diff --git a/LICENSE b/LICENSE
new file mode 100644
index 0000000000000000000000000000000000000000..409ed2ddb8193321ee9a3accba35b0e51c9530e5
--- /dev/null
+++ b/LICENSE
@@ -0,0 +1,470 @@
+This license applies to the source codes that are open sourced in connection with the DynamiCrafter.
+
+Copyright (C) 2023 THL A29 Limited, a Tencent company.
+
+Apache License
+Version 2.0, January 2004
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+
+TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION
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+Components under BSD 3-Clause License:
+------------------------------------------------
+1. numpy
+Copyright (c) 2005-2022, NumPy Developers.
+All rights reserved.
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+2. pytorch
+Copyright (c) 2016- Facebook, Inc (Adam Paszke)
+Copyright (c) 2014- Facebook, Inc (Soumith Chintala)
+Copyright (c) 2011-2014 Idiap Research Institute (Ronan Collobert)
+Copyright (c) 2012-2014 Deepmind Technologies (Koray Kavukcuoglu)
+Copyright (c) 2011-2012 NEC Laboratories America (Koray Kavukcuoglu)
+Copyright (c) 2011-2013 NYU (Clement Farabet)
+Copyright (c) 2006-2010 NEC Laboratories America (Ronan Collobert, Leon Bottou, Iain Melvin, Jason Weston)
+Copyright (c) 2006 Idiap Research Institute (Samy Bengio)
+Copyright (c) 2001-2004 Idiap Research Institute (Ronan Collobert, Samy Bengio, Johnny Mariethoz)
+
+3. torchvision
+Copyright (c) Soumith Chintala 2016,
+All rights reserved.
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+Component under Apache v2 License:
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+1. timm
+Copyright 2019 Ross Wightman
+
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+distribution, then any Derivative Works that You distribute must
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+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
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+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.
+
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+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
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+Work (including but not limited to damages for loss of goodwill,
+work stoppage, computer failure or malfunction, or any and all
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+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
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\ No newline at end of file
diff --git a/app.py b/app.py
new file mode 100644
index 0000000000000000000000000000000000000000..ab1794c969c195ce29fd566436d86c61e4e5e08e
--- /dev/null
+++ b/app.py
@@ -0,0 +1,154 @@
+import gradio as gr
+import os
+import sys
+import argparse
+import random
+from omegaconf import OmegaConf
+import torch
+import torchvision
+from pytorch_lightning import seed_everything
+from huggingface_hub import hf_hub_download
+from einops import repeat
+import torchvision.transforms as transforms
+from utils.utils import instantiate_from_config
+sys.path.insert(0, "scripts/evaluation")
+from funcs import (
+ batch_ddim_sampling,
+ load_model_checkpoint,
+ get_latent_z,
+ save_videos
+)
+
+def download_model(self):
+ REPO_ID = 'Doubiiu/DynamiCrafter'
+ filename_list = ['model.ckpt']
+ if not os.path.exists('./checkpoints/dynamicrafter_256_v1/'):
+ os.makedirs('./dynamicrafter_256_v1/')
+ for filename in filename_list:
+ local_file = os.path.join('./checkpoints/dynamicrafter_256_v1/', filename)
+ if not os.path.exists(local_file):
+ hf_hub_download(repo_id=REPO_ID, filename=filename, local_dir='./checkpoints/dynamicrafter_256_v1/', local_dir_use_symlinks=False)
+
+
+def infer(image, prompt, steps=50, cfg_scale=7.5, eta=1.0, fs=3, seed=123):
+ download_model()
+ ckpt_path='checkpoints/dynamicrafter_256_v1/model.ckpt'
+ config_file='configs/inference_256_v1.0.yaml'
+ config = OmegaConf.load(config_file)
+ model_config = config.pop("model", OmegaConf.create())
+ model_config['params']['unet_config']['params']['use_checkpoint']=False
+ model = instantiate_from_config(model_config)
+ assert os.path.exists(ckpt_path), "Error: checkpoint Not Found!"
+ model = load_model_checkpoint(model, ckpt_path)
+ model.eval()
+ model = model.cuda()
+ save_fps = 8
+
+ seed_everything(seed)
+ transform = transforms.Compose([
+ transforms.Resize(256),
+ transforms.CenterCrop(256),
+ ])
+ torch.cuda.empty_cache()
+ print('start:', prompt, time.strftime('%Y-%m-%d %H:%M:%S',time.localtime(time.time())))
+ start = time.time()
+ if steps > 60:
+ steps = 60
+
+ batch_size=1
+ channels = model.model.diffusion_model.out_channels
+ frames = model.temporal_length
+ h, w = 256 // 8, 256 // 8
+ noise_shape = [batch_size, channels, frames, h, w]
+
+ # text cond
+ text_emb = model.get_learned_conditioning([prompt])
+
+ # img cond
+ img_tensor = torch.from_numpy(image).permute(2, 0, 1).float().to(model.device)
+ img_tensor = (img_tensor / 255. - 0.5) * 2
+
+ image_tensor_resized = transform(img_tensor) #3,256,256
+ videos = image_tensor_resized.unsqueeze(0) # bchw
+
+ z = get_latent_z(model, videos.unsqueeze(2)) #bc,1,hw
+
+ img_tensor_repeat = repeat(z, 'b c t h w -> b c (repeat t) h w', repeat=frames)
+
+ cond_images = model.embedder(img_tensor.unsqueeze(0)) ## blc
+ img_emb = model.image_proj_model(cond_images)
+
+ imtext_cond = torch.cat([text_emb, img_emb], dim=1)
+
+ fs = torch.tensor([fs], dtype=torch.long, device=model.device)
+ cond = {"c_crossattn": [imtext_cond], "fs": fs, "c_concat": [img_tensor_repeat]}
+
+ ## inference
+ batch_samples = batch_ddim_sampling(model, cond, noise_shape, n_samples=1, ddim_steps=steps, ddim_eta=eta, cfg_scale=cfg_scale)
+ ## b,samples,c,t,h,w
+
+ video_path = './output.mp4'
+ save_videos(batch_samples, './', filenames=['output'], fps=save_fps)
+ model = model.cpu()
+ return video_path
+
+
+
+
+
+
+i2v_examples = [
+ ['prompts/art.png', 'man fishing in a boat at sunset', 50, 7.5, 1.0, 3, 234],
+ ['prompts/boy.png', 'boy walking on the street', 50, 7.5, 1.0, 3, 125],
+ ['prompts/dance1.jpeg', 'two people dancing', 50, 7.5, 1.0, 3, 116],
+ ['prompts/fire_and_beach.jpg', 'a campfire on the beach and the ocean waves in the background', 50, 7.5, 1.0, 3, 111],
+ ['prompts/girl3.jpeg', 'girl talking and blinking', 50, 7.5, 1.0, 3, 111],
+ ['prompts/guitar0.jpeg', 'bear playing guitar happily, snowing', 50, 7.5, 1.0, 3, 111],
+]
+css = """#input_img {max-width: 256px !important} #output_vid {max-width: 256px; max-height: 256px}"""
+
+with gr.Blocks(analytics_enabled=False, css=css) as dynamicrafter_iface:
+ gr.Markdown("<div align='center'> <h1> DynamiCrafter: Animating Open-domain Images with Video Diffusion Priors </span> </h1> \
+ <h2 style='font-weight: 450; font-size: 1rem; margin: 0rem'>\
+ <a href='https://doubiiu.github.io/'>Jinbo Xing</a>, \
+ <a href='https://menghanxia.github.io/'>Menghan Xia</a>, <a href='https://yzhang2016.github.io/'>Yong Zhang</a>, \
