994 lines
45 KiB
Python
994 lines
45 KiB
Python
# Copyright 2025 The EasyAnimate team and The HuggingFace Team.
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# 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 typing import Callable, Dict, List, Optional, Union
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import numpy as np
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import torch
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import torch.nn.functional as F
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from PIL import Image
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from transformers import (
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BertModel,
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BertTokenizer,
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Qwen2Tokenizer,
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Qwen2VLForConditionalGeneration,
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)
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from ...callbacks import MultiPipelineCallbacks, PipelineCallback
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from ...image_processor import VaeImageProcessor
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from ...models import AutoencoderKLMagvit, EasyAnimateTransformer3DModel
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from ...pipelines.pipeline_utils import DiffusionPipeline
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from ...schedulers import FlowMatchEulerDiscreteScheduler
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from ...utils import is_torch_xla_available, logging, replace_example_docstring
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from ...utils.torch_utils import randn_tensor
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from ...video_processor import VideoProcessor
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from .pipeline_output import EasyAnimatePipelineOutput
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if is_torch_xla_available():
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import torch_xla.core.xla_model as xm
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XLA_AVAILABLE = True
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else:
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XLA_AVAILABLE = False
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logger = logging.get_logger(__name__) # pylint: disable=invalid-name
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EXAMPLE_DOC_STRING = """
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Examples:
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```python
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>>> import torch
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>>> from diffusers import EasyAnimateControlPipeline
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>>> from diffusers.pipelines.easyanimate.pipeline_easyanimate_control import get_video_to_video_latent
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>>> from diffusers.utils import export_to_video, load_video
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>>> pipe = EasyAnimateControlPipeline.from_pretrained(
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... "alibaba-pai/EasyAnimateV5.1-12b-zh-Control-diffusers", torch_dtype=torch.bfloat16
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... )
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>>> pipe.to("cuda")
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>>> control_video = load_video(
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... "https://huggingface.co/alibaba-pai/EasyAnimateV5.1-12b-zh-Control/blob/main/asset/pose.mp4"
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... )
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>>> prompt = (
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... "In this sunlit outdoor garden, a beautiful woman is dressed in a knee-length, sleeveless white dress. "
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... "The hem of her dress gently sways with her graceful dance, much like a butterfly fluttering in the breeze. "
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... "Sunlight filters through the leaves, casting dappled shadows that highlight her soft features and clear eyes, "
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... "making her appear exceptionally elegant. It seems as if every movement she makes speaks of youth and vitality. "
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... "As she twirls on the grass, her dress flutters, as if the entire garden is rejoicing in her dance. "
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... "The colorful flowers around her sway in the gentle breeze, with roses, chrysanthemums, and lilies each "
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... "releasing their fragrances, creating a relaxed and joyful atmosphere."
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... )
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>>> sample_size = (672, 384)
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>>> num_frames = 49
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>>> input_video, _, _ = get_video_to_video_latent(control_video, num_frames, sample_size)
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>>> video = pipe(
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... prompt,
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... num_frames=num_frames,
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... negative_prompt="Twisted body, limb deformities, text subtitles, comics, stillness, ugliness, errors, garbled text.",
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... height=sample_size[0],
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... width=sample_size[1],
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... control_video=input_video,
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... ).frames[0]
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>>> export_to_video(video, "output.mp4", fps=8)
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```
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"""
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def preprocess_image(image, sample_size):
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"""
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Preprocess a single image (PIL.Image, numpy.ndarray, or torch.Tensor) to a resized tensor.
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"""
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if isinstance(image, torch.Tensor):
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# If input is a tensor, assume it's in CHW format and resize using interpolation
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image = torch.nn.functional.interpolate(
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image.unsqueeze(0), size=sample_size, mode="bilinear", align_corners=False
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).squeeze(0)
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elif isinstance(image, Image.Image):
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# If input is a PIL image, resize and convert to numpy array
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image = image.resize((sample_size[1], sample_size[0]))
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image = np.array(image)
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elif isinstance(image, np.ndarray):
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# If input is a numpy array, resize using PIL
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image = Image.fromarray(image).resize((sample_size[1], sample_size[0]))
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image = np.array(image)
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else:
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raise ValueError("Unsupported input type. Expected PIL.Image, numpy.ndarray, or torch.Tensor.")
