diff --git a/.gitignore b/.gitignore
new file mode 100644
index 0000000000000000000000000000000000000000..b56bb68e0d77cfe39db25adaf27c8c710577556b
--- /dev/null
+++ b/.gitignore
@@ -0,0 +1,231 @@
+**/__pycache__
+**/*.pyc
+**/*.log
+**/*.png
+**/*.jpg
+**/*.mp4
+HPSv2
+**/HPSv2
+wandb
+
+# VADER-VideoCrafter
+VADER-VideoCrafter/.DS_Store
+VADER-VideoCrafter/.vscode
+VADER-VideoCrafter/__pycache__
+VADER-VideoCrafter/*.egg-info
+VADER-VideoCrafter/checkpoints
+VADER-VideoCrafter/results
+VADER-VideoCrafter/wandb
+VADER-VideoCrafter/project_dir
+VADER-VideoCrafter/scripts/evaluation/__pycache__
+VADER-VideoCrafter/scripts/lvdm/__pycache__
+
+
+# Byte-compiled / optimized / DLL files
+VADER-Open-Sora/__pycache__/
+VADER-Open-Sora/scripts/__pycache__
+VADER-Open-Sora/opensora/__pycache__
+VADER-Open-Sora/*.py[cod]
+VADER-Open-Sora/*$py.class
+
+# C extensions
+VADER-Open-Sora/*.so
+
+# Distribution / packaging
+VADER-Open-Sora/.Python
+VADER-Open-Sora/build/
+VADER-Open-Sora/develop-eggs/
+VADER-Open-Sora/dist/
+VADER-Open-Sora/downloads/
+VADER-Open-Sora/eggs/
+VADER-Open-Sora/.eggs/
+VADER-Open-Sora/lib/
+VADER-Open-Sora/lib64/
+VADER-Open-Sora/parts/
+VADER-Open-Sora/sdist/
+VADER-Open-Sora/var/
+VADER-Open-Sora/wheels/
+VADER-Open-Sora/share/python-wheels/
+VADER-Open-Sora/*.egg-info/
+VADER-Open-Sora/.installed.cfg
+VADER-Open-Sora/*.egg
+VADER-Open-Sora/MANIFEST
+
+# PyInstaller
+# Usually these files are written by a python script from a template
+# before PyInstaller builds the exe, so as to inject date/other infos into it.
+VADER-Open-Sora/*.manifest
+VADER-Open-Sora/*.spec
+
+# Installer logs
+VADER-Open-Sora/pip-log.txt
+VADER-Open-Sora/pip-delete-this-directory.txt
+
+# Unit test / coverage reports
+VADER-Open-Sora/htmlcov/
+VADER-Open-Sora/.tox/
+VADER-Open-Sora/.nox/
+VADER-Open-Sora/.coverage
+VADER-Open-Sora/.coverage.*
+VADER-Open-Sora/.cache
+VADER-Open-Sora/nosetests.xml
+VADER-Open-Sora/coverage.xml
+VADER-Open-Sora/*.cover
+VADER-Open-Sora/*.py,cover
+VADER-Open-Sora/.hypothesis/
+VADER-Open-Sora/.pytest_cache/
+VADER-Open-Sora/cover/
+
+# Translations
+VADER-Open-Sora/*.mo
+VADER-Open-Sora/*.pot
+
+# Django stuff:
+VADER-Open-Sora/*.log
+VADER-Open-Sora/local_settings.py
+VADER-Open-Sora/db.sqlite3
+VADER-Open-Sora/db.sqlite3-journal
+
+# Flask stuff:
+VADER-Open-Sora/instance/
+VADER-Open-Sora/.webassets-cache
+
+# Scrapy stuff:
+VADER-Open-Sora/.scrapy
+
+# Sphinx documentation
+VADER-Open-Sora/docs/_build/
+
+# PyBuilder
+VADER-Open-Sora/.pybuilder/
+VADER-Open-Sora/target/
+
+# Jupyter Notebook
+VADER-Open-Sora/.ipynb_checkpoints
+
+# IPython
+VADER-Open-Sora/profile_default/
+VADER-Open-Sora/ipython_config.py
+
+# pyenv
+# For a library or package, you might want to ignore these files since the code is
+# intended to run in multiple environments; otherwise, check them in:
+# .python-version
+
+# pipenv
+# According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control.
+# However, in case of collaboration, if having platform-specific dependencies or dependencies
+# having no cross-platform support, pipenv may install dependencies that don't work, or not
+# install all needed dependencies.
+#Pipfile.lock
+
+# poetry
+# Similar to Pipfile.lock, it is generally recommended to include poetry.lock in version control.
+# This is especially recommended for binary packages to ensure reproducibility, and is more
+# commonly ignored for libraries.
+# https://python-poetry.org/docs/basic-usage/#commit-your-poetrylock-file-to-version-control
+#poetry.lock
+
+# pdm
+# Similar to Pipfile.lock, it is generally recommended to include pdm.lock in version control.
+#pdm.lock
+# pdm stores project-wide configurations in .pdm.toml, but it is recommended to not include it
+# in version control.
+# https://pdm.fming.dev/#use-with-ide
+VADER-Open-Sora/.pdm.toml
+
+# PEP 582; used by e.g. github.com/David-OConnor/pyflow and github.com/pdm-project/pdm
+VADER-Open-Sora/__pypackages__/
+
+# Celery stuff
+VADER-Open-Sora/celerybeat-schedule
+VADER-Open-Sora/celerybeat.pid
+
+# SageMath parsed files
+VADER-Open-Sora/*.sage.py
+
+# Environments
+VADER-Open-Sora/.env
+VADER-Open-Sora/.venv
+VADER-Open-Sora/env/
+VADER-Open-Sora/venv/
+VADER-Open-Sora/ENV/
+VADER-Open-Sora/env.bak/
+VADER-Open-Sora/venv.bak/
+
+
+# Spyder project settings
+VADER-Open-Sora/.spyderproject
+VADER-Open-Sora/.spyproject
+
+# Rope project settings
+VADER-Open-Sora/.ropeproject
+
+# mkdocs documentation
+VADER-Open-Sora/site
+
+# mypy
+VADER-Open-Sora/.mypy_cache/
+VADER-Open-Sora/.dmypy.json
+VADER-Open-Sora/dmypy.json
+
+# Pyre type checker
+VADER-Open-Sora/.pyre/
+
+# pytype static type analyzer
+VADER-Open-Sora/.pytype/
+
+# Cython debug symbols
+VADER-Open-Sora/cython_debug/
+
+# PyCharm
+# JetBrains specific template is maintained in a separate JetBrains.gitignore that can
+# be found at https://github.com/github/gitignore/blob/main/Global/JetBrains.gitignore
+# and can be added to the global gitignore or merged into this file. For a more nuclear
+# option (not recommended) you can uncomment the following to ignore the entire idea folder.
+VADER-Open-Sora/.idea/
+VADER-Open-Sora/.vscode/
+
+# macos
+VADER-Open-Sora/*.DS_Store
+
+# misc files
+VADER-Open-Sora/data/
+VADER-Open-Sora/dataset/
+VADER-Open-Sora/runs/
+VADER-Open-Sora/checkpoints/
+VADER-Open-Sora/outputs/
+VADER-Open-Sora/outputs
+VADER-Open-Sora/samples/
+VADER-Open-Sora/samples
+VADER-Open-Sora/logs/
+VADER-Open-Sora/pretrained_models/
+VADER-Open-Sora/pretrained_models
+VADER-Open-Sora/evaluation_results/
+VADER-Open-Sora/cache/
+VADER-Open-Sora/*.swp
+
+# Secret files
+VADER-Open-Sora/hostfile
+VADER-Open-Sora/run.sh
+VADER-Open-Sora/gradio_cached_examples/
+VADER-Open-Sora/wandb/
+
+# vae weights
+VADER-Open-Sora/eval/vae/flolpips/weights/
+
+# npm
+VADER-Open-Sora/node_modules/
+VADER-Open-Sora/package-lock.json
+VADER-Open-Sora/package.json
+
+# PLLaVA
+VADER-Open-Sora/tools/caption/pllava_dir/PLLaVA/
+
+# vbench
+VADER-Open-Sora/vbench
+VADER-Open-Sora/!eval/vbench
+VADER-Open-Sora/vbench2_beta_i2v
+
+# Video files
+VADER-Open-Sora/project_dir
\ No newline at end of file
diff --git a/Core/actpred_scorer.py b/Core/actpred_scorer.py
new file mode 100644
index 0000000000000000000000000000000000000000..acefab9f8de20eea74b51a7519ea137fd911a315
--- /dev/null
+++ b/Core/actpred_scorer.py
@@ -0,0 +1,88 @@
+
+from transformers import VideoMAEFeatureExtractor, VideoMAEForVideoClassification
+import torch
+import numpy as np
+
+class ActPredScorer(torch.nn.Module):
+
+ def __init__(self, model_name = "MCG-NJU/videomae-base-finetuned-kinetics", num_frames = 16, device = 'cuda', dtype=torch.float32):
+ super().__init__()
+ self.model = VideoMAEForVideoClassification.from_pretrained(model_name, num_frames = num_frames, torch_dtype=dtype)
+ self.feature_extractor = VideoMAEFeatureExtractor.from_pretrained(model_name)
+ self.device = device
+ self.model.to(device)
+
+ def get_target_class_idx(self, target_action):
+ def mapping_func(x):
+ if 'piano' in x:
+ return 'playing piano'
+ if 'guitar' in x:
+ return 'playing guitar'
+ if 'doughnuts' in x:
+ return 'eating doughnuts'
+ if 'beer' in x:
+ return 'drinking beer'
+ if 'badminton' in x:
+ return 'playing badminton'
+ if 'cello' in x:
+ return 'playing cello'
+ if 'scooter' in x:
+ return 'riding scooter'
+ if 'ballet' in x:
+ return 'dancing ballet'
+ if 'pancake' in x:
+ return 'flipping pancake'
+ if 'violin' in x:
+ return 'playing violin'
+ if 'wood' in x:
+ return 'chopping wood'
+ if 'watermelon' in x:
+ return 'eating watermelon'
+ if 'jogging' in x:
+ return 'jogging'
+ else:
+ print(f"Please add your action mapping to ActPredScorer. Mapping not found for {x}")
+ raise NotImplementedError
+
+
+ try:
+ target_class_idx = self.model.config.label2id[target_action]
+ except:
+ target_class_idx = self.model.config.label2id[mapping_func(target_action)]
+ return target_class_idx
+
+ def get_loss_and_score(self, norm_vid, target_action):
+ ''' video should be a torch array of dtype float, with values from 0-1, of dimension (num_frames, height, width, 3)'''
+
+ target_class_idx = self.get_target_class_idx(target_action)
+ outputs = self.model(norm_vid, labels = torch.tensor([target_class_idx]).to(self.device))
+ loss = outputs.loss
+ logits = outputs.logits
+
+ norm_logits = torch.exp(logits)/ (torch.exp(logits).sum())
+ norm_logits = norm_logits.squeeze()
+
+ score = norm_logits[target_class_idx]
+ return loss, score, self.get_pred_class(logits)
+
+ def get_pred_class(self, logits):
+ predicted_class_idx = logits.argmax(-1).item()
+ return self.model.config.id2label[predicted_class_idx]
+
+def gen_rand_labels_file(labels_list, out_file, num_labels = 50):
+ idxs = np.random.choice(len(labels_list), num_labels, replace = False)
+ rand_labels = [labels_list[i] for i in idxs]
+ rand_labels.sort()
+ with open(out_file, 'w') as f:
+ for line in rand_labels:
+ f.write(f"{line}\n")
+
+if __name__ == '__main__':
+ # import numpy as np
+ # scorer = ActPredScorer(num_frames = 7)
+ # video_torch = [torch.randn((3,256,256)).clamp(0,1) for _ in range(7)]
+ # encoding = scorer.feature_extractor(video_torch, do_rescale = False, return_tensors="pt")
+ # print(scorer.get_loss_and_score(video_torch))
+ scorer = ActPredScorer(num_frames = 7)
+ labels = scorer.model.config.id2label
+
\ No newline at end of file
diff --git a/Core/aesthetic_scorer.py b/Core/aesthetic_scorer.py
new file mode 100644
index 0000000000000000000000000000000000000000..c6f9005394d1753211b104614376d6fc9c4b526f
--- /dev/null
+++ b/Core/aesthetic_scorer.py
@@ -0,0 +1,46 @@
+# Based on https://github.com/christophschuhmann/improved-aesthetic-predictor/blob/fe88a163f4661b4ddabba0751ff645e2e620746e/simple_inference.py
+# import ipdb
+# st = ipdb.set_trace
+from importlib_resources import files
+import torch
+import torch.nn as nn
+import numpy as np
+from transformers import CLIPModel, CLIPProcessor
+from PIL import Image
+ASSETS_PATH = files("assets")
+# ASSETS_PATH = "assets"
+
+class MLPDiff(nn.Module):
+ def __init__(self):
+ super().__init__()
+ self.layers = nn.Sequential(
+ nn.Linear(768, 1024),
+ nn.Dropout(0.2),
+ nn.Linear(1024, 128),
+ nn.Dropout(0.2),
+ nn.Linear(128, 64),
+ nn.Dropout(0.1),
+ nn.Linear(64, 16),
+ nn.Linear(16, 1),
+ )
+
+
+ def forward(self, embed):
+ return self.layers(embed)
+
+
+class AestheticScorerDiff(torch.nn.Module):
+ def __init__(self, dtype):
+ super().__init__()
+ self.clip = CLIPModel.from_pretrained("openai/clip-vit-large-patch14")
+ self.mlp = MLPDiff()
+ state_dict = torch.load(ASSETS_PATH.joinpath("sac+logos+ava1-l14-linearMSE.pth"))
+ self.mlp.load_state_dict(state_dict)
+ self.dtype = dtype
+ self.eval()
+
+ def __call__(self, images):
+ device = next(self.parameters()).device
+ embed = self.clip.get_image_features(pixel_values=images)
+ embed = embed / torch.linalg.vector_norm(embed, dim=-1, keepdim=True)
+ return self.mlp(embed).squeeze(1)
diff --git a/Core/compression_scorer.py b/Core/compression_scorer.py
new file mode 100644
index 0000000000000000000000000000000000000000..a9a1dbb630958571e50f7ff69f7599ef5d5d696f
--- /dev/null
+++ b/Core/compression_scorer.py
@@ -0,0 +1,111 @@
+# Adapt from Cheng An Hsieh, et. al.: https://github.com/RewardMultiverse/reward-multiverse
+from PIL import Image
+import io
+import numpy as np
+import torch.nn as nn
+import torch
+import torchvision
+import albumentations as A
+from transformers import CLIPModel, CLIPProcessor
+# import ipdb
+# st = ipdb.set_trace
+
+def jpeg_compressibility(device):
+ def _fn(images):
+ '''
+ args:
+ images: shape NCHW
+ '''
+ org_type = images.dtype
+ if isinstance(images, torch.Tensor):
+ images = (images * 255).round().clamp(0, 255).to(torch.uint8).cpu().numpy()
+ images = images.transpose(0, 2, 3, 1) # NCHW -> NHWC
+
+ transform_images_tensor = torch.Tensor(np.array(images)).to(device, dtype=org_type)
+ transform_images_tensor = (transform_images_tensor.permute(0,3,1,2) / 255).clamp(0,1) # NHWC -> NCHW
+ transform_images_pil = [Image.fromarray(image) for image in images]
+ buffers = [io.BytesIO() for _ in transform_images_pil]
+
+ for image, buffer in zip(transform_images_pil, buffers):
+ image.save(buffer, format="JPEG", quality=95)
+
+ sizes = [buffer.tell() / 1000 for buffer in buffers]
+
+ return np.array(sizes), transform_images_tensor
+
+ return _fn
+
+
+class MLP(nn.Module):
+ def __init__(self):
+ super().__init__()
+ self.layers = nn.Sequential(
+ nn.Linear(768, 512),
+ nn.ReLU(),
+ nn.Dropout(0.2),
+ nn.Linear(512, 256),
+ nn.ReLU(),
+ nn.Dropout(0.2),
+ nn.Linear(256, 128),
+ nn.ReLU(),
+ nn.Dropout(0.2),
+ nn.Linear(128, 32),
+ nn.ReLU(),
+ nn.Dropout(0.1),
+ nn.Linear(32, 1),
+ )
+
+ def forward(self, embed):
+ return self.layers(embed)
+
+def jpegcompression_loss_fn(target=None,
+ grad_scale=0,
+ device=None,
+ accelerator=None,
+ torch_dtype=None,
+ reward_model_resume_from=None):
+ scorer = JpegCompressionScorer(dtype=torch_dtype, model_path=reward_model_resume_from).to(device, dtype=torch_dtype)
+ scorer.requires_grad_(False)
+ scorer.eval()
+ def loss_fn(im_pix_un):
+ if accelerator.mixed_precision == "fp16":
+ with accelerator.autocast():
+ rewards = scorer(im_pix_un)
+ else:
+ rewards = scorer(im_pix_un)
+
+ if target is None:
+ loss = rewards
+ else:
+ loss = abs(rewards - target)
+ return loss * grad_scale, rewards
+ return loss_fn
+
+class JpegCompressionScorer(nn.Module):
+ def __init__(self, dtype=None, model_path=None):
+ super().__init__()
+ self.clip = CLIPModel.from_pretrained("openai/clip-vit-large-patch14")
+ self.clip.requires_grad_(False)
+ self.score_generator = MLP()
+
+ if model_path:
+ state_dict = torch.load(model_path)
+ self.score_generator.load_state_dict(state_dict)
+ if dtype:
+ self.dtype = dtype
+ self.target_size = (224,224)
+ self.normalize = torchvision.transforms.Normalize(mean=[0.48145466, 0.4578275, 0.40821073],
+ std=[0.26862954, 0.26130258, 0.27577711])
+
+
+ def set_device(self, device, inference_type):
+ # self.clip.to(device, dtype = inference_type)
+ self.score_generator.to(device) # , dtype = inference_type
+
+ def __call__(self, images):
+ device = next(self.parameters()).device
+ im_pix = torchvision.transforms.Resize(self.target_size)(images)
+ im_pix = self.normalize(im_pix).to(images.dtype)
+ embed = self.clip.get_image_features(pixel_values=im_pix)
+ embed = embed / torch.linalg.vector_norm(embed, dim=-1, keepdim=True)
+ return self.score_generator(embed).squeeze(1)
\ No newline at end of file
diff --git a/Core/prompts.py b/Core/prompts.py
new file mode 100644
index 0000000000000000000000000000000000000000..bc2128b7cd2af5957992cbf491d589ff266e319d
--- /dev/null
+++ b/Core/prompts.py
@@ -0,0 +1,159 @@
+from importlib_resources import files
+import os
+import functools
+import random
+import inflect
+
+IE = inflect.engine()
+ASSETS_PATH = files("assets")
+# ASSETS_PATH = "assets"
+
+
+@functools.lru_cache(maxsize=None)
+def _load_lines(path):
+ """
+ Load lines from a file. First tries to load from `path` directly, and if that doesn't exist, searches the
+ `ddpo_pytorch/assets` directory for a file named `path`.
+ """
+ if not os.path.exists(path):
+ newpath = ASSETS_PATH.joinpath(path)
+ if not os.path.exists(newpath):
+ raise FileNotFoundError(f"Could not find {path} or assets/{path}")
+ path = newpath
+ with open(path, "r") as f:
+ return [line.strip() for line in f.readlines()]
+
+def hps_v2_all(nouns_file=None, activities_file=None):
+ return from_file("hps_v2_all.txt")
+
+def hps_custom(nouns_file=None, activities_file=None):
+ return from_file("hps_custom.txt")
+
+def hps_debug(nouns_file=None, activities_file=None):
+ return from_file("hps_debug.txt")
+
+def hps_single(nouns_file=None, activities_file=None):
+ return from_file("hps_single.txt")
+
+def kinetics_4rand(nouns_file=None, activities_file=None):
+ return from_file("kinetics_4rand.txt")
+
+def kinetics_50rand(nouns_file=None, activities_file=None):
+ return from_file("kinetics_50rand.txt")
+
+def simple_animals():
+ return from_file("simple_animals.txt")
+
+def eval_simple_animals():
+ return from_file("eval_simple_animals.txt")
+
+def eval_hps_v2_all(nouns_file=None, activities_file=None):
+ return from_file("hps_v2_all_eval.txt")
+
+def chatgpt_custom(nouns_file=None, activities_file=None):
+ return from_file("chatgpt_custom.txt")
+
+def chatgpt_custom_instruments(nouns_file=None, activities_file=None):
+ return from_file("chatgpt_custom_instruments.txt")
+
+def chatgpt_custom_human(nouns_file=None, activities_file=None):
+ return from_file("chatgpt_custom_human.txt")
+
+def chatgpt_custom_human_activity(nouns_file=None, activities_file=None):
+ return from_file("chatgpt_custom_human_activity.txt")
+
+def chatgpt_custom_animal(nouns_file=None, activities_file=None):
+ return from_file("chatgpt_custom_animal.txt")
+
+def chatgpt_custom_animal_sport(nouns_file=None, activities_file=None):
+ return from_file("chatgpt_custom_animal_sport.txt")
+
+def chatgpt_custom_animal_sportV2(nouns_file=None, activities_file=None):
+ return from_file("chatgpt_custom_animal_sportV2.txt")
+
+def chatgpt_custom_animal_clothes(nouns_file=None, activities_file=None):
+ return from_file("chatgpt_custom_animal_clothes.txt")
+
+def chatgpt_custom_animal_clothesV2(nouns_file=None, activities_file=None):
+ return from_file("chatgpt_custom_animal_clothesV2.txt")
+
+def chatgpt_custom_animal_clothesV3(nouns_file=None, activities_file=None):
+ return from_file("chatgpt_custom_animal_clothesV3.txt")
+
+def chatgpt_custom_animal_technology(nouns_file=None, activities_file=None):
+ return from_file("chatgpt_custom_animal_technology.txt")
+
+def chatgpt_custom_animal_housework(nouns_file=None, activities_file=None):
+ return from_file("chatgpt_custom_animal_housework.txt")
+
+def chatgpt_custom_animal_action(nouns_file=None, activities_file=None):
+ return from_file("chatgpt_custom_animal_action.txt")
+
+def chatgpt_custom_outdoor(nouns_file=None, activities_file=None):
+ return from_file("chatgpt_custom_outdoor.txt")
+
+def chatgpt_custom_rainy(nouns_file=None, activities_file=None):
+ return from_file("chatgpt_custom_rainy.txt")
+
+def chatgpt_custom_snowy(nouns_file=None, activities_file=None):
+ return from_file("chatgpt_custom_snowy.txt")
+
+def chatgpt_custom_dog(nouns_file=None, activities_file=None):
+ return from_file("chatgpt_custom_dog.txt")
+
+def chatgpt_custom_banana(nouns_file=None, activities_file=None):
+ return from_file("chatgpt_custom_banana.txt")
+
+def chatgpt_custom_forest(nouns_file=None, activities_file=None):
+ return from_file("chatgpt_custom_forest.txt")
+
+def chatgpt_custom_forest_vivid(nouns_file=None, activities_file=None):
+ return from_file("chatgpt_custom_forest_vivid.txt")
+
+def chatgpt_custom_cruel_animal(nouns_file=None, activities_file=None):
+ return from_file("chatgpt_custom_cruel_animal.txt")
+
+def chatgpt_custom_cruel_animal2(nouns_file=None, activities_file=None):
+ return from_file("chatgpt_custom_cruel_animal2.txt")
+
+def chatgpt_custom_bottle_glass(nouns_file=None, activities_file=None):
+ return from_file("chatgpt_custom_bottle_glass.txt")
+
+def chatgpt_custom_book_cup(nouns_file=None, activities_file=None):
+ return from_file("chatgpt_custom_book_cup.txt")
+
+def chatgpt_custom_book_cup_character(nouns_file=None, activities_file=None):
+ return from_file("chatgpt_custom_book_cup_character.txt")
+
+def chatgpt_custom_cute(nouns_file=None, activities_file=None):
+ return from_file("chatgpt_custom_cute.txt")
+
+def chatgpt_custom_ice(nouns_file=None, activities_file=None):
+ return from_file("chatgpt_custom_ice.txt")
+
+def chatgpt_custom_compression(nouns_file=None, activities_file=None):
+ return from_file("chatgpt_custom_compression.txt")
+
+def chatgpt_custom_compression_animals(nouns_file=None, activities_file=None):
+ return from_file("chatgpt_custom_compression_animals.txt")
+
+def chatgpt_custom_actpred(nouns_file=None, activities_file=None):
+ return from_file("chatgpt_custom_actpred.txt")
+
+def chatgpt_custom_actpred2(nouns_file=None, activities_file=None):
+ return from_file("chatgpt_custom_actpred2.txt")
+
+def chatgpt_custom_instruments_unseen(nouns_file=None, activities_file=None):
+ return from_file("chatgpt_custom_instruments_unseen.txt")
+
+def from_file(path, low=None, high=None, **kwargs):
+ prompts = _load_lines(path)[low:high]
+ return random.choice(prompts), {}
+
+def from_str(_str, **kwargs):
+ return _str, {}
+
+def nouns_activities(nouns_file, activities_file, **kwargs):
+ nouns = _load_lines(nouns_file)
+ activities = _load_lines(activities_file)
+ return f"{IE.a(random.choice(nouns))} {random.choice(activities)}", {}
\ No newline at end of file
diff --git a/Core/weather_scorer.py b/Core/weather_scorer.py
new file mode 100644
index 0000000000000000000000000000000000000000..29e34a89c054c9b63fca9b254dc73cad9bd48f24
--- /dev/null
+++ b/Core/weather_scorer.py
@@ -0,0 +1,161 @@
+# Copy from Cheng An Hsieh, et. al.: https://github.com/RewardMultiverse/reward-multiverse
+import torch
+import torch.nn as nn
+import torchvision
+from transformers import CLIPModel, CLIPProcessor
+
+class SimpleCNN(nn.Module): # parameter = 6333513
+ def __init__(self, num_class = None):
+ super(SimpleCNN, self).__init__()
+ self.layer1 = nn.Sequential(
+ nn.Conv2d(3, 16, kernel_size=3, stride=1, padding=1),
+ nn.ReLU(),
+ nn.MaxPool2d(kernel_size=2, stride=2))
+ self.layer2 = nn.Sequential(
+ nn.Conv2d(16, 32, kernel_size=3, stride=1, padding=1),
+ nn.ReLU(),
+ nn.MaxPool2d(kernel_size=2, stride=2))
+ self.layer3 = nn.Sequential(
+ nn.Conv2d(32, 64, kernel_size=3, stride=1, padding=1),
+ nn.ReLU(),
+ nn.MaxPool2d(kernel_size=2, stride=2))
+ self.layer4 = nn.Sequential(
+ nn.Conv2d(64, 128, kernel_size=3, stride=1, padding=1),
+ nn.ReLU(),
+ nn.MaxPool2d(kernel_size=2, stride=2))
+ self.fc1 = nn.Linear(128 * 32 * 32, 1000)
+ self.fc2 = nn.Linear(1000, num_class)
+
+ def forward(self, x):
+ x = self.layer1(x)
+ # print("x1", x.shape)
+ x = self.layer2(x)
+ # print("x2", x.shape)
+ x = self.layer3(x)
+ # print("x3", x.shape)
+ x = self.layer4(x)
+ # print("x4", x.shape)
+
+ x = x.reshape(x.size(0), -1)
+ # print("x reshape", x.shape)
+ x = torch.relu(self.fc1(x))
+ x = self.fc2(x)
+ return x
+
+
+class MLP(nn.Module):
+ def __init__(self):
+ super().__init__()
+ self.layers = nn.Sequential( # regression
+ nn.Linear(768, 1024),
+ nn.Dropout(0.2),
+ nn.Linear(1024, 128),
+ nn.Dropout(0.2),
+ nn.Linear(128, 64),
+ nn.Dropout(0.1),
+ nn.Linear(64, 16),
+ nn.Linear(16, 1),
+ nn.Sigmoid()
+ )
+
+ # self.layers = nn.Sequential( # classification
+ # nn.Linear(768, 1024),
+ # nn.Dropout(0.2),
+ # nn.Linear(1024, 128),
+ # nn.Dropout(0.2),
+ # nn.Linear(128, 64),
+ # nn.Dropout(0.1),
+ # nn.Linear(64, 16),
+ # nn.Linear(16, 2)
+ # )
+
+ def forward(self, embed):
+ return self.layers(embed)
+
+class MLP_Resnet(nn.Module):
+ def __init__(self, num_class):
+ super().__init__()
+ self.layers = nn.Sequential(
+ nn.Linear(1000, 128),
+ # nn.Dropout(0.2),
+ nn.Linear(128, 64),
+ # nn.Dropout(0.2),
+ nn.Linear(64, 16),
+ nn.Linear(16, num_class),
+ )
+
+ def forward(self, embed):
+ return self.layers(embed)
+
+
+def weather_loss_fn(target=None, # TODO: use config.task to decide returned loss_fn
+ grad_scale=0,
+ device=None,
+ accelerator=None,
+ torch_dtype=None,
+ reward_model_resume_from=None,
+ num_of_labels=None):
+ scorer = WeatherScorer(dtype=torch_dtype, model_path=reward_model_resume_from, num_class=num_of_labels).to(device, dtype=torch_dtype)
+ scorer.requires_grad_(False)
+ scorer.eval()
+
+ def loss_fn(im_pix_un):
+ if accelerator.mixed_precision == "fp16":
+ with accelerator.autocast():
+ rewards = scorer(im_pix_un)
+ else:
+ rewards = scorer(im_pix_un)
+
+ target_tensors = torch.full((rewards.shape[0],), target).to(rewards.device, dtype=rewards.dtype) # regression
+ criterion = torch.nn.MSELoss(reduction = "sum") # regression
+ # target_tensors = torch.full((rewards.shape[0],), target).to(rewards.device, dtype=torch.long) # classification
+ # criterion = nn.CrossEntropyLoss(reduction="sum") # classification
+ loss = criterion(rewards, target_tensors)
+ return loss * grad_scale, rewards #nn.Softmax(dim=-1)(rewards) # rewards (reg)
+ return loss_fn
+
+
+class WeatherModel(nn.Module):
+ def __init__(self, num_class = None):
+ super().__init__()
+ self.embed_model = torch.hub.load('pytorch/vision:v0.10.0', 'resnet18', pretrained=True)
+ self.score_model = MLP_Resnet(num_class)
+ def __call__(self, im):
+ return self.score_model(self.embed_model(im))
+
+
+class WeatherScorer(nn.Module): # Reward model
+ def __init__(self, dtype=None, model_path = None, num_class = None):
+ super().__init__()
+ self.clip = CLIPModel.from_pretrained("openai/clip-vit-large-patch14")
+ self.clip.requires_grad_(False)
+ self.clip.eval()
+ self.score_generator = MLP()
+ # self.score_generator = WeatherModel(num_class) # resnet + mlp
+ if model_path:
+ state_dict = torch.load(model_path)
+ self.score_generator.load_state_dict(state_dict)
+ self.score_generator.requires_grad_(False)
+ self.score_generator.eval()
+ # self.clip.requires_grad_(False)
+ # self.clip.eval()
+ else:
+ self.score_generator.requires_grad_(True)
+ if dtype:
+ self.dtype = dtype
+ self.target_size = (224,224) # resnet 224, cnn 512 (use 224 for both...?)
+ self.normalize = torchvision.transforms.Normalize(mean=[0.48145466, 0.4578275, 0.40821073],
+ std=[0.26862954, 0.26130258, 0.27577711])
+
+ def set_device(self, device, inference_type):
+ self.clip.to(device, dtype = inference_type) # uncomment for mlp
+ self.score_generator.to(device) # dtype = inference_dtype
+
+ def __call__(self, images):
+ device = next(self.parameters()).device
+ im_pix = torchvision.transforms.Resize(self.target_size)(images)
+ im_pix = self.normalize(im_pix).to(images.dtype)
+ embed = self.clip.get_image_features(pixel_values=im_pix)
+ embed = embed / torch.linalg.vector_norm(embed, dim=-1, keepdim=True)
+ return self.score_generator(embed).squeeze(1) # CLIP + MLP
+ # return self.score_generator(im_pix).squeeze(1) # for simpleCNN
\ No newline at end of file
diff --git a/VADER-VideoCrafter/License b/VADER-VideoCrafter/License
new file mode 100644
index 0000000000000000000000000000000000000000..e2741c4696c41ba1b3bf22748b45e9b5db09c445
--- /dev/null
+++ b/VADER-VideoCrafter/License
@@ -0,0 +1,470 @@
+This license applies to the source codes that are open sourced in connection with the VideoCrafter1.
+
+Copyright (C) 2023 THL A29 Limited, a Tencent company.
+
+Apache License
+Version 2.0, January 2004
+http://www.apache.org/licenses/
+
+TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION
+
+1. Definitions.
+
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+
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+
+Component under Apache v2 License:
+-----------------------------------------------------
+1. timm
+Copyright 2019 Ross Wightman
+
+Apache License
+Version 2.0, January 2004
+http://www.apache.org/licenses/
+
+TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION
+
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+
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+not limited to compiled object code, generated documentation,
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+
+"Work" shall mean the work of authorship, whether in Source or
+Object form, made available under the License, as indicated by a
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+(an example is provided in the Appendix below).
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+"Derivative Works" shall mean any work, whether in Source or Object
+form, that is based on (or derived from) the Work and for which the
+editorial revisions, annotations, elaborations, or other modifications
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+do not modify the License. You may add Your own attribution
+notices within Derivative Works that You distribute, alongside
+or as an addendum to the NOTICE text from the Work, provided
+that such additional attribution notices cannot be construed
+as modifying the License.
+
+You may add Your own copyright statement to Your modifications and
+may provide additional or different license terms and conditions
+for use, reproduction, or distribution of Your modifications, or
+for any such Derivative Works as a whole, provided Your use,
+reproduction, and distribution of the Work otherwise complies with
+the conditions stated in this License.
+
+5. Submission of Contributions. Unless You explicitly state otherwise,
+any Contribution intentionally submitted for inclusion in the Work
+by You to the Licensor shall be under the terms and conditions of
+this License, without any additional terms or conditions.
+Notwithstanding the above, nothing herein shall supersede or modify
+the terms of any separate license agreement you may have executed
+with Licensor regarding such Contributions.
+
+6. Trademarks. This License does not grant permission to use the trade
+names, trademarks, service marks, or product names of the Licensor,
+except as required for reasonable and customary use in describing the
+origin of the Work and reproducing the content of the NOTICE file.
+
+7. Disclaimer of Warranty. Unless required by applicable law or
+agreed to in writing, Licensor provides the Work (and each
+Contributor provides its Contributions) on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or
+implied, including, without limitation, any warranties or conditions
+of TITLE, NON-INFRINGEMENT, MERCHANTABILITY, or FITNESS FOR A
+PARTICULAR PURPOSE. You are solely responsible for determining the
+appropriateness of using or redistributing the Work and assume any
+risks associated with Your exercise of permissions under this License.
+
+8. Limitation of Liability. In no event and under no legal theory,
+whether in tort (including negligence), contract, or otherwise,
+unless required by applicable law (such as deliberate and grossly
+negligent acts) or agreed to in writing, shall any Contributor be
+liable to You for damages, including any direct, indirect, special,
+incidental, or consequential damages of any character arising as a
+result of this License or out of the use or inability to use the
+Work (including but not limited to damages for loss of goodwill,
+work stoppage, computer failure or malfunction, or any and all
+other commercial damages or losses), even if such Contributor
+has been advised of the possibility of such damages.
+
+9. Accepting Warranty or Additional Liability. While redistributing
+the Work or Derivative Works thereof, You may choose to offer,
+and charge a fee for, acceptance of support, warranty, indemnity,
+or other liability obligations and/or rights consistent with this
+License. However, in accepting such obligations, You may act only
+on Your own behalf and on Your sole responsibility, not on behalf
+of any other Contributor, and only if You agree to indemnify,
+defend, and hold each Contributor harmless for any liability
+incurred by, or claims asserted against, such Contributor by reason
+of your accepting any such warranty or additional liability.
+
+END OF TERMS AND CONDITIONS
+
+APPENDIX: How to apply the Apache License to your work.
+
+To apply the Apache License to your work, attach the following
+boilerplate notice, with the fields enclosed by brackets "[]"
+replaced with your own identifying information. (Don't include
+the brackets!) The text should be enclosed in the appropriate
+comment syntax for the file format. We also recommend that a
+file or class name and description of purpose be included on the
+same "printed page" as the copyright notice for easier
+identification within third-party archives.
+
+Copyright [yyyy] [name of copyright owner]
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
\ No newline at end of file
diff --git a/VADER-VideoCrafter/configs/inference_i2v_512_v1.0.yaml b/VADER-VideoCrafter/configs/inference_i2v_512_v1.0.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..7d490079dd655bd3e9fa7656532b2417085370e7
--- /dev/null
+++ b/VADER-VideoCrafter/configs/inference_i2v_512_v1.0.yaml
@@ -0,0 +1,83 @@
+model:
+ target: lvdm.models.ddpm3d.LatentVisualDiffusion
+ params:
+ linear_start: 0.00085
+ linear_end: 0.012
+ num_timesteps_cond: 1
+ timesteps: 1000
+ first_stage_key: video
+ cond_stage_key: caption
+ cond_stage_trainable: false
+ conditioning_key: crossattn
+ image_size:
+ - 40
+ - 64
+ channels: 4
+ scale_by_std: false
+ scale_factor: 0.18215
+ use_ema: false
+ uncond_type: empty_seq
+ use_scale: true
+ scale_b: 0.7
+ finegrained: true
+ unet_config:
+ target: lvdm.modules.networks.openaimodel3d.UNetModel
+ params:
+ in_channels: 4
+ out_channels: 4
+ model_channels: 320
+ attention_resolutions:
+ - 4
+ - 2
+ - 1
+ num_res_blocks: 2
+ channel_mult:
+ - 1
+ - 2
+ - 4
+ - 4
+ num_head_channels: 64
+ transformer_depth: 1
+ context_dim: 1024
+ use_linear: true
+ use_checkpoint: true
+ temporal_conv: true
+ temporal_attention: true
+ temporal_selfatt_only: true
+ use_relative_position: false
+ use_causal_attention: false
+ use_image_attention: true
+ temporal_length: 16
+ addition_attention: true
+ fps_cond: true
+ first_stage_config:
+ target: lvdm.models.autoencoder.AutoencoderKL
+ params:
+ embed_dim: 4
+ monitor: val/rec_loss
+ ddconfig:
+ double_z: true
+ z_channels: 4
+ resolution: 512
+ in_channels: 3
+ out_ch: 3
+ ch: 128
+ ch_mult:
+ - 1
+ - 2
+ - 4
+ - 4
+ num_res_blocks: 2
+ attn_resolutions: []
+ dropout: 0.0
+ lossconfig:
+ target: torch.nn.Identity
+ cond_stage_config:
+ target: lvdm.modules.encoders.condition.FrozenOpenCLIPEmbedder
+ params:
+ freeze: true
+ layer: penultimate
+ cond_img_config:
+ target: lvdm.modules.encoders.condition.FrozenOpenCLIPImageEmbedderV2
+ params:
+ freeze: true
\ No newline at end of file
diff --git a/VADER-VideoCrafter/configs/inference_t2v_1024_v1.0.yaml b/VADER-VideoCrafter/configs/inference_t2v_1024_v1.0.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..4cb9af1562264f63e029ce536b16f03c30f0d279
--- /dev/null
+++ b/VADER-VideoCrafter/configs/inference_t2v_1024_v1.0.yaml
@@ -0,0 +1,77 @@
+model:
+ target: lvdm.models.ddpm3d.LatentDiffusion
+ params:
+ linear_start: 0.00085
+ linear_end: 0.012
+ num_timesteps_cond: 1
+ timesteps: 1000
+ first_stage_key: video
+ cond_stage_key: caption
+ cond_stage_trainable: false
+ conditioning_key: crossattn
+ image_size:
+ - 72
+ - 128
+ channels: 4
+ scale_by_std: false
+ scale_factor: 0.18215
+ use_ema: false
+ uncond_type: empty_seq
+ use_scale: true
+ fix_scale_bug: true
+ unet_config:
+ target: lvdm.modules.networks.openaimodel3d.UNetModel
+ params:
+ in_channels: 4
+ out_channels: 4
+ model_channels: 320
+ attention_resolutions:
+ - 4
+ - 2
+ - 1
+ num_res_blocks: 2
+ channel_mult:
+ - 1
+ - 2
+ - 4
+ - 4
+ num_head_channels: 64
+ transformer_depth: 1
+ context_dim: 1024
+ use_linear: true
+ use_checkpoint: true
+ temporal_conv: false
+ temporal_attention: true
+ temporal_selfatt_only: true
+ use_relative_position: true
+ use_causal_attention: false
+ temporal_length: 16
+ addition_attention: true
+ fps_cond: true
+ first_stage_config:
+ target: lvdm.models.autoencoder.AutoencoderKL
+ params:
+ embed_dim: 4
+ monitor: val/rec_loss
+ ddconfig:
+ double_z: true
+ z_channels: 4
+ resolution: 512
+ in_channels: 3
+ out_ch: 3
+ ch: 128
+ ch_mult:
+ - 1
+ - 2
+ - 4
+ - 4
+ num_res_blocks: 2
+ attn_resolutions: []
+ dropout: 0.0
+ lossconfig:
+ target: torch.nn.Identity
+ cond_stage_config:
+ target: lvdm.modules.encoders.condition.FrozenOpenCLIPEmbedder
+ params:
+ freeze: true
+ layer: penultimate
diff --git a/VADER-VideoCrafter/configs/inference_t2v_512_v1.0.yaml b/VADER-VideoCrafter/configs/inference_t2v_512_v1.0.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..849623ebe95f6962cbb85cfa10189dd3f5b44fb4
--- /dev/null
+++ b/VADER-VideoCrafter/configs/inference_t2v_512_v1.0.yaml
@@ -0,0 +1,74 @@
+model:
+ target: lvdm.models.ddpm3d.LatentDiffusion
+ params:
+ linear_start: 0.00085
+ linear_end: 0.012
+ num_timesteps_cond: 1
+ timesteps: 1000
+ first_stage_key: video
+ cond_stage_key: caption
+ cond_stage_trainable: false
+ conditioning_key: crossattn
+ image_size:
+ - 40
+ - 64
+ channels: 4
+ scale_by_std: false
+ scale_factor: 0.18215
+ use_ema: false
+ uncond_type: empty_seq
+ unet_config:
+ target: lvdm.modules.networks.openaimodel3d.UNetModel
+ params:
+ in_channels: 4
+ out_channels: 4
+ model_channels: 320
+ attention_resolutions:
+ - 4
+ - 2
+ - 1
+ num_res_blocks: 2
+ channel_mult:
+ - 1
+ - 2
+ - 4
+ - 4
+ num_head_channels: 64
+ transformer_depth: 1
+ context_dim: 1024
+ use_linear: true
+ use_checkpoint: true
+ temporal_conv: false
+ temporal_attention: true
+ temporal_selfatt_only: true
+ use_relative_position: true
+ use_causal_attention: false
+ temporal_length: 16
+ addition_attention: true
+ first_stage_config:
+ target: lvdm.models.autoencoder.AutoencoderKL
+ params:
+ embed_dim: 4
+ monitor: val/rec_loss
+ ddconfig:
+ double_z: true
+ z_channels: 4
+ resolution: 512
+ in_channels: 3
+ out_ch: 3
+ ch: 128
+ ch_mult:
+ - 1
+ - 2
+ - 4
+ - 4
+ num_res_blocks: 2
+ attn_resolutions: []
+ dropout: 0.0
+ lossconfig:
+ target: torch.nn.Identity
+ cond_stage_config:
+ target: lvdm.modules.encoders.condition.FrozenOpenCLIPEmbedder
+ params:
+ freeze: true
+ layer: penultimate
diff --git a/VADER-VideoCrafter/configs/inference_t2v_512_v2.0.yaml b/VADER-VideoCrafter/configs/inference_t2v_512_v2.0.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..4a2e6c4e88ad32e439bb6b95b1a200d0ef104603
--- /dev/null
+++ b/VADER-VideoCrafter/configs/inference_t2v_512_v2.0.yaml
@@ -0,0 +1,77 @@
+model:
+ target: lvdm.models.ddpm3d.LatentDiffusion
+ params:
+ linear_start: 0.00085
+ linear_end: 0.012
+ num_timesteps_cond: 1
+ timesteps: 1000
+ first_stage_key: video
+ cond_stage_key: caption
+ cond_stage_trainable: false
+ conditioning_key: crossattn
+ image_size:
+ - 40
+ - 64
+ channels: 4
+ scale_by_std: false
+ scale_factor: 0.18215
+ use_ema: false
+ uncond_type: empty_seq
+ use_scale: true
+ scale_b: 0.7
+ unet_config:
+ target: lvdm.modules.networks.openaimodel3d.UNetModel
+ params:
+ in_channels: 4
+ out_channels: 4
+ model_channels: 320
+ attention_resolutions:
+ - 4
+ - 2
+ - 1
+ num_res_blocks: 2
+ channel_mult:
+ - 1
+ - 2
+ - 4
+ - 4
+ num_head_channels: 64
+ transformer_depth: 1
+ context_dim: 1024
+ use_linear: true
+ use_checkpoint: true
+ temporal_conv: true
+ temporal_attention: true
+ temporal_selfatt_only: true
+ use_relative_position: false
+ use_causal_attention: false
+ temporal_length: 16
+ addition_attention: true
+ fps_cond: true
+ first_stage_config:
+ target: lvdm.models.autoencoder.AutoencoderKL
+ params:
+ embed_dim: 4
+ monitor: val/rec_loss
+ ddconfig:
+ double_z: true
+ z_channels: 4
+ resolution: 512
+ in_channels: 3
+ out_ch: 3
+ ch: 128
+ ch_mult:
+ - 1
+ - 2
+ - 4
+ - 4
+ num_res_blocks: 2
+ attn_resolutions: []
+ dropout: 0.0
+ lossconfig:
+ target: torch.nn.Identity
+ cond_stage_config:
+ target: lvdm.modules.encoders.condition.FrozenOpenCLIPEmbedder
+ params:
+ freeze: true
+ layer: penultimate
diff --git a/VADER-VideoCrafter/lvdm/basics.py b/VADER-VideoCrafter/lvdm/basics.py
new file mode 100644
index 0000000000000000000000000000000000000000..65c771d13a7f4a932ac370f08797a8b6ba9e85ff
--- /dev/null
+++ b/VADER-VideoCrafter/lvdm/basics.py
@@ -0,0 +1,100 @@
+# adopted from
+# https://github.com/openai/improved-diffusion/blob/main/improved_diffusion/gaussian_diffusion.py
+# and
+# https://github.com/lucidrains/denoising-diffusion-pytorch/blob/7706bdfc6f527f58d33f84b7b522e61e6e3164b3/denoising_diffusion_pytorch/denoising_diffusion_pytorch.py
+# and
+# https://github.com/openai/guided-diffusion/blob/0ba878e517b276c45d1195eb29f6f5f72659a05b/guided_diffusion/nn.py
+#
+# thanks!
+
+import torch.nn as nn
+from utils.utils import instantiate_from_config
+
+
+def disabled_train(self, mode=True):
+ """Overwrite model.train with this function to make sure train/eval mode
+ does not change anymore."""
+ return self
+
+def zero_module(module):
+ """
+ Zero out the parameters of a module and return it.
+ """
+ for p in module.parameters():
+ p.detach().zero_()
+ return module
+
+def scale_module(module, scale):
+ """
+ Scale the parameters of a module and return it.
+ """
+ for p in module.parameters():
+ p.detach().mul_(scale)
+ return module
+
+
+def conv_nd(dims, *args, **kwargs):
+ """
+ Create a 1D, 2D, or 3D convolution module.
+ """
+ if dims == 1:
+ return nn.Conv1d(*args, **kwargs)
+ elif dims == 2:
+ return nn.Conv2d(*args, **kwargs)
+ elif dims == 3:
+ return nn.Conv3d(*args, **kwargs)
+ raise ValueError(f"unsupported dimensions: {dims}")
+
+
+def linear(*args, **kwargs):
+ """
+ Create a linear module.
+ """
+ return nn.Linear(*args, **kwargs)
+
+
+def avg_pool_nd(dims, *args, **kwargs):
+ """
+ Create a 1D, 2D, or 3D average pooling module.
+ """
+ if dims == 1:
+ return nn.AvgPool1d(*args, **kwargs)
+ elif dims == 2:
+ return nn.AvgPool2d(*args, **kwargs)
+ elif dims == 3:
+ return nn.AvgPool3d(*args, **kwargs)
+ raise ValueError(f"unsupported dimensions: {dims}")
+
+
+def nonlinearity(type='silu'):
+ if type == 'silu':
+ return nn.SiLU()
+ elif type == 'leaky_relu':
+ return nn.LeakyReLU()
+
+
+class GroupNormSpecific(nn.GroupNorm):
+ def forward(self, x):
+ return super().forward(x.float()).type(x.dtype)
+
+
+def normalization(channels, num_groups=32):
+ """
+ Make a standard normalization layer.
+ :param channels: number of input channels.
+ :return: an nn.Module for normalization.
+ """
+ return GroupNormSpecific(num_groups, channels)
+
+
+class HybridConditioner(nn.Module):
+
+ def __init__(self, c_concat_config, c_crossattn_config):
+ super().__init__()
+ self.concat_conditioner = instantiate_from_config(c_concat_config)
+ self.crossattn_conditioner = instantiate_from_config(c_crossattn_config)
+
+ def forward(self, c_concat, c_crossattn):
+ c_concat = self.concat_conditioner(c_concat)
+ c_crossattn = self.crossattn_conditioner(c_crossattn)
+ return {'c_concat': [c_concat], 'c_crossattn': [c_crossattn]}
\ No newline at end of file
diff --git a/VADER-VideoCrafter/lvdm/common.py b/VADER-VideoCrafter/lvdm/common.py
new file mode 100644
index 0000000000000000000000000000000000000000..1ba6c49587808d260a7404653823508076837095
--- /dev/null
+++ b/VADER-VideoCrafter/lvdm/common.py
@@ -0,0 +1,96 @@
+# Copied from VideoCrafter: https://github.com/AILab-CVC/VideoCrafter
+import math
+from inspect import isfunction
+import torch
+from torch import nn
+import torch.distributed as dist
+
+
+def gather_data(data, return_np=True):
+ ''' gather data from multiple processes to one list '''
+ data_list = [torch.zeros_like(data) for _ in range(dist.get_world_size())]
+ dist.all_gather(data_list, data) # gather not supported with NCCL
+ if return_np:
+ data_list = [data.cpu().numpy() for data in data_list]
+ return data_list
+
+def autocast(f):
+ def do_autocast(*args, **kwargs):
+ with torch.cuda.amp.autocast(enabled=True,
+ dtype=torch.get_autocast_gpu_dtype(),
+ cache_enabled=torch.is_autocast_cache_enabled()):
+ return f(*args, **kwargs)
+ return do_autocast
+
+
+def extract_into_tensor(a, t, x_shape):
+ b, *_ = t.shape
+ out = a.gather(-1, t)
+ return out.reshape(b, *((1,) * (len(x_shape) - 1)))
+
+
+def noise_like(shape, device, repeat=False):
+ repeat_noise = lambda: torch.randn((1, *shape[1:]), device=device).repeat(shape[0], *((1,) * (len(shape) - 1)))
+ noise = lambda: torch.randn(shape, device=device)
+ return repeat_noise() if repeat else noise()
+
+
+def default(val, d):
+ if exists(val):
+ return val
+ return d() if isfunction(d) else d
+
+def exists(val):
+ return val is not None
+
+def identity(*args, **kwargs):
+ return nn.Identity()
+
+def uniq(arr):
+ return{el: True for el in arr}.keys()
+
+def mean_flat(tensor):
+ """
+ Take the mean over all non-batch dimensions.
+ """
+ return tensor.mean(dim=list(range(1, len(tensor.shape))))
+
+def ismap(x):
+ if not isinstance(x, torch.Tensor):
+ return False
+ return (len(x.shape) == 4) and (x.shape[1] > 3)
+
+def isimage(x):
+ if not isinstance(x,torch.Tensor):
+ return False
+ return (len(x.shape) == 4) and (x.shape[1] == 3 or x.shape[1] == 1)
+
+def max_neg_value(t):
+ return -torch.finfo(t.dtype).max
+
+def shape_to_str(x):
+ shape_str = "x".join([str(x) for x in x.shape])
+ return shape_str
+
+def init_(tensor):
+ dim = tensor.shape[-1]
+ std = 1 / math.sqrt(dim)
+ tensor.uniform_(-std, std)
+ return tensor
+
+ckpt = torch.utils.checkpoint.checkpoint
+def checkpoint(func, inputs, params, flag):
+ """
+ Evaluate a function without caching intermediate activations, allowing for
+ reduced memory at the expense of extra compute in the backward pass.
+ :param func: the function to evaluate.
+ :param inputs: the argument sequence to pass to `func`.
+ :param params: a sequence of parameters `func` depends on but does not
+ explicitly take as arguments.
+ :param flag: if False, disable gradient checkpointing.
+ """
+ if flag:
+ return ckpt(func, *inputs, use_reentrant=False)
+ else:
+ return func(*inputs)
+
diff --git a/VADER-VideoCrafter/lvdm/distributions.py b/VADER-VideoCrafter/lvdm/distributions.py
new file mode 100644
index 0000000000000000000000000000000000000000..abe3e5477f75b142959f97dd42379f60838dd7f0
--- /dev/null
+++ b/VADER-VideoCrafter/lvdm/distributions.py
@@ -0,0 +1,96 @@
+# Copied from VideoCrafter: https://github.com/AILab-CVC/VideoCrafter
+import torch
+import numpy as np
+
+
+class AbstractDistribution:
+ def sample(self):
+ raise NotImplementedError()
+
+ def mode(self):
+ raise NotImplementedError()
+
+
+class DiracDistribution(AbstractDistribution):
+ def __init__(self, value):
+ self.value = value
+
+ def sample(self):
+ return self.value
+
+ def mode(self):
+ return self.value
+
+
+class DiagonalGaussianDistribution(object):
+ def __init__(self, parameters, deterministic=False):
+ self.parameters = parameters
+ self.mean, self.logvar = torch.chunk(parameters, 2, dim=1)
+ self.logvar = torch.clamp(self.logvar, -30.0, 20.0)
+ self.deterministic = deterministic
+ self.std = torch.exp(0.5 * self.logvar)
+ self.var = torch.exp(self.logvar)
+ if self.deterministic:
+ self.var = self.std = torch.zeros_like(self.mean).to(device=self.parameters.device)
+
+ def sample(self, noise=None):
+ if noise is None:
+ noise = torch.randn(self.mean.shape)
+
+ x = self.mean + self.std * noise.to(device=self.parameters.device)
+ return x
+
+ def kl(self, other=None):
+ if self.deterministic:
+ return torch.Tensor([0.])