+ <a href=''>Haoxin Chen</a>, <a href=''> Wangbo Yu</a>,\
+ <a href='https://github.com/hyliu'>Hanyuan Liu</a>, <a href='https://xinntao.github.io/'>Xintao Wang</a>,\
+ <a href='https://www.cse.cuhk.edu.hk/~ttwong/myself.html'>Tien-Tsin Wong</a>,\
+ <a href='https://scholar.google.com/citations?user=4oXBp9UAAAAJ&hl=zh-CN'>Ying Shan</a>\
+ </h2> \
+ <a style='font-size:18px;color: #000000' href='https://arxiv.org/abs/2310.12190'> [ArXiv] </a>\
+ <a style='font-size:18px;color: #000000' href='https://doubiiu.github.io/projects/DynamiCrafter/'> [Project Page] </a> \
+ <a style='font-size:18px;color: #000000' href='https://github.com/Doubiiu/DynamiCrafter'> [Github] </a> </div>")
+
+ with gr.Tab(label='ImageAnimation'):
+ with gr.Column():
+ with gr.Row():
+ with gr.Column():
+ with gr.Row():
+ i2v_input_image = gr.Image(label="Input Image",elem_id="input_img")
+ with gr.Row():
+ i2v_input_text = gr.Text(label='Prompts')
+ with gr.Row():
+ i2v_seed = gr.Slider(label='Random Seed', minimum=0, maximum=10000, step=1, value=123)
+ i2v_eta = gr.Slider(minimum=0.0, maximum=1.0, step=0.1, label='ETA', value=1.0, elem_id="i2v_eta")
+ i2v_cfg_scale = gr.Slider(minimum=1.0, maximum=15.0, step=0.5, label='CFG Scale', value=7.5, elem_id="i2v_cfg_scale")
+ with gr.Row():
+ i2v_steps = gr.Slider(minimum=1, maximum=60, step=1, elem_id="i2v_steps", label="Sampling steps", value=50)
+ i2v_motion = gr.Slider(minimum=1, maximum=4, step=1, elem_id="i2v_motion", label="Motion magnitude", value=3)
+ i2v_end_btn = gr.Button("Generate")
+ # with gr.Tab(label='Result'):
+ with gr.Row():
+ i2v_output_video = gr.Video(label="Generated Video",elem_id="output_vid",autoplay=True,show_share_button=True)
+
+ gr.Examples(examples=i2v_examples,
+ inputs=[i2v_input_image, i2v_input_text, i2v_steps, i2v_cfg_scale, i2v_eta, i2v_motion, i2v_seed],
+ outputs=[i2v_output_video],
+ fn = infer,
+ )
+ i2v_end_btn.click(inputs=[i2v_input_image, i2v_input_text, i2v_steps, i2v_cfg_scale, i2v_eta, i2v_motion, i2v_seed],
+ outputs=[i2v_output_video],
+ fn = infer
+ )
+
+dynamicrafter_iface.queue(max_size=12).launch(show_api=True)
\ No newline at end of file
diff --git a/configs/inference_256_v1.0.yaml b/configs/inference_256_v1.0.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..3f098796cf2e7487669b93ae3fa8e4ac2087e385
--- /dev/null
+++ b/configs/inference_256_v1.0.yaml
@@ -0,0 +1,98 @@
+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: hybrid
+ image_size: [32, 32]
+ 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: 8
+ out_channels: 4
+ model_channels: 320
+ attention_resolutions:
+ - 4
+ - 2
+ - 1
+ num_res_blocks: 2
+ channel_mult:
+ - 1
+ - 2
+ - 4
+ - 4
+ dropout: 0.1
+ 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
+ image_cross_attention: true
+ image_cross_attention_scale_learnable: true
+ default_fs: 3
+ fs_condition: 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: 256
+ 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"
+
+ img_cond_stage_config:
+ target: lvdm.modules.encoders.condition.FrozenOpenCLIPImageEmbedderV2
+ params:
+ freeze: true
+
+ image_proj_stage_config:
+ target: lvdm.modules.encoders.resampler.Resampler
+ params:
+ dim: 1024
+ depth: 4
+ dim_head: 64
+ heads: 12
+ num_queries: 16
+ embedding_dim: 1280
+ output_dim: 1024
+ ff_mult: 4
+ video_length: 16
+
diff --git a/lvdm/__pycache__/basics.cpython-39.pyc b/lvdm/__pycache__/basics.cpython-39.pyc
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diff --git a/lvdm/__pycache__/common.cpython-39.pyc b/lvdm/__pycache__/common.cpython-39.pyc
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index 0000000000000000000000000000000000000000..95f229682361ad6d3d390a401dcf34c1eae85341
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diff --git a/lvdm/__pycache__/distributions.cpython-39.pyc b/lvdm/__pycache__/distributions.cpython-39.pyc
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diff --git a/lvdm/__pycache__/ema.cpython-39.pyc b/lvdm/__pycache__/ema.cpython-39.pyc
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diff --git a/lvdm/basics.py b/lvdm/basics.py
new file mode 100644
index 0000000000000000000000000000000000000000..65c771d13a7f4a932ac370f08797a8b6ba9e85ff
--- /dev/null
+++ b/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/lvdm/common.py b/lvdm/common.py
new file mode 100644
index 0000000000000000000000000000000000000000..55a150b618e275f01d3a59ad9c7579176c4ea1b8
--- /dev/null
+++ b/lvdm/common.py
@@ -0,0 +1,94 @@
+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)
\ No newline at end of file
diff --git a/lvdm/distributions.py b/lvdm/distributions.py
new file mode 100644
index 0000000000000000000000000000000000000000..9a2a82ecace3ce27fb7816ddaf088e179c2d5ffd
--- /dev/null
+++ b/lvdm/distributions.py
@@ -0,0 +1,95 @@
+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)
+ )
\ No newline at end of file
diff --git a/lvdm/ema.py b/lvdm/ema.py
new file mode 100644
index 0000000000000000000000000000000000000000..cd2f8e3115f816b4cac674397238cd8c22de9bc2
--- /dev/null
+++ b/lvdm/ema.py
@@ -0,0 +1,76 @@
+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)
\ No newline at end of file
diff --git a/lvdm/models/__pycache__/autoencoder.cpython-39.pyc b/lvdm/models/__pycache__/autoencoder.cpython-39.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..e5513c010e880647bbc562deb3b58b5b2edd4412
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diff --git a/lvdm/models/__pycache__/ddpm3d.cpython-39.pyc b/lvdm/models/__pycache__/ddpm3d.cpython-39.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..e689d3846c82bdc4666267172b3cb137becaa2a6
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diff --git a/lvdm/models/__pycache__/utils_diffusion.cpython-39.pyc b/lvdm/models/__pycache__/utils_diffusion.cpython-39.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..1a3a5b984093eeabe4d2e29115963aa352756084
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diff --git a/lvdm/models/autoencoder.py b/lvdm/models/autoencoder.py
new file mode 100644
index 0000000000000000000000000000000000000000..d6880ff3ad6dc44447969586b807b81eccaecd35
--- /dev/null
+++ b/lvdm/models/autoencoder.py
@@ -0,0 +1,219 @@
+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
\ No newline at end of file
diff --git a/lvdm/models/ddpm3d.py b/lvdm/models/ddpm3d.py
new file mode 100644
index 0000000000000000000000000000000000000000..110a7165c62db9456876066371762dcfbcab197f
--- /dev/null
+++ b/lvdm/models/ddpm3d.py
@@ -0,0 +1,732 @@
+"""
+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.basics import disabled_train
+from lvdm.common import (
+ extract_into_tensor,
+ noise_like,
+ exists,
+ default
+)
+
+__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", "v"], 'currently only supporting "eps" and "x0" and "v"'
+ 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 # try conv?
