975 lines
46 KiB
Python
975 lines
46 KiB
Python
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# Copyright 2025 ConsisID Authors and 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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import math
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from typing import Any, Callable, Dict, List, Optional, Tuple, Union
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import numpy as np
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import PIL
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import torch
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from transformers import T5EncoderModel, T5Tokenizer
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from ...callbacks import MultiPipelineCallbacks, PipelineCallback
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from ...image_processor import PipelineImageInput
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from ...loaders import CogVideoXLoraLoaderMixin
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from ...models import AutoencoderKLCogVideoX, ConsisIDTransformer3DModel
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from ...models.embeddings import get_3d_rotary_pos_embed
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from ...pipelines.pipeline_utils import DiffusionPipeline
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from ...schedulers import CogVideoXDPMScheduler
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from ...utils import is_opencv_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 ConsisIDPipelineOutput
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if is_opencv_available():
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import cv2
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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 ConsisIDPipeline
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>>> from diffusers.pipelines.consisid.consisid_utils import prepare_face_models, process_face_embeddings_infer
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>>> from diffusers.utils import export_to_video
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>>> from huggingface_hub import snapshot_download
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>>> snapshot_download(repo_id="BestWishYsh/ConsisID-preview", local_dir="BestWishYsh/ConsisID-preview")
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>>> (
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... face_helper_1,
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... face_helper_2,
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... face_clip_model,
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... face_main_model,
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... eva_transform_mean,
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... eva_transform_std,
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... ) = prepare_face_models("BestWishYsh/ConsisID-preview", device="cuda", dtype=torch.bfloat16)
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>>> pipe = ConsisIDPipeline.from_pretrained("BestWishYsh/ConsisID-preview", torch_dtype=torch.bfloat16)
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>>> pipe.to("cuda")
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>>> # ConsisID works well with long and well-described prompts. Make sure the face in the image is clearly visible (e.g., preferably half-body or full-body).
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>>> prompt = "The video captures a boy walking along a city street, filmed in black and white on a classic 35mm camera. His expression is thoughtful, his brow slightly furrowed as if he's lost in contemplation. The film grain adds a textured, timeless quality to the image, evoking a sense of nostalgia. Around him, the cityscape is filled with vintage buildings, cobblestone sidewalks, and softly blurred figures passing by, their outlines faint and indistinct. Streetlights cast a gentle glow, while shadows play across the boy's path, adding depth to the scene. The lighting highlights the boy's subtle smile, hinting at a fleeting moment of curiosity. The overall cinematic atmosphere, complete with classic film still aesthetics and dramatic contrasts, gives the scene an evocative and introspective feel."
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>>> image = "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/consisid/consisid_input.png?download=true"
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>>> id_cond, id_vit_hidden, image, face_kps = process_face_embeddings_infer(
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... face_helper_1,
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... face_clip_model,
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... face_helper_2,
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... eva_transform_mean,
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... eva_transform_std,
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... face_main_model,
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... "cuda",
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... torch.bfloat16,
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... image,
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... is_align_face=True,
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... )
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>>> video = pipe(
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... image=image,
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... prompt=prompt,
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... num_inference_steps=50,
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... guidance_scale=6.0,
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... use_dynamic_cfg=False,
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... id_vit_hidden=id_vit_hidden,
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... id_cond=id_cond,
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... kps_cond=face_kps,
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... generator=torch.Generator("cuda").manual_seed(42),
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... )
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>>> export_to_video(video.frames[0], "output.mp4", fps=8)
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```
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"""
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def draw_kps(image_pil, kps, color_list=[(255, 0, 0), (0, 255, 0), (0, 0, 255), (255, 255, 0), (255, 0, 255)]):
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"""
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This function draws keypoints and the limbs connecting them on an image.
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Parameters:
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- image_pil (PIL.Image): Input image as a PIL object.
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- kps (list of tuples): A list of keypoints where each keypoint is a tuple of (x, y) coordinates.
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- color_list (list of tuples, optional): List of colors (in RGB format) for each keypoint. Default is a set of five
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colors.
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Returns:
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- PIL.Image: Image with the keypoints and limbs drawn.
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"""
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stickwidth = 4
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limbSeq = np.array([[0, 2], [1, 2], [3, 2], [4, 2]])
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kps = np.array(kps)
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w, h = image_pil.size
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out_img = np.zeros([h, w, 3])
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for i in range(len(limbSeq)):
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index = limbSeq[i]
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color = color_list[index[0]]
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x = kps[index][:, 0]
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y = kps[index][:, 1]
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length = ((x[0] - x[1]) ** 2 + (y[0] - y[1]) ** 2) ** 0.5
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angle = math.degrees(math.atan2(y[0] - y[1], x[0] - x[1]))
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polygon = cv2.ellipse2Poly(
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(int(np.mean(x)), int(np.mean(y))), (int(length / 2), stickwidth), int(angle), 0, 360, 1
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)
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out_img = cv2.fillConvexPoly(out_img.copy(), polygon, color)
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out_img = (out_img * 0.6).astype(np.uint8)
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for idx_kp, kp in enumerate(kps):
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color = color_list[idx_kp]
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x, y = kp
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out_img = cv2.circle(out_img.copy(), (int(x), int(y)), 10, color, -1)
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out_img_pil = PIL.Image.fromarray(out_img.astype(np.uint8))
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return out_img_pil
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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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"""
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This function calculates the resize and crop region for an image to fit a target width and height while preserving
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the aspect ratio.
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Parameters:
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- src (tuple): A tuple containing the source image's height (h) and width (w).