+ else:
+ if other is None:
+ return 0.5 * torch.sum(torch.pow(self.mean, 2)
+ + self.var - 1.0 - self.logvar,
+ dim=[1, 2, 3])
+ else:
+ return 0.5 * torch.sum(
+ torch.pow(self.mean - other.mean, 2) / other.var
+ + self.var / other.var - 1.0 - self.logvar + other.logvar,
+ dim=[1, 2, 3])
+
+ def nll(self, sample, dims=[1,2,3]):
+ if self.deterministic:
+ return torch.Tensor([0.])
+ logtwopi = np.log(2.0 * np.pi)
+ return 0.5 * torch.sum(
+ logtwopi + self.logvar + torch.pow(sample - self.mean, 2) / self.var,
+ dim=dims)
+
+ def mode(self):
+ return self.mean
+
+
+def normal_kl(mean1, logvar1, mean2, logvar2):
+ """
+ source: https://github.com/openai/guided-diffusion/blob/27c20a8fab9cb472df5d6bdd6c8d11c8f430b924/guided_diffusion/losses.py#L12
+ Compute the KL divergence between two gaussians.
+ Shapes are automatically broadcasted, so batches can be compared to
+ scalars, among other use cases.
+ """
+ tensor = None
+ for obj in (mean1, logvar1, mean2, logvar2):
+ if isinstance(obj, torch.Tensor):
+ tensor = obj
+ break
+ assert tensor is not None, "at least one argument must be a Tensor"
+
+ # Force variances to be Tensors. Broadcasting helps convert scalars to
+ # Tensors, but it does not work for torch.exp().
+ logvar1, logvar2 = [
+ x if isinstance(x, torch.Tensor) else torch.tensor(x).to(tensor)
+ for x in (logvar1, logvar2)
+ ]
+
+ return 0.5 * (
+ -1.0
+ + logvar2
+ - logvar1
+ + torch.exp(logvar1 - logvar2)
+ + ((mean1 - mean2) ** 2) * torch.exp(-logvar2)
+ )
diff --git a/VADER-VideoCrafter/lvdm/ema.py b/VADER-VideoCrafter/lvdm/ema.py
new file mode 100644
index 0000000000000000000000000000000000000000..0691aa757fb81eeaebc66e600facfb31f9e37d92
--- /dev/null
+++ b/VADER-VideoCrafter/lvdm/ema.py
@@ -0,0 +1,77 @@
+# Copied from VideoCrafter: https://github.com/AILab-CVC/VideoCrafter
+import torch
+from torch import nn
+
+
+class LitEma(nn.Module):
+ def __init__(self, model, decay=0.9999, use_num_upates=True):
+ super().__init__()
+ if decay < 0.0 or decay > 1.0:
+ raise ValueError('Decay must be between 0 and 1')
+
+ self.m_name2s_name = {}
+ self.register_buffer('decay', torch.tensor(decay, dtype=torch.float32))
+ self.register_buffer('num_updates', torch.tensor(0,dtype=torch.int) if use_num_upates
+ else torch.tensor(-1,dtype=torch.int))
+
+ for name, p in model.named_parameters():
+ if p.requires_grad:
+ #remove as '.'-character is not allowed in buffers
+ s_name = name.replace('.','')
+ self.m_name2s_name.update({name:s_name})
+ self.register_buffer(s_name,p.clone().detach().data)
+
+ self.collected_params = []
+
+ def forward(self,model):
+ decay = self.decay
+
+ if self.num_updates >= 0:
+ self.num_updates += 1
+ decay = min(self.decay,(1 + self.num_updates) / (10 + self.num_updates))
+
+ one_minus_decay = 1.0 - decay
+
+ with torch.no_grad():
+ m_param = dict(model.named_parameters())
+ shadow_params = dict(self.named_buffers())
+
+ for key in m_param:
+ if m_param[key].requires_grad:
+ sname = self.m_name2s_name[key]
+ shadow_params[sname] = shadow_params[sname].type_as(m_param[key])
+ shadow_params[sname].sub_(one_minus_decay * (shadow_params[sname] - m_param[key]))
+ else:
+ assert not key in self.m_name2s_name
+
+ def copy_to(self, model):
+ m_param = dict(model.named_parameters())
+ shadow_params = dict(self.named_buffers())
+ for key in m_param:
+ if m_param[key].requires_grad:
+ m_param[key].data.copy_(shadow_params[self.m_name2s_name[key]].data)
+ else:
+ assert not key in self.m_name2s_name
+
+ def store(self, parameters):
+ """
+ Save the current parameters for restoring later.
+ Args:
+ parameters: Iterable of `torch.nn.Parameter`; the parameters to be
+ temporarily stored.
+ """
+ self.collected_params = [param.clone() for param in parameters]
+
+ def restore(self, parameters):
+ """
+ Restore the parameters stored with the `store` method.
+ Useful to validate the model with EMA parameters without affecting the
+ original optimization process. Store the parameters before the
+ `copy_to` method. After validation (or model saving), use this to
+ restore the former parameters.
+ Args:
+ parameters: Iterable of `torch.nn.Parameter`; the parameters to be
+ updated with the stored parameters.
+ """
+ for c_param, param in zip(self.collected_params, parameters):
+ param.data.copy_(c_param.data)
diff --git a/VADER-VideoCrafter/lvdm/models/autoencoder.py b/VADER-VideoCrafter/lvdm/models/autoencoder.py
new file mode 100644
index 0000000000000000000000000000000000000000..c487e69b46f774dc1560e8c493f0cd9fe794609e
--- /dev/null
+++ b/VADER-VideoCrafter/lvdm/models/autoencoder.py
@@ -0,0 +1,220 @@
+# Copied from VideoCrafter: https://github.com/AILab-CVC/VideoCrafter
+import os
+from contextlib import contextmanager
+import torch
+import numpy as np
+from einops import rearrange
+import torch.nn.functional as F
+import pytorch_lightning as pl
+from lvdm.modules.networks.ae_modules import Encoder, Decoder
+from lvdm.distributions import DiagonalGaussianDistribution
+from utils.utils import instantiate_from_config
+
+
+class AutoencoderKL(pl.LightningModule):
+ def __init__(self,
+ ddconfig,
+ lossconfig,
+ embed_dim,
+ ckpt_path=None,
+ ignore_keys=[],
+ image_key="image",
+ colorize_nlabels=None,
+ monitor=None,
+ test=False,
+ logdir=None,
+ input_dim=4,
+ test_args=None,
+ ):
+ super().__init__()
+ self.image_key = image_key
+ self.encoder = Encoder(**ddconfig)
+ self.decoder = Decoder(**ddconfig)
+ self.loss = instantiate_from_config(lossconfig)
+ assert ddconfig["double_z"]
+ self.quant_conv = torch.nn.Conv2d(2*ddconfig["z_channels"], 2*embed_dim, 1)
+ self.post_quant_conv = torch.nn.Conv2d(embed_dim, ddconfig["z_channels"], 1)
+ self.embed_dim = embed_dim
+ self.input_dim = input_dim
+ self.test = test
+ self.test_args = test_args
+ self.logdir = logdir
+ if colorize_nlabels is not None:
+ assert type(colorize_nlabels)==int
+ self.register_buffer("colorize", torch.randn(3, colorize_nlabels, 1, 1))
+ if monitor is not None:
+ self.monitor = monitor
+ if ckpt_path is not None:
+ self.init_from_ckpt(ckpt_path, ignore_keys=ignore_keys)
+ if self.test:
+ self.init_test()
+
+ def init_test(self,):
+ self.test = True
+ save_dir = os.path.join(self.logdir, "test")
+ if 'ckpt' in self.test_args:
+ ckpt_name = os.path.basename(self.test_args.ckpt).split('.ckpt')[0] + f'_epoch{self._cur_epoch}'
+ self.root = os.path.join(save_dir, ckpt_name)
+ else:
+ self.root = save_dir
+ if 'test_subdir' in self.test_args:
+ self.root = os.path.join(save_dir, self.test_args.test_subdir)
+
+ self.root_zs = os.path.join(self.root, "zs")
+ self.root_dec = os.path.join(self.root, "reconstructions")
+ self.root_inputs = os.path.join(self.root, "inputs")
+ os.makedirs(self.root, exist_ok=True)
+
+ if self.test_args.save_z:
+ os.makedirs(self.root_zs, exist_ok=True)
+ if self.test_args.save_reconstruction:
+ os.makedirs(self.root_dec, exist_ok=True)
+ if self.test_args.save_input:
+ os.makedirs(self.root_inputs, exist_ok=True)
+ assert(self.test_args is not None)
+ self.test_maximum = getattr(self.test_args, 'test_maximum', None)
+ self.count = 0
+ self.eval_metrics = {}
+ self.decodes = []
+ self.save_decode_samples = 2048
+
+ def init_from_ckpt(self, path, ignore_keys=list()):
+ sd = torch.load(path, map_location="cpu")
+ try:
+ self._cur_epoch = sd['epoch']
+ sd = sd["state_dict"]
+ except:
+ self._cur_epoch = 'null'
+ keys = list(sd.keys())
+ for k in keys:
+ for ik in ignore_keys:
+ if k.startswith(ik):
+ print("Deleting key {} from state_dict.".format(k))
+ del sd[k]
+ self.load_state_dict(sd, strict=False)
+ # self.load_state_dict(sd, strict=True)
+ print(f"Restored from {path}")
+
+ def encode(self, x, **kwargs):
+
+ h = self.encoder(x)
+ moments = self.quant_conv(h)
+ posterior = DiagonalGaussianDistribution(moments)
+ return posterior
+
+ def decode(self, z, **kwargs):
+ z = self.post_quant_conv(z)
+ dec = self.decoder(z)
+ return dec
+
+ def forward(self, input, sample_posterior=True):
+ posterior = self.encode(input)
+ if sample_posterior:
+ z = posterior.sample()
+ else:
+ z = posterior.mode()
+ dec = self.decode(z)
+ return dec, posterior
+
+ def get_input(self, batch, k):
+ x = batch[k]
+ if x.dim() == 5 and self.input_dim == 4:
+ b,c,t,h,w = x.shape
+ self.b = b
+ self.t = t
+ x = rearrange(x, 'b c t h w -> (b t) c h w')
+
+ return x
+
+ def training_step(self, batch, batch_idx, optimizer_idx):
+ inputs = self.get_input(batch, self.image_key)
+ reconstructions, posterior = self(inputs)
+
+ if optimizer_idx == 0:
+ # train encoder+decoder+logvar
+ aeloss, log_dict_ae = self.loss(inputs, reconstructions, posterior, optimizer_idx, self.global_step,
+ last_layer=self.get_last_layer(), split="train")
+ self.log("aeloss", aeloss, prog_bar=True, logger=True, on_step=True, on_epoch=True)
+ self.log_dict(log_dict_ae, prog_bar=False, logger=True, on_step=True, on_epoch=False)
+ return aeloss
+
+ if optimizer_idx == 1:
+ # train the discriminator
+ discloss, log_dict_disc = self.loss(inputs, reconstructions, posterior, optimizer_idx, self.global_step,
+ last_layer=self.get_last_layer(), split="train")
+
+ self.log("discloss", discloss, prog_bar=True, logger=True, on_step=True, on_epoch=True)
+ self.log_dict(log_dict_disc, prog_bar=False, logger=True, on_step=True, on_epoch=False)
+ return discloss
+
+ def validation_step(self, batch, batch_idx):
+ inputs = self.get_input(batch, self.image_key)
+ reconstructions, posterior = self(inputs)
+ aeloss, log_dict_ae = self.loss(inputs, reconstructions, posterior, 0, self.global_step,
+ last_layer=self.get_last_layer(), split="val")
+
+ discloss, log_dict_disc = self.loss(inputs, reconstructions, posterior, 1, self.global_step,
+ last_layer=self.get_last_layer(), split="val")
+
+ self.log("val/rec_loss", log_dict_ae["val/rec_loss"])
+ self.log_dict(log_dict_ae)
+ self.log_dict(log_dict_disc)
+ return self.log_dict
+
+ def configure_optimizers(self):
+ lr = self.learning_rate
+ opt_ae = torch.optim.Adam(list(self.encoder.parameters())+
+ list(self.decoder.parameters())+
+ list(self.quant_conv.parameters())+
+ list(self.post_quant_conv.parameters()),
+ lr=lr, betas=(0.5, 0.9))
+ opt_disc = torch.optim.Adam(self.loss.discriminator.parameters(),
+ lr=lr, betas=(0.5, 0.9))
+ return [opt_ae, opt_disc], []
+
+ def get_last_layer(self):
+ return self.decoder.conv_out.weight
+
+ @torch.no_grad()
+ def log_images(self, batch, only_inputs=False, **kwargs):
+ log = dict()
+ x = self.get_input(batch, self.image_key)
+ x = x.to(self.device)
+ if not only_inputs:
+ xrec, posterior = self(x)
+ if x.shape[1] > 3:
+ # colorize with random projection
+ assert xrec.shape[1] > 3
+ x = self.to_rgb(x)
+ xrec = self.to_rgb(xrec)
+ log["samples"] = self.decode(torch.randn_like(posterior.sample()))
+ log["reconstructions"] = xrec
+ log["inputs"] = x
+ return log
+
+ def to_rgb(self, x):
+ assert self.image_key == "segmentation"
+ if not hasattr(self, "colorize"):
+ self.register_buffer("colorize", torch.randn(3, x.shape[1], 1, 1).to(x))
+ x = F.conv2d(x, weight=self.colorize)
+ x = 2.*(x-x.min())/(x.max()-x.min()) - 1.
+ return x
+
+class IdentityFirstStage(torch.nn.Module):
+ def __init__(self, *args, vq_interface=False, **kwargs):
+ self.vq_interface = vq_interface # TODO: Should be true by default but check to not break older stuff
+ super().__init__()
+
+ def encode(self, x, *args, **kwargs):
+ return x
+
+ def decode(self, x, *args, **kwargs):
+ return x
+
+ def quantize(self, x, *args, **kwargs):
+ if self.vq_interface:
+ return x, None, [None, None, None]
+ return x
+
+ def forward(self, x, *args, **kwargs):
+ return x
diff --git a/VADER-VideoCrafter/lvdm/models/ddpm3d.py b/VADER-VideoCrafter/lvdm/models/ddpm3d.py
new file mode 100644
index 0000000000000000000000000000000000000000..4e660774ca40f9887b6f421d829791b9c2792bf7
--- /dev/null
+++ b/VADER-VideoCrafter/lvdm/models/ddpm3d.py
@@ -0,0 +1,765 @@
+# Copied from VideoCrafter: https://github.com/AILab-CVC/VideoCrafter
+"""
+wild mixture of
+https://github.com/openai/improved-diffusion/blob/e94489283bb876ac1477d5dd7709bbbd2d9902ce/improved_diffusion/gaussian_diffusion.py
+https://github.com/lucidrains/denoising-diffusion-pytorch/blob/7706bdfc6f527f58d33f84b7b522e61e6e3164b3/denoising_diffusion_pytorch/denoising_diffusion_pytorch.py
+https://github.com/CompVis/taming-transformers
+-- merci
+"""
+
+from functools import partial
+from contextlib import contextmanager
+import numpy as np
+from tqdm import tqdm
+from einops import rearrange, repeat
+import logging
+mainlogger = logging.getLogger('mainlogger')
+import torch
+import torch.nn as nn
+from torchvision.utils import make_grid
+import pytorch_lightning as pl
+from utils.utils import instantiate_from_config
+from lvdm.ema import LitEma
+from lvdm.distributions import DiagonalGaussianDistribution
+from lvdm.models.utils_diffusion import make_beta_schedule
+from lvdm.modules.encoders.ip_resampler import ImageProjModel, Resampler
+from lvdm.basics import disabled_train
+from lvdm.common import (
+ extract_into_tensor,
+ noise_like,
+ exists,
+ default
+)
+# import ipdb
+# st = ipdb.set_trace
+
+__conditioning_keys__ = {'concat': 'c_concat',
+ 'crossattn': 'c_crossattn',
+ 'adm': 'y'}
+
+class DDPM(pl.LightningModule):
+ # classic DDPM with Gaussian diffusion, in image space
+ def __init__(self,
+ unet_config,
+ timesteps=1000,
+ beta_schedule="linear",
+ loss_type="l2",
+ ckpt_path=None,
+ ignore_keys=[],
+ load_only_unet=False,
+ monitor=None,
+ use_ema=True,
+ first_stage_key="image",
+ image_size=256,
+ channels=3,
+ log_every_t=100,
+ clip_denoised=True,
+ linear_start=1e-4,
+ linear_end=2e-2,
+ cosine_s=8e-3,
+ given_betas=None,
+ original_elbo_weight=0.,
+ v_posterior=0., # weight for choosing posterior variance as sigma = (1-v) * beta_tilde + v * beta
+ l_simple_weight=1.,
+ conditioning_key=None,
+ parameterization="eps", # all assuming fixed variance schedules
+ scheduler_config=None,
+ use_positional_encodings=False,
+ learn_logvar=False,
+ logvar_init=0.
+ ):
+ super().__init__()
+ assert parameterization in ["eps", "x0"], 'currently only supporting "eps" and "x0"'
+ self.parameterization = parameterization
+ mainlogger.info(f"{self.__class__.__name__}: Running in {self.parameterization}-prediction mode")
+ self.cond_stage_model = None
+ self.clip_denoised = clip_denoised
+ self.log_every_t = log_every_t
+ self.first_stage_key = first_stage_key
+ self.channels = channels
+ self.temporal_length = unet_config.params.temporal_length
+ self.image_size = image_size
+ if isinstance(self.image_size, int):
+ self.image_size = [self.image_size, self.image_size]
+ self.use_positional_encodings = use_positional_encodings
+ self.model = DiffusionWrapper(unet_config, conditioning_key)
+ self.use_ema = use_ema
+ if self.use_ema:
+ self.model_ema = LitEma(self.model)
+ mainlogger.info(f"Keeping EMAs of {len(list(self.model_ema.buffers()))}.")
+
+ self.use_scheduler = scheduler_config is not None
+ if self.use_scheduler:
+ self.scheduler_config = scheduler_config
+
+ self.v_posterior = v_posterior
+ self.original_elbo_weight = original_elbo_weight
+ self.l_simple_weight = l_simple_weight
+
+ if monitor is not None:
+ self.monitor = monitor
+ if ckpt_path is not None:
+ self.init_from_ckpt(ckpt_path, ignore_keys=ignore_keys, only_model=load_only_unet)
+
+ self.register_schedule(given_betas=given_betas, beta_schedule=beta_schedule, timesteps=timesteps,
+ linear_start=linear_start, linear_end=linear_end, cosine_s=cosine_s)
+
+ self.loss_type = loss_type
+
+ self.learn_logvar = learn_logvar
+ self.logvar = torch.full(fill_value=logvar_init, size=(self.num_timesteps,))
+ if self.learn_logvar:
+ self.logvar = nn.Parameter(self.logvar, requires_grad=True)
+
+
+ def register_schedule(self, given_betas=None, beta_schedule="linear", timesteps=1000,
+ linear_start=1e-4, linear_end=2e-2, cosine_s=8e-3):
+ if exists(given_betas):
+ betas = given_betas
+ else:
+ betas = make_beta_schedule(beta_schedule, timesteps, linear_start=linear_start, linear_end=linear_end,
+ cosine_s=cosine_s)
+ alphas = 1. - betas
+ alphas_cumprod = np.cumprod(alphas, axis=0)
+ alphas_cumprod_prev = np.append(1., alphas_cumprod[:-1])
+
+ timesteps, = betas.shape
+ self.num_timesteps = int(timesteps)
+ self.linear_start = linear_start
+ self.linear_end = linear_end
+ assert alphas_cumprod.shape[0] == self.num_timesteps, 'alphas have to be defined for each timestep'
+
+ to_torch = partial(torch.tensor, dtype=torch.float32)
+
+ self.register_buffer('betas', to_torch(betas))
+ self.register_buffer('alphas_cumprod', to_torch(alphas_cumprod))
+ self.register_buffer('alphas_cumprod_prev', to_torch(alphas_cumprod_prev))
+
+ # calculations for diffusion q(x_t | x_{t-1}) and others
+ self.register_buffer('sqrt_alphas_cumprod', to_torch(np.sqrt(alphas_cumprod)))
+ self.register_buffer('sqrt_one_minus_alphas_cumprod', to_torch(np.sqrt(1. - alphas_cumprod)))
+ self.register_buffer('log_one_minus_alphas_cumprod', to_torch(np.log(1. - alphas_cumprod)))
+ self.register_buffer('sqrt_recip_alphas_cumprod', to_torch(np.sqrt(1. / alphas_cumprod)))
+ self.register_buffer('sqrt_recipm1_alphas_cumprod', to_torch(np.sqrt(1. / alphas_cumprod - 1)))
+
+ # calculations for posterior q(x_{t-1} | x_t, x_0)
+ posterior_variance = (1 - self.v_posterior) * betas * (1. - alphas_cumprod_prev) / (
+ 1. - alphas_cumprod) + self.v_posterior * betas
+ # above: equal to 1. / (1. / (1. - alpha_cumprod_tm1) + alpha_t / beta_t)
+ self.register_buffer('posterior_variance', to_torch(posterior_variance))
+ # below: log calculation clipped because the posterior variance is 0 at the beginning of the diffusion chain
+ self.register_buffer('posterior_log_variance_clipped', to_torch(np.log(np.maximum(posterior_variance, 1e-20))))
+ self.register_buffer('posterior_mean_coef1', to_torch(
+ betas * np.sqrt(alphas_cumprod_prev) / (1. - alphas_cumprod)))
+ self.register_buffer('posterior_mean_coef2', to_torch(
+ (1. - alphas_cumprod_prev) * np.sqrt(alphas) / (1. - alphas_cumprod)))
+
+ if self.parameterization == "eps":
+ lvlb_weights = self.betas ** 2 / (
+ 2 * self.posterior_variance * to_torch(alphas) * (1 - self.alphas_cumprod))
+ elif self.parameterization == "x0":
+ lvlb_weights = 0.5 * np.sqrt(torch.Tensor(alphas_cumprod)) / (2. * 1 - torch.Tensor(alphas_cumprod))
+ else:
+ raise NotImplementedError("mu not supported")
+ # TODO how to choose this term
+ lvlb_weights[0] = lvlb_weights[1]
+ self.register_buffer('lvlb_weights', lvlb_weights, persistent=False)
+ assert not torch.isnan(self.lvlb_weights).all()
+
+ @contextmanager
+ def ema_scope(self, context=None):
+ if self.use_ema:
+ self.model_ema.store(self.model.parameters())
+ self.model_ema.copy_to(self.model)
+ if context is not None:
+ mainlogger.info(f"{context}: Switched to EMA weights")
+ try:
+ yield None
+ finally:
+ if self.use_ema:
+ self.model_ema.restore(self.model.parameters())
+ if context is not None:
+ mainlogger.info(f"{context}: Restored training weights")
+
+ def init_from_ckpt(self, path, ignore_keys=list(), only_model=False):
+ sd = torch.load(path, map_location="cpu")
+ if "state_dict" in list(sd.keys()):
+ sd = sd["state_dict"]
+ keys = list(sd.keys())
+ for k in keys:
+ for ik in ignore_keys:
+ if k.startswith(ik):
+ mainlogger.info("Deleting key {} from state_dict.".format(k))
+ del sd[k]
+ missing, unexpected = self.load_state_dict(sd, strict=False) if not only_model else self.model.load_state_dict(
+ sd, strict=False)
+ mainlogger.info(f"Restored from {path} with {len(missing)} missing and {len(unexpected)} unexpected keys")
+ if len(missing) > 0:
+ mainlogger.info(f"Missing Keys: {missing}")
+ if len(unexpected) > 0:
+ mainlogger.info(f"Unexpected Keys: {unexpected}")
+
+ def q_mean_variance(self, x_start, t):
+ """
+ Get the distribution q(x_t | x_0).
+ :param x_start: the [N x C x ...] tensor of noiseless inputs.
+ :param t: the number of diffusion steps (minus 1). Here, 0 means one step.
+ :return: A tuple (mean, variance, log_variance), all of x_start's shape.
+ """
+ mean = (extract_into_tensor(self.sqrt_alphas_cumprod, t, x_start.shape) * x_start)
+ variance = extract_into_tensor(1.0 - self.alphas_cumprod, t, x_start.shape)
+ log_variance = extract_into_tensor(self.log_one_minus_alphas_cumprod, t, x_start.shape)
+ return mean, variance, log_variance
+
+ def predict_start_from_noise(self, x_t, t, noise):
+ return (
+ extract_into_tensor(self.sqrt_recip_alphas_cumprod, t, x_t.shape) * x_t -
+ extract_into_tensor(self.sqrt_recipm1_alphas_cumprod, t, x_t.shape) * noise
+ )
+
+ def q_posterior(self, x_start, x_t, t):
+ posterior_mean = (
+ extract_into_tensor(self.posterior_mean_coef1, t, x_t.shape) * x_start +
+ extract_into_tensor(self.posterior_mean_coef2, t, x_t.shape) * x_t
+ )
+ posterior_variance = extract_into_tensor(self.posterior_variance, t, x_t.shape)
+ posterior_log_variance_clipped = extract_into_tensor(self.posterior_log_variance_clipped, t, x_t.shape)
+ return posterior_mean, posterior_variance, posterior_log_variance_clipped
+
+ def p_mean_variance(self, x, t, clip_denoised: bool):
+ model_out = self.model(x, t)
+ if self.parameterization == "eps":
+ x_recon = self.predict_start_from_noise(x, t=t, noise=model_out)
+ elif self.parameterization == "x0":
+ x_recon = model_out
+ if clip_denoised:
+ x_recon.clamp_(-1., 1.)
+
+ model_mean, posterior_variance, posterior_log_variance = self.q_posterior(x_start=x_recon, x_t=x, t=t)
+ return model_mean, posterior_variance, posterior_log_variance
+
+ @torch.no_grad()
+ def p_sample(self, x, t, clip_denoised=True, repeat_noise=False):
+ b, *_, device = *x.shape, x.device
+ model_mean, _, model_log_variance = self.p_mean_variance(x=x, t=t, clip_denoised=clip_denoised)
+ noise = noise_like(x.shape, device, repeat_noise)
+ # no noise when t == 0
+ nonzero_mask = (1 - (t == 0).float()).reshape(b, *((1,) * (len(x.shape) - 1)))
+ return model_mean + nonzero_mask * (0.5 * model_log_variance).exp() * noise
+
+ @torch.no_grad()
+ def p_sample_loop(self, shape, return_intermediates=False):
+ device = self.betas.device
+ b = shape[0]
+ img = torch.randn(shape, device=device)
+ intermediates = [img]
+ for i in tqdm(reversed(range(0, self.num_timesteps)), desc='Sampling t', total=self.num_timesteps):
+ img = self.p_sample(img, torch.full((b,), i, device=device, dtype=torch.long),
+ clip_denoised=self.clip_denoised)
+ if i % self.log_every_t == 0 or i == self.num_timesteps - 1:
+ intermediates.append(img)
+ if return_intermediates:
+ return img, intermediates
+ return img
+
+ @torch.no_grad()
+ def sample(self, batch_size=16, return_intermediates=False):
+ image_size = self.image_size
+ channels = self.channels
+ return self.p_sample_loop((batch_size, channels, image_size, image_size),
+ return_intermediates=return_intermediates)
+
+ def q_sample(self, x_start, t, noise=None):
+ noise = default(noise, lambda: torch.randn_like(x_start))
+ return (extract_into_tensor(self.sqrt_alphas_cumprod, t, x_start.shape) * x_start *
+ extract_into_tensor(self.scale_arr, t, x_start.shape) +
+ extract_into_tensor(self.sqrt_one_minus_alphas_cumprod, t, x_start.shape) * noise)
+
+ def get_input(self, batch, k):
+ x = batch[k]
+ x = x.to(memory_format=torch.contiguous_format).float()
+ return x
+
+ def _get_rows_from_list(self, samples):
+ n_imgs_per_row = len(samples)
+ denoise_grid = rearrange(samples, 'n b c h w -> b n c h w')
+ denoise_grid = rearrange(denoise_grid, 'b n c h w -> (b n) c h w')
+ denoise_grid = make_grid(denoise_grid, nrow=n_imgs_per_row)
+ return denoise_grid
+
+ @torch.no_grad()
+ def log_images(self, batch, N=8, n_row=2, sample=True, return_keys=None, **kwargs):
+ log = dict()
+ x = self.get_input(batch, self.first_stage_key)
+ N = min(x.shape[0], N)
+ n_row = min(x.shape[0], n_row)
+ x = x.to(self.device)[:N]
+ log["inputs"] = x
+
+ # get diffusion row
+ diffusion_row = list()
+ x_start = x[:n_row]
+
+ for t in range(self.num_timesteps):
+ if t % self.log_every_t == 0 or t == self.num_timesteps - 1:
+ t = repeat(torch.tensor([t]), '1 -> b', b=n_row)
+ t = t.to(self.device).long()
+ noise = torch.randn_like(x_start)
+ x_noisy = self.q_sample(x_start=x_start, t=t, noise=noise)
+ diffusion_row.append(x_noisy)
+
+ log["diffusion_row"] = self._get_rows_from_list(diffusion_row)
+
+ if sample:
+ # get denoise row
+ with self.ema_scope("Plotting"):
+ samples, denoise_row = self.sample(batch_size=N, return_intermediates=True)
+
+ log["samples"] = samples
+ log["denoise_row"] = self._get_rows_from_list(denoise_row)
+
+ if return_keys:
+ if np.intersect1d(list(log.keys()), return_keys).shape[0] == 0:
+ return log
+ else:
+ return {key: log[key] for key in return_keys}
+ return log
+
+
+class LatentDiffusion(DDPM):
+ """main class"""
+ def __init__(self,
+ first_stage_config,
+ cond_stage_config,
+ num_timesteps_cond=None,
+ cond_stage_key="caption",
+ cond_stage_trainable=False,
+ cond_stage_forward=None,
+ conditioning_key=None,
+ uncond_prob=0.2,
+ uncond_type="empty_seq",
+ scale_factor=1.0,
+ scale_by_std=False,
+ encoder_type="2d",
+ only_model=False,
+ use_scale=False,
+ scale_a=1,
+ scale_b=0.3,
+ mid_step=400,
+ fix_scale_bug=False,
+ *args, **kwargs):
+ self.num_timesteps_cond = default(num_timesteps_cond, 1)
+ self.scale_by_std = scale_by_std
+ assert self.num_timesteps_cond <= kwargs['timesteps']
+ # for backwards compatibility after implementation of DiffusionWrapper
+ ckpt_path = kwargs.pop("ckpt_path", None)
+ ignore_keys = kwargs.pop("ignore_keys", [])
+ conditioning_key = default(conditioning_key, 'crossattn')
+ super().__init__(conditioning_key=conditioning_key, *args, **kwargs)
+
+ self.cond_stage_trainable = cond_stage_trainable
+ self.cond_stage_key = cond_stage_key
+
+ # scale factor
+ self.use_scale=use_scale
+ if self.use_scale:
+ self.scale_a=scale_a
+ self.scale_b=scale_b
+ if fix_scale_bug:
+ scale_step=self.num_timesteps-mid_step
+ else: #bug
+ scale_step = self.num_timesteps
+
+ scale_arr1 = np.linspace(scale_a, scale_b, mid_step)
+ scale_arr2 = np.full(scale_step, scale_b)
+ scale_arr = np.concatenate((scale_arr1, scale_arr2))
+ scale_arr_prev = np.append(scale_a, scale_arr[:-1])
+ to_torch = partial(torch.tensor, dtype=torch.float32)
+ self.register_buffer('scale_arr', to_torch(scale_arr))
+
+ try:
+ self.num_downs = len(first_stage_config.params.ddconfig.ch_mult) - 1
+ except:
+ self.num_downs = 0
+ if not scale_by_std:
+ self.scale_factor = scale_factor
+ else:
+ self.register_buffer('scale_factor', torch.tensor(scale_factor))
+ self.instantiate_first_stage(first_stage_config)
+ self.instantiate_cond_stage(cond_stage_config)
+ self.first_stage_config = first_stage_config
+ self.cond_stage_config = cond_stage_config
+ self.clip_denoised = False
+
+ self.cond_stage_forward = cond_stage_forward
+ self.encoder_type = encoder_type
+ assert(encoder_type in ["2d", "3d"])
+ self.uncond_prob = uncond_prob
+ self.classifier_free_guidance = True if uncond_prob > 0 else False
+ assert(uncond_type in ["zero_embed", "empty_seq"])
+ self.uncond_type = uncond_type
+
+
+ self.restarted_from_ckpt = False
+ if ckpt_path is not None:
+ self.init_from_ckpt(ckpt_path, ignore_keys, only_model=only_model)
+ self.restarted_from_ckpt = True
+
+
+ def make_cond_schedule(self, ):
+ self.cond_ids = torch.full(size=(self.num_timesteps,), fill_value=self.num_timesteps - 1, dtype=torch.long)
+ ids = torch.round(torch.linspace(0, self.num_timesteps - 1, self.num_timesteps_cond)).long()
+ self.cond_ids[:self.num_timesteps_cond] = ids
+
+ def q_sample(self, x_start, t, noise=None):
+ noise = default(noise, lambda: torch.randn_like(x_start))
+ if self.use_scale:
+ return (extract_into_tensor(self.sqrt_alphas_cumprod, t, x_start.shape) * x_start *
+ extract_into_tensor(self.scale_arr, t, x_start.shape) +
+ extract_into_tensor(self.sqrt_one_minus_alphas_cumprod, t, x_start.shape) * noise)
+ else:
+ return (extract_into_tensor(self.sqrt_alphas_cumprod, t, x_start.shape) * x_start +
+ extract_into_tensor(self.sqrt_one_minus_alphas_cumprod, t, x_start.shape) * noise)
+
+
+ def _freeze_model(self):
+ for name, para in self.model.diffusion_model.named_parameters():
+ para.requires_grad = False
+
+ def instantiate_first_stage(self, config):
+ model = instantiate_from_config(config)
+ self.first_stage_model = model.eval()
+ self.first_stage_model.train = disabled_train
+ for param in self.first_stage_model.parameters():
+ param.requires_grad = False
+
+ def instantiate_cond_stage(self, config):
+ if not self.cond_stage_trainable:
+ model = instantiate_from_config(config)
+ self.cond_stage_model = model.eval()
+ self.cond_stage_model.train = disabled_train
+ for param in self.cond_stage_model.parameters():
+ param.requires_grad = False
+ else:
+ model = instantiate_from_config(config)
+ self.cond_stage_model = model
+
+ def get_learned_conditioning(self, c):
+ if self.cond_stage_forward is None:
+ if hasattr(self.cond_stage_model, 'encode') and callable(self.cond_stage_model.encode):
+ c = self.cond_stage_model.encode(c)
+ if isinstance(c, DiagonalGaussianDistribution):
+ c = c.mode()
+ else:
+ c = self.cond_stage_model(c)
+ else:
+ assert hasattr(self.cond_stage_model, self.cond_stage_forward)
+ c = getattr(self.cond_stage_model, self.cond_stage_forward)(c)
+ return c
+
+ def get_first_stage_encoding(self, encoder_posterior, noise=None):
+ if isinstance(encoder_posterior, DiagonalGaussianDistribution):
+ z = encoder_posterior.sample(noise=noise)
+ elif isinstance(encoder_posterior, torch.Tensor):
+ z = encoder_posterior
+ else:
+ raise NotImplementedError(f"encoder_posterior of type '{type(encoder_posterior)}' not yet implemented")
+ return self.scale_factor * z
+
+ @torch.no_grad()
+ def encode_first_stage(self, x):
+ if self.encoder_type == "2d" and x.dim() == 5:
+ b, _, t, _, _ = x.shape
+ x = rearrange(x, 'b c t h w -> (b t) c h w')
+ reshape_back = True
+ else:
+ reshape_back = False
+
+ encoder_posterior = self.first_stage_model.encode(x)
+ results = self.get_first_stage_encoding(encoder_posterior).detach()
+
+ if reshape_back:
+ results = rearrange(results, '(b t) c h w -> b c t h w', b=b,t=t)
+
+ return results
+
+ @torch.no_grad()
+ def encode_first_stage_2DAE(self, x):
+
+ b, _, t, _, _ = x.shape
+ results = torch.cat([self.get_first_stage_encoding(self.first_stage_model.encode(x[:,:,i])).detach().unsqueeze(2) for i in range(t)], dim=2)
+
+ return results
+
+ def decode_core(self, z, **kwargs):
+ if self.encoder_type == "2d" and z.dim() == 5:
+ b, _, t, _, _ = z.shape
+ z = rearrange(z, 'b c t h w -> (b t) c h w')
+ reshape_back = True
+ else:
+ reshape_back = False
+
+ z = 1. / self.scale_factor * z
+
+ results = self.first_stage_model.decode(z, **kwargs)
+
+ if reshape_back:
+ results = rearrange(results, '(b t) c h w -> b c t h w', b=b,t=t)
+ return results
+
+ @torch.no_grad()
+ def decode_first_stage(self, z, **kwargs):
+ return self.decode_core(z, **kwargs)
+
+ def apply_model(self, x_noisy, t, cond, **kwargs):
+ if isinstance(cond, dict):
+ # hybrid case, cond is exptected to be a dict
+ pass
+ else:
+ if not isinstance(cond, list):
+ cond = [cond]
+ key = 'c_concat' if self.model.conditioning_key == 'concat' else 'c_crossattn'
+ cond = {key: cond}
+
+ x_recon = self.model(x_noisy, t, **cond, **kwargs)
+
+ if isinstance(x_recon, tuple):
+ return x_recon[0]
+ else:
+ return x_recon
+
+ def _get_denoise_row_from_list(self, samples, desc=''):
+ denoise_row = []
+ for zd in tqdm(samples, desc=desc):
+ denoise_row.append(self.decode_first_stage(zd.to(self.device)))
+ n_log_timesteps = len(denoise_row)
+
+ denoise_row = torch.stack(denoise_row) # n_log_timesteps, b, C, H, W
+
+ if denoise_row.dim() == 5:
+ # img, num_imgs= n_log_timesteps * bs, grid_size=[bs,n_log_timesteps]
+ denoise_grid = rearrange(denoise_row, 'n b c h w -> b n c h w')
+ denoise_grid = rearrange(denoise_grid, 'b n c h w -> (b n) c h w')
+ denoise_grid = make_grid(denoise_grid, nrow=n_log_timesteps)
+ elif denoise_row.dim() == 6:
+ # video, grid_size=[n_log_timesteps*bs, t]
+ video_length = denoise_row.shape[3]
+ denoise_grid = rearrange(denoise_row, 'n b c t h w -> b n c t h w')
+ denoise_grid = rearrange(denoise_grid, 'b n c t h w -> (b n) c t h w')
+ denoise_grid = rearrange(denoise_grid, 'n c t h w -> (n t) c h w')
+ denoise_grid = make_grid(denoise_grid, nrow=video_length)
+ else:
+ raise ValueError
+
+ return denoise_grid
+
+
+ # @torch.no_grad()
+ def decode_first_stage_2DAE(self, z, **kwargs):
+
+ b, _, t, _, _ = z.shape
+ z = 1. / self.scale_factor * z
+ results = torch.cat([self.first_stage_model.decode(z[:,:,i], **kwargs).unsqueeze(2) for i in range(t)], dim=2)
+
+ return results
+
+
+ def p_mean_variance(self, x, c, t, clip_denoised: bool, return_x0=False, score_corrector=None, corrector_kwargs=None, **kwargs):
+ t_in = t
+ model_out = self.apply_model(x, t_in, c, **kwargs)
+
+ if score_corrector is not None:
+ assert self.parameterization == "eps"
+ model_out = score_corrector.modify_score(self, model_out, x, t, c, **corrector_kwargs)
+
+ if self.parameterization == "eps":
+ x_recon = self.predict_start_from_noise(x, t=t, noise=model_out)
+ elif self.parameterization == "x0":
+ x_recon = model_out
+ else:
+ raise NotImplementedError()
+
+ if clip_denoised:
+ x_recon.clamp_(-1., 1.)
+
+ model_mean, posterior_variance, posterior_log_variance = self.q_posterior(x_start=x_recon, x_t=x, t=t)
+
+ if return_x0:
+ return model_mean, posterior_variance, posterior_log_variance, x_recon
+ else:
+ return model_mean, posterior_variance, posterior_log_variance
+
+ @torch.no_grad()
+ def p_sample(self, x, c, t, clip_denoised=False, repeat_noise=False, return_x0=False, \
+ temperature=1., noise_dropout=0., score_corrector=None, corrector_kwargs=None, **kwargs):
+ b, *_, device = *x.shape, x.device
+ outputs = self.p_mean_variance(x=x, c=c, t=t, clip_denoised=clip_denoised, return_x0=return_x0, \
+ score_corrector=score_corrector, corrector_kwargs=corrector_kwargs, **kwargs)
+ if return_x0:
+ model_mean, _, model_log_variance, x0 = outputs
+ else:
+ model_mean, _, model_log_variance = outputs
+
+ noise = noise_like(x.shape, device, repeat_noise) * temperature
+ if noise_dropout > 0.:
+ noise = torch.nn.functional.dropout(noise, p=noise_dropout)
+ # no noise when t == 0
+ nonzero_mask = (1 - (t == 0).float()).reshape(b, *((1,) * (len(x.shape) - 1)))
+
+ if return_x0:
+ return model_mean + nonzero_mask * (0.5 * model_log_variance).exp() * noise, x0
+ else:
+ return model_mean + nonzero_mask * (0.5 * model_log_variance).exp() * noise
+
+ @torch.no_grad()
+ def p_sample_loop(self, cond, shape, return_intermediates=False, x_T=None, verbose=True, callback=None, \
+ timesteps=None, mask=None, x0=None, img_callback=None, start_T=None, log_every_t=None, **kwargs):
+
+ if not log_every_t:
+ log_every_t = self.log_every_t
+ device = self.betas.device
+ b = shape[0]
+ # sample an initial noise
+ if x_T is None:
+ img = torch.randn(shape, device=device)
+ else:
+ img = x_T
+
+ intermediates = [img]
+ if timesteps is None:
+ timesteps = self.num_timesteps
+ if start_T is not None:
+ timesteps = min(timesteps, start_T)
+
+ iterator = tqdm(reversed(range(0, timesteps)), desc='Sampling t', total=timesteps) if verbose else reversed(range(0, timesteps))
+
+ if mask is not None:
+ assert x0 is not None
+ assert x0.shape[2:3] == mask.shape[2:3] # spatial size has to match
+
+ for i in iterator:
+ ts = torch.full((b,), i, device=device, dtype=torch.long)
+ if self.shorten_cond_schedule:
+ assert self.model.conditioning_key != 'hybrid'
+ tc = self.cond_ids[ts].to(cond.device)
+ cond = self.q_sample(x_start=cond, t=tc, noise=torch.randn_like(cond))
+
+ img = self.p_sample(img, cond, ts, clip_denoised=self.clip_denoised, **kwargs)
+ if mask is not None:
+ img_orig = self.q_sample(x0, ts)
+ img = img_orig * mask + (1. - mask) * img
+
+ if i % log_every_t == 0 or i == timesteps - 1:
+ intermediates.append(img)
+ if callback: callback(i)
+ if img_callback: img_callback(img, i)
+
+ if return_intermediates:
+ return img, intermediates
+ return img
+
+
+class LatentVisualDiffusion(LatentDiffusion):
+ def __init__(self, cond_img_config, finegrained=False, random_cond=False, *args, **kwargs):
+ super().__init__(*args, **kwargs)
+ self.random_cond = random_cond
+ self.instantiate_img_embedder(cond_img_config, freeze=True)
+ num_tokens = 16 if finegrained else 4
+ self.image_proj_model = self.init_projector(use_finegrained=finegrained, num_tokens=num_tokens, input_dim=1024,\
+ cross_attention_dim=1024, dim=1280)
+
+ def instantiate_img_embedder(self, config, freeze=True):
+ embedder = instantiate_from_config(config)
+ if freeze:
+ self.embedder = embedder.eval()
+ self.embedder.train = disabled_train
+ for param in self.embedder.parameters():
+ param.requires_grad = False
+
+ def init_projector(self, use_finegrained, num_tokens, input_dim, cross_attention_dim, dim):
+ if not use_finegrained:
+ image_proj_model = ImageProjModel(clip_extra_context_tokens=num_tokens, cross_attention_dim=cross_attention_dim,
+ clip_embeddings_dim=input_dim
+ )
+ else:
+ image_proj_model = Resampler(dim=input_dim, depth=4, dim_head=64, heads=12, num_queries=num_tokens,
+ embedding_dim=dim, output_dim=cross_attention_dim, ff_mult=4
+ )
+ return image_proj_model
+
+ ## Never delete this func: it is used in log_images() and inference stage
+ def get_image_embeds(self, batch_imgs):
+ ## img: b c h w
+ img_token = self.embedder(batch_imgs)
+ img_emb = self.image_proj_model(img_token)
+ return img_emb
+
+
+class DiffusionWrapper(pl.LightningModule):
+ def __init__(self, diff_model_config, conditioning_key):
+ super().__init__()
+ self.diffusion_model = instantiate_from_config(diff_model_config)
+ self.conditioning_key = conditioning_key
+
+ def forward(self, x, t, c_concat: list = None, c_crossattn: list = None,
+ c_adm=None, s=None, mask=None, **kwargs):
+ # temporal_context = fps is foNone
+ if self.conditioning_key is None:
+ out = self.diffusion_model(x, t)
+ elif self.conditioning_key == 'concat':
+ xc = torch.cat([x] + c_concat, dim=1)
+ out = self.diffusion_model(xc, t, **kwargs)
+ elif self.conditioning_key == 'crossattn':
+ cc = torch.cat(c_crossattn, 1)
+ out = self.diffusion_model(x, t, context=cc, **kwargs)
+ elif self.conditioning_key == 'hybrid':
+ ## it is just right [b,c,t,h,w]: concatenate in channel dim
+ xc = torch.cat([x] + c_concat, dim=1)
+ cc = torch.cat(c_crossattn, 1)
+ out = self.diffusion_model(xc, t, context=cc)
+ elif self.conditioning_key == 'resblockcond':
+ cc = c_crossattn[0]
+ out = self.diffusion_model(x, t, context=cc)
+ elif self.conditioning_key == 'adm':
+ cc = c_crossattn[0]
+ out = self.diffusion_model(x, t, y=cc)
+ elif self.conditioning_key == 'hybrid-adm':
+ assert c_adm is not None
+ xc = torch.cat([x] + c_concat, dim=1)
+ cc = torch.cat(c_crossattn, 1)
+ out = self.diffusion_model(xc, t, context=cc, y=c_adm)
+ elif self.conditioning_key == 'hybrid-time':
+ assert s is not None
+ xc = torch.cat([x] + c_concat, dim=1)
+ cc = torch.cat(c_crossattn, 1)
+ out = self.diffusion_model(xc, t, context=cc, s=s)
+ elif self.conditioning_key == 'concat-time-mask':
+ # assert s is not None
+ # mainlogger.info('x & mask:',x.shape,c_concat[0].shape)
+ xc = torch.cat([x] + c_concat, dim=1)
+ out = self.diffusion_model(xc, t, context=None, s=s, mask=mask)
+ elif self.conditioning_key == 'concat-adm-mask':
+ # assert s is not None
+ # mainlogger.info('x & mask:',x.shape,c_concat[0].shape)
+ if c_concat is not None:
+ xc = torch.cat([x] + c_concat, dim=1)
+ else:
+ xc = x
+ out = self.diffusion_model(xc, t, context=None, y=s, mask=mask)
+ elif self.conditioning_key == 'hybrid-adm-mask':
+ cc = torch.cat(c_crossattn, 1)
+ if c_concat is not None:
+ xc = torch.cat([x] + c_concat, dim=1)
+ else:
+ xc = x
+ out = self.diffusion_model(xc, t, context=cc, y=s, mask=mask)
+ elif self.conditioning_key == 'hybrid-time-adm': # adm means y, e.g., class index
+ # assert s is not None
+ assert c_adm is not None
+ xc = torch.cat([x] + c_concat, dim=1)
+ cc = torch.cat(c_crossattn, 1)
+ out = self.diffusion_model(xc, t, context=cc, s=s, y=c_adm)
+ else:
+ raise NotImplementedError()
+
+ return out
\ No newline at end of file
diff --git a/VADER-VideoCrafter/lvdm/models/samplers/ddim.py b/VADER-VideoCrafter/lvdm/models/samplers/ddim.py
new file mode 100644
index 0000000000000000000000000000000000000000..bb5daf326c2a3ef3acf462c26971f32ecca19de1
--- /dev/null
+++ b/VADER-VideoCrafter/lvdm/models/samplers/ddim.py
@@ -0,0 +1,368 @@
+# Adapted from VideoCrafter: https://github.com/AILab-CVC/VideoCrafter
+import numpy as np
+from tqdm import tqdm
+import torch
+from lvdm.models.utils_diffusion import make_ddim_sampling_parameters, make_ddim_timesteps
+from lvdm.common import noise_like
+import random
+# import ipdb
+# st = ipdb.set_trace
+
+
+class DDIMSampler(object):
+ def __init__(self, model, schedule="linear", **kwargs):
+ super().__init__()
+ self.model = model
+ self.ddpm_num_timesteps = model.num_timesteps
+ self.schedule = schedule
+ self.counter = 0
+ self.backprop_mode = 'last' # default
+ self.training_mode = False # default
+
+ def register_buffer(self, name, attr):
+ if type(attr) == torch.Tensor:
+ if attr.device != torch.device("cuda"):
+ attr = attr.to(torch.device("cuda"))
+ setattr(self, name, attr)
+
+ def make_schedule(self, ddim_num_steps, ddim_discretize="uniform", ddim_eta=0., verbose=True):
+ self.ddim_timesteps = make_ddim_timesteps(ddim_discr_method=ddim_discretize, num_ddim_timesteps=ddim_num_steps,
+ num_ddpm_timesteps=self.ddpm_num_timesteps,verbose=verbose)
+ alphas_cumprod = self.model.alphas_cumprod
+ assert alphas_cumprod.shape[0] == self.ddpm_num_timesteps, 'alphas have to be defined for each timestep'
+ to_torch = lambda x: x.clone().detach().to(torch.float32).to(self.model.device)
+
+ self.register_buffer('betas', to_torch(self.model.betas))
+ self.register_buffer('alphas_cumprod', to_torch(alphas_cumprod))
+ self.register_buffer('alphas_cumprod_prev', to_torch(self.model.alphas_cumprod_prev))
+ self.use_scale = self.model.use_scale
+ # print('DDIM scale', self.use_scale)
+
+ if self.use_scale:
+ self.register_buffer('scale_arr', to_torch(self.model.scale_arr))
+ ddim_scale_arr = self.scale_arr.cpu()[self.ddim_timesteps]
+ self.register_buffer('ddim_scale_arr', ddim_scale_arr)
+ ddim_scale_arr = np.asarray([self.scale_arr.cpu()[0]] + self.scale_arr.cpu()[self.ddim_timesteps[:-1]].tolist())
+ self.register_buffer('ddim_scale_arr_prev', ddim_scale_arr)
+
+ # calculations for diffusion q(x_t | x_{t-1}) and others
+ self.register_buffer('sqrt_alphas_cumprod', to_torch(np.sqrt(alphas_cumprod.cpu())))
+ self.register_buffer('sqrt_one_minus_alphas_cumprod', to_torch(np.sqrt(1. - alphas_cumprod.cpu())))
+ self.register_buffer('log_one_minus_alphas_cumprod', to_torch(np.log(1. - alphas_cumprod.cpu())))
+ self.register_buffer('sqrt_recip_alphas_cumprod', to_torch(np.sqrt(1. / alphas_cumprod.cpu())))
+ self.register_buffer('sqrt_recipm1_alphas_cumprod', to_torch(np.sqrt(1. / alphas_cumprod.cpu() - 1)))
+
+ # ddim sampling parameters
+ ddim_sigmas, ddim_alphas, ddim_alphas_prev = make_ddim_sampling_parameters(alphacums=alphas_cumprod.cpu(),
+ ddim_timesteps=self.ddim_timesteps,
+ eta=ddim_eta,verbose=verbose)
+ self.register_buffer('ddim_sigmas', ddim_sigmas)
+ self.register_buffer('ddim_alphas', ddim_alphas)
+ self.register_buffer('ddim_alphas_prev', ddim_alphas_prev)
+ self.register_buffer('ddim_sqrt_one_minus_alphas', np.sqrt(1. - ddim_alphas))
+ sigmas_for_original_sampling_steps = ddim_eta * torch.sqrt(
+ (1 - self.alphas_cumprod_prev) / (1 - self.alphas_cumprod) * (
+ 1 - self.alphas_cumprod / self.alphas_cumprod_prev))
+ self.register_buffer('ddim_sigmas_for_original_num_steps', sigmas_for_original_sampling_steps)
+
+ # @torch.no_grad()
+ def sample(self,
+ S,
+ batch_size,
+ shape,
+ conditioning=None,
+ callback=None,
+ normals_sequence=None,
+ img_callback=None,
+ quantize_x0=False,
+ eta=0.,
+ mask=None,
+ x0=None,
+ temperature=1.,
+ noise_dropout=0.,
+ score_corrector=None,
+ corrector_kwargs=None,
+ verbose=True,
+ schedule_verbose=False,
+ x_T=None,
+ log_every_t=100,
+ unconditional_guidance_scale=1.,
+ unconditional_conditioning=None,
+ # this has to come in the same format as the conditioning, # e.g. as encoded tokens, ...
