# 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 sys from dataclasses import dataclass from typing import Any, Callable, Dict, List, Optional, Tuple, Union import numpy as np import PIL.Image import torch import torch.nn.functional as F import torchvision.transforms as T from transformers import CLIPImageProcessor, CLIPTextModel, CLIPTokenizer from diffusers.image_processor import VaeImageProcessor from diffusers.models import AutoencoderKL, ControlNetModel, UNet2DConditionModel from diffusers.models.attention_processor import Attention, AttnProcessor from diffusers.pipelines.controlnet.multicontrolnet import MultiControlNetModel from diffusers.pipelines.controlnet.pipeline_controlnet_img2img import StableDiffusionControlNetImg2ImgPipeline from diffusers.pipelines.stable_diffusion.safety_checker import StableDiffusionSafetyChecker from diffusers.schedulers import KarrasDiffusionSchedulers from diffusers.utils import BaseOutput, deprecate, logging from diffusers.utils.torch_utils import is_compiled_module, randn_tensor gmflow_dir = "/path/to/gmflow" sys.path.insert(0, gmflow_dir) from gmflow.gmflow import GMFlow # noqa: E402 from utils.utils import InputPadder # noqa: E402 logger = logging.get_logger(__name__) # pylint: disable=invalid-name def coords_grid(b, h, w, homogeneous=False, device=None): y, x = torch.meshgrid(torch.arange(h), torch.arange(w)) # [H, W] stacks = [x, y] if homogeneous: ones = torch.ones_like(x) # [H, W] stacks.append(ones) grid = torch.stack(stacks, dim=0).float() # [2, H, W] or [3, H, W] grid = grid[None].repeat(b, 1, 1, 1) # [B, 2, H, W] or [B, 3, H, W] if device is not None: grid = grid.to(device) return grid def bilinear_sample(img, sample_coords, mode="bilinear", padding_mode="zeros", return_mask=False): # img: [B, C, H, W] # sample_coords: [B, 2, H, W] in image scale if sample_coords.size(1) != 2: # [B, H, W, 2] sample_coords = sample_coords.permute(0, 3, 1, 2) b, _, h, w = sample_coords.shape # Normalize to [-1, 1] x_grid = 2 * sample_coords[:, 0] / (w - 1) - 1 y_grid = 2 * sample_coords[:, 1] / (h - 1) - 1 grid = torch.stack([x_grid, y_grid], dim=-1) # [B, H, W, 2] img = F.grid_sample(img, grid, mode=mode, padding_mode=padding_mode, align_corners=True) if return_mask: mask = (x_grid >= -1) & (y_grid >= -1) & (x_grid <= 1) & (y_grid <= 1) # [B, H, W] return img, mask return img def flow_warp(feature, flow, mask=False, mode="bilinear", padding_mode="zeros"): b, c, h, w = feature.size() assert flow.size(1) == 2 grid = coords_grid(b, h, w).to(flow.device) + flow # [B, 2, H, W] grid = grid.to(feature.dtype) return bilinear_sample(feature, grid, mode=mode, padding_mode=padding_mode, return_mask=mask) def forward_backward_consistency_check(fwd_flow, bwd_flow, alpha=0.01, beta=0.5): # fwd_flow, bwd_flow: [B, 2, H, W] # alpha and beta values are following UnFlow # (https://arxiv.org/abs/1711.07837) assert fwd_flow.dim() == 4 and bwd_flow.dim() == 4 assert fwd_flow.size(1) == 2 and bwd_flow.size(1) == 2 flow_mag = torch.norm(fwd_flow, dim=1) + torch.norm(bwd_flow, dim=1) # [B, H, W] warped_bwd_flow = flow_warp(bwd_flow, fwd_flow) # [B, 2, H, W] warped_fwd_flow = flow_warp(fwd_flow, bwd_flow) # [B, 2, H, W] diff_fwd = torch.norm(fwd_flow + warped_bwd_flow, dim=1) # [B, H, W] diff_bwd = torch.norm(bwd_flow + warped_fwd_flow, dim=1) threshold = alpha * flow_mag + beta fwd_occ = (diff_fwd > threshold).float() # [B, H, W] bwd_occ = (diff_bwd > threshold).float() return fwd_occ, bwd_occ @torch.no_grad() def get_warped_and_mask(flow_model, image1, image2, image3=None, pixel_consistency=False): if image3 is None: image3 = image1 padder = InputPadder(image1.shape, padding_factor=8) image1, image2 = padder.pad(image1[None].cuda(), image2[None].cuda()) results_dict = flow_model( image1, image2, attn_splits_list=[2], corr_radius_list=[-1], prop_radius_list=[-1], pred_bidir_flow=True ) flow_pr = results_dict["flow_preds"][-1] # [B, 2, H, W] fwd_flow = padder.unpad(flow_pr[0]).unsqueeze(0) # [1, 2, H, W] bwd_flow = padder.unpad(flow_pr[1]).unsqueeze(0) # [1, 2, H, W] fwd_occ, bwd_occ = forward_backward_consistency_check(fwd_flow, bwd_flow) # [1, H, W] float if pixel_consistency: warped_image1 = flow_warp(image1, bwd_flow) bwd_occ = torch.clamp( bwd_occ + (abs(image2 - warped_image1).mean(dim=1) > 255 * 0.25).float(), 0, 1 ).unsqueeze(0) warped_results = flow_warp(image3, bwd_flow) return warped_results, bwd_occ, bwd_flow blur = T.GaussianBlur(kernel_size=(9, 9), sigma=(18, 18)) @dataclass class TextToVideoSDPipelineOutput(BaseOutput): """ Output class for text-to-video pipelines. Args: frames (`List[np.ndarray]` or `torch.FloatTensor`) List of denoised frames (essentially images) as NumPy arrays of shape `(height, width, num_channels)` or as a `torch` tensor. The length of the list denotes the video length (the number of frames). """ frames: Union[List[np.ndarray], torch.FloatTensor] @torch.no_grad() def find_flat_region(mask): device = mask.device kernel_x = torch.Tensor([[-1, 0, 1], [-1, 0, 1], [-1, 0, 1]]).unsqueeze(0).unsqueeze(0).to(device) kernel_y = torch.Tensor([[-1, -1, -1], [0, 0, 0], [1, 1, 1]]).unsqueeze(0).unsqueeze(0).to(device) mask_ = F.pad(mask.unsqueeze(0), (1, 1, 1, 1), mode="replicate") grad_x = torch.nn.functional.conv2d(mask_, kernel_x) grad_y = torch.nn.functional.conv2d(mask_, kernel_y) return ((abs(grad_x) + abs(grad_y)) == 0).float()[0] class AttnState: STORE = 0 LOAD = 1 LOAD_AND_STORE_PREV = 2 def __init__(self): self.reset() @property def state(self): return self.