771 lines
34 KiB
Python
771 lines
34 KiB
Python
# Copyright 2023 The HuggingFace Team. All rights reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import inspect
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from dataclasses import dataclass
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from typing import Callable, Dict, List, Optional, Union
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import numpy as np
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import PIL.Image
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import torch
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from transformers import CLIPImageProcessor, CLIPVisionModelWithProjection
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from ..models.controlnext_vid_svd import ControlNeXtSDVModel
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from diffusers.image_processor import VaeImageProcessor
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from diffusers.models import AutoencoderKLTemporalDecoder, UNetSpatioTemporalConditionModel
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from diffusers.utils import BaseOutput, logging
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from diffusers.utils.torch_utils import randn_tensor
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from diffusers.pipelines.pipeline_utils import DiffusionPipeline
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from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion import retrieve_timesteps
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from ..models.unet_spatio_temporal_condition_controlnext import UNetSpatioTemporalConditionControlNeXtModel
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from ..utils.scheduling_euler_discrete_karras_fix import EulerDiscreteScheduler
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#from diffusers.pipelines.utils import PIL_INTERPOLATION, BaseOutput, logging
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from diffusers.pipelines.stable_video_diffusion.pipeline_stable_video_diffusion import StableVideoDiffusionPipeline
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from comfy.utils import ProgressBar
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import comfy.model_management as mm
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from comfy.clip_vision import clip_preprocess
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offload_device = mm.unet_offload_device()
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logger = logging.get_logger(__name__) # pylint: disable=invalid-name
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def _get_add_time_ids(
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noise_aug_strength,
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dtype,
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batch_size,
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fps=4,
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motion_bucket_id=128,
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unet=None,
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):
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add_time_ids = [fps, motion_bucket_id, noise_aug_strength]
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passed_add_embed_dim = unet.config.addition_time_embed_dim * len(add_time_ids)
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expected_add_embed_dim = unet.add_embedding.linear_1.in_features
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if expected_add_embed_dim != passed_add_embed_dim:
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raise ValueError(
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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`."
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)
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add_time_ids = torch.tensor([add_time_ids], dtype=dtype)
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# add_time_ids = add_time_ids.repeat(batch_size * num_videos_per_prompt, 1)
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return add_time_ids
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def _append_dims(x, target_dims):
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"""Appends dimensions to the end of a tensor until it has target_dims dimensions."""
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dims_to_append = target_dims - x.ndim
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if dims_to_append < 0:
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raise ValueError(f"input has {x.ndim} dims but target_dims is {target_dims}, which is less")
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return x[(...,) + (None,) * dims_to_append]
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def tensor2vid(video: torch.Tensor, processor, output_type="pt"):
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# Based on:
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# https://github.com/modelscope/modelscope/blob/1509fdb973e5871f37148a4b5e5964cafd43e64d/modelscope/pipelines/multi_modal/text_to_video_synthesis_pipeline.py#L78
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batch_size, channels, num_frames, height, width = video.shape
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outputs = []
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for batch_idx in range(batch_size):
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batch_vid = video[batch_idx].permute(1, 0, 2, 3)
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batch_output = processor.postprocess(batch_vid, output_type)
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outputs.append(batch_output)
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if output_type == "pt":
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outputs = torch.stack(outputs)
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return outputs
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@dataclass
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class StableVideoDiffusionPipelineOutput(BaseOutput):
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r"""
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Output class for zero-shot text-to-video pipeline.
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Args:
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frames (`[List[PIL.Image.Image]`, `np.ndarray`]):
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List of denoised PIL images of length `batch_size` or NumPy array of shape `(batch_size, height, width,
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num_channels)`.
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"""
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frames: Union[List[PIL.Image.Image], np.ndarray]
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class StableVideoDiffusionPipelineControlNeXt(DiffusionPipeline):
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r"""
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Pipeline to generate video from an input image using Stable Video Diffusion.
