├── .gitignore ├── model ├── video_diffusion │ ├── __init__.py │ ├── models │ │ ├── __init__.py │ │ ├── unet_3d_blocks_control.py │ │ ├── attention.py │ │ ├── resnet.py │ │ ├── unet_3d_blocks.py │ │ ├── unet_3d_condition.py │ │ └── controlnet3d.py │ └── pipelines │ │ ├── __init__.py │ │ ├── pipeline_stable_diffusion_controlnet3d.py │ │ └── pipeline_st_stable_diffusion.py └── annotator │ ├── canny │ └── __init__.py │ ├── util.py │ └── hed │ └── __init__.py ├── bear.mp4 ├── videos ├── bear.mp4 ├── canny_a_dog_comicbook.gif ├── depth_a_bear_walking_through_stars.gif └── hed_a_person_riding_a_horse_jumping_over_an_obstacle_watercolor_style.gif ├── requirements.txt ├── README.md ├── inference.py └── LICENSE /.gitignore: -------------------------------------------------------------------------------- 1 | __pycache__/ -------------------------------------------------------------------------------- /model/video_diffusion/__init__.py: -------------------------------------------------------------------------------- 1 | -------------------------------------------------------------------------------- /model/video_diffusion/models/__init__.py: -------------------------------------------------------------------------------- 1 | -------------------------------------------------------------------------------- /model/video_diffusion/pipelines/__init__.py: -------------------------------------------------------------------------------- 1 | -------------------------------------------------------------------------------- /bear.mp4: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/Weifeng-Chen/control-a-video/HEAD/bear.mp4 -------------------------------------------------------------------------------- /videos/bear.mp4: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/Weifeng-Chen/control-a-video/HEAD/videos/bear.mp4 -------------------------------------------------------------------------------- /videos/canny_a_dog_comicbook.gif: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/Weifeng-Chen/control-a-video/HEAD/videos/canny_a_dog_comicbook.gif -------------------------------------------------------------------------------- /videos/depth_a_bear_walking_through_stars.gif: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/Weifeng-Chen/control-a-video/HEAD/videos/depth_a_bear_walking_through_stars.gif -------------------------------------------------------------------------------- /requirements.txt: -------------------------------------------------------------------------------- 1 | diffusers==0.14.0 2 | transformers==4.27.3 3 | accelerate==0.18.0 4 | xformers==0.0.16 5 | imageio==2.27.0 6 | decord==0.6.0 7 | opencv-python==4.7.0.72 8 | einops==0.6.0 9 | -------------------------------------------------------------------------------- /model/annotator/canny/__init__.py: -------------------------------------------------------------------------------- 1 | import cv2 2 | 3 | 4 | class CannyDetector: 5 | def __call__(self, img, low_threshold, high_threshold): 6 | return cv2.Canny(img, low_threshold, high_threshold) 7 | -------------------------------------------------------------------------------- /videos/hed_a_person_riding_a_horse_jumping_over_an_obstacle_watercolor_style.gif: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/Weifeng-Chen/control-a-video/HEAD/videos/hed_a_person_riding_a_horse_jumping_over_an_obstacle_watercolor_style.gif -------------------------------------------------------------------------------- /model/annotator/util.py: -------------------------------------------------------------------------------- 1 | import numpy as np 2 | import cv2 3 | import os 4 | 5 | 6 | annotator_ckpts_path = os.path.join(os.path.dirname(__file__), 'ckpts') 7 | 8 | 9 | def HWC3(x): 10 | assert x.dtype == np.uint8 11 | if x.ndim == 2: 12 | x = x[:, :, None] 13 | assert x.ndim == 3 14 | H, W, C = x.shape 15 | assert C == 1 or C == 3 or C == 4 16 | if C == 3: 17 | return x 18 | if C == 1: 19 | return np.concatenate([x, x, x], axis=2) 20 | if C == 4: 21 | color = x[:, :, 0:3].astype(np.float32) 22 | alpha = x[:, :, 3:4].astype(np.float32) / 255.0 23 | y = color * alpha + 255.0 * (1.0 - alpha) 24 | y = y.clip(0, 