__state @property def timestep(self): return self.__timestep def set_timestep(self, t): self.__timestep = t def reset(self): self.__state = AttnState.STORE self.__timestep = 0 def to_load(self): self.__state = AttnState.LOAD def to_load_and_store_prev(self): self.__state = AttnState.LOAD_AND_STORE_PREV class CrossFrameAttnProcessor(AttnProcessor): """ Cross frame attention processor. Each frame attends the first frame and previous frame. Args: attn_state: Whether the model is processing the first frame or an intermediate frame """ def __init__(self, attn_state: AttnState): super().__init__() self.attn_state = attn_state self.first_maps = {} self.prev_maps = {} def __call__(self, attn: Attention, hidden_states, encoder_hidden_states=None, attention_mask=None, temb=None): # Is self attention if encoder_hidden_states is None: t = self.attn_state.timestep if self.attn_state.state == AttnState.STORE: self.first_maps[t] = hidden_states.detach() self.prev_maps[t] = hidden_states.detach() res = super().__call__(attn, hidden_states, encoder_hidden_states, attention_mask, temb) else: if self.attn_state.state == AttnState.LOAD_AND_STORE_PREV: tmp = hidden_states.detach() cross_map = torch.cat((self.first_maps[t], self.prev_maps[t]), dim=1) res = super().__call__(attn, hidden_states, cross_map, attention_mask, temb) if self.attn_state.state == AttnState.LOAD_AND_STORE_PREV: self.prev_maps[t] = tmp else: res = super().__call__(attn, hidden_states, encoder_hidden_states, attention_mask, temb) return res def prepare_image(image): if isinstance(image, torch.Tensor): # Batch single image if image.ndim == 3: image = image.unsqueeze(0) image = image.to(dtype=torch.float32) else: # preprocess image if isinstance(image, (PIL.Image.Image, np.ndarray)): image = [image] if isinstance(image, list) and isinstance(image[0], PIL.Image.Image): image = [np.array(i.convert("RGB"))[None, :] for i in image] image = np.concatenate(image, axis=0) elif isinstance(image, list) and isinstance(image[0], np.ndarray): image = np.concatenate([i[None, :] for i in image], axis=0) image = image.transpose(0, 3, 1, 2) image = torch.from_numpy(image).to(dtype=torch.float32) / 127.5 - 1.0 return image class RerenderAVideoPipeline(StableDiffusionControlNetImg2ImgPipeline): r""" Pipeline for video-to-video translation using Stable Diffusion with Rerender Algorithm. This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods the library implements for all the pipelines (such as downloading or saving, running on a particular device, etc.) In addition the pipeline inherits the following loading methods: - *Textual-Inversion*: [`loaders.TextualInversionLoaderMixin.load_textual_inversion`] Args: vae ([`AutoencoderKL`]): Variational Auto-Encoder (VAE) Model to encode and decode images to and from latent representations. text_encoder ([`CLIPTextModel`]): Frozen text-encoder. Stable Diffusion uses the text portion of [CLIP](https://huggingface.co/docs/transformers/model_doc/clip#transformers.CLIPTextModel), specifically the [clip-vit-large-patch14](https://huggingface.co/openai/clip-vit-large-patch14) variant. tokenizer (`CLIPTokenizer`): Tokenizer of class [CLIPTokenizer](https://huggingface.co/docs/transformers/v4.21.0/en/model_doc/clip#transformers.CLIPTokenizer). unet ([`UNet2DConditionModel`]): Conditional U-Net architecture to denoise the encoded image latents. controlnet ([`ControlNetModel`] or `List[ControlNetModel]`): Provides additional conditioning to the unet during the denoising process. If you set multiple ControlNets as a list, the outputs from each ControlNet are added together to create one combined additional conditioning. scheduler ([`SchedulerMixin`]): A scheduler to be used in combination with `unet` to denoise the encoded image latents. Can be one of [`DDIMScheduler`], [`LMSDiscreteScheduler`], or [`PNDMScheduler`]. safety_checker ([`StableDiffusionSafetyChecker`]): Classification module that estimates whether generated images could be considered offensive or harmful. Please, refer to the [model card](https://huggingface.co/runwayml/stable-diffusion-v1-5) for details. feature_extractor ([`CLIPImageProcessor`]): Model that extracts features from generated images to be used as inputs for the `safety_checker`. """ _optional_components = ["safety_checker", "feature_extractor"] def __init__( self, vae: AutoencoderKL, text_encoder: CLIPTextModel, tokenizer: CLIPTokenizer, unet: UNet2DConditionModel, controlnet: Union[ControlNetModel, List[ControlNetModel], Tuple[ControlNetModel], MultiControlNetModel], scheduler: KarrasDiffusionSchedulers, safety_checker: StableDiffusionSafetyChecker, feature_extractor: CLIPImageProcessor, image_encoder=None, requires_safety_checker: bool = True, ): super().