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# Convert to tensor if not already
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if not isinstance(image, torch.Tensor):
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image = torch.from_numpy(image).permute(2, 0, 1).float() / 255.0 # HWC -> CHW, normalize to [0, 1]
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return image
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def get_video_to_video_latent(input_video, num_frames, sample_size, validation_video_mask=None, ref_image=None):
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if input_video is not None:
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# Convert each frame in the list to tensor
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input_video = [preprocess_image(frame, sample_size=sample_size) for frame in input_video]
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# Stack all frames into a single tensor (F, C, H, W)
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input_video = torch.stack(input_video)[:num_frames]
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# Add batch dimension (B, F, C, H, W)
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input_video = input_video.permute(1, 0, 2, 3).unsqueeze(0)
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if validation_video_mask is not None:
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# Handle mask input
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validation_video_mask = preprocess_image(validation_video_mask, size=sample_size)
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input_video_mask = torch.where(validation_video_mask < 240 / 255.0, 0.0, 255)
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# Adjust mask dimensions to match video
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input_video_mask = input_video_mask.unsqueeze(0).unsqueeze(-1).permute([3, 0, 1, 2]).unsqueeze(0)
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input_video_mask = torch.tile(input_video_mask, [1, 1, input_video.size()[2], 1, 1])
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input_video_mask = input_video_mask.to(input_video.device, input_video.dtype)
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else:
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input_video_mask = torch.zeros_like(input_video[:, :1])
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input_video_mask[:, :, :] = 255
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else:
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input_video, input_video_mask = None, None
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if ref_image is not None:
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# Convert reference image to tensor
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ref_image = preprocess_image(ref_image, size=sample_size)
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ref_image = ref_image.permute(1, 0, 2, 3).unsqueeze(0) # Add batch dimension (B, C, H, W)
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else:
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ref_image = None
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return input_video, input_video_mask, ref_image
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# Similar to diffusers.pipelines.hunyuandit.pipeline_hunyuandit.get_resize_crop_region_for_grid
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def get_resize_crop_region_for_grid(src, tgt_width, tgt_height):
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tw = tgt_width
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th = tgt_height
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h, w = src
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r = h / w
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if r > (th / tw):
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resize_height = th
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resize_width = int(round(th / h * w))
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else:
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resize_width = tw
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resize_height = int(round(tw / w * h))
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crop_top = int(round((th - resize_height) / 2.0))
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crop_left = int(round((tw - resize_width) / 2.0))
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return (crop_top, crop_left), (crop_top + resize_height, crop_left + resize_width)
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# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.rescale_noise_cfg
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def rescale_noise_cfg(noise_cfg, noise_pred_text, guidance_rescale=0.0):
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r"""
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Rescales `noise_cfg` tensor based on `guidance_rescale` to improve image quality and fix overexposure. Based on
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Section 3.4 from [Common Diffusion Noise Schedules and Sample Steps are
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Flawed](https://huggingface.co/papers/2305.08891).
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Args:
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noise_cfg (`torch.Tensor`):
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The predicted noise tensor for the guided diffusion process.
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noise_pred_text (`torch.Tensor`):
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The predicted noise tensor for the text-guided diffusion process.
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guidance_rescale (`float`, *optional*, defaults to 0.0):
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A rescale factor applied to the noise predictions.
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Returns:
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noise_cfg (`torch.Tensor`): The rescaled noise prediction tensor.
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"""
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std_text = noise_pred_text.std(dim=list(range(1, noise_pred_text.ndim)), keepdim=True)
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std_cfg = noise_cfg.std(dim=list(range(1, noise_cfg.ndim)), keepdim=True)
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# rescale the results from guidance (fixes overexposure)
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noise_pred_rescaled = noise_cfg * (std_text / std_cfg)
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# mix with the original results from guidance by factor guidance_rescale to avoid "plain looking" images
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noise_cfg = guidance_rescale * noise_pred_rescaled + (1 - guidance_rescale) * noise_cfg
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return noise_cfg
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# Resize mask information in magvit
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def resize_mask(mask, latent, process_first_frame_only=True):
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latent_size = latent.size()
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if process_first_frame_only:
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target_size = list(latent_size[2:])
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target_size[0] = 1
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first_frame_resized = F.interpolate(
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mask[:, :, 0:1, :, :], size=target_size, mode="trilinear", align_corners=False
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)
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target_size = list(latent_size[2:])
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target_size[0] = target_size[0] - 1
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if target_size[0] != 0:
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remaining_frames_resized = F.interpolate(
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mask[:, :, 1:, :, :], size=target_size, mode="trilinear", align_corners=False
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)
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resized_mask = torch.cat([first_frame_resized, remaining_frames_resized], dim=2)
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else:
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resized_mask = first_frame_resized
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else:
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target_size = list(latent_size[2:])
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resized_mask = F.interpolate(mask, size=target_size, mode="trilinear", align_corners=False)
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return resized_mask
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# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.retrieve_timesteps
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def retrieve_timesteps(
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scheduler,
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num_inference_steps: Optional[int] = None,
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device: Optional[Union[str, torch.device]] = None,
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timesteps: Optional[List[int]] = None,
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sigmas: Optional[List[float]] = None,
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**kwargs,
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):
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r"""
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Calls the scheduler's `set_timesteps` method and retrieves timesteps from the scheduler after the call. Handles
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custom timesteps. Any kwargs will be supplied to `scheduler.set_timesteps`.
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Args:
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scheduler (`SchedulerMixin`):
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The scheduler to get timesteps from.
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num_inference_steps (`int`):
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The number of diffusion steps used when generating samples with a pre-trained model. If used, `timesteps`
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must be `None`.
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device (`str` or `torch.device`, *optional*):
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The device to which the timesteps should be moved to. If `None`, the timesteps are not moved.
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timesteps (`List[int]`, *optional*):
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Custom timesteps used to override the timestep spacing strategy of the scheduler. If `timesteps` is passed,
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`num_inference_steps` and `sigmas` must be `None`.
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sigmas (`List[float]`, *optional*):
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Custom sigmas used to override the timestep spacing strategy of the scheduler. If `sigmas` is passed,
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`num_inference_steps` and `timesteps` must be `None`.
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Returns:
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`Tuple[torch.Tensor, int]`: A tuple where the first element is the timestep schedule from the scheduler and the
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second element is the number of inference steps.
