diff --git a/.gitattributes b/.gitattributes index a6344aac8c09253b3b630fb776ae94478aa0275b..a99b24a7376037bec25ab0e18e7388c617c60307 100644 --- a/.gitattributes +++ b/.gitattributes @@ -33,3 +33,12 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text *.zip filter=lfs diff=lfs merge=lfs -text *.zst filter=lfs diff=lfs merge=lfs -text *tfevents* filter=lfs diff=lfs merge=lfs -text +animated_images/aircraft.jpg filter=lfs diff=lfs merge=lfs -text +animated_images/car.jpg filter=lfs diff=lfs merge=lfs -text +animated_images/forest.jpg filter=lfs diff=lfs merge=lfs -text +visuals/animations/dragon_glowing_eyes/dragon_glowing_eyes.gif filter=lfs diff=lfs merge=lfs -text +visuals/animations/girl_dancing_under_the_stars/girl_dancing_under_the_stars.gif filter=lfs diff=lfs merge=lfs -text +visuals/animations/people_walking/people_walking.gif filter=lfs diff=lfs merge=lfs -text +visuals/animations/sea_swell/sea_swell.gif filter=lfs diff=lfs merge=lfs -text +visuals/video_editing/edit/editing_a_corgi_walking_in_the_park_at_sunrise_oil_painting_style.gif filter=lfs diff=lfs merge=lfs -text +visuals/video_editing/origin/a_corgi_walking_in_the_park_at_sunrise_oil_painting_style.gif filter=lfs diff=lfs merge=lfs -text diff --git a/.gitignore b/.gitignore new file mode 100644 index 0000000000000000000000000000000000000000..600d2d33badf45cc068e01d2e3c837e11c417bc4 --- /dev/null +++ b/.gitignore @@ -0,0 +1 @@ +.vscode \ No newline at end of file diff --git a/LICENSE b/LICENSE new file mode 100644 index 0000000000000000000000000000000000000000..e2bf31beaba4b2365f0beea63013e4121dac261c --- /dev/null +++ b/LICENSE @@ -0,0 +1,201 @@ + Apache License + Version 2.0, January 2004 + http://www.apache.org/licenses/ + + TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION + + 1. 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We also recommend that a + file or class name and description of purpose be included on the + same "printed page" as the copyright notice for easier + identification within third-party archives. + + Copyright [XIN MA] [name of copyright owner] + + Licensed under the Apache License, Version 2.0 (the "License"); + you may not use this file except in compliance with the License. + You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + + Unless required by applicable law or agreed to in writing, software + distributed under the License is distributed on an "AS IS" BASIS, + WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + See the License for the specific language governing permissions and + limitations under the License. \ No newline at end of file diff --git a/README.md b/README.md index 32237b9c868ae6b009b915d6d09da177b50d81ba..b424fdf1cdf785f902bd25c9ed7d993a9002cd2b 100644 --- a/README.md +++ b/README.md @@ -1,12 +1,133 @@ ---- -title: Cinemo -emoji: 💻 -colorFrom: red -colorTo: blue -sdk: gradio -sdk_version: 4.39.0 -app_file: app.py -pinned: false ---- - -Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference +--- +title: Cinemo +app_file: demo.py +sdk: gradio +sdk_version: 4.37.2 +--- +## Cinemo: Consistent and Controllable Image Animation with Motion Diffusion Models
Official PyTorch Implementation + + +[![Arxiv](https://img.shields.io/badge/Arxiv-b31b1b.svg)](https://arxiv.org/abs/2407.15642) +[![Project Page](https://img.shields.io/badge/Project-Website-blue)](https://maxin-cn.github.io/cinemo_project/) + + +This repo contains pre-trained weights, and sampling code for our paper exploring image animation with motion diffusion models (Cinemo). You can find more visualizations on our [project page](https://maxin-cn.github.io/cinemo_project/). + +In this project, we propose a novel method called Cinemo, which can perform motion-controllable image animation with strong consistency and smoothness. To improve motion smoothness, Cinemo learns the distribution of motion residuals, rather than directly generating subsequent frames. Additionally, a structural similarity index-based method is proposed to control the motion intensity. Furthermore, we propose a noise refinement technique based on discrete cosine transformation to ensure temporal consistency. These three methods help Cinemo generate highly consistent, smooth, and motion-controlled image animation results. Compared to previous methods, Cinemo offers simpler and more precise user control and better generative performance. + +
+ +
+ +## News + +- (🔥 New) Jul. 23, 2024. 💥 Our paper is released on [arxiv](https://arxiv.org/abs/2407.15642). + +- (🔥 New) Jun. 2, 2024. 💥 The inference code is released. The checkpoint can be found [here](https://huggingface.co/maxin-cn/Cinemo/tree/main). + + +## Setup + +First, download and set up the repo: + +```bash +git clone https://github.com/maxin-cn/Cinemo +cd Cinemo +``` + +We provide an [`environment.yml`](environment.yml) file that can be used to create a Conda environment. If you only want +to run pre-trained models locally on CPU, you can remove the `cudatoolkit` and `pytorch-cuda` requirements from the file. + +```bash +conda env create -f environment.yml +conda activate cinemo +``` + + +## Animation + +You can sample from our **pre-trained Cinemo models** with [`animation.py`](pipelines/animation.py). Weights for our pre-trained Cinemo model can be found [here](https://huggingface.co/maxin-cn/Cinemo/tree/main). The script has various arguments for adjusting sampling steps, changing the classifier-free guidance scale, etc: + +```bash +bash pipelines/animation.sh +``` + +All related checkpoints will download automatically and then you will get the following results, + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
Input imageOutput videoInput imageOutput video
"People Walking""Sea Swell"
"Girl Dancing under the Stars""Dragon Glowing Eyes"
+ + +## Other Applications + +You can also utilize Cinemo for other applications, such as motion transfer and video editing: + +```bash +bash pipelines/video_editing.sh +``` + +All related checkpoints will download automatically and you will get the following results, + + + + + + + + + + + + + + + +
Input videoFirst frameEdited first frameOutput video
+ + + +## Citation +If you find this work useful for your research, please consider citing it. +```bibtex +@article{ma2024cinemo, + title={Cinemo: Latent Diffusion Transformer for Video Generation}, + author={Ma, Xin and Wang, Yaohui and Jia, Gengyun and Chen, Xinyuan and Li, Yuan-Fang and Chen, Cunjian and Qiao, Yu}, + journal={arXiv preprint arXiv:2407.15642}, + year={2024} +} +``` + + +## Acknowledgments +Cinemo has been greatly inspired by the following amazing works and teams: [LaVie](https://github.com/Vchitect/LaVie) and [SEINE](https://github.com/Vchitect/SEINE), we thank all the contributors for open-sourcing. + + +## License +The code and model weights are licensed under [LICENSE](LICENSE). diff --git a/__pycache__/utils.cpython-312.pyc b/__pycache__/utils.cpython-312.pyc new file mode 100644 index 0000000000000000000000000000000000000000..f09d785f6667580b87bcd6baa978851061a57ba9 Binary files /dev/null and b/__pycache__/utils.cpython-312.pyc differ diff --git a/animated_images/aircraft.jpg b/animated_images/aircraft.jpg new file mode 100644 index 0000000000000000000000000000000000000000..d532ad3d2abefb6d229accb9a86d6f81aeaae80f --- /dev/null +++ b/animated_images/aircraft.jpg @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:8c74eb22424fdcf0e40a6e22eeb497babe23d8fa9700f70ad9b9288072801bc6 +size 2722903 diff --git a/animated_images/car.jpg b/animated_images/car.jpg new file mode 100644 index 0000000000000000000000000000000000000000..4a567f7355f57e0558b60110fc528459ce99152c --- /dev/null +++ b/animated_images/car.jpg @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:331981fa29ba5d5314a3c7f42499b30cd98f8e7b01ed147626934a1d808a103b +size 1062236 diff --git a/animated_images/fireworks.jpg b/animated_images/fireworks.jpg new file mode 100644 index 0000000000000000000000000000000000000000..6c50d918e6604bc694076982de65a7f7bde2176b Binary files /dev/null and b/animated_images/fireworks.jpg differ diff --git a/animated_images/flowers.jpg b/animated_images/flowers.jpg new file mode 100644 index 0000000000000000000000000000000000000000..76cdf4993dfd35f6b84da15079de2d3984a6267c Binary files /dev/null and b/animated_images/flowers.jpg differ diff --git a/animated_images/forest.jpg b/animated_images/forest.jpg new file mode 100644 index 0000000000000000000000000000000000000000..6aae78853972a17dcc828ea807d6dd278bc138ad --- /dev/null +++ b/animated_images/forest.jpg @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:2519a6b8cd3d901388b52d9835c223dee93610d3aa4db15659003a004e2ac2eb +size 6998215 diff --git a/animated_images/shark_unwater.jpg b/animated_images/shark_unwater.jpg new file mode 100644 index 0000000000000000000000000000000000000000..2b6ea78bd0ca79b0b736223d9b8a1a3e8c85cd60 Binary files /dev/null and b/animated_images/shark_unwater.jpg differ diff --git a/configs/sample.yaml b/configs/sample.yaml new file mode 100644 index 0000000000000000000000000000000000000000..0f2cd35e7bf36c9eefe6254110f990edf9d500ed --- /dev/null +++ b/configs/sample.yaml @@ -0,0 +1,38 @@ +# ckpt +ckpt: /mnt/lustre/maxin/work/animation/animation-v6/results_qt_003-UNet-pixabaylaion-Xfor-Gc-320_512_0303000.pt +save_img_path: "./sample_videos/" + +# pretrained_model_path: "/mnt/hwfile/gcc/maxin/work/pretrained/stable-diffusion-v1-4/" +# pretrained_model_path: "maxin-cn/Cinemo" +pretrained_model_path: "./pretrained/Cinemo" + +# model config: +model: UNet +video_length: 15 +image_size: [320, 512] +# beta schedule +beta_start: 0.0001 +beta_end: 0.02 +beta_schedule: "linear" + +# model speedup +use_compile: False +use_fp16: True + +# sample config: +seed: +run_time: 0 +use_dct: True +guidance_scale: 7.5 # +motion_bucket_id: 95 # [0-19] The larger the value, the stronger the motion intensity +sample_method: 'DDIM' +num_sampling_steps: 50 +enable_vae_temporal_decoder: True +image_prompts: [ + ['aircraft.jpg', 'aircrafts flying'], + ['car.jpg' ,"car moving"], + ['fireworks.jpg', 'fireworks'], + ['flowers.jpg', 'flowers swaying'], + ['forest.jpg', 'people walking'], + ['shark_unwater.jpg', 'shark falling into the sea'], + ] \ No newline at end of file diff --git a/datasets/__pycache__/video_transforms.cpython-312.pyc b/datasets/__pycache__/video_transforms.cpython-312.pyc new file mode 100644 index 0000000000000000000000000000000000000000..df3f4d399759c307eb2ebb7d8bd49127cf0637ca Binary files /dev/null and b/datasets/__pycache__/video_transforms.cpython-312.pyc differ diff --git a/datasets/video_transforms.py b/datasets/video_transforms.py new file mode 100644 index 0000000000000000000000000000000000000000..c2bfaad9f39ac329afed6b8413f558de3ddae9eb --- /dev/null +++ b/datasets/video_transforms.py @@ -0,0 +1,748 @@ +import torch +import random +import numbers +from torchvision.transforms import RandomCrop, RandomResizedCrop +from PIL import Image +from torchvision.utils import _log_api_usage_once + +def _is_tensor_video_clip(clip): + if not torch.is_tensor(clip): + raise TypeError("clip should be Tensor. Got %s" % type(clip)) + + if not clip.ndimension() == 4: + raise ValueError("clip should be 4D. Got %dD" % clip.dim()) + + return True + + +def center_crop_arr(pil_image, image_size): + """ + Center cropping implementation from ADM. + https://github.com/openai/guided-diffusion/blob/8fb3ad9197f16bbc40620447b2742e13458d2831/guided_diffusion/image_datasets.py#L126 + """ + while min(*pil_image.size) >= 2 * image_size: + pil_image = pil_image.resize( + tuple(x // 2 for x in pil_image.size), resample=Image.BOX + ) + + scale = image_size / min(*pil_image.size) + pil_image = pil_image.resize( + tuple(round(x * scale) for x in pil_image.size), resample=Image.BICUBIC + ) + + arr = np.array(pil_image) + crop_y = (arr.shape[0] - image_size) // 2 + crop_x = (arr.shape[1] - image_size) // 2 + return Image.fromarray(arr[crop_y: crop_y + image_size, crop_x: crop_x + image_size]) + + +def crop(clip, i, j, h, w): + """ + Args: + clip (torch.tensor): Video clip to be cropped. Size is (T, C, H, W) + """ + if len(clip.size()) != 4: + raise ValueError("clip should be a 4D tensor") + return clip[..., i : i + h, j : j + w] + + +def resize(clip, target_size, interpolation_mode): + if len(target_size) != 2: + raise ValueError(f"target size should be tuple (height, width), instead got {target_size}") + return torch.nn.functional.interpolate(clip, size=target_size, mode=interpolation_mode, align_corners=False) + +def resize_scale(clip, target_size, interpolation_mode): + if len(target_size) != 2: + raise ValueError(f"target size should be tuple (height, width), instead got {target_size}") + H, W = clip.size(-2), clip.size(-1) + scale_ = target_size[0] / min(H, W) + return torch.nn.functional.interpolate(clip, scale_factor=scale_, mode=interpolation_mode, align_corners=False) + +def resize_with_scale_factor(clip, scale_factor, interpolation_mode): + return torch.nn.functional.interpolate(clip, scale_factor=scale_factor, mode=interpolation_mode, align_corners=False) + +def resize_scale_with_height(clip, target_size, interpolation_mode): + H, W = clip.size(-2), clip.size(-1) + scale_ = target_size / H + return torch.nn.functional.interpolate(clip, scale_factor=scale_, mode=interpolation_mode, align_corners=False) + +def resize_scale_with_weight(clip, target_size, interpolation_mode): + H, W = clip.size(-2), clip.size(-1) + scale_ = target_size / W + return torch.nn.functional.interpolate(clip, scale_factor=scale_, mode=interpolation_mode, align_corners=False) + + +def resized_crop(clip, i, j, h, w, size, interpolation_mode="bilinear"): + """ + Do spatial cropping and resizing to the video clip + Args: + clip (torch.tensor): Video clip to be cropped. Size is (T, C, H, W) + i (int): i in (i,j) i.e coordinates of the upper left corner. + j (int): j in (i,j) i.e coordinates of the upper left corner. + h (int): Height of the cropped region. + w (int): Width of the cropped region. + size (tuple(int, int)): height and width of resized clip + Returns: + clip (torch.tensor): Resized and cropped clip. Size is (T, C, H, W) + """ + if not _is_tensor_video_clip(clip): + raise ValueError("clip should be a 4D torch.tensor") + clip = crop(clip, i, j, h, w) + clip = resize(clip, size, interpolation_mode) + return clip + + +def center_crop(clip, crop_size): + if not _is_tensor_video_clip(clip): + raise ValueError("clip should be a 4D torch.tensor") + h, w = clip.size(-2), clip.size(-1) + # print(clip.shape) + th, tw = crop_size + if h < th or w < tw: + # print(h, w) + raise ValueError("height {} and width {} must be no smaller than crop_size".format(h, w)) + + i = int(round((h - th) / 2.0)) + j = int(round((w - tw) / 2.0)) + return crop(clip, i, j, th, tw), i, j + + +def center_crop_using_short_edge(clip): + if not _is_tensor_video_clip(clip): + raise ValueError("clip should be a 4D torch.tensor") + h, w = clip.size(-2), clip.size(-1) + if h < w: + th, tw = h, h + i = 0 + j = int(round((w - tw) / 2.0)) + else: + th, tw = w, w + i = int(round((h - th) / 2.0)) + j = 0 + return crop(clip, i, j, th, tw) + + +def random_shift_crop(clip): + ''' + Slide along the long edge, with the short edge as crop size + ''' + if not _is_tensor_video_clip(clip): + raise ValueError("clip should be a 4D torch.tensor") + h, w = clip.size(-2), clip.size(-1) + + if h <= w: + long_edge = w + short_edge = h + else: + long_edge = h + short_edge =w + + th, tw = short_edge, short_edge + + i = torch.randint(0, h - th + 1, size=(1,)).item() + j = torch.randint(0, w - tw + 1, size=(1,)).item() + return crop(clip, i, j, th, tw), i, j + +def random_crop(clip, crop_size): + if not _is_tensor_video_clip(clip): + raise ValueError("clip should be a 4D torch.tensor") + h, w = clip.size(-2), clip.size(-1) + th, tw = crop_size[-2], crop_size[-1] + + if h < th or w < tw: + raise ValueError("height {} and width {} must be no smaller than crop_size".format(h, w)) + + i = torch.randint(0, h - th + 1, size=(1,)).item() + j = torch.randint(0, w - tw + 1, size=(1,)).item() + clip_crop = crop(clip, i, j, th, tw) + return clip_crop, i, j + + +def to_tensor(clip): + """ + Convert tensor data type from uint8 to float, divide value by 255.0 and + permute the dimensions of clip tensor + Args: + clip (torch.tensor, dtype=torch.uint8): Size is (T, C, H, W) + Return: + clip (torch.tensor, dtype=torch.float): Size is (T, C, H, W) + """ + _is_tensor_video_clip(clip) + if not clip.dtype == torch.uint8: + raise TypeError("clip tensor should have data type uint8. Got %s" % str(clip.dtype)) + # return clip.float().permute(3, 0, 1, 2) / 255.0 + return clip.float() / 255.0 + + +def normalize(clip, mean, std, inplace=False): + """ + Args: + clip (torch.tensor): Video clip to be normalized. Size is (T, C, H, W) + mean (tuple): pixel RGB mean. Size is (3) + std (tuple): pixel standard deviation. Size is (3) + Returns: + normalized clip (torch.tensor): Size is (T, C, H, W) + """ + if not _is_tensor_video_clip(clip): + raise ValueError("clip should be a 4D torch.tensor") + if not inplace: + clip = clip.clone() + mean = torch.as_tensor(mean, dtype=clip.dtype, device=clip.device) + # print(mean) + std = torch.as_tensor(std, dtype=clip.dtype, device=clip.device) + clip.sub_(mean[:, None, None, None]).div_(std[:, None, None, None]) + return clip + + +def hflip(clip): + """ + Args: + clip (torch.tensor): Video clip to be normalized. Size is (T, C, H, W) + Returns: + flipped clip (torch.tensor): Size is (T, C, H, W) + """ + if not _is_tensor_video_clip(clip): + raise ValueError("clip should be a 4D torch.tensor") + return clip.flip(-1) + + +class RandomCropVideo: + def __init__(self, size): + if isinstance(size, numbers.Number): + self.size = (int(size), int(size)) + else: + self.size = size + + def __call__(self, clip): + """ + Args: + clip (torch.tensor): Video clip to be cropped. Size is (T, C, H, W) + Returns: + torch.tensor: randomly cropped video clip. + size is (T, C, OH, OW) + """ + i, j, h, w = self.get_params(clip) + return crop(clip, i, j, h, w) + + def get_params(self, clip): + h, w = clip.shape[-2:] + th, tw = self.size + + if h < th or w < tw: + raise ValueError(f"Required crop size {(th, tw)} is larger than input image size {(h, w)}") + + if w == tw and h == th: + return 0, 0, h, w + + i = torch.randint(0, h - th + 1, size=(1,)).item() + j = torch.randint(0, w - tw + 1, size=(1,)).item() + + return i, j, th, tw + + def __repr__(self) -> str: + return f"{self.__class__.__name__}(size={self.size})" + +class CenterCropResizeVideo: + ''' + First use the short side for cropping length, + center crop video, then resize to the specified size + ''' + def __init__( + self, + size, + interpolation_mode="bilinear", + ): + if isinstance(size, tuple): + if len(size) != 2: + raise ValueError(f"size should be tuple (height, width), instead got {size}") + self.size = size + else: + self.size = (size, size) + + self.interpolation_mode = interpolation_mode + + + def __call__(self, clip): + """ + Args: + clip (torch.tensor): Video clip to be cropped. Size is (T, C, H, W) + Returns: + torch.tensor: scale resized / center cropped video clip. + size is (T, C, crop_size, crop_size) + """ + # print(clip.shape) + clip_center_crop = center_crop_using_short_edge(clip) + # print(clip_center_crop.shape) 320 512 + clip_center_crop_resize = resize(clip_center_crop, target_size=self.size, interpolation_mode=self.interpolation_mode) + return clip_center_crop_resize + + def __repr__(self) -> str: + return f"{self.__class__.__name__}(size={self.size}, interpolation_mode={self.interpolation_mode}" + + +class SDXL: + def __init__( + self, + size, + interpolation_mode="bilinear", + ): + if isinstance(size, tuple): + if len(size) != 2: + raise ValueError(f"size should be tuple (height, width), instead got {size}") + self.size = size + else: + self.size = (size, size) + + self.interpolation_mode = interpolation_mode + + def __call__(self, clip): + """ + Args: + clip (torch.tensor): Video clip to be cropped. Size is (T, C, H, W) + Returns: + torch.tensor: scale resized / center cropped video clip. + size is (T, C, crop_size, crop_size) + """ + # add aditional one pixel for avoiding error in center crop + ori_h, ori_w = clip.size(-2), clip.size(-1) + tar_h, tar_w = self.size[0] + 1, self.size[1] + 1 + + # if ori_h >= tar_h and ori_w >= tar_w: + # clip_tar_crop, i, j = random_crop(clip=clip, crop_size=self.size) + # else: + # tar_h_div_ori_h = tar_h / ori_h + # tar_w_div_ori_w = tar_w / ori_w + # if tar_h_div_ori_h > tar_w_div_ori_w: + # clip = resize_with_scale_factor(clip=clip, scale_factor=tar_h_div_ori_h, interpolation_mode=self.interpolation_mode) + # else: + # clip = resize_with_scale_factor(clip=clip, scale_factor=tar_w_div_ori_w, interpolation_mode=self.interpolation_mode) + # clip_tar_crop, i, j = random_crop(clip, self.size) + if ori_h >= tar_h and ori_w >= tar_w: + tar_h_div_ori_h = tar_h / ori_h + tar_w_div_ori_w = tar_w / ori_w + if tar_h_div_ori_h > tar_w_div_ori_w: + clip = resize_with_scale_factor(clip=clip, scale_factor=tar_h_div_ori_h, interpolation_mode=self.interpolation_mode) + else: + clip = resize_with_scale_factor(clip=clip, scale_factor=tar_w_div_ori_w, interpolation_mode=self.interpolation_mode) + ori_h, ori_w = clip.size(-2), clip.size(-1) + clip_tar_crop, i, j = random_crop(clip, self.size) + else: + tar_h_div_ori_h = tar_h / ori_h + tar_w_div_ori_w = tar_w / ori_w + if tar_h_div_ori_h > tar_w_div_ori_w: + clip = resize_with_scale_factor(clip=clip, scale_factor=tar_h_div_ori_h, interpolation_mode=self.interpolation_mode) + else: + clip = resize_with_scale_factor(clip=clip, scale_factor=tar_w_div_ori_w, interpolation_mode=self.interpolation_mode) + clip_tar_crop, i, j = random_crop(clip, self.size) + return clip_tar_crop, ori_h, ori_w, i, j + + def __repr__(self) -> str: + return f"{self.__class__.__name__}(size={self.size}, interpolation_mode={self.interpolation_mode}" + + +class SDXLCenterCrop: + def __init__( + self, + size, + interpolation_mode="bilinear", + ): + if isinstance(size, tuple): + if len(size) != 2: + raise ValueError(f"size should be tuple (height, width), instead got {size}") + self.size = size + else: + self.size = (size, size) + + self.interpolation_mode = interpolation_mode + + + def __call__(self, clip): + """ + Args: + clip (torch.tensor): Video clip to be cropped. Size is (T, C, H, W) + Returns: + torch.tensor: scale resized / center cropped video clip. + size is (T, C, crop_size, crop_size) + """ + # add aditional one pixel for avoiding error in center crop + ori_h, ori_w = clip.size(-2), clip.size(-1) + tar_h, tar_w = self.size[0] + 1, self.size[1] + 1 + tar_h_div_ori_h = tar_h / ori_h + tar_w_div_ori_w = tar_w / ori_w + # print('before resize', clip.shape) + if tar_h_div_ori_h > tar_w_div_ori_w: + clip = resize_with_scale_factor(clip=clip, scale_factor=tar_h_div_ori_h, interpolation_mode=self.interpolation_mode) + # print('after h resize', clip.shape) + else: + clip = resize_with_scale_factor(clip=clip, scale_factor=tar_w_div_ori_w, interpolation_mode=self.interpolation_mode) + # print('after resize', clip.shape) + # print(clip.shape) + # clip_tar_crop, i, j = random_crop(clip, self.size) + clip_tar_crop, i, j = center_crop(clip, self.size) + # print('after crop', clip_tar_crop.shape) + + return clip_tar_crop, ori_h, ori_w, i, j + + def __repr__(self) -> str: + return f"{self.__class__.__name__}(size={self.size}, interpolation_mode={self.interpolation_mode}" + + +class InternVideo320512: + def __init__( + self, + size, + interpolation_mode="bilinear", + ): + if isinstance(size, tuple): + if len(size) != 2: + raise ValueError(f"size should be tuple (height, width), instead got {size}") + self.size = size + else: + self.size = (size, size) + + self.interpolation_mode = interpolation_mode + + + def __call__(self, clip): + """ + Args: + clip (torch.tensor): Video clip to be cropped. Size is (T, C, H, W) + Returns: + torch.tensor: scale resized / center cropped video clip. + size is (T, C, crop_size, crop_size) + """ + # add aditional one pixel for avoiding error in center crop + h, w = clip.size(-2), clip.size(-1) + # print('before resize', clip.shape) + if h < 320: + clip = resize_scale_with_height(clip=clip, target_size=321, interpolation_mode=self.interpolation_mode) + # print('after h resize', clip.shape) + if w < 512: + clip = resize_scale_with_weight(clip=clip, target_size=513, interpolation_mode=self.interpolation_mode) + # print('after w resize', clip.shape) + # print(clip.shape) + clip_center_crop = center_crop(clip, self.size) + clip_center_crop_no_subtitles = center_crop(clip, (220, 352)) + clip_center_resize = resize(clip_center_crop_no_subtitles, target_size=self.size, interpolation_mode=self.interpolation_mode) + # print(clip_center_crop.shape) + return clip_center_resize + + def __repr__(self) -> str: + return f"{self.__class__.__name__}(size={self.size}, interpolation_mode={self.interpolation_mode}" + +class CenterCropVideo: + ''' + First scale to the specified size in equal proportion to the short edge, + then center cropping + ''' + def __init__( + self, + size, + interpolation_mode="bilinear", + ): + if isinstance(size, tuple): + if len(size) != 2: + raise ValueError(f"size should be tuple (height, width), instead got {size}") + self.size = size + else: + self.size = (size, size) + + self.interpolation_mode = interpolation_mode + + + def __call__(self, clip): + """ + Args: + clip (torch.tensor): Video clip to be cropped. Size is (T, C, H, W) + Returns: + torch.tensor: scale resized / center cropped video clip. + size is (T, C, crop_size, crop_size) + """ + clip_resize = resize_scale(clip=clip, target_size=self.size, interpolation_mode=self.interpolation_mode) + clip_center_crop = center_crop(clip_resize, self.size) + return clip_center_crop + + def __repr__(self) -> str: + return f"{self.__class__.__name__}(size={self.size}, interpolation_mode={self.interpolation_mode}" + +class KineticsRandomCropResizeVideo: + ''' + Slide along the long edge, with the short edge as crop size. And resie to the desired size. + ''' + def __init__( + self, + size, + interpolation_mode="bilinear", + ): + if isinstance(size, tuple): + if len(size) != 2: + raise ValueError(f"size should be tuple (height, width), instead got {size}") + self.size = size + else: + self.size = (size, size) + + self.interpolation_mode = interpolation_mode + + def __call__(self, clip): + clip_random_crop = random_shift_crop(clip) + clip_resize = resize(clip_random_crop, self.size, self.interpolation_mode) + return clip_resize + +class ResizeVideo(): + ''' + First use the short side for cropping length, + center crop video, then resize to the specified size + ''' + def __init__( + self, + size, + interpolation_mode="bilinear", + ): + if isinstance(size, tuple): + if len(size) != 2: + raise ValueError(f"size should be tuple (height, width), instead got {size}") + self.size = size + else: + self.size = (size, size) + + self.interpolation_mode = interpolation_mode + + + def __call__(self, clip): + """ + Args: + clip (torch.tensor): Video clip to be cropped. Size is (T, C, H, W) + Returns: + torch.tensor: scale resized / center cropped video clip. + size is (T, C, crop_size, crop_size) + """ + clip_resize = resize(clip, target_size=self.size, interpolation_mode=self.interpolation_mode) + return clip_resize + + def __repr__(self) -> str: + return f"{self.__class__.__name__}(size={self.size}, interpolation_mode={self.interpolation_mode}" + +class CenterCropVideo: + def __init__( + self, + size, + interpolation_mode="bilinear", + ): + if isinstance(size, tuple): + if len(size) != 2: + raise ValueError(f"size should be tuple (height, width), instead got {size}") + self.size = size + else: + self.size = (size, size) + + self.interpolation_mode = interpolation_mode + + + def __call__(self, clip): + """ + Args: + clip (torch.tensor): Video clip to be cropped. Size is (T, C, H, W) + Returns: + torch.tensor: center cropped video clip. + size is (T, C, crop_size, crop_size) + """ + clip_center_crop = center_crop(clip, self.size) + return clip_center_crop + + def __repr__(self) -> str: + return f"{self.__class__.__name__}(size={self.size}, interpolation_mode={self.interpolation_mode}" + + +class NormalizeVideo: + """ + Normalize the video clip by mean subtraction and division by standard deviation + Args: + mean (3-tuple): pixel RGB mean + std (3-tuple): pixel RGB standard deviation + inplace (boolean): whether do in-place normalization + """ + + def __init__(self, mean, std, inplace=False): + self.mean = mean + self.std = std + self.inplace = inplace + + def __call__(self, clip): + """ + Args: + clip (torch.tensor): video clip must be normalized. Size is (C, T, H, W) + """ + return normalize(clip, self.mean, self.std, self.inplace) + + def __repr__(self) -> str: + return f"{self.__class__.__name__}(mean={self.mean}, std={self.std}, inplace={self.inplace})" + + +class ToTensorVideo: + """ + Convert tensor data type from uint8 to float, divide value by 255.0 and + permute the dimensions of clip tensor + """ + + def __init__(self): + pass + + def __call__(self, clip): + """ + Args: + clip (torch.tensor, dtype=torch.uint8): Size is (T, C, H, W) + Return: + clip (torch.tensor, dtype=torch.float): Size is (T, C, H, W) + """ + return to_tensor(clip) + + def __repr__(self) -> str: + return self.__class__.__name__ + + +class RandomHorizontalFlipVideo: + """ + Flip the video clip along the horizontal direction with a given probability + Args: + p (float): probability of the clip being flipped. Default value is 0.5 + """ + + def __init__(self, p=0.5): + self.p = p + + def __call__(self, clip): + """ + Args: + clip (torch.tensor): Size is (T, C, H, W) + Return: + clip (torch.tensor): Size is (T, C, H, W) + """ + if random.random() < self.p: + clip = hflip(clip) + return clip + + def __repr__(self) -> str: + return f"{self.__class__.__name__}(p={self.p})" + +class Compose: + """Composes several transforms together. This transform does not support torchscript. + Please, see the note below. + + Args: + transforms (list of ``Transform`` objects): list of transforms to compose. + + Example: + >>> transforms.Compose([ + >>> transforms.CenterCrop(10), + >>> transforms.PILToTensor(), + >>> transforms.ConvertImageDtype(torch.float), + >>> ]) + + .. note:: + In order to script the transformations, please use ``torch.nn.Sequential`` as below. + + >>> transforms = torch.nn.Sequential( + >>> transforms.CenterCrop(10), + >>> transforms.Normalize((0.485, 0.456, 0.406), (0.229, 0.224, 0.225)), + >>> ) + >>> scripted_transforms = torch.jit.script(transforms) + + Make sure to use only scriptable transformations, i.e. that work with ``torch.Tensor``, does not require + `lambda` functions or ``PIL.Image``. + + """ + + def __init__(self, transforms): + if not torch.jit.is_scripting() and not torch.jit.is_tracing(): + _log_api_usage_once(self) + self.transforms = transforms + + def __call__(self, img): + for t in self.transforms: + if isinstance(t, SDXLCenterCrop) or isinstance(t, SDXL): + img, ori_h, ori_w, crops_coords_top, crops_coords_left = t(img) + else: + img = t(img) + return img, ori_h, ori_w, crops_coords_top, crops_coords_left + + def __repr__(self) -> str: + format_string = self.__class__.