+ 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)
+ #count_params(self.model, verbose=True)
+ 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))
+ elif self.parameterization == "v":
+ lvlb_weights = torch.ones_like(self.betas ** 2 / (
+ 2 * self.posterior_variance * to_torch(alphas) * (1 - self.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 predict_start_from_z_and_v(self, x_t, t, v):
+ # 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)))
+ return (
+ extract_into_tensor(self.sqrt_alphas_cumprod, t, x_t.shape) * x_t -
+ extract_into_tensor(self.sqrt_one_minus_alphas_cumprod, t, x_t.shape) * v
+ )
+
+ def predict_eps_from_z_and_v(self, x_t, t, v):
+ return (
+ extract_into_tensor(self.sqrt_alphas_cumprod, t, x_t.shape) * v +
+ extract_into_tensor(self.sqrt_one_minus_alphas_cumprod, t, x_t.shape) * x_t
+ )
+
+ 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.sqrt_one_minus_alphas_cumprod, t, x_start.shape) * noise)
+
+ def get_v(self, x, noise, t):
+ return (
+ extract_into_tensor(self.sqrt_alphas_cumprod, t, x.shape) * noise -
+ extract_into_tensor(self.sqrt_one_minus_alphas_cumprod, t, x.shape) * x
+ )
+
+ 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,
+ noise_strength=0,
+ use_dynamic_rescale=False,
+ base_scale=0.7,
+ turning_step=400,
+ loop_video=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
+ self.noise_strength = noise_strength
+ self.use_dynamic_rescale = use_dynamic_rescale
+ self.loop_video = loop_video
+ 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))
+
+ if use_dynamic_rescale:
+ scale_arr1 = np.linspace(1.0, base_scale, turning_step)
+ scale_arr2 = np.full(self.num_timesteps, base_scale)
+ scale_arr = np.concatenate((scale_arr1, scale_arr2))
+ to_torch = partial(torch.tensor, dtype=torch.float32)
+ self.register_buffer('scale_arr', to_torch(scale_arr))
+
+ 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 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
+
+ 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)
+
+ # same as above but without decorator
+ def differentiable_decode_first_stage(self, z, **kwargs):
+ return self.decode_core(z, **kwargs)
+
+ def forward(self, x, c, **kwargs):
+ t = torch.randint(0, self.num_timesteps, (x.shape[0],), device=self.device).long()
+ if self.use_dynamic_rescale:
+ x = x * extract_into_tensor(self.scale_arr, t, x.shape)
+ return self.p_losses(x, c, t, **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:
+ 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
+
+
+ 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, img_cond_stage_config, image_proj_stage_config, freeze_embedder=True, *args, **kwargs):
+ super().__init__(*args, **kwargs)
+ self._init_embedder(img_cond_stage_config, freeze_embedder)
+ self.image_proj_model = instantiate_from_config(image_proj_stage_config)
+
+ def _init_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
+
+
+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, **kwargs)
+ 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, **kwargs)
+ 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
+ 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
+ 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)
+ elif self.conditioning_key == 'crossattn-adm':
+ assert c_adm is not None
+ cc = torch.cat(c_crossattn, 1)
+ out = self.diffusion_model(x, t, context=cc, y=c_adm)
+ else:
+ raise NotImplementedError()
+
+ return out
\ No newline at end of file
diff --git a/lvdm/models/samplers/__pycache__/ddim.cpython-39.pyc b/lvdm/models/samplers/__pycache__/ddim.cpython-39.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..d94203b2c221b7e1b2168b468e22fef4a7ad3f4a
Binary files /dev/null and b/lvdm/models/samplers/__pycache__/ddim.cpython-39.pyc differ
diff --git a/lvdm/models/samplers/__pycache__/ddim_multiplecond.cpython-39.pyc b/lvdm/models/samplers/__pycache__/ddim_multiplecond.cpython-39.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..0065400a48d13b73881815f3a41f371d79532230
Binary files /dev/null and b/lvdm/models/samplers/__pycache__/ddim_multiplecond.cpython-39.pyc differ
diff --git a/lvdm/models/samplers/ddim.py b/lvdm/models/samplers/ddim.py
new file mode 100644
index 0000000000000000000000000000000000000000..1fa3488e4f1e64cce1206ebb9dbf460b9ec79270
--- /dev/null
+++ b/lvdm/models/samplers/ddim.py
@@ -0,0 +1,306 @@
+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
+from lvdm.common import extract_into_tensor
+
+
+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
+
+ 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)
+
+ if self.model.use_dynamic_rescale:
+ self.ddim_scale_arr = self.model.scale_arr[self.ddim_timesteps]
+ self.ddim_scale_arr_prev = torch.cat([self.ddim_scale_arr[0:1], self.ddim_scale_arr[:-1]])
+
+ 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))
+
+ # 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,
+ precision=None,
+ fs=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)
+
+ # 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,
+ 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,
+ precision=precision,
+ fs=fs,
+ **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,precision=None,fs=None,
+ **kwargs):
+ device = self.model.betas.device
+ b = shape[0]
+ if x_T is None:
+ img = torch.randn(shape, device=device)
+ else:
+ img = x_T
+ if precision is not None:
+ if precision == 16:
+ img = img.to(dtype=torch.float16)
+
+ 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
+
+ clean_cond = kwargs.pop("clean_cond", False)
+ for i, step in enumerate(iterator):
+ index = total_steps - i - 1
+ ts = torch.full((b,), step, device=device, dtype=torch.long)
+
+ ## 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
+
+ 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,
+ mask=mask,x0=x0,fs=fs,
+ **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,mask=None,x0=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) or isinstance(c, dict):
+ e_t_cond = self.model.apply_model(x, t, c, **kwargs)
+ e_t_uncond = self.model.apply_model(x, t, unconditional_conditioning, **kwargs)
+ else:
+ raise NotImplementedError
+
+ e_t = e_t_uncond + unconditional_guidance_scale * (e_t_cond - e_t_uncond)
+
+ if self.model.parameterization == "v":
+ e_t = self.model.predict_eps_from_z_and_v(x, t, e_t)
+
+
+ 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.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
+ if self.model.parameterization != "v":
+ pred_x0 = (x - sqrt_one_minus_at * e_t) / a_t.sqrt()
+ else:
+ pred_x0 = self.model.predict_start_from_z_and_v(x, t, e_t)
+
+ if self.model.use_dynamic_rescale:
+ scale_t = torch.full(size, self.ddim_scale_arr[index], device=device)
+ prev_scale_t = torch.full(size, self.ddim_scale_arr_prev[index], device=device)
+ rescale = (prev_scale_t / scale_t)
+ pred_x0 *= rescale
+
+ 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)
+
+ x_prev = a_prev.sqrt() * pred_x0 + dir_xt + noise
+
+ return x_prev, pred_x0
+
+ @torch.no_grad()
+ def decode(self, x_latent, cond, t_start, unconditional_guidance_scale=1.0, unconditional_conditioning=None,
+ use_original_steps=False, callback=None):
+
+ 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)
+ if callback: callback(i)
+ return x_dec
+
+ @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)
+ return (extract_into_tensor(sqrt_alphas_cumprod, t, x0.shape) * x0 +
+ extract_into_tensor(sqrt_one_minus_alphas_cumprod, t, x0.shape) * noise)
diff --git a/lvdm/models/samplers/ddim_multiplecond.py b/lvdm/models/samplers/ddim_multiplecond.py
new file mode 100644
index 0000000000000000000000000000000000000000..42f46888e57706408802b530f40a0d3cae5c5eab
--- /dev/null
+++ b/lvdm/models/samplers/ddim_multiplecond.py
@@ -0,0 +1,297 @@
+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
+from lvdm.common import extract_into_tensor
+
+
+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
+
+ 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))
+
+ # 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,
+ precision=None,
+ fs=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)
+
+ # 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,
+ 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,
+ precision=precision,
+ fs=fs,
+ **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,precision=None,fs=None,
+ **kwargs):
+ device = self.model.betas.device
+ b = shape[0]
+ if x_T is None:
+ img = torch.randn(shape, device=device)
+ else:
+ img = x_T
+ if precision is not None:
+ if precision == 16:
+ img = img.to(dtype=torch.float16)
+
+
+ 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
+
+ clean_cond = kwargs.pop("clean_cond", False)
+ for i, step in enumerate(iterator):
+ index = total_steps - i - 1
+ ts = torch.full((b,), step, device=device, dtype=torch.long)
+
+ ## 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
+
+ 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,
+ mask=mask,x0=x0,fs=fs,
+ **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, cfg_img=None,mask=None,x0=None, **kwargs):
+ b, *_, device = *x.shape, x.device
+ if x.dim() == 5:
+ is_video = True
+ else:
+ is_video = False
+ if cfg_img is None:
+ cfg_img = unconditional_guidance_scale
+
+ unconditional_conditioning_img_nonetext = kwargs['unconditional_conditioning_img_nonetext']
+
+
+ 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
+ e_t_cond = self.model.apply_model(x, t, c, **kwargs)
+ e_t_uncond = self.model.apply_model(x, t, unconditional_conditioning, **kwargs)
+ e_t_uncond_img = self.model.apply_model(x, t, unconditional_conditioning_img_nonetext, **kwargs)
+ # text cfg
+ e_t = e_t_uncond + cfg_img * (e_t_uncond_img - e_t_uncond) + unconditional_guidance_scale * (e_t_cond - e_t_uncond_img)
+
+ if self.model.parameterization == "v":
+ e_t = self.model.predict_eps_from_z_and_v(x, t, e_t)
+
+
+ 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.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)
+
+ x_prev = a_prev.sqrt() * pred_x0 + dir_xt + noise
+
+ return x_prev, pred_x0
+