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"""
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if timesteps is not None and sigmas is not None:
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raise ValueError("Only one of `timesteps` or `sigmas` can be passed. Please choose one to set custom values")
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if timesteps is not None:
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accepts_timesteps = "timesteps" in set(inspect.signature(scheduler.set_timesteps).parameters.keys())
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if not accepts_timesteps:
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raise ValueError(
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f"The current scheduler class {scheduler.__class__}'s `set_timesteps` does not support custom"
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f" timestep schedules. Please check whether you are using the correct scheduler."
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)
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scheduler.set_timesteps(timesteps=timesteps, device=device, **kwargs)
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timesteps = scheduler.timesteps
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num_inference_steps = len(timesteps)
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elif sigmas is not None:
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accept_sigmas = "sigmas" in set(inspect.signature(scheduler.set_timesteps).parameters.keys())
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if not accept_sigmas:
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raise ValueError(
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f"The current scheduler class {scheduler.__class__}'s `set_timesteps` does not support custom"
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f" sigmas schedules. Please check whether you are using the correct scheduler."
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)
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scheduler.set_timesteps(sigmas=sigmas, device=device, **kwargs)
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timesteps = scheduler.timesteps
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num_inference_steps = len(timesteps)
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else:
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scheduler.set_timesteps(num_inference_steps, device=device, **kwargs)
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timesteps = scheduler.timesteps
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return timesteps, num_inference_steps
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class EasyAnimateControlPipeline(DiffusionPipeline):
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r"""
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Pipeline for text-to-video generation using EasyAnimate.
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This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods the
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library implements for all the pipelines (such as downloading or saving, running on a particular device, etc.)
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EasyAnimate uses one text encoder [qwen2 vl](https://huggingface.co/Qwen/Qwen2-VL-7B-Instruct) in V5.1.
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Args:
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vae ([`AutoencoderKLMagvit`]):
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Variational Auto-Encoder (VAE) Model to encode and decode video to and from latent representations.
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text_encoder (Optional[`~transformers.Qwen2VLForConditionalGeneration`, `~transformers.BertModel`]):
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EasyAnimate uses [qwen2 vl](https://huggingface.co/Qwen/Qwen2-VL-7B-Instruct) in V5.1.
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tokenizer (Optional[`~transformers.Qwen2Tokenizer`, `~transformers.BertTokenizer`]):
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A `Qwen2Tokenizer` or `BertTokenizer` to tokenize text.
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transformer ([`EasyAnimateTransformer3DModel`]):
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The EasyAnimate model designed by EasyAnimate Team.
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scheduler ([`FlowMatchEulerDiscreteScheduler`]):
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A scheduler to be used in combination with EasyAnimate to denoise the encoded image latents.
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"""
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model_cpu_offload_seq = "text_encoder->transformer->vae"
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_callback_tensor_inputs = ["latents", "prompt_embeds", "negative_prompt_embeds"]
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def __init__(
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self,
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vae: AutoencoderKLMagvit,
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text_encoder: Union[Qwen2VLForConditionalGeneration, BertModel],
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tokenizer: Union[Qwen2Tokenizer, BertTokenizer],
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transformer: EasyAnimateTransformer3DModel,
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scheduler: FlowMatchEulerDiscreteScheduler,
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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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text_encoder=text_encoder,
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tokenizer=tokenizer,
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transformer=transformer,
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scheduler=scheduler,
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)
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self.enable_text_attention_mask = (
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self.transformer.config.enable_text_attention_mask
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if getattr(self, "transformer", None) is not None
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else True
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)
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self.vae_spatial_compression_ratio = (
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self.vae.spatial_compression_ratio if getattr(self, "vae", None) is not None else 8
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)
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self.vae_temporal_compression_ratio = (
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self.vae.temporal_compression_ratio if getattr(self, "vae", None) is not None else 4
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)
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self.image_processor = VaeImageProcessor(vae_scale_factor=self.vae_spatial_compression_ratio)
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self.mask_processor = VaeImageProcessor(
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vae_scale_factor=self.vae_spatial_compression_ratio,
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do_normalize=False,
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do_binarize=True,
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do_convert_grayscale=True,
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)
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self.video_processor = VideoProcessor(vae_scale_factor=self.vae_spatial_compression_ratio)
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# Copied from diffusers.pipelines.easyanimate.pipeline_easyanimate.EasyAnimatePipeline.encode_prompt
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def encode_prompt(
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self,
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prompt: Union[str, List[str]],
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num_images_per_prompt: int = 1,
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do_classifier_free_guidance: bool = True,
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negative_prompt: Optional[Union[str, List[str]]] = None,
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prompt_embeds: Optional[torch.Tensor] = None,
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negative_prompt_embeds: Optional[torch.Tensor] = None,
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prompt_attention_mask: Optional[torch.Tensor] = None,
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negative_prompt_attention_mask: Optional[torch.Tensor] = None,
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device: Optional[torch.device] = None,
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dtype: Optional[torch.dtype] = None,
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max_sequence_length: int = 256,
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):
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r"""
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Encodes the prompt into text encoder hidden states.