+ **kwargs
+ ):
+
+ # check condition bs
+ if conditioning is not None:
+ if isinstance(conditioning, dict):
+ try:
+ cbs = conditioning[list(conditioning.keys())[0]].shape[0]
+ except:
+ cbs = conditioning[list(conditioning.keys())[0]][0].shape[0]
+
+ if cbs != batch_size:
+ print(f"Warning: Got {cbs} conditionings but batch-size is {batch_size}")
+ else:
+ if conditioning.shape[0] != batch_size:
+ print(f"Warning: Got {conditioning.shape[0]} conditionings but batch-size is {batch_size}")
+
+ self.make_schedule(ddim_num_steps=S, ddim_eta=eta, verbose=schedule_verbose)
+ self.ddim_num_steps = S # for ddim_sampling
+
+ # make shape
+ if len(shape) == 3:
+ C, H, W = shape
+ size = (batch_size, C, H, W)
+ elif len(shape) == 4:
+ C, T, H, W = shape
+ size = (batch_size, C, T, H, W)
+ # print(f'Data shape for DDIM sampling is {size}, eta {eta}')
+
+ samples, intermediates = self.ddim_sampling(conditioning, size, # samples: batch, c, t, h, w
+ callback=callback,
+ img_callback=img_callback,
+ quantize_denoised=quantize_x0,
+ mask=mask, x0=x0,
+ ddim_use_original_steps=False,
+ noise_dropout=noise_dropout,
+ temperature=temperature,
+ score_corrector=score_corrector,
+ corrector_kwargs=corrector_kwargs,
+ x_T=x_T,
+ log_every_t=log_every_t,
+ unconditional_guidance_scale=unconditional_guidance_scale,
+ unconditional_conditioning=unconditional_conditioning,
+ verbose=verbose,
+ **kwargs)
+ return samples, intermediates
+
+ # @torch.no_grad()
+ def ddim_sampling(self, cond, shape,
+ x_T=None, ddim_use_original_steps=False,
+ callback=None, timesteps=None, quantize_denoised=False,
+ mask=None, x0=None, img_callback=None, log_every_t=100,
+ temperature=1., noise_dropout=0., score_corrector=None, corrector_kwargs=None,
+ unconditional_guidance_scale=1., unconditional_conditioning=None, verbose=True,
+ cond_tau=1., target_size=None, start_timesteps=None,
+ **kwargs):
+ device = self.model.betas.device
+ # print('ddim device', device)
+ b = shape[0]
+ if x_T is None:
+ img = torch.randn(shape, device=device)
+ else:
+ img = x_T
+
+ if timesteps is None:
+ timesteps = self.ddpm_num_timesteps if ddim_use_original_steps else self.ddim_timesteps
+ elif timesteps is not None and not ddim_use_original_steps:
+ subset_end = int(min(timesteps / self.ddim_timesteps.shape[0], 1) * self.ddim_timesteps.shape[0]) - 1
+ timesteps = self.ddim_timesteps[:subset_end]
+
+ intermediates = {'x_inter': [img], 'pred_x0': [img]}
+ time_range = reversed(range(0,timesteps)) if ddim_use_original_steps else np.flip(timesteps)
+ total_steps = timesteps if ddim_use_original_steps else timesteps.shape[0]
+ if verbose:
+ iterator = tqdm(time_range, desc='DDIM Sampler', total=total_steps)
+ else:
+ iterator = time_range
+
+ init_x0 = False
+ clean_cond = kwargs.pop("clean_cond", False)
+
+ if self.training_mode == True:
+ if self.backprop_mode == 'last':
+ backprop_cutoff_idx = self.ddim_num_steps - 1
+ elif self.backprop_mode == 'rand':
+ backprop_cutoff_idx = random.randint(0, self.ddim_num_steps - 1)
+ elif self.backprop_mode == 'specific':
+ backprop_cutoff_idx = 15
+
+ for i, step in enumerate(iterator):
+ index = total_steps - i - 1
+ ts = torch.full((b,), step, device=device, dtype=torch.long)
+
+ if self.training_mode == True:
+ if i >= backprop_cutoff_idx:
+ for name, param in self.model.named_parameters():
+ if "lora" in name:
+ param.requires_grad = True
+ else:
+ for name, param in self.model.named_parameters():
+ param.requires_grad = False
+
+
+ if start_timesteps is not None:
+ assert x0 is not None
+ if step > start_timesteps*time_range[0]:
+ continue
+ elif not init_x0:
+ img = self.model.q_sample(x0, ts)
+ init_x0 = True
+
+ # use mask to blend noised original latent (img_orig) & new sampled latent (img)
+ if mask is not None:
+ assert x0 is not None
+ if clean_cond:
+ img_orig = x0
+ else:
+ img_orig = self.model.q_sample(x0, ts) # TODO: deterministic forward pass? <ddim inversion>
+ img = img_orig * mask + (1. - mask) * img # keep original & modify use img
+
+ index_clip = int((1 - cond_tau) * total_steps)
+ if index <= index_clip and target_size is not None:
+ target_size_ = [target_size[0], target_size[1]//8, target_size[2]//8]
+ img = torch.nn.functional.interpolate(
+ img,
+ size=target_size_,
+ mode="nearest",
+ )
+
+ forward_context = torch.autograd.graph.save_on_cpu
+ with forward_context():
+ outs = self.p_sample_ddim(img, cond, ts, index=index, use_original_steps=ddim_use_original_steps,
+ quantize_denoised=quantize_denoised, temperature=temperature,
+ noise_dropout=noise_dropout, score_corrector=score_corrector,
+ corrector_kwargs=corrector_kwargs,
+ unconditional_guidance_scale=unconditional_guidance_scale,
+ unconditional_conditioning=unconditional_conditioning,
+ x0=x0,
+ **kwargs)
+
+ img, pred_x0 = outs
+
+ if callback: callback(i)
+ if img_callback: img_callback(pred_x0, i)
+
+ if index % log_every_t == 0 or index == total_steps - 1:
+ intermediates['x_inter'].append(img)
+ intermediates['pred_x0'].append(pred_x0)
+
+ return img, intermediates
+
+ # @torch.no_grad()
+ def p_sample_ddim(self, x, c, t, index, repeat_noise=False, use_original_steps=False, quantize_denoised=False,
+ temperature=1., noise_dropout=0., score_corrector=None, corrector_kwargs=None,
+ unconditional_guidance_scale=1., unconditional_conditioning=None,
+ uc_type=None, conditional_guidance_scale_temporal=None, **kwargs):
+ b, *_, device = *x.shape, x.device
+
+ if x.dim() == 5:
+ is_video = True
+ else:
+ is_video = False
+ if unconditional_conditioning is None or unconditional_guidance_scale == 1.:
+ e_t = self.model.apply_model(x, t, c, **kwargs) # unet denoiser
+ else:
+ # with unconditional condition
+ if isinstance(c, torch.Tensor):
+ e_t = self.model.apply_model(x, t, c, **kwargs) # unet denoiser
+ e_t_uncond = self.model.apply_model(x, t, unconditional_conditioning, **kwargs)
+ elif isinstance(c, dict):
+ e_t = self.model.apply_model(x, t, c, **kwargs) # unet denoiser
+ e_t_uncond = self.model.apply_model(x, t, unconditional_conditioning, **kwargs) # unet denoiser
+ else:
+ raise NotImplementedError
+
+ if uc_type is None:
+ e_t = e_t_uncond + unconditional_guidance_scale * (e_t - e_t_uncond)
+ else:
+ if uc_type == 'cfg_original':
+ e_t = e_t + unconditional_guidance_scale * (e_t - e_t_uncond)
+ elif uc_type == 'cfg_ours':
+ e_t = e_t + unconditional_guidance_scale * (e_t_uncond - e_t)
+ else:
+ raise NotImplementedError
+
+ # temporal guidance
+ if conditional_guidance_scale_temporal is not None:
+ e_t_temporal = self.model.apply_model(x, t, c, **kwargs)
+ e_t_image = self.model.apply_model(x, t, c, no_temporal_attn=True, **kwargs)
+ e_t = e_t + conditional_guidance_scale_temporal * (e_t_temporal - e_t_image)
+
+ if score_corrector is not None:
+ assert self.model.parameterization == "eps"
+ e_t = score_corrector.modify_score(self.model, e_t, x, t, c, **corrector_kwargs)
+
+ alphas = self.model.alphas_cumprod if use_original_steps else self.ddim_alphas
+ alphas_prev = self.model.alphas_cumprod_prev if use_original_steps else self.ddim_alphas_prev
+ sqrt_one_minus_alphas = self.model.sqrt_one_minus_alphas_cumprod if use_original_steps else self.ddim_sqrt_one_minus_alphas
+ sigmas = self.model.ddim_sigmas_for_original_num_steps if use_original_steps else self.ddim_sigmas
+ # select parameters corresponding to the currently considered timestep
+
+ if is_video:
+ size = (b, 1, 1, 1, 1)
+ else:
+ size = (b, 1, 1, 1)
+ a_t = torch.full(size, alphas[index], device=device)
+ a_prev = torch.full(size, alphas_prev[index], device=device)
+ sigma_t = torch.full(size, sigmas[index], device=device)
+ sqrt_one_minus_at = torch.full(size, sqrt_one_minus_alphas[index],device=device)
+
+ # current prediction for x_0
+ pred_x0 = (x - sqrt_one_minus_at * e_t) / a_t.sqrt()
+ if quantize_denoised:
+ pred_x0, _, *_ = self.model.first_stage_model.quantize(pred_x0)
+ # direction pointing to x_t
+ dir_xt = (1. - a_prev - sigma_t**2).sqrt() * e_t
+
+ noise = sigma_t * noise_like(x.shape, device, repeat_noise) * temperature
+ if noise_dropout > 0.:
+ noise = torch.nn.functional.dropout(noise, p=noise_dropout)
+ alphas = self.model.alphas_cumprod if use_original_steps else self.ddim_alphas
+ if self.use_scale:
+ scale_arr = self.model.scale_arr if use_original_steps else self.ddim_scale_arr
+ scale_t = torch.full(size, scale_arr[index], device=device)
+ scale_arr_prev = self.model.scale_arr_prev if use_original_steps else self.ddim_scale_arr_prev
+ scale_t_prev = torch.full(size, scale_arr_prev[index], device=device)
+ pred_x0 /= scale_t
+ x_prev = a_prev.sqrt() * scale_t_prev * pred_x0 + dir_xt + noise
+ else:
+ x_prev = a_prev.sqrt() * pred_x0 + dir_xt + noise
+
+ return x_prev, pred_x0
+
+
+ @torch.no_grad()
+ def stochastic_encode(self, x0, t, use_original_steps=False, noise=None):
+ # fast, but does not allow for exact reconstruction
+ # t serves as an index to gather the correct alphas
+ if use_original_steps:
+ sqrt_alphas_cumprod = self.sqrt_alphas_cumprod
+ sqrt_one_minus_alphas_cumprod = self.sqrt_one_minus_alphas_cumprod
+ else:
+ sqrt_alphas_cumprod = torch.sqrt(self.ddim_alphas)
+ sqrt_one_minus_alphas_cumprod = self.ddim_sqrt_one_minus_alphas
+
+ if noise is None:
+ noise = torch.randn_like(x0)
+
+ def extract_into_tensor(a, t, x_shape):
+ b, *_ = t.shape
+ out = a.gather(-1, t)
+ return out.reshape(b, *((1,) * (len(x_shape) - 1)))
+
+ return (extract_into_tensor(sqrt_alphas_cumprod, t, x0.shape) * x0 +
+ extract_into_tensor(sqrt_one_minus_alphas_cumprod, t, x0.shape) * noise)
+
+ @torch.no_grad()
+ def decode(self, x_latent, cond, t_start, unconditional_guidance_scale=1.0, unconditional_conditioning=None,
+ use_original_steps=False):
+
+ timesteps = np.arange(self.ddpm_num_timesteps) if use_original_steps else self.ddim_timesteps
+ timesteps = timesteps[:t_start]
+
+ time_range = np.flip(timesteps)
+ total_steps = timesteps.shape[0]
+ print(f"Running DDIM Sampling with {total_steps} timesteps")
+
+ iterator = tqdm(time_range, desc='Decoding image', total=total_steps)
+ x_dec = x_latent
+ for i, step in enumerate(iterator):
+ index = total_steps - i - 1
+ ts = torch.full((x_latent.shape[0],), step, device=x_latent.device, dtype=torch.long)
+ x_dec, _ = self.p_sample_ddim(x_dec, cond, ts, index=index, use_original_steps=use_original_steps,
+ unconditional_guidance_scale=unconditional_guidance_scale,
+ unconditional_conditioning=unconditional_conditioning)
+ return x_dec
+
diff --git a/VADER-VideoCrafter/lvdm/models/utils_diffusion.py b/VADER-VideoCrafter/lvdm/models/utils_diffusion.py
new file mode 100644
index 0000000000000000000000000000000000000000..59403ee224726a34b89b550c7db9b2be2f1aeb58
--- /dev/null
+++ b/VADER-VideoCrafter/lvdm/models/utils_diffusion.py
@@ -0,0 +1,105 @@
+# Copied from VideoCrafter: https://github.com/AILab-CVC/VideoCrafter
+import math
+import numpy as np
+from einops import repeat
+import torch
+import torch.nn.functional as F
+
+
+def timestep_embedding(timesteps, dim, max_period=10000, repeat_only=False):
+ """
+ Create sinusoidal timestep embeddings.
+ :param timesteps: a 1-D Tensor of N indices, one per batch element.
+ These may be fractional.
+ :param dim: the dimension of the output.
+ :param max_period: controls the minimum frequency of the embeddings.
+ :return: an [N x dim] Tensor of positional embeddings.
+ """
+ if not repeat_only:
+ half = dim // 2
+ freqs = torch.exp(
+ -math.log(max_period) * torch.arange(start=0, end=half, dtype=torch.float32) / half
+ ).to(device=timesteps.device)
+ args = timesteps[:, None].float() * freqs[None]
+ embedding = torch.cat([torch.cos(args), torch.sin(args)], dim=-1)
+ if dim % 2:
+ embedding = torch.cat([embedding, torch.zeros_like(embedding[:, :1])], dim=-1)
+ else:
+ embedding = repeat(timesteps, 'b -> b d', d=dim)
+ return embedding
+
+
+def make_beta_schedule(schedule, n_timestep, linear_start=1e-4, linear_end=2e-2, cosine_s=8e-3):
+ if schedule == "linear":
+ betas = (
+ torch.linspace(linear_start ** 0.5, linear_end ** 0.5, n_timestep, dtype=torch.float64) ** 2
+ )
+
+ elif schedule == "cosine":
+ timesteps = (
+ torch.arange(n_timestep + 1, dtype=torch.float64) / n_timestep + cosine_s
+ )
+ alphas = timesteps / (1 + cosine_s) * np.pi / 2
+ alphas = torch.cos(alphas).pow(2)
+ alphas = alphas / alphas[0]
+ betas = 1 - alphas[1:] / alphas[:-1]
+ betas = np.clip(betas, a_min=0, a_max=0.999)
+
+ elif schedule == "sqrt_linear":
+ betas = torch.linspace(linear_start, linear_end, n_timestep, dtype=torch.float64)
+ elif schedule == "sqrt":
+ betas = torch.linspace(linear_start, linear_end, n_timestep, dtype=torch.float64) ** 0.5
+ else:
+ raise ValueError(f"schedule '{schedule}' unknown.")
+ return betas.numpy()
+
+
+def make_ddim_timesteps(ddim_discr_method, num_ddim_timesteps, num_ddpm_timesteps, verbose=True):
+ if ddim_discr_method == 'uniform':
+ c = num_ddpm_timesteps // num_ddim_timesteps
+ ddim_timesteps = np.asarray(list(range(0, num_ddpm_timesteps, c)))
+ elif ddim_discr_method == 'quad':
+ ddim_timesteps = ((np.linspace(0, np.sqrt(num_ddpm_timesteps * .8), num_ddim_timesteps)) ** 2).astype(int)
+ else:
+ raise NotImplementedError(f'There is no ddim discretization method called "{ddim_discr_method}"')
+
+ # assert ddim_timesteps.shape[0] == num_ddim_timesteps
+ # add one to get the final alpha values right (the ones from first scale to data during sampling)
+ steps_out = ddim_timesteps + 1
+ if verbose:
+ print(f'Selected timesteps for ddim sampler: {steps_out}')
+ return steps_out
+
+
+def make_ddim_sampling_parameters(alphacums, ddim_timesteps, eta, verbose=True):
+ # select alphas for computing the variance schedule
+ # print(f'ddim_timesteps={ddim_timesteps}, len_alphacums={len(alphacums)}')
+ alphas = alphacums[ddim_timesteps]
+ alphas_prev = np.asarray([alphacums[0]] + alphacums[ddim_timesteps[:-1]].tolist())
+
+ # according the the formula provided in https://arxiv.org/abs/2010.02502
+ sigmas = eta * np.sqrt((1 - alphas_prev) / (1 - alphas) * (1 - alphas / alphas_prev))
+ if verbose:
+ print(f'Selected alphas for ddim sampler: a_t: {alphas}; a_(t-1): {alphas_prev}')
+ print(f'For the chosen value of eta, which is {eta}, '
+ f'this results in the following sigma_t schedule for ddim sampler {sigmas}')
+ return sigmas, alphas, alphas_prev
+
+
+def betas_for_alpha_bar(num_diffusion_timesteps, alpha_bar, max_beta=0.999):
+ """
+ Create a beta schedule that discretizes the given alpha_t_bar function,
+ which defines the cumulative product of (1-beta) over time from t = [0,1].
+ :param num_diffusion_timesteps: the number of betas to produce.
+ :param alpha_bar: a lambda that takes an argument t from 0 to 1 and
+ produces the cumulative product of (1-beta) up to that
+ part of the diffusion process.
+ :param max_beta: the maximum beta to use; use values lower than 1 to
+ prevent singularities.
+ """
+ betas = []
+ for i in range(num_diffusion_timesteps):
+ t1 = i / num_diffusion_timesteps
+ t2 = (i + 1) / num_diffusion_timesteps
+ betas.append(min(1 - alpha_bar(t2) / alpha_bar(t1), max_beta))
+ return np.array(betas)
\ No newline at end of file
diff --git a/VADER-VideoCrafter/lvdm/modules/attention.py b/VADER-VideoCrafter/lvdm/modules/attention.py
new file mode 100644
index 0000000000000000000000000000000000000000..c0d2f962c6f89632353348cb6230aff62c65f85a
--- /dev/null
+++ b/VADER-VideoCrafter/lvdm/modules/attention.py
@@ -0,0 +1,476 @@
+# Copied from VideoCrafter: https://github.com/AILab-CVC/VideoCrafter
+from functools import partial
+import torch
+from torch import nn, einsum
+import torch.nn.functional as F
+from einops import rearrange, repeat
+try:
+ import xformers
+ import xformers.ops
+ XFORMERS_IS_AVAILBLE = True
+except:
+ XFORMERS_IS_AVAILBLE = False
+from lvdm.common import (
+ checkpoint,
+ exists,
+ default,
+)
+from lvdm.basics import (
+ zero_module,
+)
+
+class RelativePosition(nn.Module):
+ """ https://github.com/evelinehong/Transformer_Relative_Position_PyTorch/blob/master/relative_position.py """
+
+ def __init__(self, num_units, max_relative_position):
+ super().__init__()
+ self.num_units = num_units
+ self.max_relative_position = max_relative_position
+ self.embeddings_table = nn.Parameter(torch.Tensor(max_relative_position * 2 + 1, num_units))
+ nn.init.xavier_uniform_(self.embeddings_table)
+
+ def forward(self, length_q, length_k):
+ device = self.embeddings_table.device
+ range_vec_q = torch.arange(length_q, device=device)
+ range_vec_k = torch.arange(length_k, device=device)
+ distance_mat = range_vec_k[None, :] - range_vec_q[:, None]
+ distance_mat_clipped = torch.clamp(distance_mat, -self.max_relative_position, self.max_relative_position)
+ final_mat = distance_mat_clipped + self.max_relative_position
+ final_mat = final_mat.long()
+ embeddings = self.embeddings_table[final_mat]
+ return embeddings
+
+
+class CrossAttention(nn.Module):
+
+ def __init__(self, query_dim, context_dim=None, heads=8, dim_head=64, dropout=0.,
+ relative_position=False, temporal_length=None, img_cross_attention=False):
+ super().__init__()
+ inner_dim = dim_head * heads
+ context_dim = default(context_dim, query_dim)
+
+ self.scale = dim_head**-0.5
+ self.heads = heads
+ self.dim_head = dim_head
+ self.to_q = nn.Linear(query_dim, inner_dim, bias=False)
+ self.to_k = nn.Linear(context_dim, inner_dim, bias=False)
+ self.to_v = nn.Linear(context_dim, inner_dim, bias=False)
+ self.to_out = nn.Sequential(nn.Linear(inner_dim, query_dim), nn.Dropout(dropout))
+
+ self.image_cross_attention_scale = 1.0
+ self.text_context_len = 77
+ self.img_cross_attention = img_cross_attention
+ if self.img_cross_attention:
+ self.to_k_ip = nn.Linear(context_dim, inner_dim, bias=False)
+ self.to_v_ip = nn.Linear(context_dim, inner_dim, bias=False)
+
+ self.relative_position = relative_position
+ if self.relative_position:
+ assert(temporal_length is not None)
+ self.relative_position_k = RelativePosition(num_units=dim_head, max_relative_position=temporal_length)
+ self.relative_position_v = RelativePosition(num_units=dim_head, max_relative_position=temporal_length)
+ else:
+ ## only used for spatial attention, while NOT for temporal attention
+ if XFORMERS_IS_AVAILBLE and temporal_length is None:
+ self.forward = self.efficient_forward
+
+ def forward(self, x, context=None, mask=None):
+ h = self.heads
+
+ q = self.to_q(x)
+ context = default(context, x)
+ ## considering image token additionally
+ if context is not None and self.img_cross_attention:
+ context, context_img = context[:,:self.text_context_len,:], context[:,self.text_context_len:,:]
+ k = self.to_k(context)
+ v = self.to_v(context)
+ k_ip = self.to_k_ip(context_img)
+ v_ip = self.to_v_ip(context_img)
+ else:
+ k = self.to_k(context)
+ v = self.to_v(context)
+
+ q, k, v = map(lambda t: rearrange(t, 'b n (h d) -> (b h) n d', h=h), (q, k, v))
+ sim = torch.einsum('b i d, b j d -> b i j', q, k) * self.scale
+ if self.relative_position:
+ len_q, len_k, len_v = q.shape[1], k.shape[1], v.shape[1]
+ k2 = self.relative_position_k(len_q, len_k)
+ sim2 = einsum('b t d, t s d -> b t s', q, k2) * self.scale # TODO check
+ sim += sim2
+ del k
+
+ if exists(mask):
+ ## feasible for causal attention mask only
+ max_neg_value = -torch.finfo(sim.dtype).max
+ mask = repeat(mask, 'b i j -> (b h) i j', h=h)
+ sim.masked_fill_(~(mask>0.5), max_neg_value)
+
+ # attention, what we cannot get enough of
+ sim = sim.softmax(dim=-1)
+ out = torch.einsum('b i j, b j d -> b i d', sim, v)
+ if self.relative_position:
+ v2 = self.relative_position_v(len_q, len_v)
+ out2 = einsum('b t s, t s d -> b t d', sim, v2) # TODO check
+ out += out2
+ out = rearrange(out, '(b h) n d -> b n (h d)', h=h)
+
+ ## considering image token additionally
+ if context is not None and self.img_cross_attention:
+ k_ip, v_ip = map(lambda t: rearrange(t, 'b n (h d) -> (b h) n d', h=h), (k_ip, v_ip))
+ sim_ip = torch.einsum('b i d, b j d -> b i j', q, k_ip) * self.scale
+ del k_ip
+ sim_ip = sim_ip.softmax(dim=-1)
+ out_ip = torch.einsum('b i j, b j d -> b i d', sim_ip, v_ip)
+ out_ip = rearrange(out_ip, '(b h) n d -> b n (h d)', h=h)
+ out = out + self.image_cross_attention_scale * out_ip
+ del q
+
+ return self.to_out(out)
+
+ def efficient_forward(self, x, context=None, mask=None):
+ q = self.to_q(x)
+ context = default(context, x)
+
+ ## considering image token additionally
+ if context is not None and self.img_cross_attention:
+ context, context_img = context[:,:self.text_context_len,:], context[:,self.text_context_len:,:]
+ k = self.to_k(context)
+ v = self.to_v(context)
+ k_ip = self.to_k_ip(context_img)
+ v_ip = self.to_v_ip(context_img)
+ else:
+ k = self.to_k(context)
+ v = self.to_v(context)
+
+ b, _, _ = q.shape
+ q, k, v = map(
+ lambda t: t.unsqueeze(3)
+ .reshape(b, t.shape[1], self.heads, self.dim_head)
+ .permute(0, 2, 1, 3)
+ .reshape(b * self.heads, t.shape[1], self.dim_head)
+ .contiguous(),
+ (q, k, v),
+ )
+ # actually compute the attention, what we cannot get enough of
+ out = xformers.ops.memory_efficient_attention(q, k, v, attn_bias=None, op=None)
+
+ ## considering image token additionally
+ if context is not None and self.img_cross_attention:
+ k_ip, v_ip = map(
+ lambda t: t.unsqueeze(3)
+ .reshape(b, t.shape[1], self.heads, self.dim_head)
+ .permute(0, 2, 1, 3)
+ .reshape(b * self.heads, t.shape[1], self.dim_head)
+ .contiguous(),
+ (k_ip, v_ip),
+ )
+ out_ip = xformers.ops.memory_efficient_attention(q, k_ip, v_ip, attn_bias=None, op=None)
+ out_ip = (
+ out_ip.unsqueeze(0)
+ .reshape(b, self.heads, out.shape[1], self.dim_head)
+ .permute(0, 2, 1, 3)
+ .reshape(b, out.shape[1], self.heads * self.dim_head)
+ )
+
+ if exists(mask):
+ raise NotImplementedError
+ out = (
+ out.unsqueeze(0)
+ .reshape(b, self.heads, out.shape[1], self.dim_head)
+ .permute(0, 2, 1, 3)
+ .reshape(b, out.shape[1], self.heads * self.dim_head)
+ )
+ if context is not None and self.img_cross_attention:
+ out = out + self.image_cross_attention_scale * out_ip
+ return self.to_out(out)
+
+
+class BasicTransformerBlock(nn.Module):
+
+ def __init__(self, dim, n_heads, d_head, dropout=0., context_dim=None, gated_ff=True, checkpoint=True,
+ disable_self_attn=False, attention_cls=None, img_cross_attention=False):
+ super().__init__()
+ attn_cls = CrossAttention if attention_cls is None else attention_cls
+ self.disable_self_attn = disable_self_attn
+ self.attn1 = attn_cls(query_dim=dim, heads=n_heads, dim_head=d_head, dropout=dropout,
+ context_dim=context_dim if self.disable_self_attn else None)
+ self.ff = FeedForward(dim, dropout=dropout, glu=gated_ff)
+ self.attn2 = attn_cls(query_dim=dim, context_dim=context_dim, heads=n_heads, dim_head=d_head, dropout=dropout,
+ img_cross_attention=img_cross_attention)
+ self.norm1 = nn.LayerNorm(dim)
+ self.norm2 = nn.LayerNorm(dim)
+ self.norm3 = nn.LayerNorm(dim)
+ self.checkpoint = checkpoint
+
+ def forward(self, x, context=None, mask=None):
+ ## implementation tricks: because checkpointing doesn't support non-tensor (e.g. None or scalar) arguments
+ input_tuple = (x,) ## should not be (x), otherwise *input_tuple will decouple x into multiple arguments
+ if context is not None:
+ input_tuple = (x, context)
+ if mask is not None:
+ forward_mask = partial(self._forward, mask=mask)
+ return checkpoint(forward_mask, (x,), self.parameters(), self.checkpoint)
+ if context is not None and mask is not None:
+ input_tuple = (x, context, mask)
+ return checkpoint(self._forward, input_tuple, self.parameters(), self.checkpoint)
+
+ def _forward(self, x, context=None, mask=None):
+ x = self.attn1(self.norm1(x), context=context if self.disable_self_attn else None, mask=mask) + x
+ x = self.attn2(self.norm2(x), context=context, mask=mask) + x
+ x = self.ff(self.norm3(x)) + x
+ return x
+
+
+class SpatialTransformer(nn.Module):
+ """
+ Transformer block for image-like data in spatial axis.
+ First, project the input (aka embedding)
+ and reshape to b, t, d.
+ Then apply standard transformer action.
+ Finally, reshape to image
+ NEW: use_linear for more efficiency instead of the 1x1 convs
+ """
+
+ def __init__(self, in_channels, n_heads, d_head, depth=1, dropout=0., context_dim=None,
+ use_checkpoint=True, disable_self_attn=False, use_linear=False, img_cross_attention=False):
+ super().__init__()
+ self.in_channels = in_channels
+ inner_dim = n_heads * d_head
+ self.norm = torch.nn.GroupNorm(num_groups=32, num_channels=in_channels, eps=1e-6, affine=True)
+ if not use_linear:
+ self.proj_in = nn.Conv2d(in_channels, inner_dim, kernel_size=1, stride=1, padding=0)
+ else:
+ self.proj_in = nn.Linear(in_channels, inner_dim)
+
+ self.transformer_blocks = nn.ModuleList([
+ BasicTransformerBlock(
+ inner_dim,
+ n_heads,
+ d_head,
+ dropout=dropout,
+ context_dim=context_dim,
+ img_cross_attention=img_cross_attention,
+ disable_self_attn=disable_self_attn,
+ checkpoint=use_checkpoint) for d in range(depth)
+ ])
+ if not use_linear:
+ self.proj_out = zero_module(nn.Conv2d(inner_dim, in_channels, kernel_size=1, stride=1, padding=0))
+ else:
+ self.proj_out = zero_module(nn.Linear(inner_dim, in_channels))
+ self.use_linear = use_linear
+
+
+ def forward(self, x, context=None):
+ b, c, h, w = x.shape
+ x_in = x
+ x = self.norm(x)
+ if not self.use_linear:
+ x = self.proj_in(x)
+ x = rearrange(x, 'b c h w -> b (h w) c').contiguous()
+ if self.use_linear:
+ x = self.proj_in(x)
+ for i, block in enumerate(self.transformer_blocks):
+ x = block(x, context=context)
+ if self.use_linear:
+ x = self.proj_out(x)
+ x = rearrange(x, 'b (h w) c -> b c h w', h=h, w=w).contiguous()
+ if not self.use_linear:
+ x = self.proj_out(x)
+ return x + x_in
+
+
+class TemporalTransformer(nn.Module):
+ """
+ Transformer block for image-like data in temporal axis.
+ First, reshape to b, t, d.
+ Then apply standard transformer action.
+ Finally, reshape to image
+ """
+ def __init__(self, in_channels, n_heads, d_head, depth=1, dropout=0., context_dim=None,
+ use_checkpoint=True, use_linear=False, only_self_att=True, causal_attention=False,
+ relative_position=False, temporal_length=None):
+ super().__init__()
+ self.only_self_att = only_self_att
+ self.relative_position = relative_position
+ self.causal_attention = causal_attention
+ self.in_channels = in_channels
+ inner_dim = n_heads * d_head
+ self.norm = torch.nn.GroupNorm(num_groups=32, num_channels=in_channels, eps=1e-6, affine=True)
+ self.proj_in = nn.Conv1d(in_channels, inner_dim, kernel_size=1, stride=1, padding=0)
+ if not use_linear:
+ self.proj_in = nn.Conv1d(in_channels, inner_dim, kernel_size=1, stride=1, padding=0)
+ else:
+ self.proj_in = nn.Linear(in_channels, inner_dim)
+
+ if relative_position:
+ assert(temporal_length is not None)
+ attention_cls = partial(CrossAttention, relative_position=True, temporal_length=temporal_length)
+ else:
+ attention_cls = None
+ if self.causal_attention:
+ assert(temporal_length is not None)
+ self.mask = torch.tril(torch.ones([1, temporal_length, temporal_length]))
+
+ if self.only_self_att:
+ context_dim = None
+ self.transformer_blocks = nn.ModuleList([
+ BasicTransformerBlock(
+ inner_dim,
+ n_heads,
+ d_head,
+ dropout=dropout,
+ context_dim=context_dim,
+ attention_cls=attention_cls,
+ checkpoint=use_checkpoint) for d in range(depth)
+ ])
+ if not use_linear:
+ self.proj_out = zero_module(nn.Conv1d(inner_dim, in_channels, kernel_size=1, stride=1, padding=0))
+ else:
+ self.proj_out = zero_module(nn.Linear(inner_dim, in_channels))
+ self.use_linear = use_linear
+
+ def forward(self, x, context=None):
+ b, c, t, h, w = x.shape
+ x_in = x
+ x = self.norm(x)
+ x = rearrange(x, 'b c t h w -> (b h w) c t').contiguous()
+ if not self.use_linear:
+ x = self.proj_in(x)
+ x = rearrange(x, 'bhw c t -> bhw t c').contiguous()
+ if self.use_linear:
+ x = self.proj_in(x)
+
+ if self.causal_attention:
+ mask = self.mask.to(x.device)
+ mask = repeat(mask, 'l i j -> (l bhw) i j', bhw=b*h*w)
+ else:
+ mask = None
+
+ if self.only_self_att:
+ ## note: if no context is given, cross-attention defaults to self-attention
+ for i, block in enumerate(self.transformer_blocks):
+ x = block(x, mask=mask)
+ x = rearrange(x, '(b hw) t c -> b hw t c', b=b).contiguous()
+ else:
+ x = rearrange(x, '(b hw) t c -> b hw t c', b=b).contiguous()
+ context = rearrange(context, '(b t) l con -> b t l con', t=t).contiguous()
+ for i, block in enumerate(self.transformer_blocks):
+ # calculate each batch one by one (since number in shape could not greater then 65,535 for some package)
+ for j in range(b):
+ context_j = repeat(
+ context[j],
+ 't l con -> (t r) l con', r=(h * w) // t, t=t).contiguous()
+ ## note: causal mask will not applied in cross-attention case
+ x[j] = block(x[j], context=context_j)
+
+ if self.use_linear:
+ x = self.proj_out(x)
+ x = rearrange(x, 'b (h w) t c -> b c t h w', h=h, w=w).contiguous()
+ if not self.use_linear:
+ x = rearrange(x, 'b hw t c -> (b hw) c t').contiguous()
+ x = self.proj_out(x)
+ x = rearrange(x, '(b h w) c t -> b c t h w', b=b, h=h, w=w).contiguous()
+
+ return x + x_in
+
+
+class GEGLU(nn.Module):
+ def __init__(self, dim_in, dim_out):
+ super().__init__()
+ self.proj = nn.Linear(dim_in, dim_out * 2)
+
+ def forward(self, x):
+ x, gate = self.proj(x).chunk(2, dim=-1)
+ return x * F.gelu(gate)
+
+
+class FeedForward(nn.Module):
+ def __init__(self, dim, dim_out=None, mult=4, glu=False, dropout=0.):
+ super().__init__()
+ inner_dim = int(dim * mult)
+ dim_out = default(dim_out, dim)
+ project_in = nn.Sequential(
+ nn.Linear(dim, inner_dim),
+ nn.GELU()
+ ) if not glu else GEGLU(dim, inner_dim)
+
+ self.net = nn.Sequential(
+ project_in,
+ nn.Dropout(dropout),
+ nn.Linear(inner_dim, dim_out)
+ )
+
+ def forward(self, x):
+ return self.net(x)
+
+
+class LinearAttention(nn.Module):
+ def __init__(self, dim, heads=4, dim_head=32):
+ super().__init__()
+ self.heads = heads
+ hidden_dim = dim_head * heads
+ self.to_qkv = nn.Conv2d(dim, hidden_dim * 3, 1, bias = False)
+ self.to_out = nn.Conv2d(hidden_dim, dim, 1)
+
+ def forward(self, x):
+ b, c, h, w = x.shape
+ qkv = self.to_qkv(x)
+ q, k, v = rearrange(qkv, 'b (qkv heads c) h w -> qkv b heads c (h w)', heads = self.heads, qkv=3)
+ k = k.softmax(dim=-1)
+ context = torch.einsum('bhdn,bhen->bhde', k, v)
+ out = torch.einsum('bhde,bhdn->bhen', context, q)
+ out = rearrange(out, 'b heads c (h w) -> b (heads c) h w', heads=self.heads, h=h, w=w)
+ return self.to_out(out)
+
+
+class SpatialSelfAttention(nn.Module):
+ def __init__(self, in_channels):
+ super().__init__()
+ self.in_channels = in_channels
+
+ self.norm = torch.nn.GroupNorm(num_groups=32, num_channels=in_channels, eps=1e-6, affine=True)
+ self.q = torch.nn.Conv2d(in_channels,
+ in_channels,
+ kernel_size=1,
+ stride=1,
+ padding=0)
+ self.k = torch.nn.Conv2d(in_channels,
+ in_channels,
+ kernel_size=1,
+ stride=1,
+ padding=0)
+ self.v = torch.nn.Conv2d(in_channels,
+ in_channels,
+ kernel_size=1,
+ stride=1,
+ padding=0)
+ self.proj_out = torch.nn.Conv2d(in_channels,
+ in_channels,
+ kernel_size=1,
+ stride=1,
+ padding=0)
+
+ def forward(self, x):
+ h_ = x
+ h_ = self.norm(h_)
+ q = self.q(h_)
+ k = self.k(h_)
+ v = self.v(h_)
+
+ # compute attention
+ b,c,h,w = q.shape
+ q = rearrange(q, 'b c h w -> b (h w) c')
+ k = rearrange(k, 'b c h w -> b c (h w)')
+ w_ = torch.einsum('bij,bjk->bik', q, k)
+
+ w_ = w_ * (int(c)**(-0.5))
+ w_ = torch.nn.functional.softmax(w_, dim=2)
+
+ # attend to values
+ v = rearrange(v, 'b c h w -> b c (h w)')
+ w_ = rearrange(w_, 'b i j -> b j i')
+ h_ = torch.einsum('bij,bjk->bik', v, w_)
+ h_ = rearrange(h_, 'b c (h w) -> b c h w', h=h)
+ h_ = self.proj_out(h_)
+
+ return x+h_
diff --git a/VADER-VideoCrafter/lvdm/modules/encoders/condition.py b/VADER-VideoCrafter/lvdm/modules/encoders/condition.py
new file mode 100644
index 0000000000000000000000000000000000000000..ccc1a07dea9473778e64540639f61b512bbb6f27
--- /dev/null
+++ b/VADER-VideoCrafter/lvdm/modules/encoders/condition.py
@@ -0,0 +1,393 @@
+# Copied from VideoCrafter: https://github.com/AILab-CVC/VideoCrafter
+import torch
+import torch.nn as nn
+from torch.utils.checkpoint import checkpoint
+import kornia
+import open_clip
+from transformers import T5Tokenizer, T5EncoderModel, CLIPTokenizer, CLIPTextModel
+from lvdm.common import autocast
+from utils.utils import count_params
+
+class AbstractEncoder(nn.Module):
+ def __init__(self):
+ super().__init__()
+
+ def encode(self, *args, **kwargs):
+ raise NotImplementedError
+
+
+class IdentityEncoder(AbstractEncoder):
+
+ def encode(self, x):
+ return x
+
+
+class ClassEmbedder(nn.Module):
+ def __init__(self, embed_dim, n_classes=1000, key='class', ucg_rate=0.1):
+ super().__init__()
+ self.key = key
+ self.embedding = nn.Embedding(n_classes, embed_dim)
+ self.n_classes = n_classes
+ self.ucg_rate = ucg_rate
+
+ def forward(self, batch, key=None, disable_dropout=False):
+ if key is None:
+ key = self.key
+ # this is for use in crossattn
+ c = batch[key][:, None]
+ if self.ucg_rate > 0. and not disable_dropout:
+ mask = 1. - torch.bernoulli(torch.ones_like(c) * self.ucg_rate)
+ c = mask * c + (1 - mask) * torch.ones_like(c) * (self.n_classes - 1)
+ c = c.long()
+ c = self.embedding(c)
+ return c
+
+ def get_unconditional_conditioning(self, bs, device="cuda"):
+ uc_class = self.n_classes - 1 # 1000 classes --> 0 ... 999, one extra class for ucg (class 1000)
+ uc = torch.ones((bs,), device=device) * uc_class
+ uc = {self.key: uc}
+ return uc
+
+
+def disabled_train(self, mode=True):
+ """Overwrite model.train with this function to make sure train/eval mode
+ does not change anymore."""
+ return self
+
+
+class FrozenT5Embedder(AbstractEncoder):
+ """Uses the T5 transformer encoder for text"""
+
+ def __init__(self, version="google/t5-v1_1-large", device="cuda", max_length=77,
+ freeze=True): # others are google/t5-v1_1-xl and google/t5-v1_1-xxl
+ super().__init__()
+ self.tokenizer = T5Tokenizer.from_pretrained(version)
+ self.transformer = T5EncoderModel.from_pretrained(version)
+ self.device = device
+ self.max_length = max_length # TODO: typical value?
+ if freeze:
+ self.freeze()
+
+ def freeze(self):
+ self.transformer = self.transformer.eval()
+ # self.train = disabled_train
+ for param in self.parameters():
+ param.requires_grad = False
+
+ def forward(self, text):
+ batch_encoding = self.tokenizer(text, truncation=True, max_length=self.max_length, return_length=True,
+ return_overflowing_tokens=False, padding="max_length", return_tensors="pt")
+ tokens = batch_encoding["input_ids"].to(self.device)
+ outputs = self.transformer(input_ids=tokens)
+
+ z = outputs.last_hidden_state
+ return z
+
+ def encode(self, text):
+ return self(text)
+
+
+class FrozenCLIPEmbedder(AbstractEncoder):
+ """Uses the CLIP transformer encoder for text (from huggingface)"""
+ LAYERS = [
+ "last",
+ "pooled",
+ "hidden"
+ ]
+
+ def __init__(self, version="openai/clip-vit-large-patch14", device="cuda", max_length=77,
+ freeze=True, layer="last", layer_idx=None): # clip-vit-base-patch32
+ super().__init__()
+ assert layer in self.LAYERS
+ self.tokenizer = CLIPTokenizer.from_pretrained(version)
+ self.transformer = CLIPTextModel.from_pretrained(version)
+ self.device = device
+ self.max_length = max_length
+ if freeze:
+ self.freeze()
+ self.layer = layer
+ self.layer_idx = layer_idx
+ if layer == "hidden":
+ assert layer_idx is not None
+ assert 0 <= abs(layer_idx) <= 12
+
+ def freeze(self):
+ self.transformer = self.transformer.eval()
+ # self.train = disabled_train
+ for param in self.parameters():
+ param.requires_grad = False
+
+ def forward(self, text):
+ batch_encoding = self.tokenizer(text, truncation=True, max_length=self.max_length, return_length=True,
+ return_overflowing_tokens=False, padding="max_length", return_tensors="pt")
+ tokens = batch_encoding["input_ids"].to(self.device)
+ outputs = self.transformer(input_ids=tokens, output_hidden_states=self.layer == "hidden")
+ if self.layer == "last":
+ z = outputs.last_hidden_state
+ elif self.layer == "pooled":
+ z = outputs.pooler_output[:, None, :]
+ else:
+ z = outputs.hidden_states[self.layer_idx]
+ return z
+
+ def encode(self, text):
+ return self(text)
+
+
+class ClipImageEmbedder(nn.Module):
+ def __init__(
+ self,
+ model,
+ jit=False,
+ device='cuda' if torch.cuda.is_available() else 'cpu',
+ antialias=True,
+ ucg_rate=0.
+ ):
+ super().__init__()
+ from clip import load as load_clip
+ self.model, _ = load_clip(name=model, device=device, jit=jit)
+
+ self.antialias = antialias
+
+ self.register_buffer('mean', torch.Tensor([0.48145466, 0.4578275, 0.40821073]), persistent=False)
+ self.register_buffer('std', torch.Tensor([0.26862954, 0.26130258, 0.27577711]), persistent=False)
+ self.ucg_rate = ucg_rate
+
+ def preprocess(self, x):
+ # normalize to [0,1]
+ x = kornia.geometry.resize(x, (224, 224),
+ interpolation='bicubic', align_corners=True,
+ antialias=self.antialias)
+ x = (x + 1.) / 2.
+ # re-normalize according to clip
+ x = kornia.enhance.normalize(x, self.mean, self.std)
+ return x
+
+ def forward(self, x, no_dropout=False):
+ # x is assumed to be in range [-1,1]
+ out = self.model.encode_image(self.preprocess(x))
+ out = out.to(x.dtype)
+ if self.ucg_rate > 0. and not no_dropout:
+ out = torch.bernoulli((1. - self.ucg_rate) * torch.ones(out.shape[0], device=out.device))[:, None] * out
+ return out
+
+
+class FrozenOpenCLIPEmbedder(AbstractEncoder):
+ """
+ Uses the OpenCLIP transformer encoder for text
+ """
+ LAYERS = [
+ # "pooled",
+ "last",
+ "penultimate"
+ ]
+
+ def __init__(self, arch="ViT-H-14", version="laion2b_s32b_b79k", device="cuda", max_length=77,
+ freeze=True, layer="last"):
+ super().__init__()
+ assert layer in self.LAYERS
+ model, _, _ = open_clip.create_model_and_transforms(arch, device=torch.device('cpu'))
+ del model.visual
+ self.model = model
+
+ self.device = device
+ self.max_length = max_length
+ if freeze:
+ self.freeze()
+ self.layer = layer
+ if self.layer == "last":
+ self.layer_idx = 0
+ elif self.layer == "penultimate":
+ self.layer_idx = 1
+ else:
+ raise NotImplementedError()
+
+ def freeze(self):
+ self.model = self.model.eval()
+ for param in self.parameters():
+ param.requires_grad = False
+
+ def forward(self, text):
+ self.device = self.model.positional_embedding.device
+ tokens = open_clip.tokenize(text)
+ z = self.encode_with_transformer(tokens.to(self.device))
+ return z
+
+ def encode_with_transformer(self, text):
+ x = self.model.token_embedding(text) # [batch_size, n_ctx, d_model]
+ x = x + self.model.positional_embedding
+ x = x.permute(1, 0, 2) # NLD -> LND
+ x = self.text_transformer_forward(x, attn_mask=self.model.attn_mask)
+ x = x.permute(1, 0, 2) # LND -> NLD
+ x = self.model.ln_final(x)
+ return x
+
+ def text_transformer_forward(self, x: torch.Tensor, attn_mask=None):
+ for i, r in enumerate(self.model.transformer.resblocks):
+ if i == len(self.model.transformer.resblocks) - self.layer_idx:
+ break
+ if self.model.transformer.grad_checkpointing and not torch.jit.is_scripting():
+ x = checkpoint(r, x, attn_mask)
+ else:
+ x = r(x, attn_mask=attn_mask)
+ return x
+
+ def encode(self, text):
+ return self(text)
+
+
+class FrozenOpenCLIPImageEmbedder(AbstractEncoder):
+ """
+ Uses the OpenCLIP vision transformer encoder for images
+ """
+
+ def __init__(self, arch="ViT-H-14", version="laion2b_s32b_b79k", device="cuda", max_length=77,
+ freeze=True, layer="pooled", antialias=True, ucg_rate=0.):
+ super().__init__()
+ model, _, _ = open_clip.create_model_and_transforms(arch, device=torch.device('cpu'),
+ pretrained=version, )
+ del model.transformer
+ self.model = model
+
+ self.device = device
+ self.max_length = max_length
+ if freeze:
+ self.freeze()
+ self.layer = layer
+ if self.layer == "penultimate":
+ raise NotImplementedError()
+ self.layer_idx = 1
+
+ self.antialias = antialias
+
+ self.register_buffer('mean', torch.Tensor([0.48145466, 0.4578275, 0.40821073]), persistent=False)
+ self.register_buffer('std', torch.Tensor([0.26862954, 0.26130258, 0.27577711]), persistent=False)
+ self.ucg_rate = ucg_rate
+
+ def preprocess(self, x):
+ # normalize to [0,1]
+ x = kornia.geometry.resize(x, (224, 224),
+ interpolation='bicubic', align_corners=True,
+ antialias=self.antialias)
+ x = (x + 1.) / 2.
+ # renormalize according to clip
+ x = kornia.enhance.normalize(x, self.mean, self.std)
+ return x
+
+ def freeze(self):
+ self.model = self.model.eval()
+ for param in self.parameters():
+ param.requires_grad = False
+
+ @autocast
+ def forward(self, image, no_dropout=False):
+ z = self.encode_with_vision_transformer(image)
+ if self.ucg_rate > 0. and not no_dropout:
+ z = torch.bernoulli((1. - self.ucg_rate) * torch.ones(z.shape[0], device=z.device))[:, None] * z
+ return z
+
+ def encode_with_vision_transformer(self, img):
+ img = self.preprocess(img)
+ x = self.model.visual(img)
+ return x
+
+ def encode(self, text):
+ return self(text)
+
+
+
+class FrozenOpenCLIPImageEmbedderV2(AbstractEncoder):
+ """
+ Uses the OpenCLIP vision transformer encoder for images
+ """
+
+ def __init__(self, arch="ViT-H-14", version="laion2b_s32b_b79k", device="cuda",
+ freeze=True, layer="pooled", antialias=True):
+ super().__init__()
+ model, _, _ = open_clip.create_model_and_transforms(arch, device=torch.device('cpu'),
+ pretrained=version, )
+ del model.transformer
+ self.model = model
+ self.device = device
+
+ if freeze:
+ self.freeze()
+ self.layer = layer
+ if self.layer == "penultimate":
+ raise NotImplementedError()
+ self.layer_idx = 1
+
+ self.antialias = antialias
+ self.register_buffer('mean', torch.Tensor([0.48145466, 0.4578275, 0.40821073]), persistent=False)
+ self.register_buffer('std', torch.Tensor([0.26862954, 0.26130258, 0.27577711]), persistent=False)
+
+
+ def preprocess(self, x):
+ # normalize to [0,1]
+ x = kornia.geometry.resize(x, (224, 224),
+ interpolation='bicubic', align_corners=True,
+ antialias=self.antialias)
+ x = (x + 1.) / 2.
+ # renormalize according to clip
+ x = kornia.enhance.normalize(x, self.mean, self.std)
+ return x
+
+ def freeze(self):
+ self.model = self.model.eval()
+ for param in self.model.parameters():
+ param.requires_grad = False
+
+ def forward(self, image, no_dropout=False):
+ ## image: b c h w
+ z = self.encode_with_vision_transformer(image)
+ return z
+
+ def encode_with_vision_transformer(self, x):
+ x = self.preprocess(x)
+
+ # to patches - whether to use dual patchnorm - https://arxiv.org/abs/2302.01327v1
+ if self.model.visual.input_patchnorm:
+ # einops - rearrange(x, 'b c (h p1) (w p2) -> b (h w) (c p1 p2)')
+ x = x.reshape(x.shape[0], x.shape[1], self.model.visual.grid_size[0], self.model.visual.patch_size[0], self.model.visual.grid_size[1], self.model.visual.patch_size[1])
+ x = x.permute(0, 2, 4, 1, 3, 5)
+ x = x.reshape(x.shape[0], self.model.visual.grid_size[0] * self.model.visual.grid_size[1], -1)
+ x = self.model.visual.patchnorm_pre_ln(x)
+ x = self.model.visual.conv1(x)
+ else:
+ x = self.model.visual.conv1(x) # shape = [*, width, grid, grid]
+ x = x.reshape(x.shape[0], x.shape[1], -1) # shape = [*, width, grid ** 2]
+ x = x.permute(0, 2, 1) # shape = [*, grid ** 2, width]
+
+ # class embeddings and positional embeddings
+ x = torch.cat(
+ [self.model.visual.class_embedding.to(x.dtype) + torch.zeros(x.shape[0], 1, x.shape[-1], dtype=x.dtype, device=x.device),
+ x], dim=1) # shape = [*, grid ** 2 + 1, width]
+ x = x + self.model.visual.positional_embedding.to(x.dtype)
+
+ # a patch_dropout of 0. would mean it is disabled and this function would do nothing but return what was passed in
+ x = self.model.visual.patch_dropout(x)
+ x = self.model.visual.ln_pre(x)
+
+ x = x.permute(1, 0, 2) # NLD -> LND
+ x = self.model.visual.transformer(x)
+ x = x.permute(1, 0, 2) # LND -> NLD
+
+ return x
+
+
+class FrozenCLIPT5Encoder(AbstractEncoder):
+ def __init__(self, clip_version="openai/clip-vit-large-patch14", t5_version="google/t5-v1_1-xl", device="cuda",
+ clip_max_length=77, t5_max_length=77):
+ super().__init__()
+ self.clip_encoder = FrozenCLIPEmbedder(clip_version, device, max_length=clip_max_length)
+ self.t5_encoder = FrozenT5Embedder(t5_version, device, max_length=t5_max_length)
+ print(f"{self.clip_encoder.__class__.__name__} has {count_params(self.clip_encoder) * 1.e-6:.2f} M parameters, "
+ f"{self.t5_encoder.__class__.__name__} comes with {count_params(self.t5_encoder) * 1.e-6:.2f} M params.")