+ @torch.no_grad()
+ def decode(self, x_latent, cond, t_start, unconditional_guidance_scale=1.0, unconditional_conditioning=None,
+ use_original_steps=False, callback=None):
+
+ 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)
+ if callback: callback(i)
+ return x_dec
+
+ @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)
+ return (extract_into_tensor(sqrt_alphas_cumprod, t, x0.shape) * x0 +
+ extract_into_tensor(sqrt_one_minus_alphas_cumprod, t, x0.shape) * noise)
\ No newline at end of file
diff --git a/lvdm/models/utils_diffusion.py b/lvdm/models/utils_diffusion.py
new file mode 100644
index 0000000000000000000000000000000000000000..6a79bd4850e5e1ac3cf653cc19f5ed7941f7f77f
--- /dev/null
+++ b/lvdm/models/utils_diffusion.py
@@ -0,0 +1,104 @@
+import math
+import numpy as np
+import torch
+import torch.nn.functional as F
+from einops import repeat
+
+
+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/lvdm/modules/__pycache__/attention.cpython-39.pyc b/lvdm/modules/__pycache__/attention.cpython-39.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..a589a55c22d2dddc4c5f85bc2390bb51ca3b14cc
Binary files /dev/null and b/lvdm/modules/__pycache__/attention.cpython-39.pyc differ
diff --git a/lvdm/modules/attention.py b/lvdm/modules/attention.py
new file mode 100644
index 0000000000000000000000000000000000000000..176885d9ff6f5675c413523a38b78845ce04bd97
--- /dev/null
+++ b/lvdm/modules/attention.py
@@ -0,0 +1,514 @@
+import torch
+from torch import nn, einsum
+import torch.nn.functional as F
+from einops import rearrange, repeat
+from functools import partial
+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, video_length=None, image_cross_attention=False, image_cross_attention_scale=1.0, image_cross_attention_scale_learnable=False, text_context_len=77):
+ 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.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
+
+ self.video_length = video_length
+ self.image_cross_attention = image_cross_attention
+ self.image_cross_attention_scale = image_cross_attention_scale
+ self.text_context_len = text_context_len
+ self.image_cross_attention_scale_learnable = image_cross_attention_scale_learnable
+ if self.image_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)
+ if image_cross_attention_scale_learnable:
+ self.register_parameter('alpha', nn.Parameter(torch.tensor(0.)) )
+
+
+ def forward(self, x, context=None, mask=None):
+ spatial_self_attn = (context is None)
+ k_ip, v_ip, out_ip = None, None, None
+
+ h = self.heads
+ q = self.to_q(x)
+ context = default(context, x)
+
+ if self.image_cross_attention and not spatial_self_attn:
+ context, context_image = 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_image)
+ v_ip = self.to_v_ip(context_image)
+ else:
+ if not spatial_self_attn:
+ context = context[:,:self.text_context_len,:]
+ 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)
+
+
+ ## for image cross-attention
+ if k_ip is not None:
+ 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)
+
+
+ if out_ip is not None:
+ if self.image_cross_attention_scale_learnable:
+ out = out + self.image_cross_attention_scale * out_ip * (torch.tanh(self.alpha)+1)
+ else:
+ out = out + self.image_cross_attention_scale * out_ip
+
+ return self.to_out(out)
+
+ def efficient_forward(self, x, context=None, mask=None):
+ spatial_self_attn = (context is None)
+ k_ip, v_ip, out_ip = None, None, None
+
+ q = self.to_q(x)
+ context = default(context, x)
+
+ if self.image_cross_attention and not spatial_self_attn:
+ context, context_image = 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_image)
+ v_ip = self.to_v_ip(context_image)
+ else:
+ if not spatial_self_attn:
+ context = context[:,:self.text_context_len,:]
+ 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)
+
+ ## for image cross-attention
+ if k_ip is not None:
+ 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 out_ip is not None:
+ if self.image_cross_attention_scale_learnable:
+ out = out + self.image_cross_attention_scale * out_ip * (torch.tanh(self.alpha)+1)
+ else:
+ 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, video_length=None, image_cross_attention=False, image_cross_attention_scale=1.0, image_cross_attention_scale_learnable=False, text_context_len=77):
+ 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, video_length=video_length, image_cross_attention=image_cross_attention, image_cross_attention_scale=image_cross_attention_scale, image_cross_attention_scale_learnable=image_cross_attention_scale_learnable,text_context_len=text_context_len)
+ self.image_cross_attention = image_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, **kwargs):
+ ## 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)
+ 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, video_length=None,
+ image_cross_attention=False, image_cross_attention_scale_learnable=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)
+
+ attention_cls = None
+ self.transformer_blocks = nn.ModuleList([
+ BasicTransformerBlock(
+ inner_dim,
+ n_heads,
+ d_head,
+ dropout=dropout,
+ context_dim=context_dim,
+ disable_self_attn=disable_self_attn,
+ checkpoint=use_checkpoint,
+ attention_cls=attention_cls,
+ video_length=video_length,
+ image_cross_attention=image_cross_attention,
+ image_cross_attention_scale_learnable=image_cross_attention_scale_learnable,
+ ) 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, **kwargs):
+ 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, **kwargs)
+ 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, causal_block_size=1,
+ 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.causal_block_size = causal_block_size
+
+ 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 = partial(CrossAttention, temporal_length=temporal_length)
+ 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)
+
+ temp_mask = None
+ if self.causal_attention:
+ # slice the from mask map
+ temp_mask = self.mask[:,:t,:t].to(x.device)
+
+ if temp_mask is not None:
+ mask = temp_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/lvdm/modules/encoders/__pycache__/condition.cpython-39.pyc b/lvdm/modules/encoders/__pycache__/condition.cpython-39.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..53b6f0d02f8152932c896c505ea82aafe793f3df
Binary files /dev/null and b/lvdm/modules/encoders/__pycache__/condition.cpython-39.pyc differ
diff --git a/lvdm/modules/encoders/__pycache__/resampler.cpython-39.pyc b/lvdm/modules/encoders/__pycache__/resampler.cpython-39.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..0fd1064ba2250543baa0a4c4655ba3e0429cdf7c
Binary files /dev/null and b/lvdm/modules/encoders/__pycache__/resampler.cpython-39.pyc differ
diff --git a/lvdm/modules/encoders/condition.py b/lvdm/modules/encoders/condition.py
new file mode 100644
index 0000000000000000000000000000000000000000..443ce9a9e7e015dad5c05db737a189da83dbbb4f
--- /dev/null
+++ b/lvdm/modules/encoders/condition.py
@@ -0,0 +1,389 @@
+import torch
+import torch.nn as nn
+import kornia
+import open_clip
+from torch.utils.checkpoint import checkpoint
+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'), pretrained=version)
+ 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):
+ tokens = open_clip.tokenize(text) ## all clip models use 77 as context length
+ 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.mapper = torch.nn.Linear(1280, 1024)
+ 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.model.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]
diff --git a/lvdm/modules/encoders/resampler.py b/lvdm/modules/encoders/resampler.py
new file mode 100644
index 0000000000000000000000000000000000000000..d9abd23f681510d757acdf4dbb31794fe49ab5b1
--- /dev/null
+++ b/lvdm/modules/encoders/resampler.py
@@ -0,0 +1,145 @@
+# modified from https://github.com/mlfoundations/open_flamingo/blob/main/open_flamingo/src/helpers.py
+# and https://github.com/lucidrains/imagen-pytorch/blob/main/imagen_pytorch/imagen_pytorch.py
+# and https://github.com/tencent-ailab/IP-Adapter/blob/main/ip_adapter/resampler.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,
+ video_length=None, # using frame-wise version or not
+ ):
+ super().__init__()
+ ## queries for a single frame / image
+ self.num_queries = num_queries
+ self.video_length = video_length
+
+ ## <num_queries> queries for each frame
+ if video_length is not None:
+ num_queries = num_queries * video_length
+
+ 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) ## B (T L) C
+ 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)
+ latents = self.norm_out(latents) # B L C or B (T L) C
+
+ return latents
\ No newline at end of file
diff --git a/lvdm/modules/networks/__pycache__/ae_modules.cpython-39.pyc b/lvdm/modules/networks/__pycache__/ae_modules.cpython-39.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..cc22f1de7d72d6afe86a0495f7d2f9c1c0db04ff
Binary files /dev/null and b/lvdm/modules/networks/__pycache__/ae_modules.cpython-39.pyc differ
diff --git a/lvdm/modules/networks/__pycache__/openaimodel3d.cpython-39.pyc b/lvdm/modules/networks/__pycache__/openaimodel3d.cpython-39.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..8a542f9094add8c9448d9a61604fc6221b85ef79
Binary files /dev/null and b/lvdm/modules/networks/__pycache__/openaimodel3d.cpython-39.pyc differ
diff --git a/lvdm/modules/networks/ae_modules.py b/lvdm/modules/networks/ae_modules.py
new file mode 100644
index 0000000000000000000000000000000000000000..8565997210d89422ac09feb1bf3e71d172f6f639
--- /dev/null
+++ b/lvdm/modules/networks/ae_modules.py
@@ -0,0 +1,844 @@
+# 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
\ No newline at end of file
diff --git a/lvdm/modules/networks/openaimodel3d.py b/lvdm/modules/networks/openaimodel3d.py
new file mode 100644
index 0000000000000000000000000000000000000000..1e16147cd1a77208969c2b27ecc5634d39e8ddc3
--- /dev/null
+++ b/lvdm/modules/networks/openaimodel3d.py
@@ -0,0 +1,603 @@
+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=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.