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Args:
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prompt (`str` or `List[str]`, *optional*):
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prompt to be encoded
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device: (`torch.device`):
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torch device
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dtype (`torch.dtype`):
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torch dtype
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num_images_per_prompt (`int`):
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number of images that should be generated per prompt
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do_classifier_free_guidance (`bool`):
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whether to use classifier free guidance or not
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negative_prompt (`str` or `List[str]`, *optional*):
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The prompt or prompts not to guide the image generation. If not defined, one has to pass
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`negative_prompt_embeds` instead. Ignored when not using guidance (i.e., ignored if `guidance_scale` is
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less than `1`).
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prompt_embeds (`torch.Tensor`, *optional*):
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Pre-generated text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not
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provided, text embeddings will be generated from `prompt` input argument.
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negative_prompt_embeds (`torch.Tensor`, *optional*):
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Pre-generated negative text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt
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weighting. If not provided, negative_prompt_embeds will be generated from `negative_prompt` input
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argument.
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prompt_attention_mask (`torch.Tensor`, *optional*):
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Attention mask for the prompt. Required when `prompt_embeds` is passed directly.
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negative_prompt_attention_mask (`torch.Tensor`, *optional*):
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Attention mask for the negative prompt. Required when `negative_prompt_embeds` is passed directly.
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max_sequence_length (`int`, *optional*): maximum sequence length to use for the prompt.
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"""
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dtype = dtype or self.text_encoder.dtype
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device = device or self.text_encoder.device
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if prompt is not None and isinstance(prompt, str):
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batch_size = 1
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elif prompt is not None and isinstance(prompt, list):
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batch_size = len(prompt)
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else:
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batch_size = prompt_embeds.shape[0]
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if prompt_embeds is None:
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if isinstance(prompt, str):
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messages = [
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{
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"role": "user",
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"content": [{"type": "text", "text": prompt}],
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}
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]
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else:
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messages = [
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{
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"role": "user",
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"content": [{"type": "text", "text": _prompt}],
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}
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for _prompt in prompt
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]
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text = [
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self.tokenizer.apply_chat_template([m], tokenize=False, add_generation_prompt=True) for m in messages
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]
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text_inputs = self.tokenizer(
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text=text,
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padding="max_length",
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max_length=max_sequence_length,
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truncation=True,
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|
return_attention_mask=True,
|
|
padding_side="right",
|
|
return_tensors="pt",
|
|
)
|
|
text_inputs = text_inputs.to(self.text_encoder.device)
|
|
|
|
text_input_ids = text_inputs.input_ids
|
|
prompt_attention_mask = text_inputs.attention_mask
|
|
if self.enable_text_attention_mask:
|
|
# Inference: Generation of the output
|
|
prompt_embeds = self.text_encoder(
|
|
input_ids=text_input_ids, attention_mask=prompt_attention_mask, output_hidden_states=True
|
|
).hidden_states[-2]
|
|
else:
|
|
raise ValueError("LLM needs attention_mask")
|
|
prompt_attention_mask = prompt_attention_mask.repeat(num_images_per_prompt, 1)
|
|
|
|
prompt_embeds = prompt_embeds.to(dtype=dtype, device=device)
|
|
|
|
bs_embed, seq_len, _ = prompt_embeds.shape
|
|
# duplicate text embeddings for each generation per prompt, using mps friendly method
|
|
prompt_embeds = prompt_embeds.repeat(1, num_images_per_prompt, 1)
|
|
prompt_embeds = prompt_embeds.view(bs_embed * num_images_per_prompt, seq_len, -1)
|
|
prompt_attention_mask = prompt_attention_mask.to(device=device)
|
|
|
|
# get unconditional embeddings for classifier free guidance
|
|
if do_classifier_free_guidance and negative_prompt_embeds is None:
|
|
if negative_prompt is not None and isinstance(negative_prompt, str):
|
|
messages = [
|
|
{
|
|
"role": "user",
|
|
"content": [{"type": "text", "text": negative_prompt}],
|
|
}
|
|
]
|
|
else:
|
|
messages = [
|
|
{
|
|
"role": "user",
|
|
"content": [{"type": "text", "text": _negative_prompt}],
|
|
}
|
|
for _negative_prompt in negative_prompt
|
|
]
|
|
text = [
|
|
self.tokenizer.apply_chat_template([m], tokenize=False, add_generation_prompt=True) for m in messages
|
|
]
|
|
|
|
text_inputs = self.tokenizer(
|
|
text=text,
|
|
padding="max_length",
|
|
max_length=max_sequence_length,
|
|
truncation=True,
|
|
return_attention_mask=True,
|
|
padding_side="right",
|
|
return_tensors="pt",
|
|
)
|
|
text_inputs = text_inputs.to(self.text_encoder.device)
|
|
|
|
text_input_ids = text_inputs.input_ids
|
|
negative_prompt_attention_mask = text_inputs.attention_mask
|
|
if self.enable_text_attention_mask:
|
|
# Inference: Generation of the output
|
|
negative_prompt_embeds = self.text_encoder(
|
|
input_ids=text_input_ids,
|
|
attention_mask=negative_prompt_attention_mask,
|
|
output_hidden_states=True,
|
|
).hidden_states[-2]
|
|
else:
|
|
raise ValueError("LLM needs attention_mask")
|
|
negative_prompt_attention_mask = negative_prompt_attention_mask.repeat(num_images_per_prompt, 1)
|
|
|
|
if do_classifier_free_guidance:
|
|
# duplicate unconditional embeddings for each generation per prompt, using mps friendly method
|
|
seq_len = negative_prompt_embeds.shape[1]
|
|
|
|
negative_prompt_embeds = negative_prompt_embeds.to(dtype=dtype, device=device)
|
|
|
|
negative_prompt_embeds = negative_prompt_embeds.repeat(1, num_images_per_prompt, 1)
|
|
negative_prompt_embeds = negative_prompt_embeds.view(batch_size * num_images_per_prompt, seq_len, -1)
|
|
negative_prompt_attention_mask = negative_prompt_attention_mask.to(device=device)
|
|
|
|
return prompt_embeds, negative_prompt_embeds, prompt_attention_mask, negative_prompt_attention_mask
|
|
|
|
# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.prepare_extra_step_kwargs
|
|
def prepare_extra_step_kwargs(self, generator, eta):