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This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods
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implemented for all pipelines (downloading, saving, running on a particular device, etc.).
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Args:
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vae ([`AutoencoderKL`]):
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Variational Auto-Encoder (VAE) model to encode and decode images to and from latent representations.
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image_encoder ([`~transformers.CLIPVisionModelWithProjection`]):
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Frozen CLIP image-encoder ([laion/CLIP-ViT-H-14-laion2B-s32B-b79K](https://huggingface.co/laion/CLIP-ViT-H-14-laion2B-s32B-b79K)).
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unet ([`UNetSpatioTemporalConditionModel`]):
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A `UNetSpatioTemporalConditionModel` to denoise the encoded image latents.
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scheduler ([`EulerDiscreteScheduler`]):
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A scheduler to be used in combination with `unet` to denoise the encoded image latents.
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feature_extractor ([`~transformers.CLIPImageProcessor`]):
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A `CLIPImageProcessor` to extract features from generated images.
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"""
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model_cpu_offload_seq = "image_encoder->unet->vae"
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_callback_tensor_inputs = ["latents"]
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def __init__(
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self,
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vae: AutoencoderKLTemporalDecoder,
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image_encoder: CLIPVisionModelWithProjection,
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unet: UNetSpatioTemporalConditionControlNeXtModel,
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controlnext: ControlNeXtSDVModel,
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scheduler: EulerDiscreteScheduler,
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feature_extractor: CLIPImageProcessor,
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):
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super().__init__()
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self.register_modules(
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vae=vae,
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image_encoder=image_encoder,
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controlnext=controlnext,
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unet=unet,
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scheduler=scheduler,
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feature_extractor=feature_extractor,
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)
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self.vae_scale_factor = 2 ** (len(self.vae.config.block_out_channels) - 1)
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self.image_processor = VaeImageProcessor(vae_scale_factor=self.vae_scale_factor)
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def _encode_image(self, image, device, num_videos_per_prompt, do_classifier_free_guidance):
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dtype = next(self.image_encoder.parameters()).dtype
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# if not isinstance(image, torch.Tensor):
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# image = self.image_processor.pil_to_numpy(image)
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# image = self.image_processor.numpy_to_pt(image)
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# # We normalize the image before resizing to match with the original implementation.
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# # Then we unnormalize it after resizing.
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# image = image * 2.0 - 1.0
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# image = _resize_with_antialiasing(image, (224, 224))
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# image = (image + 1.0) / 2.0
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# # Normalize the image with for CLIP input
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# image = self.feature_extractor(
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# images=image,
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# do_normalize=True,
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# do_center_crop=False,
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# do_resize=False,
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# do_rescale=False,
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# return_tensors="pt",
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# ).pixel_values
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image = image.permute(0, 2, 3, 1)
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image = clip_preprocess(image.clone(), 224)
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image = image.to(device=device, dtype=dtype)
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self.image_encoder.to(device)
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image_embeddings = self.image_encoder(image).image_embeds
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image_embeddings = image_embeddings.unsqueeze(1)
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self.image_encoder.to(offload_device)
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# duplicate image embeddings for each generation per prompt, using mps friendly method
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bs_embed, seq_len, _ = image_embeddings.shape
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image_embeddings = image_embeddings.repeat(1, num_videos_per_prompt, 1)
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image_embeddings = image_embeddings.view(bs_embed * num_videos_per_prompt, seq_len, -1)
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if do_classifier_free_guidance:
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negative_image_embeddings = torch.zeros_like(image_embeddings)
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# For classifier free guidance, we need to do two forward passes.
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# Here we concatenate the unconditional and text embeddings into a single batch
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# to avoid doing two forward passes
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image_embeddings = torch.cat([negative_image_embeddings, image_embeddings])
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return image_embeddings
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def _encode_vae_image(
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self,
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image: torch.Tensor,
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device,
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num_videos_per_prompt,
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do_classifier_free_guidance,
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):
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image = image.to(device=device)
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image_latents = self.vae.encode(image).latent_dist.mode()
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if do_classifier_free_guidance:
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negative_image_latents = torch.zeros_like(image_latents)
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# For classifier free guidance, we need to do two forward passes.