__init__( vae, text_encoder, tokenizer, unet, controlnet, scheduler, safety_checker, feature_extractor, image_encoder, requires_safety_checker, ) if safety_checker is None and requires_safety_checker: logger.warning( f"You have disabled the safety checker for {self.__class__} by passing `safety_checker=None`. Ensure" " that you abide to the conditions of the Stable Diffusion license and do not expose unfiltered" " results in services or applications open to the public. Both the diffusers team and Hugging Face" " strongly recommend to keep the safety filter enabled in all public facing circumstances, disabling" " it only for use-cases that involve analyzing network behavior or auditing its results. For more" " information, please have a look at https://github.com/huggingface/diffusers/pull/254 ." ) if safety_checker is not None and feature_extractor is None: raise ValueError( "Make sure to define a feature extractor when loading {self.__class__} if you want to use the safety" " checker. If you do not want to use the safety checker, you can pass `'safety_checker=None'` instead." ) if isinstance(controlnet, (list, tuple)): controlnet = MultiControlNetModel(controlnet) self.register_modules( vae=vae, text_encoder=text_encoder, tokenizer=tokenizer, unet=unet, controlnet=controlnet, 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, do_convert_rgb=True) self.control_image_processor = VaeImageProcessor( vae_scale_factor=self.vae_scale_factor, do_convert_rgb=True, do_normalize=False ) self.register_to_config(requires_safety_checker=requires_safety_checker) self.attn_state = AttnState() attn_processor_dict = {} for k in unet.attn_processors.keys(): if k.startswith("up"): attn_processor_dict[k] = CrossFrameAttnProcessor(self.attn_state) else: attn_processor_dict[k] = AttnProcessor() self.unet.set_attn_processor(attn_processor_dict) flow_model = GMFlow( feature_channels=128, num_scales=1, upsample_factor=8, num_head=1, attention_type="swin", ffn_dim_expansion=4, num_transformer_layers=6, ).to("cuda") checkpoint = torch.utils.model_zoo.load_url( "https://huggingface.co/Anonymous-sub/Rerender/resolve/main/models/gmflow_sintel-0c07dcb3.pth", map_location=lambda storage, loc: storage, ) weights = checkpoint["model"] if "model" in checkpoint else checkpoint flow_model.load_state_dict(weights, strict=False) flow_model.eval() self.flow_model = flow_model # Modified from src/diffusers/pipelines/controlnet/pipeline_controlnet.StableDiffusionControlNetImg2ImgPipeline.check_inputs def check_inputs( self, prompt, callback_steps, negative_prompt=None, prompt_embeds=None, negative_prompt_embeds=None, controlnet_conditioning_scale=1.0, control_guidance_start=0.0, control_guidance_end=1.0, ): if (callback_steps is None) or ( callback_steps is not None and (not isinstance(callback_steps, int) or callback_steps <= 0) ): raise ValueError( f"`callback_steps` has to be a positive integer but is {callback_steps} of type" f" {type(callback_steps)}." ) if prompt is not None and prompt_embeds is not None: raise ValueError( f"Cannot forward both `prompt`: {prompt} and `prompt_embeds`: {prompt_embeds}. Please make sure to" " only forward one of the two." ) elif prompt is None and prompt_embeds is None: raise ValueError( "Provide either `prompt` or `prompt_embeds`. Cannot leave both `prompt` and `prompt_embeds` undefined." ) elif prompt is not None and (not isinstance(prompt, str) and not isinstance(prompt, list)): raise ValueError(f"`prompt` has to be of type `str` or `list` but is {type(prompt)}") if negative_prompt is not None and negative_prompt_embeds is not None: raise ValueError( f"Cannot forward both `negative_prompt`: {negative_prompt} and `negative_prompt_embeds`:" f" {negative_prompt_embeds}. Please make sure to only forward one of the two." ) if prompt_embeds is not None and negative_prompt_embeds is not None: if prompt_embeds.shape != negative_prompt_embeds.shape: raise ValueError( "`prompt_embeds` and `negative_prompt_embeds` must have the same shape when passed directly, but" f" got: `prompt_embeds` {prompt_embeds.shape} != `negative_prompt_embeds`" f" {negative_prompt_embeds.shape}." ) # `prompt` needs more sophisticated handling when there are multiple # conditionings. if