__name__ + "(" + for t in self.transforms: + format_string += "\n" + format_string += f" {t}" + format_string += "\n)" + return format_string + +# ------------------------------------------------------------ +# --------------------- Sampling --------------------------- +# ------------------------------------------------------------ +class TemporalRandomCrop(object): + """Temporally crop the given frame indices at a random location. + + Args: + size (int): Desired length of frames will be seen in the model. + """ + + def __init__(self, size): + self.size = size + + def __call__(self, total_frames): + rand_end = max(0, total_frames - self.size - 1) + begin_index = random.randint(0, rand_end) + end_index = min(begin_index + self.size, total_frames) + return begin_index, end_index + + +if __name__ == '__main__': + from torchvision import transforms + import torchvision.io as io + import numpy as np + from torchvision.utils import save_image + import os + + vframes, aframes, info = io.read_video( + filename='./v_Archery_g01_c03.avi', + pts_unit='sec', + output_format='TCHW' + ) + + trans = transforms.Compose([ + ToTensorVideo(), + RandomHorizontalFlipVideo(), + UCFCenterCropVideo(512), + # NormalizeVideo(mean=[0.5, 0.5, 0.5], std=[0.5, 0.5, 0.5], inplace=True), + transforms.Normalize(mean=[0.5, 0.5, 0.5], std=[0.5, 0.5, 0.5], inplace=True) + ]) + + target_video_len = 32 + frame_interval = 1 + total_frames = len(vframes) + print(total_frames) + + temporal_sample = TemporalRandomCrop(target_video_len * frame_interval) + + + # Sampling video frames + start_frame_ind, end_frame_ind = temporal_sample(total_frames) + # print(start_frame_ind) + # print(end_frame_ind) + assert end_frame_ind - start_frame_ind >= target_video_len + frame_indice = np.linspace(start_frame_ind, end_frame_ind - 1, target_video_len, dtype=int) + print(frame_indice) + + select_vframes = vframes[frame_indice] + print(select_vframes.shape) + print(select_vframes.dtype) + + select_vframes_trans = trans(select_vframes) + print(select_vframes_trans.shape) + print(select_vframes_trans.dtype) + + select_vframes_trans_int = ((select_vframes_trans * 0.5 + 0.5) * 255).to(dtype=torch.uint8) + print(select_vframes_trans_int.dtype) + print(select_vframes_trans_int.permute(0, 2, 3, 1).shape) + + io.write_video('./test.avi', select_vframes_trans_int.permute(0, 2, 3, 1), fps=8) + + for i in range(target_video_len): + save_image(select_vframes_trans[i], os.path.join('./test000', '%04d.png' % i), normalize=True, value_range=(-1, 1)) \ No newline at end of file diff --git a/demo.py b/demo.py new file mode 100644 index 0000000000000000000000000000000000000000..8aef84a2cae277eefbb4a5e89bdc1fc9d5c02600 --- /dev/null +++ b/demo.py @@ -0,0 +1,311 @@ +import gradio as gr +import os +import torch +import argparse +import torchvision + + +from pipelines.pipeline_videogen import VideoGenPipeline +from diffusers.schedulers import DDIMScheduler +from diffusers.models import AutoencoderKL +from diffusers.models import AutoencoderKLTemporalDecoder +from transformers import CLIPTokenizer, CLIPTextModel +from omegaconf import OmegaConf + +import os, sys +sys.path.append(os.path.split(sys.path[0])[0]) +from models import get_models +import imageio +from PIL import Image +import numpy as np +from datasets import video_transforms +from torchvision import transforms +from einops import rearrange, repeat +from utils import dct_low_pass_filter, exchanged_mixed_dct_freq +from copy import deepcopy +import spaces +import requests +from datetime import datetime +import random + +parser = argparse.ArgumentParser() +parser.add_argument("--config", type=str, default="./configs/sample.yaml") +args = parser.parse_args() +args = OmegaConf.load(args.config) + +torch.set_grad_enabled(False) +device = "cuda" if torch.cuda.is_available() else "cpu" +dtype = torch.float16 # torch.float16 + +unet = get_models(args).to(device, dtype=dtype) + +if args.enable_vae_temporal_decoder: + if args.use_dct: + vae_for_base_content = AutoencoderKLTemporalDecoder.from_pretrained(args.pretrained_model_path, subfolder="vae_temporal_decoder", torch_dtype=torch.float64).to(device) + else: + vae_for_base_content = AutoencoderKLTemporalDecoder.from_pretrained(args.pretrained_model_path, subfolder="vae_temporal_decoder", torch_dtype=torch.float16).to(device) + vae = deepcopy(vae_for_base_content).to(dtype=dtype) +else: + vae_for_base_content = AutoencoderKL.from_pretrained(args.pretrained_model_path, subfolder="vae",).to(device, dtype=torch.float64) + vae = deepcopy(vae_for_base_content).to(dtype=dtype) +tokenizer = CLIPTokenizer.from_pretrained(args.pretrained_model_path, subfolder="tokenizer") +text_encoder = CLIPTextModel.from_pretrained(args.pretrained_model_path, subfolder="text_encoder", torch_dtype=dtype).to(device) # huge + +# set eval mode +unet.eval() +vae.eval() +text_encoder.eval() + +basedir = os.getcwd() +savedir = os.path.join(basedir, "samples/Gradio", datetime.now().strftime("%Y-%m-%dT%H-%M-%S")) +savedir_sample = os.path.join(savedir, "sample") +os.makedirs(savedir, exist_ok=True) + +def update_and_resize_image(input_image_path, height_slider, width_slider): + if input_image_path.startswith("http://") or input_image_path.startswith("https://"): + pil_image = Image.open(requests.get(input_image_path, stream=True).raw).convert('RGB') + else: + pil_image = Image.open(input_image_path).convert('RGB') + + original_width, original_height = pil_image.size + + if original_height == height_slider and original_width == width_slider: + return gr.Image(value=np.array(pil_image)) + + ratio1 = height_slider / original_height + ratio2 = width_slider / original_width + + if ratio1 > ratio2: + new_width = int(original_width * ratio1) + new_height = int(original_height * ratio1) + else: + new_width = int(original_width * ratio2) + new_height = int(original_height * ratio2) + + pil_image = pil_image.resize((new_width, new_height), Image.LANCZOS) + + left = (new_width - width_slider) / 2 + top = (new_height - height_slider) / 2 + right = left + width_slider + bottom = top + height_slider + + pil_image = pil_image.crop((left, top, right, bottom)) + + return gr.Image(value=np.array(pil_image)) + + +def update_textbox_and_save_image(input_image, height_slider, width_slider): + pil_image = Image.fromarray(input_image.astype(np.uint8)).convert("RGB") + + original_width, original_height = pil_image.size + + ratio1 = height_slider / original_height + ratio2 = width_slider / original_width + + if ratio1 > ratio2: + new_width = int(original_width * ratio1) + new_height = int(original_height * ratio1) + else: + new_width = int(original_width * ratio2) + new_height = int(original_height * ratio2) + + pil_image = pil_image.resize((new_width, new_height), Image.LANCZOS) + + left = (new_width - width_slider) / 2 + top = (new_height - height_slider) / 2 + right = left + width_slider + bottom = top + height_slider + + pil_image = pil_image.crop((left, top, right, bottom)) + + img_path = os.path.join(savedir, "input_image.png") + pil_image.save(img_path) + + return gr.Textbox(value=img_path), gr.Image(value=np.array(pil_image)) + +def prepare_image(image, vae, transform_video, device, dtype=torch.float16): + image = torch.as_tensor(np.array(image, dtype=np.uint8, copy=True)).unsqueeze(0).permute(0, 3, 1, 2) + image = transform_video(image) + image = vae.encode(image.to(dtype=dtype, device=device)).latent_dist.sample().mul_(vae.config.scaling_factor) + image = image.unsqueeze(2) + return image + + +@spaces.GPU +def gen_video(input_image, prompt, negative_prompt, diffusion_step, height, width, scfg_scale, use_dctinit, dct_coefficients, noise_level, motion_bucket_id, seed): + + torch.manual_seed(seed) + + scheduler = DDIMScheduler.from_pretrained(args.pretrained_model_path, + subfolder="scheduler", + beta_start=args.beta_start, + beta_end=args.beta_end, + beta_schedule=args.beta_schedule) + + videogen_pipeline = VideoGenPipeline(vae=vae, + text_encoder=text_encoder, + tokenizer=tokenizer, + scheduler=scheduler, + unet=unet).to(device) + # videogen_pipeline.enable_xformers_memory_efficient_attention() + + transform_video = transforms.Compose([ + video_transforms.ToTensorVideo(), + transforms.Normalize(mean=[0.5, 0.5, 0.5], std=[0.5, 0.5, 0.5], inplace=True), + ]) + + if args.use_dct: + base_content = prepare_image(input_image, vae_for_base_content, transform_video, device, dtype=torch.float64).to(device) + else: + base_content = prepare_image(input_image, vae_for_base_content, transform_video, device, dtype=torch.float16).to(device) + + if use_dctinit: + # filter params + print("Using DCT!") + base_content_repeat = repeat(base_content, 'b c f h w -> b c (f r) h w', r=15).contiguous() + + # define filter + freq_filter = dct_low_pass_filter(dct_coefficients=base_content, percentage=dct_coefficients) + + noise = torch.randn(1, 4, 15, 40, 64).to(device) + + # add noise to base_content + diffuse_timesteps = torch.full((1,),int(noise_level)) + diffuse_timesteps = diffuse_timesteps.long() + + # 3d content + base_content_noise = scheduler.add_noise( + original_samples=base_content_repeat.to(device), + noise=noise, + timesteps=diffuse_timesteps.to(device)) + + # 3d content + latents = exchanged_mixed_dct_freq(noise=noise, + base_content=base_content_noise, + LPF_3d=freq_filter).to(dtype=torch.float16) + + base_content = base_content.to(dtype=torch.float16) + + videos = videogen_pipeline(prompt, + negative_prompt=negative_prompt, + latents=latents if use_dctinit else None, + base_content=base_content, + video_length=15, + height=height, + width=width, + num_inference_steps=diffusion_step, + guidance_scale=scfg_scale, + motion_bucket_id=100-motion_bucket_id, + enable_vae_temporal_decoder=args.enable_vae_temporal_decoder).video + + save_path = args.save_img_path + 'temp' + '.mp4' + # torchvision.io.write_video(save_path, videos[0], fps=8, video_codec='h264', options={'crf': '10'}) + imageio.mimwrite(save_path, videos[0], fps=8, quality=7) + return save_path + + +if not os.path.exists(args.save_img_path): + os.makedirs(args.save_img_path) + + +with gr.Blocks() as demo: + + gr.Markdown("
Cinemo: Consistent and Controllable Image Animation with Motion Diffusion Models
") + gr.Markdown( + """
+ [Arxiv Report] | [Project Page] | [Github]
+ """ + ) + + + with gr.Column(variant="panel"): + gr.Markdown( + """ + - Input image can be specified using the "Input Image URL" text box or uploaded by clicking or dragging the image to the "Input Image" box. + - Input image will be resized and/or center cropped to a given resolution (320 x 512) automatically. + - After setting the input image path, press the "Preview" button to visualize the resized input image. + """ + ) + + with gr.Row(): + prompt_textbox = gr.Textbox(label="Prompt", lines=1) + negative_prompt_textbox = gr.Textbox(label="Negative prompt", lines=1) + + with gr.Row(equal_height=False): + with gr.Column(): + with gr.Row(): + sample_step_slider = gr.Slider(label="Sampling steps", value=50, minimum=10, maximum=250, step=1) + + with gr.Row(): + seed_textbox = gr.Slider(label="Seed", value=100, minimum=1, maximum=int(1e8), step=1, interactive=True) + # seed_textbox = gr.Textbox(label="Seed", value=100) + # seed_button = gr.Button(value="\U0001F3B2", elem_classes="toolbutton") + # seed_button.click(fn=lambda: gr.Textbox(value=random.randint(1, int(1e8))), inputs=[], outputs=[seed_textbox]) + + with gr.Row(): + height = gr.Slider(label="Height", value=320, minimum=0, maximum=512, step=16, interactive=False) + width = gr.Slider(label="Width", value=512, minimum=0, maximum=512, step=16, interactive=False) + with gr.Row(): + txt_cfg_scale = gr.Slider(label="CFG Scale", value=7.5, minimum=1.0, maximum=20.0, step=0.1, interactive=True) + motion_bucket_id = gr.Slider(label="Motion Intensity", value=10, minimum=1, maximum=20, step=1, interactive=True) + + with gr.Row(): + use_dctinit = gr.Checkbox(label="Enable DCTInit", value=True) + dct_coefficients = gr.Slider(label="DCT Coefficients", value=0.23, minimum=0, maximum=1, step=0.01, interactive=True) + noise_level = gr.Slider(label="Noise Level", value=985, minimum=1, maximum=999, step=1, interactive=True) + + generate_button = gr.Button(value="Generate", variant='primary') + + with gr.Column(): + with gr.Row(): + input_image_path = gr.Textbox(label="Input Image URL", lines=1, scale=10, info="Press Enter or the Preview button to confirm the input image.") + preview_button = gr.Button(value="Preview") + + with gr.Row(): + input_image = gr.Image(label="Input Image", interactive=True) + input_image.upload(fn=update_textbox_and_save_image, inputs=[input_image, height, width], outputs=[input_image_path, input_image]) + result_video = gr.Video(label="Generated Animation", interactive=False, autoplay=True) + + preview_button.click(fn=update_and_resize_image, inputs=[input_image_path, height, width], outputs=[input_image]) + input_image_path.submit(fn=update_and_resize_image, inputs=[input_image_path, height, width], outputs=[input_image]) + + EXAMPLES = [ + ["./example/aircrafts_flying/0.jpg", "aircrafts flying", "", 50, 320, 512, 7.5, True, 0.23, 975, 10, 100], + ["./example/fireworks/0.jpg", "fireworks", "", 50, 320, 512, 7.5, True, 0.23, 975, 10, 100], + ["./example/flowers_swaying/0.jpg", "flowers swaying", "", 50, 320, 512, 7.5, True, 0.23, 975, 10, 100], + ["./example/girl_walking_on_the_beach/0.jpg", "girl walking on the beach", "", 50, 320, 512, 7.5, True, 0.23, 985, 10, 200], + ["./example/house_rotating/0.jpg", "house rotating", "", 50, 320, 512, 7.5, True, 0.23, 985, 10, 100], + ["./example/people_runing/0.jpg", "people runing", "", 50, 320, 512, 7.5, True, 0.23, 975, 10, 100], +] + + examples = gr.Examples( + examples = EXAMPLES, + fn = gen_video, + inputs=[input_image, prompt_textbox, negative_prompt_textbox, sample_step_slider, height, width, txt_cfg_scale, use_dctinit, dct_coefficients, noise_level, motion_bucket_id, seed_textbox], + outputs=[result_video], + # cache_examples=True, + cache_examples="lazy", + ) + + generate_button.click( + fn=gen_video, + inputs=[ + input_image, + prompt_textbox, + negative_prompt_textbox, + sample_step_slider, + height, + width, + txt_cfg_scale, + use_dctinit, + dct_coefficients, + noise_level, + motion_bucket_id, + seed_textbox, + ], + outputs=[result_video] + ) + +demo.launch(debug=False, share=True) + +# demo.launch(server_name="0.0.0.0", server_port=10034, enable_queue=True) diff --git a/environment.yml b/environment.yml new file mode 100644 index 0000000000000000000000000000000000000000..c27c8514498086601f3b061bfa6cd4a86467da5b --- /dev/null +++ b/environment.yml @@ -0,0 +1,21 @@ +name: cinemo +channels: + - pytorch + - nvidia +dependencies: + - python >= 3.10 + - pytorch >= 2.0 + - torchvision + - pytorch-cuda >= 11.7 + - pip: + - timm + - diffusers[torch]==0.24.0 + - accelerate + - python-hostlist + - tensorboard + - einops + - transformers + - av + - scikit-image + - decord + - pandas \ No newline at end of file diff --git a/example/aircrafts_flying/0.jpg b/example/aircrafts_flying/0.jpg new file mode 100644 index 0000000000000000000000000000000000000000..57f858eaadadc03718fc29a47fe44b309897a546 Binary files /dev/null and b/example/aircrafts_flying/0.jpg differ diff --git a/example/aircrafts_flying/aircrafts_flying.mp4 b/example/aircrafts_flying/aircrafts_flying.mp4 new file mode 100644 index 0000000000000000000000000000000000000000..ac7335f1e9571ba6b055a1d5f854c25c239c13f0 Binary files /dev/null and b/example/aircrafts_flying/aircrafts_flying.mp4 differ diff --git a/example/car_moving/0.jpg b/example/car_moving/0.jpg new file mode 100644 index 0000000000000000000000000000000000000000..be3c36967e33817958b52e4667eb38602ba0493e Binary files /dev/null and b/example/car_moving/0.jpg differ diff --git a/example/car_moving/car_moving.mp4 b/example/car_moving/car_moving.mp4 new file mode 100644 index 0000000000000000000000000000000000000000..27e065d671668eb799062bf052063666d817c393 Binary files /dev/null and b/example/car_moving/car_moving.mp4 differ diff --git a/example/fireworks/0.jpg b/example/fireworks/0.jpg new file mode 100644 index 0000000000000000000000000000000000000000..b5bac65dab493c2a72c4da219a03e112ee454e58 Binary files /dev/null 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torch.optim.lr_scheduler import CosineAnnealingLR + return CosineAnnealingLR(optimizer, **kwargs) + else: + raise NotImplementedError(name) + +def get_models(args): + + if 'UNet' in args.model: + return UNet3DConditionModel.from_pretrained(args.pretrained_model_path, subfolder="unet") + else: + raise '{} Model Not Supported!'.format(args.model) + \ No newline at end of file diff --git a/models/__pycache__/__init__.cpython-312.pyc b/models/__pycache__/__init__.cpython-312.pyc new file mode 100644 index 0000000000000000000000000000000000000000..2e93fc3de32f97bc04be218843a36a56a08c3da3 Binary files /dev/null and b/models/__pycache__/__init__.cpython-312.pyc differ diff --git a/models/__pycache__/attention.cpython-312.pyc b/models/__pycache__/attention.cpython-312.pyc new file mode 100644 index 0000000000000000000000000000000000000000..cb5ea4be89f6ec77740774b8a965e5833abb3086 Binary files /dev/null and b/models/__pycache__/attention.cpython-312.pyc differ diff --git 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0000000000000000000000000000000000000000..9ad9b99435ecdcc0ccbf633f5447a8afda574cd4 --- /dev/null +++ b/models/attention.py @@ -0,0 +1,476 @@ +# Copyright 2023 The HuggingFace Team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +from typing import Any, Dict, Optional + +import torch +import torch.nn.functional as F +from torch import nn + +try: + from diffusers.utils import maybe_allow_in_graph +except: + from diffusers.utils.torch_utils import maybe_allow_in_graph +from diffusers.models.activations import get_activation +from diffusers.models.attention_processor import Attention +from diffusers.models.embeddings import CombinedTimestepLabelEmbeddings +from diffusers.models.lora import LoRACompatibleLinear +from einops import rearrange, repeat + +try: + from temporal_attention import TemporalAttention, CrossAttention, PseudoCrossAttention +except: + from .temporal_attention import TemporalAttention, CrossAttention, PseudoCrossAttention + +@maybe_allow_in_graph +class BasicTransformerBlock(nn.Module): + r""" + A basic Transformer block. + + Parameters: + dim (`int`): The number of channels in the input and output. + num_attention_heads (`int`): The number of heads to use for multi-head attention. + attention_head_dim (`int`): The number of channels in each head. + dropout (`float`, *optional*, defaults to 0.0): The dropout probability to use. + cross_attention_dim (`int`, *optional*): The size of the encoder_hidden_states vector for cross attention. + only_cross_attention (`bool`, *optional*): + Whether to use only cross-attention layers. In this case two cross attention layers are used. + double_self_attention (`bool`, *optional*): + Whether to use two self-attention layers. In this case no cross attention layers are used. + activation_fn (`str`, *optional*, defaults to `"geglu"`): Activation function to be used in feed-forward. + num_embeds_ada_norm (: + obj: `int`, *optional*): The number of diffusion steps used during training. See `Transformer2DModel`. + attention_bias (: + obj: `bool`, *optional*, defaults to `False`): Configure if the attentions should contain a bias parameter. + """ + + def __init__( + self, + dim: int, + num_attention_heads: int, + attention_head_dim: int, + dropout=0.0, + cross_attention_dim: Optional[int] = None, + activation_fn: str = "geglu", + num_embeds_ada_norm: Optional[int] = None, + attention_bias: bool = False, + only_cross_attention: bool = False, + double_self_attention: bool = False, + upcast_attention: bool = False, + norm_elementwise_affine: bool = True, + norm_type: str = "layer_norm", + final_dropout: bool = False, + rotary_emb=None, + ): + super().__init__() + self.only_cross_attention = only_cross_attention + + self.use_ada_layer_norm_zero = (num_embeds_ada_norm is not None) and norm_type == "ada_norm_zero" + self.use_ada_layer_norm = (num_embeds_ada_norm is not None) and norm_type == "ada_norm" + + if norm_type in ("ada_norm", "ada_norm_zero") and num_embeds_ada_norm is None: + raise ValueError( + f"`norm_type` is set to {norm_type}, but `num_embeds_ada_norm` is not defined. Please make sure to" + f" define `num_embeds_ada_norm` if setting `norm_type` to {norm_type}." + ) + + # Define 3 blocks. Each block has its own normalization layer. + # 1. Self-Attn + if self.use_ada_layer_norm: + self.norm1 = AdaLayerNorm(dim, num_embeds_ada_norm) + elif self.use_ada_layer_norm_zero: + self.norm1 = AdaLayerNormZero(dim, num_embeds_ada_norm) + else: + self.norm1 = nn.LayerNorm(dim, elementwise_affine=norm_elementwise_affine) + self.attn1 = Attention( + query_dim=dim, + heads=num_attention_heads, + dim_head=attention_head_dim, + dropout=dropout, + bias=attention_bias, + cross_attention_dim=cross_attention_dim if only_cross_attention else None, + upcast_attention=upcast_attention, + ) + + # 2. Cross-Attn + if cross_attention_dim is not None or double_self_attention: + # We currently only use AdaLayerNormZero for self attention where there will only be one attention block. + # I.e. the number of returned modulation chunks from AdaLayerZero would not make sense if returned during + # the second cross attention block. + self.norm2 = ( + AdaLayerNorm(dim, num_embeds_ada_norm) + if self.use_ada_layer_norm + else nn.LayerNorm(dim, elementwise_affine=norm_elementwise_affine) + ) + self.attn2 = Attention( + query_dim=dim, + cross_attention_dim=cross_attention_dim if not double_self_attention else None, + heads=num_attention_heads, + dim_head=attention_head_dim, + dropout=dropout, + bias=attention_bias, + upcast_attention=upcast_attention, + ) # is self-attn if encoder_hidden_states is none + else: + self.norm2 = None + self.attn2 = None + + # 3. Temporal-Attn + self.attn_temp = TemporalAttention( + query_dim=dim, + heads=num_attention_heads, + dim_head=attention_head_dim, + dropout=dropout, + bias=attention_bias, + cross_attention_dim=None, + upcast_attention=upcast_attention, + rotary_emb=rotary_emb, + ) + self.norm_temp = AdaLayerNorm(dim, num_embeds_ada_norm) if self.use_ada_layer_norm else nn.LayerNorm(dim) + nn.init.zeros_(self.attn_temp.to_out[0].weight.data) + + # Temporal text cross attention + # self.attn_temp_text = CrossAttention(query_dim=dim, + # cross_attention_dim=cross_attention_dim, + # heads=num_attention_heads, + # dim_head=attention_head_dim, + # dropout=dropout, + # bias=attention_bias, + # upcast_attention=upcast_attention, + # ) + # self.norm_temp_text = AdaLayerNorm(dim, num_embeds_ada_norm) if self.use_ada_layer_norm else nn.LayerNorm(dim) + # nn.init.zeros_(self.attn_temp_text.to_out[0].weight.data) + + # 5. Feed-forward + self.norm3 = nn.LayerNorm(dim, elementwise_affine=norm_elementwise_affine) + self.ff = FeedForward(dim, dropout=dropout, activation_fn=activation_fn, final_dropout=final_dropout) + + # let chunk size default to None + self._chunk_size = None + self._chunk_dim = 0 + + def set_chunk_feed_forward(self, chunk_size: Optional[int], dim: int): + # Sets chunk feed-forward + self._chunk_size = chunk_size + self._chunk_dim = dim + + def forward( + self, + hidden_states: torch.FloatTensor, + attention_mask: Optional[torch.FloatTensor] = None, + encoder_hidden_states: Optional[torch.FloatTensor] = None, + encoder_attention_mask: Optional[torch.FloatTensor] = None, + timestep: Optional[torch.LongTensor] = None, + cross_attention_kwargs: Dict[str, Any] = None, + class_labels: Optional[torch.LongTensor] = None, + video_length=None, + use_image_num=None, + ): + # Notice that normalization is always applied before the real computation in the following blocks. + # 1. Self-Attention + if self.use_ada_layer_norm: + norm_hidden_states = self.norm1(hidden_states, timestep) + elif self.use_ada_layer_norm_zero: + norm_hidden_states, gate_msa, shift_mlp, scale_mlp, gate_mlp = self.norm1( + hidden_states, timestep, class_labels, hidden_dtype=hidden_states.dtype + ) + else: + norm_hidden_states = self.norm1(hidden_states) + + cross_attention_kwargs = cross_attention_kwargs if cross_attention_kwargs is not None else {} + + attn_output = self.attn1( + norm_hidden_states, + encoder_hidden_states=encoder_hidden_states if self.only_cross_attention else None, + attention_mask=attention_mask, + **cross_attention_kwargs, + ) + if self.use_ada_layer_norm_zero: + attn_output = gate_msa.unsqueeze(1) * attn_output + hidden_states = attn_output + hidden_states + + # 2. Cross-Attention + if self.attn2 is not None: + norm_hidden_states = ( + self.norm2(hidden_states, timestep) if self.use_ada_layer_norm else self.norm2(hidden_states) + ) + + attn_output = self.attn2( + norm_hidden_states, + encoder_hidden_states=encoder_hidden_states, + attention_mask=encoder_attention_mask, + **cross_attention_kwargs, + ) + hidden_states = attn_output + hidden_states + + # Temporal Attention + if self.training and use_image_num != 0: + d = hidden_states.shape[1] + hidden_states = rearrange(hidden_states, "(b f) d c -> (b d) f c", f=video_length + use_image_num).contiguous() + hidden_states_video = hidden_states[:, :video_length, :] + hidden_states_image = hidden_states[:, video_length:, :] + + # with torch.cuda.amp.autocast(dtype=torch.float32): + norm_hidden_states_video = ( + self.norm_temp(hidden_states_video, timestep) if self.use_ada_layer_norm else self.norm_temp(hidden_states_video) + ) + + hidden_states_video = self.attn_temp(norm_hidden_states_video) + hidden_states_video + + # # # Temporal Text Cross Attention + # encoder_hidden_states_reshape = rearrange(encoder_hidden_states, '(b f) d c -> b f d c', f=video_length + use_image_num).contiguous() + # encoder_hidden_states_video = encoder_hidden_states_reshape[:, 0, ...].contiguous() + # encoder_hidden_states_video = repeat(encoder_hidden_states_video, 'b d c -> (b t) d c', t=d).contiguous() + + # norm_hidden_states_video = ( + # self.norm_temp_text(hidden_states_video, timestep) if self.use_ada_layer_norm else self.norm_temp_text(hidden_states_video) + # ) + # hidden_states_video = self.attn_temp_text(norm_hidden_states_video, encoder_hidden_states=encoder_hidden_states_video) + hidden_states_video + # ################## end Temporal Text Cross Attention ################### + + hidden_states = torch.cat([hidden_states_video, hidden_states_image], dim=1) + # hidden_states = torch.cat([hidden_states_video.to(hidden_states_image.dtype), hidden_states_image], dim=1) + hidden_states = rearrange(hidden_states, "(b d) f c -> (b f) d c", d=d).contiguous() + else: + d = hidden_states.shape[1] + hidden_states = rearrange(hidden_states, "(b f) d c -> (b d) f c", f=video_length + use_image_num).contiguous() + norm_hidden_states = ( + self.norm_temp(hidden_states, timestep) if self.use_ada_layer_norm else self.norm_temp(hidden_states) + ) + hidden_states = self.attn_temp(norm_hidden_states) + hidden_states + + # # # Temporal Text Cross Attention + # encoder_hidden_states_reshape = rearrange(encoder_hidden_states, '(b f) d c -> b f d c', f=video_length + use_image_num).contiguous() + # encoder_hidden_states_video = encoder_hidden_states_reshape[:, 0, ...].contiguous() + # encoder_hidden_states_video = repeat(encoder_hidden_states_video, 'b d c -> (b t) d c', t=d).contiguous() + + # norm_hidden_states = ( + # self.norm_temp_text(hidden_states, timestep) if self.use_ada_layer_norm else self.norm_temp_text(hidden_states) + # ) + # hidden_states = self.attn_temp_text(norm_hidden_states, encoder_hidden_states=encoder_hidden_states_video) + hidden_states + # ################# end Temporal Text Cross Attention ################### + + hidden_states = rearrange(hidden_states, "(b d) f c -> (b f) d c", d=d).contiguous() + + # 3. Feed-forward + norm_hidden_states = self.norm3(hidden_states) + + if self.use_ada_layer_norm_zero: + norm_hidden_states = norm_hidden_states * (1 + scale_mlp[:, None]) + shift_mlp[:, None] + + if self._chunk_size is not None: + # "feed_forward_chunk_size" can be used to save memory + if norm_hidden_states.shape[self._chunk_dim] % self._chunk_size != 0: + raise ValueError( + f"`hidden_states` dimension to be chunked: {norm_hidden_states.shape[self._chunk_dim]} has to be divisible by chunk size: {self._chunk_size}. Make sure to set an appropriate `chunk_size` when calling `unet.enable_forward_chunking`." + ) + + num_chunks = norm_hidden_states.shape[self._chunk_dim] // self._chunk_size + ff_output = torch.cat( + [self.ff(hid_slice) for hid_slice in norm_hidden_states.chunk(num_chunks, dim=self._chunk_dim)], + dim=self._chunk_dim, + ) + else: + ff_output = self.ff(norm_hidden_states) + + if self.use_ada_layer_norm_zero: + ff_output = gate_mlp.unsqueeze(1) * ff_output + + hidden_states = ff_output + hidden_states + + return hidden_states + + +class FeedForward(nn.Module): + r""" + A feed-forward layer. + + Parameters: + dim (`int`): The number of channels in the input. + dim_out (`int`, *optional*): The number of channels in the output. If not given, defaults to `dim`. + mult (`int`, *optional*, defaults to 4): The multiplier to use for the hidden dimension. + dropout (`float`, *optional*, defaults to 0.0): The dropout probability to use. + activation_fn (`str`, *optional*, defaults to `"geglu"`): Activation function to be used in feed-forward. + final_dropout (`bool` *optional*, defaults to False): Apply a final dropout. + """ + + def __init__( + self, + dim: int, + dim_out: Optional[int] = None, + mult: int = 4, + dropout: float = 0.0, + activation_fn: str = "geglu", + final_dropout: bool = False, + ): + super().__init__() + inner_dim = int(dim * mult) + dim_out = dim_out if dim_out is not None else dim + + if activation_fn == "gelu": + act_fn = GELU(dim, inner_dim) + if activation_fn == "gelu-approximate": + act_fn = GELU(dim, inner_dim, approximate="tanh") + elif activation_fn == "geglu": + act_fn = GEGLU(dim, inner_dim) + elif activation_fn == "geglu-approximate": + act_fn = ApproximateGELU(dim, inner_dim) + + self.net = nn.ModuleList([]) + # project in + self.net.append(act_fn) + # project dropout + self.net.append(nn.Dropout(dropout)) + # project out + self.net.append(LoRACompatibleLinear(inner_dim, dim_out)) + # FF as used in Vision Transformer, MLP-Mixer, etc. have a final dropout + if final_dropout: + self.net.append(nn.Dropout(dropout)) + + def forward(self, hidden_states): + for module in self.net: + hidden_states = module(hidden_states) + return hidden_states + + +class GELU(nn.Module): + r""" + GELU activation function with tanh approximation support with `approximate="tanh"`. + """ + + def __init__(self, dim_in: int, dim_out: int, approximate: str = "none"): + super().__init__() + self.proj = nn.Linear(dim_in, dim_out) + self.approximate = approximate + + def gelu(self, gate): + if gate.device.type != "mps": + return F.gelu(gate, approximate=self.approximate) + # mps: gelu is not implemented for float16 + return F.gelu(gate.to(dtype=torch.float32), approximate=self.approximate).to(dtype=gate.dtype) + + def forward(self, hidden_states): + hidden_states = self.proj(hidden_states) + hidden_states = self.gelu(hidden_states) + return hidden_states + + +class GEGLU(nn.Module): + r""" + A variant of the gated linear unit activation function from https://arxiv.org/abs/2002.05202. + + Parameters: + dim_in (`int`): The number of channels in the input. + dim_out (`int`): The number of channels in the output. + """ + + def __init__(self, dim_in: int, dim_out: int): + super().__init__() + self.proj = LoRACompatibleLinear(dim_in, dim_out * 2) + + def gelu(self, gate): + if gate.device.type != "mps": + return F.gelu(gate) + # mps: gelu is not implemented for float16 + return F.gelu(gate.to(dtype=torch.float32)).to(dtype=gate.dtype) + + def forward(self, hidden_states): + hidden_states, gate = self.proj(hidden_states).chunk(2, dim=-1) + return hidden_states * self.gelu(gate) + + +class ApproximateGELU(nn.Module): + """ + The approximate form of Gaussian Error Linear Unit (GELU) + + For more details, see section 2: https://arxiv.org/abs/1606.08415 + """ + + def __init__(self, dim_in: int, dim_out: int): + super().__init__() + self.proj = nn.Linear(dim_in, dim_out) + + def forward(self, x): + x = self.proj(x) + return x * torch.sigmoid(1.702 * x) + + +class AdaLayerNorm(nn.Module): + """ + Norm layer modified to incorporate timestep embeddings. + """ + + def __init__(self, embedding_dim, num_embeddings): + super().__init__() + self.emb = nn.Embedding(num_embeddings, embedding_dim) + self.silu = nn.SiLU() + self.linear = nn.Linear(embedding_dim, embedding_dim * 2) + self.norm = nn.LayerNorm(embedding_dim, elementwise_affine=False) + + def forward(self, x, timestep): + emb = self.linear(self.silu(self.emb(timestep))) + scale, shift = torch.chunk(emb, 2) + x = self.norm(x) * (1 + scale) + shift + return x + + +class AdaLayerNormZero(nn.Module): + """ + Norm layer adaptive layer norm zero (adaLN-Zero). + """ + + def __init__(self, embedding_dim, num_embeddings): + super().__init__() + + self.emb = CombinedTimestepLabelEmbeddings(num_embeddings, embedding_dim) + + self.silu = nn.SiLU() + self.linear = nn.Linear(embedding_dim, 6 * embedding_dim, bias=True) + self.norm = nn.LayerNorm(embedding_dim, elementwise_affine=False, eps=1e-6) + + def forward(self, x, timestep, class_labels, hidden_dtype=None): + emb = self.linear(self.silu(self.emb(timestep, class_labels, hidden_dtype=hidden_dtype))) + shift_msa, scale_msa, gate_msa, shift_mlp, scale_mlp, gate_mlp = emb.chunk(6, dim=1) + x = self.norm(x) * (1 + scale_msa[:, None]) + shift_msa[:, None] + return x, gate_msa, shift_mlp, scale_mlp, gate_mlp + + +class AdaGroupNorm(nn.Module): + """ + GroupNorm layer modified to incorporate timestep embeddings. + """ + + def __init__( + self, embedding_dim: int, out_dim: int, num_groups: int, act_fn: Optional[str] = None, eps: float = 1e-5 + ): + super().