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- .gitignore +1 -0
- LICENSE +28 -0
- MagicAnimate/.gitattributes +35 -0
- MagicAnimate/README.md +7 -0
- MagicAnimate/appearance_encoder/config.json +66 -0
- MagicAnimate/appearance_encoder/diffusion_pytorch_model.safetensors +3 -0
- MagicAnimate/densepose_controlnet/config.json +51 -0
- MagicAnimate/densepose_controlnet/diffusion_pytorch_model.safetensors +3 -0
- MagicAnimate/temporal_attention/temporal_attention.ckpt +3 -0
- README.md +15 -0
- app.py +114 -0
- configs/inference/inference.yaml +26 -0
- configs/prompts/animation.yaml +40 -0
- demo/animate.py +195 -0
- demo/outputs/2024-01-21T03-12-04.mp4 +0 -0
- demo/outputs/2024-01-21T03-13-47.mp4 +0 -0
- demo/outputs/2024-01-21T03-16-06.mp4 +0 -0
- demo/outputs/2024-01-21T03-18-30.mp4 +0 -0
- inputs/applications/driving/densepose/dancing2.mp4 +0 -0
- inputs/applications/driving/densepose/demo4.mp4 +0 -0
- inputs/applications/driving/densepose/multi_dancing.mp4 +0 -0
- inputs/applications/driving/densepose/running.mp4 +0 -0
- inputs/applications/driving/densepose/running2.mp4 +0 -0
- inputs/applications/source_image/dalle2.jpeg +0 -0
- inputs/applications/source_image/dalle8.jpeg +0 -0
- inputs/applications/source_image/demo4.png +0 -0
- inputs/applications/source_image/monalisa.png +0 -0
- inputs/applications/source_image/multi1_source.png +0 -0
- magicanimate/models/appearance_encoder.py +1066 -0
- magicanimate/models/attention.py +320 -0
- magicanimate/models/controlnet.py +578 -0
- magicanimate/models/embeddings.py +385 -0
- magicanimate/models/motion_module.py +334 -0
- magicanimate/models/mutual_self_attention.py +642 -0
- magicanimate/models/orig_attention.py +988 -0
- magicanimate/models/resnet.py +212 -0
- magicanimate/models/stable_diffusion_controlnet_reference.py +840 -0
- magicanimate/models/unet.py +508 -0
- magicanimate/models/unet_3d_blocks.py +751 -0
- magicanimate/models/unet_controlnet.py +525 -0
- magicanimate/pipelines/animation.py +282 -0
- magicanimate/pipelines/context.py +76 -0
- magicanimate/pipelines/pipeline_animation.py +799 -0
- magicanimate/utils/dist_tools.py +105 -0
- magicanimate/utils/util.py +138 -0
- magicanimate/utils/videoreader.py +157 -0
- packages.txt +1 -0
- requirements.txt +127 -0
- samples/animation-2023-12-05T00-24-12/videos/0002_demo4.mp4 +0 -0
- samples/animation-2023-12-05T00-24-12/videos/0002_demo4/grid.mp4 +0 -0
.gitignore
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__pycache__
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LICENSE
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BSD 3-Clause License
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Copyright 2023 MagicAnimate Team All rights reserved.
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Redistribution and use in source and binary forms, with or without
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modification, are permitted provided that the following conditions are met:
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1. Redistributions of source code must retain the above copyright notice, this
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list of conditions and the following disclaimer.
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2. Redistributions in binary form must reproduce the above copyright notice,
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this list of conditions and the following disclaimer in the documentation
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and/or other materials provided with the distribution.
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3. Neither the name of the copyright holder nor the names of its
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contributors may be used to endorse or promote products derived from
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this software without specific prior written permission.
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THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
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AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
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IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
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DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE
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FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL
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DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR
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SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
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CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY,
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OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
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OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
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MagicAnimate/.gitattributes
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MagicAnimate/README.md
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---
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license: bsd-3-clause
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---
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Download MagicAnimate checkpoints by:
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```bash
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git lfs clone https://huggingface.co/zcxu-eric/MagicAnimate
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```
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MagicAnimate/appearance_encoder/config.json
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{
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MagicAnimate/appearance_encoder/diffusion_pytorch_model.safetensors
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MagicAnimate/densepose_controlnet/config.json
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}
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MagicAnimate/densepose_controlnet/diffusion_pytorch_model.safetensors
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size 1445157120
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MagicAnimate/temporal_attention/temporal_attention.ckpt
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version https://git-lfs.github.com/spec/v1
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README.md
ADDED
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---
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title: MagicAnimate
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emoji: 💃
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colorFrom: purple
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colorTo: purple
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sdk: gradio
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sdk_version: 4.10.0
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python_version: 3.11
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app_file: app.py
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models:
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- zcxu-eric/MagicAnimate
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- runwayml/stable-diffusion-v1-5
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- stabilityai/sd-vae-ft-mse
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pinned: false
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---
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app.py
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# Copyright 2023 ByteDance and/or its affiliates.
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#
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# Copyright (2023) MagicAnimate Authors
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#
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# ByteDance, its affiliates and licensors retain all intellectual
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6 |
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# property and proprietary rights in and to this material, related
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7 |
+
# documentation and any modifications thereto. Any use, reproduction,
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8 |
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# disclosure or distribution of this material and related documentation
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9 |
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# without an express license agreement from ByteDance or
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# its affiliates is strictly prohibited.
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import argparse
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import imageio
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import numpy as np
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import gradio as gr
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import os
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from PIL import Image
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from subprocess import PIPE, run
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from demo.animate import MagicAnimate
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from huggingface_hub import snapshot_download
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snapshot_download(repo_id="runwayml/stable-diffusion-v1-5", local_dir="./stable-diffusion-v1-5")
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snapshot_download(repo_id="stabilityai/sd-vae-ft-mse", local_dir="./sd-vae-ft-mse")
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snapshot_download(repo_id="zcxu-eric/MagicAnimate", local_dir="./MagicAnimate")
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+
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is_spaces = True if "SPACE_ID" in os.environ else False
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true_for_shared_ui = False #This will be true only if you are in a shared UI
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if(is_spaces):
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true_for_shared_ui = True if "zcxu-eric/magicanimate" in os.environ['SPACE_ID'] else False
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+
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+
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animator = MagicAnimate()
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+
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def animate(reference_image, motion_sequence_state, seed=1, steps=25, guidance_scale=7.5):
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return animator(reference_image, motion_sequence_state, seed, steps, guidance_scale)
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+
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with gr.Blocks() as demo:
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gr.HTML(
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"""
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<div style="display: flex; justify-content: center; align-items: center; text-align: center;">
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<a href="https://github.com/magic-research/magic-animate" style="margin-right: 20px; text-decoration: none; display: flex; align-items: center;">
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</a>
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<div>
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<h1 >MagicAnimate: Temporally Consistent Human Image Animation using Diffusion Model</h1>
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<h5 style="margin: 0;">If you like our project, please give us a star ✨ on Github for the latest update.</h5>
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<div style="display: flex; justify-content: center; align-items: center; text-align: center;>
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<a href="https://arxiv.org/abs/2311.16498"><img src="https://img.shields.io/badge/Arxiv-2311.16498-red"></a>
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<a href='https://showlab.github.io/magicanimate'><img src='https://img.shields.io/badge/Project_Page-MagicAnimate-green' alt='Project Page'></a>
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<a href='https://github.com/magic-research/magic-animate'><img src='https://img.shields.io/badge/Github-Code-blue'></a>
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</div>
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</div>
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</div>
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""")
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animation = gr.Video(format="mp4", label="Animation Results", autoplay=True)
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+
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with gr.Row():
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reference_image = gr.Image(label="Reference Image")
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motion_sequence = gr.Video(format="mp4", label="Motion Sequence",max_length=5)
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+
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with gr.Column():
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random_seed = gr.Textbox(label="Random seed", value=1, info="default: -1")
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sampling_steps = gr.Textbox(label="Sampling steps", value=25, info="default: 25")
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guidance_scale = gr.Textbox(label="Guidance scale", value=7.5, info="default: 7.5")
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submit = gr.Button("Animate")
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+
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def read_video(video):
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reader = imageio.get_reader(video)
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fps = reader.get_meta_data()['fps']
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return video
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+
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def read_image(image, size=512):
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return np.array(Image.fromarray(image).resize((size, size)))
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+
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# when user uploads a new video
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motion_sequence.upload(
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read_video,
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motion_sequence,
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motion_sequence,
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queue=False
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)
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# when `first_frame` is updated
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reference_image.upload(
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read_image,
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reference_image,
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reference_image,
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queue=False
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)
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# when the `submit` button is clicked
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submit.click(
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animate,
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[reference_image, motion_sequence, random_seed, sampling_steps, guidance_scale],
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animation
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)
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# Examples
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gr.Markdown("## Examples")
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gr.Examples(
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fn=animate,
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examples=[
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["inputs/applications/source_image/monalisa.png", "inputs/applications/driving/densepose/running.mp4"],
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["inputs/applications/source_image/demo4.png", "inputs/applications/driving/densepose/demo4.mp4"],
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["inputs/applications/source_image/dalle2.jpeg", "inputs/applications/driving/densepose/running2.mp4"],
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["inputs/applications/source_image/dalle8.jpeg", "inputs/applications/driving/densepose/dancing2.mp4"],
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["inputs/applications/source_image/multi1_source.png", "inputs/applications/driving/densepose/multi_dancing.mp4"],
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],
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inputs=[reference_image, motion_sequence],
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outputs=animation,
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cache_examples=true_for_shared_ui
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)
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+
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# demo.queue(max_size=15, api_open=False)
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demo.launch(share=True, show_api=False)
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configs/inference/inference.yaml
ADDED
@@ -0,0 +1,26 @@
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unet_additional_kwargs:
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unet_use_cross_frame_attention: false
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unet_use_temporal_attention: false
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use_motion_module: true
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motion_module_resolutions:
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+
- 1
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+
- 2
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+
- 4
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- 8
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motion_module_mid_block: false
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motion_module_decoder_only: false
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motion_module_type: Vanilla
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+
motion_module_kwargs:
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+
num_attention_heads: 8
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num_transformer_block: 1
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+
attention_block_types:
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+
- Temporal_Self
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+
- Temporal_Self
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+
temporal_position_encoding: true
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temporal_position_encoding_max_len: 24
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+
temporal_attention_dim_div: 1
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+
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+
noise_scheduler_kwargs:
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+
beta_start: 0.00085
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+
beta_end: 0.012
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+
beta_schedule: "linear"
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configs/prompts/animation.yaml
ADDED
@@ -0,0 +1,40 @@
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pretrained_model_path: "stable-diffusion-v1-5"
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pretrained_vae_path: "sd-vae-ft-mse"
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+
pretrained_controlnet_path: "MagicAnimate/densepose_controlnet"
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+
pretrained_appearance_encoder_path: "MagicAnimate/appearance_encoder"
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+
pretrained_unet_path: ""
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6 |
+
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+
motion_module: "MagicAnimate/temporal_attention/temporal_attention.ckpt"
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8 |
+
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9 |
+
savename: null
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10 |
+
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+
fusion_blocks: "midup"
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+
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13 |
+
seed: [1]
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+
steps: 25
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15 |
+
guidance_scale: 7.5
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16 |
+
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17 |
+
source_image:
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18 |
+
- "inputs/applications/source_image/monalisa.png"
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19 |
+
- "inputs/applications/source_image/demo4.png"
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20 |
+
- "inputs/applications/source_image/dalle2.jpeg"
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21 |
+
- "inputs/applications/source_image/dalle8.jpeg"
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22 |
+
- "inputs/applications/source_image/multi1_source.png"
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23 |
+
video_path:
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24 |
+
- "inputs/applications/driving/densepose/running.mp4"
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25 |
+
- "inputs/applications/driving/densepose/demo4.mp4"
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26 |
+
- "inputs/applications/driving/densepose/running2.mp4"
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27 |
+
- "inputs/applications/driving/densepose/dancing2.mp4"
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28 |
+
- "inputs/applications/driving/densepose/multi_dancing.mp4"
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29 |
+
|
30 |
+
inference_config: "configs/inference/inference.yaml"
|
31 |
+
size: 512
|
32 |
+
L: 16
|
33 |
+
S: 1
|
34 |
+
I: 0
|
35 |
+
clip: 0
|
36 |
+
offset: 0
|
37 |
+
max_length: null
|
38 |
+
video_type: "condition"
|
39 |
+
invert_video: false
|
40 |
+
save_individual_videos: false
|
demo/animate.py
ADDED
@@ -0,0 +1,195 @@
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|
1 |
+
# Copyright 2023 ByteDance and/or its affiliates.
|
2 |
+
#
|
3 |
+
# Copyright (2023) MagicAnimate Authors
|
4 |
+
#
|
5 |
+
# ByteDance, its affiliates and licensors retain all intellectual
|
6 |
+
# property and proprietary rights in and to this material, related
|
7 |
+
# documentation and any modifications thereto. Any use, reproduction,
|
8 |
+
# disclosure or distribution of this material and related documentation
|
9 |
+
# without an express license agreement from ByteDance or
|
10 |
+
# its affiliates is strictly prohibited.
|
11 |
+
import argparse
|
12 |
+
import argparse
|
13 |
+
import datetime
|
14 |
+
import inspect
|
15 |
+
import os
|
16 |
+
import numpy as np
|
17 |
+
from PIL import Image
|
18 |
+
from omegaconf import OmegaConf
|
19 |
+
from collections import OrderedDict
|
20 |
+
|
21 |
+
import torch
|
22 |
+
|
23 |
+
from diffusers import AutoencoderKL, DDIMScheduler, UniPCMultistepScheduler
|
24 |
+
|
25 |
+
from tqdm import tqdm
|
26 |
+
from transformers import CLIPTextModel, CLIPTokenizer
|
27 |
+
|
28 |
+
from magicanimate.models.unet_controlnet import UNet3DConditionModel
|
29 |
+
from magicanimate.models.controlnet import ControlNetModel
|
30 |
+
from magicanimate.models.appearance_encoder import AppearanceEncoderModel
|
31 |
+
from magicanimate.models.mutual_self_attention import ReferenceAttentionControl
|
32 |
+
from magicanimate.pipelines.pipeline_animation import AnimationPipeline
|
33 |
+
from magicanimate.utils.util import save_videos_grid
|
34 |
+
from accelerate.utils import set_seed
|
35 |
+
|
36 |
+
from magicanimate.utils.videoreader import VideoReader
|
37 |
+
|
38 |
+
from einops import rearrange, repeat
|
39 |
+
|
40 |
+
import csv, pdb, glob
|
41 |
+
from safetensors import safe_open
|
42 |
+
import math
|
43 |
+
from pathlib import Path
|
44 |
+
|
45 |
+
class MagicAnimate():
|
46 |
+
def __init__(self, config="configs/prompts/animation.yaml") -> None:
|
47 |
+
print("Initializing MagicAnimate Pipeline...")
|
48 |
+
*_, func_args = inspect.getargvalues(inspect.currentframe())
|
49 |
+
func_args = dict(func_args)
|
50 |
+
|
51 |
+
config = OmegaConf.load(config)
|
52 |
+
|
53 |
+
inference_config = OmegaConf.load(config.inference_config)
|
54 |
+
|
55 |
+
motion_module = config.motion_module
|
56 |
+
|
57 |
+
### >>> create animation pipeline >>> ###
|
58 |
+
tokenizer = CLIPTokenizer.from_pretrained(config.pretrained_model_path, subfolder="tokenizer")
|
59 |
+
text_encoder = CLIPTextModel.from_pretrained(config.pretrained_model_path, subfolder="text_encoder")
|
60 |
+
if config.pretrained_unet_path:
|
61 |
+
unet = UNet3DConditionModel.from_pretrained_2d(config.pretrained_unet_path, unet_additional_kwargs=OmegaConf.to_container(inference_config.unet_additional_kwargs))
|
62 |
+
else:
|
63 |
+
unet = UNet3DConditionModel.from_pretrained_2d(config.pretrained_model_path, subfolder="unet", unet_additional_kwargs=OmegaConf.to_container(inference_config.unet_additional_kwargs))
|
64 |
+
self.appearance_encoder = AppearanceEncoderModel.from_pretrained(config.pretrained_appearance_encoder_path, subfolder="appearance_encoder").cuda()
|
65 |
+
self.reference_control_writer = ReferenceAttentionControl(self.appearance_encoder, do_classifier_free_guidance=True, mode='write', fusion_blocks=config.fusion_blocks)
|
66 |
+
self.reference_control_reader = ReferenceAttentionControl(unet, do_classifier_free_guidance=True, mode='read', fusion_blocks=config.fusion_blocks)
|
67 |
+
if config.pretrained_vae_path is not None:
|
68 |
+
vae = AutoencoderKL.from_pretrained(config.pretrained_vae_path)
|
69 |
+
else:
|
70 |
+
vae = AutoencoderKL.from_pretrained(config.pretrained_model_path, subfolder="vae")
|
71 |
+
|
72 |
+
### Load controlnet
|
73 |
+
controlnet = ControlNetModel.from_pretrained(config.pretrained_controlnet_path)
|
74 |
+
|
75 |
+
vae.to(torch.float16)
|
76 |
+
unet.to(torch.float16)
|
77 |
+
text_encoder.to(torch.float16)
|
78 |
+
controlnet.to(torch.float16)
|
79 |
+
self.appearance_encoder.to(torch.float16)
|
80 |
+
|
81 |
+
unet.enable_xformers_memory_efficient_attention()
|
82 |
+
self.appearance_encoder.enable_xformers_memory_efficient_attention()
|
83 |
+
controlnet.enable_xformers_memory_efficient_attention()
|
84 |
+
|
85 |
+
self.pipeline = AnimationPipeline(
|
86 |
+
vae=vae, text_encoder=text_encoder, tokenizer=tokenizer, unet=unet, controlnet=controlnet,
|
87 |
+
scheduler=DDIMScheduler(**OmegaConf.to_container(inference_config.noise_scheduler_kwargs)),
|
88 |
+
# NOTE: UniPCMultistepScheduler
|
89 |
+
).to("cuda")
|
90 |
+
|
91 |
+
# 1. unet ckpt
|
92 |
+
# 1.1 motion module
|
93 |
+
motion_module_state_dict = torch.load(motion_module, map_location="cpu")
|
94 |
+
if "global_step" in motion_module_state_dict: func_args.update({"global_step": motion_module_state_dict["global_step"]})
|
95 |
+
motion_module_state_dict = motion_module_state_dict['state_dict'] if 'state_dict' in motion_module_state_dict else motion_module_state_dict
|
96 |
+
try:
|
97 |
+
# extra steps for self-trained models
|
98 |
+
state_dict = OrderedDict()
|
99 |
+
for key in motion_module_state_dict.keys():
|
100 |
+
if key.startswith("module."):
|
101 |
+
_key = key.split("module.")[-1]
|
102 |
+
state_dict[_key] = motion_module_state_dict[key]
|
103 |
+
else:
|
104 |
+
state_dict[key] = motion_module_state_dict[key]
|
105 |
+
motion_module_state_dict = state_dict
|
106 |
+
del state_dict
|
107 |
+
missing, unexpected = self.pipeline.unet.load_state_dict(motion_module_state_dict, strict=False)
|
108 |
+
assert len(unexpected) == 0
|
109 |
+
except:
|
110 |
+
_tmp_ = OrderedDict()
|
111 |
+
for key in motion_module_state_dict.keys():
|
112 |
+
if "motion_modules" in key:
|
113 |
+
if key.startswith("unet."):
|
114 |
+
_key = key.split('unet.')[-1]
|
115 |
+
_tmp_[_key] = motion_module_state_dict[key]
|
116 |
+
else:
|
117 |
+
_tmp_[key] = motion_module_state_dict[key]
|
118 |
+
missing, unexpected = unet.load_state_dict(_tmp_, strict=False)
|
119 |
+
assert len(unexpected) == 0
|
120 |
+
del _tmp_
|
121 |
+
del motion_module_state_dict
|
122 |
+
|
123 |
+
self.pipeline.to("cuda")
|
124 |
+
self.L = config.L
|
125 |
+
|
126 |
+
print("Initialization Done!")
|
127 |
+
|
128 |
+
def __call__(self, source_image, motion_sequence, random_seed, step, guidance_scale, size=512):
|
129 |
+
prompt = n_prompt = ""
|
130 |
+
random_seed = int(random_seed)
|
131 |
+
step = int(step)
|
132 |
+
guidance_scale = float(guidance_scale)
|
133 |
+
samples_per_video = []
|
134 |
+
# manually set random seed for reproduction
|
135 |
+
if random_seed != -1:
|
136 |
+
torch.manual_seed(random_seed)
|
137 |
+
set_seed(random_seed)
|
138 |
+
else:
|
139 |
+
torch.seed()
|
140 |
+
|
141 |
+
if motion_sequence.endswith('.mp4'):
|
142 |
+
control = VideoReader(motion_sequence).read()
|
143 |
+
if control[0].shape[0] != size:
|
144 |
+
control = [np.array(Image.fromarray(c).resize((size, size))) for c in control]
|
145 |
+
control = np.array(control)
|
146 |
+
|
147 |
+
if source_image.shape[0] != size:
|
148 |
+
source_image = np.array(Image.fromarray(source_image).resize((size, size)))
|
149 |
+
H, W, C = source_image.shape
|
150 |
+
|
151 |
+
init_latents = None
|
152 |
+
original_length = control.shape[0]
|
153 |
+
if control.shape[0] % self.L > 0:
|
154 |
+
control = np.pad(control, ((0, self.L-control.shape[0] % self.L), (0, 0), (0, 0), (0, 0)), mode='edge')
|
155 |
+
generator = torch.Generator(device=torch.device("cuda:0"))
|
156 |
+
generator.manual_seed(torch.initial_seed())
|
157 |
+
sample = self.pipeline(
|
158 |
+
prompt,
|
159 |
+
negative_prompt = n_prompt,
|
160 |
+
num_inference_steps = step,
|
161 |
+
guidance_scale = guidance_scale,
|
162 |
+
width = W,
|
163 |
+
height = H,
|
164 |
+
video_length = len(control),
|
165 |
+
controlnet_condition = control,
|
166 |
+
init_latents = init_latents,
|
167 |
+
generator = generator,
|
168 |
+
appearance_encoder = self.appearance_encoder,
|
169 |
+
reference_control_writer = self.reference_control_writer,
|
170 |
+
reference_control_reader = self.reference_control_reader,
|
171 |
+
source_image = source_image,
|
172 |
+
).videos
|
173 |
+
|
174 |
+
source_images = np.array([source_image] * original_length)
|
175 |
+
source_images = rearrange(torch.from_numpy(source_images), "t h w c -> 1 c t h w") / 255.0
|
176 |
+
samples_per_video.append(source_images)
|
177 |
+
|
178 |
+
control = control / 255.0
|
179 |
+
control = rearrange(control, "t h w c -> 1 c t h w")
|
180 |
+
control = torch.from_numpy(control)
|
181 |
+
samples_per_video.append(control[:, :, :original_length])
|
182 |
+
|
183 |
+
samples_per_video.append(sample[:, :, :original_length])
|
184 |
+
|
185 |
+
samples_per_video = torch.cat(samples_per_video)
|
186 |
+
|
187 |
+
time_str = datetime.datetime.now().strftime("%Y-%m-%dT%H-%M-%S")
|
188 |
+
savedir = f"demo/outputs"
|
189 |
+
animation_path = f"{savedir}/{time_str}.mp4"
|
190 |
+
|
191 |
+
os.makedirs(savedir, exist_ok=True)
|
192 |
+
save_videos_grid(samples_per_video, animation_path)
|
193 |
+
|
194 |
+
return animation_path
|
195 |
+
|
demo/outputs/2024-01-21T03-12-04.mp4
ADDED
Binary file (302 kB). View file
|
|
demo/outputs/2024-01-21T03-13-47.mp4
ADDED
Binary file (234 kB). View file
|
|
demo/outputs/2024-01-21T03-16-06.mp4
ADDED
Binary file (300 kB). View file
|
|
demo/outputs/2024-01-21T03-18-30.mp4
ADDED
Binary file (218 kB). View file
|
|
inputs/applications/driving/densepose/dancing2.mp4
ADDED
Binary file (130 kB). View file
|
|
inputs/applications/driving/densepose/demo4.mp4
ADDED
Binary file (156 kB). View file
|
|
inputs/applications/driving/densepose/multi_dancing.mp4
ADDED
Binary file (219 kB). View file
|
|
inputs/applications/driving/densepose/running.mp4
ADDED
Binary file (63.2 kB). View file
|
|
inputs/applications/driving/densepose/running2.mp4
ADDED
Binary file (152 kB). View file
|
|
inputs/applications/source_image/dalle2.jpeg
ADDED
![]() |
inputs/applications/source_image/dalle8.jpeg
ADDED
![]() |
inputs/applications/source_image/demo4.png
ADDED
![]() |
inputs/applications/source_image/monalisa.png
ADDED
![]() |
inputs/applications/source_image/multi1_source.png
ADDED
![]() |
magicanimate/models/appearance_encoder.py
ADDED
@@ -0,0 +1,1066 @@
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1 |
+
# *************************************************************************
|
2 |
+
# This file may have been modified by Bytedance Inc. (“Bytedance Inc.'s Mo-
|
3 |
+
# difications”). All Bytedance Inc.'s Modifications are Copyright (2023) B-
|
4 |
+
# ytedance Inc..
|
5 |
+
# *************************************************************************
|
6 |
+
|
7 |
+
# Copyright 2023 The HuggingFace Team. All rights reserved.
|
8 |
+
#
|
9 |
+
# Licensed under the Apache License, Version 2.0 (the "License");
|
10 |
+
# you may not use this file except in compliance with the License.
|
11 |
+
# You may obtain a copy of the License at
|
12 |
+
#
|
13 |
+
# http://www.apache.org/licenses/LICENSE-2.0
|
14 |
+
#
|
15 |
+
# Unless required by applicable law or agreed to in writing, software
|
16 |
+
# distributed under the License is distributed on an "AS IS" BASIS,
|
17 |
+
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
18 |
+
# See the License for the specific language governing permissions and
|
19 |
+
# limitations under the License.
|
20 |
+
from dataclasses import dataclass
|
21 |
+
from typing import Any, Dict, List, Optional, Tuple, Union
|
22 |
+
|
23 |
+
import torch
|
24 |
+
import torch.nn as nn
|
25 |
+
import torch.utils.checkpoint
|
26 |
+
|
27 |
+
from diffusers.configuration_utils import ConfigMixin, register_to_config
|
28 |
+
from diffusers.loaders import UNet2DConditionLoadersMixin
|
29 |
+
from diffusers.utils import BaseOutput, logging
|
30 |
+
from diffusers.models.activations import get_activation
|
31 |
+
from diffusers.models.attention_processor import (
|
32 |
+
ADDED_KV_ATTENTION_PROCESSORS,
|
33 |
+
CROSS_ATTENTION_PROCESSORS,
|
34 |
+
AttentionProcessor,
|
35 |
+
AttnAddedKVProcessor,
|
36 |
+
AttnProcessor,
|
37 |
+
)
|
38 |
+
from diffusers.models.lora import LoRALinearLayer
|
39 |
+
from diffusers.models.embeddings import (
|
40 |
+
GaussianFourierProjection,
|
41 |
+
ImageHintTimeEmbedding,
|
42 |
+
ImageProjection,
|
43 |
+
ImageTimeEmbedding,
|
44 |
+
PositionNet,
|
45 |
+
TextImageProjection,
|
46 |
+
TextImageTimeEmbedding,
|
47 |
+
TextTimeEmbedding,
|
48 |
+
TimestepEmbedding,
|
49 |
+
Timesteps,
|
50 |
+
)
|
51 |
+
from diffusers.models.modeling_utils import ModelMixin
|
52 |
+
from diffusers.models.unet_2d_blocks import (
|
53 |
+
UNetMidBlock2DCrossAttn,
|
54 |
+
UNetMidBlock2DSimpleCrossAttn,
|
55 |
+
get_down_block,
|
56 |
+
get_up_block,
|
57 |
+
)
|
58 |
+
|
59 |
+
|
60 |
+
logger = logging.get_logger(__name__) # pylint: disable=invalid-name
|
61 |
+
|
62 |
+
|
63 |
+
class Identity(torch.nn.Module):
|
64 |
+
r"""A placeholder identity operator that is argument-insensitive.
|
65 |
+
|
66 |
+
Args:
|
67 |
+
args: any argument (unused)
|
68 |
+
kwargs: any keyword argument (unused)
|
69 |
+
|
70 |
+
Shape:
|
71 |
+
- Input: :math:`(*)`, where :math:`*` means any number of dimensions.
|
72 |
+
- Output: :math:`(*)`, same shape as the input.
|
73 |
+
|
74 |
+
Examples::
|
75 |
+
|
76 |
+
>>> m = nn.Identity(54, unused_argument1=0.1, unused_argument2=False)
|
77 |
+
>>> input = torch.randn(128, 20)
|
78 |
+
>>> output = m(input)
|
79 |
+
>>> print(output.size())
|
80 |
+
torch.Size([128, 20])
|
81 |
+
|
82 |
+
"""
|
83 |
+
def __init__(self, scale=None, *args, **kwargs) -> None:
|
84 |
+
super(Identity, self).__init__()
|
85 |
+
|
86 |
+
def forward(self, input, *args, **kwargs):
|
87 |
+
return input
|
88 |
+
|
89 |
+
|
90 |
+
|
91 |
+
class _LoRACompatibleLinear(nn.Module):
|
92 |
+
"""
|
93 |
+
A Linear layer that can be used with LoRA.
|
94 |
+
"""
|
95 |
+
|
96 |
+
def __init__(self, *args, lora_layer: Optional[LoRALinearLayer] = None, **kwargs):
|
97 |
+
super().__init__(*args, **kwargs)
|
98 |
+
self.lora_layer = lora_layer
|
99 |
+
|
100 |
+
def set_lora_layer(self, lora_layer: Optional[LoRALinearLayer]):
|
101 |
+
self.lora_layer = lora_layer
|
102 |
+
|
103 |
+
def _fuse_lora(self):
|
104 |
+
pass
|
105 |
+
|
106 |
+
def _unfuse_lora(self):
|
107 |
+
pass
|
108 |
+
|
109 |
+
def forward(self, hidden_states, scale=None, lora_scale: int = 1):
|
110 |
+
return hidden_states
|
111 |
+
|
112 |
+
|
113 |
+
@dataclass
|
114 |
+
class UNet2DConditionOutput(BaseOutput):
|
115 |
+
"""
|
116 |
+
The output of [`UNet2DConditionModel`].
|
117 |
+
|
118 |
+
Args:
|
119 |
+
sample (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)`):
|
120 |
+
The hidden states output conditioned on `encoder_hidden_states` input. Output of last layer of model.
|
121 |
+
"""
|
122 |
+
|
123 |
+
sample: torch.FloatTensor = None
|
124 |
+
|
125 |
+
|
126 |
+
class AppearanceEncoderModel(ModelMixin, ConfigMixin, UNet2DConditionLoadersMixin):
|
127 |
+
r"""
|
128 |
+
A conditional 2D UNet model that takes a noisy sample, conditional state, and a timestep and returns a sample
|
129 |
+
shaped output.
|
130 |
+
|
131 |
+
This model inherits from [`ModelMixin`]. Check the superclass documentation for it's generic methods implemented
|
132 |
+
for all models (such as downloading or saving).
|
133 |
+
|
134 |
+
Parameters:
|
135 |
+
sample_size (`int` or `Tuple[int, int]`, *optional*, defaults to `None`):
|
136 |
+
Height and width of input/output sample.
|
137 |
+
in_channels (`int`, *optional*, defaults to 4): Number of channels in the input sample.
|
138 |
+
out_channels (`int`, *optional*, defaults to 4): Number of channels in the output.
|
139 |
+
center_input_sample (`bool`, *optional*, defaults to `False`): Whether to center the input sample.
|
140 |
+
flip_sin_to_cos (`bool`, *optional*, defaults to `False`):
|
141 |
+
Whether to flip the sin to cos in the time embedding.
|
142 |
+
freq_shift (`int`, *optional*, defaults to 0): The frequency shift to apply to the time embedding.
|
143 |
+
down_block_types (`Tuple[str]`, *optional*, defaults to `("CrossAttnDownBlock2D", "CrossAttnDownBlock2D", "CrossAttnDownBlock2D", "DownBlock2D")`):
|
144 |
+
The tuple of downsample blocks to use.
|
145 |
+
mid_block_type (`str`, *optional*, defaults to `"UNetMidBlock2DCrossAttn"`):
|
146 |
+
Block type for middle of UNet, it can be either `UNetMidBlock2DCrossAttn` or
|
147 |
+
`UNetMidBlock2DSimpleCrossAttn`. If `None`, the mid block layer is skipped.
|
148 |
+
up_block_types (`Tuple[str]`, *optional*, defaults to `("UpBlock2D", "CrossAttnUpBlock2D", "CrossAttnUpBlock2D", "CrossAttnUpBlock2D")`):
|
149 |
+
The tuple of upsample blocks to use.
|
150 |
+
only_cross_attention(`bool` or `Tuple[bool]`, *optional*, default to `False`):
|
151 |
+
Whether to include self-attention in the basic transformer blocks, see
|
152 |
+
[`~models.attention.BasicTransformerBlock`].
|
153 |
+
block_out_channels (`Tuple[int]`, *optional*, defaults to `(320, 640, 1280, 1280)`):
|
154 |
+
The tuple of output channels for each block.
|
155 |
+
layers_per_block (`int`, *optional*, defaults to 2): The number of layers per block.
|
156 |
+
downsample_padding (`int`, *optional*, defaults to 1): The padding to use for the downsampling convolution.
|
157 |
+
mid_block_scale_factor (`float`, *optional*, defaults to 1.0): The scale factor to use for the mid block.
|
158 |
+
act_fn (`str`, *optional*, defaults to `"silu"`): The activation function to use.
|
159 |
+
norm_num_groups (`int`, *optional*, defaults to 32): The number of groups to use for the normalization.
|
160 |
+
If `None`, normalization and activation layers is skipped in post-processing.
|
161 |
+
norm_eps (`float`, *optional*, defaults to 1e-5): The epsilon to use for the normalization.
|
162 |
+
cross_attention_dim (`int` or `Tuple[int]`, *optional*, defaults to 1280):
|
163 |
+
The dimension of the cross attention features.
|
164 |
+
transformer_layers_per_block (`int` or `Tuple[int]`, *optional*, defaults to 1):
|
165 |
+
The number of transformer blocks of type [`~models.attention.BasicTransformerBlock`]. Only relevant for
|
166 |
+
[`~models.unet_2d_blocks.CrossAttnDownBlock2D`], [`~models.unet_2d_blocks.CrossAttnUpBlock2D`],
|
167 |
+
[`~models.unet_2d_blocks.UNetMidBlock2DCrossAttn`].
|
168 |
+
encoder_hid_dim (`int`, *optional*, defaults to None):
|
169 |
+
If `encoder_hid_dim_type` is defined, `encoder_hidden_states` will be projected from `encoder_hid_dim`
|
170 |
+
dimension to `cross_attention_dim`.
|
171 |
+
encoder_hid_dim_type (`str`, *optional*, defaults to `None`):
|
172 |
+
If given, the `encoder_hidden_states` and potentially other embeddings are down-projected to text
|
173 |
+
embeddings of dimension `cross_attention` according to `encoder_hid_dim_type`.
|
174 |
+
attention_head_dim (`int`, *optional*, defaults to 8): The dimension of the attention heads.
|
175 |
+
num_attention_heads (`int`, *optional*):
|
176 |
+
The number of attention heads. If not defined, defaults to `attention_head_dim`
|
177 |
+
resnet_time_scale_shift (`str`, *optional*, defaults to `"default"`): Time scale shift config
|
178 |
+
for ResNet blocks (see [`~models.resnet.ResnetBlock2D`]). Choose from `default` or `scale_shift`.
|
179 |
+
class_embed_type (`str`, *optional*, defaults to `None`):
|
180 |
+
The type of class embedding to use which is ultimately summed with the time embeddings. Choose from `None`,
|
181 |
+
`"timestep"`, `"identity"`, `"projection"`, or `"simple_projection"`.
|
182 |
+
addition_embed_type (`str`, *optional*, defaults to `None`):
|
183 |
+
Configures an optional embedding which will be summed with the time embeddings. Choose from `None` or
|
184 |
+
"text". "text" will use the `TextTimeEmbedding` layer.
|
185 |
+
addition_time_embed_dim: (`int`, *optional*, defaults to `None`):
|
186 |
+
Dimension for the timestep embeddings.
|
187 |
+
num_class_embeds (`int`, *optional*, defaults to `None`):
|
188 |
+
Input dimension of the learnable embedding matrix to be projected to `time_embed_dim`, when performing
|
189 |
+
class conditioning with `class_embed_type` equal to `None`.
|
190 |
+
time_embedding_type (`str`, *optional*, defaults to `positional`):
|
191 |
+
The type of position embedding to use for timesteps. Choose from `positional` or `fourier`.
|
192 |
+
time_embedding_dim (`int`, *optional*, defaults to `None`):
|
193 |
+
An optional override for the dimension of the projected time embedding.
|
194 |
+
time_embedding_act_fn (`str`, *optional*, defaults to `None`):
|
195 |
+
Optional activation function to use only once on the time embeddings before they are passed to the rest of
|
196 |
+
the UNet. Choose from `silu`, `mish`, `gelu`, and `swish`.
|
197 |
+
timestep_post_act (`str`, *optional*, defaults to `None`):
|
198 |
+
The second activation function to use in timestep embedding. Choose from `silu`, `mish` and `gelu`.
|
199 |
+
time_cond_proj_dim (`int`, *optional*, defaults to `None`):
|
200 |
+
The dimension of `cond_proj` layer in the timestep embedding.
|
201 |
+
conv_in_kernel (`int`, *optional*, default to `3`): The kernel size of `conv_in` layer.
|
202 |
+
conv_out_kernel (`int`, *optional*, default to `3`): The kernel size of `conv_out` layer.
|
203 |
+
projection_class_embeddings_input_dim (`int`, *optional*): The dimension of the `class_labels` input when
|
204 |
+
`class_embed_type="projection"`. Required when `class_embed_type="projection"`.
|
205 |
+
class_embeddings_concat (`bool`, *optional*, defaults to `False`): Whether to concatenate the time
|
206 |
+
embeddings with the class embeddings.
|
207 |
+
mid_block_only_cross_attention (`bool`, *optional*, defaults to `None`):
|
208 |
+
Whether to use cross attention with the mid block when using the `UNetMidBlock2DSimpleCrossAttn`. If
|
209 |
+
`only_cross_attention` is given as a single boolean and `mid_block_only_cross_attention` is `None`, the
|
210 |
+
`only_cross_attention` value is used as the value for `mid_block_only_cross_attention`. Default to `False`
|
211 |
+
otherwise.
|
212 |
+
"""
|
213 |
+
|
214 |
+
_supports_gradient_checkpointing = True
|
215 |
+
|
216 |
+
@register_to_config
|
217 |
+
def __init__(
|
218 |
+
self,
|
219 |
+
sample_size: Optional[int] = None,
|
220 |
+
in_channels: int = 4,
|
221 |
+
out_channels: int = 4,
|
222 |
+
center_input_sample: bool = False,
|
223 |
+
flip_sin_to_cos: bool = True,
|
224 |
+
freq_shift: int = 0,
|
225 |
+
down_block_types: Tuple[str] = (
|
226 |
+
"CrossAttnDownBlock2D",
|
227 |
+
"CrossAttnDownBlock2D",
|
228 |
+
"CrossAttnDownBlock2D",
|
229 |
+
"DownBlock2D",
|
230 |
+
),
|
231 |
+
mid_block_type: Optional[str] = "UNetMidBlock2DCrossAttn",
|
232 |
+
up_block_types: Tuple[str] = ("UpBlock2D", "CrossAttnUpBlock2D", "CrossAttnUpBlock2D", "CrossAttnUpBlock2D"),
|
233 |
+
only_cross_attention: Union[bool, Tuple[bool]] = False,
|
234 |
+
block_out_channels: Tuple[int] = (320, 640, 1280, 1280),
|
235 |
+
layers_per_block: Union[int, Tuple[int]] = 2,
|
236 |
+
downsample_padding: int = 1,
|
237 |
+
mid_block_scale_factor: float = 1,
|
238 |
+
act_fn: str = "silu",
|
239 |
+
norm_num_groups: Optional[int] = 32,
|
240 |
+
norm_eps: float = 1e-5,
|
241 |
+
cross_attention_dim: Union[int, Tuple[int]] = 1280,
|
242 |
+
transformer_layers_per_block: Union[int, Tuple[int]] = 1,
|
243 |
+
encoder_hid_dim: Optional[int] = None,
|
244 |
+
encoder_hid_dim_type: Optional[str] = None,
|
245 |
+
attention_head_dim: Union[int, Tuple[int]] = 8,
|
246 |
+
num_attention_heads: Optional[Union[int, Tuple[int]]] = None,
|
247 |
+
dual_cross_attention: bool = False,
|
248 |
+
use_linear_projection: bool = False,
|
249 |
+
class_embed_type: Optional[str] = None,
|
250 |
+
addition_embed_type: Optional[str] = None,
|
251 |
+
addition_time_embed_dim: Optional[int] = None,
|
252 |
+
num_class_embeds: Optional[int] = None,
|
253 |
+
upcast_attention: bool = False,
|
254 |
+
resnet_time_scale_shift: str = "default",
|
255 |
+
resnet_skip_time_act: bool = False,
|
256 |
+
resnet_out_scale_factor: int = 1.0,
|
257 |
+
time_embedding_type: str = "positional",
|
258 |
+
time_embedding_dim: Optional[int] = None,
|
259 |
+
time_embedding_act_fn: Optional[str] = None,
|
260 |
+
timestep_post_act: Optional[str] = None,
|
261 |
+
time_cond_proj_dim: Optional[int] = None,
|
262 |
+
conv_in_kernel: int = 3,
|
263 |
+
conv_out_kernel: int = 3,
|
264 |
+
projection_class_embeddings_input_dim: Optional[int] = None,
|
265 |
+
attention_type: str = "default",
|
266 |
+
class_embeddings_concat: bool = False,
|
267 |
+
mid_block_only_cross_attention: Optional[bool] = None,
|
268 |
+
cross_attention_norm: Optional[str] = None,
|
269 |
+
addition_embed_type_num_heads=64,
|
270 |
+
):
|
271 |
+
super().__init__()
|
272 |
+
|
273 |
+
self.sample_size = sample_size
|
274 |
+
|
275 |
+
if num_attention_heads is not None:
|
276 |
+
raise ValueError(
|
277 |
+
"At the moment it is not possible to define the number of attention heads via `num_attention_heads` because of a naming issue as described in https://github.com/huggingface/diffusers/issues/2011#issuecomment-1547958131. Passing `num_attention_heads` will only be supported in diffusers v0.19."
|
278 |
+
)
|
279 |
+
|
280 |
+
# If `num_attention_heads` is not defined (which is the case for most models)
|
281 |
+
# it will default to `attention_head_dim`. This looks weird upon first reading it and it is.
|
282 |
+
# The reason for this behavior is to correct for incorrectly named variables that were introduced
|
283 |
+
# when this library was created. The incorrect naming was only discovered much later in https://github.com/huggingface/diffusers/issues/2011#issuecomment-1547958131
|
284 |
+
# Changing `attention_head_dim` to `num_attention_heads` for 40,000+ configurations is too backwards breaking
|
285 |
+
# which is why we correct for the naming here.
|
286 |
+
num_attention_heads = num_attention_heads or attention_head_dim
|
287 |
+
|
288 |
+
# Check inputs
|
289 |
+
if len(down_block_types) != len(up_block_types):
|
290 |
+
raise ValueError(
|
291 |
+
f"Must provide the same number of `down_block_types` as `up_block_types`. `down_block_types`: {down_block_types}. `up_block_types`: {up_block_types}."
|
292 |
+
)
|
293 |
+
|
294 |
+
if len(block_out_channels) != len(down_block_types):
|
295 |
+
raise ValueError(
|
296 |
+
f"Must provide the same number of `block_out_channels` as `down_block_types`. `block_out_channels`: {block_out_channels}. `down_block_types`: {down_block_types}."
|
297 |
+
)
|
298 |
+
|
299 |
+
if not isinstance(only_cross_attention, bool) and len(only_cross_attention) != len(down_block_types):
|
300 |
+
raise ValueError(
|
301 |
+
f"Must provide the same number of `only_cross_attention` as `down_block_types`. `only_cross_attention`: {only_cross_attention}. `down_block_types`: {down_block_types}."
|
302 |
+
)
|
303 |
+
|
304 |
+
if not isinstance(num_attention_heads, int) and len(num_attention_heads) != len(down_block_types):
|
305 |
+
raise ValueError(
|
306 |
+
f"Must provide the same number of `num_attention_heads` as `down_block_types`. `num_attention_heads`: {num_attention_heads}. `down_block_types`: {down_block_types}."
|
307 |
+
)
|
308 |
+
|
309 |
+
if not isinstance(attention_head_dim, int) and len(attention_head_dim) != len(down_block_types):
|
310 |
+
raise ValueError(
|
311 |
+
f"Must provide the same number of `attention_head_dim` as `down_block_types`. `attention_head_dim`: {attention_head_dim}. `down_block_types`: {down_block_types}."
|
312 |
+
)
|
313 |
+
|
314 |
+
if isinstance(cross_attention_dim, list) and len(cross_attention_dim) != len(down_block_types):
|
315 |
+
raise ValueError(
|
316 |
+
f"Must provide the same number of `cross_attention_dim` as `down_block_types`. `cross_attention_dim`: {cross_attention_dim}. `down_block_types`: {down_block_types}."
|
317 |
+
)
|
318 |
+
|
319 |
+
if not isinstance(layers_per_block, int) and len(layers_per_block) != len(down_block_types):
|
320 |
+
raise ValueError(
|
321 |
+
f"Must provide the same number of `layers_per_block` as `down_block_types`. `layers_per_block`: {layers_per_block}. `down_block_types`: {down_block_types}."
|
322 |
+
)
|
323 |
+
|
324 |
+
# input
|
325 |
+
conv_in_padding = (conv_in_kernel - 1) // 2
|
326 |
+
self.conv_in = nn.Conv2d(
|
327 |
+
in_channels, block_out_channels[0], kernel_size=conv_in_kernel, padding=conv_in_padding
|
328 |
+
)
|
329 |
+
|
330 |
+
# time
|
331 |
+
if time_embedding_type == "fourier":
|
332 |
+
time_embed_dim = time_embedding_dim or block_out_channels[0] * 2
|
333 |
+
if time_embed_dim % 2 != 0:
|
334 |
+
raise ValueError(f"`time_embed_dim` should be divisible by 2, but is {time_embed_dim}.")
|
335 |
+
self.time_proj = GaussianFourierProjection(
|
336 |
+
time_embed_dim // 2, set_W_to_weight=False, log=False, flip_sin_to_cos=flip_sin_to_cos
|
337 |
+
)
|
338 |
+
timestep_input_dim = time_embed_dim
|
339 |
+
elif time_embedding_type == "positional":
|
340 |
+
time_embed_dim = time_embedding_dim or block_out_channels[0] * 4
|
341 |
+
|
342 |
+
self.time_proj = Timesteps(block_out_channels[0], flip_sin_to_cos, freq_shift)
|
343 |
+
timestep_input_dim = block_out_channels[0]
|
344 |
+
else:
|
345 |
+
raise ValueError(
|
346 |
+
f"{time_embedding_type} does not exist. Please make sure to use one of `fourier` or `positional`."
|
347 |
+
)
|
348 |
+
|
349 |
+
self.time_embedding = TimestepEmbedding(
|
350 |
+
timestep_input_dim,
|
351 |
+
time_embed_dim,
|
352 |
+
act_fn=act_fn,
|
353 |
+
post_act_fn=timestep_post_act,
|
354 |
+
cond_proj_dim=time_cond_proj_dim,
|
355 |
+
)
|
356 |
+
|
357 |
+
if encoder_hid_dim_type is None and encoder_hid_dim is not None:
|
358 |
+
encoder_hid_dim_type = "text_proj"
|
359 |
+
self.register_to_config(encoder_hid_dim_type=encoder_hid_dim_type)
|
360 |
+
logger.info("encoder_hid_dim_type defaults to 'text_proj' as `encoder_hid_dim` is defined.")
|
361 |
+
|
362 |
+
if encoder_hid_dim is None and encoder_hid_dim_type is not None:
|
363 |
+
raise ValueError(
|
364 |
+
f"`encoder_hid_dim` has to be defined when `encoder_hid_dim_type` is set to {encoder_hid_dim_type}."
|
365 |
+
)
|
366 |
+
|
367 |
+
if encoder_hid_dim_type == "text_proj":
|
368 |
+
self.encoder_hid_proj = nn.Linear(encoder_hid_dim, cross_attention_dim)
|
369 |
+
elif encoder_hid_dim_type == "text_image_proj":
|
370 |
+
# image_embed_dim DOESN'T have to be `cross_attention_dim`. To not clutter the __init__ too much
|
371 |
+
# they are set to `cross_attention_dim` here as this is exactly the required dimension for the currently only use
|
372 |
+
# case when `addition_embed_type == "text_image_proj"` (Kadinsky 2.1)`
|
373 |
+
self.encoder_hid_proj = TextImageProjection(
|
374 |
+
text_embed_dim=encoder_hid_dim,
|
375 |
+
image_embed_dim=cross_attention_dim,
|
376 |
+
cross_attention_dim=cross_attention_dim,
|
377 |
+
)
|
378 |
+
elif encoder_hid_dim_type == "image_proj":
|
379 |
+
# Kandinsky 2.2
|
380 |
+
self.encoder_hid_proj = ImageProjection(
|
381 |
+
image_embed_dim=encoder_hid_dim,
|
382 |
+
cross_attention_dim=cross_attention_dim,
|
383 |
+
)
|
384 |
+
elif encoder_hid_dim_type is not None:
|
385 |
+
raise ValueError(
|
386 |
+
f"encoder_hid_dim_type: {encoder_hid_dim_type} must be None, 'text_proj' or 'text_image_proj'."
|
387 |
+
)
|
388 |
+
else:
|
389 |
+
self.encoder_hid_proj = None
|
390 |
+
|
391 |
+
# class embedding
|
392 |
+
if class_embed_type is None and num_class_embeds is not None:
|
393 |
+
self.class_embedding = nn.Embedding(num_class_embeds, time_embed_dim)
|
394 |
+
elif class_embed_type == "timestep":
|
395 |
+
self.class_embedding = TimestepEmbedding(timestep_input_dim, time_embed_dim, act_fn=act_fn)
|
396 |
+
elif class_embed_type == "identity":
|
397 |
+
self.class_embedding = nn.Identity(time_embed_dim, time_embed_dim)
|
398 |
+
elif class_embed_type == "projection":
|
399 |
+
if projection_class_embeddings_input_dim is None:
|
400 |
+
raise ValueError(
|
401 |
+
"`class_embed_type`: 'projection' requires `projection_class_embeddings_input_dim` be set"
|
402 |
+
)
|
403 |
+
# The projection `class_embed_type` is the same as the timestep `class_embed_type` except
|
404 |
+
# 1. the `class_labels` inputs are not first converted to sinusoidal embeddings
|
405 |
+
# 2. it projects from an arbitrary input dimension.
|
406 |
+
#
|
407 |
+
# Note that `TimestepEmbedding` is quite general, being mainly linear layers and activations.
|
408 |
+
# When used for embedding actual timesteps, the timesteps are first converted to sinusoidal embeddings.
|
409 |
+
# As a result, `TimestepEmbedding` can be passed arbitrary vectors.
|
410 |
+
self.class_embedding = TimestepEmbedding(projection_class_embeddings_input_dim, time_embed_dim)
|
411 |
+
elif class_embed_type == "simple_projection":
|
412 |
+
if projection_class_embeddings_input_dim is None:
|
413 |
+
raise ValueError(
|
414 |
+
"`class_embed_type`: 'simple_projection' requires `projection_class_embeddings_input_dim` be set"
|
415 |
+
)
|
416 |
+
self.class_embedding = nn.Linear(projection_class_embeddings_input_dim, time_embed_dim)
|
417 |
+
else:
|
418 |
+
self.class_embedding = None
|
419 |
+
|
420 |
+
if addition_embed_type == "text":
|
421 |
+
if encoder_hid_dim is not None:
|
422 |
+
text_time_embedding_from_dim = encoder_hid_dim
|
423 |
+
else:
|
424 |
+
text_time_embedding_from_dim = cross_attention_dim
|
425 |
+
|
426 |
+
self.add_embedding = TextTimeEmbedding(
|
427 |
+
text_time_embedding_from_dim, time_embed_dim, num_heads=addition_embed_type_num_heads
|
428 |
+
)
|
429 |
+
elif addition_embed_type == "text_image":
|
430 |
+
# text_embed_dim and image_embed_dim DON'T have to be `cross_attention_dim`. To not clutter the __init__ too much
|
431 |
+
# they are set to `cross_attention_dim` here as this is exactly the required dimension for the currently only use
|
432 |
+
# case when `addition_embed_type == "text_image"` (Kadinsky 2.1)`
|
433 |
+
self.add_embedding = TextImageTimeEmbedding(
|
434 |
+
text_embed_dim=cross_attention_dim, image_embed_dim=cross_attention_dim, time_embed_dim=time_embed_dim
|
435 |
+
)
|
436 |
+
elif addition_embed_type == "text_time":
|
437 |
+
self.add_time_proj = Timesteps(addition_time_embed_dim, flip_sin_to_cos, freq_shift)
|
438 |
+
self.add_embedding = TimestepEmbedding(projection_class_embeddings_input_dim, time_embed_dim)
|
439 |
+
elif addition_embed_type == "image":
|
440 |
+
# Kandinsky 2.2
|
441 |
+
self.add_embedding = ImageTimeEmbedding(image_embed_dim=encoder_hid_dim, time_embed_dim=time_embed_dim)
|
442 |
+
elif addition_embed_type == "image_hint":
|
443 |
+
# Kandinsky 2.2 ControlNet
|
444 |
+
self.add_embedding = ImageHintTimeEmbedding(image_embed_dim=encoder_hid_dim, time_embed_dim=time_embed_dim)
|
445 |
+
elif addition_embed_type is not None:
|
446 |
+
raise ValueError(f"addition_embed_type: {addition_embed_type} must be None, 'text' or 'text_image'.")
|
447 |
+
|
448 |
+
if time_embedding_act_fn is None:
|
449 |
+
self.time_embed_act = None
|
450 |
+
else:
|
451 |
+
self.time_embed_act = get_activation(time_embedding_act_fn)
|
452 |
+
|
453 |
+
self.down_blocks = nn.ModuleList([])
|
454 |
+
self.up_blocks = nn.ModuleList([])
|
455 |
+
|
456 |
+
if isinstance(only_cross_attention, bool):
|
457 |
+
if mid_block_only_cross_attention is None:
|
458 |
+
mid_block_only_cross_attention = only_cross_attention
|
459 |
+
|
460 |
+
only_cross_attention = [only_cross_attention] * len(down_block_types)
|
461 |
+
|
462 |
+
if mid_block_only_cross_attention is None:
|
463 |
+
mid_block_only_cross_attention = False
|
464 |
+
|
465 |
+
if isinstance(num_attention_heads, int):
|
466 |
+
num_attention_heads = (num_attention_heads,) * len(down_block_types)
|
467 |
+
|
468 |
+
if isinstance(attention_head_dim, int):
|
469 |
+
attention_head_dim = (attention_head_dim,) * len(down_block_types)
|
470 |
+
|
471 |
+
if isinstance(cross_attention_dim, int):
|
472 |
+
cross_attention_dim = (cross_attention_dim,) * len(down_block_types)
|
473 |
+
|
474 |
+
if isinstance(layers_per_block, int):
|
475 |
+
layers_per_block = [layers_per_block] * len(down_block_types)
|
476 |
+
|
477 |
+
if isinstance(transformer_layers_per_block, int):
|
478 |
+
transformer_layers_per_block = [transformer_layers_per_block] * len(down_block_types)
|
479 |
+
|
480 |
+
if class_embeddings_concat:
|
481 |
+
# The time embeddings are concatenated with the class embeddings. The dimension of the
|
482 |
+
# time embeddings passed to the down, middle, and up blocks is twice the dimension of the
|
483 |
+
# regular time embeddings
|
484 |
+
blocks_time_embed_dim = time_embed_dim * 2
|
485 |
+
else:
|
486 |
+
blocks_time_embed_dim = time_embed_dim
|
487 |
+
|
488 |
+
# down
|
489 |
+
output_channel = block_out_channels[0]
|
490 |
+
for i, down_block_type in enumerate(down_block_types):
|
491 |
+
input_channel = output_channel
|
492 |
+
output_channel = block_out_channels[i]
|
493 |
+
is_final_block = i == len(block_out_channels) - 1
|
494 |
+
|
495 |
+
down_block = get_down_block(
|
496 |
+
down_block_type,
|
497 |
+
num_layers=layers_per_block[i],
|
498 |
+
transformer_layers_per_block=transformer_layers_per_block[i],
|
499 |
+
in_channels=input_channel,
|
500 |
+
out_channels=output_channel,
|
501 |
+
temb_channels=blocks_time_embed_dim,
|
502 |
+
add_downsample=not is_final_block,
|
503 |
+
resnet_eps=norm_eps,
|
504 |
+
resnet_act_fn=act_fn,
|
505 |
+
resnet_groups=norm_num_groups,
|
506 |
+
cross_attention_dim=cross_attention_dim[i],
|
507 |
+
num_attention_heads=num_attention_heads[i],
|
508 |
+
downsample_padding=downsample_padding,
|
509 |
+
dual_cross_attention=dual_cross_attention,
|
510 |
+
use_linear_projection=use_linear_projection,
|
511 |
+
only_cross_attention=only_cross_attention[i],
|
512 |
+
upcast_attention=upcast_attention,
|
513 |
+
resnet_time_scale_shift=resnet_time_scale_shift,
|
514 |
+
attention_type=attention_type,
|
515 |
+
resnet_skip_time_act=resnet_skip_time_act,
|
516 |
+
resnet_out_scale_factor=resnet_out_scale_factor,
|
517 |
+
cross_attention_norm=cross_attention_norm,
|
518 |
+
attention_head_dim=attention_head_dim[i] if attention_head_dim[i] is not None else output_channel,
|
519 |
+
)
|
520 |
+
self.down_blocks.append(down_block)
|
521 |
+
|
522 |
+
# mid
|
523 |
+
if mid_block_type == "UNetMidBlock2DCrossAttn":
|
524 |
+
self.mid_block = UNetMidBlock2DCrossAttn(
|
525 |
+
transformer_layers_per_block=transformer_layers_per_block[-1],
|
526 |
+
in_channels=block_out_channels[-1],
|
527 |
+
temb_channels=blocks_time_embed_dim,
|
528 |
+
resnet_eps=norm_eps,
|
529 |
+
resnet_act_fn=act_fn,
|
530 |
+
output_scale_factor=mid_block_scale_factor,
|
531 |
+
resnet_time_scale_shift=resnet_time_scale_shift,
|
532 |
+
cross_attention_dim=cross_attention_dim[-1],
|
533 |
+
num_attention_heads=num_attention_heads[-1],
|
534 |
+
resnet_groups=norm_num_groups,
|
535 |
+
dual_cross_attention=dual_cross_attention,
|
536 |
+
use_linear_projection=use_linear_projection,
|
537 |
+
upcast_attention=upcast_attention,
|
538 |
+
attention_type=attention_type,
|
539 |
+
)
|
540 |
+
elif mid_block_type == "UNetMidBlock2DSimpleCrossAttn":
|
541 |
+
self.mid_block = UNetMidBlock2DSimpleCrossAttn(
|
542 |
+
in_channels=block_out_channels[-1],
|
543 |
+
temb_channels=blocks_time_embed_dim,
|
544 |
+
resnet_eps=norm_eps,
|
545 |
+
resnet_act_fn=act_fn,
|
546 |
+
output_scale_factor=mid_block_scale_factor,
|
547 |
+
cross_attention_dim=cross_attention_dim[-1],
|
548 |
+
attention_head_dim=attention_head_dim[-1],
|
549 |
+
resnet_groups=norm_num_groups,
|
550 |
+
resnet_time_scale_shift=resnet_time_scale_shift,
|
551 |
+
skip_time_act=resnet_skip_time_act,
|
552 |
+
only_cross_attention=mid_block_only_cross_attention,
|
553 |
+
cross_attention_norm=cross_attention_norm,
|
554 |
+
)
|
555 |
+
elif mid_block_type is None:
|
556 |
+
self.mid_block = None
|
557 |
+
else:
|
558 |
+
raise ValueError(f"unknown mid_block_type : {mid_block_type}")
|
559 |
+
|
560 |
+
# count how many layers upsample the images
|
561 |
+
self.num_upsamplers = 0
|
562 |
+
|
563 |
+
# up
|
564 |
+
reversed_block_out_channels = list(reversed(block_out_channels))
|
565 |
+
reversed_num_attention_heads = list(reversed(num_attention_heads))
|
566 |
+
reversed_layers_per_block = list(reversed(layers_per_block))
|
567 |
+
reversed_cross_attention_dim = list(reversed(cross_attention_dim))
|
568 |
+
reversed_transformer_layers_per_block = list(reversed(transformer_layers_per_block))
|
569 |
+
only_cross_attention = list(reversed(only_cross_attention))
|
570 |
+
|
571 |
+
output_channel = reversed_block_out_channels[0]
|
572 |
+
for i, up_block_type in enumerate(up_block_types):
|
573 |
+
is_final_block = i == len(block_out_channels) - 1
|
574 |
+
|
575 |
+
prev_output_channel = output_channel
|
576 |
+
output_channel = reversed_block_out_channels[i]
|
577 |
+
input_channel = reversed_block_out_channels[min(i + 1, len(block_out_channels) - 1)]
|
578 |
+
|
579 |
+
# add upsample block for all BUT final layer
|
580 |
+
if not is_final_block:
|
581 |
+
add_upsample = True
|
582 |
+
self.num_upsamplers += 1
|
583 |
+
else:
|
584 |
+
add_upsample = False
|
585 |
+
|
586 |
+
up_block = get_up_block(
|
587 |
+
up_block_type,
|
588 |
+
num_layers=reversed_layers_per_block[i] + 1,
|
589 |
+
transformer_layers_per_block=reversed_transformer_layers_per_block[i],
|
590 |
+
in_channels=input_channel,
|
591 |
+
out_channels=output_channel,
|
592 |
+
prev_output_channel=prev_output_channel,
|
593 |
+
temb_channels=blocks_time_embed_dim,
|
594 |
+
add_upsample=add_upsample,
|
595 |
+
resnet_eps=norm_eps,
|
596 |
+
resnet_act_fn=act_fn,
|
597 |
+
resnet_groups=norm_num_groups,
|
598 |
+
cross_attention_dim=reversed_cross_attention_dim[i],
|
599 |
+
num_attention_heads=reversed_num_attention_heads[i],
|
600 |
+
dual_cross_attention=dual_cross_attention,
|
601 |
+
use_linear_projection=use_linear_projection,
|
602 |
+
only_cross_attention=only_cross_attention[i],
|
603 |
+
upcast_attention=upcast_attention,
|
604 |
+
resnet_time_scale_shift=resnet_time_scale_shift,
|
605 |
+
attention_type=attention_type,
|
606 |
+
resnet_skip_time_act=resnet_skip_time_act,
|
607 |
+
resnet_out_scale_factor=resnet_out_scale_factor,
|
608 |
+
cross_attention_norm=cross_attention_norm,
|
609 |
+
attention_head_dim=attention_head_dim[i] if attention_head_dim[i] is not None else output_channel,
|
610 |
+
)
|
611 |
+
self.up_blocks.append(up_block)
|
612 |
+
prev_output_channel = output_channel
|
613 |
+
self.up_blocks[3].attentions[2].transformer_blocks[0].attn1.to_q = _LoRACompatibleLinear()
|
614 |
+
self.up_blocks[3].attentions[2].transformer_blocks[0].attn1.to_k = _LoRACompatibleLinear()
|
615 |
+
self.up_blocks[3].attentions[2].transformer_blocks[0].attn1.to_v = _LoRACompatibleLinear()
|
616 |
+
self.up_blocks[3].attentions[2].transformer_blocks[0].attn1.to_out = nn.ModuleList([Identity(), Identity()])
|
617 |
+
self.up_blocks[3].attentions[2].transformer_blocks[0].norm2 = Identity()
|
618 |
+
self.up_blocks[3].attentions[2].transformer_blocks[0].attn2 = None
|
619 |
+
self.up_blocks[3].attentions[2].transformer_blocks[0].norm3 = Identity()
|
620 |
+
self.up_blocks[3].attentions[2].transformer_blocks[0].ff = Identity()
|
621 |
+
self.up_blocks[3].attentions[2].proj_out = Identity()
|
622 |
+
|
623 |
+
if attention_type in ["gated", "gated-text-image"]:
|
624 |
+
positive_len = 768
|
625 |
+
if isinstance(cross_attention_dim, int):
|
626 |
+
positive_len = cross_attention_dim
|
627 |
+
elif isinstance(cross_attention_dim, tuple) or isinstance(cross_attention_dim, list):
|
628 |
+
positive_len = cross_attention_dim[0]
|
629 |
+
|
630 |
+
feature_type = "text-only" if attention_type == "gated" else "text-image"
|
631 |
+
self.position_net = PositionNet(
|
632 |
+
positive_len=positive_len, out_dim=cross_attention_dim, feature_type=feature_type
|
633 |
+
)
|
634 |
+
|
635 |
+
@property
|
636 |
+
def attn_processors(self) -> Dict[str, AttentionProcessor]:
|
637 |
+
r"""
|
638 |
+
Returns:
|
639 |
+
`dict` of attention processors: A dictionary containing all attention processors used in the model with
|
640 |
+
indexed by its weight name.