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- tgt_width (int): The target width to resize the image.
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- tgt_height (int): The target height to resize the image.
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Returns:
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- tuple: Two tuples representing the crop region:
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1. The top-left coordinates of the crop region.
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2. The bottom-right coordinates of the crop region.
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"""
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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.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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# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion_img2img.retrieve_latents
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def retrieve_latents(
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encoder_output: torch.Tensor, generator: Optional[torch.Generator] = None, sample_mode: str = "sample"
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):
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if hasattr(encoder_output, "latent_dist") and sample_mode == "sample":
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return encoder_output.latent_dist.sample(generator)
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elif hasattr(encoder_output, "latent_dist") and sample_mode == "argmax":
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return encoder_output.latent_dist.mode()
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elif hasattr(encoder_output, "latents"):
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return encoder_output.latents
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else:
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raise AttributeError("Could not access latents of provided encoder_output")
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class ConsisIDPipeline(DiffusionPipeline, CogVideoXLoraLoaderMixin):
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r"""
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Pipeline for image-to-video generation using ConsisID.
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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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Args:
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vae ([`AutoencoderKL`]):
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Variational Auto-Encoder (VAE) Model to encode and decode videos to and from latent representations.
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text_encoder ([`T5EncoderModel`]):
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Frozen text-encoder. ConsisID uses
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[T5](https://huggingface.co/docs/transformers/model_doc/t5#transformers.T5EncoderModel); specifically the
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[t5-v1_1-xxl](https://huggingface.co/PixArt-alpha/PixArt-alpha/tree/main/t5-v1_1-xxl) variant.
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tokenizer (`T5Tokenizer`):
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Tokenizer of class
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[T5Tokenizer](https://huggingface.co/docs/transformers/model_doc/t5#transformers.T5Tokenizer).
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transformer ([`ConsisIDTransformer3DModel`]):
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A text conditioned `ConsisIDTransformer3DModel` to denoise the encoded video latents.
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scheduler ([`SchedulerMixin`]):
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A scheduler to be used in combination with `transformer` to denoise the encoded video latents.
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"""
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_optional_components = []
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model_cpu_offload_seq = "text_encoder->transformer->vae"
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_callback_tensor_inputs = [
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"latents",
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"prompt_embeds",
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"negative_prompt_embeds",
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]
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def __init__(
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self,
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tokenizer: T5Tokenizer,
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text_encoder: T5EncoderModel,
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vae: AutoencoderKLCogVideoX,
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transformer: ConsisIDTransformer3DModel,
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scheduler: CogVideoXDPMScheduler,
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):
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super().__init__()
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self.register_modules(
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tokenizer=tokenizer,
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text_encoder=text_encoder,
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vae=vae,
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transformer=transformer,
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scheduler=scheduler,
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)
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self.vae_scale_factor_spatial = (
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2 ** (len(self.vae.config.block_out_channels) - 1) if hasattr(self, "vae") and self.vae is not None else 8
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)
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self.vae_scale_factor_temporal = (
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self.vae.config.temporal_compression_ratio if hasattr(self, "vae") and self.vae is not None else 4
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)
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self.vae_scaling_factor_image = (
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self.vae.config.scaling_factor if hasattr(self, "vae") and self.vae is not None else 0.7
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)
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self.video_processor = VideoProcessor(vae_scale_factor=self.vae_scale_factor_spatial)
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# Copied from diffusers.pipelines.cogvideo.pipeline_cogvideox.CogVideoXPipeline._get_t5_prompt_embeds
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def _get_t5_prompt_embeds(
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self,
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prompt: Union[str, List[str]] = None,
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num_videos_per_prompt: int = 1,
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max_sequence_length: int = 226,
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device: Optional[torch.device] = None,
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dtype: Optional[torch.dtype] = None,
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):
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device = device or self._execution_device
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dtype = dtype or self.text_encoder.dtype
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prompt = [prompt] if isinstance(prompt, str) else prompt
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batch_size = len(prompt)
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|
|
text_inputs = self.tokenizer(
|
||
|
|
prompt,
|
||
|
|
padding="max_length",
|
||
|
|
max_length=max_sequence_length,
|
||
|
|
truncation=True,
|
||
|
|
add_special_tokens=True,
|
||
|
|
return_tensors="pt",
|
||
|
|
)
|
||
|
|
text_input_ids = text_inputs.input_ids
|
||
|
|
untruncated_ids = self.tokenizer(prompt, padding="longest", return_tensors="pt").input_ids
|
||
|
|
|
||
|
|
if untruncated_ids.shape[-1] >= text_input_ids.shape[-1] and not torch.equal(text_input_ids, untruncated_ids):
|
||
|
|
removed_text = self.tokenizer.batch_decode(untruncated_ids[:, max_sequence_length - 1 : -1])
|
||
|
|
logger.warning(
|
||
|
|
"The following part of your input was truncated because `max_sequence_length` is set to "
|
||
|
|
f" {max_sequence_length} tokens: {removed_text}"
|
||
|
|
)
|
||
|
|
|
||
|
|
prompt_embeds = self.text_encoder(text_input_ids.to(device))[0]
|
||
|
|
prompt_embeds = prompt_embeds.to(dtype=dtype, device=device)
|
||
|
|
|
||
|
|
# duplicate text embeddings for each generation per prompt, using mps friendly method
|
||
|
|
_, seq_len, _ = prompt_embeds.shape
|
||
|
|
prompt_embeds = prompt_embeds.repeat(1, num_videos_per_prompt, 1)
|
||
|
|
prompt_embeds = prompt_embeds.view(batch_size * num_videos_per_prompt, seq_len, -1)
|
||
|
|
|
||
|
|
return prompt_embeds
|
||
|
|
|
||
|
|
# Copied from diffusers.pipelines.cogvideo.pipeline_cogvideox.CogVideoXPipeline.encode_prompt
|
||
|
|
def encode_prompt(
|
||
|
|
self,
|
||
|
|
prompt: Union[str, List[str]],
|
||
|
|
negative_prompt: Optional[Union[str, List[str]]] = None,
|
||
|
|
do_classifier_free_guidance: bool = True,
|
||
|
|
num_videos_per_prompt: int = 1,
|
||
|
|
prompt_embeds: Optional[torch.Tensor] = None,
|
||
|
|
negative_prompt_embeds: Optional[torch.Tensor] = None,
|
||
|
|
max_sequence_length: int = 226,
|
||
|
|
device: Optional[torch.device] = None,
|
||
|
|
dtype: Optional[torch.dtype] = None,
|
||
|
|
):
|
||
|
|
r"""
|
||
|
|
Encodes the prompt into text encoder hidden states.