__init__() + self.num_groups = num_groups + self.eps = eps + + if act_fn is None: + self.act = None + else: + self.act = get_activation(act_fn) + + self.linear = nn.Linear(embedding_dim, out_dim * 2) + + def forward(self, x, emb): + if self.act: + emb = self.act(emb) + emb = self.linear(emb) + emb = emb[:, :, None, None] + scale, shift = emb.chunk(2, dim=1) + + x = F.group_norm(x, self.num_groups, eps=self.eps) + x = x * (1 + scale) + shift + return x diff --git a/models/resnet.py b/models/resnet.py new file mode 100644 index 0000000000000000000000000000000000000000..7ef14625211a11ea3189a5439fc8b301df4e1bf7 --- /dev/null +++ b/models/resnet.py @@ -0,0 +1,375 @@ +# Copyright 2023 The HuggingFace Team. All rights reserved. +# `TemporalConvLayer` Copyright 2023 Alibaba DAMO-VILAB, The ModelScope Team and The HuggingFace Team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +from functools import partial +from typing import Optional + +import torch +import torch.nn as nn +import torch.nn.functional as F + +from diffusers.models.activations import get_activation +from diffusers.models.normalization import AdaGroupNorm +from diffusers.models.attention_processor import SpatialNorm +from diffusers.models.lora import LoRACompatibleConv, LoRACompatibleLinear + +from einops import rearrange + +class InflatedConv3d(nn.Conv2d): + def forward(self, x): + video_length = x.shape[2] + + x = rearrange(x, "b c f h w -> (b f) c h w") + x = super().forward(x) + x = rearrange(x, "(b f) c h w -> b c f h w", f=video_length) + + return x + + +class Upsample3D(nn.Module): + """A 2D upsampling layer with an optional convolution. + + Parameters: + channels (`int`): + number of channels in the inputs and outputs. + use_conv (`bool`, default `False`): + option to use a convolution. + use_conv_transpose (`bool`, default `False`): + option to use a convolution transpose. + out_channels (`int`, optional): + number of output channels. Defaults to `channels`. + """ + + def __init__(self, channels, use_conv=False, use_conv_transpose=False, out_channels=None, name="conv"): + super().__init__() + self.channels = channels + self.out_channels = out_channels or channels + self.use_conv = use_conv + self.use_conv_transpose = use_conv_transpose + self.name = name + + conv = None + if use_conv_transpose: + conv = nn.ConvTranspose2d(channels, self.out_channels, 4, 2, 1) + elif use_conv: + # conv = LoRACompatibleConv(self.channels, self.out_channels, 3, padding=1) + conv = InflatedConv3d(self.channels, self.out_channels, 3, padding=1) + + # TODO(Suraj, Patrick) - clean up after weight dicts are correctly renamed + if name == "conv": + self.conv = conv + else: + self.Conv2d_0 = conv + + def forward(self, hidden_states, output_size=None): + assert hidden_states.shape[1] == self.channels + + if self.use_conv_transpose: + return self.conv(hidden_states) + + # Cast to float32 to as 'upsample_nearest2d_out_frame' op does not support bfloat16 + # TODO(Suraj): Remove this cast once the issue is fixed in PyTorch + # https://github.com/pytorch/pytorch/issues/86679 + dtype = hidden_states.dtype + if dtype == torch.bfloat16: + hidden_states = hidden_states.to(torch.float32) + + # upsample_nearest_nhwc fails with large batch sizes. see https://github.com/huggingface/diffusers/issues/984 + if hidden_states.shape[0] >= 64: + hidden_states = hidden_states.contiguous() + + # if `output_size` is passed we force the interpolation output + # size and do not make use of `scale_factor=2` + if output_size is None: + hidden_states = F.interpolate(hidden_states, scale_factor=[1.0, 2.0, 2.0], mode="nearest") + else: + hidden_states = F.interpolate(hidden_states, size=output_size, mode="nearest") + + # If the input is bfloat16, we cast back to bfloat16 + if dtype == torch.bfloat16: + hidden_states = hidden_states.to(dtype) + + # TODO(Suraj, Patrick) - clean up after weight dicts are correctly renamed + if self.use_conv: + if self.name == "conv": + hidden_states = self.conv(hidden_states) + else: + hidden_states = self.Conv2d_0(hidden_states) + + return hidden_states + + +class Downsample3D(nn.Module): + """A 2D downsampling layer with an optional convolution. + + Parameters: + channels (`int`): + number of channels in the inputs and outputs. + use_conv (`bool`, default `False`): + option to use a convolution. + out_channels (`int`, optional): + number of output channels. Defaults to `channels`. + padding (`int`, default `1`): + padding for the convolution. + """ + + def __init__(self, channels, use_conv=False, out_channels=None, padding=1, name="conv"): + super().__init__() + self.channels = channels + self.out_channels = out_channels or channels + self.use_conv = use_conv + self.padding = padding + stride = 2 + self.name = name + + if use_conv: + # conv = LoRACompatibleConv(self.channels, self.out_channels, 3, stride=stride, padding=padding) + conv = InflatedConv3d(self.channels, self.out_channels, 3, stride=stride, padding=padding) + else: + assert self.channels == self.out_channels + conv = nn.AvgPool2d(kernel_size=stride, stride=stride) + + # TODO(Suraj, Patrick) - clean up after weight dicts are correctly renamed + if name == "conv": + self.Conv2d_0 = conv + self.conv = conv + elif name == "Conv2d_0": + self.conv = conv + else: + self.conv = conv + + def forward(self, hidden_states): + assert hidden_states.shape[1] == self.channels + if self.use_conv and self.padding == 0: + pad = (0, 1, 0, 1) + hidden_states = F.pad(hidden_states, pad, mode="constant", value=0) + + assert hidden_states.shape[1] == self.channels + hidden_states = self.conv(hidden_states) + + return hidden_states + +class ResnetBlock3D(nn.Module): + r""" + A Resnet block. + + Parameters: + in_channels (`int`): The number of channels in the input. + out_channels (`int`, *optional*, default to be `None`): + The number of output channels for the first conv2d layer. If None, same as `in_channels`. + dropout (`float`, *optional*, defaults to `0.0`): The dropout probability to use. + temb_channels (`int`, *optional*, default to `512`): the number of channels in timestep embedding. + groups (`int`, *optional*, default to `32`): The number of groups to use for the first normalization layer. + groups_out (`int`, *optional*, default to None): + The number of groups to use for the second normalization layer. if set to None, same as `groups`. + eps (`float`, *optional*, defaults to `1e-6`): The epsilon to use for the normalization. + non_linearity (`str`, *optional*, default to `"swish"`): the activation function to use. + time_embedding_norm (`str`, *optional*, default to `"default"` ): Time scale shift config. + By default, apply timestep embedding conditioning with a simple shift mechanism. Choose "scale_shift" or + "ada_group" for a stronger conditioning with scale and shift. + kernel (`torch.FloatTensor`, optional, default to None): FIR filter, see + [`~models.resnet.FirUpsample2D`] and [`~models.resnet.FirDownsample2D`]. + output_scale_factor (`float`, *optional*, default to be `1.0`): the scale factor to use for the output. + use_in_shortcut (`bool`, *optional*, default to `True`): + If `True`, add a 1x1 nn.conv2d layer for skip-connection. + up (`bool`, *optional*, default to `False`): If `True`, add an upsample layer. + down (`bool`, *optional*, default to `False`): If `True`, add a downsample layer. + conv_shortcut_bias (`bool`, *optional*, default to `True`): If `True`, adds a learnable bias to the + `conv_shortcut` output. + conv_2d_out_channels (`int`, *optional*, default to `None`): the number of channels in the output. + If None, same as `out_channels`. + """ + + def __init__( + self, + *, + in_channels, + out_channels=None, + conv_shortcut=False, + dropout=0.0, + temb_channels=512, + groups=32, + groups_out=None, + pre_norm=True, + eps=1e-6, + non_linearity="swish", + skip_time_act=False, + time_embedding_norm="default", # default, scale_shift, ada_group, spatial + kernel=None, + output_scale_factor=1.0, + use_in_shortcut=None, + up=False, + down=False, + conv_shortcut_bias: bool = True, + conv_2d_out_channels: Optional[int] = None, + ): + super().__init__() + self.pre_norm = pre_norm + self.pre_norm = True + self.in_channels = in_channels + out_channels = in_channels if out_channels is None else out_channels + self.out_channels = out_channels + self.use_conv_shortcut = conv_shortcut + self.up = up + self.down = down + self.output_scale_factor = output_scale_factor + self.time_embedding_norm = time_embedding_norm + self.skip_time_act = skip_time_act + + if groups_out is None: + groups_out = groups + + if self.time_embedding_norm == "ada_group": + self.norm1 = AdaGroupNorm(temb_channels, in_channels, groups, eps=eps) + elif self.time_embedding_norm == "spatial": + self.norm1 = SpatialNorm(in_channels, temb_channels) + else: + self.norm1 = torch.nn.GroupNorm(num_groups=groups, num_channels=in_channels, eps=eps, affine=True) + + # self.conv1 = LoRACompatibleConv(in_channels, out_channels, kernel_size=3, stride=1, padding=1) + self.conv1 = InflatedConv3d(in_channels, out_channels, kernel_size=3, stride=1, padding=1) + + if temb_channels is not None: + if self.time_embedding_norm == "default": + self.time_emb_proj = LoRACompatibleLinear(temb_channels, out_channels) + elif self.time_embedding_norm == "scale_shift": + self.time_emb_proj = LoRACompatibleLinear(temb_channels, 2 * out_channels) + elif self.time_embedding_norm == "ada_group" or self.time_embedding_norm == "spatial": + self.time_emb_proj = None + else: + raise ValueError(f"unknown time_embedding_norm : {self.time_embedding_norm} ") + else: + self.time_emb_proj = None + + if self.time_embedding_norm == "ada_group": + self.norm2 = AdaGroupNorm(temb_channels, out_channels, groups_out, eps=eps) + elif self.time_embedding_norm == "spatial": + self.norm2 = SpatialNorm(out_channels, temb_channels) + else: + self.norm2 = torch.nn.GroupNorm(num_groups=groups_out, num_channels=out_channels, eps=eps, affine=True) + + self.dropout = torch.nn.Dropout(dropout) + conv_2d_out_channels = conv_2d_out_channels or out_channels + # self.conv2 = LoRACompatibleConv(out_channels, conv_2d_out_channels, kernel_size=3, stride=1, padding=1) + self.conv2 = InflatedConv3d(out_channels, conv_2d_out_channels, kernel_size=3, stride=1, padding=1) + + self.nonlinearity = get_activation(non_linearity) + + self.upsample = self.downsample = None + if self.up: + if kernel == "fir": + fir_kernel = (1, 3, 3, 1) + self.upsample = lambda x: upsample_2d(x, kernel=fir_kernel) + elif kernel == "sde_vp": + self.upsample = partial(F.interpolate, scale_factor=2.0, mode="nearest") + else: + self.upsample = Upsample3D(in_channels, use_conv=False) + elif self.down: + if kernel == "fir": + fir_kernel = (1, 3, 3, 1) + self.downsample = lambda x: downsample_2d(x, kernel=fir_kernel) + elif kernel == "sde_vp": + self.downsample = partial(F.avg_pool2d, kernel_size=2, stride=2) + else: + self.downsample = Downsample3D(in_channels, use_conv=False, padding=1, name="op") + + self.use_in_shortcut = self.in_channels != conv_2d_out_channels if use_in_shortcut is None else use_in_shortcut + + self.conv_shortcut = None + if self.use_in_shortcut: + # self.conv_shortcut = LoRACompatibleConv( + # in_channels, conv_2d_out_channels, kernel_size=1, stride=1, padding=0, bias=conv_shortcut_bias + # ) + self.conv_shortcut = InflatedConv3d( + in_channels, conv_2d_out_channels, kernel_size=1, stride=1, padding=0, bias=conv_shortcut_bias + ) + + def forward(self, input_tensor, temb): + hidden_states = input_tensor + + if self.time_embedding_norm == "ada_group" or self.time_embedding_norm == "spatial": + hidden_states = self.norm1(hidden_states, temb) + else: + hidden_states = self.norm1(hidden_states) + + hidden_states = self.nonlinearity(hidden_states) + + if self.upsample is not None: + # upsample_nearest_nhwc fails with large batch sizes. see https://github.com/huggingface/diffusers/issues/984 + if hidden_states.shape[0] >= 64: + input_tensor = input_tensor.contiguous() + hidden_states = hidden_states.contiguous() + input_tensor = self.upsample(input_tensor) + hidden_states = self.upsample(hidden_states) + elif self.downsample is not None: + input_tensor = self.downsample(input_tensor) + hidden_states = self.downsample(hidden_states) + + hidden_states = self.conv1(hidden_states) + + # print(self.time_emb_proj) # LoRACompatibleLinear(in_features=1280, out_features=320, bias=True) + # print(self.nonlinearity) # SiLU() + + if self.time_emb_proj is not None: + if not self.skip_time_act: + temb = self.nonlinearity(temb) + temb = self.time_emb_proj(temb) + # temb = temb[:, :, None, None, None] + # if self.training: + # temb = rearrange(temb, 'b f d -> b d f')[..., None, None] + # else: + # temb = temb[:, :, None, None, None] + temb = temb[:, :, None, None, None] + # print(temb.shape) + + if temb is not None and self.time_embedding_norm == "default": + # print(hidden_states.shape) + hidden_states = hidden_states + temb + + # torch.Size([2, 320, 21, 32, 32]) + # torch.Size([2, 320, 1, 1, 1]) + # torch.Size([2, 320, 21, 32, 32]) + # torch.Size([2, 320, 1, 1, 1]) + # torch.Size([2, 640, 21, 16, 16]) + # torch.Size([2, 640, 1, 1, 1]) + # torch.Size([2, 640, 21, 16, 16]) + # torch.Size([2, 640, 1, 1, 1]) + # torch.Size([2, 1280, 21, 8, 8]) + # torch.Size([2, 1280, 1, 1, 1]) + # torch.Size([2, 1280, 21, 8, 8]) + # torch.Size([2, 1280, 1, 1, 1]) + # torch.Size([2, 1280, 21, 4, 4]) + # torch.Size([2, 1280, 1, 1, 1]) + + if self.time_embedding_norm == "ada_group" or self.time_embedding_norm == "spatial": + hidden_states = self.norm2(hidden_states, temb) + else: + hidden_states = self.norm2(hidden_states) + + if temb is not None and self.time_embedding_norm == "scale_shift": + scale, shift = torch.chunk(temb, 2, dim=1) + hidden_states = hidden_states * (1 + scale) + shift + + hidden_states = self.nonlinearity(hidden_states) + + hidden_states = self.dropout(hidden_states) + hidden_states = self.conv2(hidden_states) + + if self.conv_shortcut is not None: + input_tensor = self.conv_shortcut(input_tensor) + + output_tensor = (input_tensor + hidden_states) / self.output_scale_factor + + return output_tensor \ No newline at end of file diff --git a/models/rotary_embedding_torch_mx.py b/models/rotary_embedding_torch_mx.py new file mode 100644 index 0000000000000000000000000000000000000000..f51cd1203f07127b55f3e05bfb90f1aadf2df8b8 --- /dev/null +++ b/models/rotary_embedding_torch_mx.py @@ -0,0 +1,167 @@ +from math import pi, log + +import torch +from torch import nn, einsum + +from einops import rearrange, repeat + +# helper functions + +def exists(val): + return val is not None + +def broadcat(tensors, dim = -1): + num_tensors = len(tensors) + shape_lens = set(list(map(lambda t: len(t.shape), tensors))) + assert len(shape_lens) == 1, 'tensors must all have the same number of dimensions' + shape_len = list(shape_lens)[0] + + dim = (dim + shape_len) if dim < 0 else dim + dims = list(zip(*map(lambda t: list(t.shape), tensors))) + + expandable_dims = [(i, val) for i, val in enumerate(dims) if i != dim] + assert all([*map(lambda t: len(set(t[1])) <= 2, expandable_dims)]), 'invalid dimensions for broadcastable concatentation' + max_dims = list(map(lambda t: (t[0], max(t[1])), expandable_dims)) + expanded_dims = list(map(lambda t: (t[0], (t[1],) * num_tensors), max_dims)) + expanded_dims.insert(dim, (dim, dims[dim])) + expandable_shapes = list(zip(*map(lambda t: t[1], expanded_dims))) + tensors = list(map(lambda t: t[0].expand(*t[1]), zip(tensors, expandable_shapes))) + return torch.cat(tensors, dim = dim) + +# rotary embedding helper functions + +def rotate_half(x): + x = rearrange(x, '... (d r) -> ... d r', r = 2) + x1, x2 = x.unbind(dim = -1) + x = torch.stack((-x2, x1), dim = -1) + return rearrange(x, '... d r -> ... (d r)') + +def apply_rotary_emb(freqs, t, start_index = 0, scale = 1.): + freqs = freqs.to(t) + rot_dim = freqs.shape[-1] + end_index = start_index + rot_dim + assert rot_dim <= t.shape[-1], f'feature dimension {t.shape[-1]} is not of sufficient size to rotate in all the positions {rot_dim}' + t_left, t, t_right = t[..., :start_index], t[..., start_index:end_index], t[..., end_index:] + t = (t * freqs.cos() * scale) + (rotate_half(t) * freqs.sin() * scale) + return torch.cat((t_left, t, t_right), dim = -1) + +# learned rotation helpers + +def apply_learned_rotations(rotations, t, start_index = 0, freq_ranges = None): + if exists(freq_ranges): + rotations = einsum('..., f -> ... f', rotations, freq_ranges) + rotations = rearrange(rotations, '... r f -> ... (r f)') + + rotations = repeat(rotations, '... n -> ... (n r)', r = 2) + return apply_rotary_emb(rotations, t, start_index = start_index) + +# classes + +class RotaryEmbedding(nn.Module): + def __init__( + self, + dim, + custom_freqs = None, + freqs_for = 'lang', + theta = 10000, + max_freq = 10, + num_freqs = 1, + learned_freq = False, + use_xpos = False, + xpos_scale_base = 512, + interpolate_factor = 1., + theta_rescale_factor = 1. + ): + super().__init__() + # proposed by reddit user bloc97, to rescale rotary embeddings to longer sequence length without fine-tuning + # has some connection to NTK literature + # https://www.reddit.com/r/LocalLLaMA/comments/14lz7j5/ntkaware_scaled_rope_allows_llama_models_to_have/ + theta *= theta_rescale_factor ** (dim / (dim - 2)) + + if exists(custom_freqs): + freqs = custom_freqs + elif freqs_for == 'lang': + freqs = 1. / (theta ** (torch.arange(0, dim, 2)[:(dim // 2)].float() / dim)) + elif freqs_for == 'pixel': + freqs = torch.linspace(1., max_freq / 2, dim // 2) * pi + elif freqs_for == 'constant': + freqs = torch.ones(num_freqs).float() + else: + raise ValueError(f'unknown modality {freqs_for}') + + self.cache = dict() + self.cache_scale = dict() + # self.freqs = nn.Parameter(freqs, requires_grad = learned_freq) + self.register_buffer('freqs', freqs) + + # interpolation factors + + assert interpolate_factor >= 1. + self.interpolate_factor = interpolate_factor + + # xpos + + self.use_xpos = use_xpos + if not use_xpos: + self.register_buffer('scale', None) + return + + scale = (torch.arange(0, dim, 2) + 0.4 * dim) / (1.4 * dim) + self.scale_base = xpos_scale_base + self.register_buffer('scale', scale) + + def get_seq_pos(self, seq_len, device, dtype, offset = 0): + return (torch.arange(seq_len, device = device, dtype = dtype) + offset) / self.interpolate_factor + + def rotate_queries_or_keys(self, t, seq_dim = -2, offset = 0): + assert not self.use_xpos, 'you must use `.rotate_queries_and_keys` method instead and pass in both queries and keys, for length extrapolatable rotary embeddings' + device, dtype, seq_len = t.device, t.dtype, t.shape[seq_dim] + freqs = self.forward(lambda: self.get_seq_pos(seq_len, device = device, dtype = dtype, offset = offset), cache_key = f'freqs:{seq_len}|offset:{offset}') + return apply_rotary_emb(freqs, t) + + def rotate_queries_and_keys(self, q, k, seq_dim = -2): + assert self.use_xpos + device, dtype, seq_len = q.device, q.dtype, q.shape[seq_dim] + seq = self.get_seq_pos(seq_len, dtype = dtype, device = device) + freqs = self.forward(lambda: seq, cache_key = f'freqs:{seq_len}') + scale = self.get_scale(lambda: seq, cache_key = f'scale:{seq_len}').to(dtype) + rotated_q = apply_rotary_emb(freqs, q, scale = scale) + rotated_k = apply_rotary_emb(freqs, k, scale = scale ** -1) + return rotated_q, rotated_k + + def get_scale(self, t, cache_key = None): + assert self.use_xpos + + if exists(cache_key) and cache_key in self.cache: + return self.cache[cache_key] + + if callable(t): + t = t() + + scale = 1. + if self.use_xpos: + power = (t - len(t) // 2) / self.scale_base + scale = self.scale ** rearrange(power, 'n -> n 1') + scale = torch.cat((scale, scale), dim = -1) + + if exists(cache_key): + self.cache[cache_key] = scale + + return scale + + def forward(self, t, cache_key = None): + if exists(cache_key) and cache_key in self.cache: + return self.cache[cache_key] + + if callable(t): + t = t() + + freqs = self.freqs + + freqs = torch.einsum('..., f -> ... f', t.type(freqs.dtype), freqs) + freqs = repeat(freqs, '... n -> ... (n r)', r = 2) + + if exists(cache_key): + self.cache[cache_key] = freqs + + return freqs diff --git a/models/temporal_attention.py b/models/temporal_attention.py new file mode 100644 index 0000000000000000000000000000000000000000..4a0014869fd86d71cea4422debb2d9d74cc384fd --- /dev/null +++ b/models/temporal_attention.py @@ -0,0 +1,541 @@ +import torch +from torch import nn +from typing import Optional +from dataclasses import dataclass +from diffusers.utils import BaseOutput +from diffusers.utils.import_utils import is_xformers_available +import torch.nn.functional as F +from einops import rearrange, repeat +import math + +@dataclass +class Transformer3DModelOutput(BaseOutput): + sample: torch.FloatTensor + + +if is_xformers_available(): + import xformers + import xformers.ops +else: + xformers = None + +def exists(x): + return x is not None + +class CrossAttention(nn.Module): + r""" + copy from diffuser 0.11.1 + A cross attention layer. + Parameters: + query_dim (`int`): The number of channels in the query. + cross_attention_dim (`int`, *optional*): + The number of channels in the encoder_hidden_states. If not given, defaults to `query_dim`. + heads (`int`, *optional*, defaults to 8): The number of heads to use for multi-head attention. + dim_head (`int`, *optional*, defaults to 64): The number of channels in each head. + dropout (`float`, *optional*, defaults to 0.0): The dropout probability to use. + bias (`bool`, *optional*, defaults to False): + Set to `True` for the query, key, and value linear layers to contain a bias parameter. + """ + + def __init__( + self, + query_dim: int, + cross_attention_dim: Optional[int] = None, + heads: int = 8, + dim_head: int = 64, + dropout: float = 0.0, + bias=False, + upcast_attention: bool = False, + upcast_softmax: bool = False, + added_kv_proj_dim: Optional[int] = None, + norm_num_groups: Optional[int] = None, + use_relative_position: bool = False, + ): + super().__init__() + # print('num head', heads) + inner_dim = dim_head * heads + cross_attention_dim = cross_attention_dim if cross_attention_dim is not None else query_dim + self.upcast_attention = upcast_attention + self.upcast_softmax = upcast_softmax + + self.scale = dim_head**-0.5 + + self.heads = heads + self.dim_head = dim_head + # for slice_size > 0 the attention score computation + # is split across the batch axis to save memory + # You can set slice_size with `set_attention_slice` + self.sliceable_head_dim = heads + self._slice_size = None + self._use_memory_efficient_attention_xformers = False # No use xformers for temporal attention + self.added_kv_proj_dim = added_kv_proj_dim + + if norm_num_groups is not None: + self.group_norm = nn.GroupNorm(num_channels=inner_dim, num_groups=norm_num_groups, eps=1e-5, affine=True) + else: + self.group_norm = None + + self.to_q = nn.Linear(query_dim, inner_dim, bias=bias) + self.to_k = nn.Linear(cross_attention_dim, inner_dim, bias=bias) + self.to_v = nn.Linear(cross_attention_dim, inner_dim, bias=bias) + + if self.added_kv_proj_dim is not None: + self.add_k_proj = nn.Linear(added_kv_proj_dim, cross_attention_dim) + self.add_v_proj = nn.Linear(added_kv_proj_dim, cross_attention_dim) + + self.to_out = nn.ModuleList([]) + self.to_out.append(nn.Linear(inner_dim, query_dim)) + self.to_out.append(nn.Dropout(dropout)) + + def reshape_heads_to_batch_dim(self, tensor): + batch_size, seq_len, dim = tensor.shape + head_size = self.heads + tensor = tensor.reshape(batch_size, seq_len, head_size, dim // head_size) + tensor = tensor.permute(0, 2, 1, 3).reshape(batch_size * head_size, seq_len, dim // head_size) + return tensor + + def reshape_batch_dim_to_heads(self, tensor): + batch_size, seq_len, dim = tensor.shape + head_size = self.heads + tensor = tensor.reshape(batch_size // head_size, head_size, seq_len, dim) + tensor = tensor.permute(0, 2, 1, 3).reshape(batch_size // head_size, seq_len, dim * head_size) + return tensor + + def reshape_for_scores(self, tensor): + # split heads and dims + # tensor should be [b (h w)] f (d nd) + batch_size, seq_len, dim = tensor.shape + head_size = self.heads + tensor = tensor.reshape(batch_size, seq_len, head_size, dim // head_size) + tensor = tensor.permute(0, 2, 1, 3).contiguous() + return tensor + + def same_batch_dim_to_heads(self, tensor): + batch_size, head_size, seq_len, dim = tensor.shape # [b (h w)] nd f d + tensor = tensor.reshape(batch_size, seq_len, dim * head_size) + return tensor + + def set_attention_slice(self, slice_size): + if slice_size is not None and slice_size > self.sliceable_head_dim: + raise ValueError(f"slice_size {slice_size} has to be smaller or equal to {self.sliceable_head_dim}.") + + self._slice_size = slice_size + + def forward(self, hidden_states, encoder_hidden_states=None, attention_mask=None, use_image_num=None): + batch_size, sequence_length, _ = hidden_states.shape + + encoder_hidden_states = encoder_hidden_states + + if self.group_norm is not None: + hidden_states = self.group_norm(hidden_states.transpose(1, 2)).transpose(1, 2) + + query = self.to_q(hidden_states) # [b (h w)] f (nd * d) + + # print('before reshpape query shape', query.shape) + dim = query.shape[-1] + query = self.reshape_heads_to_batch_dim(query) # [b (h w) nd] f d + # print('after reshape query shape', query.shape) + + if self.added_kv_proj_dim is not None: + key = self.to_k(hidden_states) + value = self.to_v(hidden_states) + encoder_hidden_states_key_proj = self.add_k_proj(encoder_hidden_states) + encoder_hidden_states_value_proj = self.add_v_proj(encoder_hidden_states) + + key = self.reshape_heads_to_batch_dim(key) + value = self.reshape_heads_to_batch_dim(value) + encoder_hidden_states_key_proj = self.reshape_heads_to_batch_dim(encoder_hidden_states_key_proj) + encoder_hidden_states_value_proj = self.reshape_heads_to_batch_dim(encoder_hidden_states_value_proj) + + key = torch.concat([encoder_hidden_states_key_proj, key], dim=1) + value = torch.concat([encoder_hidden_states_value_proj, value], dim=1) + else: + encoder_hidden_states = encoder_hidden_states if encoder_hidden_states is not None else hidden_states + key = self.to_k(encoder_hidden_states) + value = self.to_v(encoder_hidden_states) + + key = self.reshape_heads_to_batch_dim(key) + value = self.reshape_heads_to_batch_dim(value) + + if attention_mask is not None: + if attention_mask.shape[-1] != query.shape[1]: + target_length = query.shape[1] + attention_mask = F.pad(attention_mask, (0, target_length), value=0.0) + attention_mask = attention_mask.repeat_interleave(self.heads, dim=0) + + # do not use xformers for temporal attention + # # attention, what we cannot get enough of + # if self._use_memory_efficient_attention_xformers: + # hidden_states = self._memory_efficient_attention_xformers(query, key, value, attention_mask) + # # Some versions of xformers return output in fp32, cast it back to the dtype of the input + # hidden_states = hidden_states.to(query.dtype) + # else: + # if self._slice_size is None or query.shape[0] // self._slice_size == 1: + # hidden_states = self._attention(query, key, value, attention_mask) + # else: + # hidden_states = self._sliced_attention(query, key, value, sequence_length, dim, attention_mask) + hidden_states = self._attention(query, key, value, attention_mask) + + # linear proj + hidden_states = self.to_out[0](hidden_states) + + # dropout + hidden_states = self.to_out[1](hidden_states) + return hidden_states + + + def _attention(self, query, key, value, attention_mask=None): + if self.upcast_attention: + query = query.float() + key = key.float() + + attention_scores = torch.baddbmm( + torch.empty(query.shape[0], query.shape[1], key.shape[1], dtype=query.dtype, device=query.device), + query, + key.transpose(-1, -2), + beta=0, + alpha=self.scale, + ) + + # print('query shape', query.shape) + # print('key shape', key.shape) + # print('value shape', value.shape) + + if attention_mask is not None: + # print('attention_mask', attention_mask.shape) + # print('attention_scores', attention_scores.shape) + # exit() + attention_scores = attention_scores + attention_mask + + if self.upcast_softmax: + attention_scores = attention_scores.float() + + attention_probs = attention_scores.softmax(dim=-1) + # print(attention_probs.shape) + + # cast back to the original dtype + attention_probs = attention_probs.to(value.dtype) + # print(attention_probs.shape) + + # compute attention output + hidden_states = torch.bmm(attention_probs, value) + # print(hidden_states.shape) + + # reshape hidden_states + hidden_states = self.reshape_batch_dim_to_heads(hidden_states) + # print(hidden_states.shape) + # exit() + return hidden_states + + def _sliced_attention(self, query, key, value, sequence_length, dim, attention_mask): + batch_size_attention = query.shape[0] + hidden_states = torch.zeros( + (batch_size_attention, sequence_length, dim // self.heads), device=query.device, dtype=query.dtype + ) + slice_size = self._slice_size if self._slice_size is not None else hidden_states.shape[0] + for i in range(hidden_states.shape[0] // slice_size): + start_idx = i * slice_size + end_idx = (i + 1) * slice_size + + query_slice = query[start_idx:end_idx] + key_slice = key[start_idx:end_idx] + + if self.upcast_attention: + query_slice = query_slice.float() + key_slice = key_slice.float() + + attn_slice = torch.baddbmm( + torch.empty(slice_size, query.shape[1], key.shape[1], dtype=query_slice.dtype, device=query.device), + query_slice, + key_slice.transpose(-1, -2), + beta=0, + alpha=self.scale, + ) + + if attention_mask is not None: + attn_slice = attn_slice + attention_mask[start_idx:end_idx] + + if self.upcast_softmax: + attn_slice = attn_slice.float() + + attn_slice = attn_slice.softmax(dim=-1) + + # cast back to the original dtype + attn_slice = attn_slice.to(value.dtype) + attn_slice = torch.bmm(attn_slice, value[start_idx:end_idx]) + + hidden_states[start_idx:end_idx] = attn_slice + + # reshape hidden_states + hidden_states = self.reshape_batch_dim_to_heads(hidden_states) + return hidden_states + + def _memory_efficient_attention_xformers(self, query, key, value, attention_mask): + # TODO attention_mask + query = query.contiguous() + key = key.contiguous() + value = value.contiguous() + # print(query.shape) + # print(key.shape) + # print(value.shape) + hidden_states = xformers.ops.memory_efficient_attention(query, key, value, attn_bias=attention_mask) + # print(hidden_states.shape) + hidden_states = self.reshape_batch_dim_to_heads(hidden_states) + # print(hidden_states.shape) + # exit() + return hidden_states + +class TemporalAttention(CrossAttention): + def __init__(self, + query_dim: int, + cross_attention_dim: Optional[int] = None, + heads: int = 8, + dim_head: int = 64, + dropout: float = 0.0, + bias=False, + upcast_attention: bool = False, + upcast_softmax: bool = False, + added_kv_proj_dim: Optional[int] = None, + norm_num_groups: Optional[int] = None, + rotary_emb=None): + super().