# Copyright 2023 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. import inspect from dataclasses import dataclass from typing import Callable, Dict, List, Optional, Union import cv2, os, sys import numpy as np import PIL.Image import torch import torch.nn as nn from transformers import CLIPImageProcessor, CLIPVisionModelWithProjection from diffusers.image_processor import VaeImageProcessor from diffusers.schedulers import EulerDiscreteScheduler from diffusers.models import AutoencoderKLTemporalDecoder from diffusers.utils import BaseOutput, logging from diffusers.utils.torch_utils import randn_tensor from diffusers import DiffusionPipeline # Import files from the local folder root_path = os.path.abspath('.') sys.path.append(root_path) from utils.img_utils import tensor2np from svd.temporal_controlnet import ControlNetModel from svd.unet_spatio_temporal_condition import UNetSpatioTemporalConditionModel logger = logging.get_logger(__name__) # pylint: disable=invalid-name def _append_dims(x, target_dims): """Appends dimensions to the end of a tensor until it has target_dims dimensions.""" dims_to_append = target_dims - x.ndim if dims_to_append < 0: raise ValueError(f"input has {x.ndim} dims but target_dims is {target_dims}, which is less") return x[(...,) + (None,) * dims_to_append] def tensor2vid(video: torch.Tensor, processor, output_type="np"): # Based on: # https://github.com/modelscope/modelscope/blob/1509fdb973e5871f37148a4b5e5964cafd43e64d/modelscope/pipelines/multi_modal/text_to_video_synthesis_pipeline.py#L78 batch_size, channels, num_frames, height, width = video.shape outputs = [] for batch_idx in range(batch_size): batch_vid = video[batch_idx].permute(1, 0, 2, 3) batch_output = processor.postprocess(batch_vid, output_type) outputs.append(batch_output) return outputs @dataclass class StableVideoDiffusionPipelineOutput(BaseOutput): r""" Output class for zero-shot text-to-video pipeline. Args: frames (`[List[PIL.Image.Image]`, `np.ndarray`]): List of denoised PIL images of length `batch_size` or NumPy array of shape `(batch_size, height, width, num_channels)`. """ frames: Union[List[PIL.Image.Image], np.ndarray] class StableVideoDiffusionControlNetPipeline(DiffusionPipeline): r""" Pipeline to generate video from an input image using Stable Video Diffusion. This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods implemented for all pipelines (downloading, saving, running on a particular device, etc.). Args: vae ([`AutoencoderKL`]): Variational Auto-Encoder (VAE) model to encode and decode images to and from latent representations. image_encoder ([`~transformers.CLIPVisionModelWithProjection`]): Frozen CLIP image-encoder ([laion/CLIP-ViT-H-14-laion2B-s32B-b79K](https://huggingface.co/laion/CLIP-ViT-H-14-laion2B-s32B-b79K)). unet ([`UNetSpatioTemporalConditionModel`]): A `UNetSpatioTemporalConditionModel` to denoise the encoded image latents. scheduler ([`EulerDiscreteScheduler`]): A scheduler to be used in combination with `unet` to denoise the encoded image latents. feature_extractor ([`~transformers.CLIPImageProcessor`]): A `CLIPImageProcessor` to extract features from generated images. """ model_cpu_offload_seq = "image_encoder->unet->vae" _callback_tensor_inputs = ["latents"] def __init__( self, vae: AutoencoderKLTemporalDecoder, image_encoder: CLIPVisionModelWithProjection, unet: UNetSpatioTemporalConditionModel, scheduler: EulerDiscreteScheduler, feature_extractor: CLIPImageProcessor, ): super().__init__() # TODO: multi-controlnet consideration self.register_modules( vae = vae, image_encoder = image_encoder, unet = unet, scheduler = scheduler, 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, do_convert_rgb=True) # The vae_scale_factor is for image dimension, not for image size self.control_image_processor = VaeImageProcessor( vae_scale_factor=self.vae_scale_factor, do_convert_rgb=True, do_normalize=False ) def encode_clip(self, image, prompt, use_text, text_encoder, device, num_videos_per_prompt, do_classifier_free_guidance, use_instructpix2pix): dtype = next(self.image_encoder.parameters()).dtype if not isinstance(image, torch.Tensor): image = self.image_processor.pil_to_numpy(image) # Map [0, 255] to [0, 1] range image = self.image_processor.numpy_to_pt(image) # We