+
+ def encode(self, text):
+ return self(text)
+
+ def forward(self, text):
+ clip_z = self.clip_encoder.encode(text)
+ t5_z = self.t5_encoder.encode(text)
+ return [clip_z, t5_z]
\ No newline at end of file
diff --git a/VADER-VideoCrafter/lvdm/modules/encoders/ip_resampler.py b/VADER-VideoCrafter/lvdm/modules/encoders/ip_resampler.py
new file mode 100644
index 0000000000000000000000000000000000000000..d274f5aa77af36f5a12522a6e0254893ef6879d6
--- /dev/null
+++ b/VADER-VideoCrafter/lvdm/modules/encoders/ip_resampler.py
@@ -0,0 +1,137 @@
+# Copied from VideoCrafter: https://github.com/AILab-CVC/VideoCrafter
+# modified from https://github.com/mlfoundations/open_flamingo/blob/main/open_flamingo/src/helpers.py
+import math
+import torch
+import torch.nn as nn
+
+
+class ImageProjModel(nn.Module):
+ """Projection Model"""
+ def __init__(self, cross_attention_dim=1024, clip_embeddings_dim=1024, clip_extra_context_tokens=4):
+ super().__init__()
+ self.cross_attention_dim = cross_attention_dim
+ self.clip_extra_context_tokens = clip_extra_context_tokens
+ self.proj = nn.Linear(clip_embeddings_dim, self.clip_extra_context_tokens * cross_attention_dim)
+ self.norm = nn.LayerNorm(cross_attention_dim)
+
+ def forward(self, image_embeds):
+ #embeds = image_embeds
+ embeds = image_embeds.type(list(self.proj.parameters())[0].dtype)
+ clip_extra_context_tokens = self.proj(embeds).reshape(-1, self.clip_extra_context_tokens, self.cross_attention_dim)
+ clip_extra_context_tokens = self.norm(clip_extra_context_tokens)
+ return clip_extra_context_tokens
+
+# FFN
+def FeedForward(dim, mult=4):
+ inner_dim = int(dim * mult)
+ return nn.Sequential(
+ nn.LayerNorm(dim),
+ nn.Linear(dim, inner_dim, bias=False),
+ nn.GELU(),
+ nn.Linear(inner_dim, dim, bias=False),
+ )
+
+
+def reshape_tensor(x, heads):
+ bs, length, width = x.shape
+ #(bs, length, width) --> (bs, length, n_heads, dim_per_head)
+ x = x.view(bs, length, heads, -1)
+ # (bs, length, n_heads, dim_per_head) --> (bs, n_heads, length, dim_per_head)
+ x = x.transpose(1, 2)
+ # (bs, n_heads, length, dim_per_head) --> (bs*n_heads, length, dim_per_head)
+ x = x.reshape(bs, heads, length, -1)
+ return x
+
+
+class PerceiverAttention(nn.Module):
+ def __init__(self, *, dim, dim_head=64, heads=8):
+ super().__init__()
+ self.scale = dim_head**-0.5
+ self.dim_head = dim_head
+ self.heads = heads
+ inner_dim = dim_head * heads
+
+ self.norm1 = nn.LayerNorm(dim)
+ self.norm2 = nn.LayerNorm(dim)
+
+ self.to_q = nn.Linear(dim, inner_dim, bias=False)
+ self.to_kv = nn.Linear(dim, inner_dim * 2, bias=False)
+ self.to_out = nn.Linear(inner_dim, dim, bias=False)
+
+
+ def forward(self, x, latents):
+ """
+ Args:
+ x (torch.Tensor): image features
+ shape (b, n1, D)
+ latent (torch.Tensor): latent features
+ shape (b, n2, D)
+ """
+ x = self.norm1(x)
+ latents = self.norm2(latents)
+
+ b, l, _ = latents.shape
+
+ q = self.to_q(latents)
+ kv_input = torch.cat((x, latents), dim=-2)
+ k, v = self.to_kv(kv_input).chunk(2, dim=-1)
+
+ q = reshape_tensor(q, self.heads)
+ k = reshape_tensor(k, self.heads)
+ v = reshape_tensor(v, self.heads)
+
+ # attention
+ scale = 1 / math.sqrt(math.sqrt(self.dim_head))
+ weight = (q * scale) @ (k * scale).transpose(-2, -1) # More stable with f16 than dividing afterwards
+ weight = torch.softmax(weight.float(), dim=-1).type(weight.dtype)
+ out = weight @ v
+
+ out = out.permute(0, 2, 1, 3).reshape(b, l, -1)
+
+ return self.to_out(out)
+
+
+class Resampler(nn.Module):
+ def __init__(
+ self,
+ dim=1024,
+ depth=8,
+ dim_head=64,
+ heads=16,
+ num_queries=8,
+ embedding_dim=768,
+ output_dim=1024,
+ ff_mult=4,
+ ):
+ super().__init__()
+
+ self.latents = nn.Parameter(torch.randn(1, num_queries, dim) / dim**0.5)
+
+ self.proj_in = nn.Linear(embedding_dim, dim)
+
+ self.proj_out = nn.Linear(dim, output_dim)
+ self.norm_out = nn.LayerNorm(output_dim)
+
+ self.layers = nn.ModuleList([])
+ for _ in range(depth):
+ self.layers.append(
+ nn.ModuleList(
+ [
+ PerceiverAttention(dim=dim, dim_head=dim_head, heads=heads),
+ FeedForward(dim=dim, mult=ff_mult),
+ ]
+ )
+ )
+
+ def forward(self, x):
+
+ latents = self.latents.repeat(x.size(0), 1, 1)
+
+ x = self.proj_in(x)
+
+ for attn, ff in self.layers:
+ latents = attn(x, latents) + latents
+ latents = ff(latents) + latents
+
+ latents = self.proj_out(latents)
+ return self.norm_out(latents)
\ No newline at end of file
diff --git a/VADER-VideoCrafter/lvdm/modules/networks/ae_modules.py b/VADER-VideoCrafter/lvdm/modules/networks/ae_modules.py
new file mode 100644
index 0000000000000000000000000000000000000000..210762e56ec00bfd12c720f10bb2d96a3f402d71
--- /dev/null
+++ b/VADER-VideoCrafter/lvdm/modules/networks/ae_modules.py
@@ -0,0 +1,846 @@
+# Copied from VideoCrafter: https://github.com/AILab-CVC/VideoCrafter
+# pytorch_diffusion + derived encoder decoder
+import math
+import torch
+import numpy as np
+import torch.nn as nn
+from einops import rearrange
+from utils.utils import instantiate_from_config
+from lvdm.modules.attention import LinearAttention
+
+def nonlinearity(x):
+ # swish
+ return x*torch.sigmoid(x)
+
+
+def Normalize(in_channels, num_groups=32):
+ return torch.nn.GroupNorm(num_groups=num_groups, num_channels=in_channels, eps=1e-6, affine=True)
+
+
+
+class LinAttnBlock(LinearAttention):
+ """to match AttnBlock usage"""
+ def __init__(self, in_channels):
+ super().__init__(dim=in_channels, heads=1, dim_head=in_channels)
+
+
+class AttnBlock(nn.Module):
+ def __init__(self, in_channels):
+ super().__init__()
+ self.in_channels = in_channels
+
+ self.norm = Normalize(in_channels)
+ self.q = torch.nn.Conv2d(in_channels,
+ in_channels,
+ kernel_size=1,
+ stride=1,
+ padding=0)
+ self.k = torch.nn.Conv2d(in_channels,
+ in_channels,
+ kernel_size=1,
+ stride=1,
+ padding=0)
+ self.v = torch.nn.Conv2d(in_channels,
+ in_channels,
+ kernel_size=1,
+ stride=1,
+ padding=0)
+ self.proj_out = torch.nn.Conv2d(in_channels,
+ in_channels,
+ kernel_size=1,
+ stride=1,
+ padding=0)
+
+ def forward(self, x):
+ h_ = x
+ h_ = self.norm(h_)
+ q = self.q(h_)
+ k = self.k(h_)
+ v = self.v(h_)
+
+ # compute attention
+ b,c,h,w = q.shape
+ q = q.reshape(b,c,h*w) # bcl
+ q = q.permute(0,2,1) # bcl -> blc l=hw
+ k = k.reshape(b,c,h*w) # bcl
+
+ w_ = torch.bmm(q,k) # b,hw,hw w[b,i,j]=sum_c q[b,i,c]k[b,c,j]
+ w_ = w_ * (int(c)**(-0.5))
+ w_ = torch.nn.functional.softmax(w_, dim=2)
+
+ # attend to values
+ v = v.reshape(b,c,h*w)
+ w_ = w_.permute(0,2,1) # b,hw,hw (first hw of k, second of q)
+ h_ = torch.bmm(v,w_) # b, c,hw (hw of q) h_[b,c,j] = sum_i v[b,c,i] w_[b,i,j]
+ h_ = h_.reshape(b,c,h,w)
+
+ h_ = self.proj_out(h_)
+
+ return x+h_
+
+def make_attn(in_channels, attn_type="vanilla"):
+ assert attn_type in ["vanilla", "linear", "none"], f'attn_type {attn_type} unknown'
+ #print(f"making attention of type '{attn_type}' with {in_channels} in_channels")
+ if attn_type == "vanilla":
+ return AttnBlock(in_channels)
+ elif attn_type == "none":
+ return nn.Identity(in_channels)
+ else:
+ return LinAttnBlock(in_channels)
+
+class Downsample(nn.Module):
+ def __init__(self, in_channels, with_conv):
+ super().__init__()
+ self.with_conv = with_conv
+ self.in_channels = in_channels
+ if self.with_conv:
+ # no asymmetric padding in torch conv, must do it ourselves
+ self.conv = torch.nn.Conv2d(in_channels,
+ in_channels,
+ kernel_size=3,
+ stride=2,
+ padding=0)
+ def forward(self, x):
+ if self.with_conv:
+ pad = (0,1,0,1)
+ x = torch.nn.functional.pad(x, pad, mode="constant", value=0)
+ x = self.conv(x)
+ else:
+ x = torch.nn.functional.avg_pool2d(x, kernel_size=2, stride=2)
+ return x
+
+class Upsample(nn.Module):
+ def __init__(self, in_channels, with_conv):
+ super().__init__()
+ self.with_conv = with_conv
+ self.in_channels = in_channels
+ if self.with_conv:
+ self.conv = torch.nn.Conv2d(in_channels,
+ in_channels,
+ kernel_size=3,
+ stride=1,
+ padding=1)
+
+ def forward(self, x):
+ x = torch.nn.functional.interpolate(x, scale_factor=2.0, mode="nearest")
+ if self.with_conv:
+ x = self.conv(x)
+ return x
+
+def get_timestep_embedding(timesteps, embedding_dim):
+ """
+ This matches the implementation in Denoising Diffusion Probabilistic Models:
+ From Fairseq.
+ Build sinusoidal embeddings.
+ This matches the implementation in tensor2tensor, but differs slightly
+ from the description in Section 3.5 of "Attention Is All You Need".
+ """
+ assert len(timesteps.shape) == 1
+
+ half_dim = embedding_dim // 2
+ emb = math.log(10000) / (half_dim - 1)
+ emb = torch.exp(torch.arange(half_dim, dtype=torch.float32) * -emb)
+ emb = emb.to(device=timesteps.device)
+ emb = timesteps.float()[:, None] * emb[None, :]
+ emb = torch.cat([torch.sin(emb), torch.cos(emb)], dim=1)
+ if embedding_dim % 2 == 1: # zero pad
+ emb = torch.nn.functional.pad(emb, (0,1,0,0))
+ return emb
+
+
+
+class ResnetBlock(nn.Module):
+ def __init__(self, *, in_channels, out_channels=None, conv_shortcut=False,
+ dropout, temb_channels=512):
+ super().__init__()
+ self.in_channels = in_channels
+ out_channels = in_channels if out_channels is None else out_channels
+ self.out_channels = out_channels
+ self.use_conv_shortcut = conv_shortcut
+
+ self.norm1 = Normalize(in_channels)
+ self.conv1 = torch.nn.Conv2d(in_channels,
+ out_channels,
+ kernel_size=3,
+ stride=1,
+ padding=1)
+ if temb_channels > 0:
+ self.temb_proj = torch.nn.Linear(temb_channels,
+ out_channels)
+ self.norm2 = Normalize(out_channels)
+ self.dropout = torch.nn.Dropout(dropout)
+ self.conv2 = torch.nn.Conv2d(out_channels,
+ out_channels,
+ kernel_size=3,
+ stride=1,
+ padding=1)
+ if self.in_channels != self.out_channels:
+ if self.use_conv_shortcut:
+ self.conv_shortcut = torch.nn.Conv2d(in_channels,
+ out_channels,
+ kernel_size=3,
+ stride=1,
+ padding=1)
+ else:
+ self.nin_shortcut = torch.nn.Conv2d(in_channels,
+ out_channels,
+ kernel_size=1,
+ stride=1,
+ padding=0)
+
+ def forward(self, x, temb):
+ h = x
+ h = self.norm1(h)
+ h = nonlinearity(h)
+ h = self.conv1(h)
+
+ if temb is not None:
+ h = h + self.temb_proj(nonlinearity(temb))[:,:,None,None]
+
+ h = self.norm2(h)
+ h = nonlinearity(h)
+ h = self.dropout(h)
+ h = self.conv2(h)
+
+ if self.in_channels != self.out_channels:
+ if self.use_conv_shortcut:
+ x = self.conv_shortcut(x)
+ else:
+ x = self.nin_shortcut(x)
+
+ return x+h
+
+class Model(nn.Module):
+ def __init__(self, *, ch, out_ch, ch_mult=(1,2,4,8), num_res_blocks,
+ attn_resolutions, dropout=0.0, resamp_with_conv=True, in_channels,
+ resolution, use_timestep=True, use_linear_attn=False, attn_type="vanilla"):
+ super().__init__()
+ if use_linear_attn: attn_type = "linear"
+ self.ch = ch
+ self.temb_ch = self.ch*4
+ self.num_resolutions = len(ch_mult)
+ self.num_res_blocks = num_res_blocks
+ self.resolution = resolution
+ self.in_channels = in_channels
+
+ self.use_timestep = use_timestep
+ if self.use_timestep:
+ # timestep embedding
+ self.temb = nn.Module()
+ self.temb.dense = nn.ModuleList([
+ torch.nn.Linear(self.ch,
+ self.temb_ch),
+ torch.nn.Linear(self.temb_ch,
+ self.temb_ch),
+ ])
+
+ # downsampling
+ self.conv_in = torch.nn.Conv2d(in_channels,
+ self.ch,
+ kernel_size=3,
+ stride=1,
+ padding=1)
+
+ curr_res = resolution
+ in_ch_mult = (1,)+tuple(ch_mult)
+ self.down = nn.ModuleList()
+ for i_level in range(self.num_resolutions):
+ block = nn.ModuleList()
+ attn = nn.ModuleList()
+ block_in = ch*in_ch_mult[i_level]
+ block_out = ch*ch_mult[i_level]
+ for i_block in range(self.num_res_blocks):
+ block.append(ResnetBlock(in_channels=block_in,
+ out_channels=block_out,
+ temb_channels=self.temb_ch,
+ dropout=dropout))
+ block_in = block_out
+ if curr_res in attn_resolutions:
+ attn.append(make_attn(block_in, attn_type=attn_type))
+ down = nn.Module()
+ down.block = block
+ down.attn = attn
+ if i_level != self.num_resolutions-1:
+ down.downsample = Downsample(block_in, resamp_with_conv)
+ curr_res = curr_res // 2
+ self.down.append(down)
+
+ # middle
+ self.mid = nn.Module()
+ self.mid.block_1 = ResnetBlock(in_channels=block_in,
+ out_channels=block_in,
+ temb_channels=self.temb_ch,
+ dropout=dropout)
+ self.mid.attn_1 = make_attn(block_in, attn_type=attn_type)
+ self.mid.block_2 = ResnetBlock(in_channels=block_in,
+ out_channels=block_in,
+ temb_channels=self.temb_ch,
+ dropout=dropout)
+
+ # upsampling
+ self.up = nn.ModuleList()
+ for i_level in reversed(range(self.num_resolutions)):
+ block = nn.ModuleList()
+ attn = nn.ModuleList()
+ block_out = ch*ch_mult[i_level]
+ skip_in = ch*ch_mult[i_level]
+ for i_block in range(self.num_res_blocks+1):
+ if i_block == self.num_res_blocks:
+ skip_in = ch*in_ch_mult[i_level]
+ block.append(ResnetBlock(in_channels=block_in+skip_in,
+ out_channels=block_out,
+ temb_channels=self.temb_ch,
+ dropout=dropout))
+ block_in = block_out
+ if curr_res in attn_resolutions:
+ attn.append(make_attn(block_in, attn_type=attn_type))
+ up = nn.Module()
+ up.block = block
+ up.attn = attn
+ if i_level != 0:
+ up.upsample = Upsample(block_in, resamp_with_conv)
+ curr_res = curr_res * 2
+ self.up.insert(0, up) # prepend to get consistent order
+
+ # end
+ self.norm_out = Normalize(block_in)
+ self.conv_out = torch.nn.Conv2d(block_in,
+ out_ch,
+ kernel_size=3,
+ stride=1,
+ padding=1)
+
+ def forward(self, x, t=None, context=None):
+ #assert x.shape[2] == x.shape[3] == self.resolution
+ if context is not None:
+ # assume aligned context, cat along channel axis
+ x = torch.cat((x, context), dim=1)
+ if self.use_timestep:
+ # timestep embedding
+ assert t is not None
+ temb = get_timestep_embedding(t, self.ch)
+ temb = self.temb.dense[0](temb)
+ temb = nonlinearity(temb)
+ temb = self.temb.dense[1](temb)
+ else:
+ temb = None
+
+ # downsampling
+ hs = [self.conv_in(x)]
+ for i_level in range(self.num_resolutions):
+ for i_block in range(self.num_res_blocks):
+ h = self.down[i_level].block[i_block](hs[-1], temb)
+ if len(self.down[i_level].attn) > 0:
+ h = self.down[i_level].attn[i_block](h)
+ hs.append(h)
+ if i_level != self.num_resolutions-1:
+ hs.append(self.down[i_level].downsample(hs[-1]))
+
+ # middle
+ h = hs[-1]
+ h = self.mid.block_1(h, temb)
+ h = self.mid.attn_1(h)
+ h = self.mid.block_2(h, temb)
+
+ # upsampling
+ for i_level in reversed(range(self.num_resolutions)):
+ for i_block in range(self.num_res_blocks+1):
+ h = self.up[i_level].block[i_block](
+ torch.cat([h, hs.pop()], dim=1), temb)
+ if len(self.up[i_level].attn) > 0:
+ h = self.up[i_level].attn[i_block](h)
+ if i_level != 0:
+ h = self.up[i_level].upsample(h)
+
+ # end
+ h = self.norm_out(h)
+ h = nonlinearity(h)
+ h = self.conv_out(h)
+ return h
+
+ def get_last_layer(self):
+ return self.conv_out.weight
+
+
+class Encoder(nn.Module):
+ def __init__(self, *, ch, out_ch, ch_mult=(1,2,4,8), num_res_blocks,
+ attn_resolutions, dropout=0.0, resamp_with_conv=True, in_channels,
+ resolution, z_channels, double_z=True, use_linear_attn=False, attn_type="vanilla",
+ **ignore_kwargs):
+ super().__init__()
+ if use_linear_attn: attn_type = "linear"
+ self.ch = ch
+ self.temb_ch = 0
+ self.num_resolutions = len(ch_mult)
+ self.num_res_blocks = num_res_blocks
+ self.resolution = resolution
+ self.in_channels = in_channels
+
+ # downsampling
+ self.conv_in = torch.nn.Conv2d(in_channels,
+ self.ch,
+ kernel_size=3,
+ stride=1,
+ padding=1)
+
+ curr_res = resolution
+ in_ch_mult = (1,)+tuple(ch_mult)
+ self.in_ch_mult = in_ch_mult
+ self.down = nn.ModuleList()
+ for i_level in range(self.num_resolutions):
+ block = nn.ModuleList()
+ attn = nn.ModuleList()
+ block_in = ch*in_ch_mult[i_level]
+ block_out = ch*ch_mult[i_level]
+ for i_block in range(self.num_res_blocks):
+ block.append(ResnetBlock(in_channels=block_in,
+ out_channels=block_out,
+ temb_channels=self.temb_ch,
+ dropout=dropout))
+ block_in = block_out
+ if curr_res in attn_resolutions:
+ attn.append(make_attn(block_in, attn_type=attn_type))
+ down = nn.Module()
+ down.block = block
+ down.attn = attn
+ if i_level != self.num_resolutions-1:
+ down.downsample = Downsample(block_in, resamp_with_conv)
+ curr_res = curr_res // 2
+ self.down.append(down)
+
+ # middle
+ self.mid = nn.Module()
+ self.mid.block_1 = ResnetBlock(in_channels=block_in,
+ out_channels=block_in,
+ temb_channels=self.temb_ch,
+ dropout=dropout)
+ self.mid.attn_1 = make_attn(block_in, attn_type=attn_type)
+ self.mid.block_2 = ResnetBlock(in_channels=block_in,
+ out_channels=block_in,
+ temb_channels=self.temb_ch,
+ dropout=dropout)
+
+ # end
+ self.norm_out = Normalize(block_in)
+ self.conv_out = torch.nn.Conv2d(block_in,
+ 2*z_channels if double_z else z_channels,
+ kernel_size=3,
+ stride=1,
+ padding=1)
+
+ def forward(self, x):
+ # timestep embedding
+ temb = None
+
+ # print(f'encoder-input={x.shape}')
+ # downsampling
+ hs = [self.conv_in(x)]
+ # print(f'encoder-conv in feat={hs[0].shape}')
+ for i_level in range(self.num_resolutions):
+ for i_block in range(self.num_res_blocks):
+ h = self.down[i_level].block[i_block](hs[-1], temb)
+ # print(f'encoder-down feat={h.shape}')
+ if len(self.down[i_level].attn) > 0:
+ h = self.down[i_level].attn[i_block](h)
+ hs.append(h)
+ if i_level != self.num_resolutions-1:
+ # print(f'encoder-downsample (input)={hs[-1].shape}')
+ hs.append(self.down[i_level].downsample(hs[-1]))
+ # print(f'encoder-downsample (output)={hs[-1].shape}')
+
+ # middle
+ h = hs[-1]
+ h = self.mid.block_1(h, temb)
+ # print(f'encoder-mid1 feat={h.shape}')
+ h = self.mid.attn_1(h)
+ h = self.mid.block_2(h, temb)
+ # print(f'encoder-mid2 feat={h.shape}')
+
+ # end
+ h = self.norm_out(h)
+ h = nonlinearity(h)
+ h = self.conv_out(h)
+ # print(f'end feat={h.shape}')
+ return h
+
+
+class Decoder(nn.Module):
+ def __init__(self, *, ch, out_ch, ch_mult=(1,2,4,8), num_res_blocks,
+ attn_resolutions, dropout=0.0, resamp_with_conv=True, in_channels,
+ resolution, z_channels, give_pre_end=False, tanh_out=False, use_linear_attn=False,
+ attn_type="vanilla", **ignorekwargs):
+ super().__init__()
+ if use_linear_attn: attn_type = "linear"
+ self.ch = ch
+ self.temb_ch = 0
+ self.num_resolutions = len(ch_mult)
+ self.num_res_blocks = num_res_blocks
+ self.resolution = resolution
+ self.in_channels = in_channels
+ self.give_pre_end = give_pre_end
+ self.tanh_out = tanh_out
+
+ # compute in_ch_mult, block_in and curr_res at lowest res
+ in_ch_mult = (1,)+tuple(ch_mult)
+ block_in = ch*ch_mult[self.num_resolutions-1]
+ curr_res = resolution // 2**(self.num_resolutions-1)
+ self.z_shape = (1,z_channels,curr_res,curr_res)
+ print("AE working on z of shape {} = {} dimensions.".format(
+ self.z_shape, np.prod(self.z_shape)))
+
+ # z to block_in
+ self.conv_in = torch.nn.Conv2d(z_channels,
+ block_in,
+ kernel_size=3,
+ stride=1,
+ padding=1)
+
+ # middle
+ self.mid = nn.Module()
+ self.mid.block_1 = ResnetBlock(in_channels=block_in,
+ out_channels=block_in,
+ temb_channels=self.temb_ch,
+ dropout=dropout)
+ self.mid.attn_1 = make_attn(block_in, attn_type=attn_type)
+ self.mid.block_2 = ResnetBlock(in_channels=block_in,
+ out_channels=block_in,
+ temb_channels=self.temb_ch,
+ dropout=dropout)
+
+ # upsampling
+ self.up = nn.ModuleList()
+ for i_level in reversed(range(self.num_resolutions)):
+ block = nn.ModuleList()
+ attn = nn.ModuleList()
+ block_out = ch*ch_mult[i_level]
+ for i_block in range(self.num_res_blocks+1):
+ block.append(ResnetBlock(in_channels=block_in,
+ out_channels=block_out,
+ temb_channels=self.temb_ch,
+ dropout=dropout))
+ block_in = block_out
+ if curr_res in attn_resolutions:
+ attn.append(make_attn(block_in, attn_type=attn_type))
+ up = nn.Module()
+ up.block = block
+ up.attn = attn
+ if i_level != 0:
+ up.upsample = Upsample(block_in, resamp_with_conv)
+ curr_res = curr_res * 2
+ self.up.insert(0, up) # prepend to get consistent order
+
+ # end
+ self.norm_out = Normalize(block_in)
+ self.conv_out = torch.nn.Conv2d(block_in,
+ out_ch,
+ kernel_size=3,
+ stride=1,
+ padding=1)
+
+ def forward(self, z):
+ #assert z.shape[1:] == self.z_shape[1:]
+ self.last_z_shape = z.shape
+
+ # print(f'decoder-input={z.shape}')
+ # timestep embedding
+ temb = None
+
+ # z to block_in
+ h = self.conv_in(z)
+ # print(f'decoder-conv in feat={h.shape}')
+
+ # middle
+ h = self.mid.block_1(h, temb)
+ h = self.mid.attn_1(h)
+ h = self.mid.block_2(h, temb)
+ # print(f'decoder-mid feat={h.shape}')
+
+ # upsampling
+ for i_level in reversed(range(self.num_resolutions)):
+ for i_block in range(self.num_res_blocks+1):
+ h = self.up[i_level].block[i_block](h, temb)
+ if len(self.up[i_level].attn) > 0:
+ h = self.up[i_level].attn[i_block](h)
+ # print(f'decoder-up feat={h.shape}')
+ if i_level != 0:
+ h = self.up[i_level].upsample(h)
+ # print(f'decoder-upsample feat={h.shape}')
+
+ # end
+ if self.give_pre_end:
+ return h
+
+ h = self.norm_out(h)
+ h = nonlinearity(h)
+ h = self.conv_out(h)
+ # print(f'decoder-conv_out feat={h.shape}')
+ if self.tanh_out:
+ h = torch.tanh(h)
+ return h
+
+
+class SimpleDecoder(nn.Module):
+ def __init__(self, in_channels, out_channels, *args, **kwargs):
+ super().__init__()
+ self.model = nn.ModuleList([nn.Conv2d(in_channels, in_channels, 1),
+ ResnetBlock(in_channels=in_channels,
+ out_channels=2 * in_channels,
+ temb_channels=0, dropout=0.0),
+ ResnetBlock(in_channels=2 * in_channels,
+ out_channels=4 * in_channels,
+ temb_channels=0, dropout=0.0),
+ ResnetBlock(in_channels=4 * in_channels,
+ out_channels=2 * in_channels,
+ temb_channels=0, dropout=0.0),
+ nn.Conv2d(2*in_channels, in_channels, 1),
+ Upsample(in_channels, with_conv=True)])
+ # end
+ self.norm_out = Normalize(in_channels)
+ self.conv_out = torch.nn.Conv2d(in_channels,
+ out_channels,
+ kernel_size=3,
+ stride=1,
+ padding=1)
+
+ def forward(self, x):
+ for i, layer in enumerate(self.model):
+ if i in [1,2,3]:
+ x = layer(x, None)
+ else:
+ x = layer(x)
+
+ h = self.norm_out(x)
+ h = nonlinearity(h)
+ x = self.conv_out(h)
+ return x
+
+
+class UpsampleDecoder(nn.Module):
+ def __init__(self, in_channels, out_channels, ch, num_res_blocks, resolution,
+ ch_mult=(2,2), dropout=0.0):
+ super().__init__()
+ # upsampling
+ self.temb_ch = 0
+ self.num_resolutions = len(ch_mult)
+ self.num_res_blocks = num_res_blocks
+ block_in = in_channels
+ curr_res = resolution // 2 ** (self.num_resolutions - 1)
+ self.res_blocks = nn.ModuleList()
+ self.upsample_blocks = nn.ModuleList()
+ for i_level in range(self.num_resolutions):
+ res_block = []
+ block_out = ch * ch_mult[i_level]
+ for i_block in range(self.num_res_blocks + 1):
+ res_block.append(ResnetBlock(in_channels=block_in,
+ out_channels=block_out,
+ temb_channels=self.temb_ch,
+ dropout=dropout))
+ block_in = block_out
+ self.res_blocks.append(nn.ModuleList(res_block))
+ if i_level != self.num_resolutions - 1:
+ self.upsample_blocks.append(Upsample(block_in, True))
+ curr_res = curr_res * 2
+
+ # end
+ self.norm_out = Normalize(block_in)
+ self.conv_out = torch.nn.Conv2d(block_in,
+ out_channels,
+ kernel_size=3,
+ stride=1,
+ padding=1)
+
+ def forward(self, x):
+ # upsampling
+ h = x
+ for k, i_level in enumerate(range(self.num_resolutions)):
+ for i_block in range(self.num_res_blocks + 1):
+ h = self.res_blocks[i_level][i_block](h, None)
+ if i_level != self.num_resolutions - 1:
+ h = self.upsample_blocks[k](h)
+ h = self.norm_out(h)
+ h = nonlinearity(h)
+ h = self.conv_out(h)
+ return h
+
+
+class LatentRescaler(nn.Module):
+ def __init__(self, factor, in_channels, mid_channels, out_channels, depth=2):
+ super().__init__()
+ # residual block, interpolate, residual block
+ self.factor = factor
+ self.conv_in = nn.Conv2d(in_channels,
+ mid_channels,
+ kernel_size=3,
+ stride=1,
+ padding=1)
+ self.res_block1 = nn.ModuleList([ResnetBlock(in_channels=mid_channels,
+ out_channels=mid_channels,
+ temb_channels=0,
+ dropout=0.0) for _ in range(depth)])
+ self.attn = AttnBlock(mid_channels)
+ self.res_block2 = nn.ModuleList([ResnetBlock(in_channels=mid_channels,
+ out_channels=mid_channels,
+ temb_channels=0,
+ dropout=0.0) for _ in range(depth)])
+
+ self.conv_out = nn.Conv2d(mid_channels,
+ out_channels,
+ kernel_size=1,
+ )
+
+ def forward(self, x):
+ x = self.conv_in(x)
+ for block in self.res_block1:
+ x = block(x, None)
+ x = torch.nn.functional.interpolate(x, size=(int(round(x.shape[2]*self.factor)), int(round(x.shape[3]*self.factor))))
+ x = self.attn(x)
+ for block in self.res_block2:
+ x = block(x, None)
+ x = self.conv_out(x)
+ return x
+
+
+class MergedRescaleEncoder(nn.Module):
+ def __init__(self, in_channels, ch, resolution, out_ch, num_res_blocks,
+ attn_resolutions, dropout=0.0, resamp_with_conv=True,
+ ch_mult=(1,2,4,8), rescale_factor=1.0, rescale_module_depth=1):
+ super().__init__()
+ intermediate_chn = ch * ch_mult[-1]
+ self.encoder = Encoder(in_channels=in_channels, num_res_blocks=num_res_blocks, ch=ch, ch_mult=ch_mult,
+ z_channels=intermediate_chn, double_z=False, resolution=resolution,
+ attn_resolutions=attn_resolutions, dropout=dropout, resamp_with_conv=resamp_with_conv,
+ out_ch=None)
+ self.rescaler = LatentRescaler(factor=rescale_factor, in_channels=intermediate_chn,
+ mid_channels=intermediate_chn, out_channels=out_ch, depth=rescale_module_depth)
+
+ def forward(self, x):
+ x = self.encoder(x)
+ x = self.rescaler(x)
+ return x
+
+
+class MergedRescaleDecoder(nn.Module):
+ def __init__(self, z_channels, out_ch, resolution, num_res_blocks, attn_resolutions, ch, ch_mult=(1,2,4,8),
+ dropout=0.0, resamp_with_conv=True, rescale_factor=1.0, rescale_module_depth=1):
+ super().__init__()
+ tmp_chn = z_channels*ch_mult[-1]
+ self.decoder = Decoder(out_ch=out_ch, z_channels=tmp_chn, attn_resolutions=attn_resolutions, dropout=dropout,
+ resamp_with_conv=resamp_with_conv, in_channels=None, num_res_blocks=num_res_blocks,
+ ch_mult=ch_mult, resolution=resolution, ch=ch)
+ self.rescaler = LatentRescaler(factor=rescale_factor, in_channels=z_channels, mid_channels=tmp_chn,
+ out_channels=tmp_chn, depth=rescale_module_depth)
+
+ def forward(self, x):
+ x = self.rescaler(x)
+ x = self.decoder(x)
+ return x
+
+
+class Upsampler(nn.Module):
+ def __init__(self, in_size, out_size, in_channels, out_channels, ch_mult=2):
+ super().__init__()
+ assert out_size >= in_size
+ num_blocks = int(np.log2(out_size//in_size))+1
+ factor_up = 1.+ (out_size % in_size)
+ print(f"Building {self.__class__.__name__} with in_size: {in_size} --> out_size {out_size} and factor {factor_up}")
+ self.rescaler = LatentRescaler(factor=factor_up, in_channels=in_channels, mid_channels=2*in_channels,
+ out_channels=in_channels)
+ self.decoder = Decoder(out_ch=out_channels, resolution=out_size, z_channels=in_channels, num_res_blocks=2,
+ attn_resolutions=[], in_channels=None, ch=in_channels,
+ ch_mult=[ch_mult for _ in range(num_blocks)])
+
+ def forward(self, x):
+ x = self.rescaler(x)
+ x = self.decoder(x)
+ return x
+
+
+class Resize(nn.Module):
+ def __init__(self, in_channels=None, learned=False, mode="bilinear"):
+ super().__init__()
+ self.with_conv = learned
+ self.mode = mode
+ if self.with_conv:
+ print(f"Note: {self.__class__.__name} uses learned downsampling and will ignore the fixed {mode} mode")
+ raise NotImplementedError()
+ assert in_channels is not None
+ # no asymmetric padding in torch conv, must do it ourselves
+ self.conv = torch.nn.Conv2d(in_channels,
+ in_channels,
+ kernel_size=4,
+ stride=2,
+ padding=1)
+
+ def forward(self, x, scale_factor=1.0):
+ if scale_factor==1.0:
+ return x
+ else:
+ x = torch.nn.functional.interpolate(x, mode=self.mode, align_corners=False, scale_factor=scale_factor)
+ return x
+
+class FirstStagePostProcessor(nn.Module):
+
+ def __init__(self, ch_mult:list, in_channels,
+ pretrained_model:nn.Module=None,
+ reshape=False,
+ n_channels=None,
+ dropout=0.,
+ pretrained_config=None):
+ super().__init__()
+ if pretrained_config is None:
+ assert pretrained_model is not None, 'Either "pretrained_model" or "pretrained_config" must not be None'
+ self.pretrained_model = pretrained_model
+ else:
+ assert pretrained_config is not None, 'Either "pretrained_model" or "pretrained_config" must not be None'
+ self.instantiate_pretrained(pretrained_config)
+
+ self.do_reshape = reshape
+
+ if n_channels is None:
+ n_channels = self.pretrained_model.encoder.ch
+
+ self.proj_norm = Normalize(in_channels,num_groups=in_channels//2)
+ self.proj = nn.Conv2d(in_channels,n_channels,kernel_size=3,
+ stride=1,padding=1)
+
+ blocks = []
+ downs = []
+ ch_in = n_channels
+ for m in ch_mult:
+ blocks.append(ResnetBlock(in_channels=ch_in,out_channels=m*n_channels,dropout=dropout))
+ ch_in = m * n_channels
+ downs.append(Downsample(ch_in, with_conv=False))
+
+ self.model = nn.ModuleList(blocks)
+ self.downsampler = nn.ModuleList(downs)
+
+
+ def instantiate_pretrained(self, config):
+ model = instantiate_from_config(config)
+ self.pretrained_model = model.eval()
+ # self.pretrained_model.train = False
+ for param in self.pretrained_model.parameters():
+ param.requires_grad = False
+
+
+ @torch.no_grad()
+ def encode_with_pretrained(self,x):
+ c = self.pretrained_model.encode(x)
+ if isinstance(c, DiagonalGaussianDistribution):
+ c = c.mode()
+ return c
+
+ def forward(self,x):
+ z_fs = self.encode_with_pretrained(x)
+ z = self.proj_norm(z_fs)
+ z = self.proj(z)
+ z = nonlinearity(z)
+
+ for submodel, downmodel in zip(self.model,self.downsampler):
+ z = submodel(z,temb=None)
+ z = downmodel(z)
+
+ if self.do_reshape:
+ z = rearrange(z,'b c h w -> b (h w) c')
+ return z
+
diff --git a/VADER-VideoCrafter/lvdm/modules/networks/openaimodel3d.py b/VADER-VideoCrafter/lvdm/modules/networks/openaimodel3d.py
new file mode 100644
index 0000000000000000000000000000000000000000..b6882d01be932f949b7ae790917d48f3a152305b
--- /dev/null
+++ b/VADER-VideoCrafter/lvdm/modules/networks/openaimodel3d.py
@@ -0,0 +1,578 @@
+# Copied from VideoCrafter: https://github.com/AILab-CVC/VideoCrafter
+from functools import partial
+from abc import abstractmethod
+import torch
+import torch.nn as nn
+from einops import rearrange
+import torch.nn.functional as F
+from lvdm.models.utils_diffusion import timestep_embedding
+from lvdm.common import checkpoint
+from lvdm.basics import (
+ zero_module,
+ conv_nd,
+ linear,
+ avg_pool_nd,
+ normalization
+)
+from lvdm.modules.attention import SpatialTransformer, TemporalTransformer
+
+
+class TimestepBlock(nn.Module):
+ """
+ Any module where forward() takes timestep embeddings as a second argument.
+ """
+ @abstractmethod
+ def forward(self, x, emb):
+ """
+ Apply the module to `x` given `emb` timestep embeddings.
+ """
+
+
+class TimestepEmbedSequential(nn.Sequential, TimestepBlock):
+ """
+ A sequential module that passes timestep embeddings to the children that
+ support it as an extra input.
+ """
+
+ def forward(self, x, emb, context=None, batch_size=None):
+ for layer in self:
+ if isinstance(layer, TimestepBlock):
+ x = layer(x, emb, batch_size)
+ elif isinstance(layer, SpatialTransformer):
+ x = layer(x, context)
+ elif isinstance(layer, TemporalTransformer):
+ x = rearrange(x, '(b f) c h w -> b c f h w', b=batch_size)
+ x = layer(x, context)
+ x = rearrange(x, 'b c f h w -> (b f) c h w')
+ else:
+ x = layer(x,)
+ return x
+
+
+class Downsample(nn.Module):
+ """
+ A downsampling layer with an optional convolution.
+ :param channels: channels in the inputs and outputs.
+ :param use_conv: a bool determining if a convolution is applied.
+ :param dims: determines if the signal is 1D, 2D, or 3D. If 3D, then
+ downsampling occurs in the inner-two dimensions.
+ """
+
+ def __init__(self, channels, use_conv, dims=2, out_channels=None, padding=1):
+ super().__init__()
+ self.channels = channels
+ self.out_channels = out_channels or channels
+ self.use_conv = use_conv
+ self.dims = dims
+ stride = 2 if dims != 3 else (1, 2, 2)
+ if use_conv:
+ self.op = conv_nd(
+ dims, self.channels, self.out_channels, 3, stride=stride, padding=padding
+ )
+ else:
+ assert self.channels == self.out_channels
+ self.op = avg_pool_nd(dims, kernel_size=stride, stride=stride)
+
+ def forward(self, x):
+ assert x.shape[1] == self.channels
+ return self.op(x)
+
+
+class Upsample(nn.Module):
+ """
+ An upsampling layer with an optional convolution.
+ :param channels: channels in the inputs and outputs.
+ :param use_conv: a bool determining if a convolution is applied.
+ :param dims: determines if the signal is 1D, 2D, or 3D. If 3D, then
+ upsampling occurs in the inner-two dimensions.
+ """
+
+ def __init__(self, channels, use_conv, dims=2, out_channels=None, padding=1):
+ super().__init__()
+ self.channels = channels
+ self.out_channels = out_channels or channels
+ self.use_conv = use_conv
+ self.dims = dims
+ if use_conv:
+ self.conv = conv_nd(dims, self.channels, self.out_channels, 3, padding=padding)
+
+ def forward(self, x):
+ assert x.shape[1] == self.channels
+ if self.dims == 3:
+ x = F.interpolate(x, (x.shape[2], x.shape[3] * 2, x.shape[4] * 2), mode='nearest')
+ else:
+ x = F.interpolate(x, scale_factor=2, mode='nearest')
+ if self.use_conv:
+ x = self.conv(x)
+ return x
+
+
+class ResBlock(TimestepBlock):
+ """
+ A residual block that can optionally change the number of channels.
+ :param channels: the number of input channels.
+ :param emb_channels: the number of timestep embedding channels.
+ :param dropout: the rate of dropout.
+ :param out_channels: if specified, the number of out channels.
+ :param use_conv: if True and out_channels is specified, use a spatial
+ convolution instead of a smaller 1x1 convolution to change the
+ channels in the skip connection.
+ :param dims: determines if the signal is 1D, 2D, or 3D.
+ :param up: if True, use this block for upsampling.
+ :param down: if True, use this block for downsampling.
+ """
+
+ def __init__(
+ self,
+ channels,
+ emb_channels,
+ dropout,
+ out_channels=None,
+ use_scale_shift_norm=False,
+ dims=2,
+ use_checkpoint=False,
+ use_conv=False,
+ up=False,
+ down=False,
+ use_temporal_conv=False,
+ tempspatial_aware=False
+ ):
+ super().__init__()
+ self.channels = channels
+ self.emb_channels = emb_channels
+ self.dropout = dropout
+ self.out_channels = out_channels or channels
+ self.use_conv = use_conv
+ self.use_checkpoint = use_checkpoint
+ self.use_scale_shift_norm = use_scale_shift_norm
+ self.use_temporal_conv = use_temporal_conv
+
+ self.in_layers = nn.Sequential(
+ normalization(channels),
+ nn.SiLU(),
+ conv_nd(dims, channels, self.out_channels, 3, padding=1),
+ )
+
+ self.updown = up or down
+
+ if up:
+ self.h_upd = Upsample(channels, False, dims)
+ self.x_upd = Upsample(channels, False, dims)
+ elif down:
+ self.h_upd = Downsample(channels, False, dims)
+ self.x_upd = Downsample(channels, False, dims)
+ else:
+ self.h_upd = self.x_upd = nn.Identity()
+
+ self.emb_layers = nn.Sequential(
+ nn.SiLU(),
+ nn.Linear(
+ emb_channels,
+ 2 * self.out_channels if use_scale_shift_norm else self.out_channels,
+ ),
+ )
+ self.out_layers = nn.Sequential(
+ normalization(self.out_channels),
+ nn.SiLU(),
+ nn.Dropout(p=dropout),
+ zero_module(nn.Conv2d(self.out_channels, self.out_channels, 3, padding=1)),
+ )
+
+ if self.out_channels == channels:
+ self.skip_connection = nn.Identity()
+ elif use_conv:
+ self.skip_connection = conv_nd(dims, channels, self.out_channels, 3, padding=1)
+ else:
+ self.skip_connection = conv_nd(dims, channels, self.out_channels, 1)
+
+ if self.use_temporal_conv:
+ self.temopral_conv = TemporalConvBlock(
+ self.out_channels,
+ self.out_channels,
+ dropout=0.1,
+ spatial_aware=tempspatial_aware
+ )
+
+ def forward(self, x, emb, batch_size=None):
+ """
+ Apply the block to a Tensor, conditioned on a timestep embedding.
+ :param x: an [N x C x ...] Tensor of features.
+ :param emb: an [N x emb_channels] Tensor of timestep embeddings.
+ :return: an [N x C x ...] Tensor of outputs.
+ """
+ input_tuple = (x, emb,)
+ if batch_size:
+ forward_batchsize = partial(self._forward, batch_size=batch_size)
+ return checkpoint(forward_batchsize, input_tuple, self.parameters(), self.use_checkpoint)
+ return checkpoint(self._forward, input_tuple, self.parameters(), self.use_checkpoint)
+
+ def _forward(self, x, emb, batch_size=None,):
+ if self.updown:
+ in_rest, in_conv = self.in_layers[:-1], self.in_layers[-1]
+ h = in_rest(x)
+ h = self.h_upd(h)
+ x = self.x_upd(x)
+ h = in_conv(h)
+ else:
+ h = self.in_layers(x)
+ emb_out = self.emb_layers(emb).type(h.dtype)
+ while len(emb_out.shape) < len(h.shape):
+ emb_out = emb_out[..., None]
+ if self.use_scale_shift_norm:
+ out_norm, out_rest = self.out_layers[0], self.out_layers[1:]
+ scale, shift = torch.chunk(emb_out, 2, dim=1)
+ h = out_norm(h) * (1 + scale) + shift
+ h = out_rest(h)
+ else:
+ h = h + emb_out
+ h = self.out_layers(h)
+ h = self.skip_connection(x) + h
+
+ if self.use_temporal_conv and batch_size:
+ h = rearrange(h, '(b t) c h w -> b c t h w', b=batch_size)
+ h = self.temopral_conv(h)
+ h = rearrange(h, 'b c t h w -> (b t) c h w')
+ return h
+
+
+class TemporalConvBlock(nn.Module):
+ """
+ Adapted from modelscope: https://github.com/modelscope/modelscope/blob/master/modelscope/models/multi_modal/video_synthesis/unet_sd.py
+ """
+
+ def __init__(self, in_channels, out_channels=None, dropout=0.0, spatial_aware=False):
+ super(TemporalConvBlock, self).__init__()
+ if out_channels is None:
+ out_channels = in_channels
+ self.in_channels = in_channels
+ self.out_channels = out_channels
+ kernel_shape = (3, 1, 1) if not spatial_aware else (3, 3, 3)
+ padding_shape = (1, 0, 0) if not spatial_aware else (1, 1, 1)
+
+ # conv layers
+ self.conv1 = nn.Sequential(
+ nn.GroupNorm(32, in_channels), nn.SiLU(),
+ nn.Conv3d(in_channels, out_channels, kernel_shape, padding=padding_shape))
+ self.conv2 = nn.Sequential(
+ nn.GroupNorm(32, out_channels), nn.SiLU(), nn.Dropout(dropout),
+ nn.Conv3d(out_channels, in_channels, kernel_shape, padding=padding_shape))
+ self.conv3 = nn.Sequential(
+ nn.GroupNorm(32, out_channels), nn.SiLU(), nn.Dropout(dropout),
+ nn.Conv3d(out_channels, in_channels, (3, 1, 1), padding=(1, 0, 0)))
+ self.conv4 = nn.Sequential(
+ nn.GroupNorm(32, out_channels), nn.SiLU(), nn.Dropout(dropout),
+ nn.Conv3d(out_channels, in_channels, (3, 1, 1), padding=(1, 0, 0)))
+
+ # zero out the last layer params,so the conv block is identity
+ nn.init.zeros_(self.conv4[-1].weight)
+ nn.init.zeros_(self.conv4[-1].bias)
+
+ def forward(self, x):
+ identity = x
+ x = self.conv1(x)
+ x = self.conv2(x)
+ x = self.conv3(x)
+ x = self.conv4(x)
+
+ return x + identity
+
+
+class UNetModel(nn.Module):
+ """
+ The full UNet model with attention and timestep embedding.
+ :param in_channels: in_channels in the input Tensor.
+ :param model_channels: base channel count for the model.
+ :param out_channels: channels in the output Tensor.
+ :param num_res_blocks: number of residual blocks per downsample.
+ :param attention_resolutions: a collection of downsample rates at which
+ attention will take place. May be a set, list, or tuple.
+ For example, if this contains 4, then at 4x downsampling, attention
+ will be used.
+ :param dropout: the dropout probability.
+ :param channel_mult: channel multiplier for each level of the UNet.
+ :param conv_resample: if True, use learned convolutions for upsampling and
+ downsampling.
+ :param dims: determines if the signal is 1D, 2D, or 3D.
+ :param num_classes: if specified (as an int), then this model will be
+ class-conditional with `num_classes` classes.
+ :param use_checkpoint: use gradient checkpointing to reduce memory usage.
+ :param num_heads: the number of attention heads in each attention layer.
+ :param num_heads_channels: if specified, ignore num_heads and instead use
+ a fixed channel width per attention head.
+ :param num_heads_upsample: works with num_heads to set a different number
+ of heads for upsampling. Deprecated.
+ :param use_scale_shift_norm: use a FiLM-like conditioning mechanism.
+ :param resblock_updown: use residual blocks for up/downsampling.
+ """
+
+ def __init__(self,
+ in_channels,
+ model_channels,
+ out_channels,
+ num_res_blocks,
+ attention_resolutions,
+ dropout=0.0,
+ channel_mult=(1, 2, 4, 8),
+ conv_resample=True,
+ dims=2,
+ context_dim=None,
+ use_scale_shift_norm=False,
+ resblock_updown=False,
+ num_heads=-1,
+ num_head_channels=-1,
+ transformer_depth=1,
+ use_linear=False,
+ use_checkpoint=False,
+ temporal_conv=False,
+ tempspatial_aware=False,
+ temporal_attention=True,
+ temporal_selfatt_only=True,
+ use_relative_position=True,
+ use_causal_attention=False,
+ temporal_length=None,
+ use_fp16=False,
+ addition_attention=False,
+ use_image_attention=False,
+ temporal_transformer_depth=1,
+ fps_cond=False,
+ ):
+ super(UNetModel, self).__init__()
+ if num_heads == -1:
+ assert num_head_channels != -1, 'Either num_heads or num_head_channels has to be set'
+ if num_head_channels == -1:
+ assert num_heads != -1, 'Either num_heads or num_head_channels has to be set'
+
+ self.in_channels = in_channels
+ self.model_channels = model_channels
+ self.out_channels = out_channels
+ self.num_res_blocks = num_res_blocks
+ self.attention_resolutions = attention_resolutions
+ self.dropout = dropout
+ self.channel_mult = channel_mult
+ self.conv_resample = conv_resample
+ self.temporal_attention = temporal_attention
+ time_embed_dim = model_channels * 4
+ self.use_checkpoint = use_checkpoint
+ self.dtype = torch.float16 if use_fp16 else torch.float32
+ self.addition_attention=addition_attention
+ self.use_image_attention = use_image_attention
+ self.fps_cond=fps_cond
+
+
+
+ self.time_embed = nn.Sequential(
+ linear(model_channels, time_embed_dim),
+ nn.SiLU(),
+ linear(time_embed_dim, time_embed_dim),
+ )
+ if self.fps_cond:
+ self.fps_embedding = nn.Sequential(
+ linear(model_channels, time_embed_dim),
+ nn.SiLU(),
+ linear(time_embed_dim, time_embed_dim),
+ )
+
+ self.input_blocks = nn.ModuleList(
+ [
+ TimestepEmbedSequential(conv_nd(dims, in_channels, model_channels, 3, padding=1))
+ ]
+ )
+ if self.addition_attention:
+ self.init_attn=TimestepEmbedSequential(
+ TemporalTransformer(
+ model_channels,
+ n_heads=8,
+ d_head=num_head_channels,
+ depth=transformer_depth,
+ context_dim=context_dim,
+ use_checkpoint=use_checkpoint, only_self_att=temporal_selfatt_only,
+ causal_attention=use_causal_attention, relative_position=use_relative_position,
+ temporal_length=temporal_length))
+
+ input_block_chans = [model_channels]
+ ch = model_channels
+ ds = 1
+ for level, mult in enumerate(channel_mult):
+ for _ in range(num_res_blocks):
+ layers = [
+ ResBlock(ch, time_embed_dim, dropout,
+ out_channels=mult * model_channels, dims=dims, use_checkpoint=use_checkpoint,
+ use_scale_shift_norm=use_scale_shift_norm, tempspatial_aware=tempspatial_aware,
+ use_temporal_conv=temporal_conv
+ )
+ ]
+ ch = mult * model_channels
+ if ds in attention_resolutions:
+ if num_head_channels == -1:
+ dim_head = ch // num_heads
+ else:
+ num_heads = ch // num_head_channels
+ dim_head = num_head_channels
+ layers.append(
+ SpatialTransformer(ch, num_heads, dim_head,
+ depth=transformer_depth, context_dim=context_dim, use_linear=use_linear,
+ use_checkpoint=use_checkpoint, disable_self_attn=False,
+ img_cross_attention=self.use_image_attention
+ )
+ )
+ if self.temporal_attention:
+ layers.append(
+ TemporalTransformer(ch, num_heads, dim_head,
+ depth=temporal_transformer_depth, context_dim=context_dim, use_linear=use_linear,
+ use_checkpoint=use_checkpoint, only_self_att=temporal_selfatt_only,
+ causal_attention=use_causal_attention, relative_position=use_relative_position,
+ temporal_length=temporal_length
+ )
+ )
+ self.input_blocks.append(TimestepEmbedSequential(*layers))
+ input_block_chans.append(ch)
+ if level != len(channel_mult) - 1:
+ out_ch = ch
+ self.input_blocks.append(
+ TimestepEmbedSequential(
+ ResBlock(ch, time_embed_dim, dropout,
+ out_channels=out_ch, dims=dims, use_checkpoint=use_checkpoint,
+ use_scale_shift_norm=use_scale_shift_norm,
+ down=True
+ )
+ if resblock_updown
+ else Downsample(ch, conv_resample, dims=dims, out_channels=out_ch)
+ )
+ )
+ ch = out_ch
+ input_block_chans.append(ch)
+ ds *= 2
+
+ if num_head_channels == -1:
+ dim_head = ch // num_heads
+ else:
+ num_heads = ch // num_head_channels
+ dim_head = num_head_channels
+ layers = [
+ ResBlock(ch, time_embed_dim, dropout,
+ dims=dims, use_checkpoint=use_checkpoint,
+ use_scale_shift_norm=use_scale_shift_norm, tempspatial_aware=tempspatial_aware,
+ use_temporal_conv=temporal_conv
+ ),
+ SpatialTransformer(ch, num_heads, dim_head,
+ depth=transformer_depth, context_dim=context_dim, use_linear=use_linear,
+ use_checkpoint=use_checkpoint, disable_self_attn=False,
+ img_cross_attention=self.use_image_attention
+ )
+ ]
+ if self.temporal_attention:
+ layers.append(
+ TemporalTransformer(ch, num_heads, dim_head,
+ depth=temporal_transformer_depth, context_dim=context_dim, use_linear=use_linear,
+ use_checkpoint=use_checkpoint, only_self_att=temporal_selfatt_only,
+ causal_attention=use_causal_attention, relative_position=use_relative_position,
+ temporal_length=temporal_length
+ )
+ )
+ layers.append(
+ ResBlock(ch, time_embed_dim, dropout,
+ dims=dims, use_checkpoint=use_checkpoint,
+ use_scale_shift_norm=use_scale_shift_norm, tempspatial_aware=tempspatial_aware,
+ use_temporal_conv=temporal_conv
+ )
+ )
+ self.middle_block = TimestepEmbedSequential(*layers)
+
+ self.output_blocks = nn.ModuleList([])
+ for level, mult in list(enumerate(channel_mult))[::-1]:
+ for i in range(num_res_blocks + 1):
+ ich = input_block_chans.pop()
+ layers = [
+ ResBlock(ch + ich, time_embed_dim, dropout,
+ out_channels=mult * model_channels, dims=dims, use_checkpoint=use_checkpoint,
+ use_scale_shift_norm=use_scale_shift_norm, tempspatial_aware=tempspatial_aware,
+ use_temporal_conv=temporal_conv
+ )
+ ]
+ ch = model_channels * mult
+ if ds in attention_resolutions:
+ if num_head_channels == -1:
+ dim_head = ch // num_heads
+ else:
+ num_heads = ch // num_head_channels
+ dim_head = num_head_channels
+ layers.append(
+ SpatialTransformer(ch, num_heads, dim_head,
+ depth=transformer_depth, context_dim=context_dim, use_linear=use_linear,
+ use_checkpoint=use_checkpoint, disable_self_attn=False,
+ img_cross_attention=self.use_image_attention
+ )
+ )
+ if self.temporal_attention:
+ layers.append(
+ TemporalTransformer(ch, num_heads, dim_head,
+ depth=temporal_transformer_depth, context_dim=context_dim, use_linear=use_linear,
+ use_checkpoint=use_checkpoint, only_self_att=temporal_selfatt_only,
+ causal_attention=use_causal_attention, relative_position=use_relative_position,
+ temporal_length=temporal_length
+ )
+ )
+ if level and i == num_res_blocks:
+ out_ch = ch
+ layers.append(
+ ResBlock(ch, time_embed_dim, dropout,
+ out_channels=out_ch, dims=dims, use_checkpoint=use_checkpoint,
+ use_scale_shift_norm=use_scale_shift_norm,
+ up=True
+ )
+ if resblock_updown
+ else Upsample(ch, conv_resample, dims=dims, out_channels=out_ch)
+ )
+ ds //= 2
+ self.output_blocks.append(TimestepEmbedSequential(*layers))
+
+ self.out = nn.Sequential(
+ normalization(ch),
+ nn.SiLU(),
+ zero_module(conv_nd(dims, model_channels, out_channels, 3, padding=1)),
+ )
+
+ def forward(self, x, timesteps, context=None, features_adapter=None, fps=16, **kwargs):
+ t_emb = timestep_embedding(timesteps, self.model_channels, repeat_only=False)
+ emb = self.time_embed(t_emb)
+
+ if self.fps_cond:
+ if type(fps) == int:
+ fps = torch.full_like(timesteps, fps)
+ fps_emb = timestep_embedding(fps,self.model_channels, repeat_only=False)
+ emb += self.fps_embedding(fps_emb)
+
+ b,_,t,_,_ = x.shape
+ ## repeat t times for context [(b t) 77 768] & time embedding
+ context = context.repeat_interleave(repeats=t, dim=0)
+ emb = emb.repeat_interleave(repeats=t, dim=0)
+
+ ## always in shape (b t) c h w, except for temporal layer
+ x = rearrange(x, 'b c t h w -> (b t) c h w')
+
+ h = x.type(self.dtype)
+ adapter_idx = 0
+ hs = []
+ for id, module in enumerate(self.input_blocks):
+ h = module(h, emb, context=context, batch_size=b)
+ if id ==0 and self.addition_attention:
+ h = self.init_attn(h, emb, context=context, batch_size=b)
+ ## plug-in adapter features
+ if ((id+1)%3 == 0) and features_adapter is not None:
+ h = h + features_adapter[adapter_idx]
+ adapter_idx += 1
+ hs.append(h)
+ if features_adapter is not None:
+ assert len(features_adapter)==adapter_idx, 'Wrong features_adapter'
+
+ h = self.middle_block(h, emb, context=context, batch_size=b)
+ for module in self.output_blocks:
+ h = torch.cat([h, hs.pop()], dim=1)
+ h = module(h, emb, context=context, batch_size=b)
+ h = h.type(x.dtype)
+ y = self.out(h)
+
+ # reshape back to (b c t h w)
+ y = rearrange(y, '(b t) c h w -> b c t h w', b=b)
+ return y
+
\ No newline at end of file
diff --git a/VADER-VideoCrafter/lvdm/modules/x_transformer.py b/VADER-VideoCrafter/lvdm/modules/x_transformer.py
new file mode 100644
index 0000000000000000000000000000000000000000..f252ab4032a78407ed487495807940c4ba802ffa
--- /dev/null
+++ b/VADER-VideoCrafter/lvdm/modules/x_transformer.py
@@ -0,0 +1,640 @@
+"""shout-out to https://github.com/lucidrains/x-transformers/tree/main/x_transformers"""
+from functools import partial
+from inspect import isfunction
+from collections import namedtuple
+from einops import rearrange, repeat
+import torch
+from torch import nn, einsum
+import torch.nn.functional as F
+
+# constants
+DEFAULT_DIM_HEAD = 64
+
+Intermediates = namedtuple('Intermediates', [
+ 'pre_softmax_attn',
+ 'post_softmax_attn'
+])
+
+LayerIntermediates = namedtuple('Intermediates', [
+ 'hiddens',
+ 'attn_intermediates'
+])
+
+
+class AbsolutePositionalEmbedding(nn.Module):
+ def __init__(self, dim, max_seq_len):
+ super().__init__()
+ self.emb = nn.Embedding(max_seq_len, dim)
+ self.init_()
+
+ def init_(self):
+ nn.init.normal_(self.emb.weight, std=0.02)
+
+ def forward(self, x):
+ n = torch.arange(x.shape[1], device=x.device)
+ return self.emb(n)[None, :, :]
+
+
+class FixedPositionalEmbedding(nn.Module):
+ def __init__(self, dim):
+ super().__init__()
+ inv_freq = 1. / (10000 ** (torch.arange(0, dim, 2).float() / dim))
+ self.register_buffer('inv_freq', inv_freq)
+
+ def forward(self, x, seq_dim=1, offset=0):
+ t = torch.arange(x.shape[seq_dim], device=x.device).type_as(self.inv_freq) + offset
+ sinusoid_inp = torch.einsum('i , j -> i j', t, self.inv_freq)
+ emb = torch.cat((sinusoid_inp.sin(), sinusoid_inp.cos()), dim=-1)
+ return emb[None, :, :]
+
+
+# helpers
+
+def exists(val):
+ return val is not None
+
+
+def default(val, d):
+ if exists(val):
+ return val
+ return d() if isfunction(d) else d
+
+
+def always(val):
+ def inner(*args, **kwargs):
+ return val
+ return inner
+
+
+def not_equals(val):
+ def inner(x):
+ return x != val
+ return inner
+
+
+def equals(val):
+ def inner(x):
+ return x == val
+ return inner
+
+
+def max_neg_value(tensor):
+ return -torch.finfo(tensor.dtype).max
+
+
+# keyword argument helpers
+
+def pick_and_pop(keys, d):
+ values = list(map(lambda key: d.pop(key), keys))
+ return dict(zip(keys, values))
+
+
+def group_dict_by_key(cond, d):
+ return_val = [dict(), dict()]
+ for key in d.keys():
+ match = bool(cond(key))
+ ind = int(not match)
+ return_val[ind][key] = d[key]
+ return (*return_val,)
+
+
+def string_begins_with(prefix, str):
+ return str.startswith(prefix)
+
+
+def group_by_key_prefix(prefix, d):
+ return group_dict_by_key(partial(string_begins_with, prefix), d)
+
+
+def groupby_prefix_and_trim(prefix, d):
+ kwargs_with_prefix, kwargs = group_dict_by_key(partial(string_begins_with, prefix), d)
+ kwargs_without_prefix = dict(map(lambda x: (x[0][len(prefix):], x[1]), tuple(kwargs_with_prefix.items())))
+ return kwargs_without_prefix, kwargs
+
+
+# classes
+class Scale(nn.Module):
+ def __init__(self, value, fn):
+ super().__init__()
+ self.value = value
+ self.fn = fn
+
+ def forward(self, x, **kwargs):
+ x, *rest = self.fn(x, **kwargs)
+ return (x * self.value, *rest)
+
+
+class Rezero(nn.Module):
+ def __init__(self, fn):
+ super().__init__()
+ self.fn = fn
+ self.g = nn.Parameter(torch.zeros(1))
+
+ def forward(self, x, **kwargs):
+ x, *rest = self.fn(x, **kwargs)
+ return (x * self.g, *rest)
+
+
+class ScaleNorm(nn.Module):
+ def __init__(self, dim, eps=1e-5):
+ super().__init__()
+ self.scale = dim ** -0.5
+ self.eps = eps
+ self.g = nn.Parameter(torch.ones(1))
+
+ def forward(self, x):
+ norm = torch.norm(x, dim=-1, keepdim=True) * self.scale
+ return x / norm.clamp(min=self.eps) * self.g
+
+
+class RMSNorm(nn.Module):
+ def __init__(self, dim, eps=1e-8):
+ super().__init__()
+ self.scale = dim ** -0.5
+ self.eps = eps
+ self.g = nn.Parameter(torch.ones(dim))
+
+ def forward(self, x):
+ norm = torch.norm(x, dim=-1, keepdim=True) * self.scale
+ return x / norm.clamp(min=self.eps) * self.g
+
+
+class Residual(nn.Module):
+ def forward(self, x, residual):
+ return x + residual
+
+
+class GRUGating(nn.Module):
+ def __init__(self, dim):
+ super().__init__()
+ self.gru = nn.GRUCell(dim, dim)
+
+ def forward(self, x, residual):
+ gated_output = self.gru(
+ rearrange(x, 'b n d -> (b n) d'),
+ rearrange(residual, 'b n d -> (b n) d')
+ )
+
+ return gated_output.reshape_as(x)
+
+
+# feedforward
+
+class GEGLU(nn.Module):
+ def __init__(self, dim_in, dim_out):
+ super().__init__()
+ self.proj = nn.Linear(dim_in, dim_out * 2)
+
+ def forward(self, x):
+ x, gate = self.proj(x).chunk(2, dim=-1)
+ return x * F.gelu(gate)
+
+
+class FeedForward(nn.Module):
+ def __init__(self, dim, dim_out=None, mult=4, glu=False, dropout=0.):
+ super().__init__()
+ inner_dim = int(dim * mult)
+ dim_out = default(dim_out, dim)
+ project_in = nn.Sequential(
+ nn.Linear(dim, inner_dim),
+ nn.GELU()
+ ) if not glu else GEGLU(dim, inner_dim)
+
+ self.net = nn.Sequential(
+ project_in,
+ nn.Dropout(dropout),
+ nn.Linear(inner_dim, dim_out)
+ )
+
+ def forward(self, x):
+ return self.net(x)
+
+
+# attention.