__init__(query_dim, cross_attention_dim, heads, dim_head, dropout, bias, upcast_attention, upcast_softmax, added_kv_proj_dim, norm_num_groups) + # relative time positional embeddings + self.time_rel_pos_bias = RelativePositionBias(heads=heads, max_distance=32) # realistically will not be able to generate that many frames of video... yet + self.rotary_emb = rotary_emb + # self.rotary_emb = RotaryEmbedding(32) + + def forward(self, hidden_states, encoder_hidden_states=None, attention_mask=None): + time_rel_pos_bias = self.time_rel_pos_bias(hidden_states.shape[1], device=hidden_states.device) + batch_size, sequence_length, _ = hidden_states.shape + + encoder_hidden_states = encoder_hidden_states + + if self.group_norm is not None: + hidden_states = self.group_norm(hidden_states.transpose(1, 2)).transpose(1, 2) + + query = self.to_q(hidden_states) # [b (h w)] f (nd * d) + dim = query.shape[-1] + + if self.added_kv_proj_dim is not None: + key = self.to_k(hidden_states) + value = self.to_v(hidden_states) + encoder_hidden_states_key_proj = self.add_k_proj(encoder_hidden_states) + encoder_hidden_states_value_proj = self.add_v_proj(encoder_hidden_states) + + key = self.reshape_heads_to_batch_dim(key) + value = self.reshape_heads_to_batch_dim(value) + encoder_hidden_states_key_proj = self.reshape_heads_to_batch_dim(encoder_hidden_states_key_proj) + encoder_hidden_states_value_proj = self.reshape_heads_to_batch_dim(encoder_hidden_states_value_proj) + + key = torch.concat([encoder_hidden_states_key_proj, key], dim=1) + value = torch.concat([encoder_hidden_states_value_proj, value], dim=1) + else: + encoder_hidden_states = encoder_hidden_states if encoder_hidden_states is not None else hidden_states + key = self.to_k(encoder_hidden_states) + value = self.to_v(encoder_hidden_states) + + if attention_mask is not None: + if attention_mask.shape[-1] != query.shape[1]: + target_length = query.shape[1] + attention_mask = F.pad(attention_mask, (0, target_length), value=0.0) + attention_mask = attention_mask.repeat_interleave(self.heads, dim=0) + + # Do not use xformers for temporal attention + # attention, what we cannot get enough of + # if self._use_memory_efficient_attention_xformers: + # hidden_states = self._memory_efficient_attention_xformers(query, key, value, attention_mask) + # # Some versions of xformers return output in fp32, cast it back to the dtype of the input + # hidden_states = hidden_states.to(query.dtype) + # else: + # if self._slice_size is None or query.shape[0] // self._slice_size == 1: + # hidden_states = self._attention(query, key, value, attention_mask, time_rel_pos_bias) + # else: + # hidden_states = self._sliced_attention(query, key, value, sequence_length, dim, attention_mask) + + if self._slice_size is None or query.shape[0] // self._slice_size == 1: + hidden_states = self._attention(query, key, value, attention_mask, time_rel_pos_bias) + else: + hidden_states = self._sliced_attention(query, key, value, sequence_length, dim, attention_mask) + + # linear proj + hidden_states = self.to_out[0](hidden_states) + + # dropout + hidden_states = self.to_out[1](hidden_states) + return hidden_states + + + def _attention(self, query, key, value, attention_mask=None, time_rel_pos_bias=None): + if self.upcast_attention: + query = query.float() + key = key.float() + + # print('query shape', query.shape) + # print('key shape', key.shape) + # print('value shape', value.shape) + # reshape for adding time positional bais + query = self.scale * rearrange(query, 'b f (h d) -> b h f d', h=self.heads) # d: dim_head; n: heads + key = rearrange(key, 'b f (h d) -> b h f d', h=self.heads) # d: dim_head; n: heads + value = rearrange(value, 'b f (h d) -> b h f d', h=self.heads) # d: dim_head; n: heads + # print('query shape', query.shape) + # print('key shape', key.shape) + # print('value shape', value.shape) + + # torch.baddbmm only accepte 3-D tensor + # https://runebook.dev/zh/docs/pytorch/generated/torch.baddbmm + # attention_scores = self.scale * torch.matmul(query, key.transpose(-1, -2)) + if exists(self.rotary_emb): + query = self.rotary_emb.rotate_queries_or_keys(query) + key = self.rotary_emb.rotate_queries_or_keys(key) + + attention_scores = torch.einsum('... h i d, ... h j d -> ... h i j', query, key) + # print('attention_scores shape', attention_scores.shape) + # print('time_rel_pos_bias shape', time_rel_pos_bias.shape) + # print('attention_mask shape', attention_mask.shape) + + attention_scores = attention_scores + time_rel_pos_bias + # print(attention_scores.shape) + + # bert from huggin face + # attention_scores = attention_scores / math.sqrt(self.dim_head) + + # # Normalize the attention scores to probabilities. + # attention_probs = nn.functional.softmax(attention_scores, dim=-1) + + if attention_mask is not None: + # add attention mask + attention_scores = attention_scores + attention_mask + + # vdm + attention_scores = attention_scores - attention_scores.amax(dim = -1, keepdim = True).detach() + + # # Mask out future positions (causal mask) + # mask = torch.triu(torch.ones(16, 16), diagonal=1).to(device=attention_scores.device, dtype=attention_scores.dtype) # + # attention_scores.masked_fill_(mask == 1, float('-inf')) + + # # # disable the fisrt frame + # mask = torch.zeros(16, 16).to(device=attention_scores.device, dtype=attention_scores.dtype) + # mask[:, :1] = 1 + # mask[0, 0] = 0 + # attention_scores.masked_fill_(mask == 1, float('-inf')) + + # only enable the first frame to internact with others frames + # mask = torch.zeros(16, 16).to(device=attention_scores.device, dtype=attention_scores.dtype) + # mask[:1, 1:] = 1 + # attention_scores.masked_fill_(mask == 1, float('-inf')) + + attention_probs = nn.functional.softmax(attention_scores, dim=-1) + # print(attention_probs[0][0]) + + # cast back to the original dtype + attention_probs = attention_probs.to(value.dtype) + + # compute attention output + # hidden_states = torch.matmul(attention_probs, value) + hidden_states = torch.einsum('... h i j, ... h j d -> ... h i d', attention_probs, value) + # print(hidden_states.shape) + # hidden_states = self.same_batch_dim_to_heads(hidden_states) + hidden_states = rearrange(hidden_states, 'b h f d -> b f (h d)') + # print(hidden_states.shape) + # exit() + return hidden_states + +class RelativePositionBias(nn.Module): + def __init__( + self, + heads=8, + num_buckets=32, + max_distance=128, + ): + super().__init__() + self.num_buckets = num_buckets + self.max_distance = max_distance + self.relative_attention_bias = nn.Embedding(num_buckets, heads) + + @staticmethod + def _relative_position_bucket(relative_position, num_buckets=32, max_distance=128): + ret = 0 + n = -relative_position + + num_buckets //= 2 + ret += (n < 0).long() * num_buckets + n = torch.abs(n) + + max_exact = num_buckets // 2 + is_small = n < max_exact + + val_if_large = max_exact + ( + torch.log(n.float() / max_exact) / math.log(max_distance / max_exact) * (num_buckets - max_exact) + ).long() + val_if_large = torch.min(val_if_large, torch.full_like(val_if_large, num_buckets - 1)) + + ret += torch.where(is_small, n, val_if_large) + return ret + + def forward(self, n, device): + q_pos = torch.arange(n, dtype = torch.long, device = device) + k_pos = torch.arange(n, dtype = torch.long, device = device) + rel_pos = rearrange(k_pos, 'j -> 1 j') - rearrange(q_pos, 'i -> i 1') + rp_bucket = self._relative_position_bucket(rel_pos, num_buckets = self.num_buckets, max_distance = self.max_distance) + values = self.relative_attention_bias(rp_bucket) + return rearrange(values, 'i j h -> h i j') # num_heads, num_frames, num_frames + +class PseudoCrossAttention(CrossAttention): + def forward(self, hidden_states, encoder_hidden_states=None, base_content=None, attention_mask=None, video_length=None): + batch_size, sequence_length, _ = hidden_states.shape + video_length = 17 + + if self.group_norm is not None: + hidden_states = self.group_norm(hidden_states.transpose(1, 2)).transpose(1, 2) + + query = self.to_q(hidden_states) + + dim = query.shape[-1] + query = self.reshape_heads_to_batch_dim(query) + + if self.added_kv_proj_dim is not None: + raise NotImplementedError + + encoder_hidden_states = encoder_hidden_states if encoder_hidden_states is not None else hidden_states + key = self.to_k(encoder_hidden_states) + value = self.to_v(encoder_hidden_states) + + key = rearrange(key, "(b f) d c -> b f d c", f=video_length).contiguous() + key[:, 1:] = key[:, 1:] + key[:, :1] + key = rearrange(key, "b f d c -> (b f) d c").contiguous() + + value = rearrange(value, "(b f) d c -> b f d c", f=video_length).contiguous() + value[:, 1:] = value[:, 1:] + value[:, :1] + value = rearrange(value, "b f d c -> (b f) d c").contiguous() + + key = self.reshape_heads_to_batch_dim(key) + value = self.reshape_heads_to_batch_dim(value) + + if attention_mask is not None: + if attention_mask.shape[-1] != query.shape[1]: + target_length = query.shape[1] + attention_mask = F.pad(attention_mask, (0, target_length), value=0.0) + attention_mask = attention_mask.repeat_interleave(self.heads, dim=0) + + # attention, what we cannot get enough of + if self._use_memory_efficient_attention_xformers: + hidden_states = self._memory_efficient_attention_xformers(query, key, value, attention_mask) + # Some versions of xformers return output in fp32, cast it back to the dtype of the input + hidden_states = hidden_states.to(query.dtype) + else: + if self._slice_size is None or query.shape[0] // self._slice_size == 1: + hidden_states = self._attention(query, key, value, attention_mask) + else: + hidden_states = self._sliced_attention(query, key, value, sequence_length, dim, attention_mask) + + # hidden_states = rearrange(hidden_states, '(b f) d c -> b f d c', f=video_length).contiguous() + # hidden_states[:, :1, ...] = base_content + # hidden_states = rearrange(hidden_states, 'b f d c -> (b f) d c') + + # linear proj + hidden_states = self.to_out[0](hidden_states) + + # dropout + hidden_states = self.to_out[1](hidden_states) + return hidden_states + \ No newline at end of file diff --git a/models/transformer_3d.py b/models/transformer_3d.py new file mode 100644 index 0000000000000000000000000000000000000000..881a824cdf9f4cae156ba3c5d33faa9409b2fd3f --- /dev/null +++ b/models/transformer_3d.py @@ -0,0 +1,368 @@ +# Copyright 2023 The HuggingFace Team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +from dataclasses import dataclass +from typing import Any, Dict, Optional + +import torch +import torch.nn.functional as F +from torch import nn + +from diffusers.configuration_utils import ConfigMixin, register_to_config +from diffusers.models.embeddings import ImagePositionalEmbeddings +from diffusers.utils import BaseOutput, deprecate +from diffusers.models.embeddings import PatchEmbed +from diffusers.models.lora import LoRACompatibleConv, LoRACompatibleLinear +from diffusers.models.modeling_utils import ModelMixin +from einops import rearrange, repeat + +try: + from attention import BasicTransformerBlock +except: + from .attention import BasicTransformerBlock + +@dataclass +class Transformer3DModelOutput(BaseOutput): + """ + The output of [`Transformer2DModel`]. + + Args: + 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): + The hidden states output conditioned on the `encoder_hidden_states` input. If discrete, returns probability + distributions for the unnoised latent pixels. + """ + + sample: torch.FloatTensor + + +class Transformer3DModel(ModelMixin, ConfigMixin): + """ + A 2D Transformer model for image-like data. + + Parameters: + num_attention_heads (`int`, *optional*, defaults to 16): The number of heads to use for multi-head attention. + attention_head_dim (`int`, *optional*, defaults to 88): The number of channels in each head. + in_channels (`int`, *optional*): + The number of channels in the input and output (specify if the input is **continuous**). + num_layers (`int`, *optional*, defaults to 1): The number of layers of Transformer blocks to use. + dropout (`float`, *optional*, defaults to 0.0): The dropout probability to use. + cross_attention_dim (`int`, *optional*): The number of `encoder_hidden_states` dimensions to use. + sample_size (`int`, *optional*): The width of the latent images (specify if the input is **discrete**). + This is fixed during training since it is used to learn a number of position embeddings. + num_vector_embeds (`int`, *optional*): + The number of classes of the vector embeddings of the latent pixels (specify if the input is **discrete**). + Includes the class for the masked latent pixel. + activation_fn (`str`, *optional*, defaults to `"geglu"`): Activation function to use in feed-forward. + num_embeds_ada_norm ( `int`, *optional*): + The number of diffusion steps used during training. Pass if at least one of the norm_layers is + `AdaLayerNorm`. This is fixed during training since it is used to learn a number of embeddings that are + added to the hidden states. + + During inference, you can denoise for up to but not more steps than `num_embeds_ada_norm`. + attention_bias (`bool`, *optional*): + Configure if the `TransformerBlocks` attention should contain a bias parameter. + """ + + @register_to_config + def __init__( + self, + num_attention_heads: int = 16, + attention_head_dim: int = 88, + in_channels: Optional[int] = None, + out_channels: Optional[int] = None, + num_layers: int = 1, + dropout: float = 0.0, + norm_num_groups: int = 32, + cross_attention_dim: Optional[int] = None, + attention_bias: bool = False, + sample_size: Optional[int] = None, + num_vector_embeds: Optional[int] = None, + patch_size: Optional[int] = None, + activation_fn: str = "geglu", + num_embeds_ada_norm: Optional[int] = None, + use_linear_projection: bool = False, + only_cross_attention: bool = False, + upcast_attention: bool = False, + norm_type: str = "layer_norm", + norm_elementwise_affine: bool = True, + rotary_emb=None, + ): + super().__init__() + self.use_linear_projection = use_linear_projection + self.num_attention_heads = num_attention_heads + self.attention_head_dim = attention_head_dim + inner_dim = num_attention_heads * attention_head_dim + + # 1. Transformer2DModel can process both standard continuous images of shape `(batch_size, num_channels, width, height)` as well as quantized image embeddings of shape `(batch_size, num_image_vectors)` + # Define whether input is continuous or discrete depending on configuration + self.is_input_continuous = (in_channels is not None) and (patch_size is None) + self.is_input_vectorized = num_vector_embeds is not None + self.is_input_patches = in_channels is not None and patch_size is not None + + if norm_type == "layer_norm" and num_embeds_ada_norm is not None: + deprecation_message = ( + f"The configuration file of this model: {self.__class__} is outdated. `norm_type` is either not set or" + " incorrectly set to `'layer_norm'`.Make sure to set `norm_type` to `'ada_norm'` in the config." + " Please make sure to update the config accordingly as leaving `norm_type` might led to incorrect" + " results in future versions. If you have downloaded this checkpoint from the Hugging Face Hub, it" + " would be very nice if you could open a Pull request for the `transformer/config.json` file" + ) + deprecate("norm_type!=num_embeds_ada_norm", "1.0.0", deprecation_message, standard_warn=False) + norm_type = "ada_norm" + + if self.is_input_continuous and self.is_input_vectorized: + raise ValueError( + f"Cannot define both `in_channels`: {in_channels} and `num_vector_embeds`: {num_vector_embeds}. Make" + " sure that either `in_channels` or `num_vector_embeds` is None." + ) + elif self.is_input_vectorized and self.is_input_patches: + raise ValueError( + f"Cannot define both `num_vector_embeds`: {num_vector_embeds} and `patch_size`: {patch_size}. Make" + " sure that either `num_vector_embeds` or `num_patches` is None." + ) + elif not self.is_input_continuous and not self.is_input_vectorized and not self.is_input_patches: + raise ValueError( + f"Has to define `in_channels`: {in_channels}, `num_vector_embeds`: {num_vector_embeds}, or patch_size:" + f" {patch_size}. Make sure that `in_channels`, `num_vector_embeds` or `num_patches` is not None." + ) + + # 2. Define input layers + if self.is_input_continuous: + self.in_channels = in_channels + + self.norm = torch.nn.GroupNorm(num_groups=norm_num_groups, num_channels=in_channels, eps=1e-6, affine=True) + if use_linear_projection: + self.proj_in = LoRACompatibleLinear(in_channels, inner_dim) + else: + self.proj_in = LoRACompatibleConv(in_channels, inner_dim, kernel_size=1, stride=1, padding=0) + elif self.is_input_vectorized: + assert sample_size is not None, "Transformer2DModel over discrete input must provide sample_size" + assert num_vector_embeds is not None, "Transformer2DModel over discrete input must provide num_embed" + + self.height = sample_size + self.width = sample_size + self.num_vector_embeds = num_vector_embeds + self.num_latent_pixels = self.height * self.width + + self.latent_image_embedding = ImagePositionalEmbeddings( + num_embed=num_vector_embeds, embed_dim=inner_dim, height=self.height, width=self.width + ) + elif self.is_input_patches: + assert sample_size is not None, "Transformer2DModel over patched input must provide sample_size" + + self.height = sample_size + self.width = sample_size + + self.patch_size = patch_size + self.pos_embed = PatchEmbed( + height=sample_size, + width=sample_size, + patch_size=patch_size, + in_channels=in_channels, + embed_dim=inner_dim, + ) + + # 3. Define transformers blocks + self.transformer_blocks = nn.ModuleList( + [ + BasicTransformerBlock( + inner_dim, + num_attention_heads, + attention_head_dim, + dropout=dropout, + cross_attention_dim=cross_attention_dim, + activation_fn=activation_fn, + num_embeds_ada_norm=num_embeds_ada_norm, + attention_bias=attention_bias, + only_cross_attention=only_cross_attention, + upcast_attention=upcast_attention, + norm_type=norm_type, + norm_elementwise_affine=norm_elementwise_affine, + rotary_emb=rotary_emb, + ) + for d in range(num_layers) + ] + ) + + # 4. Define output layers + self.out_channels = in_channels if out_channels is None else out_channels + if self.is_input_continuous: + # TODO: should use out_channels for continuous projections + if use_linear_projection: + self.proj_out = LoRACompatibleLinear(inner_dim, in_channels) + else: + self.proj_out = LoRACompatibleConv(inner_dim, in_channels, kernel_size=1, stride=1, padding=0) + elif self.is_input_vectorized: + self.norm_out = nn.LayerNorm(inner_dim) + self.out = nn.Linear(inner_dim, self.num_vector_embeds - 1) + elif self.is_input_patches: + self.norm_out = nn.LayerNorm(inner_dim, elementwise_affine=False, eps=1e-6) + self.proj_out_1 = nn.Linear(inner_dim, 2 * inner_dim) + self.proj_out_2 = nn.Linear(inner_dim, patch_size * patch_size * self.out_channels) + + def forward( + self, + hidden_states: torch.Tensor, + encoder_hidden_states: Optional[torch.Tensor] = None, + timestep: Optional[torch.LongTensor] = None, + class_labels: Optional[torch.LongTensor] = None, + cross_attention_kwargs: Dict[str, Any] = None, + attention_mask: Optional[torch.Tensor] = None, + encoder_attention_mask: Optional[torch.Tensor] = None, + return_dict: bool = True, + use_image_num=None, + ): + """ + The [`Transformer2DModel`] forward method. + + Args: + hidden_states (`torch.LongTensor` of shape `(batch size, num latent pixels)` if discrete, `torch.FloatTensor` of shape `(batch size, channel, height, width)` if continuous): + Input `hidden_states`. + encoder_hidden_states ( `torch.FloatTensor` of shape `(batch size, sequence len, embed dims)`, *optional*): + Conditional embeddings for cross attention layer. If not given, cross-attention defaults to + self-attention. + timestep ( `torch.LongTensor`, *optional*): + Used to indicate denoising step. Optional timestep to be applied as an embedding in `AdaLayerNorm`. + class_labels ( `torch.LongTensor` of shape `(batch size, num classes)`, *optional*): + Used to indicate class labels conditioning. Optional class labels to be applied as an embedding in + `AdaLayerZeroNorm`. + encoder_attention_mask ( `torch.Tensor`, *optional*): + Cross-attention mask applied to `encoder_hidden_states`. Two formats supported: + + * Mask `(batch, sequence_length)` True = keep, False = discard. + * Bias `(batch, 1, sequence_length)` 0 = keep, -10000 = discard. + + If `ndim == 2`: will be interpreted as a mask, then converted into a bias consistent with the format + above. This bias will be added to the cross-attention scores. + return_dict (`bool`, *optional*, defaults to `True`): + Whether or not to return a [`~models.unet_2d_condition.UNet2DConditionOutput`] instead of a plain + tuple. + + Returns: + If `return_dict` is True, an [`~models.transformer_2d.Transformer2DModelOutput`] is returned, otherwise a + `tuple` where the first element is the sample tensor. + """ + # ensure attention_mask is a bias, and give it a singleton query_tokens dimension. + # we may have done this conversion already, e.g. if we came here via UNet2DConditionModel#forward. + # we can tell by counting dims; if ndim == 2: it's a mask rather than a bias. + # expects mask of shape: + # [batch, key_tokens] + # adds singleton query_tokens dimension: + # [batch, 1, key_tokens] + # this helps to broadcast it as a bias over attention scores, which will be in one of the following shapes: + # [batch, heads, query_tokens, key_tokens] (e.g. torch sdp attn) + # [batch * heads, query_tokens, key_tokens] (e.g. xformers or classic attn) + if attention_mask is not None and attention_mask.ndim == 2: + # assume that mask is expressed as: + # (1 = keep, 0 = discard) + # convert mask into a bias that can be added to attention scores: + # (keep = +0, discard = -10000.0) + attention_mask = (1 - attention_mask.to(hidden_states.dtype)) * -10000.0 + attention_mask = attention_mask.unsqueeze(1) + + # convert encoder_attention_mask to a bias the same way we do for attention_mask + if encoder_attention_mask is not None and encoder_attention_mask.ndim == 2: + encoder_attention_mask = (1 - encoder_attention_mask.to(hidden_states.dtype)) * -10000.0 + encoder_attention_mask = encoder_attention_mask.unsqueeze(1) + + # 1. Input + if self.is_input_continuous: # True + + assert hidden_states.dim() == 5, f"Expected hidden_states to have ndim=5, but got ndim={hidden_states.dim()}." + if self.training and use_image_num != 0: + video_length = hidden_states.shape[2] - use_image_num + hidden_states = rearrange(hidden_states, "b c f h w -> (b f) c h w").contiguous() + encoder_hidden_states_length = encoder_hidden_states.shape[1] + encoder_hidden_states_video = encoder_hidden_states[:, :encoder_hidden_states_length - use_image_num, ...] + encoder_hidden_states_video = repeat(encoder_hidden_states_video, 'b m n c -> b (m f) n c', f=video_length).contiguous() + encoder_hidden_states_image = encoder_hidden_states[:, encoder_hidden_states_length - use_image_num:, ...] + encoder_hidden_states = torch.cat([encoder_hidden_states_video, encoder_hidden_states_image], dim=1) + encoder_hidden_states = rearrange(encoder_hidden_states, 'b m n c -> (b m) n c').contiguous() + else: + video_length = hidden_states.shape[2] + hidden_states = rearrange(hidden_states, "b c f h w -> (b f) c h w").contiguous() + encoder_hidden_states = repeat(encoder_hidden_states, 'b n c -> (b f) n c', f=video_length).contiguous() + + batch, _, height, width = hidden_states.shape + residual = hidden_states + + hidden_states = self.norm(hidden_states) + if not self.use_linear_projection: + hidden_states = self.proj_in(hidden_states) + inner_dim = hidden_states.shape[1] + hidden_states = hidden_states.permute(0, 2, 3, 1).reshape(batch, height * width, inner_dim) + else: + inner_dim = hidden_states.shape[1] + hidden_states = hidden_states.permute(0, 2, 3, 1).reshape(batch, height * width, inner_dim) + hidden_states = self.proj_in(hidden_states) + elif self.is_input_vectorized: + hidden_states = self.latent_image_embedding(hidden_states) + elif self.is_input_patches: + hidden_states = self.pos_embed(hidden_states) + + # 2. Blocks + for block in self.transformer_blocks: + hidden_states = block( + hidden_states, + attention_mask=attention_mask, + encoder_hidden_states=encoder_hidden_states, + encoder_attention_mask=encoder_attention_mask, + timestep=timestep, + cross_attention_kwargs=cross_attention_kwargs, + class_labels=class_labels, + video_length=video_length, + use_image_num=use_image_num, + ) + + # 3. Output + if self.is_input_continuous: + if not self.use_linear_projection: + hidden_states = hidden_states.reshape(batch, height, width, inner_dim).permute(0, 3, 1, 2).contiguous() + hidden_states = self.proj_out(hidden_states) + else: + hidden_states = self.proj_out(hidden_states) + hidden_states = hidden_states.reshape(batch, height, width, inner_dim).permute(0, 3, 1, 2).contiguous() + + output = hidden_states + residual + elif self.is_input_vectorized: + hidden_states = self.norm_out(hidden_states) + logits = self.out(hidden_states) + # (batch, self.num_vector_embeds - 1, self.num_latent_pixels) + logits = logits.permute(0, 2, 1) + + # log(p(x_0)) + output = F.log_softmax(logits.double(), dim=1).float() + elif self.is_input_patches: + # TODO: cleanup! + conditioning = self.transformer_blocks[0].norm1.emb( + timestep, class_labels, hidden_dtype=hidden_states.dtype + ) + shift, scale = self.proj_out_1(F.silu(conditioning)).chunk(2, dim=1) + hidden_states = self.norm_out(hidden_states) * (1 + scale[:, None]) + shift[:, None] + hidden_states = self.proj_out_2(hidden_states) + + # unpatchify + height = width = int(hidden_states.shape[1] ** 0.5) + hidden_states = hidden_states.reshape( + shape=(-1, height, width, self.patch_size, self.patch_size, self.out_channels) + ) + hidden_states = torch.einsum("nhwpqc->nchpwq", hidden_states) + output = hidden_states.reshape( + shape=(-1, self.out_channels, height * self.patch_size, width * self.patch_size) + ) + + output = rearrange(output, "(b f) c h w -> b c f h w", f=video_length + use_image_num).contiguous() + if not return_dict: + return (output,) + + return Transformer3DModelOutput(sample=output) diff --git a/models/unet.py b/models/unet.py new file mode 100644 index 0000000000000000000000000000000000000000..7ae50a26a9b18f3f9a6f5d2e103b55af104cea2e --- /dev/null +++ b/models/unet.py @@ -0,0 +1,819 @@ +# Copyright 2023 The HuggingFace Team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +from dataclasses import dataclass +from typing import Any, Dict, List, Optional, Tuple, Union +import os +import json +import torch +import torch.nn as nn +import torch.utils.checkpoint +from einops import repeat + +from diffusers.configuration_utils import ConfigMixin, register_to_config +from diffusers.loaders import UNet2DConditionLoadersMixin +from diffusers.utils import BaseOutput, logging +from diffusers.models.activations import get_activation +from diffusers.models.attention_processor import AttentionProcessor, AttnProcessor +from diffusers.models.embeddings import ( + GaussianFourierProjection, + TimestepEmbedding, + Timesteps, +) +from diffusers.models.modeling_utils import ModelMixin +# from rotary_embedding_torch import RotaryEmbedding + +try: + from unet_blocks import (UNetMidBlock3DCrossAttn, + get_down_block, get_up_block, + CrossAttnDownBlock3D, + DownBlock3D, + CrossAttnUpBlock3D, + UpBlock3D) + from resnet import InflatedConv3d + from rotary_embedding_torch_mx import RotaryEmbedding +except: + from .unet_blocks import (UNetMidBlock3DCrossAttn, + get_down_block, get_up_block, + CrossAttnDownBlock3D, + DownBlock3D, + CrossAttnUpBlock3D, + UpBlock3D) + from .resnet import InflatedConv3d + from .rotary_embedding_torch_mx import RotaryEmbedding + +logger = logging.get_logger(__name__) # pylint: disable=invalid-name + + +@dataclass +class UNet3DConditionOutput(BaseOutput): + """ + The output of [`UNet2DConditionModel`]. + + Args: + sample (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)`): + The hidden states output conditioned on `encoder_hidden_states` input. Output of last layer of model. + """ + + sample: torch.FloatTensor = None + + +class UNet3DConditionModel(ModelMixin, ConfigMixin, UNet2DConditionLoadersMixin): + + _supports_gradient_checkpointing = True + + @register_to_config + def __init__( + self, + sample_size: Optional[int] = None, + in_channels: int = 4, + out_channels: int = 4, + center_input_sample: bool = False, + flip_sin_to_cos: bool = True, + freq_shift: int = 0, + down_block_types: Tuple[str] = ( + "CrossAttnDownBlock3D", + "CrossAttnDownBlock3D", + "CrossAttnDownBlock3D", + "DownBlock3D", + ), + mid_block_type: Optional[str] = "UNetMidBlock3DCrossAttn", + up_block_types: Tuple[str] = ( + "UpBlock3D", + "CrossAttnUpBlock3D", + "CrossAttnUpBlock3D", + "CrossAttnUpBlock3D" + ), + only_cross_attention: Union[bool, Tuple[bool]] = False, # xl false + block_out_channels: Tuple[int] = (320, 640, 1280, 1280), + layers_per_block: Union[int, Tuple[int]] = 2, + downsample_padding: int = 1, + mid_block_scale_factor: float = 1, + act_fn: str = "silu", + norm_num_groups: Optional[int] = 32, + norm_eps: float = 1e-5, + cross_attention_dim: Union[int, Tuple[int]] = 1280, + transformer_layers_per_block: Union[int, Tuple[int]] = 1, + encoder_hid_dim: Optional[int] = None, # xl null + encoder_hid_dim_type: Optional[str] = None, # xl null + attention_head_dim: Union[int, Tuple[int]] = 8, + num_attention_heads: Optional[Union[int, Tuple[int]]] = None, # xl null + dual_cross_attention: bool = False, # false + use_linear_projection: bool = False, # xl true + class_embed_type: Optional[str] = None, # null + addition_embed_type: Optional[str] = None, # xl text_time + addition_time_embed_dim: Optional[int] = None, # xl 256 + num_class_embeds: Optional[int] = None, # xl null + upcast_attention: bool = False, # xl false + resnet_time_scale_shift: str = "default", # xl default + resnet_skip_time_act: bool = False, # xl false + resnet_out_scale_factor: int = 1.0, # xl 1.0 + time_embedding_type: str = "positional", # xl positional + time_embedding_dim: Optional[int] = None, # xl null + time_embedding_act_fn: Optional[str] = None, # xl null + timestep_post_act: Optional[str] = None, # xl null + time_cond_proj_dim: Optional[int] = None, # null + conv_in_kernel: int = 3, # xl 3 + conv_out_kernel: int = 3, # xl 3 + projection_class_embeddings_input_dim: Optional[int] = None, # 2816 + class_embeddings_concat: bool = False, # xl false + mid_block_only_cross_attention: Optional[bool] = None, # null + cross_attention_norm: Optional[str] = None, # null + addition_embed_type_num_heads=64, + ): + super().