+ :param use_temporal_conv: if True, use the temporal convolution.
+ :param use_image_dataset: if True, the temporal parameters will not be optimized.
+ """
+
+ 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
+ th_kernel_shape = (3, 1, 1) if not spatial_aware else (3, 3, 1)
+ th_padding_shape = (1, 0, 0) if not spatial_aware else (1, 1, 0)
+ tw_kernel_shape = (3, 1, 1) if not spatial_aware else (3, 1, 3)
+ tw_padding_shape = (1, 0, 0) if not spatial_aware else (1, 0, 1)
+
+ # conv layers
+ self.conv1 = nn.Sequential(
+ nn.GroupNorm(32, in_channels), nn.SiLU(),
+ nn.Conv3d(in_channels, out_channels, th_kernel_shape, padding=th_padding_shape))
+ self.conv2 = nn.Sequential(
+ nn.GroupNorm(32, out_channels), nn.SiLU(), nn.Dropout(dropout),
+ nn.Conv3d(out_channels, in_channels, tw_kernel_shape, padding=tw_padding_shape))
+ self.conv3 = nn.Sequential(
+ nn.GroupNorm(32, out_channels), nn.SiLU(), nn.Dropout(dropout),
+ nn.Conv3d(out_channels, in_channels, th_kernel_shape, padding=th_padding_shape))
+ self.conv4 = nn.Sequential(
+ nn.GroupNorm(32, out_channels), nn.SiLU(), nn.Dropout(dropout),
+ nn.Conv3d(out_channels, in_channels, tw_kernel_shape, padding=tw_padding_shape))
+
+ # 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 identity + x
+
+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.
+ :param use_new_attention_order: use a different attention pattern for potentially
+ increased efficiency.
+ """
+
+ 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,
+ use_relative_position=True,
+ use_causal_attention=False,
+ temporal_length=None,
+ use_fp16=False,
+ addition_attention=False,
+ temporal_selfatt_only=True,
+ image_cross_attention=False,
+ image_cross_attention_scale_learnable=False,
+ default_fs=4,
+ fs_condition=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
+ temporal_self_att_only = True
+ self.addition_attention = addition_attention
+ self.temporal_length = temporal_length
+ self.image_cross_attention = image_cross_attention
+ self.image_cross_attention_scale_learnable = image_cross_attention_scale_learnable
+ self.default_fs = default_fs
+ self.fs_condition = fs_condition
+
+ ## Time embedding blocks
+ self.time_embed = nn.Sequential(
+ linear(model_channels, time_embed_dim),
+ nn.SiLU(),
+ linear(time_embed_dim, time_embed_dim),
+ )
+ if fs_condition:
+ self.framestride_embed = nn.Sequential(
+ linear(model_channels, time_embed_dim),
+ nn.SiLU(),
+ linear(time_embed_dim, time_embed_dim),
+ )
+ nn.init.zeros_(self.framestride_embed[-1].weight)
+ nn.init.zeros_(self.framestride_embed[-1].bias)
+
+ ## Input Block
+ 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=False, 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,
+ video_length=temporal_length, image_cross_attention=self.image_cross_attention,
+ image_cross_attention_scale_learnable=self.image_cross_attention_scale_learnable,
+ )
+ )
+ if self.temporal_attention:
+ layers.append(
+ TemporalTransformer(ch, num_heads, dim_head,
+ depth=transformer_depth, context_dim=context_dim, use_linear=use_linear,
+ use_checkpoint=use_checkpoint, only_self_att=temporal_self_att_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, video_length=temporal_length,
+ image_cross_attention=self.image_cross_attention,image_cross_attention_scale_learnable=self.image_cross_attention_scale_learnable
+ )
+ ]
+ if self.temporal_attention:
+ layers.append(
+ TemporalTransformer(ch, num_heads, dim_head,
+ depth=transformer_depth, context_dim=context_dim, use_linear=use_linear,
+ use_checkpoint=use_checkpoint, only_self_att=temporal_self_att_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
+ )
+ )
+
+ ## Middle Block
+ self.middle_block = TimestepEmbedSequential(*layers)
+
+ ## Output Block
+ 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, video_length=temporal_length,
+ image_cross_attention=self.image_cross_attention,image_cross_attention_scale_learnable=self.image_cross_attention_scale_learnable
+ )
+ )
+ if self.temporal_attention:
+ layers.append(
+ TemporalTransformer(ch, num_heads, dim_head,
+ depth=transformer_depth, context_dim=context_dim, use_linear=use_linear,
+ use_checkpoint=use_checkpoint, only_self_att=temporal_self_att_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, fs=None, **kwargs):
+ b,_,t,_,_ = x.shape
+ t_emb = timestep_embedding(timesteps, self.model_channels, repeat_only=False).type(x.dtype)
+ emb = self.time_embed(t_emb)
+
+ ## repeat t times for context [(b t) 77 768] & time embedding
+ ## check if we use per-frame image conditioning
+ _, l_context, _ = context.shape
+ if l_context == 77 + t*16: ## !!! HARD CODE here
+ context_text, context_img = context[:,:77,:], context[:,77:,:]
+ context_text = context_text.repeat_interleave(repeats=t, dim=0)
+ context_img = rearrange(context_img, 'b (t l) c -> (b t) l c', t=t)
+ context = torch.cat([context_text, context_img], dim=1)
+ else:
+ 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')
+
+ ## combine emb
+ if self.fs_condition:
+ if fs is None:
+ fs = torch.tensor(
+ [self.default_fs] * b, dtype=torch.long, device=x.device)
+ fs_emb = timestep_embedding(fs, self.model_channels, repeat_only=False).type(x.dtype)
+ fs_embed = self.framestride_embed(fs_emb)
+ fs_embed = fs_embed.repeat_interleave(repeats=t, dim=0)
+ emb = emb + fs_embed
+
+ 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/lvdm/modules/x_transformer.py b/lvdm/modules/x_transformer.py
new file mode 100644
index 0000000000000000000000000000000000000000..5321012f860a8fb06850c1ddf495db934addecae
--- /dev/null
+++ b/lvdm/modules/x_transformer.py
@@ -0,0 +1,639 @@
+"""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
\ No newline at end of file
diff --git a/prompts/art.png b/prompts/art.png
new file mode 100644
index 0000000000000000000000000000000000000000..5b69f493c94b89450b6e8f626d6c08eb013eceac
Binary files /dev/null and b/prompts/art.png differ
diff --git a/prompts/bear.png b/prompts/bear.png
new file mode 100644
index 0000000000000000000000000000000000000000..35424acd32d4e8d91e253673e9a28645bde0dee3
Binary files /dev/null and b/prompts/bear.png differ
diff --git a/prompts/boy.png b/prompts/boy.png
new file mode 100644
index 0000000000000000000000000000000000000000..c337e2311442be27ecd887be3b1b439936216646
Binary files /dev/null and b/prompts/boy.png differ
diff --git a/prompts/dance1.jpeg b/prompts/dance1.jpeg
new file mode 100644
index 0000000000000000000000000000000000000000..8d5e21af912236669313cd61d3e6541d242206d6
Binary files /dev/null and b/prompts/dance1.jpeg differ
diff --git a/prompts/fire_and_beach.jpg b/prompts/fire_and_beach.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..f0caead29e5b3fabc70765ccce563093f6375ebd
Binary files /dev/null and b/prompts/fire_and_beach.jpg differ
diff --git a/prompts/girl2.jpeg b/prompts/girl2.jpeg
new file mode 100644
index 0000000000000000000000000000000000000000..1de1cb0681fec0216e519ed57b01c16a04169886
Binary files /dev/null and b/prompts/girl2.jpeg differ
diff --git a/prompts/girl3.jpeg b/prompts/girl3.jpeg
new file mode 100644
index 0000000000000000000000000000000000000000..b5ce4b233a316c25d4518ece50f6a40b885499d3
Binary files /dev/null and b/prompts/girl3.jpeg differ
diff --git a/prompts/guitar0.jpeg b/prompts/guitar0.jpeg
new file mode 100644
index 0000000000000000000000000000000000000000..fb8643aa4c4a9aa9ff248b7e7b8f1b75341d400b
Binary files /dev/null and b/prompts/guitar0.jpeg differ
diff --git a/prompts/test_prompts.txt b/prompts/test_prompts.txt
new file mode 100644
index 0000000000000000000000000000000000000000..792073e075abb2f56dbe9c85560d39cd6ab489f0
--- /dev/null
+++ b/prompts/test_prompts.txt
@@ -0,0 +1,8 @@
+man fishing in a boat at sunset
+a brown bear is walking in a zoo enclosure, some rocks around
+boy walking on the street
+two people dancing
+a campfire on the beach and the ocean waves in the background
+girl with fires and smoke on his head
+girl talking and blinking
+bear playing guitar happily, snowing
\ No newline at end of file
diff --git a/requirements.txt b/requirements.txt
new file mode 100644
index 0000000000000000000000000000000000000000..071c197eb8e9868367cf9e6112e2f3aa5b674550
--- /dev/null
+++ b/requirements.txt
@@ -0,0 +1,23 @@
+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==1.8.3
+PyYAML==6.0
+setuptools==65.6.3
+torch==2.0.0
+torchvision
+tqdm==4.65.0
+transformers==4.25.1
+moviepy
+av
+xformers
+gradio
+timm
+scikit-learn
+open_clip_torch==2.22.0
+kornia
\ No newline at end of file
diff --git a/scripts/evaluation/__pycache__/funcs.cpython-39.pyc b/scripts/evaluation/__pycache__/funcs.cpython-39.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..5ddc6deec9c795beb9816694c9dded8b991a6932
Binary files /dev/null and b/scripts/evaluation/__pycache__/funcs.cpython-39.pyc differ
diff --git a/scripts/evaluation/ddp_wrapper.py b/scripts/evaluation/ddp_wrapper.py
new file mode 100644
index 0000000000000000000000000000000000000000..e5187ab2c2a444f40c6c4fec5438a4f6a428b943
--- /dev/null
+++ b/scripts/evaluation/ddp_wrapper.py
@@ -0,0 +1,47 @@
+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()
+
+ inference_args.savedir = inference_args.savedir+str('_seed')+str(inference_args.seed)
+ 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/scripts/evaluation/funcs.py b/scripts/evaluation/funcs.py
new file mode 100644
index 0000000000000000000000000000000000000000..416fe355b7701d62ff773703a53b0f040cee3c9e
--- /dev/null
+++ b/scripts/evaluation/funcs.py
@@ -0,0 +1,205 @@
+import os, sys, glob
+import numpy as np
+from collections import OrderedDict
+from decord import VideoReader, cpu
+import cv2
+
+import torch
+import torchvision
+sys.path.insert(1, os.path.join(sys.path[0], '..', '..'))