+class Attention(nn.Module):
+ def __init__(
+ self,
+ dim,
+ dim_head=DEFAULT_DIM_HEAD,
+ heads=8,
+ causal=False,
+ mask=None,
+ talking_heads=False,
+ sparse_topk=None,
+ use_entmax15=False,
+ num_mem_kv=0,
+ dropout=0.,
+ on_attn=False
+ ):
+ super().__init__()
+ if use_entmax15:
+ raise NotImplementedError("Check out entmax activation instead of softmax activation!")
+ self.scale = dim_head ** -0.5
+ self.heads = heads
+ self.causal = causal
+ self.mask = mask
+
+ inner_dim = dim_head * heads
+
+ self.to_q = nn.Linear(dim, inner_dim, bias=False)
+ self.to_k = nn.Linear(dim, inner_dim, bias=False)
+ self.to_v = nn.Linear(dim, inner_dim, bias=False)
+ self.dropout = nn.Dropout(dropout)
+
+ # talking heads
+ self.talking_heads = talking_heads
+ if talking_heads:
+ self.pre_softmax_proj = nn.Parameter(torch.randn(heads, heads))
+ self.post_softmax_proj = nn.Parameter(torch.randn(heads, heads))
+
+ # explicit topk sparse attention
+ self.sparse_topk = sparse_topk
+
+ # entmax
+ #self.attn_fn = entmax15 if use_entmax15 else F.softmax
+ self.attn_fn = F.softmax
+
+ # add memory key / values
+ self.num_mem_kv = num_mem_kv
+ if num_mem_kv > 0:
+ self.mem_k = nn.Parameter(torch.randn(heads, num_mem_kv, dim_head))
+ self.mem_v = nn.Parameter(torch.randn(heads, num_mem_kv, dim_head))
+
+ # attention on attention
+ self.attn_on_attn = on_attn
+ self.to_out = nn.Sequential(nn.Linear(inner_dim, dim * 2), nn.GLU()) if on_attn else nn.Linear(inner_dim, dim)
+
+ def forward(
+ self,
+ x,
+ context=None,
+ mask=None,
+ context_mask=None,
+ rel_pos=None,
+ sinusoidal_emb=None,
+ prev_attn=None,
+ mem=None
+ ):
+ b, n, _, h, talking_heads, device = *x.shape, self.heads, self.talking_heads, x.device
+ kv_input = default(context, x)
+
+ q_input = x
+ k_input = kv_input
+ v_input = kv_input
+
+ if exists(mem):
+ k_input = torch.cat((mem, k_input), dim=-2)
+ v_input = torch.cat((mem, v_input), dim=-2)
+
+ if exists(sinusoidal_emb):
+ # in shortformer, the query would start at a position offset depending on the past cached memory
+ offset = k_input.shape[-2] - q_input.shape[-2]
+ q_input = q_input + sinusoidal_emb(q_input, offset=offset)
+ k_input = k_input + sinusoidal_emb(k_input)
+
+ q = self.to_q(q_input)
+ k = self.to_k(k_input)
+ v = self.to_v(v_input)
+
+ q, k, v = map(lambda t: rearrange(t, 'b n (h d) -> b h n d', h=h), (q, k, v))
+
+ input_mask = None
+ if any(map(exists, (mask, context_mask))):
+ q_mask = default(mask, lambda: torch.ones((b, n), device=device).bool())
+ k_mask = q_mask if not exists(context) else context_mask
+ k_mask = default(k_mask, lambda: torch.ones((b, k.shape[-2]), device=device).bool())
+ q_mask = rearrange(q_mask, 'b i -> b () i ()')
+ k_mask = rearrange(k_mask, 'b j -> b () () j')
+ input_mask = q_mask * k_mask
+
+ if self.num_mem_kv > 0:
+ mem_k, mem_v = map(lambda t: repeat(t, 'h n d -> b h n d', b=b), (self.mem_k, self.mem_v))
+ k = torch.cat((mem_k, k), dim=-2)
+ v = torch.cat((mem_v, v), dim=-2)
+ if exists(input_mask):
+ input_mask = F.pad(input_mask, (self.num_mem_kv, 0), value=True)
+
+ dots = einsum('b h i d, b h j d -> b h i j', q, k) * self.scale
+ mask_value = max_neg_value(dots)
+
+ if exists(prev_attn):
+ dots = dots + prev_attn
+
+ pre_softmax_attn = dots
+
+ if talking_heads:
+ dots = einsum('b h i j, h k -> b k i j', dots, self.pre_softmax_proj).contiguous()
+
+ if exists(rel_pos):
+ dots = rel_pos(dots)
+
+ if exists(input_mask):
+ dots.masked_fill_(~input_mask, mask_value)
+ del input_mask
+
+ if self.causal:
+ i, j = dots.shape[-2:]
+ r = torch.arange(i, device=device)
+ mask = rearrange(r, 'i -> () () i ()') < rearrange(r, 'j -> () () () j')
+ mask = F.pad(mask, (j - i, 0), value=False)
+ dots.masked_fill_(mask, mask_value)
+ del mask
+
+ if exists(self.sparse_topk) and self.sparse_topk < dots.shape[-1]:
+ top, _ = dots.topk(self.sparse_topk, dim=-1)
+ vk = top[..., -1].unsqueeze(-1).expand_as(dots)
+ mask = dots < vk
+ dots.masked_fill_(mask, mask_value)
+ del mask
+
+ attn = self.attn_fn(dots, dim=-1)
+ post_softmax_attn = attn
+
+ attn = self.dropout(attn)
+
+ if talking_heads:
+ attn = einsum('b h i j, h k -> b k i j', attn, self.post_softmax_proj).contiguous()
+
+ out = einsum('b h i j, b h j d -> b h i d', attn, v)
+ out = rearrange(out, 'b h n d -> b n (h d)')
+
+ intermediates = Intermediates(
+ pre_softmax_attn=pre_softmax_attn,
+ post_softmax_attn=post_softmax_attn
+ )
+
+ return self.to_out(out), intermediates
+
+
+class AttentionLayers(nn.Module):
+ def __init__(
+ self,
+ dim,
+ depth,
+ heads=8,
+ causal=False,
+ cross_attend=False,
+ only_cross=False,
+ use_scalenorm=False,
+ use_rmsnorm=False,
+ use_rezero=False,
+ rel_pos_num_buckets=32,
+ rel_pos_max_distance=128,
+ position_infused_attn=False,
+ custom_layers=None,
+ sandwich_coef=None,
+ par_ratio=None,
+ residual_attn=False,
+ cross_residual_attn=False,
+ macaron=False,
+ pre_norm=True,
+ gate_residual=False,
+ **kwargs
+ ):
+ super().__init__()
+ ff_kwargs, kwargs = groupby_prefix_and_trim('ff_', kwargs)
+ attn_kwargs, _ = groupby_prefix_and_trim('attn_', kwargs)
+
+ dim_head = attn_kwargs.get('dim_head', DEFAULT_DIM_HEAD)
+
+ self.dim = dim
+ self.depth = depth
+ self.layers = nn.ModuleList([])
+
+ self.has_pos_emb = position_infused_attn
+ self.pia_pos_emb = FixedPositionalEmbedding(dim) if position_infused_attn else None
+ self.rotary_pos_emb = always(None)
+
+ assert rel_pos_num_buckets <= rel_pos_max_distance, 'number of relative position buckets must be less than the relative position max distance'
+ self.rel_pos = None
+
+ self.pre_norm = pre_norm
+
+ self.residual_attn = residual_attn
+ self.cross_residual_attn = cross_residual_attn
+
+ norm_class = ScaleNorm if use_scalenorm else nn.LayerNorm
+ norm_class = RMSNorm if use_rmsnorm else norm_class
+ norm_fn = partial(norm_class, dim)
+
+ norm_fn = nn.Identity if use_rezero else norm_fn
+ branch_fn = Rezero if use_rezero else None
+
+ if cross_attend and not only_cross:
+ default_block = ('a', 'c', 'f')
+ elif cross_attend and only_cross:
+ default_block = ('c', 'f')
+ else:
+ default_block = ('a', 'f')
+
+ if macaron:
+ default_block = ('f',) + default_block
+
+ if exists(custom_layers):
+ layer_types = custom_layers
+ elif exists(par_ratio):
+ par_depth = depth * len(default_block)
+ assert 1 < par_ratio <= par_depth, 'par ratio out of range'
+ default_block = tuple(filter(not_equals('f'), default_block))
+ par_attn = par_depth // par_ratio
+ depth_cut = par_depth * 2 // 3 # 2 / 3 attention layer cutoff suggested by PAR paper
+ par_width = (depth_cut + depth_cut // par_attn) // par_attn
+ assert len(default_block) <= par_width, 'default block is too large for par_ratio'
+ par_block = default_block + ('f',) * (par_width - len(default_block))
+ par_head = par_block * par_attn
+ layer_types = par_head + ('f',) * (par_depth - len(par_head))
+ elif exists(sandwich_coef):
+ assert sandwich_coef > 0 and sandwich_coef <= depth, 'sandwich coefficient should be less than the depth'
+ layer_types = ('a',) * sandwich_coef + default_block * (depth - sandwich_coef) + ('f',) * sandwich_coef
+ else:
+ layer_types = default_block * depth
+
+ self.layer_types = layer_types
+ self.num_attn_layers = len(list(filter(equals('a'), layer_types)))
+
+ for layer_type in self.layer_types:
+ if layer_type == 'a':
+ layer = Attention(dim, heads=heads, causal=causal, **attn_kwargs)
+ elif layer_type == 'c':
+ layer = Attention(dim, heads=heads, **attn_kwargs)
+ elif layer_type == 'f':
+ layer = FeedForward(dim, **ff_kwargs)
+ layer = layer if not macaron else Scale(0.5, layer)
+ else:
+ raise Exception(f'invalid layer type {layer_type}')
+
+ if isinstance(layer, Attention) and exists(branch_fn):
+ layer = branch_fn(layer)
+
+ if gate_residual:
+ residual_fn = GRUGating(dim)
+ else:
+ residual_fn = Residual()
+
+ self.layers.append(nn.ModuleList([
+ norm_fn(),
+ layer,
+ residual_fn
+ ]))
+
+ def forward(
+ self,
+ x,
+ context=None,
+ mask=None,
+ context_mask=None,
+ mems=None,
+ return_hiddens=False
+ ):
+ hiddens = []
+ intermediates = []
+ prev_attn = None
+ prev_cross_attn = None
+
+ mems = mems.copy() if exists(mems) else [None] * self.num_attn_layers
+
+ for ind, (layer_type, (norm, block, residual_fn)) in enumerate(zip(self.layer_types, self.layers)):
+ is_last = ind == (len(self.layers) - 1)
+
+ if layer_type == 'a':
+ hiddens.append(x)
+ layer_mem = mems.pop(0)
+
+ residual = x
+
+ if self.pre_norm:
+ x = norm(x)
+
+ if layer_type == 'a':
+ out, inter = block(x, mask=mask, sinusoidal_emb=self.pia_pos_emb, rel_pos=self.rel_pos,
+ prev_attn=prev_attn, mem=layer_mem)
+ elif layer_type == 'c':
+ out, inter = block(x, context=context, mask=mask, context_mask=context_mask, prev_attn=prev_cross_attn)
+ elif layer_type == 'f':
+ out = block(x)
+
+ x = residual_fn(out, residual)
+
+ if layer_type in ('a', 'c'):
+ intermediates.append(inter)
+
+ if layer_type == 'a' and self.residual_attn:
+ prev_attn = inter.pre_softmax_attn
+ elif layer_type == 'c' and self.cross_residual_attn:
+ prev_cross_attn = inter.pre_softmax_attn
+
+ if not self.pre_norm and not is_last:
+ x = norm(x)
+
+ if return_hiddens:
+ intermediates = LayerIntermediates(
+ hiddens=hiddens,
+ attn_intermediates=intermediates
+ )
+
+ return x, intermediates
+
+ return x
+
+
+class Encoder(AttentionLayers):
+ def __init__(self, **kwargs):
+ assert 'causal' not in kwargs, 'cannot set causality on encoder'
+ super().__init__(causal=False, **kwargs)
+
+
+
+class TransformerWrapper(nn.Module):
+ def __init__(
+ self,
+ *,
+ num_tokens,
+ max_seq_len,
+ attn_layers,
+ emb_dim=None,
+ max_mem_len=0.,
+ emb_dropout=0.,
+ num_memory_tokens=None,
+ tie_embedding=False,
+ use_pos_emb=True
+ ):
+ super().__init__()
+ assert isinstance(attn_layers, AttentionLayers), 'attention layers must be one of Encoder or Decoder'
+
+ dim = attn_layers.dim
+ emb_dim = default(emb_dim, dim)
+
+ self.max_seq_len = max_seq_len
+ self.max_mem_len = max_mem_len
+ self.num_tokens = num_tokens
+
+ self.token_emb = nn.Embedding(num_tokens, emb_dim)
+ self.pos_emb = AbsolutePositionalEmbedding(emb_dim, max_seq_len) if (
+ use_pos_emb and not attn_layers.has_pos_emb) else always(0)
+ self.emb_dropout = nn.Dropout(emb_dropout)
+
+ self.project_emb = nn.Linear(emb_dim, dim) if emb_dim != dim else nn.Identity()
+ self.attn_layers = attn_layers
+ self.norm = nn.LayerNorm(dim)
+
+ self.init_()
+
+ self.to_logits = nn.Linear(dim, num_tokens) if not tie_embedding else lambda t: t @ self.token_emb.weight.t()
+
+ # memory tokens (like [cls]) from Memory Transformers paper
+ num_memory_tokens = default(num_memory_tokens, 0)
+ self.num_memory_tokens = num_memory_tokens
+ if num_memory_tokens > 0:
+ self.memory_tokens = nn.Parameter(torch.randn(num_memory_tokens, dim))
+
+ # let funnel encoder know number of memory tokens, if specified
+ if hasattr(attn_layers, 'num_memory_tokens'):
+ attn_layers.num_memory_tokens = num_memory_tokens
+
+ def init_(self):
+ nn.init.normal_(self.token_emb.weight, std=0.02)
+
+ def forward(
+ self,
+ x,
+ return_embeddings=False,
+ mask=None,
+ return_mems=False,
+ return_attn=False,
+ mems=None,
+ **kwargs
+ ):
+ b, n, device, num_mem = *x.shape, x.device, self.num_memory_tokens
+ x = self.token_emb(x)
+ x += self.pos_emb(x)
+ x = self.emb_dropout(x)
+
+ x = self.project_emb(x)
+
+ if num_mem > 0:
+ mem = repeat(self.memory_tokens, 'n d -> b n d', b=b)
+ x = torch.cat((mem, x), dim=1)
+
+ # auto-handle masking after appending memory tokens
+ if exists(mask):
+ mask = F.pad(mask, (num_mem, 0), value=True)
+
+ x, intermediates = self.attn_layers(x, mask=mask, mems=mems, return_hiddens=True, **kwargs)
+ x = self.norm(x)
+
+ mem, x = x[:, :num_mem], x[:, num_mem:]
+
+ out = self.to_logits(x) if not return_embeddings else x
+
+ if return_mems:
+ hiddens = intermediates.hiddens
+ new_mems = list(map(lambda pair: torch.cat(pair, dim=-2), zip(mems, hiddens))) if exists(mems) else hiddens
+ new_mems = list(map(lambda t: t[..., -self.max_mem_len:, :].detach(), new_mems))
+ return out, new_mems
+
+ if return_attn:
+ attn_maps = list(map(lambda t: t.post_softmax_attn, intermediates.attn_intermediates))
+ return out, attn_maps
+
+ return out
+
diff --git a/VADER-VideoCrafter/readme.md b/VADER-VideoCrafter/readme.md
new file mode 100644
index 0000000000000000000000000000000000000000..33ea67e1979971c67cbfdbd7e3c54fa6b889f2c3
--- /dev/null
+++ b/VADER-VideoCrafter/readme.md
@@ -0,0 +1,138 @@
+<div align="center">
+
+<!-- TITLE -->
+# 🌟**VADER-VideoCrafter**
+</div>
+
+
+
+We **highly recommend** proceeding with the VADER-VideoCrafter model first, which performs better than the other two.
+
+## ⚙️ Installation
+Assuming you are in the `VADER/` directory, you are able to create a Conda environments for VADER-VideoCrafter using the following commands:
+```bash
+cd VADER-VideoCrafter
+conda create -n vader_videocrafter python=3.10
+conda activate vader_videocrafter
+conda install pytorch==2.3.0 torchvision==0.18.0 torchaudio==2.3.0 pytorch-cuda=12.1 -c pytorch -c nvidia
+conda install xformers -c xformers
+pip install -r requirements.txt
+git clone https://github.com/tgxs002/HPSv2.git
+cd HPSv2/
+pip install -e .
+cd ..
+```
+
+
+- We are using the pretrained Text-to-Video [VideoCrafter2](https://huggingface.co/VideoCrafter/VideoCrafter2/blob/main/model.ckpt) model via Hugging Face. If you unfortunately find the model is not automatically downloaded when you running inference or training script, you can manually download it and put the `model.ckpt` in `VADER/VADER-VideoCrafter/checkpoints/base_512_v2/model.ckpt`.
+
+
+## 📺 Inference
+Please run `accelerate config` as the first step to configure accelerator settings. If you are not familiar with the accelerator configuration, you can refer to VADER-VideoCrafter [documentation](../documentation/VADER-VideoCrafter.md).
+
+Assuming you are in the `VADER/` directory, you are able to do inference using the following commands:
+```bash
+cd VADER-VideoCrafter
+sh scripts/run_text2video_inference.sh
+```
+- We have tested on PyTorch 2.3.0 and CUDA 12.1. The inferece script works on a single GPU with 16GBs VRAM, when we set `val_batch_size=1` and use `fp16` mixed precision. It should also work with recent PyTorch and CUDA versions.
+- `VADER/VADER-VideoCrafter/scripts/main/train_t2v_lora.py` is a script for inference of the VideoCrafter2 using VADER via LoRA.
+ - Most of the arguments are the same as the training process. The main difference is that `--inference_only` should be set to `True`.
+ - `--lora_ckpt_path` is required to set to the path of the pretrained LoRA model. Otherwise, the original VideoCrafter model will be used for inference.
+
+## 🔧 Training
+Please run `accelerate config` as the first step to configure accelerator settings. If you are not familiar with the accelerator configuration, you can refer to VADER-VideoCrafter [documentation](../documentation/VADER-VideoCrafter.md).
+
+Assuming you are in the `VADER/` directory, you are able to train the model using the following commands:
+
+```bash
+cd VADER-VideoCrafter
+sh scripts/run_text2video_train.sh
+```
+- Our experiments are conducted on PyTorch 2.3.0 and CUDA 12.1 while using 4 A6000s (48GB RAM). It should also work with recent PyTorch and CUDA versions. The training script have been tested on a single GPU with 16GBs VRAM, when we set `train_batch_size=1 val_batch_size=1` and use `fp16` mixed precision.
+- `VADER/VADER-VideoCrafter/scripts/main/train_t2v_lora.py` is also a script for fine-tuning the VideoCrafter2 using VADER via LoRA.
+ - You can read the VADER-VideoCrafter [documentation](../documentation/VADER-VideoCrafter.md) to understand the usage of arguments.
+
+## 💡 Tutorial
+This section is to provide a tutorial on how to implement the VADER method on VideoCrafter by yourself. We will provide a step-by-step guide to help you understand the implementation details. Thus, you can easily adapt the VADER method to later versions of VideCrafter. This tutorial is based on the VideoCrafter2.
+
+### Step 1: Install the dependencies
+First, you need to install the dependencies according to the [VideoCrafter](https://github.com/AILab-CVC/VideoCrafter) repository. You can also follow the instructions in the repository to install the dependencies.
+```bash
+conda create -n vader_videocrafter python=3.8.5
+conda activate vader_videocrafter
+pip install -r requirements.txt
+```
+
+You have to download pretrained Text-to-Video [VideoCrafter2](https://huggingface.co/VideoCrafter/VideoCrafter2/blob/main/model.ckpt) model via Hugging Face, and put the `model.ckpt` in the downloaded VideoCrafter directionary as `VideoCrafter/checkpoints/base_512_v2/model.ckpt`.
+
+There are a list of extra dependencies that you need to install for VADER. You can install them by running the following command.
+```bash
+# Install the HPS
+git clone https://github.com/tgxs002/HPSv2.git
+cd HPSv2/
+pip install -e .
+cd ..
+
+# Install the dependencies
+pip install albumentations \
+peft \
+bitsandbytes \
+accelerate \
+inflect \
+wandb \
+ipdb \
+pytorch_lightning
+```
+
+### Step 2: Transfer VADER scripts
+You can copy our `VADER/VADER-VideoCrafter/scripts/main/train_t2v_lora.py` to the `VideoCrafter/scripts/evaluation/` directory of VideoCrafter. It is better to copy our `run_text2video_train.sh` and `run_text2video_inference.sh` to the directionary `VideoCrafter/scripts/` as well. Then, you need to copy All the files in `VADER/Core/` and `VADER/assets/` to the parent directory of VideoCrafter, which means `Core/`, `assets` and `VideoCrafter/` should be in the same directory. Now, you may have a directory structure like:
+```bash
+.
+├── Core
+│ ├── ...
+├── VideoCrafter
+│ ├── scripts
+│ │ ├── evaluation
+│ │ │ ├── train_t2v_lora.py
+│ │ ├── run_text2video_train.sh
+│ │ ├── run_text2video_inference.sh
+│ ├── checkpoints
+│ │ ├── base_512_v2
+│ │ │ ├── model.ckpt
+├── assets
+│ ├── ...
+```
+
+### Step 3: Modify the VideoCrafter code
+You need to modify the VideoCrafter code to adapt the VADER method. You can follow the instructions below to modify the code.
+
+- Modify the `batch_ddim_sampling()` function in `VideoCrafter/scripts/evaluation/funcs.py` as our implementation in `VADER/VADER-VideoCrafter/scripts/main/funcs.py`.
+- Modify the `DDIMSampler.__init__()`, `DDIMSampler.sample()` and `DDIMSampler.ddim_sampling` functions in `VideoCrafter\lvdm\models\samplers\ddim.py` as our implementation in `VADER/VADER-VideoCrafter\lvdm\models\samplers\ddim.py`.
+- Comment out the `@torch.no_grad()` before `DDIMSampler.sample()`, `DDIMSampler.ddim_sampling`, and `DDIMSampler.p_sample_ddim()` in `VideoCrafter\lvdm\models\samplers\ddim.py`. Also, comment out the `@torch.no_grad()` before `LatentDiffusion.decode_first_stage_2DAE()` in `VideoCrafter\lvdm\models\ddpm3d.py`.
+- Because we have commented out the `@torch.no_grad()`, you can add `with torch.no_grad():` at some places in `VideoCrater/scripts/evaluation/inference.py` to avoid the gradient calculation.
+
+### Step 4: Ready to Train
+Now you have all the files in the right place and modified the VideoCrafter source code. You can run the training script by running the following command.
+```bash
+cd VideoCrafter
+
+# training
+sh scripts/run_text2video_train.sh
+
+# or inference
+sh scripts/run_text2video_inference.sh
+```
+
+
+## Acknowledgement
+
+Our codebase is directly built on top of [VideoCrafter](https://github.com/AILab-CVC/VideoCrafter), [Open-Sora](https://github.com/hpcaitech/Open-Sora), and [Animate Anything](https://github.com/alibaba/animate-anything/). We would like to thank the authors for open-sourcing their code.
+
+## Citation
+
+If you find this work useful in your research, please cite:
+
+```bibtex
+
+```
diff --git a/VADER-VideoCrafter/requirements.txt b/VADER-VideoCrafter/requirements.txt
new file mode 100644
index 0000000000000000000000000000000000000000..0f89a58109182c6b921a60671336fd9aa1f0750e
--- /dev/null
+++ b/VADER-VideoCrafter/requirements.txt
@@ -0,0 +1,28 @@
+decord==0.6.0
+einops==0.3.0
+imageio==2.9.0
+numpy==1.24.2
+omegaconf==2.1.1
+opencv_python
+pandas==2.0.0
+Pillow==9.5.0
+pytorch_lightning==2.3.1
+PyYAML==6.0
+setuptools==65.6.3
+tqdm==4.65.0
+transformers==4.25.1
+moviepy==1.0.3
+av==12.2.0
+gradio
+timm==1.0.7
+scikit-learn==1.5.0
+open_clip_torch==2.22.0
+kornia==0.7.3
+albumentations==1.3.1
+peft==0.11.1
+bitsandbytes==0.42.0
+accelerate==0.31.0
+inflect==7.3.0
+wandb==0.17.3
+ipdb==0.13.13
+huggingface-hub==0.23.4
\ No newline at end of file
diff --git a/VADER-VideoCrafter/scripts/main/ddp_wrapper.py b/VADER-VideoCrafter/scripts/main/ddp_wrapper.py
new file mode 100644
index 0000000000000000000000000000000000000000..67cf97522b1f56e7a20d476373d99ca02b6e5dca
--- /dev/null
+++ b/VADER-VideoCrafter/scripts/main/ddp_wrapper.py
@@ -0,0 +1,47 @@
+# Copied from VideoCrafter: https://github.com/AILab-CVC/VideoCrafter
+import datetime
+import argparse, importlib
+from pytorch_lightning import seed_everything
+
+import torch
+import torch.distributed as dist
+
+def setup_dist(local_rank):
+ if dist.is_initialized():
+ return
+ torch.cuda.set_device(local_rank)
+ torch.distributed.init_process_group('nccl', init_method='env://')
+
+
+def get_dist_info():
+ if dist.is_available():
+ initialized = dist.is_initialized()
+ else:
+ initialized = False
+ if initialized:
+ rank = dist.get_rank()
+ world_size = dist.get_world_size()
+ else:
+ rank = 0
+ world_size = 1
+ return rank, world_size
+
+
+if __name__ == '__main__':
+ now = datetime.datetime.now().strftime("%Y-%m-%d-%H-%M-%S")
+ parser = argparse.ArgumentParser()
+ parser.add_argument("--module", type=str, help="module name", default="inference")
+ parser.add_argument("--local_rank", type=int, nargs="?", help="for ddp", default=0)
+ args, unknown = parser.parse_known_args()
+ inference_api = importlib.import_module(args.module, package=None)
+
+ inference_parser = inference_api.get_parser()
+ inference_args, unknown = inference_parser.parse_known_args()
+
+ seed_everything(inference_args.seed)
+ setup_dist(args.local_rank)
+ torch.backends.cudnn.benchmark = True
+ rank, gpu_num = get_dist_info()
+
+ print("@CoLVDM Inference [rank%d]: %s"%(rank, now))
+ inference_api.run_inference(inference_args, gpu_num, rank)
\ No newline at end of file
diff --git a/VADER-VideoCrafter/scripts/main/funcs.py b/VADER-VideoCrafter/scripts/main/funcs.py
new file mode 100644
index 0000000000000000000000000000000000000000..e04714020150f9c64df679bc83a070db2491609f
--- /dev/null
+++ b/VADER-VideoCrafter/scripts/main/funcs.py
@@ -0,0 +1,231 @@
+# Adapted from VideoCrafter: https://github.com/AILab-CVC/VideoCrafter
+import os, sys, glob
+import numpy as np
+from collections import OrderedDict
+from decord import VideoReader, cpu
+import cv2
+import random
+
+import torch
+import torchvision
+sys.path.insert(1, os.path.join(sys.path[0], '..', '..'))
+from lvdm.models.samplers.ddim import DDIMSampler
+# import ipdb
+# st = ipdb.set_trace
+
+def batch_ddim_sampling(model, cond, noise_shape, n_samples=1, ddim_steps=50, ddim_eta=1.0,\
+ cfg_scale=1.0, temporal_cfg_scale=None, backprop_mode=None, decode_frame='-1', **kwargs):
+ ddim_sampler = DDIMSampler(model)
+ if backprop_mode is not None: # it is for training now, backprop_mode != None also means vader training mode
+ ddim_sampler.backprop_mode = backprop_mode
+ ddim_sampler.training_mode = True
+ uncond_type = model.uncond_type
+ batch_size = noise_shape[0]
+
+ ## construct unconditional guidance
+ if cfg_scale != 1.0:
+ if uncond_type == "empty_seq":
+ prompts = batch_size * [""]
+
+ uc_emb = model.get_learned_conditioning(prompts)
+ elif uncond_type == "zero_embed":
+ c_emb = cond["c_crossattn"][0] if isinstance(cond, dict) else cond
+ uc_emb = torch.zeros_like(c_emb)
+
+ ## process image embedding token
+ if hasattr(model, 'embedder'):
+ uc_img = torch.zeros(noise_shape[0],3,224,224).to(model.device)
+ ## img: b c h w >> b l c
+ uc_img = model.get_image_embeds(uc_img)
+ uc_emb = torch.cat([uc_emb, uc_img], dim=1)
+
+ if isinstance(cond, dict):
+ uc = {key:cond[key] for key in cond.keys()}
+ uc.update({'c_crossattn': [uc_emb]})
+ else:
+ uc = uc_emb
+ else:
+ uc = None
+
+ x_T = None
+ batch_variants = []
+
+ for _ in range(n_samples):
+ if ddim_sampler is not None:
+ kwargs.update({"clean_cond": True})
+ samples, _ = ddim_sampler.sample(S=ddim_steps, # samples: batch, c, t, h, w
+ conditioning=cond,
+ batch_size=noise_shape[0],
+ shape=noise_shape[1:],
+ verbose=False,
+ unconditional_guidance_scale=cfg_scale,
+ unconditional_conditioning=uc,
+ eta=ddim_eta,
+ temporal_length=noise_shape[2],
+ conditional_guidance_scale_temporal=temporal_cfg_scale,
+ x_T=x_T,
+ **kwargs
+ )
+
+ ## reconstruct from latent to pixel space
+ if backprop_mode is not None: # it is for training now. Use one frame randomly to save memory
+ try:
+ decode_frame=int(decode_frame)
+ #it's a int
+ except:
+ pass
+ if type(decode_frame) == int:
+ frame_index = random.randint(0,samples.shape[2]-1) if decode_frame == -1 else decode_frame # samples: batch, c, t, h, w
+ batch_images = model.decode_first_stage_2DAE(samples[:,:,frame_index:frame_index+1,:,:])
+ elif decode_frame in ['alt', 'all']:
+ idxs = range(0, samples.shape[2], 2) if decode_frame == 'alt' else range(samples.shape[2])
+ batch_images = model.decode_first_stage_2DAE(samples[:,:,idxs,:,:])
+
+
+ else: # inference mode
+ batch_images = model.decode_first_stage_2DAE(samples)
+ batch_variants.append(batch_images)
+
+ ## batch, <samples>, c, t, h, w
+ batch_variants = torch.stack(batch_variants, dim=1)
+ return batch_variants
+
+
+def get_filelist(data_dir, ext='*'):
+ file_list = glob.glob(os.path.join(data_dir, '*.%s'%ext))
+ file_list.sort()
+ return file_list
+
+def get_dirlist(path):
+ list = []
+ if (os.path.exists(path)):
+ files = os.listdir(path)
+ for file in files:
+ m = os.path.join(path,file)
+ if (os.path.isdir(m)):
+ list.append(m)
+ list.sort()
+ return list
+
+
+def load_model_checkpoint(model, ckpt):
+ def load_checkpoint(model, ckpt, full_strict):
+ state_dict = torch.load(ckpt, map_location="cpu")
+ try:
+ ## deepspeed
+ new_pl_sd = OrderedDict()
+ for key in state_dict['module'].keys():
+ new_pl_sd[key[16:]]=state_dict['module'][key]
+ model.load_state_dict(new_pl_sd, strict=full_strict)
+ except:
+ if "state_dict" in list(state_dict.keys()):
+ state_dict = state_dict["state_dict"]
+ model.load_state_dict(state_dict, strict=full_strict)
+ return model
+ load_checkpoint(model, ckpt, full_strict=True)
+ print('>>> model checkpoint loaded.')
+ return model
+
+
+def load_prompts(prompt_file):
+ f = open(prompt_file, 'r')
+ prompt_list = []
+ for idx, line in enumerate(f.readlines()):
+ l = line.strip()
+ if len(l) != 0:
+ prompt_list.append(l)
+ f.close()
+ return prompt_list
+
+
+def load_video_batch(filepath_list, frame_stride, video_size=(256,256), video_frames=16):
+ '''
+ Notice about some special cases:
+ 1. video_frames=-1 means to take all the frames (with fs=1)
+ 2. when the total video frames is less than required, padding strategy will be used (repreated last frame)
+ '''
+ fps_list = []
+ batch_tensor = []
+ assert frame_stride > 0, "valid frame stride should be a positive interge!"
+ for filepath in filepath_list:
+ padding_num = 0
+ vidreader = VideoReader(filepath, ctx=cpu(0), width=video_size[1], height=video_size[0])
+ fps = vidreader.get_avg_fps()
+ total_frames = len(vidreader)
+ max_valid_frames = (total_frames-1) // frame_stride + 1
+ if video_frames < 0:
+ ## all frames are collected: fs=1 is a must
+ required_frames = total_frames
+ frame_stride = 1
+ else:
+ required_frames = video_frames
+ query_frames = min(required_frames, max_valid_frames)
+ frame_indices = [frame_stride*i for i in range(query_frames)]
+
+ ## [t,h,w,c] -> [c,t,h,w]
+ frames = vidreader.get_batch(frame_indices)
+ frame_tensor = torch.tensor(frames.asnumpy()).permute(3, 0, 1, 2).float()
+ frame_tensor = (frame_tensor / 255. - 0.5) * 2
+ if max_valid_frames < required_frames:
+ padding_num = required_frames - max_valid_frames
+ frame_tensor = torch.cat([frame_tensor, *([frame_tensor[:,-1:,:,:]]*padding_num)], dim=1)
+ print(f'{os.path.split(filepath)[1]} is not long enough: {padding_num} frames padded.')
+ batch_tensor.append(frame_tensor)
+ sample_fps = int(fps/frame_stride)
+ fps_list.append(sample_fps)
+
+ return torch.stack(batch_tensor, dim=0)
+
+from PIL import Image
+def load_image_batch(filepath_list, image_size=(256,256)):
+ batch_tensor = []
+ for filepath in filepath_list:
+ _, filename = os.path.split(filepath)
+ _, ext = os.path.splitext(filename)
+ if ext == '.mp4':
+ vidreader = VideoReader(filepath, ctx=cpu(0), width=image_size[1], height=image_size[0])
+ frame = vidreader.get_batch([0])
+ img_tensor = torch.tensor(frame.asnumpy()).squeeze(0).permute(2, 0, 1).float()
+ elif ext == '.png' or ext == '.jpg':
+ img = Image.open(filepath).convert("RGB")
+ rgb_img = np.array(img, np.float32)
+ #bgr_img = cv2.imread(filepath, cv2.IMREAD_COLOR)
+ #bgr_img = cv2.cvtColor(bgr_img, cv2.COLOR_BGR2RGB)
+ rgb_img = cv2.resize(rgb_img, (image_size[1],image_size[0]), interpolation=cv2.INTER_LINEAR)
+ img_tensor = torch.from_numpy(rgb_img).permute(2, 0, 1).float()
+ else:
+ print(f'ERROR: <{ext}> image loading only support format: [mp4], [png], [jpg]')
+ raise NotImplementedError
+ img_tensor = (img_tensor / 255. - 0.5) * 2
+ batch_tensor.append(img_tensor)
+ return torch.stack(batch_tensor, dim=0)
+
+
+def save_videos(batch_tensors, savedir, filenames, fps=10):
+ # b,samples,c,t,h,w
+ n_samples = batch_tensors.shape[1]
+ for idx, vid_tensor in enumerate(batch_tensors):
+ video = vid_tensor.detach().cpu()
+ video = torch.clamp(video.float(), -1., 1.)
+ video = video.permute(2, 0, 1, 3, 4) # t,n,c,h,w
+ frame_grids = [torchvision.utils.make_grid(framesheet, nrow=int(n_samples)) for framesheet in video] #[3, 1*h, n*w]
+ grid = torch.stack(frame_grids, dim=0) # stack in temporal dim [t, 3, n*h, w]
+ grid = (grid + 1.0) / 2.0
+ grid = (grid * 255).to(torch.uint8).permute(0, 2, 3, 1)
+ savepath = os.path.join(savedir, f"{filenames[idx]}.mp4")
+ torchvision.io.write_video(savepath, grid, fps=fps, video_codec='h264', options={'crf': '10'})
+
+def get_videos(batch_tensors, fps=10):
+ # b,samples,c,t,h,w
+ n_samples = batch_tensors.shape[1]
+ vid_tensor = batch_tensors[0]
+ video = vid_tensor.detach().cpu()
+ video = torch.clamp(video.float(), -1., 1.)
+ video = video.permute(2, 0, 1, 3, 4) # t,n,c,h,w
+ frame_grids = [torchvision.utils.make_grid(framesheet, nrow=int(n_samples)) for framesheet in video] #[3, 1*h, n*w]
+ grid = torch.stack(frame_grids, dim=0) # stack in temporal dim [t, 3, n*h, w]
+ grid = (grid + 1.0) / 2.0
+ grid = (grid * 255).to(torch.uint8).permute(0, 2, 3, 1)
+
+ return grid
+
diff --git a/VADER-VideoCrafter/scripts/main/train_t2v_lora.py b/VADER-VideoCrafter/scripts/main/train_t2v_lora.py
new file mode 100644
index 0000000000000000000000000000000000000000..30d4dfc7f4008dee7bb82dfaf123c5f4b3629c88
--- /dev/null
+++ b/VADER-VideoCrafter/scripts/main/train_t2v_lora.py
@@ -0,0 +1,817 @@
+import argparse, os, sys, glob, yaml, math, random
+sys.path.append('../') # setting path to get Core and assets
+
+import datetime, time
+import numpy as np
+from omegaconf import OmegaConf
+from collections import OrderedDict
+from tqdm import trange, tqdm
+from einops import repeat
+from einops import rearrange, repeat
+from functools import partial
+import torch
+from pytorch_lightning import seed_everything
+
+from funcs import load_model_checkpoint, load_prompts, load_image_batch, get_filelist, save_videos, get_videos
+from funcs import batch_ddim_sampling
+from utils.utils import instantiate_from_config
+
+import peft
+import torchvision
+from transformers.utils import ContextManagers
+from transformers import AutoProcessor, AutoModel, AutoImageProcessor, AutoModelForObjectDetection, AutoModelForZeroShotObjectDetection
+from Core.aesthetic_scorer import AestheticScorerDiff
+from Core.actpred_scorer import ActPredScorer
+from Core.weather_scorer import WeatherScorer
+from Core.compression_scorer import JpegCompressionScorer, jpeg_compressibility
+import Core.prompts as prompts_file
+from hpsv2.src.open_clip import create_model_and_transforms, get_tokenizer
+import hpsv2
+import bitsandbytes as bnb
+from accelerate import Accelerator
+from accelerate.logging import get_logger
+from accelerate.utils import gather_object
+import torch.distributed as dist
+import logging
+import gc
+from PIL import Image
+import io
+import albumentations as A
+from huggingface_hub import snapshot_download
+import cv2
+# import ipdb
+# st = ipdb.set_trace
+
+
+logger = get_logger(__name__, log_level="INFO") # get logger for current module
+
+def create_logging(logging, logger, accelerator):
+ logging.basicConfig(
+ format="%(asctime)s - %(levelname)s - %(name)s - %(message)s",
+ datefmt="%m/%d/%Y %H:%M:%S",
+ level=logging.INFO,
+ )
+ logger.info(accelerator.state, main_process_only=False)
+
+def create_output_folders(output_dir, run_name):
+ out_dir = os.path.join(output_dir, run_name)
+ os.makedirs(out_dir, exist_ok=True)
+ os.makedirs(f"{out_dir}/samples", exist_ok=True)
+ return out_dir
+
+def str2bool(v):
+ if isinstance(v, bool):
+ return v
+ if v.lower() in ('yes', 'true', 't', 'y', '1'):
+ return True
+ elif v.lower() in ('no', 'false', 'f', 'n', '0'):
+ return False
+ else:
+ raise argparse.ArgumentTypeError('Boolean value expected.')
+
+def get_parser():
+ parser = argparse.ArgumentParser()
+ parser.add_argument("--seed", type=int, default=20230211, help="seed for seed_everything")
+ parser.add_argument("--mode", default="base", type=str, help="which kind of inference mode: {'base', 'i2v'}")
+ parser.add_argument("--ckpt_path", type=str, default='VADER-VideoCrafter/checkpoints/base_512_v2/model.ckpt', help="checkpoint path")
+ parser.add_argument("--config", type=str, default='VADER-VideoCrafter/configs/inference_t2v_512_v2.0.yaml', help="config (yaml) path")
+ parser.add_argument("--savefps", type=str, default=10, help="video fps to generate")
+ parser.add_argument("--n_samples", type=int, default=1, help="num of samples per prompt",)
+ parser.add_argument("--ddim_steps", type=int, default=50, help="steps of ddim if positive, otherwise use DDPM",)
+ parser.add_argument("--ddim_eta", type=float, default=1.0, help="eta for ddim sampling (0.0 yields deterministic sampling)",)
+ parser.add_argument("--height", type=int, default=512, help="image height, in pixel space")
+ parser.add_argument("--width", type=int, default=512, help="image width, in pixel space")
+ parser.add_argument("--frames", type=int, default=-1, help="frames num to inference")
+ parser.add_argument("--fps", type=int, default=24)
+ parser.add_argument("--unconditional_guidance_scale", type=float, default=1.0, help="prompt classifier-free guidance")
+ parser.add_argument("--unconditional_guidance_scale_temporal", type=float, default=None, help="temporal consistency guidance")
+ ## for conditional i2v only
+ parser.add_argument("--cond_input", type=str, default=None, help="data dir of conditional input")
+ ## for training
+ parser.add_argument("--lr", type=float, default=2e-4, help="learning rate")
+ parser.add_argument("--val_batch_size", type=int, default=1, help="batch size for validation")
+ parser.add_argument("--num_val_runs", type=int, default=1, help="total number of validation samples = num_val_runs * num_gpus * num_val_batch")
+ parser.add_argument("--train_batch_size", type=int, default=1, help="batch size for training")
+ parser.add_argument("--reward_fn", type=str, default="aesthetic", help="reward function: 'aesthetic', 'hps', 'aesthetic_hps', 'pick_score', 'rainy', 'snowy', 'objectDetection', 'actpred', 'compression'")
+ parser.add_argument("--compression_model_path", type=str, default='assets/compression_reward.pt', help="compression model path") # The compression model is used only when reward_fn is 'compression'
+ # The "book." is for grounding-dino model . Remember to add "." at the end of the object name for grounding-dino model.
+ # But for yolos model, do not add "." at the end of the object name. Instead, you should set the object name to "book" for example.
+ parser.add_argument("--target_object", type=str, default="book", help="target object for object detection reward function")
+ parser.add_argument("--detector_model", type=str, default="yolos-base", help="object detection model",
+ choices=["yolos-base", "yolos-tiny", "grounding-dino-base", "grounding-dino-tiny"])
+ parser.add_argument("--hps_version", type=str, default="v2.1", help="hps version: 'v2.0', 'v2.1'")
+ parser.add_argument("--prompt_fn", type=str, default="hps_custom", help="prompt function")
+ parser.add_argument("--nouns_file", type=str, default="simple_animals.txt", help="nouns file")
+ parser.add_argument("--activities_file", type=str, default="activities.txt", help="activities file")
+ parser.add_argument("--num_train_epochs", type=int, default=200, help="number of training epochs")
+ parser.add_argument("--max_train_steps", type=int, default=10000, help="max training steps")
+ parser.add_argument("--backprop_mode", type=str, default="last", help="backpropagation mode: 'last', 'rand', 'specific'") # backprop_mode != None also means training mode for batch_ddim_sampling
+ parser.add_argument("--gradient_accumulation_steps", type=int, default=1, help="gradient accumulation steps")
+ parser.add_argument("--mixed_precision", type=str, default='fp16', help="mixed precision training: 'no', 'fp8', 'fp16', 'bf16'")
+ parser.add_argument("--project_dir", type=str, default="VADER-VideoCrafter/project_dir", help="project directory")
+ parser.add_argument("--validation_steps", type=int, default=1, help="The frequency of validation, e.g., 1 means validate every 1*accelerator.num_processes steps")
+ parser.add_argument("--checkpointing_steps", type=int, default=1, help="The frequency of checkpointing")
+ parser.add_argument("--wandb_entity", type=str, default="", help="wandb entity")
+ parser.add_argument("--debug", type=str2bool, default=False, help="debug mode")
+ parser.add_argument("--max_grad_norm", type=float, default=1.0, help="max gradient norm")
+ parser.add_argument("--use_AdamW8bit", type=str2bool, default=False, help="use AdamW8bit optimizer")
+ parser.add_argument("--is_sample_preview", type=str2bool, default=True, help="sample preview during training")
+ parser.add_argument("--decode_frame", type=str, default="-1", help="decode frame: '-1', 'fml', 'all', 'alt'") # it could also be any number str like '3', '10'. alt: alternate frames, fml: first, middle, last frames, all: all frames. '-1': random frame
+ parser.add_argument("--inference_only", type=str2bool, default=True, help="only do inference")
+ parser.add_argument("--lora_ckpt_path", type=str, default=None, help="LoRA checkpoint path")
+ parser.add_argument("--lora_rank", type=int, default=16, help="LoRA rank")
+
+ return parser
+
+
+def aesthetic_loss_fn(aesthetic_target=None,
+ grad_scale=0,
+ device=None,
+ torch_dtype=None):
+ '''
+ Args:
+ aesthetic_target: float, the target value of the aesthetic score. it is 10 in this experiment
+ grad_scale: float, the scale of the gradient. it is 0.1 in this experiment
+ device: torch.device, the device to run the model.
+ torch_dtype: torch.dtype, the data type of the model.
+
+ Returns:
+ loss_fn: function, the loss function of the aesthetic reward function.
+ '''
+ target_size = (224, 224)
+ normalize = torchvision.transforms.Normalize(mean=[0.48145466, 0.4578275, 0.40821073],
+ std=[0.26862954, 0.26130258, 0.27577711])
+
+ scorer = AestheticScorerDiff(dtype=torch_dtype).to(device, dtype=torch_dtype)
+ scorer.requires_grad_(False)
+
+ def loss_fn(im_pix_un):
+ im_pix = ((im_pix_un / 2) + 0.5).clamp(0, 1)
+ im_pix = torchvision.transforms.Resize(target_size)(im_pix)
+ im_pix = normalize(im_pix).to(im_pix_un.dtype)
+ rewards = scorer(im_pix)
+ if aesthetic_target is None: # default maximization
+ loss = -1 * rewards
+ else:
+ # using L1 to keep on same scale
+ loss = abs(rewards - aesthetic_target)
+ return loss.mean() * grad_scale, rewards.mean()
+ return loss_fn
+
+
+def hps_loss_fn(inference_dtype=None, device=None, hps_version="v2.0"):
+ '''
+ Args:
+ inference_dtype: torch.dtype, the data type of the model.