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# Here we concatenate the unconditional and text embeddings into a single batch
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# to avoid doing two forward passes
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image_latents = torch.cat([negative_image_latents, image_latents])
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# duplicate image_latents for each generation per prompt, using mps friendly method
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image_latents = image_latents.repeat(num_videos_per_prompt, 1, 1, 1)
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return image_latents
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def _get_add_time_ids(
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self,
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fps,
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motion_bucket_id,
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noise_aug_strength,
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dtype,
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batch_size,
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num_videos_per_prompt,
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do_classifier_free_guidance,
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):
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add_time_ids = [fps, motion_bucket_id, noise_aug_strength]
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passed_add_embed_dim = self.unet.config.addition_time_embed_dim * len(add_time_ids)
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expected_add_embed_dim = self.unet.add_embedding.linear_1.in_features
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if expected_add_embed_dim != passed_add_embed_dim:
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raise ValueError(
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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`."
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)
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add_time_ids = torch.tensor([add_time_ids], dtype=dtype)
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add_time_ids = add_time_ids.repeat(batch_size * num_videos_per_prompt, 1)
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if do_classifier_free_guidance:
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add_time_ids = torch.cat([add_time_ids, add_time_ids])
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return add_time_ids
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def decode_latents(self, latents, num_frames, decode_chunk_size=14):
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# [batch, frames, channels, height, width] -> [batch*frames, channels, height, width]
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latents = latents.flatten(0, 1)
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latents = 1 / self.vae.config.scaling_factor * latents
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accepts_num_frames = "num_frames" in set(inspect.signature(self.vae.forward).parameters.keys())
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# decode decode_chunk_size frames at a time to avoid OOM
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frames = []
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for i in range(0, latents.shape[0], decode_chunk_size):
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num_frames_in = latents[i : i + decode_chunk_size].shape[0]
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decode_kwargs = {}
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# if accepts_num_frames:
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# # we only pass num_frames_in if it's expected
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# decode_kwargs["num_frames"] = num_frames_in
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decode_kwargs["num_frames"] = num_frames_in
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frame = self.vae.decode(latents[i : i + decode_chunk_size], **decode_kwargs).sample
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frames.append(frame)
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frames = torch.cat(frames, dim=0)
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# [batch*frames, channels, height, width] -> [batch, channels, frames, height, width]
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frames = frames.reshape(-1, num_frames, *frames.shape[1:]).permute(0, 2, 1, 3, 4)
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# we always cast to float32 as this does not cause significant overhead and is compatible with bfloat16
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frames = frames.float()
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return frames
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def check_inputs(self, image, height, width):
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if (
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not isinstance(image, torch.Tensor)
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and not isinstance(image, PIL.Image.Image)
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and not isinstance(image, list)
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):
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raise ValueError(
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"`image` has to be of type `torch.FloatTensor` or `PIL.Image.Image` or `List[PIL.Image.Image]` but is"
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f" {type(image)}"
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)
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if height % 8 != 0 or width % 8 != 0:
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raise ValueError(f"`height` and `width` have to be divisible by 8 but are {height} and {width}.")
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def prepare_latents(
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self,
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batch_size,
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num_frames,
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num_channels_latents,
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height,
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width,
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dtype,
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device,
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generator,
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latents=None,
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):
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shape = (
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batch_size,
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num_frames,
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num_channels_latents // 2,
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height // self.vae_scale_factor,
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width // self.vae_scale_factor,
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)
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if isinstance(generator, list) and len(generator) != batch_size:
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raise ValueError(
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f"You have passed a list of generators of length {len(generator)}, but requested an effective batch"
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f" size of {batch_size}. Make sure the batch size matches the length of the generators."
