erge branch 'main' into text2igm

README.md

@@ -16,10 +16,11 @@ You need a webcam to run this demo. 🤗
 
 ## Running Locally
 
-You need CUDA and Python 3.10 or a Mac with an M1/M2/M3 chip
-`TIMEOUT`: limit user session timeout
-`SAFETY_CHECKER`: disabled if you want NSFW filter off  
-`MAX_QUEUE_SIZE`: limit number of users on current app instance
+You need CUDA and Python 3.10, Mac with an M1/M2/M3 chip or Intel Arc GPU
+
+`TIMEOUT`: limit user session timeout  
+`SAFETY_CHECKER`: disabled if you want NSFW filter off   
+`MAX_QUEUE_SIZE`: limit number of users on current app instance  
 
 ### image to image
 

app-img2img.py

@@ -9,9 +9,14 @@ from fastapi.middleware.cors import CORSMiddleware
 from fastapi.responses import StreamingResponse, JSONResponse
 from fastapi.staticfiles import StaticFiles
 
-from diffusers import DiffusionPipeline, AutoencoderTiny
+from diffusers import AutoPipelineForImage2Image, AutoencoderTiny
 from compel import Compel
 import torch
+
+try:
+    import intel_extension_for_pytorch as ipex
+except:
+    pass
 from PIL import Image
 import numpy as np
 import gradio as gr
@@ -27,11 +32,14 @@ SAFETY_CHECKER = os.environ.get("SAFETY_CHECKER", None)
 WIDTH = 512
 HEIGHT = 512
 # disable tiny autoencoder for better quality speed tradeoff
-USE_TINY_AUTOENCODER=True
+USE_TINY_AUTOENCODER = True
 
 # check if MPS is available OSX only M1/M2/M3 chips
 mps_available = hasattr(torch.backends, "mps") and torch.backends.mps.is_available()
-device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+xpu_available = hasattr(torch, "xpu") and torch.xpu.is_available()
+device = torch.device(
+    "cuda" if torch.cuda.is_available() else "xpu" if xpu_available else "cpu"
+)
 torch_device = device
 
 # change to torch.float16 to save GPU memory
@@ -48,17 +56,13 @@ if mps_available:
     torch_dtype = torch.float32
 
 if SAFETY_CHECKER == "True":
-    pipe = DiffusionPipeline.from_pretrained(
+    pipe = AutoPipelineForImage2Image.from_pretrained(
         "SimianLuo/LCM_Dreamshaper_v7",
-        custom_pipeline="latent_consistency_img2img.py",
-        custom_revision="main",
     )
 else:
-    pipe = DiffusionPipeline.from_pretrained(
+    pipe = AutoPipelineForImage2Image.from_pretrained(
         "SimianLuo/LCM_Dreamshaper_v7",
         safety_checker=None,
-        custom_pipeline="latent_consistency_img2img.py",
-        custom_revision="main",
     )
 
 if USE_TINY_AUTOENCODER:
@@ -66,13 +70,13 @@ if USE_TINY_AUTOENCODER:
         "madebyollin/taesd", torch_dtype=torch_dtype, use_safetensors=True
     )
 pipe.set_progress_bar_config(disable=True)
-pipe.to(torch_device=torch_device, torch_dtype=torch_dtype).to(device)
+pipe.to(device=torch_device, dtype=torch_dtype).to(device)
 pipe.unet.to(memory_format=torch.channels_last)
 
 if psutil.virtual_memory().total < 64 * 1024**3:
     pipe.enable_attention_slicing()
 
-if not mps_available:
+if not mps_available and not xpu_available:
     pipe.unet = torch.compile(pipe.unet, mode="reduce-overhead", fullgraph=True)
     pipe(prompt="warmup", image=[Image.new("RGB", (512, 512))])
 
@@ -93,7 +97,9 @@ class InputParams(BaseModel):
     height: int = HEIGHT
 
 
-def predict(input_image: Image.Image, params: InputParams, prompt_embeds: torch.Tensor = None):
+def predict(
+    input_image: Image.Image, params: InputParams, prompt_embeds: torch.Tensor = None
+):
     generator = torch.manual_seed(params.seed)
     # Can be set to 1~50 steps. LCM support fast inference even <= 4 steps. Recommend: 1~8 steps.
     num_inference_steps = 3
@@ -106,7 +112,7 @@ def predict(input_image: Image.Image, params: InputParams, prompt_embeds: torch.
         guidance_scale=params.guidance_scale,
         width=params.width,
         height=params.height,
-        lcm_origin_steps=50,
+        original_inference_steps=50,
         output_type="pil",
     )
     nsfw_content_detected = (
@@ -176,6 +182,7 @@ async def stream(user_id: uuid.UUID):
     try:
         user_queue = user_queue_map[uid]
         queue = user_queue["queue"]
+
         async def generate():
             last_prompt: str = None
             prompt_embeds: torch.Tensor = None

app-txt2img.py

@@ -12,6 +12,11 @@ from fastapi.staticfiles import StaticFiles
 from diffusers import DiffusionPipeline, AutoencoderTiny
 from compel import Compel
 import torch
+
+try:
+    import intel_extension_for_pytorch as ipex
+except:
+    pass
 from PIL import Image
 import numpy as np
 import gradio as gr
@@ -25,14 +30,17 @@ import psutil
 MAX_QUEUE_SIZE = int(os.environ.get("MAX_QUEUE_SIZE", 0))
 TIMEOUT = float(os.environ.get("TIMEOUT", 0))
 SAFETY_CHECKER = os.environ.get("SAFETY_CHECKER", None)
-WIDTH = 512
-HEIGHT = 512
+WIDTH = 768
+HEIGHT = 768
 # disable tiny autoencoder for better quality speed tradeoff
-USE_TINY_AUTOENCODER=True
+USE_TINY_AUTOENCODER = False
 
 # check if MPS is available OSX only M1/M2/M3 chips
 mps_available = hasattr(torch.backends, "mps") and torch.backends.mps.is_available()
-device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+xpu_available = hasattr(torch, "xpu") and torch.xpu.is_available()
+device = torch.device(
+    "cuda" if torch.cuda.is_available() else "xpu" if xpu_available else "cpu"
+)
 torch_device = device
 # change to torch.float16 to save GPU memory
 torch_dtype = torch.float32
@@ -50,29 +58,25 @@ if mps_available:
 if SAFETY_CHECKER == "True":
     pipe = DiffusionPipeline.from_pretrained(
         "SimianLuo/LCM_Dreamshaper_v7",
-        custom_pipeline="latent_consistency_txt2img.py",
-        custom_revision="main",
     )
 else:
     pipe = DiffusionPipeline.from_pretrained(
         "SimianLuo/LCM_Dreamshaper_v7",
         safety_checker=None,
-        custom_pipeline="latent_consistency_txt2img.py",
-        custom_revision="main",
     )
 if USE_TINY_AUTOENCODER:
     pipe.vae = AutoencoderTiny.from_pretrained(
         "madebyollin/taesd", torch_dtype=torch_dtype, use_safetensors=True
     )
 pipe.set_progress_bar_config(disable=True)
-pipe.to(torch_device=torch_device, torch_dtype=torch_dtype).to(device)
+pipe.to(device=torch_device, dtype=torch_dtype).to(device)
 pipe.unet.to(memory_format=torch.channels_last)
 
 # check if computer has less than 64GB of RAM using sys or os
 if psutil.virtual_memory().total < 64 * 1024**3:
     pipe.enable_attention_slicing()
 
-if not mps_available:
+if not mps_available and not xpu_available:
     pipe.unet = torch.compile(pipe.unet, mode="reduce-overhead", fullgraph=True)
     pipe(prompt="warmup", num_inference_steps=1, guidance_scale=8.0)
 
@@ -83,6 +87,7 @@ compel_proc = Compel(
 )
 user_queue_map = {}
 
+
 class InputParams(BaseModel):
     prompt: str
     seed: int = 2159232
@@ -90,6 +95,7 @@ class InputParams(BaseModel):
     width: int = WIDTH
     height: int = HEIGHT
 
+
 def predict(params: InputParams):
     generator = torch.manual_seed(params.seed)
     prompt_embeds = compel_proc(params.prompt)
@@ -102,7 +108,7 @@ def predict(params: InputParams):
         guidance_scale=params.guidance_scale,
         width=params.width,
         height=params.height,
-        lcm_origin_steps=50,
+        original_inference_steps=50,
         output_type="pil",
     )
     nsfw_content_detected = (
@@ -124,6 +130,7 @@ app.add_middleware(
     allow_headers=["*"],
 )
 
+
 @app.websocket("/ws")
 async def websocket_endpoint(websocket: WebSocket):
     await websocket.accept()

img2img/index.html

@@ -257,7 +257,7 @@
                     <output class="text-xs w-[50px] text-center font-light px-1 py-1 border border-gray-700 rounded-md">
                         8.0</output>
                     <label class="text-sm font-medium" for="strength">Strength</label>
-                    <input type="range" id="strength" name="strength" min="0.20" max="1" step="0.001" value="0.50"
+                    <input type="range" id="strength" name="strength" min="0.02" max="1" step="0.001" value="0.50"
                         oninput="this.nextElementSibling.value = Number(this.value).toFixed(2)">
                     <output class="text-xs w-[50px] text-center font-light px-1 py-1 border border-gray-700 rounded-md">
                         0.5</output>