|
641 |
+
"""
|
642 |
+
# set recursively
|
643 |
+
processors = {}
|
644 |
+
|
645 |
+
def fn_recursive_add_processors(name: str, module: torch.nn.Module, processors: Dict[str, AttentionProcessor]):
|
646 |
+
if hasattr(module, "get_processor"):
|
647 |
+
processors[f"{name}.processor"] = module.get_processor(return_deprecated_lora=True)
|
648 |
+
|
649 |
+
for sub_name, child in module.named_children():
|
650 |
+
fn_recursive_add_processors(f"{name}.{sub_name}", child, processors)
|
651 |
+
|
652 |
+
return processors
|
653 |
+
|
654 |
+
for name, module in self.named_children():
|
655 |
+
fn_recursive_add_processors(name, module, processors)
|
656 |
+
|
657 |
+
return processors
|
658 |
+
|
659 |
+
def set_attn_processor(self, processor: Union[AttentionProcessor, Dict[str, AttentionProcessor]]):
|
660 |
+
r"""
|
661 |
+
Sets the attention processor to use to compute attention.
|
662 |
+
|
663 |
+
Parameters:
|
664 |
+
processor (`dict` of `AttentionProcessor` or only `AttentionProcessor`):
|
665 |
+
The instantiated processor class or a dictionary of processor classes that will be set as the processor
|
666 |
+
for **all** `Attention` layers.
|
667 |
+
|
668 |
+
If `processor` is a dict, the key needs to define the path to the corresponding cross attention
|
669 |
+
processor. This is strongly recommended when setting trainable attention processors.
|
670 |
+
|
671 |
+
"""
|
672 |
+
count = len(self.attn_processors.keys())
|
673 |
+
|
674 |
+
if isinstance(processor, dict) and len(processor) != count:
|
675 |
+
raise ValueError(
|
676 |
+
f"A dict of processors was passed, but the number of processors {len(processor)} does not match the"
|
677 |
+
f" number of attention layers: {count}. Please make sure to pass {count} processor classes."
|
678 |
+
)
|
679 |
+
|
680 |
+
def fn_recursive_attn_processor(name: str, module: torch.nn.Module, processor):
|
681 |
+
if hasattr(module, "set_processor"):
|
682 |
+
if not isinstance(processor, dict):
|
683 |
+
module.set_processor(processor)
|
684 |
+
else:
|
685 |
+
module.set_processor(processor.pop(f"{name}.processor"))
|
686 |
+
|
687 |
+
for sub_name, child in module.named_children():
|
688 |
+
fn_recursive_attn_processor(f"{name}.{sub_name}", child, processor)
|
689 |
+
|
690 |
+
for name, module in self.named_children():
|
691 |
+
fn_recursive_attn_processor(name, module, processor)
|
692 |
+
|
693 |
+
def set_default_attn_processor(self):
|
694 |
+
"""
|
695 |
+
Disables custom attention processors and sets the default attention implementation.
|
696 |
+
"""
|
697 |
+
if all(proc.__class__ in ADDED_KV_ATTENTION_PROCESSORS for proc in self.attn_processors.values()):
|
698 |
+
processor = AttnAddedKVProcessor()
|
699 |
+
elif all(proc.__class__ in CROSS_ATTENTION_PROCESSORS for proc in self.attn_processors.values()):
|
700 |
+
processor = AttnProcessor()
|
701 |
+
else:
|
702 |
+
raise ValueError(
|
703 |
+
f"Cannot call `set_default_attn_processor` when attention processors are of type {next(iter(self.attn_processors.values()))}"
|
704 |
+
)
|
705 |
+
|
706 |
+
self.set_attn_processor(processor)
|
707 |
+
|
708 |
+
def set_attention_slice(self, slice_size):
|
709 |
+
r"""
|
710 |
+
Enable sliced attention computation.
|
711 |
+
|
712 |
+
When this option is enabled, the attention module splits the input tensor in slices to compute attention in
|
713 |
+
several steps. This is useful for saving some memory in exchange for a small decrease in speed.
|
714 |
+
|
715 |
+
Args:
|
716 |
+
slice_size (`str` or `int` or `list(int)`, *optional*, defaults to `"auto"`):
|
717 |
+
When `"auto"`, input to the attention heads is halved, so attention is computed in two steps. If
|
718 |
+
`"max"`, maximum amount of memory is saved by running only one slice at a time. If a number is
|
719 |
+
provided, uses as many slices as `attention_head_dim // slice_size`. In this case, `attention_head_dim`
|
720 |
+
must be a multiple of `slice_size`.
|
721 |
+
"""
|
722 |
+
sliceable_head_dims = []
|
723 |
+
|
724 |
+
def fn_recursive_retrieve_sliceable_dims(module: torch.nn.Module):
|
725 |
+
if hasattr(module, "set_attention_slice"):
|
726 |
+
sliceable_head_dims.append(module.sliceable_head_dim)
|
727 |
+
|
728 |
+
for child in module.children():
|
729 |
+
fn_recursive_retrieve_sliceable_dims(child)
|
730 |
+
|
731 |
+
# retrieve number of attention layers
|
732 |
+
for module in self.children():
|
733 |
+
fn_recursive_retrieve_sliceable_dims(module)
|
734 |
+
|
735 |
+
num_sliceable_layers = len(sliceable_head_dims)
|
736 |
+
|
737 |
+
if slice_size == "auto":
|
738 |
+
# half the attention head size is usually a good trade-off between
|
739 |
+
# speed and memory
|
740 |
+
slice_size = [dim // 2 for dim in sliceable_head_dims]
|
741 |
+
elif slice_size == "max":
|
742 |
+
# make smallest slice possible
|
743 |
+
slice_size = num_sliceable_layers * [1]
|
744 |
+
|
745 |
+
slice_size = num_sliceable_layers * [slice_size] if not isinstance(slice_size, list) else slice_size
|
746 |
+
|
747 |
+
if len(slice_size) != len(sliceable_head_dims):
|
748 |
+
raise ValueError(
|
749 |
+
f"You have provided {len(slice_size)}, but {self.config} has {len(sliceable_head_dims)} different"
|
750 |
+
f" attention layers. Make sure to match `len(slice_size)` to be {len(sliceable_head_dims)}."
|
751 |
+
)
|
752 |
+
|
753 |
+
for i in range(len(slice_size)):
|
754 |
+
size = slice_size[i]
|
755 |
+
dim = sliceable_head_dims[i]
|
756 |
+
if size is not None and size > dim:
|
757 |
+
raise ValueError(f"size {size} has to be smaller or equal to {dim}.")
|
758 |
+
|
759 |
+
# Recursively walk through all the children.
|
760 |
+
# Any children which exposes the set_attention_slice method
|
761 |
+
# gets the message
|
762 |
+
def fn_recursive_set_attention_slice(module: torch.nn.Module, slice_size: List[int]):
|
763 |
+
if hasattr(module, "set_attention_slice"):
|
764 |
+
module.set_attention_slice(slice_size.pop())
|
765 |
+
|
766 |
+
for child in module.children():
|
767 |
+
fn_recursive_set_attention_slice(child, slice_size)
|
768 |
+
|
769 |
+
reversed_slice_size = list(reversed(slice_size))
|
770 |
+
for module in self.children():
|
771 |
+
fn_recursive_set_attention_slice(module, reversed_slice_size)
|
772 |
+
|
773 |
+
def _set_gradient_checkpointing(self, module, value=False):
|
774 |
+
if hasattr(module, "gradient_checkpointing"):
|
775 |
+
module.gradient_checkpointing = value
|
776 |
+
|
777 |
+
def forward(
|
778 |
+
self,
|
779 |
+
sample: torch.FloatTensor,
|
780 |
+
timestep: Union[torch.Tensor, float, int],
|
781 |
+
encoder_hidden_states: torch.Tensor,
|
782 |
+
class_labels: Optional[torch.Tensor] = None,
|
783 |
+
timestep_cond: Optional[torch.Tensor] = None,
|
784 |
+
attention_mask: Optional[torch.Tensor] = None,
|
785 |
+
cross_attention_kwargs: Optional[Dict[str, Any]] = None,
|
786 |
+
added_cond_kwargs: Optional[Dict[str, torch.Tensor]] = None,
|
787 |
+
down_block_additional_residuals: Optional[Tuple[torch.Tensor]] = None,
|
788 |
+
mid_block_additional_residual: Optional[torch.Tensor] = None,
|
789 |
+
encoder_attention_mask: Optional[torch.Tensor] = None,
|
790 |
+
return_dict: bool = True,
|
791 |
+
) -> Union[UNet2DConditionOutput, Tuple]:
|
792 |
+
r"""
|
793 |
+
The [`UNet2DConditionModel`] forward method.
|
794 |
+
|
795 |
+
Args:
|
796 |
+
sample (`torch.FloatTensor`):
|
797 |
+
The noisy input tensor with the following shape `(batch, channel, height, width)`.
|
798 |
+
timestep (`torch.FloatTensor` or `float` or `int`): The number of timesteps to denoise an input.
|
799 |
+
encoder_hidden_states (`torch.FloatTensor`):
|
800 |
+
The encoder hidden states with shape `(batch, sequence_length, feature_dim)`.
|
801 |
+
encoder_attention_mask (`torch.Tensor`):
|
802 |
+
A cross-attention mask of shape `(batch, sequence_length)` is applied to `encoder_hidden_states`. If
|
803 |
+
`True` the mask is kept, otherwise if `False` it is discarded. Mask will be converted into a bias,
|
804 |
+
which adds large negative values to the attention scores corresponding to "discard" tokens.
|
805 |
+
return_dict (`bool`, *optional*, defaults to `True`):
|
806 |
+
Whether or not to return a [`~models.unet_2d_condition.UNet2DConditionOutput`] instead of a plain
|
807 |
+
tuple.
|
808 |
+
cross_attention_kwargs (`dict`, *optional*):
|
809 |
+
A kwargs dictionary that if specified is passed along to the [`AttnProcessor`].
|
810 |
+
added_cond_kwargs: (`dict`, *optional*):
|
811 |
+
A kwargs dictionary containin additional embeddings that if specified are added to the embeddings that
|
812 |
+
are passed along to the UNet blocks.
|
813 |
+
|
814 |
+
Returns:
|
815 |
+
[`~models.unet_2d_condition.UNet2DConditionOutput`] or `tuple`:
|
816 |
+
If `return_dict` is True, an [`~models.unet_2d_condition.UNet2DConditionOutput`] is returned, otherwise
|
817 |
+
a `tuple` is returned where the first element is the sample tensor.
|
818 |
+
"""
|
819 |
+
# By default samples have to be AT least a multiple of the overall upsampling factor.
|
820 |
+
# The overall upsampling factor is equal to 2 ** (# num of upsampling layers).
|
821 |
+
# However, the upsampling interpolation output size can be forced to fit any upsampling size
|
822 |
+
# on the fly if necessary.
|
823 |
+
default_overall_up_factor = 2**self.num_upsamplers
|
824 |
+
|
825 |
+
# upsample size should be forwarded when sample is not a multiple of `default_overall_up_factor`
|
826 |
+
forward_upsample_size = False
|
827 |
+
upsample_size = None
|
828 |
+
|
829 |
+
if any(s % default_overall_up_factor != 0 for s in sample.shape[-2:]):
|
830 |
+
logger.info("Forward upsample size to force interpolation output size.")
|
831 |
+
forward_upsample_size = True
|
832 |
+
|
833 |
+
if attention_mask is not None:
|
834 |
+
attention_mask = (1 - attention_mask.to(sample.dtype)) * -10000.0
|
835 |
+
attention_mask = attention_mask.unsqueeze(1)
|
836 |
+
|
837 |
+
# convert encoder_attention_mask to a bias the same way we do for attention_mask
|
838 |
+
if encoder_attention_mask is not None:
|
839 |
+
encoder_attention_mask = (1 - encoder_attention_mask.to(sample.dtype)) * -10000.0
|
840 |
+
encoder_attention_mask = encoder_attention_mask.unsqueeze(1)
|
841 |
+
|
842 |
+
# 0. center input if necessary
|
843 |
+
if self.config.center_input_sample:
|
844 |
+
sample = 2 * sample - 1.0
|
845 |
+
|
846 |
+
# 1. time
|
847 |
+
timesteps = timestep
|
848 |
+
if not torch.is_tensor(timesteps):
|
849 |
+
# TODO: this requires sync between CPU and GPU. So try to pass timesteps as tensors if you can
|
850 |
+
# This would be a good case for the `match` statement (Python 3.10+)
|
851 |
+
is_mps = sample.device.type == "mps"
|
852 |
+
if isinstance(timestep, float):
|
853 |
+
dtype = torch.float32 if is_mps else torch.float64
|
854 |
+
else:
|
855 |
+
dtype = torch.int32 if is_mps else torch.int64
|
856 |
+
timesteps = torch.tensor([timesteps], dtype=dtype, device=sample.device)
|
857 |
+
elif len(timesteps.shape) == 0:
|
858 |
+
timesteps = timesteps[None].to(sample.device)
|
859 |
+
|
860 |
+
# broadcast to batch dimension in a way that's compatible with ONNX/Core ML
|
861 |
+
timesteps = timesteps.expand(sample.shape[0])
|
862 |
+
|
863 |
+
t_emb = self.time_proj(timesteps)
|
864 |
+
|
865 |
+
# `Timesteps` does not contain any weights and will always return f32 tensors
|
866 |
+
# but time_embedding might actually be running in fp16. so we need to cast here.
|
867 |
+
# there might be better ways to encapsulate this.
|
868 |
+
t_emb = t_emb.to(dtype=sample.dtype)
|
869 |
+
|
870 |
+
emb = self.time_embedding(t_emb, timestep_cond)
|
871 |
+
aug_emb = None
|
872 |
+
|
873 |
+
if self.class_embedding is not None:
|
874 |
+
if class_labels is None:
|
875 |
+
raise ValueError("class_labels should be provided when num_class_embeds > 0")
|
876 |
+
|
877 |
+
if self.config.class_embed_type == "timestep":
|
878 |
+
class_labels = self.time_proj(class_labels)
|
879 |
+
|
880 |
+
# `Timesteps` does not contain any weights and will always return f32 tensors
|
881 |
+
# there might be better ways to encapsulate this.
|
882 |
+
class_labels = class_labels.to(dtype=sample.dtype)
|
883 |
+
|
884 |
+
class_emb = self.class_embedding(class_labels).to(dtype=sample.dtype)
|
885 |
+
|
886 |
+
if self.config.class_embeddings_concat:
|
887 |
+
emb = torch.cat([emb, class_emb], dim=-1)
|
888 |
+
else:
|
889 |
+
emb = emb + class_emb
|
890 |
+
|
891 |
+
if self.config.addition_embed_type == "text":
|
892 |
+
aug_emb = self.add_embedding(encoder_hidden_states)
|
893 |
+
elif self.config.addition_embed_type == "text_image":
|
894 |
+
# Kandinsky 2.1 - style
|
895 |
+
if "image_embeds" not in added_cond_kwargs:
|
896 |
+
raise ValueError(
|
897 |
+
f"{self.__class__} has the config param `addition_embed_type` set to 'text_image' which requires the keyword argument `image_embeds` to be passed in `added_cond_kwargs`"
|
898 |
+
)
|
899 |
+
|
900 |
+
image_embs = added_cond_kwargs.get("image_embeds")
|
901 |
+
text_embs = added_cond_kwargs.get("text_embeds", encoder_hidden_states)
|
902 |
+
aug_emb = self.add_embedding(text_embs, image_embs)
|
903 |
+
elif self.config.addition_embed_type == "text_time":
|
904 |
+
# SDXL - style
|
905 |
+
if "text_embeds" not in added_cond_kwargs:
|
906 |
+
raise ValueError(
|
907 |
+
f"{self.__class__} has the config param `addition_embed_type` set to 'text_time' which requires the keyword argument `text_embeds` to be passed in `added_cond_kwargs`"
|
908 |
+
)
|
909 |
+
text_embeds = added_cond_kwargs.get("text_embeds")
|
910 |
+
if "time_ids" not in added_cond_kwargs:
|
911 |
+
raise ValueError(
|
912 |
+
f"{self.__class__} has the config param `addition_embed_type` set to 'text_time' which requires the keyword argument `time_ids` to be passed in `added_cond_kwargs`"
|
913 |
+
)
|
914 |
+
time_ids = added_cond_kwargs.get("time_ids")
|
915 |
+
time_embeds = self.add_time_proj(time_ids.flatten())
|
916 |
+
time_embeds = time_embeds.reshape((text_embeds.shape[0], -1))
|
917 |
+
|
918 |
+
add_embeds = torch.concat([text_embeds, time_embeds], dim=-1)
|
919 |
+
add_embeds = add_embeds.to(emb.dtype)
|
920 |
+
aug_emb = self.add_embedding(add_embeds)
|
921 |
+
elif self.config.addition_embed_type == "image":
|
922 |
+
# Kandinsky 2.2 - style
|
923 |
+
if "image_embeds" not in added_cond_kwargs:
|
924 |
+
raise ValueError(
|
925 |
+
f"{self.__class__} has the config param `addition_embed_type` set to 'image' which requires the keyword argument `image_embeds` to be passed in `added_cond_kwargs`"
|
926 |
+
)
|
927 |
+
image_embs = added_cond_kwargs.get("image_embeds")
|
928 |
+
aug_emb = self.add_embedding(image_embs)
|
929 |
+
elif self.config.addition_embed_type == "image_hint":
|
930 |
+
# Kandinsky 2.2 - style
|
931 |
+
if "image_embeds" not in added_cond_kwargs or "hint" not in added_cond_kwargs:
|
932 |
+
raise ValueError(
|
933 |
+
f"{self.__class__} has the config param `addition_embed_type` set to 'image_hint' which requires the keyword arguments `image_embeds` and `hint` to be passed in `added_cond_kwargs`"
|
934 |
+
)
|
935 |
+
image_embs = added_cond_kwargs.get("image_embeds")
|
936 |
+
hint = added_cond_kwargs.get("hint")
|
937 |
+
aug_emb, hint = self.add_embedding(image_embs, hint)
|
938 |
+
sample = torch.cat([sample, hint], dim=1)
|
939 |
+
|
940 |
+
emb = emb + aug_emb if aug_emb is not None else emb
|
941 |
+
|
942 |
+
if self.time_embed_act is not None:
|
943 |
+
emb = self.time_embed_act(emb)
|
944 |
+
|
945 |
+
if self.encoder_hid_proj is not None and self.config.encoder_hid_dim_type == "text_proj":
|
946 |
+
encoder_hidden_states = self.encoder_hid_proj(encoder_hidden_states)
|
947 |
+
elif self.encoder_hid_proj is not None and self.config.encoder_hid_dim_type == "text_image_proj":
|
948 |
+
# Kadinsky 2.1 - style
|
949 |
+
if "image_embeds" not in added_cond_kwargs:
|
950 |
+
raise ValueError(
|
951 |
+
f"{self.__class__} has the config param `encoder_hid_dim_type` set to 'text_image_proj' which requires the keyword argument `image_embeds` to be passed in `added_conditions`"
|
952 |
+
)
|
953 |
+
|
954 |
+
image_embeds = added_cond_kwargs.get("image_embeds")
|
955 |
+
encoder_hidden_states = self.encoder_hid_proj(encoder_hidden_states, image_embeds)
|
956 |
+
elif self.encoder_hid_proj is not None and self.config.encoder_hid_dim_type == "image_proj":
|
957 |
+
# Kandinsky 2.2 - style
|
958 |
+
if "image_embeds" not in added_cond_kwargs:
|
959 |
+
raise ValueError(
|
960 |
+
f"{self.__class__} has the config param `encoder_hid_dim_type` set to 'image_proj' which requires the keyword argument `image_embeds` to be passed in `added_conditions`"
|
961 |
+
)
|
962 |
+
image_embeds = added_cond_kwargs.get("image_embeds")
|
963 |
+
encoder_hidden_states = self.encoder_hid_proj(image_embeds)
|
964 |
+
# 2. pre-process
|
965 |
+
sample = self.conv_in(sample)
|
966 |
+
|
967 |
+
# 2.5 GLIGEN position net
|
968 |
+
if cross_attention_kwargs is not None and cross_attention_kwargs.get("gligen", None) is not None:
|
969 |
+
cross_attention_kwargs = cross_attention_kwargs.copy()
|
970 |
+
gligen_args = cross_attention_kwargs.pop("gligen")
|
971 |
+
cross_attention_kwargs["gligen"] = {"objs": self.position_net(**gligen_args)}
|
972 |
+
|
973 |
+
# 3. down
|
974 |
+
|
975 |
+
is_controlnet = mid_block_additional_residual is not None and down_block_additional_residuals is not None
|
976 |
+
is_adapter = mid_block_additional_residual is None and down_block_additional_residuals is not None
|
977 |
+
|
978 |
+
down_block_res_samples = (sample,)
|
979 |
+
for downsample_block in self.down_blocks:
|
980 |
+
if hasattr(downsample_block, "has_cross_attention") and downsample_block.has_cross_attention:
|
981 |
+
# For t2i-adapter CrossAttnDownBlock2D
|
982 |
+
additional_residuals = {}
|
983 |
+
if is_adapter and len(down_block_additional_residuals) > 0:
|
984 |
+
additional_residuals["additional_residuals"] = down_block_additional_residuals.pop(0)
|
985 |
+
|
986 |
+
sample, res_samples = downsample_block(
|
987 |
+
hidden_states=sample,
|
988 |
+
temb=emb,
|
989 |
+
encoder_hidden_states=encoder_hidden_states,
|
990 |
+
attention_mask=attention_mask,
|
991 |
+
cross_attention_kwargs=cross_attention_kwargs,
|
992 |
+
encoder_attention_mask=encoder_attention_mask,
|
993 |
+
**additional_residuals,
|
994 |
+
)
|
995 |
+
else:
|
996 |
+
sample, res_samples = downsample_block(hidden_states=sample, temb=emb)
|
997 |
+
|
998 |
+
if is_adapter and len(down_block_additional_residuals) > 0:
|
999 |
+
sample += down_block_additional_residuals.pop(0)
|
1000 |
+
|
1001 |
+
down_block_res_samples += res_samples
|
1002 |
+
|
1003 |
+
if is_controlnet:
|
1004 |
+
new_down_block_res_samples = ()
|
1005 |
+
|
1006 |
+
for down_block_res_sample, down_block_additional_residual in zip(
|
1007 |
+
down_block_res_samples, down_block_additional_residuals
|
1008 |
+
):
|
1009 |
+
down_block_res_sample = down_block_res_sample + down_block_additional_residual
|
1010 |
+
new_down_block_res_samples = new_down_block_res_samples + (down_block_res_sample,)
|
1011 |
+
|
1012 |
+
down_block_res_samples = new_down_block_res_samples
|
1013 |
+
|
1014 |
+
# 4. mid
|
1015 |
+
if self.mid_block is not None:
|
1016 |
+
sample = self.mid_block(
|
1017 |
+
sample,
|
1018 |
+
emb,
|
1019 |
+
encoder_hidden_states=encoder_hidden_states,
|
1020 |
+
attention_mask=attention_mask,
|
1021 |
+
cross_attention_kwargs=cross_attention_kwargs,
|
1022 |
+
encoder_attention_mask=encoder_attention_mask,
|
1023 |
+
)
|
1024 |
+
# To support T2I-Adapter-XL
|
1025 |
+
if (
|
1026 |
+
is_adapter
|
1027 |
+
and len(down_block_additional_residuals) > 0
|
1028 |
+
and sample.shape == down_block_additional_residuals[0].shape
|
1029 |
+
):
|
1030 |
+
sample += down_block_additional_residuals.pop(0)
|
1031 |
+
|
1032 |
+
if is_controlnet:
|
1033 |
+
sample = sample + mid_block_additional_residual
|
1034 |
+
|
1035 |
+
# 5. up
|
1036 |
+
for i, upsample_block in enumerate(self.up_blocks):
|
1037 |
+
is_final_block = i == len(self.up_blocks) - 1
|
1038 |
+
|
1039 |
+
res_samples = down_block_res_samples[-len(upsample_block.resnets) :]
|
1040 |
+
down_block_res_samples = down_block_res_samples[: -len(upsample_block.resnets)]
|
1041 |
+
|
1042 |
+
# if we have not reached the final block and need to forward the
|
1043 |
+
# upsample size, we do it here
|
1044 |
+
if not is_final_block and forward_upsample_size:
|
1045 |
+
upsample_size = down_block_res_samples[-1].shape[2:]
|
1046 |
+
|
1047 |
+
if hasattr(upsample_block, "has_cross_attention") and upsample_block.has_cross_attention:
|
1048 |
+
sample = upsample_block(
|
1049 |
+
hidden_states=sample,
|
1050 |
+
temb=emb,
|
1051 |
+
res_hidden_states_tuple=res_samples,
|
1052 |
+
encoder_hidden_states=encoder_hidden_states,
|
1053 |
+
cross_attention_kwargs=cross_attention_kwargs,
|
1054 |
+
upsample_size=upsample_size,
|
1055 |
+
attention_mask=attention_mask,
|
1056 |
+
encoder_attention_mask=encoder_attention_mask,
|
1057 |
+
)
|
1058 |
+
else:
|
1059 |
+
sample = upsample_block(
|
1060 |
+
hidden_states=sample, temb=emb, res_hidden_states_tuple=res_samples, upsample_size=upsample_size
|
1061 |
+
)
|
1062 |
+
|
1063 |
+
if not return_dict:
|
1064 |
+
return (sample,)
|
1065 |
+
|
1066 |
+
return UNet2DConditionOutput(sample=sample)
|
magicanimate/models/attention.py
ADDED
@@ -0,0 +1,320 @@
|
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|
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|
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|
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|
|
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|
|
|
|
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|
|
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|
|
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|
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|
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|
|
|
|
|
|
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|
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|
|
|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
# *************************************************************************
|
2 |
+
# This file may have been modified by Bytedance Inc. (“Bytedance Inc.'s Mo-
|
3 |
+
# difications”). All Bytedance Inc.'s Modifications are Copyright (2023) B-
|
4 |
+
# ytedance Inc..
|
5 |
+
# *************************************************************************
|
6 |
+
|
7 |
+
# Copyright 2023 The HuggingFace Team. All rights reserved.
|
8 |
+
#
|
9 |
+
# Licensed under the Apache License, Version 2.0 (the "License");
|
10 |
+
# you may not use this file except in compliance with the License.
|
11 |
+
# You may obtain a copy of the License at
|
12 |
+
#
|
13 |
+
# http://www.apache.org/licenses/LICENSE-2.0
|
14 |
+
#
|
15 |
+
# Unless required by applicable law or agreed to in writing, software
|
16 |
+
# distributed under the License is distributed on an "AS IS" BASIS,
|
17 |
+
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
18 |
+
# See the License for the specific language governing permissions and
|
19 |
+
# limitations under the License.
|
20 |
+
from dataclasses import dataclass
|
21 |
+
from typing import Optional
|
22 |
+
|
23 |
+
import torch
|
24 |
+
import torch.nn.functional as F
|
25 |
+
from torch import nn
|
26 |
+
|
27 |
+
from diffusers.configuration_utils import ConfigMixin, register_to_config
|
28 |
+
from diffusers.models.modeling_utils import ModelMixin
|
29 |
+
from diffusers.utils import BaseOutput
|
30 |
+
from diffusers.utils.import_utils import is_xformers_available
|
31 |
+
from diffusers.models.attention import FeedForward, AdaLayerNorm
|
32 |
+
from diffusers.models.attention import Attention as CrossAttention
|
33 |
+
|
34 |
+
from einops import rearrange, repeat
|
35 |
+
|
36 |
+
@dataclass
|
37 |
+
class Transformer3DModelOutput(BaseOutput):
|
38 |
+
sample: torch.FloatTensor
|
39 |
+
|
40 |
+
|
41 |
+
if is_xformers_available():
|
42 |
+
import xformers
|
43 |
+
import xformers.ops
|
44 |
+
else:
|
45 |
+
xformers = None
|
46 |
+
|
47 |
+
|
48 |
+
class Transformer3DModel(ModelMixin, ConfigMixin):
|
49 |
+
@register_to_config
|
50 |
+
def __init__(
|
51 |
+
self,
|
52 |
+
num_attention_heads: int = 16,
|
53 |
+
attention_head_dim: int = 88,
|
54 |
+
in_channels: Optional[int] = None,
|
55 |
+
num_layers: int = 1,
|
56 |
+
dropout: float = 0.0,
|
57 |
+
norm_num_groups: int = 32,
|
58 |
+
cross_attention_dim: Optional[int] = None,
|
59 |
+
attention_bias: bool = False,
|
60 |
+
activation_fn: str = "geglu",
|
61 |
+
num_embeds_ada_norm: Optional[int] = None,
|
62 |
+
use_linear_projection: bool = False,
|
63 |
+
only_cross_attention: bool = False,
|
64 |
+
upcast_attention: bool = False,
|
65 |
+
|
66 |
+
unet_use_cross_frame_attention=None,
|
67 |
+
unet_use_temporal_attention=None,
|
68 |
+
):
|
69 |
+
super().__init__()
|
70 |
+
self.use_linear_projection = use_linear_projection
|
71 |
+
self.num_attention_heads = num_attention_heads
|
72 |
+
self.attention_head_dim = attention_head_dim
|
73 |
+
inner_dim = num_attention_heads * attention_head_dim
|
74 |
+
|
75 |
+
# Define input layers
|
76 |
+
self.in_channels = in_channels
|
77 |
+
|
78 |
+
self.norm = torch.nn.GroupNorm(num_groups=norm_num_groups, num_channels=in_channels, eps=1e-6, affine=True)
|
79 |
+
if use_linear_projection:
|
80 |
+
self.proj_in = nn.Linear(in_channels, inner_dim)
|
81 |
+
else:
|
82 |
+
self.proj_in = nn.Conv2d(in_channels, inner_dim, kernel_size=1, stride=1, padding=0)
|
83 |
+
|
84 |
+
# Define transformers blocks
|
85 |
+
self.transformer_blocks = nn.ModuleList(
|
86 |
+
[
|
87 |
+
BasicTransformerBlock(
|
88 |
+
inner_dim,
|
89 |
+
num_attention_heads,
|
90 |
+
attention_head_dim,
|
91 |
+
dropout=dropout,
|
92 |
+
cross_attention_dim=cross_attention_dim,
|
93 |
+
activation_fn=activation_fn,
|
94 |
+
num_embeds_ada_norm=num_embeds_ada_norm,
|
95 |
+
attention_bias=attention_bias,
|
96 |
+
only_cross_attention=only_cross_attention,
|
97 |
+
upcast_attention=upcast_attention,
|
98 |
+
|
99 |
+
unet_use_cross_frame_attention=unet_use_cross_frame_attention,
|
100 |
+
unet_use_temporal_attention=unet_use_temporal_attention,
|
101 |
+
)
|
102 |
+
for d in range(num_layers)
|
103 |
+
]
|
104 |
+
)
|
105 |
+
|
106 |
+
# 4. Define output layers
|
107 |
+
if use_linear_projection:
|
108 |
+
self.proj_out = nn.Linear(in_channels, inner_dim)
|
109 |
+
else:
|
110 |
+
self.proj_out = nn.Conv2d(inner_dim, in_channels, kernel_size=1, stride=1, padding=0)
|
111 |
+
|
112 |
+
def forward(self, hidden_states, encoder_hidden_states=None, timestep=None, return_dict: bool = True):
|
113 |
+
# Input
|
114 |
+
assert hidden_states.dim() == 5, f"Expected hidden_states to have ndim=5, but got ndim={hidden_states.dim()}."
|
115 |
+
video_length = hidden_states.shape[2]
|
116 |
+
hidden_states = rearrange(hidden_states, "b c f h w -> (b f) c h w")
|
117 |
+
# JH: need not repeat when a list of prompts are given
|
118 |
+
if encoder_hidden_states.shape[0] != hidden_states.shape[0]:
|
119 |
+
encoder_hidden_states = repeat(encoder_hidden_states, 'b n c -> (b f) n c', f=video_length)
|
120 |
+
|
121 |
+
batch, channel, height, weight = hidden_states.shape
|
122 |
+
residual = hidden_states
|
123 |
+
|
124 |
+
hidden_states = self.norm(hidden_states)
|
125 |
+
if not self.use_linear_projection:
|
126 |
+
hidden_states = self.proj_in(hidden_states)
|
127 |
+
inner_dim = hidden_states.shape[1]
|
128 |
+
hidden_states = hidden_states.permute(0, 2, 3, 1).reshape(batch, height * weight, inner_dim)
|
129 |
+
else:
|
130 |
+
inner_dim = hidden_states.shape[1]
|
131 |
+
hidden_states = hidden_states.permute(0, 2, 3, 1).reshape(batch, height * weight, inner_dim)
|
132 |
+
hidden_states = self.proj_in(hidden_states)
|
133 |
+
|
134 |
+
# Blocks
|
135 |
+
for block in self.transformer_blocks:
|
136 |
+
hidden_states = block(
|
137 |
+
hidden_states,
|
138 |
+
encoder_hidden_states=encoder_hidden_states,
|
139 |
+
timestep=timestep,
|
140 |
+
video_length=video_length
|
141 |
+
)
|
142 |
+
|
143 |
+
# Output
|
144 |
+
if not self.use_linear_projection:
|
145 |
+
hidden_states = (
|
146 |
+
hidden_states.reshape(batch, height, weight, inner_dim).permute(0, 3, 1, 2).contiguous()
|
147 |
+
)
|
148 |
+
hidden_states = self.proj_out(hidden_states)
|
149 |
+
else:
|
150 |
+
hidden_states = self.proj_out(hidden_states)
|
151 |
+
hidden_states = (
|
152 |
+
hidden_states.reshape(batch, height, weight, inner_dim).permute(0, 3, 1, 2).contiguous()
|
153 |
+
)
|
154 |
+
|
155 |
+
output = hidden_states + residual
|
156 |
+
|
157 |
+
output = rearrange(output, "(b f) c h w -> b c f h w", f=video_length)
|
158 |
+
if not return_dict:
|
159 |
+
return (output,)
|
160 |
+
|
161 |
+
return Transformer3DModelOutput(sample=output)
|
162 |
+
|
163 |
+
|
164 |
+
class BasicTransformerBlock(nn.Module):
|
165 |
+
def __init__(
|
166 |
+
self,
|
167 |
+
dim: int,
|
168 |
+
num_attention_heads: int,
|
169 |
+
attention_head_dim: int,
|
170 |
+
dropout=0.0,
|
171 |
+
cross_attention_dim: Optional[int] = None,
|
172 |
+
activation_fn: str = "geglu",
|
173 |
+
num_embeds_ada_norm: Optional[int] = None,
|
174 |
+
attention_bias: bool = False,
|
175 |
+
only_cross_attention: bool = False,
|
176 |
+
upcast_attention: bool = False,
|
177 |
+
|
178 |
+
unet_use_cross_frame_attention = None,
|
179 |
+
unet_use_temporal_attention = None,
|
180 |
+
):
|
181 |
+
super().__init__()
|
182 |
+
self.only_cross_attention = only_cross_attention
|
183 |
+
self.use_ada_layer_norm = num_embeds_ada_norm is not None
|
184 |
+
self.unet_use_cross_frame_attention = unet_use_cross_frame_attention
|
185 |
+
self.unet_use_temporal_attention = unet_use_temporal_attention
|
186 |
+
|
187 |
+
# SC-Attn
|
188 |
+
assert unet_use_cross_frame_attention is not None
|
189 |
+
if unet_use_cross_frame_attention:
|
190 |
+
self.attn1 = SparseCausalAttention2D(
|
191 |
+
query_dim=dim,
|
192 |
+
heads=num_attention_heads,
|
193 |
+
dim_head=attention_head_dim,
|
194 |
+
dropout=dropout,
|
195 |
+
bias=attention_bias,
|
196 |
+
cross_attention_dim=cross_attention_dim if only_cross_attention else None,
|
197 |
+
upcast_attention=upcast_attention,
|
198 |
+
)
|
199 |
+
else:
|
200 |
+
self.attn1 = CrossAttention(
|
201 |
+
query_dim=dim,
|
202 |
+
heads=num_attention_heads,
|
203 |
+
dim_head=attention_head_dim,
|
204 |
+
dropout=dropout,
|
205 |
+
bias=attention_bias,
|
206 |
+
upcast_attention=upcast_attention,
|
207 |
+
)
|
208 |
+
self.norm1 = AdaLayerNorm(dim, num_embeds_ada_norm) if self.use_ada_layer_norm else nn.LayerNorm(dim)
|
209 |
+
|
210 |
+
# Cross-Attn
|
211 |
+
if cross_attention_dim is not None:
|
212 |
+
self.attn2 = CrossAttention(
|
213 |
+
query_dim=dim,
|
214 |
+
cross_attention_dim=cross_attention_dim,
|
215 |
+
heads=num_attention_heads,
|
216 |
+
dim_head=attention_head_dim,
|
217 |
+
dropout=dropout,
|
218 |
+
bias=attention_bias,
|
219 |
+
upcast_attention=upcast_attention,
|
220 |
+
)
|
221 |
+
else:
|
222 |
+
self.attn2 = None
|
223 |
+
|
224 |
+
if cross_attention_dim is not None:
|
225 |
+
self.norm2 = AdaLayerNorm(dim, num_embeds_ada_norm) if self.use_ada_layer_norm else nn.LayerNorm(dim)
|
226 |
+
else:
|
227 |
+
self.norm2 = None
|
228 |
+
|
229 |
+
# Feed-forward
|
230 |
+
self.ff = FeedForward(dim, dropout=dropout, activation_fn=activation_fn)
|
231 |
+
self.norm3 = nn.LayerNorm(dim)
|
232 |
+
self.use_ada_layer_norm_zero = False
|
233 |
+
|
234 |
+
# Temp-Attn
|
235 |
+
assert unet_use_temporal_attention is not None
|
236 |
+
if unet_use_temporal_attention:
|
237 |
+
self.attn_temp = CrossAttention(
|
238 |
+
query_dim=dim,
|
239 |
+
heads=num_attention_heads,
|
240 |
+
dim_head=attention_head_dim,
|
241 |
+
dropout=dropout,
|
242 |
+
bias=attention_bias,
|
243 |
+
upcast_attention=upcast_attention,
|
244 |
+
)
|
245 |
+
nn.init.zeros_(self.attn_temp.to_out[0].weight.data)
|
246 |
+
self.norm_temp = AdaLayerNorm(dim, num_embeds_ada_norm) if self.use_ada_layer_norm else nn.LayerNorm(dim)
|
247 |
+
|
248 |
+
def set_use_memory_efficient_attention_xformers(self, use_memory_efficient_attention_xformers: bool, *args, **kwargs):
|
249 |
+
if not is_xformers_available():
|
250 |
+
print("Here is how to install it")
|
251 |
+
raise ModuleNotFoundError(
|
252 |
+
"Refer to https://github.com/facebookresearch/xformers for more information on how to install"
|
253 |
+
" xformers",
|
254 |
+
name="xformers",
|
255 |
+
)
|
256 |
+
elif not torch.cuda.is_available():
|
257 |
+
raise ValueError(
|
258 |
+
"torch.cuda.is_available() should be True but is False. xformers' memory efficient attention is only"
|
259 |
+
" available for GPU "
|
260 |
+
)
|
261 |
+
else:
|
262 |
+
try:
|
263 |
+
# Make sure we can run the memory efficient attention
|
264 |
+
_ = xformers.ops.memory_efficient_attention(
|
265 |
+
torch.randn((1, 2, 40), device="cuda"),
|
266 |
+
torch.randn((1, 2, 40), device="cuda"),
|
267 |
+
torch.randn((1, 2, 40), device="cuda"),
|
268 |
+
)
|
269 |
+
except Exception as e:
|
270 |
+
raise e
|
271 |
+
self.attn1._use_memory_efficient_attention_xformers = use_memory_efficient_attention_xformers
|
272 |
+
if self.attn2 is not None:
|
273 |
+
self.attn2._use_memory_efficient_attention_xformers = use_memory_efficient_attention_xformers
|
274 |
+
# self.attn_temp._use_memory_efficient_attention_xformers = use_memory_efficient_attention_xformers
|
275 |
+
|
276 |
+
def forward(self, hidden_states, encoder_hidden_states=None, timestep=None, attention_mask=None, video_length=None):
|
277 |
+
# SparseCausal-Attention
|
278 |
+
norm_hidden_states = (
|
279 |
+
self.norm1(hidden_states, timestep) if self.use_ada_layer_norm else self.norm1(hidden_states)
|
280 |
+
)
|
281 |
+
|
282 |
+
# if self.only_cross_attention:
|
283 |
+
# hidden_states = (
|
284 |
+
# self.attn1(norm_hidden_states, encoder_hidden_states, attention_mask=attention_mask) + hidden_states
|
285 |
+
# )
|
286 |
+
# else:
|
287 |
+
# hidden_states = self.attn1(norm_hidden_states, attention_mask=attention_mask, video_length=video_length) + hidden_states
|
288 |
+
|
289 |
+
# pdb.set_trace()
|
290 |
+
if self.unet_use_cross_frame_attention:
|
291 |
+
hidden_states = self.attn1(norm_hidden_states, attention_mask=attention_mask, video_length=video_length) + hidden_states
|
292 |
+
else:
|
293 |
+
hidden_states = self.attn1(norm_hidden_states, attention_mask=attention_mask) + hidden_states
|
294 |
+
|
295 |
+
if self.attn2 is not None:
|
296 |
+
# Cross-Attention
|
297 |
+
norm_hidden_states = (
|
298 |
+
self.norm2(hidden_states, timestep) if self.use_ada_layer_norm else self.norm2(hidden_states)
|
299 |
+
)
|
300 |
+
hidden_states = (
|
301 |
+
self.attn2(
|
302 |
+
norm_hidden_states, encoder_hidden_states=encoder_hidden_states, attention_mask=attention_mask
|
303 |
+
)
|
304 |
+
+ hidden_states
|
305 |
+
)
|
306 |
+
|
307 |
+
# Feed-forward
|
308 |
+
hidden_states = self.ff(self.norm3(hidden_states)) + hidden_states
|
309 |
+
|
310 |
+
# Temporal-Attention
|
311 |
+
if self.unet_use_temporal_attention:
|
312 |
+
d = hidden_states.shape[1]
|
313 |
+
hidden_states = rearrange(hidden_states, "(b f) d c -> (b d) f c", f=video_length)
|
314 |
+
norm_hidden_states = (
|
315 |
+
self.norm_temp(hidden_states, timestep) if self.use_ada_layer_norm else self.norm_temp(hidden_states)
|
316 |
+
)
|
317 |
+
hidden_states = self.attn_temp(norm_hidden_states) + hidden_states
|
318 |
+
hidden_states = rearrange(hidden_states, "(b d) f c -> (b f) d c", d=d)
|
319 |
+
|
320 |
+
return hidden_states
|
magicanimate/models/controlnet.py
ADDED
@@ -0,0 +1,578 @@
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|
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|
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|
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|
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|
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|
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|
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|
|
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|
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|
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|
|
|
|
|
|
|
|
|
|
|
1 |
+
# *************************************************************************
|
2 |
+
# This file may have been modified by Bytedance Inc. (“Bytedance Inc.'s Mo-
|
3 |
+
# difications”). All Bytedance Inc.'s Modifications are Copyright (2023) B-
|
4 |
+
# ytedance Inc..
|
5 |
+
# *************************************************************************
|
6 |
+
|
7 |
+
# Copyright 2023 The HuggingFace Team. All rights reserved.
|
8 |
+
#
|
9 |
+
# Licensed under the Apache License, Version 2.0 (the "License");
|
10 |
+
# you may not use this file except in compliance with the License.
|
11 |
+
# You may obtain a copy of the License at
|
12 |
+
#
|
13 |
+
# http://www.apache.org/licenses/LICENSE-2.0
|
14 |
+
#
|
15 |
+
# Unless required by applicable law or agreed to in writing, software
|
16 |
+
# distributed under the License is distributed on an "AS IS" BASIS,
|
17 |
+
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
18 |
+
# See the License for the specific language governing permissions and
|
19 |
+
# limitations under the License.
|
20 |
+
from dataclasses import dataclass
|
21 |
+
from typing import Any, Dict, List, Optional, Tuple, Union
|
22 |
+
|
23 |
+
import torch
|
24 |
+
from torch import nn
|
25 |
+
from torch.nn import functional as F
|
26 |
+
|
27 |
+
from diffusers.configuration_utils import ConfigMixin, register_to_config
|
28 |
+
from diffusers.utils import BaseOutput, logging
|
29 |
+
from .embeddings import TimestepEmbedding, Timesteps
|
30 |
+
from diffusers.models.modeling_utils import ModelMixin
|
31 |
+
from diffusers.models.unet_2d_blocks import (
|
32 |
+
CrossAttnDownBlock2D,
|
33 |
+
DownBlock2D,
|
34 |
+
UNetMidBlock2DCrossAttn,
|
35 |
+
get_down_block,
|
36 |
+
)
|
37 |
+
from diffusers.models.unet_2d_condition import UNet2DConditionModel
|
38 |
+
|
39 |
+
|
40 |
+
logger = logging.get_logger(__name__) # pylint: disable=invalid-name
|
41 |
+
|
42 |
+
|
43 |
+
@dataclass
|
44 |
+
class ControlNetOutput(BaseOutput):
|
45 |
+
down_block_res_samples: Tuple[torch.Tensor]
|
46 |
+
mid_block_res_sample: torch.Tensor
|
47 |
+
|
48 |
+
|
49 |
+
class ControlNetConditioningEmbedding(nn.Module):
|
50 |
+
"""
|
51 |
+
Quoting from https://arxiv.org/abs/2302.05543: "Stable Diffusion uses a pre-processing method similar to VQ-GAN
|
52 |
+
[11] to convert the entire dataset of 512 × 512 images into smaller 64 × 64 “latent images” for stabilized
|
53 |
+
training. This requires ControlNets to convert image-based conditions to 64 × 64 feature space to match the
|
54 |
+
convolution size. We use a tiny network E(·) of four convolution layers with 4 × 4 kernels and 2 × 2 strides
|
55 |
+
(activated by ReLU, channels are 16, 32, 64, 128, initialized with Gaussian weights, trained jointly with the full
|
56 |
+
model) to encode image-space conditions ... into feature maps ..."
|
57 |
+
"""
|
58 |
+
|
59 |
+
def __init__(
|
60 |
+
self,
|
61 |
+
conditioning_embedding_channels: int,
|
62 |
+
conditioning_channels: int = 3,
|
63 |
+
block_out_channels: Tuple[int] = (16, 32, 96, 256),
|
64 |
+
):
|
65 |
+
super().__init__()
|
66 |
+
|
67 |
+
self.conv_in = nn.Conv2d(conditioning_channels, block_out_channels[0], kernel_size=3, padding=1)
|
68 |
+
|
69 |
+
self.blocks = nn.ModuleList([])
|
70 |
+
|
71 |
+
for i in range(len(block_out_channels) - 1):
|
72 |
+
channel_in = block_out_channels[i]
|
73 |
+
channel_out = block_out_channels[i + 1]
|
74 |
+
self.blocks.append(nn.Conv2d(channel_in, channel_in, kernel_size=3, padding=1))
|
75 |
+
self.blocks.append(nn.Conv2d(channel_in, channel_out, kernel_size=3, padding=1, stride=2))
|
76 |
+
|
77 |
+
self.conv_out = zero_module(
|
78 |
+
nn.Conv2d(block_out_channels[-1], conditioning_embedding_channels, kernel_size=3, padding=1)
|
79 |
+
)
|
80 |
+
|
81 |
+
def forward(self, conditioning):
|
82 |
+
embedding = self.conv_in(conditioning)
|
83 |
+
embedding = F.silu(embedding)
|
84 |
+
|
85 |
+
for block in self.blocks:
|
86 |
+
embedding = block(embedding)
|
87 |
+
embedding = F.silu(embedding)
|
88 |
+
|
89 |
+
embedding = self.conv_out(embedding)
|
90 |
+
|
91 |
+
return embedding
|
92 |
+
|
93 |
+
|
94 |
+
class ControlNetModel(ModelMixin, ConfigMixin):
|
95 |
+
_supports_gradient_checkpointing = True
|
96 |
+
|
97 |
+
@register_to_config
|
98 |
+
def __init__(
|
99 |
+
self,
|
100 |
+
in_channels: int = 4,
|
101 |
+
flip_sin_to_cos: bool = True,
|
102 |
+
freq_shift: int = 0,
|
103 |
+
down_block_types: Tuple[str] = (
|
104 |
+
"CrossAttnDownBlock2D",
|
105 |
+
"CrossAttnDownBlock2D",
|
106 |
+
"CrossAttnDownBlock2D",
|
107 |
+
"DownBlock2D",
|
108 |
+
),
|
109 |
+
only_cross_attention: Union[bool, Tuple[bool]] = False,
|
110 |
+
block_out_channels: Tuple[int] = (320, 640, 1280, 1280),
|
111 |
+
layers_per_block: int = 2,
|
112 |
+
downsample_padding: int = 1,
|
113 |
+
mid_block_scale_factor: float = 1,
|
114 |
+
act_fn: str = "silu",
|
115 |
+
norm_num_groups: Optional[int] = 32,
|
116 |
+
norm_eps: float = 1e-5,
|
117 |
+
cross_attention_dim: int = 1280,
|
118 |
+
attention_head_dim: Union[int, Tuple[int]] = 8,
|
119 |
+
use_linear_projection: bool = False,
|
120 |
+
class_embed_type: Optional[str] = None,
|
121 |
+
num_class_embeds: Optional[int] = None,
|
122 |
+
upcast_attention: bool = False,
|
123 |
+
resnet_time_scale_shift: str = "default",
|
124 |
+
projection_class_embeddings_input_dim: Optional[int] = None,
|
125 |
+
controlnet_conditioning_channel_order: str = "rgb",
|
126 |
+
conditioning_embedding_out_channels: Optional[Tuple[int]] = (16, 32, 96, 256),
|
127 |
+
):
|
128 |
+
super().__init__()
|
129 |
+
|
130 |
+
# Check inputs
|
131 |
+
if len(block_out_channels) != len(down_block_types):
|
132 |
+
raise ValueError(
|
133 |
+
f"Must provide the same number of `block_out_channels` as `down_block_types`. `block_out_channels`: {block_out_channels}. `down_block_types`: {down_block_types}."
|
134 |
+
)
|
135 |
+
|
136 |
+
if not isinstance(only_cross_attention, bool) and len(only_cross_attention) != len(down_block_types):
|
137 |
+
raise ValueError(
|
138 |
+
f"Must provide the same number of `only_cross_attention` as `down_block_types`. `only_cross_attention`: {only_cross_attention}. `down_block_types`: {down_block_types}."
|
139 |
+
)
|
140 |
+
|
141 |
+
if not isinstance(attention_head_dim, int) and len(attention_head_dim) != len(down_block_types):
|
142 |
+
raise ValueError(
|
143 |
+
f"Must provide the same number of `attention_head_dim` as `down_block_types`. `attention_head_dim`: {attention_head_dim}. `down_block_types`: {down_block_types}."
|
144 |
+
)
|
145 |
+
|
146 |
+
# input
|
147 |
+
conv_in_kernel = 3
|
148 |
+
conv_in_padding = (conv_in_kernel - 1) // 2
|
149 |
+
self.conv_in = nn.Conv2d(
|
150 |
+
in_channels, block_out_channels[0], kernel_size=conv_in_kernel, padding=conv_in_padding
|
151 |
+
)
|
152 |
+
|
153 |
+
# time
|
154 |
+
time_embed_dim = block_out_channels[0] * 4
|
155 |
+
|
156 |
+
self.time_proj = Timesteps(block_out_channels[0], flip_sin_to_cos, freq_shift)
|
157 |
+
timestep_input_dim = block_out_channels[0]
|
158 |
+
|
159 |
+
self.time_embedding = TimestepEmbedding(
|
160 |
+
timestep_input_dim,
|
161 |
+
time_embed_dim,
|
162 |
+
act_fn=act_fn,
|
163 |
+
)
|
164 |
+
|
165 |
+
# class embedding
|
166 |
+
if class_embed_type is None and num_class_embeds is not None:
|
167 |
+
self.class_embedding = nn.Embedding(num_class_embeds, time_embed_dim)
|
168 |
+
elif class_embed_type == "timestep":
|
169 |
+
self.class_embedding = TimestepEmbedding(timestep_input_dim, time_embed_dim)
|
170 |
+
elif class_embed_type == "identity":
|
171 |
+
self.class_embedding = nn.Identity(time_embed_dim, time_embed_dim)
|
172 |
+
elif class_embed_type == "projection":
|
173 |
+
if projection_class_embeddings_input_dim is None:
|
174 |
+
raise ValueError(
|
175 |
+
"`class_embed_type`: 'projection' requires `projection_class_embeddings_input_dim` be set"
|
176 |
+
)
|
177 |
+
# The projection `class_embed_type` is the same as the timestep `class_embed_type` except
|
178 |
+
# 1. the `class_labels` inputs are not first converted to sinusoidal embeddings
|
179 |
+
# 2. it projects from an arbitrary input dimension.
|
180 |
+
#
|
181 |
+
# Note that `TimestepEmbedding` is quite general, being mainly linear layers and activations.
|
182 |
+
# When used for embedding actual timesteps, the timesteps are first converted to sinusoidal embeddings.
|
183 |
+
# As a result, `TimestepEmbedding` can be passed arbitrary vectors.
|
184 |
+
self.class_embedding = TimestepEmbedding(projection_class_embeddings_input_dim, time_embed_dim)
|
185 |
+
else:
|
186 |
+
self.class_embedding = None
|
187 |
+
|
188 |
+
# control net conditioning embedding
|
189 |
+
self.controlnet_cond_embedding = ControlNetConditioningEmbedding(
|
190 |
+
conditioning_embedding_channels=block_out_channels[0],
|
191 |
+
block_out_channels=conditioning_embedding_out_channels,
|
192 |
+
)
|
193 |
+
|
194 |
+
self.down_blocks = nn.ModuleList([])
|
195 |
+
self.controlnet_down_blocks = nn.ModuleList([])
|
196 |
+
|
197 |
+
if isinstance(only_cross_attention, bool):
|
198 |
+
only_cross_attention = [only_cross_attention] * len(down_block_types)
|
199 |
+
|
200 |
+
if isinstance(attention_head_dim, int):
|
201 |
+
attention_head_dim = (attention_head_dim,) * len(down_block_types)
|
202 |
+
|
203 |
+
# down
|
204 |
+
output_channel = block_out_channels[0]
|
205 |
+
|
206 |
+
controlnet_block = nn.Conv2d(output_channel, output_channel, kernel_size=1)
|
207 |
+
controlnet_block = zero_module(controlnet_block)
|
208 |
+
self.controlnet_down_blocks.append(controlnet_block)
|
209 |
+
|
210 |
+
for i, down_block_type in enumerate(down_block_types):
|
211 |
+
input_channel = output_channel
|
212 |
+
output_channel = block_out_channels[i]
|
213 |
+
is_final_block = i == len(block_out_channels) - 1
|
214 |
+
|
215 |
+
down_block = get_down_block(
|
216 |
+
down_block_type,
|
217 |
+
num_layers=layers_per_block,
|
218 |
+
in_channels=input_channel,
|
219 |
+
out_channels=output_channel,
|
220 |
+
temb_channels=time_embed_dim,
|
221 |
+
add_downsample=not is_final_block,
|
222 |
+
resnet_eps=norm_eps,
|
223 |
+
resnet_act_fn=act_fn,
|
224 |
+
resnet_groups=norm_num_groups,
|
225 |
+
cross_attention_dim=cross_attention_dim,
|
226 |
+
num_attention_heads=attention_head_dim[i],
|
227 |
+
downsample_padding=downsample_padding,
|
228 |
+
use_linear_projection=use_linear_projection,
|
229 |
+
only_cross_attention=only_cross_attention[i],
|
230 |
+
upcast_attention=upcast_attention,
|
231 |
+
resnet_time_scale_shift=resnet_time_scale_shift,
|
232 |
+
)
|
233 |
+
self.down_blocks.append(down_block)
|
234 |
+
|
235 |
+
for _ in range(layers_per_block):
|
236 |
+
controlnet_block = nn.Conv2d(output_channel, output_channel, kernel_size=1)
|
237 |
+
controlnet_block = zero_module(controlnet_block)
|
238 |
+
self.controlnet_down_blocks.append(controlnet_block)
|
239 |
+
|
240 |
+
if not is_final_block:
|
241 |
+
controlnet_block = nn.Conv2d(output_channel, output_channel, kernel_size=1)
|
242 |
+
controlnet_block = zero_module(controlnet_block)
|
243 |
+
self.controlnet_down_blocks.append(controlnet_block)
|
244 |
+
|
245 |
+
# mid
|
246 |
+
mid_block_channel = block_out_channels[-1]
|
247 |
+
|
248 |
+
controlnet_block = nn.Conv2d(mid_block_channel, mid_block_channel, kernel_size=1)
|
249 |
+
controlnet_block = zero_module(controlnet_block)
|
250 |
+
self.controlnet_mid_block = controlnet_block
|
251 |
+
|
252 |
+
self.mid_block = UNetMidBlock2DCrossAttn(
|
253 |
+
in_channels=mid_block_channel,
|
254 |
+
temb_channels=time_embed_dim,
|
255 |
+
resnet_eps=norm_eps,
|
256 |
+
resnet_act_fn=act_fn,
|
257 |
+
output_scale_factor=mid_block_scale_factor,
|
258 |
+
resnet_time_scale_shift=resnet_time_scale_shift,
|
259 |
+
cross_attention_dim=cross_attention_dim,
|
260 |
+
num_attention_heads=attention_head_dim[-1],
|
261 |
+
resnet_groups=norm_num_groups,
|
262 |
+
use_linear_projection=use_linear_projection,
|
263 |
+
upcast_attention=upcast_attention,
|
264 |
+
)
|
265 |
+
|
266 |
+
@classmethod
|
267 |
+
def from_unet(
|
268 |
+
cls,
|
269 |
+
unet: UNet2DConditionModel,
|
270 |
+
controlnet_conditioning_channel_order: str = "rgb",
|
271 |
+
conditioning_embedding_out_channels: Optional[Tuple[int]] = (16, 32, 96, 256),
|
272 |
+
load_weights_from_unet: bool = True,
|
273 |
+
):
|
274 |
+
r"""
|
275 |
+
Instantiate Controlnet class from UNet2DConditionModel.
|
276 |
+
|
277 |
+
Parameters:
|
278 |
+
unet (`UNet2DConditionModel`):
|
279 |
+
UNet model which weights are copied to the ControlNet. Note that all configuration options are also
|
280 |
+
copied where applicable.
|
281 |
+
"""
|
282 |
+
controlnet = cls(
|
283 |
+
in_channels=unet.config.in_channels,
|
284 |
+
flip_sin_to_cos=unet.config.flip_sin_to_cos,
|
285 |
+
freq_shift=unet.config.freq_shift,
|
286 |
+
down_block_types=unet.config.down_block_types,
|
287 |
+
only_cross_attention=unet.config.only_cross_attention,
|
288 |
+
block_out_channels=unet.config.block_out_channels,
|
289 |
+
layers_per_block=unet.config.layers_per_block,
|
290 |
+
downsample_padding=unet.config.downsample_padding,
|
291 |
+
mid_block_scale_factor=unet.config.mid_block_scale_factor,
|
292 |
+
act_fn=unet.config.act_fn,
|
293 |
+
norm_num_groups=unet.config.norm_num_groups,
|
294 |
+
norm_eps=unet.config.norm_eps,
|
295 |
+
cross_attention_dim=unet.config.cross_attention_dim,
|
296 |
+
attention_head_dim=unet.config.attention_head_dim,
|
297 |
+
use_linear_projection=unet.config.use_linear_projection,
|
298 |
+
class_embed_type=unet.config.class_embed_type,
|
299 |
+
num_class_embeds=unet.config.num_class_embeds,
|
300 |
+
upcast_attention=unet.config.upcast_attention,
|
301 |
+
resnet_time_scale_shift=unet.config.resnet_time_scale_shift,
|
302 |
+
projection_class_embeddings_input_dim=unet.config.projection_class_embeddings_input_dim,
|
303 |
+
controlnet_conditioning_channel_order=controlnet_conditioning_channel_order,
|
304 |
+
conditioning_embedding_out_channels=conditioning_embedding_out_channels,
|
305 |
+
)
|
306 |
+
|
307 |
+
if load_weights_from_unet:
|
308 |
+
controlnet.conv_in.load_state_dict(unet.conv_in.state_dict())
|
309 |
+
controlnet.time_proj.load_state_dict(unet.time_proj.state_dict())
|
310 |
+
controlnet.time_embedding.load_state_dict(unet.time_embedding.state_dict())
|
311 |
+
|
312 |
+
if controlnet.class_embedding:
|
313 |
+
controlnet.class_embedding.load_state_dict(unet.class_embedding.state_dict())
|
314 |
+
|
315 |
+
controlnet.down_blocks.load_state_dict(unet.down_blocks.state_dict())
|
316 |
+
controlnet.mid_block.load_state_dict(unet.mid_block.state_dict())
|
317 |
+
|
318 |
+
return controlnet
|
319 |
+
|
320 |
+
# @property
|
321 |
+
# # Copied from diffusers.models.unet_2d_condition.UNet2DConditionModel.attn_processors
|
322 |
+
# def attn_processors(self) -> Dict[str, AttentionProcessor]:
|
323 |
+
# r"""
|
324 |
+
# Returns:
|
325 |
+
# `dict` of attention processors: A dictionary containing all attention processors used in the model with
|
326 |
+
# indexed by its weight name.
|
327 |
+
# """
|
328 |
+
# # set recursively
|
329 |
+
# processors = {}
|
330 |
+
|
331 |
+
# def fn_recursive_add_processors(name: str, module: torch.nn.Module, processors: Dict[str, AttentionProcessor]):
|
332 |
+
# if hasattr(module, "set_processor"):
|
333 |
+
# processors[f"{name}.processor"] = module.processor
|
334 |
+
|
335 |
+
# for sub_name, child in module.named_children():
|
336 |
+
# fn_recursive_add_processors(f"{name}.{sub_name}", child, processors)
|
337 |
+
|
338 |
+
# return processors
|
339 |
+
|
340 |
+
# for name, module in self.named_children():
|
341 |
+
# fn_recursive_add_processors(name, module, processors)
|
342 |
+
|
343 |
+
# return processors
|
344 |
+
|
345 |
+
# # Copied from diffusers.models.unet_2d_condition.UNet2DConditionModel.set_attn_processor
|
346 |
+
# def set_attn_processor(self, processor: Union[AttentionProcessor, Dict[str, AttentionProcessor]]):
|
347 |
+
# r"""
|
348 |
+
# Parameters:
|
349 |
+
# `processor (`dict` of `AttentionProcessor` or `AttentionProcessor`):
|
350 |
+
# The instantiated processor class or a dictionary of processor classes that will be set as the processor
|
351 |
+
# of **all** `Attention` layers.
|
352 |
+
# In case `processor` is a dict, the key needs to define the path to the corresponding cross attention processor. This is strongly recommended when setting trainable attention processors.:
|
353 |
+
|
354 |
+
# """
|
355 |
+
# count = len(self.attn_processors.keys())
|
356 |
+
|
357 |
+
# if isinstance(processor, dict) and len(processor) != count:
|
358 |
+
# raise ValueError(
|
359 |
+
# f"A dict of processors was passed, but the number of processors {len(processor)} does not match the"
|
360 |
+
# f" number of attention layers: {count}. Please make sure to pass {count} processor classes."
|
361 |
+
# )
|
362 |
+
|
363 |
+
# def fn_recursive_attn_processor(name: str, module: torch.nn.Module, processor):
|
364 |
+
# if hasattr(module, "set_processor"):
|
365 |
+
# if not isinstance(processor, dict):
|
366 |
+
# module.set_processor(processor)
|
367 |
+
# else:
|
368 |
+
# module.set_processor(processor.pop(f"{name}.processor"))
|
369 |
+
|
370 |
+
# for sub_name, child in module.named_children():
|
371 |
+
# fn_recursive_attn_processor(f"{name}.{sub_name}", child, processor)
|
372 |
+
|
373 |
+
# for name, module in self.named_children():
|
374 |
+
# fn_recursive_attn_processor(name, module, processor)
|
375 |
+
|
376 |
+
# # Copied from diffusers.models.unet_2d_condition.UNet2DConditionModel.set_default_attn_processor
|
377 |
+
# def set_default_attn_processor(self):
|
378 |
+
# """
|
379 |
+
# Disables custom attention processors and sets the default attention implementation.
|
380 |
+
# """
|
381 |
+
# self.set_attn_processor(AttnProcessor())
|
382 |
+
|
383 |
+
# Copied from diffusers.models.unet_2d_condition.UNet2DConditionModel.set_attention_slice
|
384 |
+
def set_attention_slice(self, slice_size):
|
385 |
+
r"""
|
386 |
+
Enable sliced attention computation.