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)
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if latents is None:
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latents = randn_tensor(shape, generator=generator, device=device, dtype=dtype)
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else:
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latents = latents.to(device)
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# scale the initial noise by the standard deviation required by the scheduler
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latents = latents * self.scheduler.init_noise_sigma
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return latents
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@property
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def guidance_scale(self):
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return self._guidance_scale
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# here `guidance_scale` is defined analog to the guidance weight `w` of equation (2)
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# of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1`
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# corresponds to doing no classifier free guidance.
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@property
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def do_classifier_free_guidance(self):
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return self._guidance_scale >= 1 and self.unet.config.time_cond_proj_dim is None
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@property
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def num_timesteps(self):
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return self._num_timesteps
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@torch.no_grad()
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def __call__(
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self,
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image: Union[PIL.Image.Image, List[PIL.Image.Image], torch.FloatTensor],
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controlnext_condition:Optional[torch.FloatTensor] = None,
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height: int = 576,
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width: int = 1024,
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num_frames: Optional[int] = None,
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num_inference_steps: int = 25,
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min_guidance_scale: float = 1.0,
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max_guidance_scale: float = 3.0,
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fps: int = 7,
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motion_bucket_id: int = 127,
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noise_aug_strength: int = 0.02,
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sigmas: Optional[List[float]] = None,
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decode_chunk_size: Optional[int] = None,
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num_videos_per_prompt: Optional[int] = 1,
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generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None,
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latents: Optional[torch.FloatTensor] = None,
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output_type: Optional[str] = "pil",
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callback_on_step_end: Optional[Callable[[int, int, Dict], None]] = None,
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callback_on_step_end_tensor_inputs: List[str] = ["latents"],
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return_dict: bool = True,
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controlnext_cond_scale=1.0,
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batch_size=1,
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overlap=5,
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frames_per_batch = 14,
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):
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r"""
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The call function to the pipeline for generation.
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Args:
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image (`PIL.Image.Image` or `List[PIL.Image.Image]` or `torch.FloatTensor`):
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Image or images to guide image generation. If you provide a tensor, it needs to be compatible with
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[`CLIPImageProcessor`](https://huggingface.co/lambdalabs/sd-image-variations-diffusers/blob/main/feature_extractor/preprocessor_config.json).
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height (`int`, *optional*, defaults to `self.unet.config.sample_size * self.vae_scale_factor`):
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The height in pixels of the generated image.
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width (`int`, *optional*, defaults to `self.unet.config.sample_size * self.vae_scale_factor`):
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The width in pixels of the generated image.
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num_frames (`int`, *optional*):
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The number of video frames to generate. Defaults to 14 for `stable-video-diffusion-img2vid` and to 25 for `stable-video-diffusion-img2vid-xt`
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num_inference_steps (`int`, *optional*, defaults to 25):
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The number of denoising steps. More denoising steps usually lead to a higher quality image at the
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expense of slower inference. This parameter is modulated by `strength`.
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min_guidance_scale (`float`, *optional*, defaults to 1.0):
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The minimum guidance scale. Used for the classifier free guidance with first frame.
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max_guidance_scale (`float`, *optional*, defaults to 3.0):
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The maximum guidance scale. Used for the classifier free guidance with last frame.
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fps (`int`, *optional*, defaults to 7):
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Frames per second. The rate at which the generated images shall be exported to a video after generation.
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Note that Stable Diffusion Video's UNet was micro-conditioned on fps-1 during training.
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motion_bucket_id (`int`, *optional*, defaults to 127):
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The motion bucket ID. Used as conditioning for the generation. The higher the number the more motion will be in the video.
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noise_aug_strength (`int`, *optional*, defaults to 0.02):
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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.
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decode_chunk_size (`int`, *optional*):
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The number of frames to decode at a time. The higher the chunk size, the higher the temporal consistency
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between frames, but also the higher the memory consumption. By default, the decoder will decode all frames at once
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for maximal quality. Reduce `decode_chunk_size` to reduce memory usage.