latent_consistency_img2img.py

@@ -1,934 +0,0 @@
-# Copyright 2023 Stanford University Team and The HuggingFace Team. All rights reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-# DISCLAIMER: This code is strongly influenced by https://github.com/pesser/pytorch_diffusion
-# and https://github.com/hojonathanho/diffusion
-
-import math
-from dataclasses import dataclass
-from typing import Any, Dict, List, Optional, Tuple, Union
-
-import numpy as np
-import PIL.Image
-import torch
-from transformers import CLIPImageProcessor, CLIPTextModel, CLIPTokenizer
-
-from diffusers import (
-    AutoencoderTiny,
-    AutoencoderKL,
-    ConfigMixin,
-    DiffusionPipeline,
-    SchedulerMixin,
-    UNet2DConditionModel,
-    logging,
-)
-from diffusers.configuration_utils import register_to_config
-from diffusers.image_processor import PipelineImageInput, VaeImageProcessor
-from diffusers.pipelines.stable_diffusion import StableDiffusionPipelineOutput
-from diffusers.pipelines.stable_diffusion.safety_checker import (
-    StableDiffusionSafetyChecker,
-)
-from diffusers.utils import BaseOutput
-from diffusers.utils.torch_utils import randn_tensor
-
-
-logger = logging.get_logger(__name__)  # pylint: disable=invalid-name
-
-
-class LatentConsistencyModelImg2ImgPipeline(DiffusionPipeline):
-    _optional_components = ["scheduler"]
-
-    def __init__(
-        self,
-        vae: AutoencoderKL,
-        text_encoder: CLIPTextModel,
-        tokenizer: CLIPTokenizer,
-        unet: UNet2DConditionModel,
-        scheduler: "LCMSchedulerWithTimestamp",
-        safety_checker: StableDiffusionSafetyChecker,
-        feature_extractor: CLIPImageProcessor,
-        requires_safety_checker: bool = True,
-    ):
-        super().__init__()
-
-        scheduler = (
-            scheduler
-            if scheduler is not None
-            else LCMSchedulerWithTimestamp(
-                beta_start=0.00085,
-                beta_end=0.0120,
-                beta_schedule="scaled_linear",
-                prediction_type="epsilon",
-            )
-        )
-
-        self.register_modules(
-            vae=vae,
-            text_encoder=text_encoder,
-            tokenizer=tokenizer,
-            unet=unet,
-            scheduler=scheduler,
-            safety_checker=safety_checker,
-            feature_extractor=feature_extractor,
-        )
-        self.vae_scale_factor = 2 ** (len(self.vae.config.block_out_channels) - 1)
-        self.image_processor = VaeImageProcessor(vae_scale_factor=self.vae_scale_factor)
-
-    def _encode_prompt(
-        self,
-        prompt,
-        device,
-        num_images_per_prompt,
-        prompt_embeds: None,
-    ):
-        r"""
-        Encodes the prompt into text encoder hidden states.
-        Args:
-            prompt (`str` or `List[str]`, *optional*):
-                prompt to be encoded
-            device: (`torch.device`):
-                torch device
-            num_images_per_prompt (`int`):
-                number of images that should be generated per prompt
-            prompt_embeds (`torch.FloatTensor`, *optional*):
-                Pre-generated text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not
-                provided, text embeddings will be generated from `prompt` input argument.
-        """
-
-        if prompt is not None and isinstance(prompt, str):
-            pass
-        elif prompt is not None and isinstance(prompt, list):
-            len(prompt)
-        else:
-            prompt_embeds.shape[0]
-
-        if prompt_embeds is None:
-            text_inputs = self.tokenizer(
-                prompt,
-                padding="max_length",
-                max_length=self.tokenizer.model_max_length,
-                truncation=True,
-                return_tensors="pt",
-            )
-            text_input_ids = text_inputs.input_ids
-            untruncated_ids = self.tokenizer(
-                prompt, padding="longest", return_tensors="pt"
-            ).input_ids
-
-            if untruncated_ids.shape[-1] >= text_input_ids.shape[
-                -1
-            ] and not torch.equal(text_input_ids, untruncated_ids):
-                removed_text = self.tokenizer.batch_decode(
-                    untruncated_ids[:, self.tokenizer.model_max_length - 1 : -1]
-                )
-                logger.warning(
-                    "The following part of your input was truncated because CLIP can only handle sequences up to"
-                    f" {self.tokenizer.model_max_length} tokens: {removed_text}"
-                )
-
-            if (
-                hasattr(self.text_encoder.config, "use_attention_mask")
-                and self.text_encoder.config.use_attention_mask
-            ):
-                attention_mask = text_inputs.attention_mask.to(device)
-            else:
-                attention_mask = None
-
-            prompt_embeds = self.text_encoder(
-                text_input_ids.to(device),
-                attention_mask=attention_mask,
-            )
-            prompt_embeds = prompt_embeds[0]
-
-        if self.text_encoder is not None:
-            prompt_embeds_dtype = self.text_encoder.dtype
-        elif self.unet is not None:
-            prompt_embeds_dtype = self.unet.dtype
-        else:
-            prompt_embeds_dtype = prompt_embeds.dtype
-
-        prompt_embeds = prompt_embeds.to(dtype=prompt_embeds_dtype, device=device)
-
-        bs_embed, seq_len, _ = prompt_embeds.shape
-        # duplicate text embeddings for each generation per prompt, using mps friendly method
-        prompt_embeds = prompt_embeds.repeat(1, num_images_per_prompt, 1)
-        prompt_embeds = prompt_embeds.view(
-            bs_embed * num_images_per_prompt, seq_len, -1
-        )
-
-        # Don't need to get uncond prompt embedding because of LCM Guided Distillation
-        return prompt_embeds
-
-    def run_safety_checker(self, image, device, dtype):
-        if self.safety_checker is None:
-            has_nsfw_concept = None
-        else:
-            if torch.is_tensor(image):
-                feature_extractor_input = self.image_processor.postprocess(
-                    image, output_type="pil"
-                )
-            else:
-                feature_extractor_input = self.image_processor.numpy_to_pil(image)
-            safety_checker_input = self.feature_extractor(
-                feature_extractor_input, return_tensors="pt"
-            ).to(device)
-            image, has_nsfw_concept = self.safety_checker(
-                images=image, clip_input=safety_checker_input.pixel_values.to(dtype)
-            )
-        return image, has_nsfw_concept
-
-    def prepare_latents(
-        self,
-        image,
-        timestep,
-        batch_size,
-        num_channels_latents,
-        height,
-        width,
-        dtype,
-        device,
-        latents=None,
-        generator=None,
-    ):
-        shape = (
-            batch_size,
-            num_channels_latents,
-            height // self.vae_scale_factor,
-            width // self.vae_scale_factor,
-        )
-
-        if not isinstance(image, (torch.Tensor, PIL.Image.Image, list)):
-            raise ValueError(
-                f"`image` has to be of type `torch.Tensor`, `PIL.Image.Image` or list but is {type(image)}"
-            )
-
-        image = image.to(device=device, dtype=dtype)
-
-        # batch_size = batch_size * num_images_per_prompt
-        if image.shape[1] == 4:
-            init_latents = image
-
-        else:
-            if isinstance(generator, list) and len(generator) != batch_size:
-                raise ValueError(
-                    f"You have passed a list of generators of length {len(generator)}, but requested an effective batch"
-                    f" size of {batch_size}. Make sure the batch size matches the length of the generators."
-                )
-
-            elif isinstance(generator, list):
-                if isinstance(self.vae, AutoencoderTiny):
-                    init_latents = [
-                        self.vae.encode(image[i : i + 1]).latents
-                        for i in range(batch_size)
-                    ]
-                else:
-                    init_latents = [
-                        self.vae.encode(image[i : i + 1]).latent_dist.sample(generator[i])
-                        for i in range(batch_size)
-                    ]
-                init_latents = torch.cat(init_latents, dim=0)
-            else:
-                if isinstance(self.vae, AutoencoderTiny):
-                    init_latents = self.vae.encode(image).latents
-                else:
-                    init_latents = self.vae.encode(image).latent_dist.sample(generator)
-
-            init_latents = self.vae.config.scaling_factor * init_latents
-
-        if (
-            batch_size > init_latents.shape[0]
-            and batch_size % init_latents.shape[0] == 0
-        ):
-            # expand init_latents for batch_size
-            (
-                f"You have passed {batch_size} text prompts (`prompt`), but only {init_latents.shape[0]} initial"
-                " images (`image`). Initial images are now duplicating to match the number of text prompts. Note"
-                " that this behavior is deprecated and will be removed in a version 1.0.0. Please make sure to update"
-                " your script to pass as many initial images as text prompts to suppress this warning."
-            )
-            # deprecate("len(prompt) != len(image)", "1.0.0", deprecation_message, standard_warn=False)
-            additional_image_per_prompt = batch_size // init_latents.shape[0]
-            init_latents = torch.cat(