|
387 |
+
|
388 |
+
When this option is enabled, the attention module will split the input tensor in slices, to compute attention
|
389 |
+
in several steps. This is useful to save some memory in exchange for a small speed decrease.
|
390 |
+
|
391 |
+
Args:
|
392 |
+
slice_size (`str` or `int` or `list(int)`, *optional*, defaults to `"auto"`):
|
393 |
+
When `"auto"`, halves the input to the attention heads, so attention will be computed in two steps. If
|
394 |
+
`"max"`, maximum amount of memory will be saved by running only one slice at a time. If a number is
|
395 |
+
provided, uses as many slices as `attention_head_dim // slice_size`. In this case, `attention_head_dim`
|
396 |
+
must be a multiple of `slice_size`.
|
397 |
+
"""
|
398 |
+
sliceable_head_dims = []
|
399 |
+
|
400 |
+
def fn_recursive_retrieve_sliceable_dims(module: torch.nn.Module):
|
401 |
+
if hasattr(module, "set_attention_slice"):
|
402 |
+
sliceable_head_dims.append(module.sliceable_head_dim)
|
403 |
+
|
404 |
+
for child in module.children():
|
405 |
+
fn_recursive_retrieve_sliceable_dims(child)
|
406 |
+
|
407 |
+
# retrieve number of attention layers
|
408 |
+
for module in self.children():
|
409 |
+
fn_recursive_retrieve_sliceable_dims(module)
|
410 |
+
|
411 |
+
num_sliceable_layers = len(sliceable_head_dims)
|
412 |
+
|
413 |
+
if slice_size == "auto":
|
414 |
+
# half the attention head size is usually a good trade-off between
|
415 |
+
# speed and memory
|
416 |
+
slice_size = [dim // 2 for dim in sliceable_head_dims]
|
417 |
+
elif slice_size == "max":
|
418 |
+
# make smallest slice possible
|
419 |
+
slice_size = num_sliceable_layers * [1]
|
420 |
+
|
421 |
+
slice_size = num_sliceable_layers * [slice_size] if not isinstance(slice_size, list) else slice_size
|
422 |
+
|
423 |
+
if len(slice_size) != len(sliceable_head_dims):
|
424 |
+
raise ValueError(
|
425 |
+
f"You have provided {len(slice_size)}, but {self.config} has {len(sliceable_head_dims)} different"
|
426 |
+
f" attention layers. Make sure to match `len(slice_size)` to be {len(sliceable_head_dims)}."
|
427 |
+
)
|
428 |
+
|
429 |
+
for i in range(len(slice_size)):
|
430 |
+
size = slice_size[i]
|
431 |
+
dim = sliceable_head_dims[i]
|
432 |
+
if size is not None and size > dim:
|
433 |
+
raise ValueError(f"size {size} has to be smaller or equal to {dim}.")
|
434 |
+
|
435 |
+
# Recursively walk through all the children.
|
436 |
+
# Any children which exposes the set_attention_slice method
|
437 |
+
# gets the message
|
438 |
+
def fn_recursive_set_attention_slice(module: torch.nn.Module, slice_size: List[int]):
|
439 |
+
if hasattr(module, "set_attention_slice"):
|
440 |
+
module.set_attention_slice(slice_size.pop())
|
441 |
+
|
442 |
+
for child in module.children():
|
443 |
+
fn_recursive_set_attention_slice(child, slice_size)
|
444 |
+
|
445 |
+
reversed_slice_size = list(reversed(slice_size))
|
446 |
+
for module in self.children():
|
447 |
+
fn_recursive_set_attention_slice(module, reversed_slice_size)
|
448 |
+
|
449 |
+
def _set_gradient_checkpointing(self, module, value=False):
|
450 |
+
if isinstance(module, (CrossAttnDownBlock2D, DownBlock2D)):
|
451 |
+
module.gradient_checkpointing = value
|
452 |
+
|
453 |
+
def forward(
|
454 |
+
self,
|
455 |
+
sample: torch.FloatTensor,
|
456 |
+
timestep: Union[torch.Tensor, float, int],
|
457 |
+
encoder_hidden_states: torch.Tensor,
|
458 |
+
controlnet_cond: torch.FloatTensor,
|
459 |
+
conditioning_scale: float = 1.0,
|
460 |
+
class_labels: Optional[torch.Tensor] = None,
|
461 |
+
timestep_cond: Optional[torch.Tensor] = None,
|
462 |
+
attention_mask: Optional[torch.Tensor] = None,
|
463 |
+
cross_attention_kwargs: Optional[Dict[str, Any]] = None,
|
464 |
+
return_dict: bool = True,
|
465 |
+
) -> Union[ControlNetOutput, Tuple]:
|
466 |
+
# check channel order
|
467 |
+
channel_order = self.config.controlnet_conditioning_channel_order
|
468 |
+
|
469 |
+
if channel_order == "rgb":
|
470 |
+
# in rgb order by default
|
471 |
+
...
|
472 |
+
elif channel_order == "bgr":
|
473 |
+
controlnet_cond = torch.flip(controlnet_cond, dims=[1])
|
474 |
+
else:
|
475 |
+
raise ValueError(f"unknown `controlnet_conditioning_channel_order`: {channel_order}")
|
476 |
+
|
477 |
+
# prepare attention_mask
|
478 |
+
if attention_mask is not None:
|
479 |
+
attention_mask = (1 - attention_mask.to(sample.dtype)) * -10000.0
|
480 |
+
attention_mask = attention_mask.unsqueeze(1)
|
481 |
+
|
482 |
+
# 1. time
|
483 |
+
timesteps = timestep
|
484 |
+
if not torch.is_tensor(timesteps):
|
485 |
+
# TODO: this requires sync between CPU and GPU. So try to pass timesteps as tensors if you can
|
486 |
+
# This would be a good case for the `match` statement (Python 3.10+)
|
487 |
+
is_mps = sample.device.type == "mps"
|
488 |
+
if isinstance(timestep, float):
|
489 |
+
dtype = torch.float32 if is_mps else torch.float64
|
490 |
+
else:
|
491 |
+
dtype = torch.int32 if is_mps else torch.int64
|
492 |
+
timesteps = torch.tensor([timesteps], dtype=dtype, device=sample.device)
|
493 |
+
elif len(timesteps.shape) == 0:
|
494 |
+
timesteps = timesteps[None].to(sample.device)
|
495 |
+
|
496 |
+
# broadcast to batch dimension in a way that's compatible with ONNX/Core ML
|
497 |
+
timesteps = timesteps.expand(sample.shape[0])
|
498 |
+
|
499 |
+
t_emb = self.time_proj(timesteps)
|
500 |
+
|
501 |
+
# timesteps does not contain any weights and will always return f32 tensors
|
502 |
+
# but time_embedding might actually be running in fp16. so we need to cast here.
|
503 |
+
# there might be better ways to encapsulate this.
|
504 |
+
t_emb = t_emb.to(dtype=self.dtype)
|
505 |
+
|
506 |
+
emb = self.time_embedding(t_emb, timestep_cond)
|
507 |
+
|
508 |
+
if self.class_embedding is not None:
|
509 |
+
if class_labels is None:
|
510 |
+
raise ValueError("class_labels should be provided when num_class_embeds > 0")
|
511 |
+
|
512 |
+
if self.config.class_embed_type == "timestep":
|
513 |
+
class_labels = self.time_proj(class_labels)
|
514 |
+
|
515 |
+
class_emb = self.class_embedding(class_labels).to(dtype=self.dtype)
|
516 |
+
emb = emb + class_emb
|
517 |
+
|
518 |
+
# 2. pre-process
|
519 |
+
sample = self.conv_in(sample)
|
520 |
+
|
521 |
+
controlnet_cond = self.controlnet_cond_embedding(controlnet_cond)
|
522 |
+
|
523 |
+
sample += controlnet_cond
|
524 |
+
|
525 |
+
# 3. down
|
526 |
+
down_block_res_samples = (sample,)
|
527 |
+
for downsample_block in self.down_blocks:
|
528 |
+
if hasattr(downsample_block, "has_cross_attention") and downsample_block.has_cross_attention:
|
529 |
+
sample, res_samples = downsample_block(
|
530 |
+
hidden_states=sample,
|
531 |
+
temb=emb,
|
532 |
+
encoder_hidden_states=encoder_hidden_states,
|
533 |
+
attention_mask=attention_mask,
|
534 |
+
# cross_attention_kwargs=cross_attention_kwargs,
|
535 |
+
)
|
536 |
+
else:
|
537 |
+
sample, res_samples = downsample_block(hidden_states=sample, temb=emb)
|
538 |
+
|
539 |
+
down_block_res_samples += res_samples
|
540 |
+
|
541 |
+
# 4. mid
|
542 |
+
if self.mid_block is not None:
|
543 |
+
sample = self.mid_block(
|
544 |
+
sample,
|
545 |
+
emb,
|
546 |
+
encoder_hidden_states=encoder_hidden_states,
|
547 |
+
attention_mask=attention_mask,
|
548 |
+
# cross_attention_kwargs=cross_attention_kwargs,
|
549 |
+
)
|
550 |
+
|
551 |
+
# 5. Control net blocks
|
552 |
+
|
553 |
+
controlnet_down_block_res_samples = ()
|
554 |
+
|
555 |
+
for down_block_res_sample, controlnet_block in zip(down_block_res_samples, self.controlnet_down_blocks):
|
556 |
+
down_block_res_sample = controlnet_block(down_block_res_sample)
|
557 |
+
controlnet_down_block_res_samples += (down_block_res_sample,)
|
558 |
+
|
559 |
+
down_block_res_samples = controlnet_down_block_res_samples
|
560 |
+
|
561 |
+
mid_block_res_sample = self.controlnet_mid_block(sample)
|
562 |
+
|
563 |
+
# 6. scaling
|
564 |
+
down_block_res_samples = [sample * conditioning_scale for sample in down_block_res_samples]
|
565 |
+
mid_block_res_sample *= conditioning_scale
|
566 |
+
|
567 |
+
if not return_dict:
|
568 |
+
return (down_block_res_samples, mid_block_res_sample)
|
569 |
+
|
570 |
+
return ControlNetOutput(
|
571 |
+
down_block_res_samples=down_block_res_samples, mid_block_res_sample=mid_block_res_sample
|
572 |
+
)
|
573 |
+
|
574 |
+
|
575 |
+
def zero_module(module):
|
576 |
+
for p in module.parameters():
|
577 |
+
nn.init.zeros_(p)
|
578 |
+
return module
|
magicanimate/models/embeddings.py
ADDED
@@ -0,0 +1,385 @@
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|
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|
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|
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|
|
|
|
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|
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|
|
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|
|
|
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|
|
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|
|
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|
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|
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|
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|
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|
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|
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|
|
|
|
|
|
|
|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
# *************************************************************************
|
2 |
+
# This file may have been modified by Bytedance Inc. (“Bytedance Inc.'s Mo-
|
3 |
+
# difications”). All Bytedance Inc.'s Modifications are Copyright (2023) B-
|
4 |
+
# ytedance Inc..
|
5 |
+
# *************************************************************************
|
6 |
+
|
7 |
+
# Copyright 2023 The HuggingFace Team. All rights reserved.
|
8 |
+
#
|
9 |
+
# Licensed under the Apache License, Version 2.0 (the "License");
|
10 |
+
# you may not use this file except in compliance with the License.
|
11 |
+
# You may obtain a copy of the License at
|
12 |
+
#
|
13 |
+
# http://www.apache.org/licenses/LICENSE-2.0
|
14 |
+
#
|
15 |
+
# Unless required by applicable law or agreed to in writing, software
|
16 |
+
# distributed under the License is distributed on an "AS IS" BASIS,
|
17 |
+
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
18 |
+
# See the License for the specific language governing permissions and
|
19 |
+
# limitations under the License.
|
20 |
+
import math
|
21 |
+
from typing import Optional
|
22 |
+
|
23 |
+
import numpy as np
|
24 |
+
import torch
|
25 |
+
from torch import nn
|
26 |
+
|
27 |
+
|
28 |
+
def get_timestep_embedding(
|
29 |
+
timesteps: torch.Tensor,
|
30 |
+
embedding_dim: int,
|
31 |
+
flip_sin_to_cos: bool = False,
|
32 |
+
downscale_freq_shift: float = 1,
|
33 |
+
scale: float = 1,
|
34 |
+
max_period: int = 10000,
|
35 |
+
):
|
36 |
+
"""
|
37 |
+
This matches the implementation in Denoising Diffusion Probabilistic Models: Create sinusoidal timestep embeddings.
|
38 |
+
|
39 |
+
:param timesteps: a 1-D Tensor of N indices, one per batch element.
|
40 |
+
These may be fractional.
|
41 |
+
:param embedding_dim: the dimension of the output. :param max_period: controls the minimum frequency of the
|
42 |
+
embeddings. :return: an [N x dim] Tensor of positional embeddings.
|
43 |
+
"""
|
44 |
+
assert len(timesteps.shape) == 1, "Timesteps should be a 1d-array"
|
45 |
+
|
46 |
+
half_dim = embedding_dim // 2
|
47 |
+
exponent = -math.log(max_period) * torch.arange(
|
48 |
+
start=0, end=half_dim, dtype=torch.float32, device=timesteps.device
|
49 |
+
)
|
50 |
+
exponent = exponent / (half_dim - downscale_freq_shift)
|
51 |
+
|
52 |
+
emb = torch.exp(exponent)
|
53 |
+
emb = timesteps[:, None].float() * emb[None, :]
|
54 |
+
|
55 |
+
# scale embeddings
|
56 |
+
emb = scale * emb
|
57 |
+
|
58 |
+
# concat sine and cosine embeddings
|
59 |
+
emb = torch.cat([torch.sin(emb), torch.cos(emb)], dim=-1)
|
60 |
+
|
61 |
+
# flip sine and cosine embeddings
|
62 |
+
if flip_sin_to_cos:
|
63 |
+
emb = torch.cat([emb[:, half_dim:], emb[:, :half_dim]], dim=-1)
|
64 |
+
|
65 |
+
# zero pad
|
66 |
+
if embedding_dim % 2 == 1:
|
67 |
+
emb = torch.nn.functional.pad(emb, (0, 1, 0, 0))
|
68 |
+
return emb
|
69 |
+
|
70 |
+
|
71 |
+
def get_2d_sincos_pos_embed(embed_dim, grid_size, cls_token=False, extra_tokens=0):
|
72 |
+
"""
|
73 |
+
grid_size: int of the grid height and width return: pos_embed: [grid_size*grid_size, embed_dim] or
|
74 |
+
[1+grid_size*grid_size, embed_dim] (w/ or w/o cls_token)
|
75 |
+
"""
|
76 |
+
grid_h = np.arange(grid_size, dtype=np.float32)
|
77 |
+
grid_w = np.arange(grid_size, dtype=np.float32)
|
78 |
+
grid = np.meshgrid(grid_w, grid_h) # here w goes first
|
79 |
+
grid = np.stack(grid, axis=0)
|
80 |
+
|
81 |
+
grid = grid.reshape([2, 1, grid_size, grid_size])
|
82 |
+
pos_embed = get_2d_sincos_pos_embed_from_grid(embed_dim, grid)
|
83 |
+
if cls_token and extra_tokens > 0:
|
84 |
+
pos_embed = np.concatenate([np.zeros([extra_tokens, embed_dim]), pos_embed], axis=0)
|
85 |
+
return pos_embed
|
86 |
+
|
87 |
+
|
88 |
+
def get_2d_sincos_pos_embed_from_grid(embed_dim, grid):
|
89 |
+
if embed_dim % 2 != 0:
|
90 |
+
raise ValueError("embed_dim must be divisible by 2")
|
91 |
+
|
92 |
+
# use half of dimensions to encode grid_h
|
93 |
+
emb_h = get_1d_sincos_pos_embed_from_grid(embed_dim // 2, grid[0]) # (H*W, D/2)
|
94 |
+
emb_w = get_1d_sincos_pos_embed_from_grid(embed_dim // 2, grid[1]) # (H*W, D/2)
|
95 |
+
|
96 |
+
emb = np.concatenate([emb_h, emb_w], axis=1) # (H*W, D)
|
97 |
+
return emb
|
98 |
+
|
99 |
+
|
100 |
+
def get_1d_sincos_pos_embed_from_grid(embed_dim, pos):
|
101 |
+
"""
|
102 |
+
embed_dim: output dimension for each position pos: a list of positions to be encoded: size (M,) out: (M, D)
|
103 |
+
"""
|
104 |
+
if embed_dim % 2 != 0:
|
105 |
+
raise ValueError("embed_dim must be divisible by 2")
|
106 |
+
|
107 |
+
omega = np.arange(embed_dim // 2, dtype=np.float64)
|
108 |
+
omega /= embed_dim / 2.0
|
109 |
+
omega = 1.0 / 10000**omega # (D/2,)
|
110 |
+
|
111 |
+
pos = pos.reshape(-1) # (M,)
|
112 |
+
out = np.einsum("m,d->md", pos, omega) # (M, D/2), outer product
|
113 |
+
|
114 |
+
emb_sin = np.sin(out) # (M, D/2)
|
115 |
+
emb_cos = np.cos(out) # (M, D/2)
|
116 |
+
|
117 |
+
emb = np.concatenate([emb_sin, emb_cos], axis=1) # (M, D)
|
118 |
+
return emb
|
119 |
+
|
120 |
+
|
121 |
+
class PatchEmbed(nn.Module):
|
122 |
+
"""2D Image to Patch Embedding"""
|
123 |
+
|
124 |
+
def __init__(
|
125 |
+
self,
|
126 |
+
height=224,
|
127 |
+
width=224,
|
128 |
+
patch_size=16,
|
129 |
+
in_channels=3,
|
130 |
+
embed_dim=768,
|
131 |
+
layer_norm=False,
|
132 |
+
flatten=True,
|
133 |
+
bias=True,
|
134 |
+
):
|
135 |
+
super().__init__()
|
136 |
+
|
137 |
+
num_patches = (height // patch_size) * (width // patch_size)
|
138 |
+
self.flatten = flatten
|
139 |
+
self.layer_norm = layer_norm
|
140 |
+
|
141 |
+
self.proj = nn.Conv2d(
|
142 |
+
in_channels, embed_dim, kernel_size=(patch_size, patch_size), stride=patch_size, bias=bias
|
143 |
+
)
|
144 |
+
if layer_norm:
|
145 |
+
self.norm = nn.LayerNorm(embed_dim, elementwise_affine=False, eps=1e-6)
|
146 |
+
else:
|
147 |
+
self.norm = None
|
148 |
+
|
149 |
+
pos_embed = get_2d_sincos_pos_embed(embed_dim, int(num_patches**0.5))
|
150 |
+
self.register_buffer("pos_embed", torch.from_numpy(pos_embed).float().unsqueeze(0), persistent=False)
|
151 |
+
|
152 |
+
def forward(self, latent):
|
153 |
+
latent = self.proj(latent)
|
154 |
+
if self.flatten:
|
155 |
+
latent = latent.flatten(2).transpose(1, 2) # BCHW -> BNC
|
156 |
+
if self.layer_norm:
|
157 |
+
latent = self.norm(latent)
|
158 |
+
return latent + self.pos_embed
|
159 |
+
|
160 |
+
|
161 |
+
class TimestepEmbedding(nn.Module):
|
162 |
+
def __init__(
|
163 |
+
self,
|
164 |
+
in_channels: int,
|
165 |
+
time_embed_dim: int,
|
166 |
+
act_fn: str = "silu",
|
167 |
+
out_dim: int = None,
|
168 |
+
post_act_fn: Optional[str] = None,
|
169 |
+
cond_proj_dim=None,
|
170 |
+
):
|
171 |
+
super().__init__()
|
172 |
+
|
173 |
+
self.linear_1 = nn.Linear(in_channels, time_embed_dim)
|
174 |
+
|
175 |
+
if cond_proj_dim is not None:
|
176 |
+
self.cond_proj = nn.Linear(cond_proj_dim, in_channels, bias=False)
|
177 |
+
else:
|
178 |
+
self.cond_proj = None
|
179 |
+
|
180 |
+
if act_fn == "silu":
|
181 |
+
self.act = nn.SiLU()
|
182 |
+
elif act_fn == "mish":
|
183 |
+
self.act = nn.Mish()
|
184 |
+
elif act_fn == "gelu":
|
185 |
+
self.act = nn.GELU()
|
186 |
+
else:
|
187 |
+
raise ValueError(f"{act_fn} does not exist. Make sure to define one of 'silu', 'mish', or 'gelu'")
|
188 |
+
|
189 |
+
if out_dim is not None:
|
190 |
+
time_embed_dim_out = out_dim
|
191 |
+
else:
|
192 |
+
time_embed_dim_out = time_embed_dim
|
193 |
+
self.linear_2 = nn.Linear(time_embed_dim, time_embed_dim_out)
|
194 |
+
|
195 |
+
if post_act_fn is None:
|
196 |
+
self.post_act = None
|
197 |
+
elif post_act_fn == "silu":
|
198 |
+
self.post_act = nn.SiLU()
|
199 |
+
elif post_act_fn == "mish":
|
200 |
+
self.post_act = nn.Mish()
|
201 |
+
elif post_act_fn == "gelu":
|
202 |
+
self.post_act = nn.GELU()
|
203 |
+
else:
|
204 |
+
raise ValueError(f"{post_act_fn} does not exist. Make sure to define one of 'silu', 'mish', or 'gelu'")
|
205 |
+
|
206 |
+
def forward(self, sample, condition=None):
|
207 |
+
if condition is not None:
|
208 |
+
sample = sample + self.cond_proj(condition)
|
209 |
+
sample = self.linear_1(sample)
|
210 |
+
|
211 |
+
if self.act is not None:
|
212 |
+
sample = self.act(sample)
|
213 |
+
|
214 |
+
sample = self.linear_2(sample)
|
215 |
+
|
216 |
+
if self.post_act is not None:
|
217 |
+
sample = self.post_act(sample)
|
218 |
+
return sample
|
219 |
+
|
220 |
+
|
221 |
+
class Timesteps(nn.Module):
|
222 |
+
def __init__(self, num_channels: int, flip_sin_to_cos: bool, downscale_freq_shift: float):
|
223 |
+
super().__init__()
|
224 |
+
self.num_channels = num_channels
|
225 |
+
self.flip_sin_to_cos = flip_sin_to_cos
|
226 |
+
self.downscale_freq_shift = downscale_freq_shift
|
227 |
+
|
228 |
+
def forward(self, timesteps):
|
229 |
+
t_emb = get_timestep_embedding(
|
230 |
+
timesteps,
|
231 |
+
self.num_channels,
|
232 |
+
flip_sin_to_cos=self.flip_sin_to_cos,
|
233 |
+
downscale_freq_shift=self.downscale_freq_shift,
|
234 |
+
)
|
235 |
+
return t_emb
|
236 |
+
|
237 |
+
|
238 |
+
class GaussianFourierProjection(nn.Module):
|
239 |
+
"""Gaussian Fourier embeddings for noise levels."""
|
240 |
+
|
241 |
+
def __init__(
|
242 |
+
self, embedding_size: int = 256, scale: float = 1.0, set_W_to_weight=True, log=True, flip_sin_to_cos=False
|
243 |
+
):
|
244 |
+
super().__init__()
|
245 |
+
self.weight = nn.Parameter(torch.randn(embedding_size) * scale, requires_grad=False)
|
246 |
+
self.log = log
|
247 |
+
self.flip_sin_to_cos = flip_sin_to_cos
|
248 |
+
|
249 |
+
if set_W_to_weight:
|
250 |
+
# to delete later
|
251 |
+
self.W = nn.Parameter(torch.randn(embedding_size) * scale, requires_grad=False)
|
252 |
+
|
253 |
+
self.weight = self.W
|
254 |
+
|
255 |
+
def forward(self, x):
|
256 |
+
if self.log:
|
257 |
+
x = torch.log(x)
|
258 |
+
|
259 |
+
x_proj = x[:, None] * self.weight[None, :] * 2 * np.pi
|
260 |
+
|
261 |
+
if self.flip_sin_to_cos:
|
262 |
+
out = torch.cat([torch.cos(x_proj), torch.sin(x_proj)], dim=-1)
|
263 |
+
else:
|
264 |
+
out = torch.cat([torch.sin(x_proj), torch.cos(x_proj)], dim=-1)
|
265 |
+
return out
|
266 |
+
|
267 |
+
|
268 |
+
class ImagePositionalEmbeddings(nn.Module):
|
269 |
+
"""
|
270 |
+
Converts latent image classes into vector embeddings. Sums the vector embeddings with positional embeddings for the
|
271 |
+
height and width of the latent space.
|
272 |
+
|
273 |
+
For more details, see figure 10 of the dall-e paper: https://arxiv.org/abs/2102.12092
|
274 |
+
|
275 |
+
For VQ-diffusion:
|
276 |
+
|
277 |
+
Output vector embeddings are used as input for the transformer.
|
278 |
+
|
279 |
+
Note that the vector embeddings for the transformer are different than the vector embeddings from the VQVAE.
|
280 |
+
|
281 |
+
Args:
|
282 |
+
num_embed (`int`):
|
283 |
+
Number of embeddings for the latent pixels embeddings.
|
284 |
+
height (`int`):
|
285 |
+
Height of the latent image i.e. the number of height embeddings.
|
286 |
+
width (`int`):
|
287 |
+
Width of the latent image i.e. the number of width embeddings.
|
288 |
+
embed_dim (`int`):
|
289 |
+
Dimension of the produced vector embeddings. Used for the latent pixel, height, and width embeddings.
|
290 |
+
"""
|
291 |
+
|
292 |
+
def __init__(
|
293 |
+
self,
|
294 |
+
num_embed: int,
|
295 |
+
height: int,
|
296 |
+
width: int,
|
297 |
+
embed_dim: int,
|
298 |
+
):
|
299 |
+
super().__init__()
|
300 |
+
|
301 |
+
self.height = height
|
302 |
+
self.width = width
|
303 |
+
self.num_embed = num_embed
|
304 |
+
self.embed_dim = embed_dim
|
305 |
+
|
306 |
+
self.emb = nn.Embedding(self.num_embed, embed_dim)
|
307 |
+
self.height_emb = nn.Embedding(self.height, embed_dim)
|
308 |
+
self.width_emb = nn.Embedding(self.width, embed_dim)
|
309 |
+
|
310 |
+
def forward(self, index):
|
311 |
+
emb = self.emb(index)
|
312 |
+
|
313 |
+
height_emb = self.height_emb(torch.arange(self.height, device=index.device).view(1, self.height))
|
314 |
+
|
315 |
+
# 1 x H x D -> 1 x H x 1 x D
|
316 |
+
height_emb = height_emb.unsqueeze(2)
|
317 |
+
|
318 |
+
width_emb = self.width_emb(torch.arange(self.width, device=index.device).view(1, self.width))
|
319 |
+
|
320 |
+
# 1 x W x D -> 1 x 1 x W x D
|
321 |
+
width_emb = width_emb.unsqueeze(1)
|
322 |
+
|
323 |
+
pos_emb = height_emb + width_emb
|
324 |
+
|
325 |
+
# 1 x H x W x D -> 1 x L xD
|
326 |
+
pos_emb = pos_emb.view(1, self.height * self.width, -1)
|
327 |
+
|
328 |
+
emb = emb + pos_emb[:, : emb.shape[1], :]
|
329 |
+
|
330 |
+
return emb
|
331 |
+
|
332 |
+
|
333 |
+
class LabelEmbedding(nn.Module):
|
334 |
+
"""
|
335 |
+
Embeds class labels into vector representations. Also handles label dropout for classifier-free guidance.
|
336 |
+
|
337 |
+
Args:
|
338 |
+
num_classes (`int`): The number of classes.
|
339 |
+
hidden_size (`int`): The size of the vector embeddings.
|
340 |
+
dropout_prob (`float`): The probability of dropping a label.
|
341 |
+
"""
|
342 |
+
|
343 |
+
def __init__(self, num_classes, hidden_size, dropout_prob):
|
344 |
+
super().__init__()
|
345 |
+
use_cfg_embedding = dropout_prob > 0
|
346 |
+
self.embedding_table = nn.Embedding(num_classes + use_cfg_embedding, hidden_size)
|
347 |
+
self.num_classes = num_classes
|
348 |
+
self.dropout_prob = dropout_prob
|
349 |
+
|
350 |
+
def token_drop(self, labels, force_drop_ids=None):
|
351 |
+
"""
|
352 |
+
Drops labels to enable classifier-free guidance.
|
353 |
+
"""
|
354 |
+
if force_drop_ids is None:
|
355 |
+
drop_ids = torch.rand(labels.shape[0], device=labels.device) < self.dropout_prob
|
356 |
+
else:
|
357 |
+
drop_ids = torch.tensor(force_drop_ids == 1)
|
358 |
+
labels = torch.where(drop_ids, self.num_classes, labels)
|
359 |
+
return labels
|
360 |
+
|
361 |
+
def forward(self, labels, force_drop_ids=None):
|
362 |
+
use_dropout = self.dropout_prob > 0
|
363 |
+
if (self.training and use_dropout) or (force_drop_ids is not None):
|
364 |
+
labels = self.token_drop(labels, force_drop_ids)
|
365 |
+
embeddings = self.embedding_table(labels)
|
366 |
+
return embeddings
|
367 |
+
|
368 |
+
|
369 |
+
class CombinedTimestepLabelEmbeddings(nn.Module):
|
370 |
+
def __init__(self, num_classes, embedding_dim, class_dropout_prob=0.1):
|
371 |
+
super().__init__()
|
372 |
+
|
373 |
+
self.time_proj = Timesteps(num_channels=256, flip_sin_to_cos=True, downscale_freq_shift=1)
|
374 |
+
self.timestep_embedder = TimestepEmbedding(in_channels=256, time_embed_dim=embedding_dim)
|
375 |
+
self.class_embedder = LabelEmbedding(num_classes, embedding_dim, class_dropout_prob)
|
376 |
+
|
377 |
+
def forward(self, timestep, class_labels, hidden_dtype=None):
|
378 |
+
timesteps_proj = self.time_proj(timestep)
|
379 |
+
timesteps_emb = self.timestep_embedder(timesteps_proj.to(dtype=hidden_dtype)) # (N, D)
|
380 |
+
|
381 |
+
class_labels = self.class_embedder(class_labels) # (N, D)
|
382 |
+
|
383 |
+
conditioning = timesteps_emb + class_labels # (N, D)
|
384 |
+
|
385 |
+
return conditioning
|
magicanimate/models/motion_module.py
ADDED
@@ -0,0 +1,334 @@
|
|
|
|
|
|
|
|
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|
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|
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|
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|
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|
|
|
|
|
|
|
|
|
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|
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|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
# *************************************************************************
|
2 |
+
# This file may have been modified by Bytedance Inc. (“Bytedance Inc.'s Mo-
|
3 |
+
# difications”). All Bytedance Inc.'s Modifications are Copyright (2023) B-
|
4 |
+
# ytedance Inc..
|
5 |
+
# *************************************************************************
|
6 |
+
|
7 |
+
# Adapted from https://github.com/guoyww/AnimateDiff
|
8 |
+
from dataclasses import dataclass
|
9 |
+
|
10 |
+
import torch
|
11 |
+
import torch.nn.functional as F
|
12 |
+
from torch import nn
|
13 |
+
|
14 |
+
from diffusers.utils import BaseOutput
|
15 |
+
from diffusers.utils.import_utils import is_xformers_available
|
16 |
+
from diffusers.models.attention import FeedForward
|
17 |
+
from magicanimate.models.orig_attention import CrossAttention
|
18 |
+
|
19 |
+
from einops import rearrange, repeat
|
20 |
+
import math
|
21 |
+
|
22 |
+
|
23 |
+
def zero_module(module):
|
24 |
+
# Zero out the parameters of a module and return it.
|
25 |
+
for p in module.parameters():
|
26 |
+
p.detach().zero_()
|
27 |
+
return module
|
28 |
+
|
29 |
+
|
30 |
+
@dataclass
|
31 |
+
class TemporalTransformer3DModelOutput(BaseOutput):
|
32 |
+
sample: torch.FloatTensor
|
33 |
+
|
34 |
+
|
35 |
+
if is_xformers_available():
|
36 |
+
import xformers
|
37 |
+
import xformers.ops
|
38 |
+
else:
|
39 |
+
xformers = None
|
40 |
+
|
41 |
+
|
42 |
+
def get_motion_module(
|
43 |
+
in_channels,
|
44 |
+
motion_module_type: str,
|
45 |
+
motion_module_kwargs: dict
|
46 |
+
):
|
47 |
+
if motion_module_type == "Vanilla":
|
48 |
+
return VanillaTemporalModule(in_channels=in_channels, **motion_module_kwargs,)
|
49 |
+
else:
|
50 |
+
raise ValueError
|
51 |
+
|
52 |
+
|
53 |
+
class VanillaTemporalModule(nn.Module):
|
54 |
+
def __init__(
|
55 |
+
self,
|
56 |
+
in_channels,
|
57 |
+
num_attention_heads = 8,
|
58 |
+
num_transformer_block = 2,
|
59 |
+
attention_block_types =( "Temporal_Self", "Temporal_Self" ),
|
60 |
+
cross_frame_attention_mode = None,
|
61 |
+
temporal_position_encoding = False,
|
62 |
+
temporal_position_encoding_max_len = 24,
|
63 |
+
temporal_attention_dim_div = 1,
|
64 |
+
zero_initialize = True,
|
65 |
+
):
|
66 |
+
super().__init__()
|
67 |
+
|
68 |
+
self.temporal_transformer = TemporalTransformer3DModel(
|
69 |
+
in_channels=in_channels,
|
70 |
+
num_attention_heads=num_attention_heads,
|
71 |
+
attention_head_dim=in_channels // num_attention_heads // temporal_attention_dim_div,
|
72 |
+
num_layers=num_transformer_block,
|
73 |
+
attention_block_types=attention_block_types,
|
74 |
+
cross_frame_attention_mode=cross_frame_attention_mode,
|
75 |
+
temporal_position_encoding=temporal_position_encoding,
|
76 |
+
temporal_position_encoding_max_len=temporal_position_encoding_max_len,
|
77 |
+
)
|
78 |
+
|
79 |
+
if zero_initialize:
|
80 |
+
self.temporal_transformer.proj_out = zero_module(self.temporal_transformer.proj_out)
|
81 |
+
|
82 |
+
def forward(self, input_tensor, temb, encoder_hidden_states, attention_mask=None, anchor_frame_idx=None):
|
83 |
+
hidden_states = input_tensor
|
84 |
+
hidden_states = self.temporal_transformer(hidden_states, encoder_hidden_states, attention_mask)
|
85 |
+
|
86 |
+
output = hidden_states
|
87 |
+
return output
|
88 |
+
|
89 |
+
|
90 |
+
class TemporalTransformer3DModel(nn.Module):
|
91 |
+
def __init__(
|
92 |
+
self,
|
93 |
+
in_channels,
|
94 |
+
num_attention_heads,
|
95 |
+
attention_head_dim,
|
96 |
+
|
97 |
+
num_layers,
|
98 |
+
attention_block_types = ( "Temporal_Self", "Temporal_Self", ),
|
99 |
+
dropout = 0.0,
|
100 |
+
norm_num_groups = 32,
|
101 |
+
cross_attention_dim = 768,
|
102 |
+
activation_fn = "geglu",
|
103 |
+
attention_bias = False,
|
104 |
+
upcast_attention = False,
|
105 |
+
|
106 |
+
cross_frame_attention_mode = None,
|
107 |
+
temporal_position_encoding = False,
|
108 |
+
temporal_position_encoding_max_len = 24,
|
109 |
+
):
|
110 |
+
super().__init__()
|
111 |
+
|
112 |
+
inner_dim = num_attention_heads * attention_head_dim
|
113 |
+
|
114 |
+
self.norm = torch.nn.GroupNorm(num_groups=norm_num_groups, num_channels=in_channels, eps=1e-6, affine=True)
|
115 |
+
self.proj_in = nn.Linear(in_channels, inner_dim)
|
116 |
+
|
117 |
+
self.transformer_blocks = nn.ModuleList(
|
118 |
+
[
|
119 |
+
TemporalTransformerBlock(
|
120 |
+
dim=inner_dim,
|
121 |
+
num_attention_heads=num_attention_heads,
|
122 |
+
attention_head_dim=attention_head_dim,
|
123 |
+
attention_block_types=attention_block_types,
|
124 |
+
dropout=dropout,
|
125 |
+
norm_num_groups=norm_num_groups,
|
126 |
+
cross_attention_dim=cross_attention_dim,
|
127 |
+
activation_fn=activation_fn,
|
128 |
+
attention_bias=attention_bias,
|
129 |
+
upcast_attention=upcast_attention,
|
130 |
+
cross_frame_attention_mode=cross_frame_attention_mode,
|
131 |
+
temporal_position_encoding=temporal_position_encoding,
|
132 |
+
temporal_position_encoding_max_len=temporal_position_encoding_max_len,
|
133 |
+
)
|
134 |
+
for d in range(num_layers)
|
135 |
+
]
|
136 |
+
)
|
137 |
+
self.proj_out = nn.Linear(inner_dim, in_channels)
|
138 |
+
|
139 |
+
def forward(self, hidden_states, encoder_hidden_states=None, attention_mask=None):
|
140 |
+
assert hidden_states.dim() == 5, f"Expected hidden_states to have ndim=5, but got ndim={hidden_states.dim()}."
|
141 |
+
video_length = hidden_states.shape[2]
|
142 |
+
hidden_states = rearrange(hidden_states, "b c f h w -> (b f) c h w")
|
143 |
+
|
144 |
+
batch, channel, height, weight = hidden_states.shape
|
145 |
+
residual = hidden_states
|
146 |
+
|
147 |
+
hidden_states = self.norm(hidden_states)
|
148 |
+
inner_dim = hidden_states.shape[1]
|
149 |
+
hidden_states = hidden_states.permute(0, 2, 3, 1).reshape(batch, height * weight, inner_dim)
|
150 |
+
hidden_states = self.proj_in(hidden_states)
|
151 |
+
|
152 |
+
# Transformer Blocks
|
153 |
+
for block in self.transformer_blocks:
|
154 |
+
hidden_states = block(hidden_states, encoder_hidden_states=encoder_hidden_states, video_length=video_length)
|
155 |
+
|
156 |
+
# output
|
157 |
+
hidden_states = self.proj_out(hidden_states)
|
158 |
+
hidden_states = hidden_states.reshape(batch, height, weight, inner_dim).permute(0, 3, 1, 2).contiguous()
|
159 |
+
|
160 |
+
output = hidden_states + residual
|
161 |
+
output = rearrange(output, "(b f) c h w -> b c f h w", f=video_length)
|
162 |
+
|
163 |
+
return output
|
164 |
+
|
165 |
+
|
166 |
+
class TemporalTransformerBlock(nn.Module):
|
167 |
+
def __init__(
|
168 |
+
self,
|
169 |
+
dim,
|
170 |
+
num_attention_heads,
|
171 |
+
attention_head_dim,
|
172 |
+
attention_block_types = ( "Temporal_Self", "Temporal_Self", ),
|
173 |
+
dropout = 0.0,
|
174 |
+
norm_num_groups = 32,
|
175 |
+
cross_attention_dim = 768,
|
176 |
+
activation_fn = "geglu",
|
177 |
+
attention_bias = False,
|
178 |
+
upcast_attention = False,
|
179 |
+
cross_frame_attention_mode = None,
|
180 |
+
temporal_position_encoding = False,
|
181 |
+
temporal_position_encoding_max_len = 24,
|
182 |
+
):
|
183 |
+
super().__init__()
|
184 |
+
|
185 |
+
attention_blocks = []
|
186 |
+
norms = []
|
187 |
+
|
188 |
+
for block_name in attention_block_types:
|
189 |
+
attention_blocks.append(
|
190 |
+
VersatileAttention(
|
191 |
+
attention_mode=block_name.split("_")[0],
|
192 |
+
cross_attention_dim=cross_attention_dim if block_name.endswith("_Cross") else None,
|
193 |
+
|
194 |
+
query_dim=dim,
|
195 |
+
heads=num_attention_heads,
|
196 |
+
dim_head=attention_head_dim,
|
197 |
+
dropout=dropout,
|
198 |
+
bias=attention_bias,
|
199 |
+
upcast_attention=upcast_attention,
|
200 |
+
|
201 |
+
cross_frame_attention_mode=cross_frame_attention_mode,
|
202 |
+
temporal_position_encoding=temporal_position_encoding,
|
203 |
+
temporal_position_encoding_max_len=temporal_position_encoding_max_len,
|
204 |
+
)
|
205 |
+
)
|
206 |
+
norms.append(nn.LayerNorm(dim))
|
207 |
+
|
208 |
+
self.attention_blocks = nn.ModuleList(attention_blocks)
|
209 |
+
self.norms = nn.ModuleList(norms)
|
210 |
+
|
211 |
+
self.ff = FeedForward(dim, dropout=dropout, activation_fn=activation_fn)
|
212 |
+
self.ff_norm = nn.LayerNorm(dim)
|
213 |
+
|
214 |
+
|
215 |
+
def forward(self, hidden_states, encoder_hidden_states=None, attention_mask=None, video_length=None):
|
216 |
+
for attention_block, norm in zip(self.attention_blocks, self.norms):
|
217 |
+
norm_hidden_states = norm(hidden_states)
|
218 |
+
hidden_states = attention_block(
|
219 |
+
norm_hidden_states,
|
220 |
+
encoder_hidden_states=encoder_hidden_states if attention_block.is_cross_attention else None,
|
221 |
+
video_length=video_length,
|
222 |
+
) + hidden_states
|
223 |
+
|
224 |
+
hidden_states = self.ff(self.ff_norm(hidden_states)) + hidden_states
|
225 |
+
|
226 |
+
output = hidden_states
|
227 |
+
return output
|
228 |
+
|
229 |
+
|
230 |
+
class PositionalEncoding(nn.Module):
|
231 |
+
def __init__(
|
232 |
+
self,
|
233 |
+
d_model,
|
234 |
+
dropout = 0.,
|
235 |
+
max_len = 24
|
236 |
+
):
|
237 |
+
super().__init__()
|
238 |
+
self.dropout = nn.Dropout(p=dropout)
|
239 |
+
position = torch.arange(max_len).unsqueeze(1)
|
240 |
+
div_term = torch.exp(torch.arange(0, d_model, 2) * (-math.log(10000.0) / d_model))
|
241 |
+
pe = torch.zeros(1, max_len, d_model)
|
242 |
+
pe[0, :, 0::2] = torch.sin(position * div_term)
|
243 |
+
pe[0, :, 1::2] = torch.cos(position * div_term)
|
244 |
+
self.register_buffer('pe', pe)
|
245 |
+
|
246 |
+
def forward(self, x):
|
247 |
+
x = x + self.pe[:, :x.size(1)]
|
248 |
+
return self.dropout(x)
|
249 |
+
|
250 |
+
|
251 |
+
class VersatileAttention(CrossAttention):
|
252 |
+
def __init__(
|
253 |
+
self,
|
254 |
+
attention_mode = None,
|
255 |
+
cross_frame_attention_mode = None,
|
256 |
+
temporal_position_encoding = False,
|
257 |
+
temporal_position_encoding_max_len = 24,
|
258 |
+
*args, **kwargs
|
259 |
+
):
|
260 |
+
super().__init__(*args, **kwargs)
|
261 |
+
assert attention_mode == "Temporal"
|
262 |
+
|
263 |
+
self.attention_mode = attention_mode
|
264 |
+
self.is_cross_attention = kwargs["cross_attention_dim"] is not None
|
265 |
+
|
266 |
+
self.pos_encoder = PositionalEncoding(
|
267 |
+
kwargs["query_dim"],
|
268 |
+
dropout=0.,
|
269 |
+
max_len=temporal_position_encoding_max_len
|
270 |
+
) if (temporal_position_encoding and attention_mode == "Temporal") else None
|
271 |
+
|
272 |
+
def extra_repr(self):
|
273 |
+
return f"(Module Info) Attention_Mode: {self.attention_mode}, Is_Cross_Attention: {self.is_cross_attention}"
|
274 |
+
|
275 |
+
def forward(self, hidden_states, encoder_hidden_states=None, attention_mask=None, video_length=None):
|
276 |
+
batch_size, sequence_length, _ = hidden_states.shape
|
277 |
+
|
278 |
+
if self.attention_mode == "Temporal":
|
279 |
+
d = hidden_states.shape[1]
|
280 |
+
hidden_states = rearrange(hidden_states, "(b f) d c -> (b d) f c", f=video_length)
|
281 |
+
|
282 |
+
if self.pos_encoder is not None:
|
283 |
+
hidden_states = self.pos_encoder(hidden_states)
|
284 |
+
|
285 |
+
encoder_hidden_states = repeat(encoder_hidden_states, "b n c -> (b d) n c", d=d) if encoder_hidden_states is not None else encoder_hidden_states
|
286 |
+
else:
|
287 |
+
raise NotImplementedError
|
288 |
+
|
289 |
+
encoder_hidden_states = encoder_hidden_states
|
290 |
+
|
291 |
+
if self.group_norm is not None:
|
292 |
+
hidden_states = self.group_norm(hidden_states.transpose(1, 2)).transpose(1, 2)
|
293 |
+
|
294 |
+
query = self.to_q(hidden_states)
|
295 |
+
dim = query.shape[-1]
|
296 |
+
query = self.reshape_heads_to_batch_dim(query)
|
297 |
+
|
298 |
+
if self.added_kv_proj_dim is not None:
|
299 |
+
raise NotImplementedError
|
300 |
+
|
301 |
+
encoder_hidden_states = encoder_hidden_states if encoder_hidden_states is not None else hidden_states
|
302 |
+
key = self.to_k(encoder_hidden_states)
|
303 |
+
value = self.to_v(encoder_hidden_states)
|
304 |
+
|
305 |
+
key = self.reshape_heads_to_batch_dim(key)
|
306 |
+
value = self.reshape_heads_to_batch_dim(value)
|
307 |
+
|
308 |
+
if attention_mask is not None:
|
309 |
+
if attention_mask.shape[-1] != query.shape[1]:
|
310 |
+
target_length = query.shape[1]
|
311 |
+
attention_mask = F.pad(attention_mask, (0, target_length), value=0.0)
|
312 |
+
attention_mask = attention_mask.repeat_interleave(self.heads, dim=0)
|
313 |
+
|
314 |
+
# attention, what we cannot get enough of
|
315 |
+
if self._use_memory_efficient_attention_xformers:
|
316 |
+
hidden_states = self._memory_efficient_attention_xformers(query, key, value, attention_mask)
|
317 |
+
# Some versions of xformers return output in fp32, cast it back to the dtype of the input
|
318 |
+
hidden_states = hidden_states.to(query.dtype)
|
319 |
+
else:
|
320 |
+
if self._slice_size is None or query.shape[0] // self._slice_size == 1:
|
321 |
+
hidden_states = self._attention(query, key, value, attention_mask)
|
322 |
+
else:
|
323 |
+
hidden_states = self._sliced_attention(query, key, value, sequence_length, dim, attention_mask)
|
324 |
+
|
325 |
+
# linear proj
|
326 |
+
hidden_states = self.to_out[0](hidden_states)
|
327 |
+
|
328 |
+
# dropout
|
329 |
+
hidden_states = self.to_out[1](hidden_states)
|
330 |
+
|
331 |
+
if self.attention_mode == "Temporal":
|
332 |
+
hidden_states = rearrange(hidden_states, "(b d) f c -> (b f) d c", d=d)