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num_videos_per_prompt (`int`, *optional*, defaults to 1):
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The number of images to generate per prompt.
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generator (`torch.Generator` or `List[torch.Generator]`, *optional*):
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A [`torch.Generator`](https://pytorch.org/docs/stable/generated/torch.Generator.html) to make
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generation deterministic.
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latents (`torch.FloatTensor`, *optional*):
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Pre-generated noisy latents sampled from a Gaussian distribution, to be used as inputs for image
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generation. Can be used to tweak the same generation with different prompts. If not provided, a latents
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tensor is generated by sampling using the supplied random `generator`.
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output_type (`str`, *optional*, defaults to `"pil"`):
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The output format of the generated image. Choose between `PIL.Image` or `np.array`.
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callback_on_step_end (`Callable`, *optional*):
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A function that calls at the end of each denoising steps during the inference. The function is called
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with the following arguments: `callback_on_step_end(self: DiffusionPipeline, step: int, timestep: int,
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callback_kwargs: Dict)`. `callback_kwargs` will include a list of all tensors as specified by
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`callback_on_step_end_tensor_inputs`.
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callback_on_step_end_tensor_inputs (`List`, *optional*):
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The list of tensor inputs for the `callback_on_step_end` function. The tensors specified in the list
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will be passed as `callback_kwargs` argument. You will only be able to include variables listed in the
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`._callback_tensor_inputs` attribute of your pipeline class.
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return_dict (`bool`, *optional*, defaults to `True`):
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Whether or not to return a [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] instead of a
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plain tuple.
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Returns:
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[`~pipelines.stable_diffusion.StableVideoDiffusionPipelineOutput`] or `tuple`:
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If `return_dict` is `True`, [`~pipelines.stable_diffusion.StableVideoDiffusionPipelineOutput`] is returned,
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otherwise a `tuple` is returned where the first element is a list of list with the generated frames.
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Examples:
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```py
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from diffusers import StableVideoDiffusionPipeline
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from diffusers.utils import load_image, export_to_video
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pipe = StableVideoDiffusionPipeline.from_pretrained("stabilityai/stable-video-diffusion-img2vid-xt", torch_dtype=torch.float16, variant="fp16")
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pipe.to("cuda")
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image = load_image("https://lh3.googleusercontent.com/y-iFOHfLTwkuQSUegpwDdgKmOjRSTvPxat63dQLB25xkTs4lhIbRUFeNBWZzYf370g=s1200")
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image = image.resize((1024, 576))
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frames = pipe(image, num_frames=25, decode_chunk_size=8).frames[0]
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export_to_video(frames, "generated.mp4", fps=7)
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```
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"""
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# 0. Default height and width to unet
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height = height or self.unet.config.sample_size * self.vae_scale_factor
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width = width or self.unet.config.sample_size * self.vae_scale_factor
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num_frames = num_frames if num_frames is not None else self.unet.config.num_frames
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decode_chunk_size = decode_chunk_size if decode_chunk_size is not None else num_frames
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frames_per_batch = min(frames_per_batch, num_frames)
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# 1. Check inputs. Raise error if not correct
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self.check_inputs(image, height, width)
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# 2. Define call parameters
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#if isinstance(image, PIL.Image.Image):
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# batch_size = 1
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#elif isinstance(image, list):
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# batch_size = len(image)
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#else:
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# batch_size = image.shape[0]
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device = self._execution_device
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# here `guidance_scale` is defined analog to the guidance weight `w` of equation (2)
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# of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1`
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# corresponds to doing no classifier free guidance.
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do_classifier_free_guidance = max_guidance_scale >= 1.0
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# 3. Encode input image
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image_embeddings = self._encode_image(image, device, num_videos_per_prompt, do_classifier_free_guidance)
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# NOTE: Stable Diffusion Video was conditioned on fps - 1, which
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# is why it is reduced here.
