mirror of
https://github.com/huggingface/diffusers.git
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04f4bd54ea
* introduce videoprocessor. * fix quality * address yiyi's feedback * fix preprocess_video call. * video_processor -> image_processor * fix * fix more. * quality * image_processor -> video_processor * support List[List[PIL.Image.Image]] * change to video_processor. * documentation * Apply suggestions from code review * changes * remove print. * refactor video processor (part # 7776) (#7861) * update * update remove deprecate * Update src/diffusers/video_processor.py * update * Apply suggestions from code review * deprecate list of 5d for video and list of 4d for image + apply other feedbacks * up --------- Co-authored-by: Sayak Paul <spsayakpaul@gmail.com> * add doc. * tensor2vid -> postprocess_video. * refactor preprocess with preprocess_video * set default values. * empty commit * more refactoring of prepare_latents in animatediff vid2vid * checking documentation * remove documentation for now. * fix animatediff sdxl * fix test failure [part of video processor PR] (#7905) up * remove preceed_with_frames. * doc * fix * fix * remove video input as a single-frame video. --------- Co-authored-by: YiYi Xu <yixu310@gmail.com>
170 lines
7.3 KiB
Python
170 lines
7.3 KiB
Python
# coding=utf-8
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# Copyright 2024 HuggingFace Inc.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import unittest
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import numpy as np
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import PIL.Image
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import torch
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from parameterized import parameterized
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from diffusers.video_processor import VideoProcessor
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np.random.seed(0)
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torch.manual_seed(0)
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class VideoProcessorTest(unittest.TestCase):
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def get_dummy_sample(self, input_type):
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batch_size = 1
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num_frames = 5
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num_channels = 3
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height = 8
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width = 8
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def generate_image():
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return PIL.Image.fromarray(np.random.randint(0, 256, size=(height, width, num_channels)).astype("uint8"))
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def generate_4d_array():
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return np.random.rand(num_frames, height, width, num_channels)
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def generate_5d_array():
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return np.random.rand(batch_size, num_frames, height, width, num_channels)
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def generate_4d_tensor():
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return torch.rand(num_frames, num_channels, height, width)
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def generate_5d_tensor():
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return torch.rand(batch_size, num_frames, num_channels, height, width)
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if input_type == "list_images":
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sample = [generate_image() for _ in range(num_frames)]
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elif input_type == "list_list_images":
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sample = [[generate_image() for _ in range(num_frames)] for _ in range(num_frames)]
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elif input_type == "list_4d_np":
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sample = [generate_4d_array() for _ in range(num_frames)]
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elif input_type == "list_list_4d_np":
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sample = [[generate_4d_array() for _ in range(num_frames)] for _ in range(num_frames)]
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elif input_type == "list_5d_np":
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sample = [generate_5d_array() for _ in range(num_frames)]
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elif input_type == "5d_np":
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sample = generate_5d_array()
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elif input_type == "list_4d_pt":
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sample = [generate_4d_tensor() for _ in range(num_frames)]
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elif input_type == "list_list_4d_pt":
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sample = [[generate_4d_tensor() for _ in range(num_frames)] for _ in range(num_frames)]
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elif input_type == "list_5d_pt":
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sample = [generate_5d_tensor() for _ in range(num_frames)]
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elif input_type == "5d_pt":
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sample = generate_5d_tensor()
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return sample
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def to_np(self, video):
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# List of images.
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if isinstance(video[0], PIL.Image.Image):
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video = np.stack([np.array(i) for i in video], axis=0)
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# List of list of images.
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elif isinstance(video, list) and isinstance(video[0][0], PIL.Image.Image):
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frames = []
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for vid in video:
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all_current_frames = np.stack([np.array(i) for i in vid], axis=0)
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frames.append(all_current_frames)
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video = np.stack([np.array(frame) for frame in frames], axis=0)
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# List of 4d/5d {ndarrays, torch tensors}.