|
|
# prepare extra kwargs for the scheduler step, since not all schedulers have the same signature
|
|
# eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers.
|
|
# eta corresponds to η in DDIM paper: https://huggingface.co/papers/2010.02502
|
|
# and should be between [0, 1]
|
|
|
|
accepts_eta = "eta" in set(inspect.signature(self.scheduler.step).parameters.keys())
|
|
extra_step_kwargs = {}
|
|
if accepts_eta:
|
|
extra_step_kwargs["eta"] = eta
|
|
|
|
# check if the scheduler accepts generator
|
|
accepts_generator = "generator" in set(inspect.signature(self.scheduler.step).parameters.keys())
|
|
if accepts_generator:
|
|
extra_step_kwargs["generator"] = generator
|
|
return extra_step_kwargs
|
|
|
|
def check_inputs(
|
|
self,
|
|
prompt,
|
|
height,
|
|
width,
|
|
negative_prompt=None,
|
|
prompt_embeds=None,
|
|
negative_prompt_embeds=None,
|
|
prompt_attention_mask=None,
|
|
negative_prompt_attention_mask=None,
|
|
callback_on_step_end_tensor_inputs=None,
|
|
):
|
|
if height % 16 != 0 or width % 16 != 0:
|
|
raise ValueError(f"`height` and `width` have to be divisible by 16 but are {height} and {width}.")
|
|
|
|
if callback_on_step_end_tensor_inputs is not None and not all(
|
|
k in self._callback_tensor_inputs for k in callback_on_step_end_tensor_inputs
|
|
):
|
|
raise ValueError(
|
|
f"`callback_on_step_end_tensor_inputs` has to be in {self._callback_tensor_inputs}, but found {[k for k in callback_on_step_end_tensor_inputs if k not in self._callback_tensor_inputs]}"
|
|
)
|
|
|
|
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 prompt_embeds is not None and prompt_attention_mask is None:
|
|
raise ValueError("Must provide `prompt_attention_mask` when specifying `prompt_embeds`.")
|
|
|
|
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 negative_prompt_embeds is not None and negative_prompt_attention_mask is None:
|
|
raise ValueError("Must provide `negative_prompt_attention_mask` when specifying `negative_prompt_embeds`.")
|
|
|
|
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}."
|
|
)
|
|
|
|
def prepare_latents(
|
|
self, batch_size, num_channels_latents, num_frames, height, width, dtype, device, generator, latents=None
|
|
):
|
|
if latents is not None:
|
|
return latents.to(device=device, dtype=dtype)
|
|
|
|
shape = (
|
|
batch_size,
|
|
num_channels_latents,
|
|
(num_frames - 1) // self.vae_temporal_compression_ratio + 1,
|
|
height // self.vae_spatial_compression_ratio,
|
|
width // self.vae_spatial_compression_ratio,
|
|
)
|
|
|
|
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."
|
|
)
|
|
|
|
latents = randn_tensor(shape, generator=generator, device=device, dtype=dtype)
|
|
# scale the initial noise by the standard deviation required by the scheduler
|
|
if hasattr(self.scheduler, "init_noise_sigma"):
|
|
latents = latents * self.scheduler.init_noise_sigma
|
|
return latents
|
|
|
|
def prepare_control_latents(
|
|
self, control, control_image, batch_size, height, width, dtype, device, generator, do_classifier_free_guidance
|
|
):
|
|
# resize the control to latents shape as we concatenate the control to the latents
|
|
# we do that before converting to dtype to avoid breaking in case we're using cpu_offload
|
|
# and half precision
|
|
|
|
if control is not None:
|
|
control = control.to(device=device, dtype=dtype)
|
|
bs = 1
|
|
new_control = []
|
|
for i in range(0, control.shape[0], bs):
|
|
control_bs = control[i : i + bs]
|
|
control_bs = self.vae.encode(control_bs)[0]
|
|
control_bs = control_bs.mode()
|
|
new_control.append(control_bs)
|
|
control = torch.cat(new_control, dim=0)
|
|
control = control * self.vae.config.scaling_factor
|
|
|
|
if control_image is not None:
|
|
control_image = control_image.to(device=device, dtype=dtype)
|
|
bs = 1
|
|
new_control_pixel_values = []
|
|
for i in range(0, control_image.shape[0], bs):
|
|
control_pixel_values_bs = control_image[i : i + bs]
|
|
control_pixel_values_bs = self.vae.encode(control_pixel_values_bs)[0]
|
|
control_pixel_values_bs = control_pixel_values_bs.mode()
|
|
new_control_pixel_values.append(control_pixel_values_bs)
|
|
control_image_latents = torch.cat(new_control_pixel_values, dim=0)
|
|
control_image_latents = control_image_latents * self.vae.config.scaling_factor
|
|
else:
|
|
control_image_latents = None
|
|
|
|
return control, control_image_latents
|
|
|
|
@property
|
|
def guidance_scale(self):
|
|
return self._guidance_scale
|
|
|
|
@property
|
|
def guidance_rescale(self):