-                [init_latents] * additional_image_per_prompt, dim=0
-            )
-        elif (
-            batch_size > init_latents.shape[0]
-            and batch_size % init_latents.shape[0] != 0
-        ):
-            raise ValueError(
-                f"Cannot duplicate `image` of batch size {init_latents.shape[0]} to {batch_size} text prompts."
-            )
-        else:
-            init_latents = torch.cat([init_latents], dim=0)
-
-        shape = init_latents.shape
-        noise = randn_tensor(shape, generator=generator, device=device, dtype=dtype)
-
-        # get latents
-        init_latents = self.scheduler.add_noise(init_latents, noise, timestep)
-        latents = init_latents
-
-        if latents is None:
-            latents = torch.randn(shape, dtype=dtype).to(device)
-        else:
-            latents = latents.to(device)
-        # scale the initial noise by the standard deviation required by the scheduler
-        latents = latents * self.scheduler.init_noise_sigma
-        return latents
-
-    def get_w_embedding(self, w, embedding_dim=512, dtype=torch.float32):
-        """
-        see https://github.com/google-research/vdm/blob/dc27b98a554f65cdc654b800da5aa1846545d41b/model_vdm.py#L298
-        Args:
-        timesteps: torch.Tensor: generate embedding vectors at these timesteps
-        embedding_dim: int: dimension of the embeddings to generate
-        dtype: data type of the generated embeddings
-        Returns:
-        embedding vectors with shape `(len(timesteps), embedding_dim)`
-        """
-        assert len(w.shape) == 1
-        w = w * 1000.0
-
-        half_dim = embedding_dim // 2
-        emb = torch.log(torch.tensor(10000.0)) / (half_dim - 1)
-        emb = torch.exp(torch.arange(half_dim, dtype=dtype) * -emb)
-        emb = w.to(dtype)[:, 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))
-        assert emb.shape == (w.shape[0], embedding_dim)
-        return emb
-
-    def get_timesteps(self, num_inference_steps, strength, device):
-        # get the original timestep using init_timestep
-        init_timestep = min(int(num_inference_steps * strength), num_inference_steps)
-
-        t_start = max(num_inference_steps - init_timestep, 0)
-        timesteps = self.scheduler.timesteps[t_start * self.scheduler.order :]
-
-        return timesteps, num_inference_steps - t_start
-
-    @torch.no_grad()
-    def __call__(
-        self,
-        prompt: Union[str, List[str]] = None,
-        image: PipelineImageInput = None,
-        strength: float = 0.8,
-        height: Optional[int] = 768,
-        width: Optional[int] = 768,
-        guidance_scale: float = 7.5,
-        num_images_per_prompt: Optional[int] = 1,
-        latents: Optional[torch.FloatTensor] = None,
-        generator: Optional[torch.Generator] = None,
-        num_inference_steps: int = 4,
-        lcm_origin_steps: int = 50,
-        prompt_embeds: Optional[torch.FloatTensor] = None,
-        output_type: Optional[str] = "pil",
-        return_dict: bool = True,
-        cross_attention_kwargs: Optional[Dict[str, Any]] = None,
-    ):
-        # 0. Default height and width to unet
-        height = height or self.unet.config.sample_size * self.vae_scale_factor
-        width = width or self.unet.config.sample_size * self.vae_scale_factor
-
-        # 2. Define call parameters
-        if prompt is not None and isinstance(prompt, str):
-            batch_size = 1
-        elif prompt is not None and isinstance(prompt, list):
-            batch_size = len(prompt)
-        else:
-            batch_size = prompt_embeds.shape[0]
-
-        device = self._execution_device
-        # do_classifier_free_guidance = guidance_scale > 0.0  # In LCM Implementation:  cfg_noise = noise_cond + cfg_scale * (noise_cond - noise_uncond) , (cfg_scale > 0.0 using CFG)
-
-        # 3. Encode input prompt
-        prompt_embeds = self._encode_prompt(
-            prompt,
-            device,
-            num_images_per_prompt,
-            prompt_embeds=prompt_embeds,
-        )
-
-        # 3.5 encode image
-        image = self.image_processor.preprocess(image)
-
-        # 4. Prepare timesteps
-        self.scheduler.set_timesteps(strength, num_inference_steps, lcm_origin_steps)
-        # timesteps = self.scheduler.timesteps
-        # timesteps, num_inference_steps = self.get_timesteps(num_inference_steps, 1.0, device)
-        timesteps = self.scheduler.timesteps
-        latent_timestep = timesteps[:1].repeat(batch_size * num_images_per_prompt)
-
-        print("timesteps: ", timesteps)
-
-        # 5. Prepare latent variable
-        num_channels_latents = self.unet.config.in_channels
-        latents = self.prepare_latents(
-            image,
-            latent_timestep,
-            batch_size * num_images_per_prompt,
-            num_channels_latents,
-            height,
-            width,
-            prompt_embeds.dtype,
-            device,
-            latents,
-            generator
-        )
-        bs = batch_size * num_images_per_prompt
-
-        # 6. Get Guidance Scale Embedding
-        w = torch.tensor(guidance_scale).repeat(bs)
-        w_embedding = self.get_w_embedding(w, embedding_dim=256).to(
-            device=device, dtype=latents.dtype
-        )
-
-        # 7. LCM MultiStep Sampling Loop:
-        with self.progress_bar(total=num_inference_steps) as progress_bar:
-            for i, t in enumerate(timesteps):
-                ts = torch.full((bs,), t, device=device, dtype=torch.long)
-                latents = latents.to(prompt_embeds.dtype)
-
-                # model prediction (v-prediction, eps, x)
-                model_pred = self.unet(
-                    latents,
-                    ts,
-                    timestep_cond=w_embedding,
-                    encoder_hidden_states=prompt_embeds,
-                    cross_attention_kwargs=cross_attention_kwargs,
-                    return_dict=False,
-                )[0]
-
-                # compute the previous noisy sample x_t -> x_t-1
-                latents, denoised = self.scheduler.step(
-                    model_pred, i, t, latents, return_dict=False
-                )
-
-                # # call the callback, if provided
-                # if i == len(timesteps) - 1:
-                progress_bar.update()
-
-        denoised = denoised.to(prompt_embeds.dtype)
-        if not output_type == "latent":
-            image = self.vae.decode(
-                denoised / self.vae.config.scaling_factor, return_dict=False
-            )[0]
-            image, has_nsfw_concept = self.run_safety_checker(
-                image, device, prompt_embeds.dtype
-            )
-        else:
-            image = denoised
-            has_nsfw_concept = None
-
-        if has_nsfw_concept is None:
-            do_denormalize = [True] * image.shape[0]
-        else:
-            do_denormalize = [not has_nsfw for has_nsfw in has_nsfw_concept]
-
-        image = self.image_processor.postprocess(
-            image, output_type=output_type, do_denormalize=do_denormalize
-        )
-
-        if not return_dict:
-            return (image, has_nsfw_concept)
-
-        return StableDiffusionPipelineOutput(
-            images=image, nsfw_content_detected=has_nsfw_concept
-        )
-
-
-@dataclass
-# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->DDIM
-class LCMSchedulerOutput(BaseOutput):
-    """
-    Output class for the scheduler's `step` function output.
-    Args:
-        prev_sample (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)` for images):
-            Computed sample `(x_{t-1})` of previous timestep. `prev_sample` should be used as next model input in the
-            denoising loop.
-        pred_original_sample (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)` for images):
-            The predicted denoised sample `(x_{0})` based on the model output from the current timestep.
-            `pred_original_sample` can be used to preview progress or for guidance.
-    """
-
-    prev_sample: torch.FloatTensor
-    denoised: Optional[torch.FloatTensor] = None
-
-
-# Copied from diffusers.schedulers.scheduling_ddpm.betas_for_alpha_bar
-def betas_for_alpha_bar(
-    num_diffusion_timesteps,
-    max_beta=0.999,
-    alpha_transform_type="cosine",
-):
-    """
-    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].
-    Contains a function alpha_bar that takes an argument t and transforms it to the cumulative product of (1-beta) up
-    to that part of the diffusion process.
-    Args:
-        num_diffusion_timesteps (`int`): the number of betas to produce.
-        max_beta (`float`): the maximum beta to use; use values lower than 1 to
-                     prevent singularities.
-        alpha_transform_type (`str`, *optional*, default to `cosine`): the type of noise schedule for alpha_bar.
-                     Choose from `cosine` or `exp`
-    Returns:
-        betas (`np.ndarray`): the betas used by the scheduler to step the model outputs
-    """
-    if alpha_transform_type == "cosine":
-
-        def alpha_bar_fn(t):
-            return math.cos((t + 0.008) / 1.008 * math.pi / 2) ** 2
-
-    elif alpha_transform_type == "exp":
-
-        def alpha_bar_fn(t):
-            return math.exp(t * -12.0)
-
-    else:
-        raise ValueError(f"Unsupported alpha_tranform_type: {alpha_transform_type}")
-
-    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_fn(t2) / alpha_bar_fn(t1), max_beta))
-    return torch.tensor(betas, dtype=torch.float32)
-
-
-def rescale_zero_terminal_snr(betas):
-    """
-    Rescales betas to have zero terminal SNR Based on https://arxiv.org/pdf/2305.08891.pdf (Algorithm 1)
-    Args:
-        betas (`torch.FloatTensor`):