+ device: torch.device, the device to run the model.
+ hps_version: str, the version of the HPS model. It is "v2.0" or "v2.1" in this experiment.
+
+ Returns:
+ loss_fn: function, the loss function of the HPS reward function.
+ '''
+ model_name = "ViT-H-14"
+
+ model, preprocess_train, preprocess_val = create_model_and_transforms(
+ model_name,
+ 'laion2B-s32B-b79K',
+ precision=inference_dtype,
+ device=device,
+ jit=False,
+ force_quick_gelu=False,
+ force_custom_text=False,
+ force_patch_dropout=False,
+ force_image_size=None,
+ pretrained_image=False,
+ image_mean=None,
+ image_std=None,
+ light_augmentation=True,
+ aug_cfg={},
+ output_dict=True,
+ with_score_predictor=False,
+ with_region_predictor=False
+ )
+
+ tokenizer = get_tokenizer(model_name)
+
+ if hps_version == "v2.0": # if there is a error, please download the model manually and set the path
+ checkpoint_path = f"{os.path.expanduser('~')}/.cache/huggingface/hub/models--xswu--HPSv2/snapshots/697403c78157020a1ae59d23f111aa58ced35b0a/HPS_v2_compressed.pt"
+ else: # hps_version == "v2.1"
+ checkpoint_path = f"{os.path.expanduser('~')}/.cache/huggingface/hub/models--xswu--HPSv2/snapshots/697403c78157020a1ae59d23f111aa58ced35b0a/HPS_v2.1_compressed.pt"
+ # force download of model via score
+ hpsv2.score([], "", hps_version=hps_version)
+
+ checkpoint = torch.load(checkpoint_path, map_location=device)
+ model.load_state_dict(checkpoint['state_dict'])
+ tokenizer = get_tokenizer(model_name)
+ model = model.to(device, dtype=inference_dtype)
+ model.eval()
+
+ target_size = (224, 224)
+ normalize = torchvision.transforms.Normalize(mean=[0.48145466, 0.4578275, 0.40821073],
+ std=[0.26862954, 0.26130258, 0.27577711])
+
+ def loss_fn(im_pix, prompts):
+ im_pix = ((im_pix / 2) + 0.5).clamp(0, 1)
+ x_var = torchvision.transforms.Resize(target_size)(im_pix)
+ x_var = normalize(x_var).to(im_pix.dtype)
+ caption = tokenizer(prompts)
+ caption = caption.to(device)
+ outputs = model(x_var, caption)
+ image_features, text_features = outputs["image_features"], outputs["text_features"]
+ logits = image_features @ text_features.T
+ scores = torch.diagonal(logits)
+ loss = 1.0 - scores
+ return loss.mean(), scores.mean()
+
+ return loss_fn
+
+def aesthetic_hps_loss_fn(aesthetic_target=None,
+ grad_scale=0,
+ inference_dtype=None,
+ device=None,
+ hps_version="v2.0"):
+ '''
+ Args:
+ aesthetic_target: float, the target value of the aesthetic score. it is 10 in this experiment
+ grad_scale: float, the scale of the gradient. it is 0.1 in this experiment
+ inference_dtype: torch.dtype, the data type of the model.
+ device: torch.device, the device to run the model.
+ hps_version: str, the version of the HPS model. It is "v2.0" or "v2.1" in this experiment.
+
+ Returns:
+ loss_fn: function, the loss function of a combination of aesthetic and HPS reward function.
+ '''
+ # HPS
+ model_name = "ViT-H-14"
+
+ model, preprocess_train, preprocess_val = create_model_and_transforms(
+ model_name,
+ 'laion2B-s32B-b79K',
+ precision=inference_dtype,
+ device=device,
+ jit=False,
+ force_quick_gelu=False,
+ force_custom_text=False,
+ force_patch_dropout=False,
+ force_image_size=None,
+ pretrained_image=False,
+ image_mean=None,
+ image_std=None,
+ light_augmentation=True,
+ aug_cfg={},
+ output_dict=True,
+ with_score_predictor=False,
+ with_region_predictor=False
+ )
+
+ # tokenizer = get_tokenizer(model_name)
+
+ if hps_version == "v2.0": # if there is a error, please download the model manually and set the path
+ checkpoint_path = f"{os.path.expanduser('~')}/.cache/huggingface/hub/models--xswu--HPSv2/snapshots/697403c78157020a1ae59d23f111aa58ced35b0a/HPS_v2_compressed.pt"
+ else: # hps_version == "v2.1"
+ checkpoint_path = f"{os.path.expanduser('~')}/.cache/huggingface/hub/models--xswu--HPSv2/snapshots/697403c78157020a1ae59d23f111aa58ced35b0a/HPS_v2.1_compressed.pt"
+ # force download of model via score
+ hpsv2.score([], "", hps_version=hps_version)
+
+ checkpoint = torch.load(checkpoint_path, map_location=device)
+ model.load_state_dict(checkpoint['state_dict'])
+ tokenizer = get_tokenizer(model_name)
+ model = model.to(device, dtype=inference_dtype)
+ model.eval()
+
+ target_size = (224, 224)
+ normalize = torchvision.transforms.Normalize(mean=[0.48145466, 0.4578275, 0.40821073],
+ std=[0.26862954, 0.26130258, 0.27577711])
+ # Aesthetic
+ scorer = AestheticScorerDiff(dtype=inference_dtype).to(device, dtype=inference_dtype)
+ scorer.requires_grad_(False)
+
+ def loss_fn(im_pix_un, prompts):
+ # Aesthetic
+ im_pix = ((im_pix_un / 2) + 0.5).clamp(0, 1)
+ im_pix = torchvision.transforms.Resize(target_size)(im_pix)
+ im_pix = normalize(im_pix).to(im_pix_un.dtype)
+
+ aesthetic_rewards = scorer(im_pix)
+ if aesthetic_target is None: # default maximization
+ aesthetic_loss = -1 * aesthetic_rewards
+ else:
+ # using L1 to keep on same scale
+ aesthetic_loss = abs(aesthetic_rewards - aesthetic_target)
+ aesthetic_loss = aesthetic_loss.mean() * grad_scale
+ aesthetic_rewards = aesthetic_rewards.mean()
+
+ # HPS
+ caption = tokenizer(prompts)
+ caption = caption.to(device)
+ outputs = model(im_pix, caption)
+ image_features, text_features = outputs["image_features"], outputs["text_features"]
+ logits = image_features @ text_features.T
+ scores = torch.diagonal(logits)
+ hps_loss = abs(1.0 - scores)
+ hps_loss = hps_loss.mean()
+ hps_rewards = scores.mean()
+
+ loss = (1.5 * aesthetic_loss + hps_loss) /2 # 1.5 is a hyperparameter. Set it to 1.5 because experimentally hps_loss is 1.5 times larger than aesthetic_loss
+ rewards = (aesthetic_rewards + 15 * hps_rewards) / 2 # 15 is a hyperparameter. Set it to 15 because experimentally aesthetic_rewards is 15 times larger than hps_reward
+ return loss, rewards
+
+ return loss_fn
+
+def pick_score_loss_fn(inference_dtype=None, device=None):
+ '''
+ Args:
+ inference_dtype: torch.dtype, the data type of the model.
+ device: torch.device, the device to run the model.
+
+ Returns:
+ loss_fn: function, the loss function of the PickScore reward function.
+ '''
+ processor_name_or_path = "laion/CLIP-ViT-H-14-laion2B-s32B-b79K"
+ model_pretrained_name_or_path = "yuvalkirstain/PickScore_v1"
+ processor = AutoProcessor.from_pretrained(processor_name_or_path, torch_dtype=inference_dtype)
+ model = AutoModel.from_pretrained(model_pretrained_name_or_path, torch_dtype=inference_dtype).eval().to(device)
+ model.requires_grad_(False)
+
+ def loss_fn(im_pix_un, prompts): # im_pix_un: b,c,h,w
+ im_pix = ((im_pix_un / 2) + 0.5).clamp(0, 1)
+
+ # reproduce the pick_score preprocessing
+ im_pix = im_pix * 255 # b,c,h,w
+
+ if im_pix.shape[2] < im_pix.shape[3]:
+ height = 224
+ width = im_pix.shape[3] * height // im_pix.shape[2] # keep the aspect ratio, so the width is w * 224/h
+ else:
+ width = 224
+ height = im_pix.shape[2] * width // im_pix.shape[3] # keep the aspect ratio, so the height is h * 224/w
+
+ # interpolation and antialiasing should be the same as below
+ im_pix = torchvision.transforms.Resize((height, width),
+ interpolation=torchvision.transforms.InterpolationMode.BICUBIC,
+ antialias=True)(im_pix)
+ im_pix = im_pix.permute(0, 2, 3, 1) # b,c,h,w -> (b,h,w,c)
+ # crop the center 224x224
+ startx = width//2 - (224//2)
+ starty = height//2 - (224//2)
+ im_pix = im_pix[:, starty:starty+224, startx:startx+224, :]
+ # do rescale and normalize as CLIP
+ im_pix = im_pix * 0.00392156862745098 # rescale factor
+ mean = torch.tensor([0.48145466, 0.4578275, 0.40821073]).to(device)
+ std = torch.tensor([0.26862954, 0.26130258, 0.27577711]).to(device)
+ im_pix = (im_pix - mean) / std
+ im_pix = im_pix.permute(0, 3, 1, 2) # BHWC -> BCHW
+
+ text_inputs = processor(
+ text=prompts,
+ padding=True,
+ truncation=True,
+ max_length=77,
+ return_tensors="pt",
+ ).to(device)
+
+
+ # embed
+ image_embs = model.get_image_features(pixel_values=im_pix)
+ image_embs = image_embs / torch.norm(image_embs, dim=-1, keepdim=True)
+
+ text_embs = model.get_text_features(**text_inputs)
+ text_embs = text_embs / torch.norm(text_embs, dim=-1, keepdim=True)
+
+ # score
+ scores = model.logit_scale.exp() * (text_embs @ image_embs.T)[0]
+ loss = abs(1.0 - scores / 100.0)
+ return loss.mean(), scores.mean()
+
+ return loss_fn
+
+def weather_loss_fn(inference_dtype=None, device=None, weather="rainy", target=None, grad_scale=0):
+ '''
+ Args:
+ inference_dtype: torch.dtype, the data type of the model.
+ device: torch.device, the device to run the model.
+ weather: str, the weather condition. It is "rainy" or "snowy" in this experiment.
+ target: float, the target value of the weather score. It is 1.0 in this experiment.
+ grad_scale: float, the scale of the gradient. It is 1 in this experiment.
+
+ Returns:
+ loss_fn: function, the loss function of the weather reward function.
+ '''
+ if weather == "rainy":
+ reward_model_path = "../assets/rainy_reward.pt"
+ elif weather == "snowy":
+ reward_model_path = "../assets/snowy_reward.pt"
+ else:
+ raise NotImplementedError
+ scorer = WeatherScorer(dtype=inference_dtype, model_path=reward_model_path).to(device, dtype=inference_dtype)
+ scorer.requires_grad_(False)
+ scorer.eval()
+ def loss_fn(im_pix_un):
+ im_pix = ((im_pix_un + 1) / 2).clamp(0, 1) # from [-1, 1] to [0, 1]
+ rewards = scorer(im_pix)
+
+ if target is None:
+ loss = rewards
+ else:
+ loss = abs(rewards - target)
+
+ return loss.mean() * grad_scale, rewards.mean()
+ return loss_fn
+
+def objectDetection_loss_fn(inference_dtype=None, device=None, targetObject='dog.', model_name='grounding-dino-base'):
+ '''
+ This reward function is used to remove the target object from the generated video.
+ We use yolo-s-tiny model to detect the target object in the generated video.
+
+ Args:
+ inference_dtype: torch.dtype, the data type of the model.
+ device: torch.device, the device to run the model.
+ targetObject: str, the object to detect. It is "dog" in this experiment.
+
+ Returns:
+ loss_fn: function, the loss function of the object detection reward function.
+ '''
+ if model_name == "yolos-base":
+ image_processor = AutoImageProcessor.from_pretrained("hustvl/yolos-base", torch_dtype=inference_dtype)
+ model = AutoModelForObjectDetection.from_pretrained("hustvl/yolos-base", torch_dtype=inference_dtype).to(device)
+ # check if "." in the targetObject name for yolos model
+ if "." in targetObject:
+ raise ValueError("The targetObject name should not contain '.' for yolos-base model.")
+ elif model_name == "yolos-tiny":
+ image_processor = AutoImageProcessor.from_pretrained("hustvl/yolos-tiny", torch_dtype=inference_dtype)
+ model = AutoModelForObjectDetection.from_pretrained("hustvl/yolos-tiny", torch_dtype=inference_dtype).to(device)
+ # check if "." in the targetObject name for yolos model
+ if "." in targetObject:
+ raise ValueError("The targetObject name should not contain '.' for yolos-tiny model.")
+ elif model_name == "grounding-dino-base":
+ image_processor = AutoProcessor.from_pretrained("IDEA-Research/grounding-dino-base", torch_dtype=inference_dtype)
+ model = AutoModelForZeroShotObjectDetection.from_pretrained("IDEA-Research/grounding-dino-base",torch_dtype=inference_dtype).to(device)
+ # check if "." in the targetObject name for grounding-dino model
+ if "." not in targetObject:
+ raise ValueError("The targetObject name should contain '.' for grounding-dino-base model.")
+ elif model_name == "grounding-dino-tiny":
+ image_processor = AutoProcessor.from_pretrained("IDEA-Research/grounding-dino-tiny", torch_dtype=inference_dtype)
+ model = AutoModelForZeroShotObjectDetection.from_pretrained("IDEA-Research/grounding-dino-tiny", torch_dtype=inference_dtype).to(device)
+ # check if "." in the targetObject name for grounding-dino model
+ if "." not in targetObject:
+ raise ValueError("The targetObject name should contain '.' for grounding-dino-tiny model.")
+ else:
+ raise NotImplementedError
+
+ model.requires_grad_(False)
+ model.eval()
+
+ def loss_fn(im_pix_un): # im_pix_un: b,c,h,w
+ images = ((im_pix_un / 2) + 0.5).clamp(0.0, 1.0)
+
+ # reproduce the yolo preprocessing
+ height = 512
+ width = 512 * images.shape[3] // images.shape[2] # keep the aspect ratio, so the width is 512 * w/h
+ images = torchvision.transforms.Resize((height, width), antialias=False)(images)
+ images = images.permute(0, 2, 3, 1) # b,c,h,w -> (b,h,w,c)
+
+ image_mean = torch.tensor([0.485, 0.456, 0.406]).to(device)
+ image_std = torch.tensor([0.229, 0.224, 0.225]).to(device)
+
+ images = (images - image_mean) / image_std
+ normalized_image = images.permute(0,3,1,2) # NHWC -> NCHW
+
+ # Process images
+ if model_name == "yolos-base" or model_name == "yolos-tiny":
+ outputs = model(pixel_values=normalized_image)
+ else: # grounding-dino model
+ inputs = image_processor(text=targetObject, return_tensors="pt").to(device)
+ outputs = model(pixel_values=normalized_image, input_ids=inputs.input_ids)
+
+ # Get target sizes for each image
+ target_sizes = torch.tensor([normalized_image[0].shape[1:]]*normalized_image.shape[0]).to(device)
+
+ # Post-process results for each image
+ if model_name == "yolos-base" or model_name == "yolos-tiny":
+ results = image_processor.post_process_object_detection(outputs, threshold=0.2, target_sizes=target_sizes)
+ else: # grounding-dino model
+ results = image_processor.post_process_grounded_object_detection(
+ outputs,
+ inputs.input_ids,
+ box_threshold=0.4,
+ text_threshold=0.3,
+ target_sizes=target_sizes
+ )
+
+ sum_avg_scores = 0
+ for i, result in enumerate(results):
+ if model_name == "yolos-base" or model_name == "yolos-tiny":
+ id = model.config.label2id[targetObject]
+ # get index of targetObject's label
+ index = torch.where(result["labels"] == id)
+ if len(index[0]) == 0: # index: ([],[]) so index[0] is the first list
+ sum_avg_scores = torch.sum(outputs.logits - outputs.logits) # set sum_avg_scores to 0
+ continue
+ scores = result["scores"][index]
+ else: # grounding-dino model
+ if result["scores"].shape[0] == 0:
+ sum_avg_scores = torch.sum(outputs.last_hidden_state - outputs.last_hidden_state) # set sum_avg_scores to 0
+ continue
+ scores = result["scores"]
+ sum_avg_scores = sum_avg_scores + (torch.sum(scores) / scores.shape[0])
+
+ loss = sum_avg_scores / len(results)
+ reward = 1 - loss
+
+ return loss, reward
+ return loss_fn
+
+def compression_loss_fn(inference_dtype=None, device=None, target=None, grad_scale=0, model_path=None):
+ '''
+ Args:
+ inference_dtype: torch.dtype, the data type of the model.
+ device: torch.device, the device to run the model.
+ model_path: str, the path of the compression model.
+
+ Returns:
+ loss_fn: function, the loss function of the compression reward function.
+ '''
+ scorer = JpegCompressionScorer(dtype=inference_dtype, model_path=model_path).to(device, dtype=inference_dtype)
+ scorer.requires_grad_(False)
+ scorer.eval()
+ def loss_fn(im_pix_un):
+ im_pix = ((im_pix_un + 1) / 2).clamp(0, 1)
+ rewards = scorer(im_pix)
+
+ if target is None:
+ loss = rewards
+ else:
+ loss = abs(rewards - target)
+ return loss.mean() * grad_scale, rewards.mean()
+
+ return loss_fn
+
+def actpred_loss_fn(inference_dtype=None, device=None, num_frames = 14, target_size=224):
+ scorer = ActPredScorer(device=device, num_frames = num_frames, dtype=inference_dtype)
+ scorer.requires_grad_(False)
+
+ def preprocess_img(img):
+ img = ((img/2) + 0.5).clamp(0,1)
+ img = torchvision.transforms.Resize((target_size, target_size), antialias = True)(img)
+ img = torchvision.transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])(img)
+ return img
+ def loss_fn(vid, target_action_label):
+ vid = torch.cat([preprocess_img(img).unsqueeze(0) for img in vid])[None]
+ return scorer.get_loss_and_score(vid, target_action_label)
+
+ return loss_fn
+
+
+def should_sample(global_step, validation_steps, is_sample_preview):
+ return (global_step % validation_steps == 0 or global_step ==1) \
+ and is_sample_preview
+
+
+def run_training(args, peft_model, **kwargs):
+ ## ---------------------step 1: accelerator setup---------------------------
+ accelerator = Accelerator( # Initialize Accelerator
+ gradient_accumulation_steps=args.gradient_accumulation_steps,
+ mixed_precision=args.mixed_precision,
+ project_dir=args.project_dir
+
+ )
+ output_dir = args.project_dir
+
+ # Make one log on every process with the configuration for debugging.
+ create_logging(logging, logger, accelerator)
+
+ # ## ------------------------step 2: model config-----------------------------
+ # # download the checkpoint for VideoCrafter2 model
+ # ckpt_dir = args.ckpt_path.split('/') # args.ckpt='checkpoints/base_512_v2/model.ckpt' -> 'checkpoints/base_512_v2'
+ # ckpt_dir = '/'.join(ckpt_dir[:-1])
+ # snapshot_download(repo_id='VideoCrafter/VideoCrafter2', local_dir =ckpt_dir)
+
+ # # load the model
+ # config = OmegaConf.load(args.config)
+ # model_config = config.pop("model", OmegaConf.create())
+ # model = instantiate_from_config(model_config)
+
+ # assert os.path.exists(args.ckpt_path), f"Error: checkpoint [{args.ckpt_path}] Not Found!"
+ # model = load_model_checkpoint(model, args.ckpt_path)
+
+
+ # # convert first_stage_model and cond_stage_model to torch.float16 if mixed_precision is True
+ # if args.mixed_precision != 'no':
+ # model.first_stage_model = model.first_stage_model.half()
+ # model.cond_stage_model = model.cond_stage_model.half()
+
+ # # step 2.1: add LoRA using peft
+ # config = peft.LoraConfig(
+ # r=args.lora_rank,
+ # target_modules=["to_k", "to_v", "to_q"], # only diffusion_model has these modules
+ # lora_dropout=0.01,
+ # )
+
+ # peft_model = peft.get_peft_model(model, config)
+
+ # peft_model.print_trainable_parameters()
+
+ # # load the pretrained LoRA model
+ # if args.lora_ckpt_path is not None:
+ # if args.lora_ckpt_path == "huggingface-hps-aesthetic": # download the pretrained LoRA model from huggingface
+ # snapshot_download(repo_id='zheyangqin/VADER', local_dir ='VADER-VideoCrafter/checkpoints/pretrained_lora')
+ # args.lora_ckpt_path = 'VADER-VideoCrafter/checkpoints/pretrained_lora/vader_videocrafter_hps_aesthetic.pt'
+ # elif args.lora_ckpt_path == "huggingface-pickscore": # download the pretrained LoRA model from huggingface
+ # snapshot_download(repo_id='zheyangqin/VADER', local_dir ='VADER-VideoCrafter/checkpoints/pretrained_lora')
+ # args.lora_ckpt_path = 'VADER-VideoCrafter/checkpoints/pretrained_lora/vader_videocrafter_pickscore.pt'
+ # # load the pretrained LoRA model
+ # peft.set_peft_model_state_dict(peft_model, torch.load(args.lora_ckpt_path))
+
+ # Inference Step: only do inference and save the videos. Skip this step if it is training
+ # ==================================================================
+ if args.inference_only:
+ peft_model = accelerator.prepare(peft_model)
+ # sample shape
+ assert (args.height % 16 == 0) and (args.width % 16 == 0), "Error: image size [h,w] should be multiples of 16!"
+ # latent noise shape
+ h, w = args.height // 8, args.width // 8
+ if isinstance(peft_model, torch.nn.parallel.DistributedDataParallel):
+ frames = peft_model.module.temporal_length if args.frames < 0 else args.frames
+ channels = peft_model.module.channels
+ else:
+ frames = peft_model.temporal_length if args.frames < 0 else args.frames
+ channels = peft_model.channels
+
+ ## Inference step 2: run Inference over samples
+ logger.info("***** Running inference *****")
+
+ first_epoch = 0
+ global_step = 0
+
+
+ ## Inference Step 3: generate new validation videos
+ with torch.no_grad():
+
+ # set random seed for each process
+ random.seed(args.seed)
+ torch.manual_seed(args.seed)
+
+ prompts_all = [args.prompt_str]
+ val_prompt = list(prompts_all)
+
+ assert len(val_prompt) == 1, "Error: only one prompt is allowed for inference in gradio!"
+
+ # store output of generations in dict
+ results=dict(filenames=[],dir_name=[], prompt=[])
+
+ # Inference Step 3.1: forward pass
+ batch_size = len(val_prompt)
+ noise_shape = [batch_size, channels, frames, h, w]
+
+ fps = torch.tensor([args.fps]*batch_size).to(accelerator.device).long()
+
+ prompts = val_prompt
+ if isinstance(prompts, str):
+ prompts = [prompts]
+
+
+ with accelerator.autocast(): # mixed precision
+ if isinstance(peft_model, torch.nn.parallel.DistributedDataParallel):
+ text_emb = peft_model.module.get_learned_conditioning(prompts).to(accelerator.device)
+ else:
+ text_emb = peft_model.get_learned_conditioning(prompts).to(accelerator.device)
+
+ if args.mode == 'base':
+ cond = {"c_crossattn": [text_emb], "fps": fps}
+ else: # TODO: implement i2v mode training in the future
+ raise NotImplementedError
+
+ # Inference Step 3.2: inference, batch_samples shape: batch, <samples>, c, t, h, w
+ # no backprop_mode=args.backprop_mode because it is inference process
+ if isinstance(peft_model, torch.nn.parallel.DistributedDataParallel):
+ batch_samples = batch_ddim_sampling(peft_model.module, cond, noise_shape, args.n_samples, \
+ args.ddim_steps, args.ddim_eta, args.unconditional_guidance_scale, None, decode_frame=args.decode_frame, **kwargs)
+ else:
+ batch_samples = batch_ddim_sampling(peft_model, cond, noise_shape, args.n_samples, \
+ args.ddim_steps, args.ddim_eta, args.unconditional_guidance_scale, None, decode_frame=args.decode_frame, **kwargs)
+
+ # batch_samples: b,samples,c,t,h,w
+ dir_name = os.path.join(output_dir, "samples")
+ # filenames should be related to the gpu index
+ # get timestamps for filenames to avoid overwriting
+ # current_time = datetime.datetime.now().strftime("%Y%m%d%H%M%S")
+ filenames = [f"temporal"] # only one sample
+ # if dir_name is not exists, create it
+ os.makedirs(dir_name, exist_ok=True)
+
+ save_videos(batch_samples, dir_name, filenames, fps=args.savefps)
+
+ results["filenames"].extend(filenames)
+ results["dir_name"].extend([dir_name]*len(filenames))
+ results["prompt"].extend(prompts)
+ results=[ results ] # transform to list, otherwise gather_object() will not collect correctly
+
+ # Inference Step 3.3: collect inference results and save the videos to wandb
+ # collect inference results from all the GPUs
+ results_gathered=gather_object(results)
+
+ if accelerator.is_main_process:
+ filenames = []
+ dir_name = []
+ prompts = []
+ for i in range(len(results_gathered)):
+ filenames.extend(results_gathered[i]["filenames"])
+ dir_name.extend(results_gathered[i]["dir_name"])
+ prompts.extend(results_gathered[i]["prompt"])
+
+ logger.info("Validation sample saved!")
+
+ # # batch size is 1, so only one video is generated
+
+ # video = get_videos(batch_samples)
+
+ # # read the video from the saved path
+ video_path = os.path.join(dir_name[0], filenames[0]+".mp4")
+
+
+
+ # release memory
+ del batch_samples
+ torch.cuda.empty_cache()
+ gc.collect()
+
+ return video_path
+
+ # end of inference only, training script continues
+ # ==================================================================
+
+
+def setup_model(lora_ckpt_path="huggingface-pickscore", lora_rank=16):
+ parser = get_parser()
+ args = parser.parse_args()
+
+ ## ------------------------step 2: model config-----------------------------
+ # download the checkpoint for VideoCrafter2 model
+ ckpt_dir = args.ckpt_path.split('/') # args.ckpt='checkpoints/base_512_v2/model.ckpt' -> 'checkpoints/base_512_v2'
+ ckpt_dir = '/'.join(ckpt_dir[:-1])
+ snapshot_download(repo_id='VideoCrafter/VideoCrafter2', local_dir =ckpt_dir)
+
+ # load the model
+ config = OmegaConf.load(args.config)
+ model_config = config.pop("model", OmegaConf.create())
+ model = instantiate_from_config(model_config)
+
+ assert os.path.exists(args.ckpt_path), f"Error: checkpoint [{args.ckpt_path}] Not Found!"
+ model = load_model_checkpoint(model, args.ckpt_path)
+
+ # convert first_stage_model and cond_stage_model to torch.float16 if mixed_precision is True
+ if args.mixed_precision != 'no':
+ model.first_stage_model = model.first_stage_model.half()
+ model.cond_stage_model = model.cond_stage_model.half()
+
+ # step 2.1: add LoRA using peft
+ config = peft.LoraConfig(
+ r=args.lora_rank,
+ target_modules=["to_k", "to_v", "to_q"], # only diffusion_model has these modules
+ lora_dropout=0.01,
+ )
+
+ peft_model = peft.get_peft_model(model, config)
+
+ peft_model.print_trainable_parameters()
+
+ # load the pretrained LoRA model
+ if lora_ckpt_path != "Base Model":
+ if lora_ckpt_path == "huggingface-hps-aesthetic": # download the pretrained LoRA model from huggingface
+ snapshot_download(repo_id='zheyangqin/VADER', local_dir ='VADER-VideoCrafter/checkpoints/pretrained_lora')
+ lora_ckpt_path = 'VADER-VideoCrafter/checkpoints/pretrained_lora/vader_videocrafter_hps_aesthetic.pt'
+ elif lora_ckpt_path == "huggingface-pickscore": # download the pretrained LoRA model from huggingface
+ snapshot_download(repo_id='zheyangqin/VADER', local_dir ='VADER-VideoCrafter/checkpoints/pretrained_lora')
+ lora_ckpt_path = 'VADER-VideoCrafter/checkpoints/pretrained_lora/vader_videocrafter_pickscore.pt'
+ # load the pretrained LoRA model
+ peft.set_peft_model_state_dict(peft_model, torch.load(lora_ckpt_path))
+
+ print("Model setup complete!")
+ return peft_model
+
+
+def main_fn(prompt, seed=200, height=320, width=512, unconditional_guidance_scale=12, ddim_steps=25, ddim_eta=1.0,
+ frames=24, savefps=10, model=None):
+
+ parser = get_parser()
+ args = parser.parse_args()
+
+
+
+ # overwrite the default arguments
+ args.prompt_str = prompt
+ args.seed = seed
+ args.height = height
+ args.width = width
+ args.unconditional_guidance_scale = unconditional_guidance_scale
+ args.ddim_steps = ddim_steps
+ args.ddim_eta = ddim_eta
+ args.frames = frames
+ args.savefps = savefps
+
+ seed_everything(args.seed)
+
+ video_path = run_training(args, model)
+
+ return video_path
+
diff --git a/VADER-VideoCrafter/scripts/run_text2video_inference.sh b/VADER-VideoCrafter/scripts/run_text2video_inference.sh
new file mode 100644
index 0000000000000000000000000000000000000000..02f771622ccf7fac0964092d5078602775787862
--- /dev/null
+++ b/VADER-VideoCrafter/scripts/run_text2video_inference.sh
@@ -0,0 +1,30 @@
+ckpt='checkpoints/base_512_v2/model.ckpt'
+config='configs/inference_t2v_512_v2.0.yaml'
+PORT=$((20000 + RANDOM % 10000))
+
+accelerate launch --multi_gpu --main_process_port $PORT scripts/main/train_t2v_lora.py \
+--seed 200 \
+--mode 'base' \
+--ckpt_path $ckpt \
+--config $config \
+--height 320 --width 512 \
+--unconditional_guidance_scale 12.0 \
+--ddim_steps 25 \
+--ddim_eta 1.0 \
+--frames 24 \
+--prompt_fn 'chatgpt_custom_cute' \
+--val_batch_size 1 \
+--num_val_runs 1 \
+--lora_rank 16 \
+--inference_only True \
+--project_dir ./project_dir/inference \
+--lora_ckpt_path huggingface-pickscore \
+--is_sample_preview True
+
+
+
+ckpt='checkpoints/base_512_v2/model.ckpt'
+config='configs/inference_t2v_512_v2.0.yaml'
+PORT=$((20000 + RANDOM % 10000))
+
+accelerate launch --multi_gpu --main_process_port 15009 scripts/main/train_t2v_lora.py --seed 200 --mode 'base' --ckpt_path 'checkpoints/base_512_v2/model.ckpt' --config 'configs/inference_t2v_512_v2.0.yaml' --height 320 --width 512 --unconditional_guidance_scale 12.0 --ddim_steps 25 --ddim_eta 1.0 --frames 24 --prompt_fn 'chatgpt_custom_cute' --val_batch_size 1 --num_val_runs 1 --lora_rank 16 --inference_only True --project_dir ./project_dir/inference --lora_ckpt_path huggingface-pickscore --is_sample_preview True
diff --git a/VADER-VideoCrafter/scripts/run_text2video_train.sh b/VADER-VideoCrafter/scripts/run_text2video_train.sh
new file mode 100644
index 0000000000000000000000000000000000000000..7c982c94b11e3fce97a36849cadc3ab512a3b072
--- /dev/null
+++ b/VADER-VideoCrafter/scripts/run_text2video_train.sh
@@ -0,0 +1,28 @@
+ckpt='checkpoints/base_512_v2/model.ckpt'
+config='configs/inference_t2v_512_v2.0.yaml'
+PORT=$((20000 + RANDOM % 10000))
+
+accelerate launch --multi_gpu --main_process_port $PORT scripts/main/train_t2v_lora.py \
+--seed 300 \
+--mode 'base' \
+--ckpt_path $ckpt \
+--config $config \
+--height 320 --width 512 \
+--unconditional_guidance_scale 12.0 \
+--ddim_steps 25 \
+--ddim_eta 1.0 \
+--frames 12 \
+--prompt_fn 'chatgpt_custom_instruments' \
+--gradient_accumulation_steps 8 \
+--num_train_epochs 200 \
+--train_batch_size 1 \
+--val_batch_size 1 \
+--num_val_runs 1 \
+--reward_fn 'aesthetic_hps' \
+--decode_frame '-1' \
+--hps_version 'v2.1' \
+--lr 0.0002 \
+--validation_steps 10 \
+--lora_rank 16 \
+--is_sample_preview True
+
diff --git a/VADER-VideoCrafter/utils/utils.py b/VADER-VideoCrafter/utils/utils.py
new file mode 100644
index 0000000000000000000000000000000000000000..c73b93e006c4250161b427e4d1fff512ca046f7c
--- /dev/null
+++ b/VADER-VideoCrafter/utils/utils.py
@@ -0,0 +1,77 @@
+import importlib
+import numpy as np
+import cv2
+import torch
+import torch.distributed as dist
+
+
+def count_params(model, verbose=False):
+ total_params = sum(p.numel() for p in model.parameters())
+ if verbose:
+ print(f"{model.__class__.__name__} has {total_params*1.e-6:.2f} M params.")
+ return total_params
+
+
+def check_istarget(name, para_list):
+ """
+ name: full name of source para
+ para_list: partial name of target para
+ """
+ istarget=False
+ for para in para_list:
+ if para in name:
+ return True
+ return istarget
+
+
+def instantiate_from_config(config):
+ if not "target" in config:
+ if config == '__is_first_stage__':
+ return None
+ elif config == "__is_unconditional__":
+ return None
+ raise KeyError("Expected key `target` to instantiate.")
+ return get_obj_from_str(config["target"])(**config.get("params", dict()))
+
+
+def get_obj_from_str(string, reload=False):
+ module, cls = string.rsplit(".", 1)
+ if reload:
+ module_imp = importlib.import_module(module)
+ importlib.reload(module_imp)
+ return getattr(importlib.import_module(module, package=None), cls)
+
+
+def load_npz_from_dir(data_dir):
+ data = [np.load(os.path.join(data_dir, data_name))['arr_0'] for data_name in os.listdir(data_dir)]
+ data = np.concatenate(data, axis=0)
+ return data
+
+
+def load_npz_from_paths(data_paths):
+ data = [np.load(data_path)['arr_0'] for data_path in data_paths]
+ data = np.concatenate(data, axis=0)
+ return data
+
+
+def resize_numpy_image(image, max_resolution=512 * 512, resize_short_edge=None):
+ h, w = image.shape[:2]
+ if resize_short_edge is not None:
+ k = resize_short_edge / min(h, w)
+ else:
+ k = max_resolution / (h * w)
+ k = k**0.5
+ h = int(np.round(h * k / 64)) * 64
+ w = int(np.round(w * k / 64)) * 64
+ image = cv2.resize(image, (w, h), interpolation=cv2.INTER_LANCZOS4)
+ return image
+
+
+def setup_dist(args):
+ if dist.is_initialized():
+ return
+ torch.cuda.set_device(args.local_rank)
+ torch.distributed.init_process_group(
+ 'nccl',
+ init_method='env://'
+ )
\ No newline at end of file
diff --git a/app.py b/app.py
index cbffdf1ba490e3ae1fb244c10909cccfa7652993..abc0cddec12c0e709857cd3c55e42b4106fcb1b3 100644
--- a/app.py
+++ b/app.py
@@ -1,7 +1,210 @@
import gradio as gr
+import os
-def greet(name):
- return "Hello " + name + "!!"
+import sys
+sys.path.append('./VADER-VideoCrafter/scripts/main')
+sys.path.append('./VADER-VideoCrafter/scripts')
+sys.path.append('./VADER-VideoCrafter')
-demo = gr.Interface(fn=greet, inputs="text", outputs="text")
-demo.launch()
\ No newline at end of file
+from train_t2v_lora import main_fn, setup_model
+
+model = None # Placeholder for model
+
+def gradio_main_fn(prompt, seed, height, width, unconditional_guidance_scale, ddim_steps, ddim_eta,
+ frames, savefps):
+ global model
+ if model is None:
+ return "Model is not loaded. Please load the model first."
+ video_path = main_fn(prompt=prompt,
+ seed=int(seed),
+ height=int(height),
+ width=int(width),
+ unconditional_guidance_scale=float(unconditional_guidance_scale),
+ ddim_steps=int(ddim_steps),
+ ddim_eta=float(ddim_eta),
+ frames=int(frames),
+ savefps=int(savefps),
+ model=model)
+
+ return video_path
+
+def reset_fn():
+ return ("A mermaid with flowing hair and a shimmering tail discovers a hidden underwater kingdom adorned with coral palaces, glowing pearls, and schools of colorful fish, encountering both wonders and dangers along the way.",
+ 200, 320, 512, 12.0, 25, 1.0, 24, 16, 10, "huggingface-pickscore")
+
+def update_lora_rank(lora_model):
+ if lora_model == "huggingface-pickscore":
+ return gr.update(value=16)
+ elif lora_model == "huggingface-hps-aesthetic":
+ return gr.update(value=8)
+ else: # "Base Model"
+ return gr.update(value=0)
+
+def update_dropdown(lora_rank):
+ if lora_rank == 16:
+ return gr.update(value="huggingface-pickscore")
+ elif lora_rank == 8:
+ return gr.update(value="huggingface-hps-aesthetic")
+ else: # 0
+ return gr.update(value="Base Model")
+
+
+def setup_model_progress(lora_model, lora_rank):
+ global model
+
+ # Disable buttons and show loading indicator
+ yield (gr.update(interactive=False), gr.update(interactive=False), gr.update(interactive=False), "Loading model...")
+
+ model = setup_model(lora_model, lora_rank) # Ensure you pass the necessary parameters to the setup_model function
+
+ # Enable buttons after loading and update indicator
+ yield (gr.update(interactive=True), gr.update(interactive=True), gr.update(interactive=True), "Model loaded successfully")
+
+css = """
+.centered {
+ display: flex;
+ justify-content: center;
+}
+"""
+
+
+with gr.Blocks(css=css) as demo:
+ with gr.Row():
+ with gr.Column():
+ gr.HTML(
+ """
+ <h1 style='text-align: center; font-size: 3.2em; margin-bottom: 0.5em; font-family: Arial, sans-serif; margin: 20px;'>
+ Video Diffusion Alignment via Reward Gradient
+ </h1>
+ """
+ )
+ gr.HTML(
+ """
+ <style>
+ body {
+ font-family: Arial, sans-serif;
+ text-align: center;
+ margin: 50px;
+ }
+ a {
+ text-decoration: none !important;
+ color: black !important;
+ }
+
+ </style>
+ <body>
+ <div style="font-size: 1.4em; margin-bottom: 0.5em; ">
+ <a href="https://mihirp1998.github.io">Mihir Prabhudesai</a><sup>*</sup>
+ <a href="https://russellmendonca.github.io/">Russell Mendonca</a><sup>*</sup>
+ <a href="mailto: zheyangqin.qzy@gmail.com">Zheyang Qin</a><sup>*</sup>
+ <a href="https://www.cs.cmu.edu/~katef/">Katerina Fragkiadaki</a><sup></sup>
+ <a href="https://www.cs.cmu.edu/~dpathak/">Deepak Pathak</a><sup></sup>
+
+
+ </div>
+ <div style="font-size: 1.3em; font-style: italic;">
+ Carnegie Mellon University
+ </div>
+ </body>
+ """
+ )
+ gr.HTML(
+ """
+ <head>
+ <link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/6.0.0-beta3/css/all.min.css">
+
+ <style>
+ .button-container {
+ display: flex;
+ justify-content: center;
+ gap: 10px;
+ margin-top: 10px;
+ }
+
+ .button-container a {
+ display: inline-flex;
+ align-items: center;
+ padding: 10px 20px;
+ border-radius: 30px;
+ border: 1px solid #ccc;
+ text-decoration: none;
+ color: #333 !important;
+ font-size: 16px;
+ text-decoration: none !important;
+ }
+
+ .button-container a i {
+ margin-right: 8px;
+ }
+ </style>
+ </head>
+
+ <div class="button-container">
+ <a href="https://arxiv.org/abs/2407.08737" class="btn btn-outline-primary">
+ <i class="fa-solid fa-file-pdf"></i> Paper
+ </a>
+ <a href="https://vader-vid.github.io/" class="btn btn-outline-danger">
+ <i class="fa-solid fa-video"></i> Website
+ <a href="https://github.com/mihirp1998/VADER" class="btn btn-outline-secondary">
+ <i class="fa-brands fa-github"></i> Code
+ </a>
+ </div>
+ """
+ )
+
+ with gr.Row(elem_classes="centered"):
+ with gr.Column(scale=0.6):
+ output_video = gr.Video()
+
+ with gr.Row():
+ lora_model = gr.Dropdown(
+ label="VADER Model",
+ choices=["huggingface-pickscore", "huggingface-hps-aesthetic", "Base Model"],
+ value="huggingface-pickscore"
+ )
+ lora_rank = gr.Slider(minimum=0, maximum=16, label="LoRA Rank", step = 8, value=16)
+ load_btn = gr.Button("Load Model")
+ # Add a label to show the loading indicator
+ loading_indicator = gr.Label(value="", label="Loading Indicator")
+
+ prompt = gr.Textbox(placeholder="Enter prompt text here", lines=4, label="Text Prompt",
+ value="A mermaid with flowing hair and a shimmering tail discovers a hidden underwater kingdom adorned with coral palaces, glowing pearls, and schools of colorful fish, encountering both wonders and dangers along the way.")
+
+ seed = gr.Slider(minimum=0, maximum=65536, label="Seed", step = 1, value=200)
+
+ run_btn = gr.Button("Run Inference")
+
+
+ with gr.Row():
+ height = gr.Slider(minimum=0, maximum=1024, label="Height", step = 16, value=320)
+ width = gr.Slider(minimum=0, maximum=1024, label="Width", step = 16, value=512)
+
+ with gr.Row():
+ frames = gr.Slider(minimum=0, maximum=50, label="Frames", step = 1, value=24)
+ savefps = gr.Slider(minimum=0, maximum=60, label="Save FPS", step = 1, value=10)
+
+
+ with gr.Row():
+ DDIM_Steps = gr.Slider(minimum=0, maximum=100, label="DDIM Steps", step = 1, value=25)
+ unconditional_guidance_scale = gr.Slider(minimum=0, maximum=50, label="Guidance Scale", step = 0.1, value=12.0)
+ DDIM_Eta = gr.Slider(minimum=0, maximum=1, label="DDIM Eta", step = 0.01, value=1.0)
+
+ # reset button
+ reset_btn = gr.Button("Reset")
+
+ reset_btn.click(fn=reset_fn, outputs=[prompt, seed, height, width, unconditional_guidance_scale, DDIM_Steps, DDIM_Eta, frames, lora_rank, savefps, lora_model])
+
+
+
+ load_btn.click(fn=setup_model_progress, inputs=[lora_model, lora_rank], outputs=[load_btn, run_btn, reset_btn, loading_indicator])
+ run_btn.click(fn=gradio_main_fn,
+ inputs=[prompt, seed, height, width, unconditional_guidance_scale, DDIM_Steps, DDIM_Eta, frames, savefps],
+ outputs=output_video
+ )
+
+ lora_model.change(fn=update_lora_rank, inputs=lora_model, outputs=lora_rank)
+ lora_rank.change(fn=update_dropdown, inputs=lora_rank, outputs=lora_model)
+
+demo.launch()
+
+# main_fn(prompt="A mermaid with flowing hair and a shimmering tail discovers a hidden underwater kingdom adorned with coral palaces, glowing pearls, and schools of colorful fish, encountering both wonders and dangers along the way.",)
\ No newline at end of file
diff --git a/assets/activities.txt b/assets/activities.txt
new file mode 100644
index 0000000000000000000000000000000000000000..abea0458a5836b50ec85da2f732ff3a7d63b8c3a
--- /dev/null
+++ b/assets/activities.txt
@@ -0,0 +1,3 @@
+washing the dishes
+riding a bike
+playing chess
\ No newline at end of file
diff --git a/assets/chatgpt_custom.txt b/assets/chatgpt_custom.txt
new file mode 100644
index 0000000000000000000000000000000000000000..1552981a94e6cdbdd11e8e2c3951c92c28e7c820
--- /dev/null
+++ b/assets/chatgpt_custom.txt
@@ -0,0 +1,50 @@
+A dog catching a red frisbee in mid-air with the sun setting behind, casting a golden glow.
+A dog fetching a stick thrown into a crystal-clear lake surrounded by trees.
+A dog running alongside a bicycle on a park trail lined with golden autumn leaves.
+A dog leaping over a fence to catch a ball in a yard illuminated by string lights.
+A dog performing agility course obstacles with precision in a park decorated with lanterns.
+A dog running through a tunnel on an agility course with soft, colorful lighting.
+A dog playing tug-of-war with a human in the backyard under a canopy of twinkling lights.
+A dog retrieving a ball from the water in a pond surrounded by blooming lilies.
+A dog weaving through poles on an agility course with soft lighting creating a magical atmosphere.
+A dog running to catch a thrown toy in the air with vibrant flowers in the background.
+A dog diving into a pool to retrieve a toy with sunlight sparkling on the water.
+A dog catching a ball on the run during a game of fetch in a picturesque park.
+A dog jumping through a hoop in an agility course with colorful flags.
+A dog playing with a toy in a garden filled with blooming flowers.
+A dog retrieving a frisbee from the air in a park under a bright blue sky.
+A dog running at full speed across a field with a rainbow overhead.
+A dog chasing a thrown stick in a field with golden sunlight.
+A dog performing tricks in a park filled with children and laughter.
+A dog chasing its tail in a backyard with colorful flowers.
+A dog cuddling with a human under a tree with soft, ambient lighting.
+A dog and cat playing together in a garden with soft morning light filtering through the trees.
+A dog and cat chasing each other around the living room with fairy lights twinkling.
+A dog and cat lounging together under a tree with soft, ambient lighting.
+A dog and cat playing with a toy in a cozy, warmly lit room.
+A dog and cat cuddling with a human on a couch with a fireplace glowing.
+A cat jumping onto a high shelf to reach a dangling toy mouse, with soft light filtering through the window.
+A cat chasing a laser pointer around the living room with fairy lights twinkling in the background.
+A cat climbing up a curtain to reach a window ledge overlooking a moonlit garden.
+A cat playing with a toy on a string hanging from a door, with soft ambient lighting creating a cozy atmosphere.
+A cat pouncing on a moving object on the floor with gentle candlelight flickering nearby.
+A cat batting at a moving object on a string, with a fireplace crackling in the background.
+A cat chasing after a butterfly in the garden with soft morning light filtering through the trees.
+A cat stalking a bird from behind a bush in the yard, with evening light casting long shadows.
+A cat swiping at a moving toy on the floor with a cozy, warmly lit room.
+A cat jumping from one surface to another on a bookshelf with soft lighting.
+A cat chasing a toy car on the floor with sunlight streaming through the window.
+A cat leaping to catch a feather toy hanging from a door with sunlight streaming in.
+A cat playing with a ball in the living room with soft ambient lighting.
+A cat batting at a dangling feather toy in a cozy, warmly lit room.
+A cat chasing a string in a room filled with soft, ambient light.
+A cat playing hide and seek with a toy in the living room with a warm glow.
+A cat lounging in a sunbeam with a toy nearby.
+A cat curling up in a cozy bed with soft ambient light around.
+A cat playing with a small ball in a room with a warm, inviting atmosphere.
+A cat and dog playing together in a garden with soft morning light filtering through the trees.
+A cat and dog chasing each other around the living room with fairy lights twinkling.
+A cat and dog lounging together under a tree with soft, ambient lighting.
+A cat and dog playing with a toy in a cozy, warmly lit room.
+A cat and dog cuddling with a human on a couch with a fireplace glowing.
+A cat jumping onto a high shelf to reach a dangling toy mouse, with soft light filtering through the window.
\ No newline at end of file
diff --git a/assets/chatgpt_custom_actpred.txt b/assets/chatgpt_custom_actpred.txt
new file mode 100644
index 0000000000000000000000000000000000000000..d1c2ae795a67914da2f1ee6cfd1168e1dbab73c7
--- /dev/null
+++ b/assets/chatgpt_custom_actpred.txt
@@ -0,0 +1,41 @@
+A young girl playing the piano in a cozy living room.
+A boy playing guitar on a sunny beach.
+A woman chopping wood in a forest.
+A girl jogging along a scenic trail.
+A boy playing the piano in a school auditorium.
+A young woman playing guitar by a campfire.
+A woman jogging in a park at sunrise.
+A young girl chopping wood in a rustic backyard.
+A boy jogging along a beach at sunset.
+A man playing the piano in a modern living room.
+A woman playing guitar at a local music festival.
+A boy chopping wood in a snowy forest.
+A young man jogging through a busy city street.
+A woman playing the piano in an elegant ballroom.
+A man playing guitar in a cozy café.
+A boy jogging on a mountain trail.
+A young girl playing the piano in a music classroom.
+A man chopping wood in a rural countryside.
+A woman jogging along a riverbank.
+A boy playing guitar on a rooftop terrace.
+A young man chopping wood in a quiet forest.
+A woman playing the piano in a beautifully decorated hall.
+A man jogging in a suburban neighborhood.
+A girl playing guitar at a summer camp.
+A young woman jogging on a scenic beach.
+A man playing the piano at a fancy restaurant.
+A woman chopping wood in her backyard.
+A young girl jogging in a peaceful park.
+A boy playing guitar in a busy city square.
+A woman playing guitar in a cozy living room.
+A girl jogging through a bustling market.
+A boy playing the piano at a local talent show.
+A young man chopping wood at a cabin retreat.
+A young girl jogging in a colorful garden.
+A man playing guitar at a rooftop party.
+A woman playing the piano in a vintage-style parlor.
+A boy chopping wood on a farm.
+A young man jogging in an urban park.
+A woman playing guitar in a chic coffee shop.
+A man jogging along a cliffside trail.
+A young girl playing the piano in a sunlit room.
\ No newline at end of file
diff --git a/assets/chatgpt_custom_actpred2.txt b/assets/chatgpt_custom_actpred2.txt
new file mode 100644
index 0000000000000000000000000000000000000000..b0be8e8e4f92e39ac71669b82a3a6bf972fd1555
--- /dev/null
+++ b/assets/chatgpt_custom_actpred2.txt
@@ -0,0 +1,50 @@
+A young girl playing the piano in a cozy living room.
+A boy playing guitar on a sunny beach.
+A man playing the violin in a bustling city square.
+A woman chopping wood in a forest.
+A girl jogging along a scenic trail.
+A boy playing the piano in a school auditorium.
+A young woman playing guitar by a campfire.
+A man playing the violin in a grand concert hall.
+A woman jogging in a park at sunrise.
+A young girl chopping wood in a rustic backyard.
+A boy jogging along a beach at sunset.
+A man playing the piano in a modern living room.
+A woman playing guitar at a local music festival.
+A young girl playing the violin in a tranquil garden.
+A boy chopping wood in a snowy forest.
+A young man jogging through a busy city street.
+A woman playing the piano in an elegant ballroom.
+A man playing guitar in a cozy café.
+A girl playing the violin in a historic church.
+A boy jogging on a mountain trail.
+A young girl playing the piano in a music classroom.
+A man chopping wood in a rural countryside.
+A woman jogging along a riverbank.
+A boy playing guitar on a rooftop terrace.
+A young girl playing the violin in a flower-filled meadow.
+A young man chopping wood in a quiet forest.
+A woman playing the piano in a beautifully decorated hall.
+A man jogging in a suburban neighborhood.
+A girl playing guitar at a summer camp.
+A boy playing the violin in a quaint library.
+A young woman jogging on a scenic beach.
+A man playing the piano at a fancy restaurant.
+A woman chopping wood in her backyard.
+A young girl jogging in a peaceful park.
+A boy playing guitar in a busy city square.
+A man playing the violin in a modern art gallery.
+A woman playing guitar in a cozy living room.
+A girl jogging through a bustling market.
+A boy playing the piano at a local talent show.
+A young man chopping wood at a cabin retreat.
+A woman playing the violin on a balcony overlooking a city.
+A young girl jogging in a colorful garden.
+A man playing guitar at a rooftop party.
+A woman playing the piano in a vintage-style parlor.
+A boy chopping wood on a farm.
+A girl playing the violin in a serene forest.
+A young man jogging in an urban park.
+A woman playing guitar in a chic coffee shop.
+A man jogging along a cliffside trail.
+A young girl playing the piano in a sunlit room.
\ No newline at end of file
diff --git a/assets/chatgpt_custom_animal.txt b/assets/chatgpt_custom_animal.txt
new file mode 100644
index 0000000000000000000000000000000000000000..ebb742b6c12e12c03089493337d938a25ed6be68
--- /dev/null
+++ b/assets/chatgpt_custom_animal.txt
@@ -0,0 +1,24 @@
+A happy dog running in a sunny park, with children playing in the background and birds flying above.
+A cozy cat sleeping on a couch, surrounded by soft pillows and a warm blanket, with a gentle breeze coming through an open window.
+A graceful bird flying across a clear blue sky, with fluffy white clouds and distant mountains in the background.
+An excited dog fetching a bright red frisbee and bringing it back to its smiling owner in a green park.
+A curious cat climbing a tall tree to chase a quick squirrel in a thick forest.
+A big elephant spraying water with its trunk, making a rainbow in the bright sunlight at a calm waterhole.
+A friendly dog playing with a curious cat in a colorful garden full of blooming flowers and buzzing bees.
+A busy bird feeding its hungry chicks in a cozy nest on a tall tree, with the morning sun shining.
+A strong lion and a graceful lioness resting together in the shade of a big tree on a wide grassland.
+A playful penguin sliding down a snowy hill with its friends, the snow sparkling under a bright winter sun.
+A clever monkey riding a small bicycle through a lively village, drawing the amazed looks of local children.
+A tall giraffe bending down to drink water from a clear river, surrounded by green trees and colorful birds.
+A majestic whale swimming in the deep blue ocean during a beautiful sunset, with the sky painted in orange and pink colors.
+A peaceful deer eating grass in a thick forest, with sunlight filtering through the trees creating a magical feeling.
+A determined turtle crawling on a sandy beach, with waves gently crashing in the background under a bright, clear sky.
+A joyful dog playing in the snow, leaving paw prints and trying to catch snowflakes on its nose.
+A cautious cat hiding from the rain under a tree, its fur slightly wet and raindrops making ripples in puddles.
+A lively horse running through a field of blooming flowers in spring, the landscape full of colors and sweet smells.
+A sad puppy looking sorry after being scolded, its ears down and eyes wet with tears in a quiet room.