|
|
return self._guidance_rescale
|
|
|
|
# here `guidance_scale` is defined analog to the guidance weight `w` of equation (2)
|
|
# of the Imagen paper: https://huggingface.co/papers/2205.11487 . `guidance_scale = 1`
|
|
# corresponds to doing no classifier free guidance.
|
|
@property
|
|
def do_classifier_free_guidance(self):
|
|
return self._guidance_scale > 1
|
|
|
|
@property
|
|
def num_timesteps(self):
|
|
return self._num_timesteps
|
|
|
|
@property
|
|
def interrupt(self):
|
|
return self._interrupt
|
|
|
|
@torch.no_grad()
|
|
@replace_example_docstring(EXAMPLE_DOC_STRING)
|
|
def __call__(
|
|
self,
|
|
prompt: Union[str, List[str]] = None,
|
|
num_frames: Optional[int] = 49,
|
|
height: Optional[int] = 512,
|
|
width: Optional[int] = 512,
|
|
control_video: Union[torch.FloatTensor] = None,
|
|
control_camera_video: Union[torch.FloatTensor] = None,
|
|
ref_image: Union[torch.FloatTensor] = None,
|
|
num_inference_steps: Optional[int] = 50,
|
|
guidance_scale: Optional[float] = 5.0,
|
|
negative_prompt: Optional[Union[str, List[str]]] = None,
|
|
num_images_per_prompt: Optional[int] = 1,
|
|
eta: Optional[float] = 0.0,
|
|
generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None,
|
|
latents: Optional[torch.Tensor] = None,
|
|
prompt_embeds: Optional[torch.Tensor] = None,
|
|
negative_prompt_embeds: Optional[torch.Tensor] = None,
|
|
prompt_attention_mask: Optional[torch.Tensor] = None,
|
|
negative_prompt_attention_mask: Optional[torch.Tensor] = None,
|
|
output_type: Optional[str] = "pil",
|
|
return_dict: bool = True,
|
|
callback_on_step_end: Optional[
|
|
Union[Callable[[int, int, Dict], None], PipelineCallback, MultiPipelineCallbacks]
|
|
] = None,
|
|
callback_on_step_end_tensor_inputs: List[str] = ["latents"],
|
|
guidance_rescale: float = 0.0,
|
|
timesteps: Optional[List[int]] = None,
|
|
):
|
|
r"""
|
|
Generates images or video using the EasyAnimate pipeline based on the provided prompts.
|
|
|
|
Examples:
|
|
prompt (`str` or `List[str]`, *optional*):
|
|
Text prompts to guide the image or video generation. If not provided, use `prompt_embeds` instead.
|
|
num_frames (`int`, *optional*):
|
|
Length of the generated video (in frames).
|
|
height (`int`, *optional*):
|
|
Height of the generated image in pixels.
|
|
width (`int`, *optional*):
|
|
Width of the generated image in pixels.
|
|
num_inference_steps (`int`, *optional*, defaults to 50):
|
|
Number of denoising steps during generation. More steps generally yield higher quality images but slow
|
|
down inference.
|
|
guidance_scale (`float`, *optional*, defaults to 5.0):
|
|
Encourages the model to align outputs with prompts. A higher value may decrease image quality.
|
|
negative_prompt (`str` or `List[str]`, *optional*):
|
|
Prompts indicating what to exclude in generation. If not specified, use `negative_prompt_embeds`.
|
|
num_images_per_prompt (`int`, *optional*, defaults to 1):
|
|
Number of images to generate for each prompt.
|
|
eta (`float`, *optional*, defaults to 0.0):
|
|
Applies to DDIM scheduling. Controlled by the eta parameter from the related literature.
|
|
generator (`torch.Generator` or `List[torch.Generator]`, *optional*):
|
|
A generator to ensure reproducibility in image generation.
|
|
latents (`torch.Tensor`, *optional*):
|
|
Predefined latent tensors to condition generation.
|
|
prompt_embeds (`torch.Tensor`, *optional*):
|
|
Text embeddings for the prompts. Overrides prompt string inputs for more flexibility.
|
|
negative_prompt_embeds (`torch.Tensor`, *optional*):
|
|
Embeddings for negative prompts. Overrides string inputs if defined.
|
|
prompt_attention_mask (`torch.Tensor`, *optional*):
|
|
Attention mask for the primary prompt embeddings.
|
|
negative_prompt_attention_mask (`torch.Tensor`, *optional*):
|
|
Attention mask for negative prompt embeddings.
|
|
output_type (`str`, *optional*, defaults to "latent"):
|
|
Format of the generated output, either as a PIL image or as a NumPy array.
|
|
return_dict (`bool`, *optional*, defaults to `True`):
|
|
If `True`, returns a structured output. Otherwise returns a simple tuple.
|
|
callback_on_step_end (`Callable`, *optional*):
|
|
Functions called at the end of each denoising step.