normalize the image before resizing to match with the original implementation. # Then, we unnormalize it after resizing. image = image * 2.0 - 1.0 # [-1, 1] range image = _resize_with_antialiasing(image, (224, 224)) # Resize to square image image = (image + 1.0) / 2.0 # [0, 1] range # Normalize the image with for CLIP input image = self.feature_extractor( images=image, do_normalize=True, do_center_crop=False, do_resize=False, do_rescale=False, return_tensors="pt", ).pixel_values # The value range is a little deviated now, and I got [-1.76, 2.15] for one sample image = image.to(device=device, dtype=dtype) image_embeddings = self.image_encoder(image).image_embeds image_embeddings = image_embeddings.unsqueeze(1) # duplicate image embeddings for each generation per prompt, using mps friendly method bs_embed, seq_len, _ = image_embeddings.shape image_embeddings = image_embeddings.repeat(1, num_videos_per_prompt, 1) encoder_hidden_states = image_embeddings.view(bs_embed * num_videos_per_prompt, seq_len, -1) # Prepare for the text embeddings if needed if use_text: text_embeddings = text_encoder(prompt)[0] # Concat two embeddings together on dim 1 encoder_hidden_states = torch.cat((text_embeddings, encoder_hidden_states), dim=1) # Layer norm on the last dim TODO: 这里order小改了一下顺序，变成先encoder hidden states了 layer_norm = nn.LayerNorm((78, 1024)).to(device=device, dtype=dtype) encoder_hidden_states = layer_norm(encoder_hidden_states) if do_classifier_free_guidance: negative_encoder_hidden_states = torch.zeros_like(encoder_hidden_states) # For classifier free guidance, we need to do two forward passes. # Here we concatenate the unconditional and text embeddings into a single batch # to avoid doing two forward passes if use_instructpix2pix: encoder_hidden_states = torch.cat([encoder_hidden_states, negative_encoder_hidden_states, negative_encoder_hidden_states]) else: encoder_hidden_states = torch.cat([negative_encoder_hidden_states, encoder_hidden_states]) return encoder_hidden_states def _encode_vae_image( self, image: torch.Tensor, device, num_videos_per_prompt, do_classifier_free_guidance, use_instructpix2pix, ): image = image.to(device=device) image_latents = self.vae.encode(image).latent_dist.mode() if do_classifier_free_guidance: negative_image_latents = torch.zeros_like(image_latents) # For classifier free guidance, we need to do two forward passes. # Here we concatenate the unconditional and text embeddings into a single batch # to avoid doing two forward passes if use_instructpix2pix: image_latents = torch.cat([image_latents, image_latents, negative_image_latents]) else: image_latents = torch.cat([negative_image_latents, image_latents]) # duplicate image_latents for each generation per prompt, using mps friendly method image_latents = image_latents.repeat(num_videos_per_prompt, 1, 1, 1) return image_latents def _get_add_time_ids( self, fps, motion_bucket_id, noise_aug_strength, dtype, batch_size, num_videos_per_prompt, do_classifier_free_guidance, guess_mode, use_instructpix2pix, ): # Define the default values from SVD add_time_ids = [fps, motion_bucket_id, noise_aug_strength] # Sanity Check passed_add_embed_dim = self.unet.config.addition_time_embed_dim * len(add_time_ids) expected_add_embed_dim = self.unet.add_embedding.linear_1.in_features if expected_add_embed_dim != passed_add_embed_dim: raise ValueError( f"Model expects an added time embedding vector of length {expected_add_embed_dim}, but a vector of {passed_add_embed_dim} was created. The model has an incorrect config. Please check `unet.config.time_embedding_type` and `text_encoder_2.config.projection_dim`." ) add_time_ids = torch.tensor([add_time_ids], dtype=dtype) add_time_ids = add_time_ids.repeat(batch_size * num_videos_per_prompt, 1) if do_classifier_free_guidance: if use_instructpix2pix: add_time_ids = torch.cat([add_time_ids, add_time_ids, add_time_ids]) else: add_time_ids = torch.cat([add_time_ids, add_time_ids]) # Return the info return add_time_ids def decode_latents(self, latents, num_frames, decode_chunk_size=14): # [batch, frames, channels, height, width] -> [batch*frames, channels, height, width] latents = latents.flatten(0, 1) latents = 1 / self.vae.config.scaling_factor * latents