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# See: https://github.com/Stability-AI/generative-models/blob/ed0997173f98eaf8f4edf7ba5fe8f15c6b877fd3/scripts/sampling/simple_video_sample.py#L188
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fps = fps - 1
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# 4. Encode input image using VAE
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image = self.image_processor.preprocess(image, height=height, width=width)
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noise = randn_tensor(image.shape, generator=generator, device=image.device, dtype=image.dtype)
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image = image + noise_aug_strength * noise #
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# needs_upcasting = (self.vae.dtype == torch.float16 or self.vae.dtype == torch.bfloat16) and self.vae.config.force_upcast
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# if needs_upcasting:
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# self_vae_dtype = self.vae.dtype
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# self.vae.to(dtype=torch.float32)
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image_latents = self._encode_vae_image(image, device, num_videos_per_prompt, do_classifier_free_guidance)
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image_latents = image_latents.to(image_embeddings.dtype)
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# cast back to fp16 if needed
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# if needs_upcasting:
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# self.vae.to(dtype=self_vae_dtype)
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# Repeat the image latents for each frame so we can concatenate them with the noise
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# image_latents [batch, channels, height, width] ->[batch, num_frames, channels, height, width]
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image_latents = image_latents.unsqueeze(1).repeat(1, num_frames, 1, 1, 1)
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#image_latents = torch.cat([image_latents] * 2) if do_classifier_free_guidance else image_latents
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# 5. Get Added Time IDs
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added_time_ids = self._get_add_time_ids(
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fps,
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motion_bucket_id,
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noise_aug_strength,
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image_embeddings.dtype,
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batch_size,
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num_videos_per_prompt,
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do_classifier_free_guidance,
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)
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added_time_ids = added_time_ids.to(device)
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# 4. Prepare timesteps
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if sigmas is not None:
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timesteps, num_inference_steps = retrieve_timesteps(self.scheduler, num_inference_steps, device, None, sigmas)
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else:
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timesteps, num_inference_steps = retrieve_timesteps(self.scheduler, num_inference_steps, device, None)
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# 5. Prepare latent variables
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|
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num_channels_latents = self.unet.config.in_channels
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latents = self.prepare_latents(
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batch_size * num_videos_per_prompt,
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num_frames,
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num_channels_latents,
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height,
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width,
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image_embeddings.dtype,
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device,
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generator,
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latents,
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)
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#prepare controlnext condition
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|
controlnext_condition = self.image_processor.preprocess(controlnext_condition, height=height, width=width)
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controlnext_condition = (controlnext_condition + 1.0) / 2
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controlnext_condition = controlnext_condition.unsqueeze(0)
|
|
if do_classifier_free_guidance:
|
|
controlnext_condition = torch.cat([controlnext_condition] * 2)
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|