__init__() + + self.sample_size = sample_size + + if num_attention_heads is not None: + raise ValueError( + "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." + ) + + # If `num_attention_heads` is not defined (which is the case for most models) + # it will default to `attention_head_dim`. This looks weird upon first reading it and it is. + # The reason for this behavior is to correct for incorrectly named variables that were introduced + # when this library was created. The incorrect naming was only discovered much later in https://github.com/huggingface/diffusers/issues/2011#issuecomment-1547958131 + # Changing `attention_head_dim` to `num_attention_heads` for 40,000+ configurations is too backwards breaking + # which is why we correct for the naming here. + num_attention_heads = num_attention_heads or attention_head_dim + + # Check inputs + if len(down_block_types) != len(up_block_types): + raise ValueError( + 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}." + ) + + if len(block_out_channels) != len(down_block_types): + raise ValueError( + 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}." + ) + + if not isinstance(only_cross_attention, bool) and len(only_cross_attention) != len(down_block_types): + raise ValueError( + 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}." + ) + + if not isinstance(num_attention_heads, int) and len(num_attention_heads) != len(down_block_types): + raise ValueError( + 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}." + ) + + if not isinstance(attention_head_dim, int) and len(attention_head_dim) != len(down_block_types): + raise ValueError( + 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}." + ) + + if isinstance(cross_attention_dim, list) and len(cross_attention_dim) != len(down_block_types): + raise ValueError( + 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}." + ) + + if not isinstance(layers_per_block, int) and len(layers_per_block) != len(down_block_types): + raise ValueError( + 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}." + ) + + # input + conv_in_padding = (conv_in_kernel - 1) // 2 + self.conv_in = InflatedConv3d(in_channels, block_out_channels[0], kernel_size=conv_in_kernel, padding=conv_in_padding) + + # time + if time_embedding_type == "fourier": + time_embed_dim = time_embedding_dim or block_out_channels[0] * 2 + if time_embed_dim % 2 != 0: + raise ValueError(f"`time_embed_dim` should be divisible by 2, but is {time_embed_dim}.") + self.time_proj = GaussianFourierProjection( + time_embed_dim // 2, set_W_to_weight=False, log=False, flip_sin_to_cos=flip_sin_to_cos + ) + timestep_input_dim = time_embed_dim + elif time_embedding_type == "positional": # we are here + time_embed_dim = time_embedding_dim or block_out_channels[0] * 4 + + self.time_proj = Timesteps(block_out_channels[0], flip_sin_to_cos, freq_shift) + timestep_input_dim = block_out_channels[0] + else: + raise ValueError( + f"{time_embedding_type} does not exist. Please make sure to use one of `fourier` or `positional`." + ) + + self.time_embedding = TimestepEmbedding( + timestep_input_dim, + time_embed_dim, + act_fn=act_fn, + post_act_fn=timestep_post_act, + cond_proj_dim=time_cond_proj_dim, + ) + + + if addition_embed_type == "motion_ids": # we need this in our situation + self.add_time_proj = Timesteps(addition_time_embed_dim, flip_sin_to_cos, freq_shift) + self.add_embedding = TimestepEmbedding(addition_time_embed_dim, time_embed_dim) + # nn.init.zeros_(self.add_embedding.linear_1.weight.data) + nn.init.zeros_(self.add_embedding.linear_2.weight.data) + elif addition_embed_type is not None: + print("Not use any addition embed type!") + # raise ValueError(f"addition_embed_type: {addition_embed_type} must be None, 'text' or 'text_image'.") + + + self.down_blocks = nn.ModuleList([]) + self.up_blocks = nn.ModuleList([]) + + if isinstance(only_cross_attention, bool): # only_cross_attention is False + if mid_block_only_cross_attention is None: # mid_block_only_cross_attention is None + mid_block_only_cross_attention = only_cross_attention # Thus, mid_block_only_cross_attention is False + + only_cross_attention = [only_cross_attention] * len(down_block_types) + + if mid_block_only_cross_attention is None: + mid_block_only_cross_attention = False + + if isinstance(num_attention_heads, int): # num_attention_heads is None + num_attention_heads = (num_attention_heads,) * len(down_block_types) + + if isinstance(attention_head_dim, int): + attention_head_dim = (attention_head_dim,) * len(down_block_types) + + if isinstance(cross_attention_dim, int): + cross_attention_dim = (cross_attention_dim,) * len(down_block_types) + + if isinstance(layers_per_block, int): + layers_per_block = [layers_per_block] * len(down_block_types) + + if isinstance(transformer_layers_per_block, int): + transformer_layers_per_block = [transformer_layers_per_block] * len(down_block_types) + + if class_embeddings_concat: + # The time embeddings are concatenated with the class embeddings. The dimension of the + # time embeddings passed to the down, middle, and up blocks is twice the dimension of the + # regular time embeddings + blocks_time_embed_dim = time_embed_dim * 2 + else: + blocks_time_embed_dim = time_embed_dim + + + # rotary_emb = RotaryEmbedding(dim = 32, + # interpolate_factor = 2.) + rotary_emb = RotaryEmbedding(dim = 32) + # rotary_emb = None + + # down + output_channel = block_out_channels[0] + for i, down_block_type in enumerate(down_block_types): + input_channel = output_channel + output_channel = block_out_channels[i] + is_final_block = i == len(block_out_channels) - 1 + + down_block = get_down_block( + down_block_type, + num_layers=layers_per_block[i], + transformer_layers_per_block=transformer_layers_per_block[i], + in_channels=input_channel, + out_channels=output_channel, + temb_channels=blocks_time_embed_dim, + add_downsample=not is_final_block, + resnet_eps=norm_eps, + resnet_act_fn=act_fn, + resnet_groups=norm_num_groups, + cross_attention_dim=cross_attention_dim[i], + num_attention_heads=num_attention_heads[i], + downsample_padding=downsample_padding, + dual_cross_attention=dual_cross_attention, + use_linear_projection=use_linear_projection, + only_cross_attention=only_cross_attention[i], + upcast_attention=upcast_attention, + resnet_time_scale_shift=resnet_time_scale_shift, + resnet_skip_time_act=resnet_skip_time_act, + resnet_out_scale_factor=resnet_out_scale_factor, + cross_attention_norm=cross_attention_norm, + attention_head_dim=attention_head_dim[i] if attention_head_dim[i] is not None else output_channel, + rotary_emb=rotary_emb, + ) + self.down_blocks.append(down_block) + + # mid + if mid_block_type == "UNetMidBlock3DCrossAttn": + self.mid_block = UNetMidBlock3DCrossAttn( + transformer_layers_per_block=transformer_layers_per_block[-1], + in_channels=block_out_channels[-1], + temb_channels=blocks_time_embed_dim, + resnet_eps=norm_eps, + resnet_act_fn=act_fn, + output_scale_factor=mid_block_scale_factor, + resnet_time_scale_shift=resnet_time_scale_shift, + cross_attention_dim=cross_attention_dim[-1], + num_attention_heads=num_attention_heads[-1], + resnet_groups=norm_num_groups, + dual_cross_attention=dual_cross_attention, + use_linear_projection=use_linear_projection, + upcast_attention=upcast_attention, + rotary_emb=rotary_emb, + ) + elif mid_block_type is None: + self.mid_block = None + else: + raise ValueError(f"unknown mid_block_type : {mid_block_type}") + + # count how many layers upsample the images + self.num_upsamplers = 0 + + # up + reversed_block_out_channels = list(reversed(block_out_channels)) + reversed_num_attention_heads = list(reversed(num_attention_heads)) + reversed_layers_per_block = list(reversed(layers_per_block)) + reversed_cross_attention_dim = list(reversed(cross_attention_dim)) + reversed_transformer_layers_per_block = list(reversed(transformer_layers_per_block)) + only_cross_attention = list(reversed(only_cross_attention)) + + output_channel = reversed_block_out_channels[0] + for i, up_block_type in enumerate(up_block_types): + is_final_block = i == len(block_out_channels) - 1 + + prev_output_channel = output_channel + output_channel = reversed_block_out_channels[i] + input_channel = reversed_block_out_channels[min(i + 1, len(block_out_channels) - 1)] + + # add upsample block for all BUT final layer + if not is_final_block: + add_upsample = True + self.num_upsamplers += 1 + else: + add_upsample = False + + up_block = get_up_block( + up_block_type, + num_layers=reversed_layers_per_block[i] + 1, + transformer_layers_per_block=reversed_transformer_layers_per_block[i], + in_channels=input_channel, + out_channels=output_channel, + prev_output_channel=prev_output_channel, + temb_channels=blocks_time_embed_dim, + add_upsample=add_upsample, + resnet_eps=norm_eps, + resnet_act_fn=act_fn, + resnet_groups=norm_num_groups, + cross_attention_dim=reversed_cross_attention_dim[i], + num_attention_heads=reversed_num_attention_heads[i], + dual_cross_attention=dual_cross_attention, + use_linear_projection=use_linear_projection, + only_cross_attention=only_cross_attention[i], + upcast_attention=upcast_attention, + resnet_time_scale_shift=resnet_time_scale_shift, + resnet_skip_time_act=resnet_skip_time_act, + resnet_out_scale_factor=resnet_out_scale_factor, + cross_attention_norm=cross_attention_norm, + attention_head_dim=attention_head_dim[i] if attention_head_dim[i] is not None else output_channel, + rotary_emb=rotary_emb, + ) + self.up_blocks.append(up_block) + prev_output_channel = output_channel + + # out + if norm_num_groups is not None: + self.conv_norm_out = nn.GroupNorm( + num_channels=block_out_channels[0], num_groups=norm_num_groups, eps=norm_eps + ) + + self.conv_act = get_activation(act_fn) + + else: + self.conv_norm_out = None + self.conv_act = None + + conv_out_padding = (conv_out_kernel - 1) // 2 + self.conv_out = InflatedConv3d(block_out_channels[0], out_channels, kernel_size=conv_out_kernel, padding=conv_out_padding) + + # clip image preject + # self.clip_img_proj = nn.Linear(768, 768, bias=True) + # self.add_time_proj_silu = nn.SiLU() + + + @property + def attn_processors(self) -> Dict[str, AttentionProcessor]: + r""" + Returns: + `dict` of attention processors: A dictionary containing all attention processors used in the model with + indexed by its weight name. + """ + # set recursively + processors = {} + + def fn_recursive_add_processors(name: str, module: torch.nn.Module, processors: Dict[str, AttentionProcessor]): + if hasattr(module, "set_processor"): + processors[f"{name}.processor"] = module.processor + + for sub_name, child in module.named_children(): + fn_recursive_add_processors(f"{name}.{sub_name}", child, processors) + + return processors + + for name, module in self.named_children(): + fn_recursive_add_processors(name, module, processors) + + return processors + + def set_attn_processor(self, processor: Union[AttentionProcessor, Dict[str, AttentionProcessor]]): + r""" + Sets the attention processor to use to compute attention. + + Parameters: + processor (`dict` of `AttentionProcessor` or only `AttentionProcessor`): + The instantiated processor class or a dictionary of processor classes that will be set as the processor + for **all** `Attention` layers. + + If `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. + + """ + count = len(self.attn_processors.keys()) + + if isinstance(processor, dict) and len(processor) != count: + raise ValueError( + f"A dict of processors was passed, but the number of processors {len(processor)} does not match the" + f" number of attention layers: {count}. Please make sure to pass {count} processor classes." + ) + + def fn_recursive_attn_processor(name: str, module: torch.nn.Module, processor): + if hasattr(module, "set_processor"): + if not isinstance(processor, dict): + module.set_processor(processor) + else: + module.set_processor(processor.pop(f"{name}.processor")) + + for sub_name, child in module.named_children(): + fn_recursive_attn_processor(f"{name}.{sub_name}", child, processor) + + for name, module in self.named_children(): + fn_recursive_attn_processor(name, module, processor) + + def set_default_attn_processor(self): + """ + Disables custom attention processors and sets the default attention implementation. + """ + self.set_attn_processor(AttnProcessor()) + + def set_attention_slice(self, slice_size): + r""" + Enable sliced attention computation. + + When this option is enabled, the attention module splits the input tensor in slices to compute attention in + several steps. This is useful for saving some memory in exchange for a small decrease in speed. + + Args: + slice_size (`str` or `int` or `list(int)`, *optional*, defaults to `"auto"`): + When `"auto"`, input to the attention heads is halved, so attention is computed in two steps. If + `"max"`, maximum amount of memory is saved by running only one slice at a time. If a number is + provided, uses as many slices as `attention_head_dim // slice_size`. In this case, `attention_head_dim` + must be a multiple of `slice_size`. + """ + sliceable_head_dims = [] + + def fn_recursive_retrieve_sliceable_dims(module: torch.nn.Module): + if hasattr(module, "set_attention_slice"): + sliceable_head_dims.append(module.sliceable_head_dim) + + for child in module.children(): + fn_recursive_retrieve_sliceable_dims(child) + + # retrieve number of attention layers + for module in self.children(): + fn_recursive_retrieve_sliceable_dims(module) + + num_sliceable_layers = len(sliceable_head_dims) + + if slice_size == "auto": + # half the attention head size is usually a good trade-off between + # speed and memory + slice_size = [dim // 2 for dim in sliceable_head_dims] + elif slice_size == "max": + # make smallest slice possible + slice_size = num_sliceable_layers * [1] + + slice_size = num_sliceable_layers * [slice_size] if not isinstance(slice_size, list) else slice_size + + if len(slice_size) != len(sliceable_head_dims): + raise ValueError( + f"You have provided {len(slice_size)}, but {self.config} has {len(sliceable_head_dims)} different" + f" attention layers. Make sure to match `len(slice_size)` to be {len(sliceable_head_dims)}." + ) + + for i in range(len(slice_size)): + size = slice_size[i] + dim = sliceable_head_dims[i] + if size is not None and size > dim: + raise ValueError(f"size {size} has to be smaller or equal to {dim}.") + + # Recursively walk through all the children. + # Any children which exposes the set_attention_slice method + # gets the message + def fn_recursive_set_attention_slice(module: torch.nn.Module, slice_size: List[int]): + if hasattr(module, "set_attention_slice"): + module.set_attention_slice(slice_size.pop()) + + for child in module.children(): + fn_recursive_set_attention_slice(child, slice_size) + + reversed_slice_size = list(reversed(slice_size)) + for module in self.children(): + fn_recursive_set_attention_slice(module, reversed_slice_size) + + def _set_gradient_checkpointing(self, module, value=False): + if isinstance(module, (CrossAttnDownBlock3D, DownBlock3D, CrossAttnUpBlock3D, UpBlock3D)): + module.gradient_checkpointing = value + + def forward( + self, + sample: torch.FloatTensor, + timestep: Union[torch.Tensor, float, int], + encoder_hidden_states: torch.Tensor, + encoder_img_hidden_states: Optional[torch.Tensor] = None, + class_labels: Optional[torch.Tensor] = None, + timestep_cond: Optional[torch.Tensor] = None, + attention_mask: Optional[torch.Tensor] = None, + base_content: Optional[torch.Tensor] = None, + cross_attention_kwargs: Optional[Dict[str, Any]] = None, + added_motion_ids: Optional[Dict[str, Any]] = None, + added_cond_kwargs: Optional[Dict[str, torch.Tensor]] = None, + down_block_additional_residuals: Optional[Tuple[torch.Tensor]] = None, + mid_block_additional_residual: Optional[torch.Tensor] = None, + encoder_attention_mask: Optional[torch.Tensor] = None, + return_dict: bool = True, + use_image_num: int = 0, + ) -> Union[UNet3DConditionOutput, Tuple]: + r""" + The [`UNet2DConditionModel`] forward method. + + Args: + sample (`torch.FloatTensor`): + The noisy input tensor with the following shape `(batch, channel, height, width)`. + timestep (`torch.FloatTensor` or `float` or `int`): The number of timesteps to denoise an input. + encoder_hidden_states (`torch.FloatTensor`): + The encoder hidden states with shape `(batch, sequence_length, feature_dim)`. + encoder_attention_mask (`torch.Tensor`): + A cross-attention mask of shape `(batch, sequence_length)` is applied to `encoder_hidden_states`. If + `True` the mask is kept, otherwise if `False` it is discarded. Mask will be converted into a bias, + which adds large negative values to the attention scores corresponding to "discard" tokens. + return_dict (`bool`, *optional*, defaults to `True`): + Whether or not to return a [`~models.unet_2d_condition.UNet3DConditionOutput`] instead of a plain + tuple. + cross_attention_kwargs (`dict`, *optional*): + A kwargs dictionary that if specified is passed along to the [`AttnProcessor`]. + added_cond_kwargs: (`dict`, *optional*): + A kwargs dictionary containin additional embeddings that if specified are added to the embeddings that + are passed along to the UNet blocks. + + Returns: + [`~models.unet_2d_condition.UNet2DConditionOutput`] or `tuple`: + If `return_dict` is True, an [`~models.unet_2d_condition.UNet2DConditionOutput`] is returned, otherwise + a `tuple` is returned where the first element is the sample tensor. + """ + # By default samples have to be AT least a multiple of the overall upsampling factor. + # The overall upsampling factor is equal to 2 ** (# num of upsampling layers). + # However, the upsampling interpolation output size can be forced to fit any upsampling size + # on the fly if necessary. + default_overall_up_factor = 2**self.num_upsamplers + + # upsample size should be forwarded when sample is not a multiple of `default_overall_up_factor` + forward_upsample_size = False + upsample_size = None + + if any(s % default_overall_up_factor != 0 for s in sample.shape[-2:]): + logger.info("Forward upsample size to force interpolation output size.") + forward_upsample_size = True + + # ensure attention_mask is a bias, and give it a singleton query_tokens dimension + # expects mask of shape: + # [batch, key_tokens] + # adds singleton query_tokens dimension: + # [batch, 1, key_tokens] + # this helps to broadcast it as a bias over attention scores, which will be in one of the following shapes: + # [batch, heads, query_tokens, key_tokens] (e.g. torch sdp attn) + # [batch * heads, query_tokens, key_tokens] (e.g. xformers or classic attn) + if attention_mask is not None: + # assume that mask is expressed as: + # (1 = keep, 0 = discard) + # convert mask into a bias that can be added to attention scores: + # (keep = +0, discard = -10000.0) + attention_mask = (1 - attention_mask.to(sample.dtype)) * -10000.0 + attention_mask = attention_mask.unsqueeze(1) + + # convert encoder_attention_mask to a bias the same way we do for attention_mask + if encoder_attention_mask is not None: + encoder_attention_mask = (1 - encoder_attention_mask.to(sample.dtype)) * -10000.0 + encoder_attention_mask = encoder_attention_mask.unsqueeze(1) + + # 0. center input if necessary + if self.config.center_input_sample: + sample = 2 * sample - 1.0 + + # 1. time + timesteps = timestep + if not torch.is_tensor(timesteps): + # TODO: this requires sync between CPU and GPU. So try to pass timesteps as tensors if you can + # This would be a good case for the `match` statement (Python 3.10+) + is_mps = sample.device.type == "mps" + if isinstance(timestep, float): + dtype = torch.float32 if is_mps else torch.float64 + else: + dtype = torch.int32 if is_mps else torch.int64 + timesteps = torch.tensor([timesteps], dtype=dtype, device=sample.device) + elif len(timesteps.shape) == 0: + timesteps = timesteps[None].to(sample.device) + + # broadcast to batch dimension in a way that's compatible with ONNX/Core ML + timesteps = timesteps.expand(sample.shape[0]) + + t_emb = self.time_proj(timesteps) + + # `Timesteps` does not contain any weights and will always return f32 tensors + # but time_embedding might actually be running in fp16. so we need to cast here. + # there might be better ways to encapsulate this. + t_emb = t_emb.to(dtype=sample.dtype) + + emb = self.time_embedding(t_emb, timestep_cond) + aug_emb = None + + if self.config.addition_embed_type == "motion_ids": # we are here + motion_ids_embeds = self.add_time_proj(added_motion_ids.flatten()).to(dtype=emb.dtype) + aug_emb = self.add_embedding(motion_ids_embeds) + + emb = emb + aug_emb if aug_emb is not None else emb # emb is timesteps; aug_emb is the combination of text_embeds and time_embeds + + # 2. pre-process + sample = base_content + sample + sample = torch.cat([base_content, sample], dim=2) + sample = self.conv_in(sample) + + # 3. down + # print(encoder_hidden_states.shape) # shape [2, 77, 2048] + is_controlnet = mid_block_additional_residual is not None and down_block_additional_residuals is not None + is_adapter = mid_block_additional_residual is None and down_block_additional_residuals is not None + + down_block_res_samples = (sample,) + for downsample_block in self.down_blocks: + if hasattr(downsample_block, "has_cross_attention") and downsample_block.has_cross_attention: + # For t2i-adapter CrossAttnDownBlock2D + additional_residuals = {} + if is_adapter and len(down_block_additional_residuals) > 0: + additional_residuals["additional_residuals"] = down_block_additional_residuals.pop(0) + + sample, res_samples = downsample_block( + hidden_states=sample, + temb=emb, + encoder_hidden_states=encoder_hidden_states, + attention_mask=attention_mask, + cross_attention_kwargs=cross_attention_kwargs, + encoder_attention_mask=encoder_attention_mask, + use_image_num=use_image_num, + **additional_residuals, + ) + else: + sample, res_samples = downsample_block(hidden_states=sample, temb=emb) + + if is_adapter and len(down_block_additional_residuals) > 0: + sample += down_block_additional_residuals.pop(0) + + down_block_res_samples += res_samples + + if is_controlnet: + new_down_block_res_samples = () + + for down_block_res_sample, down_block_additional_residual in zip( + down_block_res_samples, down_block_additional_residuals + ): + down_block_res_sample = down_block_res_sample + down_block_additional_residual + new_down_block_res_samples = new_down_block_res_samples + (down_block_res_sample,) + + down_block_res_samples = new_down_block_res_samples + + # 4. mid + if self.mid_block is not None: + sample = self.mid_block( + sample, + emb, + encoder_hidden_states=encoder_hidden_states, + attention_mask=attention_mask, + cross_attention_kwargs=cross_attention_kwargs, + encoder_attention_mask=encoder_attention_mask, + use_image_num=use_image_num, + ) + + if is_controlnet: + sample = sample + mid_block_additional_residual + + # 5. up + for i, upsample_block in enumerate(self.up_blocks): + is_final_block = i == len(self.up_blocks) - 1 + + res_samples = down_block_res_samples[-len(upsample_block.resnets) :] + down_block_res_samples = down_block_res_samples[: -len(upsample_block.resnets)] + + # if we have not reached the final block and need to forward the + # upsample size, we do it here + if not is_final_block and forward_upsample_size: + upsample_size = down_block_res_samples[-1].shape[2:] + + if hasattr(upsample_block, "has_cross_attention") and upsample_block.has_cross_attention: + sample = upsample_block( + hidden_states=sample, + temb=emb, + res_hidden_states_tuple=res_samples, + encoder_hidden_states=encoder_hidden_states, + cross_attention_kwargs=cross_attention_kwargs, + upsample_size=upsample_size, + attention_mask=attention_mask, + encoder_attention_mask=encoder_attention_mask, + use_image_num=use_image_num, + ) + else: + sample = upsample_block( + hidden_states=sample, temb=emb, res_hidden_states_tuple=res_samples, upsample_size=upsample_size + ) + + # 6. post-process + if self.conv_norm_out: + sample = self.conv_norm_out(sample) + sample = self.conv_act(sample) + sample = self.conv_out(sample)[:, :, 1:, ...] + + if not return_dict: + return (sample,) + + return UNet3DConditionOutput(sample=sample) + + def forward_with_cfg(self, + x, + t, + encoder_hidden_states = None, + added_cond_kwargs = None, + class_labels: Optional[torch.Tensor] = None, + cfg_scale=7.0, + use_fp16=False): + """ + Forward pass of DiT, but also batches the unconditional forward pass for classifier-free guidance. + """ + # https://github.com/openai/glide-text2im/blob/main/notebooks/text2im.ipynb + half = x[: len(x) // 2] + combined = torch.cat([half, half], dim=0) + if use_fp16: + combined = combined.to(dtype=torch.float16) + model_out = self.forward(combined, t, encoder_hidden_states, class_labels, added_cond_kwargs=added_cond_kwargs).sample + # For exact reproducibility reasons, we apply classifier-free guidance on only + # three channels by default. The standard approach to cfg applies it to all channels. + # This can be done by uncommenting the following line and commenting-out the line following that. + eps, rest = model_out[:, :4], model_out[:, 4:] + # eps, rest = model_out[:, :3], model_out[:, 3:] # b c f h w + cond_eps, uncond_eps = torch.split(eps, len(eps) // 2, dim=0) + half_eps = uncond_eps + cfg_scale * (cond_eps - uncond_eps) + eps = torch.cat([half_eps, half_eps], dim=0) + return torch.cat([eps, rest], dim=1) + + # @classmethod + # def from_pretrained_2d(cls, pretrained_model_path, subfolder=None): + # if subfolder is not None: + # pretrained_model_path = os.path.join(pretrained_model_path, subfolder) + + + # config_file = os.path.join(pretrained_model_path, 'config.json') + # if not os.path.isfile(config_file): + # raise RuntimeError(f"{config_file} does not exist") + # with open(config_file, "r") as f: + # config = json.load(f) + # config["_class_name"] = cls.__name__ + # config["down_block_types"] = [ + # "CrossAttnDownBlock3D", + # "CrossAttnDownBlock3D", + # "CrossAttnDownBlock3D", + # "DownBlock3D" + # ] + + # config["mid_block_type"] = "UNetMidBlock3DCrossAttn" + + # config["up_block_types"] = [ + # "UpBlock3D", + # "CrossAttnUpBlock3D", + # "CrossAttnUpBlock3D", + # "CrossAttnUpBlock3D" + # ] + + # # animation training method + # config["addition_embed_type"] = "motion_ids" + # # config["addition_embed_type"] = "" + # # config["addition_embed_type_num_heads"] = 64 + # config["addition_time_embed_dim"] = 256 + # # config["projection_class_embeddings_input_dim"] = 2816 + + # from diffusers.utils import WEIGHTS_NAME # diffusion_pytorch_model.bin + + # model = cls.from_config(config) + # return model \ No newline at end of file diff --git a/models/unet_blocks.py b/models/unet_blocks.py new file mode 100644 index 0000000000000000000000000000000000000000..449083fd4f054fa616241148d49eeab5edf730af --- /dev/null +++ b/models/unet_blocks.py @@ -0,0 +1,758 @@ +# Copyright 2023 The HuggingFace Team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +from typing import Any, Dict, Optional, Tuple + +import numpy as np +import torch +import torch.nn.functional as F +from torch import nn + +from diffusers.utils import is_torch_version, logging + +try: + from transformer_3d import Transformer3DModel + from resnet import Downsample3D, ResnetBlock3D, Upsample3D +except: + from .transformer_3d import Transformer3DModel + from .resnet import Downsample3D, ResnetBlock3D, Upsample3D + +logger = logging.get_logger(__name__) # pylint: disable=invalid-name + + +def get_down_block( + down_block_type, + num_layers, + in_channels, + out_channels, + temb_channels, + add_downsample, + resnet_eps, + resnet_act_fn, + transformer_layers_per_block=1, + num_attention_heads=None, + resnet_groups=None, + cross_attention_dim=None, + downsample_padding=None, + dual_cross_attention=False, + use_linear_projection=False, + only_cross_attention=False, + upcast_attention=False, + resnet_time_scale_shift="default", + resnet_skip_time_act=False, + resnet_out_scale_factor=1.0, + cross_attention_norm=None, + attention_head_dim=None, + downsample_type=None, + rotary_emb=None, +): + # If attn head dim is not defined, we default it to the number of heads + if attention_head_dim is None: + logger.warn( + f"It is recommended to provide `attention_head_dim` when calling `get_down_block`. Defaulting `attention_head_dim` to {num_attention_heads}." + ) + attention_head_dim = num_attention_heads + + down_block_type = down_block_type[7:] if down_block_type.startswith("UNetRes") else down_block_type + if down_block_type == "DownBlock3D": + return DownBlock3D( + num_layers=num_layers, + in_channels=in_channels, + out_channels=out_channels, + temb_channels=temb_channels, + add_downsample=add_downsample, + resnet_eps=resnet_eps, + resnet_act_fn=resnet_act_fn, + resnet_groups=resnet_groups, + downsample_padding=downsample_padding, + resnet_time_scale_shift=resnet_time_scale_shift, + ) + elif down_block_type == "CrossAttnDownBlock3D": + if cross_attention_dim is None: + raise ValueError("cross_attention_dim must be specified for CrossAttnDownBlock3D") + return CrossAttnDownBlock3D( + num_layers=num_layers, + transformer_layers_per_block=transformer_layers_per_block, + in_channels=in_channels, + out_channels=out_channels, + temb_channels=temb_channels, + add_downsample=add_downsample, + resnet_eps=resnet_eps, + resnet_act_fn=resnet_act_fn, + resnet_groups=resnet_groups, + downsample_padding=downsample_padding, + cross_attention_dim=cross_attention_dim, + num_attention_heads=num_attention_heads, + dual_cross_attention=dual_cross_attention, + use_linear_projection=use_linear_projection, + only_cross_attention=only_cross_attention, + upcast_attention=upcast_attention, + resnet_time_scale_shift=resnet_time_scale_shift, + rotary_emb=rotary_emb, + ) + raise ValueError(f"{down_block_type} does not exist.") + + +def get_up_block( + up_block_type, + num_layers, + in_channels, + out_channels, + prev_output_channel, + temb_channels, + add_upsample, + resnet_eps, + resnet_act_fn, + transformer_layers_per_block=1, + num_attention_heads=None, + resnet_groups=None, + cross_attention_dim=None, + dual_cross_attention=False, + use_linear_projection=False, + only_cross_attention=False, + upcast_attention=False, + resnet_time_scale_shift="default", + resnet_skip_time_act=False, + resnet_out_scale_factor=1.0, + cross_attention_norm=None, + attention_head_dim=None, + upsample_type=None, + rotary_emb=None, +): + # If attn head dim is not defined, we default it to the number of heads + if attention_head_dim is None: + logger.warn( + f"It is recommended to provide `attention_head_dim` when calling `get_up_block`. Defaulting `attention_head_dim` to {num_attention_heads}." + ) + attention_head_dim = num_attention_heads + + up_block_type = up_block_type[7:] if up_block_type.startswith("UNetRes") else up_block_type + if up_block_type == "UpBlock3D": + return UpBlock3D( + num_layers=num_layers, + in_channels=in_channels, + out_channels=out_channels, + prev_output_channel=prev_output_channel, + temb_channels=temb_channels, + add_upsample=add_upsample, + resnet_eps=resnet_eps, + resnet_act_fn=resnet_act_fn, + resnet_groups=resnet_groups, + resnet_time_scale_shift=resnet_time_scale_shift, + ) + elif up_block_type == "CrossAttnUpBlock3D": + if cross_attention_dim is None: + raise ValueError("cross_attention_dim must be specified for CrossAttnUpBlock3D") + return CrossAttnUpBlock3D( + num_layers=num_layers, + transformer_layers_per_block=transformer_layers_per_block, + in_channels=in_channels, + out_channels=out_channels, + prev_output_channel=prev_output_channel, + temb_channels=temb_channels, + add_upsample=add_upsample, + resnet_eps=resnet_eps, + resnet_act_fn=resnet_act_fn, + resnet_groups=resnet_groups, + cross_attention_dim=cross_attention_dim, + num_attention_heads=num_attention_heads, + dual_cross_attention=dual_cross_attention, + use_linear_projection=use_linear_projection, + only_cross_attention=only_cross_attention, + upcast_attention=upcast_attention, + resnet_time_scale_shift=resnet_time_scale_shift, + rotary_emb=rotary_emb, + ) + raise ValueError(f"{up_block_type} does not exist.") + +class UNetMidBlock3DCrossAttn(nn.Module): + def __init__( + self, + in_channels: int, + temb_channels: int, + dropout: float = 0.0, + num_layers: int = 1, + transformer_layers_per_block: int = 1, + resnet_eps: float = 1e-6, + resnet_time_scale_shift: str = "default", + resnet_act_fn: str = "swish", + resnet_groups: int = 32, + resnet_pre_norm: bool = True, + num_attention_heads=1, + output_scale_factor=1.0, + cross_attention_dim=1280, + dual_cross_attention=False, + use_linear_projection=False, + upcast_attention=False, + rotary_emb=None, + ): + super().__init__() + + self.has_cross_attention = True + self.num_attention_heads = num_attention_heads + resnet_groups = resnet_groups if resnet_groups is not None else min(in_channels // 4, 32) + + # there is always at least one resnet + resnets = [ + ResnetBlock3D( + in_channels=in_channels, + out_channels=in_channels, + temb_channels=temb_channels, + eps=resnet_eps, + groups=resnet_groups, + dropout=dropout, + time_embedding_norm=resnet_time_scale_shift, + non_linearity=resnet_act_fn, + output_scale_factor=output_scale_factor, + pre_norm=resnet_pre_norm, + ) + ] + attentions = [] + + for _ in range(num_layers): + + attentions.append( + Transformer3DModel( + num_attention_heads, + in_channels // num_attention_heads, + in_channels=in_channels, + num_layers=transformer_layers_per_block, + cross_attention_dim=cross_attention_dim, + norm_num_groups=resnet_groups, + use_linear_projection=use_linear_projection, + upcast_attention=upcast_attention, + rotary_emb=rotary_emb, + ) + ) + + resnets.append( + ResnetBlock3D( + in_channels=in_channels, + out_channels=in_channels, + temb_channels=temb_channels, + eps=resnet_eps, + groups=resnet_groups, + dropout=dropout, + time_embedding_norm=resnet_time_scale_shift, + non_linearity=resnet_act_fn, + output_scale_factor=output_scale_factor, + pre_norm=resnet_pre_norm, + ) + ) + + self.attentions = nn.ModuleList(attentions) + self.resnets = nn.ModuleList(resnets) + + def forward( + self, + hidden_states: torch.FloatTensor, + temb: Optional[torch.FloatTensor] = None, + encoder_hidden_states: Optional[torch.FloatTensor] = None, + attention_mask: Optional[torch.FloatTensor] = None, + cross_attention_kwargs: Optional[Dict[str, Any]] = None, + encoder_attention_mask: Optional[torch.FloatTensor] = None, + use_image_num=None, + ) -> torch.FloatTensor: + hidden_states = self.resnets[0](hidden_states, temb) + for attn, resnet in zip(self.attentions, self.resnets[1:]): + hidden_states = attn( + hidden_states, + encoder_hidden_states=encoder_hidden_states, + cross_attention_kwargs=cross_attention_kwargs, + attention_mask=attention_mask, + encoder_attention_mask=encoder_attention_mask, + return_dict=False, + use_image_num=use_image_num, + )[0] + hidden_states = resnet(hidden_states, temb) + + return hidden_states + + +class CrossAttnDownBlock3D(nn.Module): + def __init__( + self, + in_channels: int, + out_channels: int, + temb_channels: int, + dropout: float = 0.0, + num_layers: int = 1, + transformer_layers_per_block: int = 1, + resnet_eps: float = 1e-6, + resnet_time_scale_shift: str = "default", + resnet_act_fn: str = "swish", + resnet_groups: int = 32, + resnet_pre_norm: bool = True, + num_attention_heads=1, + cross_attention_dim=1280, + output_scale_factor=1.0, + downsample_padding=1, + add_downsample=True, + dual_cross_attention=False, + use_linear_projection=False, + only_cross_attention=False, + upcast_attention=False, + rotary_emb=None, + ): + super().