+An excited kitten playing with a ball of yarn, moving quickly and nimbly, in a cozy living room full of toys.
+A curious elephant exploring its surroundings, its trunk gently touching things and eyes wide with wonder in a dense jungle.
+A group of dolphins jumping out of the water, the sun setting behind them and casting a golden light on the waves.
+A flock of birds flying during autumn, with colorful leaves swirling around them in the cool air.
+A content panda eating bamboo in a peaceful forest, with a gentle waterfall flowing in the background.
\ No newline at end of file
diff --git a/assets/chatgpt_custom_animal_action.txt b/assets/chatgpt_custom_animal_action.txt
new file mode 100644
index 0000000000000000000000000000000000000000..a52fea40204bb4a4c17c8bb3ba562be8ed5467fb
--- /dev/null
+++ b/assets/chatgpt_custom_animal_action.txt
@@ -0,0 +1,40 @@
+Cat riding a skateboard.
+Dog riding a skateboard.
+Rabbit riding a skateboard.
+Raccoon riding a skateboard.
+Monkey riding a skateboard.
+Hedgehog riding a skateboard.
+Bat riding a skateboard.
+Mouse riding a skateboard.
+Cat working on a laptop.
+Dog working on a laptop.
+Rabbit working on a laptop.
+Raccoon working on a laptop.
+Monkey working on a laptop.
+Hedgehog working on a laptop.
+Bat working on a laptop.
+Mouse working on a laptop.
+Cat reading a book.
+Dog reading a book.
+Rabbit reading a book.
+Raccoon reading a book.
+Monkey reading a book.
+Hedgehog reading a book.
+Bat reading a book.
+Mouse reading a book.
+Cat eating an apple.
+Dog eating an apple.
+Rabbit eating an apple.
+Raccoon eating an apple.
+Monkey eating an apple.
+Hedgehog eating an apple.
+Bat eating an apple.
+Mouse eating an apple.
+Cat eating watermelon.
+Dog eating watermelon.
+Rabbit eating watermelon.
+Raccoon eating watermelon.
+Monkey eating watermelon.
+Hedgehog eating watermelon.
+Bat eating watermelon.
+Mouse eating watermelon.
\ No newline at end of file
diff --git a/assets/chatgpt_custom_animal_clothes.txt b/assets/chatgpt_custom_animal_clothes.txt
new file mode 100644
index 0000000000000000000000000000000000000000..429755e6b8871d944445d180d2cc1d2fc77665ee
--- /dev/null
+++ b/assets/chatgpt_custom_animal_clothes.txt
@@ -0,0 +1,50 @@
+A dog in a red shirt, running in a park.
+A cat in a blue shirt, sitting on a windowsill.
+A rabbit in a green shirt, pretending to cook.
+A bear in a yellow shirt, splashing in puddles.
+A penguin in a red scarf, waddling on ice.
+A squirrel in a blue scarf, collecting acorns.
+A monkey in a green scarf, juggling balls.
+A lion in a yellow scarf, sitting on a rock.
+A dolphin in a red hat, jumping out of the water.
+A turtle in a blue hat, relaxing on a beach.
+A dog in a firefighter hat, standing by a toy fire truck.
+A cat in a ballet hat, practicing dance moves.
+A rabbit in a magician's hat, pulling a carrot from a hat.
+A bear in a cowboy hat, pretending to chop wood.
+A penguin in a Santa hat, sliding on ice.
+A squirrel in a knight's hat, holding a tiny sword.
+A monkey in a detective hat, holding a magnifying glass.
+A lion in a wizard's hat, looking at the stars.
+A dolphin in a red shirt, hitting a ball with its nose.
+A turtle in a blue shirt, holding a small wand.
+A dog in a green shirt, skateboarding in a park.
+A cat in a yellow shirt, sleeping on a bed.
+A rabbit in a red shirt, picking flowers.
+A bear in a blue shirt, fishing by a river.
+A penguin in a green shirt, walking to an office.
+A squirrel in a yellow shirt, collecting nuts.
+A monkey in a red shirt, playing on a jungle gym.
+A lion in a blue shirt, playing catch.
+A dolphin in a green shirt, swimming near a boat.
+A turtle in a yellow shirt, hiking on a trail.
+A dog in a red hat, walking down an aisle.
+A cat in a blue hat, sitting at a table.
+A rabbit in a green hat, holding a diploma.
+A bear in a yellow hat, trick-or-treating.
+A penguin in a red scarf, decorating a Christmas tree.
+A squirrel in a blue scarf, celebrating a birthday.
+A monkey in a green scarf, holding confetti.
+A lion in a yellow scarf, sitting by a cake.
+A dolphin in a red hat, jumping in a pool.
+A turtle in a blue hat, at a garden party.
+A dog in a red jacket, playing in the snow.
+A cat in a blue jacket, lounging under an umbrella.
+A rabbit in a green jacket, hopping in a garden.
+A bear in a yellow jacket, sitting by a campfire.
+A penguin in a red jacket, skating on ice.
+A squirrel in a blue jacket, enjoying a sunny day.
+A monkey in a green jacket, swinging from trees.
+A lion in a yellow jacket, building a sandcastle.
+A dolphin in a red jacket, jumping through hoops.
+A turtle in a blue jacket, celebrating a holiday.
\ No newline at end of file
diff --git a/assets/chatgpt_custom_animal_clothesV2.txt b/assets/chatgpt_custom_animal_clothesV2.txt
new file mode 100644
index 0000000000000000000000000000000000000000..2325f8318c5a5bc8217efae33e26e79a0bad7660
--- /dev/null
+++ b/assets/chatgpt_custom_animal_clothesV2.txt
@@ -0,0 +1,50 @@
+The dog is wearing a red shirt and eating from a bowl.
+The cat is wearing a blue shirt and playing with yarn.
+The rabbit is wearing a green shirt and reading a book.
+The bear is wearing a yellow shirt and painting a picture.
+The raccoon is wearing a red dress and sliding down a tree.
+The squirrel is wearing a blue dress and holding a nut.
+The monkey is wearing a green dress and climbing a tree.
+The lion is wearing a yellow dress and lying on a rock.
+The fox is wearing a red hat and balancing a ball.
+The panda is wearing a blue hat and looking at flowers.
+The dog is wearing a firefighter hat and holding a hose.
+The cat is wearing a ballet hat and standing on one foot.
+The rabbit is wearing a magician's hat and holding a wand.
+The bear is wearing a cowboy hat and tipping its hat.
+The raccoon is wearing a Santa hat and carrying a gift.
+The squirrel is wearing a knight's hat and holding a toy sword.
+The monkey is wearing a detective hat and looking through a magnifying glass.
+The lion is wearing a wizard's hat and holding a book.
+The fox is wearing a red shirt and splashing water.
+The panda is wearing a blue shirt and blowing bubbles.
+The dog is wearing a green shirt and digging a hole.
+The cat is wearing a yellow shirt and napping on a cushion.
+The rabbit is wearing a red shirt and watering plants.
+The bear is wearing a blue shirt and eating honey.
+The raccoon is wearing a green shirt and building a small fort.
+The squirrel is wearing a yellow shirt and sitting on a branch.
+The monkey is wearing a red shirt and peeling a banana.
+The lion is wearing a blue shirt and looking at the sky.
+The fox is wearing a green shirt and sitting in a garden.
+The panda is wearing a yellow shirt and walking in a forest
+The dog is wearing a red hat and carrying a flower.
+The cat is wearing a blue hat and sitting at a table.
+The rabbit is wearing a green hat and holding a diploma.
+The bear is wearing a yellow hat and holding a pumpkin.
+The raccoon is wearing a red dress and hanging ornaments.
+The squirrel is wearing a blue dress and eating cake.
+The monkey is wearing a green dress and blowing a whistle.
+The lion is wearing a yellow dress and standing next to a cake.
+The fox is wearing a red hat and playing with leaves.
+The panda is wearing a blue hat and holding a balloon.
+The dog is wearing a red coat and sniffing the snow.
+The cat is wearing a blue coat and sunbathing.
+The rabbit is wearing a green coat and picking flowers.
+The bear is wearing a yellow coat and roasting marshmallows.
+The raccoon is wearing a red coat and holding a snowball.
+The squirrel is wearing a blue coat and looking at leaves.
+The monkey is wearing a green coat and eating fruit.
+The lion is wearing a yellow coat and digging in the sand.
+The fox is wearing a red coat and jumping in a pile of leaves.
+The panda is wearing a blue coat and sitting by a fire.
\ No newline at end of file
diff --git a/assets/chatgpt_custom_animal_clothesV3.txt b/assets/chatgpt_custom_animal_clothesV3.txt
new file mode 100644
index 0000000000000000000000000000000000000000..f50634304ab37d82df93ce2d3741c5ee5ac411ad
--- /dev/null
+++ b/assets/chatgpt_custom_animal_clothesV3.txt
@@ -0,0 +1,96 @@
+Cat in a red jacket
+Cat in a blue jacket
+Cat in a yellow jacket
+Cat in a black jacket
+Cat in a red shirt
+Cat in a blue shirt
+Cat in a yellow shirt
+Cat in a black shirt
+Cat in a red dress
+Cat in a blue dress
+Cat in a yellow dress
+Cat in a black dress
+Dog in a red jacket
+Dog in a blue jacket
+Dog in a yellow jacket
+Dog in a black jacket
+Dog in a red shirt
+Dog in a blue shirt
+Dog in a yellow shirt
+Dog in a black shirt
+Dog in a red dress
+Dog in a blue dress
+Dog in a yellow dress
+Dog in a black dress
+Rabbit in a red jacket
+Rabbit in a blue jacket
+Rabbit in a yellow jacket
+Rabbit in a black jacket
+Rabbit in a red shirt
+Rabbit in a blue shirt
+Rabbit in a yellow shirt
+Rabbit in a black shirt
+Rabbit in a red dress
+Rabbit in a blue dress
+Rabbit in a yellow dress
+Rabbit in a black dress
+Raccoon in a red jacket
+Raccoon in a blue jacket
+Raccoon in a yellow jacket
+Raccoon in a black jacket
+Raccoon in a red shirt
+Raccoon in a blue shirt
+Raccoon in a yellow shirt
+Raccoon in a black shirt
+Raccoon in a red dress
+Raccoon in a blue dress
+Raccoon in a yellow dress
+Raccoon in a black dress
+Monkey in a red jacket
+Monkey in a blue jacket
+Monkey in a yellow jacket
+Monkey in a black jacket
+Monkey in a red shirt
+Monkey in a blue shirt
+Monkey in a yellow shirt
+Monkey in a black shirt
+Monkey in a red dress
+Monkey in a blue dress
+Monkey in a yellow dress
+Monkey in a black dress
+Hedgehog in a red jacket
+Hedgehog in a blue jacket
+Hedgehog in a yellow jacket
+Hedgehog in a black jacket
+Hedgehog in a red shirt
+Hedgehog in a blue shirt
+Hedgehog in a yellow shirt
+Hedgehog in a black shirt
+Hedgehog in a red dress
+Hedgehog in a blue dress
+Hedgehog in a yellow dress
+Hedgehog in a black dress
+Mouse in a red jacket
+Mouse in a blue jacket
+Mouse in a yellow jacket
+Mouse in a black jacket
+Mouse in a red shirt
+Mouse in a blue shirt
+Mouse in a yellow shirt
+Mouse in a black shirt
+Mouse in a red dress
+Mouse in a blue dress
+Mouse in a yellow dress
+Mouse in a black dress
+Panda in a red jacket
+Panda in a blue jacket
+Panda in a yellow jacket
+Panda in a black jacket
+Panda in a red shirt
+Panda in a blue shirt
+Panda in a yellow shirt
+Panda in a black shirt
+Panda in a red dress
+Panda in a blue dress
+Panda in a yellow dress
+Panda in a black dress
\ No newline at end of file
diff --git a/assets/chatgpt_custom_animal_housework.txt b/assets/chatgpt_custom_animal_housework.txt
new file mode 100644
index 0000000000000000000000000000000000000000..622c1f6f89d9c3806fa4c9de5e6ea21e9de77de7
--- /dev/null
+++ b/assets/chatgpt_custom_animal_housework.txt
@@ -0,0 +1,150 @@
+The monkey is watering the plants.
+The raccoon is vacuuming the carpet.
+The cat is sweeping the floor.
+The rabbit is wiping the windows.
+The hedgehog is washing the car.
+The dog is wiping the dining table.
+The bat is dusting the shelves.
+The monkey is watering the garden.
+The raccoon is vacuuming the stairs.
+The cat is sweeping the porch.
+The rabbit is wiping the windowsills.
+The hedgehog is washing the car windows.
+The dog is wiping the kitchen table.
+The bat is dusting the bookshelf.
+The monkey is watering the indoor plants.
+The raccoon is vacuuming the bedroom.
+The cat is sweeping the garage.
+The rabbit is wiping the patio doors.
+The hedgehog is washing the car hood.
+The dog is wiping the coffee table.
+The bat is dusting the mantle.
+The monkey is watering the hanging plants.
+The raccoon is vacuuming the living room rug.
+The cat is sweeping the basement.
+The rabbit is wiping the bedroom windows.
+The hedgehog is washing the car tires.
+The dog is wiping the side table.
+The bat is dusting the picture frames.
+The monkey is watering the flower pots.
+The raccoon is vacuuming the hallway.
+The cat is sweeping the deck.
+The rabbit is wiping the kitchen windows.
+The hedgehog is washing the car bumper.
+The dog is wiping the dining chairs.
+The bat is dusting the TV stand.
+The monkey is watering the balcony plants.
+The raccoon is vacuuming the study.
+The cat is sweeping the driveway.
+The rabbit is wiping the glass doors.
+The hedgehog is washing the car rear window.
+The dog is wiping the dining table before dinner.
+The bat is dusting the living room shelves.
+The monkey is watering the backyard garden.
+The raccoon is vacuuming the hallway carpet.
+The cat is sweeping the patio.
+The rabbit is wiping the sliding doors.
+The hedgehog is washing the car front grille.
+The dog is wiping the kitchen table after a meal.
+The bat is dusting the study shelves.
+The monkey is watering the windowsill plants.
+The raccoon is vacuuming the nursery.
+The cat is sweeping the front porch.
+The rabbit is wiping the exterior windows.
+The hedgehog is washing the car side mirrors.
+The dog is wiping the dining table after dinner.
+The bat is dusting the bedroom nightstands.
+The monkey is watering the kitchen herb garden.
+The raccoon is vacuuming the playroom.
+The cat is sweeping the mudroom.
+The rabbit is wiping the windows with a squeegee.
+The hedgehog is washing the car headlights.
+The dog is wiping the dining table after breakfast.
+The bat is dusting the entertainment center.
+The monkey is watering the sunroom plants.
+The raccoon is vacuuming the family room.
+The cat is sweeping the garden shed.
+The rabbit is wiping the windows in the family room.
+The hedgehog is washing the car roof.
+The dog is wiping the living room table.
+The bat is dusting the home office shelves.
+The monkey is watering the front yard flowers.
+The raccoon is vacuuming the home theater.
+The cat is sweeping the steps.
+The rabbit is wiping the kitchen windows with a sponge.
+The hedgehog is washing the car front bumper.
+The dog is wiping the dining table after lunch.
+The bat is dusting the mantelpiece.
+The monkey is watering the windowsill herbs.
+The raccoon is vacuuming the dining room.
+The cat is sweeping the entryway.
+The rabbit is wiping the interior windows.
+The hedgehog is washing the car fenders.
+The dog is wiping the dining table before a meal.
+The bat is dusting the entertainment center.
+The monkey is watering the patio plants.
+The raccoon is vacuuming the basement.
+The cat is sweeping the sidewalk.
+The rabbit is wiping the blinds.
+The hedgehog is washing the car tires.
+The dog is wiping the kitchen table before setting it.
+The bat is dusting the bookshelf.
+The monkey is watering the office plants.
+The raccoon is vacuuming the guest room.
+The cat is sweeping the garden shed.
+The rabbit is wiping the playroom windows.
+The hedgehog is washing the car roof.
+The dog is wiping the dining table after breakfast.
+The bat is dusting the picture frames.
+The monkey is watering the porch plants.
+The raccoon is vacuuming the family room carpet.
+The cat is sweeping the garage.
+The rabbit is wiping the sunroom windows.
+The hedgehog is washing the car hood.
+The dog is wiping the kitchen table after lunch.
+The bat is dusting the nightstands.
+The monkey is watering the balcony plants.
+The raccoon is vacuuming the home theater carpet.
+The cat is sweeping the front steps.
+The rabbit is wiping the living room windows.
+The hedgehog is washing the car side mirrors.
+The dog is wiping the dining table before dinner.
+The bat is dusting the study shelves.
+The monkey is watering the backyard flowers.
+The raccoon is vacuuming the bedroom carpet.
+The cat is sweeping the patio.
+The rabbit is wiping the glass doors.
+The hedgehog is washing the car front grille.
+The dog is wiping the dining table after a meal.
+The bat is dusting the bookshelf.
+The monkey is watering the hallway plants.
+The raccoon is vacuuming the living room carpet.
+The cat is sweeping the garage floor.
+The rabbit is wiping the patio doors.
+The hedgehog is washing the car bumper.
+The dog is wiping the dining table before setting it.
+The bat is dusting the family room shelves.
+The monkey is watering the kitchen herbs.
+The raccoon is vacuuming the playroom carpet.
+The cat is sweeping the front porch.
+The rabbit is wiping the windowsills.
+The hedgehog is washing the car hood.
+The dog is wiping the dining chairs.
+The bat is dusting the TV stand.
+The monkey is watering the garden.
+The raccoon is vacuuming the stairs.
+The cat is sweeping the basement.
+The rabbit is wiping the kitchen windows.
+The hedgehog is washing the car rear window.
+The dog is wiping the dining table before dinner.
+The bat is dusting the nightstands.
+The monkey is watering the flower pots.
+The raccoon is vacuuming the hallway.
+The cat is sweeping the deck.
+The rabbit is wiping the kitchen windows.
+The hedgehog is washing the car tires.
+The dog is wiping the dining chairs.
+The bat is dusting the TV stand.
+The monkey is watering the indoor plants.
+The raccoon is vacuuming the bedroom carpet.
+The cat is sweeping the floor.
\ No newline at end of file
diff --git a/assets/chatgpt_custom_animal_sport.txt b/assets/chatgpt_custom_animal_sport.txt
new file mode 100644
index 0000000000000000000000000000000000000000..30dc538f5edf5e67350d1d8e33b18abf25faaf08
--- /dev/null
+++ b/assets/chatgpt_custom_animal_sport.txt
@@ -0,0 +1,25 @@
+A dog playing soccer in a green field, wearing a red jersey and kicking a ball towards a goal.
+A cat playing tennis on a sunny court, wearing a white visor and swinging a racket to hit the ball.
+A kangaroo boxing in a ring, wearing blue boxing gloves and shorts, jumping and throwing punches.
+A bear lifting weights in a gym, wearing a headband and wristbands, straining as it lifts a heavy barbell.
+A penguin ice skating in an indoor rink, wearing a bright scarf and gracefully gliding on the ice.
+A rabbit running in a track race, wearing running shoes and a number bib, sprinting towards the finish line.
+A horse playing polo on a green field, wearing a helmet and riding gear, skillfully hitting the ball with a mallet.
+A squirrel surfing on a big wave, wearing a wetsuit and balancing on a surfboard with skill.
+A monkey climbing a rock wall, wearing climbing shoes and a harness, reaching for the next hold with determination.
+An elephant playing basketball in a playground, wearing a jersey and shooting the ball into the hoop with its trunk.
+A group of dogs playing football, all wearing team jerseys and helmets, running and tackling each other on the field.
+A pair of cats playing beach volleyball, wearing sunglasses and swim trunks, jumping to spike the ball over the net.
+A herd of elephants playing rugby, wearing matching uniforms and charging towards the goal line.
+A flock of birds playing badminton in a park, wearing tiny sweatbands and fluttering to hit the shuttlecock.
+A team of rabbits playing baseball, wearing caps and gloves, one rabbit pitching the ball while another gets ready to bat.
+A fox skateboarding in a skate park, wearing a helmet and knee pads, performing tricks on the ramps.
+A cheetah racing in a motocross event, wearing a racing suit and helmet, speeding over dirt hills and jumps.
+A raccoon doing parkour in an urban environment, wearing a bandana and wristbands, skillfully jumping between buildings.
+A dolphin windsurfing on the ocean, wearing a bright life vest and maneuvering the board with precision.
+A tiger bungee jumping off a bridge, wearing a safety harness and roaring as it plunges towards the river below.
+A polar bear swimming competitively in a pool, wearing goggles and a swim cap, racing towards the finish line.
+A seal playing water polo, wearing a swim cap and maneuvering the ball with its flippers in a heated match.
+A duck rowing a boat in a lake, wearing a life jacket and paddling vigorously with its team.
+A turtle snorkeling in a coral reef, wearing a snorkel mask and fins, exploring the colorful underwater world.
+A frog kayaking down a river, wearing a helmet and life vest, skillfully navigating through the rapids.
\ No newline at end of file
diff --git a/assets/chatgpt_custom_animal_sportV2.txt b/assets/chatgpt_custom_animal_sportV2.txt
new file mode 100644
index 0000000000000000000000000000000000000000..2738ce9e849240247edd4ede67627fb92c2e3326
--- /dev/null
+++ b/assets/chatgpt_custom_animal_sportV2.txt
@@ -0,0 +1,50 @@
+A dog playing soccer in a green field, wearing a red jersey and kicking a ball towards a goal.
+A cat playing tennis on a sunny court, wearing a white visor and swinging a racket to hit the ball.
+A kangaroo boxing in a ring, wearing blue boxing gloves and shorts, jumping and throwing punches.
+A bear lifting weights in a gym, wearing a headband and wristbands, straining as it lifts a heavy barbell.
+A penguin ice skating in an indoor rink, wearing a bright scarf and gracefully gliding on the ice.
+A rabbit running in a track race, wearing running shoes and a number bib, sprinting towards the finish line.
+A sea otter surfing on a big wave, wearing a wetsuit and balancing on a surfboard with ease.
+A monkey climbing a rock wall, wearing climbing shoes and a harness, reaching for the next hold with determination.
+An elephant playing basketball in a playground, wearing a jersey and shooting the ball into the hoop with its trunk.
+A kangaroo playing rugby, wearing a jersey and running with the ball towards the goal line.
+A group of dogs playing football, all wearing team jerseys and helmets, running and tackling each other on the field.
+A pair of cats playing beach volleyball, wearing sunglasses and swim trunks, jumping to spike the ball over the net.
+A herd of elephants playing rugby, wearing matching uniforms and charging towards the goal line.
+A team of rabbits playing baseball, wearing caps and gloves, one rabbit pitching the ball while another gets ready to bat.
+A team of squirrels playing soccer, wearing colorful jerseys and skillfully passing the ball to each other.
+A fox skateboarding in a skate park, wearing a helmet and knee pads, performing tricks on the ramps.
+A cheetah racing in a motocross event, wearing a racing suit and helmet, speeding over dirt hills and jumps.
+A raccoon doing parkour in an urban environment, wearing a bandana and wristbands, skillfully jumping between buildings.
+A dolphin windsurfing on the ocean, wearing a bright life vest and maneuvering the board with precision.
+A squirrel bungee jumping off a bridge, wearing a safety harness and looking excited as it bounces.
+A polar bear swimming competitively in a pool, wearing goggles and a swim cap, racing towards the finish line.
+A seal playing water polo, wearing a swim cap and maneuvering the ball with its flippers in a heated match.
+A duck rowing a boat in a calm lake, wearing a life jacket and paddling vigorously with its webbed feet.
+A turtle snorkeling in a coral reef, wearing a snorkel mask and fins, exploring the colorful underwater world.
+A frog kayaking down a river, wearing a helmet and life vest, skillfully navigating through the rapids.
+A dog sledding in a snowy landscape, wearing a harness and pulling a sled through the snow.
+A rabbit skiing down a snowy hill, wearing a winter hat and goggles, expertly navigating the slopes.
+A penguin sliding down an icy slope, wearing a scarf and having fun in the winter wonderland.
+A polar bear playing ice hockey, wearing a jersey and skates, skillfully handling the puck with its stick.
+A cat snowboarding on a mountain, wearing a colorful jacket and performing tricks on the snow.
+A dog playing frisbee in a park, wearing a bandana and leaping high to catch the flying disc.
+A cat performing gymnastics, wearing a leotard and balancing gracefully on a beam.
+A rabbit participating in an agility course, wearing a harness and quickly navigating through the obstacles.
+A bear doing archery in a field, wearing a quiver and aiming carefully at the target.
+A kangaroo playing basketball, wearing a jersey and dribbling the ball before making a jump shot.
+A group of dogs playing basketball, wearing team jerseys and passing the ball to each other.
+A pair of cats playing doubles tennis, wearing matching visors and coordinating their shots.
+A team of rabbits playing soccer, wearing colorful jerseys and skillfully dribbling the ball.
+A pair of kangaroos playing doubles badminton, wearing wristbands and jumping to hit the shuttlecock.
+A team of monkeys playing volleyball, wearing headbands and high-fiving each other after a point.
+A cheetah running in a marathon, wearing running shoes and a number bib, speeding towards the finish line.
+A raccoon skateboarding down a city street, wearing a helmet and knee pads, performing tricks on the pavement.
+A dolphin doing synchronized swimming, wearing a swim cap and gracefully moving in sync with others.
+A squirrel doing a high-wire act in a circus, wearing a tiny costume and balancing skillfully on the rope.
+A monkey doing bungee jumping, wearing a safety harness and looking thrilled as it bounces up and down.
+A sea lion surfing on the ocean waves, wearing a wetsuit and riding the surfboard with expertise.
+A duck swimming in a lake race, wearing a small cap and paddling swiftly with its webbed feet.
+A turtle diving in a pool, wearing goggles and fins, gracefully exploring the underwater environment.
+A frog rowing a boat in a calm river, wearing a life jacket and paddling smoothly with its strong legs.
+A seal doing synchronized swimming, wearing a swim cap and performing elegant moves in the water.
\ No newline at end of file
diff --git a/assets/chatgpt_custom_animal_technology.txt b/assets/chatgpt_custom_animal_technology.txt
new file mode 100644
index 0000000000000000000000000000000000000000..ec743a90a816c7725d3f110cdcf584f1b3ef5947
--- /dev/null
+++ b/assets/chatgpt_custom_animal_technology.txt
@@ -0,0 +1,50 @@
+The dog is using a computer, typing on the keyboard.
+The cat is talking on a phone.
+The rabbit is playing a video game with a controller.
+The bear is watching TV.
+The raccoon is taking a selfie with a phone.
+The squirrel is wearing headphones and listening to music.
+The monkey is using a tablet.
+The lion is using a laptop.
+The fox is changing TV channels with a remote control.
+The panda is checking the time on a smartwatch.
+The dog is browsing on a tablet.
+The cat is taking a photo with a camera.
+The rabbit is video calling a friend on a laptop.
+The bear is listening to music on a speaker.
+The raccoon is charging a phone.
+The squirrel is reading an e-book on a tablet.
+The monkey is typing on a laptop.
+The lion is taking a video with a phone.
+The fox is setting an alarm on a smartwatch.
+The panda is playing a game on a tablet.
+The dog is watching videos on a phone.
+The cat is using a fitness tracker.
+The rabbit is browsing the internet on a computer.
+The bear is listening to a podcast on a phone.
+The raccoon is texting on a phone.
+The squirrel is watching a movie on a tablet.
+The monkey is scrolling through social media on a phone.
+The lion is video calling a friend on a laptop.
+The fox is using a calculator on a phone.
+The panda is playing a video game with a controller.
+The dog is checking email on a computer.
+The cat is listening to music with earbuds.
+The rabbit is using a webcam for a video chat.
+The bear is typing a document on a laptop.
+The raccoon is taking a photo with a tablet.
+The squirrel is using a microwave to heat food.
+The monkey is reading a digital book on an e-reader.
+The lion is taking a picture with a smartphone.
+The fox is watching a show on a tablet.
+The panda is checking the weather on a smartphone.
+The dog is using a laptop to write a story.
+The cat is using a smart light to change colors.
+The rabbit is listening to an audiobook on a phone.
+The bear is making a video call on a tablet.
+The raccoon is setting a reminder on a smartwatch.
+The squirrel is using a digital thermometer to check the temperature.
+The monkey is editing a photo on a laptop.
+The lion is playing music on a Bluetooth speaker.
+The fox is setting a timer on a smartphone.
+The panda is watching a tutorial on a computer.
\ No newline at end of file
diff --git a/assets/chatgpt_custom_banana.txt b/assets/chatgpt_custom_banana.txt
new file mode 100644
index 0000000000000000000000000000000000000000..059a474863dcbf7e4cc2fc4926f8e836e3151109
--- /dev/null
+++ b/assets/chatgpt_custom_banana.txt
@@ -0,0 +1,50 @@
+A banana and a vase of flowers on a marble kitchen counter.
+A banana and a cup of tea on a wooden desk.
+A banana and a water bottle on a park bench.
+A banana and a small pot of herbs on a windowsill.
+A banana and a wicker basket on a picnic table.
+A banana and a soft blanket on a bookshelf.
+A banana and a lamp on a nightstand.
+A banana and a latte on a café table.
+A banana and sunglasses on a car dashboard.
+A banana and fine china on a dining table.
+A banana and a notebook on a classroom desk.
+A banana and a water bottle on a gym bench.
+A banana and seashells on a beach towel.
+A banana and a potted plant on a windowsill.
+A banana and candles on a fireplace mantel.
+A banana and flowers in a bicycle basket.
+A banana and old books on a library table.
+A banana and a remote control on a couch armrest.
+A banana and vegetables on a kitchen island.
+A banana and a small vase of daisies on a restaurant table.
+A banana and a book on a park table.
+A banana and sheet music on a grand piano.
+A banana and roses on a garden bench.
+A banana and a water bottle on a yoga mat.
+A banana and a cup of pens on a teacher’s desk.
+A banana and a glass of water on a bedside table.
+A banana and paintbrushes on a workbench.
+A banana and a playbill on a theater seat.
+A banana and a small bag of snacks on a park fountain edge.
+A banana and a fishing rod on a boat deck.
+A banana and other fruits on a street vendor’s cart.
+A banana and a glass of wine on a rooftop terrace table.
+A banana and a bag of popcorn on a stadium seat.
+A banana and a coffee mug on a computer desk.
+A banana and a book on a garden swing.
+A banana and a magazine on a city bus seat.
+A banana and a travel brochure on a hotel lobby table.
+A banana and a stack of notebooks on a classroom bookshelf.
+A banana and a cappuccino on a café counter.
+A banana and a water bottle on a treadmill console.
+A banana and a bowl of nuts on a restaurant bar counter.
+A banana and a thermos of tea on a park path bench.
+A banana and a magazine on a hospital waiting room chair.
+A banana and a suitcase on a train station bench.
+A banana and a basket of snacks on a beach picnic blanket.
+A banana and a program on a concert hall seat.
+A banana and a small vase of flowers on a kitchen window ledge.
+A banana and a sketchbook on an artist’s worktable.
+A banana and a water bottle on a hiking trail bench.
+A banana and a cup of tea on a coffee table.
\ No newline at end of file
diff --git a/assets/chatgpt_custom_book_cup.txt b/assets/chatgpt_custom_book_cup.txt
new file mode 100644
index 0000000000000000000000000000000000000000..9293cf3f8598b53bc862c502992fc6aedc99c5ba
--- /dev/null
+++ b/assets/chatgpt_custom_book_cup.txt
@@ -0,0 +1,25 @@
+A book and a cup of coffee on a cozy armchair.
+A book and a cup of tea on a windowsill with raindrops.
+A book and a cup of hot chocolate on a wooden desk.
+A book and a cup of herbal tea on a bedside table.
+A book and a cup of coffee on a park bench under a tree.
+A book and a cup of tea on a picnic blanket in a meadow.
+A book and a cup of coffee on a café table by the window.
+A book and a cup of tea on a porch swing.
+A book and a cup of coffee on a kitchen counter with a fruit bowl.
+A book and a cup of tea on a library table surrounded by shelves of books.
+A book and a cup of hot chocolate on a coffee table by the fireplace.
+A book and a cup of tea on a garden bench among blooming flowers.
+A book and a cup of coffee on a balcony with a city view.
+A book and a cup of tea on a nightstand with a lamp.
+A book and a cup of herbal tea on a yoga mat in a serene room.
+A book and a cup of coffee on a desk in a home office.
+A book and a cup of tea on a park bench by a pond.
+A book and a cup of coffee on a café patio with string lights.
+A book and a cup of tea on a picnic table in the forest.
+A book and a cup of hot chocolate on a windowsill with a snowy view.
+A book and a cup of tea on a hammock in the garden.
+A book and a cup of coffee on a rustic wooden table in a cabin.
+A book and a cup of tea on a bench in a botanical garden.
+A book and a cup of coffee on a study table with a globe.
+A book and a cup of tea on a blanket in a sunflower field.
\ No newline at end of file
diff --git a/assets/chatgpt_custom_book_cup_character.txt b/assets/chatgpt_custom_book_cup_character.txt
new file mode 100644
index 0000000000000000000000000000000000000000..40912840d331e60f18fe2e7298a7bf4d7a5504b6
--- /dev/null
+++ b/assets/chatgpt_custom_book_cup_character.txt
@@ -0,0 +1,25 @@
+A woman reading a book and sipping a cup of coffee on a cozy armchair.
+A man reading a book and drinking a cup of tea on a windowsill with raindrops.
+A girl with a book and a cup of hot chocolate on a wooden desk.
+A man reading a book and enjoying a cup of herbal tea on a bedside table.
+A girl reading a book and holding a cup of coffee on a park bench under a tree.
+A woman lying on a picnic blanket in a meadow with a book and a cup of tea.
+A man reading a book and sipping a cup of coffee on a café table by the window.
+A girl swinging on a porch swing with a book and a cup of tea.
+A woman reading a book and enjoying a cup of coffee on a kitchen counter with a fruit bowl.
+A man surrounded by books, reading with a cup of tea on a library table.
+A child reading a book and drinking a cup of hot chocolate on a coffee table by the fireplace.
+A woman sitting on a garden bench, reading a book and sipping a cup of tea among blooming flowers.
+A man reading a book and enjoying a cup of coffee on a balcony with a city view.
+A woman reading a book and drinking a cup of tea on a nightstand with a lamp.
+A woman reading a book and sipping a cup of herbal tea on a yoga mat in a serene room.
+A man reading a book and drinking a cup of coffee on a desk in a home office.
+A man reading a book and enjoying a cup of tea on a park bench by a pond.
+A couple reading books and sipping cups of coffee on a café patio with string lights.
+A woman reading a book and drinking a cup of tea on a picnic table in the forest.
+A child reading a book and drinking a cup of hot chocolate on a windowsill with a snowy view.
+A woman lounging in a hammock with a book and a cup of tea in the garden.
+A man reading a book and sipping a cup of coffee on a rustic wooden table in a cabin.
+A woman reading a book and drinking a cup of tea on a bench in a botanical garden.
+A child reading a book and enjoying a cup of coffee on a study table with a globe.
+A girl lying on a blanket in a sunflower field with a book and a cup of tea.
\ No newline at end of file
diff --git a/assets/chatgpt_custom_bottle_glass.txt b/assets/chatgpt_custom_bottle_glass.txt
new file mode 100644
index 0000000000000000000000000000000000000000..a786829bcecabe1cc3497db728d9eacb0cd368d3
--- /dev/null
+++ b/assets/chatgpt_custom_bottle_glass.txt
@@ -0,0 +1,50 @@
+A wine glass and a bottle on a dining table set for a romantic dinner.
+A wine glass and a bottle on a picnic blanket in a sunny meadow.
+A wine glass and a bottle on a coffee table in a cozy living room.
+A wine glass and a bottle on a kitchen island during a dinner party.
+A wine glass and a bottle on a balcony table overlooking the city.
+A wine glass and a bottle on a restaurant table with a candle.
+A wine glass and a bottle on a garden table under a pergola.
+A wine glass and a bottle on a beach towel by the ocean.
+A wine glass and a bottle on a wooden picnic table in the forest.
+A wine glass and a bottle on a patio table at sunset.
+A wine glass and a bottle on a small table in a wine cellar.
+A wine glass and a bottle on a bar counter in a chic lounge.
+A wine glass and a bottle on a blanket under a starlit sky.
+A wine glass and a bottle on a stone wall in a vineyard.
+A wine glass and a bottle on a windowsill with a city view.
+A wine glass and a bottle on a garden bench surrounded by flowers.
+A wine glass and a bottle on a dining table with a cheese platter.
+A wine glass and a bottle on a coffee table beside a roaring fire.
+A wine glass and a bottle on a boat deck with the sea in the background.
+A wine glass and a bottle on a terrace with a mountain view.
+A wine glass and a bottle on a bar cart in a stylish living room.
+A wine glass and a bottle on a round table in a cozy nook.
+A wine glass and a bottle on a table set for a garden party.
+A wine glass and a bottle on a balcony with a scenic view.
+A wine glass and a bottle on a dining table with a holiday centerpiece.
+A wine glass and a bottle on a side table at a pool party.
+A wine glass and a bottle on a picnic table at a lakeside.
+A wine glass and a bottle on a kitchen counter with snacks.
+A wine glass and a bottle on a wooden table in a rustic cabin.
+A wine glass and a bottle on a patio table with string lights above.
+A wine glass and a bottle on a stone table in a courtyard.
+A wine glass and a bottle on a table in a cozy café.
+A wine glass and a bottle on a blanket in a flower field.
+A wine glass and a bottle on a rooftop terrace at night.
+A wine glass and a bottle on a table in a gazebo.
+A wine glass and a bottle on a dining table with an elegant tablecloth.
+A wine glass and a bottle on a picnic table with a view of the hills.
+A wine glass and a bottle on a lounge chair by the pool.
+A wine glass and a bottle on a wooden bench in a botanical garden.
+A wine glass and a bottle on a table at an outdoor concert.
+A wine glass and a bottle on a windowsill with a sunset view.
+A wine glass and a bottle on a dining table with fresh flowers.
+A wine glass and a bottle on a table in a country house.
+A wine glass and a bottle on a patio table with a view of the vineyard.
+A wine glass and a bottle on a café table with a street view.
+A wine glass and a bottle on a picnic blanket by a river.
+A wine glass and a bottle on a table in a seaside restaurant.
+A wine glass and a bottle on a blanket in a park.
+A wine glass and a bottle on a dining table in a cozy kitchen.
+A wine glass and a bottle on a rooftop garden with fairy lights.
\ No newline at end of file
diff --git a/assets/chatgpt_custom_compression.txt b/assets/chatgpt_custom_compression.txt
new file mode 100644
index 0000000000000000000000000000000000000000..06e213f055e71e96064e31b5fa8e2eea34d87dc3
--- /dev/null
+++ b/assets/chatgpt_custom_compression.txt
@@ -0,0 +1,32 @@
+A group of birds is singing melodiously from the branches of a tall pine tree.
+A raccoon is exploring a hollow log, the forest floor covered in a soft carpet of leaves with a light fog settling in.
+A pair of owls is watching over their nest, with the ground covered in a light dusting of snow.
+Beagle walking on a rainy street, splashing through puddles.
+Persian cat lounging by a window, watching outside.
+Border collie carrying a watering can in a sunlit garden.
+A flock of colorful birds singing in a tree.
+A majestic eagle gliding over snow-capped mountains with a clear blue sky as the backdrop.
+A peacock spreading its magnificent, colorful feathers in a park.
+Seagulls flying and calling over a busy harbor with boats and ships docked.
+A colorful parrot perched on a tropical tree branch, talking and mimicking sounds with lush green foliage in the background.
+A vibrant parrot meticulously building its nest in a tall tree.
+A fluffy Persian cat peacefully napping in a sunbeam streaming through a window in a cozy room.
+A curious Siamese cat scaling a tall bookshelf, surrounded by books and trinkets, in a warm, softly lit study.
+A frisky kitten pouncing and playing in a pile of autumn leaves.
+A cute rabbit munching on a bright orange carrot in a cozy garden.
+A white rabbit hopping through a snowy landscape, leaving little paw prints in the fresh snow under a clear, crisp winter sky.
+A flamingo standing gracefully in a shallow lagoon, its pink feathers vibrant against the blue water.
+A hummingbird hovering near a bright red flower, its wings a blur as it drinks nectar.
+A flock of cranes taking flight from a serene wetland.
+A kingfisher diving into a clear river to catch a fish, the water splashing around it.
+A pair of swans gliding smoothly across a tranquil lake with autumn foliage reflected in the water.
+A woodpecker diligently tapping on the trunk of an old oak tree in a dense forest.
+A group of penguins huddled together on an icy Antarctic shore, with the ocean in the background.
+A sparrow building its nest in the eaves of a rustic farmhouse, using twigs and straw.
+A majestic hawk perched on a cliff edge, scanning the valley below for prey.
+A robin singing cheerfully from a snow-dusted fence post in a winter garden.
+A pelican soaring above the waves, with a coastal town visible in the distance.
+A cardinal sitting on a snowy branch, its bright red feathers standing out against the white landscape.
+A group of ducklings following their mother across a calm pond, lily pads floating nearby.
+An eagle's nest high in a rocky mountain crag, with eaglets peeking over the edge.
+A pair of lovebirds snuggling close together on a leafy branch.
\ No newline at end of file
diff --git a/assets/chatgpt_custom_compression_animals.txt b/assets/chatgpt_custom_compression_animals.txt
new file mode 100644
index 0000000000000000000000000000000000000000..6248f05127c43a69f201ee6cce44c58e66816f2f
--- /dev/null
+++ b/assets/chatgpt_custom_compression_animals.txt
@@ -0,0 +1,90 @@
+A lion roaring in the savannah.
+A lioness hunting in the grasslands.
+A pride of lions resting under a tree.
+A lion cub playing in the wild.
+A lion drinking water from a river.
+A lion walking through the tall grass.
+A lion basking in the sun.
+A lion chasing a gazelle.
+A lion grooming its cub.
+A lion standing on a rock overlooking the savannah.
+An elephant bathing in a river.
+A herd of elephants crossing the savannah.
+A baby elephant playing with its trunk.
+An elephant feeding on tree leaves.
+An elephant spraying water with its trunk.
+An elephant walking through the forest.
+An elephant family interacting.
+An elephant trumpeting loudly.
+An elephant pulling down a tree branch.
+An elephant resting in the shade.
+A panda eating bamboo.
+A panda climbing a tree.
+A panda playing in the snow.
+A panda mother with her cub.
+A panda rolling down a hill.
+A panda resting in a tree.
+A panda exploring its enclosure.
+A panda chewing on leaves.
+A panda playing with a ball.
+A panda taking a nap.
+A kangaroo hopping across the plains.
+A mother kangaroo with a joey in her pouch.
+A kangaroo boxing with another kangaroo.
+A kangaroo grazing on grass.
+A kangaroo resting under a tree.
+A kangaroo jumping over a fence.
+A kangaroo drinking water from a pond.
+A kangaroo scratching its ear.
+A kangaroo exploring the bushland.
+A kangaroo looking out from a hilltop.
+A giraffe eating leaves from a tall tree.
+A giraffe walking across the savannah.
+A baby giraffe standing next to its mother.
+A giraffe drinking water from a pond.
+A giraffe running in the wild.
+A giraffe interacting with other animals.
+A giraffe bending down to drink.
+A giraffe using its long neck to reach leaves.
+A giraffe resting in the shade.
+A giraffe exploring its habitat.
+A tiger stalking through the jungle.
+A tiger swimming in a river.
+A tiger roaring loudly.
+A tiger mother with her cubs.
+A tiger hunting in the grass.
+A tiger climbing a tree.
+A tiger playing with its cubs.
+A tiger resting on a rock.
+A tiger patrolling its territory.
+A tiger drinking from a stream.
+A parrot mimicking human speech.
+A parrot eating fruit in the rainforest.
+A parrot flying in the jungle.
+A parrot grooming its feathers.
+A parrot perched on a branch.
+A parrot interacting with other birds.
+A parrot playing with a toy.
+A parrot spreading its colorful wings.
+A parrot drinking water from a bowl.
+A parrot dancing to music.
+A robin singing on a tree branch.
+A robin feeding on the ground.
+A robin building a nest.
+A robin feeding its chicks.
+A robin hopping on a lawn.
+A robin interacting with other birds.
+A robin taking a bath in a birdbath.
+A robin perched on a fence.
+A robin flying through a garden.
+A robin resting in the shade.
+A sparrow hopping on the ground.
+A sparrow building a nest.
+A sparrow singing on a tree branch.
+A sparrow feeding its chicks.
+A sparrow searching for food in the grass.
+A sparrow taking a dust bath.
+A sparrow flying through a garden.
+A sparrow perching on a fence.
+A sparrow drinking from a birdbath.
+A sparrow interacting with other small birds.
\ No newline at end of file
diff --git a/assets/chatgpt_custom_cruel_animal.txt b/assets/chatgpt_custom_cruel_animal.txt
new file mode 100644
index 0000000000000000000000000000000000000000..7a5dc1c492e51660161e19f7e4183329a526d2a5
--- /dev/null
+++ b/assets/chatgpt_custom_cruel_animal.txt
@@ -0,0 +1,20 @@
+A lion is majestically standing on a rocky outcrop, surveying its territory with the golden light of dawn casting a regal glow on its mane.
+A lioness is gently nuzzling her cubs in the tall grass, the setting sun creating a warm, serene atmosphere with a rainbow in the distance.
+A lion is roaring powerfully at the edge of the savanna, the sound echoing through the vast landscape under a dramatic sunset sky.
+A pride of lions is resting under the shade of an acacia tree, the midday sun filtering through the leaves and creating a peaceful scene.
+A lion is drinking from a clear watering hole, the water sparkling under the bright sun with a faint rainbow appearing in the mist.
+A python is coiled around a tree branch, its scales glistening in the dappled sunlight of the dense jungle.
+A python is slithering through the underbrush, its movements silent and fluid as the morning fog begins to lift.
+A python is basking on a sun-warmed rock, its eyes scanning the surroundings with a rainbow appearing in the distant mist.
+A python is stealthily approaching a small stream, its body moving gracefully through the tall grass as the sun sets in a blaze of color.
+A python is wrapped around a large tree trunk, the forest alive with the sounds of twilight and a light fog settling in.
+A dragon is soaring through the sky, its wings casting a shadow over the landscape as the sun sets in a fiery display.
+A dragon is perched atop a mountain peak, its scales shimmering in the moonlight as it gazes down at the world below.
+A dragon is breathing fire into the night sky, the flames illuminating its powerful form with the stars twinkling above.
+A dragon is curled up in its lair, the treasure around it glinting in the soft light of dawn with a rainbow visible through the cave entrance.
+A dragon is flying low over a calm lake, the water reflecting its majestic form with a light fog rising from the surface.
+A tyrannosaurus is roaring in a lush prehistoric forest, the sound reverberating through the trees as the sun rises.
+A tyrannosaurus is hunting near a riverbank, its powerful legs moving silently through the tall ferns with a light fog in the background.
+A tyrannosaurus is standing on a hill, its silhouette framed against the setting sun with a rainbow appearing in the distant sky.
+A tyrannosaurus is drinking from a clear stream, the water sparkling in the midday sun with prehistoric plants surrounding the scene.
+A tyrannosaurus is stalking through a dense jungle, the moonlight casting eerie shadows on its massive form as the ground is lightly dusted with mist.
\ No newline at end of file
diff --git a/assets/chatgpt_custom_cruel_animal2.txt b/assets/chatgpt_custom_cruel_animal2.txt
new file mode 100644
index 0000000000000000000000000000000000000000..d09c103bc15c15944bd1b5b87771db1b1d4c72da
--- /dev/null
+++ b/assets/chatgpt_custom_cruel_animal2.txt
@@ -0,0 +1,30 @@
+A unicorn is charging through a burning forest, its mane and tail flowing as the flames create a dramatic and fiery backdrop.
+A unicorn is standing on a cliff's edge, rearing up against the backdrop of a blazing sunset, its horn gleaming in the intense light.
+A unicorn is galloping across a scorched plain, the ground still smoldering from a recent fire, its hooves kicking up sparks as it runs.
+A unicorn is battling a fierce beast in the midst of a volcanic eruption, the lava glowing ominously around them.
+A unicorn is leaping over a wall of fire, its silhouette stark against the roaring flames as it escapes danger.
+A Pegasus is soaring through the sky above a volcano, its wings cutting through the smoke and ash with the molten lava glowing below.
+A Pegasus is flying low over a desert, the hot wind ruffling its feathers as it races against a backdrop of towering sand dunes.
+A Pegasus is hovering above a battlefield, its eyes scanning the chaos below as the setting sun casts a fiery glow.
+A Pegasus is emerging from a dense cloud of smoke, its wings beating powerfully as it flies towards the burning horizon.
+A Pegasus is gliding through the night sky, lit only by the intense glow of a distant wildfire.
+A Stegosaurus is defending itself from a predator in a hot, arid landscape, its tail swinging powerfully under the blazing sun.
+A Stegosaurus is grazing near a bubbling hot spring, the steam rising around it in the intense midday heat.
+A Stegosaurus is battling another dinosaur in a dusty canyon, the heat shimmering in the air as they clash.
+A Stegosaurus is trudging through a volcanic plain, the ground cracking and smoldering beneath its heavy steps.
+A Stegosaurus is standing its ground in a dry riverbed, the intense heat creating waves in the air as it faces off against a threat.
+A Tyrannosaurus is roaring triumphantly on a rocky outcrop, the sun setting behind it in a blaze of red and orange.
+A Tyrannosaurus is stalking its prey through a scorched forest, the air thick with smoke and the ground littered with burning embers.
+A Tyrannosaurus is locked in a fierce battle with another dinosaur, their roars echoing through a hot, dry valley.
+A Tyrannosaurus is standing tall on a volcanic ridge, the molten lava flowing below as ash rains down from the sky.
+A Tyrannosaurus is chasing a herd of dinosaurs across a dusty plain, the heat and dust creating a dramatic, intense scene.
+A dragon is unleashing a torrent of fire on a desolate landscape, the ground cracking and burning under the intense heat.
+A dragon is flying over a city in flames, its wings casting a shadow against the inferno below.
+A dragon is perched on a mountain peak, breathing fire into the sky as the molten lava flows down the slopes.
+A dragon is battling a rival dragon in mid-air, their fiery breaths illuminating the night as they clash.
+A dragon is emerging from a cave in the heart of a volcano, its scales glowing with the heat of the molten rock around it.
+A Mosasaur is breaking the surface of a turbulent, lava-heated ocean, its massive jaws snapping at the air as steam rises around it.
+A Mosasaur is battling a giant squid in the depths of a volcanic underwater trench, the glow of the molten rock illuminating their struggle.
+A Mosasaur is swimming through the hot, sulfurous waters near an underwater volcano, its powerful tail propelling it swiftly through the currents.
+A Mosasaur is breaching the surface near a tropical island, the setting sun casting a fiery glow over the scene as it searches for prey.
+A Mosasaur is navigating through a sea of floating volcanic rock, the intense heat creating a dramatic and dangerous environment as it hunts.
diff --git a/assets/chatgpt_custom_cute.txt b/assets/chatgpt_custom_cute.txt
new file mode 100644
index 0000000000000000000000000000000000000000..8a1ebc4e77adc4e20e84a92d2004e74b8f9434e6
--- /dev/null
+++ b/assets/chatgpt_custom_cute.txt
@@ -0,0 +1,100 @@
+A young prince battling a fierce, fire-breathing dragon amid thunderous roars, stormy skies, and blazing flames to save a princess from a tall, enchanted tower wrapped in vines and glowing runes.
+A talking frog with a regal voice and a golden crown helps a lost princess navigate a mystical forest filled with sparkling flowers and twinkling fairy lights to find her way back to her grand castle.
+A fairy godmother, shimmering with glittering wings and a glowing wand, transforms a humble pumpkin into an opulent, golden carriage pulled by white horses with jeweled harnesses for a magical night at the ball.
+A mischievous elf with a cheeky grin and a twinkle in his eye leads children through a mysterious forest of towering, twisted trees, glowing mushrooms, and whispering shadows, revealing hidden secrets and treasures.
+A brave knight clad in gleaming armor and a flowing cape faces off against a wicked witch with a crooked staff and a cackling laugh in an enchanted forest swirling with dark magic and eerie mists.
+A mermaid with flowing hair and a shimmering tail discovers a hidden underwater kingdom adorned with coral palaces, glowing pearls, and schools of colorful fish, encountering both wonders and dangers along the way.
+A young girl befriends a gentle giant in a land of enormous plants and creatures, where they embark on adventures through fields of giant flowers, towering mushrooms, and sparkling waterfalls.
+A magical duel between a good wizard with a flowing beard and a staff of light and an evil sorcerer cloaked in shadows and wielding dark magic, set against a backdrop of a stormy sky and crackling energy.
+A princess and her animal friends, including talking birds and dancing deer, prepare for a royal ball in a castle filled with glittering chandeliers, flowing gowns, and enchanting music.
+A group of fairies with iridescent wings and glowing wands work together to save their enchanted forest from a dark curse, flying through a landscape of ancient trees, sparkling streams, and hidden groves.
+A young hero finds a magic lamp in a dusty, ancient cave, releasing a wish-granting genie with a booming voice and a swirling, ethereal form, leading to adventures filled with treasure and peril.
+A talking cat with a sharp wit and a stylish hat leads a humble miller’s son through a series of clever schemes, bringing them both fortune and fame in a kingdom of grand castles and bustling marketplaces.
+A brave girl uses her wits and courage to outsmart a cunning wolf in a dark and spooky forest, filled with eerie sounds, rustling leaves, and hidden dangers.
+A dragon with shimmering scales and a unicorn with a glowing horn form an unlikely friendship, exploring a magical land of floating islands, sparkling rivers, and enchanted meadows.
+A clever tailor tricks a towering giant to save his village, using quick thinking and clever traps in a landscape of rolling hills, quaint cottages, and towering mountains.
+A magical tree with golden leaves and whispering branches grants wishes to kind-hearted villagers, its presence bringing warmth and wonder to a serene and picturesque village.
+A young prince, transformed into a frog by an evil spell, embarks on a quest to break the curse, hopping through enchanted forests, glittering streams, and ancient ruins.
+A curious girl falls down a rabbit hole into a whimsical wonderland filled with talking animals, enchanted tea parties, and peculiar characters, each more fantastical than the last.
+A knight, with a sword of light and a heart of gold, rescues a village from an army of trolls, battling through rocky landscapes, dark caves, and roaring waterfalls.
+A princess discovers she has magical powers, learning to control them through trials and adventures in a world of ancient magic, mystical creatures, and hidden realms.
+A brave child faces a monster under the bed, discovering a secret world of friendly creatures and fantastical adventures hidden beneath the ordinary.
+A friendly ghost with a soft, glowing aura helps children find a hidden treasure in an old, creaky mansion filled with dusty books, secret passages, and mysterious artifacts.