controlnext_condition = controlnext_condition.to(device, latents.dtype)
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controlnext_condition_all = controlnext_condition * controlnext_cond_scale
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latents_all = latents
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|
|
|
# 7. Prepare guidance scale
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|
guidance_scale = torch.linspace(min_guidance_scale, max_guidance_scale, frames_per_batch).unsqueeze(0)
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guidance_scale = guidance_scale.to(device, latents.dtype)
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|
guidance_scale = guidance_scale.repeat(batch_size * num_videos_per_prompt, 1)
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|
guidance_scale = _append_dims(guidance_scale, latents.ndim)
|
|
|
|
self._guidance_scale = guidance_scale
|
|
|
|
noise_aug_strength = 0.02 #"¯\_(ツ)_/¯
|
|
added_time_ids = _get_add_time_ids(
|
|
noise_aug_strength,
|
|
image_embeddings.dtype,
|
|
batch_size,
|
|
6,
|
|
128,
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|
unet=self.unet,
|
|
)
|
|
if do_classifier_free_guidance:
|
|
added_time_ids = torch.cat([added_time_ids] * 2)
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|
added_time_ids = added_time_ids.to(latents.device)
|
|
|
|
|
|
# 8. Denoising loop
|
|
num_warmup_steps = len(timesteps) - num_inference_steps * self.scheduler.order
|
|
self._num_timesteps = len(timesteps)
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|
comfy_pbar = ProgressBar(num_inference_steps)
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|
with self.progress_bar(total=num_inference_steps) as progress_bar:
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|
for i, t in enumerate(timesteps):
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|
pred_tmp = torch.zeros_like(latents_all)
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|
counter = torch.zeros((latents.shape[0], num_frames, 1, 1, 1 )).to(device=latents.device)
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|
for batch, ind_start_idx in enumerate(range(0, num_frames-overlap, frames_per_batch-overlap)):
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|
self.scheduler._step_index = None
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|
if ind_start_idx + frames_per_batch > num_frames:
|
|
ind_start = num_frames - frames_per_batch
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|
else:
|
|
ind_start = ind_start_idx
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|
latents = latents_all[:,ind_start:ind_start+frames_per_batch].contiguous()
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|
controlnext_condition = controlnext_condition_all[:,ind_start:ind_start+frames_per_batch].contiguous()
|
|
|
|
controlnext_condition[:, 0, ...] = controlnext_condition_all[:, 0, ...]
|
|
|
|
# expand the latents if we are doing classifier free guidance
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|
latent_model_input = torch.cat([latents] * 2) if do_classifier_free_guidance else latents
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|
latent_model_input = self.scheduler.scale_model_input(latent_model_input, t)
|
|
|
|
controlnext_output = self.controlnext(
|
|
controlnext_condition,
|
|
t,
|
|
)
|
|
if do_classifier_free_guidance:
|
|
N = controlnext_output['output'].shape[0]
|
|
controlnext_output['scale'] = torch.tensor(controlnext_output['scale']).to(latent_model_input).repeat(N)[:, None, None, None]
|
|
controlnext_output['scale'][:N // 2] *= 0
|
|
|
|
|
|
# Concatenate image_latents over channels dimention
|
|
latent_model_input = torch.cat([latent_model_input, image_latents[:,ind_start:ind_start+frames_per_batch].contiguous()], dim=2)
|
|
|
|
|
|
# predict the noise residual
|
|
noise_pred = self.unet(
|
|
latent_model_input,
|
|
t,
|
|
encoder_hidden_states=image_embeddings,
|
|
added_time_ids=added_time_ids,
|
|
conditional_controls=controlnext_output,
|
|
return_dict=False,
|
|
)[0]
|
|
|
|
# perform guidance
|
|
if do_classifier_free_guidance:
|
|
noise_pred_uncond, noise_pred_cond = noise_pred.chunk(2)
|
|
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 ind_start == 0:
|
|
pred_tmp[:,ind_start:ind_start+frames_per_batch] += latents
|
|
counter[:,ind_start:ind_start+frames_per_batch] += 1
|
|
else:
|
|
pred_tmp[:,ind_start + 1 : ind_start+frames_per_batch] += latents[:, 1:, ...]
|
|
counter[:,ind_start + 1:ind_start+frames_per_batch] += 1
|
|
pred_tmp /= counter
|
|
latents_all = pred_tmp
|
|
|
|
if i == len(timesteps) - 1 or ((i + 1) > num_warmup_steps and (i + 1) % self.scheduler.order == 0):
|
|
progress_bar.update()
|
|
comfy_pbar.update(1)
|
|
latents = latents_all
|
|
|
|
if not output_type == "latent":
|
|
# cast back to fp16 if needed
|
|
#if needs_upcasting:
|
|
# self.vae.to(dtype=self_vae_dtype)
|
|
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
|
|
def _resize_with_antialiasing(input, size, interpolation="bicubic", align_corners=True):
|
|
|
|
if input.ndim == 3:
|
|
input = input.unsqueeze(0) # Add a batch dimension
|
|
|
|
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
|