isinstance(self.controlnet, MultiControlNetModel): if isinstance(prompt, list): logger.warning( f"You have {len(self.controlnet.nets)} ControlNets and you have passed {len(prompt)}" " prompts. The conditionings will be fixed across the prompts." ) is_compiled = hasattr(F, "scaled_dot_product_attention") and isinstance( self.controlnet, torch._dynamo.eval_frame.OptimizedModule ) # Check `controlnet_conditioning_scale` if ( isinstance(self.controlnet, ControlNetModel) or is_compiled and isinstance(self.controlnet._orig_mod, ControlNetModel) ): if not isinstance(controlnet_conditioning_scale, float): raise TypeError("For single controlnet: `controlnet_conditioning_scale` must be type `float`.") elif ( isinstance(self.controlnet, MultiControlNetModel) or is_compiled and isinstance(self.controlnet._orig_mod, MultiControlNetModel) ): if isinstance(controlnet_conditioning_scale, list): if any(isinstance(i, list) for i in controlnet_conditioning_scale): raise ValueError("A single batch of multiple conditionings are supported at the moment.") elif isinstance(controlnet_conditioning_scale, list) and len(controlnet_conditioning_scale) != len( self.controlnet.nets ): raise ValueError( "For multiple controlnets: When `controlnet_conditioning_scale` is specified as `list`, it must have" " the same length as the number of controlnets" ) else: assert False if len(control_guidance_start) != len(control_guidance_end): raise ValueError( f"`control_guidance_start` has {len(control_guidance_start)} elements, but `control_guidance_end` has {len(control_guidance_end)} elements. Make sure to provide the same number of elements to each list." ) if isinstance(self.controlnet, MultiControlNetModel): if len(control_guidance_start) != len(self.controlnet.nets): raise ValueError( f"`control_guidance_start`: {control_guidance_start} has {len(control_guidance_start)} elements but there are {len(self.controlnet.nets)} controlnets available. Make sure to provide {len(self.controlnet.nets)}." ) for start, end in zip(control_guidance_start, control_guidance_end): if start >= end: raise ValueError( f"control guidance start: {start} cannot be larger or equal to control guidance end: {end}." ) if start < 0.0: raise ValueError(f"control guidance start: {start} can't be smaller than 0.") if end > 1.0: raise ValueError(f"control guidance end: {end} can't be larger than 1.0.") # Copied from diffusers.pipelines.controlnet.pipeline_controlnet.StableDiffusionControlNetPipeline.prepare_image def prepare_control_image( self, image, width, height, batch_size, num_images_per_prompt, device, dtype, do_classifier_free_guidance=False, guess_mode=False, ): image = self.control_image_processor.preprocess(image, height=height, width=width).to(dtype=torch.float32) image_batch_size = image.shape[0] if image_batch_size == 1: repeat_by = batch_size else: # image batch size is the same as prompt batch size repeat_by = num_images_per_prompt image = image.repeat_interleave(repeat_by, dim=0) image = image.to(device=device, dtype=dtype) if do_classifier_free_guidance and not guess_mode: image = torch.cat([image] * 2) return image # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion_img2img.StableDiffusionImg2ImgPipeline.get_timesteps 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 # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion_img2img.StableDiffusionImg2ImgPipeline.prepare_latents def prepare_latents(self, image, timestep, batch_size, num_images_per_prompt, dtype, device, generator=None): 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): 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: 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 deprecation_message = ( 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 return latents @torch.no_grad() def __call__( self, prompt: Union[str, List[str]] = None, frames: Union[List[np.ndarray], torch.FloatTensor] = None, control_frames: Union[List[np.ndarray], torch.FloatTensor] = None, strength: float = 0.8, num_inference_steps: int = 50, guidance_scale: float = 7.5, negative_prompt: Optional[Union[str, List[str]]] = None, eta: float = 0.0, generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None, latents: Optional[torch.FloatTensor] = None, prompt_embeds: Optional[torch.FloatTensor] = None, negative_prompt_embeds: Optional[torch.FloatTensor] = None, output_type: Optional[str] = "pil", return_dict: bool = True, callback: Optional[Callable[[int, int, torch.FloatTensor], None]] = None, callback_steps: int = 1, cross_attention_kwargs: Optional[Dict[str, Any]] = None, controlnet_conditioning_scale: Union[float, List[float]] = 0.8, guess_mode: bool = False, control_guidance_start: Union[float, List[float]] = 0.0, control_guidance_end: Union[float, List[float]] = 1.0, warp_start: Union[float, List[float]] = 0.0, warp_end: Union[float, List[float]] = 0.3, mask_start: Union[float, List[float]] = 0.5, mask_end: Union[float, List[float]] = 0.8, smooth_boundary: bool = True, mask_strength: Union[float, List[float]] = 0.5, inner_strength: Union[float, List[float]] = 0.9, ): r""" Function invoked when calling the pipeline for generation. Args: prompt (`str` or `List[str]`, *optional*): The prompt or prompts to guide the image generation. If not defined, one has to pass `prompt_embeds`. instead. frames (`List[np.ndarray]` or `torch.FloatTensor`): The input images to be used as the starting point for the image generation process. control_frames (`List[np.ndarray]` or `torch.FloatTensor`): The ControlNet input images condition to provide guidance to the `unet` for generation. strength ('float'): SDEdit strength. num_inference_steps (`int`, *optional*, defaults to 50): The number of denoising steps. More denoising steps usually lead to a higher quality image at the expense of slower inference. guidance_scale (`float`, *optional*, defaults to 7.5): Guidance scale as defined in [Classifier-Free Diffusion Guidance](https://arxiv.org/abs/2207.12598). `guidance_scale` is defined as `w` of equation 2. of [Imagen Paper](https://arxiv.org/pdf/2205.11487.pdf). Guidance scale is enabled by setting `guidance_scale > 1`. Higher guidance scale encourages to generate images that are closely linked to the text `prompt`, usually at the expense of lower image quality. negative_prompt (`str` or `List[str]`, *optional*): The prompt or prompts not to guide the image generation. If not defined, one has to pass `negative_prompt_embeds` instead. Ignored when not using guidance (i.e., ignored if `guidance_scale` is less than `1`). eta (`float`, *optional*, defaults to 0.0): Corresponds to parameter eta (η) in the DDIM paper: https://arxiv.org/abs/2010.02502. Only applies to [`schedulers.DDIMScheduler`], will be ignored for others. generator (`torch.Generator` or `List[torch.Generator]`, *optional*): One or a list of [torch generator(s)](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 will ge generated by sampling using the supplied random `generator`. 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. negative_prompt_embeds (`torch.FloatTensor`, *optional*): Pre-generated negative text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not provided, negative_prompt_embeds will be generated from `negative_prompt` input argument. output_type (`str`, *optional*, defaults to `"pil"`): The output format of the generate image. Choose between [PIL](https://pillow.readthedocs.io/en/stable/): `PIL.Image.Image` or `np.array`. return_dict (`bool`, *optional*, defaults to `True`): Whether or not to return a [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] instead of a plain tuple. callback (`Callable`, *optional*): A function that will be called every `callback_steps` steps during inference. The function will be called with the following arguments: `callback(step: int, timestep: int, latents: torch.FloatTensor)`. callback_steps (`int`, *optional*, defaults to 1): The frequency at which the `callback` function will be called. If not specified, the callback will be called at every step. cross_attention_kwargs (`dict`, *optional*): A kwargs dictionary that if specified is passed along to the `AttentionProcessor` as defined under `self.processor` in [diffusers.models.attention_processor](https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/attention_processor.py). controlnet_conditioning_scale (`float` or `List[float]`, *optional*, defaults to 1.0): The outputs of the controlnet are multiplied by `controlnet_conditioning_scale` before they are added to the residual in the original unet. If multiple ControlNets are specified in init, you can set the corresponding scale as a list. Note that by default, we use a smaller conditioning scale for inpainting than for [`~StableDiffusionControlNetPipeline.__call__`]. guess_mode (`bool`, *optional*, defaults to `False`): In this mode, the ControlNet encoder will try best to recognize the content of the input image even if you remove all prompts. The `guidance_scale` between 3.0 and 5.0 is recommended. control_guidance_start (`float` or `List[float]`, *optional*, defaults to 0.0): The percentage of total steps at which the controlnet starts applying. control_guidance_end (`float` or `List[float]`, *optional*, defaults to 1.0): The percentage of total steps at which the controlnet stops applying. warp_start (`float`): Shape-aware fusion start timestep. warp_end (`float`): Shape-aware fusion end timestep. mask_start (`float`): Pixel-aware fusion start timestep. mask_end (`float`):Pixel-aware fusion end timestep. smooth_boundary (`bool`): Smooth fusion boundary. Set `True` to prevent artifacts at boundary. mask_strength (`float`): Pixel-aware fusion