|
|
callback_on_step_end_tensor_inputs (`List[str]`, *optional*):
|
|
Tensor names to be included in callback function calls.
|
|
guidance_rescale (`float`, *optional*, defaults to 0.0):
|
|
Adjusts noise levels based on guidance scale.
|
|
|
|
Returns:
|
|
[`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] or `tuple`:
|
|
If `return_dict` is `True`, [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] is returned,
|
|
otherwise a `tuple` is returned where the first element is a list with the generated images and the
|
|
second element is a list of `bool`s indicating whether the corresponding generated image contains
|
|
"not-safe-for-work" (nsfw) content.
|
|
"""
|
|
|
|
if isinstance(callback_on_step_end, (PipelineCallback, MultiPipelineCallbacks)):
|
|
callback_on_step_end_tensor_inputs = callback_on_step_end.tensor_inputs
|
|
|
|
# 0. default height and width
|
|
height = int((height // 16) * 16)
|
|
width = int((width // 16) * 16)
|
|
|
|
# 1. Check inputs. Raise error if not correct
|
|
self.check_inputs(
|
|
prompt,
|
|
height,
|
|
width,
|
|
negative_prompt,
|
|
prompt_embeds,
|
|
negative_prompt_embeds,
|
|
prompt_attention_mask,
|
|
negative_prompt_attention_mask,
|
|
callback_on_step_end_tensor_inputs,
|
|
)
|
|
self._guidance_scale = guidance_scale
|
|
self._guidance_rescale = guidance_rescale
|
|
self._interrupt = False
|
|
|
|
# 2. Define call parameters
|
|
if prompt is not None and isinstance(prompt, str):
|
|
batch_size = 1
|
|
elif prompt is not None and isinstance(prompt, list):
|
|
batch_size = len(prompt)
|
|
else:
|
|
batch_size = prompt_embeds.shape[0]
|
|
|
|
device = self._execution_device
|
|
if self.text_encoder is not None:
|
|
dtype = self.text_encoder.dtype
|
|
else:
|
|
dtype = self.transformer.dtype
|
|
|
|
# 3. Encode input prompt
|
|
(
|
|
prompt_embeds,
|
|
negative_prompt_embeds,
|
|
prompt_attention_mask,
|
|
negative_prompt_attention_mask,
|
|
) = self.encode_prompt(
|
|
prompt=prompt,
|
|
device=device,
|
|
dtype=dtype,
|
|
num_images_per_prompt=num_images_per_prompt,
|
|
do_classifier_free_guidance=self.do_classifier_free_guidance,
|
|
negative_prompt=negative_prompt,
|
|
prompt_embeds=prompt_embeds,
|
|
negative_prompt_embeds=negative_prompt_embeds,
|
|
prompt_attention_mask=prompt_attention_mask,
|
|
negative_prompt_attention_mask=negative_prompt_attention_mask,
|
|
text_encoder_index=0,
|
|
)
|
|
|
|
# 4. Prepare timesteps
|
|
if isinstance(self.scheduler, FlowMatchEulerDiscreteScheduler):
|
|
timesteps, num_inference_steps = retrieve_timesteps(
|
|
self.scheduler, num_inference_steps, device, timesteps, mu=1
|
|
)
|
|
else:
|
|
timesteps, num_inference_steps = retrieve_timesteps(self.scheduler, num_inference_steps, device, timesteps)
|
|
timesteps = self.scheduler.timesteps
|
|
|
|
# 5. Prepare latent variables
|
|
num_channels_latents = self.vae.config.latent_channels
|
|
latents = self.prepare_latents(
|
|
batch_size * num_images_per_prompt,
|
|
num_channels_latents,
|
|
num_frames,
|
|
height,
|
|
width,
|
|
dtype,
|
|
device,
|
|
generator,
|
|
latents,
|
|
)
|
|
|
|
if control_camera_video is not None:
|
|
control_video_latents = resize_mask(control_camera_video, latents, process_first_frame_only=True)
|
|
control_video_latents = control_video_latents * 6
|
|
control_latents = (
|
|
torch.cat([control_video_latents] * 2) if self.do_classifier_free_guidance else control_video_latents
|
|
).to(device, dtype)
|
|
elif control_video is not None:
|
|
batch_size, channels, num_frames, height_video, width_video = control_video.shape
|
|
control_video = self.image_processor.preprocess(
|
|
control_video.permute(0, 2, 1, 3, 4).reshape(
|
|
batch_size * num_frames, channels, height_video, width_video
|
|
),
|
|
height=height,
|
|
width=width,
|
|
)
|
|
control_video = control_video.to(dtype=torch.float32)
|
|
control_video = control_video.reshape(batch_size, num_frames, channels, height, width).permute(
|
|
0, 2, 1, 3, 4
|
|
)
|
|
control_video_latents = self.prepare_control_latents(
|
|
None,
|
|
control_video,
|
|
batch_size,
|
|
height,
|
|
width,
|
|
dtype,
|
|
device,
|
|
generator,
|
|
self.do_classifier_free_guidance,
|
|
)[1]
|
|
control_latents = (
|
|
torch.cat([control_video_latents] * 2) if self.do_classifier_free_guidance else control_video_latents
|
|
).to(device, dtype)
|
|
else:
|
|
control_video_latents = torch.zeros_like(latents).to(device, dtype)