-            the betas that the scheduler is being initialized with.
-    Returns:
-        `torch.FloatTensor`: rescaled betas with zero terminal SNR
-    """
-    # Convert betas to alphas_bar_sqrt
-    alphas = 1.0 - betas
-    alphas_cumprod = torch.cumprod(alphas, dim=0)
-    alphas_bar_sqrt = alphas_cumprod.sqrt()
-
-    # Store old values.
-    alphas_bar_sqrt_0 = alphas_bar_sqrt[0].clone()
-    alphas_bar_sqrt_T = alphas_bar_sqrt[-1].clone()
-
-    # Shift so the last timestep is zero.
-    alphas_bar_sqrt -= alphas_bar_sqrt_T
-
-    # Scale so the first timestep is back to the old value.
-    alphas_bar_sqrt *= alphas_bar_sqrt_0 / (alphas_bar_sqrt_0 - alphas_bar_sqrt_T)
-
-    # Convert alphas_bar_sqrt to betas
-    alphas_bar = alphas_bar_sqrt**2  # Revert sqrt
-    alphas = alphas_bar[1:] / alphas_bar[:-1]  # Revert cumprod
-    alphas = torch.cat([alphas_bar[0:1], alphas])
-    betas = 1 - alphas
-
-    return betas
-
-
-class LCMSchedulerWithTimestamp(SchedulerMixin, ConfigMixin):
-    """
-    This class modifies LCMScheduler to add a timestamp argument to set_timesteps
-
-
-    `LCMScheduler` extends the denoising procedure introduced in denoising diffusion probabilistic models (DDPMs) with
-    non-Markovian guidance.
-    This model inherits from [`SchedulerMixin`] and [`ConfigMixin`]. Check the superclass documentation for the generic
-    methods the library implements for all schedulers such as loading and saving.
-    Args:
-        num_train_timesteps (`int`, defaults to 1000):
-            The number of diffusion steps to train the model.
-        beta_start (`float`, defaults to 0.0001):
-            The starting `beta` value of inference.
-        beta_end (`float`, defaults to 0.02):
-            The final `beta` value.
-        beta_schedule (`str`, defaults to `"linear"`):
-            The beta schedule, a mapping from a beta range to a sequence of betas for stepping the model. Choose from
-            `linear`, `scaled_linear`, or `squaredcos_cap_v2`.
-        trained_betas (`np.ndarray`, *optional*):
-            Pass an array of betas directly to the constructor to bypass `beta_start` and `beta_end`.
-        clip_sample (`bool`, defaults to `True`):
-            Clip the predicted sample for numerical stability.
-        clip_sample_range (`float`, defaults to 1.0):
-            The maximum magnitude for sample clipping. Valid only when `clip_sample=True`.
-        set_alpha_to_one (`bool`, defaults to `True`):
-            Each diffusion step uses the alphas product value at that step and at the previous one. For the final step
-            there is no previous alpha. When this option is `True` the previous alpha product is fixed to `1`,
-            otherwise it uses the alpha value at step 0.
-        steps_offset (`int`, defaults to 0):
-            An offset added to the inference steps. You can use a combination of `offset=1` and
-            `set_alpha_to_one=False` to make the last step use step 0 for the previous alpha product like in Stable
-            Diffusion.
-        prediction_type (`str`, defaults to `epsilon`, *optional*):
-            Prediction type of the scheduler function; can be `epsilon` (predicts the noise of the diffusion process),
-            `sample` (directly predicts the noisy sample`) or `v_prediction` (see section 2.4 of [Imagen
-            Video](https://imagen.research.google/video/paper.pdf) paper).
-        thresholding (`bool`, defaults to `False`):
-            Whether to use the "dynamic thresholding" method. This is unsuitable for latent-space diffusion models such
-            as Stable Diffusion.
-        dynamic_thresholding_ratio (`float`, defaults to 0.995):
-            The ratio for the dynamic thresholding method. Valid only when `thresholding=True`.
-        sample_max_value (`float`, defaults to 1.0):
-            The threshold value for dynamic thresholding. Valid only when `thresholding=True`.
-        timestep_spacing (`str`, defaults to `"leading"`):
-            The way the timesteps should be scaled. Refer to Table 2 of the [Common Diffusion Noise Schedules and
-            Sample Steps are Flawed](https://huggingface.co/papers/2305.08891) for more information.
-        rescale_betas_zero_snr (`bool`, defaults to `False`):
-            Whether to rescale the betas to have zero terminal SNR. This enables the model to generate very bright and
-            dark samples instead of limiting it to samples with medium brightness. Loosely related to
-            [`--offset_noise`](https://github.com/huggingface/diffusers/blob/74fd735eb073eb1d774b1ab4154a0876eb82f055/examples/dreambooth/train_dreambooth.py#L506).
-    """
-
-    # _compatibles = [e.name for e in KarrasDiffusionSchedulers]
-    order = 1
-
-    @register_to_config
-    def __init__(
-        self,
-        num_train_timesteps: int = 1000,
-        beta_start: float = 0.0001,
-        beta_end: float = 0.02,
-        beta_schedule: str = "linear",
-        trained_betas: Optional[Union[np.ndarray, List[float]]] = None,
-        clip_sample: bool = True,
-        set_alpha_to_one: bool = True,
-        steps_offset: int = 0,
-        prediction_type: str = "epsilon",
-        thresholding: bool = False,
-        dynamic_thresholding_ratio: float = 0.995,
-        clip_sample_range: float = 1.0,
-        sample_max_value: float = 1.0,
-        timestep_spacing: str = "leading",
-        rescale_betas_zero_snr: bool = False,
-    ):
-        if trained_betas is not None:
-            self.betas = torch.tensor(trained_betas, dtype=torch.float32)
-        elif beta_schedule == "linear":
-            self.betas = torch.linspace(
-                beta_start, beta_end, num_train_timesteps, dtype=torch.float32
-            )
-        elif beta_schedule == "scaled_linear":
-            # this schedule is very specific to the latent diffusion model.
-            self.betas = (
-                torch.linspace(
-                    beta_start**0.5,
-                    beta_end**0.5,
-                    num_train_timesteps,
-                    dtype=torch.float32,
-                )
-                ** 2
-            )
-        elif beta_schedule == "squaredcos_cap_v2":
-            # Glide cosine schedule
-            self.betas = betas_for_alpha_bar(num_train_timesteps)
-        else:
-            raise NotImplementedError(
-                f"{beta_schedule} does is not implemented for {self.__class__}"
-            )
-
-        # Rescale for zero SNR
-        if rescale_betas_zero_snr:
-            self.betas = rescale_zero_terminal_snr(self.betas)
-
-        self.alphas = 1.0 - self.betas
-        self.alphas_cumprod = torch.cumprod(self.alphas, dim=0)
-
-        # At every step in ddim, we are looking into the previous alphas_cumprod
-        # For the final step, there is no previous alphas_cumprod because we are already at 0
-        # `set_alpha_to_one` decides whether we set this parameter simply to one or
-        # whether we use the final alpha of the "non-previous" one.
-        self.final_alpha_cumprod = (
-            torch.tensor(1.0) if set_alpha_to_one else self.alphas_cumprod[0]
-        )
-
-        # standard deviation of the initial noise distribution
-        self.init_noise_sigma = 1.0
-
-        # setable values
-        self.num_inference_steps = None
-        self.timesteps = torch.from_numpy(
-            np.arange(0, num_train_timesteps)[::-1].copy().astype(np.int64)
-        )
-
-    def scale_model_input(
-        self, sample: torch.FloatTensor, timestep: Optional[int] = None
-    ) -> torch.FloatTensor:
-        """
-        Ensures interchangeability with schedulers that need to scale the denoising model input depending on the
-        current timestep.
-        Args:
-            sample (`torch.FloatTensor`):
-                The input sample.
-            timestep (`int`, *optional*):
-                The current timestep in the diffusion chain.
-        Returns:
-            `torch.FloatTensor`:
-                A scaled input sample.
-        """
-        return sample
-
-    def _get_variance(self, timestep, prev_timestep):
-        alpha_prod_t = self.alphas_cumprod[timestep]
-        alpha_prod_t_prev = (
-            self.alphas_cumprod[prev_timestep]
-            if prev_timestep >= 0
-            else self.final_alpha_cumprod
-        )
-        beta_prod_t = 1 - alpha_prod_t
-        beta_prod_t_prev = 1 - alpha_prod_t_prev
-
-        variance = (beta_prod_t_prev / beta_prod_t) * (
-            1 - alpha_prod_t / alpha_prod_t_prev
-        )
-
-        return variance
-
-    # Copied from diffusers.schedulers.scheduling_ddpm.DDPMScheduler._threshold_sample
-    def _threshold_sample(self, sample: torch.FloatTensor) -> torch.FloatTensor:
-        """
-        "Dynamic thresholding: At each sampling step we set s to a certain percentile absolute pixel value in xt0 (the
-        prediction of x_0 at timestep t), and if s > 1, then we threshold xt0 to the range [-s, s] and then divide by
-        s. Dynamic thresholding pushes saturated pixels (those near -1 and 1) inwards, thereby actively preventing
-        pixels from saturation at each step. We find that dynamic thresholding results in significantly better
-        photorealism as well as better image-text alignment, especially when using very large guidance weights."
-        https://arxiv.org/abs/2205.11487
-        """
-        dtype = sample.dtype
-        batch_size, channels, height, width = sample.shape
-
-        if dtype not in (torch.float32, torch.float64):
-            sample = (
-                sample.float()
-            )  # upcast for quantile calculation, and clamp not implemented for cpu half