strength. inner_strength (`float`): Pixel-aware fusion detail level. Examples: Returns: [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] or `tuple`: [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] if `return_dict` is True, otherwise a `tuple. When returning a tuple, the first element is a list with the generated images, and the second element is a list of `bool`s denoting whether the corresponding generated image likely represents "not-safe-for-work" (nsfw) content, according to the `safety_checker`. """ controlnet = self.controlnet._orig_mod if is_compiled_module(self.controlnet) else self.controlnet # align format for control guidance if not isinstance(control_guidance_start, list) and isinstance(control_guidance_end, list): control_guidance_start = len(control_guidance_end) * [control_guidance_start] elif not isinstance(control_guidance_end, list) and isinstance(control_guidance_start, list): control_guidance_end = len(control_guidance_start) * [control_guidance_end] elif not isinstance(control_guidance_start, list) and not isinstance(control_guidance_end, list): mult = len(controlnet.nets) if isinstance(controlnet, MultiControlNetModel) else 1 control_guidance_start, control_guidance_end = ( mult * [control_guidance_start], mult * [control_guidance_end], ) # 1. Check inputs. Raise error if not correct self.check_inputs( prompt, callback_steps, negative_prompt, prompt_embeds, negative_prompt_embeds, controlnet_conditioning_scale, control_guidance_start, control_guidance_end, ) # 2. Define call parameters # Currently we only support 1 prompt if prompt is not None and isinstance(prompt, str): batch_size = 1 elif prompt is not None and isinstance(prompt, list): assert False else: assert False num_images_per_prompt = 1 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 = guidance_scale > 1.0 if isinstance(controlnet, MultiControlNetModel) and isinstance(controlnet_conditioning_scale, float): controlnet_conditioning_scale = [controlnet_conditioning_scale] * len(controlnet.nets) global_pool_conditions = ( controlnet.config.global_pool_conditions if isinstance(controlnet, ControlNetModel) else controlnet.nets[0].config.global_pool_conditions ) guess_mode = guess_mode or global_pool_conditions # 3. Encode input prompt text_encoder_lora_scale = ( cross_attention_kwargs.get("scale", None) if cross_attention_kwargs is not None else None ) prompt_embeds = self._encode_prompt( prompt, device, num_images_per_prompt, do_classifier_free_guidance, negative_prompt, prompt_embeds=prompt_embeds, negative_prompt_embeds=negative_prompt_embeds, lora_scale=text_encoder_lora_scale, ) # 4. Process the first frame height, width = None, None output_frames = [] self.attn_state.reset() # 4.1 prepare frames image = self.image_processor.preprocess(frames[0]).to(dtype=torch.float32) first_image = image[0] # C, H, W # 4.2 Prepare controlnet_conditioning_image # Currently we only support single control if isinstance(controlnet, ControlNetModel): control_image = self.prepare_control_image( image=control_frames[0], width=width, height=height, batch_size=batch_size, num_images_per_prompt=1, device=device, dtype=controlnet.dtype, do_classifier_free_guidance=do_classifier_free_guidance, guess_mode=guess_mode, ) else: assert False # 4.3 Prepare timesteps self.scheduler.set_timesteps(num_inference_steps, device=device) timesteps, cur_num_inference_steps = self.get_timesteps(num_inference_steps, strength, device) latent_timestep = timesteps[:1].repeat(batch_size) # 4.4 Prepare latent variables latents = self.prepare_latents( image, latent_timestep, batch_size, num_images_per_prompt, prompt_embeds.dtype, device, generator, ) # 4.5 Prepare extra step kwargs. TODO: Logic should ideally just be moved out of the pipeline extra_step_kwargs = self.prepare_extra_step_kwargs(generator, eta) # 4.6 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) first_x0_list = [] # 4.7 Denoising loop num_warmup_steps = len(timesteps) - cur_num_inference_steps * self.scheduler.order with self.progress_bar(total=cur_num_inference_steps) as progress_bar: for i, t in enumerate(timesteps): self.attn_state.set_timestep(t.item()) # expand the latents if we are doing classifier free guidance 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) # controlnet(s) inference if guess_mode and do_classifier_free_guidance: # Infer ControlNet only for the conditional batch. control_model_input = latents control_model_input = self.scheduler.scale_model_input(control_model_input, t) controlnet_prompt_embeds = prompt_embeds.chunk(2)[1] else: control_model_input = latent_model_input controlnet_prompt_embeds = prompt_embeds 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] down_block_res_samples, mid_block_res_sample = self.controlnet( control_model_input, t, encoder_hidden_states=controlnet_prompt_embeds, controlnet_cond=control_image, conditioning_scale=cond_scale, guess_mode=guess_mode, return_dict=False, ) if guess_mode and do_classifier_free_guidance: # 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( latent_model_input, t, encoder_hidden_states=prompt_embeds, cross_attention_kwargs=cross_attention_kwargs, down_block_additional_residuals=down_block_res_samples, mid_block_additional_residual=mid_block_res_sample, return_dict=False, )[0] # perform guidance if do_classifier_free_guidance: noise_pred_uncond, noise_pred_text = noise_pred.chunk(2) noise_pred = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond) alpha_prod_t = self.scheduler.alphas_cumprod[t] beta_prod_t = 1 - alpha_prod_t pred_x0 = (latents - beta_prod_t ** (0.5) * noise_pred) / alpha_prod_t ** (0.5) first_x0 = pred_x0.detach() first_x0_list.append(first_x0) # compute the previous noisy sample x_t -> x_t-1 latents = self.scheduler.step(noise_pred, t, latents, **extra_step_kwargs, return_dict=False)[0] # call the callback, if provided if i == len(timesteps) - 1 or ((i + 1) > num_warmup_steps and (i + 1) % self.scheduler.order == 0): progress_bar.update() if callback is not None and i % callback_steps == 0: callback(i, t, latents) if not output_type == "latent": image = self.vae.decode(latents / self.vae.config.scaling_factor, return_dict=False)[0] else: image = latents first_result = image prev_result = image do_denormalize = [True] * image.shape[0] image = self.image_processor.postprocess(image, output_type=output_type, do_denormalize=do_denormalize) output_frames.append(image[0]) # 5. Process each frame for idx in range(1, len(frames)): image = frames[idx] prev_image = frames[idx - 1] control_image = control_frames[idx] # 5.1 prepare frames image = self.image_processor.preprocess(image).to(dtype=torch.float32) prev_image = self.image_processor.preprocess(prev_image).to(dtype=torch.float32) warped_0, bwd_occ_0, bwd_flow_0 = get_warped_and_mask( self.flow_model, first_image, image[0], first_result, False ) blend_mask_0 = blur(F.max_pool2d(bwd_occ_0, kernel_size=9, stride=1, padding=4)) blend_mask_0 = torch.clamp(blend_mask_0 + bwd_occ_0, 0, 1) warped_pre, bwd_occ_pre, bwd_flow_pre = get_warped_and_mask( self.flow_model, prev_image[0], image[0], prev_result, False ) blend_mask_pre = blur(F.max_pool2d(bwd_occ_pre, kernel_size=9, stride=1, padding=4)) blend_mask_pre = torch.clamp(blend_mask_pre + bwd_occ_pre, 0, 1) warp_mask = 1 - F.max_pool2d(blend_mask_0, kernel_size=8) warp_flow = F.interpolate(bwd_flow_0 / 8.0, scale_factor=1.0 / 8, mode="bilinear") # 5.2 Prepare controlnet_conditioning_image # Currently we only support single control if isinstance(controlnet, ControlNetModel): control_image = self.prepare_control_image( image=control_image, width=width, height=height, batch_size=batch_size, num_images_per_prompt=1, device=device, dtype=controlnet.dtype, do_classifier_free_guidance=do_classifier_free_guidance, guess_mode=guess_mode, ) else: assert False # 5.3 Prepare timesteps self.scheduler.set_timesteps(num_inference_steps, device=device) timesteps, cur_num_inference_steps = self.get_timesteps(num_inference_steps, strength, device) latent_timestep = timesteps[:1].repeat(batch_size) skip_t = int(num_inference_steps * (1 - strength)) warp_start_t = int(warp_start * num_inference_steps) warp_end_t = int(warp_end * num_inference_steps) mask_start_t = int(mask_start * num_inference_steps) mask_end_t = int(mask_end * num_inference_steps) # 5.4 Prepare latent variables init_latents = self.prepare_latents( image, latent_timestep, batch_size, num_images_per_prompt, prompt_embeds.dtype, device, generator, ) # 5.5 Prepare extra step kwargs. TODO: Logic should ideally just be moved out of the pipeline extra_step_kwargs = self.prepare_extra_step_kwargs(generator, eta) # 5.6 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) # 5.7 Denoising loop num_warmup_steps = len(timesteps) - cur_num_inference_steps * self.scheduler.order def denoising_loop(latents, mask=None, xtrg=None, noise_rescale=None): dir_xt = 0 latents_dtype = latents.dtype with self.progress_bar(total=cur_num_inference_steps) as progress_bar: for i, t in enumerate(timesteps): self.attn_state.set_timestep(t.item()) if i + skip_t >= mask_start_t and i + skip_t <= mask_end_t and xtrg is not None: rescale = torch.maximum(1.0 - mask, (1 - mask**2) ** 0.5 * inner_strength) if noise_rescale is not None: rescale = (1.0 - mask) * (1 - noise_rescale) + rescale * noise_rescale noise = randn_tensor(xtrg.shape, generator=generator, device=device, dtype=xtrg.dtype) latents_ref = self.scheduler.add_noise(xtrg, noise, t) latents = latents_ref * mask + (1.0 - mask) * (latents - dir_xt) + rescale * dir_xt latents = latents.to(latents_dtype) # expand the latents if we are doing classifier free guidance 