|
333 |
+
|
334 |
+
return hidden_states
|
magicanimate/models/mutual_self_attention.py
ADDED
@@ -0,0 +1,642 @@
|
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1 |
+
# Copyright 2023 ByteDance and/or its affiliates.
|
2 |
+
#
|
3 |
+
# Copyright (2023) MagicAnimate Authors
|
4 |
+
#
|
5 |
+
# ByteDance, its affiliates and licensors retain all intellectual
|
6 |
+
# property and proprietary rights in and to this material, related
|
7 |
+
# documentation and any modifications thereto. Any use, reproduction,
|
8 |
+
# disclosure or distribution of this material and related documentation
|
9 |
+
# without an express license agreement from ByteDance or
|
10 |
+
# its affiliates is strictly prohibited.
|
11 |
+
|
12 |
+
import torch
|
13 |
+
import torch.nn.functional as F
|
14 |
+
|
15 |
+
from einops import rearrange
|
16 |
+
from typing import Any, Callable, Dict, List, Optional, Tuple, Union
|
17 |
+
|
18 |
+
from diffusers.models.attention import BasicTransformerBlock
|
19 |
+
from magicanimate.models.attention import BasicTransformerBlock as _BasicTransformerBlock
|
20 |
+
from diffusers.models.unet_2d_blocks import CrossAttnDownBlock2D, CrossAttnUpBlock2D, DownBlock2D, UpBlock2D
|
21 |
+
from .stable_diffusion_controlnet_reference import torch_dfs
|
22 |
+
|
23 |
+
|
24 |
+
class AttentionBase:
|
25 |
+
def __init__(self):
|
26 |
+
self.cur_step = 0
|
27 |
+
self.num_att_layers = -1
|
28 |
+
self.cur_att_layer = 0
|
29 |
+
|
30 |
+
def after_step(self):
|
31 |
+
pass
|
32 |
+
|
33 |
+
def __call__(self, q, k, v, sim, attn, is_cross, place_in_unet, num_heads, **kwargs):
|
34 |
+
out = self.forward(q, k, v, sim, attn, is_cross, place_in_unet, num_heads, **kwargs)
|
35 |
+
self.cur_att_layer += 1
|
36 |
+
if self.cur_att_layer == self.num_att_layers:
|
37 |
+
self.cur_att_layer = 0
|
38 |
+
self.cur_step += 1
|
39 |
+
# after step
|
40 |
+
self.after_step()
|
41 |
+
return out
|
42 |
+
|
43 |
+
def forward(self, q, k, v, sim, attn, is_cross, place_in_unet, num_heads, **kwargs):
|
44 |
+
out = torch.einsum('b i j, b j d -> b i d', attn, v)
|
45 |
+
out = rearrange(out, '(b h) n d -> b n (h d)', h=num_heads)
|
46 |
+
return out
|
47 |
+
|
48 |
+
def reset(self):
|
49 |
+
self.cur_step = 0
|
50 |
+
self.cur_att_layer = 0
|
51 |
+
|
52 |
+
|
53 |
+
class MutualSelfAttentionControl(AttentionBase):
|
54 |
+
|
55 |
+
def __init__(self, total_steps=50, hijack_init_state=True, with_negative_guidance=False, appearance_control_alpha=0.5, mode='enqueue'):
|
56 |
+
"""
|
57 |
+
Mutual self-attention control for Stable-Diffusion MODEl
|
58 |
+
Args:
|
59 |
+
total_steps: the total number of steps
|
60 |
+
"""
|
61 |
+
super().__init__()
|
62 |
+
self.total_steps = total_steps
|
63 |
+
self.hijack = hijack_init_state
|
64 |
+
self.with_negative_guidance = with_negative_guidance
|
65 |
+
|
66 |
+
# alpha: mutual self attention intensity
|
67 |
+
# TODO: make alpha learnable
|
68 |
+
self.alpha = appearance_control_alpha
|
69 |
+
self.GLOBAL_ATTN_QUEUE = []
|
70 |
+
assert mode in ['enqueue', 'dequeue']
|
71 |
+
MODE = mode
|
72 |
+
|
73 |
+
def attn_batch(self, q, k, v, num_heads, **kwargs):
|
74 |
+
"""
|
75 |
+
Performing attention for a batch of queries, keys, and values
|
76 |
+
"""
|
77 |
+
b = q.shape[0] // num_heads
|
78 |
+
q = rearrange(q, "(b h) n d -> h (b n) d", h=num_heads)
|
79 |
+
k = rearrange(k, "(b h) n d -> h (b n) d", h=num_heads)
|
80 |
+
v = rearrange(v, "(b h) n d -> h (b n) d", h=num_heads)
|
81 |
+
|
82 |
+
sim = torch.einsum("h i d, h j d -> h i j", q, k) * kwargs.get("scale")
|
83 |
+
attn = sim.softmax(-1)
|
84 |
+
out = torch.einsum("h i j, h j d -> h i d", attn, v)
|
85 |
+
out = rearrange(out, "h (b n) d -> b n (h d)", b=b)
|
86 |
+
return out
|
87 |
+
|
88 |
+
def mutual_self_attn(self, q, k, v, num_heads, **kwargs):
|
89 |
+
q_tgt, q_src = q.chunk(2)
|
90 |
+
k_tgt, k_src = k.chunk(2)
|
91 |
+
v_tgt, v_src = v.chunk(2)
|
92 |
+
|
93 |
+
# out_tgt = self.attn_batch(q_tgt, k_src, v_src, num_heads, **kwargs) * self.alpha + \
|
94 |
+
# self.attn_batch(q_tgt, k_tgt, v_tgt, num_heads, **kwargs) * (1 - self.alpha)
|
95 |
+
out_tgt = self.attn_batch(q_tgt, torch.cat([k_tgt, k_src], dim=1), torch.cat([v_tgt, v_src], dim=1), num_heads, **kwargs)
|
96 |
+
out_src = self.attn_batch(q_src, k_src, v_src, num_heads, **kwargs)
|
97 |
+
out = torch.cat([out_tgt, out_src], dim=0)
|
98 |
+
return out
|
99 |
+
|
100 |
+
def mutual_self_attn_wq(self, q, k, v, sim, attn, is_cross, place_in_unet, num_heads, **kwargs):
|
101 |
+
if self.MODE == 'dequeue' and len(self.kv_queue) > 0:
|
102 |
+
k_src, v_src = self.kv_queue.pop(0)
|
103 |
+
out = self.attn_batch(q, torch.cat([k, k_src], dim=1), torch.cat([v, v_src], dim=1), num_heads, **kwargs)
|
104 |
+
return out
|
105 |
+
else:
|
106 |
+
self.kv_queue.append([k.clone(), v.clone()])
|
107 |
+
return super().forward(q, k, v, sim, attn, is_cross, place_in_unet, num_heads, **kwargs)
|
108 |
+
|
109 |
+
def get_queue(self):
|
110 |
+
return self.GLOBAL_ATTN_QUEUE
|
111 |
+
|
112 |
+
def set_queue(self, attn_queue):
|
113 |
+
self.GLOBAL_ATTN_QUEUE = attn_queue
|
114 |
+
|
115 |
+
def clear_queue(self):
|
116 |
+
self.GLOBAL_ATTN_QUEUE = []
|
117 |
+
|
118 |
+
def to(self, dtype):
|
119 |
+
self.GLOBAL_ATTN_QUEUE = [p.to(dtype) for p in self.GLOBAL_ATTN_QUEUE]
|
120 |
+
|
121 |
+
def forward(self, q, k, v, sim, attn, is_cross, place_in_unet, num_heads, **kwargs):
|
122 |
+
"""
|
123 |
+
Attention forward function
|
124 |
+
"""
|
125 |
+
return super().forward(q, k, v, sim, attn, is_cross, place_in_unet, num_heads, **kwargs)
|
126 |
+
|
127 |
+
|
128 |
+
class ReferenceAttentionControl():
|
129 |
+
|
130 |
+
def __init__(self,
|
131 |
+
unet,
|
132 |
+
mode="write",
|
133 |
+
do_classifier_free_guidance=False,
|
134 |
+
attention_auto_machine_weight = float('inf'),
|
135 |
+
gn_auto_machine_weight = 1.0,
|
136 |
+
style_fidelity = 1.0,
|
137 |
+
reference_attn=True,
|
138 |
+
reference_adain=False,
|
139 |
+
fusion_blocks="midup",
|
140 |
+
batch_size=1,
|
141 |
+
) -> None:
|
142 |
+
# 10. Modify self attention and group norm
|
143 |
+
self.unet = unet
|
144 |
+
assert mode in ["read", "write"]
|
145 |
+
assert fusion_blocks in ["midup", "full"]
|
146 |
+
self.reference_attn = reference_attn
|
147 |
+
self.reference_adain = reference_adain
|
148 |
+
self.fusion_blocks = fusion_blocks
|
149 |
+
self.register_reference_hooks(
|
150 |
+
mode,
|
151 |
+
do_classifier_free_guidance,
|
152 |
+
attention_auto_machine_weight,
|
153 |
+
gn_auto_machine_weight,
|
154 |
+
style_fidelity,
|
155 |
+
reference_attn,
|
156 |
+
reference_adain,
|
157 |
+
fusion_blocks,
|
158 |
+
batch_size=batch_size,
|
159 |
+
)
|
160 |
+
|
161 |
+
def register_reference_hooks(
|
162 |
+
self,
|
163 |
+
mode,
|
164 |
+
do_classifier_free_guidance,
|
165 |
+
attention_auto_machine_weight,
|
166 |
+
gn_auto_machine_weight,
|
167 |
+
style_fidelity,
|
168 |
+
reference_attn,
|
169 |
+
reference_adain,
|
170 |
+
dtype=torch.float16,
|
171 |
+
batch_size=1,
|
172 |
+
num_images_per_prompt=1,
|
173 |
+
device=torch.device("cpu"),
|
174 |
+
fusion_blocks='midup',
|
175 |
+
):
|
176 |
+
MODE = mode
|
177 |
+
do_classifier_free_guidance = do_classifier_free_guidance
|
178 |
+
attention_auto_machine_weight = attention_auto_machine_weight
|
179 |
+
gn_auto_machine_weight = gn_auto_machine_weight
|
180 |
+
style_fidelity = style_fidelity
|
181 |
+
reference_attn = reference_attn
|
182 |
+
reference_adain = reference_adain
|
183 |
+
fusion_blocks = fusion_blocks
|
184 |
+
num_images_per_prompt = num_images_per_prompt
|
185 |
+
dtype=dtype
|
186 |
+
if do_classifier_free_guidance:
|
187 |
+
uc_mask = (
|
188 |
+
torch.Tensor([1] * batch_size * num_images_per_prompt * 16 + [0] * batch_size * num_images_per_prompt * 16)
|
189 |
+
.to(device)
|
190 |
+
.bool()
|
191 |
+
)
|
192 |
+
else:
|
193 |
+
uc_mask = (
|
194 |
+
torch.Tensor([0] * batch_size * num_images_per_prompt * 2)
|
195 |
+
.to(device)
|
196 |
+
.bool()
|
197 |
+
)
|
198 |
+
|
199 |
+
def hacked_basic_transformer_inner_forward(
|
200 |
+
self,
|
201 |
+
hidden_states: torch.FloatTensor,
|
202 |
+
attention_mask: Optional[torch.FloatTensor] = None,
|
203 |
+
encoder_hidden_states: Optional[torch.FloatTensor] = None,
|
204 |
+
encoder_attention_mask: Optional[torch.FloatTensor] = None,
|
205 |
+
timestep: Optional[torch.LongTensor] = None,
|
206 |
+
cross_attention_kwargs: Dict[str, Any] = None,
|
207 |
+
class_labels: Optional[torch.LongTensor] = None,
|
208 |
+
video_length=None,
|
209 |
+
):
|
210 |
+
if self.use_ada_layer_norm:
|
211 |
+
norm_hidden_states = self.norm1(hidden_states, timestep)
|
212 |
+
elif self.use_ada_layer_norm_zero:
|
213 |
+
norm_hidden_states, gate_msa, shift_mlp, scale_mlp, gate_mlp = self.norm1(
|
214 |
+
hidden_states, timestep, class_labels, hidden_dtype=hidden_states.dtype
|
215 |
+
)
|
216 |
+
else:
|
217 |
+
norm_hidden_states = self.norm1(hidden_states)
|
218 |
+
|
219 |
+
# 1. Self-Attention
|
220 |
+
cross_attention_kwargs = cross_attention_kwargs if cross_attention_kwargs is not None else {}
|
221 |
+
if self.only_cross_attention:
|
222 |
+
attn_output = self.attn1(
|
223 |
+
norm_hidden_states,
|
224 |
+
encoder_hidden_states=encoder_hidden_states if self.only_cross_attention else None,
|
225 |
+
attention_mask=attention_mask,
|
226 |
+
**cross_attention_kwargs,
|
227 |
+
)
|
228 |
+
else:
|
229 |
+
if MODE == "write":
|
230 |
+
self.bank.append(norm_hidden_states.clone())
|
231 |
+
attn_output = self.attn1(
|
232 |
+
norm_hidden_states,
|
233 |
+
encoder_hidden_states=encoder_hidden_states if self.only_cross_attention else None,
|
234 |
+
attention_mask=attention_mask,
|
235 |
+
**cross_attention_kwargs,
|
236 |
+
)
|
237 |
+
if MODE == "read":
|
238 |
+
self.bank = [rearrange(d.unsqueeze(1).repeat(1, video_length, 1, 1), "b t l c -> (b t) l c")[:hidden_states.shape[0]] for d in self.bank]
|
239 |
+
hidden_states_uc = self.attn1(norm_hidden_states,
|
240 |
+
encoder_hidden_states=torch.cat([norm_hidden_states] + self.bank, dim=1),
|
241 |
+
attention_mask=attention_mask) + hidden_states
|
242 |
+
hidden_states_c = hidden_states_uc.clone()
|
243 |
+
_uc_mask = uc_mask.clone()
|
244 |
+
if do_classifier_free_guidance:
|
245 |
+
if hidden_states.shape[0] != _uc_mask.shape[0]:
|
246 |
+
_uc_mask = (
|
247 |
+
torch.Tensor([1] * (hidden_states.shape[0]//2) + [0] * (hidden_states.shape[0]//2))
|
248 |
+
.to(device)
|
249 |
+
.bool()
|
250 |
+
)
|
251 |
+
hidden_states_c[_uc_mask] = self.attn1(
|
252 |
+
norm_hidden_states[_uc_mask],
|
253 |
+
encoder_hidden_states=norm_hidden_states[_uc_mask],
|
254 |
+
attention_mask=attention_mask,
|
255 |
+
) + hidden_states[_uc_mask]
|
256 |
+
hidden_states = hidden_states_c.clone()
|
257 |
+
|
258 |
+
self.bank.clear()
|
259 |
+
if self.attn2 is not None:
|
260 |
+
# Cross-Attention
|
261 |
+
norm_hidden_states = (
|
262 |
+
self.norm2(hidden_states, timestep) if self.use_ada_layer_norm else self.norm2(hidden_states)
|
263 |
+
)
|
264 |
+
hidden_states = (
|
265 |
+
self.attn2(
|
266 |
+
norm_hidden_states, encoder_hidden_states=encoder_hidden_states, attention_mask=attention_mask
|
267 |
+
)
|
268 |
+
+ hidden_states
|
269 |
+
)
|
270 |
+
|
271 |
+
# Feed-forward
|
272 |
+
hidden_states = self.ff(self.norm3(hidden_states)) + hidden_states
|
273 |
+
|
274 |
+
# Temporal-Attention
|
275 |
+
if self.unet_use_temporal_attention:
|
276 |
+
d = hidden_states.shape[1]
|
277 |
+
hidden_states = rearrange(hidden_states, "(b f) d c -> (b d) f c", f=video_length)
|
278 |
+
norm_hidden_states = (
|
279 |
+
self.norm_temp(hidden_states, timestep) if self.use_ada_layer_norm else self.norm_temp(hidden_states)
|
280 |
+
)
|
281 |
+
hidden_states = self.attn_temp(norm_hidden_states) + hidden_states
|
282 |
+
hidden_states = rearrange(hidden_states, "(b d) f c -> (b f) d c", d=d)
|
283 |
+
|
284 |
+
return hidden_states
|
285 |
+
|
286 |
+
if self.use_ada_layer_norm_zero:
|
287 |
+
attn_output = gate_msa.unsqueeze(1) * attn_output
|
288 |
+
hidden_states = attn_output + hidden_states
|
289 |
+
|
290 |
+
if self.attn2 is not None:
|
291 |
+
norm_hidden_states = (
|
292 |
+
self.norm2(hidden_states, timestep) if self.use_ada_layer_norm else self.norm2(hidden_states)
|
293 |
+
)
|
294 |
+
|
295 |
+
# 2. Cross-Attention
|
296 |
+
attn_output = self.attn2(
|
297 |
+
norm_hidden_states,
|
298 |
+
encoder_hidden_states=encoder_hidden_states,
|
299 |
+
attention_mask=encoder_attention_mask,
|
300 |
+
**cross_attention_kwargs,
|
301 |
+
)
|
302 |
+
hidden_states = attn_output + hidden_states
|
303 |
+
|
304 |
+
# 3. Feed-forward
|
305 |
+
norm_hidden_states = self.norm3(hidden_states)
|
306 |
+
|
307 |
+
if self.use_ada_layer_norm_zero:
|
308 |
+
norm_hidden_states = norm_hidden_states * (1 + scale_mlp[:, None]) + shift_mlp[:, None]
|
309 |
+
|
310 |
+
ff_output = self.ff(norm_hidden_states)
|
311 |
+
|
312 |
+
if self.use_ada_layer_norm_zero:
|
313 |
+
ff_output = gate_mlp.unsqueeze(1) * ff_output
|
314 |
+
|
315 |
+
hidden_states = ff_output + hidden_states
|
316 |
+
|
317 |
+
return hidden_states
|
318 |
+
|
319 |
+
def hacked_mid_forward(self, *args, **kwargs):
|
320 |
+
eps = 1e-6
|
321 |
+
x = self.original_forward(*args, **kwargs)
|
322 |
+
if MODE == "write":
|
323 |
+
if gn_auto_machine_weight >= self.gn_weight:
|
324 |
+
var, mean = torch.var_mean(x, dim=(2, 3), keepdim=True, correction=0)
|
325 |
+
self.mean_bank.append(mean)
|
326 |
+
self.var_bank.append(var)
|
327 |
+
if MODE == "read":
|
328 |
+
if len(self.mean_bank) > 0 and len(self.var_bank) > 0:
|
329 |
+
var, mean = torch.var_mean(x, dim=(2, 3), keepdim=True, correction=0)
|
330 |
+
std = torch.maximum(var, torch.zeros_like(var) + eps) ** 0.5
|
331 |
+
mean_acc = sum(self.mean_bank) / float(len(self.mean_bank))
|
332 |
+
var_acc = sum(self.var_bank) / float(len(self.var_bank))
|
333 |
+
std_acc = torch.maximum(var_acc, torch.zeros_like(var_acc) + eps) ** 0.5
|
334 |
+
x_uc = (((x - mean) / std) * std_acc) + mean_acc
|
335 |
+
x_c = x_uc.clone()
|
336 |
+
if do_classifier_free_guidance and style_fidelity > 0:
|
337 |
+
x_c[uc_mask] = x[uc_mask]
|
338 |
+
x = style_fidelity * x_c + (1.0 - style_fidelity) * x_uc
|
339 |
+
self.mean_bank = []
|
340 |
+
self.var_bank = []
|
341 |
+
return x
|
342 |
+
|
343 |
+
def hack_CrossAttnDownBlock2D_forward(
|
344 |
+
self,
|
345 |
+
hidden_states: torch.FloatTensor,
|
346 |
+
temb: Optional[torch.FloatTensor] = None,
|
347 |
+
encoder_hidden_states: Optional[torch.FloatTensor] = None,
|
348 |
+
attention_mask: Optional[torch.FloatTensor] = None,
|
349 |
+
cross_attention_kwargs: Optional[Dict[str, Any]] = None,
|
350 |
+
encoder_attention_mask: Optional[torch.FloatTensor] = None,
|
351 |
+
):
|
352 |
+
eps = 1e-6
|
353 |
+
|
354 |
+
# TODO(Patrick, William) - attention mask is not used
|
355 |
+
output_states = ()
|
356 |
+
|
357 |
+
for i, (resnet, attn) in enumerate(zip(self.resnets, self.attentions)):
|
358 |
+
hidden_states = resnet(hidden_states, temb)
|
359 |
+
hidden_states = attn(
|
360 |
+
hidden_states,
|
361 |
+
encoder_hidden_states=encoder_hidden_states,
|
362 |
+
cross_attention_kwargs=cross_attention_kwargs,
|
363 |
+
attention_mask=attention_mask,
|
364 |
+
encoder_attention_mask=encoder_attention_mask,
|
365 |
+
return_dict=False,
|
366 |
+
)[0]
|
367 |
+
if MODE == "write":
|
368 |
+
if gn_auto_machine_weight >= self.gn_weight:
|
369 |
+
var, mean = torch.var_mean(hidden_states, dim=(2, 3), keepdim=True, correction=0)
|
370 |
+
self.mean_bank.append([mean])
|
371 |
+
self.var_bank.append([var])
|
372 |
+
if MODE == "read":
|
373 |
+
if len(self.mean_bank) > 0 and len(self.var_bank) > 0:
|
374 |
+
var, mean = torch.var_mean(hidden_states, dim=(2, 3), keepdim=True, correction=0)
|
375 |
+
std = torch.maximum(var, torch.zeros_like(var) + eps) ** 0.5
|
376 |
+
mean_acc = sum(self.mean_bank[i]) / float(len(self.mean_bank[i]))
|
377 |
+
var_acc = sum(self.var_bank[i]) / float(len(self.var_bank[i]))
|
378 |
+
std_acc = torch.maximum(var_acc, torch.zeros_like(var_acc) + eps) ** 0.5
|
379 |
+
hidden_states_uc = (((hidden_states - mean) / std) * std_acc) + mean_acc
|
380 |
+
hidden_states_c = hidden_states_uc.clone()
|
381 |
+
if do_classifier_free_guidance and style_fidelity > 0:
|
382 |
+
hidden_states_c[uc_mask] = hidden_states[uc_mask].to(hidden_states_c.dtype)
|
383 |
+
hidden_states = style_fidelity * hidden_states_c + (1.0 - style_fidelity) * hidden_states_uc
|
384 |
+
|
385 |
+
output_states = output_states + (hidden_states,)
|
386 |
+
|
387 |
+
if MODE == "read":
|
388 |
+
self.mean_bank = []
|
389 |
+
self.var_bank = []
|
390 |
+
|
391 |
+
if self.downsamplers is not None:
|
392 |
+
for downsampler in self.downsamplers:
|
393 |
+
hidden_states = downsampler(hidden_states)
|
394 |
+
|
395 |
+
output_states = output_states + (hidden_states,)
|
396 |
+
|
397 |
+
return hidden_states, output_states
|
398 |
+
|
399 |
+
def hacked_DownBlock2D_forward(self, hidden_states, temb=None):
|
400 |
+
eps = 1e-6
|
401 |
+
|
402 |
+
output_states = ()
|
403 |
+
|
404 |
+
for i, resnet in enumerate(self.resnets):
|
405 |
+
hidden_states = resnet(hidden_states, temb)
|
406 |
+
|
407 |
+
if MODE == "write":
|
408 |
+
if gn_auto_machine_weight >= self.gn_weight:
|
409 |
+
var, mean = torch.var_mean(hidden_states, dim=(2, 3), keepdim=True, correction=0)
|
410 |
+
self.mean_bank.append([mean])
|
411 |
+
self.var_bank.append([var])
|
412 |
+
if MODE == "read":
|
413 |
+
if len(self.mean_bank) > 0 and len(self.var_bank) > 0:
|
414 |
+
var, mean = torch.var_mean(hidden_states, dim=(2, 3), keepdim=True, correction=0)
|
415 |
+
std = torch.maximum(var, torch.zeros_like(var) + eps) ** 0.5
|
416 |
+
mean_acc = sum(self.mean_bank[i]) / float(len(self.mean_bank[i]))
|
417 |
+
var_acc = sum(self.var_bank[i]) / float(len(self.var_bank[i]))
|
418 |
+
std_acc = torch.maximum(var_acc, torch.zeros_like(var_acc) + eps) ** 0.5
|
419 |
+
hidden_states_uc = (((hidden_states - mean) / std) * std_acc) + mean_acc
|
420 |
+
hidden_states_c = hidden_states_uc.clone()
|
421 |
+
if do_classifier_free_guidance and style_fidelity > 0:
|
422 |
+
hidden_states_c[uc_mask] = hidden_states[uc_mask].to(hidden_states_c.dtype)
|
423 |
+
hidden_states = style_fidelity * hidden_states_c + (1.0 - style_fidelity) * hidden_states_uc
|
424 |
+
|
425 |
+
output_states = output_states + (hidden_states,)
|
426 |
+
|
427 |
+
if MODE == "read":
|
428 |
+
self.mean_bank = []
|
429 |
+
self.var_bank = []
|
430 |
+
|
431 |
+
if self.downsamplers is not None:
|
432 |
+
for downsampler in self.downsamplers:
|
433 |
+
hidden_states = downsampler(hidden_states)
|
434 |
+
|
435 |
+
output_states = output_states + (hidden_states,)
|
436 |
+
|
437 |
+
return hidden_states, output_states
|
438 |
+
|
439 |
+
def hacked_CrossAttnUpBlock2D_forward(
|
440 |
+
self,
|
441 |
+
hidden_states: torch.FloatTensor,
|
442 |
+
res_hidden_states_tuple: Tuple[torch.FloatTensor, ...],
|
443 |
+
temb: Optional[torch.FloatTensor] = None,
|
444 |
+
encoder_hidden_states: Optional[torch.FloatTensor] = None,
|
445 |
+
cross_attention_kwargs: Optional[Dict[str, Any]] = None,
|
446 |
+
upsample_size: Optional[int] = None,
|
447 |
+
attention_mask: Optional[torch.FloatTensor] = None,
|
448 |
+
encoder_attention_mask: Optional[torch.FloatTensor] = None,
|
449 |
+
):
|
450 |
+
eps = 1e-6
|
451 |
+
# TODO(Patrick, William) - attention mask is not used
|
452 |
+
for i, (resnet, attn) in enumerate(zip(self.resnets, self.attentions)):
|
453 |
+
# pop res hidden states
|
454 |
+
res_hidden_states = res_hidden_states_tuple[-1]
|
455 |
+
res_hidden_states_tuple = res_hidden_states_tuple[:-1]
|
456 |
+
hidden_states = torch.cat([hidden_states, res_hidden_states], dim=1)
|
457 |
+
hidden_states = resnet(hidden_states, temb)
|
458 |
+
hidden_states = attn(
|
459 |
+
hidden_states,
|
460 |
+
encoder_hidden_states=encoder_hidden_states,
|
461 |
+
cross_attention_kwargs=cross_attention_kwargs,
|
462 |
+
attention_mask=attention_mask,
|
463 |
+
encoder_attention_mask=encoder_attention_mask,
|
464 |
+
return_dict=False,
|
465 |
+
)[0]
|
466 |
+
|
467 |
+
if MODE == "write":
|
468 |
+
if gn_auto_machine_weight >= self.gn_weight:
|
469 |
+
var, mean = torch.var_mean(hidden_states, dim=(2, 3), keepdim=True, correction=0)
|
470 |
+
self.mean_bank.append([mean])
|
471 |
+
self.var_bank.append([var])
|
472 |
+
if MODE == "read":
|
473 |
+
if len(self.mean_bank) > 0 and len(self.var_bank) > 0:
|
474 |
+
var, mean = torch.var_mean(hidden_states, dim=(2, 3), keepdim=True, correction=0)
|
475 |
+
std = torch.maximum(var, torch.zeros_like(var) + eps) ** 0.5
|
476 |
+
mean_acc = sum(self.mean_bank[i]) / float(len(self.mean_bank[i]))
|
477 |
+
var_acc = sum(self.var_bank[i]) / float(len(self.var_bank[i]))
|
478 |
+
std_acc = torch.maximum(var_acc, torch.zeros_like(var_acc) + eps) ** 0.5
|
479 |
+
hidden_states_uc = (((hidden_states - mean) / std) * std_acc) + mean_acc
|
480 |
+
hidden_states_c = hidden_states_uc.clone()
|
481 |
+
if do_classifier_free_guidance and style_fidelity > 0:
|
482 |
+
hidden_states_c[uc_mask] = hidden_states[uc_mask].to(hidden_states_c.dtype)
|
483 |
+
hidden_states = style_fidelity * hidden_states_c + (1.0 - style_fidelity) * hidden_states_uc
|
484 |
+
|
485 |
+
if MODE == "read":
|
486 |
+
self.mean_bank = []
|
487 |
+
self.var_bank = []
|
488 |
+
|
489 |
+
if self.upsamplers is not None:
|
490 |
+
for upsampler in self.upsamplers:
|
491 |
+
hidden_states = upsampler(hidden_states, upsample_size)
|
492 |
+
|
493 |
+
return hidden_states
|
494 |
+
|
495 |
+
def hacked_UpBlock2D_forward(self, hidden_states, res_hidden_states_tuple, temb=None, upsample_size=None):
|
496 |
+
eps = 1e-6
|
497 |
+
for i, resnet in enumerate(self.resnets):
|
498 |
+
# pop res hidden states
|
499 |
+
res_hidden_states = res_hidden_states_tuple[-1]
|
500 |
+
res_hidden_states_tuple = res_hidden_states_tuple[:-1]
|
501 |
+
hidden_states = torch.cat([hidden_states, res_hidden_states], dim=1)
|
502 |
+
hidden_states = resnet(hidden_states, temb)
|
503 |
+
|
504 |
+
if MODE == "write":
|
505 |
+
if gn_auto_machine_weight >= self.gn_weight:
|
506 |
+
var, mean = torch.var_mean(hidden_states, dim=(2, 3), keepdim=True, correction=0)
|
507 |
+
self.mean_bank.append([mean])
|
508 |
+
self.var_bank.append([var])
|
509 |
+
if MODE == "read":
|
510 |
+
if len(self.mean_bank) > 0 and len(self.var_bank) > 0:
|
511 |
+
var, mean = torch.var_mean(hidden_states, dim=(2, 3), keepdim=True, correction=0)
|
512 |
+
std = torch.maximum(var, torch.zeros_like(var) + eps) ** 0.5
|
513 |
+
mean_acc = sum(self.mean_bank[i]) / float(len(self.mean_bank[i]))
|
514 |
+
var_acc = sum(self.var_bank[i]) / float(len(self.var_bank[i]))
|
515 |
+
std_acc = torch.maximum(var_acc, torch.zeros_like(var_acc) + eps) ** 0.5
|
516 |
+
hidden_states_uc = (((hidden_states - mean) / std) * std_acc) + mean_acc
|
517 |
+
hidden_states_c = hidden_states_uc.clone()
|
518 |
+
if do_classifier_free_guidance and style_fidelity > 0:
|
519 |
+
hidden_states_c[uc_mask] = hidden_states[uc_mask].to(hidden_states_c.dtype)
|
520 |
+
hidden_states = style_fidelity * hidden_states_c + (1.0 - style_fidelity) * hidden_states_uc
|
521 |
+
|
522 |
+
if MODE == "read":
|
523 |
+
self.mean_bank = []
|
524 |
+
self.var_bank = []
|
525 |
+
|
526 |
+
if self.upsamplers is not None:
|
527 |
+
for upsampler in self.upsamplers:
|
528 |
+
hidden_states = upsampler(hidden_states, upsample_size)
|
529 |
+
|
530 |
+
return hidden_states
|
531 |
+
|
532 |
+
if self.reference_attn:
|
533 |
+
if self.fusion_blocks == "midup":
|
534 |
+
attn_modules = [module for module in (torch_dfs(self.unet.mid_block)+torch_dfs(self.unet.up_blocks)) if isinstance(module, BasicTransformerBlock) or isinstance(module, _BasicTransformerBlock)]
|
535 |
+
elif self.fusion_blocks == "full":
|
536 |
+
attn_modules = [module for module in torch_dfs(self.unet) if isinstance(module, BasicTransformerBlock) or isinstance(module, _BasicTransformerBlock)]
|
537 |
+
attn_modules = sorted(attn_modules, key=lambda x: -x.norm1.normalized_shape[0])
|
538 |
+
|
539 |
+
for i, module in enumerate(attn_modules):
|
540 |
+
module._original_inner_forward = module.forward
|
541 |
+
module.forward = hacked_basic_transformer_inner_forward.__get__(module, BasicTransformerBlock)
|
542 |
+
module.bank = []
|
543 |
+
module.attn_weight = float(i) / float(len(attn_modules))
|
544 |
+
|
545 |
+
if self.reference_adain:
|
546 |
+
gn_modules = [self.unet.mid_block]
|
547 |
+
self.unet.mid_block.gn_weight = 0
|
548 |
+
|
549 |
+
down_blocks = self.unet.down_blocks
|
550 |
+
for w, module in enumerate(down_blocks):
|
551 |
+
module.gn_weight = 1.0 - float(w) / float(len(down_blocks))
|
552 |
+
gn_modules.append(module)
|
553 |
+
|
554 |
+
up_blocks = self.unet.up_blocks
|
555 |
+
for w, module in enumerate(up_blocks):
|
556 |
+
module.gn_weight = float(w) / float(len(up_blocks))
|
557 |
+
gn_modules.append(module)
|
558 |
+
|
559 |
+
for i, module in enumerate(gn_modules):
|
560 |
+
if getattr(module, "original_forward", None) is None:
|
561 |
+
module.original_forward = module.forward
|
562 |
+
if i == 0:
|
563 |
+
# mid_block
|
564 |
+
module.forward = hacked_mid_forward.__get__(module, torch.nn.Module)
|
565 |
+
elif isinstance(module, CrossAttnDownBlock2D):
|
566 |
+
module.forward = hack_CrossAttnDownBlock2D_forward.__get__(module, CrossAttnDownBlock2D)
|
567 |
+
elif isinstance(module, DownBlock2D):
|
568 |
+
module.forward = hacked_DownBlock2D_forward.__get__(module, DownBlock2D)
|
569 |
+
elif isinstance(module, CrossAttnUpBlock2D):
|
570 |
+
module.forward = hacked_CrossAttnUpBlock2D_forward.__get__(module, CrossAttnUpBlock2D)
|
571 |
+
elif isinstance(module, UpBlock2D):
|
572 |
+
module.forward = hacked_UpBlock2D_forward.__get__(module, UpBlock2D)
|
573 |
+
module.mean_bank = []
|
574 |
+
module.var_bank = []
|
575 |
+
module.gn_weight *= 2
|
576 |
+
|
577 |
+
def update(self, writer, dtype=torch.float16):
|
578 |
+
if self.reference_attn:
|
579 |
+
if self.fusion_blocks == "midup":
|
580 |
+
reader_attn_modules = [module for module in (torch_dfs(self.unet.mid_block)+torch_dfs(self.unet.up_blocks)) if isinstance(module, _BasicTransformerBlock)]
|
581 |
+
writer_attn_modules = [module for module in (torch_dfs(writer.unet.mid_block)+torch_dfs(writer.unet.up_blocks)) if isinstance(module, BasicTransformerBlock)]
|
582 |
+
elif self.fusion_blocks == "full":
|
583 |
+
reader_attn_modules = [module for module in torch_dfs(self.unet) if isinstance(module, _BasicTransformerBlock)]
|
584 |
+
writer_attn_modules = [module for module in torch_dfs(writer.unet) if isinstance(module, BasicTransformerBlock)]
|
585 |
+
reader_attn_modules = sorted(reader_attn_modules, key=lambda x: -x.norm1.normalized_shape[0])
|
586 |
+
writer_attn_modules = sorted(writer_attn_modules, key=lambda x: -x.norm1.normalized_shape[0])
|
587 |
+
for r, w in zip(reader_attn_modules, writer_attn_modules):
|
588 |
+
r.bank = [v.clone().to(dtype) for v in w.bank]
|
589 |
+
# w.bank.clear()
|
590 |
+
if self.reference_adain:
|
591 |
+
reader_gn_modules = [self.unet.mid_block]
|
592 |
+
|
593 |
+
down_blocks = self.unet.down_blocks
|
594 |
+
for w, module in enumerate(down_blocks):
|
595 |
+
reader_gn_modules.append(module)
|
596 |
+
|
597 |
+
up_blocks = self.unet.up_blocks
|
598 |
+
for w, module in enumerate(up_blocks):
|
599 |
+
reader_gn_modules.append(module)
|
600 |
+
|
601 |
+
writer_gn_modules = [writer.unet.mid_block]
|
602 |
+
|
603 |
+
down_blocks = writer.unet.down_blocks
|
604 |
+
for w, module in enumerate(down_blocks):
|
605 |
+
writer_gn_modules.append(module)
|
606 |
+
|
607 |
+
up_blocks = writer.unet.up_blocks
|
608 |
+
for w, module in enumerate(up_blocks):
|
609 |
+
writer_gn_modules.append(module)
|
610 |
+
|
611 |
+
for r, w in zip(reader_gn_modules, writer_gn_modules):
|
612 |
+
if len(w.mean_bank) > 0 and isinstance(w.mean_bank[0], list):
|
613 |
+
r.mean_bank = [[v.clone().to(dtype) for v in vl] for vl in w.mean_bank]
|
614 |
+
r.var_bank = [[v.clone().to(dtype) for v in vl] for vl in w.var_bank]
|
615 |
+
else:
|
616 |
+
r.mean_bank = [v.clone().to(dtype) for v in w.mean_bank]
|
617 |
+
r.var_bank = [v.clone().to(dtype) for v in w.var_bank]
|
618 |
+
|
619 |
+
def clear(self):
|
620 |
+
if self.reference_attn:
|
621 |
+
if self.fusion_blocks == "midup":
|
622 |
+
reader_attn_modules = [module for module in (torch_dfs(self.unet.mid_block)+torch_dfs(self.unet.up_blocks)) if isinstance(module, BasicTransformerBlock) or isinstance(module, _BasicTransformerBlock)]
|
623 |
+
elif self.fusion_blocks == "full":
|
624 |
+
reader_attn_modules = [module for module in torch_dfs(self.unet) if isinstance(module, BasicTransformerBlock) or isinstance(module, _BasicTransformerBlock)]
|
625 |
+
reader_attn_modules = sorted(reader_attn_modules, key=lambda x: -x.norm1.normalized_shape[0])
|
626 |
+
for r in reader_attn_modules:
|
627 |
+
r.bank.clear()
|
628 |
+
if self.reference_adain:
|
629 |
+
reader_gn_modules = [self.unet.mid_block]
|
630 |
+
|
631 |
+
down_blocks = self.unet.down_blocks
|
632 |
+
for w, module in enumerate(down_blocks):
|
633 |
+
reader_gn_modules.append(module)
|
634 |
+
|
635 |
+
up_blocks = self.unet.up_blocks
|
636 |
+
for w, module in enumerate(up_blocks):
|
637 |
+
reader_gn_modules.append(module)
|
638 |
+
|
639 |
+
for r in reader_gn_modules:
|
640 |
+
r.mean_bank.clear()
|
641 |
+
r.var_bank.clear()
|
642 |
+
|
magicanimate/models/orig_attention.py
ADDED
@@ -0,0 +1,988 @@
|
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|
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|
|
|
|
|
|
|
|
1 |
+
# *************************************************************************
|
2 |
+
# This file may have been modified by Bytedance Inc. (“Bytedance Inc.'s Mo-
|
3 |
+
# difications”). All Bytedance Inc.'s Modifications are Copyright (2023) B-
|
4 |
+
# ytedance Inc..
|
5 |
+
# *************************************************************************
|
6 |
+
|
7 |
+
# Copyright 2022 The HuggingFace Team. All rights reserved.
|
8 |
+
#
|
9 |
+
# Licensed under the Apache License, Version 2.0 (the "License");
|
10 |
+
# you may not use this file except in compliance with the License.
|
11 |
+
# You may obtain a copy of the License at
|
12 |
+
#
|
13 |
+
# http://www.apache.org/licenses/LICENSE-2.0
|
14 |
+
#
|
15 |
+
# Unless required by applicable law or agreed to in writing, software
|
16 |
+
# distributed under the License is distributed on an "AS IS" BASIS,
|
17 |
+
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
18 |
+
# See the License for the specific language governing permissions and
|
19 |
+
# limitations under the License.
|
20 |
+
import math
|
21 |
+
from dataclasses import dataclass
|
22 |
+
from typing import Optional
|
23 |
+
|
24 |
+
import torch
|
25 |
+
import torch.nn.functional as F
|
26 |
+
from torch import nn
|
27 |
+
|
28 |
+
from diffusers.configuration_utils import ConfigMixin, register_to_config
|
29 |
+
from diffusers.models.modeling_utils import ModelMixin
|
30 |
+
from diffusers.models.embeddings import ImagePositionalEmbeddings
|
31 |
+
from diffusers.utils import BaseOutput
|
32 |
+
from diffusers.utils.import_utils import is_xformers_available
|
33 |
+
|
34 |
+
|
35 |
+
@dataclass
|
36 |
+
class Transformer2DModelOutput(BaseOutput):
|
37 |
+
"""
|
38 |
+
Args:
|
39 |
+
sample (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)` or `(batch size, num_vector_embeds - 1, num_latent_pixels)` if [`Transformer2DModel`] is discrete):
|
40 |
+
Hidden states conditioned on `encoder_hidden_states` input. If discrete, returns probability distributions
|
41 |
+
for the unnoised latent pixels.
|
42 |
+
"""
|
43 |
+
|
44 |
+
sample: torch.FloatTensor
|
45 |
+
|
46 |
+
|
47 |
+
if is_xformers_available():
|
48 |
+
import xformers
|
49 |
+
import xformers.ops
|
50 |
+
else:
|
51 |
+
xformers = None
|
52 |
+
|
53 |
+
|
54 |
+
class Transformer2DModel(ModelMixin, ConfigMixin):
|
55 |
+
"""
|
56 |
+
Transformer model for image-like data. Takes either discrete (classes of vector embeddings) or continuous (actual
|
57 |
+
embeddings) inputs.
|
58 |
+
|
59 |
+
When input is continuous: First, project the input (aka embedding) and reshape to b, t, d. Then apply standard
|
60 |
+
transformer action. Finally, reshape to image.
|
61 |
+
|
62 |
+
When input is discrete: First, input (classes of latent pixels) is converted to embeddings and has positional
|
63 |
+
embeddings applied, see `ImagePositionalEmbeddings`. Then apply standard transformer action. Finally, predict
|
64 |
+
classes of unnoised image.
|
65 |
+
|
66 |
+
Note that it is assumed one of the input classes is the masked latent pixel. The predicted classes of the unnoised
|
67 |
+
image do not contain a prediction for the masked pixel as the unnoised image cannot be masked.
|
68 |
+
|
69 |
+
Parameters:
|
70 |
+
num_attention_heads (`int`, *optional*, defaults to 16): The number of heads to use for multi-head attention.
|
71 |
+
attention_head_dim (`int`, *optional*, defaults to 88): The number of channels in each head.
|
72 |
+
in_channels (`int`, *optional*):
|
73 |
+
Pass if the input is continuous. The number of channels in the input and output.
|
74 |
+
num_layers (`int`, *optional*, defaults to 1): The number of layers of Transformer blocks to use.
|
75 |
+
dropout (`float`, *optional*, defaults to 0.0): The dropout probability to use.
|
76 |
+
cross_attention_dim (`int`, *optional*): The number of encoder_hidden_states dimensions to use.
|
77 |
+
sample_size (`int`, *optional*): Pass if the input is discrete. The width of the latent images.
|
78 |
+
Note that this is fixed at training time as it is used for learning a number of position embeddings. See
|
79 |
+
`ImagePositionalEmbeddings`.
|
80 |
+
num_vector_embeds (`int`, *optional*):
|
81 |
+
Pass if the input is discrete. The number of classes of the vector embeddings of the latent pixels.
|
82 |
+
Includes the class for the masked latent pixel.
|
83 |
+
activation_fn (`str`, *optional*, defaults to `"geglu"`): Activation function to be used in feed-forward.
|
84 |
+
num_embeds_ada_norm ( `int`, *optional*): Pass if at least one of the norm_layers is `AdaLayerNorm`.
|
85 |
+
The number of diffusion steps used during training. Note that this is fixed at training time as it is used
|
86 |
+
to learn a number of embeddings that are added to the hidden states. During inference, you can denoise for
|
87 |
+
up to but not more than steps than `num_embeds_ada_norm`.
|
88 |
+
attention_bias (`bool`, *optional*):
|
89 |
+
Configure if the TransformerBlocks' attention should contain a bias parameter.
|
90 |
+
"""
|
91 |
+
|
92 |
+
@register_to_config
|
93 |
+
def __init__(
|
94 |
+
self,
|
95 |
+
num_attention_heads: int = 16,
|
96 |
+
attention_head_dim: int = 88,
|
97 |
+
in_channels: Optional[int] = None,
|
98 |
+
num_layers: int = 1,
|
99 |
+
dropout: float = 0.0,
|
100 |
+
norm_num_groups: int = 32,
|
101 |
+
cross_attention_dim: Optional[int] = None,
|
102 |
+
attention_bias: bool = False,
|
103 |
+
sample_size: Optional[int] = None,
|
104 |
+
num_vector_embeds: Optional[int] = None,
|
105 |
+
activation_fn: str = "geglu",
|
106 |
+
num_embeds_ada_norm: Optional[int] = None,
|
107 |
+
use_linear_projection: bool = False,
|
108 |
+
only_cross_attention: bool = False,
|
109 |
+
upcast_attention: bool = False,
|
110 |
+
):
|
111 |
+
super().__init__()
|
112 |
+
self.use_linear_projection = use_linear_projection
|
113 |
+
self.num_attention_heads = num_attention_heads
|
114 |
+
self.attention_head_dim = attention_head_dim
|
115 |
+
inner_dim = num_attention_heads * attention_head_dim
|
116 |
+
|
117 |
+
# 1. Transformer2DModel can process both standard continous images of shape `(batch_size, num_channels, width, height)` as well as quantized image embeddings of shape `(batch_size, num_image_vectors)`
|
118 |
+
# Define whether input is continuous or discrete depending on configuration
|
119 |
+
self.is_input_continuous = in_channels is not None
|
120 |
+
self.is_input_vectorized = num_vector_embeds is not None
|
121 |
+
|
122 |
+
if self.is_input_continuous and self.is_input_vectorized:
|
123 |
+
raise ValueError(
|
124 |
+
f"Cannot define both `in_channels`: {in_channels} and `num_vector_embeds`: {num_vector_embeds}. Make"
|
125 |
+
" sure that either `in_channels` or `num_vector_embeds` is None."
|
126 |
+
)
|
127 |
+
elif not self.is_input_continuous and not self.is_input_vectorized:
|
128 |
+
raise ValueError(
|
129 |
+
f"Has to define either `in_channels`: {in_channels} or `num_vector_embeds`: {num_vector_embeds}. Make"
|
130 |
+
" sure that either `in_channels` or `num_vector_embeds` is not None."
|
131 |
+
)
|
132 |
+
|
133 |
+
# 2. Define input layers
|
134 |
+
if self.is_input_continuous:
|
135 |
+
self.in_channels = in_channels
|
136 |
+
|
137 |
+
self.norm = torch.nn.GroupNorm(num_groups=norm_num_groups, num_channels=in_channels, eps=1e-6, affine=True)
|
138 |
+
if use_linear_projection:
|
139 |
+
self.proj_in = nn.Linear(in_channels, inner_dim)
|
140 |
+
else:
|
141 |
+
self.proj_in = nn.Conv2d(in_channels, inner_dim, kernel_size=1, stride=1, padding=0)
|
142 |
+
elif self.is_input_vectorized:
|
143 |
+
assert sample_size is not None, "Transformer2DModel over discrete input must provide sample_size"
|
144 |
+
assert num_vector_embeds is not None, "Transformer2DModel over discrete input must provide num_embed"
|
145 |
+
|
146 |
+
self.height = sample_size
|
147 |
+
self.width = sample_size
|
148 |
+
self.num_vector_embeds = num_vector_embeds
|
149 |
+
self.num_latent_pixels = self.height * self.width
|
150 |
+
|
151 |
+
self.latent_image_embedding = ImagePositionalEmbeddings(
|
152 |
+
num_embed=num_vector_embeds, embed_dim=inner_dim, height=self.height, width=self.width
|
153 |
+
)
|
154 |
+
|
155 |
+
# 3. Define transformers blocks
|
156 |
+
self.transformer_blocks = nn.ModuleList(
|
157 |
+
[
|
158 |
+
BasicTransformerBlock(
|
159 |
+
inner_dim,
|
160 |
+
num_attention_heads,
|
161 |
+
attention_head_dim,
|
162 |
+
dropout=dropout,
|
163 |
+
cross_attention_dim=cross_attention_dim,
|
164 |
+
activation_fn=activation_fn,
|
165 |
+
num_embeds_ada_norm=num_embeds_ada_norm,
|
166 |
+
attention_bias=attention_bias,
|
167 |
+
only_cross_attention=only_cross_attention,
|
168 |
+
upcast_attention=upcast_attention,
|
169 |
+
)
|
170 |
+
for d in range(num_layers)
|
171 |
+
]
|
172 |
+
)
|
173 |
+
|
174 |
+
# 4. Define output layers
|
175 |
+
if self.is_input_continuous:
|
176 |
+
if use_linear_projection:
|
177 |
+
self.proj_out = nn.Linear(in_channels, inner_dim)
|
178 |
+
else:
|
179 |
+
self.proj_out = nn.Conv2d(inner_dim, in_channels, kernel_size=1, stride=1, padding=0)
|
180 |
+
elif self.is_input_vectorized:
|
181 |
+
self.norm_out = nn.LayerNorm(inner_dim)
|
182 |
+
self.out = nn.Linear(inner_dim, self.num_vector_embeds - 1)
|
183 |
+
|
184 |
+
def forward(self, hidden_states, encoder_hidden_states=None, timestep=None, return_dict: bool = True):
|
185 |
+
"""
|
186 |
+
Args:
|
187 |
+
hidden_states ( When discrete, `torch.LongTensor` of shape `(batch size, num latent pixels)`.
|
188 |
+
When continous, `torch.FloatTensor` of shape `(batch size, channel, height, width)`): Input
|
189 |
+
hidden_states
|
190 |
+
encoder_hidden_states ( `torch.LongTensor` of shape `(batch size, encoder_hidden_states dim)`, *optional*):
|
191 |
+
Conditional embeddings for cross attention layer. If not given, cross-attention defaults to
|
192 |
+
self-attention.
|
193 |
+
timestep ( `torch.long`, *optional*):
|
194 |
+
Optional timestep to be applied as an embedding in AdaLayerNorm's. Used to indicate denoising step.
|
195 |
+
return_dict (`bool`, *optional*, defaults to `True`):
|
196 |
+
Whether or not to return a [`models.unet_2d_condition.UNet2DConditionOutput`] instead of a plain tuple.
|
197 |
+
|
198 |
+
Returns:
|
199 |
+
[`~models.attention.Transformer2DModelOutput`] or `tuple`: [`~models.attention.Transformer2DModelOutput`]
|
200 |
+
if `return_dict` is True, otherwise a `tuple`. When returning a tuple, the first element is the sample
|
201 |
+
tensor.
|
202 |
+
"""
|
203 |
+
# 1. Input
|
204 |
+
if self.is_input_continuous:
|
205 |
+
batch, channel, height, weight = hidden_states.shape
|
206 |
+
residual = hidden_states
|
207 |
+
|
208 |
+
hidden_states = self.norm(hidden_states)
|
209 |
+
if not self.use_linear_projection:
|
210 |
+
hidden_states = self.proj_in(hidden_states)
|
211 |
+
inner_dim = hidden_states.shape[1]
|
212 |
+
hidden_states = hidden_states.permute(0, 2, 3, 1).reshape(batch, height * weight, inner_dim)
|
213 |
+
else:
|
214 |
+
inner_dim = hidden_states.shape[1]
|
215 |
+
hidden_states = hidden_states.permute(0, 2, 3, 1).reshape(batch, height * weight, inner_dim)
|
216 |
+
hidden_states = self.proj_in(hidden_states)
|
217 |
+
elif self.is_input_vectorized:
|
218 |
+
hidden_states = self.latent_image_embedding(hidden_states)
|
219 |
+
|
220 |
+
# 2. Blocks
|
221 |
+
for block in self.transformer_blocks:
|
222 |
+
hidden_states = block(hidden_states, encoder_hidden_states=encoder_hidden_states, timestep=timestep)
|
223 |
+
|
224 |
+
# 3. Output
|
225 |
+
if self.is_input_continuous:
|
226 |
+
if not self.use_linear_projection:
|
227 |
+
hidden_states = (
|
228 |
+
hidden_states.reshape(batch, height, weight, inner_dim).permute(0, 3, 1, 2).contiguous()
|
229 |
+
)
|
230 |
+
hidden_states = self.proj_out(hidden_states)
|
231 |
+
else:
|
232 |
+
hidden_states = self.proj_out(hidden_states)
|
233 |
+
hidden_states = (
|
234 |
+
hidden_states.reshape(batch, height, weight, inner_dim).permute(0, 3, 1, 2).contiguous()
|
235 |
+
)
|
236 |
+
|
237 |
+
output = hidden_states + residual
|
238 |
+
elif self.is_input_vectorized:
|
239 |
+
hidden_states = self.norm_out(hidden_states)
|
240 |
+
logits = self.out(hidden_states)
|
241 |
+
# (batch, self.num_vector_embeds - 1, self.num_latent_pixels)
|
242 |
+
logits = logits.permute(0, 2, 1)
|
243 |
+
|
244 |
+
# log(p(x_0))
|
245 |
+
output = F.log_softmax(logits.double(), dim=1).float()
|
246 |
+
|
247 |
+
if not return_dict:
|
248 |
+
return (output,)
|
249 |
+
|
250 |
+
return Transformer2DModelOutput(sample=output)
|
251 |
+
|
252 |
+
|
253 |
+
class AttentionBlock(nn.Module):
|
254 |
+
"""
|
255 |
+
An attention block that allows spatial positions to attend to each other. Originally ported from here, but adapted
|
256 |
+
to the N-d case.
|
257 |
+
https://github.com/hojonathanho/diffusion/blob/1e0dceb3b3495bbe19116a5e1b3596cd0706c543/diffusion_tf/models/unet.py#L66.
|
258 |
+
Uses three q, k, v linear layers to compute attention.
|
259 |
+
|
260 |
+
Parameters:
|
261 |
+
channels (`int`): The number of channels in the input and output.
|
262 |
+
num_head_channels (`int`, *optional*):
|
263 |
+
The number of channels in each head. If None, then `num_heads` = 1.
|
264 |
+
norm_num_groups (`int`, *optional*, defaults to 32): The number of groups to use for group norm.
|
265 |
+
rescale_output_factor (`float`, *optional*, defaults to 1.0): The factor to rescale the output by.
|
266 |
+
eps (`float`, *optional*, defaults to 1e-5): The epsilon value to use for group norm.
|
267 |
+
"""
|
268 |
+
|
269 |
+
# IMPORTANT;TODO(Patrick, William) - this class will be deprecated soon. Do not use it anymore
|
270 |
+
|
271 |
+
def __init__(
|
272 |
+
self,
|
273 |
+
channels: int,
|
274 |
+
num_head_channels: Optional[int] = None,
|
275 |
+
norm_num_groups: int = 32,
|
276 |
+
rescale_output_factor: float = 1.0,
|
277 |
+
eps: float = 1e-5,
|
278 |
+
):
|
279 |
+
super().__init__()
|
280 |
+
self.channels = channels
|
281 |
+
|
282 |
+
self.num_heads = channels // num_head_channels if num_head_channels is not None else 1
|
283 |
+
self.num_head_size = num_head_channels
|
284 |
+
self.group_norm = nn.GroupNorm(num_channels=channels, num_groups=norm_num_groups, eps=eps, affine=True)
|
285 |
+
|
286 |
+
# define q,k,v as linear layers
|
287 |
+
self.query = nn.Linear(channels, channels)
|
288 |
+
self.key = nn.Linear(channels, channels)
|
289 |
+
self.value = nn.Linear(channels, channels)
|
290 |
+
|
291 |
+
self.rescale_output_factor = rescale_output_factor
|
292 |
+
self.proj_attn = nn.Linear(channels, channels, 1)
|
293 |
+
|
294 |
+
self._use_memory_efficient_attention_xformers = False
|
295 |
+
|
296 |
+
def set_use_memory_efficient_attention_xformers(self, use_memory_efficient_attention_xformers: bool, *args, **kwargs):
|
297 |
+
if not is_xformers_available():
|
298 |
+
raise ModuleNotFoundError(
|
299 |
+
"Refer to https://github.com/facebookresearch/xformers for more information on how to install"
|
300 |
+
" xformers",
|
301 |
+
name="xformers",
|
302 |
+
)
|
303 |
+
elif not torch.cuda.is_available():
|
304 |
+
raise ValueError(
|
305 |
+
"torch.cuda.is_available() should be True but is False. xformers' memory efficient attention is only"
|
306 |
+
" available for GPU "
|
307 |
+
)
|
308 |
+
else:
|
309 |
+
try:
|
310 |
+
# Make sure we can run the memory efficient attention
|
311 |
+
_ = xformers.ops.memory_efficient_attention(
|
312 |
+
torch.randn((1, 2, 40), device="cuda"),
|
313 |
+
torch.randn((1, 2, 40), device="cuda"),
|
314 |
+
torch.randn((1, 2, 40), device="cuda"),
|
315 |
+
)
|
316 |
+
except Exception as e:
|
317 |
+
raise e
|
318 |
+
self._use_memory_efficient_attention_xformers = use_memory_efficient_attention_xformers
|
319 |
+
|
320 |
+
def reshape_heads_to_batch_dim(self, tensor):
|
321 |
+
batch_size, seq_len, dim = tensor.shape
|
322 |
+
head_size = self.num_heads
|
323 |
+
tensor = tensor.reshape(batch_size, seq_len, head_size, dim // head_size)
|
324 |
+
tensor = tensor.permute(0, 2, 1, 3).reshape(batch_size * head_size, seq_len, dim // head_size)
|
325 |
+
return tensor
|
326 |
+
|
327 |
+
def reshape_batch_dim_to_heads(self, tensor):
|
328 |
+
batch_size, seq_len, dim = tensor.shape
|
329 |
+
head_size = self.num_heads
|
330 |
+
tensor = tensor.reshape(batch_size // head_size, head_size, seq_len, dim)
|
331 |
+
tensor = tensor.permute(0, 2, 1, 3).reshape(batch_size // head_size, seq_len, dim * head_size)
|
332 |
+
return tensor
|
333 |
+
|
334 |
+
def forward(self, hidden_states):
|
335 |
+
residual = hidden_states
|
336 |
+
batch, channel, height, width = hidden_states.shape
|
337 |
+
|
338 |
+
# norm
|
339 |
+
hidden_states = self.group_norm(hidden_states)
|
340 |
+
|
341 |
+
hidden_states = hidden_states.view(batch, channel, height * width).transpose(1, 2)
|
342 |
+
|
343 |
+
# proj to q, k, v
|
344 |
+
query_proj = self.query(hidden_states)
|
345 |
+
key_proj = self.key(hidden_states)
|
346 |
+
value_proj = self.value(hidden_states)
|
347 |
+
|
348 |
+
scale = 1 / math.sqrt(self.channels / self.num_heads)
|
349 |
+
|
350 |
+
query_proj = self.reshape_heads_to_batch_dim(query_proj)
|
351 |
+
key_proj = self.reshape_heads_to_batch_dim(key_proj)
|
352 |
+
value_proj = self.reshape_heads_to_batch_dim(value_proj)
|
353 |
+
|
354 |
+
if self._use_memory_efficient_attention_xformers:
|
355 |
+
# Memory efficient attention
|
356 |
+
hidden_states = xformers.ops.memory_efficient_attention(query_proj, key_proj, value_proj, attn_bias=None)
|
357 |
+
hidden_states = hidden_states.to(query_proj.dtype)
|
358 |
+
else:
|
359 |
+
attention_scores = torch.baddbmm(
|
360 |
+
torch.empty(
|
361 |
+
query_proj.shape[0],
|
362 |
+
query_proj.shape[1],
|
363 |
+
key_proj.shape[1],
|
364 |
+
dtype=query_proj.dtype,
|
365 |
+
device=query_proj.device,
|
366 |
+
),
|
367 |
+
query_proj,
|
368 |
+
key_proj.transpose(-1, -2),
|
369 |
+
beta=0,
|
370 |
+
alpha=scale,
|
371 |
+
)
|
372 |
+
attention_probs = torch.softmax(attention_scores.float(), dim=-1).type(attention_scores.dtype)
|
373 |
+
hidden_states = torch.bmm(attention_probs, value_proj)
|
374 |
+
|
375 |
+
# reshape hidden_states
|
376 |
+
hidden_states = self.reshape_batch_dim_to_heads(hidden_states)
|
377 |
+
|
378 |
+
# compute next hidden_states
|
379 |
+
hidden_states = self.proj_attn(hidden_states)
|
380 |
+
|
381 |
+
hidden_states = hidden_states.transpose(-1, -2).reshape(batch, channel, height, width)
|
382 |
+
|
383 |
+
# res connect and rescale
|
384 |
+
hidden_states = (hidden_states + residual) / self.rescale_output_factor
|
385 |
+
return hidden_states
|
386 |
+
|
387 |
+
|
388 |
+
class BasicTransformerBlock(nn.Module):
|
389 |
+
r"""
|
390 |
+
A basic Transformer block.
|
391 |
+
|
392 |
+
Parameters:
|
393 |
+
dim (`int`): The number of channels in the input and output.
|
394 |
+
num_attention_heads (`int`): The number of heads to use for multi-head attention.
|
395 |
+
attention_head_dim (`int`): The number of channels in each head.
|
396 |
+
dropout (`float`, *optional*, defaults to 0.0): The dropout probability to use.
|
397 |
+
cross_attention_dim (`int`, *optional*): The size of the encoder_hidden_states vector for cross attention.
|
398 |
+
activation_fn (`str`, *optional*, defaults to `"geglu"`): Activation function to be used in feed-forward.
|
399 |
+
num_embeds_ada_norm (:
|
400 |
+
obj: `int`, *optional*): The number of diffusion steps used during training. See `Transformer2DModel`.
|
401 |
+
attention_bias (:
|
402 |
+
obj: `bool`, *optional*, defaults to `False`): Configure if the attentions should contain a bias parameter.
|
403 |
+
"""
|
404 |
+
|
405 |
+
def __init__(
|
406 |
+
self,
|
407 |
+
dim: int,
|
408 |
+
num_attention_heads: int,
|
409 |
+
attention_head_dim: int,
|
410 |
+
dropout=0.0,
|
411 |
+
cross_attention_dim: Optional[int] = None,
|
412 |
+
activation_fn: str = "geglu",
|
413 |
+
num_embeds_ada_norm: Optional[int] = None,
|
414 |
+
attention_bias: bool = False,
|
415 |
+
only_cross_attention: bool = False,
|
416 |
+
upcast_attention: bool = False,
|
417 |
+
):
|
418 |
+
super().__init__()
|
419 |
+
self.only_cross_attention = only_cross_attention
|
420 |
+
self.use_ada_layer_norm = num_embeds_ada_norm is not None
|
421 |
+
|
422 |
+
# 1. Self-Attn
|
423 |
+
self.attn1 = CrossAttention(
|
424 |
+
query_dim=dim,
|
425 |
+
heads=num_attention_heads,
|
426 |
+
dim_head=attention_head_dim,
|
427 |
+
dropout=dropout,
|
428 |
+
bias=attention_bias,
|
429 |
+
cross_attention_dim=cross_attention_dim if only_cross_attention else None,
|
430 |
+
upcast_attention=upcast_attention,
|
431 |
+
) # is a self-attention
|
432 |
+
self.ff = FeedForward(dim, dropout=dropout, activation_fn=activation_fn)
|
433 |
+
|
434 |
+
# 2. Cross-Attn
|
435 |
+
if cross_attention_dim is not None:
|
436 |
+
self.attn2 = CrossAttention(
|
437 |
+
query_dim=dim,
|
438 |
+
cross_attention_dim=cross_attention_dim,
|
439 |
+
heads=num_attention_heads,
|
440 |
+
dim_head=attention_head_dim,
|
441 |
+
dropout=dropout,
|
442 |
+
bias=attention_bias,
|
443 |
+
upcast_attention=upcast_attention,
|
444 |
+
) # is self-attn if encoder_hidden_states is none
|
445 |
+
else:
|
446 |
+
self.attn2 = None
|
447 |
+
|
448 |
+
self.norm1 = AdaLayerNorm(dim, num_embeds_ada_norm) if self.use_ada_layer_norm else nn.LayerNorm(dim)
|
449 |
+
|
450 |
+
if cross_attention_dim is not None:
|
451 |
+
self.norm2 = AdaLayerNorm(dim, num_embeds_ada_norm) if self.use_ada_layer_norm else nn.LayerNorm(dim)
|
452 |
+
else:
|
453 |
+
self.norm2 = None
|
454 |
+
|
455 |
+
# 3. Feed-forward
|
456 |
+
self.norm3 = nn.LayerNorm(dim)
|
457 |
+
|
458 |
+
def set_use_memory_efficient_attention_xformers(self, use_memory_efficient_attention_xformers: bool, *args, **kwargs):
|
459 |
+
if not is_xformers_available():
|
460 |
+
print("Here is how to install it")
|
461 |
+
raise ModuleNotFoundError(
|
462 |
+
"Refer to https://github.com/facebookresearch/xformers for more information on how to install"
|
463 |
+
" xformers",
|
464 |
+
name="xformers",
|
465 |
+
)
|
466 |
+
elif not torch.cuda.is_available():
|
467 |
+
raise ValueError(
|
468 |
+
"torch.cuda.is_available() should be True but is False. xformers' memory efficient attention is only"
|
469 |
+
" available for GPU "
|
470 |
+
)
|
471 |
+
else:
|
472 |
+
try:
|
473 |
+
# Make sure we can run the memory efficient attention
|
474 |
+
_ = xformers.ops.memory_efficient_attention(
|
475 |
+
torch.randn((1, 2, 40), device="cuda"),
|
476 |
+
torch.randn((1, 2, 40), device="cuda"),
|
477 |
+
torch.randn((1, 2, 40), device="cuda"),
|
478 |
+
)
|
479 |
+
except Exception as e:
|
480 |
+
raise e
|
481 |
+
self.attn1._use_memory_efficient_attention_xformers = use_memory_efficient_attention_xformers
|
482 |
+
if self.attn2 is not None:
|
483 |
+
self.attn2._use_memory_efficient_attention_xformers = use_memory_efficient_attention_xformers
|
484 |
+
|
485 |
+
def forward(self, hidden_states, encoder_hidden_states=None, timestep=None, attention_mask=None):
|
486 |
+
# 1. Self-Attention
|
487 |
+
norm_hidden_states = (
|
488 |
+
self.norm1(hidden_states, timestep) if self.use_ada_layer_norm else self.norm1(hidden_states)
|
489 |
+
)
|
490 |
+
|
491 |
+
if self.only_cross_attention:
|
492 |
+
hidden_states = (
|
493 |
+
self.attn1(norm_hidden_states, encoder_hidden_states, attention_mask=attention_mask) + hidden_states
|
494 |
+
)
|
495 |
+
else:
|
496 |
+
hidden_states = self.attn1(norm_hidden_states, attention_mask=attention_mask) + hidden_states
|
497 |
+
|
498 |
+
if self.attn2 is not None:
|
499 |
+
# 2. Cross-Attention
|
500 |
+
norm_hidden_states = (
|
501 |
+
self.norm2(hidden_states, timestep) if self.use_ada_layer_norm else self.norm2(hidden_states)
|
502 |
+
)
|
503 |
+
hidden_states = (
|
504 |
+
self.attn2(
|
505 |
+
norm_hidden_states, encoder_hidden_states=encoder_hidden_states, attention_mask=attention_mask
|
506 |
+
)
|
507 |
+
+ hidden_states
|
508 |
+
)
|
509 |
+
|
510 |
+
# 3. Feed-forward
|
511 |
+
hidden_states = self.ff(self.norm3(hidden_states)) + hidden_states
|
512 |
+
|
513 |
+
return hidden_states
|
514 |
+
|
515 |
+
|
516 |
+
class CrossAttention(nn.Module):
|
517 |
+
r"""
|
518 |
+
A cross attention layer.
|
519 |
+
|
520 |
+
Parameters:
|
521 |
+
query_dim (`int`): The number of channels in the query.
|
522 |
+
cross_attention_dim (`int`, *optional*):
|
523 |
+
The number of channels in the encoder_hidden_states. If not given, defaults to `query_dim`.
|
524 |
+
heads (`int`, *optional*, defaults to 8): The number of heads to use for multi-head attention.
|
525 |
+
dim_head (`int`, *optional*, defaults to 64): The number of channels in each head.
|
526 |
+
dropout (`float`, *optional*, defaults to 0.0): The dropout probability to use.
|
527 |
+
bias (`bool`, *optional*, defaults to False):
|
528 |
+
Set to `True` for the query, key, and value linear layers to contain a bias parameter.
|
529 |
+
"""
|
530 |
+
|
531 |
+
def __init__(
|
532 |
+
self,
|
533 |
+
query_dim: int,
|
534 |
+
cross_attention_dim: Optional[int] = None,
|
535 |
+
heads: int = 8,
|
536 |
+
dim_head: int = 64,
|
537 |
+
dropout: float = 0.0,
|
538 |
+
bias=False,
|
539 |
+
upcast_attention: bool = False,
|
540 |
+
upcast_softmax: bool = False,
|
541 |
+
added_kv_proj_dim: Optional[int] = None,
|
542 |
+
norm_num_groups: Optional[int] = None,
|
543 |
+
):
|
544 |
+
super().__init__()
|
545 |
+
inner_dim = dim_head * heads
|
546 |
+
cross_attention_dim = cross_attention_dim if cross_attention_dim is not None else query_dim
|
547 |
+
self.upcast_attention = upcast_attention
|
548 |
+
self.upcast_softmax = upcast_softmax
|
549 |
+
|
550 |
+
self.scale = dim_head**-0.5
|
551 |
+
|
552 |
+
self.heads = heads
|
553 |
+
# for slice_size > 0 the attention score computation
|
554 |
+
# is split across the batch axis to save memory
|
555 |
+
# You can set slice_size with `set_attention_slice`
|
556 |
+
self.sliceable_head_dim = heads
|
557 |
+
self._slice_size = None
|
558 |
+
self._use_memory_efficient_attention_xformers = False
|
559 |
+
self.added_kv_proj_dim = added_kv_proj_dim
|
560 |
+
|
561 |
+
if norm_num_groups is not None:
|
562 |
+
self.group_norm = nn.GroupNorm(num_channels=inner_dim, num_groups=norm_num_groups, eps=1e-5, affine=True)
|
563 |
+
else:
|
564 |
+
self.group_norm = None
|
565 |
+
|
566 |
+
self.to_q = nn.Linear(query_dim, inner_dim, bias=bias)
|
567 |
+
self.to_k = nn.Linear(cross_attention_dim, inner_dim, bias=bias)
|
568 |
+
self.to_v = nn.Linear(cross_attention_dim, inner_dim, bias=bias)
|
569 |
+
|
570 |
+
if self.added_kv_proj_dim is not None:
|
571 |
+
self.add_k_proj = nn.Linear(added_kv_proj_dim, cross_attention_dim)
|
572 |
+
self.add_v_proj = nn.Linear(added_kv_proj_dim, cross_attention_dim)
|
573 |
+
|
574 |
+
self.to_out = nn.ModuleList([])
|
575 |
+
self.to_out.append(nn.Linear(inner_dim, query_dim))
|
576 |
+
self.to_out.append(nn.Dropout(dropout))
|
577 |
+
|
578 |
+
def reshape_heads_to_batch_dim(self, tensor):
|
579 |
+
batch_size, seq_len, dim = tensor.shape
|
580 |
+
head_size = self.heads
|
581 |
+
tensor = tensor.reshape(batch_size, seq_len, head_size, dim // head_size)
|
582 |
+
tensor = tensor.permute(0, 2, 1, 3).reshape(batch_size * head_size, seq_len, dim // head_size)
|
583 |
+
return tensor
|
584 |
+
|
585 |
+
def reshape_batch_dim_to_heads(self, tensor):
|
586 |
+
batch_size, seq_len, dim = tensor.shape
|
587 |
+
head_size = self.heads
|
588 |
+
tensor = tensor.reshape(batch_size // head_size, head_size, seq_len, dim)
|
589 |
+
tensor = tensor.permute(0, 2, 1, 3).reshape(batch_size // head_size, seq_len, dim * head_size)
|
590 |
+
return tensor
|
591 |
+
|
592 |
+
def set_attention_slice(self, slice_size):
|
593 |
+
if slice_size is not None and slice_size > self.sliceable_head_dim:
|
594 |
+
raise ValueError(f"slice_size {slice_size} has to be smaller or equal to {self.sliceable_head_dim}.")
|
595 |
+
|
596 |
+
self._slice_size = slice_size
|
597 |
+
|
598 |
+
def forward(self, hidden_states, encoder_hidden_states=None, attention_mask=None):
|
599 |
+
batch_size, sequence_length, _ = hidden_states.shape
|
600 |
+
|
601 |
+
encoder_hidden_states = encoder_hidden_states
|
602 |
+
|
603 |
+
if self.group_norm is not None:
|
604 |
+
hidden_states = self.group_norm(hidden_states.transpose(1, 2)).transpose(1, 2)
|
605 |
+
|
606 |
+
query = self.to_q(hidden_states)
|
607 |
+
dim = query.shape[-1]
|
608 |
+
query = self.reshape_heads_to_batch_dim(query)
|
609 |
+
|
610 |
+
if self.added_kv_proj_dim is not None:
|
611 |
+
key = self.to_k(hidden_states)
|
612 |
+
value = self.to_v(hidden_states)
|
613 |
+
encoder_hidden_states_key_proj = self.add_k_proj(encoder_hidden_states)
|
614 |
+
encoder_hidden_states_value_proj = self.add_v_proj(encoder_hidden_states)
|
615 |
+
|
616 |
+
key = self.reshape_heads_to_batch_dim(key)
|
617 |
+
value = self.reshape_heads_to_batch_dim(value)
|
618 |
+
encoder_hidden_states_key_proj = self.reshape_heads_to_batch_dim(encoder_hidden_states_key_proj)
|
619 |
+
encoder_hidden_states_value_proj = self.reshape_heads_to_batch_dim(encoder_hidden_states_value_proj)
|
620 |
+
|
621 |
+
key = torch.concat([encoder_hidden_states_key_proj, key], dim=1)
|
622 |
+
value = torch.concat([encoder_hidden_states_value_proj, value], dim=1)
|
623 |
+
else:
|
624 |
+
encoder_hidden_states = encoder_hidden_states if encoder_hidden_states is not None else hidden_states
|
625 |
+
key = self.to_k(encoder_hidden_states)
|
626 |
+
value = self.to_v(encoder_hidden_states)
|
627 |
+
|
628 |
+
key = self.reshape_heads_to_batch_dim(key)
|
629 |
+
value = self.reshape_heads_to_batch_dim(value)
|
630 |
+
|
631 |
+
if attention_mask is not None:
|
632 |
+
if attention_mask.shape[-1] != query.shape[1]:
|
633 |
+
target_length = query.shape[1]
|
634 |
+
attention_mask = F.pad(attention_mask, (0, target_length), value=0.0)
|
635 |
+
attention_mask = attention_mask.repeat_interleave(self.heads, dim=0)
|
636 |
+
|
637 |
+
# attention, what we cannot get enough of
|
638 |
+
if self._use_memory_efficient_attention_xformers:
|
639 |
+
hidden_states = self._memory_efficient_attention_xformers(query, key, value, attention_mask)
|
640 |
+
# Some versions of xformers return output in fp32, cast it back to the dtype of the input
|
641 |
+
hidden_states = hidden_states.to(query.dtype)
|
642 |
+
else:
|
643 |
+
if self._slice_size is None or query.shape[0] // self._slice_size == 1:
|
644 |
+
hidden_states = self._attention(query, key, value, attention_mask)
|
645 |
+
else:
|
646 |
+
hidden_states = self._sliced_attention(query, key, value, sequence_length, dim, attention_mask)
|
647 |
+
|
648 |
+
# linear proj
|
649 |
+
hidden_states = self.to_out[0](hidden_states)
|
650 |
+
|
651 |
+
# dropout
|
652 |
+
hidden_states = self.to_out[1](hidden_states)
|
653 |
+
return hidden_states
|
654 |
+
|
655 |
+
def _attention(self, query, key, value, attention_mask=None):
|
656 |
+
if self.upcast_attention:
|
657 |
+
query = query.float()
|
658 |
+
key = key.float()
|
659 |
+
|
660 |
+
attention_scores = torch.baddbmm(
|
661 |
+
torch.empty(query.shape[0], query.shape[1], key.shape[1], dtype=query.dtype, device=query.device),
|
662 |
+
query,
|
663 |
+
key.transpose(-1, -2),
|
664 |
+
beta=0,
|
665 |
+
alpha=self.scale,
|
666 |
+
)
|
667 |
+
|
668 |
+
if attention_mask is not None:
|
669 |
+
attention_scores = attention_scores + attention_mask
|
670 |
+
|
671 |
+
if self.upcast_softmax:
|
672 |
+
attention_scores = attention_scores.float()
|
673 |
+
|
674 |
+
attention_probs = attention_scores.softmax(dim=-1)
|
675 |
+
|
676 |
+
# cast back to the original dtype
|
677 |
+
attention_probs = attention_probs.to(value.dtype)
|
678 |
+
|
679 |
+
# compute attention output
|
680 |
+
hidden_states = torch.bmm(attention_probs, value)
|
681 |
+
|
682 |
+
# reshape hidden_states
|
683 |
+
hidden_states = self.reshape_batch_dim_to_heads(hidden_states)
|
684 |
+
return hidden_states
|
685 |
+
|
686 |
+
def _sliced_attention(self, query, key, value, sequence_length, dim, attention_mask):
|
687 |
+
batch_size_attention = query.shape[0]
|
688 |
+
hidden_states = torch.zeros(
|
689 |
+
(batch_size_attention, sequence_length, dim // self.heads), device=query.device, dtype=query.dtype
|
690 |
+
)
|
691 |
+
slice_size = self._slice_size if self._slice_size is not None else hidden_states.shape[0]
|
692 |
+
for i in range(hidden_states.shape[0] // slice_size):
|
693 |
+
start_idx = i * slice_size
|
694 |
+
end_idx = (i + 1) * slice_size
|
695 |
+
|
696 |
+
query_slice = query[start_idx:end_idx]
|
697 |
+
key_slice = key[start_idx:end_idx]
|
698 |
+
|
699 |
+
if self.upcast_attention:
|
700 |
+
query_slice = query_slice.float()
|
701 |
+
key_slice = key_slice.float()
|
702 |
+
|
703 |
+
attn_slice = torch.baddbmm(
|
704 |
+
torch.empty(slice_size, query.shape[1], key.shape[1], dtype=query_slice.dtype, device=query.device),
|
705 |
+
query_slice,
|
706 |
+
key_slice.transpose(-1, -2),
|
707 |
+
beta=0,
|
708 |
+
alpha=self.scale,
|
709 |
+
)
|
710 |
+
|
711 |
+
if attention_mask is not None:
|
712 |
+
attn_slice = attn_slice + attention_mask[start_idx:end_idx]
|
713 |
+
|
714 |
+
if self.upcast_softmax:
|
715 |
+
attn_slice = attn_slice.float()
|
716 |
+
|
717 |
+
attn_slice = attn_slice.softmax(dim=-1)
|
718 |
+
|
719 |
+
# cast back to the original dtype
|
720 |
+
attn_slice = attn_slice.to(value.dtype)
|
721 |
+
attn_slice = torch.bmm(attn_slice, value[start_idx:end_idx])
|
722 |
+
|
723 |
+
hidden_states[start_idx:end_idx] = attn_slice
|
724 |
+
|
725 |
+
# reshape hidden_states
|
726 |
+
hidden_states = self.reshape_batch_dim_to_heads(hidden_states)
|
727 |
+
return hidden_states
|
728 |
+
|
729 |
+
def _memory_efficient_attention_xformers(self, query, key, value, attention_mask):
|
730 |
+
# TODO attention_mask
|
731 |
+
query = query.contiguous()
|
732 |
+
key = key.contiguous()
|
733 |
+
value = value.contiguous()
|
734 |
+
hidden_states = xformers.ops.memory_efficient_attention(query, key, value, attn_bias=attention_mask)
|
735 |
+
hidden_states = self.reshape_batch_dim_to_heads(hidden_states)
|
736 |
+
return hidden_states
|
737 |
+
|
738 |
+
|
739 |
+
class FeedForward(nn.Module):
|
740 |
+
r"""
|
741 |
+
A feed-forward layer.
|
742 |
+
|
743 |
+
Parameters:
|
744 |
+
dim (`int`): The number of channels in the input.