accepts_num_frames = "num_frames" in set(inspect.signature(self.vae.forward).parameters.keys()) # decode decode_chunk_size frames at a time to avoid OOM frames = [] for i in range(0, latents.shape[0], decode_chunk_size): num_frames_in = latents[i : i + decode_chunk_size].shape[0] decode_kwargs = {} if accepts_num_frames: # we only pass num_frames_in if it's expected decode_kwargs["num_frames"] = num_frames_in frame = self.vae.decode(latents[i : i + decode_chunk_size], **decode_kwargs).sample frames.append(frame) frames = torch.cat(frames, dim=0) # [batch*frames, channels, height, width] -> [batch, channels, frames, height, width] frames = frames.reshape(-1, num_frames, *frames.shape[1:]).permute(0, 2, 1, 3, 4) # we always cast to float32 as this does not cause significant overhead and is compatible with bfloat16 frames = frames.float() return frames def check_inputs(self, image, height, width, ): if ( not isinstance(image, torch.Tensor) and not isinstance(image, PIL.Image.Image) and not isinstance(image, list) ): raise ValueError( "`image` has to be of type `torch.FloatTensor` or `PIL.Image.Image` or `List[PIL.Image.Image]` but is" f" {type(image)}" ) if height % 8 != 0 or width % 8 != 0: raise ValueError(f"`height` and `width` have to be divisible by 8 but are {height} and {width}.") # TODO: I didn't test input for controlnet_conditioning_scale, control_guidance_start, and control_guidance_end def prepare_latents( self, batch_size, num_frames, num_channels_latents, height, width, dtype, device, generator, latents=None, ): shape = ( batch_size, num_frames, num_channels_latents // 2, height // self.vae_scale_factor, width // self.vae_scale_factor, ) 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." ) if latents is None: latents = randn_tensor(shape, generator=generator, device=device, dtype=dtype) 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 @property def guidance_scale(self): return self._guidance_scale # We don't directly have do_classifier_free_guidance function, we judge simply by max_guidance @property def num_timesteps(self): return self._num_timesteps def prepare_condition_image( self, condition_img, width, height, batch_size, num_videos_per_prompt, device, dtype, do_classifier_free_guidance=False, guess_mode=False, ): # The input of condition_img is already in the range [0, 1] condition_img = torch.from_numpy(condition_img) # hwc -> chw condition_img = condition_img.to(torch.float16).to(self._execution_device) # Set this in default # CFG will be done in main function, not here now return condition_img # [0, 1] range && Torch data type @torch.no_grad() def __call__( self, image: Union[PIL.Image.Image, List[PIL.Image.Image], torch.FloatTensor], condition_img: np, controlnet: ControlNetModel, prompt = None, use_text: bool = False, text_encoder = None, height: int = 576, width: int = 1024, num_frames: Optional[int] = None, num_inference_steps: int = 25, min_guidance_scale: float = 1.0, max_guidance_scale: float = 3.0, fps: int = 7, motion_bucket_id: int = 127, # controlnet_image_index: Optional[int] = [0], # coordinate_values = None, noise_aug_strength: int = 0.02, decode_chunk_size: Optional[int] = None, num_videos_per_prompt: Optional[int] = 1, generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None, latents: Optional[torch.FloatTensor] = None, output_type: Optional[str] = "pil", callback_on_step_end: Optional[Callable[[int, int, Dict], None]] = None, callback_on_step_end_tensor_inputs: List[str] = ["latents"], return_dict: bool = True, controlnet_conditioning_scale: Union[float, List[float]] = 1.0, use_instructpix2pix: bool = False, control_guidance_start: Union[float, List[float]] = 0.0, control_guidance_end: Union[float, List[float]] = 1.0, inner_conditioning_scale: float = 1.0, guess_mode: bool = True, image_guidance_scale: float = 7.5, ): r""" The call function to the pipeline for generation. Args: image (`PIL.Image.Image` or `List[PIL.Image.Image]` or `torch.FloatTensor`): Image or images to guide image generation. If you provide a tensor, it needs to be compatible with [`CLIPImageProcessor`](https://huggingface.co/lambdalabs/sd-image-variations-diffusers/blob/main/feature_extractor/preprocessor_config.json). height (`int`, *optional*, defaults