|
||
|
|
|
||
|
|
Args:
|
||
|
|
prompt (`str` or `List[str]`, *optional*):
|
||
|
|
prompt to be encoded
|
||
|
|
negative_prompt (`str` or `List[str]`, *optional*):
|
||
|
|
The prompt or prompts not to guide the image generation. If not defined, one has to pass
|
||
|
|
`negative_prompt_embeds` instead. Ignored when not using guidance (i.e., ignored if `guidance_scale` is
|
||
|
|
less than `1`).
|
||
|
|
do_classifier_free_guidance (`bool`, *optional*, defaults to `True`):
|
||
|
|
Whether to use classifier free guidance or not.
|
||
|
|
num_videos_per_prompt (`int`, *optional*, defaults to 1):
|
||
|
|
Number of videos that should be generated per prompt. torch device to place the resulting embeddings on
|
||
|
|
prompt_embeds (`torch.Tensor`, *optional*):
|
||
|
|
Pre-generated text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not
|
||
|
|
provided, text embeddings will be generated from `prompt` input argument.
|
||
|
|
negative_prompt_embeds (`torch.Tensor`, *optional*):
|
||
|
|
Pre-generated negative text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt
|
||
|
|
weighting. If not provided, negative_prompt_embeds will be generated from `negative_prompt` input
|
||
|
|
argument.
|
||
|
|
device: (`torch.device`, *optional*):
|
||
|
|
torch device
|
||
|
|
dtype: (`torch.dtype`, *optional*):
|
||
|
|
torch dtype
|
||
|
|
"""
|
||
|
|
device = device or self._execution_device
|
||
|
|
|
||
|
|
prompt = [prompt] if isinstance(prompt, str) else prompt
|
||
|
|
if prompt is not None:
|
||
|
|
batch_size = len(prompt)
|
||
|
|
else:
|
||
|
|
batch_size = prompt_embeds.shape[0]
|
||
|
|
|
||
|
|
if prompt_embeds is None:
|
||
|
|
prompt_embeds = self._get_t5_prompt_embeds(
|
||
|
|
prompt=prompt,
|
||
|
|
num_videos_per_prompt=num_videos_per_prompt,
|
||
|
|
max_sequence_length=max_sequence_length,
|
||
|
|
device=device,
|
||
|
|
dtype=dtype,
|
||
|
|
)
|
||
|
|
|
||
|
|
if do_classifier_free_guidance and negative_prompt_embeds is None:
|
||
|
|
negative_prompt = negative_prompt or ""
|
||
|
|
negative_prompt = batch_size * [negative_prompt] if isinstance(negative_prompt, str) else negative_prompt
|
||
|
|
|
||
|
|
if prompt is not None and type(prompt) is not type(negative_prompt):
|
||
|
|
raise TypeError(
|
||
|
|
f"`negative_prompt` should be the same type to `prompt`, but got {type(negative_prompt)} !="
|
||
|
|
f" {type(prompt)}."
|
||
|
|
)
|
||
|
|
elif batch_size != len(negative_prompt):
|
||
|
|
raise ValueError(
|
||
|
|
f"`negative_prompt`: {negative_prompt} has batch size {len(negative_prompt)}, but `prompt`:"
|
||
|
|
f" {prompt} has batch size {batch_size}. Please make sure that passed `negative_prompt` matches"
|
||
|
|
" the batch size of `prompt`."
|
||
|
|
)
|
||
|
|
|
||
|
|
negative_prompt_embeds = self._get_t5_prompt_embeds(
|
||
|
|
prompt=negative_prompt,
|
||
|
|
num_videos_per_prompt=num_videos_per_prompt,
|
||
|
|
max_sequence_length=max_sequence_length,
|
||
|
|
device=device,
|
||
|
|
dtype=dtype,
|
||
|
|
)
|
||
|
|
|
||
|
|
return prompt_embeds, negative_prompt_embeds
|
||
|
|
|
||
|
|
def prepare_latents(
|
||
|
|
self,
|
||
|
|
image: torch.Tensor,
|
||
|
|
batch_size: int = 1,
|
||
|
|
num_channels_latents: int = 16,
|
||
|
|
num_frames: int = 13,
|
||
|
|
height: int = 60,
|
||
|
|
width: int = 90,
|
||
|
|
dtype: Optional[torch.dtype] = None,
|
||
|
|
device: Optional[torch.device] = None,
|
||
|
|
generator: Optional[torch.Generator] = None,
|
||
|
|
latents: Optional[torch.Tensor] = None,
|
||
|
|
kps_cond: Optional[torch.Tensor] = None,
|
||
|
|
):
|
||
|
|
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."