|
745 |
+
dim_out (`int`, *optional*): The number of channels in the output. If not given, defaults to `dim`.
|
746 |
+
mult (`int`, *optional*, defaults to 4): The multiplier to use for the hidden dimension.
|
747 |
+
dropout (`float`, *optional*, defaults to 0.0): The dropout probability to use.
|
748 |
+
activation_fn (`str`, *optional*, defaults to `"geglu"`): Activation function to be used in feed-forward.
|
749 |
+
"""
|
750 |
+
|
751 |
+
def __init__(
|
752 |
+
self,
|
753 |
+
dim: int,
|
754 |
+
dim_out: Optional[int] = None,
|
755 |
+
mult: int = 4,
|
756 |
+
dropout: float = 0.0,
|
757 |
+
activation_fn: str = "geglu",
|
758 |
+
):
|
759 |
+
super().__init__()
|
760 |
+
inner_dim = int(dim * mult)
|
761 |
+
dim_out = dim_out if dim_out is not None else dim
|
762 |
+
|
763 |
+
if activation_fn == "gelu":
|
764 |
+
act_fn = GELU(dim, inner_dim)
|
765 |
+
elif activation_fn == "geglu":
|
766 |
+
act_fn = GEGLU(dim, inner_dim)
|
767 |
+
elif activation_fn == "geglu-approximate":
|
768 |
+
act_fn = ApproximateGELU(dim, inner_dim)
|
769 |
+
|
770 |
+
self.net = nn.ModuleList([])
|
771 |
+
# project in
|
772 |
+
self.net.append(act_fn)
|
773 |
+
# project dropout
|
774 |
+
self.net.append(nn.Dropout(dropout))
|
775 |
+
# project out
|
776 |
+
self.net.append(nn.Linear(inner_dim, dim_out))
|
777 |
+
|
778 |
+
def forward(self, hidden_states):
|
779 |
+
for module in self.net:
|
780 |
+
hidden_states = module(hidden_states)
|
781 |
+
return hidden_states
|
782 |
+
|
783 |
+
|
784 |
+
class GELU(nn.Module):
|
785 |
+
r"""
|
786 |
+
GELU activation function
|
787 |
+
"""
|
788 |
+
|
789 |
+
def __init__(self, dim_in: int, dim_out: int):
|
790 |
+
super().__init__()
|
791 |
+
self.proj = nn.Linear(dim_in, dim_out)
|
792 |
+
|
793 |
+
def gelu(self, gate):
|
794 |
+
if gate.device.type != "mps":
|
795 |
+
return F.gelu(gate)
|
796 |
+
# mps: gelu is not implemented for float16
|
797 |
+
return F.gelu(gate.to(dtype=torch.float32)).to(dtype=gate.dtype)
|
798 |
+
|
799 |
+
def forward(self, hidden_states):
|
800 |
+
hidden_states = self.proj(hidden_states)
|
801 |
+
hidden_states = self.gelu(hidden_states)
|
802 |
+
return hidden_states
|
803 |
+
|
804 |
+
|
805 |
+
# feedforward
|
806 |
+
class GEGLU(nn.Module):
|
807 |
+
r"""
|
808 |
+
A variant of the gated linear unit activation function from https://arxiv.org/abs/2002.05202.
|
809 |
+
|
810 |
+
Parameters:
|
811 |
+
dim_in (`int`): The number of channels in the input.
|
812 |
+
dim_out (`int`): The number of channels in the output.
|
813 |
+
"""
|
814 |
+
|
815 |
+
def __init__(self, dim_in: int, dim_out: int):
|
816 |
+
super().__init__()
|
817 |
+
self.proj = nn.Linear(dim_in, dim_out * 2)
|
818 |
+
|
819 |
+
def gelu(self, gate):
|
820 |
+
if gate.device.type != "mps":
|
821 |
+
return F.gelu(gate)
|
822 |
+
# mps: gelu is not implemented for float16
|
823 |
+
return F.gelu(gate.to(dtype=torch.float32)).to(dtype=gate.dtype)
|
824 |
+
|
825 |
+
def forward(self, hidden_states):
|
826 |
+
hidden_states, gate = self.proj(hidden_states).chunk(2, dim=-1)
|
827 |
+
return hidden_states * self.gelu(gate)
|
828 |
+
|
829 |
+
|
830 |
+
class ApproximateGELU(nn.Module):
|
831 |
+
"""
|
832 |
+
The approximate form of Gaussian Error Linear Unit (GELU)
|
833 |
+
|
834 |
+
For more details, see section 2: https://arxiv.org/abs/1606.08415
|
835 |
+
"""
|
836 |
+
|
837 |
+
def __init__(self, dim_in: int, dim_out: int):
|
838 |
+
super().__init__()
|
839 |
+
self.proj = nn.Linear(dim_in, dim_out)
|
840 |
+
|
841 |
+
def forward(self, x):
|
842 |
+
x = self.proj(x)
|
843 |
+
return x * torch.sigmoid(1.702 * x)
|
844 |
+
|
845 |
+
|
846 |
+
class AdaLayerNorm(nn.Module):
|
847 |
+
"""
|
848 |
+
Norm layer modified to incorporate timestep embeddings.
|
849 |
+
"""
|
850 |
+
|
851 |
+
def __init__(self, embedding_dim, num_embeddings):
|
852 |
+
super().__init__()
|
853 |
+
self.emb = nn.Embedding(num_embeddings, embedding_dim)
|
854 |
+
self.silu = nn.SiLU()
|
855 |
+
self.linear = nn.Linear(embedding_dim, embedding_dim * 2)
|
856 |
+
self.norm = nn.LayerNorm(embedding_dim, elementwise_affine=False)
|
857 |
+
|
858 |
+
def forward(self, x, timestep):
|
859 |
+
emb = self.linear(self.silu(self.emb(timestep)))
|
860 |
+
scale, shift = torch.chunk(emb, 2)
|
861 |
+
x = self.norm(x) * (1 + scale) + shift
|
862 |
+
return x
|
863 |
+
|
864 |
+
|
865 |
+
class DualTransformer2DModel(nn.Module):
|
866 |
+
"""
|
867 |
+
Dual transformer wrapper that combines two `Transformer2DModel`s for mixed inference.
|
868 |
+
|
869 |
+
Parameters:
|
870 |
+
num_attention_heads (`int`, *optional*, defaults to 16): The number of heads to use for multi-head attention.
|
871 |
+
attention_head_dim (`int`, *optional*, defaults to 88): The number of channels in each head.
|
872 |
+
in_channels (`int`, *optional*):
|
873 |
+
Pass if the input is continuous. The number of channels in the input and output.
|
874 |
+
num_layers (`int`, *optional*, defaults to 1): The number of layers of Transformer blocks to use.
|
875 |
+
dropout (`float`, *optional*, defaults to 0.1): The dropout probability to use.
|
876 |
+
cross_attention_dim (`int`, *optional*): The number of encoder_hidden_states dimensions to use.
|
877 |
+
sample_size (`int`, *optional*): Pass if the input is discrete. The width of the latent images.
|
878 |
+
Note that this is fixed at training time as it is used for learning a number of position embeddings. See
|
879 |
+
`ImagePositionalEmbeddings`.
|
880 |
+
num_vector_embeds (`int`, *optional*):
|
881 |
+
Pass if the input is discrete. The number of classes of the vector embeddings of the latent pixels.
|
882 |
+
Includes the class for the masked latent pixel.
|
883 |
+
activation_fn (`str`, *optional*, defaults to `"geglu"`): Activation function to be used in feed-forward.
|
884 |
+
num_embeds_ada_norm ( `int`, *optional*): Pass if at least one of the norm_layers is `AdaLayerNorm`.
|
885 |
+
The number of diffusion steps used during training. Note that this is fixed at training time as it is used
|
886 |
+
to learn a number of embeddings that are added to the hidden states. During inference, you can denoise for
|
887 |
+
up to but not more than steps than `num_embeds_ada_norm`.
|
888 |
+
attention_bias (`bool`, *optional*):
|
889 |
+
Configure if the TransformerBlocks' attention should contain a bias parameter.
|
890 |
+
"""
|
891 |
+
|
892 |
+
def __init__(
|
893 |
+
self,
|
894 |
+
num_attention_heads: int = 16,
|
895 |
+
attention_head_dim: int = 88,
|
896 |
+
in_channels: Optional[int] = None,
|
897 |
+
num_layers: int = 1,
|
898 |
+
dropout: float = 0.0,
|
899 |
+
norm_num_groups: int = 32,
|
900 |
+
cross_attention_dim: Optional[int] = None,
|
901 |
+
attention_bias: bool = False,
|
902 |
+
sample_size: Optional[int] = None,
|
903 |
+
num_vector_embeds: Optional[int] = None,
|
904 |
+
activation_fn: str = "geglu",
|
905 |
+
num_embeds_ada_norm: Optional[int] = None,
|
906 |
+
):
|
907 |
+
super().__init__()
|
908 |
+
self.transformers = nn.ModuleList(
|
909 |
+
[
|
910 |
+
Transformer2DModel(
|
911 |
+
num_attention_heads=num_attention_heads,
|
912 |
+
attention_head_dim=attention_head_dim,
|
913 |
+
in_channels=in_channels,
|
914 |
+
num_layers=num_layers,
|
915 |
+
dropout=dropout,
|
916 |
+
norm_num_groups=norm_num_groups,
|
917 |
+
cross_attention_dim=cross_attention_dim,
|
918 |
+
attention_bias=attention_bias,
|
919 |
+
sample_size=sample_size,
|
920 |
+
num_vector_embeds=num_vector_embeds,
|
921 |
+
activation_fn=activation_fn,
|
922 |
+
num_embeds_ada_norm=num_embeds_ada_norm,
|
923 |
+
)
|
924 |
+
for _ in range(2)
|
925 |
+
]
|
926 |
+
)
|
927 |
+
|
928 |
+
# Variables that can be set by a pipeline:
|
929 |
+
|
930 |
+
# The ratio of transformer1 to transformer2's output states to be combined during inference
|
931 |
+
self.mix_ratio = 0.5
|
932 |
+
|
933 |
+
# The shape of `encoder_hidden_states` is expected to be
|
934 |
+
# `(batch_size, condition_lengths[0]+condition_lengths[1], num_features)`
|
935 |
+
self.condition_lengths = [77, 257]
|
936 |
+
|
937 |
+
# Which transformer to use to encode which condition.
|
938 |
+
# E.g. `(1, 0)` means that we'll use `transformers[1](conditions[0])` and `transformers[0](conditions[1])`
|
939 |
+
self.transformer_index_for_condition = [1, 0]
|
940 |
+
|
941 |
+
def forward(
|
942 |
+
self, hidden_states, encoder_hidden_states, timestep=None, attention_mask=None, return_dict: bool = True
|
943 |
+
):
|
944 |
+
"""
|
945 |
+
Args:
|
946 |
+
hidden_states ( When discrete, `torch.LongTensor` of shape `(batch size, num latent pixels)`.
|
947 |
+
When continuous, `torch.FloatTensor` of shape `(batch size, channel, height, width)`): Input
|
948 |
+
hidden_states
|
949 |
+
encoder_hidden_states ( `torch.LongTensor` of shape `(batch size, encoder_hidden_states dim)`, *optional*):
|
950 |
+
Conditional embeddings for cross attention layer. If not given, cross-attention defaults to
|
951 |
+
self-attention.
|
952 |
+
timestep ( `torch.long`, *optional*):
|
953 |
+
Optional timestep to be applied as an embedding in AdaLayerNorm's. Used to indicate denoising step.
|
954 |
+
attention_mask (`torch.FloatTensor`, *optional*):
|
955 |
+
Optional attention mask to be applied in CrossAttention
|
956 |
+
return_dict (`bool`, *optional*, defaults to `True`):
|
957 |
+
Whether or not to return a [`models.unet_2d_condition.UNet2DConditionOutput`] instead of a plain tuple.
|
958 |
+
|
959 |
+
Returns:
|
960 |
+
[`~models.attention.Transformer2DModelOutput`] or `tuple`: [`~models.attention.Transformer2DModelOutput`]
|
961 |
+
if `return_dict` is True, otherwise a `tuple`. When returning a tuple, the first element is the sample
|
962 |
+
tensor.
|
963 |
+
"""
|
964 |
+
input_states = hidden_states
|
965 |
+
|
966 |
+
encoded_states = []
|
967 |
+
tokens_start = 0
|
968 |
+
# attention_mask is not used yet
|
969 |
+
for i in range(2):
|
970 |
+
# for each of the two transformers, pass the corresponding condition tokens
|
971 |
+
condition_state = encoder_hidden_states[:, tokens_start : tokens_start + self.condition_lengths[i]]
|
972 |
+
transformer_index = self.transformer_index_for_condition[i]
|
973 |
+
encoded_state = self.transformers[transformer_index](
|
974 |
+
input_states,
|
975 |
+
encoder_hidden_states=condition_state,
|
976 |
+
timestep=timestep,
|
977 |
+
return_dict=False,
|
978 |
+
)[0]
|
979 |
+
encoded_states.append(encoded_state - input_states)
|
980 |
+
tokens_start += self.condition_lengths[i]
|
981 |
+
|
982 |
+
output_states = encoded_states[0] * self.mix_ratio + encoded_states[1] * (1 - self.mix_ratio)
|
983 |
+
output_states = output_states + input_states
|
984 |
+
|
985 |
+
if not return_dict:
|
986 |
+
return (output_states,)
|
987 |
+
|
988 |
+
return Transformer2DModelOutput(sample=output_states)
|
magicanimate/models/resnet.py
ADDED
@@ -0,0 +1,212 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
# *************************************************************************
|
2 |
+
# This file may have been modified by Bytedance Inc. (“Bytedance Inc.'s Mo-
|
3 |
+
# difications”). All Bytedance Inc.'s Modifications are Copyright (2023) B-
|
4 |
+
# ytedance Inc..
|
5 |
+
# *************************************************************************
|
6 |
+
|
7 |
+
# Adapted from https://github.com/guoyww/AnimateDiff
|
8 |
+
|
9 |
+
# Copyright 2023 The HuggingFace Team. All rights reserved.
|
10 |
+
# `TemporalConvLayer` Copyright 2023 Alibaba DAMO-VILAB, The ModelScope Team and The HuggingFace Team. All rights reserved.
|
11 |
+
#
|
12 |
+
# Licensed under the Apache License, Version 2.0 (the "License");
|
13 |
+
# you may not use this file except in compliance with the License.
|
14 |
+
# You may obtain a copy of the License at
|
15 |
+
#
|
16 |
+
# http://www.apache.org/licenses/LICENSE-2.0
|
17 |
+
#
|
18 |
+
# Unless required by applicable law or agreed to in writing, software
|
19 |
+
# distributed under the License is distributed on an "AS IS" BASIS,
|
20 |
+
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
21 |
+
# See the License for the specific language governing permissions and
|
22 |
+
# limitations under the License.
|
23 |
+
import torch
|
24 |
+
import torch.nn as nn
|
25 |
+
import torch.nn.functional as F
|
26 |
+
|
27 |
+
from einops import rearrange
|
28 |
+
|
29 |
+
|
30 |
+
class InflatedConv3d(nn.Conv2d):
|
31 |
+
def forward(self, x):
|
32 |
+
video_length = x.shape[2]
|
33 |
+
|
34 |
+
x = rearrange(x, "b c f h w -> (b f) c h w")
|
35 |
+
x = super().forward(x)
|
36 |
+
x = rearrange(x, "(b f) c h w -> b c f h w", f=video_length)
|
37 |
+
|
38 |
+
return x
|
39 |
+
|
40 |
+
|
41 |
+
class Upsample3D(nn.Module):
|
42 |
+
def __init__(self, channels, use_conv=False, use_conv_transpose=False, out_channels=None, name="conv"):
|
43 |
+
super().__init__()
|
44 |
+
self.channels = channels
|
45 |
+
self.out_channels = out_channels or channels
|
46 |
+
self.use_conv = use_conv
|
47 |
+
self.use_conv_transpose = use_conv_transpose
|
48 |
+
self.name = name
|
49 |
+
|
50 |
+
conv = None
|
51 |
+
if use_conv_transpose:
|
52 |
+
raise NotImplementedError
|
53 |
+
elif use_conv:
|
54 |
+
self.conv = InflatedConv3d(self.channels, self.out_channels, 3, padding=1)
|
55 |
+
|
56 |
+
def forward(self, hidden_states, output_size=None):
|
57 |
+
assert hidden_states.shape[1] == self.channels
|
58 |
+
|
59 |
+
if self.use_conv_transpose:
|
60 |
+
raise NotImplementedError
|
61 |
+
|
62 |
+
# Cast to float32 to as 'upsample_nearest2d_out_frame' op does not support bfloat16
|
63 |
+
dtype = hidden_states.dtype
|
64 |
+
if dtype == torch.bfloat16:
|
65 |
+
hidden_states = hidden_states.to(torch.float32)
|
66 |
+
|
67 |
+
# upsample_nearest_nhwc fails with large batch sizes. see https://github.com/huggingface/diffusers/issues/984
|
68 |
+
if hidden_states.shape[0] >= 64:
|
69 |
+
hidden_states = hidden_states.contiguous()
|
70 |
+
|
71 |
+
# if `output_size` is passed we force the interpolation output
|
72 |
+
# size and do not make use of `scale_factor=2`
|
73 |
+
if output_size is None:
|
74 |
+
hidden_states = F.interpolate(hidden_states, scale_factor=[1.0, 2.0, 2.0], mode="nearest")
|
75 |
+
else:
|
76 |
+
hidden_states = F.interpolate(hidden_states, size=output_size, mode="nearest")
|
77 |
+
|
78 |
+
# If the input is bfloat16, we cast back to bfloat16
|
79 |
+
if dtype == torch.bfloat16:
|
80 |
+
hidden_states = hidden_states.to(dtype)
|
81 |
+
|
82 |
+
hidden_states = self.conv(hidden_states)
|
83 |
+
|
84 |
+
return hidden_states
|
85 |
+
|
86 |
+
|
87 |
+
class Downsample3D(nn.Module):
|
88 |
+
def __init__(self, channels, use_conv=False, out_channels=None, padding=1, name="conv"):
|
89 |
+
super().__init__()
|
90 |
+
self.channels = channels
|
91 |
+
self.out_channels = out_channels or channels
|
92 |
+
self.use_conv = use_conv
|
93 |
+
self.padding = padding
|
94 |
+
stride = 2
|
95 |
+
self.name = name
|
96 |
+
|
97 |
+
if use_conv:
|
98 |
+
self.conv = InflatedConv3d(self.channels, self.out_channels, 3, stride=stride, padding=padding)
|
99 |
+
else:
|
100 |
+
raise NotImplementedError
|
101 |
+
|
102 |
+
def forward(self, hidden_states):
|
103 |
+
assert hidden_states.shape[1] == self.channels
|
104 |
+
if self.use_conv and self.padding == 0:
|
105 |
+
raise NotImplementedError
|
106 |
+
|
107 |
+
assert hidden_states.shape[1] == self.channels
|
108 |
+
hidden_states = self.conv(hidden_states)
|
109 |
+
|
110 |
+
return hidden_states
|
111 |
+
|
112 |
+
|
113 |
+
class ResnetBlock3D(nn.Module):
|
114 |
+
def __init__(
|
115 |
+
self,
|
116 |
+
*,
|
117 |
+
in_channels,
|
118 |
+
out_channels=None,
|
119 |
+
conv_shortcut=False,
|
120 |
+
dropout=0.0,
|
121 |
+
temb_channels=512,
|
122 |
+
groups=32,
|
123 |
+
groups_out=None,
|
124 |
+
pre_norm=True,
|
125 |
+
eps=1e-6,
|
126 |
+
non_linearity="swish",
|
127 |
+
time_embedding_norm="default",
|
128 |
+
output_scale_factor=1.0,
|
129 |
+
use_in_shortcut=None,
|
130 |
+
):
|
131 |
+
super().__init__()
|
132 |
+
self.pre_norm = pre_norm
|
133 |
+
self.pre_norm = True
|
134 |
+
self.in_channels = in_channels
|
135 |
+
out_channels = in_channels if out_channels is None else out_channels
|
136 |
+
self.out_channels = out_channels
|
137 |
+
self.use_conv_shortcut = conv_shortcut
|
138 |
+
self.time_embedding_norm = time_embedding_norm
|
139 |
+
self.output_scale_factor = output_scale_factor
|
140 |
+
|
141 |
+
if groups_out is None:
|
142 |
+
groups_out = groups
|
143 |
+
|
144 |
+
self.norm1 = torch.nn.GroupNorm(num_groups=groups, num_channels=in_channels, eps=eps, affine=True)
|
145 |
+
|
146 |
+
self.conv1 = InflatedConv3d(in_channels, out_channels, kernel_size=3, stride=1, padding=1)
|
147 |
+
|
148 |
+
if temb_channels is not None:
|
149 |
+
if self.time_embedding_norm == "default":
|
150 |
+
time_emb_proj_out_channels = out_channels
|
151 |
+
elif self.time_embedding_norm == "scale_shift":
|
152 |
+
time_emb_proj_out_channels = out_channels * 2
|
153 |
+
else:
|
154 |
+
raise ValueError(f"unknown time_embedding_norm : {self.time_embedding_norm} ")
|
155 |
+
|
156 |
+
self.time_emb_proj = torch.nn.Linear(temb_channels, time_emb_proj_out_channels)
|
157 |
+
else:
|
158 |
+
self.time_emb_proj = None
|
159 |
+
|
160 |
+
self.norm2 = torch.nn.GroupNorm(num_groups=groups_out, num_channels=out_channels, eps=eps, affine=True)
|
161 |
+
self.dropout = torch.nn.Dropout(dropout)
|
162 |
+
self.conv2 = InflatedConv3d(out_channels, out_channels, kernel_size=3, stride=1, padding=1)
|
163 |
+
|
164 |
+
if non_linearity == "swish":
|
165 |
+
self.nonlinearity = lambda x: F.silu(x)
|
166 |
+
elif non_linearity == "mish":
|
167 |
+
self.nonlinearity = Mish()
|
168 |
+
elif non_linearity == "silu":
|
169 |
+
self.nonlinearity = nn.SiLU()
|
170 |
+
|
171 |
+
self.use_in_shortcut = self.in_channels != self.out_channels if use_in_shortcut is None else use_in_shortcut
|
172 |
+
|
173 |
+
self.conv_shortcut = None
|
174 |
+
if self.use_in_shortcut:
|
175 |
+
self.conv_shortcut = InflatedConv3d(in_channels, out_channels, kernel_size=1, stride=1, padding=0)
|
176 |
+
|
177 |
+
def forward(self, input_tensor, temb):
|
178 |
+
hidden_states = input_tensor
|
179 |
+
|
180 |
+
hidden_states = self.norm1(hidden_states)
|
181 |
+
hidden_states = self.nonlinearity(hidden_states)
|
182 |
+
|
183 |
+
hidden_states = self.conv1(hidden_states)
|
184 |
+
|
185 |
+
if temb is not None:
|
186 |
+
temb = self.time_emb_proj(self.nonlinearity(temb))[:, :, None, None, None]
|
187 |
+
|
188 |
+
if temb is not None and self.time_embedding_norm == "default":
|
189 |
+
hidden_states = hidden_states + temb
|
190 |
+
|
191 |
+
hidden_states = self.norm2(hidden_states)
|
192 |
+
|
193 |
+
if temb is not None and self.time_embedding_norm == "scale_shift":
|
194 |
+
scale, shift = torch.chunk(temb, 2, dim=1)
|
195 |
+
hidden_states = hidden_states * (1 + scale) + shift
|
196 |
+
|
197 |
+
hidden_states = self.nonlinearity(hidden_states)
|
198 |
+
|
199 |
+
hidden_states = self.dropout(hidden_states)
|
200 |
+
hidden_states = self.conv2(hidden_states)
|
201 |
+
|
202 |
+
if self.conv_shortcut is not None:
|
203 |
+
input_tensor = self.conv_shortcut(input_tensor)
|
204 |
+
|
205 |
+
output_tensor = (input_tensor + hidden_states) / self.output_scale_factor
|
206 |
+
|
207 |
+
return output_tensor
|
208 |
+
|
209 |
+
|
210 |
+
class Mish(torch.nn.Module):
|
211 |
+
def forward(self, hidden_states):
|
212 |
+
return hidden_states * torch.tanh(torch.nn.functional.softplus(hidden_states))
|
magicanimate/models/stable_diffusion_controlnet_reference.py
ADDED
@@ -0,0 +1,840 @@
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|
|
|
1 |
+
# *************************************************************************
|
2 |
+
# This file may have been modified by Bytedance Inc. (“Bytedance Inc.'s Mo-
|
3 |
+
# difications”). All Bytedance Inc.'s Modifications are Copyright (2023) B-
|
4 |
+
# ytedance Inc..
|
5 |
+
# *************************************************************************
|
6 |
+
|
7 |
+
# Inspired by: https://github.com/Mikubill/sd-webui-controlnet/discussions/1236 and https://github.com/Mikubill/sd-webui-controlnet/discussions/1280
|
8 |
+
from typing import Any, Callable, Dict, List, Optional, Tuple, Union
|
9 |
+
|
10 |
+
import numpy as np
|
11 |
+
import PIL.Image
|
12 |
+
import torch
|
13 |
+
|
14 |
+
from diffusers import StableDiffusionControlNetPipeline
|
15 |
+
from diffusers.models import ControlNetModel
|
16 |
+
from diffusers.models.attention import BasicTransformerBlock
|
17 |
+
from diffusers.models.unet_2d_blocks import CrossAttnDownBlock2D, CrossAttnUpBlock2D, DownBlock2D, UpBlock2D
|
18 |
+
from diffusers.pipelines.controlnet.multicontrolnet import MultiControlNetModel
|
19 |
+
from diffusers.pipelines.stable_diffusion import StableDiffusionPipelineOutput
|
20 |
+
from diffusers.utils import logging
|
21 |
+
from diffusers.utils.torch_utils import is_compiled_module, randn_tensor
|
22 |
+
|
23 |
+
logger = logging.get_logger(__name__) # pylint: disable=invalid-name
|
24 |
+
|
25 |
+
EXAMPLE_DOC_STRING = """
|
26 |
+
Examples:
|
27 |
+
```py
|
28 |
+
>>> import cv2
|
29 |
+
>>> import torch
|
30 |
+
>>> import numpy as np
|
31 |
+
>>> from PIL import Image
|
32 |
+
>>> from diffusers import UniPCMultistepScheduler
|
33 |
+
>>> from diffusers.utils import load_image
|
34 |
+
|
35 |
+
>>> input_image = load_image("https://hf.co/datasets/huggingface/documentation-images/resolve/main/diffusers/input_image_vermeer.png")
|
36 |
+
|
37 |
+
>>> # get canny image
|
38 |
+
>>> image = cv2.Canny(np.array(input_image), 100, 200)
|
39 |
+
>>> image = image[:, :, None]
|
40 |
+
>>> image = np.concatenate([image, image, image], axis=2)
|
41 |
+
>>> canny_image = Image.fromarray(image)
|
42 |
+
|
43 |
+
>>> controlnet = ControlNetModel.from_pretrained("lllyasviel/sd-controlnet-canny", torch_dtype=torch.float16)
|
44 |
+
>>> pipe = StableDiffusionControlNetReferencePipeline.from_pretrained(
|
45 |
+
"runwayml/stable-diffusion-v1-5",
|
46 |
+
controlnet=controlnet,
|
47 |
+
safety_checker=None,
|
48 |
+
torch_dtype=torch.float16
|
49 |
+
).to('cuda:0')
|
50 |
+
|
51 |
+
>>> pipe.scheduler = UniPCMultistepScheduler.from_config(pipe_controlnet.scheduler.config)
|
52 |
+
|
53 |
+
>>> result_img = pipe(ref_image=input_image,
|
54 |
+
prompt="1girl",
|
55 |
+
image=canny_image,
|
56 |
+
num_inference_steps=20,
|
57 |
+
reference_attn=True,
|
58 |
+
reference_adain=True).images[0]
|
59 |
+
|
60 |
+
>>> result_img.show()
|
61 |
+
```
|
62 |
+
"""
|
63 |
+
|
64 |
+
|
65 |
+
def torch_dfs(model: torch.nn.Module):
|
66 |
+
result = [model]
|
67 |
+
for child in model.children():
|
68 |
+
result += torch_dfs(child)
|
69 |
+
return result
|
70 |
+
|
71 |
+
|
72 |
+
class StableDiffusionControlNetReferencePipeline(StableDiffusionControlNetPipeline):
|
73 |
+
def prepare_ref_latents(self, refimage, batch_size, dtype, device, generator, do_classifier_free_guidance):
|
74 |
+
refimage = refimage.to(device=device, dtype=dtype)
|
75 |
+
|
76 |
+
# encode the mask image into latents space so we can concatenate it to the latents
|
77 |
+
if isinstance(generator, list):
|
78 |
+
ref_image_latents = [
|
79 |
+
self.vae.encode(refimage[i : i + 1]).latent_dist.sample(generator=generator[i])
|
80 |
+
for i in range(batch_size)
|
81 |
+
]
|
82 |
+
ref_image_latents = torch.cat(ref_image_latents, dim=0)
|
83 |
+
else:
|
84 |
+
ref_image_latents = self.vae.encode(refimage).latent_dist.sample(generator=generator)
|
85 |
+
ref_image_latents = self.vae.config.scaling_factor * ref_image_latents
|
86 |
+
|
87 |
+
# duplicate mask and ref_image_latents for each generation per prompt, using mps friendly method
|
88 |
+
if ref_image_latents.shape[0] < batch_size:
|
89 |
+
if not batch_size % ref_image_latents.shape[0] == 0:
|
90 |
+
raise ValueError(
|
91 |
+
"The passed images and the required batch size don't match. Images are supposed to be duplicated"
|
92 |
+
f" to a total batch size of {batch_size}, but {ref_image_latents.shape[0]} images were passed."
|
93 |
+
" Make sure the number of images that you pass is divisible by the total requested batch size."
|
94 |
+
)
|
95 |
+
ref_image_latents = ref_image_latents.repeat(batch_size // ref_image_latents.shape[0], 1, 1, 1)
|
96 |
+
|
97 |
+
ref_image_latents = torch.cat([ref_image_latents] * 2) if do_classifier_free_guidance else ref_image_latents
|
98 |
+
|
99 |
+
# aligning device to prevent device errors when concating it with the latent model input
|
100 |
+
ref_image_latents = ref_image_latents.to(device=device, dtype=dtype)
|
101 |
+
return ref_image_latents
|
102 |
+
|
103 |
+
@torch.no_grad()
|
104 |
+
def __call__(
|
105 |
+
self,
|
106 |
+
prompt: Union[str, List[str]] = None,
|
107 |
+
image: Union[
|
108 |
+
torch.FloatTensor,
|
109 |
+
PIL.Image.Image,
|
110 |
+
np.ndarray,
|
111 |
+
List[torch.FloatTensor],
|
112 |
+
List[PIL.Image.Image],
|
113 |
+
List[np.ndarray],
|
114 |
+
] = None,
|
115 |
+
ref_image: Union[torch.FloatTensor, PIL.Image.Image] = None,
|
116 |
+
height: Optional[int] = None,
|
117 |
+
width: Optional[int] = None,
|
118 |
+
num_inference_steps: int = 50,
|
119 |
+
guidance_scale: float = 7.5,
|
120 |
+
negative_prompt: Optional[Union[str, List[str]]] = None,
|
121 |
+
num_images_per_prompt: Optional[int] = 1,
|
122 |
+
eta: float = 0.0,
|
123 |
+
generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None,
|
124 |
+
latents: Optional[torch.FloatTensor] = None,
|
125 |
+
prompt_embeds: Optional[torch.FloatTensor] = None,
|
126 |
+
negative_prompt_embeds: Optional[torch.FloatTensor] = None,
|
127 |
+
output_type: Optional[str] = "pil",
|
128 |
+
return_dict: bool = True,
|
129 |
+
callback: Optional[Callable[[int, int, torch.FloatTensor], None]] = None,
|
130 |
+
callback_steps: int = 1,
|
131 |
+
cross_attention_kwargs: Optional[Dict[str, Any]] = None,
|
132 |
+
controlnet_conditioning_scale: Union[float, List[float]] = 1.0,
|
133 |
+
guess_mode: bool = False,
|
134 |
+
attention_auto_machine_weight: float = 1.0,
|
135 |
+
gn_auto_machine_weight: float = 1.0,
|
136 |
+
style_fidelity: float = 0.5,
|
137 |
+
reference_attn: bool = True,
|
138 |
+
reference_adain: bool = True,
|
139 |
+
):
|
140 |
+
r"""
|
141 |
+
Function invoked when calling the pipeline for generation.
|
142 |
+
|
143 |
+
Args:
|
144 |
+
prompt (`str` or `List[str]`, *optional*):
|
145 |
+
The prompt or prompts to guide the image generation. If not defined, one has to pass `prompt_embeds`.
|
146 |
+
instead.
|
147 |
+
image (`torch.FloatTensor`, `PIL.Image.Image`, `np.ndarray`, `List[torch.FloatTensor]`, `List[PIL.Image.Image]`, `List[np.ndarray]`,:
|
148 |
+
`List[List[torch.FloatTensor]]`, `List[List[np.ndarray]]` or `List[List[PIL.Image.Image]]`):
|
149 |
+
The ControlNet input condition. ControlNet uses this input condition to generate guidance to Unet. If
|
150 |
+
the type is specified as `Torch.FloatTensor`, it is passed to ControlNet as is. `PIL.Image.Image` can
|
151 |
+
also be accepted as an image. The dimensions of the output image defaults to `image`'s dimensions. If
|
152 |
+
height and/or width are passed, `image` is resized according to them. If multiple ControlNets are
|
153 |
+
specified in init, images must be passed as a list such that each element of the list can be correctly
|
154 |
+
batched for input to a single controlnet.
|
155 |
+
ref_image (`torch.FloatTensor`, `PIL.Image.Image`):
|
156 |
+
The Reference Control input condition. Reference Control uses this input condition to generate guidance to Unet. If
|
157 |
+
the type is specified as `Torch.FloatTensor`, it is passed to Reference Control as is. `PIL.Image.Image` can
|
158 |
+
also be accepted as an image.
|
159 |
+
height (`int`, *optional*, defaults to self.unet.config.sample_size * self.vae_scale_factor):
|
160 |
+
The height in pixels of the generated image.
|
161 |
+
width (`int`, *optional*, defaults to self.unet.config.sample_size * self.vae_scale_factor):
|
162 |
+
The width in pixels of the generated image.
|
163 |
+
num_inference_steps (`int`, *optional*, defaults to 50):
|
164 |
+
The number of denoising steps. More denoising steps usually lead to a higher quality image at the
|
165 |
+
expense of slower inference.
|
166 |
+
guidance_scale (`float`, *optional*, defaults to 7.5):
|
167 |
+
Guidance scale as defined in [Classifier-Free Diffusion Guidance](https://arxiv.org/abs/2207.12598).
|
168 |
+
`guidance_scale` is defined as `w` of equation 2. of [Imagen
|
169 |
+
Paper](https://arxiv.org/pdf/2205.11487.pdf). Guidance scale is enabled by setting `guidance_scale >
|
170 |
+
1`. Higher guidance scale encourages to generate images that are closely linked to the text `prompt`,
|
171 |
+
usually at the expense of lower image quality.
|
172 |
+
negative_prompt (`str` or `List[str]`, *optional*):
|
173 |
+
The prompt or prompts not to guide the image generation. If not defined, one has to pass
|
174 |
+
`negative_prompt_embeds` instead. Ignored when not using guidance (i.e., ignored if `guidance_scale` is
|
175 |
+
less than `1`).
|
176 |
+
num_images_per_prompt (`int`, *optional*, defaults to 1):
|
177 |
+
The number of images to generate per prompt.
|
178 |
+
eta (`float`, *optional*, defaults to 0.0):
|
179 |
+
Corresponds to parameter eta (η) in the DDIM paper: https://arxiv.org/abs/2010.02502. Only applies to
|
180 |
+
[`schedulers.DDIMScheduler`], will be ignored for others.
|
181 |
+
generator (`torch.Generator` or `List[torch.Generator]`, *optional*):
|
182 |
+
One or a list of [torch generator(s)](https://pytorch.org/docs/stable/generated/torch.Generator.html)
|
183 |
+
to make generation deterministic.
|
184 |
+
latents (`torch.FloatTensor`, *optional*):
|
185 |
+
Pre-generated noisy latents, sampled from a Gaussian distribution, to be used as inputs for image
|
186 |
+
generation. Can be used to tweak the same generation with different prompts. If not provided, a latents
|
187 |
+
tensor will ge generated by sampling using the supplied random `generator`.
|
188 |
+
prompt_embeds (`torch.FloatTensor`, *optional*):
|
189 |
+
Pre-generated text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not
|
190 |
+
provided, text embeddings will be generated from `prompt` input argument.
|
191 |
+
negative_prompt_embeds (`torch.FloatTensor`, *optional*):
|
192 |
+
Pre-generated negative text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt
|
193 |
+
weighting. If not provided, negative_prompt_embeds will be generated from `negative_prompt` input
|
194 |
+
argument.
|
195 |
+
output_type (`str`, *optional*, defaults to `"pil"`):
|
196 |
+
The output format of the generate image. Choose between
|
197 |
+
[PIL](https://pillow.readthedocs.io/en/stable/): `PIL.Image.Image` or `np.array`.
|
198 |
+
return_dict (`bool`, *optional*, defaults to `True`):
|
199 |
+
Whether or not to return a [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] instead of a
|
200 |
+
plain tuple.
|
201 |
+
callback (`Callable`, *optional*):
|
202 |
+
A function that will be called every `callback_steps` steps during inference. The function will be
|
203 |
+
called with the following arguments: `callback(step: int, timestep: int, latents: torch.FloatTensor)`.
|
204 |
+
callback_steps (`int`, *optional*, defaults to 1):
|
205 |
+
The frequency at which the `callback` function will be called. If not specified, the callback will be
|
206 |
+
called at every step.
|
207 |
+
cross_attention_kwargs (`dict`, *optional*):
|
208 |
+
A kwargs dictionary that if specified is passed along to the `AttentionProcessor` as defined under
|
209 |
+
`self.processor` in
|
210 |
+
[diffusers.models.attention_processor](https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/attention_processor.py).
|
211 |
+
controlnet_conditioning_scale (`float` or `List[float]`, *optional*, defaults to 1.0):
|
212 |
+
The outputs of the controlnet are multiplied by `controlnet_conditioning_scale` before they are added
|
213 |
+
to the residual in the original unet. If multiple ControlNets are specified in init, you can set the
|
214 |
+
corresponding scale as a list.
|
215 |
+
guess_mode (`bool`, *optional*, defaults to `False`):
|
216 |
+
In this mode, the ControlNet encoder will try best to recognize the content of the input image even if
|
217 |
+
you remove all prompts. The `guidance_scale` between 3.0 and 5.0 is recommended.
|
218 |
+
attention_auto_machine_weight (`float`):
|
219 |
+
Weight of using reference query for self attention's context.
|
220 |
+
If attention_auto_machine_weight=1.0, use reference query for all self attention's context.
|
221 |
+
gn_auto_machine_weight (`float`):
|
222 |
+
Weight of using reference adain. If gn_auto_machine_weight=2.0, use all reference adain plugins.
|
223 |
+
style_fidelity (`float`):
|
224 |
+
style fidelity of ref_uncond_xt. If style_fidelity=1.0, control more important,
|
225 |
+
elif style_fidelity=0.0, prompt more important, else balanced.
|
226 |
+
reference_attn (`bool`):
|
227 |
+
Whether to use reference query for self attention's context.
|
228 |
+
reference_adain (`bool`):
|
229 |
+
Whether to use reference adain.
|
230 |
+
|
231 |
+
Examples:
|
232 |
+
|
233 |
+
Returns:
|
234 |
+
[`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] or `tuple`:
|
235 |
+
[`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] if `return_dict` is True, otherwise a `tuple.
|
236 |
+
When returning a tuple, the first element is a list with the generated images, and the second element is a
|
237 |
+
list of `bool`s denoting whether the corresponding generated image likely represents "not-safe-for-work"
|
238 |
+
(nsfw) content, according to the `safety_checker`.
|
239 |
+
"""
|
240 |
+
assert reference_attn or reference_adain, "`reference_attn` or `reference_adain` must be True."
|
241 |
+
|
242 |
+
# 1. Check inputs. Raise error if not correct
|
243 |
+
self.check_inputs(
|
244 |
+
prompt,
|
245 |
+
image,
|
246 |
+
callback_steps,
|
247 |
+
negative_prompt,
|
248 |
+
prompt_embeds,
|
249 |
+
negative_prompt_embeds,
|
250 |
+
controlnet_conditioning_scale,
|
251 |
+
)
|
252 |
+
|
253 |
+
# 2. Define call parameters
|
254 |
+
if prompt is not None and isinstance(prompt, str):
|
255 |
+
batch_size = 1
|
256 |
+
elif prompt is not None and isinstance(prompt, list):
|
257 |
+
batch_size = len(prompt)
|
258 |
+
else:
|
259 |
+
batch_size = prompt_embeds.shape[0]
|
260 |
+
|
261 |
+
device = self._execution_device
|
262 |
+
# here `guidance_scale` is defined analog to the guidance weight `w` of equation (2)
|
263 |
+
# of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1`
|
264 |
+
# corresponds to doing no classifier free guidance.
|
265 |
+
do_classifier_free_guidance = guidance_scale > 1.0
|
266 |
+
|
267 |
+
controlnet = self.controlnet._orig_mod if is_compiled_module(self.controlnet) else self.controlnet
|
268 |
+
|
269 |
+
if isinstance(controlnet, MultiControlNetModel) and isinstance(controlnet_conditioning_scale, float):
|
270 |
+
controlnet_conditioning_scale = [controlnet_conditioning_scale] * len(controlnet.nets)
|
271 |
+
|
272 |
+
global_pool_conditions = (
|
273 |
+
controlnet.config.global_pool_conditions
|
274 |
+
if isinstance(controlnet, ControlNetModel)
|
275 |
+
else controlnet.nets[0].config.global_pool_conditions
|
276 |
+
)
|
277 |
+
guess_mode = guess_mode or global_pool_conditions
|
278 |
+
|
279 |
+
# 3. Encode input prompt
|
280 |
+
text_encoder_lora_scale = (
|
281 |
+
cross_attention_kwargs.get("scale", None) if cross_attention_kwargs is not None else None
|
282 |
+
)
|
283 |
+
prompt_embeds = self._encode_prompt(
|
284 |
+
prompt,
|
285 |
+
device,
|
286 |
+
num_images_per_prompt,
|
287 |
+
do_classifier_free_guidance,
|
288 |
+
negative_prompt,
|
289 |
+
prompt_embeds=prompt_embeds,
|
290 |
+
negative_prompt_embeds=negative_prompt_embeds,
|
291 |
+
lora_scale=text_encoder_lora_scale,
|
292 |
+
)
|
293 |
+
|
294 |
+
# 4. Prepare image
|
295 |
+
if isinstance(controlnet, ControlNetModel):
|
296 |
+
image = self.prepare_image(
|
297 |
+
image=image,
|
298 |
+
width=width,
|
299 |
+
height=height,
|
300 |
+
batch_size=batch_size * num_images_per_prompt,
|
301 |
+
num_images_per_prompt=num_images_per_prompt,
|
302 |
+
device=device,
|
303 |
+
dtype=controlnet.dtype,
|
304 |
+
do_classifier_free_guidance=do_classifier_free_guidance,
|
305 |
+
guess_mode=guess_mode,
|
306 |
+
)
|
307 |
+
height, width = image.shape[-2:]
|
308 |
+
elif isinstance(controlnet, MultiControlNetModel):
|
309 |
+
images = []
|
310 |
+
|
311 |
+
for image_ in image:
|
312 |
+
image_ = self.prepare_image(
|
313 |
+
image=image_,
|
314 |
+
width=width,
|
315 |
+
height=height,
|
316 |
+
batch_size=batch_size * num_images_per_prompt,
|
317 |
+
num_images_per_prompt=num_images_per_prompt,
|
318 |
+
device=device,
|
319 |
+
dtype=controlnet.dtype,
|
320 |
+
do_classifier_free_guidance=do_classifier_free_guidance,
|
321 |
+
guess_mode=guess_mode,
|
322 |
+
)
|
323 |
+
|
324 |
+
images.append(image_)
|
325 |
+
|
326 |
+
image = images
|
327 |
+
height, width = image[0].shape[-2:]
|
328 |
+
else:
|
329 |
+
assert False
|
330 |
+
|
331 |
+
# 5. Preprocess reference image
|
332 |
+
ref_image = self.prepare_image(
|
333 |
+
image=ref_image,
|
334 |
+
width=width,
|
335 |
+
height=height,
|
336 |
+
batch_size=batch_size * num_images_per_prompt,
|
337 |
+
num_images_per_prompt=num_images_per_prompt,
|
338 |
+
device=device,
|
339 |
+
dtype=prompt_embeds.dtype,
|
340 |
+
)
|
341 |
+
|
342 |
+
# 6. Prepare timesteps
|
343 |
+
self.scheduler.set_timesteps(num_inference_steps, device=device)
|
344 |
+
timesteps = self.scheduler.timesteps
|
345 |
+
|
346 |
+
# 7. Prepare latent variables
|
347 |
+
num_channels_latents = self.unet.config.in_channels
|
348 |
+
latents = self.prepare_latents(
|
349 |
+
batch_size * num_images_per_prompt,
|
350 |
+
num_channels_latents,
|
351 |
+
height,
|
352 |
+
width,
|
353 |
+
prompt_embeds.dtype,
|
354 |
+
device,
|
355 |
+
generator,
|
356 |
+
latents,
|
357 |
+
)
|
358 |
+
|
359 |
+
# 8. Prepare reference latent variables
|
360 |
+
ref_image_latents = self.prepare_ref_latents(
|
361 |
+
ref_image,
|
362 |
+
batch_size * num_images_per_prompt,
|
363 |
+
prompt_embeds.dtype,
|
364 |
+
device,
|
365 |
+
generator,
|
366 |
+
do_classifier_free_guidance,
|
367 |
+
)
|
368 |
+
|
369 |
+
# 9. Prepare extra step kwargs. TODO: Logic should ideally just be moved out of the pipeline
|
370 |
+
extra_step_kwargs = self.prepare_extra_step_kwargs(generator, eta)
|
371 |
+
|
372 |
+
# 10. Modify self attention and group norm
|
373 |
+
MODE = "write"
|
374 |
+
uc_mask = (
|
375 |
+
torch.Tensor([1] * batch_size * num_images_per_prompt + [0] * batch_size * num_images_per_prompt)
|
376 |
+
.type_as(ref_image_latents)
|
377 |
+
.bool()
|
378 |
+
)
|
379 |
+
|
380 |
+
def hacked_basic_transformer_inner_forward(
|
381 |
+
self,
|
382 |
+
hidden_states: torch.FloatTensor,
|
383 |
+
attention_mask: Optional[torch.FloatTensor] = None,
|
384 |
+
encoder_hidden_states: Optional[torch.FloatTensor] = None,
|
385 |
+
encoder_attention_mask: Optional[torch.FloatTensor] = None,
|
386 |
+
timestep: Optional[torch.LongTensor] = None,
|
387 |
+
cross_attention_kwargs: Dict[str, Any] = None,
|
388 |
+
class_labels: Optional[torch.LongTensor] = None,
|
389 |
+
):
|
390 |
+
if self.use_ada_layer_norm:
|
391 |
+
norm_hidden_states = self.norm1(hidden_states, timestep)
|
392 |
+
elif self.use_ada_layer_norm_zero:
|
393 |
+
norm_hidden_states, gate_msa, shift_mlp, scale_mlp, gate_mlp = self.norm1(
|
394 |
+
hidden_states, timestep, class_labels, hidden_dtype=hidden_states.dtype
|
395 |
+
)
|
396 |
+
else:
|
397 |
+
norm_hidden_states = self.norm1(hidden_states)
|
398 |
+
|
399 |
+
# 1. Self-Attention
|
400 |
+
cross_attention_kwargs = cross_attention_kwargs if cross_attention_kwargs is not None else {}
|
401 |
+
if self.only_cross_attention:
|
402 |
+
attn_output = self.attn1(
|
403 |
+
norm_hidden_states,
|
404 |
+
encoder_hidden_states=encoder_hidden_states if self.only_cross_attention else None,
|
405 |
+
attention_mask=attention_mask,
|
406 |
+
**cross_attention_kwargs,
|
407 |
+
)
|
408 |
+
else:
|
409 |
+
if MODE == "write":
|
410 |
+
self.bank.append(norm_hidden_states.detach().clone())
|
411 |
+
attn_output = self.attn1(
|
412 |
+
norm_hidden_states,
|
413 |
+
encoder_hidden_states=encoder_hidden_states if self.only_cross_attention else None,
|
414 |
+
attention_mask=attention_mask,
|
415 |
+
**cross_attention_kwargs,
|
416 |
+
)
|
417 |
+
if MODE == "read":
|
418 |
+
if attention_auto_machine_weight > self.attn_weight:
|
419 |
+
attn_output_uc = self.attn1(
|
420 |
+
norm_hidden_states,
|
421 |
+
encoder_hidden_states=torch.cat([norm_hidden_states] + self.bank, dim=1),
|
422 |
+
# attention_mask=attention_mask,
|
423 |
+
**cross_attention_kwargs,
|
424 |
+
)
|
425 |
+
attn_output_c = attn_output_uc.clone()
|
426 |
+
if do_classifier_free_guidance and style_fidelity > 0:
|
427 |
+
attn_output_c[uc_mask] = self.attn1(
|
428 |
+
norm_hidden_states[uc_mask],
|
429 |
+
encoder_hidden_states=norm_hidden_states[uc_mask],
|
430 |
+
**cross_attention_kwargs,
|
431 |
+
)
|
432 |
+
attn_output = style_fidelity * attn_output_c + (1.0 - style_fidelity) * attn_output_uc
|
433 |
+
self.bank.clear()
|
434 |
+
else:
|
435 |
+
attn_output = self.attn1(
|
436 |
+
norm_hidden_states,
|
437 |
+
encoder_hidden_states=encoder_hidden_states if self.only_cross_attention else None,
|
438 |
+
attention_mask=attention_mask,
|
439 |
+
**cross_attention_kwargs,
|
440 |
+
)
|
441 |
+
if self.use_ada_layer_norm_zero:
|
442 |
+
attn_output = gate_msa.unsqueeze(1) * attn_output
|
443 |
+
hidden_states = attn_output + hidden_states
|
444 |
+
|
445 |
+
if self.attn2 is not None:
|
446 |
+
norm_hidden_states = (
|
447 |
+
self.norm2(hidden_states, timestep) if self.use_ada_layer_norm else self.norm2(hidden_states)
|
448 |
+
)
|
449 |
+
|
450 |
+
# 2. Cross-Attention
|
451 |
+
attn_output = self.attn2(
|
452 |
+
norm_hidden_states,
|
453 |
+
encoder_hidden_states=encoder_hidden_states,
|
454 |
+
attention_mask=encoder_attention_mask,
|
455 |
+
**cross_attention_kwargs,
|
456 |
+
)
|
457 |
+
hidden_states = attn_output + hidden_states
|
458 |
+
|
459 |
+
# 3. Feed-forward
|
460 |
+
norm_hidden_states = self.norm3(hidden_states)
|
461 |
+
|
462 |
+
if self.use_ada_layer_norm_zero:
|
463 |
+
norm_hidden_states = norm_hidden_states * (1 + scale_mlp[:, None]) + shift_mlp[:, None]
|
464 |
+
|
465 |
+
ff_output = self.ff(norm_hidden_states)
|
466 |
+
|
467 |
+
if self.use_ada_layer_norm_zero:
|
468 |
+
ff_output = gate_mlp.unsqueeze(1) * ff_output
|
469 |
+
|
470 |
+
hidden_states = ff_output + hidden_states
|
471 |
+
|
472 |
+
return hidden_states
|
473 |
+
|
474 |
+
def hacked_mid_forward(self, *args, **kwargs):
|
475 |
+
eps = 1e-6
|
476 |
+
x = self.original_forward(*args, **kwargs)
|
477 |
+
if MODE == "write":
|
478 |
+
if gn_auto_machine_weight >= self.gn_weight:
|
479 |
+
var, mean = torch.var_mean(x, dim=(2, 3), keepdim=True, correction=0)
|
480 |
+
self.mean_bank.append(mean)
|
481 |
+
self.var_bank.append(var)
|
482 |
+
if MODE == "read":
|
483 |
+
if len(self.mean_bank) > 0 and len(self.var_bank) > 0:
|
484 |
+
var, mean = torch.var_mean(x, dim=(2, 3), keepdim=True, correction=0)
|
485 |
+
std = torch.maximum(var, torch.zeros_like(var) + eps) ** 0.5
|
486 |
+
mean_acc = sum(self.mean_bank) / float(len(self.mean_bank))
|
487 |
+
var_acc = sum(self.var_bank) / float(len(self.var_bank))
|
488 |
+
std_acc = torch.maximum(var_acc, torch.zeros_like(var_acc) + eps) ** 0.5
|
489 |
+
x_uc = (((x - mean) / std) * std_acc) + mean_acc
|
490 |
+
x_c = x_uc.clone()
|
491 |
+
if do_classifier_free_guidance and style_fidelity > 0:
|
492 |
+
x_c[uc_mask] = x[uc_mask]
|
493 |
+
x = style_fidelity * x_c + (1.0 - style_fidelity) * x_uc
|
494 |
+
self.mean_bank = []
|
495 |
+
self.var_bank = []
|
496 |
+
return x
|
497 |
+
|
498 |
+
def hack_CrossAttnDownBlock2D_forward(
|
499 |
+
self,
|
500 |
+
hidden_states: torch.FloatTensor,
|
501 |
+
temb: Optional[torch.FloatTensor] = None,
|
502 |
+
encoder_hidden_states: Optional[torch.FloatTensor] = None,
|
503 |
+
attention_mask: Optional[torch.FloatTensor] = None,
|
504 |
+
cross_attention_kwargs: Optional[Dict[str, Any]] = None,
|
505 |
+
encoder_attention_mask: Optional[torch.FloatTensor] = None,
|
506 |
+
):
|
507 |
+
eps = 1e-6
|
508 |
+
|
509 |
+
# TODO(Patrick, William) - attention mask is not used
|
510 |
+
output_states = ()
|
511 |
+
|
512 |
+
for i, (resnet, attn) in enumerate(zip(self.resnets, self.attentions)):
|
513 |
+
hidden_states = resnet(hidden_states, temb)
|
514 |
+
hidden_states = attn(
|
515 |
+
hidden_states,
|
516 |
+
encoder_hidden_states=encoder_hidden_states,
|
517 |
+
cross_attention_kwargs=cross_attention_kwargs,
|
518 |
+
attention_mask=attention_mask,
|
519 |
+
encoder_attention_mask=encoder_attention_mask,
|
520 |
+
return_dict=False,
|
521 |
+
)[0]
|
522 |
+
if MODE == "write":
|
523 |
+
if gn_auto_machine_weight >= self.gn_weight:
|
524 |
+
var, mean = torch.var_mean(hidden_states, dim=(2, 3), keepdim=True, correction=0)
|
525 |
+
self.mean_bank.append([mean])
|
526 |
+
self.var_bank.append([var])
|
527 |
+
if MODE == "read":
|
528 |
+
if len(self.mean_bank) > 0 and len(self.var_bank) > 0:
|
529 |
+
var, mean = torch.var_mean(hidden_states, dim=(2, 3), keepdim=True, correction=0)
|
530 |
+
std = torch.maximum(var, torch.zeros_like(var) + eps) ** 0.5
|
531 |
+
mean_acc = sum(self.mean_bank[i]) / float(len(self.mean_bank[i]))
|
532 |
+
var_acc = sum(self.var_bank[i]) / float(len(self.var_bank[i]))
|
533 |
+
std_acc = torch.maximum(var_acc, torch.zeros_like(var_acc) + eps) ** 0.5
|
534 |
+
hidden_states_uc = (((hidden_states - mean) / std) * std_acc) + mean_acc
|
535 |
+
hidden_states_c = hidden_states_uc.clone()
|
536 |
+
if do_classifier_free_guidance and style_fidelity > 0:
|
537 |
+
hidden_states_c[uc_mask] = hidden_states[uc_mask]
|
538 |
+
hidden_states = style_fidelity * hidden_states_c + (1.0 - style_fidelity) * hidden_states_uc
|
539 |
+
|
540 |
+
output_states = output_states + (hidden_states,)
|
541 |
+
|
542 |
+
if MODE == "read":
|
543 |
+
self.mean_bank = []
|
544 |
+
self.var_bank = []
|
545 |
+
|
546 |
+
if self.downsamplers is not None:
|
547 |
+
for downsampler in self.downsamplers:
|
548 |
+
hidden_states = downsampler(hidden_states)
|
549 |
+
|
550 |
+
output_states = output_states + (hidden_states,)
|
551 |
+
|
552 |
+
return hidden_states, output_states
|
553 |
+
|
554 |
+
def hacked_DownBlock2D_forward(self, hidden_states, temb=None):
|
555 |
+
eps = 1e-6
|
556 |
+
|
557 |
+
output_states = ()
|
558 |
+
|
559 |
+
for i, resnet in enumerate(self.resnets):
|
560 |
+
hidden_states = resnet(hidden_states, temb)
|
561 |
+
|
562 |
+
if MODE == "write":
|
563 |
+
if gn_auto_machine_weight >= self.gn_weight:
|
564 |
+
var, mean = torch.var_mean(hidden_states, dim=(2, 3), keepdim=True, correction=0)
|
565 |
+
self.mean_bank.append([mean])
|
566 |
+
self.var_bank.append([var])
|
567 |
+
if MODE == "read":
|
568 |
+
if len(self.mean_bank) > 0 and len(self.var_bank) > 0:
|
569 |
+
var, mean = torch.var_mean(hidden_states, dim=(2, 3), keepdim=True, correction=0)
|
570 |
+
std = torch.maximum(var, torch.zeros_like(var) + eps) ** 0.5
|
571 |
+
mean_acc = sum(self.mean_bank[i]) / float(len(self.mean_bank[i]))
|
572 |
+
var_acc = sum(self.var_bank[i]) / float(len(self.var_bank[i]))
|
573 |
+
std_acc = torch.maximum(var_acc, torch.zeros_like(var_acc) + eps) ** 0.5
|
574 |
+
hidden_states_uc = (((hidden_states - mean) / std) * std_acc) + mean_acc
|
575 |
+
hidden_states_c = hidden_states_uc.clone()
|
576 |
+
if do_classifier_free_guidance and style_fidelity > 0:
|
577 |
+
hidden_states_c[uc_mask] = hidden_states[uc_mask]
|
578 |
+
hidden_states = style_fidelity * hidden_states_c + (1.0 - style_fidelity) * hidden_states_uc
|
579 |
+
|
580 |
+
output_states = output_states + (hidden_states,)
|
581 |
+
|
582 |
+
if MODE == "read":
|
583 |
+
self.mean_bank = []
|
584 |
+
self.var_bank = []
|
585 |
+
|
586 |
+
if self.downsamplers is not None:
|
587 |
+
for downsampler in self.downsamplers:
|
588 |
+
hidden_states = downsampler(hidden_states)
|
589 |
+
|
590 |
+
output_states = output_states + (hidden_states,)
|
591 |
+
|
592 |
+
return hidden_states, output_states
|
593 |
+
|
594 |
+
def hacked_CrossAttnUpBlock2D_forward(
|
595 |
+
self,
|
596 |
+
hidden_states: torch.FloatTensor,
|
597 |
+
res_hidden_states_tuple: Tuple[torch.FloatTensor, ...],
|
598 |
+
temb: Optional[torch.FloatTensor] = None,
|
599 |
+
encoder_hidden_states: Optional[torch.FloatTensor] = None,
|
600 |
+
cross_attention_kwargs: Optional[Dict[str, Any]] = None,
|
601 |
+
upsample_size: Optional[int] = None,
|
602 |
+
attention_mask: Optional[torch.FloatTensor] = None,
|
603 |
+
encoder_attention_mask: Optional[torch.FloatTensor] = None,
|
604 |
+
):
|
605 |
+
eps = 1e-6
|
606 |
+
# TODO(Patrick, William) - attention mask is not used
|
607 |
+
for i, (resnet, attn) in enumerate(zip(self.resnets, self.attentions)):
|
608 |
+
# pop res hidden states
|
609 |
+
res_hidden_states = res_hidden_states_tuple[-1]
|
610 |
+
res_hidden_states_tuple = res_hidden_states_tuple[:-1]
|
611 |
+
hidden_states = torch.cat([hidden_states, res_hidden_states], dim=1)
|
612 |
+
hidden_states = resnet(hidden_states, temb)
|
613 |
+
hidden_states = attn(
|
614 |
+
hidden_states,
|
615 |
+
encoder_hidden_states=encoder_hidden_states,
|
616 |
+
cross_attention_kwargs=cross_attention_kwargs,
|
617 |
+
attention_mask=attention_mask,
|
618 |
+
encoder_attention_mask=encoder_attention_mask,
|
619 |
+
return_dict=False,
|
620 |
+
)[0]
|
621 |
+
|
622 |
+
if MODE == "write":
|
623 |
+
if gn_auto_machine_weight >= self.gn_weight:
|
624 |
+
var, mean = torch.var_mean(hidden_states, dim=(2, 3), keepdim=True, correction=0)
|
625 |
+
self.mean_bank.append([mean])
|
626 |
+
self.var_bank.append([var])
|
627 |
+
if MODE == "read":
|
628 |
+
if len(self.mean_bank) > 0 and len(self.var_bank) > 0:
|
629 |
+
var, mean = torch.var_mean(hidden_states, dim=(2, 3), keepdim=True, correction=0)
|
630 |
+
std = torch.maximum(var, torch.zeros_like(var) + eps) ** 0.5
|
631 |
+
mean_acc = sum(self.mean_bank[i]) / float(len(self.mean_bank[i]))
|
632 |
+
var_acc = sum(self.var_bank[i]) / float(len(self.var_bank[i]))
|
633 |
+
std_acc = torch.maximum(var_acc, torch.zeros_like(var_acc) + eps) ** 0.5
|
634 |
+
hidden_states_uc = (((hidden_states - mean) / std) * std_acc) + mean_acc
|
635 |
+
hidden_states_c = hidden_states_uc.clone()
|
636 |
+
if do_classifier_free_guidance and style_fidelity > 0:
|
637 |
+
hidden_states_c[uc_mask] = hidden_states[uc_mask]
|
638 |
+
hidden_states = style_fidelity * hidden_states_c + (1.0 - style_fidelity) * hidden_states_uc
|
639 |
+
|
640 |
+
if MODE == "read":
|
641 |
+
self.mean_bank = []
|
642 |
+
self.var_bank = []
|
643 |
+
|
644 |
+
if self.upsamplers is not None:
|
645 |
+
for upsampler in self.upsamplers:
|
646 |
+
hidden_states = upsampler(hidden_states, upsample_size)
|
647 |
+
|
648 |
+
return hidden_states
|
649 |
+
|
650 |
+
def hacked_UpBlock2D_forward(self, hidden_states, res_hidden_states_tuple, temb=None, upsample_size=None):
|
651 |
+
eps = 1e-6
|
652 |
+
for i, resnet in enumerate(self.resnets):
|
653 |
+
# pop res hidden states
|
654 |
+
res_hidden_states = res_hidden_states_tuple[-1]
|
655 |
+
res_hidden_states_tuple = res_hidden_states_tuple[:-1]
|
656 |
+
hidden_states = torch.cat([hidden_states, res_hidden_states], dim=1)
|
657 |
+
hidden_states = resnet(hidden_states, temb)
|
658 |
+
|
659 |
+
if MODE == "write":
|
660 |
+
if gn_auto_machine_weight >= self.gn_weight:
|
661 |
+
var, mean = torch.var_mean(hidden_states, dim=(2, 3), keepdim=True, correction=0)
|
662 |
+
self.mean_bank.append([mean])
|
663 |
+
self.var_bank.append([var])
|
664 |
+
if MODE == "read":
|
665 |
+
if len(self.mean_bank) > 0 and len(self.var_bank) > 0:
|
666 |
+
var, mean = torch.var_mean(hidden_states, dim=(2, 3), keepdim=True, correction=0)
|
667 |
+
std = torch.maximum(var, torch.zeros_like(var) + eps) ** 0.5
|
668 |
+
mean_acc = sum(self.mean_bank[i]) / float(len(self.mean_bank[i]))
|
669 |
+
var_acc = sum(self.var_bank[i]) / float(len(self.var_bank[i]))
|
670 |
+
std_acc = torch.maximum(var_acc, torch.zeros_like(var_acc) + eps) ** 0.5
|
671 |
+
hidden_states_uc = (((hidden_states - mean) / std) * std_acc) + mean_acc
|
672 |
+
hidden_states_c = hidden_states_uc.clone()
|
673 |
+
if do_classifier_free_guidance and style_fidelity > 0:
|
674 |
+
hidden_states_c[uc_mask] = hidden_states[uc_mask]
|
675 |
+
hidden_states = style_fidelity * hidden_states_c + (1.0 - style_fidelity) * hidden_states_uc
|
676 |
+
|
677 |
+
if MODE == "read":
|
678 |
+
self.mean_bank = []
|
679 |
+
self.var_bank = []
|
680 |
+
|
681 |
+
if self.upsamplers is not None:
|
682 |
+
for upsampler in self.upsamplers:
|
683 |
+
hidden_states = upsampler(hidden_states, upsample_size)
|
684 |
+
|
685 |
+
return hidden_states
|
686 |
+
|
687 |
+
if reference_attn:
|
688 |
+
attn_modules = [module for module in torch_dfs(self.unet) if isinstance(module, BasicTransformerBlock)]
|
689 |
+
attn_modules = sorted(attn_modules, key=lambda x: -x.norm1.normalized_shape[0])
|
690 |
+
|
691 |
+
for i, module in enumerate(attn_modules):
|
692 |
+
module._original_inner_forward = module.forward
|
693 |
+
module.forward = hacked_basic_transformer_inner_forward.__get__(module, BasicTransformerBlock)
|
694 |
+
module.bank = []
|
695 |
+
module.attn_weight = float(i) / float(len(attn_modules))
|
696 |
+
|
697 |
+
if reference_adain:
|
698 |
+
gn_modules = [self.unet.mid_block]
|
699 |
+
self.unet.mid_block.gn_weight = 0
|
700 |
+
|
701 |
+
down_blocks = self.unet.down_blocks
|
702 |
+
for w, module in enumerate(down_blocks):
|
703 |
+
module.gn_weight = 1.0 - float(w) / float(len(down_blocks))
|
704 |
+
gn_modules.append(module)
|
705 |
+
|
706 |
+
up_blocks = self.unet.up_blocks
|
707 |
+
for w, module in enumerate(up_blocks):
|
708 |
+
module.gn_weight = float(w) / float(len(up_blocks))
|
709 |
+
gn_modules.append(module)
|
710 |
+
|
711 |
+
for i, module in enumerate(gn_modules):
|
712 |
+
if getattr(module, "original_forward", None) is None:
|
713 |
+
module.original_forward = module.forward
|
714 |
+
if i == 0:
|
715 |
+
# mid_block
|
716 |
+
module.forward = hacked_mid_forward.__get__(module, torch.nn.Module)
|
717 |
+
elif isinstance(module, CrossAttnDownBlock2D):
|
718 |
+
module.forward = hack_CrossAttnDownBlock2D_forward.__get__(module, CrossAttnDownBlock2D)
|
719 |
+
elif isinstance(module, DownBlock2D):
|
720 |
+
module.forward = hacked_DownBlock2D_forward.__get__(module, DownBlock2D)
|
721 |
+
elif isinstance(module, CrossAttnUpBlock2D):
|
722 |
+
module.forward = hacked_CrossAttnUpBlock2D_forward.__get__(module, CrossAttnUpBlock2D)
|
723 |
+
elif isinstance(module, UpBlock2D):
|
724 |
+
module.forward = hacked_UpBlock2D_forward.__get__(module, UpBlock2D)
|
725 |
+
module.mean_bank = []
|
726 |
+
module.var_bank = []
|
727 |
+
module.gn_weight *= 2
|
728 |
+
|
729 |
+
# 11. Denoising loop
|
730 |
+
num_warmup_steps = len(timesteps) - num_inference_steps * self.scheduler.order
|
731 |
+
with self.progress_bar(total=num_inference_steps) as progress_bar:
|
732 |
+
for i, t in enumerate(timesteps):
|
733 |
+
# expand the latents if we are doing classifier free guidance
|
734 |
+
latent_model_input = torch.cat([latents] * 2) if do_classifier_free_guidance else latents
|
735 |
+
latent_model_input = self.scheduler.scale_model_input(latent_model_input, t)
|
736 |
+
|
737 |
+
# controlnet(s) inference
|
738 |
+
if guess_mode and do_classifier_free_guidance:
|
739 |
+
# Infer ControlNet only for the conditional batch.
|
740 |
+
control_model_input = latents
|
741 |
+
control_model_input = self.scheduler.scale_model_input(control_model_input, t)
|
742 |
+
controlnet_prompt_embeds = prompt_embeds.chunk(2)[1]
|
743 |
+
else:
|
744 |
+
control_model_input = latent_model_input
|
745 |
+
controlnet_prompt_embeds = prompt_embeds
|
746 |
+
|
747 |
+
down_block_res_samples, mid_block_res_sample = self.controlnet(
|
748 |
+
control_model_input,
|
749 |
+
t,
|
750 |
+
encoder_hidden_states=controlnet_prompt_embeds,
|
751 |
+
controlnet_cond=image,
|
752 |
+
conditioning_scale=controlnet_conditioning_scale,
|
753 |
+
guess_mode=guess_mode,
|
754 |
+
return_dict=False,
|
755 |
+
)
|
756 |
+
|
757 |
+
if guess_mode and do_classifier_free_guidance:
|
758 |
+
# Infered ControlNet only for the conditional batch.
|
759 |
+
# To apply the output of ControlNet to both the unconditional and conditional batches,
|
760 |
+
# add 0 to the unconditional batch to keep it unchanged.
|
761 |
+
down_block_res_samples = [torch.cat([torch.zeros_like(d), d]) for d in down_block_res_samples]
|
762 |
+
mid_block_res_sample = torch.cat([torch.zeros_like(mid_block_res_sample), mid_block_res_sample])
|
763 |
+
|
764 |
+
# ref only part
|
765 |
+
noise = randn_tensor(
|
766 |
+
ref_image_latents.shape, generator=generator, device=device, dtype=ref_image_latents.dtype
|
767 |
+
)
|
768 |
+
ref_xt = self.scheduler.add_noise(
|
769 |
+
ref_image_latents,
|
770 |
+
noise,
|
771 |
+
t.reshape(
|
772 |
+
1,
|
773 |
+
),
|
774 |
+
)
|
775 |
+
ref_xt = self.scheduler.scale_model_input(ref_xt, t)
|
776 |
+
|
777 |
+
MODE = "write"
|
778 |
+
self.unet(
|
779 |
+
ref_xt,
|
780 |
+
t,
|
781 |
+
encoder_hidden_states=prompt_embeds,
|
782 |
+
cross_attention_kwargs=cross_attention_kwargs,
|
783 |
+
return_dict=False,
|
784 |
+
)
|
785 |
+
|
786 |
+
# predict the noise residual
|
787 |
+
MODE = "read"
|
788 |
+
noise_pred = self.unet(
|
789 |
+
latent_model_input,
|
790 |
+
t,
|
791 |
+
encoder_hidden_states=prompt_embeds,
|
792 |
+
cross_attention_kwargs=cross_attention_kwargs,
|
793 |
+
down_block_additional_residuals=down_block_res_samples,
|
794 |
+
mid_block_additional_residual=mid_block_res_sample,
|
795 |
+
return_dict=False,
|
796 |
+
)[0]
|
797 |
+
|
798 |
+
# perform guidance
|
799 |
+
if do_classifier_free_guidance:
|
800 |
+
noise_pred_uncond, noise_pred_text = noise_pred.chunk(2)
|
801 |
+
noise_pred = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
|
802 |
+
|
803 |
+
# compute the previous noisy sample x_t -> x_t-1
|
804 |
+
latents = self.scheduler.step(noise_pred, t, latents, **extra_step_kwargs, return_dict=False)[0]
|
805 |
+
|
806 |
+
# call the callback, if provided
|
807 |
+
if i == len(timesteps) - 1 or ((i + 1) > num_warmup_steps and (i + 1) % self.scheduler.order == 0):
|
808 |
+
progress_bar.update()
|
809 |
+
if callback is not None and i % callback_steps == 0:
|
810 |
+
callback(i, t, latents)
|
811 |
+
|
812 |
+
# If we do sequential model offloading, let's offload unet and controlnet
|
813 |
+
# manually for max memory savings
|
814 |
+
if hasattr(self, "final_offload_hook") and self.final_offload_hook is not None:
|
815 |
+
self.unet.to("cpu")
|
816 |
+
self.controlnet.to("cpu")
|
817 |
+
torch.cuda.empty_cache()
|
818 |
+
|
819 |
+
if not output_type == "latent":
|
820 |
+
image = self.vae.decode(latents / self.vae.config.scaling_factor, return_dict=False)[0]
|
821 |
+
image, has_nsfw_concept = self.run_safety_checker(image, device, prompt_embeds.dtype)
|
822 |
+
else:
|
823 |
+
image = latents
|
824 |
+
has_nsfw_concept = None
|
825 |
+
|
826 |
+
if has_nsfw_concept is None:
|
827 |
+
do_denormalize = [True] * image.shape[0]
|
828 |
+
else:
|
829 |
+
do_denormalize = [not has_nsfw for has_nsfw in has_nsfw_concept]
|
830 |
+
|
831 |
+
image = self.image_processor.postprocess(image, output_type=output_type, do_denormalize=do_denormalize)
|
832 |
+
|
833 |
+
# Offload last model to CPU
|
834 |
+
if hasattr(self, "final_offload_hook") and self.final_offload_hook is not None:
|
835 |
+
self.final_offload_hook.offload()
|
836 |
+
|
837 |
+
if not return_dict:
|
838 |
+
return (image, has_nsfw_concept)
|
839 |
+
|
840 |
+
return StableDiffusionPipelineOutput(images=image, nsfw_content_detected=has_nsfw_concept)
|
magicanimate/models/unet.py
ADDED
@@ -0,0 +1,508 @@
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|
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|
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|
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|
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|
|
|
|
|
|
|
|
|
|
|
1 |
+
# *************************************************************************
|
2 |
+
# This file may have been modified by Bytedance Inc. (“Bytedance Inc.'s Mo-
|
3 |
+
# difications”). All Bytedance Inc.'s Modifications are Copyright (2023) B-
|
4 |
+
# ytedance Inc..
|
5 |
+
# *************************************************************************
|
6 |
+
|
7 |
+
# Adapted from https://github.com/guoyww/AnimateDiff
|
8 |
+
|
9 |
+
# Copyright 2023 The HuggingFace Team. All rights reserved.
|
10 |
+
#
|
11 |
+
# Licensed under the Apache License, Version 2.0 (the "License");
|
12 |
+
# you may not use this file except in compliance with the License.
|
13 |
+
# You may obtain a copy of the License at
|
14 |
+
#
|
15 |
+
# http://www.apache.org/licenses/LICENSE-2.0
|
16 |
+
#
|
17 |
+
# Unless required by applicable law or agreed to in writing, software
|
18 |
+
# distributed under the License is distributed on an "AS IS" BASIS,
|
19 |
+
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
20 |
+
# See the License for the specific language governing permissions and
|
21 |
+
# limitations under the License.
|
22 |
+
from dataclasses import dataclass
|
23 |
+
from typing import List, Optional, Tuple, Union
|
24 |
+
|
25 |
+
import os
|
26 |
+
import json
|
27 |
+
import pdb
|
28 |
+
|
29 |
+
import torch
|
30 |
+
import torch.nn as nn
|
31 |
+
import torch.utils.checkpoint
|
32 |
+
|
33 |
+
from diffusers.configuration_utils import ConfigMixin, register_to_config
|
34 |
+
from diffusers.models.modeling_utils import ModelMixin
|
35 |
+
from diffusers.utils import BaseOutput, logging
|
36 |
+
from diffusers.models.embeddings import TimestepEmbedding, Timesteps
|
37 |
+
from .unet_3d_blocks import (
|
38 |
+
CrossAttnDownBlock3D,
|
39 |
+
CrossAttnUpBlock3D,
|
40 |
+
DownBlock3D,
|
41 |
+
UNetMidBlock3DCrossAttn,
|
42 |
+
UpBlock3D,
|
43 |
+
get_down_block,
|
44 |
+
get_up_block,
|
45 |
+
)
|
46 |
+
from .resnet import InflatedConv3d
|
47 |
+
|
48 |
+
|
49 |
+
logger = logging.get_logger(__name__) # pylint: disable=invalid-name
|
50 |
+
|
51 |
+
|
52 |
+
@dataclass
|
53 |
+
class UNet3DConditionOutput(BaseOutput):
|
54 |
+
sample: torch.FloatTensor
|
55 |
+
|
56 |
+
|
57 |
+
class UNet3DConditionModel(ModelMixin, ConfigMixin):
|
58 |
+
_supports_gradient_checkpointing = True
|
59 |
+
|
60 |
+
@register_to_config
|
61 |
+
def __init__(
|
62 |
+
self,
|
63 |
+
sample_size: Optional[int] = None,
|
64 |
+
in_channels: int = 4,
|
65 |
+
out_channels: int = 4,
|
66 |
+
center_input_sample: bool = False,
|
67 |
+
flip_sin_to_cos: bool = True,
|
68 |
+
freq_shift: int = 0,
|
69 |
+
down_block_types: Tuple[str] = (
|
70 |
+
"CrossAttnDownBlock3D",
|
71 |
+
"CrossAttnDownBlock3D",
|
72 |
+
"CrossAttnDownBlock3D",
|
73 |
+
"DownBlock3D",
|
74 |
+
),
|
75 |
+
mid_block_type: str = "UNetMidBlock3DCrossAttn",
|
76 |
+
up_block_types: Tuple[str] = (
|
77 |
+
"UpBlock3D",
|
78 |
+
"CrossAttnUpBlock3D",
|
79 |
+
"CrossAttnUpBlock3D",
|
80 |
+
"CrossAttnUpBlock3D"
|
81 |
+
),
|
82 |
+
only_cross_attention: Union[bool, Tuple[bool]] = False,
|
83 |
+
block_out_channels: Tuple[int] = (320, 640, 1280, 1280),
|
84 |
+
layers_per_block: int = 2,
|
85 |
+
downsample_padding: int = 1,
|
86 |
+
mid_block_scale_factor: float = 1,
|
87 |
+
act_fn: str = "silu",
|
88 |
+
norm_num_groups: int = 32,
|
89 |
+
norm_eps: float = 1e-5,
|
90 |
+
cross_attention_dim: int = 1280,
|
91 |
+
attention_head_dim: Union[int, Tuple[int]] = 8,
|
92 |
+
dual_cross_attention: bool = False,
|
93 |
+
use_linear_projection: bool = False,
|
94 |
+
class_embed_type: Optional[str] = None,
|
95 |
+
num_class_embeds: Optional[int] = None,
|
96 |
+
upcast_attention: bool = False,
|
97 |
+
resnet_time_scale_shift: str = "default",
|
98 |
+
|
99 |
+
# Additional
|
100 |
+
use_motion_module = False,
|
101 |
+
motion_module_resolutions = ( 1,2,4,8 ),
|
102 |
+
motion_module_mid_block = False,
|
103 |
+
motion_module_decoder_only = False,
|
104 |
+
motion_module_type = None,
|
105 |
+
motion_module_kwargs = {},
|
106 |
+
unet_use_cross_frame_attention = None,
|
107 |
+
unet_use_temporal_attention = None,
|
108 |
+
):
|
109 |
+
super().__init__()
|
110 |
+
|
111 |
+
self.sample_size = sample_size
|
112 |
+
time_embed_dim = block_out_channels[0] * 4
|
113 |
+
|
114 |
+
# input
|
115 |
+
self.conv_in = InflatedConv3d(in_channels, block_out_channels[0], kernel_size=3, padding=(1, 1))
|
116 |
+
|
117 |
+
# time
|
118 |
+
self.time_proj = Timesteps(block_out_channels[0], flip_sin_to_cos, freq_shift)
|
119 |
+
timestep_input_dim = block_out_channels[0]
|
120 |
+
|
121 |
+
self.time_embedding = TimestepEmbedding(timestep_input_dim, time_embed_dim)
|
122 |
+
|
123 |
+
# class embedding
|
124 |
+
if class_embed_type is None and num_class_embeds is not None:
|
125 |
+
self.class_embedding = nn.Embedding(num_class_embeds, time_embed_dim)
|
126 |
+
elif class_embed_type == "timestep":
|
127 |
+
self.class_embedding = TimestepEmbedding(timestep_input_dim, time_embed_dim)
|
128 |
+
elif class_embed_type == "identity":
|
129 |
+
self.class_embedding = nn.Identity(time_embed_dim, time_embed_dim)
|
130 |
+
else:
|
131 |
+
self.class_embedding = None
|
132 |
+
|
133 |
+
self.down_blocks = nn.ModuleList([])
|
134 |
+
self.mid_block = None
|
135 |
+
self.up_blocks = nn.ModuleList([])
|
136 |
+
|
137 |
+
if isinstance(only_cross_attention, bool):
|
138 |
+
only_cross_attention = [only_cross_attention] * len(down_block_types)
|
139 |
+
|
140 |
+
if isinstance(attention_head_dim, int):
|
141 |
+
attention_head_dim = (attention_head_dim,) * len(down_block_types)
|
142 |
+
|
143 |
+
# down
|
144 |
+
output_channel = block_out_channels[0]
|
145 |
+
for i, down_block_type in enumerate(down_block_types):
|
146 |
+
res = 2 ** i
|
147 |
+
input_channel = output_channel
|
148 |
+
output_channel = block_out_channels[i]
|
149 |
+
is_final_block = i == len(block_out_channels) - 1
|
150 |
+
|
151 |
+
down_block = get_down_block(
|
152 |
+
down_block_type,
|
153 |
+
num_layers=layers_per_block,
|
154 |
+
in_channels=input_channel,
|
155 |
+
out_channels=output_channel,
|
156 |
+
temb_channels=time_embed_dim,
|
157 |
+
add_downsample=not is_final_block,
|
158 |
+
resnet_eps=norm_eps,
|
159 |
+
resnet_act_fn=act_fn,
|
160 |
+
resnet_groups=norm_num_groups,
|
161 |
+
cross_attention_dim=cross_attention_dim,
|
162 |
+
attn_num_head_channels=attention_head_dim[i],
|
163 |
+
downsample_padding=downsample_padding,
|
164 |
+
dual_cross_attention=dual_cross_attention,
|
165 |
+
use_linear_projection=use_linear_projection,
|
166 |
+
only_cross_attention=only_cross_attention[i],
|
167 |
+
upcast_attention=upcast_attention,
|
168 |
+
resnet_time_scale_shift=resnet_time_scale_shift,
|
169 |
+
|
170 |
+
unet_use_cross_frame_attention=unet_use_cross_frame_attention,
|
171 |
+
unet_use_temporal_attention=unet_use_temporal_attention,
|
172 |
+
|
173 |
+
use_motion_module=use_motion_module and (res in motion_module_resolutions) and (not motion_module_decoder_only),
|
174 |
+
motion_module_type=motion_module_type,
|
175 |
+
motion_module_kwargs=motion_module_kwargs,
|
176 |
+
)
|
177 |
+
self.down_blocks.append(down_block)
|
178 |
+
|
179 |
+
# mid
|
180 |
+
if mid_block_type == "UNetMidBlock3DCrossAttn":
|
181 |
+
self.mid_block = UNetMidBlock3DCrossAttn(
|
182 |
+
in_channels=block_out_channels[-1],
|
183 |
+
temb_channels=time_embed_dim,
|
184 |
+
resnet_eps=norm_eps,
|
185 |
+
resnet_act_fn=act_fn,
|
186 |
+
output_scale_factor=mid_block_scale_factor,
|
187 |
+
resnet_time_scale_shift=resnet_time_scale_shift,
|
188 |
+
cross_attention_dim=cross_attention_dim,
|
189 |
+
attn_num_head_channels=attention_head_dim[-1],
|
190 |
+
resnet_groups=norm_num_groups,
|
191 |
+
dual_cross_attention=dual_cross_attention,
|
192 |
+
use_linear_projection=use_linear_projection,
|
193 |
+
upcast_attention=upcast_attention,
|
194 |
+
|
195 |
+
unet_use_cross_frame_attention=unet_use_cross_frame_attention,
|
196 |
+
unet_use_temporal_attention=unet_use_temporal_attention,
|
197 |
+
|
198 |
+
use_motion_module=use_motion_module and motion_module_mid_block,
|
199 |
+
motion_module_type=motion_module_type,
|
200 |
+
motion_module_kwargs=motion_module_kwargs,
|
201 |
+
)
|
202 |
+
else:
|
203 |
+
raise ValueError(f"unknown mid_block_type : {mid_block_type}")
|
204 |
+
|
205 |
+
# count how many layers upsample the videos
|
206 |
+
self.num_upsamplers = 0
|
207 |
+
|
208 |
+
# up
|
209 |
+
reversed_block_out_channels = list(reversed(block_out_channels))
|
210 |
+
reversed_attention_head_dim = list(reversed(attention_head_dim))
|
211 |
+
only_cross_attention = list(reversed(only_cross_attention))
|
212 |
+
output_channel = reversed_block_out_channels[0]
|
213 |
+
for i, up_block_type in enumerate(up_block_types):
|
214 |
+
res = 2 ** (3 - i)
|
215 |
+
is_final_block = i == len(block_out_channels) - 1
|
216 |
+
|
217 |
+
prev_output_channel = output_channel
|
218 |
+
output_channel = reversed_block_out_channels[i]
|
219 |
+
input_channel = reversed_block_out_channels[min(i + 1, len(block_out_channels) - 1)]
|
220 |
+
|
221 |
+
# add upsample block for all BUT final layer
|
222 |
+
if not is_final_block:
|
223 |
+
add_upsample = True
|
224 |
+
self.num_upsamplers += 1
|
225 |
+
else:
|
226 |
+
add_upsample = False
|
227 |
+
|
228 |
+
up_block = get_up_block(
|
229 |
+
up_block_type,
|
230 |
+
num_layers=layers_per_block + 1,
|
231 |
+
in_channels=input_channel,
|
232 |
+
out_channels=output_channel,
|
233 |
+
prev_output_channel=prev_output_channel,
|
234 |
+
temb_channels=time_embed_dim,
|
235 |
+
add_upsample=add_upsample,
|
236 |
+
resnet_eps=norm_eps,
|
237 |
+
resnet_act_fn=act_fn,
|
238 |
+
resnet_groups=norm_num_groups,
|
239 |
+
cross_attention_dim=cross_attention_dim,
|
240 |
+
attn_num_head_channels=reversed_attention_head_dim[i],
|
241 |
+
dual_cross_attention=dual_cross_attention,
|
242 |
+
use_linear_projection=use_linear_projection,
|
243 |
+
only_cross_attention=only_cross_attention[i],
|
244 |
+
upcast_attention=upcast_attention,
|
245 |
+
resnet_time_scale_shift=resnet_time_scale_shift,
|
246 |
+
|
247 |
+
unet_use_cross_frame_attention=unet_use_cross_frame_attention,
|
248 |
+
unet_use_temporal_attention=unet_use_temporal_attention,
|
249 |
+
|
250 |
+
use_motion_module=use_motion_module and (res in motion_module_resolutions),
|
251 |
+
motion_module_type=motion_module_type,
|
252 |
+
motion_module_kwargs=motion_module_kwargs,
|
253 |
+
)
|
254 |
+
self.up_blocks.append(up_block)
|
255 |
+
prev_output_channel = output_channel
|
256 |
+
|
257 |
+
# out
|
258 |
+
self.conv_norm_out = nn.GroupNorm(num_channels=block_out_channels[0], num_groups=norm_num_groups, eps=norm_eps)
|
259 |
+
self.conv_act = nn.SiLU()
|
260 |
+
self.conv_out = InflatedConv3d(block_out_channels[0], out_channels, kernel_size=3, padding=1)
|
261 |
+
|
262 |
+
def set_attention_slice(self, slice_size):
|
263 |
+
r"""
|
264 |
+
Enable sliced attention computation.
|
265 |
+
|
266 |
+
When this option is enabled, the attention module will split the input tensor in slices, to compute attention
|
267 |
+
in several steps. This is useful to save some memory in exchange for a small speed decrease.
|
268 |
+
|
269 |
+
Args:
|
270 |
+
slice_size (`str` or `int` or `list(int)`, *optional*, defaults to `"auto"`):
|
271 |
+
When `"auto"`, halves the input to the attention heads, so attention will be computed in two steps. If
|
272 |
+
`"max"`, maxium amount of memory will be saved by running only one slice at a time. If a number is
|
273 |
+
provided, uses as many slices as `attention_head_dim // slice_size`. In this case, `attention_head_dim`
|
274 |
+
must be a multiple of `slice_size`.
|
275 |
+
"""
|
276 |
+
sliceable_head_dims = []
|
277 |
+
|
278 |
+
def fn_recursive_retrieve_slicable_dims(module: torch.nn.Module):
|
279 |
+
if hasattr(module, "set_attention_slice"):
|
280 |
+
sliceable_head_dims.append(module.sliceable_head_dim)
|
281 |
+
|
282 |
+
for child in module.children():
|
283 |
+
fn_recursive_retrieve_slicable_dims(child)
|
284 |
+
|
285 |
+
# retrieve number of attention layers
|
286 |
+
for module in self.children():
|
287 |
+
fn_recursive_retrieve_slicable_dims(module)
|
288 |
+
|
289 |
+
num_slicable_layers = len(sliceable_head_dims)
|
290 |
+
|
291 |
+
if slice_size == "auto":
|
292 |
+
# half the attention head size is usually a good trade-off between
|
293 |
+
# speed and memory
|
294 |
+
slice_size = [dim // 2 for dim in sliceable_head_dims]
|
295 |
+
elif slice_size == "max":
|
296 |
+
# make smallest slice possible
|
297 |
+
slice_size = num_slicable_layers * [1]
|
298 |
+
|
299 |
+
slice_size = num_slicable_layers * [slice_size] if not isinstance(slice_size, list) else slice_size
|
300 |
+
|
301 |
+
if len(slice_size) != len(sliceable_head_dims):
|
302 |
+
raise ValueError(
|
303 |
+
f"You have provided {len(slice_size)}, but {self.config} has {len(sliceable_head_dims)} different"
|
304 |
+
f" attention layers. Make sure to match `len(slice_size)` to be {len(sliceable_head_dims)}."
|
305 |
+
)
|
306 |
+
|
307 |
+
for i in range(len(slice_size)):
|
308 |
+
size = slice_size[i]
|
309 |
+
dim = sliceable_head_dims[i]
|
310 |
+
if size is not None and size > dim:
|
311 |
+
raise ValueError(f"size {size} has to be smaller or equal to {dim}.")
|
312 |
+
|
313 |
+
# Recursively walk through all the children.
|
314 |
+
# Any children which exposes the set_attention_slice method
|
315 |
+
# gets the message
|
316 |
+
def fn_recursive_set_attention_slice(module: torch.nn.Module, slice_size: List[int]):
|
317 |
+
if hasattr(module, "set_attention_slice"):
|
318 |
+
module.set_attention_slice(slice_size.pop())
|
319 |
+
|
320 |
+
for child in module.children():
|
321 |
+
fn_recursive_set_attention_slice(child, slice_size)
|
322 |
+
|
323 |
+
reversed_slice_size = list(reversed(slice_size))
|
324 |
+
for module in self.children():
|
325 |
+
fn_recursive_set_attention_slice(module, reversed_slice_size)
|
326 |
+
|
327 |
+
def _set_gradient_checkpointing(self, module, value=False):
|
328 |
+
if isinstance(module, (CrossAttnDownBlock3D, DownBlock3D, CrossAttnUpBlock3D, UpBlock3D)):
|
329 |
+
module.gradient_checkpointing = value
|
330 |
+
|
331 |
+
def forward(
|
332 |
+
self,
|
333 |
+
sample: torch.FloatTensor,
|
334 |
+
timestep: Union[torch.Tensor, float, int],
|
335 |
+
encoder_hidden_states: torch.Tensor,
|
336 |
+
class_labels: Optional[torch.Tensor] = None,
|
337 |
+
attention_mask: Optional[torch.Tensor] = None,
|
338 |
+
return_dict: bool = True,
|
339 |
+
) -> Union[UNet3DConditionOutput, Tuple]:
|
340 |
+
r"""
|
341 |
+
Args:
|
342 |
+
sample (`torch.FloatTensor`): (batch, channel, height, width) noisy inputs tensor
|
343 |
+
timestep (`torch.FloatTensor` or `float` or `int`): (batch) timesteps
|
344 |
+
encoder_hidden_states (`torch.FloatTensor`): (batch, sequence_length, feature_dim) encoder hidden states
|
345 |
+
return_dict (`bool`, *optional*, defaults to `True`):
|
346 |
+
Whether or not to return a [`models.unet_2d_condition.UNet2DConditionOutput`] instead of a plain tuple.
|
347 |
+
|
348 |
+
Returns:
|
349 |
+
[`~models.unet_2d_condition.UNet2DConditionOutput`] or `tuple`:
|
350 |
+
[`~models.unet_2d_condition.UNet2DConditionOutput`] if `return_dict` is True, otherwise a `tuple`. When
|
351 |
+
returning a tuple, the first element is the sample tensor.
|
352 |
+
"""
|
353 |
+
# By default samples have to be AT least a multiple of the overall upsampling factor.
|
354 |
+
# The overall upsampling factor is equal to 2 ** (# num of upsampling layears).
|
355 |
+
# However, the upsampling interpolation output size can be forced to fit any upsampling size
|
356 |
+
# on the fly if necessary.
|
357 |
+
default_overall_up_factor = 2**self.num_upsamplers
|
358 |
+
|
359 |
+
# upsample size should be forwarded when sample is not a multiple of `default_overall_up_factor`
|
360 |
+
forward_upsample_size = False
|
361 |
+
upsample_size = None
|
362 |
+
|
363 |
+
if any(s % default_overall_up_factor != 0 for s in sample.shape[-2:]):
|
364 |
+
logger.info("Forward upsample size to force interpolation output size.")
|
365 |
+
forward_upsample_size = True
|
366 |
+
|
367 |
+
# prepare attention_mask
|
368 |
+
if attention_mask is not None:
|
369 |
+
attention_mask = (1 - attention_mask.to(sample.dtype)) * -10000.0
|
370 |
+
attention_mask = attention_mask.unsqueeze(1)
|
371 |
+
|
372 |
+
# center input if necessary
|
373 |
+
if self.config.center_input_sample:
|
374 |
+
sample = 2 * sample - 1.0
|
375 |
+
|
376 |
+
# time
|
377 |
+
timesteps = timestep
|
378 |
+
if not torch.is_tensor(timesteps):
|
379 |
+
# This would be a good case for the `match` statement (Python 3.10+)
|
380 |
+
is_mps = sample.device.type == "mps"
|
381 |
+
if isinstance(timestep, float):
|
382 |
+
dtype = torch.float32 if is_mps else torch.float64
|
383 |
+
else:
|
384 |
+
dtype = torch.int32 if is_mps else torch.int64
|
385 |
+
timesteps = torch.tensor([timesteps], dtype=dtype, device=sample.device)
|
386 |
+
elif len(timesteps.shape) == 0:
|
387 |
+
timesteps = timesteps[None].to(sample.device)
|
388 |
+
|
389 |
+
# broadcast to batch dimension in a way that's compatible with ONNX/Core ML
|
390 |
+
timesteps = timesteps.expand(sample.shape[0])
|
391 |
+
|
392 |
+
t_emb = self.time_proj(timesteps)
|
393 |
+
|
394 |
+
# timesteps does not contain any weights and will always return f32 tensors
|
395 |
+
# but time_embedding might actually be running in fp16. so we need to cast here.
|
396 |
+
# there might be better ways to encapsulate this.
|
397 |
+
t_emb = t_emb.to(dtype=self.dtype)
|
398 |
+
emb = self.time_embedding(t_emb)
|
399 |
+
|
400 |
+
if self.class_embedding is not None:
|
401 |
+
if class_labels is None:
|
402 |
+
raise ValueError("class_labels should be provided when num_class_embeds > 0")
|
403 |
+
|
404 |
+
if self.config.class_embed_type == "timestep":
|
405 |
+
class_labels = self.time_proj(class_labels)
|
406 |
+
|
407 |
+
class_emb = self.class_embedding(class_labels).to(dtype=self.dtype)
|
408 |
+
emb = emb + class_emb
|
409 |
+
|
410 |
+
# pre-process
|
411 |
+
sample = self.conv_in(sample)
|
412 |
+
|
413 |
+
# down
|
414 |
+
down_block_res_samples = (sample,)
|
415 |
+
for downsample_block in self.down_blocks:
|
416 |
+
if hasattr(downsample_block, "has_cross_attention") and downsample_block.has_cross_attention:
|
417 |
+
sample, res_samples = downsample_block(
|
418 |
+
hidden_states=sample,
|
419 |
+
temb=emb,
|
420 |
+
encoder_hidden_states=encoder_hidden_states,
|
421 |
+
attention_mask=attention_mask,
|
422 |
+
)
|
423 |
+
else:
|
424 |
+
sample, res_samples = downsample_block(hidden_states=sample, temb=emb, encoder_hidden_states=encoder_hidden_states)
|
425 |
+
|
426 |
+
down_block_res_samples += res_samples
|
427 |
+
|
428 |
+
# mid
|
429 |
+
sample = self.mid_block(
|
430 |
+
sample, emb, encoder_hidden_states=encoder_hidden_states, attention_mask=attention_mask
|
431 |
+
)
|
432 |
+
|
433 |
+
# up
|
434 |
+
for i, upsample_block in enumerate(self.up_blocks):
|
435 |
+
is_final_block = i == len(self.up_blocks) - 1
|
436 |
+
|
437 |
+
res_samples = down_block_res_samples[-len(upsample_block.resnets) :]
|
438 |
+
down_block_res_samples = down_block_res_samples[: -len(upsample_block.resnets)]
|
439 |
+
|
440 |
+
# if we have not reached the final block and need to forward the
|
441 |
+
# upsample size, we do it here
|
442 |
+
if not is_final_block and forward_upsample_size:
|
443 |
+
upsample_size = down_block_res_samples[-1].shape[2:]
|
444 |
+
|
445 |
+
if hasattr(upsample_block, "has_cross_attention") and upsample_block.has_cross_attention:
|
446 |
+
sample = upsample_block(
|
447 |
+
hidden_states=sample,
|
448 |
+
temb=emb,
|
449 |
+
res_hidden_states_tuple=res_samples,
|
450 |
+
encoder_hidden_states=encoder_hidden_states,
|
451 |
+
upsample_size=upsample_size,
|
452 |
+
attention_mask=attention_mask,
|
453 |
+
)
|
454 |
+
else:
|
455 |
+
sample = upsample_block(
|
456 |
+
hidden_states=sample, temb=emb, res_hidden_states_tuple=res_samples, upsample_size=upsample_size, encoder_hidden_states=encoder_hidden_states,
|
457 |
+
)
|
458 |
+
|
459 |
+
# post-process
|
460 |
+
sample = self.conv_norm_out(sample)
|
461 |
+
sample = self.conv_act(sample)
|
462 |
+
sample = self.conv_out(sample)
|
463 |
+
|
464 |
+
if not return_dict:
|
465 |
+
return (sample,)
|
466 |
+
|
467 |
+
return UNet3DConditionOutput(sample=sample)
|
468 |
+
|
469 |
+
@classmethod
|
470 |
+
def from_pretrained_2d(cls, pretrained_model_path, subfolder=None, unet_additional_kwargs=None):
|
471 |
+
if subfolder is not None:
|
472 |
+
pretrained_model_path = os.path.join(pretrained_model_path, subfolder)
|
473 |
+
print(f"loaded temporal unet's pretrained weights from {pretrained_model_path} ...")
|
474 |
+
|
475 |
+
config_file = os.path.join(pretrained_model_path, 'config.json')
|
476 |
+
if not os.path.isfile(config_file):
|
477 |
+
raise RuntimeError(f"{config_file} does not exist")
|
478 |
+
with open(config_file, "r") as f:
|
479 |
+
config = json.load(f)
|
480 |
+
config["_class_name"] = cls.__name__
|
481 |
+
config["down_block_types"] = [
|
482 |
+
"CrossAttnDownBlock3D",
|
483 |
+
"CrossAttnDownBlock3D",
|
484 |
+
"CrossAttnDownBlock3D",
|
485 |
+
"DownBlock3D"
|
486 |
+
]
|
487 |
+
config["up_block_types"] = [
|
488 |
+
"UpBlock3D",
|
489 |
+
"CrossAttnUpBlock3D",
|
490 |
+
"CrossAttnUpBlock3D",
|
491 |
+
"CrossAttnUpBlock3D"
|
492 |
+
]
|
493 |
+
|
494 |
+
from diffusers.utils import WEIGHTS_NAME
|
495 |
+
model = cls.from_config(config, **unet_additional_kwargs)
|
496 |
+
model_file = os.path.join(pretrained_model_path, WEIGHTS_NAME)
|
497 |
+
if not os.path.isfile(model_file):
|
498 |
+
raise RuntimeError(f"{model_file} does not exist")
|
499 |
+
state_dict = torch.load(model_file, map_location="cpu")
|
500 |
+
|
501 |
+
m, u = model.load_state_dict(state_dict, strict=False)
|
502 |
+
print(f"### missing keys: {len(m)}; \n### unexpected keys: {len(u)};")
|
503 |
+
# print(f"### missing keys:\n{m}\n### unexpected keys:\n{u}\n")
|
504 |
+
|
505 |
+
params = [p.numel() if "temporal" in n else 0 for n, p in model.named_parameters()]
|
506 |
+
print(f"### Temporal Module Parameters: {sum(params) / 1e6} M")
|
507 |
+
|
508 |
+
return model
|
magicanimate/models/unet_3d_blocks.py
ADDED
@@ -0,0 +1,751 @@
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|
|
1 |
+
# *************************************************************************
|
2 |
+
# This file may have been modified by Bytedance Inc. (“Bytedance Inc.'s Mo-
|
3 |
+
# difications”). All Bytedance Inc.'s Modifications are Copyright (2023) B-
|
4 |
+
# ytedance Inc..
|
5 |
+
# *************************************************************************
|
6 |
+
|
7 |
+
# Adapted from https://github.com/guoyww/AnimateDiff
|
8 |
+
|
9 |
+
# Copyright 2023 The HuggingFace Team. All rights reserved.
|
10 |
+
#
|
11 |
+
# Licensed under the Apache License, Version 2.0 (the "License");
|
12 |
+
# you may not use this file except in compliance with the License.
|
13 |
+
# You may obtain a copy of the License at
|
14 |
+
#
|
15 |
+
# http://www.apache.org/licenses/LICENSE-2.0
|
16 |
+
#
|
17 |
+
# Unless required by applicable law or agreed to in writing, software
|
18 |
+
# distributed under the License is distributed on an "AS IS" BASIS,
|
19 |
+
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
20 |
+
# See the License for the specific language governing permissions and
|
21 |
+
# limitations under the License.
|
22 |
+
import torch
|
23 |
+
from torch import nn
|
24 |
+
|
25 |
+
from .attention import Transformer3DModel
|
26 |
+
from .resnet import Downsample3D, ResnetBlock3D, Upsample3D
|
27 |
+
from .motion_module import get_motion_module
|
28 |
+
|
29 |
+
|
30 |
+
def get_down_block(
|
31 |
+
down_block_type,
|
32 |
+
num_layers,
|
33 |
+
in_channels,
|
34 |
+
out_channels,
|
35 |
+
temb_channels,
|
36 |
+
add_downsample,
|
37 |
+
resnet_eps,
|
38 |
+
resnet_act_fn,
|
39 |
+
attn_num_head_channels,
|
40 |
+
resnet_groups=None,
|
41 |
+
cross_attention_dim=None,
|
42 |
+
downsample_padding=None,
|
43 |
+
dual_cross_attention=False,
|
44 |
+
use_linear_projection=False,
|
45 |
+
only_cross_attention=False,
|
46 |
+
upcast_attention=False,
|
47 |
+
resnet_time_scale_shift="default",
|
48 |
+
|
49 |
+
unet_use_cross_frame_attention=None,
|
50 |
+
unet_use_temporal_attention=None,
|
51 |
+
|
52 |
+
use_motion_module=None,
|
53 |
+
|
54 |
+
motion_module_type=None,
|
55 |
+
motion_module_kwargs=None,
|
56 |
+
):
|
57 |
+
down_block_type = down_block_type[7:] if down_block_type.startswith("UNetRes") else down_block_type
|
58 |
+
if down_block_type == "DownBlock3D":
|
59 |
+
return DownBlock3D(
|
60 |
+
num_layers=num_layers,
|
61 |
+
in_channels=in_channels,
|
62 |
+
out_channels=out_channels,
|
63 |
+
temb_channels=temb_channels,
|
64 |
+
add_downsample=add_downsample,
|
65 |
+
resnet_eps=resnet_eps,
|
66 |
+
resnet_act_fn=resnet_act_fn,
|
67 |
+
resnet_groups=resnet_groups,
|
68 |
+
downsample_padding=downsample_padding,
|
69 |
+
resnet_time_scale_shift=resnet_time_scale_shift,
|
70 |
+
|
71 |
+
use_motion_module=use_motion_module,
|
72 |
+
motion_module_type=motion_module_type,
|
73 |
+
motion_module_kwargs=motion_module_kwargs,
|
74 |
+
)
|
75 |
+
elif down_block_type == "CrossAttnDownBlock3D":
|
76 |
+
if cross_attention_dim is None:
|
77 |
+
raise ValueError("cross_attention_dim must be specified for CrossAttnDownBlock3D")
|
78 |
+
return CrossAttnDownBlock3D(
|
79 |
+
num_layers=num_layers,
|
80 |
+
in_channels=in_channels,
|
81 |
+
out_channels=out_channels,
|
82 |
+
temb_channels=temb_channels,
|
83 |
+
add_downsample=add_downsample,
|
84 |
+
resnet_eps=resnet_eps,
|
85 |
+
resnet_act_fn=resnet_act_fn,
|
86 |
+
resnet_groups=resnet_groups,
|
87 |
+
downsample_padding=downsample_padding,
|
88 |
+
cross_attention_dim=cross_attention_dim,
|
89 |
+
attn_num_head_channels=attn_num_head_channels,
|
90 |
+
dual_cross_attention=dual_cross_attention,
|
91 |
+
use_linear_projection=use_linear_projection,
|
92 |
+
only_cross_attention=only_cross_attention,
|
93 |
+
upcast_attention=upcast_attention,
|
94 |
+
resnet_time_scale_shift=resnet_time_scale_shift,
|
95 |
+
|
96 |
+
unet_use_cross_frame_attention=unet_use_cross_frame_attention,
|
97 |
+
unet_use_temporal_attention=unet_use_temporal_attention,
|
98 |
+
|
99 |
+
use_motion_module=use_motion_module,
|
100 |
+
motion_module_type=motion_module_type,
|
101 |
+
motion_module_kwargs=motion_module_kwargs,
|
102 |
+
)
|
103 |
+
raise ValueError(f"{down_block_type} does not exist.")
|
104 |
+
|
105 |
+
|
106 |
+
def get_up_block(
|
107 |
+
up_block_type,
|
108 |
+
num_layers,
|
109 |
+
in_channels,
|
110 |
+
out_channels,
|
111 |
+
prev_output_channel,
|
112 |
+
temb_channels,
|
113 |
+
add_upsample,
|
114 |
+
resnet_eps,
|
115 |
+
resnet_act_fn,
|
116 |
+
attn_num_head_channels,
|
117 |
+
resnet_groups=None,
|
118 |
+
cross_attention_dim=None,
|
119 |
+
dual_cross_attention=False,
|
120 |
+
use_linear_projection=False,
|
121 |
+
only_cross_attention=False,
|
122 |
+
upcast_attention=False,
|
123 |
+
resnet_time_scale_shift="default",
|
124 |
+
|
125 |
+
unet_use_cross_frame_attention=None,
|
126 |
+
unet_use_temporal_attention=None,
|
127 |
+
|
128 |
+
use_motion_module=None,
|
129 |
+
motion_module_type=None,
|
130 |
+
motion_module_kwargs=None,
|
131 |
+
):
|
132 |
+
up_block_type = up_block_type[7:] if up_block_type.startswith("UNetRes") else up_block_type
|
133 |
+
if up_block_type == "UpBlock3D":
|
134 |
+
return UpBlock3D(
|
135 |
+
num_layers=num_layers,
|
136 |
+
in_channels=in_channels,
|
137 |
+
out_channels=out_channels,
|
138 |
+
prev_output_channel=prev_output_channel,
|
139 |
+
temb_channels=temb_channels,
|
140 |
+
add_upsample=add_upsample,
|
141 |
+
resnet_eps=resnet_eps,
|
142 |
+
resnet_act_fn=resnet_act_fn,
|
143 |
+
resnet_groups=resnet_groups,
|
144 |
+
resnet_time_scale_shift=resnet_time_scale_shift,
|
145 |
+
|
146 |
+
use_motion_module=use_motion_module,
|
147 |
+
motion_module_type=motion_module_type,
|
148 |
+
motion_module_kwargs=motion_module_kwargs,
|
149 |
+
)
|
150 |
+
elif up_block_type == "CrossAttnUpBlock3D":
|
151 |
+
if cross_attention_dim is None:
|
152 |
+
raise ValueError("cross_attention_dim must be specified for CrossAttnUpBlock3D")
|
153 |
+
return CrossAttnUpBlock3D(
|
154 |
+
num_layers=num_layers,
|
155 |
+
in_channels=in_channels,
|
156 |
+
out_channels=out_channels,
|
157 |
+
prev_output_channel=prev_output_channel,
|
158 |
+
temb_channels=temb_channels,
|
159 |
+
add_upsample=add_upsample,
|
160 |
+
resnet_eps=resnet_eps,
|
161 |
+
resnet_act_fn=resnet_act_fn,
|
162 |
+
resnet_groups=resnet_groups,
|
163 |
+
cross_attention_dim=cross_attention_dim,
|
164 |
+
attn_num_head_channels=attn_num_head_channels,
|
165 |
+
dual_cross_attention=dual_cross_attention,
|
166 |
+
use_linear_projection=use_linear_projection,
|
167 |
+
only_cross_attention=only_cross_attention,
|
168 |
+
upcast_attention=upcast_attention,
|
169 |
+
resnet_time_scale_shift=resnet_time_scale_shift,
|
170 |
+
|
171 |
+
unet_use_cross_frame_attention=unet_use_cross_frame_attention,
|
172 |
+
unet_use_temporal_attention=unet_use_temporal_attention,
|
173 |
+
|
174 |
+
use_motion_module=use_motion_module,
|
175 |
+
motion_module_type=motion_module_type,
|
176 |
+
motion_module_kwargs=motion_module_kwargs,
|
177 |
+
)
|
178 |
+
raise ValueError(f"{up_block_type} does not exist.")
|
179 |
+
|
180 |
+
|
181 |
+
class UNetMidBlock3DCrossAttn(nn.Module):
|
182 |
+
def __init__(
|
183 |
+
self,
|
184 |
+
in_channels: int,
|
185 |
+
temb_channels: int,
|
186 |
+
dropout: float = 0.0,
|
187 |
+
num_layers: int = 1,
|
188 |
+
resnet_eps: float = 1e-6,
|
189 |
+
resnet_time_scale_shift: str = "default",
|
190 |
+
resnet_act_fn: str = "swish",
|
191 |
+
resnet_groups: int = 32,
|
192 |
+
resnet_pre_norm: bool = True,
|
193 |
+
attn_num_head_channels=1,
|
194 |
+
output_scale_factor=1.0,
|
195 |
+
cross_attention_dim=1280,
|
196 |
+
dual_cross_attention=False,
|
197 |
+
use_linear_projection=False,
|
198 |
+
upcast_attention=False,
|
199 |
+
|
200 |
+
unet_use_cross_frame_attention=None,
|
201 |
+
unet_use_temporal_attention=None,
|
202 |
+
|
203 |
+
use_motion_module=None,
|
204 |
+
|
205 |
+
motion_module_type=None,
|
206 |
+
motion_module_kwargs=None,
|
207 |
+
):
|
208 |
+
super().__init__()
|
209 |
+
|
210 |
+
self.has_cross_attention = True
|
211 |
+
self.attn_num_head_channels = attn_num_head_channels
|
212 |
+
resnet_groups = resnet_groups if resnet_groups is not None else min(in_channels // 4, 32)
|
213 |
+
|
214 |
+
# there is always at least one resnet
|
215 |
+
resnets = [
|
216 |
+
ResnetBlock3D(
|
217 |
+
in_channels=in_channels,
|
218 |
+
out_channels=in_channels,
|
219 |
+
temb_channels=temb_channels,
|
220 |
+
eps=resnet_eps,
|
221 |
+
groups=resnet_groups,
|
222 |
+
dropout=dropout,
|
223 |
+
time_embedding_norm=resnet_time_scale_shift,
|
224 |
+
non_linearity=resnet_act_fn,
|
225 |
+
output_scale_factor=output_scale_factor,
|
226 |
+
pre_norm=resnet_pre_norm,
|
227 |
+
)
|
228 |
+
]
|
229 |
+
attentions = []
|
230 |
+
motion_modules = []
|
231 |
+
|
232 |
+
for _ in range(num_layers):
|
233 |
+
if dual_cross_attention:
|
234 |
+
raise NotImplementedError
|
235 |
+
attentions.append(
|
236 |
+
Transformer3DModel(
|
237 |
+
attn_num_head_channels,
|
238 |
+
in_channels // attn_num_head_channels,
|
239 |
+
in_channels=in_channels,
|
240 |
+
num_layers=1,
|
241 |
+
cross_attention_dim=cross_attention_dim,
|
242 |
+
norm_num_groups=resnet_groups,
|
243 |
+
use_linear_projection=use_linear_projection,
|
244 |
+
upcast_attention=upcast_attention,
|
245 |
+
|
246 |
+
unet_use_cross_frame_attention=unet_use_cross_frame_attention,
|
247 |
+
unet_use_temporal_attention=unet_use_temporal_attention,
|
248 |
+
)
|
249 |
+
)
|
250 |
+
motion_modules.append(
|
251 |
+
get_motion_module(
|
252 |
+
in_channels=in_channels,
|
253 |
+
motion_module_type=motion_module_type,
|
254 |
+
motion_module_kwargs=motion_module_kwargs,
|
255 |
+
) if use_motion_module else None
|
256 |
+
)
|
257 |
+
resnets.append(
|
258 |
+
ResnetBlock3D(
|
259 |
+
in_channels=in_channels,
|
260 |
+
out_channels=in_channels,
|
261 |
+
temb_channels=temb_channels,
|
262 |
+
eps=resnet_eps,
|
263 |
+
groups=resnet_groups,
|
264 |
+
dropout=dropout,
|
265 |
+
time_embedding_norm=resnet_time_scale_shift,
|
266 |
+
non_linearity=resnet_act_fn,
|
267 |
+
output_scale_factor=output_scale_factor,
|
268 |
+
pre_norm=resnet_pre_norm,
|
269 |
+
)
|
270 |
+
)
|
271 |
+
|
272 |
+
self.attentions = nn.ModuleList(attentions)
|
273 |
+
self.resnets = nn.ModuleList(resnets)
|
274 |
+
self.motion_modules = nn.ModuleList(motion_modules)
|
275 |
+
|
276 |
+
def forward(self, hidden_states, temb=None, encoder_hidden_states=None, attention_mask=None):
|
277 |
+
hidden_states = self.resnets[0](hidden_states, temb)
|
278 |
+
for attn, resnet, motion_module in zip(self.attentions, self.resnets[1:], self.motion_modules):
|
279 |
+
hidden_states = attn(hidden_states, encoder_hidden_states=encoder_hidden_states).sample
|
280 |
+
hidden_states = motion_module(hidden_states, temb, encoder_hidden_states=encoder_hidden_states) if motion_module is not None else hidden_states
|
281 |
+
hidden_states = resnet(hidden_states, temb)
|
282 |
+
|
283 |
+
return hidden_states
|
284 |
+
|
285 |
+
|
286 |
+
class CrossAttnDownBlock3D(nn.Module):
|
287 |
+
def __init__(
|
288 |
+
self,
|
289 |
+
in_channels: int,
|
290 |
+
out_channels: int,
|
291 |
+
temb_channels: int,
|
292 |
+
dropout: float = 0.0,
|
293 |
+
num_layers: int = 1,
|
294 |
+
resnet_eps: float = 1e-6,
|
295 |
+
resnet_time_scale_shift: str = "default",
|
296 |
+
resnet_act_fn: str = "swish",
|
297 |
+
resnet_groups: int = 32,
|
298 |
+
resnet_pre_norm: bool = True,
|
299 |
+
attn_num_head_channels=1,
|
300 |
+
cross_attention_dim=1280,
|
301 |
+
output_scale_factor=1.0,
|
302 |
+
downsample_padding=1,
|
303 |
+
add_downsample=True,
|
304 |
+
dual_cross_attention=False,
|
305 |
+
use_linear_projection=False,
|
306 |
+
only_cross_attention=False,
|
307 |
+
upcast_attention=False,
|
308 |
+
|
309 |
+
unet_use_cross_frame_attention=None,
|
310 |
+
unet_use_temporal_attention=None,
|
311 |
+
|
312 |
+
use_motion_module=None,
|
313 |
+
|
314 |
+
motion_module_type=None,
|
315 |
+
motion_module_kwargs=None,
|
316 |
+
):
|
317 |
+
super().__init__()
|
318 |
+
resnets = []
|
319 |
+
attentions = []
|
320 |
+
motion_modules = []
|
321 |
+
|
322 |
+
self.has_cross_attention = True
|
323 |
+
self.attn_num_head_channels = attn_num_head_channels
|
324 |
+
|
325 |
+
for i in range(num_layers):
|
326 |
+
in_channels = in_channels if i == 0 else out_channels
|
327 |
+
resnets.append(
|
328 |
+
ResnetBlock3D(
|
329 |
+
in_channels=in_channels,
|
330 |
+
out_channels=out_channels,
|
331 |
+
temb_channels=temb_channels,
|
332 |
+
eps=resnet_eps,
|
333 |
+
groups=resnet_groups,
|
334 |
+
dropout=dropout,
|
335 |
+
time_embedding_norm=resnet_time_scale_shift,
|
336 |
+
non_linearity=resnet_act_fn,
|
337 |
+
output_scale_factor=output_scale_factor,
|
338 |
+
pre_norm=resnet_pre_norm,
|
339 |
+
)
|
340 |
+
)
|
341 |
+
if dual_cross_attention:
|
342 |
+
raise NotImplementedError
|
343 |
+
attentions.append(
|
344 |
+
Transformer3DModel(
|
345 |
+
attn_num_head_channels,
|
346 |
+
out_channels // attn_num_head_channels,
|
347 |
+
in_channels=out_channels,
|
348 |
+
num_layers=1,
|
349 |
+
cross_attention_dim=cross_attention_dim,
|
350 |
+
norm_num_groups=resnet_groups,
|
351 |
+
use_linear_projection=use_linear_projection,
|
352 |
+
only_cross_attention=only_cross_attention,
|
353 |
+
upcast_attention=upcast_attention,
|
354 |
+
|
355 |
+
unet_use_cross_frame_attention=unet_use_cross_frame_attention,
|
356 |
+
unet_use_temporal_attention=unet_use_temporal_attention,
|
357 |
+
)
|
358 |
+
)
|
359 |
+
motion_modules.append(
|
360 |
+
get_motion_module(
|
361 |
+
in_channels=out_channels,
|
362 |
+
motion_module_type=motion_module_type,
|
363 |
+
motion_module_kwargs=motion_module_kwargs,
|
364 |
+
) if use_motion_module else None
|
365 |
+
)
|
366 |
+
|
367 |
+
self.attentions = nn.ModuleList(attentions)
|
368 |
+
self.resnets = nn.ModuleList(resnets)
|
369 |
+
self.motion_modules = nn.ModuleList(motion_modules)
|
370 |
+
|
371 |
+
if add_downsample:
|
372 |
+
self.downsamplers = nn.ModuleList(
|
373 |
+
[
|
374 |
+
Downsample3D(
|
375 |
+
out_channels, use_conv=True, out_channels=out_channels, padding=downsample_padding, name="op"
|
376 |
+
)
|
377 |
+
]
|
378 |
+
)
|
379 |
+
else:
|
380 |
+
self.downsamplers = None
|
381 |
+
|
382 |
+
self.gradient_checkpointing = False
|
383 |
+
|
384 |
+
def forward(self, hidden_states, temb=None, encoder_hidden_states=None, attention_mask=None):
|
385 |
+
output_states = ()
|
386 |
+
|
387 |
+
for resnet, attn, motion_module in zip(self.resnets, self.attentions, self.motion_modules):
|
388 |
+
if self.training and self.gradient_checkpointing:
|
389 |
+
|
390 |
+
def create_custom_forward(module, return_dict=None):
|
391 |
+
def custom_forward(*inputs):
|
392 |
+
if return_dict is not None:
|
393 |
+
return module(*inputs, return_dict=return_dict)
|
394 |
+
else:
|
395 |
+
return module(*inputs)
|
396 |
+
|
397 |
+
return custom_forward
|
398 |
+
|
399 |
+
hidden_states = torch.utils.checkpoint.checkpoint(create_custom_forward(resnet), hidden_states, temb)
|
400 |
+
hidden_states = torch.utils.checkpoint.checkpoint(
|
401 |
+
create_custom_forward(attn, return_dict=False),
|
402 |
+
hidden_states,
|
403 |
+
encoder_hidden_states,
|
404 |
+
)[0]
|
405 |
+
if motion_module is not None:
|
406 |
+
hidden_states = torch.utils.checkpoint.checkpoint(create_custom_forward(motion_module), hidden_states.requires_grad_(), temb, encoder_hidden_states)
|
407 |
+
|
408 |
+
else:
|
409 |
+
hidden_states = resnet(hidden_states, temb)
|
410 |
+
hidden_states = attn(hidden_states, encoder_hidden_states=encoder_hidden_states).sample
|
411 |
+
|
412 |
+
# add motion module
|
413 |
+
hidden_states = motion_module(hidden_states, temb, encoder_hidden_states=encoder_hidden_states) if motion_module is not None else hidden_states
|
414 |
+
|
415 |
+
output_states += (hidden_states,)
|
416 |
+
|
417 |
+
if self.downsamplers is not None:
|
418 |
+
for downsampler in self.downsamplers:
|
419 |
+
hidden_states = downsampler(hidden_states)
|
420 |
+
|
421 |
+
output_states += (hidden_states,)
|
422 |
+
|
423 |
+
return hidden_states, output_states
|
424 |
+
|
425 |
+
|
426 |
+
class DownBlock3D(nn.Module):
|
427 |
+
def __init__(
|
428 |
+
self,
|
429 |
+
in_channels: int,
|
430 |
+
out_channels: int,
|
431 |
+
temb_channels: int,
|
432 |
+
dropout: float = 0.0,
|
433 |
+
num_layers: int = 1,
|
434 |
+
resnet_eps: float = 1e-6,
|
435 |
+
resnet_time_scale_shift: str = "default",
|
436 |
+
resnet_act_fn: str = "swish",
|
437 |
+
resnet_groups: int = 32,
|
438 |
+
resnet_pre_norm: bool = True,
|
439 |
+
output_scale_factor=1.0,
|
440 |
+
add_downsample=True,
|
441 |
+
downsample_padding=1,
|
442 |
+
|
443 |
+
use_motion_module=None,
|
444 |
+
motion_module_type=None,
|
445 |
+
motion_module_kwargs=None,
|
446 |
+
):
|
447 |
+
super().__init__()
|
448 |
+
resnets = []
|
449 |
+
motion_modules = []
|
450 |
+
|
451 |
+
for i in range(num_layers):
|
452 |
+
in_channels = in_channels if i == 0 else out_channels
|
453 |
+
resnets.append(
|
454 |
+
ResnetBlock3D(
|
455 |
+
in_channels=in_channels,
|
456 |
+
out_channels=out_channels,
|
457 |
+
temb_channels=temb_channels,
|
458 |
+
eps=resnet_eps,
|
459 |
+
groups=resnet_groups,
|
460 |
+
dropout=dropout,
|
461 |
+
time_embedding_norm=resnet_time_scale_shift,
|
462 |
+
non_linearity=resnet_act_fn,
|
463 |
+
output_scale_factor=output_scale_factor,
|
464 |
+
pre_norm=resnet_pre_norm,
|
465 |
+
)
|
466 |
+
)
|
467 |
+
motion_modules.append(
|
468 |
+
get_motion_module(
|
469 |
+
in_channels=out_channels,
|
470 |
+
motion_module_type=motion_module_type,
|
471 |
+
motion_module_kwargs=motion_module_kwargs,
|
472 |
+
) if use_motion_module else None
|
473 |
+
)
|
474 |
+
|
475 |
+
self.resnets = nn.ModuleList(resnets)
|
476 |
+
self.motion_modules = nn.ModuleList(motion_modules)
|
477 |
+
|
478 |
+
if add_downsample:
|
479 |
+
self.downsamplers = nn.ModuleList(
|
480 |
+
[
|
481 |
+
Downsample3D(
|
482 |
+
out_channels, use_conv=True, out_channels=out_channels, padding=downsample_padding, name="op"
|
483 |
+
)
|
484 |
+
]
|
485 |
+
)
|
486 |
+
else:
|
487 |
+
self.downsamplers = None
|
488 |
+
|
489 |
+
self.gradient_checkpointing = False
|
490 |
+
|
491 |
+
def forward(self, hidden_states, temb=None, encoder_hidden_states=None):
|
492 |
+
output_states = ()
|
493 |
+
|
494 |
+
for resnet, motion_module in zip(self.resnets, self.motion_modules):
|
495 |
+
if self.training and self.gradient_checkpointing:
|
496 |
+
def create_custom_forward(module):
|
497 |
+
def custom_forward(*inputs):
|
498 |
+
return module(*inputs)
|
499 |
+
|
500 |
+
return custom_forward
|
501 |
+
|
502 |
+
hidden_states = torch.utils.checkpoint.checkpoint(create_custom_forward(resnet), hidden_states, temb)
|
503 |
+
if motion_module is not None:
|
504 |
+
hidden_states = torch.utils.checkpoint.checkpoint(create_custom_forward(motion_module), hidden_states.requires_grad_(), temb, encoder_hidden_states)
|
505 |
+
else:
|
506 |
+
hidden_states = resnet(hidden_states, temb)
|
507 |
+
|
508 |
+
# add motion module
|
509 |
+
hidden_states = motion_module(hidden_states, temb, encoder_hidden_states=encoder_hidden_states) if motion_module is not None else hidden_states
|
510 |
+
|
511 |
+
output_states += (hidden_states,)
|
512 |
+
|
513 |
+
if self.downsamplers is not None:
|
514 |
+
for downsampler in self.downsamplers:
|
515 |
+
hidden_states = downsampler(hidden_states)
|
516 |
+
|
517 |
+
output_states += (hidden_states,)
|
518 |
+
|
519 |
+
return hidden_states, output_states
|
520 |
+
|
521 |
+
|
522 |
+
class CrossAttnUpBlock3D(nn.Module):
|
523 |
+
def __init__(
|
524 |
+
self,
|
525 |
+
in_channels: int,
|
526 |
+
out_channels: int,
|
527 |
+
prev_output_channel: int,
|
528 |
+
temb_channels: int,
|
529 |
+
dropout: float = 0.0,
|
530 |
+
num_layers: int = 1,
|
531 |
+
resnet_eps: float = 1e-6,
|
532 |
+
resnet_time_scale_shift: str = "default",
|
533 |
+
resnet_act_fn: str = "swish",
|
534 |
+
resnet_groups: int = 32,
|
535 |
+
resnet_pre_norm: bool = True,
|
536 |
+
attn_num_head_channels=1,
|
537 |
+
cross_attention_dim=1280,
|
538 |
+
output_scale_factor=1.0,
|
539 |
+
add_upsample=True,
|
540 |
+
dual_cross_attention=False,
|
541 |
+
use_linear_projection=False,
|
542 |
+
only_cross_attention=False,
|
543 |
+
upcast_attention=False,
|
544 |
+
|
545 |
+
unet_use_cross_frame_attention=None,
|
546 |
+
unet_use_temporal_attention=None,
|
547 |
+
|
548 |
+
use_motion_module=None,
|
549 |
+
|
550 |
+
motion_module_type=None,
|
551 |
+
motion_module_kwargs=None,
|
552 |
+
):
|
553 |
+
super().__init__()
|
554 |
+
resnets = []
|
555 |
+
attentions = []
|
556 |
+
motion_modules = []
|
557 |
+
|
558 |
+
self.has_cross_attention = True
|
559 |
+
self.attn_num_head_channels = attn_num_head_channels
|
560 |
+
|
561 |
+
for i in range(num_layers):
|
562 |
+
res_skip_channels = in_channels if (i == num_layers - 1) else out_channels
|
563 |
+
resnet_in_channels = prev_output_channel if i == 0 else out_channels
|
564 |
+
|
565 |
+
resnets.append(
|
566 |
+
ResnetBlock3D(
|
567 |
+
in_channels=resnet_in_channels + res_skip_channels,
|
568 |
+
out_channels=out_channels,
|
569 |
+
temb_channels=temb_channels,
|
570 |
+
eps=resnet_eps,
|
571 |
+
groups=resnet_groups,
|
572 |
+
dropout=dropout,
|
573 |
+
time_embedding_norm=resnet_time_scale_shift,
|
574 |
+
non_linearity=resnet_act_fn,
|
575 |
+
output_scale_factor=output_scale_factor,
|
576 |
+
pre_norm=resnet_pre_norm,
|
577 |
+
)
|
578 |
+
)
|
579 |
+
if dual_cross_attention:
|
580 |
+
raise NotImplementedError
|
581 |
+
attentions.append(
|
582 |
+
Transformer3DModel(
|
583 |
+
attn_num_head_channels,
|
584 |
+
out_channels // attn_num_head_channels,
|
585 |
+
in_channels=out_channels,
|
586 |
+
num_layers=1,
|
587 |
+
cross_attention_dim=cross_attention_dim,
|
588 |
+
norm_num_groups=resnet_groups,
|
589 |
+
use_linear_projection=use_linear_projection,
|
590 |
+
only_cross_attention=only_cross_attention,
|
591 |
+
upcast_attention=upcast_attention,
|
592 |
+
|
593 |
+
unet_use_cross_frame_attention=unet_use_cross_frame_attention,
|
594 |
+
unet_use_temporal_attention=unet_use_temporal_attention,
|
595 |
+
)
|
596 |
+
)
|
597 |
+
motion_modules.append(
|
598 |
+
get_motion_module(
|
599 |
+
in_channels=out_channels,
|
600 |
+
motion_module_type=motion_module_type,
|
601 |
+
motion_module_kwargs=motion_module_kwargs,
|
602 |
+
) if use_motion_module else None
|
603 |
+
)
|
604 |
+
|
605 |
+
self.attentions = nn.ModuleList(attentions)
|
606 |
+
self.resnets = nn.ModuleList(resnets)
|
607 |
+
self.motion_modules = nn.ModuleList(motion_modules)
|
608 |
+
|
609 |
+
if add_upsample:
|
610 |
+
self.upsamplers = nn.ModuleList([Upsample3D(out_channels, use_conv=True, out_channels=out_channels)])
|
611 |
+
else:
|
612 |
+
self.upsamplers = None
|
613 |
+
|
614 |
+
self.gradient_checkpointing = False
|
615 |
+
|
616 |
+
def forward(
|
617 |
+
self,
|
618 |
+
hidden_states,
|
619 |
+
res_hidden_states_tuple,
|
620 |
+
temb=None,
|
621 |
+
encoder_hidden_states=None,
|
622 |
+
upsample_size=None,
|
623 |
+
attention_mask=None,
|
624 |
+
):
|
625 |
+
for resnet, attn, motion_module in zip(self.resnets, self.attentions, self.motion_modules):
|
626 |
+
# pop res hidden states
|
627 |
+
res_hidden_states = res_hidden_states_tuple[-1]
|
628 |
+
res_hidden_states_tuple = res_hidden_states_tuple[:-1]
|
629 |
+
hidden_states = torch.cat([hidden_states, res_hidden_states], dim=1)
|
630 |
+
|
631 |
+
if self.training and self.gradient_checkpointing:
|
632 |
+
|
633 |
+
def create_custom_forward(module, return_dict=None):
|
634 |
+
def custom_forward(*inputs):
|
635 |
+
if return_dict is not None:
|
636 |
+
return module(*inputs, return_dict=return_dict)
|
637 |
+
else:
|
638 |
+
return module(*inputs)
|
639 |
+
|
640 |
+
return custom_forward
|
641 |
+
|
642 |
+
hidden_states = torch.utils.checkpoint.checkpoint(create_custom_forward(resnet), hidden_states, temb)
|
643 |
+
hidden_states = torch.utils.checkpoint.checkpoint(
|
644 |
+
create_custom_forward(attn, return_dict=False),
|
645 |
+
hidden_states,
|
646 |
+
encoder_hidden_states,
|
647 |
+
)[0]
|
648 |
+
if motion_module is not None:
|
649 |
+
hidden_states = torch.utils.checkpoint.checkpoint(create_custom_forward(motion_module), hidden_states.requires_grad_(), temb, encoder_hidden_states)
|
650 |
+
|
651 |
+
else:
|
652 |
+
hidden_states = resnet(hidden_states, temb)
|
653 |
+
hidden_states = attn(hidden_states, encoder_hidden_states=encoder_hidden_states).sample
|
654 |
+
|
655 |
+
# add motion module
|
656 |
+
hidden_states = motion_module(hidden_states, temb, encoder_hidden_states=encoder_hidden_states) if motion_module is not None else hidden_states
|
657 |
+
|
658 |
+
if self.upsamplers is not None:
|
659 |
+
for upsampler in self.upsamplers:
|
660 |
+
hidden_states = upsampler(hidden_states, upsample_size)
|
661 |
+
|
662 |
+
return hidden_states
|
663 |
+
|
664 |
+
|
665 |
+
class UpBlock3D(nn.Module):
|
666 |
+
def __init__(
|
667 |
+
self,
|
668 |
+
in_channels: int,
|
669 |
+
prev_output_channel: int,
|
670 |
+
out_channels: int,
|
671 |
+
temb_channels: int,
|
672 |
+
dropout: float = 0.0,
|
673 |
+
num_layers: int = 1,
|
674 |
+
resnet_eps: float = 1e-6,
|
675 |
+
resnet_time_scale_shift: str = "default",
|
676 |
+
resnet_act_fn: str = "swish",
|
677 |
+
resnet_groups: int = 32,
|
678 |
+
resnet_pre_norm: bool = True,
|
679 |
+
output_scale_factor=1.0,
|
680 |
+
add_upsample=True,
|
681 |
+
|
682 |
+
use_motion_module=None,
|
683 |
+
motion_module_type=None,
|
684 |
+
motion_module_kwargs=None,
|
685 |
+
):
|
686 |
+
super().__init__()
|
687 |
+
resnets = []
|
688 |
+
motion_modules = []
|
689 |
+
|
690 |
+
for i in range(num_layers):
|
691 |
+
res_skip_channels = in_channels if (i == num_layers - 1) else out_channels
|
692 |
+
resnet_in_channels = prev_output_channel if i == 0 else out_channels
|
693 |
+
|
694 |
+
resnets.append(
|
695 |
+
ResnetBlock3D(
|
696 |
+
in_channels=resnet_in_channels + res_skip_channels,
|
697 |
+
out_channels=out_channels,
|
698 |
+
temb_channels=temb_channels,
|
699 |
+
eps=resnet_eps,
|
700 |
+
groups=resnet_groups,
|
701 |
+
dropout=dropout,
|
702 |
+
time_embedding_norm=resnet_time_scale_shift,
|
703 |
+
non_linearity=resnet_act_fn,
|
704 |
+
output_scale_factor=output_scale_factor,
|
705 |
+
pre_norm=resnet_pre_norm,
|
706 |
+
)
|
707 |
+
)
|
708 |
+
motion_modules.append(
|
709 |
+
get_motion_module(
|
710 |
+
in_channels=out_channels,
|
711 |
+
motion_module_type=motion_module_type,
|
712 |
+
motion_module_kwargs=motion_module_kwargs,
|
713 |
+
) if use_motion_module else None
|
714 |
+
)
|
715 |
+
|
716 |
+
self.resnets = nn.ModuleList(resnets)
|
717 |
+
self.motion_modules = nn.ModuleList(motion_modules)
|
718 |
+
|
719 |
+
if add_upsample:
|
720 |
+
self.upsamplers = nn.ModuleList([Upsample3D(out_channels, use_conv=True, out_channels=out_channels)])
|
721 |
+
else:
|
722 |
+
self.upsamplers = None
|
723 |
+
|
724 |
+
self.gradient_checkpointing = False
|
725 |
+
|
726 |
+
def forward(self, hidden_states, res_hidden_states_tuple, temb=None, upsample_size=None, encoder_hidden_states=None,):
|
727 |
+
for resnet, motion_module in zip(self.resnets, self.motion_modules):
|
728 |
+
# pop res hidden states
|
729 |
+
res_hidden_states = res_hidden_states_tuple[-1]
|
730 |
+
res_hidden_states_tuple = res_hidden_states_tuple[:-1]
|
731 |
+
hidden_states = torch.cat([hidden_states, res_hidden_states], dim=1)
|
732 |
+
|
733 |
+
if self.training and self.gradient_checkpointing:
|
734 |
+
def create_custom_forward(module):
|
735 |
+
def custom_forward(*inputs):
|
736 |
+
return module(*inputs)
|
737 |
+
|
738 |
+
return custom_forward
|
739 |
+
|
740 |
+
hidden_states = torch.utils.checkpoint.checkpoint(create_custom_forward(resnet), hidden_states, temb)
|
741 |
+
if motion_module is not None:
|
742 |
+
hidden_states = torch.utils.checkpoint.checkpoint(create_custom_forward(motion_module), hidden_states.requires_grad_(), temb, encoder_hidden_states)
|
743 |
+
else:
|
744 |
+
hidden_states = resnet(hidden_states, temb)
|
745 |
+
hidden_states = motion_module(hidden_states, temb, encoder_hidden_states=encoder_hidden_states) if motion_module is not None else hidden_states
|
746 |
+
|
747 |
+
if self.upsamplers is not None:
|
748 |
+
for upsampler in self.upsamplers:
|
749 |
+
hidden_states = upsampler(hidden_states, upsample_size)
|
750 |
+
|
751 |
+
return hidden_states
|
magicanimate/models/unet_controlnet.py
ADDED
@@ -0,0 +1,525 @@
|
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|
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|
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|
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|
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|
|
|
|
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|
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|
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|
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|
|
|
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|
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|
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|
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|
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|
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|
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|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