-
-        # Flatten sample for doing quantile calculation along each image
-        sample = sample.reshape(batch_size, channels * height * width)
-
-        abs_sample = sample.abs()  # "a certain percentile absolute pixel value"
-
-        s = torch.quantile(abs_sample, self.config.dynamic_thresholding_ratio, dim=1)
-        s = torch.clamp(
-            s, min=1, max=self.config.sample_max_value
-        )  # When clamped to min=1, equivalent to standard clipping to [-1, 1]
-
-        s = s.unsqueeze(1)  # (batch_size, 1) because clamp will broadcast along dim=0
-        sample = (
-            torch.clamp(sample, -s, s) / s
-        )  # "we threshold xt0 to the range [-s, s] and then divide by s"
-
-        sample = sample.reshape(batch_size, channels, height, width)
-        sample = sample.to(dtype)
-
-        return sample
-
-    def set_timesteps(
-        self,
-        stength,
-        num_inference_steps: int,
-        lcm_origin_steps: int,
-        device: Union[str, torch.device] = None,
-    ):
-        """
-        Sets the discrete timesteps used for the diffusion chain (to be run before inference).
-        Args:
-            num_inference_steps (`int`):
-                The number of diffusion steps used when generating samples with a pre-trained model.
-        """
-
-        if num_inference_steps > self.config.num_train_timesteps:
-            raise ValueError(
-                f"`num_inference_steps`: {num_inference_steps} cannot be larger than `self.config.train_timesteps`:"
-                f" {self.config.num_train_timesteps} as the unet model trained with this scheduler can only handle"
-                f" maximal {self.config.num_train_timesteps} timesteps."
-            )
-
-        self.num_inference_steps = num_inference_steps
-
-        # LCM Timesteps Setting:  # Linear Spacing
-        c = self.config.num_train_timesteps // lcm_origin_steps
-        lcm_origin_timesteps = (
-            np.asarray(list(range(1, int(lcm_origin_steps * stength) + 1))) * c - 1
-        )  # LCM Training  Steps Schedule
-        skipping_step = len(lcm_origin_timesteps) // num_inference_steps
-        timesteps = lcm_origin_timesteps[::-skipping_step][
-            :num_inference_steps
-        ]  # LCM Inference Steps Schedule
-
-        self.timesteps = torch.from_numpy(timesteps.copy()).to(device)
-
-    def get_scalings_for_boundary_condition_discrete(self, t):
-        self.sigma_data = 0.5  # Default: 0.5
-
-        # By dividing 0.1: This is almost a delta function at t=0.
-        c_skip = self.sigma_data**2 / ((t / 0.1) ** 2 + self.sigma_data**2)
-        c_out = (t / 0.1) / ((t / 0.1) ** 2 + self.sigma_data**2) ** 0.5
-        return c_skip, c_out
-
-    def step(
-        self,
-        model_output: torch.FloatTensor,
-        timeindex: int,
-        timestep: int,
-        sample: torch.FloatTensor,
-        eta: float = 0.0,
-        use_clipped_model_output: bool = False,
-        generator=None,
-        variance_noise: Optional[torch.FloatTensor] = None,
-        return_dict: bool = True,
-    ) -> Union[LCMSchedulerOutput, Tuple]:
-        """
-        Predict the sample from the previous timestep by reversing the SDE. This function propagates the diffusion
-        process from the learned model outputs (most often the predicted noise).
-        Args:
-            model_output (`torch.FloatTensor`):
-                The direct output from learned diffusion model.
-            timestep (`float`):
-                The current discrete timestep in the diffusion chain.
-            sample (`torch.FloatTensor`):
-                A current instance of a sample created by the diffusion process.
-            eta (`float`):
-                The weight of noise for added noise in diffusion step.
-            use_clipped_model_output (`bool`, defaults to `False`):
-                If `True`, computes "corrected" `model_output` from the clipped predicted original sample. Necessary
-                because predicted original sample is clipped to [-1, 1] when `self.config.clip_sample` is `True`. If no
-                clipping has happened, "corrected" `model_output` would coincide with the one provided as input and
-                `use_clipped_model_output` has no effect.
-            generator (`torch.Generator`, *optional*):
-                A random number generator.
-            variance_noise (`torch.FloatTensor`):
-                Alternative to generating noise with `generator` by directly providing the noise for the variance
-                itself. Useful for methods such as [`CycleDiffusion`].
-            return_dict (`bool`, *optional*, defaults to `True`):
-                Whether or not to return a [`~schedulers.scheduling_lcm.LCMSchedulerOutput`] or `tuple`.
-        Returns:
-            [`~schedulers.scheduling_utils.LCMSchedulerOutput`] or `tuple`:
-                If return_dict is `True`, [`~schedulers.scheduling_lcm.LCMSchedulerOutput`] is returned, otherwise a
-                tuple is returned where the first element is the sample tensor.
-        """
-        if self.num_inference_steps is None:
-            raise ValueError(
-                "Number of inference steps is 'None', you need to run 'set_timesteps' after creating the scheduler"
-            )
-
-        # 1. get previous step value
-        prev_timeindex = timeindex + 1
-        if prev_timeindex < len(self.timesteps):
-            prev_timestep = self.timesteps[prev_timeindex]
-        else:
-            prev_timestep = timestep
-
-        # 2. compute alphas, betas
-        alpha_prod_t = self.alphas_cumprod[timestep]
-        alpha_prod_t_prev = (
-            self.alphas_cumprod[prev_timestep]
-            if prev_timestep >= 0
-            else self.final_alpha_cumprod
-        )
-
-        beta_prod_t = 1 - alpha_prod_t
-        beta_prod_t_prev = 1 - alpha_prod_t_prev
-
-        # 3. Get scalings for boundary conditions
-        c_skip, c_out = self.get_scalings_for_boundary_condition_discrete(timestep)
-
-        # 4. Different Parameterization:
-        parameterization = self.config.prediction_type
-
-        if parameterization == "epsilon":  # noise-prediction
-            pred_x0 = (sample - beta_prod_t.sqrt() * model_output) / alpha_prod_t.sqrt()
-
-        elif parameterization == "sample":  # x-prediction
-            pred_x0 = model_output
-
-        elif parameterization == "v_prediction":  # v-prediction
-            pred_x0 = alpha_prod_t.sqrt() * sample - beta_prod_t.sqrt() * model_output
-
-        # 4. Denoise model output using boundary conditions
-        denoised = c_out * pred_x0 + c_skip * sample
-
-        # 5. Sample z ~ N(0, I), For MultiStep Inference
-        # Noise is not used for one-step sampling.
-        if len(self.timesteps) > 1:
-            noise = torch.randn(model_output.shape).to(model_output.device)
-            prev_sample = (
-                alpha_prod_t_prev.sqrt() * denoised + beta_prod_t_prev.sqrt() * noise
-            )
-        else:
-            prev_sample = denoised
-
-        if not return_dict:
-            return (prev_sample, denoised)
-
-        return LCMSchedulerOutput(prev_sample=prev_sample, denoised=denoised)
-
-    # Copied from diffusers.schedulers.scheduling_ddpm.DDPMScheduler.add_noise
-    def add_noise(
-        self,
-        original_samples: torch.FloatTensor,
-        noise: torch.FloatTensor,
-        timesteps: torch.IntTensor,
-    ) -> torch.FloatTensor:
-        # Make sure alphas_cumprod and timestep have same device and dtype as original_samples
-        alphas_cumprod = self.alphas_cumprod.to(
-            device=original_samples.device, dtype=original_samples.dtype
-        )
-        timesteps = timesteps.to(original_samples.device)
-
-        sqrt_alpha_prod = alphas_cumprod[timesteps] ** 0.5
-        sqrt_alpha_prod = sqrt_alpha_prod.flatten()
-        while len(sqrt_alpha_prod.shape) < len(original_samples.shape):
-            sqrt_alpha_prod = sqrt_alpha_prod.unsqueeze(-1)
-
-        sqrt_one_minus_alpha_prod = (1 - alphas_cumprod[timesteps]) ** 0.5
-        sqrt_one_minus_alpha_prod = sqrt_one_minus_alpha_prod.flatten()
-        while len(sqrt_one_minus_alpha_prod.shape) < len(original_samples.shape):
-            sqrt_one_minus_alpha_prod = sqrt_one_minus_alpha_prod.unsqueeze(-1)
-
-        noisy_samples = (
-            sqrt_alpha_prod * original_samples + sqrt_one_minus_alpha_prod * noise
-        )
-        return noisy_samples
-
-    # Copied from diffusers.schedulers.scheduling_ddpm.DDPMScheduler.get_velocity
-    def get_velocity(
-        self,
-        sample: torch.FloatTensor,
-        noise: torch.FloatTensor,
-        timesteps: torch.IntTensor,
-    ) -> torch.FloatTensor:
-        # Make sure alphas_cumprod and timestep have same device and dtype as sample
-        alphas_cumprod = self.alphas_cumprod.to(
-            device=sample.device, dtype=sample.dtype
-        )
-        timesteps = timesteps.to(sample.device)
-
-        sqrt_alpha_prod = alphas_cumprod[timesteps] ** 0.5
-        sqrt_alpha_prod = sqrt_alpha_prod.flatten()
-        while len(sqrt_alpha_prod.shape) < len(sample.shape):
-            sqrt_alpha_prod = sqrt_alpha_prod.unsqueeze(-1)
-
-        sqrt_one_minus_alpha_prod = (1 - alphas_cumprod[timesteps]) ** 0.5
-        sqrt_one_minus_alpha_prod = sqrt_one_minus_alpha_prod.flatten()
-        while len(sqrt_one_minus_alpha_prod.shape) < len(sample.shape):
-            sqrt_one_minus_alpha_prod = sqrt_one_minus_alpha_prod.unsqueeze(-1)
-
-        velocity = sqrt_alpha_prod * noise - sqrt_one_minus_alpha_prod * sample
-        return velocity
-
-    def __len__(self):
-        return self.config.num_train_timesteps