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elif isinstance(video, list) and isinstance(video[0], (torch.Tensor, np.ndarray)):
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if isinstance(video[0], np.ndarray):
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video = np.stack(video, axis=0) if video[0].ndim == 4 else np.concatenate(video, axis=0)
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else:
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if video[0].ndim == 4:
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video = np.stack([i.cpu().numpy().transpose(0, 2, 3, 1) for i in video], axis=0)
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elif video[0].ndim == 5:
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video = np.concatenate([i.cpu().numpy().transpose(0, 1, 3, 4, 2) for i in video], axis=0)
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# List of list of 4d/5d {ndarrays, torch tensors}.
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elif (
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isinstance(video, list)
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and isinstance(video[0], list)
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and isinstance(video[0][0], (torch.Tensor, np.ndarray))
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):
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all_frames = []
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for list_of_videos in video:
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temp_frames = []
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for vid in list_of_videos:
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if vid.ndim == 4:
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current_vid_frames = np.stack(
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[i if isinstance(i, np.ndarray) else i.cpu().numpy().transpose(1, 2, 0) for i in vid],
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axis=0,
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)
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elif vid.ndim == 5:
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current_vid_frames = np.concatenate(
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[i if isinstance(i, np.ndarray) else i.cpu().numpy().transpose(0, 2, 3, 1) for i in vid],
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axis=0,
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)
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temp_frames.append(current_vid_frames)
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temp_frames = np.stack(temp_frames, axis=0)
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all_frames.append(temp_frames)
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video = np.concatenate(all_frames, axis=0)
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# Just 5d {ndarrays, torch tensors}.
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elif isinstance(video, (torch.Tensor, np.ndarray)) and video.ndim == 5:
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video = video if isinstance(video, np.ndarray) else video.cpu().numpy().transpose(0, 1, 3, 4, 2)
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return video
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@parameterized.expand(["list_images", "list_list_images"])
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def test_video_processor_pil(self, input_type):
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video_processor = VideoProcessor(do_resize=False, do_normalize=True)
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input = self.get_dummy_sample(input_type=input_type)
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for output_type in ["pt", "np", "pil"]:
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out = video_processor.postprocess_video(video_processor.preprocess_video(input), output_type=output_type)
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out_np = self.to_np(out)
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input_np = self.to_np(input).astype("float32") / 255.0 if output_type != "pil" else self.to_np(input)
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assert np.abs(input_np - out_np).max() < 1e-6, f"Decoded output does not match input for {output_type=}"
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@parameterized.expand(["list_4d_np", "list_5d_np", "5d_np"])
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def test_video_processor_np(self, input_type):
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video_processor = VideoProcessor(do_resize=False, do_normalize=True)
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input = self.get_dummy_sample(input_type=input_type)
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for output_type in ["pt", "np", "pil"]:
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out = video_processor.postprocess_video(video_processor.preprocess_video(input), output_type=output_type)
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out_np = self.to_np(out)
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input_np = (
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(self.to_np(input) * 255.0).round().astype("uint8") if output_type == "pil" else self.to_np(input)
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)
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assert np.abs(input_np - out_np).max() < 1e-6, f"Decoded output does not match input for {output_type=}"
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@parameterized.expand(["list_4d_pt", "list_5d_pt", "5d_pt"])
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def test_video_processor_pt(self, input_type):
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video_processor = VideoProcessor(do_resize=False, do_normalize=True)
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input = self.get_dummy_sample(input_type=input_type)
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for output_type in ["pt", "np", "pil"]:
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out = video_processor.postprocess_video(video_processor.preprocess_video(input), output_type=output_type)
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out_np = self.to_np(out)
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input_np = (
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(self.to_np(input) * 255.0).round().astype("uint8") if output_type == "pil" else self.to_np(input)
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)
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assert np.abs(input_np - out_np).max() < 1e-6, f"Decoded output does not match input for {output_type=}"
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