+from lvdm.models.samplers.ddim import DDIMSampler
+from einops import rearrange
+
+
+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, **kwargs):
+ ddim_sampler = DDIMSampler(model)
+ uncond_type = model.uncond_type
+ batch_size = noise_shape[0]
+ fs = cond["fs"]
+ del cond["fs"]
+ ## construct unconditional guidance
+ if cfg_scale != 1.0:
+ if uncond_type == "empty_seq":
+ prompts = batch_size * [""]
+ #prompts = N * T * [""] ## if is_imgbatch=True
+ 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.embedder(uc_img)
+ uc_img = model.image_proj_model(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,
+ 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,
+ fs=fs,
+ **kwargs
+ )
+ ## reconstruct from latent to pixel space
+ batch_images = model.decode_first_stage(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_latent_z(model, videos):
+ b, c, t, h, w = videos.shape
+ x = rearrange(videos, 'b c t h w -> (b t) c h w')
+ z = model.encode_first_stage(x)
+ z = rearrange(z, '(b t) c h w -> b c t h w', b=b, t=t)
+ return z
\ No newline at end of file
diff --git a/scripts/evaluation/inference.py b/scripts/evaluation/inference.py
new file mode 100644
index 0000000000000000000000000000000000000000..4fd463d35f0e57537f7199a8e8ed751901bc7c36
--- /dev/null
+++ b/scripts/evaluation/inference.py
@@ -0,0 +1,329 @@
+import argparse, os, sys, glob
+import datetime, time
+from omegaconf import OmegaConf
+from tqdm import tqdm
+from einops import rearrange, repeat
+from collections import OrderedDict
+
+import torch
+import torchvision
+import torchvision.transforms as transforms
+from pytorch_lightning import seed_everything
+from PIL import Image
+sys.path.insert(1, os.path.join(sys.path[0], '..', '..'))
+from lvdm.models.samplers.ddim import DDIMSampler
+from lvdm.models.samplers.ddim_multiplecond import DDIMSampler as DDIMSampler_multicond
+from utils.utils import instantiate_from_config
+
+
+def get_filelist(data_dir, postfixes):
+ patterns = [os.path.join(data_dir, f"*.{postfix}") for postfix in postfixes]
+ file_list = []
+ for pattern in patterns:
+ file_list.extend(glob.glob(pattern))
+ file_list.sort()
+ return file_list
+
+def load_model_checkpoint(model, ckpt):
+ state_dict = torch.load(ckpt, map_location="cpu")
+ if "state_dict" in list(state_dict.keys()):
+ state_dict = state_dict["state_dict"]
+ model.load_state_dict(state_dict, strict=True)
+ else:
+ # 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)
+ 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_data_prompts(data_dir, video_size=(256,256), video_frames=16, gfi=False):
+ transform = transforms.Compose([
+ transforms.Resize(min(video_size)),
+ transforms.CenterCrop(video_size),
+ transforms.ToTensor(),
+ transforms.Normalize(mean=(0.5, 0.5, 0.5), std=(0.5, 0.5, 0.5))])
+ ## load prompts
+ prompt_file = get_filelist(data_dir, ['txt'])
+ assert len(prompt_file) > 0, "Error: found NO prompt file!"
+ ###### default prompt
+ default_idx = 0
+ default_idx = min(default_idx, len(prompt_file)-1)
+ if len(prompt_file) > 1:
+ print(f"Warning: multiple prompt files exist. The one {os.path.split(prompt_file[default_idx])[1]} is used.")
+ ## only use the first one (sorted by name) if multiple exist
+
+ ## load video
+ file_list = get_filelist(data_dir, ['jpg', 'png', 'jpeg', 'JPEG', 'PNG'])
+ # assert len(file_list) == n_samples, "Error: data and prompts are NOT paired!"
+ data_list = []
+ filename_list = []
+ prompt_list = load_prompts(prompt_file[default_idx])
+ n_samples = len(prompt_list)
+ for idx in range(n_samples):
+ image = Image.open(file_list[idx]).convert('RGB')
+ image_tensor = transform(image).unsqueeze(1) # [c,1,h,w]
+ frame_tensor = repeat(image_tensor, 'c t h w -> c (repeat t) h w', repeat=video_frames)
+
+ data_list.append(frame_tensor)
+ _, filename = os.path.split(file_list[idx])
+ filename_list.append(filename)
+
+ return filename_list, data_list, prompt_list
+
+
+def save_results(prompt, samples, filename, fakedir, fps=8, loop=False):
+ filename = filename.split('.')[0]+'.mp4'
+ prompt = prompt[0] if isinstance(prompt, list) else prompt
+
+ ## save video
+ videos = [samples]
+ savedirs = [fakedir]
+ for idx, video in enumerate(videos):
+ if video is None:
+ continue
+ # b,c,t,h,w
+ video = video.detach().cpu()
+ video = torch.clamp(video.float(), -1., 1.)
+ n = video.shape[0]
+ video = video.permute(2, 0, 1, 3, 4) # t,n,c,h,w
+ if loop:
+ video = video[:-1,...]
+
+ frame_grids = [torchvision.utils.make_grid(framesheet, nrow=int(n), padding=0) for framesheet in video] #[3, 1*h, n*w]
+ grid = torch.stack(frame_grids, dim=0) # stack in temporal dim [t, 3, h, n*w]
+ grid = (grid + 1.0) / 2.0
+ grid = (grid * 255).to(torch.uint8).permute(0, 2, 3, 1)
+ path = os.path.join(savedirs[idx], filename)
+ torchvision.io.write_video(path, grid, fps=fps, video_codec='h264', options={'crf': '10'}) ## crf indicates the quality
+
+
+def save_results_seperate(prompt, samples, filename, fakedir, fps=10, loop=False):
+ prompt = prompt[0] if isinstance(prompt, list) else prompt
+
+ ## save video
+ videos = [samples]
+ savedirs = [fakedir]
+ for idx, video in enumerate(videos):
+ if video is None:
+ continue
+ # b,c,t,h,w
+ video = video.detach().cpu()
+ if loop: # remove the last frame
+ video = video[:,:,:-1,...]
+ video = torch.clamp(video.float(), -1., 1.)
+ n = video.shape[0]
+ for i in range(n):
+ grid = video[i,...]