to `self.unet.config.sample_size * self.vae_scale_factor`): The height in pixels of the generated image. width (`int`, *optional*, defaults to `self.unet.config.sample_size * self.vae_scale_factor`): The width in pixels of the generated image. num_frames (`int`, *optional*): The number of video frames to generate. Defaults to 14 for `stable-video-diffusion-img2vid` and to 25 for `stable-video-diffusion-img2vid-xt` num_inference_steps (`int`, *optional*, defaults to 25): The number of denoising steps. More denoising steps usually lead to a higher quality image at the expense of slower inference. This parameter is modulated by `strength`. min_guidance_scale (`float`, *optional*, defaults to 1.0): The minimum guidance scale. Used for the classifier free guidance with first frame. max_guidance_scale (`float`, *optional*, defaults to 3.0): The maximum guidance scale. Used for the classifier free guidance with last frame. fps (`int`, *optional*, defaults to 7): Frames per second. The rate at which the generated images shall be exported to a video after generation. Note that Stable Diffusion Video's UNet was micro-conditioned on fps-1 during training. motion_bucket_id (`int`, *optional*, defaults to 127): The motion bucket ID. Used as conditioning for the generation. The higher the number the more motion will be in the video. noise_aug_strength (`int`, *optional*, defaults to 0.02): The amount of noise added to the init image, the higher it is the less the video will look like the init image. Increase it for more motion. decode_chunk_size (`int`, *optional*): The number of frames to decode at a time. The higher the chunk size, the higher the temporal consistency between frames, but also the higher the memory consumption. By default, the decoder will decode all frames at once for maximal quality. Reduce `decode_chunk_size` to reduce memory usage. num_videos_per_prompt (`int`, *optional*, defaults to 1): The number of images to generate per prompt. generator (`torch.Generator` or `List[torch.Generator]`, *optional*): A [`torch.Generator`](https://pytorch.org/docs/stable/generated/torch.Generator.html) to make generation deterministic. latents (`torch.FloatTensor`, *optional*): Pre-generated noisy latents sampled from a Gaussian distribution, to be used as inputs for image generation. Can be used to tweak the same generation with different prompts. If not provided, a latents tensor is generated by sampling using the supplied random `generator`. output_type (`str`, *optional*, defaults to `"pil"`): The output format of the generated image. Choose between `PIL.Image` or `np.array`. callback_on_step_end (`Callable`, *optional*): A function that calls at the end of each denoising steps during the inference. The function is called with the following arguments: `callback_on_step_end(self: DiffusionPipeline, step: int, timestep: int, callback_kwargs: Dict)`. `callback_kwargs` will include a list of all tensors as specified by `callback_on_step_end_tensor_inputs`. callback_on_step_end_tensor_inputs (`List`, *optional*): The list of tensor inputs for the `callback_on_step_end` function. The tensors specified in the list will be passed as `callback_kwargs` argument. You will only be able to include variables listed in the `._callback_tensor_inputs` attribute of your pipeline class. return_dict (`bool`, *optional*, defaults to `True`): Whether or not to return a [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] instead of a plain tuple. Returns: [`~pipelines.stable_diffusion.StableVideoDiffusionPipelineOutput`] or `tuple`: If `return_dict` is `True`, [`~pipelines.stable_diffusion.StableVideoDiffusionPipelineOutput`] is returned, otherwise a `tuple` is returned where the first element is a list of list with the generated frames. Examples: ```py from diffusers import StableVideoDiffusionPipeline from diffusers.utils import load_image, export_to_video pipe = StableVideoDiffusionPipeline.from_pretrained("stabilityai/stable-video-diffusion-img2vid-xt", torch_dtype=torch.float16, variant="fp16") pipe.to("cuda") image = load_image("https://lh3.googleusercontent.com/y-iFOHfLTwkuQSUegpwDdgKmOjRSTvPxat63dQLB25xkTs4lhIbRUFeNBWZzYf370g=s1200") image = image.resize((1024, 576)) frames = pipe(image, num_frames=25, decode_chunk_size=8).frames[0] export_to_video(frames, "generated.mp4", fps=7) ``` """ # align format for control guidance mult = 1 control_guidance_start, control_guidance_end = ( mult * [control_guidance_start], mult * [control_guidance_end], ) # 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 # It seems that self.unet.config.sample_size * self.vae_scale_factor is a default image size input setting num_frames = num_frames if num_frames is not None else self.unet.config.num_frames decode_chunk_size = decode_chunk_size if decode_chunk_size is not None else num_frames # 1. Check inputs. Raise error if not correct self.check_inputs(image, height, width) # 2. Define call parameters if isinstance(image, PIL.Image.Image): batch_size = 1 elif isinstance(image, list): batch_size = len(image) else: batch_size = image.shape[0] device = self._execution_device # here `guidance_scale` is defined analog to the guidance weight `w` of equation (2) # of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1` # corresponds to doing no classifier free guidance. do_classifier_free_guidance = max_guidance_scale > 1.0 if do_classifier_free_guidance: print("We will use CFG!!!") # 3. Encode input image encoder_hidden_states = self.encode_clip(image, prompt, use_text, text_encoder, device, num_videos_per_prompt, do_classifier_free_guidance, use_instructpix2pix) # NOTE: Stable Diffusion Video was conditioned on fps - 1, which # is why it is reduced here. # See: https://github.com/Stability-AI/generative-models/blob/ed0997173f98eaf8f4edf7ba5fe8f15c6b877fd3/scripts/sampling/simple_video_sample.py#L188 fps = fps - 1 # 4. Encode input image using VAE image = self.image_processor.preprocess(image, height=height, width=width) # [0, 255] to [-1, 1] noise = randn_tensor(image.shape, generator=generator, device=image.device, dtype=image.dtype) image = image + noise_aug_strength * noise needs_upcasting = self.vae.dtype == torch.float16 and self.vae.config.force_upcast if needs_upcasting: self.vae.to(dtype=torch.float32) image_latents = self._encode_vae_image(image, device, num_videos_per_prompt, do_classifier_free_guidance, use_instructpix2pix) image_latents = image_latents.to(encoder_hidden_states.dtype) # cast back to fp16 if needed if needs_upcasting: self.vae.to(dtype=torch.float16) # Repeat the image latents for each frame so we can concatenate them with the noise # image_latents [batch, channels, height, width] ->[batch, num_frames, channels, height, width] image_latents = image_latents.unsqueeze(1).repeat(1, num_frames, 1, 1, 1) # 4.5 Prepare control image (Will need to consider multiControlNet) condition_img = self.prepare_condition_image( condition_img = condition_img, width = width, height = height, batch_size = batch_size * num_videos_per_prompt, num_videos_per_prompt = num_videos_per_prompt, device = device, dtype = controlnet.dtype, do_classifier_free_guidance = do_classifier_free_guidance, guess_mode = guess_mode, ) # [0, 255] to [0, 1] range # 5. Get Added Time IDs added_time_ids = self._get_add_time_ids( fps, motion_bucket_id, noise_aug_strength, # coordinate_values, encoder_hidden_states.dtype, batch_size, num_videos_per_prompt, do_classifier_free_guidance, guess_mode = guess_mode, use_instructpix2pix = use_instructpix2pix, ) added_time_ids = added_time_ids.to(device) # 4. Prepare timesteps self.scheduler.set_timesteps(num_inference_steps, device=device) timesteps = self.scheduler.timesteps # 5. Prepare latent variables num_channels_latents = self.unet.config.in_channels latents = self.prepare_latents( batch_size * num_videos_per_prompt, num_frames, num_channels_latents, height, width, encoder_hidden_states.dtype, device, generator, latents, ) # Nosiy latents across all frames needed # 7. Prepare guidance scale guidance_scale = torch.linspace(min_guidance_scale, max_guidance_scale, num_frames).unsqueeze(0) guidance_scale = guidance_scale.to(device, latents.dtype) guidance_scale = guidance_scale.repeat(batch_size * num_videos_per_prompt, 1) guidance_scale = _append_dims(guidance_scale, latents.ndim) self._guidance_scale = guidance_scale # 7.5 Create tensor stating which controlnets to keep controlnet_keep = [] for i in range(len(timesteps)): keeps = [ 1.0 - float(i / len(timesteps) < s or (i + 1) / len(timesteps) > e) for s, e in zip(control_guidance_start, control_guidance_end) ] controlnet_keep.append(keeps[0] if isinstance(controlnet, ControlNetModel) else keeps) # 8. Denoising loop num_warmup_steps = len(timesteps) - num_inference_steps * self.scheduler.order self._num_timesteps = len(timesteps) with self.progress_bar(total=num_inference_steps) as progress_bar: for i, t in enumerate(timesteps): # print("This is timestep ", t) # expand the latents if we are doing classifier free guidance if use_instructpix2pix: latent_model_input = torch.cat([latents] * 3) if do_classifier_free_guidance else latents else: latent_model_input = torch.cat([latents] * 2) if do_classifier_free_guidance else latents latent_model_input = self.scheduler.scale_model_input(latent_model_input, t) # I think that this is where sequential generation takes influence # Concatenate image_latents over channels dimension for video diffusion purposes latent_model_input = torch.cat([latent_model_input, image_latents], dim=2) # image_latents is fixed and latent_model_input will be based on latents which is updated frequently # ControlNet Scale if isinstance(controlnet_keep[i], list): cond_scale = [c * s for c, s in zip(controlnet_conditioning_scale, controlnet_keep[i])] else: controlnet_cond_scale = controlnet_conditioning_scale if isinstance(controlnet_cond_scale, list): controlnet_cond_scale = controlnet_cond_scale[0] cond_scale = controlnet_cond_scale * controlnet_keep[i] # assert condition_img.shape[1] >= len(controlnet_image_index) # VAE encode controlnet_cond = self.vae.encode(condition_img).latent_dist.mode() if do_classifier_free_guidance: if use_instructpix2pix: controlnet_cond = torch.cat([controlnet_cond, controlnet_cond, controlnet_cond]) # controlnet_conditioning_mask = torch.cat([controlnet_conditioning_mask, controlnet_conditioning_mask, controlnet_conditioning_mask]) else: controlnet_cond = torch.cat([controlnet_cond, controlnet_cond]) # controlnet_conditioning_mask = torch.cat([controlnet_conditioning_mask, controlnet_conditioning_mask]) down_block_res_samples, mid_block_res_sample = controlnet( sample = latent_model_input, timestep = t, encoder_hidden_states = encoder_hidden_states, added_time_ids = added_time_ids, controlnet_cond = controlnet_cond, return_dict = False, inner_conditioning_scale = inner_conditioning_scale, # Inner conditioning scale conditioning_scale = cond_scale, # Outer conditioning scale guess_mode = guess_mode, ) if guess_mode and do_classifier_free_guidance: # Won't consider this one, since we don't use guess mode # Infered ControlNet only for the conditional batch. # To apply the output of ControlNet to both the unconditional and conditional batches, # add 0 to the unconditional batch to keep it unchanged. down_block_res_samples = [torch.cat([torch.zeros_like(d), d]) for d in down_block_res_samples] mid_block_res_sample = torch.cat([torch.zeros_like(mid_block_res_sample), mid_block_res_sample]) # predict the noise residual noise_pred = self.unet( sample = latent_model_input, # [batch, frames, 4*2, height, width] timestep = t, encoder_hidden_states = encoder_hidden_states, added_time_ids = added_time_ids, down_block_additional_residuals = down_block_res_samples, mid_block_additional_residual = mid_block_res_sample, return_dict = False, )[0] # image_embeddings is used for cross attention metioned in the paper # perform guidance if do_classifier_free_guidance: if use_instructpix2pix: noise_pred_1st_frame, noise_pred_cond, noise_pred_uncond = noise_pred.chunk(3) # There are two noises here: one is unconditional and one is conditional noise_pred = noise_pred_uncond + \ self.guidance_scale * (noise_pred_cond - noise_pred_uncond) + \ image_guidance_scale * (noise_pred_cond - noise_pred_1st_frame) # InstructPix2Pix is (noise_pred_1st_frame - noise_pred_cond) else: noise_pred_uncond, noise_pred_cond = noise_pred.chunk(2) # There are two noises here: one is unconditional and one is conditional noise_pred = noise_pred_uncond + self.guidance_scale * (noise_pred_cond - noise_pred_uncond) # compute the previous noisy sample x_t -> x_t-1 latents = self.scheduler.step(noise_pred, t, latents).prev_sample if callback_on_step_end is not None: callback_kwargs = {} for k in callback_on_step_end_tensor_inputs: callback_kwargs[k] = locals()[k] callback_outputs = callback_on_step_end(self, i, t, callback_kwargs) latents = callback_outputs.pop("latents", latents) if i == len(timesteps) - 1 or ((i + 1) > num_warmup_steps and (i + 1) % self.scheduler.order == 0): progress_bar.update() if not output_type == "latent": # cast back to fp16 if needed if needs_upcasting: self.vae.to(dtype=torch.float16) frames = self.decode_latents(latents, num_frames, decode_chunk_size) frames = tensor2vid(frames, self.image_processor, output_type=output_type) else: frames = latents self.maybe_free_model_hooks() if not return_dict: return frames return StableVideoDiffusionPipelineOutput(frames=frames) # resizing utils # TODO: clean up later (put to shared utils file) def _resize_with_antialiasing(input, size, interpolation="bicubic", align_corners=True): h, w = input.shape[-2:] factors = (h / size[0], w / size[1]) # First, we have to determine sigma # Taken from skimage: https://github.com/scikit-image/scikit-image/blob/v0.19.2/skimage/transform/_warps.py#L171 sigmas = ( max((factors[0] - 1.0) / 2.0, 0.001), max((factors[1] - 1.0) / 2.0, 0.001), ) # Now kernel size. Good results are for 3 sigma, but that is kind of slow. Pillow uses 1 sigma # https://github.com/python-pillow/Pillow/blob/master/src/libImaging/Resample.c#L206 # But they do it in the 2 passes, which gives better results. Let's try 2 sigmas for now ks = int(max(2.0 * 2 * sigmas[0], 3)), int(max(2.0 * 2 * sigmas[1], 3)) # Make sure it is odd if (ks[0] % 2) == 0: ks = ks[0] + 1, ks[1] if (ks[1] % 2) == 0: ks = ks[0], ks[1] + 1 input = _gaussian_blur2d(input, ks, sigmas) output = torch.nn.functional.interpolate(input, size=size, mode=interpolation, align_corners=align_corners) return output def _compute_padding(kernel_size): """Compute padding tuple.""" # 4 or 6 ints: (padding_left, padding_right,padding_top,padding_bottom) # https://pytorch.org/docs/stable/nn.html#torch.nn.functional.pad if len(kernel_size) < 2: raise AssertionError(kernel_size) computed = [k - 1 for k in kernel_size] # for even kernels we need to do asymmetric padding :( out_padding = 2 * len(kernel_size) * [0] for i in range(len(kernel_size)): computed_tmp = computed[-(i + 1)] pad_front = computed_tmp // 2 pad_rear = computed_tmp - pad_front out_padding[2 * i + 0] = pad_front out_padding[2 * i + 1] = pad_rear return out_padding def _filter2d(input, kernel): # prepare kernel b, c, h, w = input.shape tmp_kernel = kernel[:, None, ...].to(device=input.device, dtype=input.dtype) tmp_kernel = tmp_kernel.expand(-1, c, -1, -1) height, width = tmp_kernel.shape[-2:] padding_shape: list[int] = _compute_padding([height, width]) input = torch.nn.functional.pad(input, padding_shape, mode="reflect") # kernel and input tensor reshape to align element-wise or batch-wise params tmp_kernel = tmp_kernel.reshape(-1, 1, height, width) input = input.view(-1, tmp_kernel.size(0), input.size(-2), input.size(-1)) # convolve the tensor with the kernel. output = torch.nn.functional.conv2d(input, tmp_kernel, groups=tmp_kernel.size(0), padding=0, stride=1) out = output.view(b, c, h, w) return out def _gaussian(window_size: int, sigma): if isinstance(sigma, float): sigma = torch.tensor([[sigma]]) batch_size = sigma.shape[0] x = (torch.arange(window_size, device=sigma.device, dtype=sigma.dtype) - window_size // 2).expand(batch_size, -1) if window_size % 2 == 0: x = x + 0.5 gauss = torch.exp(-x.pow(2.0) / (2 * sigma.pow(2.0))) return gauss / gauss.sum(-1, keepdim=True) def _gaussian_blur2d(input, kernel_size, sigma): if isinstance(sigma, tuple): sigma = torch.tensor([sigma], dtype=input.dtype) else: sigma = sigma.to(dtype=input.dtype) ky, kx = int(kernel_size[0]), int(kernel_size[1]) bs = sigma.shape[0] kernel_x = _gaussian(kx, sigma[:, 1].view(bs, 1)) kernel_y = _gaussian(ky, sigma[:, 0].view(bs, 1)) out_x = _filter2d(input, kernel_x[..., None, :]) out = _filter2d(out_x, kernel_y[..., None]) return out def is_compiled_module(module) -> bool: """Check whether the module was compiled with torch.compile()""" if is_torch_version("<", "2.0.0") or not hasattr(torch, "_dynamo"): return False return isinstance(module, torch._dynamo.eval_frame.OptimizedModule)