latent_consistency_txt2img.py

@@ -1,836 +0,0 @@
-# Copyright 2023 Stanford University Team and The HuggingFace Team. All rights reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-# DISCLAIMER: This code is strongly influenced by https://github.com/pesser/pytorch_diffusion
-# and https://github.com/hojonathanho/diffusion
-
-import math
-from dataclasses import dataclass
-from typing import Any, Dict, List, Optional, Tuple, Union
-
-import numpy as np
-import torch
-from transformers import CLIPImageProcessor, CLIPTextModel, CLIPTokenizer
-
-from diffusers import (
-    AutoencoderKL,
-    ConfigMixin,
-    DiffusionPipeline,
-    SchedulerMixin,
-    UNet2DConditionModel,
-    logging,
-)
-from diffusers.configuration_utils import register_to_config
-from diffusers.image_processor import VaeImageProcessor
-from diffusers.pipelines.stable_diffusion import StableDiffusionPipelineOutput
-from diffusers.pipelines.stable_diffusion.safety_checker import (
-    StableDiffusionSafetyChecker,
-)
-from diffusers.utils import BaseOutput
-
-
-logger = logging.get_logger(__name__)  # pylint: disable=invalid-name
-
-
-class LatentConsistencyModelPipeline(DiffusionPipeline):
-    _optional_components = ["scheduler"]
-
-    def __init__(
-        self,
-        vae: AutoencoderKL,
-        text_encoder: CLIPTextModel,
-        tokenizer: CLIPTokenizer,
-        unet: UNet2DConditionModel,
-        scheduler: "LCMScheduler",
-        safety_checker: StableDiffusionSafetyChecker,
-        feature_extractor: CLIPImageProcessor,
-        requires_safety_checker: bool = True,
-    ):
-        super().__init__()
-
-        scheduler = (
-            scheduler
-            if scheduler is not None
-            else LCMScheduler(
-                beta_start=0.00085,
-                beta_end=0.0120,
-                beta_schedule="scaled_linear",
-                prediction_type="epsilon",
-            )
-        )
-
-        self.register_modules(
-            vae=vae,
-            text_encoder=text_encoder,
-            tokenizer=tokenizer,
-            unet=unet,
-            scheduler=scheduler,
-            safety_checker=safety_checker,
-            feature_extractor=feature_extractor,
-        )
-        self.vae_scale_factor = 2 ** (len(self.vae.config.block_out_channels) - 1)
-        self.image_processor = VaeImageProcessor(vae_scale_factor=self.vae_scale_factor)
-
-    def _encode_prompt(
-        self,
-        prompt,
-        device,
-        num_images_per_prompt,
-        prompt_embeds: None,
-    ):
-        r"""
-        Encodes the prompt into text encoder hidden states.
-        Args:
-            prompt (`str` or `List[str]`, *optional*):
-                prompt to be encoded
-            device: (`torch.device`):
-                torch device
-            num_images_per_prompt (`int`):
-                number of images that should be generated per prompt
-            prompt_embeds (`torch.FloatTensor`, *optional*):
-                Pre-generated text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not
-                provided, text embeddings will be generated from `prompt` input argument.
-        """
-
-        if prompt is not None and isinstance(prompt, str):
-            pass
-        elif prompt is not None and isinstance(prompt, list):
-            len(prompt)
-        else:
-            prompt_embeds.shape[0]
-
-        if prompt_embeds is None:
-            text_inputs = self.tokenizer(
-                prompt,
-                padding="max_length",
-                max_length=self.tokenizer.model_max_length,
-                truncation=True,
-                return_tensors="pt",
-            )
-            text_input_ids = text_inputs.input_ids
-            untruncated_ids = self.tokenizer(
-                prompt, padding="longest", return_tensors="pt"
-            ).input_ids
-
-            if untruncated_ids.shape[-1] >= text_input_ids.shape[
-                -1
-            ] and not torch.equal(text_input_ids, untruncated_ids):
-                removed_text = self.tokenizer.batch_decode(
-                    untruncated_ids[:, self.tokenizer.model_max_length - 1 : -1]
-                )
-                logger.warning(
-                    "The following part of your input was truncated because CLIP can only handle sequences up to"
-                    f" {self.tokenizer.model_max_length} tokens: {removed_text}"
-                )
-
-            if (
-                hasattr(self.text_encoder.config, "use_attention_mask")
-                and self.text_encoder.config.use_attention_mask
-            ):
-                attention_mask = text_inputs.attention_mask.to(device)
-            else:
-                attention_mask = None
-
-            prompt_embeds = self.text_encoder(
-                text_input_ids.to(device),
-                attention_mask=attention_mask,
-            )
-            prompt_embeds = prompt_embeds[0]
-
-        if self.text_encoder is not None:
-            prompt_embeds_dtype = self.text_encoder.dtype
-        elif self.unet is not None:
-            prompt_embeds_dtype = self.unet.dtype
-        else:
-            prompt_embeds_dtype = prompt_embeds.dtype
-
-        prompt_embeds = prompt_embeds.to(dtype=prompt_embeds_dtype, device=device)
-
-        bs_embed, seq_len, _ = prompt_embeds.shape
-        # duplicate text embeddings for each generation per prompt, using mps friendly method
-        prompt_embeds = prompt_embeds.repeat(1, num_images_per_prompt, 1)
-        prompt_embeds = prompt_embeds.view(
-            bs_embed * num_images_per_prompt, seq_len, -1
-        )
-
-        # Don't need to get uncond prompt embedding because of LCM Guided Distillation
-        return prompt_embeds
-
-    def run_safety_checker(self, image, device, dtype):
-        if self.safety_checker is None:
-            has_nsfw_concept = None
-        else:
-            if torch.is_tensor(image):
-                feature_extractor_input = self.image_processor.postprocess(
-                    image, output_type="pil"
-                )
-            else:
-                feature_extractor_input = self.image_processor.numpy_to_pil(image)
-            safety_checker_input = self.feature_extractor(
-                feature_extractor_input, return_tensors="pt"
-            ).to(device)
-            image, has_nsfw_concept = self.safety_checker(
-                images=image, clip_input=safety_checker_input.pixel_values.to(dtype)
-            )
-        return image, has_nsfw_concept
-
-    def prepare_latents(
-        self,
-        batch_size,
-        num_channels_latents,
-        height,
-        width,
-        dtype,
-        device,
-        latents=None,
-        generator=None,
-    ):
-        shape = (
-            batch_size,
-            num_channels_latents,
-            height // self.vae_scale_factor,
-            width // self.vae_scale_factor,
-        )
-        if generator is None:
-            generator = torch.Generator()
-            generator.manual_seed(torch.randint(0, 2 ** 32, (1,)).item())
-
-        if latents is None:
-            latents = torch.randn(shape, dtype=dtype, generator=generator).to(device)
-        else:
-            latents = latents.to(device)
-        # scale the initial noise by the standard deviation required by the scheduler
-        latents = latents * self.scheduler.init_noise_sigma
-        return latents
-
-    def get_w_embedding(self, w, embedding_dim=512, dtype=torch.float32):
-        """
-        see https://github.com/google-research/vdm/blob/dc27b98a554f65cdc654b800da5aa1846545d41b/model_vdm.py#L298
-        Args:
-        timesteps: torch.Tensor: generate embedding vectors at these timesteps
-        embedding_dim: int: dimension of the embeddings to generate
-        dtype: data type of the generated embeddings
-        Returns:
-        embedding vectors with shape `(len(timesteps), embedding_dim)`
-        """
-        assert len(w.shape) == 1
-        w = w * 1000.0
-
-        half_dim = embedding_dim // 2
-        emb = torch.log(torch.tensor(10000.0)) / (half_dim - 1)
-        emb = torch.exp(torch.arange(half_dim, dtype=dtype) * -emb)
-        emb = w.to(dtype)[:, 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))
-        assert emb.shape == (w.shape[0], embedding_dim)
-        return emb
-
-    @torch.no_grad()
-    def __call__(
-        self,
-        prompt: Union[str, List[str]] = None,
-        height: Optional[int] = 768,
-        width: Optional[int] = 768,
-        guidance_scale: float = 7.5,
-        num_images_per_prompt: Optional[int] = 1,
-        latents: Optional[torch.FloatTensor] = None,
-        generator: Optional[torch.Generator] = None,
-        num_inference_steps: int = 4,
-        lcm_origin_steps: int = 50,
-        prompt_embeds: Optional[torch.FloatTensor] = None,
-        output_type: Optional[str] = "pil",
-        return_dict: bool = True,
-        cross_attention_kwargs: Optional[Dict[str, Any]] = None,
-    ):
-        # 0. Default height and width to unet
-        height = height or self.unet.config.sample_size * self.vae_scale_factor
-        width = width or self.unet.config.sample_size * self.vae_scale_factor
-
-        # 2. Define call parameters
-        if prompt is not None and isinstance(prompt, str):
-            batch_size = 1
-        elif prompt is not None and isinstance(prompt, list):
-            batch_size = len(prompt)
-        else:
-            batch_size = prompt_embeds.shape[0]
-
-        device = self._execution_device
-        # do_classifier_free_guidance = guidance_scale > 0.0  # In LCM Implementation:  cfg_noise = noise_cond + cfg_scale * (noise_cond - noise_uncond) , (cfg_scale > 0.0 using CFG)
-
-        # 3. Encode input prompt
-        prompt_embeds = self._encode_prompt(
-            prompt,
-            device,
-            num_images_per_prompt,
-            prompt_embeds=prompt_embeds,
-        )
-
-        # 4. Prepare timesteps
-        self.scheduler.set_timesteps(num_inference_steps, lcm_origin_steps)
-        timesteps = self.scheduler.timesteps
-
-        # 5. Prepare latent variable
-        num_channels_latents = self.unet.config.in_channels
-        latents = self.prepare_latents(
-            batch_size * num_images_per_prompt,
-            num_channels_latents,
-            height,
-            width,
-            prompt_embeds.dtype,
-            device,
-            latents,
-            generator
-        )
-        bs = batch_size * num_images_per_prompt
-
-        # 6. Get Guidance Scale Embedding
-        w = torch.tensor(guidance_scale).repeat(bs)
-        w_embedding = self.get_w_embedding(w, embedding_dim=256).to(
-            device=device, dtype=latents.dtype
-        )
-
-        # 7. LCM MultiStep Sampling Loop:
-        with self.progress_bar(total=num_inference_steps) as progress_bar:
-            for i, t in enumerate(timesteps):
-                ts = torch.full((bs,), t, device=device, dtype=torch.long)
-                latents = latents.to(prompt_embeds.dtype)
-
-                # model prediction (v-prediction, eps, x)
-                model_pred = self.unet(
-                    latents,
-                    ts,
-                    timestep_cond=w_embedding,
-                    encoder_hidden_states=prompt_embeds,
-                    cross_attention_kwargs=cross_attention_kwargs,
-                    return_dict=False,
-                )[0]
-
-                # compute the previous noisy sample x_t -> x_t-1
-                latents, denoised = self.scheduler.step(