__init__() + resnets = [] + attentions = [] + + self.has_cross_attention = True + self.num_attention_heads = num_attention_heads + + for i in range(num_layers): + in_channels = in_channels if i == 0 else out_channels + resnets.append( + ResnetBlock3D( + in_channels=in_channels, + out_channels=out_channels, + temb_channels=temb_channels, + eps=resnet_eps, + groups=resnet_groups, + dropout=dropout, + time_embedding_norm=resnet_time_scale_shift, + non_linearity=resnet_act_fn, + output_scale_factor=output_scale_factor, + pre_norm=resnet_pre_norm, + ) + ) + + attentions.append( + Transformer3DModel( + num_attention_heads, + out_channels // num_attention_heads, + in_channels=out_channels, + num_layers=transformer_layers_per_block, + cross_attention_dim=cross_attention_dim, + norm_num_groups=resnet_groups, + use_linear_projection=use_linear_projection, + only_cross_attention=only_cross_attention, + upcast_attention=upcast_attention, + rotary_emb=rotary_emb, + ) + ) + + self.attentions = nn.ModuleList(attentions) + self.resnets = nn.ModuleList(resnets) + + if add_downsample: + self.downsamplers = nn.ModuleList( + [ + Downsample3D( + out_channels, use_conv=True, out_channels=out_channels, padding=downsample_padding, name="op" + ) + ] + ) + else: + self.downsamplers = None + + self.gradient_checkpointing = False + + def forward( + self, + hidden_states: torch.FloatTensor, + temb: Optional[torch.FloatTensor] = None, + encoder_hidden_states: Optional[torch.FloatTensor] = None, + attention_mask: Optional[torch.FloatTensor] = None, + cross_attention_kwargs: Optional[Dict[str, Any]] = None, + encoder_attention_mask: Optional[torch.FloatTensor] = None, + additional_residuals=None, + use_image_num=None, + ): + output_states = () + + blocks = list(zip(self.resnets, self.attentions)) + + for i, (resnet, attn) in enumerate(blocks): + if self.training and self.gradient_checkpointing: + + def create_custom_forward(module, return_dict=None): + def custom_forward(*inputs): + if return_dict is not None: + return module(*inputs, return_dict=return_dict) + else: + return module(*inputs) + + return custom_forward + + def create_custom_forward_attn(module, return_dict=None, use_image_num=None): + def custom_forward(*inputs): + if return_dict is not None: + return module(*inputs, return_dict=return_dict, use_image_num=use_image_num) + else: + return module(*inputs, use_image_num=use_image_num) + + return custom_forward + + ckpt_kwargs: Dict[str, Any] = {"use_reentrant": False} if is_torch_version(">=", "1.11.0") else {} + hidden_states = torch.utils.checkpoint.checkpoint( + create_custom_forward(resnet), + hidden_states, + temb, + **ckpt_kwargs, + ) + hidden_states = torch.utils.checkpoint.checkpoint( + create_custom_forward_attn(attn, return_dict=False, use_image_num=use_image_num), + hidden_states, + encoder_hidden_states, + None, # timestep + None, # class_labels + cross_attention_kwargs, + attention_mask, + encoder_attention_mask, + **ckpt_kwargs, + )[0] + else: + hidden_states = resnet(hidden_states, temb) + hidden_states = attn( + hidden_states, + encoder_hidden_states=encoder_hidden_states, + cross_attention_kwargs=cross_attention_kwargs, + attention_mask=attention_mask, + encoder_attention_mask=encoder_attention_mask, + return_dict=False, + use_image_num=use_image_num, + )[0] + + # apply additional residuals to the output of the last pair of resnet and attention blocks + if i == len(blocks) - 1 and additional_residuals is not None: + hidden_states = hidden_states + additional_residuals + + output_states = output_states + (hidden_states,) + + if self.downsamplers is not None: + for downsampler in self.downsamplers: + hidden_states = downsampler(hidden_states) + + output_states = output_states + (hidden_states,) + + return hidden_states, output_states + + +class DownBlock3D(nn.Module): + def __init__( + self, + in_channels: int, + out_channels: int, + temb_channels: int, + dropout: float = 0.0, + num_layers: int = 1, + resnet_eps: float = 1e-6, + resnet_time_scale_shift: str = "default", + resnet_act_fn: str = "swish", + resnet_groups: int = 32, + resnet_pre_norm: bool = True, + output_scale_factor=1.0, + add_downsample=True, + downsample_padding=1, + ): + super().__init__() + resnets = [] + + for i in range(num_layers): + in_channels = in_channels if i == 0 else out_channels + resnets.append( + ResnetBlock3D( + in_channels=in_channels, + out_channels=out_channels, + temb_channels=temb_channels, + eps=resnet_eps, + groups=resnet_groups, + dropout=dropout, + time_embedding_norm=resnet_time_scale_shift, + non_linearity=resnet_act_fn, + output_scale_factor=output_scale_factor, + pre_norm=resnet_pre_norm, + ) + ) + + self.resnets = nn.ModuleList(resnets) + + if add_downsample: + self.downsamplers = nn.ModuleList( + [ + Downsample3D( + out_channels, use_conv=True, out_channels=out_channels, padding=downsample_padding, name="op" + ) + ] + ) + else: + self.downsamplers = None + + self.gradient_checkpointing = False + + def forward(self, hidden_states, temb=None): + output_states = () + + for resnet in self.resnets: + if self.training and self.gradient_checkpointing: + + def create_custom_forward(module): + def custom_forward(*inputs): + return module(*inputs) + + return custom_forward + + if is_torch_version(">=", "1.11.0"): + hidden_states = torch.utils.checkpoint.checkpoint( + create_custom_forward(resnet), hidden_states, temb, use_reentrant=False + ) + else: + hidden_states = torch.utils.checkpoint.checkpoint( + create_custom_forward(resnet), hidden_states, temb + ) + else: + hidden_states = resnet(hidden_states, temb) + + output_states = output_states + (hidden_states,) + + if self.downsamplers is not None: + for downsampler in self.downsamplers: + hidden_states = downsampler(hidden_states) + + output_states = output_states + (hidden_states,) + + return hidden_states, output_states + +class CrossAttnUpBlock3D(nn.Module): + def __init__( + self, + in_channels: int, + out_channels: int, + prev_output_channel: int, + temb_channels: int, + dropout: float = 0.0, + num_layers: int = 1, + transformer_layers_per_block: int = 1, + resnet_eps: float = 1e-6, + resnet_time_scale_shift: str = "default", + resnet_act_fn: str = "swish", + resnet_groups: int = 32, + resnet_pre_norm: bool = True, + num_attention_heads=1, + cross_attention_dim=1280, + output_scale_factor=1.0, + add_upsample=True, + dual_cross_attention=False, + use_linear_projection=False, + only_cross_attention=False, + upcast_attention=False, + rotary_emb=None, + ): + super().__init__() + resnets = [] + attentions = [] + + self.has_cross_attention = True + self.num_attention_heads = num_attention_heads + + for i in range(num_layers): + res_skip_channels = in_channels if (i == num_layers - 1) else out_channels + resnet_in_channels = prev_output_channel if i == 0 else out_channels + + resnets.append( + ResnetBlock3D( + in_channels=resnet_in_channels + res_skip_channels, + out_channels=out_channels, + temb_channels=temb_channels, + eps=resnet_eps, + groups=resnet_groups, + dropout=dropout, + time_embedding_norm=resnet_time_scale_shift, + non_linearity=resnet_act_fn, + output_scale_factor=output_scale_factor, + pre_norm=resnet_pre_norm, + ) + ) + + attentions.append( + Transformer3DModel( + num_attention_heads, + out_channels // num_attention_heads, + in_channels=out_channels, + num_layers=transformer_layers_per_block, + cross_attention_dim=cross_attention_dim, + norm_num_groups=resnet_groups, + use_linear_projection=use_linear_projection, + only_cross_attention=only_cross_attention, + upcast_attention=upcast_attention, + rotary_emb=rotary_emb, + ) + ) + + self.attentions = nn.ModuleList(attentions) + self.resnets = nn.ModuleList(resnets) + + if add_upsample: # True + self.upsamplers = nn.ModuleList([Upsample3D(out_channels, use_conv=True, out_channels=out_channels)]) + else: + self.upsamplers = None + + self.gradient_checkpointing = False + + def forward( + self, + hidden_states: torch.FloatTensor, + res_hidden_states_tuple: Tuple[torch.FloatTensor, ...], + temb: Optional[torch.FloatTensor] = None, + encoder_hidden_states: Optional[torch.FloatTensor] = None, + cross_attention_kwargs: Optional[Dict[str, Any]] = None, + upsample_size: Optional[int] = None, + attention_mask: Optional[torch.FloatTensor] = None, + encoder_attention_mask: Optional[torch.FloatTensor] = None, + use_image_num=None, + ): + for resnet, attn in zip(self.resnets, self.attentions): + # pop res hidden states + res_hidden_states = res_hidden_states_tuple[-1] + res_hidden_states_tuple = res_hidden_states_tuple[:-1] + # print('hidden_states', hidden_states.shape) + # print('res_hidden_states', res_hidden_states.shape) + hidden_states = torch.cat([hidden_states, res_hidden_states], dim=1) + + if self.training and self.gradient_checkpointing: + + def create_custom_forward(module, return_dict=None): + def custom_forward(*inputs): + if return_dict is not None: + return module(*inputs, return_dict=return_dict) + else: + return module(*inputs) + + return custom_forward + + def create_custom_forward_attn(module, return_dict=None, use_image_num=None): + def custom_forward(*inputs): + if return_dict is not None: + return module(*inputs, return_dict=return_dict, use_image_num=use_image_num) + else: + return module(*inputs, use_image_num=use_image_num) + + return custom_forward + + ckpt_kwargs: Dict[str, Any] = {"use_reentrant": False} if is_torch_version(">=", "1.11.0") else {} + hidden_states = torch.utils.checkpoint.checkpoint( + create_custom_forward(resnet), + hidden_states, + temb, + **ckpt_kwargs, + ) + hidden_states = torch.utils.checkpoint.checkpoint( + create_custom_forward_attn(attn, return_dict=False, use_image_num=use_image_num), + hidden_states, + encoder_hidden_states, + None, # timestep + None, # class_labels + cross_attention_kwargs, + attention_mask, + encoder_attention_mask, + **ckpt_kwargs, + )[0] + else: + hidden_states = resnet(hidden_states, temb) + hidden_states = attn( + hidden_states, + encoder_hidden_states=encoder_hidden_states, + cross_attention_kwargs=cross_attention_kwargs, + attention_mask=attention_mask, + encoder_attention_mask=encoder_attention_mask, + return_dict=False, + use_image_num=use_image_num, + )[0] + if self.upsamplers is not None: + for upsampler in self.upsamplers: + hidden_states = upsampler(hidden_states, upsample_size) + + return hidden_states + + +class UpBlock3D(nn.Module): + def __init__( + self, + in_channels: int, + prev_output_channel: int, + out_channels: int, + temb_channels: int, + dropout: float = 0.0, + num_layers: int = 1, + resnet_eps: float = 1e-6, + resnet_time_scale_shift: str = "default", + resnet_act_fn: str = "swish", + resnet_groups: int = 32, + resnet_pre_norm: bool = True, + output_scale_factor=1.0, + add_upsample=True, + ): + super().__init__() + resnets = [] + + for i in range(num_layers): + res_skip_channels = in_channels if (i == num_layers - 1) else out_channels + resnet_in_channels = prev_output_channel if i == 0 else out_channels + + resnets.append( + ResnetBlock3D( + in_channels=resnet_in_channels + res_skip_channels, + out_channels=out_channels, + temb_channels=temb_channels, + eps=resnet_eps, + groups=resnet_groups, + dropout=dropout, + time_embedding_norm=resnet_time_scale_shift, + non_linearity=resnet_act_fn, + output_scale_factor=output_scale_factor, + pre_norm=resnet_pre_norm, + ) + ) + + self.resnets = nn.ModuleList(resnets) + + if add_upsample: + self.upsamplers = nn.ModuleList([Upsample3D(out_channels, use_conv=True, out_channels=out_channels)]) + else: + self.upsamplers = None + + self.gradient_checkpointing = False + + def forward(self, hidden_states, res_hidden_states_tuple, temb=None, upsample_size=None): + for resnet in self.resnets: + # pop res hidden states + res_hidden_states = res_hidden_states_tuple[-1] + res_hidden_states_tuple = res_hidden_states_tuple[:-1] + hidden_states = torch.cat([hidden_states, res_hidden_states], dim=1) + + if self.training and self.gradient_checkpointing: + + def create_custom_forward(module): + def custom_forward(*inputs): + return module(*inputs) + + return custom_forward + + if is_torch_version(">=", "1.11.0"): + hidden_states = torch.utils.checkpoint.checkpoint( + create_custom_forward(resnet), hidden_states, temb, use_reentrant=False + ) + else: + hidden_states = torch.utils.checkpoint.checkpoint( + create_custom_forward(resnet), hidden_states, temb + ) + else: + hidden_states = resnet(hidden_states, temb) + + if self.upsamplers is not None: + for upsampler in self.upsamplers: + hidden_states = upsampler(hidden_states, upsample_size) + + return hidden_states \ No newline at end of file diff --git a/pipelines/__init__.py b/pipelines/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/pipelines/__pycache__/__init__.cpython-312.pyc b/pipelines/__pycache__/__init__.cpython-312.pyc new file mode 100644 index 0000000000000000000000000000000000000000..0cc1cc139f273c26dbac925d2c60b14884e71119 Binary files /dev/null and b/pipelines/__pycache__/__init__.cpython-312.pyc differ diff --git a/pipelines/__pycache__/pipeline_videogen.cpython-312.pyc b/pipelines/__pycache__/pipeline_videogen.cpython-312.pyc new file mode 100644 index 0000000000000000000000000000000000000000..d62c8d6befc3f76030408cf5c16dd0154598f781 Binary files /dev/null and b/pipelines/__pycache__/pipeline_videogen.cpython-312.pyc differ diff --git a/pipelines/animation.py b/pipelines/animation.py new file mode 100644 index 0000000000000000000000000000000000000000..c013b566e5a8e2ea6ea81e2a1b15a7a5a37bbb3a --- /dev/null +++ b/pipelines/animation.py @@ -0,0 +1,137 @@ +import os +import torch +import argparse +import torchvision + +from pipeline_videogen import VideoGenPipeline +from diffusers.schedulers import DDIMScheduler +from diffusers.models import AutoencoderKL +from diffusers.models import AutoencoderKLTemporalDecoder +from transformers import CLIPTokenizer, CLIPTextModel +from omegaconf import OmegaConf + +import os, sys +sys.path.append(os.path.split(sys.path[0])[0]) +from models import get_models +import imageio +from PIL import Image +import numpy as np +from datasets import video_transforms +from torchvision import transforms +from einops import rearrange, repeat +from utils import dct_low_pass_filter, exchanged_mixed_dct_freq +from copy import deepcopy + +def prepare_image(path, vae, transform_video, device, dtype=torch.float16): + with open(path, 'rb') as f: + image = Image.open(f).convert('RGB') + image = torch.as_tensor(np.array(image, dtype=np.uint8, copy=True)).unsqueeze(0).permute(0, 3, 1, 2) + image, ori_h, ori_w, crops_coords_top, crops_coords_left = transform_video(image) + image = vae.encode(image.to(dtype=dtype, device=device)).latent_dist.sample().mul_(vae.config.scaling_factor) + image = image.unsqueeze(2) + return image + +def main(args): + + if args.seed: + torch.manual_seed(args.seed) + torch.set_grad_enabled(False) + device = "cuda" if torch.cuda.is_available() else "cpu" + dtype = torch.float16 # torch.float16 + + unet = get_models(args).to(device, dtype=dtype) + + if args.enable_vae_temporal_decoder: + if args.use_dct: + vae_for_base_content = AutoencoderKLTemporalDecoder.from_pretrained(args.pretrained_model_path, subfolder="vae_temporal_decoder", torch_dtype=torch.float64).to(device) + else: + vae_for_base_content = AutoencoderKLTemporalDecoder.from_pretrained(args.pretrained_model_path, subfolder="vae_temporal_decoder", torch_dtype=torch.float16).to(device) + vae = deepcopy(vae_for_base_content).to(dtype=dtype) + else: + vae_for_base_content = AutoencoderKL.from_pretrained(args.pretrained_model_path, subfolder="vae",).to(device, dtype=torch.float64) + vae = deepcopy(vae_for_base_content).to(dtype=dtype) + tokenizer = CLIPTokenizer.from_pretrained(args.pretrained_model_path, subfolder="tokenizer") + text_encoder = CLIPTextModel.from_pretrained(args.pretrained_model_path, subfolder="text_encoder", torch_dtype=dtype).to(device) # huge + + # set eval mode + unet.eval() + vae.eval() + text_encoder.eval() + + scheduler = DDIMScheduler.from_pretrained(args.pretrained_model_path, + subfolder="scheduler", + beta_start=args.beta_start, + beta_end=args.beta_end, + beta_schedule=args.beta_schedule) + + + videogen_pipeline = VideoGenPipeline(vae=vae, + text_encoder=text_encoder, + tokenizer=tokenizer, + scheduler=scheduler, + unet=unet).to(device) + # videogen_pipeline.enable_xformers_memory_efficient_attention() + # videogen_pipeline.enable_vae_slicing() + + if not os.path.exists(args.save_img_path): + os.makedirs(args.save_img_path) + + transform_video = video_transforms.Compose([ + video_transforms.ToTensorVideo(), + video_transforms.SDXLCenterCrop((args.image_size[0], args.image_size[1])), # center crop using shor edge, then resize + transforms.Normalize(mean=[0.5, 0.5, 0.5], std=[0.5, 0.5, 0.5], inplace=True), + ]) + + for i, (image, prompt) in enumerate(args.image_prompts): + if args.use_dct: + base_content = prepare_image("./animated_images/" + image, vae_for_base_content, transform_video, device, dtype=torch.float64).to(device) + else: + base_content = prepare_image("./animated_images/" + image, vae_for_base_content, transform_video, device, dtype=torch.float16).to(device) + + if args.use_dct: + # filter params + print("Using DCT!") + base_content_repeat = repeat(base_content, 'b c f h w -> b c (f r) h w', r=15).contiguous() + + # define filter + freq_filter = dct_low_pass_filter(dct_coefficients=base_content, + percentage=0.23) + + noise = torch.randn(1, 4, 15, 40, 64).to(device) + + # add noise to base_content + diffuse_timesteps = torch.full((1,),int(975)) + diffuse_timesteps = diffuse_timesteps.long() + + # 3d content + base_content_noise = scheduler.add_noise( + original_samples=base_content_repeat.to(device), + noise=noise, + timesteps=diffuse_timesteps.to(device)) + + # 3d content + latents = exchanged_mixed_dct_freq(noise=noise, + base_content=base_content_noise, + LPF_3d=freq_filter).to(dtype=torch.float16) + + base_content = base_content.to(dtype=torch.float16) + + videos = videogen_pipeline(prompt, + latents=latents if args.use_dct else None, + base_content=base_content, + video_length=args.video_length, + height=args.image_size[0], + width=args.image_size[1], + num_inference_steps=args.num_sampling_steps, + guidance_scale=args.guidance_scale, + motion_bucket_id=args.motion_bucket_id, + enable_vae_temporal_decoder=args.enable_vae_temporal_decoder).video + + imageio.mimwrite(args.save_img_path + prompt.replace(' ', '_') + '_%04d' % i + '_%04d' % args.run_time + '-imageio.mp4', videos[0], fps=8, quality=8) # highest quality is 10, lowest is 0 + +if __name__ == "__main__": + parser = argparse.ArgumentParser() + parser.add_argument("--config", type=str, default="./configs/sample.yaml") + args = parser.parse_args() + + main(OmegaConf.load(args.config)) diff --git a/pipelines/animation.sh b/pipelines/animation.sh new file mode 100644 index 0000000000000000000000000000000000000000..dece9b0349006f0c8eba35fc6749e52fce79eac9 --- /dev/null +++ b/pipelines/animation.sh @@ -0,0 +1,2 @@ +export CUDA_VISIBLE_DEVICES=4 +python pipelines/animation.py --config configs/sample.yaml \ No newline at end of file diff --git a/pipelines/pipeline_inversion.py b/pipelines/pipeline_inversion.py new file mode 100644 index 0000000000000000000000000000000000000000..c585bc345e255f6ad4a7478af044dc13fc5d88a0 --- /dev/null +++ b/pipelines/pipeline_inversion.py @@ -0,0 +1,748 @@ +# Copyright 2023 The HuggingFace Team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import inspect +from typing import Any, Callable, Dict, List, Optional, Union +import einops +import torch +from packaging import version +from transformers import CLIPFeatureExtractor, CLIPTextModel, CLIPTokenizer + +from diffusers.configuration_utils import FrozenDict +from diffusers.models import AutoencoderKL +from diffusers.schedulers import KarrasDiffusionSchedulers +from diffusers.utils import ( + deprecate, + is_accelerate_available, + is_accelerate_version, + logging, + replace_example_docstring, + BaseOutput, +) +try: + from diffusers.utils import randn_tensor +except: + from diffusers.utils.torch_utils import randn_tensor +from dataclasses import dataclass + +import os, sys +sys.path.append(os.path.split(sys.path[0])[0]) +from models.unet import UNet3DConditionModel + +import numpy as np + +try: + from diffusers.pipeline_utils import DiffusionPipeline # 0.21.0 +except: + from diffusers.pipelines.pipeline_utils import DiffusionPipeline # 0.25.0 + +@dataclass +class StableDiffusionPipelineOutput(BaseOutput): + video: torch.Tensor + +logger = logging.get_logger(__name__) # pylint: disable=invalid-name + +EXAMPLE_DOC_STRING = """ + Examples: + ```py + >>> import torch + >>> from diffusers import StableDiffusionPipeline + + >>> pipe = StableDiffusionPipeline.from_pretrained("runwayml/stable-diffusion-v1-5", torch_dtype=torch.float16) + >>> pipe = pipe.to("cuda") + + >>> prompt = "a photo of an astronaut riding a horse on mars" + >>> image = pipe(prompt).images[0] + ``` +""" + + +class VideoGenInversionPipeline(DiffusionPipeline): + r""" + Pipeline for text-to-image generation using Stable Diffusion. + + This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods the + library implements for all the pipelines (such as downloading or saving, running on a particular device, etc.) + + Args: + vae ([`AutoencoderKL`]): + Variational Auto-Encoder (VAE) Model to encode and decode images to and from latent representations. + text_encoder ([`CLIPTextModel`]): + Frozen text-encoder. Stable Diffusion uses the text portion of + [CLIP](https://huggingface.co/docs/transformers/model_doc/clip#transformers.CLIPTextModel), specifically + the [clip-vit-large-patch14](https://huggingface.co/openai/clip-vit-large-patch14) variant. + tokenizer (`CLIPTokenizer`): + Tokenizer of class + [CLIPTokenizer](https://huggingface.co/docs/transformers/v4.21.0/en/model_doc/clip#transformers.CLIPTokenizer). + unet ([`UNet2DConditionModel`]): Conditional U-Net architecture to denoise the encoded image latents. + scheduler ([`SchedulerMixin`]): + A scheduler to be used in combination with `unet` to denoise the encoded image latents. Can be one of + [`DDIMScheduler`], [`LMSDiscreteScheduler`], or [`PNDMScheduler`]. + safety_checker ([`StableDiffusionSafetyChecker`]): + Classification module that estimates whether generated images could be considered offensive or harmful. + Please, refer to the [model card](https://huggingface.co/runwayml/stable-diffusion-v1-5) for details. + feature_extractor ([`CLIPFeatureExtractor`]): + Model that extracts features from generated images to be used as inputs for the `safety_checker`. + """ + _optional_components = ["safety_checker", "feature_extractor"] + + def __init__( + self, + vae: AutoencoderKL, + text_encoder: CLIPTextModel, + tokenizer: CLIPTokenizer, + unet: UNet3DConditionModel, + scheduler: KarrasDiffusionSchedulers, + ): + super().__init__() + + if hasattr(scheduler.config, "steps_offset") and scheduler.config.steps_offset != 1: + deprecation_message = ( + f"The configuration file of this scheduler: {scheduler} is outdated. `steps_offset`" + f" should be set to 1 instead of {scheduler.config.steps_offset}. Please make sure " + "to update the config accordingly as leaving `steps_offset` might led to incorrect results" + " in future versions. If you have downloaded this checkpoint from the Hugging Face Hub," + " it would be very nice if you could open a Pull request for the `scheduler/scheduler_config.json`" + " file" + ) + deprecate("steps_offset!=1", "1.0.0", deprecation_message, standard_warn=False) + new_config = dict(scheduler.config) + new_config["steps_offset"] = 1 + scheduler._internal_dict = FrozenDict(new_config) + + if hasattr(scheduler.config, "clip_sample") and scheduler.config.clip_sample is True: + deprecation_message = ( + f"The configuration file of this scheduler: {scheduler} has not set the configuration `clip_sample`." + " `clip_sample` should be set to False in the configuration file. Please make sure to update the" + " config accordingly as not setting `clip_sample` in the config might lead to incorrect results in" + " future versions. If you have downloaded this checkpoint from the Hugging Face Hub, it would be very" + " nice if you could open a Pull request for the `scheduler/scheduler_config.json` file" + ) + deprecate("clip_sample not set", "1.0.0", deprecation_message, standard_warn=False) + new_config = dict(scheduler.config) + new_config["clip_sample"] = False + scheduler._internal_dict = FrozenDict(new_config) + + + + is_unet_version_less_0_9_0 = hasattr(unet.config, "_diffusers_version") and version.parse( + version.parse(unet.config._diffusers_version).base_version + ) < version.parse("0.9.0.dev0") + is_unet_sample_size_less_64 = hasattr(unet.config, "sample_size") and unet.config.sample_size < 64 + if is_unet_version_less_0_9_0 and is_unet_sample_size_less_64: + deprecation_message = ( + "The configuration file of the unet has set the default `sample_size` to smaller than" + " 64 which seems highly unlikely. If your checkpoint is a fine-tuned version of any of the" + " following: \n- CompVis/stable-diffusion-v1-4 \n- CompVis/stable-diffusion-v1-3 \n-" + " CompVis/stable-diffusion-v1-2 \n- CompVis/stable-diffusion-v1-1 \n- runwayml/stable-diffusion-v1-5" + " \n- runwayml/stable-diffusion-inpainting \n you should change 'sample_size' to 64 in the" + " configuration file. Please make sure to update the config accordingly as leaving `sample_size=32`" + " in the config might lead to incorrect results in future versions. If you have downloaded this" + " checkpoint from the Hugging Face Hub, it would be very nice if you could open a Pull request for" + " the `unet/config.json` file" + ) + deprecate("sample_size<64", "1.0.0", deprecation_message, standard_warn=False) + new_config = dict(unet.config) + new_config["sample_size"] = 64 + unet._internal_dict = FrozenDict(new_config) + + self.register_modules( + vae=vae, + text_encoder=text_encoder, + tokenizer=tokenizer, + unet=unet, + scheduler=scheduler, + ) + self.vae_scale_factor = 2 ** (len(self.vae.config.block_out_channels) - 1) + # self.register_to_config(requires_safety_checker=requires_safety_checker) + + def enable_vae_slicing(self): + r""" + Enable sliced VAE decoding. + + When this option is enabled, the VAE will split the input tensor in slices to compute decoding in several + steps. This is useful to save some memory and allow larger batch sizes. + """ + self.vae.enable_slicing() + + def disable_vae_slicing(self): + r""" + Disable sliced VAE decoding. If `enable_vae_slicing` was previously invoked, this method will go back to + computing decoding in one step. + """ + self.vae.disable_slicing() + + def enable_vae_tiling(self): + r""" + Enable tiled VAE decoding. + + When this option is enabled, the VAE will split the input tensor into tiles to compute decoding and encoding in + several steps. This is useful to save a large amount of memory and to allow the processing of larger images. + """ + self.vae.enable_tiling() + + def disable_vae_tiling(self): + r""" + Disable tiled VAE decoding. If `enable_vae_tiling` was previously invoked, this method will go back to + computing decoding in one step. + """ + self.vae.disable_tiling() + + def enable_sequential_cpu_offload(self, gpu_id=0): + r""" + Offloads all models to CPU using accelerate, significantly reducing memory usage. When called, unet, + text_encoder, vae and safety checker have their state dicts saved to CPU and then are moved to a + `torch.device('meta') and loaded to GPU only when their specific submodule has its `forward` method called. + Note that offloading happens on a submodule basis. Memory savings are higher than with + `enable_model_cpu_offload`, but performance is lower. + """ + if is_accelerate_available() and is_accelerate_version(">=", "0.14.0"): + from accelerate import cpu_offload + else: + raise ImportError("`enable_sequential_cpu_offload` requires `accelerate v0.14.0` or higher") + + device = torch.device(f"cuda:{gpu_id}") + + if self.device.type != "cpu": + self.to("cpu", silence_dtype_warnings=True) + torch.cuda.empty_cache() # otherwise we don't see the memory savings (but they probably exist) + + for cpu_offloaded_model in [self.unet, self.text_encoder, self.vae]: + cpu_offload(cpu_offloaded_model, device) + + # if self.safety_checker is not None: + # cpu_offload(self.safety_checker, execution_device=device, offload_buffers=True) + + def enable_model_cpu_offload(self, gpu_id=0): + r""" + Offloads all models to CPU using accelerate, reducing memory usage with a low impact on performance. Compared + to `enable_sequential_cpu_offload`, this method moves one whole model at a time to the GPU when its `forward` + method is called, and the model remains in GPU until the next model runs. Memory savings are lower than with + `enable_sequential_cpu_offload`, but performance is much better due to the iterative execution of the `unet`. + """ + if is_accelerate_available() and is_accelerate_version(">=", "0.17.0.dev0"): + from accelerate import cpu_offload_with_hook + else: + raise ImportError("`enable_model_offload` requires `accelerate v0.17.0` or higher.") + + device = torch.device(f"cuda:{gpu_id}") + + if self.device.type != "cpu": + self.to("cpu", silence_dtype_warnings=True) + torch.cuda.empty_cache() # otherwise we don't see the memory savings (but they probably exist) + + hook = None + for cpu_offloaded_model in [self.text_encoder, self.unet, self.vae]: + _, hook = cpu_offload_with_hook(cpu_offloaded_model, device, prev_module_hook=hook) + + # if self.safety_checker is not None: + # _, hook = cpu_offload_with_hook(self.safety_checker, device, prev_module_hook=hook) + + # We'll offload the last model manually. + self.final_offload_hook = hook + + @property + def _execution_device(self): + r""" + Returns the device on which the pipeline's models will be executed. After calling + `pipeline.enable_sequential_cpu_offload()` the execution device can only be inferred from Accelerate's module + hooks. + """ + if not hasattr(self.unet, "_hf_hook"): + return self.device + for module in self.unet.modules(): + if ( + hasattr(module, "_hf_hook") + and hasattr(module._hf_hook, "execution_device") + and module._hf_hook.execution_device is not None + ): + return torch.device(module._hf_hook.execution_device) + return self.device + + def _encode_prompt( + self, + prompt, + device, + num_images_per_prompt, + do_classifier_free_guidance, + negative_prompt=None, + prompt_embeds: Optional[torch.FloatTensor] = None, + negative_prompt_embeds: Optional[torch.FloatTensor] = None, + ): + r""" + Encodes the prompt into text encoder hidden states. + + Args: + prompt (`str` or `List[str]`, *optional*): + prompt to be encoded + device: (`torch.device`): + torch device + num_images_per_prompt (`int`): + number of images that should be generated per prompt + do_classifier_free_guidance (`bool`): + whether to use classifier free guidance or not + negative_prompt (`str` or `List[str]`, *optional*): + The prompt or prompts not to guide the image generation. If not defined, one has to pass + `negative_prompt_embeds`. instead. If not defined, one has to pass `negative_prompt_embeds`. instead. + Ignored when not using guidance (i.e., ignored if `guidance_scale` is less than `1`). + prompt_embeds (`torch.FloatTensor`, *optional*): + Pre-generated text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not + provided, text embeddings will be generated from `prompt` input argument. + negative_prompt_embeds (`torch.FloatTensor`, *optional*): + Pre-generated negative text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt + weighting. If not provided, negative_prompt_embeds will be generated from `negative_prompt` input + argument. + """ + if prompt is not None and isinstance(prompt, str): + batch_size = 1 + elif prompt is not None and isinstance(prompt, list): + batch_size = len(prompt) + else: + batch_size = prompt_embeds.shape[0] + + if prompt_embeds is None: + text_inputs = self.tokenizer( + prompt, + padding="max_length", + max_length=self.tokenizer.model_max_length, + truncation=True, + return_tensors="pt", + ) + text_input_ids = text_inputs.input_ids + untruncated_ids = self.tokenizer(prompt, padding="longest", return_tensors="pt").input_ids + + if untruncated_ids.shape[-1] >= text_input_ids.shape[-1] and not torch.equal( + text_input_ids, untruncated_ids + ): + removed_text = self.tokenizer.batch_decode( + untruncated_ids[:, self.tokenizer.model_max_length - 1 : -1] + ) + logger.warning( + "The following part of your input was truncated because CLIP can only handle sequences up to" + f" {self.tokenizer.model_max_length} tokens: {removed_text}" + ) + + if hasattr(self.text_encoder.config, "use_attention_mask") and self.text_encoder.config.use_attention_mask: + attention_mask = text_inputs.attention_mask.to(device) + else: + attention_mask = None + + prompt_embeds = self.text_encoder( + text_input_ids.to(device), + attention_mask=attention_mask, + ) + prompt_embeds = prompt_embeds[0] + + prompt_embeds = prompt_embeds.to(dtype=self.text_encoder.dtype, device=device) + + bs_embed, seq_len, _ = prompt_embeds.shape + # duplicate text embeddings for each generation per prompt, using mps friendly method + prompt_embeds = prompt_embeds.repeat(1, num_images_per_prompt, 1) + prompt_embeds = prompt_embeds.view(bs_embed * num_images_per_prompt, seq_len, -1) + + # get unconditional embeddings for classifier free guidance + if do_classifier_free_guidance and negative_prompt_embeds is None: + uncond_tokens: List[str] + if negative_prompt is None: + uncond_tokens = [""] * batch_size + elif type(prompt) is not type(negative_prompt): + raise TypeError( + f"`negative_prompt` should be the same type to `prompt`, but got {type(negative_prompt)} !