+A talking squirrel with a mischievous grin leads an adventure through a magical forest filled with towering trees, glowing flowers, and hidden secrets, revealing the wonders of nature.
+A young hero discovers a secret portal to a fairy-tale world, stepping into a land of enchanted castles, mythical creatures, and epic quests, each more thrilling than the last.
+A group of animals, including a wise owl, a brave rabbit, and a clever fox, work together to outsmart a cunning predator, using their unique skills in a lush and vibrant forest.
+A magical creature, with iridescent scales and sparkling eyes, helps a prince find a lost kingdom, guiding him through enchanted landscapes, ancient ruins, and mystical challenges.
+A girl with magical shoes that can take her anywhere she wishes embarks on adventures through fantastical lands, from floating islands to glowing forests and ancient cities.
+A prince and princess work together to break an ancient curse, facing trials and challenges in a world of dark forests, enchanted castles, and hidden magic.
+A young girl befriends a dragon, learning to fly on its back through skies filled with stars, across mountains, and over sparkling seas, discovering the wonders of the world.
+A magical library where books come to life, revealing stories of heroes, monsters, and magical lands, each book an adventure waiting to be explored.
+A group of children find a secret door to a land of ice and snow, filled with sparkling ice castles, snow-covered forests, and magical creatures, embarking on a wintery adventure.
+A fairy, with delicate wings and a glowing aura, grants wishes to kind-hearted villagers, bringing joy and magic to their everyday lives in a charming and idyllic village.
+A young hero solves riddles and puzzles to save a kingdom, using wit and bravery to navigate enchanted forests, ancient ruins, and mystical trials.
+A group of friends go on a quest to find a lost crown, facing challenges and adventures in a world of enchanted forests, hidden valleys, and ancient castles.
+A magical garden with talking flowers, dancing butterflies, and singing birds, where each step reveals a new wonder and a hidden secret.
+A prince, disguised as a pauper, discovers true friendship and loyalty, experiencing life in a bustling village filled with colorful characters and heartwarming moments.
+A young girl finds a magic mirror that shows the future, using its power to prevent disasters and bring happiness to her kingdom, facing challenges and adventures along the way.
+A knight faces trials of strength, courage, and wisdom to prove his worthiness to the king, journeying through enchanted forests, mystical mountains, and ancient ruins.
+A princess, disguised as a commoner, explores the world beyond her castle, discovering the joys and challenges of ordinary life in a vibrant and diverse kingdom.
+A magical creature, with glowing eyes and a mystical aura, guides a hero through a dangerous journey, offering wisdom and protection in a world of enchanted forests, ancient ruins, and hidden magic.
+A group of children befriends a fairy, helping her save her home from dark forces, embarking on adventures through magical forests, sparkling streams, and hidden groves.
+A young hero discovers a hidden realm of giants, navigating a world of enormous trees, towering mountains, and giant creatures, each step an adventure.
+A talking bird with shimmering feathers and a melodious voice leads an adventure to find a legendary treasure, guiding through enchanted forests, ancient ruins, and mystical challenges.
+A prince and his loyal steed on a quest to find a magical artifact, journeying through enchanted forests, mystical mountains, and hidden valleys, each step filled with wonder and danger.
+A fairy-tale wedding with enchanted guests, magical decorations, and a grand celebration, where every moment is filled with wonder, joy, and enchantment.
+A group of animals, including a wise owl, a brave rabbit, and a clever fox, work together to restore a magical forest, overcoming challenges and discovering hidden wonders.
+A young girl discovers a hidden talent that saves her kingdom, using her newfound abilities to face challenges and bring happiness to her people, in a world of magic and wonder.
+A brave knight challenges a sorcerer to save a princess, battling through enchanted forests, ancient ruins, and mystical trials, each step filled with danger and adventure.
+A group of friends discover a magical world through an old wardrobe, stepping into a land of enchanted forests, mythical creatures, and epic quests, each adventure more thrilling than the last.
+A young hero finds a dragon egg and raises the dragon, embarking on adventures through enchanted forests, mystical mountains, and ancient ruins, each step filled with wonder and danger.
+A tiny mouse in a colorful dress prepares a surprise birthday party for her forest friends, decorating a cozy treehouse with balloons, ribbons, and a giant cake.
+A baby dragon with bright, curious eyes and tiny wings learns to fly for the first time with the help of a friendly bird.
+A group of playful fairies throw a tea party in a meadow filled with blooming flowers, where teacups and pastries float in the air and butterflies dance around.
+A young princess and her pet bunny explore a magical garden filled with talking flowers, friendly bees, and a sparkling pond.
+A group of woodland animals, including a hedgehog, a squirrel, and a deer, build a cozy little house together, complete with tiny furniture and flower decorations.
+A young fairy with glittering wings sprinkles magic dust on a sleepy village, bringing dreams of happy adventures to all the children.
+A kitten with a tiny crown goes on a magical adventure through a land of oversized furniture and enchanted toys.
+A pair of ducklings embark on a journey to find the rainbow’s end, waddling through lush meadows, sparkling streams, and colorful gardens.
+A little girl discovers a hidden door in her backyard that leads to a world where all her stuffed animals come to life and have their own adventures.
+A group of baby animals, including a fox, a raccoon, and an owl, play hide-and-seek in a forest filled with giggles, rustling leaves, and sparkling fairy lights.
+A tiny gnome with a big hat and a friendly smile helps a lost bird find its way home through a forest of giant mushrooms and glowing flowers.
+A young unicorn with a rainbow-colored mane discovers a magical meadow where she meets other baby unicorns and they play together under a glittering sky.
+A princess and her fluffy puppy go on a picnic by a sparkling lake, with a basket filled with delicious treats and a blanket adorned with magical patterns.
+A group of baby dragons discover a field of blooming flowers and play a game of tag, their tiny wings fluttering and their giggles filling the air.
+A young girl befriends a tiny fairy who lives in a daisy, and they explore a world where flowers are houses and leaves are slides.
+A baby panda discovers a magical bamboo forest where each stalk glows softly, and he makes friends with a wise old turtle.
+A young princess and her kitten have a slumber party in a castle, complete with pillow forts, twinkling lights, and bedtime stories.
+A group of ducklings find a hidden pond with lily pads that light up, and they play a game of hopscotch across the glowing water.
+A curious bunny finds a magical book that brings his drawings to life, and he creates a world filled with friendly creatures and fun adventures.
+A pair of baby hedgehogs roll around in a field of dandelions, laughing and playing as the fluffy seeds float around them.
+A little girl and her teddy bear find a secret garden where every flower tells a story, and they spend the day listening to the magical tales.
+A baby deer with big, bright eyes explores a forest filled with sparkling fireflies and discovers a hidden clearing where all the animals gather for a dance.
+A young fairy with a flower petal dress hosts a playdate with other fairies in a meadow filled with bouncing butterflies and singing birds.
+A kitten with a golden bell on her collar explores a magical attic where old toys come to life and have their own whimsical adventures.
+A young prince and his baby dragon friend build a sandcastle on a beach with seashell towers and a moat that sparkles with magic.
+A group of baby animals, including a bunny, a squirrel, and a fox, have a picnic in a meadow filled with blooming flowers and fluttering butterflies.
+A tiny elf with a green hat and curly shoes explores a magical mushroom village, meeting other friendly elves and discovering hidden treasures.
+A young girl finds a magical mirror that shows her the secret lives of her toys, and she spends the day watching their adorable adventures.
+A baby owl with big, curious eyes learns to fly under the guidance of a wise old owl, soaring through a forest filled with twinkling stars and glowing flowers.
+A young fairy with a wand that glows softly helps a group of animals prepare for a nighttime festival, decorating the forest with lanterns and sparkling lights.
+A kitten and a puppy, both wearing tiny crowns, embark on a royal adventure through a castle filled with secret passages and hidden treasures.
+A group of baby animals find a magical treehouse with a slide made of rainbows and a swing that goes higher than the clouds.
+A young girl and her pet bunny discover a secret garden where every flower has a friendly face and sings a happy song.
+A baby dragon with glittering scales finds a hidden pond where he meets other baby dragons and they play a game of splash and chase.
+A young princess and her pet unicorn explore a magical forest where every tree has a door that leads to a different fairy tale world.
+A group of baby animals, including a hedgehog, a fox, and a squirrel, discover a hidden clearing where they find a magical seesaw that floats in the air.
+A young fairy with a crown of flowers and a wand made of twigs helps her forest friends prepare for a grand feast under the stars.
+A kitten with a ribbon around her neck finds a magical garden where butterflies glow in the dark and flowers hum soft lullabies.
+A young girl and her teddy bear go on a magical adventure through a world where every toy comes to life and has its own story to tell.
+A baby panda with a playful personality finds a bamboo forest where each stalk glows softly, and he makes friends with a wise old turtle.
+A young princess and her fluffy puppy have a slumber party in a castle, complete with pillow forts, twinkling lights, and bedtime stories.
+A group of ducklings find a hidden pond with lily pads that light up, and they play a game of hopscotch across the glowing water.
+A curious bunny finds a magical book that brings his drawings to life, and he creates a world filled with friendly creatures and fun adventures.
+A pair of baby hedgehogs roll around in a field of dandelions, laughing and playing as the fluffy seeds float around them.
+A little girl and her teddy bear find a secret garden where every flower tells a story, and they spend the day listening to the magical tales.
+A baby deer with big, bright eyes explores a forest filled with sparkling fireflies and discovers a hidden clearing where all the animals gather for a dance.
+A young fairy with a flower petal dress hosts a playdate with other fairies in a meadow filled with bouncing butterflies and singing birds.
+A kitten with a golden bell on her collar explores a magical attic where old toys come to life and have their own whimsical adventures.
+A young prince and his baby dragon friend build a sandcastle on a beach with seashell towers and a moat that sparkles with magic.
+A group of baby animals, including a bunny, a squirrel, and a fox, have a picnic in a meadow filled with blooming flowers and fluttering butterflies.
\ No newline at end of file
diff --git a/assets/chatgpt_custom_dog.txt b/assets/chatgpt_custom_dog.txt
new file mode 100644
index 0000000000000000000000000000000000000000..77fc3ede6896b9557c159e35f0f47fd1cc35f5f5
--- /dev/null
+++ b/assets/chatgpt_custom_dog.txt
@@ -0,0 +1,50 @@
+A cat and a dog sitting together on a cozy couch.
+A cat and a dog playing with a ball in the backyard.
+A cat and a dog sharing a sunny spot on the living room floor.
+A cat and a dog sleeping side by side in a pet bed.
+A cat and a dog looking out the window at birds.
+A cat and a dog eating from their bowls in the kitchen.
+A cat and a dog exploring a garden with blooming flowers.
+A cat and a dog lying on a rug in front of a fireplace.
+A cat and a dog sitting together on a park bench.
+A cat and a dog playing with a toy in the living room.
+A cat and a dog relaxing on a porch with a view of the yard.
+A cat and a dog sitting on a windowsill watching the rain.
+A cat and a dog cuddled up on a bed with soft blankets.
+A cat and a dog sniffing around a picnic area in the park.
+A cat and a dog sitting on a deck overlooking a lake.
+A cat and a dog lying together under a tree in the yard.
+A cat and a dog playing with a feather toy in the living room.
+A cat and a dog sitting on a staircase looking at each other.
+A cat and a dog lounging on a sunlit patio.
+A cat and a dog exploring a wooded area together.
+A cat and a dog sitting side by side on a balcony.
+A cat and a dog sharing a pet bed in the bedroom.
+A cat and a dog playing chase around the house.
+A cat and a dog sitting by the front door waiting for their owner.
+A cat and a dog relaxing on a hammock in the backyard.
+A cat and a dog watching fish swim in an aquarium.
+A cat and a dog lying on a carpet in a cozy den.
+A cat and a dog playing in a pile of autumn leaves.
+A cat and a dog sitting together on a garden bench.
+A cat and a dog exploring a new room together.
+A cat and a dog sitting under a table during dinner time.
+A cat and a dog playing with a stuffed animal in the living room.
+A cat and a dog sitting together on a dock by a pond.
+A cat and a dog lounging on the grass in the backyard.
+A cat and a dog sitting on the porch steps looking out at the yard.
+A cat and a dog napping together in a sunbeam.
+A cat and a dog sniffing around a garden bed.
+A cat and a dog sitting together on a park bench under a tree.
+A cat and a dog playing with a rope toy in the living room.
+A cat and a dog relaxing on a swing on the porch.
+A cat and a dog exploring a new environment together.
+A cat and a dog sitting on a bench at the beach.
+A cat and a dog cuddling together on a chair.
+A cat and a dog playing in a sandbox in the yard.
+A cat and a dog lying on a blanket in the park.
+A cat and a dog sitting on a windowsill watching the world go by.
+A cat and a dog sniffing around a flower bed in the yard.
+A cat and a dog relaxing together on a patio.
+A cat and a dog playing hide and seek in the house.
+A cat and a dog sitting together on a garden swing.
\ No newline at end of file
diff --git a/assets/chatgpt_custom_forest.txt b/assets/chatgpt_custom_forest.txt
new file mode 100644
index 0000000000000000000000000000000000000000..434b6e005f2a68e7405388c59cb920e23b3a7a9e
--- /dev/null
+++ b/assets/chatgpt_custom_forest.txt
@@ -0,0 +1,50 @@
+A deer is grazing on grass in a sunlit clearing.
+A rabbit is hopping through a patch of wildflowers.
+A squirrel is gathering acorns near a large oak tree.
+A fox is stealthily moving through the underbrush.
+A family of ducks is swimming in a tranquil forest pond.
+A bear is catching fish in a fast-flowing stream.
+A raccoon is climbing a tree to reach a bird's nest.
+A group of birds is singing from the branches of a tall pine tree.
+A hedgehog is curling up among the fallen leaves.
+A pair of owls is perched on a high branch, watching the forest below.
+A family of deer is resting under the shade of a large tree.
+A bat is hanging upside down from a tree branch, sleeping.
+A wolf is howling at the moon in a clearing.
+A family of rabbits is playing near their burrow.
+A fox is chasing a butterfly through the grass.
+A squirrel is running along the branches of a tall tree.
+A raccoon is washing its paws in a small stream.
+A bear is scratching its back against a tree trunk.
+A group of ducks is waddling along the forest floor.
+A hedgehog is sniffing around for food in the underbrush.
+A pair of owls is hooting softly to each other.
+A deer is drinking from a clear forest stream.
+A bat is fluttering through the trees as dusk falls.
+A wolf is stalking through the forest, searching for prey.
+A family of rabbits is nibbling on clover in a meadow.
+A fox is resting on a rock, basking in the sun.
+A squirrel is leaping from branch to branch.
+A raccoon is exploring a hollow log.
+A bear is eating berries from a bush.
+A group of ducks is quacking and flapping their wings in the pond.
+A hedgehog is foraging for insects in the leaf litter.
+A pair of owls is watching over their nest.
+A deer is wandering through a field of tall grass.
+A bat is catching insects in mid-air.
+A wolf is drinking from a forest stream.
+A family of rabbits is hiding in the tall grass.
+A fox is exploring a fallen log.
+A squirrel is storing nuts in a tree hollow.
+A raccoon is peering into a small cave.
+A bear is lying down in a sunny spot, taking a nap.
+A group of ducks is preening their feathers on the shore of the pond.
+A hedgehog is curling up in a patch of moss.
+A pair of owls is swooping down to catch their dinner.
+A deer is scratching its ear with its hind leg.
+A bat is roosting in a tree cavity.
+A wolf is leading its pack through the forest.
+A family of rabbits is playing hide and seek among the bushes.
+A fox is sniffing the air, alert for any danger.
+A squirrel is enjoying an acorn on a high branch.
+A raccoon is rummaging through the underbrush for food.
\ No newline at end of file
diff --git a/assets/chatgpt_custom_forest_vivid.txt b/assets/chatgpt_custom_forest_vivid.txt
new file mode 100644
index 0000000000000000000000000000000000000000..6155102b612cd6c83f4a27f4f08e5c3ca69f58b3
--- /dev/null
+++ b/assets/chatgpt_custom_forest_vivid.txt
@@ -0,0 +1,50 @@
+A deer is peacefully grazing on vibrant green grass in a sunlit clearing, surrounded by glowing fireflies, with a rainbow arching across the sky.
+A rabbit is joyfully hopping through a patch of colorful wildflowers, with the warm golden light of the setting sun streaming through the trees.
+A squirrel is busily gathering shiny acorns near a majestic, ancient oak tree, with a soft morning fog rolling in.
+A fox is stealthily moving through the underbrush, the moonlight casting a mystical glow on its fur as the night sky clears after a rain.
+A family of ducks is swimming gracefully in a tranquil forest pond, the water sparkling under the midday sun with a faint rainbow in the mist.
+A bear is skillfully catching fish in a fast-flowing, crystal-clear stream, with the first light of dawn breaking through the trees.
+A raccoon is climbing a tall tree to reach a bird's nest, the tree's branches swaying gently in the breeze as a light snowfall begins.
+A group of birds is singing melodiously from the branches of a tall pine tree, the sky behind them painted with the colors of sunset and streaked with a vibrant rainbow.
+A hedgehog is curling up among the fallen autumn leaves, the warm light of the setting sun creating a cozy atmosphere with a hint of fog.
+A pair of owls is perched on a high branch, watching the forest below as the stars begin to twinkle in the clear night sky after a light snow.
+A family of deer is resting under the shade of a large tree, with soft beams of sunlight filtering through the branches and a rainbow arcing overhead.
+A bat is hanging upside down from a tree branch, peacefully sleeping as the twilight sky deepens with the last rays of the sun.
+A wolf is howling at the moon in a moonlit clearing, the stars sparkling like diamonds above and the ground lightly dusted with snow.
+A family of rabbits is playfully hopping near their burrow, the meadow bathed in the soft glow of dusk with mist rising from the ground.
+A fox is joyfully chasing a butterfly through the tall grass, with the sunlight creating a magical aura around them and a rainbow appearing in the distance.
+A squirrel is energetically running along the branches of a tall tree, the leaves rustling softly as the first light of dawn breaks.
+A raccoon is washing its paws in a sparkling, gently flowing stream, surrounded by lush greenery with a light fog settling over the water.
+A bear is scratching its back against a tree trunk, the tree’s bark illuminated by the afternoon sun and a rainbow appearing after a brief shower.
+A group of ducks is waddling along the forest floor, the sunlight filtering through the canopy creating a dappled pattern on the ground with a hint of fog.
+A hedgehog is sniffing around for food in the underbrush, the forest floor covered in soft, mossy patches as the sun sets in a blaze of color.
+A pair of owls is hooting softly to each other under the starlit sky, their eyes reflecting the moonlight with the ground covered in a light dusting of snow.
+A deer is drinking from a clear forest stream, the water sparkling as the sun shines brightly above and a rainbow appears in the mist.
+A bat is fluttering through the trees as dusk falls, the sky painted in shades of purple and pink with a light fog settling in.
+A wolf is stalking through the forest, searching for prey, with the full moon casting a silver glow and the ground lightly covered in snow.
+A family of rabbits is nibbling on clover in a meadow, surrounded by a sea of colorful wildflowers as a rainbow arches overhead.
+A fox is resting on a sun-warmed rock, basking in the soft afternoon light with a gentle mist rising around it.
+A squirrel is leaping from branch to branch, the sunlight highlighting its agile movements with a rainbow appearing in the distance.
+A raccoon is exploring a hollow log, the forest floor covered in a soft carpet of leaves with a light fog settling in.
+A bear is eating juicy berries from a bush, the sunlight creating a warm, inviting atmosphere with a hint of fog.
+A group of ducks is quacking and flapping their wings in the pond, the water sparkling in the sunlight with a rainbow in the background.
+A hedgehog is foraging for insects in the leaf litter, the soft light of dawn illuminating the scene with a gentle mist.
+A pair of owls is watching over their nest, the forest quiet and peaceful under the starlit sky with the ground covered in a light dusting of snow.
+A deer is wandering through a field of tall grass, the sunset casting a golden glow over the landscape with a rainbow arching overhead.
+A bat is catching insects in mid-air, the twilight sky providing a dramatic backdrop with a light fog settling in.
+A wolf is drinking from a forest stream, the moonlight creating a serene and mystical atmosphere with the ground lightly covered in snow.
+A family of rabbits is hiding in the tall grass, the setting sun creating a warm and cozy scene with a hint of fog.
+A fox is exploring a fallen log, the forest bathed in the soft light of early morning with a rainbow appearing after a brief shower.
+A squirrel is storing nuts in a tree hollow, the branches swaying gently in the breeze with a light fog rolling in.
+A raccoon is peering into a small cave, the forest alive with the sounds of twilight and the ground lightly dusted with snow.
+A bear is lying down in a sunny spot, taking a nap, with the sun casting a golden glow and a rainbow appearing after a brief shower.
+A group of ducks is preening their feathers on the shore of the pond, the water reflecting the clear blue sky with a hint of fog.
+A hedgehog is curling up in a patch of moss, the forest floor covered in a soft, green carpet with the first light of dawn breaking.
+A pair of owls is swooping down to catch their dinner, the moonlight creating dramatic shadows with the ground lightly covered in snow.
+A deer is scratching its ear with its hind leg, the sunlight creating a warm and peaceful scene with a rainbow in the background.
+A bat is roosting in a tree cavity, the night sky filled with stars and the ground lightly covered in snow.
+A wolf is leading its pack through the forest, the moonlight creating a mysterious and powerful atmosphere with a light fog.
+A family of rabbits is playing hide and seek among the bushes, the forest alive with the sounds of nature and the first light of dawn breaking.
+A fox is sniffing the air, alert for any danger, with the forest bathed in the soft light of dawn and a rainbow in the distance.
+A squirrel is enjoying an acorn on a high branch, the sunlight highlighting its contented expression with a light fog settling in.
+A raccoon is rummaging through the underbrush for food, the forest alive with the sounds of twilight and the ground lightly covered in snow.
\ No newline at end of file
diff --git a/assets/chatgpt_custom_human.txt b/assets/chatgpt_custom_human.txt
new file mode 100644
index 0000000000000000000000000000000000000000..8a43a65bd98c5de518828ba00e244521802c1a0d
--- /dev/null
+++ b/assets/chatgpt_custom_human.txt
@@ -0,0 +1,50 @@
+A person playing a guitar by a campfire under a starry sky.
+A person riding a bicycle along a tree-lined path with golden autumn leaves falling.
+A person reading a book on a park bench surrounded by blooming flowers.
+A person doing yoga on a beach at sunrise with gentle waves in the background.
+A person painting on an easel in a meadow with colorful wildflowers.
+A person running through a field with a kite soaring high in the sky.
+A person having a picnic under a cherry blossom tree in full bloom.
+A person kayaking on a serene lake with mountains in the distance.
+A person playing the violin by a riverside with sunlight reflecting off the water.
+A person practicing tai chi in a garden with morning mist rising.
+A person dancing ballet in an elegant room with soft, golden lighting.
+A person playing the piano in a grand hall with beams of sunlight streaming through.
+A person sculpting clay in a studio with fairy lights and soft music playing.
+A person writing poetry in a cozy room with a fireplace crackling nearby.
+A person singing on a stage with twinkling lights and a dreamy backdrop.
+A person drawing in a sketchbook while sitting on a balcony overlooking a sunset.
+A person weaving on a loom in a rustic cabin with warm lighting.
+A person playing the cello in a sunlit room with large windows.
+A person acting on a theater stage with dramatic lighting and set design.
+A person creating a mosaic in a garden filled with vibrant flowers.
+A person baking bread in a rustic kitchen with sunlight streaming through the windows.
+A person picking fresh vegetables in a garden with morning dew glistening.
+A person having a tea party in a garden with a beautifully set table and delicate china.
+A person cooking a meal over an open fire at a campsite surrounded by nature.
+A person decorating a cake in a cozy kitchen with warm, ambient lighting.
+A person making homemade pasta in a sunlit kitchen with fresh ingredients.
+A person enjoying a picnic with a spread of delicious food under a shady tree.
+A person serving drinks at a quaint outdoor café with a charming atmosphere.
+A person harvesting fruit in an orchard with sunlight filtering through the trees.
+A person grilling food at a backyard barbecue with friends and family gathered around.
+A person playing a board game with friends in a cozy living room.
+A person fishing at a tranquil lake with the morning mist rising.
+A person hiking through a forest with sunlight streaming through the canopy.
+A person swimming in a clear, blue pool with a waterfall in the background.
+A person practicing archery in a meadow with a beautiful backdrop.
+A person roller skating along a beachside boardwalk at sunset.
+A person playing badminton in a park with soft evening light.
+A person meditating on a mountaintop with a breathtaking view.
+A person flying a drone over a scenic landscape with rolling hills.
+A person playing volleyball on a sandy beach with the waves crashing nearby.
+A person lounging in a hammock between two trees with a book in hand.
+A person soaking in a hot tub under the stars with soft lighting around.
+A person napping in a sunbeam on a cozy window seat with a blanket.
+A person enjoying a bubble bath in a luxurious bathroom with candles and rose petals.
+A person watching a movie on a projector in a backyard with twinkling lights.
+A person getting a massage in a serene spa setting with calming music.
+A person sipping tea on a porch with a scenic mountain view.
+A person practicing deep breathing exercises on a cliff overlooking the ocean.
+A person listening to music with headphones while lying in a field of flowers.
+A person stargazing with a telescope in an open field under a clear night sky.
\ No newline at end of file
diff --git a/assets/chatgpt_custom_human_activity.txt b/assets/chatgpt_custom_human_activity.txt
new file mode 100644
index 0000000000000000000000000000000000000000..661035b0d5f93ae82b3aca6cafb8c5e4cc2bd181
--- /dev/null
+++ b/assets/chatgpt_custom_human_activity.txt
@@ -0,0 +1,50 @@
+A person washing dishes in a kitchen, wearing gloves and scrubbing a plate under running water.
+A person vacuuming a living room, carefully moving the vacuum cleaner over the carpet.
+A person folding laundry in a bedroom, neatly stacking clothes on a bed.
+A person cooking dinner in a kitchen, chopping vegetables on a cutting board.
+A person painting a wall in a living room, using a roller and wearing overalls.
+A person mowing the lawn in a backyard, pushing a lawnmower across the grass.
+A person ironing clothes in a laundry room, pressing a shirt with an iron.
+A person making a bed in a bedroom, straightening the sheets and fluffing the pillows.
+A person cleaning windows in a living room, using a spray bottle and a cloth.
+A person organizing a closet, arranging clothes on hangers and placing shoes on a rack.
+A person jogging in a park, wearing running shoes and listening to music with headphones.
+A person gardening in a backyard, planting flowers in a flowerbed.
+A person cycling on a bike path, wearing a helmet and riding a bicycle.
+A person fishing at a lake, casting a fishing rod and waiting patiently.
+A person hiking on a mountain trail, carrying a backpack and using trekking poles.
+A person having a picnic in a park, laying out a blanket and setting up food.
+A person walking a dog on a leash in a neighborhood, stopping occasionally to let the dog sniff.
+A person playing basketball at an outdoor court, dribbling the ball and shooting at the hoop.
+A person flying a kite on a beach, holding the string and running to keep the kite in the air.
+A person swimming in a pool, doing freestyle strokes and wearing goggles.
+A chef preparing a meal in a restaurant kitchen, slicing ingredients and cooking on a stove.
+A teacher writing on a chalkboard in a classroom, explaining a lesson to students.
+A doctor examining a patient in an office, using a stethoscope and checking vital signs.
+A mechanic fixing a car in a garage, using tools to work on the engine.
+A construction worker building a wall, laying bricks and applying mortar.
+A librarian organizing books on shelves, checking titles and arranging them alphabetically.
+A cashier scanning items at a grocery store, ringing up purchases and handling money.
+A barista making coffee in a café, steaming milk and brewing espresso.
+A firefighter putting out a fire, aiming a hose at flames and wearing protective gear.
+A hairdresser cutting a client's hair in a salon, using scissors and a comb.
+An artist painting a canvas in a studio, mixing colors on a palette and applying brushstrokes.
+A musician playing a guitar on a stage, strumming chords and singing into a microphone.
+A writer typing on a laptop at a desk, focused on creating a story.
+A photographer taking pictures in a park, adjusting the camera and framing shots.
+A dancer practicing ballet in a dance studio, performing pirouettes and wearing ballet shoes.
+A sculptor carving a statue out of clay, shaping details with sculpting tools.
+A filmmaker directing a scene on a movie set, giving instructions to actors and adjusting the camera.
+A graphic designer working on a computer, creating digital illustrations and layouts.
+A singer recording in a music studio, singing into a microphone and wearing headphones.
+A florist arranging a bouquet, selecting flowers and trimming stems.
+A student studying at a desk, reading a textbook and taking notes.
+A person learning to play the piano, practicing scales and reading sheet music.
+A child painting in an art class, using watercolors and a brush on paper.
+A group of people attending a workshop, listening to a speaker and taking notes.
+A tutor helping a student with homework, explaining concepts and solving problems together.
+A scientist conducting an experiment in a lab, mixing chemicals and recording observations.
+A person practicing yoga in a studio, performing various poses and focusing on breathing.
+A student presenting a project in a classroom, speaking in front of the class and showing visuals.
+A person learning to drive, sitting in a car with an instructor and practicing maneuvers.
+A person assembling a puzzle, fitting pieces together on a table.
\ No newline at end of file
diff --git a/assets/chatgpt_custom_human_fashion.txt b/assets/chatgpt_custom_human_fashion.txt
new file mode 100644
index 0000000000000000000000000000000000000000..ecfcab66edb7678eb237847723050d7e2d0fad1c
--- /dev/null
+++ b/assets/chatgpt_custom_human_fashion.txt
@@ -0,0 +1,25 @@
+A woman in a stylish dress walking down a busy street, with high-end stores and neon lights in the background.
+A man in a trendy suit taking a selfie in a city square, surrounded by modern buildings and a fountain.
+An elegant woman enjoying dinner at a high-end restaurant, with exquisite tableware and delicious food on the table.
+A gentleman toasting during a candlelit dinner, with soft lighting and artistic decorations in the background.
+A woman in professional sportswear running on a treadmill in a luxury gym, with modern fitness equipment and city views through large windows.
+A man lifting weights in the free weights area of an upscale gym, with clean mirrors and various machines in the background.
+A woman sunbathing by the pool of a luxury hotel, with a clear pool and comfortable loungers nearby.
+A man overlooking the cityscape from the balcony of a five-star hotel, holding a glass of champagne.
+A woman in a stunning evening gown walking the red carpet, surrounded by flashing cameras and photographers.
+A man in a black tuxedo mingling with guests at a luxurious banquet, with elegant decorations and chandeliers in the background.
+A woman shopping in a high-end mall, carrying several designer shopping bags, with shiny marble floors and luxurious stores in the background.
+A man trying on a suit in the men's section of an upscale department store, surrounded by neatly arranged clothes and mirrors.
+A fashionable woman enjoying a latte at an upscale café, with beautiful coffee art and pastries on the table.
+A gentleman reading in an artfully designed café, next to elegantly decorated bookshelves and soft lighting.
+A well-dressed man posing in front of a luxury car, with a modern cityscape in the background.
+A stylish woman gracefully getting out of a luxury car, surrounded by high-end office buildings and designer stores.
+A stylish woman having afternoon tea in a lavish tearoom, with fine china, assorted pastries, and a beautiful chandelier overhead.
+A gentleman in a tailored suit enjoying a cup of tea and scones, seated at a beautifully set table with floral arrangements.
+A woman in a fashionable outfit browsing books in an upscale bookstore, with cozy reading nooks and artfully arranged shelves.
+A man in a smart casual outfit sitting in a plush armchair, reading a book with a cup of coffee on a side table.
+A woman in a fashionable sundress enjoying the breeze on the deck of a luxury yacht, with the open sea and clear sky around her.
+A man in a casual yet chic outfit steering a luxury yacht, with other elegantly dressed guests relaxing on the deck.
+A woman in a stylish outfit admiring a painting in a modern art gallery, with minimalist decor and spotlights on the artworks.
+A man in an elegant suit discussing an abstract sculpture with a curator, surrounded by contemporary art pieces.
+A man in a formal suit enjoying a gourmet meal at a rooftop restaurant, with ambient lighting and a live pianist playing.
\ No newline at end of file
diff --git a/assets/chatgpt_custom_ice.txt b/assets/chatgpt_custom_ice.txt
new file mode 100644
index 0000000000000000000000000000000000000000..5a264416eec21178de65a7ed5b97a02b97194095
--- /dev/null
+++ b/assets/chatgpt_custom_ice.txt
@@ -0,0 +1,16 @@
+The Ice Queen stands in her shimmering palace, her gown blending with the frosty surroundings as she surveys her kingdom.
+A brave explorer, bundled in furs, navigates a mystical ice world, discovering hidden castles and frozen landscapes.
+An ice sculptor chisels a massive block in a workshop filled with intricate sculptures, his breath visible in the chilly air.
+A snow princess stands on the balcony of her ice castle, her hair adorned with delicate snowflakes, overlooking her serene realm.
+An icy warrior clad in glistening armor patrols the perimeter of a fortress, ready to defend against intruders.
+A glacial princess wanders through her crystal cave, her touch leaving a trail of frost as she explores her frozen domain.
+A brave explorer chips away at a wall of ice, revealing a hidden chamber with glowing ice crystals.
+A girl gently touches an ice wall, feeling the cold surface under her fingertips as it glows softly.
+A child slides across a frozen lake, her laughter echoing through the snowy landscape.
+A teenager carves a detailed figure out of an ice block, her concentration visible in the cold air.
+A girl gently touches an ice wall, marveling at its smooth, cold surface as she explores the ice castle.
+An explorer carefully climbs the side of an ice tower, his hands gripping the icy ledges as he ascends.
+A mage raises her staff, casting an ice spell that causes the castle walls to shimmer with new frost.
+The Ice Queen walks gracefully through the grand hall, her footsteps echoing on the crystalline floor.
+A scout lights an ice lantern, its glow illuminating the dark passageways of the ice castle.
+A girl discovers a hidden passage behind an ice statue, her curiosity leading her deeper into the ice castle's mysteries.
\ No newline at end of file
diff --git a/assets/chatgpt_custom_instruments.txt b/assets/chatgpt_custom_instruments.txt
new file mode 100644
index 0000000000000000000000000000000000000000..1ff0562d863e17ebb81b9044fe381e2af6f7063d
--- /dev/null
+++ b/assets/chatgpt_custom_instruments.txt
@@ -0,0 +1,50 @@
+A dog playing a piano with its paws in a living room illuminated by fairy lights.
+A dog strumming a guitar while sitting on a park bench at sunset.
+A dog tapping a drum set in a backyard with twinkling lights overhead.
+A dog blowing into a trumpet in a garden filled with blooming flowers.
+A dog playing a saxophone under a tree with soft, ambient lighting.
+A dog hitting xylophone keys in a cozy room with a fireplace crackling.
+A dog playing a harmonica on a porch with the sun setting in the background.
+A dog drumming on a set of bongos at a picnic in the park.
+A dog strumming a ukulele on a beach at dusk.
+A dog playing a tambourine while sitting on a blanket in a meadow.
+A cat playing a harp in a sunlit room with soft, flowing curtains.
+A cat tapping piano keys with precision in a warmly lit living room.
+A cat strumming a tiny guitar on a windowsill overlooking a garden.
+A cat plucking at a banjo in a room filled with cozy, ambient light.
+A cat playing a flute on a balcony under a moonlit sky.
+A cat tapping on a set of drums in a room with twinkling fairy lights.
+A cat playing a violin in a garden with morning dew sparkling.
+A cat hitting the chimes in a yard with a gentle breeze.
+A cat playing a cello in a room with soft, golden light.
+A cat strumming a sitar in a peaceful, cozy corner.
+A rabbit tapping the keys of a tiny piano in a meadow filled with wildflowers.
+A rabbit playing a miniature guitar under a tree with soft light filtering through.
+A rabbit hitting the drums with its paws in a garden with blooming flowers.
+A rabbit playing a flute on a wooden deck at sunset.
+A rabbit strumming a ukulele while sitting on a picnic blanket in the park.
+A rabbit tapping a xylophone in a backyard with twinkling string lights.
+A rabbit playing a harmonica in a cozy corner with soft ambient lighting.
+A rabbit drumming on a set of bongos in a sunlit field.
+A rabbit playing a tambourine in a garden with colorful flowers.
+A rabbit tapping the chimes in a yard filled with sunlight.
+A raccoon playing a piano with its nimble fingers in a moonlit backyard.
+A raccoon strumming a guitar while perched on a tree branch.
+A raccoon tapping a drum set under a starry night sky.
+A raccoon blowing into a saxophone in a forest clearing with fireflies.
+A raccoon playing a harmonica while sitting on a park bench at dusk.
+A raccoon hitting the xylophone in a garden with soft lighting.
+A raccoon playing a flute on a rooftop under a bright, full moon.
+A raccoon drumming on a set of bongos in a cozy, warmly lit room.
+A raccoon strumming a ukulele while sitting on a log by a campfire.
+A raccoon playing a tambourine in a field with a beautiful sunset.
+A dog and cat playing a duet on the piano in a room filled with fairy lights.
+A dog strumming a guitar while a rabbit taps the drums in a garden.
+A cat playing the violin while a raccoon strums a guitar under a tree.
+A rabbit and raccoon playing a duet on the xylophone in a meadow.
+A dog and raccoon playing harmonicas together in a cozy, warmly lit room.
+A cat and rabbit playing the flute and ukulele, respectively, in a sunlit garden.
+A dog tapping the bongos while a cat strums a tiny guitar on a picnic blanket.
+A raccoon blowing into a saxophone while a rabbit plays the tambourine in a field.
+A dog and rabbit playing a duet on the piano in a room with a crackling fireplace.
+A cat strumming a guitar while a raccoon taps the chimes in a moonlit backyard.
\ No newline at end of file
diff --git a/assets/chatgpt_custom_instruments_unseen.txt b/assets/chatgpt_custom_instruments_unseen.txt
new file mode 100644
index 0000000000000000000000000000000000000000..9b45a83a502e669e709ce4fec4bed62cccf18791
--- /dev/null
+++ b/assets/chatgpt_custom_instruments_unseen.txt
@@ -0,0 +1,55 @@
+A dog playing a harp in a dimly lit room with candles flickering around.
+A cat playing a double bass in a sunny garden surrounded by butterflies.
+A dog tapping a xylophone in a bright room filled with hanging plants.
+A cat blowing into a trumpet on a rooftop as the sun sets in the background.
+A rabbit strumming a guitar on a hill overlooking a scenic lake at dusk.
+A dog playing an accordion in a bustling city park during autumn.
+A cat plucking a ukulele in a cozy nook with a gentle rain visible outside the window.
+A rabbit playing a flute on a bridge over a tranquil pond.
+A raccoon playing a guitar under a blossoming cherry tree.
+A dog strumming a guitar on a snowy porch with soft winter light.
+A cat playing a keyboard in a colorful alley adorned with street art.
+A rabbit tapping a drum in a lush greenhouse.
+A dog blowing into a horn at the edge of a forest during a foggy morning.
+A cat playing a piano in a small, quaint chapel.
+A raccoon playing a violin in a vintage room filled with antique furniture.
+A dog playing a saxophone by a quiet stream with autumn leaves falling.
+A cat strumming a guitar on a balcony overlooking a bustling cityscape at night.
+A rabbit playing a trumpet in a meadow during a vibrant sunrise.
+A raccoon tapping a xylophone in a misty garden at dawn.
+A dog playing a keyboard in a futuristic room with glowing neon lights.
+A dog strumming an acoustic guitar by a lakeside campfire under the stars.
+A cat playing an electric guitar in a loft with industrial-style decor and soft, multicolored lights.
+A rabbit playing a classical guitar in a botanical garden surrounded by exotic flowers.
+A raccoon strumming a bass guitar in a cozy basement with vintage band posters on the walls.
+A dog playing a slide guitar on a porch during a gentle rainstorm.
+A cat plucking a twelve-string guitar on the roof during a clear sunset.
+A rabbit strumming a flamenco guitar in a vibrant street during a local festival.
+A dog playing a grand piano in a grand ballroom with chandeliers and elegant dancers.
+A cat tapping the keys of a digital piano in a modern apartment overlooking a bustling city.
+A rabbit playing an upright piano in a quaint cafe while patrons enjoy their coffee.
+A raccoon playing a baby grand piano on a theater stage with dramatic lighting.
+A dog performing on a piano in a jazz bar with a dimly lit, intimate atmosphere.
+A cat composing on a keyboard in a home studio filled with various musical instruments.
+A rabbit playing a miniature piano in a children’s library during storytime.
+A raccoon experimenting with sounds on a keyboard in a museum exhibit about the history of music.
+A dog playing bongos on a beach during a sunset.
+A cat tapping bongos in a cozy living room with a fireplace.
+A rabbit playing bongos in a park during a sunny afternoon.
+A raccoon drumming on bongos under a starry night sky.
+A dog tapping bongos in a backyard with twinkling fairy lights.
+A cat strumming a bass guitar in a modern living room.
+A rabbit playing an acoustic guitar by a riverside during a calm evening.
+A raccoon plucking a guitar in a forest clearing.
+A dog playing a baby grand piano in a sunlit conservatory.
+A cat playing a digital piano in a tech-themed studio.
+A rabbit tapping bongos at a picnic under a big oak tree.
+A raccoon strumming a guitar on a hillside with wildflowers.
+A dog playing bongos at a summer festival with colorful decorations.
+A cat playing an acoustic guitar in a moonlit garden.
+A rabbit tapping bongos in a meadow at dawn.
+A raccoon strumming a bass guitar in a vintage-themed café.
+A dog playing a piano in a music classroom with students watching.
+A cat tapping bongos on a balcony overlooking a city at night.
+A rabbit strumming a guitar in a quiet forest glade.
+A raccoon playing an electric bass in a garage band setting.
\ No newline at end of file
diff --git a/assets/chatgpt_custom_outdoor.txt b/assets/chatgpt_custom_outdoor.txt
new file mode 100644
index 0000000000000000000000000000000000000000..1eda4527aa77213d8370ef7e58d38fba737fa53f
--- /dev/null
+++ b/assets/chatgpt_custom_outdoor.txt
@@ -0,0 +1,50 @@
+A peaceful forest with tall trees and a small wooden cabin.
+A blooming meadow filled with colorful wildflowers and a single oak tree.
+A bustling city park with children playing and a fountain in the center.
+A serene beach with soft sand and a lighthouse in the distance.
+A quiet mountain trail with rocky terrain and a wooden bench.
+A picturesque lake with clear water and a small rowboat.
+A charming village square with cobblestone streets and a market stall.
+A tranquil garden with blooming roses and a stone pathway.
+A vibrant orchard with apple trees and a rustic wooden cart.
+A lush valley with green hills and a grazing herd of sheep.
+A majestic waterfall cascading into a clear pool with rocks.
+A quiet countryside road lined with wildflowers and a wooden fence.
+A beautiful vineyard with rows of grapevines and a quaint farmhouse.
+A peaceful riverbank with tall grasses and a small fishing pier.
+A scenic overlook with a view of rolling hills and a picnic table.
+A colorful carnival with bright lights and a Ferris wheel.
+A bustling farmer’s market with fresh produce and lively vendors.
+A serene bamboo forest with tall stalks and a winding path.
+A historic castle with towering walls and a drawbridge.
+A cozy village street with charming houses and a lamppost.
+A rustic barn in an open field with hay bales stacked outside.
+A calm river with a stone bridge and ducks swimming.
+A desert landscape with sandy dunes and a single cactus.
+A sunny park with children playing and a kite flying.
+A vibrant sunflower field with tall sunflowers and a scarecrow.
+A rocky cliff overlooking the ocean with seagulls flying.
+A peaceful meadow with grazing deer and wildflowers.
+A charming harbor with sailboats and a lighthouse.
+A dense forest with a carpet of ferns and a hidden stream.
+A quaint village with a cobblestone path and flower boxes.
+A serene pond with lily pads and a wooden dock.
+A lush garden with a variety of flowers and a birdbath.
+A colorful coral reef with tropical fish and seaweed.
+A picturesque valley with rolling hills and a clear river.
+A tranquil lakeside with canoes and a wooden pier.
+A vibrant market street with vendors and colorful awnings.
+A historic village square with an old clock tower.
+A quiet trail through a dense forest with moss-covered trees.
+A scenic mountain pass with a winding road and tall pines.
+A bustling port with fishing boats and seagulls.
+A peaceful woodland glade with a bubbling brook.
+A charming cottage garden with blooming flowers and a picket fence.
+A serene riverside with willow trees and a stone bench.
+A sunny vineyard with ripe grapes and a rustic barn.
+A lush forest path with wildflowers and tall trees.
+A picturesque beach with gentle waves and seashells.
+A vibrant city street with cafes and outdoor seating.
+A calm meadow with a stone path and butterflies.
+A scenic countryside road with a wooden fence and grazing cows.
+A peaceful park with a pond and paddleboats.
\ No newline at end of file
diff --git a/assets/chatgpt_custom_rainy.txt b/assets/chatgpt_custom_rainy.txt
new file mode 100644
index 0000000000000000000000000000000000000000..2490caf52aea669481321fdd37061d133ab60b59
--- /dev/null
+++ b/assets/chatgpt_custom_rainy.txt
@@ -0,0 +1,24 @@
+A street with children playing near a playground.
+A street with trees lining the sidewalk.
+A street with reflections of streetlights on the wet road.
+A street with a busker playing guitar on the corner.
+A street with puddles forming on the pavement.
+A street with a newsstand and people reading newspapers.
+A street with a market and vendors selling fruits.
+A street with people walking.
+A street with a mural painted on a building wall.
+A street with outdoor cafes and people dining.
+A street with a dog park and owners walking their dogs.
+A street with pedestrians and a coffee shop.
+A street with cars driving slowly.
+A street with a park and benches along the sidewalk.
+A street with a fountain in the middle of a square.
+A street with a bicycle rental station.
+A street with a flower shop and colorful displays.
+A street with parked cars and a row of houses.
+A street with a view of a distant skyline.
+A street with a bus stop and people waiting.
+A street with street performers entertaining a crowd.
+A street with a bookstore and a display of bestsellers.
+A street with a bakery and people buying pastries.
+A street with a bicycle lane and cyclists riding by.
\ No newline at end of file
diff --git a/assets/chatgpt_custom_snowy.txt b/assets/chatgpt_custom_snowy.txt
new file mode 100644
index 0000000000000000000000000000000000000000..41b64cae72b2134b13f749cfa595813d1698fe12
--- /dev/null
+++ b/assets/chatgpt_custom_snowy.txt
@@ -0,0 +1,69 @@
+A street with children playing near a playground.
+A street with trees lining the sidewalk.
+A street with reflections of streetlights on the wet road.
+A street with a busker playing guitar on the corner.
+A street with puddles forming on the pavement.
+A street with a newsstand and people reading newspapers.
+A street with a market and vendors selling fruits.
+A street with people walking.
+A street with a mural painted on a building wall.
+A street with outdoor cafes and people dining.
+A street with a dog park and owners walking their dogs.
+A street with pedestrians and a coffee shop.
+A street with cars driving slowly.
+A street with a park and benches along the sidewalk.
+A street with a fountain in the middle of a square.
+A street with a bicycle rental station.
+A street with a flower shop and colorful displays.
+A street with parked cars and a row of houses.
+A street with a view of a distant skyline.
+A street with a bus stop and people waiting.
+A street with street performers entertaining a crowd.
+A street with a bookstore and a display of bestsellers.
+A street with a bakery and people buying pastries.
+A street with a bicycle lane and cyclists riding by.
+A peaceful forest with tall trees and a small wooden cabin.
+A blooming meadow filled with colorful wildflowers and a single oak tree.
+A bustling city park with children playing and a fountain in the center.
+A quiet mountain trail with rocky terrain and a wooden bench.
+A picturesque lake with clear water and a small rowboat.
+A charming village square with cobblestone streets and a market stall.
+A tranquil garden with blooming roses and a stone pathway.
+A vibrant orchard with apple trees and a rustic wooden cart.
+A lush valley with green hills and a grazing herd of sheep.
+A majestic waterfall cascading into a clear pool with rocks.
+A quiet countryside road lined with wildflowers and a wooden fence.
+A beautiful vineyard with rows of grapevines and a quaint farmhouse.
+A peaceful riverbank with tall grasses and a small fishing pier.
+A scenic overlook with a view of rolling hills and a picnic table.
+A colorful carnival with bright lights and a Ferris wheel.
+A bustling farmer’s market with fresh produce and lively vendors.
+A serene bamboo forest with tall stalks and a winding path.
+A historic castle with towering walls and a drawbridge.
+A cozy village street with charming houses and a lamppost.
+A rustic barn in an open field with hay bales stacked outside.
+A calm river with a stone bridge and ducks swimming.
+A desert landscape with sandy dunes and a single cactus.
+A sunny park with children playing and a kite flying.
+A vibrant sunflower field with tall sunflowers and a scarecrow.
+A peaceful meadow with grazing deer and wildflowers.
+A charming harbor with sailboats and a lighthouse.
+A dense forest with a carpet of ferns and a hidden stream.
+A quaint village with a cobblestone path and flower boxes.
+A serene pond with lily pads and a wooden dock.
+A lush garden with a variety of flowers and a birdbath.
+A picturesque valley with rolling hills and a clear river.
+A tranquil lakeside with canoes and a wooden pier.
+A vibrant market street with vendors and colorful awnings.
+A historic village square with an old clock tower.
+A quiet trail through a dense forest with moss-covered trees.
+A scenic mountain pass with a winding road and tall pines.
+A peaceful woodland glade with a bubbling brook.
+A charming cottage garden with blooming flowers and a picket fence.
+A serene riverside with willow trees and a stone bench.
+A sunny vineyard with ripe grapes and a rustic barn.
+A lush forest path with wildflowers and tall trees.
+A vibrant city street with cafes and outdoor seating.
+A calm meadow with a stone path and butterflies.
+A scenic countryside road with a wooden fence and grazing cows.
+A peaceful park with a pond and paddleboats.
\ No newline at end of file
diff --git a/assets/compression_reward.pt b/assets/compression_reward.pt
new file mode 100644
index 0000000000000000000000000000000000000000..489dff8cfcdcf9a5eb3dfa0f78954c69215a512c
--- /dev/null
+++ b/assets/compression_reward.pt
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:4834909306ec3fd210805ad9eed6506338417ecf898aab7fa2d13f9c32ea2b2d
+size 1857371
diff --git a/assets/hps_custom.txt b/assets/hps_custom.txt
new file mode 100644
index 0000000000000000000000000000000000000000..b22a4d9fa9af713e67d2ca4df7402870d44ed6c8
--- /dev/null
+++ b/assets/hps_custom.txt
@@ -0,0 +1,50 @@
+A man taking a drink from a water fountain.
+A smiling man is cooking in his kitchen.
+A man smiles as he stirs his food in the pot.
+Several people around some motorcycles on the side of a road.
+A black and white cat looking out a window over another cat.
+A woman in a purple top pulling food out of a oven.
+A car driving past a parked motorcycle.
+A blue airplane in a blue, cloudless sky.
+A man and his dog riding on a bike.
+A brown and black dog sticking its head out a window.
+An airplane flying past the Moon in the sky.
+People leaning out the windows of a train as it goes through the countryside.
+there are many people trying to avoid the rain
+A woman eating vegetables in front of a stove.
+A man hanging his head out of the side of a train.
+A man getting food ready while people watch.
+A woman taking a photo over the shoulder of a man on a bike.
+A plane flies in the sky in front of a silhouette of a moon.
+there are many men playing soccer in a field.
+A woman forks vegetables out of a bowl into her mouth.
+A woman taking a picture of herself in a mirror.
+A couple of men riding a motorcycle down a street.
+A jet flies in the distance with the moon in the background.
+there is a man wearing a suit sitting on a bench
+Man talking on personal cell phone on a yellow and black bench.
+Two people on a motorcycle with tone taking a photo.
+View of tall city buildings with cars and people walking by.
+Woman walking down the side walk of a busy night city.
+Woman eating an assortment of mixed vegetables in a bowl.
+some people driving down the road with their bikes
+A brown cat crouches and arches its back in a white sink.
+Meats being prepared for cooking on kitchen counter.
+Large dog looking at television show in living room.
+A man driving a motorcycle with a woman holding a cell phone.
+A young woman standing in a kitchen eats a plate of vegetables.
+The motorcyclist in a helmet is looking over the side of a bridge.
+At night on a street with a group of a bicycle riders riding down the road together.
+some people holding umbrellas and standing by a car in the rain
+A person is riding his motorcycle on the dirt road.
+Eight jars are being filled with orange slices.
+there are two woman that are riding motorcycles
+There is a cyclist riding above all the pigeons.
+Some people are enjoying time on a beach.
+A woman eating fresh vegetables from a bowl.
+Man is riding a board near a field
+A man on a motorcycle riding in the desert.
+A group of young bicyclists on a city street at night.
+two men on a scooter riding down the roadway
+A man in a helmet and jacket riding a motorcycle in the desert.
+A woman holding a colorful kite on top of a green field.
\ No newline at end of file
diff --git a/assets/rainy_reward.pt b/assets/rainy_reward.pt
new file mode 100644
index 0000000000000000000000000000000000000000..81f122ca26116b3204478658f184ca9eb7218088
--- /dev/null
+++ b/assets/rainy_reward.pt
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:bfa1e0f38d6a8ed08637a72a141ea26fefe05632f5c160e952aa903f24ff80d3
+size 3715200
diff --git a/assets/sac+logos+ava1-l14-linearMSE.pth b/assets/sac+logos+ava1-l14-linearMSE.pth
new file mode 100644
index 0000000000000000000000000000000000000000..aae5780851125baf1a30834c3a715d3866858a4d
--- /dev/null
+++ b/assets/sac+logos+ava1-l14-linearMSE.pth
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:21dd590f3ccdc646f0d53120778b296013b096a035a2718c9cb0d511bff0f1e0
+size 3714759
diff --git a/assets/simple_animals.txt b/assets/simple_animals.txt
new file mode 100644
index 0000000000000000000000000000000000000000..bc9e1176a2eb831541d98dcd810b674a36602a78
--- /dev/null
+++ b/assets/simple_animals.txt
@@ -0,0 +1,45 @@
+cat
+dog
+horse
+monkey
+rabbit
+zebra
+spider
+bird
+sheep
+deer
+cow
+goat
+lion
+tiger
+bear
+raccoon
+fox
+wolf
+lizard
+beetle
+ant
+butterfly
+fish
+shark
+whale
+dolphin
+squirrel
+mouse
+rat
+snake
+turtle
+frog
+chicken
+duck
+goose
+bee
+pig
+turkey
+fly
+llama
+camel
+bat
+gorilla
+hedgehog
+kangaroo
diff --git a/assets/snowy_reward.pt b/assets/snowy_reward.pt
new file mode 100644
index 0000000000000000000000000000000000000000..a338ae4abf5b4e3ba97c07ea14f50c1639a5dc54
--- /dev/null
+++ b/assets/snowy_reward.pt
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:dae251d2b71dd08b8d43f9ea8b2550ac60159ed02f54bede6162b0108d54f991
+size 3715200