-                    model_pred, i, t, latents, return_dict=False
-                )
-
-                # # call the callback, if provided
-                # if i == len(timesteps) - 1:
-                progress_bar.update()
-
-        denoised = denoised.to(prompt_embeds.dtype)
-        if not output_type == "latent":
-            image = self.vae.decode(
-                denoised / self.vae.config.scaling_factor, return_dict=False
-            )[0]
-            image, has_nsfw_concept = self.run_safety_checker(
-                image, device, prompt_embeds.dtype
-            )
-        else:
-            image = denoised
-            has_nsfw_concept = None
-
-        if has_nsfw_concept is None:
-            do_denormalize = [True] * image.shape[0]
-        else:
-            do_denormalize = [not has_nsfw for has_nsfw in has_nsfw_concept]
-
-        image = self.image_processor.postprocess(
-            image, output_type=output_type, do_denormalize=do_denormalize
-        )
-
-        if not return_dict:
-            return (image, has_nsfw_concept)
-
-        return StableDiffusionPipelineOutput(
-            images=image, nsfw_content_detected=has_nsfw_concept
-        )
-
-
-@dataclass
-# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->DDIM
-class LCMSchedulerOutput(BaseOutput):
-    """
-    Output class for the scheduler's `step` function output.
-    Args:
-        prev_sample (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)` for images):
-            Computed sample `(x_{t-1})` of previous timestep. `prev_sample` should be used as next model input in the
-            denoising loop.
-        pred_original_sample (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)` for images):
-            The predicted denoised sample `(x_{0})` based on the model output from the current timestep.
-            `pred_original_sample` can be used to preview progress or for guidance.
-    """
-
-    prev_sample: torch.FloatTensor
-    denoised: Optional[torch.FloatTensor] = None
-
-
-# Copied from diffusers.schedulers.scheduling_ddpm.betas_for_alpha_bar
-def betas_for_alpha_bar(
-    num_diffusion_timesteps,
-    max_beta=0.999,
-    alpha_transform_type="cosine",
-):
-    """
-    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].
-    Contains a function alpha_bar that takes an argument t and transforms it to the cumulative product of (1-beta) up
-    to that part of the diffusion process.
-    Args:
-        num_diffusion_timesteps (`int`): the number of betas to produce.
-        max_beta (`float`): the maximum beta to use; use values lower than 1 to
-                     prevent singularities.
-        alpha_transform_type (`str`, *optional*, default to `cosine`): the type of noise schedule for alpha_bar.
-                     Choose from `cosine` or `exp`
-    Returns:
-        betas (`np.ndarray`): the betas used by the scheduler to step the model outputs
-    """
-    if alpha_transform_type == "cosine":
-
-        def alpha_bar_fn(t):
-            return math.cos((t + 0.008) / 1.008 * math.pi / 2) ** 2
-
-    elif alpha_transform_type == "exp":
-
-        def alpha_bar_fn(t):
-            return math.exp(t * -12.0)
-
-    else:
-        raise ValueError(f"Unsupported alpha_tranform_type: {alpha_transform_type}")
-
-    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_fn(t2) / alpha_bar_fn(t1), max_beta))
-    return torch.tensor(betas, dtype=torch.float32)
-
-
-def rescale_zero_terminal_snr(betas):
-    """
-    Rescales betas to have zero terminal SNR Based on https://arxiv.org/pdf/2305.08891.pdf (Algorithm 1)
-    Args:
-        betas (`torch.FloatTensor`):
-            the betas that the scheduler is being initialized with.
-    Returns:
-        `torch.FloatTensor`: rescaled betas with zero terminal SNR
-    """
-    # Convert betas to alphas_bar_sqrt
-    alphas = 1.0 - betas
-    alphas_cumprod = torch.cumprod(alphas, dim=0)
-    alphas_bar_sqrt = alphas_cumprod.sqrt()
-
-    # Store old values.
-    alphas_bar_sqrt_0 = alphas_bar_sqrt[0].clone()
-    alphas_bar_sqrt_T = alphas_bar_sqrt[-1].clone()
-
-    # Shift so the last timestep is zero.
-    alphas_bar_sqrt -= alphas_bar_sqrt_T
-
-    # Scale so the first timestep is back to the old value.
-    alphas_bar_sqrt *= alphas_bar_sqrt_0 / (alphas_bar_sqrt_0 - alphas_bar_sqrt_T)
-
-    # Convert alphas_bar_sqrt to betas
-    alphas_bar = alphas_bar_sqrt**2  # Revert sqrt
-    alphas = alphas_bar[1:] / alphas_bar[:-1]  # Revert cumprod
-    alphas = torch.cat([alphas_bar[0:1], alphas])
-    betas = 1 - alphas
-
-    return betas
-
-
-class LCMScheduler(SchedulerMixin, ConfigMixin):
-    """
-    `LCMScheduler` extends the denoising procedure introduced in denoising diffusion probabilistic models (DDPMs) with
-    non-Markovian guidance.
-    This model inherits from [`SchedulerMixin`] and [`ConfigMixin`]. Check the superclass documentation for the generic
-    methods the library implements for all schedulers such as loading and saving.
-    Args:
-        num_train_timesteps (`int`, defaults to 1000):
-            The number of diffusion steps to train the model.
-        beta_start (`float`, defaults to 0.0001):
-            The starting `beta` value of inference.
-        beta_end (`float`, defaults to 0.02):
-            The final `beta` value.
-        beta_schedule (`str`, defaults to `"linear"`):
-            The beta schedule, a mapping from a beta range to a sequence of betas for stepping the model. Choose from
-            `linear`, `scaled_linear`, or `squaredcos_cap_v2`.
-        trained_betas (`np.ndarray`, *optional*):
-            Pass an array of betas directly to the constructor to bypass `beta_start` and `beta_end`.
-        clip_sample (`bool`, defaults to `True`):
-            Clip the predicted sample for numerical stability.
-        clip_sample_range (`float`, defaults to 1.0):
-            The maximum magnitude for sample clipping. Valid only when `clip_sample=True`.
-        set_alpha_to_one (`bool`, defaults to `True`):
-            Each diffusion step uses the alphas product value at that step and at the previous one. For the final step
-            there is no previous alpha. When this option is `True` the previous alpha product is fixed to `1`,
-            otherwise it uses the alpha value at step 0.
-        steps_offset (`int`, defaults to 0):
-            An offset added to the inference steps. You can use a combination of `offset=1` and
-            `set_alpha_to_one=False` to make the last step use step 0 for the previous alpha product like in Stable
-            Diffusion.
-        prediction_type (`str`, defaults to `epsilon`, *optional*):
-            Prediction type of the scheduler function; can be `epsilon` (predicts the noise of the diffusion process),
-            `sample` (directly predicts the noisy sample`) or `v_prediction` (see section 2.4 of [Imagen
-            Video](https://imagen.research.google/video/paper.pdf) paper).
-        thresholding (`bool`, defaults to `False`):
-            Whether to use the "dynamic thresholding" method. This is unsuitable for latent-space diffusion models such
-            as Stable Diffusion.
-        dynamic_thresholding_ratio (`float`, defaults to 0.995):
-            The ratio for the dynamic thresholding method. Valid only when `thresholding=True`.
-        sample_max_value (`float`, defaults to 1.0):
-            The threshold value for dynamic thresholding. Valid only when `thresholding=True`.
-        timestep_spacing (`str`, defaults to `"leading"`):
-            The way the timesteps should be scaled. Refer to Table 2 of the [Common Diffusion Noise Schedules and
-            Sample Steps are Flawed](https://huggingface.co/papers/2305.08891) for more information.
-        rescale_betas_zero_snr (`bool`, defaults to `False`):
-            Whether to rescale the betas to have zero terminal SNR. This enables the model to generate very bright and
-            dark samples instead of limiting it to samples with medium brightness. Loosely related to
-            [`--offset_noise`](https://github.com/huggingface/diffusers/blob/74fd735eb073eb1d774b1ab4154a0876eb82f055/examples/dreambooth/train_dreambooth.py#L506).
-    """
-
-    # _compatibles = [e.name for e in KarrasDiffusionSchedulers]
-    order = 1
-
-    @register_to_config
-    def __init__(
-        self,
-        num_train_timesteps: int = 1000,
-        beta_start: float = 0.0001,
-        beta_end: float = 0.02,
-        beta_schedule: str = "linear",
-        trained_betas: Optional[Union[np.ndarray, List[float]]] = None,
-        clip_sample: bool = True,
-        set_alpha_to_one: bool = True,
-        steps_offset: int = 0,
-        prediction_type: str = "epsilon",
-        thresholding: bool = False,
-        dynamic_thresholding_ratio: float = 0.995,
-        clip_sample_range: float = 1.0,
-        sample_max_value: float = 1.0,
-        timestep_spacing: str = "leading",
-        rescale_betas_zero_snr: bool = False,
-    ):
-        if trained_betas is not None:
-            self.betas = torch.tensor(trained_betas, dtype=torch.float32)
-        elif beta_schedule == "linear":
-            self.betas = torch.linspace(
-                beta_start, beta_end, num_train_timesteps, dtype=torch.float32
-            )
-        elif beta_schedule == "scaled_linear":
-            # this schedule is very specific to the latent diffusion model.
-            self.betas = (
-                torch.linspace(
-                    beta_start**0.5,
-                    beta_end**0.5,
-                    num_train_timesteps,
-                    dtype=torch.float32,
-                )
-                ** 2
-            )
-        elif beta_schedule == "squaredcos_cap_v2":
-            # Glide cosine schedule
-            self.betas = betas_for_alpha_bar(num_train_timesteps)
-        else:
-            raise NotImplementedError(
-                f"{beta_schedule} does is not implemented for {self.__class__}"
-            )
-
-        # Rescale for zero SNR
-        if rescale_betas_zero_snr:
-            self.betas = rescale_zero_terminal_snr(self.betas)
-
-        self.alphas = 1.0 - self.betas
-        self.alphas_cumprod = torch.cumprod(self.alphas, dim=0)
-
-        # At every step in ddim, we are looking into the previous alphas_cumprod
-        # For the final step, there is no previous alphas_cumprod because we are already at 0
-        # `set_alpha_to_one` decides whether we set this parameter simply to one or
-        # whether we use the final alpha of the "non-previous" one.
-        self.final_alpha_cumprod = (
-            torch.tensor(1.0) if set_alpha_to_one else self.alphas_cumprod[0]
-        )
-
-        # standard deviation of the initial noise distribution
-        self.init_noise_sigma = 1.0
-
-        # setable values
-        self.num_inference_steps = None
-        self.timesteps = torch.from_numpy(
-            np.arange(0, num_train_timesteps)[::-1].copy().astype(np.int64)
-        )
-
-    def scale_model_input(
-        self, sample: torch.FloatTensor, timestep: Optional[int] = None
-    ) -> torch.FloatTensor:
-        """
-        Ensures interchangeability with schedulers that need to scale the denoising model input depending on the
-        current timestep.
-        Args:
-            sample (`torch.FloatTensor`):
-                The input sample.
-            timestep (`int`, *optional*):
-                The current timestep in the diffusion chain.
-        Returns:
-            `torch.FloatTensor`:
-                A scaled input sample.
-        """
-        return sample
-
-    def _get_variance(self, timestep, prev_timestep):
-        alpha_prod_t = self.alphas_cumprod[timestep]
-        alpha_prod_t_prev = (
-            self.alphas_cumprod[prev_timestep]
-            if prev_timestep >= 0
-            else self.final_alpha_cumprod
-        )
-        beta_prod_t = 1 - alpha_prod_t
-        beta_prod_t_prev = 1 - alpha_prod_t_prev
-
-        variance = (beta_prod_t_prev / beta_prod_t) * (
-            1 - alpha_prod_t / alpha_prod_t_prev
-        )
-
-        return variance
-
-    # Copied from diffusers.schedulers.scheduling_ddpm.DDPMScheduler._threshold_sample
-    def _threshold_sample(self, sample: torch.FloatTensor) -> torch.FloatTensor:
-        """
-        "Dynamic thresholding: At each sampling step we set s to a certain percentile absolute pixel value in xt0 (the
-        prediction of x_0 at timestep t), and if s > 1, then we threshold xt0 to the range [-s, s] and then divide by
-        s. Dynamic thresholding pushes saturated pixels (those near -1 and 1) inwards, thereby actively preventing
-        pixels from saturation at each step. We find that dynamic thresholding results in significantly better
-        photorealism as well as better image-text alignment, especially when using very large guidance weights."
-        https://arxiv.org/abs/2205.11487
-        """
-        dtype = sample.dtype
-        batch_size, channels, height, width = sample.shape
-
-        if dtype not in (torch.float32, torch.float64):
-            sample = (
-                sample.float()
-            )  # upcast for quantile calculation, and clamp not implemented for cpu half
-
-        # Flatten sample for doing quantile calculation along each image
-        sample = sample.reshape(batch_size, channels * height * width)
-
-        abs_sample = sample.abs()  # "a certain percentile absolute pixel value"
-
-        s = torch.quantile(abs_sample, self.config.dynamic_thresholding_ratio, dim=1)
-        s = torch.clamp(
-            s, min=1, max=self.config.sample_max_value
-        )  # When clamped to min=1, equivalent to standard clipping to [-1, 1]
-
-        s = s.unsqueeze(1)  # (batch_size, 1) because clamp will broadcast along dim=0
-        sample = (
-            torch.clamp(sample, -s, s) / s
-        )  # "we threshold xt0 to the range [-s, s] and then divide by s"
-
-        sample = sample.reshape(batch_size, channels, height, width)
-        sample = sample.to(dtype)
-
-        return sample
-
-    def set_timesteps(
-        self,
-        num_inference_steps: int,
-        lcm_origin_steps: int,
-        device: Union[str, torch.device] = None,
-    ):
-        """
-        Sets the discrete timesteps used for the diffusion chain (to be run before inference).
-        Args:
-            num_inference_steps (`int`):
-                The number of diffusion steps used when generating samples with a pre-trained model.
-        """
-
-        if num_inference_steps > self.config.num_train_timesteps:
-            raise ValueError(
-                f"`num_inference_steps`: {num_inference_steps} cannot be larger than `self.config.train_timesteps`:"
-                f" {self.config.num_train_timesteps} as the unet model trained with this scheduler can only handle"
-                f" maximal {self.config.num_train_timesteps} timesteps."
-            )
-
-        self.num_inference_steps = num_inference_steps
-
-        # LCM Timesteps Setting:  # Linear Spacing
-        c = self.config.num_train_timesteps // lcm_origin_steps
-        lcm_origin_timesteps = (
-            np.asarray(list(range(1, lcm_origin_steps + 1))) * c - 1
-        )  # LCM Training  Steps Schedule
-        skipping_step = len(lcm_origin_timesteps) // num_inference_steps
-        timesteps = lcm_origin_timesteps[::-skipping_step][
-            :num_inference_steps
-        ]  # LCM Inference Steps Schedule
-
-        self.timesteps = torch.from_numpy(timesteps.copy()).to(device)
-
-    def get_scalings_for_boundary_condition_discrete(self, t):
-        self.sigma_data = 0.5  # Default: 0.5
-
-        # By dividing 0.1: This is almost a delta function at t=0.
-        c_skip = self.sigma_data**2 / ((t / 0.1) ** 2 + self.sigma_data**2)
-        c_out = (t / 0.1) / ((t / 0.1) ** 2 + self.sigma_data**2) ** 0.5
-        return c_skip, c_out
-
-    def step(
-        self,
-        model_output: torch.FloatTensor,
-        timeindex: int,
-        timestep: int,
-        sample: torch.FloatTensor,
-        eta: float = 0.0,
-        use_clipped_model_output: bool = False,
-        generator=None,
-        variance_noise: Optional[torch.FloatTensor] = None,
-        return_dict: bool = True,
-    ) -> Union[LCMSchedulerOutput, Tuple]:
-        """
-        Predict the sample from the previous timestep by reversing the SDE. This function propagates the diffusion
-        process from the learned model outputs (most often the predicted noise).
-        Args:
-            model_output (`torch.FloatTensor`):
-                The direct output from learned diffusion model.
-            timestep (`float`):
-                The current discrete timestep in the diffusion chain.
-            sample (`torch.FloatTensor`):
-                A current instance of a sample created by the diffusion process.
-            eta (`float`):
-                The weight of noise for added noise in diffusion step.
-            use_clipped_model_output (`bool`, defaults to `False`):
-                If `True`, computes "corrected" `model_output` from the clipped predicted original sample. Necessary
-                because predicted original sample is clipped to [-1, 1] when `self.config.clip_sample` is `True`. If no
-                clipping has happened, "corrected" `model_output` would coincide with the one provided as input and
-                `use_clipped_model_output` has no effect.
-            generator (`torch.Generator`, *optional*):
-                A random number generator.
-            variance_noise (`torch.FloatTensor`):
-                Alternative to generating noise with `generator` by directly providing the noise for the variance
-                itself. Useful for methods such as [`CycleDiffusion`].
-            return_dict (`bool`, *optional*, defaults to `True`):
-                Whether or not to return a [`~schedulers.scheduling_lcm.LCMSchedulerOutput`] or `tuple`.
-        Returns:
-            [`~schedulers.scheduling_utils.LCMSchedulerOutput`] or `tuple`:
-                If return_dict is `True`, [`~schedulers.scheduling_lcm.LCMSchedulerOutput`] is returned, otherwise a
-                tuple is returned where the first element is the sample tensor.
-        """
-        if self.num_inference_steps is None:
-            raise ValueError(
-                "Number of inference steps is 'None', you need to run 'set_timesteps' after creating the scheduler"
-            )
-
-        # 1. get previous step value
-        prev_timeindex = timeindex + 1
-        if prev_timeindex < len(self.timesteps):
-            prev_timestep = self.timesteps[prev_timeindex]
-        else:
-            prev_timestep = timestep
-
-        # 2. compute alphas, betas
-        alpha_prod_t = self.alphas_cumprod[timestep]
-        alpha_prod_t_prev = (
-            self.alphas_cumprod[prev_timestep]
-            if prev_timestep >= 0
-            else self.final_alpha_cumprod
-        )
-
-        beta_prod_t = 1 - alpha_prod_t
-        beta_prod_t_prev = 1 - alpha_prod_t_prev
-
-        # 3. Get scalings for boundary conditions
-        c_skip, c_out = self.get_scalings_for_boundary_condition_discrete(timestep)
-
-        # 4. Different Parameterization:
-        parameterization = self.config.prediction_type
-
-        if parameterization == "epsilon":  # noise-prediction
-            pred_x0 = (sample - beta_prod_t.sqrt() * model_output) / alpha_prod_t.sqrt()
-
-        elif parameterization == "sample":  # x-prediction
-            pred_x0 = model_output
-
-        elif parameterization == "v_prediction":  # v-prediction
-            pred_x0 = alpha_prod_t.sqrt() * sample - beta_prod_t.sqrt() * model_output
-
-        # 4. Denoise model output using boundary conditions
-        denoised = c_out * pred_x0 + c_skip * sample
-
-        # 5. Sample z ~ N(0, I), For MultiStep Inference
-        # Noise is not used for one-step sampling.
-        if len(self.timesteps) > 1:
-            noise = torch.randn(model_output.shape).to(model_output.device)
-            prev_sample = (
-                alpha_prod_t_prev.sqrt() * denoised + beta_prod_t_prev.sqrt() * noise
-            )
-        else:
-            prev_sample = denoised
-
-        if not return_dict:
-            return (prev_sample, denoised)
-
-        return LCMSchedulerOutput(prev_sample=prev_sample, denoised=denoised)
-
-    # Copied from diffusers.schedulers.scheduling_ddpm.DDPMScheduler.add_noise
-    def add_noise(
-        self,
-        original_samples: torch.FloatTensor,
-        noise: torch.FloatTensor,
-        timesteps: torch.IntTensor,
-    ) -> torch.FloatTensor:
-        # Make sure alphas_cumprod and timestep have same device and dtype as original_samples
-        alphas_cumprod = self.alphas_cumprod.to(
-            device=original_samples.device, dtype=original_samples.dtype
-        )
-        timesteps = timesteps.to(original_samples.device)
-
-        sqrt_alpha_prod = alphas_cumprod[timesteps] ** 0.5
-        sqrt_alpha_prod = sqrt_alpha_prod.flatten()
-        while len(sqrt_alpha_prod.shape) < len(original_samples.shape):
-            sqrt_alpha_prod = sqrt_alpha_prod.unsqueeze(-1)
-
-        sqrt_one_minus_alpha_prod = (1 - alphas_cumprod[timesteps]) ** 0.5
-        sqrt_one_minus_alpha_prod = sqrt_one_minus_alpha_prod.flatten()
-        while len(sqrt_one_minus_alpha_prod.shape) < len(original_samples.shape):
-            sqrt_one_minus_alpha_prod = sqrt_one_minus_alpha_prod.unsqueeze(-1)
-
-        noisy_samples = (
-            sqrt_alpha_prod * original_samples + sqrt_one_minus_alpha_prod * noise
-        )
-        return noisy_samples
-
-    # Copied from diffusers.schedulers.scheduling_ddpm.DDPMScheduler.get_velocity
-    def get_velocity(
-        self,
-        sample: torch.FloatTensor,
-        noise: torch.FloatTensor,
-        timesteps: torch.IntTensor,
-    ) -> torch.FloatTensor:
-        # Make sure alphas_cumprod and timestep have same device and dtype as sample
-        alphas_cumprod = self.alphas_cumprod.to(
-            device=sample.device, dtype=sample.dtype
-        )
-        timesteps = timesteps.to(sample.device)
-
-        sqrt_alpha_prod = alphas_cumprod[timesteps] ** 0.5
-        sqrt_alpha_prod = sqrt_alpha_prod.flatten()
-        while len(sqrt_alpha_prod.shape) < len(sample.shape):
-            sqrt_alpha_prod = sqrt_alpha_prod.unsqueeze(-1)
-
-        sqrt_one_minus_alpha_prod = (1 - alphas_cumprod[timesteps]) ** 0.5
-        sqrt_one_minus_alpha_prod = sqrt_one_minus_alpha_prod.flatten()
-        while len(sqrt_one_minus_alpha_prod.shape) < len(sample.shape):
-            sqrt_one_minus_alpha_prod = sqrt_one_minus_alpha_prod.unsqueeze(-1)
-
-        velocity = sqrt_alpha_prod * noise - sqrt_one_minus_alpha_prod * sample
-        return velocity
-
-    def __len__(self):
-        return self.config.num_train_timesteps

requirements.txt

@@ -1,4 +1,4 @@
-diffusers==0.21.4 
+diffusers==0.22.1
 transformers==4.34.1
 gradio==3.50.2
 --extra-index-url https://download.pytorch.org/whl/cu121