=" + f" {type(prompt)}." + ) + elif isinstance(negative_prompt, str): + uncond_tokens = [negative_prompt] + elif batch_size != len(negative_prompt): + raise ValueError( + f"`negative_prompt`: {negative_prompt} has batch size {len(negative_prompt)}, but `prompt`:" + f" {prompt} has batch size {batch_size}. Please make sure that passed `negative_prompt` matches" + " the batch size of `prompt`." + ) + else: + uncond_tokens = negative_prompt + + max_length = prompt_embeds.shape[1] + uncond_input = self.tokenizer( + uncond_tokens, + padding="max_length", + max_length=max_length, + truncation=True, + return_tensors="pt", + ) + + if hasattr(self.text_encoder.config, "use_attention_mask") and self.text_encoder.config.use_attention_mask: + attention_mask = uncond_input.attention_mask.to(device) + else: + attention_mask = None + + negative_prompt_embeds = self.text_encoder( + uncond_input.input_ids.to(device), + attention_mask=attention_mask, + ) + negative_prompt_embeds = negative_prompt_embeds[0] + + if do_classifier_free_guidance: + # duplicate unconditional embeddings for each generation per prompt, using mps friendly method + seq_len = negative_prompt_embeds.shape[1] + + negative_prompt_embeds = negative_prompt_embeds.to(dtype=self.text_encoder.dtype, device=device) + + negative_prompt_embeds = negative_prompt_embeds.repeat(1, num_images_per_prompt, 1) + negative_prompt_embeds = negative_prompt_embeds.view(batch_size * num_images_per_prompt, seq_len, -1) + + # For classifier free guidance, we need to do two forward passes. + # Here we concatenate the unconditional and text embeddings into a single batch + # to avoid doing two forward passes + prompt_embeds = torch.cat([negative_prompt_embeds, prompt_embeds]) + + return prompt_embeds + + # def run_safety_checker(self, image, device, dtype): + # if self.safety_checker is not None: + # safety_checker_input = self.feature_extractor(self.numpy_to_pil(image), return_tensors="pt").to(device) + # image, has_nsfw_concept = self.safety_checker( + # images=image, clip_input=safety_checker_input.pixel_values.to(dtype) + # ) + # else: + # has_nsfw_concept = None + # return image, has_nsfw_concept + + # def decode_latents(self, latents): + # print(latents.shape) + # video_length = latents.shape[2] + # latents = einops.rearrange(latents, "b c f h w -> (b f) c h w").contiguous() + # video = self.vae.decode(latents / 0.18215).sample + # video = einops.rearrange(video, "(b f) c h w -> b f c h w", f=video_length).contiguous() + # return video + + def decode_latents(self, latents): + video_length = latents.shape[2] + latents = 1 / 0.18215 * latents + latents = einops.rearrange(latents, "b c f h w -> (b f) c h w") + video = self.vae.decode(latents).sample + video = einops.rearrange(video, "(b f) c h w -> b f h w c", f=video_length) + # video = (video / 2 + 0.5).clamp(0, 1) + video = ((video / 2 + 0.5) * 255).add_(0.5).clamp_(0, 255).to(dtype=torch.uint8).cpu().contiguous() + # we always cast to float32 as this does not cause significant overhead and is compatible with bfloa16 + # video = video.cpu().float() + return video + + # def decode_latents(self, latents): + # latents = 1 / self.vae.config.scaling_factor * latents + # image = self.vae.decode(latents).sample + # image = (image / 2 + 0.5).clamp(0, 1) + # # we always cast to float32 as this does not cause significant overhead and is compatible with bfloat16 + # image = image.cpu().permute(0, 2, 3, 1).float().numpy() + # return image + + def prepare_extra_step_kwargs(self, generator, eta): + # prepare extra kwargs for the scheduler step, since not all schedulers have the same signature + # eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers. + # eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502 + # and should be between [0, 1] + + accepts_eta = "eta" in set(inspect.signature(self.scheduler.step).parameters.keys()) + extra_step_kwargs = {} + if accepts_eta: + extra_step_kwargs["eta"] = eta + + # check if the scheduler accepts generator + accepts_generator = "generator" in set(inspect.signature(self.scheduler.step).parameters.keys()) + if accepts_generator: + extra_step_kwargs["generator"] = generator + return extra_step_kwargs + + def check_inputs( + self, + prompt, + height, + width, + callback_steps, + negative_prompt=None, + prompt_embeds=None, + negative_prompt_embeds=None, + ): + if height % 8 != 0 or width % 8 != 0: + raise ValueError(f"`height` and `width` have to be divisible by 8 but are {height} and {width}.") + + if (callback_steps is None) or ( + callback_steps is not None and (not isinstance(callback_steps, int) or callback_steps <= 0) + ): + raise ValueError( + f"`callback_steps` has to be a positive integer but is {callback_steps} of type" + f" {type(callback_steps)}." + ) + + if prompt is not None and prompt_embeds is not None: + raise ValueError( + f"Cannot forward both `prompt`: {prompt} and `prompt_embeds`: {prompt_embeds}. Please make sure to" + " only forward one of the two." + ) + elif prompt is None and prompt_embeds is None: + raise ValueError( + "Provide either `prompt` or `prompt_embeds`. Cannot leave both `prompt` and `prompt_embeds` undefined." + ) + elif prompt is not None and (not isinstance(prompt, str) and not isinstance(prompt, list)): + raise ValueError(f"`prompt` has to be of type `str` or `list` but is {type(prompt)}") + + if negative_prompt is not None and negative_prompt_embeds is not None: + raise ValueError( + f"Cannot forward both `negative_prompt`: {negative_prompt} and `negative_prompt_embeds`:" + f" {negative_prompt_embeds}. Please make sure to only forward one of the two." + ) + + if prompt_embeds is not None and negative_prompt_embeds is not None: + if prompt_embeds.shape != negative_prompt_embeds.shape: + raise ValueError( + "`prompt_embeds` and `negative_prompt_embeds` must have the same shape when passed directly, but" + f" got: `prompt_embeds` {prompt_embeds.shape} != `negative_prompt_embeds`" + f" {negative_prompt_embeds.shape}." + ) + + def prepare_latents(self, batch_size, num_channels_latents, video_length, height, width, dtype, device, generator, latents=None): + shape = (batch_size, num_channels_latents, video_length, height // self.vae_scale_factor, width // self.vae_scale_factor) + if isinstance(generator, list) and len(generator) != batch_size: + raise ValueError( + f"You have passed a list of generators of length {len(generator)}, but requested an effective batch" + f" size of {batch_size}. Make sure the batch size matches the length of the generators." + ) + + if latents is None: + latents = randn_tensor(shape, generator=generator, device=device, dtype=dtype) + else: + latents = latents.to(device) + + # scale the initial noise by the standard deviation required by the scheduler + latents = latents * self.scheduler.init_noise_sigma + return latents + + @torch.no_grad() + @replace_example_docstring(EXAMPLE_DOC_STRING) + def __call__( + self, + prompt: Union[str, List[str]] = None, + height: Optional[int] = None, + width: Optional[int] = None, + base_content: Optional[torch.Tensor] = None, + video_length: int = 16, + num_inference_steps: int = 50, + guidance_scale: float = 7.5, + negative_prompt: Optional[Union[str, List[str]]] = None, + num_images_per_prompt: Optional[int] = 1, + eta: float = 0.0, + motion_bucket_id: int = 25, # [0-99] + generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None, + latents: Optional[torch.FloatTensor] = None, + prompt_embeds: Optional[torch.FloatTensor] = None, + negative_prompt_embeds: Optional[torch.FloatTensor] = None, + output_type: Optional[str] = "pil", + return_dict: bool = True, + callback: Optional[Callable[[int, int, torch.FloatTensor], None]] = None, + callback_steps: int = 1, + cross_attention_kwargs: Optional[Dict[str, Any]] = None, + ): + r""" + Function invoked when calling the pipeline for generation. + + Args: + prompt (`str` or `List[str]`, *optional*): + The prompt or prompts to guide the image generation. If not defined, one has to pass `prompt_embeds`. + instead. + height (`int`, *optional*, defaults to self.unet.config.sample_size * self.vae_scale_factor): + The height in pixels of the generated image. + width (`int`, *optional*, defaults to self.unet.config.sample_size * self.vae_scale_factor): + The width in pixels of the generated image. + num_inference_steps (`int`, *optional*, defaults to 50): + The number of denoising steps. More denoising steps usually lead to a higher quality image at the + expense of slower inference. + guidance_scale (`float`, *optional*, defaults to 7.5): + Guidance scale as defined in [Classifier-Free Diffusion Guidance](https://arxiv.org/abs/2207.12598). + `guidance_scale` is defined as `w` of equation 2. of [Imagen + Paper](https://arxiv.org/pdf/2205.11487.pdf). Guidance scale is enabled by setting `guidance_scale > + 1`. Higher guidance scale encourages to generate images that are closely linked to the text `prompt`, + usually at the expense of lower image quality. + negative_prompt (`str` or `List[str]`, *optional*): + The prompt or prompts not to guide the image generation. If not defined, one has to pass + `negative_prompt_embeds`. instead. If not defined, one has to pass `negative_prompt_embeds`. instead. + Ignored when not using guidance (i.e., ignored if `guidance_scale` is less than `1`). + num_images_per_prompt (`int`, *optional*, defaults to 1): + The number of images to generate per prompt. + eta (`float`, *optional*, defaults to 0.0): + Corresponds to parameter eta (η) in the DDIM paper: https://arxiv.org/abs/2010.02502. Only applies to + [`schedulers.DDIMScheduler`], will be ignored for others. + generator (`torch.Generator` or `List[torch.Generator]`, *optional*): + One or a list of [torch generator(s)](https://pytorch.org/docs/stable/generated/torch.Generator.html) + to make generation deterministic. + latents (`torch.FloatTensor`, *optional*): + Pre-generated noisy latents, sampled from a Gaussian distribution, to be used as inputs for image + generation. Can be used to tweak the same generation with different prompts. If not provided, a latents + tensor will ge generated by sampling using the supplied random `generator`. + prompt_embeds (`torch.FloatTensor`, *optional*): + Pre-generated text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not + provided, text embeddings will be generated from `prompt` input argument. + negative_prompt_embeds (`torch.FloatTensor`, *optional*): + Pre-generated negative text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt + weighting. If not provided, negative_prompt_embeds will be generated from `negative_prompt` input + argument. + output_type (`str`, *optional*, defaults to `"pil"`): + The output format of the generate image. Choose between + [PIL](https://pillow.readthedocs.io/en/stable/): `PIL.Image.Image` or `np.array`. + return_dict (`bool`, *optional*, defaults to `True`): + Whether or not to return a [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] instead of a + plain tuple. + callback (`Callable`, *optional*): + A function that will be called every `callback_steps` steps during inference. The function will be + called with the following arguments: `callback(step: int, timestep: int, latents: torch.FloatTensor)`. + callback_steps (`int`, *optional*, defaults to 1): + The frequency at which the `callback` function will be called. If not specified, the callback will be + called at every step. + cross_attention_kwargs (`dict`, *optional*): + A kwargs dictionary that if specified is passed along to the `AttnProcessor` as defined under + `self.processor` in + [diffusers.cross_attention](https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/cross_attention.py). + + Examples: + + Returns: + [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] or `tuple`: + [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] if `return_dict` is True, otherwise a `tuple. + When returning a tuple, the first element is a list with the generated images, and the second element is a + list of `bool`s denoting whether the corresponding generated image likely represents "not-safe-for-work" + (nsfw) content, according to the `safety_checker`. + """ + # 0. Default height and width to unet + height = height or self.unet.config.sample_size * self.vae_scale_factor + width = width or self.unet.config.sample_size * self.vae_scale_factor + + # 1. Check inputs. Raise error if not correct + self.check_inputs( + prompt, height, width, callback_steps, negative_prompt, prompt_embeds, negative_prompt_embeds + ) + + # 2. Define call parameters + if prompt is not None and isinstance(prompt, str): + batch_size = 1 + elif prompt is not None and isinstance(prompt, list): + batch_size = len(prompt) + else: + batch_size = prompt_embeds.shape[0] + + device = self._execution_device + # here `guidance_scale` is defined analog to the guidance weight `w` of equation (2) + # of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1` + # corresponds to doing no classifier free guidance. + do_classifier_free_guidance = guidance_scale > 1.0 + + # 3. Encode input prompt + prompt_embeds = self._encode_prompt( + prompt, + device, + num_images_per_prompt, + do_classifier_free_guidance, + negative_prompt, + prompt_embeds=prompt_embeds, + negative_prompt_embeds=negative_prompt_embeds, + ) + + # 4. Prepare timesteps + self.scheduler.set_timesteps(num_inference_steps, device=device) + timesteps = self.scheduler.timesteps + + # 5. Prepare latent variables + num_channels_latents = self.unet.config.in_channels + latents = self.prepare_latents( + batch_size * num_images_per_prompt, + num_channels_latents, + video_length, + height, + width, + prompt_embeds.dtype, + device, + generator, + latents, + ) + + # 6. Prepare extra step kwargs. TODO: Logic should ideally just be moved out of the pipeline + extra_step_kwargs = self.prepare_extra_step_kwargs(generator, eta) + + motion_bucket_id = torch.tensor([motion_bucket_id], dtype=latents.dtype, device=latents.device) + + # 7. Denoising loop + num_warmup_steps = len(timesteps) - num_inference_steps * self.scheduler.order + timesteps = reversed(timesteps) + # print("Valid timesteps: ", timesteps) + with self.progress_bar(total=num_inference_steps) as progress_bar: + for i, t in enumerate(timesteps): + latent_model_input = torch.cat([latents] * 2) if do_classifier_free_guidance else latents + latent_model_input = self.scheduler.scale_model_input(latent_model_input, t) + + if do_classifier_free_guidance: + base_content_model_input = torch.cat([base_content, base_content], dim=0) + motion_bucket_id_model_input = torch.cat([motion_bucket_id, motion_bucket_id], dim=0) + else: + base_content_model_input = base_content + motion_bucket_id_model_input = motion_bucket_id + + # predict the noise residual + noise_pred = self.unet( + latent_model_input, + t, + encoder_hidden_states=prompt_embeds, + base_content=base_content_model_input, + added_motion_ids=motion_bucket_id_model_input, + # cross_attention_kwargs=cross_attention_kwargs, + ).sample + + # perform guidance + if do_classifier_free_guidance: + noise_pred_uncond, noise_pred_text = noise_pred.chunk(2) + noise_pred = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond) + + # compute the previous noisy sample x_t -> x_t-1 + latents, _ = self.next_step(noise_pred, t, latents, verbose=False) + + # call the callback, if provided + if i == len(timesteps) - 1 or ((i + 1) > num_warmup_steps and (i + 1) % self.scheduler.order == 0): + progress_bar.update() + if callback is not None and i % callback_steps == 0: + callback(i, t, latents) + + + # 8. Post-processing + # video = self.decode_latents(latents) + + return StableDiffusionPipelineOutput(video=latents) + + def next_step( + self, + model_output: torch.FloatTensor, + timestep: int, + x: torch.FloatTensor, + eta=0., + verbose=False + ): + """ + Inverse sampling for DDIM Inversion + """ + if verbose: + print("timestep: ", timestep) + next_step = timestep + timestep = min(timestep - self.scheduler.config.num_train_timesteps // self.scheduler.num_inference_steps, 999) + alpha_prod_t = self.scheduler.alphas_cumprod[timestep] if timestep >= 0 else self.scheduler.final_alpha_cumprod + alpha_prod_t_next = self.scheduler.alphas_cumprod[next_step] + beta_prod_t = 1 - alpha_prod_t + pred_x0 = (x - beta_prod_t**0.5 * model_output) / alpha_prod_t**0.5 + pred_dir = (1 - alpha_prod_t_next)**0.5 * model_output + x_next = alpha_prod_t_next**0.5 * pred_x0 + pred_dir + return x_next, pred_x0 \ No newline at end of file diff --git a/pipelines/pipeline_videogen.py b/pipelines/pipeline_videogen.py new file mode 100644 index 0000000000000000000000000000000000000000..8034d25770d10ea1e878c01e8abc7b54735dddc5 --- /dev/null +++ b/pipelines/pipeline_videogen.py @@ -0,0 +1,756 @@ +# Copyright 2023 The HuggingFace Team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import inspect +from typing import Any, Callable, Dict, List, Optional, Union +import einops +import torch +from packaging import version +from transformers import CLIPFeatureExtractor, CLIPTextModel, CLIPTokenizer + +from diffusers.configuration_utils import FrozenDict +from diffusers.models import AutoencoderKL +from diffusers.schedulers import KarrasDiffusionSchedulers +from diffusers.utils import ( + deprecate, + is_accelerate_available, + is_accelerate_version, + logging, + replace_example_docstring, + BaseOutput, +) +try: + from diffusers.utils import randn_tensor +except: + from diffusers.utils.torch_utils import randn_tensor +# from diffusers.pipeline_utils import DiffusionPipeline +from diffusers.pipelines.pipeline_utils import DiffusionPipeline +from dataclasses import dataclass + +import os, sys +sys.path.append(os.path.split(sys.path[0])[0]) + +from models.unet import UNet3DConditionModel + +import numpy as np + +@dataclass +class StableDiffusionPipelineOutput(BaseOutput): + video: torch.Tensor + +logger = logging.get_logger(__name__) # pylint: disable=invalid-name + +EXAMPLE_DOC_STRING = """ + Examples: + ```py + >>> import torch + >>> from diffusers import StableDiffusionPipeline + + >>> pipe = StableDiffusionPipeline.from_pretrained("runwayml/stable-diffusion-v1-5", torch_dtype=torch.float16) + >>> pipe = pipe.to("cuda") + + >>> prompt = "a photo of an astronaut riding a horse on mars" + >>> image = pipe(prompt).images[0] + ``` +""" + + +class VideoGenPipeline(DiffusionPipeline): + r""" + Pipeline for text-to-image generation using Stable Diffusion. + + This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods the + library implements for all the pipelines (such as downloading or saving, running on a particular device, etc.) + + Args: + vae ([`AutoencoderKL`]): + Variational Auto-Encoder (VAE) Model to encode and decode images to and from latent representations. + text_encoder ([`CLIPTextModel`]): + Frozen text-encoder. Stable Diffusion uses the text portion of + [CLIP](https://huggingface.co/docs/transformers/model_doc/clip#transformers.CLIPTextModel), specifically + the [clip-vit-large-patch14](https://huggingface.co/openai/clip-vit-large-patch14) variant. + tokenizer (`CLIPTokenizer`): + Tokenizer of class + [CLIPTokenizer](https://huggingface.co/docs/transformers/v4.21.0/en/model_doc/clip#transformers.CLIPTokenizer). + unet ([`UNet2DConditionModel`]): Conditional U-Net architecture to denoise the encoded image latents. + scheduler ([`SchedulerMixin`]): + A scheduler to be used in combination with `unet` to denoise the encoded image latents. Can be one of + [`DDIMScheduler`], [`LMSDiscreteScheduler`], or [`PNDMScheduler`]. + safety_checker ([`StableDiffusionSafetyChecker`]): + Classification module that estimates whether generated images could be considered offensive or harmful. + Please, refer to the [model card](https://huggingface.co/runwayml/stable-diffusion-v1-5) for details. + feature_extractor ([`CLIPFeatureExtractor`]): + Model that extracts features from generated images to be used as inputs for the `safety_checker`. + """ + _optional_components = ["safety_checker", "feature_extractor"] + + def __init__( + self, + vae: AutoencoderKL, + text_encoder: CLIPTextModel, + tokenizer: CLIPTokenizer, + unet: UNet3DConditionModel, + scheduler: KarrasDiffusionSchedulers, + ): + super().__init__() + + if hasattr(scheduler.config, "steps_offset") and scheduler.config.steps_offset != 1: + deprecation_message = ( + f"The configuration file of this scheduler: {scheduler} is outdated. `steps_offset`" + f" should be set to 1 instead of {scheduler.config.steps_offset}. Please make sure " + "to update the config accordingly as leaving `steps_offset` might led to incorrect results" + " in future versions. If you have downloaded this checkpoint from the Hugging Face Hub," + " it would be very nice if you could open a Pull request for the `scheduler/scheduler_config.json`" + " file" + ) + deprecate("steps_offset!=1", "1.0.0", deprecation_message, standard_warn=False) + new_config = dict(scheduler.config) + new_config["steps_offset"] = 1 + scheduler._internal_dict = FrozenDict(new_config) + + if hasattr(scheduler.config, "clip_sample") and scheduler.config.clip_sample is True: + deprecation_message = ( + f"The configuration file of this scheduler: {scheduler} has not set the configuration `clip_sample`." + " `clip_sample` should be set to False in the configuration file. Please make sure to update the" + " config accordingly as not setting `clip_sample` in the config might lead to incorrect results in" + " future versions. If you have downloaded this checkpoint from the Hugging Face Hub, it would be very" + " nice if you could open a Pull request for the `scheduler/scheduler_config.json` file" + ) + deprecate("clip_sample not set", "1.0.0", deprecation_message, standard_warn=False) + new_config = dict(scheduler.config) + new_config["clip_sample"] = False + scheduler._internal_dict = FrozenDict(new_config) + + + + is_unet_version_less_0_9_0 = hasattr(unet.config, "_diffusers_version") and version.parse( + version.parse(unet.config._diffusers_version).base_version + ) < version.parse("0.9.0.dev0") + is_unet_sample_size_less_64 = hasattr(unet.config, "sample_size") and unet.config.sample_size < 64 + if is_unet_version_less_0_9_0 and is_unet_sample_size_less_64: + deprecation_message = ( + "The configuration file of the unet has set the default `sample_size` to smaller than" + " 64 which seems highly unlikely. If your checkpoint is a fine-tuned version of any of the" + " following: \n- CompVis/stable-diffusion-v1-4 \n- CompVis/stable-diffusion-v1-3 \n-" + " CompVis/stable-diffusion-v1-2 \n- CompVis/stable-diffusion-v1-1 \n- runwayml/stable-diffusion-v1-5" + " \n- runwayml/stable-diffusion-inpainting \n you should change 'sample_size' to 64 in the" + " configuration file. Please make sure to update the config accordingly as leaving `sample_size=32`" + " in the config might lead to incorrect results in future versions. If you have downloaded this" + " checkpoint from the Hugging Face Hub, it would be very nice if you could open a Pull request for" + " the `unet/config.json` file" + ) + deprecate("sample_size<64", "1.0.0", deprecation_message, standard_warn=False) + new_config = dict(unet.config) + new_config["sample_size"] = 64 + unet._internal_dict = FrozenDict(new_config) + + self.register_modules( + vae=vae, + text_encoder=text_encoder, + tokenizer=tokenizer, + unet=unet, + scheduler=scheduler, + ) + self.vae_scale_factor = 2 ** (len(self.vae.config.block_out_channels) - 1) + # self.register_to_config(requires_safety_checker=requires_safety_checker) + + def enable_vae_slicing(self): + r""" + Enable sliced VAE decoding. + + When this option is enabled, the VAE will split the input tensor in slices to compute decoding in several + steps. This is useful to save some memory and allow larger batch sizes. + """ + self.vae.enable_slicing() + + def disable_vae_slicing(self): + r""" + Disable sliced VAE decoding. If `enable_vae_slicing` was previously invoked, this method will go back to + computing decoding in one step. + """ + self.vae.disable_slicing() + + def enable_vae_tiling(self): + r""" + Enable tiled VAE decoding. + + When this option is enabled, the VAE will split the input tensor into tiles to compute decoding and encoding in + several steps. This is useful to save a large amount of memory and to allow the processing of larger images. + """ + self.vae.enable_tiling() + + def disable_vae_tiling(self): + r""" + Disable tiled VAE decoding. If `enable_vae_tiling` was previously invoked, this method will go back to + computing decoding in one step. + """ + self.vae.disable_tiling() + + def enable_sequential_cpu_offload(self, gpu_id=0): + r""" + Offloads all models to CPU using accelerate, significantly reducing memory usage. When called, unet, + text_encoder, vae and safety checker have their state dicts saved to CPU and then are moved to a + `torch.device('meta') and loaded to GPU only when their specific submodule has its `forward` method called. + Note that offloading happens on a submodule basis. Memory savings are higher than with + `enable_model_cpu_offload`, but performance is lower. + """ + if is_accelerate_available() and is_accelerate_version(">=", "0.14.0"): + from accelerate import cpu_offload + else: + raise ImportError("`enable_sequential_cpu_offload` requires `accelerate v0.14.0` or higher") + + device = torch.device(f"cuda:{gpu_id}") + + if self.device.type != "cpu": + self.to("cpu", silence_dtype_warnings=True) + torch.cuda.empty_cache() # otherwise we don't see the memory savings (but they probably exist) + + for cpu_offloaded_model in [self.unet, self.text_encoder, self.vae]: + cpu_offload(cpu_offloaded_model, device) + + # if self.safety_checker is not None: + # cpu_offload(self.safety_checker, execution_device=device, offload_buffers=True) + + def enable_model_cpu_offload(self, gpu_id=0): + r""" + Offloads all models to CPU using accelerate, reducing memory usage with a low impact on performance. Compared + to `enable_sequential_cpu_offload`, this method moves one whole model at a time to the GPU when its `forward` + method is called, and the model remains in GPU until the next model runs. Memory savings are lower than with + `enable_sequential_cpu_offload`, but performance is much better due to the iterative execution of the `unet`. + """ + if is_accelerate_available() and is_accelerate_version(">=", "0.17.0.dev0"): + from accelerate import cpu_offload_with_hook + else: + raise ImportError("`enable_model_offload` requires `accelerate v0.17.0` or higher.") + + device = torch.device(f"cuda:{gpu_id}") + + if self.device.type != "cpu": + self.to("cpu", silence_dtype_warnings=True) + torch.cuda.empty_cache() # otherwise we don't see the memory savings (but they probably exist) + + hook = None + for cpu_offloaded_model in [self.text_encoder, self.unet, self.vae]: + _, hook = cpu_offload_with_hook(cpu_offloaded_model, device, prev_module_hook=hook) + + # if self.safety_checker is not None: + # _, hook = cpu_offload_with_hook(self.safety_checker, device, prev_module_hook=hook) + + # We'll offload the last model manually. + self.final_offload_hook = hook + + @property + def _execution_device(self): + r""" + Returns the device on which the pipeline's models will be executed. After calling + `pipeline.enable_sequential_cpu_offload()` the execution device can only be inferred from Accelerate's module + hooks. + """ + if not hasattr(self.unet, "_hf_hook"): + return self.device + for module in self.unet.modules(): + if ( + hasattr(module, "_hf_hook") + and hasattr(module._hf_hook, "execution_device") + and module._hf_hook.execution_device is not None + ): + return torch.device(module._hf_hook.execution_device) + return self.device + + def _encode_prompt( + self, + prompt, + device, + num_images_per_prompt, + do_classifier_free_guidance, + negative_prompt=None, + prompt_embeds: Optional[torch.FloatTensor] = None, + negative_prompt_embeds: Optional[torch.FloatTensor] = None, + ): + r""" + Encodes the prompt into text encoder hidden states. + + Args: + prompt (`str` or `List[str]`, *optional*): + prompt to be encoded + device: (`torch.device`): + torch device + num_images_per_prompt (`int`): + number of images that should be generated per prompt + do_classifier_free_guidance (`bool`): + whether to use classifier free guidance or not + negative_prompt (`str` or `List[str]`, *optional*): + The prompt or prompts not to guide the image generation. If not defined, one has to pass + `negative_prompt_embeds`. instead. If not defined, one has to pass `negative_prompt_embeds`. instead. + Ignored when not using guidance (i.e., ignored if `guidance_scale` is less than `1`). + prompt_embeds (`torch.FloatTensor`, *optional*): + Pre-generated text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not + provided, text embeddings will be generated from `prompt` input argument. + negative_prompt_embeds (`torch.FloatTensor`, *optional*): + Pre-generated negative text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt + weighting. If not provided, negative_prompt_embeds will be generated from `negative_prompt` input + argument. + """ + if prompt is not None and isinstance(prompt, str): + batch_size = 1 + elif prompt is not None and isinstance(prompt, list): + batch_size = len(prompt) + else: + batch_size = prompt_embeds.shape[0] + + if prompt_embeds is None: + text_inputs = self.tokenizer( + prompt, + padding="max_length", + max_length=self.tokenizer.model_max_length, + truncation=True, + return_tensors="pt", + ) + text_input_ids = text_inputs.input_ids + untruncated_ids = self.tokenizer(prompt, padding="longest", return_tensors="pt").input_ids + + if untruncated_ids.shape[-1] >= text_input_ids.shape[-1] and not torch.equal( + text_input_ids, untruncated_ids + ): + removed_text = self.tokenizer.batch_decode( + untruncated_ids[:, self.tokenizer.model_max_length - 1 : -1] + ) + logger.warning( + "The following part of your input was truncated because CLIP can only handle sequences up to" + f" {self.tokenizer.model_max_length} tokens: {removed_text}" + ) + + if hasattr(self.text_encoder.config, "use_attention_mask") and self.text_encoder.config.use_attention_mask: + attention_mask = text_inputs.attention_mask.to(device) + else: + attention_mask = None + + prompt_embeds = self.text_encoder( + text_input_ids.to(device), + attention_mask=attention_mask, + ) + prompt_embeds = prompt_embeds[0] + + prompt_embeds = prompt_embeds.to(dtype=self.text_encoder.dtype, device=device) + + bs_embed, seq_len, _ = prompt_embeds.shape + # duplicate text embeddings for each generation per prompt, using mps friendly method + prompt_embeds = prompt_embeds.repeat(1, num_images_per_prompt, 1) + prompt_embeds = prompt_embeds.view(bs_embed * num_images_per_prompt, seq_len, -1) + + # get unconditional embeddings for classifier free guidance + if do_classifier_free_guidance and negative_prompt_embeds is None: + uncond_tokens: List[str] + if negative_prompt is None: + uncond_tokens = [""] * batch_size + elif type(prompt) is not type(negative_prompt): + raise TypeError( + f"`negative_prompt` should be the same type to `prompt`, but got {type(negative_prompt)} !