|
||
|
|
)
|
||
|
|
|
||
|
|
num_frames = (num_frames - 1) // self.vae_scale_factor_temporal + 1
|
||
|
|
shape = (
|
||
|
|
batch_size,
|
||
|
|
num_frames,
|
||
|
|
num_channels_latents,
|
||
|
|
height // self.vae_scale_factor_spatial,
|
||
|
|
width // self.vae_scale_factor_spatial,
|
||
|
|
)
|
||
|
|
|
||
|
|
image = image.unsqueeze(2) # [B, C, F, H, W]
|
||
|
|
|
||
|
|
if isinstance(generator, list):
|
||
|
|
image_latents = [
|
||
|
|
retrieve_latents(self.vae.encode(image[i].unsqueeze(0)), generator[i]) for i in range(batch_size)
|
||
|
|
]
|
||
|
|
if kps_cond is not None:
|
||
|
|
kps_cond = kps_cond.unsqueeze(2)
|
||
|
|
kps_cond_latents = [
|
||
|
|
retrieve_latents(self.vae.encode(kps_cond[i].unsqueeze(0)), generator[i])
|
||
|
|
for i in range(batch_size)
|
||
|
|
]
|
||
|
|
else:
|
||
|
|
image_latents = [retrieve_latents(self.vae.encode(img.unsqueeze(0)), generator) for img in image]
|
||
|
|
if kps_cond is not None:
|
||
|
|
kps_cond = kps_cond.unsqueeze(2)
|
||
|
|
kps_cond_latents = [retrieve_latents(self.vae.encode(img.unsqueeze(0)), generator) for img in kps_cond]
|
||
|
|
|
||
|
|
image_latents = torch.cat(image_latents, dim=0).to(dtype).permute(0, 2, 1, 3, 4) # [B, F, C, H, W]
|
||
|
|
image_latents = self.vae_scaling_factor_image * image_latents
|
||
|
|
|
||
|
|
if kps_cond is not None:
|
||
|
|
kps_cond_latents = torch.cat(kps_cond_latents, dim=0).to(dtype).permute(0, 2, 1, 3, 4) # [B, F, C, H, W]
|
||
|
|
kps_cond_latents = self.vae_scaling_factor_image * kps_cond_latents
|
||
|
|
|
||
|
|
padding_shape = (
|
||
|
|
batch_size,
|
||
|
|
num_frames - 2,
|
||
|
|
num_channels_latents,
|
||
|
|
height // self.vae_scale_factor_spatial,
|
||
|
|
width // self.vae_scale_factor_spatial,
|
||
|
|
)
|
||
|
|
else:
|
||
|
|
padding_shape = (
|
||
|
|
batch_size,
|
||
|
|
num_frames - 1,
|
||
|
|
num_channels_latents,
|
||
|
|
height // self.vae_scale_factor_spatial,
|
||
|
|
width // self.vae_scale_factor_spatial,
|
||
|
|
)
|
||
|
|
|
||
|
|
latent_padding = torch.zeros(padding_shape, device=device, dtype=dtype)
|
||
|
|
if kps_cond is not None:
|
||
|
|
image_latents = torch.cat([image_latents, kps_cond_latents, latent_padding], dim=1)
|
||
|
|
else:
|
||
|
|
image_latents = torch.cat([image_latents, latent_padding], dim=1)
|
||
|
|
|
||
|
|
if latents is None:
|
||
|
|
latents = randn_tensor(shape, generator=generator, device=device, dtype=dtype)
|
||
|
|
else:
|
||
|
|
latents = latents.to(device)
|
||
|
|
|
||
|
|
# scale the initial noise by the standard deviation required by the scheduler
|
||
|
|
latents = latents * self.scheduler.init_noise_sigma
|
||
|
|
return latents, image_latents
|
||
|
|
|
||
|
|
# Copied from diffusers.pipelines.cogvideo.pipeline_cogvideox.CogVideoXPipeline.decode_latents
|
||
|
|
def decode_latents(self, latents: torch.Tensor) -> torch.Tensor:
|
||
|
|
latents = latents.permute(0, 2, 1, 3, 4) # [batch_size, num_channels, num_frames, height, width]
|
||
|
|
latents = 1 / self.vae_scaling_factor_image * latents
|
||
|
|
|
||
|
|
frames = self.vae.decode(latents).sample
|
||
|
|
return frames
|
||
|
|
|
||
|
|
# Copied from diffusers.pipelines.animatediff.pipeline_animatediff_video2video.AnimateDiffVideoToVideoPipeline.get_timesteps
|
||
|
|
def get_timesteps(self, num_inference_steps, timesteps, strength, device):
|
||
|
|
# get the original timestep using init_timestep
|
||
|
|
init_timestep = min(int(num_inference_steps * strength), num_inference_steps)
|
||
|
|
|
||
|
|
t_start = max(num_inference_steps - init_timestep, 0)
|
||
|
|
timesteps = timesteps[t_start * self.scheduler.order :]
|
||
|
|
|
||
|
|
return timesteps, num_inference_steps - t_start
|
||
|
|
|
||
|
|
# 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,
|
||
|
|
image,
|
||
|
|
prompt,
|
||
|
|
height,
|
||
|
|
width,
|
||
|
|
negative_prompt,
|
||
|
|
callback_on_step_end_tensor_inputs,
|
||
|
|
latents=None,
|
||
|
|
prompt_embeds=None,
|
||
|
|
negative_prompt_embeds=None,
|
||
|
|
):
|
||
|
|
if (
|
||
|
|
not isinstance(image, torch.Tensor)
|
||
|
|
and not isinstance(image, PIL.Image.Image)
|
||
|
|
and not isinstance(image, list)
|
||
|
|
):
|
||
|
|
raise ValueError(
|
||
|
|
"`image` has to be of type `torch.Tensor` or `PIL.Image.Image` or `List[PIL.Image.Image]` but is"
|
||
|
|
f" {type(image)}"
|
||
|
|
)
|
||
|
|
|
||
|
|
if height % 8 != 0 or width % 8 != 0:
|
||
|
|
raise ValueError(f"`height` and `width` have to be divisible by 8 but are {height} and {width}.")