+ grid = (grid + 1.0) / 2.0
+ grid = (grid * 255).to(torch.uint8).permute(1, 2, 3, 0) #thwc
+ path = os.path.join(savedirs[idx].replace('samples', 'samples_separate'), f'{filename.split(".")[0]}_sample{i}.mp4')
+ torchvision.io.write_video(path, grid, fps=fps, video_codec='h264', options={'crf': '10'})
+
+def get_latent_z(model, videos):
+ b, c, t, h, w = videos.shape
+ x = rearrange(videos, 'b c t h w -> (b t) c h w')
+ z = model.encode_first_stage(x)
+ z = rearrange(z, '(b t) c h w -> b c t h w', b=b, t=t)
+ return z
+
+
+def image_guided_synthesis(model, prompts, videos, noise_shape, n_samples=1, ddim_steps=50, ddim_eta=1., \
+ unconditional_guidance_scale=1.0, cfg_img=None, fs=None, text_input=False, multiple_cond_cfg=False, loop=False, gfi=False, **kwargs):
+ ddim_sampler = DDIMSampler(model) if not multiple_cond_cfg else DDIMSampler_multicond(model)
+ batch_size = noise_shape[0]
+ fs = torch.tensor([fs] * batch_size, dtype=torch.long, device=model.device)
+
+ if not text_input:
+ prompts = [""]*batch_size
+
+ img = videos[:,:,0] #bchw
+ img_emb = model.embedder(img) ## blc
+ img_emb = model.image_proj_model(img_emb)
+
+ cond_emb = model.get_learned_conditioning(prompts)
+ cond = {"c_crossattn": [torch.cat([cond_emb,img_emb], dim=1)]}
+ if model.model.conditioning_key == 'hybrid':
+ z = get_latent_z(model, videos) # b c t h w
+ if loop or gfi:
+ img_cat_cond = torch.zeros_like(z)
+ img_cat_cond[:,:,0,:,:] = z[:,:,0,:,:]
+ img_cat_cond[:,:,-1,:,:] = z[:,:,-1,:,:]
+ else:
+ img_cat_cond = z[:,:,:1,:,:]
+ img_cat_cond = repeat(img_cat_cond, 'b c t h w -> b c (repeat t) h w', repeat=z.shape[2])
+ cond["c_concat"] = [img_cat_cond] # b c 1 h w
+
+ if unconditional_guidance_scale != 1.0:
+ if model.uncond_type == "empty_seq":
+ prompts = batch_size * [""]
+ uc_emb = model.get_learned_conditioning(prompts)
+ elif model.uncond_type == "zero_embed":
+ uc_emb = torch.zeros_like(cond_emb)
+ uc_img_emb = model.embedder(torch.zeros_like(img)) ## b l c
+ uc_img_emb = model.image_proj_model(uc_img_emb)
+ uc = {"c_crossattn": [torch.cat([uc_emb,uc_img_emb],dim=1)]}
+ if model.model.conditioning_key == 'hybrid':
+ uc["c_concat"] = [img_cat_cond]
+ else:
+ uc = None
+
+ ## we need one more unconditioning image=yes, text=""
+ if multiple_cond_cfg and cfg_img != 1.0:
+ uc_2 = {"c_crossattn": [torch.cat([uc_emb,img_emb],dim=1)]}
+ if model.model.conditioning_key == 'hybrid':
+ uc_2["c_concat"] = [img_cat_cond]
+ kwargs.update({"unconditional_conditioning_img_nonetext": uc_2})
+ else:
+ kwargs.update({"unconditional_conditioning_img_nonetext": None})
+
+ z0 = None
+ cond_mask = None
+
+ batch_variants = []
+ for _ in range(n_samples):
+
+ if z0 is not None:
+ cond_z0 = z0.clone()
+ kwargs.update({"clean_cond": True})
+ else:
+ cond_z0 = None
+ if ddim_sampler is not None:
+
+ samples, _ = ddim_sampler.sample(S=ddim_steps,
+ conditioning=cond,
+ batch_size=batch_size,
+ shape=noise_shape[1:],
+ verbose=False,
+ unconditional_guidance_scale=unconditional_guidance_scale,
+ unconditional_conditioning=uc,
+ eta=ddim_eta,
+ cfg_img=cfg_img,
+ mask=cond_mask,
+ x0=cond_z0,
+ fs=fs,
+ **kwargs
+ )
+
+ ## reconstruct from latent to pixel space
+ batch_images = model.decode_first_stage(samples)
+ batch_variants.append(batch_images)
+ ## variants, batch, c, t, h, w
+ batch_variants = torch.stack(batch_variants)
+ return batch_variants.permute(1, 0, 2, 3, 4, 5)
+
+
+def run_inference(args, gpu_num, gpu_no):
+ ## model config
+ config = OmegaConf.load(args.config)
+ model_config = config.pop("model", OmegaConf.create())
+
+ ## set use_checkpoint as False as when using deepspeed, it encounters an error "deepspeed backend not set"
+ model_config['params']['unet_config']['params']['use_checkpoint'] = False
+ model = instantiate_from_config(model_config)
+ model = model.cuda(gpu_no)
+
+ assert os.path.exists(args.ckpt_path), "Error: checkpoint Not Found!"
+ model = load_model_checkpoint(model, args.ckpt_path)
+ model.eval()
+
+ ## run over data
+ assert (args.height % 16 == 0) and (args.width % 16 == 0), "Error: image size [h,w] should be multiples of 16!"
+ assert args.bs == 1, "Current implementation only support [batch size = 1]!"
+ ## latent noise shape
+ h, w = args.height // 8, args.width // 8
+ channels = model.model.diffusion_model.out_channels
+ n_frames = args.video_length
+ print(f'Inference with {n_frames} frames')
+ noise_shape = [args.bs, channels, n_frames, h, w]
+
+ fakedir = os.path.join(args.savedir, "samples")
+ fakedir_separate = os.path.join(args.savedir, "samples_separate")
+
+ # os.makedirs(fakedir, exist_ok=True)
+ os.makedirs(fakedir_separate, exist_ok=True)
+
+ ## prompt file setting
+ assert os.path.exists(args.prompt_dir), "Error: prompt file Not Found!"
+ filename_list, data_list, prompt_list = load_data_prompts(args.prompt_dir, video_size=(args.height, args.width), video_frames=n_frames, gfi=args.gfi)
+ num_samples = len(prompt_list)
+ samples_split = num_samples // gpu_num
+ print('Prompts testing [rank:%d] %d/%d samples loaded.'%(gpu_no, samples_split, num_samples))
+ #indices = random.choices(list(range(0, num_samples)), k=samples_per_device)
+ indices = list(range(samples_split*gpu_no, samples_split*(gpu_no+1)))
+ prompt_list_rank = [prompt_list[i] for i in indices]
+ data_list_rank = [data_list[i] for i in indices]
+ filename_list_rank = [filename_list[i] for i in indices]
+
+ start = time.time()
+ with torch.no_grad(), torch.cuda.amp.autocast():
+ for idx, indice in tqdm(enumerate(range(0, len(prompt_list_rank), args.bs)), desc='Sample Batch'):
+ prompts = prompt_list_rank[indice:indice+args.bs]
+ videos = data_list_rank[indice:indice+args.bs]
+ filenames = filename_list_rank[indice:indice+args.bs]
+ if isinstance(videos, list):
+ videos = torch.stack(videos, dim=0).to("cuda")
+ else:
+ videos = videos.unsqueeze(0).to("cuda")
+
+ batch_samples = image_guided_synthesis(model, prompts, videos, noise_shape, args.n_samples, args.ddim_steps, args.ddim_eta, \
+ args.unconditional_guidance_scale, args.cfg_img, args.frame_stride, args.text_input, args.multiple_cond_cfg, args.loop, args.gfi)
+
+ ## save each example individually
+ for nn, samples in enumerate(batch_samples):
+ ## samples : [n_samples,c,t,h,w]
+ prompt = prompts[nn]
+ filename = filenames[nn]
+ # save_results(prompt, samples, filename, fakedir, fps=8, loop=args.loop)
+ save_results_seperate(prompt, samples, filename, fakedir, fps=8, loop=args.loop)
+
+ print(f"Saved in {args.savedir}. Time used: {(time.time() - start):.2f} seconds")
+
+
+def get_parser():
+ parser = argparse.ArgumentParser()
+ parser.add_argument("--savedir", type=str, default=None, help="results saving path")
+ parser.add_argument("--ckpt_path", type=str, default=None, help="checkpoint path")
+ parser.add_argument("--config", type=str, help="config (yaml) path")
+ parser.add_argument("--prompt_dir", type=str, default=None, help="a data dir containing videos and prompts")
+ 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("--bs", type=int, default=1, help="batch size for inference, should be one")
+ 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("--frame_stride", type=int, default=3, choices=[1, 2, 3, 4, 5, 6], help="frame stride control for results, smaller value->smaller motion magnitude and more stable, and vice versa")