|
|
control_latents = (
|
|
torch.cat([control_video_latents] * 2) if self.do_classifier_free_guidance else control_video_latents
|
|
).to(device, dtype)
|
|
|
|
if ref_image is not None:
|
|
batch_size, channels, num_frames, height_video, width_video = ref_image.shape
|
|
ref_image = self.image_processor.preprocess(
|
|
ref_image.permute(0, 2, 1, 3, 4).reshape(batch_size * num_frames, channels, height_video, width_video),
|
|
height=height,
|
|
width=width,
|
|
)
|
|
ref_image = ref_image.to(dtype=torch.float32)
|
|
ref_image = ref_image.reshape(batch_size, num_frames, channels, height, width).permute(0, 2, 1, 3, 4)
|
|
|
|
ref_image_latents = self.prepare_control_latents(
|
|
None,
|
|
ref_image,
|
|
batch_size,
|
|
height,
|
|
width,
|
|
prompt_embeds.dtype,
|
|
device,
|
|
generator,
|
|
self.do_classifier_free_guidance,
|
|
)[1]
|
|
|
|
ref_image_latents_conv_in = torch.zeros_like(latents)
|
|
if latents.size()[2] != 1:
|
|
ref_image_latents_conv_in[:, :, :1] = ref_image_latents
|
|
ref_image_latents_conv_in = (
|
|
torch.cat([ref_image_latents_conv_in] * 2)
|
|
if self.do_classifier_free_guidance
|
|
else ref_image_latents_conv_in
|
|
).to(device, dtype)
|
|
control_latents = torch.cat([control_latents, ref_image_latents_conv_in], dim=1)
|
|
else:
|
|
ref_image_latents_conv_in = torch.zeros_like(latents)
|
|
ref_image_latents_conv_in = (
|
|
torch.cat([ref_image_latents_conv_in] * 2)
|
|
if self.do_classifier_free_guidance
|
|
else ref_image_latents_conv_in
|
|
).to(device, dtype)
|
|
control_latents = torch.cat([control_latents, ref_image_latents_conv_in], dim=1)
|
|
|
|
# 6. 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)
|
|
|
|
if self.do_classifier_free_guidance:
|
|
prompt_embeds = torch.cat([negative_prompt_embeds, prompt_embeds])
|
|
prompt_attention_mask = torch.cat([negative_prompt_attention_mask, prompt_attention_mask])
|
|
|
|
# To latents.device
|
|
prompt_embeds = prompt_embeds.to(device=device)
|
|
prompt_attention_mask = prompt_attention_mask.to(device=device)
|
|
|
|
# 7. Denoising loop
|
|
num_warmup_steps = len(timesteps) - num_inference_steps * self.scheduler.order
|
|
self._num_timesteps = len(timesteps)
|
|
with self.progress_bar(total=num_inference_steps) as progress_bar:
|
|
for i, t in enumerate(timesteps):
|
|
if self.interrupt:
|
|
continue
|
|
|
|
# expand the latents if we are doing classifier free guidance
|
|
latent_model_input = torch.cat([latents] * 2) if self.do_classifier_free_guidance else latents
|
|
if hasattr(self.scheduler, "scale_model_input"):
|
|
latent_model_input = self.scheduler.scale_model_input(latent_model_input, t)
|
|
|
|
# expand scalar t to 1-D tensor to match the 1st dim of latent_model_input
|
|
t_expand = torch.tensor([t] * latent_model_input.shape[0], device=device).to(
|
|
dtype=latent_model_input.dtype
|
|
)
|
|
# predict the noise residual
|
|
noise_pred = self.transformer(
|
|
latent_model_input,
|
|
t_expand,
|
|
encoder_hidden_states=prompt_embeds,
|
|
control_latents=control_latents,
|
|
return_dict=False,
|
|
)[0]
|
|
if noise_pred.size()[1] != self.vae.config.latent_channels:
|
|
noise_pred, _ = noise_pred.chunk(2, dim=1)
|
|
|
|
# perform guidance
|
|
if self.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)
|
|
|
|
if self.do_classifier_free_guidance and guidance_rescale > 0.0:
|
|
# Based on 3.4. in https://huggingface.co/papers/2305.08891
|
|
noise_pred = rescale_noise_cfg(noise_pred, noise_pred_text, guidance_rescale=guidance_rescale)
|
|
|
|
# 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]
|
|
|
|
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)
|
|
prompt_embeds = callback_outputs.pop("prompt_embeds", prompt_embeds)
|
|
negative_prompt_embeds = callback_outputs.pop("negative_prompt_embeds", negative_prompt_embeds)
|
|
|
|
if i == len(timesteps) - 1 or ((i + 1) > num_warmup_steps and (i + 1) % self.scheduler.order == 0):
|
|
progress_bar.update()
|
|
|
|
if XLA_AVAILABLE:
|
|
xm.mark_step()
|
|
|
|
# Convert to tensor
|
|
if not output_type == "latent":
|
|
video = self.decode_latents(latents)
|
|
video = self.video_processor.postprocess_video(video=video, output_type=output_type)
|
|
else:
|
|
video = latents
|
|
|
|
# Offload all models
|
|
self.maybe_free_model_hooks()
|
|
|
|
if not return_dict:
|
|
return (video,)
|
|
|
|
return EasyAnimatePipelineOutput(frames=video)
|