|
||
|
|
|
||
|
|
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 is not None and negative_prompt_embeds is not None:
|
||
|
|
raise ValueError(
|
||
|
|
f"Cannot forward both `prompt`: {prompt} and `negative_prompt_embeds`:"
|
||
|
|
f" {negative_prompt_embeds}. Please make sure to only forward one of the two."
|
||
|
|
)
|
||
|
|
|
||
|
|
if negative_prompt is not None and negative_prompt_embeds is not None:
|
||
|
|
raise ValueError(
|
||
|
|
f"Cannot forward both `negative_prompt`: {negative_prompt} and `negative_prompt_embeds`:"
|
||
|
|
f" {negative_prompt_embeds}. Please make sure to only forward one of the two."
|
||
|
|
)
|
||
|
|
|
||
|
|
if prompt_embeds is not None and negative_prompt_embeds is not None:
|
||
|
|
if prompt_embeds.shape != negative_prompt_embeds.shape:
|
||
|
|
raise ValueError(
|
||
|
|
"`prompt_embeds` and `negative_prompt_embeds` must have the same shape when passed directly, but"
|
||
|
|
f" got: `prompt_embeds` {prompt_embeds.shape} != `negative_prompt_embeds`"
|
||
|
|
f" {negative_prompt_embeds.shape}."
|
||
|
|
)
|
||
|
|
|
||
|
|
def _prepare_rotary_positional_embeddings(
|
||
|
|
self,
|
||
|
|
height: int,
|
||
|
|
width: int,
|
||
|
|
num_frames: int,
|
||
|
|
device: torch.device,
|
||
|
|
) -> Tuple[torch.Tensor, torch.Tensor]:
|
||
|
|
grid_height = height // (self.vae_scale_factor_spatial * self.transformer.config.patch_size)
|
||
|
|
grid_width = width // (self.vae_scale_factor_spatial * self.transformer.config.patch_size)
|
||
|
|
base_size_width = self.transformer.config.sample_width // self.transformer.config.patch_size
|
||
|
|
base_size_height = self.transformer.config.sample_height // self.transformer.config.patch_size
|
||
|
|
|
||
|
|
grid_crops_coords = get_resize_crop_region_for_grid(
|
||
|
|
(grid_height, grid_width), base_size_width, base_size_height
|
||
|
|
)
|
||
|
|
freqs_cos, freqs_sin = get_3d_rotary_pos_embed(
|
||
|
|
embed_dim=self.transformer.config.attention_head_dim,
|
||
|
|
crops_coords=grid_crops_coords,
|
||
|
|
grid_size=(grid_height, grid_width),
|
||
|
|
temporal_size=num_frames,
|
||
|
|
device=device,
|
||
|
|
)
|
||
|
|
|
||
|
|
return freqs_cos, freqs_sin
|
||
|
|
|
||
|
|
@property
|
||
|
|
def guidance_scale(self):
|
||
|
|
return self._guidance_scale
|
||
|
|
|
||
|
|
@property
|
||
|
|
def num_timesteps(self):
|
||
|
|
return self._num_timesteps
|
||
|
|
|
||
|
|
@property
|
||
|
|
def attention_kwargs(self):
|
||
|
|
return self._attention_kwargs
|
||
|
|
|
||
|
|
@property
|
||
|
|
def interrupt(self):
|
||
|
|
return self._interrupt
|
||
|
|
|
||
|
|
@torch.no_grad()
|
||
|
|
@replace_example_docstring(EXAMPLE_DOC_STRING)
|
||
|
|
def __call__(
|
||
|
|
self,
|
||
|
|
image: PipelineImageInput,
|
||
|
|
prompt: Optional[Union[str, List[str]]] = None,
|
||
|
|
negative_prompt: Optional[Union[str, List[str]]] = None,
|
||
|
|
height: int = 480,
|
||
|
|
width: int = 720,
|
||
|
|
num_frames: int = 49,
|
||
|
|
num_inference_steps: int = 50,
|
||
|
|
guidance_scale: float = 6.0,
|
||
|
|
use_dynamic_cfg: bool = False,
|
||
|
|
num_videos_per_prompt: int = 1,
|
||
|
|
eta: float = 0.0,
|
||
|
|
generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None,
|
||
|
|
latents: Optional[torch.FloatTensor] = None,
|
||
|
|
prompt_embeds: Optional[torch.FloatTensor] = None,
|
||
|
|
negative_prompt_embeds: Optional[torch.FloatTensor] = None,
|
||
|
|
output_type: str = "pil",
|
||
|
|
return_dict: bool = True,
|
||
|
|
attention_kwargs: Optional[Dict[str, Any]] = None,
|
||
|
|
callback_on_step_end: Optional[
|
||
|
|
Union[Callable[[int, int, Dict], None], PipelineCallback, MultiPipelineCallbacks]
|
||
|
|
] = None,
|
||
|
|
callback_on_step_end_tensor_inputs: List[str] = ["latents"],
|
||
|
|
max_sequence_length: int = 226,
|
||
|
|
id_vit_hidden: Optional[torch.Tensor] = None,
|
||
|
|
id_cond: Optional[torch.Tensor] = None,
|
||
|
|
kps_cond: Optional[torch.Tensor] = None,
|
||
|
|
) -> Union[ConsisIDPipelineOutput, Tuple]:
|
||
|
|
"""
|
||
|
|
Function invoked when calling the pipeline for generation.