|
|
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|
|
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|
|
|
|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
# *************************************************************************
|
2 |
+
# This file may have been modified by Bytedance Inc. (“Bytedance Inc.'s Mo-
|
3 |
+
# difications”). All Bytedance Inc.'s Modifications are Copyright (2023) B-
|
4 |
+
# ytedance Inc..
|
5 |
+
# *************************************************************************
|
6 |
+
|
7 |
+
# Copyright 2023 The HuggingFace Team. All rights reserved.
|
8 |
+
#
|
9 |
+
# Licensed under the Apache License, Version 2.0 (the "License");
|
10 |
+
# you may not use this file except in compliance with the License.
|
11 |
+
# You may obtain a copy of the License at
|
12 |
+
#
|
13 |
+
# http://www.apache.org/licenses/LICENSE-2.0
|
14 |
+
#
|
15 |
+
# Unless required by applicable law or agreed to in writing, software
|
16 |
+
# distributed under the License is distributed on an "AS IS" BASIS,
|
17 |
+
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
18 |
+
# See the License for the specific language governing permissions and
|
19 |
+
# limitations under the License.
|
20 |
+
from dataclasses import dataclass
|
21 |
+
from typing import List, Optional, Tuple, Union
|
22 |
+
|
23 |
+
import os
|
24 |
+
import json
|
25 |
+
|
26 |
+
import torch
|
27 |
+
import torch.nn as nn
|
28 |
+
import torch.utils.checkpoint
|
29 |
+
|
30 |
+
from diffusers.configuration_utils import ConfigMixin, register_to_config
|
31 |
+
from diffusers.models.modeling_utils import ModelMixin
|
32 |
+
from diffusers.utils import BaseOutput, logging
|
33 |
+
from diffusers.models.embeddings import TimestepEmbedding, Timesteps
|
34 |
+
from magicanimate.models.unet_3d_blocks import (
|
35 |
+
CrossAttnDownBlock3D,
|
36 |
+
CrossAttnUpBlock3D,
|
37 |
+
DownBlock3D,
|
38 |
+
UNetMidBlock3DCrossAttn,
|
39 |
+
UpBlock3D,
|
40 |
+
get_down_block,
|
41 |
+
get_up_block,
|
42 |
+
)
|
43 |
+
from .resnet import InflatedConv3d
|
44 |
+
|
45 |
+
|
46 |
+
logger = logging.get_logger(__name__) # pylint: disable=invalid-name
|
47 |
+
|
48 |
+
|
49 |
+
@dataclass
|
50 |
+
class UNet3DConditionOutput(BaseOutput):
|
51 |
+
sample: torch.FloatTensor
|
52 |
+
|
53 |
+
|
54 |
+
class UNet3DConditionModel(ModelMixin, ConfigMixin):
|
55 |
+
_supports_gradient_checkpointing = True
|
56 |
+
|
57 |
+
@register_to_config
|
58 |
+
def __init__(
|
59 |
+
self,
|
60 |
+
sample_size: Optional[int] = None,
|
61 |
+
in_channels: int = 4,
|
62 |
+
out_channels: int = 4,
|
63 |
+
center_input_sample: bool = False,
|
64 |
+
flip_sin_to_cos: bool = True,
|
65 |
+
freq_shift: int = 0,
|
66 |
+
down_block_types: Tuple[str] = (
|
67 |
+
"CrossAttnDownBlock3D",
|
68 |
+
"CrossAttnDownBlock3D",
|
69 |
+
"CrossAttnDownBlock3D",
|
70 |
+
"DownBlock3D",
|
71 |
+
),
|
72 |
+
mid_block_type: str = "UNetMidBlock3DCrossAttn",
|
73 |
+
up_block_types: Tuple[str] = (
|
74 |
+
"UpBlock3D",
|
75 |
+
"CrossAttnUpBlock3D",
|
76 |
+
"CrossAttnUpBlock3D",
|
77 |
+
"CrossAttnUpBlock3D"
|
78 |
+
),
|
79 |
+
only_cross_attention: Union[bool, Tuple[bool]] = False,
|
80 |
+
block_out_channels: Tuple[int] = (320, 640, 1280, 1280),
|
81 |
+
layers_per_block: int = 2,
|
82 |
+
downsample_padding: int = 1,
|
83 |
+
mid_block_scale_factor: float = 1,
|
84 |
+
act_fn: str = "silu",
|
85 |
+
norm_num_groups: int = 32,
|
86 |
+
norm_eps: float = 1e-5,
|
87 |
+
cross_attention_dim: int = 1280,
|
88 |
+
attention_head_dim: Union[int, Tuple[int]] = 8,
|
89 |
+
dual_cross_attention: bool = False,
|
90 |
+
use_linear_projection: bool = False,
|
91 |
+
class_embed_type: Optional[str] = None,
|
92 |
+
num_class_embeds: Optional[int] = None,
|
93 |
+
upcast_attention: bool = False,
|
94 |
+
resnet_time_scale_shift: str = "default",
|
95 |
+
|
96 |
+
# Additional
|
97 |
+
use_motion_module = False,
|
98 |
+
motion_module_resolutions = ( 1,2,4,8 ),
|
99 |
+
motion_module_mid_block = False,
|
100 |
+
motion_module_decoder_only = False,
|
101 |
+
motion_module_type = None,
|
102 |
+
motion_module_kwargs = {},
|
103 |
+
unet_use_cross_frame_attention = None,
|
104 |
+
unet_use_temporal_attention = None,
|
105 |
+
):
|
106 |
+
super().__init__()
|
107 |
+
|
108 |
+
self.sample_size = sample_size
|
109 |
+
time_embed_dim = block_out_channels[0] * 4
|
110 |
+
|
111 |
+
# input
|
112 |
+
self.conv_in = InflatedConv3d(in_channels, block_out_channels[0], kernel_size=3, padding=(1, 1))
|
113 |
+
|
114 |
+
# time
|
115 |
+
self.time_proj = Timesteps(block_out_channels[0], flip_sin_to_cos, freq_shift)
|
116 |
+
timestep_input_dim = block_out_channels[0]
|
117 |
+
|
118 |
+
self.time_embedding = TimestepEmbedding(timestep_input_dim, time_embed_dim)
|
119 |
+
|
120 |
+
# class embedding
|
121 |
+
if class_embed_type is None and num_class_embeds is not None:
|
122 |
+
self.class_embedding = nn.Embedding(num_class_embeds, time_embed_dim)
|
123 |
+
elif class_embed_type == "timestep":
|
124 |
+
self.class_embedding = TimestepEmbedding(timestep_input_dim, time_embed_dim)
|
125 |
+
elif class_embed_type == "identity":
|
126 |
+
self.class_embedding = nn.Identity(time_embed_dim, time_embed_dim)
|
127 |
+
else:
|
128 |
+
self.class_embedding = None
|
129 |
+
|
130 |
+
self.down_blocks = nn.ModuleList([])
|
131 |
+
self.mid_block = None
|
132 |
+
self.up_blocks = nn.ModuleList([])
|
133 |
+
|
134 |
+
if isinstance(only_cross_attention, bool):
|
135 |
+
only_cross_attention = [only_cross_attention] * len(down_block_types)
|
136 |
+
|
137 |
+
if isinstance(attention_head_dim, int):
|
138 |
+
attention_head_dim = (attention_head_dim,) * len(down_block_types)
|
139 |
+
|
140 |
+
# down
|
141 |
+
output_channel = block_out_channels[0]
|
142 |
+
for i, down_block_type in enumerate(down_block_types):
|
143 |
+
res = 2 ** i
|
144 |
+
input_channel = output_channel
|
145 |
+
output_channel = block_out_channels[i]
|
146 |
+
is_final_block = i == len(block_out_channels) - 1
|
147 |
+
|
148 |
+
down_block = get_down_block(
|
149 |
+
down_block_type,
|
150 |
+
num_layers=layers_per_block,
|
151 |
+
in_channels=input_channel,
|
152 |
+
out_channels=output_channel,
|
153 |
+
temb_channels=time_embed_dim,
|
154 |
+
add_downsample=not is_final_block,
|
155 |
+
resnet_eps=norm_eps,
|
156 |
+
resnet_act_fn=act_fn,
|
157 |
+
resnet_groups=norm_num_groups,
|
158 |
+
cross_attention_dim=cross_attention_dim,
|
159 |
+
attn_num_head_channels=attention_head_dim[i],
|
160 |
+
downsample_padding=downsample_padding,
|
161 |
+
dual_cross_attention=dual_cross_attention,
|
162 |
+
use_linear_projection=use_linear_projection,
|
163 |
+
only_cross_attention=only_cross_attention[i],
|
164 |
+
upcast_attention=upcast_attention,
|
165 |
+
resnet_time_scale_shift=resnet_time_scale_shift,
|
166 |
+
|
167 |
+
unet_use_cross_frame_attention=unet_use_cross_frame_attention,
|
168 |
+
unet_use_temporal_attention=unet_use_temporal_attention,
|
169 |
+
|
170 |
+
use_motion_module=use_motion_module and (res in motion_module_resolutions) and (not motion_module_decoder_only),
|
171 |
+
motion_module_type=motion_module_type,
|
172 |
+
motion_module_kwargs=motion_module_kwargs,
|
173 |
+
)
|
174 |
+
self.down_blocks.append(down_block)
|
175 |
+
|
176 |
+
# mid
|
177 |
+
if mid_block_type == "UNetMidBlock3DCrossAttn":
|
178 |
+
self.mid_block = UNetMidBlock3DCrossAttn(
|
179 |
+
in_channels=block_out_channels[-1],
|
180 |
+
temb_channels=time_embed_dim,
|
181 |
+
resnet_eps=norm_eps,
|
182 |
+
resnet_act_fn=act_fn,
|
183 |
+
output_scale_factor=mid_block_scale_factor,
|
184 |
+
resnet_time_scale_shift=resnet_time_scale_shift,
|
185 |
+
cross_attention_dim=cross_attention_dim,
|
186 |
+
attn_num_head_channels=attention_head_dim[-1],
|
187 |
+
resnet_groups=norm_num_groups,
|
188 |
+
dual_cross_attention=dual_cross_attention,
|
189 |
+
use_linear_projection=use_linear_projection,
|
190 |
+
upcast_attention=upcast_attention,
|
191 |
+
|
192 |
+
unet_use_cross_frame_attention=unet_use_cross_frame_attention,
|
193 |
+
unet_use_temporal_attention=unet_use_temporal_attention,
|
194 |
+
|
195 |
+
use_motion_module=use_motion_module and motion_module_mid_block,
|
196 |
+
motion_module_type=motion_module_type,
|
197 |
+
motion_module_kwargs=motion_module_kwargs,
|
198 |
+
)
|
199 |
+
else:
|
200 |
+
raise ValueError(f"unknown mid_block_type : {mid_block_type}")
|
201 |
+
|
202 |
+
# count how many layers upsample the videos
|
203 |
+
self.num_upsamplers = 0
|
204 |
+
|
205 |
+
# up
|
206 |
+
reversed_block_out_channels = list(reversed(block_out_channels))
|
207 |
+
reversed_attention_head_dim = list(reversed(attention_head_dim))
|
208 |
+
only_cross_attention = list(reversed(only_cross_attention))
|
209 |
+
output_channel = reversed_block_out_channels[0]
|
210 |
+
for i, up_block_type in enumerate(up_block_types):
|
211 |
+
res = 2 ** (3 - i)
|
212 |
+
is_final_block = i == len(block_out_channels) - 1
|
213 |
+
|
214 |
+
prev_output_channel = output_channel
|
215 |
+
output_channel = reversed_block_out_channels[i]
|
216 |
+
input_channel = reversed_block_out_channels[min(i + 1, len(block_out_channels) - 1)]
|
217 |
+
|
218 |
+
# add upsample block for all BUT final layer
|
219 |
+
if not is_final_block:
|
220 |
+
add_upsample = True
|
221 |
+
self.num_upsamplers += 1
|
222 |
+
else:
|
223 |
+
add_upsample = False
|
224 |
+
|
225 |
+
up_block = get_up_block(
|
226 |
+
up_block_type,
|
227 |
+
num_layers=layers_per_block + 1,
|
228 |
+
in_channels=input_channel,
|
229 |
+
out_channels=output_channel,
|
230 |
+
prev_output_channel=prev_output_channel,
|
231 |
+
temb_channels=time_embed_dim,
|
232 |
+
add_upsample=add_upsample,
|
233 |
+
resnet_eps=norm_eps,
|
234 |
+
resnet_act_fn=act_fn,
|
235 |
+
resnet_groups=norm_num_groups,
|
236 |
+
cross_attention_dim=cross_attention_dim,
|
237 |
+
attn_num_head_channels=reversed_attention_head_dim[i],
|
238 |
+
dual_cross_attention=dual_cross_attention,
|
239 |
+
use_linear_projection=use_linear_projection,
|
240 |
+
only_cross_attention=only_cross_attention[i],
|
241 |
+
upcast_attention=upcast_attention,
|
242 |
+
resnet_time_scale_shift=resnet_time_scale_shift,
|
243 |
+
|
244 |
+
unet_use_cross_frame_attention=unet_use_cross_frame_attention,
|
245 |
+
unet_use_temporal_attention=unet_use_temporal_attention,
|
246 |
+
|
247 |
+
use_motion_module=use_motion_module and (res in motion_module_resolutions),
|
248 |
+
motion_module_type=motion_module_type,
|
249 |
+
motion_module_kwargs=motion_module_kwargs,
|
250 |
+
)
|
251 |
+
self.up_blocks.append(up_block)
|
252 |
+
prev_output_channel = output_channel
|
253 |
+
|
254 |
+
# out
|
255 |
+
self.conv_norm_out = nn.GroupNorm(num_channels=block_out_channels[0], num_groups=norm_num_groups, eps=norm_eps)
|
256 |
+
self.conv_act = nn.SiLU()
|
257 |
+
self.conv_out = InflatedConv3d(block_out_channels[0], out_channels, kernel_size=3, padding=1)
|
258 |
+
|
259 |
+
def set_attention_slice(self, slice_size):
|
260 |
+
r"""
|
261 |
+
Enable sliced attention computation.
|
262 |
+
|
263 |
+
When this option is enabled, the attention module will split the input tensor in slices, to compute attention
|
264 |
+
in several steps. This is useful to save some memory in exchange for a small speed decrease.
|
265 |
+
|
266 |
+
Args:
|
267 |
+
slice_size (`str` or `int` or `list(int)`, *optional*, defaults to `"auto"`):
|
268 |
+
When `"auto"`, halves the input to the attention heads, so attention will be computed in two steps. If
|
269 |
+
`"max"`, maxium amount of memory will be saved by running only one slice at a time. If a number is
|
270 |
+
provided, uses as many slices as `attention_head_dim // slice_size`. In this case, `attention_head_dim`
|
271 |
+
must be a multiple of `slice_size`.
|
272 |
+
"""
|
273 |
+
sliceable_head_dims = []
|
274 |
+
|
275 |
+
def fn_recursive_retrieve_slicable_dims(module: torch.nn.Module):
|
276 |
+
if hasattr(module, "set_attention_slice"):
|
277 |
+
sliceable_head_dims.append(module.sliceable_head_dim)
|
278 |
+
|
279 |
+
for child in module.children():
|
280 |
+
fn_recursive_retrieve_slicable_dims(child)
|
281 |
+
|
282 |
+
# retrieve number of attention layers
|
283 |
+
for module in self.children():
|
284 |
+
fn_recursive_retrieve_slicable_dims(module)
|
285 |
+
|
286 |
+
num_slicable_layers = len(sliceable_head_dims)
|
287 |
+
|
288 |
+
if slice_size == "auto":
|
289 |
+
# half the attention head size is usually a good trade-off between
|
290 |
+
# speed and memory
|
291 |
+
slice_size = [dim // 2 for dim in sliceable_head_dims]
|
292 |
+
elif slice_size == "max":
|
293 |
+
# make smallest slice possible
|
294 |
+
slice_size = num_slicable_layers * [1]
|
295 |
+
|
296 |
+
slice_size = num_slicable_layers * [slice_size] if not isinstance(slice_size, list) else slice_size
|
297 |
+
|
298 |
+
if len(slice_size) != len(sliceable_head_dims):
|
299 |
+
raise ValueError(
|
300 |
+
f"You have provided {len(slice_size)}, but {self.config} has {len(sliceable_head_dims)} different"
|
301 |
+
f" attention layers. Make sure to match `len(slice_size)` to be {len(sliceable_head_dims)}."
|
302 |
+
)
|
303 |
+
|
304 |
+
for i in range(len(slice_size)):
|
305 |
+
size = slice_size[i]
|
306 |
+
dim = sliceable_head_dims[i]
|
307 |
+
if size is not None and size > dim:
|
308 |
+
raise ValueError(f"size {size} has to be smaller or equal to {dim}.")
|
309 |
+
|
310 |
+
# Recursively walk through all the children.
|
311 |
+
# Any children which exposes the set_attention_slice method
|
312 |
+
# gets the message
|
313 |
+
def fn_recursive_set_attention_slice(module: torch.nn.Module, slice_size: List[int]):
|
314 |
+
if hasattr(module, "set_attention_slice"):
|
315 |
+
module.set_attention_slice(slice_size.pop())
|
316 |
+
|
317 |
+
for child in module.children():
|
318 |
+
fn_recursive_set_attention_slice(child, slice_size)
|
319 |
+
|
320 |
+
reversed_slice_size = list(reversed(slice_size))
|
321 |
+
for module in self.children():
|
322 |
+
fn_recursive_set_attention_slice(module, reversed_slice_size)
|
323 |
+
|
324 |
+
def _set_gradient_checkpointing(self, module, value=False):
|
325 |
+
if isinstance(module, (CrossAttnDownBlock3D, DownBlock3D, CrossAttnUpBlock3D, UpBlock3D)):
|
326 |
+
module.gradient_checkpointing = value
|
327 |
+
|
328 |
+
def forward(
|
329 |
+
self,
|
330 |
+
sample: torch.FloatTensor,
|
331 |
+
timestep: Union[torch.Tensor, float, int],
|
332 |
+
encoder_hidden_states: torch.Tensor,
|
333 |
+
class_labels: Optional[torch.Tensor] = None,
|
334 |
+
attention_mask: Optional[torch.Tensor] = None,
|
335 |
+
# for controlnet
|
336 |
+
down_block_additional_residuals: Optional[Tuple[torch.Tensor]] = None,
|
337 |
+
mid_block_additional_residual: Optional[torch.Tensor] = None,
|
338 |
+
return_dict: bool = True,
|
339 |
+
) -> Union[UNet3DConditionOutput, Tuple]:
|
340 |
+
r"""
|
341 |
+
Args:
|
342 |
+
sample (`torch.FloatTensor`): (batch, channel, height, width) noisy inputs tensor
|
343 |
+
timestep (`torch.FloatTensor` or `float` or `int`): (batch) timesteps
|
344 |
+
encoder_hidden_states (`torch.FloatTensor`): (batch, sequence_length, feature_dim) encoder hidden states
|
345 |
+
return_dict (`bool`, *optional*, defaults to `True`):
|
346 |
+
Whether or not to return a [`models.unet_2d_condition.UNet2DConditionOutput`] instead of a plain tuple.
|
347 |
+
|
348 |
+
Returns:
|
349 |
+
[`~models.unet_2d_condition.UNet2DConditionOutput`] or `tuple`:
|
350 |
+
[`~models.unet_2d_condition.UNet2DConditionOutput`] if `return_dict` is True, otherwise a `tuple`. When
|
351 |
+
returning a tuple, the first element is the sample tensor.
|
352 |
+
"""
|
353 |
+
# By default samples have to be AT least a multiple of the overall upsampling factor.
|
354 |
+
# The overall upsampling factor is equal to 2 ** (# num of upsampling layears).
|
355 |
+
# However, the upsampling interpolation output size can be forced to fit any upsampling size
|
356 |
+
# on the fly if necessary.
|
357 |
+
default_overall_up_factor = 2**self.num_upsamplers
|
358 |
+
|
359 |
+
# upsample size should be forwarded when sample is not a multiple of `default_overall_up_factor`
|
360 |
+
forward_upsample_size = False
|
361 |
+
upsample_size = None
|
362 |
+
|
363 |
+
if any(s % default_overall_up_factor != 0 for s in sample.shape[-2:]):
|
364 |
+
logger.info("Forward upsample size to force interpolation output size.")
|
365 |
+
forward_upsample_size = True
|
366 |
+
|
367 |
+
# prepare attention_mask
|
368 |
+
if attention_mask is not None:
|
369 |
+
attention_mask = (1 - attention_mask.to(sample.dtype)) * -10000.0
|
370 |
+
attention_mask = attention_mask.unsqueeze(1)
|
371 |
+
|
372 |
+
# center input if necessary
|
373 |
+
if self.config.center_input_sample:
|
374 |
+
sample = 2 * sample - 1.0
|
375 |
+
|
376 |
+
# time
|
377 |
+
timesteps = timestep
|
378 |
+
if not torch.is_tensor(timesteps):
|
379 |
+
# This would be a good case for the `match` statement (Python 3.10+)
|
380 |
+
is_mps = sample.device.type == "mps"
|
381 |
+
if isinstance(timestep, float):
|
382 |
+
dtype = torch.float32 if is_mps else torch.float64
|
383 |
+
else:
|
384 |
+
dtype = torch.int32 if is_mps else torch.int64
|
385 |
+
timesteps = torch.tensor([timesteps], dtype=dtype, device=sample.device)
|
386 |
+
elif len(timesteps.shape) == 0:
|
387 |
+
timesteps = timesteps[None].to(sample.device)
|
388 |
+
|
389 |
+
# broadcast to batch dimension in a way that's compatible with ONNX/Core ML
|
390 |
+
timesteps = timesteps.expand(sample.shape[0])
|
391 |
+
|
392 |
+
t_emb = self.time_proj(timesteps)
|
393 |
+
|
394 |
+
# timesteps does not contain any weights and will always return f32 tensors
|
395 |
+
# but time_embedding might actually be running in fp16. so we need to cast here.
|
396 |
+
# there might be better ways to encapsulate this.
|
397 |
+
t_emb = t_emb.to(dtype=self.dtype)
|
398 |
+
emb = self.time_embedding(t_emb)
|
399 |
+
|
400 |
+
if self.class_embedding is not None:
|
401 |
+
if class_labels is None:
|
402 |
+
raise ValueError("class_labels should be provided when num_class_embeds > 0")
|
403 |
+
|
404 |
+
if self.config.class_embed_type == "timestep":
|
405 |
+
class_labels = self.time_proj(class_labels)
|
406 |
+
|
407 |
+
class_emb = self.class_embedding(class_labels).to(dtype=self.dtype)
|
408 |
+
emb = emb + class_emb
|
409 |
+
|
410 |
+
# pre-process
|
411 |
+
sample = self.conv_in(sample)
|
412 |
+
|
413 |
+
# down
|
414 |
+
is_controlnet = mid_block_additional_residual is not None and down_block_additional_residuals is not None
|
415 |
+
|
416 |
+
down_block_res_samples = (sample,)
|
417 |
+
for downsample_block in self.down_blocks:
|
418 |
+
if hasattr(downsample_block, "has_cross_attention") and downsample_block.has_cross_attention:
|
419 |
+
sample, res_samples = downsample_block(
|
420 |
+
hidden_states=sample,
|
421 |
+
temb=emb,
|
422 |
+
encoder_hidden_states=encoder_hidden_states,
|
423 |
+
attention_mask=attention_mask,
|
424 |
+
)
|
425 |
+
else:
|
426 |
+
sample, res_samples = downsample_block(hidden_states=sample, temb=emb, encoder_hidden_states=encoder_hidden_states)
|
427 |
+
|
428 |
+
down_block_res_samples += res_samples
|
429 |
+
|
430 |
+
if is_controlnet:
|
431 |
+
new_down_block_res_samples = ()
|
432 |
+
|
433 |
+
for down_block_res_sample, down_block_additional_residual in zip(
|
434 |
+
down_block_res_samples, down_block_additional_residuals
|
435 |
+
):
|
436 |
+
down_block_res_sample = down_block_res_sample + down_block_additional_residual
|
437 |
+
new_down_block_res_samples = new_down_block_res_samples + (down_block_res_sample,)
|
438 |
+
|
439 |
+
down_block_res_samples = new_down_block_res_samples
|
440 |
+
|
441 |
+
# mid
|
442 |
+
sample = self.mid_block(
|
443 |
+
sample, emb, encoder_hidden_states=encoder_hidden_states, attention_mask=attention_mask
|
444 |
+
)
|
445 |
+
|
446 |
+
if is_controlnet:
|
447 |
+
sample = sample + mid_block_additional_residual
|
448 |
+
|
449 |
+
# up
|
450 |
+
for i, upsample_block in enumerate(self.up_blocks):
|
451 |
+
is_final_block = i == len(self.up_blocks) - 1
|
452 |
+
|
453 |
+
res_samples = down_block_res_samples[-len(upsample_block.resnets) :]
|
454 |
+
down_block_res_samples = down_block_res_samples[: -len(upsample_block.resnets)]
|
455 |
+
|
456 |
+
# if we have not reached the final block and need to forward the
|
457 |
+
# upsample size, we do it here
|
458 |
+
if not is_final_block and forward_upsample_size:
|
459 |
+
upsample_size = down_block_res_samples[-1].shape[2:]
|
460 |
+
|
461 |
+
if hasattr(upsample_block, "has_cross_attention") and upsample_block.has_cross_attention:
|
462 |
+
sample = upsample_block(
|
463 |
+
hidden_states=sample,
|
464 |
+
temb=emb,
|
465 |
+
res_hidden_states_tuple=res_samples,
|
466 |
+
encoder_hidden_states=encoder_hidden_states,
|
467 |
+
upsample_size=upsample_size,
|
468 |
+
attention_mask=attention_mask,
|
469 |
+
)
|
470 |
+
else:
|
471 |
+
sample = upsample_block(
|
472 |
+
hidden_states=sample, temb=emb, res_hidden_states_tuple=res_samples, upsample_size=upsample_size, encoder_hidden_states=encoder_hidden_states,
|
473 |
+
)
|
474 |
+
|
475 |
+
# post-process
|
476 |
+
sample = self.conv_norm_out(sample)
|
477 |
+
sample = self.conv_act(sample)
|
478 |
+
sample = self.conv_out(sample)
|
479 |
+
|
480 |
+
if not return_dict:
|
481 |
+
return (sample,)
|
482 |
+
|
483 |
+
return UNet3DConditionOutput(sample=sample)
|
484 |
+
|
485 |
+
@classmethod
|
486 |
+
def from_pretrained_2d(cls, pretrained_model_path, subfolder=None, unet_additional_kwargs=None):
|
487 |
+
if subfolder is not None:
|
488 |
+
pretrained_model_path = os.path.join(pretrained_model_path, subfolder)
|
489 |
+
print(f"loaded temporal unet's pretrained weights from {pretrained_model_path} ...")
|
490 |
+
|
491 |
+
config_file = os.path.join(pretrained_model_path, 'config.json')
|
492 |
+
if not os.path.isfile(config_file):
|
493 |
+
raise RuntimeError(f"{config_file} does not exist")
|
494 |
+
with open(config_file, "r") as f:
|
495 |
+
config = json.load(f)
|
496 |
+
config["_class_name"] = cls.__name__
|
497 |
+
config["down_block_types"] = [
|
498 |
+
"CrossAttnDownBlock3D",
|
499 |
+
"CrossAttnDownBlock3D",
|
500 |
+
"CrossAttnDownBlock3D",
|
501 |
+
"DownBlock3D"
|
502 |
+
]
|
503 |
+
config["up_block_types"] = [
|
504 |
+
"UpBlock3D",
|
505 |
+
"CrossAttnUpBlock3D",
|
506 |
+
"CrossAttnUpBlock3D",
|
507 |
+
"CrossAttnUpBlock3D"
|
508 |
+
]
|
509 |
+
# config["mid_block_type"] = "UNetMidBlock3DCrossAttn"
|
510 |
+
|
511 |
+
from diffusers.utils import WEIGHTS_NAME
|
512 |
+
model = cls.from_config(config, **unet_additional_kwargs)
|
513 |
+
model_file = os.path.join(pretrained_model_path, WEIGHTS_NAME)
|
514 |
+
if not os.path.isfile(model_file):
|
515 |
+
raise RuntimeError(f"{model_file} does not exist")
|
516 |
+
state_dict = torch.load(model_file, map_location="cpu")
|
517 |
+
|
518 |
+
m, u = model.load_state_dict(state_dict, strict=False)
|
519 |
+
print(f"### missing keys: {len(m)}; \n### unexpected keys: {len(u)};")
|
520 |
+
# print(f"### missing keys:\n{m}\n### unexpected keys:\n{u}\n")
|
521 |
+
|
522 |
+
params = [p.numel() if "temporal" in n else 0 for n, p in model.named_parameters()]
|
523 |
+
print(f"### Temporal Module Parameters: {sum(params) / 1e6} M")
|
524 |
+
|
525 |
+
return model
|
magicanimate/pipelines/animation.py
ADDED
@@ -0,0 +1,282 @@
|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
# Copyright 2023 ByteDance and/or its affiliates.
|
2 |
+
#
|
3 |
+
# Copyright (2023) MagicAnimate Authors
|
4 |
+
#
|
5 |
+
# ByteDance, its affiliates and licensors retain all intellectual
|
6 |
+
# property and proprietary rights in and to this material, related
|
7 |
+
# documentation and any modifications thereto. Any use, reproduction,
|
8 |
+
# disclosure or distribution of this material and related documentation
|
9 |
+
# without an express license agreement from ByteDance or
|
10 |
+
# its affiliates is strictly prohibited.
|
11 |
+
import argparse
|
12 |
+
import datetime
|
13 |
+
import inspect
|
14 |
+
import os
|
15 |
+
import random
|
16 |
+
import numpy as np
|
17 |
+
|
18 |
+
from PIL import Image
|
19 |
+
from omegaconf import OmegaConf
|
20 |
+
from collections import OrderedDict
|
21 |
+
|
22 |
+
import torch
|
23 |
+
import torch.distributed as dist
|
24 |
+
|
25 |
+
from diffusers import AutoencoderKL, DDIMScheduler, UniPCMultistepScheduler
|
26 |
+
|
27 |
+
from tqdm import tqdm
|
28 |
+
from transformers import CLIPTextModel, CLIPTokenizer
|
29 |
+
|
30 |
+
from magicanimate.models.unet_controlnet import UNet3DConditionModel
|
31 |
+
from magicanimate.models.controlnet import ControlNetModel
|
32 |
+
from magicanimate.models.appearance_encoder import AppearanceEncoderModel
|
33 |
+
from magicanimate.models.mutual_self_attention import ReferenceAttentionControl
|
34 |
+
from magicanimate.pipelines.pipeline_animation import AnimationPipeline
|
35 |
+
from magicanimate.utils.util import save_videos_grid
|
36 |
+
from magicanimate.utils.dist_tools import distributed_init
|
37 |
+
from accelerate.utils import set_seed
|
38 |
+
|
39 |
+
from magicanimate.utils.videoreader import VideoReader
|
40 |
+
|
41 |
+
from einops import rearrange
|
42 |
+
|
43 |
+
from pathlib import Path
|
44 |
+
|
45 |
+
|
46 |
+
def main(args):
|
47 |
+
|
48 |
+
*_, func_args = inspect.getargvalues(inspect.currentframe())
|
49 |
+
func_args = dict(func_args)
|
50 |
+
|
51 |
+
config = OmegaConf.load(args.config)
|
52 |
+
|
53 |
+
# Initialize distributed training
|
54 |
+
device = torch.device(f"cuda:{args.rank}")
|
55 |
+
dist_kwargs = {"rank":args.rank, "world_size":args.world_size, "dist":args.dist}
|
56 |
+
|
57 |
+
if config.savename is None:
|
58 |
+
time_str = datetime.datetime.now().strftime("%Y-%m-%dT%H-%M-%S")
|
59 |
+
savedir = f"samples/{Path(args.config).stem}-{time_str}"
|
60 |
+
else:
|
61 |
+
savedir = f"samples/{config.savename}"
|
62 |
+
|
63 |
+
if args.dist:
|
64 |
+
dist.broadcast_object_list([savedir], 0)
|
65 |
+
dist.barrier()
|
66 |
+
|
67 |
+
if args.rank == 0:
|
68 |
+
os.makedirs(savedir, exist_ok=True)
|
69 |
+
|
70 |
+
inference_config = OmegaConf.load(config.inference_config)
|
71 |
+
|
72 |
+
motion_module = config.motion_module
|
73 |
+
|
74 |
+
### >>> create animation pipeline >>> ###
|
75 |
+
tokenizer = CLIPTokenizer.from_pretrained(config.pretrained_model_path, subfolder="tokenizer")
|
76 |
+
text_encoder = CLIPTextModel.from_pretrained(config.pretrained_model_path, subfolder="text_encoder")
|
77 |
+
if config.pretrained_unet_path:
|
78 |
+
unet = UNet3DConditionModel.from_pretrained_2d(config.pretrained_unet_path, unet_additional_kwargs=OmegaConf.to_container(inference_config.unet_additional_kwargs))
|
79 |
+
else:
|
80 |
+
unet = UNet3DConditionModel.from_pretrained_2d(config.pretrained_model_path, subfolder="unet", unet_additional_kwargs=OmegaConf.to_container(inference_config.unet_additional_kwargs))
|
81 |
+
appearance_encoder = AppearanceEncoderModel.from_pretrained(config.pretrained_appearance_encoder_path, subfolder="appearance_encoder").to(device)
|
82 |
+
reference_control_writer = ReferenceAttentionControl(appearance_encoder, do_classifier_free_guidance=True, mode='write', fusion_blocks=config.fusion_blocks)
|
83 |
+
reference_control_reader = ReferenceAttentionControl(unet, do_classifier_free_guidance=True, mode='read', fusion_blocks=config.fusion_blocks)
|
84 |
+
if config.pretrained_vae_path is not None:
|
85 |
+
vae = AutoencoderKL.from_pretrained(config.pretrained_vae_path)
|
86 |
+
else:
|
87 |
+
vae = AutoencoderKL.from_pretrained(config.pretrained_model_path, subfolder="vae")
|
88 |
+
|
89 |
+
### Load controlnet
|
90 |
+
controlnet = ControlNetModel.from_pretrained(config.pretrained_controlnet_path)
|
91 |
+
|
92 |
+
unet.enable_xformers_memory_efficient_attention()
|
93 |
+
appearance_encoder.enable_xformers_memory_efficient_attention()
|
94 |
+
controlnet.enable_xformers_memory_efficient_attention()
|
95 |
+
|
96 |
+
vae.to(torch.float16)
|
97 |
+
unet.to(torch.float16)
|
98 |
+
text_encoder.to(torch.float16)
|
99 |
+
appearance_encoder.to(torch.float16)
|
100 |
+
controlnet.to(torch.float16)
|
101 |
+
|
102 |
+
pipeline = AnimationPipeline(
|
103 |
+
vae=vae, text_encoder=text_encoder, tokenizer=tokenizer, unet=unet, controlnet=controlnet,
|
104 |
+
scheduler=DDIMScheduler(**OmegaConf.to_container(inference_config.noise_scheduler_kwargs)),
|
105 |
+
# NOTE: UniPCMultistepScheduler
|
106 |
+
)
|
107 |
+
|
108 |
+
# 1. unet ckpt
|
109 |
+
# 1.1 motion module
|
110 |
+
motion_module_state_dict = torch.load(motion_module, map_location="cpu")
|
111 |
+
if "global_step" in motion_module_state_dict: func_args.update({"global_step": motion_module_state_dict["global_step"]})
|
112 |
+
motion_module_state_dict = motion_module_state_dict['state_dict'] if 'state_dict' in motion_module_state_dict else motion_module_state_dict
|
113 |
+
try:
|
114 |
+
# extra steps for self-trained models
|
115 |
+
state_dict = OrderedDict()
|
116 |
+
for key in motion_module_state_dict.keys():
|
117 |
+
if key.startswith("module."):
|
118 |
+
_key = key.split("module.")[-1]
|
119 |
+
state_dict[_key] = motion_module_state_dict[key]
|
120 |
+
else:
|
121 |
+
state_dict[key] = motion_module_state_dict[key]
|
122 |
+
motion_module_state_dict = state_dict
|
123 |
+
del state_dict
|
124 |
+
missing, unexpected = pipeline.unet.load_state_dict(motion_module_state_dict, strict=False)
|
125 |
+
assert len(unexpected) == 0
|
126 |
+
except:
|
127 |
+
_tmp_ = OrderedDict()
|
128 |
+
for key in motion_module_state_dict.keys():
|
129 |
+
if "motion_modules" in key:
|
130 |
+
if key.startswith("unet."):
|
131 |
+
_key = key.split('unet.')[-1]
|
132 |
+
_tmp_[_key] = motion_module_state_dict[key]
|
133 |
+
else:
|
134 |
+
_tmp_[key] = motion_module_state_dict[key]
|
135 |
+
missing, unexpected = unet.load_state_dict(_tmp_, strict=False)
|
136 |
+
assert len(unexpected) == 0
|
137 |
+
del _tmp_
|
138 |
+
del motion_module_state_dict
|
139 |
+
|
140 |
+
pipeline.to(device)
|
141 |
+
### <<< create validation pipeline <<< ###
|
142 |
+
|
143 |
+
random_seeds = config.get("seed", [-1])
|
144 |
+
random_seeds = [random_seeds] if isinstance(random_seeds, int) else list(random_seeds)
|
145 |
+
random_seeds = random_seeds * len(config.source_image) if len(random_seeds) == 1 else random_seeds
|
146 |
+
|
147 |
+
# input test videos (either source video/ conditions)
|
148 |
+
|
149 |
+
test_videos = config.video_path
|
150 |
+
source_images = config.source_image
|
151 |
+
num_actual_inference_steps = config.get("num_actual_inference_steps", config.steps)
|
152 |
+
|
153 |
+
# read size, step from yaml file
|
154 |
+
sizes = [config.size] * len(test_videos)
|
155 |
+
steps = [config.S] * len(test_videos)
|
156 |
+
|
157 |
+
config.random_seed = []
|
158 |
+
prompt = n_prompt = ""
|
159 |
+
for idx, (source_image, test_video, random_seed, size, step) in tqdm(
|
160 |
+
enumerate(zip(source_images, test_videos, random_seeds, sizes, steps)),
|
161 |
+
total=len(test_videos),
|
162 |
+
disable=(args.rank!=0)
|
163 |
+
):
|
164 |
+
samples_per_video = []
|
165 |
+
samples_per_clip = []
|
166 |
+
# manually set random seed for reproduction
|
167 |
+
if random_seed != -1:
|
168 |
+
torch.manual_seed(random_seed)
|
169 |
+
set_seed(random_seed)
|
170 |
+
else:
|
171 |
+
torch.seed()
|
172 |
+
config.random_seed.append(torch.initial_seed())
|
173 |
+
|
174 |
+
if test_video.endswith('.mp4'):
|
175 |
+
control = VideoReader(test_video).read()
|
176 |
+
if control[0].shape[0] != size:
|
177 |
+
control = [np.array(Image.fromarray(c).resize((size, size))) for c in control]
|
178 |
+
if config.max_length is not None:
|
179 |
+
control = control[config.offset: (config.offset+config.max_length)]
|
180 |
+
control = np.array(control)
|
181 |
+
|
182 |
+
if source_image.endswith(".mp4"):
|
183 |
+
source_image = np.array(Image.fromarray(VideoReader(source_image).read()[0]).resize((size, size)))
|
184 |
+
else:
|
185 |
+
source_image = np.array(Image.open(source_image).resize((size, size)))
|
186 |
+
H, W, C = source_image.shape
|
187 |
+
|
188 |
+
print(f"current seed: {torch.initial_seed()}")
|
189 |
+
init_latents = None
|
190 |
+
|
191 |
+
# print(f"sampling {prompt} ...")
|
192 |
+
original_length = control.shape[0]
|
193 |
+
if control.shape[0] % config.L > 0:
|
194 |
+
control = np.pad(control, ((0, config.L-control.shape[0] % config.L), (0, 0), (0, 0), (0, 0)), mode='edge')
|
195 |
+
generator = torch.Generator(device=torch.device("cuda:0"))
|
196 |
+
generator.manual_seed(torch.initial_seed())
|
197 |
+
sample = pipeline(
|
198 |
+
prompt,
|
199 |
+
negative_prompt = n_prompt,
|
200 |
+
num_inference_steps = config.steps,
|
201 |
+
guidance_scale = config.guidance_scale,
|
202 |
+
width = W,
|
203 |
+
height = H,
|
204 |
+
video_length = len(control),
|
205 |
+
controlnet_condition = control,
|
206 |
+
init_latents = init_latents,
|
207 |
+
generator = generator,
|
208 |
+
num_actual_inference_steps = num_actual_inference_steps,
|
209 |
+
appearance_encoder = appearance_encoder,
|
210 |
+
reference_control_writer = reference_control_writer,
|
211 |
+
reference_control_reader = reference_control_reader,
|
212 |
+
source_image = source_image,
|
213 |
+
**dist_kwargs,
|
214 |
+
).videos
|
215 |
+
|
216 |
+
if args.rank == 0:
|
217 |
+
source_images = np.array([source_image] * original_length)
|
218 |
+
source_images = rearrange(torch.from_numpy(source_images), "t h w c -> 1 c t h w") / 255.0
|
219 |
+
samples_per_video.append(source_images)
|
220 |
+
|
221 |
+
control = control / 255.0
|
222 |
+
control = rearrange(control, "t h w c -> 1 c t h w")
|
223 |
+
control = torch.from_numpy(control)
|
224 |
+
samples_per_video.append(control[:, :, :original_length])
|
225 |
+
|
226 |
+
samples_per_video.append(sample[:, :, :original_length])
|
227 |
+
|
228 |
+
samples_per_video = torch.cat(samples_per_video)
|
229 |
+
|
230 |
+
video_name = os.path.basename(test_video)[:-4]
|
231 |
+
source_name = os.path.basename(config.source_image[idx]).split(".")[0]
|
232 |
+
save_videos_grid(samples_per_video[-1:], f"{savedir}/videos/{source_name}_{video_name}.mp4")
|
233 |
+
save_videos_grid(samples_per_video, f"{savedir}/videos/{source_name}_{video_name}/grid.mp4")
|
234 |
+
|
235 |
+
if config.save_individual_videos:
|
236 |
+
save_videos_grid(samples_per_video[1:2], f"{savedir}/videos/{source_name}_{video_name}/ctrl.mp4")
|
237 |
+
save_videos_grid(samples_per_video[0:1], f"{savedir}/videos/{source_name}_{video_name}/orig.mp4")
|
238 |
+
|
239 |
+
if args.dist:
|
240 |
+
dist.barrier()
|
241 |
+
|
242 |
+
if args.rank == 0:
|
243 |
+
OmegaConf.save(config, f"{savedir}/config.yaml")
|
244 |
+
|
245 |
+
|
246 |
+
def distributed_main(device_id, args):
|
247 |
+
args.rank = device_id
|
248 |
+
args.device_id = device_id
|
249 |
+
if torch.cuda.is_available():
|
250 |
+
torch.cuda.set_device(args.device_id)
|
251 |
+
torch.cuda.init()
|
252 |
+
distributed_init(args)
|
253 |
+
main(args)
|
254 |
+
|
255 |
+
|
256 |
+
def run(args):
|
257 |
+
|
258 |
+
if args.dist:
|
259 |
+
args.world_size = max(1, torch.cuda.device_count())
|
260 |
+
assert args.world_size <= torch.cuda.device_count()
|
261 |
+
|
262 |
+
if args.world_size > 0 and torch.cuda.device_count() > 1:
|
263 |
+
port = random.randint(10000, 20000)
|
264 |
+
args.init_method = f"tcp://localhost:{port}"
|
265 |
+
torch.multiprocessing.spawn(
|
266 |
+
fn=distributed_main,
|
267 |
+
args=(args,),
|
268 |
+
nprocs=args.world_size,
|
269 |
+
)
|
270 |
+
else:
|
271 |
+
main(args)
|
272 |
+
|
273 |
+
|
274 |
+
if __name__ == "__main__":
|
275 |
+
parser = argparse.ArgumentParser()
|
276 |
+
parser.add_argument("--config", type=str, required=True)
|
277 |
+
parser.add_argument("--dist", action="store_true", required=False)
|
278 |
+
parser.add_argument("--rank", type=int, default=0, required=False)
|
279 |
+
parser.add_argument("--world_size", type=int, default=1, required=False)
|
280 |
+
|
281 |
+
args = parser.parse_args()
|
282 |
+
run(args)
|
magicanimate/pipelines/context.py
ADDED
@@ -0,0 +1,76 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
# *************************************************************************
|
2 |
+
# This file may have been modified by Bytedance Inc. (“Bytedance Inc.'s Mo-
|
3 |
+
# difications”). All Bytedance Inc.'s Modifications are Copyright (2023) B-
|
4 |
+
# ytedance Inc..
|
5 |
+
# *************************************************************************
|
6 |
+
|
7 |
+
# Adapted from https://github.com/s9roll7/animatediff-cli-prompt-travel/tree/main
|
8 |
+
import numpy as np
|
9 |
+
from typing import Callable, Optional, List
|
10 |
+
|
11 |
+
|
12 |
+
def ordered_halving(val):
|
13 |
+
bin_str = f"{val:064b}"
|
14 |
+
bin_flip = bin_str[::-1]
|
15 |
+
as_int = int(bin_flip, 2)
|
16 |
+
|
17 |
+
return as_int / (1 << 64)
|
18 |
+
|
19 |
+
|
20 |
+
def uniform(
|
21 |
+
step: int = ...,
|
22 |
+
num_steps: Optional[int] = None,
|
23 |
+
num_frames: int = ...,
|
24 |
+
context_size: Optional[int] = None,
|
25 |
+
context_stride: int = 3,
|
26 |
+
context_overlap: int = 4,
|
27 |
+
closed_loop: bool = True,
|
28 |
+
):
|
29 |
+
if num_frames <= context_size:
|
30 |
+
yield list(range(num_frames))
|
31 |
+
return
|
32 |
+
|
33 |
+
context_stride = min(context_stride, int(np.ceil(np.log2(num_frames / context_size))) + 1)
|
34 |
+
|
35 |
+
for context_step in 1 << np.arange(context_stride):
|
36 |
+
pad = int(round(num_frames * ordered_halving(step)))
|
37 |
+
for j in range(
|
38 |
+
int(ordered_halving(step) * context_step) + pad,
|
39 |
+
num_frames + pad + (0 if closed_loop else -context_overlap),
|
40 |
+
(context_size * context_step - context_overlap),
|
41 |
+
):
|
42 |
+
yield [e % num_frames for e in range(j, j + context_size * context_step, context_step)]
|
43 |
+
|
44 |
+
|
45 |
+
def get_context_scheduler(name: str) -> Callable:
|
46 |
+
if name == "uniform":
|
47 |
+
return uniform
|
48 |
+
else:
|
49 |
+
raise ValueError(f"Unknown context_overlap policy {name}")
|
50 |
+
|
51 |
+
|
52 |
+
def get_total_steps(
|
53 |
+
scheduler,
|
54 |
+
timesteps: List[int],
|
55 |
+
num_steps: Optional[int] = None,
|
56 |
+
num_frames: int = ...,
|
57 |
+
context_size: Optional[int] = None,
|
58 |
+
context_stride: int = 3,
|
59 |
+
context_overlap: int = 4,
|
60 |
+
closed_loop: bool = True,
|
61 |
+
):
|
62 |
+
return sum(
|
63 |
+
len(
|
64 |
+
list(
|
65 |
+
scheduler(
|
66 |
+
i,
|
67 |
+
num_steps,
|
68 |
+
num_frames,
|
69 |
+
context_size,
|
70 |
+
context_stride,
|
71 |
+
context_overlap,
|
72 |
+
)
|
73 |
+
)
|
74 |
+
)
|
75 |
+
for i in range(len(timesteps))
|
76 |
+
)
|
magicanimate/pipelines/pipeline_animation.py
ADDED
@@ -0,0 +1,799 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
1 |
+
# *************************************************************************
|
2 |
+
# This file may have been modified by Bytedance Inc. (“Bytedance Inc.'s Mo-
|
3 |
+
# difications”). All Bytedance Inc.'s Modifications are Copyright (2023) B-
|
4 |
+
# ytedance Inc..
|
5 |
+
# *************************************************************************
|
6 |
+
|
7 |
+
# Adapted from https://github.com/showlab/Tune-A-Video/blob/main/tuneavideo/pipelines/pipeline_tuneavideo.py
|
8 |
+
|
9 |
+
# Copyright 2023 The HuggingFace Team. All rights reserved.
|
10 |
+
#
|
11 |
+
# Licensed under the Apache License, Version 2.0 (the "License");
|
12 |
+
# you may not use this file except in compliance with the License.
|
13 |
+
# You may obtain a copy of the License at
|
14 |
+
#
|
15 |
+
# http://www.apache.org/licenses/LICENSE-2.0
|
16 |
+
#
|
17 |
+
# Unless required by applicable law or agreed to in writing, software
|
18 |
+
# distributed under the License is distributed on an "AS IS" BASIS,
|
19 |
+
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
20 |
+
# See the License for the specific language governing permissions and
|
21 |
+
# limitations under the License.
|
22 |
+
"""
|
23 |
+
TODO:
|
24 |
+
1. support multi-controlnet
|
25 |
+
2. [DONE] support DDIM inversion
|
26 |
+
3. support Prompt-to-prompt
|
27 |
+
"""
|
28 |
+
|
29 |
+
import inspect, math
|
30 |
+
from typing import Callable, List, Optional, Union
|
31 |
+
from dataclasses import dataclass
|
32 |
+
from PIL import Image
|
33 |
+
import numpy as np
|
34 |
+
import torch
|
35 |
+
import torch.distributed as dist
|
36 |
+
from tqdm import tqdm
|
37 |
+
from diffusers.utils import is_accelerate_available
|
38 |
+
from packaging import version
|
39 |
+
from transformers import CLIPTextModel, CLIPTokenizer
|
40 |
+
|
41 |
+
from diffusers.configuration_utils import FrozenDict
|
42 |
+
from diffusers.models import AutoencoderKL
|
43 |
+
from diffusers.pipeline_utils import DiffusionPipeline
|
44 |
+
from diffusers.schedulers import (
|
45 |
+
DDIMScheduler,
|
46 |
+
DPMSolverMultistepScheduler,
|
47 |
+
EulerAncestralDiscreteScheduler,
|
48 |
+
EulerDiscreteScheduler,
|
49 |
+
LMSDiscreteScheduler,
|
50 |
+
PNDMScheduler,
|
51 |
+
)
|
52 |
+
from diffusers.utils import deprecate, logging, BaseOutput
|
53 |
+
|
54 |
+
from einops import rearrange
|
55 |
+
|
56 |
+
from magicanimate.models.unet_controlnet import UNet3DConditionModel
|
57 |
+
from magicanimate.models.controlnet import ControlNetModel
|
58 |
+
from magicanimate.models.mutual_self_attention import ReferenceAttentionControl
|
59 |
+
from magicanimate.pipelines.context import (
|
60 |
+
get_context_scheduler,
|
61 |
+
get_total_steps
|
62 |
+
)
|
63 |
+
from magicanimate.utils.util import get_tensor_interpolation_method
|
64 |
+
|
65 |
+
logger = logging.get_logger(__name__) # pylint: disable=invalid-name
|
66 |
+
|
67 |
+
|
68 |
+
@dataclass
|
69 |
+
class AnimationPipelineOutput(BaseOutput):
|
70 |
+
videos: Union[torch.Tensor, np.ndarray]
|
71 |
+
|
72 |
+
|
73 |
+
class AnimationPipeline(DiffusionPipeline):
|
74 |
+
_optional_components = []
|
75 |
+
|
76 |
+
def __init__(
|
77 |
+
self,
|
78 |
+
vae: AutoencoderKL,
|
79 |
+
text_encoder: CLIPTextModel,
|
80 |
+
tokenizer: CLIPTokenizer,
|
81 |
+
unet: UNet3DConditionModel,
|
82 |
+
controlnet: ControlNetModel,
|
83 |
+
scheduler: Union[
|
84 |
+
DDIMScheduler,
|
85 |
+
PNDMScheduler,
|
86 |
+
LMSDiscreteScheduler,
|
87 |
+
EulerDiscreteScheduler,
|
88 |
+
EulerAncestralDiscreteScheduler,
|
89 |
+
DPMSolverMultistepScheduler,
|
90 |
+
],
|
91 |
+
):
|
92 |
+
super().__init__()
|
93 |
+
|
94 |
+
if hasattr(scheduler.config, "steps_offset") and scheduler.config.steps_offset != 1:
|
95 |
+
deprecation_message = (
|
96 |
+
f"The configuration file of this scheduler: {scheduler} is outdated. `steps_offset`"
|
97 |
+
f" should be set to 1 instead of {scheduler.config.steps_offset}. Please make sure "
|
98 |
+
"to update the config accordingly as leaving `steps_offset` might led to incorrect results"
|
99 |
+
" in future versions. If you have downloaded this checkpoint from the Hugging Face Hub,"
|
100 |
+
" it would be very nice if you could open a Pull request for the `scheduler/scheduler_config.json`"
|
101 |
+
" file"
|
102 |
+
)
|
103 |
+
deprecate("steps_offset!=1", "1.0.0", deprecation_message, standard_warn=False)
|
104 |
+
new_config = dict(scheduler.config)
|
105 |
+
new_config["steps_offset"] = 1
|
106 |
+
scheduler._internal_dict = FrozenDict(new_config)
|
107 |
+
|
108 |
+
if hasattr(scheduler.config, "clip_sample") and scheduler.config.clip_sample is True:
|
109 |
+
deprecation_message = (
|
110 |
+
f"The configuration file of this scheduler: {scheduler} has not set the configuration `clip_sample`."