=" + f" {type(prompt)}." + ) + elif isinstance(negative_prompt, str): + uncond_tokens = [negative_prompt] + elif batch_size != len(negative_prompt): + raise ValueError( + f"`negative_prompt`: {negative_prompt} has batch size {len(negative_prompt)}, but `prompt`:" + f" {prompt} has batch size {batch_size}. Please make sure that passed `negative_prompt` matches" + " the batch size of `prompt`." + ) + else: + uncond_tokens = negative_prompt + + max_length = prompt_embeds.shape[1] + uncond_input = self.tokenizer( + uncond_tokens, + padding="max_length", + max_length=max_length, + truncation=True, + return_tensors="pt", + ) + + if hasattr(self.text_encoder.config, "use_attention_mask") and self.text_encoder.config.use_attention_mask: + attention_mask = uncond_input.attention_mask.to(device) + else: + attention_mask = None + + negative_prompt_embeds = self.text_encoder( + uncond_input.input_ids.to(device), + attention_mask=attention_mask, + ) + negative_prompt_embeds = negative_prompt_embeds[0] + + if do_classifier_free_guidance: + # duplicate unconditional embeddings for each generation per prompt, using mps friendly method + seq_len = negative_prompt_embeds.shape[1] + + negative_prompt_embeds = negative_prompt_embeds.to(dtype=self.text_encoder.dtype, device=device) + + negative_prompt_embeds = negative_prompt_embeds.repeat(1, num_images_per_prompt, 1) + negative_prompt_embeds = negative_prompt_embeds.view(batch_size * num_images_per_prompt, seq_len, -1) + + # For classifier free guidance, we need to do two forward passes. + # Here we concatenate the unconditional and text embeddings into a single batch + # to avoid doing two forward passes + prompt_embeds = torch.cat([negative_prompt_embeds, prompt_embeds]) + + return prompt_embeds + + # def run_safety_checker(self, image, device, dtype): + # if self.safety_checker is not None: + # safety_checker_input = self.feature_extractor(self.numpy_to_pil(image), return_tensors="pt").to(device) + # image, has_nsfw_concept = self.safety_checker( + # images=image, clip_input=safety_checker_input.pixel_values.to(dtype) + # ) + # else: + # has_nsfw_concept = None + # return image, has_nsfw_concept + + # def decode_latents(self, latents): + # print(latents.shape) + # video_length = latents.shape[2] + # latents = einops.rearrange(latents, "b c f h w -> (b f) c h w").contiguous() + # video = self.vae.decode(latents / 0.18215).sample + # video = einops.rearrange(video, "(b f) c h w -> b f c h w", f=video_length).contiguous() + # return video + + def decode_latents(self, latents, base_content): + video_length = latents.shape[2] + latents = 1 / 0.18215 * latents + base_content = 1 / 0.18215 * base_content + latents = base_content + latents + latents = torch.cat([base_content, latents], dim=2) + latents = einops.rearrange(latents, "b c f h w -> (b f) c h w") + video = self.vae.decode(latents).sample + video = einops.rearrange(video, "(b f) c h w -> b f h w c", f=video_length+1) + # video = (video / 2 + 0.5).clamp(0, 1) + video = ((video / 2 + 0.5) * 255).add_(0.5).clamp_(0, 255).to(dtype=torch.uint8).cpu().contiguous() + # we always cast to float32 as this does not cause significant overhead and is compatible with bfloa16 + # video = video.cpu().float() + return video + + def decode_latents_with_temporal_decoder(self, latents, base_content): + video_length = latents.shape[2] + latents = 1 / self.vae.config.scaling_factor * latents + base_content = 1 / self.vae.config.scaling_factor * base_content + latents = base_content + latents + latents = torch.cat([base_content, latents], dim=2) + latents = einops.rearrange(latents, "b c f h w -> (b f) c h w") + video = [] + + decode_chunk_size = 14 + for frame_idx in range(0, latents.shape[0], decode_chunk_size): + num_frames_in = latents[frame_idx : frame_idx + decode_chunk_size].shape[0] + + decode_kwargs = {} + decode_kwargs["num_frames"] = num_frames_in + + video.append(self.vae.decode(latents[frame_idx:frame_idx+decode_chunk_size], **decode_kwargs).sample) + + video = torch.cat(video) + video = einops.rearrange(video, "(b f) c h w -> b f h w c", f=video_length+1) + video = ((video / 2.0 + 0.5).clamp(0, 1) * 255).to(dtype=torch.uint8).cpu().contiguous() + # we always cast to float32 as this does not cause significant overhead and is compatible with bfloa16 + return video + + # def decode_latents(self, latents): + # latents = 1 / self.vae.config.scaling_factor * latents + # image = self.vae.decode(latents).sample + # image = (image / 2 + 0.5).clamp(0, 1) + # # we always cast to float32 as this does not cause significant overhead and is compatible with bfloat16 + # image = image.cpu().permute(0, 2, 3, 1).float().numpy() + # return image + + def prepare_extra_step_kwargs(self, generator, eta): + # prepare extra kwargs for the scheduler step, since not all schedulers have the same signature + # eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers. + # eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502 + # and should be between [0, 1] + + accepts_eta = "eta" in set(inspect.signature(self.scheduler.step).parameters.keys()) + extra_step_kwargs = {} + if accepts_eta: + extra_step_kwargs["eta"] = eta + + # check if the scheduler accepts generator + accepts_generator = "generator" in set(inspect.signature(self.scheduler.step).parameters.keys()) + if accepts_generator: + extra_step_kwargs["generator"] = generator + return extra_step_kwargs + + def check_inputs( + self, + prompt, + height, + width, + callback_steps, + negative_prompt=None, + prompt_embeds=None, + negative_prompt_embeds=None, + ): + if height % 8 != 0 or width % 8 != 0: + raise ValueError(f"`height` and `width` have to be divisible by 8 but are {height} and {width}.") + + if (callback_steps is None) or ( + callback_steps is not None and (not isinstance(callback_steps, int) or callback_steps <= 0) + ): + raise ValueError( + f"`callback_steps` has to be a positive integer but is {callback_steps} of type" + f" {type(callback_steps)}." + ) + + if prompt is not None and prompt_embeds is not None: + raise ValueError( + f"Cannot forward both `prompt`: {prompt} and `prompt_embeds`: {prompt_embeds}. Please make sure to" + " only forward one of the two." + ) + elif prompt is None and prompt_embeds is None: + raise ValueError( + "Provide either `prompt` or `prompt_embeds`. Cannot leave both `prompt` and `prompt_embeds` undefined." + ) + elif prompt is not None and (not isinstance(prompt, str) and not isinstance(prompt, list)): + raise ValueError(f"`prompt` has to be of type `str` or `list` but is {type(prompt)}") + + if negative_prompt is not None and negative_prompt_embeds is not None: + raise ValueError( + f"Cannot forward both `negative_prompt`: {negative_prompt} and `negative_prompt_embeds`:" + f" {negative_prompt_embeds}. Please make sure to only forward one of the two." + ) + + if prompt_embeds is not None and negative_prompt_embeds is not None: + if prompt_embeds.shape != negative_prompt_embeds.shape: + raise ValueError( + "`prompt_embeds` and `negative_prompt_embeds` must have the same shape when passed directly, but" + f" got: `prompt_embeds` {prompt_embeds.shape} != `negative_prompt_embeds`" + f" {negative_prompt_embeds.shape}." + ) + + def prepare_latents(self, batch_size, num_channels_latents, video_length, height, width, dtype, device, generator, latents=None): + shape = (batch_size, num_channels_latents, video_length, height // self.vae_scale_factor, width // self.vae_scale_factor) + if isinstance(generator, list) and len(generator) != batch_size: + raise ValueError( + f"You have passed a list of generators of length {len(generator)}, but requested an effective batch" + f" size of {batch_size}. Make sure the batch size matches the length of the generators." + ) + + if latents is None: + latents = randn_tensor(shape, generator=generator, device=device, dtype=dtype) + else: + latents = latents.to(device) + + # scale the initial noise by the standard deviation required by the scheduler + latents = latents * self.scheduler.init_noise_sigma + return latents + + @torch.no_grad() + @replace_example_docstring(EXAMPLE_DOC_STRING) + def __call__( + self, + prompt: Union[str, List[str]] = None, + height: Optional[int] = None, + width: Optional[int] = None, + base_content: Optional[torch.Tensor] = None, + video_length: int = 16, + num_inference_steps: int = 50, + guidance_scale: float = 7.5, + negative_prompt: Optional[Union[str, List[str]]] = None, + num_images_per_prompt: Optional[int] = 1, + eta: float = 0.0, + motion_bucket_id: int = 25, # [0-99] + generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None, + latents: Optional[torch.FloatTensor] = None, + prompt_embeds: Optional[torch.FloatTensor] = None, + negative_prompt_embeds: Optional[torch.FloatTensor] = None, + output_type: Optional[str] = "pil", + return_dict: bool = True, + callback: Optional[Callable[[int, int, torch.FloatTensor], None]] = None, + callback_steps: int = 1, + cross_attention_kwargs: Optional[Dict[str, Any]] = None, + enable_vae_temporal_decoder: bool = False, + ): + r""" + Function invoked when calling the pipeline for generation. + + Args: + prompt (`str` or `List[str]`, *optional*): + The prompt or prompts to guide the image generation. If not defined, one has to pass `prompt_embeds`. + instead. + height (`int`, *optional*, defaults to self.unet.config.sample_size * self.vae_scale_factor): + The height in pixels of the generated image. + width (`int`, *optional*, defaults to self.unet.config.sample_size * self.vae_scale_factor): + The width in pixels of the generated image. + num_inference_steps (`int`, *optional*, defaults to 50): + The number of denoising steps. More denoising steps usually lead to a higher quality image at the + expense of slower inference. + guidance_scale (`float`, *optional*, defaults to 7.5): + Guidance scale as defined in [Classifier-Free Diffusion Guidance](https://arxiv.org/abs/2207.12598). + `guidance_scale` is defined as `w` of equation 2. of [Imagen + Paper](https://arxiv.org/pdf/2205.11487.pdf). Guidance scale is enabled by setting `guidance_scale > + 1`. Higher guidance scale encourages to generate images that are closely linked to the text `prompt`, + usually at the expense of lower image quality. + negative_prompt (`str` or `List[str]`, *optional*): + The prompt or prompts not to guide the image generation. If not defined, one has to pass + `negative_prompt_embeds`. instead. If not defined, one has to pass `negative_prompt_embeds`. instead. + Ignored when not using guidance (i.e., ignored if `guidance_scale` is less than `1`). + num_images_per_prompt (`int`, *optional*, defaults to 1): + The number of images to generate per prompt. + eta (`float`, *optional*, defaults to 0.0): + Corresponds to parameter eta (η) in the DDIM paper: https://arxiv.org/abs/2010.02502. Only applies to + [`schedulers.DDIMScheduler`], will be ignored for others. + generator (`torch.Generator` or `List[torch.Generator]`, *optional*): + One or a list of [torch generator(s)](https://pytorch.org/docs/stable/generated/torch.Generator.html) + to make generation deterministic. + latents (`torch.FloatTensor`, *optional*): + Pre-generated noisy latents, sampled from a Gaussian distribution, to be used as inputs for image + generation. Can be used to tweak the same generation with different prompts. If not provided, a latents + tensor will ge generated by sampling using the supplied random `generator`. + prompt_embeds (`torch.FloatTensor`, *optional*): + Pre-generated text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not + provided, text embeddings will be generated from `prompt` input argument. + negative_prompt_embeds (`torch.FloatTensor`, *optional*): + Pre-generated negative text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt + weighting. If not provided, negative_prompt_embeds will be generated from `negative_prompt` input + argument. + output_type (`str`, *optional*, defaults to `"pil"`): + The output format of the generate image. Choose between + [PIL](https://pillow.readthedocs.io/en/stable/): `PIL.Image.Image` or `np.array`. + return_dict (`bool`, *optional*, defaults to `True`): + Whether or not to return a [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] instead of a + plain tuple. + callback (`Callable`, *optional*): + A function that will be called every `callback_steps` steps during inference. The function will be + called with the following arguments: `callback(step: int, timestep: int, latents: torch.FloatTensor)`. + callback_steps (`int`, *optional*, defaults to 1): + The frequency at which the `callback` function will be called. If not specified, the callback will be + called at every step. + cross_attention_kwargs (`dict`, *optional*): + A kwargs dictionary that if specified is passed along to the `AttnProcessor` as defined under + `self.processor` in + [diffusers.cross_attention](https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/cross_attention.py). + + Examples: + + Returns: + [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] or `tuple`: + [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] if `return_dict` is True, otherwise a `tuple. + When returning a tuple, the first element is a list with the generated images, and the second element is a + list of `bool`s denoting whether the corresponding generated image likely represents "not-safe-for-work" + (nsfw) content, according to the `safety_checker`. + """ + # 0. Default height and width to unet + height = height or self.unet.config.sample_size * self.vae_scale_factor + width = width or self.unet.config.sample_size * self.vae_scale_factor + + # 1. Check inputs. Raise error if not correct + self.check_inputs( + prompt, height, width, callback_steps, negative_prompt, prompt_embeds, negative_prompt_embeds + ) + + # 2. Define call parameters + if prompt is not None and isinstance(prompt, str): + batch_size = 1 + elif prompt is not None and isinstance(prompt, list): + batch_size = len(prompt) + else: + batch_size = prompt_embeds.shape[0] + + device = self._execution_device + # here `guidance_scale` is defined analog to the guidance weight `w` of equation (2) + # of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1` + # corresponds to doing no classifier free guidance. + do_classifier_free_guidance = guidance_scale > 1.0 + + # 3. Encode input prompt + prompt_embeds = self._encode_prompt( + prompt, + device, + num_images_per_prompt, + do_classifier_free_guidance, + negative_prompt, + prompt_embeds=prompt_embeds, + negative_prompt_embeds=negative_prompt_embeds, + ) + + # 4. Prepare timesteps + self.scheduler.set_timesteps(num_inference_steps, device=device) + timesteps = self.scheduler.timesteps + + # 5. Prepare latent variables + num_channels_latents = self.unet.config.in_channels + latents = self.prepare_latents( + batch_size * num_images_per_prompt, + num_channels_latents, + video_length, + height, + width, + prompt_embeds.dtype, + device, + generator, + latents, + ) + + # 6. Prepare extra step kwargs. TODO: Logic should ideally just be moved out of the pipeline + extra_step_kwargs = self.prepare_extra_step_kwargs(generator, eta) + + # motion bucket id + motion_bucket_id = torch.tensor([motion_bucket_id], dtype=latents.dtype).to(device=latents.device) + + # 7. Denoising loop + num_warmup_steps = len(timesteps) - num_inference_steps * self.scheduler.order + with self.progress_bar(total=num_inference_steps) as progress_bar: + for i, t in enumerate(timesteps): + # expand the latents if we are doing classifier free guidance + latent_model_input = torch.cat([latents] * 2) if do_classifier_free_guidance else latents + latent_model_input = self.scheduler.scale_model_input(latent_model_input, t) + + if do_classifier_free_guidance: + base_content_model_input = torch.cat([base_content, base_content], dim=0) + motion_bucket_id_model_input = torch.cat([motion_bucket_id, motion_bucket_id], dim=0) + else: + base_content_model_input = base_content + motion_bucket_id_model_input = motion_bucket_id + + # predict the noise residual + noise_pred = self.unet( + latent_model_input, + t, + encoder_hidden_states=prompt_embeds, + base_content=base_content_model_input, + added_motion_ids=motion_bucket_id_model_input, + # cross_attention_kwargs=cross_attention_kwargs, + ).sample + + # perform guidance + if do_classifier_free_guidance: + noise_pred_uncond, noise_pred_text = noise_pred.chunk(2) + noise_pred = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond) + + # compute the previous noisy sample x_t -> x_t-1 + latents = self.scheduler.step(noise_pred, t, latents, **extra_step_kwargs).prev_sample + + # call the callback, if provided + if i == len(timesteps) - 1 or ((i + 1) > num_warmup_steps and (i + 1) % self.scheduler.order == 0): + progress_bar.update() + if callback is not None and i % callback_steps == 0: + callback(i, t, latents) + + if output_type == 'latents': + return StableDiffusionPipelineOutput(video=latents) + + # 8. Post-processing + if enable_vae_temporal_decoder: + video = self.decode_latents_with_temporal_decoder(latents, base_content) + else: + video = self.decode_latents(latents, base_content) + + return StableDiffusionPipelineOutput(video=video) \ No newline at end of file diff --git a/pipelines/video_editing.sh b/pipelines/video_editing.sh new file mode 100644 index 0000000000000000000000000000000000000000..3ea9dd18828c52001db98f3df33e58ae243867ac --- /dev/null +++ b/pipelines/video_editing.sh @@ -0,0 +1,2 @@ +export CUDA_VISIBLE_DEVICES=6 +python pipelines/video_editting.py --config configs/sample.yaml \ No newline at end of file diff --git a/pipelines/video_editting.py b/pipelines/video_editting.py new file mode 100644 index 0000000000000000000000000000000000000000..0b41d9a7d8a8263157b474e60bcf1a142eb7760a --- /dev/null +++ b/pipelines/video_editting.py @@ -0,0 +1,200 @@ +import os +import torch +import argparse +import torchvision + +from pipeline_videogen import VideoGenPipeline +from pipelines.pipeline_inversion import VideoGenInversionPipeline + +from diffusers.schedulers import DDIMScheduler +from diffusers.models import AutoencoderKL +from diffusers.models import AutoencoderKLTemporalDecoder +from transformers import CLIPTokenizer, CLIPTextModel +from omegaconf import OmegaConf + +import os, sys +sys.path.append(os.path.split(sys.path[0])[0]) +from utils import find_model +from models import get_models +import imageio +import decord +import numpy as np +from copy import deepcopy +from PIL import Image +from datasets import video_transforms +from torchvision import transforms + +def prepare_image(path, vae, transform_video, device, dtype=torch.float16): + with open(path, 'rb') as f: + image = Image.open(f).convert('RGB') + image = torch.as_tensor(np.array(image, dtype=np.uint8, copy=True)).unsqueeze(0).permute(0, 3, 1, 2) + image, ori_h, ori_w, crops_coords_top, crops_coords_left = transform_video(image) + image = vae.encode(image.to(dtype=dtype, device=device)).latent_dist.sample().mul_(vae.config.scaling_factor) + image = image.unsqueeze(2) + return image + +def separation_content_motion(video_clip): + """ + Separate content and motion in a given video. + Args: + video_clip: A given video clip, shape [B, C, F, H, W] + + Return: + base_frame: Base frame, shape [B, C, 1, H, W] + motions: Motions based on base frame, shape [B, C, F-1, H, W] + """ + # Selecting the first frame from each video in the batch as the base frame + base_frame = video_clip[:, :, :1, :, :] + + # Calculating the motion (difference between each frame and the base frame) + motions = video_clip[:, :, 1:, :, :] - base_frame + + return base_frame, motions + + +class DecordInit(object): + """Using Decord(https://github.com/dmlc/decord) to initialize the video_reader.""" + + def __init__(self, num_threads=1): + self.num_threads = num_threads + self.ctx = decord.cpu(0) + + def __call__(self, filename): + """Perform the Decord initialization. + Args: + results (dict): The resulting dict to be modified and passed + to the next transform in pipeline. + """ + reader = decord.VideoReader(filename, + ctx=self.ctx, + num_threads=self.num_threads) + return reader + + def __repr__(self): + repr_str = (f'{self.__class__.__name__}(' + f'sr={self.sr},' + f'num_threads={self.num_threads})') + return repr_str + + +def main(args): + # torch.manual_seed(args.seed) + torch.set_grad_enabled(False) + device = "cuda" if torch.cuda.is_available() else "cpu" + dtype = torch.float16 # torch.float16 + + unet = get_models(args).to(device, dtype=torch.float16) + state_dict = find_model(args.ckpt) + unet.load_state_dict(state_dict) + + if args.enable_vae_temporal_decoder: + if args.use_dct: + vae_for_base_content = AutoencoderKLTemporalDecoder.from_pretrained(args.pretrained_model_path, subfolder="vae_temporal_decoder", torch_dtype=torch.float64).to(device) + else: + vae_for_base_content = AutoencoderKLTemporalDecoder.from_pretrained(args.pretrained_model_path, subfolder="vae_temporal_decoder", torch_dtype=torch.float16).to(device) + vae = deepcopy(vae_for_base_content).to(dtype=dtype) + else: + vae_for_base_content = AutoencoderKL.from_pretrained(args.pretrained_model_path, subfolder="vae",).to(device, dtype=torch.float64) + vae = deepcopy(vae_for_base_content).to(dtype=dtype) + + tokenizer = CLIPTokenizer.from_pretrained(args.pretrained_model_path, subfolder="tokenizer") + text_encoder = CLIPTextModel.from_pretrained(args.pretrained_model_path, subfolder="text_encoder", torch_dtype=torch.float16).to(device) + + # set eval mode + unet.eval() + vae.eval() + text_encoder.eval() + + scheduler_inversion = DDIMScheduler.from_pretrained(args.pretrained_model_path, + subfolder="scheduler", + beta_start=args.beta_start, + beta_end=args.beta_end, + beta_schedule=args.beta_schedule,) + + scheduler = DDIMScheduler.from_pretrained(args.pretrained_model_path, + subfolder="scheduler", + beta_start=args.beta_start, + beta_end=args.beta_end, + # beta_end=0.017, + beta_schedule=args.beta_schedule,) + + videogen_pipeline = VideoGenPipeline(vae=vae, + text_encoder=text_encoder, + tokenizer=tokenizer, + scheduler=scheduler_inversion, + unet=unet).to(device) + + videogen_pipeline_inversion = VideoGenInversionPipeline(vae=vae, + text_encoder=text_encoder, + tokenizer=tokenizer, + scheduler=scheduler, + unet=unet).to(device) + # videogen_pipeline.enable_xformers_memory_efficient_attention() + # videogen_pipeline.enable_vae_slicing() + + transform_video = video_transforms.Compose([ + video_transforms.ToTensorVideo(), + video_transforms.SDXLCenterCrop((args.image_size[0], args.image_size[1])), # center crop using shor edge, then resize + transforms.Normalize(mean=[0.5, 0.5, 0.5], std=[0.5, 0.5, 0.5], inplace=True), + ]) + + + # video_path = './video_editing/A_man_walking_on_the_beach.mp4' + video_path = './video_editing/a_corgi_walking_in_the_park_at_sunrise_oil_painting_style.mp4' + + + video_reader = DecordInit() + video = video_reader(video_path) + frame_indice = np.linspace(0, 15, 16, dtype=int) + video = torch.from_numpy(video.get_batch(frame_indice).asnumpy()).permute(0, 3, 1, 2).contiguous() + video = video / 255.0 + video = video * 2.0 - 1.0 + latents = vae.encode(video.to(dtype=torch.float16, device=device)).latent_dist.sample().mul_(vae.config.scaling_factor).unsqueeze(0).permute(0, 2, 1, 3, 4) + + base_content, motion_latents = separation_content_motion(latents) + + # image_path = "./video_editing/a_man_walking_in_the_park.png" + image_path = "./video_editing/a_cute_corgi_walking_in_the_park.png" + edit_content = prepare_image(image_path, vae, transform_video, device, dtype=torch.float16).to(device) + + if not os.path.exists(args.save_img_path): + os.makedirs(args.save_img_path) + + # prompt_inversion = 'a man walking on the beach' + prompt_inversion = 'a corgi walking in the park at sunrise, oil painting style' + latents = videogen_pipeline_inversion(prompt_inversion, + latents=motion_latents, + base_content=base_content, + video_length=args.video_length, + height=args.image_size[0], + width=args.image_size[1], + num_inference_steps=args.num_sampling_steps, + guidance_scale=1.0, + # guidance_scale=args.guidance_scale, + motion_bucket_id=args.motion_bucket_id, + output_type="latent").video + + # prompt = 'a man walking in the park' + prompt = 'a corgi walking in the park at sunrise, oil painting style' + videos = videogen_pipeline(prompt, + latents=latents, + base_content=edit_content, + video_length=args.video_length, + height=args.image_size[0], + width=args.image_size[1], + num_inference_steps=args.num_sampling_steps, + guidance_scale=1.0, + # guidance_scale=args.guidance_scale, + motion_bucket_id=args.motion_bucket_id, + enable_vae_temporal_decoder=args.enable_vae_temporal_decoder).video + imageio.mimwrite(args.save_img_path + prompt.replace(' ', '_') + '_%04d' % args.run_time + '-imageio.mp4', videos[0], fps=8, quality=8) # highest quality is 10, lowest is 0 + print('save path {}'.format(args.save_img_path)) + +if __name__ == "__main__": + parser = argparse.ArgumentParser() + parser.add_argument("--config", type=str, default="./configs/sample.yaml") + args = parser.parse_args() + + main(OmegaConf.load(args.config)) + + diff --git a/pretrained/Cinemo/model_index.json b/pretrained/Cinemo/model_index.json new file mode 100644 index 0000000000000000000000000000000000000000..72f7b0487b783996b5c6c9e8d9cafbbf8fd3a3c8 --- /dev/null +++ b/pretrained/Cinemo/model_index.json @@ -0,0 +1,32 @@ +{ + "_class_name": "StableDiffusionPipeline", + "_diffusers_version": "0.2.2", + "feature_extractor": [ + "transformers", + "CLIPImageProcessor" + ], + "safety_checker": [ + "stable_diffusion", + "StableDiffusionSafetyChecker" + ], + "scheduler": [ + "diffusers", + "PNDMScheduler" + ], + "text_encoder": [ + "transformers", + "CLIPTextModel" + ], + "tokenizer": [ + "transformers", + "CLIPTokenizer" + ], + "unet": [ + "diffusers", + "UNet2DConditionModel" + ], + "vae": [ + "diffusers", + "AutoencoderKL" + ] +} diff --git a/pretrained/Cinemo/scheduler/scheduler_config.json b/pretrained/Cinemo/scheduler/scheduler_config.json new file mode 100644 index 0000000000000000000000000000000000000000..1a3e6d1a17dd6e722b4d4297023f979170219680 --- /dev/null +++ b/pretrained/Cinemo/scheduler/scheduler_config.json @@ -0,0 +1,13 @@ +{ + "_class_name": "PNDMScheduler", + "_diffusers_version": "0.7.0.dev0", + "beta_end": 0.012, + "beta_schedule": "scaled_linear", + "beta_start": 0.00085, + "num_train_timesteps": 1000, + "set_alpha_to_one": false, + "skip_prk_steps": true, + "steps_offset": 1, + "trained_betas": null, + "clip_sample": false +} diff --git a/pretrained/Cinemo/text_encoder/config.json b/pretrained/Cinemo/text_encoder/config.json new file mode 100644 index 0000000000000000000000000000000000000000..df430a86cefe4115495e64c47936850adaeac0e9 --- /dev/null +++ b/pretrained/Cinemo/text_encoder/config.json @@ -0,0 +1,24 @@ +{ + "_name_or_path": "openai/clip-vit-large-patch14", + "architectures": [ + "CLIPTextModel" + ], + "attention_dropout": 0.0, + "bos_token_id": 0, + "dropout": 0.0, + "eos_token_id": 2, + "hidden_act": "quick_gelu", + "hidden_size": 768, + "initializer_factor": 1.0, + "initializer_range": 0.02, + "intermediate_size": 3072, + "layer_norm_eps": 1e-05, + "max_position_embeddings": 77, + "model_type": "clip_text_model", + "num_attention_heads": 12, + "num_hidden_layers": 12, + "pad_token_id": 1, + "torch_dtype": "float32", + "transformers_version": "4.21.0.dev0", + "vocab_size": 49408 +} diff --git a/pretrained/Cinemo/text_encoder/pytorch_model.bin b/pretrained/Cinemo/text_encoder/pytorch_model.bin new file mode 100644 index 0000000000000000000000000000000000000000..b7d12c62d43f947efeeba58b833ed8182541a73f --- /dev/null +++ b/pretrained/Cinemo/text_encoder/pytorch_model.bin @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:770a47a9ffdcfda0b05506a7888ed714d06131d60267e6cf52765d61cf59fd67 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+import math +import torch +import logging +import subprocess +import numpy as np +import torch.distributed as dist + +# from torch._six import inf +from torch import inf +from PIL import Image +from typing import Union, Iterable +from collections import OrderedDict +from torch.utils.tensorboard import SummaryWriter +from typing import Dict +import torch_dct + +from diffusers.utils import is_bs4_available, is_ftfy_available + +import html +import re +import urllib.parse as ul + +if is_bs4_available(): + from bs4 import BeautifulSoup + +if is_ftfy_available(): + import ftfy + +import torch.fft as fft + +_tensor_or_tensors = Union[torch.Tensor, Iterable[torch.Tensor]] + + +################################################################################# +# Testing Utils # +################################################################################# + +def find_model(model_name): + """ + Finds a pre-trained model + """ + assert os.path.isfile(model_name), f'Could not find DiT checkpoint at {model_name}' + checkpoint = torch.load(model_name, map_location=lambda storage, loc: storage) + + if "ema" in checkpoint: # supports checkpoints from train.py + print('Using ema ckpt!') + checkpoint = checkpoint["ema"] + else: + checkpoint = checkpoint["model"] + print("Using model ckpt!") + return checkpoint + +def save_video_grid(video, nrow=None): + b, t, h, w, c = video.shape + + if nrow is None: + nrow = math.ceil(math.sqrt(b)) + ncol = math.ceil(b / nrow) + padding = 1 + video_grid = torch.zeros((t, (padding + h) * nrow + padding, + (padding + w) * ncol + padding, c), dtype=torch.uint8) + + # print(video_grid.shape) + for i in range(b): + r = i // ncol + c = i % ncol + start_r = (padding + h) * r + start_c = (padding + w) * c + video_grid[:, start_r:start_r + h, start_c:start_c + w] = video[i] + + return video_grid + +def save_videos_grid_tav(videos: torch.Tensor, path: str, rescale=False, nrow=None, fps=8): + from einops import rearrange + import imageio + import torchvision + + b, _, _, _, _ = videos.shape + if nrow is None: + nrow = math.ceil(math.sqrt(b)) + videos = rearrange(videos, "b c t h w -> t b c h w") + outputs = [] + for x in videos: + x = torchvision.utils.make_grid(x, nrow=nrow) + x = x.transpose(0, 1).transpose(1, 2).squeeze(-1) + if rescale: + x = (x + 1.0) / 2.0 # -1,1 -> 0,1 + x = (x * 255).numpy().astype(np.uint8) + outputs.append(x) + + # os.makedirs(os.path.dirname(path), exist_ok=True) + imageio.mimsave(path, outputs, fps=fps) + + +################################################################################# +# MMCV Utils # +################################################################################# + + +def collect_env(): + # Copyright (c) OpenMMLab. All rights reserved. + from mmcv.utils import collect_env as collect_base_env + from mmcv.utils import get_git_hash + """Collect the information of the running environments.""" + + env_info = collect_base_env() + env_info['MMClassification'] = get_git_hash()[:7] + + for name, val in env_info.items(): + print(f'{name}: {val}') + + print(torch.cuda.get_arch_list()) + print(torch.version.cuda) + + +################################################################################# +# DCT Functions # +################################################################################# + +def dct_low_pass_filter(dct_coefficients, percentage=0.3): # 2d [b c f h w] + """ + Applies a low pass filter to the given DCT coefficients. + + :param dct_coefficients: 2D tensor of DCT coefficients + :param percentage: percentage of coefficients to keep (between 0 and 1) + :return: 2D tensor of DCT coefficients after applying the low pass filter + """ + # Determine the cutoff indices for both dimensions + cutoff_x = int(dct_coefficients.shape[-2] * percentage) + cutoff_y = int(dct_coefficients.shape[-1] * percentage) + + # Create a mask with the same shape as the DCT coefficients + mask = torch.zeros_like(dct_coefficients) + # Set the top-left corner of the mask to 1 (the low-frequency area) + mask[:, :, :, :cutoff_x, :cutoff_y] = 1 + + return mask + +def normalize(tensor): + """将Tensor归一化到[0, 1]范围内。""" + min_val = tensor.min() + max_val = tensor.max() + normalized = (tensor - min_val) / (max_val - min_val) + return normalized + +def denormalize(tensor, max_val_target, min_val_target): + """将Tensor从[0, 1]范围反归一化到目标的[min_val_target, max_val_target]范围。""" + denormalized = tensor * (max_val_target - min_val_target) + min_val_target + return denormalized + +def exchanged_mixed_dct_freq(noise, base_content, LPF_3d, normalized=False): + # noise dct + noise_freq = torch_dct.dct_3d(noise, 'ortho') + + # frequency + HPF_3d = 1 - LPF_3d + noise_freq_high = noise_freq * HPF_3d + + # base frame dct 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