|
||
|
|
|
||
|
|
Args:
|
||
|
|
image (`PipelineImageInput`):
|
||
|
|
The input image to condition the generation on. Must be an image, a list of images or a `torch.Tensor`.
|
||
|
|
prompt (`str` or `List[str]`, *optional*):
|
||
|
|
The prompt or prompts to guide the image generation. If not defined, one has to pass `prompt_embeds`.
|
||
|
|
instead.
|
||
|
|
negative_prompt (`str` or `List[str]`, *optional*):
|
||
|
|
The prompt or prompts not to guide the image generation. If not defined, one has to pass
|
||
|
|
`negative_prompt_embeds` instead. Ignored when not using guidance (i.e., ignored if `guidance_scale` is
|
||
|
|
less than `1`).
|
||
|
|
height (`int`, *optional*, defaults to self.transformer.config.sample_height * self.vae_scale_factor_spatial):
|
||
|
|
The height in pixels of the generated image. This is set to 480 by default for the best results.
|
||
|
|
width (`int`, *optional*, defaults to self.transformer.config.sample_height * self.vae_scale_factor_spatial):
|
||
|
|
The width in pixels of the generated image. This is set to 720 by default for the best results.
|
||
|
|
num_frames (`int`, defaults to `49`):
|
||
|
|
Number of frames to generate. Must be divisible by self.vae_scale_factor_temporal. Generated video will
|
||
|
|
contain 1 extra frame because ConsisID is conditioned with (num_seconds * fps + 1) frames where
|
||
|
|
num_seconds is 6 and fps is 4. However, since videos can be saved at any fps, the only condition that
|
||
|
|
needs to be satisfied is that of divisibility mentioned above.
|
||
|
|
num_inference_steps (`int`, *optional*, defaults to 50):
|
||
|
|
The number of denoising steps. More denoising steps usually lead to a higher quality image at the
|
||
|
|
expense of slower inference.
|
||
|
|
guidance_scale (`float`, *optional*, defaults to 6):
|
||
|
|
Guidance scale as defined in [Classifier-Free Diffusion
|
||
|
|
Guidance](https://huggingface.co/papers/2207.12598). `guidance_scale` is defined as `w` of equation 2.
|
||
|
|
of [Imagen Paper](https://huggingface.co/papers/2205.11487). Guidance scale is enabled by setting
|
||
|
|
`guidance_scale > 1`. Higher guidance scale encourages to generate images that are closely linked to
|
||
|
|
the text `prompt`, usually at the expense of lower image quality.
|
||
|
|
use_dynamic_cfg (`bool`, *optional*, defaults to `False`):
|
||
|
|
If True, dynamically adjusts the guidance scale during inference. This allows the model to use a
|
||
|
|
progressive guidance scale, improving the balance between text-guided generation and image quality over
|
||
|
|
the course of the inference steps. Typically, early inference steps use a higher guidance scale for
|
||
|
|
more faithful image generation, while later steps reduce it for more diverse and natural results.
|
||
|
|
num_videos_per_prompt (`int`, *optional*, defaults to 1):
|
||
|
|
The number of videos to generate per prompt.
|
||
|
|
generator (`torch.Generator` or `List[torch.Generator]`, *optional*):
|
||
|
|
One or a list of [torch generator(s)](https://pytorch.org/docs/stable/generated/torch.Generator.html)
|
||
|
|
to make generation deterministic.
|
||
|
|
latents (`torch.FloatTensor`, *optional*):
|
||
|
|
Pre-generated noisy latents, sampled from a Gaussian distribution, to be used as inputs for image
|
||
|
|
generation. Can be used to tweak the same generation with different prompts. If not provided, a latents
|
||
|
|
tensor will ge generated by sampling using the supplied random `generator`.
|
||
|
|
prompt_embeds (`torch.FloatTensor`, *optional*):
|
||
|
|
Pre-generated text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not
|
||
|
|
provided, text embeddings will be generated from `prompt` input argument.
|
||
|
|
negative_prompt_embeds (`torch.FloatTensor`, *optional*):
|
||
|
|
Pre-generated negative text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt
|
||
|
|
weighting. If not provided, negative_prompt_embeds will be generated from `negative_prompt` input
|
||
|
|
argument.
|
||
|
|
output_type (`str`, *optional*, defaults to `"pil"`):
|
||
|
|
The output format of the generate image. Choose between
|
||
|
|
[PIL](https://pillow.readthedocs.io/en/stable/): `PIL.Image.Image` or `np.array`.
|
||
|
|
return_dict (`bool`, *optional*, defaults to `True`):
|
||
|
|
Whether or not to return a [`~pipelines.stable_diffusion_xl.StableDiffusionXLPipelineOutput`] instead
|
||
|
|
of a plain tuple.