|
111 |
+
" `clip_sample` should be set to False in the configuration file. Please make sure to update the"
|
112 |
+
" config accordingly as not setting `clip_sample` in the config might lead to incorrect results in"
|
113 |
+
" future versions. If you have downloaded this checkpoint from the Hugging Face Hub, it would be very"
|
114 |
+
" nice if you could open a Pull request for the `scheduler/scheduler_config.json` file"
|
115 |
+
)
|
116 |
+
deprecate("clip_sample not set", "1.0.0", deprecation_message, standard_warn=False)
|
117 |
+
new_config = dict(scheduler.config)
|
118 |
+
new_config["clip_sample"] = False
|
119 |
+
scheduler._internal_dict = FrozenDict(new_config)
|
120 |
+
|
121 |
+
is_unet_version_less_0_9_0 = hasattr(unet.config, "_diffusers_version") and version.parse(
|
122 |
+
version.parse(unet.config._diffusers_version).base_version
|
123 |
+
) < version.parse("0.9.0.dev0")
|
124 |
+
is_unet_sample_size_less_64 = hasattr(unet.config, "sample_size") and unet.config.sample_size < 64
|
125 |
+
if is_unet_version_less_0_9_0 and is_unet_sample_size_less_64:
|
126 |
+
deprecation_message = (
|
127 |
+
"The configuration file of the unet has set the default `sample_size` to smaller than"
|
128 |
+
" 64 which seems highly unlikely. If your checkpoint is a fine-tuned version of any of the"
|
129 |
+
" following: \n- CompVis/stable-diffusion-v1-4 \n- CompVis/stable-diffusion-v1-3 \n-"
|
130 |
+
" CompVis/stable-diffusion-v1-2 \n- CompVis/stable-diffusion-v1-1 \n- runwayml/stable-diffusion-v1-5"
|
131 |
+
" \n- runwayml/stable-diffusion-inpainting \n you should change 'sample_size' to 64 in the"
|
132 |
+
" configuration file. Please make sure to update the config accordingly as leaving `sample_size=32`"
|
133 |
+
" in the config might lead to incorrect results in future versions. If you have downloaded this"
|
134 |
+
" checkpoint from the Hugging Face Hub, it would be very nice if you could open a Pull request for"
|
135 |
+
" the `unet/config.json` file"
|
136 |
+
)
|
137 |
+
deprecate("sample_size<64", "1.0.0", deprecation_message, standard_warn=False)
|
138 |
+
new_config = dict(unet.config)
|
139 |
+
new_config["sample_size"] = 64
|
140 |
+
unet._internal_dict = FrozenDict(new_config)
|
141 |
+
|
142 |
+
self.register_modules(
|
143 |
+
vae=vae,
|
144 |
+
text_encoder=text_encoder,
|
145 |
+
tokenizer=tokenizer,
|
146 |
+
unet=unet,
|
147 |
+
controlnet=controlnet,
|
148 |
+
scheduler=scheduler,
|
149 |
+
)
|
150 |
+
self.vae_scale_factor = 2 ** (len(self.vae.config.block_out_channels) - 1)
|
151 |
+
|
152 |
+
def enable_vae_slicing(self):
|
153 |
+
self.vae.enable_slicing()
|
154 |
+
|
155 |
+
def disable_vae_slicing(self):
|
156 |
+
self.vae.disable_slicing()
|
157 |
+
|
158 |
+
def enable_sequential_cpu_offload(self, gpu_id=0):
|
159 |
+
if is_accelerate_available():
|
160 |
+
from accelerate import cpu_offload
|
161 |
+
else:
|
162 |
+
raise ImportError("Please install accelerate via `pip install accelerate`")
|
163 |
+
|
164 |
+
device = torch.device(f"cuda:{gpu_id}")
|
165 |
+
|
166 |
+
for cpu_offloaded_model in [self.unet, self.text_encoder, self.vae]:
|
167 |
+
if cpu_offloaded_model is not None:
|
168 |
+
cpu_offload(cpu_offloaded_model, device)
|
169 |
+
|
170 |
+
|
171 |
+
@property
|
172 |
+
def _execution_device(self):
|
173 |
+
if self.device != torch.device("meta") or not hasattr(self.unet, "_hf_hook"):
|
174 |
+
return self.device
|
175 |
+
for module in self.unet.modules():
|
176 |
+
if (
|
177 |
+
hasattr(module, "_hf_hook")
|
178 |
+
and hasattr(module._hf_hook, "execution_device")
|
179 |
+
and module._hf_hook.execution_device is not None
|
180 |
+
):
|
181 |
+
return torch.device(module._hf_hook.execution_device)
|
182 |
+
return self.device
|
183 |
+
|
184 |
+
def _encode_prompt(self, prompt, device, num_videos_per_prompt, do_classifier_free_guidance, negative_prompt):
|
185 |
+
batch_size = len(prompt) if isinstance(prompt, list) else 1
|
186 |
+
|
187 |
+
text_inputs = self.tokenizer(
|
188 |
+
prompt,
|
189 |
+
padding="max_length",
|
190 |
+
max_length=self.tokenizer.model_max_length,
|
191 |
+
truncation=True,
|
192 |
+
return_tensors="pt",
|
193 |
+
)
|
194 |
+
text_input_ids = text_inputs.input_ids
|
195 |
+
untruncated_ids = self.tokenizer(prompt, padding="longest", return_tensors="pt").input_ids
|
196 |
+
|
197 |
+
if untruncated_ids.shape[-1] >= text_input_ids.shape[-1] and not torch.equal(text_input_ids, untruncated_ids):
|
198 |
+
removed_text = self.tokenizer.batch_decode(untruncated_ids[:, self.tokenizer.model_max_length - 1 : -1])
|
199 |
+
logger.warning(
|
200 |
+
"The following part of your input was truncated because CLIP can only handle sequences up to"
|
201 |
+
f" {self.tokenizer.model_max_length} tokens: {removed_text}"
|
202 |
+
)
|
203 |
+
|
204 |
+
if hasattr(self.text_encoder.config, "use_attention_mask") and self.text_encoder.config.use_attention_mask:
|
205 |
+
attention_mask = text_inputs.attention_mask.to(device)
|
206 |
+
else:
|
207 |
+
attention_mask = None
|
208 |
+
|
209 |
+
text_embeddings = self.text_encoder(
|
210 |
+
text_input_ids.to(device),
|
211 |
+
attention_mask=attention_mask,
|
212 |
+
)
|
213 |
+
text_embeddings = text_embeddings[0]
|
214 |
+
|
215 |
+
# duplicate text embeddings for each generation per prompt, using mps friendly method
|
216 |
+
bs_embed, seq_len, _ = text_embeddings.shape
|
217 |
+
text_embeddings = text_embeddings.repeat(1, num_videos_per_prompt, 1)
|
218 |
+
text_embeddings = text_embeddings.view(bs_embed * num_videos_per_prompt, seq_len, -1)
|
219 |
+
|
220 |
+
# get unconditional embeddings for classifier free guidance
|
221 |
+
if do_classifier_free_guidance:
|
222 |
+
uncond_tokens: List[str]
|
223 |
+
if negative_prompt is None:
|
224 |
+
uncond_tokens = [""] * batch_size
|
225 |
+
elif type(prompt) is not type(negative_prompt):
|
226 |
+
raise TypeError(
|
227 |
+
f"`negative_prompt` should be the same type to `prompt`, but got {type(negative_prompt)} !="
|
228 |
+
f" {type(prompt)}."
|
229 |
+
)
|
230 |
+
elif isinstance(negative_prompt, str):
|
231 |
+
uncond_tokens = [negative_prompt]
|
232 |
+
elif batch_size != len(negative_prompt):
|
233 |
+
raise ValueError(
|
234 |
+
f"`negative_prompt`: {negative_prompt} has batch size {len(negative_prompt)}, but `prompt`:"
|
235 |
+
f" {prompt} has batch size {batch_size}. Please make sure that passed `negative_prompt` matches"
|
236 |
+
" the batch size of `prompt`."
|
237 |
+
)
|
238 |
+
else:
|
239 |
+
uncond_tokens = negative_prompt
|
240 |
+
|
241 |
+
max_length = text_input_ids.shape[-1]
|
242 |
+
uncond_input = self.tokenizer(
|
243 |
+
uncond_tokens,
|
244 |
+
padding="max_length",
|
245 |
+
max_length=max_length,
|
246 |
+
truncation=True,
|
247 |
+
return_tensors="pt",
|
248 |
+
)
|
249 |
+
|
250 |
+
if hasattr(self.text_encoder.config, "use_attention_mask") and self.text_encoder.config.use_attention_mask:
|
251 |
+
attention_mask = uncond_input.attention_mask.to(device)
|
252 |
+
else:
|
253 |
+
attention_mask = None
|
254 |
+
|
255 |
+
uncond_embeddings = self.text_encoder(
|
256 |
+
uncond_input.input_ids.to(device),
|
257 |
+
attention_mask=attention_mask,
|
258 |
+
)
|
259 |
+
uncond_embeddings = uncond_embeddings[0]
|
260 |
+
|
261 |
+
# duplicate unconditional embeddings for each generation per prompt, using mps friendly method
|
262 |
+
seq_len = uncond_embeddings.shape[1]
|
263 |
+
uncond_embeddings = uncond_embeddings.repeat(1, num_videos_per_prompt, 1)
|
264 |
+
uncond_embeddings = uncond_embeddings.view(batch_size * num_videos_per_prompt, seq_len, -1)
|
265 |
+
|
266 |
+
# For classifier free guidance, we need to do two forward passes.
|
267 |
+
# Here we concatenate the unconditional and text embeddings into a single batch
|
268 |
+
# to avoid doing two forward passes
|
269 |
+
text_embeddings = torch.cat([uncond_embeddings, text_embeddings])
|
270 |
+
|
271 |
+
return text_embeddings
|
272 |
+
|
273 |
+
def decode_latents(self, latents, rank, decoder_consistency=None):
|
274 |
+
video_length = latents.shape[2]
|
275 |
+
latents = 1 / 0.18215 * latents
|
276 |
+
latents = rearrange(latents, "b c f h w -> (b f) c h w")
|
277 |
+
# video = self.vae.decode(latents).sample
|
278 |
+
video = []
|
279 |
+
for frame_idx in tqdm(range(latents.shape[0]), disable=(rank!=0)):
|
280 |
+
if decoder_consistency is not None:
|
281 |
+
video.append(decoder_consistency(latents[frame_idx:frame_idx+1]))
|
282 |
+
else:
|
283 |
+
video.append(self.vae.decode(latents[frame_idx:frame_idx+1]).sample)
|
284 |
+
video = torch.cat(video)
|
285 |
+
video = rearrange(video, "(b f) c h w -> b c f h w", f=video_length)
|
286 |
+
video = (video / 2 + 0.5).clamp(0, 1)
|
287 |
+
# we always cast to float32 as this does not cause significant overhead and is compatible with bfloa16
|
288 |
+
video = video.cpu().float().numpy()
|
289 |
+
return video
|
290 |
+
|
291 |
+
def prepare_extra_step_kwargs(self, generator, eta):
|
292 |
+
# prepare extra kwargs for the scheduler step, since not all schedulers have the same signature
|
293 |
+
# eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers.
|
294 |
+
# eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502
|
295 |
+
# and should be between [0, 1]
|
296 |
+
|
297 |
+
accepts_eta = "eta" in set(inspect.signature(self.scheduler.step).parameters.keys())
|
298 |
+
extra_step_kwargs = {}
|
299 |
+
if accepts_eta:
|
300 |
+
extra_step_kwargs["eta"] = eta
|
301 |
+
|
302 |
+
# check if the scheduler accepts generator
|
303 |
+
accepts_generator = "generator" in set(inspect.signature(self.scheduler.step).parameters.keys())
|
304 |
+
if accepts_generator:
|
305 |
+
extra_step_kwargs["generator"] = generator
|
306 |
+
return extra_step_kwargs
|
307 |
+
|
308 |
+
def check_inputs(self, prompt, height, width, callback_steps):
|
309 |
+
if not isinstance(prompt, str) and not isinstance(prompt, list):
|
310 |
+
raise ValueError(f"`prompt` has to be of type `str` or `list` but is {type(prompt)}")
|
311 |
+
|
312 |
+
if height % 8 != 0 or width % 8 != 0:
|
313 |
+
raise ValueError(f"`height` and `width` have to be divisible by 8 but are {height} and {width}.")
|
314 |
+
|
315 |
+
if (callback_steps is None) or (
|
316 |
+
callback_steps is not None and (not isinstance(callback_steps, int) or callback_steps <= 0)
|
317 |
+
):
|
318 |
+
raise ValueError(
|
319 |
+
f"`callback_steps` has to be a positive integer but is {callback_steps} of type"
|
320 |
+
f" {type(callback_steps)}."
|
321 |
+
)
|
322 |
+
|
323 |
+
def prepare_latents(self, batch_size, num_channels_latents, video_length, height, width, dtype, device, generator, latents=None, clip_length=16):
|
324 |
+
shape = (batch_size, num_channels_latents, clip_length, height // self.vae_scale_factor, width // self.vae_scale_factor)
|
325 |
+
if isinstance(generator, list) and len(generator) != batch_size:
|
326 |
+
raise ValueError(
|
327 |
+
f"You have passed a list of generators of length {len(generator)}, but requested an effective batch"
|
328 |
+
f" size of {batch_size}. Make sure the batch size matches the length of the generators."
|
329 |
+
)
|
330 |
+
if latents is None:
|
331 |
+
rand_device = "cpu" if device.type == "mps" else device
|
332 |
+
|
333 |
+
if isinstance(generator, list):
|
334 |
+
latents = [
|
335 |
+
torch.randn(shape, generator=generator[i], device=rand_device, dtype=dtype)
|
336 |
+
for i in range(batch_size)
|
337 |
+
]
|
338 |
+
latents = torch.cat(latents, dim=0).to(device)
|
339 |
+
else:
|
340 |
+
latents = torch.randn(shape, generator=generator, device=rand_device, dtype=dtype).to(device)
|
341 |
+
|
342 |
+
latents = latents.repeat(1, 1, video_length//clip_length, 1, 1)
|
343 |
+
else:
|
344 |
+
if latents.shape != shape:
|
345 |
+
raise ValueError(f"Unexpected latents shape, got {latents.shape}, expected {shape}")
|
346 |
+
latents = latents.to(device)
|
347 |
+
|
348 |
+
# scale the initial noise by the standard deviation required by the scheduler
|
349 |
+
latents = latents * self.scheduler.init_noise_sigma
|
350 |
+
return latents
|
351 |
+
|
352 |
+
def prepare_condition(self, condition, num_videos_per_prompt, device, dtype, do_classifier_free_guidance):
|
353 |
+
# prepare conditions for controlnet
|
354 |
+
condition = torch.from_numpy(condition.copy()).to(device=device, dtype=dtype) / 255.0
|
355 |
+
condition = torch.stack([condition for _ in range(num_videos_per_prompt)], dim=0)
|
356 |
+
condition = rearrange(condition, 'b f h w c -> (b f) c h w').clone()
|
357 |
+
if do_classifier_free_guidance:
|
358 |
+
condition = torch.cat([condition] * 2)
|
359 |
+
return condition
|
360 |
+
|
361 |
+
def next_step(
|
362 |
+
self,
|
363 |
+
model_output: torch.FloatTensor,
|
364 |
+
timestep: int,
|
365 |
+
x: torch.FloatTensor,
|
366 |
+
eta=0.,
|
367 |
+
verbose=False
|
368 |
+
):
|
369 |
+
"""
|
370 |
+
Inverse sampling for DDIM Inversion
|
371 |
+
"""
|
372 |
+
if verbose:
|
373 |
+
print("timestep: ", timestep)
|
374 |
+
next_step = timestep
|
375 |
+
timestep = min(timestep - self.scheduler.config.num_train_timesteps // self.scheduler.num_inference_steps, 999)
|
376 |
+
alpha_prod_t = self.scheduler.alphas_cumprod[timestep] if timestep >= 0 else self.scheduler.final_alpha_cumprod
|
377 |
+
alpha_prod_t_next = self.scheduler.alphas_cumprod[next_step]
|
378 |
+
beta_prod_t = 1 - alpha_prod_t
|
379 |
+
pred_x0 = (x - beta_prod_t**0.5 * model_output) / alpha_prod_t**0.5
|
380 |
+
pred_dir = (1 - alpha_prod_t_next)**0.5 * model_output
|
381 |
+
x_next = alpha_prod_t_next**0.5 * pred_x0 + pred_dir
|
382 |
+
return x_next, pred_x0
|
383 |
+
|
384 |
+
@torch.no_grad()
|
385 |
+
def images2latents(self, images, dtype):
|
386 |
+
"""
|
387 |
+
Convert RGB image to VAE latents
|
388 |
+
"""
|
389 |
+
device = self._execution_device
|
390 |
+
images = torch.from_numpy(images).float().to(dtype) / 127.5 - 1
|
391 |
+
images = rearrange(images, "f h w c -> f c h w").to(device)
|
392 |
+
latents = []
|
393 |
+
for frame_idx in range(images.shape[0]):
|
394 |
+
latents.append(self.vae.encode(images[frame_idx:frame_idx+1])['latent_dist'].mean * 0.18215)
|
395 |
+
latents = torch.cat(latents)
|
396 |
+
return latents
|
397 |
+
|
398 |
+
@torch.no_grad()
|
399 |
+
def invert(
|
400 |
+
self,
|
401 |
+
image: torch.Tensor,
|
402 |
+
prompt,
|
403 |
+
num_inference_steps=20,
|
404 |
+
num_actual_inference_steps=10,
|
405 |
+
eta=0.0,
|
406 |
+
return_intermediates=False,
|
407 |
+
**kwargs):
|
408 |
+
"""
|
409 |
+
Adapted from: https://github.com/Yujun-Shi/DragDiffusion/blob/main/drag_pipeline.py#L440
|
410 |
+
invert a real image into noise map with determinisc DDIM inversion
|
411 |
+
"""
|
412 |
+
device = self._execution_device
|
413 |
+
batch_size = image.shape[0]
|
414 |
+
if isinstance(prompt, list):
|
415 |
+
if batch_size == 1:
|
416 |
+
image = image.expand(len(prompt), -1, -1, -1)
|
417 |
+
elif isinstance(prompt, str):
|
418 |
+
if batch_size > 1:
|
419 |
+
prompt = [prompt] * batch_size
|
420 |
+
|
421 |
+
# text embeddings
|
422 |
+
text_input = self.tokenizer(
|
423 |
+
prompt,
|
424 |
+
padding="max_length",
|
425 |
+
max_length=77,
|
426 |
+
return_tensors="pt"
|
427 |
+
)
|
428 |
+
text_embeddings = self.text_encoder(text_input.input_ids.to(device))[0]
|
429 |
+
print("input text embeddings :", text_embeddings.shape)
|
430 |
+
# define initial latents
|
431 |
+
latents = self.images2latents(image)
|
432 |
+
|
433 |
+
print("latents shape: ", latents.shape)
|
434 |
+
# interative sampling
|
435 |
+
self.scheduler.set_timesteps(num_inference_steps)
|
436 |
+
print("Valid timesteps: ", reversed(self.scheduler.timesteps))
|
437 |
+
latents_list = [latents]
|
438 |
+
pred_x0_list = [latents]
|
439 |
+
for i, t in enumerate(tqdm(reversed(self.scheduler.timesteps), desc="DDIM Inversion")):
|
440 |
+
|
441 |
+
if num_actual_inference_steps is not None and i >= num_actual_inference_steps:
|
442 |
+
continue
|
443 |
+
model_inputs = latents
|
444 |
+
|
445 |
+
# predict the noise
|
446 |
+
# NOTE: the u-net here is UNet3D, therefore the model_inputs need to be of shape (b c f h w)
|
447 |
+
model_inputs = rearrange(model_inputs, "f c h w -> 1 c f h w")
|
448 |
+
noise_pred = self.unet(model_inputs, t, encoder_hidden_states=text_embeddings).sample
|
449 |
+
noise_pred = rearrange(noise_pred, "b c f h w -> (b f) c h w")
|
450 |
+
|
451 |
+
# compute the previous noise sample x_t-1 -> x_t
|
452 |
+
latents, pred_x0 = self.next_step(noise_pred, t, latents)
|
453 |
+
latents_list.append(latents)
|
454 |
+
pred_x0_list.append(pred_x0)
|
455 |
+
|
456 |
+
if return_intermediates:
|
457 |
+
# return the intermediate laters during inversion
|
458 |
+
return latents, latents_list
|
459 |
+
return latents
|
460 |
+
|
461 |
+
def interpolate_latents(self, latents: torch.Tensor, interpolation_factor:int, device ):
|
462 |
+
if interpolation_factor < 2:
|
463 |
+
return latents
|
464 |
+
|
465 |
+
new_latents = torch.zeros(
|
466 |
+
(latents.shape[0],latents.shape[1],((latents.shape[2]-1) * interpolation_factor)+1, latents.shape[3],latents.shape[4]),
|
467 |
+
device=latents.device,
|
468 |
+
dtype=latents.dtype,
|
469 |
+
)
|
470 |
+
|
471 |
+
org_video_length = latents.shape[2]
|
472 |
+
rate = [i/interpolation_factor for i in range(interpolation_factor)][1:]
|
473 |
+
|
474 |
+
new_index = 0
|
475 |
+
|
476 |
+
v0 = None
|
477 |
+
v1 = None
|
478 |
+
|
479 |
+
for i0,i1 in zip( range( org_video_length ),range( org_video_length )[1:] ):
|
480 |
+
v0 = latents[:,:,i0,:,:]
|
481 |
+
v1 = latents[:,:,i1,:,:]
|
482 |
+
|
483 |
+
new_latents[:,:,new_index,:,:] = v0
|
484 |
+
new_index += 1
|
485 |
+
|
486 |
+
for f in rate:
|
487 |
+
v = get_tensor_interpolation_method()(v0.to(device=device),v1.to(device=device),f)
|
488 |
+
new_latents[:,:,new_index,:,:] = v.to(latents.device)
|
489 |
+
new_index += 1
|
490 |
+
|
491 |
+
new_latents[:,:,new_index,:,:] = v1
|
492 |
+
new_index += 1
|
493 |
+
|
494 |
+
return new_latents
|
495 |
+
|
496 |
+
def select_controlnet_res_samples(self, controlnet_res_samples_cache_dict, context, do_classifier_free_guidance, b, f):
|
497 |
+
_down_block_res_samples = []
|
498 |
+
_mid_block_res_sample = []
|
499 |
+
for i in np.concatenate(np.array(context)):
|
500 |
+
_down_block_res_samples.append(controlnet_res_samples_cache_dict[i][0])
|
501 |
+
_mid_block_res_sample.append(controlnet_res_samples_cache_dict[i][1])
|
502 |
+
down_block_res_samples = [[] for _ in range(len(controlnet_res_samples_cache_dict[i][0]))]
|
503 |
+
for res_t in _down_block_res_samples:
|
504 |
+
for i, res in enumerate(res_t):
|
505 |
+
down_block_res_samples[i].append(res)
|
506 |
+
down_block_res_samples = [torch.cat(res) for res in down_block_res_samples]
|
507 |
+
mid_block_res_sample = torch.cat(_mid_block_res_sample)
|
508 |
+
|
509 |
+
# reshape controlnet output to match the unet3d inputs
|
510 |
+
b = b // 2 if do_classifier_free_guidance else b
|
511 |
+
_down_block_res_samples = []
|
512 |
+
for sample in down_block_res_samples:
|
513 |
+
sample = rearrange(sample, '(b f) c h w -> b c f h w', b=b, f=f)
|
514 |
+
if do_classifier_free_guidance:
|
515 |
+
sample = sample.repeat(2, 1, 1, 1, 1)
|
516 |
+
_down_block_res_samples.append(sample)
|
517 |
+
down_block_res_samples = _down_block_res_samples
|
518 |
+
mid_block_res_sample = rearrange(mid_block_res_sample, '(b f) c h w -> b c f h w', b=b, f=f)
|
519 |
+
if do_classifier_free_guidance:
|
520 |
+
mid_block_res_sample = mid_block_res_sample.repeat(2, 1, 1, 1, 1)
|
521 |
+
|
522 |
+
return down_block_res_samples, mid_block_res_sample
|
523 |
+
|
524 |
+
@torch.no_grad()
|
525 |
+
def __call__(
|
526 |
+
self,
|
527 |
+
prompt: Union[str, List[str]],
|
528 |
+
video_length: Optional[int],
|
529 |
+
height: Optional[int] = None,
|
530 |
+
width: Optional[int] = None,
|
531 |
+
num_inference_steps: int = 50,
|
532 |
+
guidance_scale: float = 7.5,
|
533 |
+
negative_prompt: Optional[Union[str, List[str]]] = None,
|
534 |
+
num_videos_per_prompt: Optional[int] = 1,
|
535 |
+
eta: float = 0.0,
|
536 |
+
generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None,
|
537 |
+
latents: Optional[torch.FloatTensor] = None,
|
538 |
+
output_type: Optional[str] = "tensor",
|
539 |
+
return_dict: bool = True,
|
540 |
+
callback: Optional[Callable[[int, int, torch.FloatTensor], None]] = None,
|
541 |
+
callback_steps: Optional[int] = 1,
|
542 |
+
controlnet_condition: list = None,
|
543 |
+
controlnet_conditioning_scale: float = 1.0,
|
544 |
+
context_frames: int = 16,
|
545 |
+
context_stride: int = 1,
|
546 |
+
context_overlap: int = 4,
|
547 |
+
context_batch_size: int = 1,
|
548 |
+
context_schedule: str = "uniform",
|
549 |
+
init_latents: Optional[torch.FloatTensor] = None,
|
550 |
+
num_actual_inference_steps: Optional[int] = None,
|
551 |
+
appearance_encoder = None,
|
552 |
+
reference_control_writer = None,
|
553 |
+
reference_control_reader = None,
|
554 |
+
source_image: str = None,
|
555 |
+
decoder_consistency = None,
|
556 |
+
**kwargs,
|
557 |
+
):
|
558 |
+
"""
|
559 |
+
New args:
|
560 |
+
- controlnet_condition : condition map (e.g., depth, canny, keypoints) for controlnet
|
561 |
+
- controlnet_conditioning_scale : conditioning scale for controlnet
|
562 |
+
- init_latents : initial latents to begin with (used along with invert())
|
563 |
+
- num_actual_inference_steps : number of actual inference steps (while total steps is num_inference_steps)
|
564 |
+
"""
|
565 |
+
controlnet = self.controlnet
|
566 |
+
|
567 |
+
# Default height and width to unet
|
568 |
+
height = height or self.unet.config.sample_size * self.vae_scale_factor
|
569 |
+
width = width or self.unet.config.sample_size * self.vae_scale_factor
|
570 |
+
|
571 |
+
# Check inputs. Raise error if not correct
|
572 |
+
self.check_inputs(prompt, height, width, callback_steps)
|
573 |
+
|
574 |
+
# Define call parameters
|
575 |
+
# batch_size = 1 if isinstance(prompt, str) else len(prompt)
|
576 |
+
batch_size = 1
|
577 |
+
if latents is not None:
|
578 |
+
batch_size = latents.shape[0]
|
579 |
+
if isinstance(prompt, list):
|
580 |
+
batch_size = len(prompt)
|
581 |
+
|
582 |
+
device = self._execution_device
|
583 |
+
# here `guidance_scale` is defined analog to the guidance weight `w` of equation (2)
|
584 |
+
# of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1`
|
585 |
+
# corresponds to doing no classifier free guidance.
|
586 |
+
do_classifier_free_guidance = guidance_scale > 1.0
|
587 |
+
|
588 |
+
# Encode input prompt
|
589 |
+
prompt = prompt if isinstance(prompt, list) else [prompt] * batch_size
|
590 |
+
if negative_prompt is not None:
|
591 |
+
negative_prompt = negative_prompt if isinstance(negative_prompt, list) else [negative_prompt] * batch_size
|
592 |
+
text_embeddings = self._encode_prompt(
|
593 |
+
prompt, device, num_videos_per_prompt, do_classifier_free_guidance, negative_prompt
|
594 |
+
)
|
595 |
+
text_embeddings = torch.cat([text_embeddings] * context_batch_size)
|
596 |
+
|
597 |
+
reference_control_writer = ReferenceAttentionControl(appearance_encoder, do_classifier_free_guidance=True, mode='write', batch_size=context_batch_size)
|
598 |
+
reference_control_reader = ReferenceAttentionControl(self.unet, do_classifier_free_guidance=True, mode='read', batch_size=context_batch_size)
|
599 |
+
|
600 |
+
is_dist_initialized = kwargs.get("dist", False)
|
601 |
+
rank = kwargs.get("rank", 0)
|
602 |
+
world_size = kwargs.get("world_size", 1)
|
603 |
+
|
604 |
+
# Prepare video
|
605 |
+
assert num_videos_per_prompt == 1 # FIXME: verify if num_videos_per_prompt > 1 works
|
606 |
+
assert batch_size == 1 # FIXME: verify if batch_size > 1 works
|
607 |
+
control = self.prepare_condition(
|
608 |
+
condition=controlnet_condition,
|
609 |
+
device=device,
|
610 |
+
dtype=controlnet.dtype,
|
611 |
+
num_videos_per_prompt=num_videos_per_prompt,
|
612 |
+
do_classifier_free_guidance=do_classifier_free_guidance,
|
613 |
+
)
|
614 |
+
controlnet_uncond_images, controlnet_cond_images = control.chunk(2)
|
615 |
+
|
616 |
+
# Prepare timesteps
|
617 |
+
self.scheduler.set_timesteps(num_inference_steps, device=device)
|
618 |
+
timesteps = self.scheduler.timesteps
|
619 |
+
|
620 |
+
# Prepare latent variables
|
621 |
+
if init_latents is not None:
|
622 |
+
latents = rearrange(init_latents, "(b f) c h w -> b c f h w", f=video_length)
|
623 |
+
else:
|
624 |
+
num_channels_latents = self.unet.in_channels
|
625 |
+
latents = self.prepare_latents(
|
626 |
+
batch_size * num_videos_per_prompt,
|
627 |
+
num_channels_latents,
|
628 |
+
video_length,
|
629 |
+
height,
|
630 |
+
width,
|
631 |
+
text_embeddings.dtype,
|
632 |
+
device,
|
633 |
+
generator,
|
634 |
+
latents,
|
635 |
+
)
|
636 |
+
latents_dtype = latents.dtype
|
637 |
+
|
638 |
+
# Prepare extra step kwargs.
|
639 |
+
extra_step_kwargs = self.prepare_extra_step_kwargs(generator, eta)
|
640 |
+
|
641 |
+
# Prepare text embeddings for controlnet
|
642 |
+
controlnet_text_embeddings = text_embeddings.repeat_interleave(video_length, 0)
|
643 |
+
_, controlnet_text_embeddings_c = controlnet_text_embeddings.chunk(2)
|
644 |
+
|
645 |
+
controlnet_res_samples_cache_dict = {i:None for i in range(video_length)}
|
646 |
+
|
647 |
+
# For img2img setting
|
648 |
+
if num_actual_inference_steps is None:
|
649 |
+
num_actual_inference_steps = num_inference_steps
|
650 |
+
|
651 |
+
if isinstance(source_image, str):
|
652 |
+
ref_image_latents = self.images2latents(np.array(Image.open(source_image).resize((width, height)))[None, :], latents_dtype).cuda()
|
653 |
+
elif isinstance(source_image, np.ndarray):
|
654 |
+
ref_image_latents = self.images2latents(source_image[None, :], latents_dtype).cuda()
|
655 |
+
|
656 |
+
context_scheduler = get_context_scheduler(context_schedule)
|
657 |
+
|
658 |
+
# Denoising loop
|
659 |
+
for i, t in tqdm(enumerate(timesteps), total=len(timesteps), disable=(rank!=0)):
|
660 |
+
if num_actual_inference_steps is not None and i < num_inference_steps - num_actual_inference_steps:
|
661 |
+
continue
|
662 |
+
|
663 |
+
noise_pred = torch.zeros(
|
664 |
+
(latents.shape[0] * (2 if do_classifier_free_guidance else 1), *latents.shape[1:]),
|
665 |
+
device=latents.device,
|
666 |
+
dtype=latents.dtype,
|
667 |
+
)
|
668 |
+
counter = torch.zeros(
|
669 |
+
(1, 1, latents.shape[2], 1, 1), device=latents.device, dtype=latents.dtype
|
670 |
+
)
|
671 |
+
|
672 |
+
appearance_encoder(
|
673 |
+
ref_image_latents.repeat(context_batch_size * (2 if do_classifier_free_guidance else 1), 1, 1, 1),
|
674 |
+
t,
|
675 |
+
encoder_hidden_states=text_embeddings,
|
676 |
+
return_dict=False,
|
677 |
+
)
|
678 |
+
|
679 |
+
context_queue = list(context_scheduler(
|
680 |
+
0, num_inference_steps, latents.shape[2], context_frames, context_stride, 0
|
681 |
+
))
|
682 |
+
num_context_batches = math.ceil(len(context_queue) / context_batch_size)
|
683 |
+
for i in range(num_context_batches):
|
684 |
+
context = context_queue[i*context_batch_size: (i+1)*context_batch_size]
|
685 |
+
# expand the latents if we are doing classifier free guidance
|
686 |
+
controlnet_latent_input = (
|
687 |
+
torch.cat([latents[:, :, c] for c in context])
|
688 |
+
.to(device)
|
689 |
+
)
|
690 |
+
controlnet_latent_input = self.scheduler.scale_model_input(controlnet_latent_input, t)
|
691 |
+
|
692 |
+
# prepare inputs for controlnet
|
693 |
+
b, c, f, h, w = controlnet_latent_input.shape
|
694 |
+
controlnet_latent_input = rearrange(controlnet_latent_input, "b c f h w -> (b f) c h w")
|
695 |
+
|
696 |
+
# controlnet inference
|
697 |
+
down_block_res_samples, mid_block_res_sample = self.controlnet(
|
698 |
+
controlnet_latent_input,
|
699 |
+
t,
|
700 |
+
encoder_hidden_states=torch.cat([controlnet_text_embeddings_c[c] for c in context]),
|
701 |
+
controlnet_cond=torch.cat([controlnet_cond_images[c] for c in context]),
|
702 |
+
conditioning_scale=controlnet_conditioning_scale,
|
703 |
+
return_dict=False,
|
704 |
+
)
|
705 |
+
|
706 |
+
for j, k in enumerate(np.concatenate(np.array(context))):
|
707 |
+
controlnet_res_samples_cache_dict[k] = ([sample[j:j+1] for sample in down_block_res_samples], mid_block_res_sample[j:j+1])
|
708 |
+
|
709 |
+
context_queue = list(context_scheduler(
|
710 |
+
0, num_inference_steps, latents.shape[2], context_frames, context_stride, context_overlap
|
711 |
+
))
|
712 |
+
|
713 |
+
num_context_batches = math.ceil(len(context_queue) / context_batch_size)
|
714 |
+
global_context = []
|
715 |
+
for i in range(num_context_batches):
|
716 |
+
global_context.append(context_queue[i*context_batch_size: (i+1)*context_batch_size])
|
717 |
+
|
718 |
+
for context in global_context[rank::world_size]:
|
719 |
+
# expand the latents if we are doing classifier free guidance
|
720 |
+
latent_model_input = (
|
721 |
+
torch.cat([latents[:, :, c] for c in context])
|
722 |
+
.to(device)
|
723 |
+
.repeat(2 if do_classifier_free_guidance else 1, 1, 1, 1, 1)
|
724 |
+
)
|
725 |
+
latent_model_input = self.scheduler.scale_model_input(latent_model_input, t)
|
726 |
+
|
727 |
+
b, c, f, h, w = latent_model_input.shape
|
728 |
+
down_block_res_samples, mid_block_res_sample = self.select_controlnet_res_samples(
|
729 |
+
controlnet_res_samples_cache_dict,
|
730 |
+
context,
|
731 |
+
do_classifier_free_guidance,
|
732 |
+
b, f
|
733 |
+
)
|
734 |
+
|
735 |
+
reference_control_reader.update(reference_control_writer)
|
736 |
+
|
737 |
+
# predict the noise residual
|
738 |
+
pred = self.unet(
|
739 |
+
latent_model_input,
|
740 |
+
t,
|
741 |
+
encoder_hidden_states=text_embeddings[:b],
|
742 |
+
down_block_additional_residuals=down_block_res_samples,
|
743 |
+
mid_block_additional_residual=mid_block_res_sample,
|
744 |
+
return_dict=False,
|
745 |
+
)[0]
|
746 |
+
|
747 |
+
reference_control_reader.clear()
|
748 |
+
|
749 |
+
pred_uc, pred_c = pred.chunk(2)
|
750 |
+
pred = torch.cat([pred_uc.unsqueeze(0), pred_c.unsqueeze(0)])
|
751 |
+
for j, c in enumerate(context):
|
752 |
+
noise_pred[:, :, c] = noise_pred[:, :, c] + pred[:, j]
|
753 |
+
counter[:, :, c] = counter[:, :, c] + 1
|
754 |
+
|
755 |
+
if is_dist_initialized:
|
756 |
+
noise_pred_gathered = [torch.zeros_like(noise_pred) for _ in range(world_size)]
|
757 |
+
if rank == 0:
|
758 |
+
dist.gather(tensor=noise_pred, gather_list=noise_pred_gathered, dst=0)
|
759 |
+
else:
|
760 |
+
dist.gather(tensor=noise_pred, gather_list=[], dst=0)
|
761 |
+
dist.barrier()
|
762 |
+
|
763 |
+
if rank == 0:
|
764 |
+
for k in range(1, world_size):
|
765 |
+
for context in global_context[k::world_size]:
|
766 |
+
for j, c in enumerate(context):
|
767 |
+
noise_pred[:, :, c] = noise_pred[:, :, c] + noise_pred_gathered[k][:, :, c]
|
768 |
+
counter[:, :, c] = counter[:, :, c] + 1
|
769 |
+
|
770 |
+
# perform guidance
|
771 |
+
if do_classifier_free_guidance:
|
772 |
+
noise_pred_uncond, noise_pred_text = (noise_pred / counter).chunk(2)
|
773 |
+
noise_pred = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
|
774 |
+
|
775 |
+
# compute the previous noisy sample x_t -> x_t-1
|
776 |
+
latents = self.scheduler.step(noise_pred, t, latents, **extra_step_kwargs).prev_sample
|
777 |
+
|
778 |
+
if is_dist_initialized:
|
779 |
+
dist.broadcast(latents, 0)
|
780 |
+
dist.barrier()
|
781 |
+
|
782 |
+
reference_control_writer.clear()
|
783 |
+
|
784 |
+
interpolation_factor = 1
|
785 |
+
latents = self.interpolate_latents(latents, interpolation_factor, device)
|
786 |
+
# Post-processing
|
787 |
+
video = self.decode_latents(latents, rank, decoder_consistency=decoder_consistency)
|
788 |
+
|
789 |
+
if is_dist_initialized:
|
790 |
+
dist.barrier()
|
791 |
+
|
792 |
+
# Convert to tensor
|
793 |
+
if output_type == "tensor":
|
794 |
+
video = torch.from_numpy(video)
|
795 |
+
|
796 |
+
if not return_dict:
|
797 |
+
return video
|
798 |
+
|
799 |
+
return AnimationPipelineOutput(videos=video)
|
magicanimate/utils/dist_tools.py
ADDED
@@ -0,0 +1,105 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
# Copyright 2023 ByteDance and/or its affiliates.
|
2 |
+
#
|
3 |
+
# Copyright (2023) MagicAnimate Authors
|
4 |
+
#
|
5 |
+
# ByteDance, its affiliates and licensors retain all intellectual
|
6 |
+
# property and proprietary rights in and to this material, related
|
7 |
+
# documentation and any modifications thereto. Any use, reproduction,
|
8 |
+
# disclosure or distribution of this material and related documentation
|
9 |
+
# without an express license agreement from ByteDance or
|
10 |
+
# its affiliates is strictly prohibited.
|
11 |
+
import os
|
12 |
+
import socket
|
13 |
+
import warnings
|
14 |
+
import torch
|
15 |
+
from torch import distributed as dist
|
16 |
+
|
17 |
+
|
18 |
+
def distributed_init(args):
|
19 |
+
|
20 |
+
if dist.is_initialized():
|
21 |
+
warnings.warn("Distributed is already initialized, cannot initialize twice!")
|
22 |
+
args.rank = dist.get_rank()
|
23 |
+
else:
|
24 |
+
print(
|
25 |
+
f"Distributed Init (Rank {args.rank}): "
|
26 |
+
f"{args.init_method}"
|
27 |
+
)
|
28 |
+
dist.init_process_group(
|
29 |
+
backend='nccl',
|
30 |
+
init_method=args.init_method,
|
31 |
+
world_size=args.world_size,
|
32 |
+
rank=args.rank,
|
33 |
+
)
|
34 |
+
print(
|
35 |
+
f"Initialized Host {socket.gethostname()} as Rank "
|
36 |
+
f"{args.rank}"
|
37 |
+
)
|
38 |
+
|
39 |
+
if "MASTER_ADDR" not in os.environ or "MASTER_PORT" not in os.environ:
|
40 |
+
# Set for onboxdataloader support
|
41 |
+
split = args.init_method.split("//")
|
42 |
+
assert len(split) == 2, (
|
43 |
+
"host url for distributed should be split by '//' "
|
44 |
+
+ "into exactly two elements"
|
45 |
+
)
|
46 |
+
|
47 |
+
split = split[1].split(":")
|
48 |
+
assert (
|
49 |
+
len(split) == 2
|
50 |
+
), "host url should be of the form <host_url>:<host_port>"
|
51 |
+
os.environ["MASTER_ADDR"] = split[0]
|
52 |
+
os.environ["MASTER_PORT"] = split[1]
|
53 |
+
|
54 |
+
# perform a dummy all-reduce to initialize the NCCL communicator
|
55 |
+
dist.all_reduce(torch.zeros(1).cuda())
|
56 |
+
|
57 |
+
suppress_output(is_master())
|
58 |
+
args.rank = dist.get_rank()
|
59 |
+
return args.rank
|
60 |
+
|
61 |
+
|
62 |
+
def get_rank():
|
63 |
+
if not dist.is_available():
|
64 |
+
return 0
|
65 |
+
if not dist.is_nccl_available():
|
66 |
+
return 0
|
67 |
+
if not dist.is_initialized():
|
68 |
+
return 0
|
69 |
+
return dist.get_rank()
|
70 |
+
|
71 |
+
|
72 |
+
def is_master():
|
73 |
+
return get_rank() == 0
|
74 |
+
|
75 |
+
|
76 |
+
def synchronize():
|
77 |
+
if dist.is_initialized():
|
78 |
+
dist.barrier()
|
79 |
+
|
80 |
+
|
81 |
+
def suppress_output(is_master):
|
82 |
+
"""Suppress printing on the current device. Force printing with `force=True`."""
|
83 |
+
import builtins as __builtin__
|
84 |
+
|
85 |
+
builtin_print = __builtin__.print
|
86 |
+
|
87 |
+
def print(*args, **kwargs):
|
88 |
+
force = kwargs.pop("force", False)
|
89 |
+
if is_master or force:
|
90 |
+
builtin_print(*args, **kwargs)
|
91 |
+
|
92 |
+
__builtin__.print = print
|
93 |
+
|
94 |
+
import warnings
|
95 |
+
|
96 |
+
builtin_warn = warnings.warn
|
97 |
+
|
98 |
+
def warn(*args, **kwargs):
|
99 |
+
force = kwargs.pop("force", False)
|
100 |
+
if is_master or force:
|
101 |
+
builtin_warn(*args, **kwargs)
|
102 |
+
|
103 |
+
# Log warnings only once
|
104 |
+
warnings.warn = warn
|
105 |
+
warnings.simplefilter("once", UserWarning)
|
magicanimate/utils/util.py
ADDED
@@ -0,0 +1,138 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
# *************************************************************************
|
2 |
+
# This file may have been modified by Bytedance Inc. (“Bytedance Inc.'s Mo-
|
3 |
+
# difications”). All Bytedance Inc.'s Modifications are Copyright (2023) B-
|
4 |
+
# ytedance Inc..
|
5 |
+
# *************************************************************************
|
6 |
+
|
7 |
+
# Adapted from https://github.com/guoyww/AnimateDiff
|
8 |
+
import os
|
9 |
+
import imageio
|
10 |
+
import numpy as np
|
11 |
+
|
12 |
+
import torch
|
13 |
+
import torchvision
|
14 |
+
|
15 |
+
from PIL import Image
|
16 |
+
from typing import Union
|
17 |
+
from tqdm import tqdm
|
18 |
+
from einops import rearrange
|
19 |
+
|
20 |
+
|
21 |
+
def save_videos_grid(videos: torch.Tensor, path: str, rescale=False, n_rows=6, fps=25):
|
22 |
+
videos = rearrange(videos, "b c t h w -> t b c h w")
|
23 |
+
outputs = []
|
24 |
+
for x in videos:
|
25 |
+
x = torchvision.utils.make_grid(x, nrow=n_rows)
|
26 |
+
x = x.transpose(0, 1).transpose(1, 2).squeeze(-1)
|
27 |
+
if rescale:
|
28 |
+
x = (x + 1.0) / 2.0 # -1,1 -> 0,1
|
29 |
+
x = (x * 255).numpy().astype(np.uint8)
|
30 |
+
outputs.append(x)
|
31 |
+
|
32 |
+
os.makedirs(os.path.dirname(path), exist_ok=True)
|
33 |
+
imageio.mimsave(path, outputs, fps=fps)
|
34 |
+
|
35 |
+
def save_images_grid(images: torch.Tensor, path: str):
|
36 |
+
assert images.shape[2] == 1 # no time dimension
|
37 |
+
images = images.squeeze(2)
|
38 |
+
grid = torchvision.utils.make_grid(images)
|
39 |
+
grid = (grid * 255).numpy().transpose(1, 2, 0).astype(np.uint8)
|
40 |
+
os.makedirs(os.path.dirname(path), exist_ok=True)
|
41 |
+
Image.fromarray(grid).save(path)
|
42 |
+
|
43 |
+
# DDIM Inversion
|
44 |
+
@torch.no_grad()
|
45 |
+
def init_prompt(prompt, pipeline):
|
46 |
+
uncond_input = pipeline.tokenizer(
|
47 |
+
[""], padding="max_length", max_length=pipeline.tokenizer.model_max_length,
|
48 |
+
return_tensors="pt"
|
49 |
+
)
|
50 |
+
uncond_embeddings = pipeline.text_encoder(uncond_input.input_ids.to(pipeline.device))[0]
|
51 |
+
text_input = pipeline.tokenizer(
|
52 |
+
[prompt],
|
53 |
+
padding="max_length",
|
54 |
+
max_length=pipeline.tokenizer.model_max_length,
|
55 |
+
truncation=True,
|
56 |
+
return_tensors="pt",
|
57 |
+
)
|
58 |
+
text_embeddings = pipeline.text_encoder(text_input.input_ids.to(pipeline.device))[0]
|
59 |
+
context = torch.cat([uncond_embeddings, text_embeddings])
|
60 |
+
|
61 |
+
return context
|
62 |
+
|
63 |
+
|
64 |
+
def next_step(model_output: Union[torch.FloatTensor, np.ndarray], timestep: int,
|
65 |
+
sample: Union[torch.FloatTensor, np.ndarray], ddim_scheduler):
|
66 |
+
timestep, next_timestep = min(
|
67 |
+
timestep - ddim_scheduler.config.num_train_timesteps // ddim_scheduler.num_inference_steps, 999), timestep
|
68 |
+
alpha_prod_t = ddim_scheduler.alphas_cumprod[timestep] if timestep >= 0 else ddim_scheduler.final_alpha_cumprod
|
69 |
+
alpha_prod_t_next = ddim_scheduler.alphas_cumprod[next_timestep]
|
70 |
+
beta_prod_t = 1 - alpha_prod_t
|
71 |
+
next_original_sample = (sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5
|
72 |
+
next_sample_direction = (1 - alpha_prod_t_next) ** 0.5 * model_output
|
73 |
+
next_sample = alpha_prod_t_next ** 0.5 * next_original_sample + next_sample_direction
|
74 |
+
return next_sample
|
75 |
+
|
76 |
+
|
77 |
+
def get_noise_pred_single(latents, t, context, unet):
|
78 |
+
noise_pred = unet(latents, t, encoder_hidden_states=context)["sample"]
|
79 |
+
return noise_pred
|
80 |
+
|
81 |
+
|
82 |
+
@torch.no_grad()
|
83 |
+
def ddim_loop(pipeline, ddim_scheduler, latent, num_inv_steps, prompt):
|
84 |
+
context = init_prompt(prompt, pipeline)
|
85 |
+
uncond_embeddings, cond_embeddings = context.chunk(2)
|
86 |
+
all_latent = [latent]
|
87 |
+
latent = latent.clone().detach()
|
88 |
+
for i in tqdm(range(num_inv_steps)):
|
89 |
+
t = ddim_scheduler.timesteps[len(ddim_scheduler.timesteps) - i - 1]
|
90 |
+
noise_pred = get_noise_pred_single(latent, t, cond_embeddings, pipeline.unet)
|
91 |
+
latent = next_step(noise_pred, t, latent, ddim_scheduler)
|
92 |
+
all_latent.append(latent)
|
93 |
+
return all_latent
|
94 |
+
|
95 |
+
|
96 |
+
@torch.no_grad()
|
97 |
+
def ddim_inversion(pipeline, ddim_scheduler, video_latent, num_inv_steps, prompt=""):
|
98 |
+
ddim_latents = ddim_loop(pipeline, ddim_scheduler, video_latent, num_inv_steps, prompt)
|
99 |
+
return ddim_latents
|
100 |
+
|
101 |
+
|
102 |
+
def video2images(path, step=4, length=16, start=0):
|
103 |
+
reader = imageio.get_reader(path)
|
104 |
+
frames = []
|
105 |
+
for frame in reader:
|
106 |
+
frames.append(np.array(frame))
|
107 |
+
frames = frames[start::step][:length]
|
108 |
+
return frames
|
109 |
+
|
110 |
+
|
111 |
+
def images2video(video, path, fps=8):
|
112 |
+
imageio.mimsave(path, video, fps=fps)
|
113 |
+
return
|
114 |
+
|
115 |
+
|
116 |
+
tensor_interpolation = None
|
117 |
+
|
118 |
+
def get_tensor_interpolation_method():
|
119 |
+
return tensor_interpolation
|
120 |
+
|
121 |
+
def set_tensor_interpolation_method(is_slerp):
|
122 |
+
global tensor_interpolation
|
123 |
+
tensor_interpolation = slerp if is_slerp else linear
|
124 |
+
|
125 |
+
def linear(v1, v2, t):
|
126 |
+
return (1.0 - t) * v1 + t * v2
|
127 |
+
|
128 |
+
def slerp(
|
129 |
+
v0: torch.Tensor, v1: torch.Tensor, t: float, DOT_THRESHOLD: float = 0.9995
|
130 |
+
) -> torch.Tensor:
|
131 |
+
u0 = v0 / v0.norm()
|
132 |
+
u1 = v1 / v1.norm()
|
133 |
+
dot = (u0 * u1).sum()
|
134 |
+
if dot.abs() > DOT_THRESHOLD:
|
135 |
+
#logger.info(f'warning: v0 and v1 close to parallel, using linear interpolation instead.')
|
136 |
+
return (1.0 - t) * v0 + t * v1
|
137 |
+
omega = dot.acos()
|
138 |
+
return (((1.0 - t) * omega).sin() * v0 + (t * omega).sin() * v1) / omega.sin()
|
magicanimate/utils/videoreader.py
ADDED
@@ -0,0 +1,157 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
# *************************************************************************
|
2 |
+
# This file may have been modified by Bytedance Inc. (“Bytedance Inc.'s Mo-
|
3 |
+
# difications”). All Bytedance Inc.'s Modifications are Copyright (2023) B-
|
4 |
+
# ytedance Inc..
|
5 |
+
# *************************************************************************
|
6 |
+
|
7 |
+
# Copyright 2022 ByteDance and/or its affiliates.
|
8 |
+
#
|
9 |
+
# Copyright (2022) PV3D Authors
|
10 |
+
#
|
11 |
+
# ByteDance, its affiliates and licensors retain all intellectual
|
12 |
+
# property and proprietary rights in and to this material, related
|
13 |
+
# documentation and any modifications thereto. Any use, reproduction,
|
14 |
+
# disclosure or distribution of this material and related documentation
|
15 |
+
# without an express license agreement from ByteDance or
|
16 |
+
# its affiliates is strictly prohibited.
|
17 |
+
import av, gc
|
18 |
+
import torch
|
19 |
+
import warnings
|
20 |
+
import numpy as np
|
21 |
+
|
22 |
+
|
23 |
+
_CALLED_TIMES = 0
|
24 |
+
_GC_COLLECTION_INTERVAL = 20
|
25 |
+
|
26 |
+
|
27 |
+
# remove warnings
|
28 |
+
av.logging.set_level(av.logging.ERROR)
|
29 |
+
|
30 |
+
|
31 |
+
class VideoReader():
|
32 |
+
"""
|
33 |
+
Simple wrapper around PyAV that exposes a few useful functions for
|
34 |
+
dealing with video reading. PyAV is a pythonic binding for the ffmpeg libraries.
|
35 |
+
Acknowledgement: Codes are borrowed from Bruno Korbar
|
36 |
+
"""
|
37 |
+
def __init__(self, video, num_frames=float("inf"), decode_lossy=False, audio_resample_rate=None, bi_frame=False):
|
38 |
+
"""
|
39 |
+
Arguments:
|
40 |
+
video_path (str): path or byte of the video to be loaded
|
41 |
+
"""
|
42 |
+
self.container = av.open(video)
|
43 |
+
self.num_frames = num_frames
|
44 |
+
self.bi_frame = bi_frame
|
45 |
+
|
46 |
+
self.resampler = None
|
47 |
+
if audio_resample_rate is not None:
|
48 |
+
self.resampler = av.AudioResampler(rate=audio_resample_rate)
|
49 |
+
|
50 |
+
if self.container.streams.video:
|
51 |
+
# enable multi-threaded video decoding
|
52 |
+
if decode_lossy:
|
53 |
+
warnings.warn('VideoReader| thread_type==AUTO can yield potential frame dropping!', RuntimeWarning)
|
54 |
+
self.container.streams.video[0].thread_type = 'AUTO'
|
55 |
+
self.video_stream = self.container.streams.video[0]
|
56 |
+
else:
|
57 |
+
self.video_stream = None
|
58 |
+
|
59 |
+
self.fps = self._get_video_frame_rate()
|
60 |
+
|
61 |
+
def seek(self, pts, backward=True, any_frame=False):
|
62 |
+
stream = self.video_stream
|
63 |
+
self.container.seek(pts, any_frame=any_frame, backward=backward, stream=stream)
|
64 |
+
|
65 |
+
def _occasional_gc(self):
|
66 |
+
# there are a lot of reference cycles in PyAV, so need to manually call
|
67 |
+
# the garbage collector from time to time
|
68 |
+
global _CALLED_TIMES, _GC_COLLECTION_INTERVAL
|
69 |
+
_CALLED_TIMES += 1
|
70 |
+
if _CALLED_TIMES % _GC_COLLECTION_INTERVAL == _GC_COLLECTION_INTERVAL - 1:
|
71 |
+
gc.collect()
|
72 |
+
|
73 |
+
def _read_video(self, offset):
|
74 |
+
self._occasional_gc()
|
75 |
+
|
76 |
+
pts = self.container.duration * offset
|
77 |
+
time_ = pts / float(av.time_base)
|
78 |
+
self.container.seek(int(pts))
|
79 |
+
|
80 |
+
video_frames = []
|
81 |
+
count = 0
|
82 |
+
for _, frame in enumerate(self._iter_frames()):
|
83 |
+
if frame.pts * frame.time_base >= time_:
|
84 |
+
video_frames.append(frame)
|
85 |
+
if count >= self.num_frames - 1:
|
86 |
+
break
|
87 |
+
count += 1
|
88 |
+
return video_frames
|
89 |
+
|
90 |
+
def _iter_frames(self):
|
91 |
+
for packet in self.container.demux(self.video_stream):
|
92 |
+
for frame in packet.decode():
|
93 |
+
yield frame
|
94 |
+
|
95 |
+
def _compute_video_stats(self):
|
96 |
+
if self.video_stream is None or self.container is None:
|
97 |
+
return 0
|
98 |
+
num_of_frames = self.container.streams.video[0].frames
|
99 |
+
if num_of_frames == 0:
|
100 |
+
num_of_frames = self.fps * float(self.container.streams.video[0].duration*self.video_stream.time_base)
|
101 |
+
self.seek(0, backward=False)
|
102 |
+
count = 0
|
103 |
+
time_base = 512
|
104 |
+
for p in self.container.decode(video=0):
|
105 |
+
count = count + 1
|
106 |
+
if count == 1:
|
107 |
+
start_pts = p.pts
|
108 |
+
elif count == 2:
|
109 |
+
time_base = p.pts - start_pts
|
110 |
+
break
|
111 |
+
return start_pts, time_base, num_of_frames
|
112 |
+
|
113 |
+
def _get_video_frame_rate(self):
|
114 |
+
return float(self.container.streams.video[0].guessed_rate)
|
115 |
+
|
116 |
+
def sample(self, debug=False):
|
117 |
+
|
118 |
+
if self.container is None:
|
119 |
+
raise RuntimeError('video stream not found')
|
120 |
+
sample = dict()
|
121 |
+
_, _, total_num_frames = self._compute_video_stats()
|
122 |
+
offset = torch.randint(max(1, total_num_frames-self.num_frames-1), [1]).item()
|
123 |
+
video_frames = self._read_video(offset/total_num_frames)
|
124 |
+
video_frames = np.array([np.uint8(f.to_rgb().to_ndarray()) for f in video_frames])
|
125 |
+
sample["frames"] = video_frames
|
126 |
+
sample["frame_idx"] = [offset]
|
127 |
+
|
128 |
+
if self.bi_frame:
|
129 |
+
frames = [np.random.beta(2, 1, size=1), np.random.beta(1, 2, size=1)]
|
130 |
+
frames = [int(frames[0] * self.num_frames), int(frames[1] * self.num_frames)]
|
131 |
+
frames.sort()
|
132 |
+
video_frames = np.array([video_frames[min(frames)], video_frames[max(frames)]])
|
133 |
+
Ts= [min(frames) / (self.num_frames - 1), max(frames) / (self.num_frames - 1)]
|
134 |
+
sample["frames"] = video_frames
|
135 |
+
sample["real_t"] = torch.tensor(Ts, dtype=torch.float32)
|
136 |
+
sample["frame_idx"] = [offset+min(frames), offset+max(frames)]
|
137 |
+
return sample
|
138 |
+
|
139 |
+
return sample
|
140 |
+
|
141 |
+
def read_frames(self, frame_indices):
|
142 |
+
self.num_frames = frame_indices[1] - frame_indices[0]
|
143 |
+
video_frames = self._read_video(frame_indices[0]/self.get_num_frames())
|
144 |
+
video_frames = np.array([
|
145 |
+
np.uint8(video_frames[0].to_rgb().to_ndarray()),
|
146 |
+
np.uint8(video_frames[-1].to_rgb().to_ndarray())
|
147 |
+
])
|
148 |
+
return video_frames
|
149 |
+
|
150 |
+
def read(self):
|
151 |
+
video_frames = self._read_video(0)
|
152 |
+
video_frames = np.array([np.uint8(f.to_rgb().to_ndarray()) for f in video_frames])
|
153 |
+
return video_frames
|
154 |
+
|
155 |
+
def get_num_frames(self):
|
156 |
+
_, _, total_num_frames = self._compute_video_stats()
|
157 |
+
return total_num_frames
|
packages.txt
ADDED
@@ -0,0 +1 @@
|
|
|
|
|
1 |
+
ffmpeg
|
requirements.txt
ADDED
@@ -0,0 +1,127 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
absl-py==1.4.0
|
2 |
+
accelerate==0.22.0
|
3 |
+
aiofiles==23.2.1
|
4 |
+
aiohttp==3.8.5
|
5 |
+
aiosignal==1.3.1
|
6 |
+
altair==5.0.1
|
7 |
+
annotated-types==0.5.0
|
8 |
+
antlr4-python3-runtime==4.9.3
|
9 |
+
anyio==3.7.1
|
10 |
+
async-timeout==4.0.3
|
11 |
+
attrs==23.1.0
|
12 |
+
cachetools==5.3.1
|
13 |
+
certifi==2023.7.22
|
14 |
+
charset-normalizer==3.2.0
|
15 |
+
click==8.1.7
|
16 |
+
cmake==3.27.2
|
17 |
+
contourpy==1.1.0
|
18 |
+
cycler==0.11.0
|
19 |
+
datasets==2.14.4
|
20 |
+
dill==0.3.7
|
21 |
+
einops==0.6.1
|
22 |
+
exceptiongroup==1.1.3
|
23 |
+
fastapi==0.103.0
|
24 |
+
ffmpy==0.3.1
|
25 |
+
filelock==3.12.2
|
26 |
+
fonttools==4.42.1
|
27 |
+
frozenlist==1.4.0
|
28 |
+
fsspec==2023.6.0
|
29 |
+
google-auth==2.22.0
|
30 |
+
google-auth-oauthlib==1.0.0
|
31 |
+
gradio==3.41.2
|
32 |
+
gradio-client==0.5.0
|
33 |
+
grpcio==1.57.0
|
34 |
+
h11==0.14.0
|
35 |
+
httpcore==0.17.3
|
36 |
+
httpx==0.24.1
|
37 |
+
huggingface-hub==0.16.4
|
38 |
+
idna==3.4
|
39 |
+
importlib-metadata==6.8.0
|
40 |
+
importlib-resources==6.0.1
|
41 |
+
jinja2==3.1.2
|
42 |
+
joblib==1.3.2
|
43 |
+
jsonschema==4.19.0
|
44 |
+
jsonschema-specifications==2023.7.1
|
45 |
+
kiwisolver==1.4.5
|
46 |
+
lightning-utilities==0.9.0
|
47 |
+
lit==16.0.6
|
48 |
+
markdown==3.4.4
|
49 |
+
markupsafe==2.1.3
|
50 |
+
matplotlib==3.7.2
|
51 |
+
mpmath==1.3.0
|
52 |
+
multidict==6.0.4
|
53 |
+
multiprocess==0.70.15
|
54 |
+
networkx==3.1
|
55 |
+
numpy==1.24.4
|
56 |
+
nvidia-cublas-cu11==11.10.3.66
|
57 |
+
nvidia-cuda-cupti-cu11==11.7.101
|
58 |
+
nvidia-cuda-nvrtc-cu11==11.7.99
|
59 |
+
nvidia-cuda-runtime-cu11==11.7.99
|
60 |
+
nvidia-cudnn-cu11==8.5.0.96
|
61 |
+
nvidia-cufft-cu11==10.9.0.58
|
62 |
+
nvidia-curand-cu11==10.2.10.91
|
63 |
+
nvidia-cusolver-cu11==11.4.0.1
|
64 |
+
nvidia-cusparse-cu11==11.7.4.91
|
65 |
+
nvidia-nccl-cu11==2.14.3
|
66 |
+
nvidia-nvtx-cu11==11.7.91
|
67 |
+
oauthlib==3.2.2
|
68 |
+
omegaconf==2.3.0
|
69 |
+
opencv-python==4.8.0.76
|
70 |
+
orjson==3.9.5
|
71 |
+
pandas==2.0.3
|
72 |
+
pillow==9.5.0
|
73 |
+
pkgutil-resolve-name==1.3.10
|
74 |
+
protobuf==4.24.2
|
75 |
+
psutil==5.9.5
|
76 |
+
pyarrow==13.0.0
|
77 |
+
pyasn1==0.5.0
|
78 |
+
pyasn1-modules==0.3.0
|
79 |
+
pydantic==2.3.0
|
80 |
+
pydantic-core==2.6.3
|
81 |
+
pydub==0.25.1
|
82 |
+
pyparsing==3.0.9
|
83 |
+
python-multipart==0.0.6
|
84 |
+
pytorch-lightning==2.0.7
|
85 |
+
pytz==2023.3
|
86 |
+
pyyaml==6.0.1
|
87 |
+
referencing==0.30.2
|
88 |
+
regex==2023.8.8
|
89 |
+
requests==2.31.0
|
90 |
+
requests-oauthlib==1.3.1
|
91 |
+
rpds-py==0.9.2
|
92 |
+
rsa==4.9
|
93 |
+
safetensors==0.3.3
|
94 |
+
semantic-version==2.10.0
|
95 |
+
sniffio==1.3.0
|
96 |
+
starlette==0.27.0
|
97 |
+
sympy==1.12
|
98 |
+
tensorboard==2.14.0
|
99 |
+
tensorboard-data-server==0.7.1
|
100 |
+
tokenizers==0.13.3
|
101 |
+
toolz==0.12.0
|
102 |
+
torchmetrics==1.1.0
|
103 |
+
tqdm==4.66.1
|
104 |
+
transformers==4.32.0
|
105 |
+
triton==2.0.0
|
106 |
+
tzdata==2023.3
|
107 |
+
urllib3==1.26.16
|
108 |
+
uvicorn==0.23.2
|
109 |
+
websockets==11.0.3
|
110 |
+
werkzeug==2.3.7
|
111 |
+
xxhash==3.3.0
|
112 |
+
yarl==1.9.2
|
113 |
+
zipp==3.16.2
|
114 |
+
decord
|
115 |
+
imageio==2.9.0
|
116 |
+
imageio-ffmpeg==0.4.3
|
117 |
+
timm
|
118 |
+
scipy
|
119 |
+
scikit-image
|
120 |
+
av
|
121 |
+
imgaug
|
122 |
+
lpips
|
123 |
+
ffmpeg-python
|
124 |
+
torch==2.0.1
|
125 |
+
torchvision==0.15.2
|
126 |
+
xformers==0.0.22
|
127 |
+
diffusers==0.21.4
|
samples/animation-2023-12-05T00-24-12/videos/0002_demo4.mp4
ADDED
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|
samples/animation-2023-12-05T00-24-12/videos/0002_demo4/grid.mp4
ADDED
Binary file (398 kB). View file
|
|