+ parser.add_argument("--unconditional_guidance_scale", type=float, default=1.0, help="prompt classifier-free guidance")
+ parser.add_argument("--seed", type=int, default=123, help="seed for seed_everything")
+ parser.add_argument("--video_length", type=int, default=16, help="inference video length")
+ parser.add_argument("--negative_prompt", action='store_true', default=False, help="negative prompt")
+ parser.add_argument("--text_input", action='store_true', default=False, help="input text to I2V model or not")
+ parser.add_argument("--multiple_cond_cfg", action='store_true', default=False, help="use multi-condition cfg or not")
+ parser.add_argument("--cfg_img", type=float, default=None, help="guidance scale for image conditioning")
+
+ ## currently not support looping video and generative frame interpolation
+ parser.add_argument("--loop", action='store_true', default=False, help="generate looping videos or not")
+ parser.add_argument("--gfi", action='store_true', default=False, help="generate generative frame interpolation (gfi) or not")
+ return parser
+
+
+if __name__ == '__main__':
+ now = datetime.datetime.now().strftime("%Y-%m-%d-%H-%M-%S")
+ print("@CoVideoGen cond-Inference: %s"%now)
+ parser = get_parser()
+ args = parser.parse_args()
+
+ seed_everything(args.seed)
+ rank, gpu_num = 0, 1
+ run_inference(args, gpu_num, rank)
\ No newline at end of file
diff --git a/scripts/gradio/__pycache__/i2v_test.cpython-39.pyc b/scripts/gradio/__pycache__/i2v_test.cpython-39.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..5c45f203dad5483362fb23d0d32d32f53e3eec06
Binary files /dev/null and b/scripts/gradio/__pycache__/i2v_test.cpython-39.pyc differ
diff --git a/scripts/gradio/i2v_test.py b/scripts/gradio/i2v_test.py
new file mode 100644
index 0000000000000000000000000000000000000000..ccaafb7b340af612f68d0ee7ef9b48b40ca5ea2b
--- /dev/null
+++ b/scripts/gradio/i2v_test.py
@@ -0,0 +1,102 @@
+import os
+import time
+from omegaconf import OmegaConf
+import torch
+from scripts.evaluation.funcs import load_model_checkpoint, save_videos, batch_ddim_sampling, get_latent_z
+from utils.utils import instantiate_from_config
+from huggingface_hub import hf_hub_download
+from einops import repeat
+import torchvision.transforms as transforms
+from pytorch_lightning import seed_everything
+
+
+class Image2Video():
+ def __init__(self,result_dir='./tmp/',gpu_num=1) -> None:
+ self.download_model()
+ self.result_dir = result_dir
+ if not os.path.exists(self.result_dir):
+ os.mkdir(self.result_dir)
+ ckpt_path='checkpoints/dynamicrafter_256_v1/model.ckpt'
+ config_file='configs/inference_256_v1.0.yaml'
+ config = OmegaConf.load(config_file)
+ model_config = config.pop("model", OmegaConf.create())
+ model_config['params']['unet_config']['params']['use_checkpoint']=False
+ model_list = []
+ for gpu_id in range(gpu_num):
+ model = instantiate_from_config(model_config)
+ # model = model.cuda(gpu_id)
+ assert os.path.exists(ckpt_path), "Error: checkpoint Not Found!"
+ model = load_model_checkpoint(model, ckpt_path)
+ model.eval()
+ model_list.append(model)
+ self.model_list = model_list
+ self.save_fps = 8
+
+ def get_image(self, image, prompt, steps=50, cfg_scale=7.5, eta=1.0, fs=3, seed=123):
+ seed_everything(seed)
+ transform = transforms.Compose([
+ transforms.Resize(256),
+ transforms.CenterCrop(256),
+ ])
+ torch.cuda.empty_cache()
+ print('start:', prompt, time.strftime('%Y-%m-%d %H:%M:%S',time.localtime(time.time())))
+ start = time.time()
+ gpu_id=0
+ if steps > 60:
+ steps = 60
+ model = self.model_list[gpu_id]
+ model = model.cuda()
+ batch_size=1
+ channels = model.model.diffusion_model.out_channels
+ frames = model.temporal_length
+ h, w = 256 // 8, 256 // 8
+ noise_shape = [batch_size, channels, frames, h, w]
+
+ # text cond
+ text_emb = model.get_learned_conditioning([prompt])
+
+ # img cond
+ img_tensor = torch.from_numpy(image).permute(2, 0, 1).float().to(model.device)
+ img_tensor = (img_tensor / 255. - 0.5) * 2
+
+ image_tensor_resized = transform(img_tensor) #3,256,256
+ videos = image_tensor_resized.unsqueeze(0) # bchw
+
+ z = get_latent_z(model, videos.unsqueeze(2)) #bc,1,hw
+
+ img_tensor_repeat = repeat(z, 'b c t h w -> b c (repeat t) h w', repeat=frames)
+
+ cond_images = model.embedder(img_tensor.unsqueeze(0)) ## blc
+ img_emb = model.image_proj_model(cond_images)
+
+ imtext_cond = torch.cat([text_emb, img_emb], dim=1)
+
+ fs = torch.tensor([fs], dtype=torch.long, device=model.device)
+ cond = {"c_crossattn": [imtext_cond], "fs": fs, "c_concat": [img_tensor_repeat]}
+
+ ## inference
+ batch_samples = batch_ddim_sampling(model, cond, noise_shape, n_samples=1, ddim_steps=steps, ddim_eta=eta, cfg_scale=cfg_scale)
+ ## b,samples,c,t,h,w
+ prompt_str = prompt.replace("/", "_slash_") if "/" in prompt else prompt
+ prompt_str = prompt_str.replace(" ", "_") if " " in prompt else prompt_str
+ prompt_str=prompt_str[:40]
+
+ save_videos(batch_samples, self.result_dir, filenames=[prompt_str], fps=self.save_fps)
+ print(f"Saved in {prompt_str}. Time used: {(time.time() - start):.2f} seconds")
+ model = model.cpu()
+ return os.path.join(self.result_dir, f"{prompt_str}.mp4")
+
+ def download_model(self):
+ REPO_ID = 'Doubiiu/DynamiCrafter'
+ filename_list = ['model.ckpt']
+ if not os.path.exists('./checkpoints/dynamicrafter_256_v1/'):
+ os.makedirs('./dynamicrafter_256_v1/')
+ for filename in filename_list:
+ local_file = os.path.join('./checkpoints/dynamicrafter_256_v1/', filename)
+ if not os.path.exists(local_file):
+ hf_hub_download(repo_id=REPO_ID, filename=filename, local_dir='./checkpoints/dynamicrafter_256_v1/', local_dir_use_symlinks=False)
+
+if __name__ == '__main__':
+ i2v = Image2Video()
+ video_path = i2v.get_image('prompts/art.png','man fishing in a boat at sunset')
+ print('done', video_path)
\ No newline at end of file
diff --git a/scripts/run.sh b/scripts/run.sh
new file mode 100644
index 0000000000000000000000000000000000000000..73550d073abd546f8d5f7939c7bcced829a70636
--- /dev/null
+++ b/scripts/run.sh
@@ -0,0 +1,25 @@
+name="dynamicrafter_256"
+
+ckpt='checkpoints/dynamicrafter_256_v1/model.ckpt'
+config='configs/inference_256_v1.0.yaml'
+
+prompt_dir="prompts/"
+res_dir="results"
+
+CUDA_VISIBLE_DEVICES=0 python3 scripts/evaluation/inference.py \
+--seed 123 \
+--ckpt_path $ckpt \
+--config $config \
+--savedir $res_dir/$name \
+--n_samples 1 \
+--bs 1 --height 256 --width 256 \
+--unconditional_guidance_scale 7.5 \
+--ddim_steps 50 \
+--ddim_eta 1.0 \
+--prompt_dir $prompt_dir \
+--text_input \
+--video_length 16 \
+--frame_stride 3
+
+## multi-cond CFG: the <unconditional_guidance_scale> is s_txt, <cfg_img> is s_img
+#--multiple_cond_cfg --cfg_img 7.5
\ No newline at end of file
diff --git a/utils/__pycache__/utils.cpython-39.pyc b/utils/__pycache__/utils.cpython-39.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..b1e28105d38250b218e943a22cc16351637ff045
Binary files /dev/null and b/utils/__pycache__/utils.cpython-39.pyc differ
diff --git a/utils/utils.py b/utils/utils.py
new file mode 100644
index 0000000000000000000000000000000000000000..ef116e56f6f61dce0cf4942986ac659bf93fcd24
--- /dev/null
+++ b/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