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) # controlnet(s) inference if guess_mode and do_classifier_free_guidance: # Infer ControlNet only for the conditional batch. control_model_input = latents control_model_input = self.scheduler.scale_model_input(control_model_input, t) controlnet_prompt_embeds = prompt_embeds.chunk(2)[1] else: control_model_input = latent_model_input controlnet_prompt_embeds = prompt_embeds 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] down_block_res_samples, mid_block_res_sample = self.controlnet( control_model_input, t, encoder_hidden_states=controlnet_prompt_embeds, controlnet_cond=control_image, conditioning_scale=cond_scale, guess_mode=guess_mode, return_dict=False, ) if guess_mode and do_classifier_free_guidance: # 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( latent_model_input, t, encoder_hidden_states=prompt_embeds, cross_attention_kwargs=cross_attention_kwargs, down_block_additional_residuals=down_block_res_samples, mid_block_additional_residual=mid_block_res_sample, return_dict=False, )[0] # perform guidance if do_classifier_free_guidance: noise_pred_uncond, noise_pred_text = noise_pred.chunk(2) noise_pred = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond) # Get pred_x0 from scheduler alpha_prod_t = self.scheduler.alphas_cumprod[t] beta_prod_t = 1 - alpha_prod_t pred_x0 = (latents - beta_prod_t ** (0.5) * noise_pred) / alpha_prod_t ** (0.5) if i + skip_t >= warp_start_t and i + skip_t <= warp_end_t: # warp x_0 pred_x0 = ( flow_warp(first_x0_list[i], warp_flow, mode="nearest") * warp_mask + (1 - warp_mask) * pred_x0 ) # get x_t from x_0 latents = self.scheduler.add_noise(pred_x0, noise_pred, t).to(latents_dtype) prev_t = t - self.scheduler.config.num_train_timesteps // self.scheduler.num_inference_steps if i == len(timesteps) - 1: alpha_t_prev = 1.0 else: alpha_t_prev = self.scheduler.alphas_cumprod[prev_t] dir_xt = (1.0 - alpha_t_prev) ** 0.5 * noise_pred # compute the previous noisy sample x_t -> x_t-1 latents = self.scheduler.step(noise_pred, t, latents, **extra_step_kwargs, return_dict=False)[ 0 ] # call the callback, if provided if i == len(timesteps) - 1 or ( (i + 1) > num_warmup_steps and (i + 1) % self.scheduler.order == 0 ): progress_bar.update() if callback is not None and i % callback_steps == 0: callback(i, t, latents) return latents if mask_start_t <= mask_end_t: self.attn_state.to_load() else: self.attn_state.to_load_and_store_prev() latents = denoising_loop(init_latents) if mask_start_t <= mask_end_t: direct_result = self.vae.decode(latents / self.vae.config.scaling_factor, return_dict=False)[0] blend_results = (1 - blend_mask_pre) * warped_pre + blend_mask_pre * direct_result blend_results = (1 - blend_mask_0) * warped_0 + blend_mask_0 * blend_results bwd_occ = 1 - torch.clamp(1 - bwd_occ_pre + 1 - bwd_occ_0, 0, 1) blend_mask = blur(F.max_pool2d(bwd_occ, kernel_size=9, stride=1, padding=4)) blend_mask = 1 - torch.clamp(blend_mask + bwd_occ, 0, 1) blend_results = blend_results.to(latents.dtype) xtrg = self.vae.encode(blend_results).latent_dist.sample(generator) xtrg = self.vae.config.scaling_factor * xtrg blend_results_rec = self.vae.decode(xtrg / self.vae.config.scaling_factor, return_dict=False)[0] xtrg_rec = self.vae.encode(blend_results_rec).latent_dist.sample(generator) xtrg_rec = self.vae.config.scaling_factor * xtrg_rec xtrg_ = xtrg + (xtrg - xtrg_rec) blend_results_rec_new = self.vae.decode(xtrg_ / self.vae.config.scaling_factor, return_dict=False)[0] tmp = (abs(blend_results_rec_new - blend_results).mean(dim=1, keepdims=True) > 0.25).float() mask_x = F.max_pool2d( (F.interpolate(tmp, scale_factor=1 / 8.0, mode="bilinear") > 0).float(), kernel_size=3, stride=1, padding=1, ) mask = 1 - F.max_pool2d(1 - blend_mask, kernel_size=8) # * (1-mask_x) if smooth_boundary: noise_rescale = find_flat_region(mask) else: noise_rescale = torch.ones_like(mask) xtrg = (xtrg + (1 - mask_x) * (xtrg - xtrg_rec)) * mask xtrg = xtrg.to(latents.dtype) self.scheduler.set_timesteps(num_inference_steps, device=device) timesteps, cur_num_inference_steps = self.get_timesteps(num_inference_steps, strength, device) self.attn_state.to_load_and_store_prev() latents = denoising_loop(init_latents, mask * mask_strength, xtrg, noise_rescale) if not output_type == "latent": image = self.vae.decode(latents / self.vae.config.scaling_factor, return_dict=False)[0] else: image = latents prev_result = image do_denormalize = [True] * image.shape[0] image = self.image_processor.postprocess(image, output_type=output_type, do_denormalize=do_denormalize) output_frames.append(image[0]) # Offload last model to CPU if hasattr(self, "final_offload_hook") and self.final_offload_hook is not None: self.final_offload_hook.offload() if not return_dict: return output_frames return TextToVideoSDPipelineOutput(frames=output_frames)