|
||
|
|
attention_kwargs (`dict`, *optional*):
|
||
|
|
A kwargs dictionary that if specified is passed along to the `AttentionProcessor` as defined under
|
||
|
|
`self.processor` in
|
||
|
|
[diffusers.models.attention_processor](https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/attention_processor.py).
|
||
|
|
callback_on_step_end (`Callable`, *optional*):
|
||
|
|
A function that calls at the end of each denoising steps during the inference. The function is called
|
||
|
|
with the following arguments: `callback_on_step_end(self: DiffusionPipeline, step: int, timestep: int,
|
||
|
|
callback_kwargs: Dict)`. `callback_kwargs` will include a list of all tensors as specified by
|
||
|
|
`callback_on_step_end_tensor_inputs`.
|
||
|
|
callback_on_step_end_tensor_inputs (`List`, *optional*):
|
||
|
|
The list of tensor inputs for the `callback_on_step_end` function. The tensors specified in the list
|
||
|
|
will be passed as `callback_kwargs` argument. You will only be able to include variables listed in the
|
||
|
|
`._callback_tensor_inputs` attribute of your pipeline class.
|
||
|
|
max_sequence_length (`int`, defaults to `226`):
|
||
|
|
Maximum sequence length in encoded prompt. Must be consistent with
|
||
|
|
`self.transformer.config.max_text_seq_length` otherwise may lead to poor results.
|
||
|
|
id_vit_hidden (`Optional[torch.Tensor]`, *optional*):
|
||
|
|
The tensor representing the hidden features extracted from the face model, which are used to condition
|
||
|
|
the local facial extractor. This is crucial for the model to obtain high-frequency information of the
|
||
|
|
face. If not provided, the local facial extractor will not run normally.
|
||
|
|
id_cond (`Optional[torch.Tensor]`, *optional*):
|
||
|
|
The tensor representing the hidden features extracted from the clip model, which are used to condition
|
||
|
|
the local facial extractor. This is crucial for the model to edit facial features If not provided, the
|
||
|
|
local facial extractor will not run normally.
|
||
|
|
kps_cond (`Optional[torch.Tensor]`, *optional*):
|
||
|
|
A tensor that determines whether the global facial extractor use keypoint information for conditioning.
|
||
|
|
If provided, this tensor controls whether facial keypoints such as eyes, nose, and mouth landmarks are
|
||
|
|
used during the generation process. This helps ensure the model retains more facial low-frequency
|
||
|
|
information.
|
||
|
|
|
||
|
|
Examples:
|
||
|
|
|
||
|
|
Returns:
|
||
|
|
[`~pipelines.consisid.pipeline_output.ConsisIDPipelineOutput`] or `tuple`:
|
||
|
|
[`~pipelines.consisid.pipeline_output.ConsisIDPipelineOutput`] if `return_dict` is True, otherwise a
|
||
|
|
`tuple`. When returning a tuple, the first element is a list with the generated images.
|
||
|
|
"""
|
||
|
|
|
||
|
|
if isinstance(callback_on_step_end, (PipelineCallback, MultiPipelineCallbacks)):
|
||
|
|
callback_on_step_end_tensor_inputs = callback_on_step_end.tensor_inputs
|
||
|
|
|
||
|
|
height = height or self.transformer.config.sample_height * self.vae_scale_factor_spatial
|
||
|
|
width = width or self.transformer.config.sample_width * self.vae_scale_factor_spatial
|
||
|
|
num_frames = num_frames or self.transformer.config.sample_frames
|
||
|
|
|
||
|
|
num_videos_per_prompt = 1
|
||
|
|
|
||
|
|
# 1. Check inputs. Raise error if not correct
|
||
|
|
self.check_inputs(
|
||
|
|
image=image,
|
||
|
|
prompt=prompt,
|
||
|
|
height=height,
|
||
|
|
width=width,
|
||
|
|
negative_prompt=negative_prompt,
|
||
|
|
callback_on_step_end_tensor_inputs=callback_on_step_end_tensor_inputs,
|
||
|
|
latents=latents,
|
||
|
|
prompt_embeds=prompt_embeds,
|
||
|
|
negative_prompt_embeds=negative_prompt_embeds,
|
||
|
|
)
|
||
|
|
self._guidance_scale = guidance_scale
|
||
|
|
self._attention_kwargs = attention_kwargs
|
||
|
|
self._interrupt = False
|
||
|
|
|
||
|
|
# 2. Default 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
|
||
|
|
|
||
|
|
# 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.
|
||
|
|
do_classifier_free_guidance = guidance_scale > 1.0
|
||
|
|
|
||
|
|
# 3. Encode input prompt
|
||
|
|
prompt_embeds, negative_prompt_embeds = self.encode_prompt(
|
||
|
|
prompt=prompt,
|
||
|
|
negative_prompt=negative_prompt,
|
||
|
|
do_classifier_free_guidance=do_classifier_free_guidance,
|
||
|
|
num_videos_per_prompt=num_videos_per_prompt,
|
||
|
|
prompt_embeds=prompt_embeds,
|
||
|
|
negative_prompt_embeds=negative_prompt_embeds,
|
||
|
|
max_sequence_length=max_sequence_length,
|
||
|
|
device=device,
|
||
|
|
)
|
||
|
|
if do_classifier_free_guidance:
|
||
|
|
prompt_embeds = torch.cat([negative_prompt_embeds, prompt_embeds], dim=0)
|
||
|
|
|
||
|
|
# 4. Prepare timesteps
|
||
|
|
timesteps, num_inference_steps = retrieve_timesteps(self.scheduler, num_inference_steps, device)
|
||
|
|
self._num_timesteps = len(timesteps)
|
||
|
|
|
||
|
|
# 5. Prepare latents
|
||
|
|
is_kps = getattr(self.transformer.config, "is_kps", False)
|
||
|
|
kps_cond = kps_cond if is_kps else None
|
||
|
|
if kps_cond is not None:
|
||
|
|
kps_cond = draw_kps(image, kps_cond)
|
||
|
|
kps_cond = self.video_processor.preprocess(kps_cond, height=height, width=width).to(
|
||
|
|
device, dtype=prompt_embeds.dtype
|
||
|
|
)
|
||
|
|
|
||
|
|
image = self.video_processor.preprocess(image, height=height, width=width).to(
|
||
|
|
device, dtype=prompt_embeds.dtype
|
||
|
|
)
|
||
|
|
|
||
|
|
latent_channels = self.transformer.config.in_channels // 2
|
||
|
|
latents, image_latents = self.prepare_latents(
|
||
|
|
image,
|
||
|
|
batch_size * num_videos_per_prompt,
|
||
|
|
latent_channels,
|
||
|
|
num_frames,
|
||
|
|
height,
|
||
|
|
width,
|
||
|
|
prompt_embeds.dtype,
|
||
|
|
device,
|
||
|
|
generator,
|
||
|
|
latents,
|
||
|
|
kps_cond,
|
||
|
|
)
|
||
|
|
|
||
|
|
# 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)
|
||
|
|
|
||
|
|
# 7. Create rotary embeds if required
|
||
|
|
image_rotary_emb = (
|
||
|
|
self._prepare_rotary_positional_embeddings(height, width, latents.size(1), device)
|
||
|
|
if self.transformer.config.use_rotary_positional_embeddings
|
||
|
|
else None
|
||
|
|
)
|
||
|
|
|
||
|
|
# 8. Denoising loop
|
||
|
|
num_warmup_steps = max(len(timesteps) - num_inference_steps * self.scheduler.order, 0)
|
||
|
|
|
||
|
|
with self.progress_bar(total=num_inference_steps) as progress_bar:
|
||
|
|
# for DPM-solver++
|
||
|
|
old_pred_original_sample = None
|
||
|
|
timesteps_cpu = timesteps.cpu()
|
||
|
|
for i, t in enumerate(timesteps):
|
||
|
|
if self.interrupt:
|
||
|
|
continue
|
||
|
|
|
||
|
|
latent_model_input = torch.cat([latents] * 2) if do_classifier_free_guidance else latents
|
||
|
|
latent_model_input = self.scheduler.scale_model_input(latent_model_input, t)
|
||
|
|
|
||
|
|
latent_image_input = torch.cat([image_latents] * 2) if do_classifier_free_guidance else image_latents
|
||
|
|
latent_model_input = torch.cat([latent_model_input, latent_image_input], dim=2)
|
||
|
|
|
||
|
|
# broadcast to batch dimension in a way that's compatible with ONNX/Core ML
|
||
|
|
timestep = t.expand(latent_model_input.shape[0])
|
||
|
|
|
||
|
|
# predict noise model_output
|
||
|
|
noise_pred = self.transformer(
|
||
|
|
hidden_states=latent_model_input,
|
||
|
|
encoder_hidden_states=prompt_embeds,
|
||
|
|
timestep=timestep,
|
||
|
|
image_rotary_emb=image_rotary_emb,
|
||
|
|
attention_kwargs=attention_kwargs,
|
||
|
|
return_dict=False,
|
||
|
|
id_vit_hidden=id_vit_hidden,
|
||
|
|
id_cond=id_cond,
|
||
|
|
)[0]
|
||
|
|
noise_pred = noise_pred.float()
|
||
|
|
|
||
|
|
# perform guidance
|
||
|
|
if use_dynamic_cfg:
|
||
|
|
self._guidance_scale = 1 + guidance_scale * (
|
||
|
|
(
|
||
|
|
1
|
||
|
|
- math.cos(
|
||
|
|
math.pi
|
||
|
|
* ((num_inference_steps - timesteps_cpu[i].item()) / num_inference_steps) ** 5.0
|
||
|
|
)
|
||
|
|
)
|
||
|
|
/ 2
|
||
|
|
)
|
||
|
|
if do_classifier_free_guidance:
|
||
|
|
noise_pred_uncond, noise_pred_text = noise_pred.chunk(2)
|
||
|
|
noise_pred = noise_pred_uncond + self.guidance_scale * (noise_pred_text - noise_pred_uncond)
|
||
|
|
|
||
|
|
# compute the previous noisy sample x_t -> x_t-1
|
||
|
|
if not isinstance(self.scheduler, CogVideoXDPMScheduler):
|
||
|
|
latents = self.scheduler.step(noise_pred, t, latents, **extra_step_kwargs, return_dict=False)[0]
|
||
|
|
else:
|
||
|
|
latents, old_pred_original_sample = self.scheduler.step(
|
||
|
|
noise_pred,
|
||
|
|
old_pred_original_sample,
|
||
|
|
t,
|
||
|
|
timesteps[i - 1] if i > 0 else None,
|
||
|
|
latents,
|
||
|
|
**extra_step_kwargs,
|
||
|
|
return_dict=False,
|
||
|
|
)
|
||
|
|
latents = latents.to(prompt_embeds.dtype)
|
||
|
|
|
||
|
|
# call the callback, if provided
|
||
|
|
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 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 ConsisIDPipelineOutput(frames=video)
|