255).astype(np.uint8) 25 | return y 26 | 27 | 28 | def resize_image(input_image, resolution): 29 | H, W, C = input_image.shape 30 | H = float(H) 31 | W = float(W) 32 | k = float(resolution) / min(H, W) 33 | H *= k 34 | W *= k 35 | H = int(np.round(H / 64.0)) * 64 36 | W = int(np.round(W / 64.0)) * 64 37 | img = cv2.resize(input_image, (W, H), interpolation=cv2.INTER_LANCZOS4 if k > 1 else cv2.INTER_AREA) 38 | return img 39 | -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | # control-a-video 2 | 3 | Official Implementation of ["Control-A-Video: Controllable Text-to-Video Generation with Diffusion Models"](https://arxiv.org/abs/2305.13840) 4 | - [Project Page](https://controlavideo.github.io) 5 | - [Online Demo](https://huggingface.co/spaces/wf-genius/Control-A-Video) 6 | 7 | Similar to Controlnet, We otain the condition maps from another video, and we support three kinds of control maps at this time. 8 | 9 | |depth control| canny control | hed control | 10 | |:-:|:-:|:-:| 11 | |
a bear walking through stars,artstation |
a dog, comicbook style |
person riding horse, watercolor| 12 | 13 | 14 | # Setup 15 | 16 | The model has been tesed in torch version: `1.13.1+cu117`, simply run 17 | ``` 18 | pip3 install -r requirements.txt 19 | ``` 20 | 21 | # Usage 22 | 23 | ## 1. Quick Use 24 | We provide a demo for quick testing in this repo, simply running: 25 | 26 | ``` 27 | python3 inference.py --prompt "a bear walking through stars, artstation" --input_video bear.mp4 --control_mode depth 28 | ``` 29 | 30 | Args: 31 | - `--input_video`: path of input video(mp4 format). 32 | - `--num_sample_frames`: nums of frames to generate. (recommend > 8). 33 | - `--each_sample_frame`: sampling frames for each time. (for auto-regressive generateion.) 34 | - `--sampling_rate`: skip sampling from the input video. 35 | 36 | - `--control_mode`: allows for different control, currently support **`canny`, `depth`, `hed`**. (you need to download the weight of **hed** annotator from [link](https://huggingface.co/wf-genius/controlavideo-hed/resolve/main/hed-network.pth) and put it in work space.) 37 | - `--video_scale`: guidance scale of video consistency, borrows from GEN-1. (don't be too large, 1~2 work well, set 0 to disable it.) 38 | - `--init_noise_thres`: the propoed threshold of residual-based noise init. (range from 0 to 1, larger value leads to more smooth but may introduce artifacts.) 39 | 40 | - `--inference_step, --guidance_scale, --height, --width, --prompt`: same as other T2I model. 41 | 42 | If the automatic downloading not work, the models weights can be downloaded from: [depth_control_model](https://huggingface.co/wf-genius/controlavideo-depth), [canny_control_model](https://huggingface.co/wf-genius/controlavideo-canny), [hed_control_model](https://huggingface.co/wf-genius/controlavideo-hed). 43 | 44 | ## 2. Auto-Regressive Generation 45 | Our model firstly generates the first frame. Once We get the first frame, we generate the subsquent frames conditioned on the first frame. Thus, it will allow our model to generate longer videos auto-regressive. (This operation is still under experiment and it may collaspe after 3 or 4 iterations.) 46 | ``` 47 | python3 inference.py --prompt "a bear walking through stars, artstation" --input_video bear.mp4 --control_mode depth --num_sample_frames 16 --each_sample_frame 8 48 | ``` 49 | Note that `num_sample_frames` should be multiple of `each_sample_frame`. 50 | 51 | ## Replace the 2d model (Experimentally) 52 | Since we freeze the 2d model, you can replace it with any other model based on `stable-diffusion-v1-5` to generate custom-style videos. 53 | 54 | ``` 55 | state_dict_path = os.path.join(pipeline_model_path, "unet", "diffusion_pytorch_model.bin") 56 | state_dict = torch.load(state_dict_path, map_location="cpu") 57 | video_controlnet_pipe.unet.load_2d_state_dict(state_dict=state_dict) # reload 2d model. 58 | ``` 59 | 60 | # Citation 61 | ``` 62 | @misc{chen2023controlavideo, 63 | title={Control-A-Video: Controllable Text-to-Video Generation with Diffusion Models}, 64 | author={Weifeng Chen and Jie Wu and Pan Xie and Hefeng Wu and Jiashi Li and Xin Xia and Xuefeng Xiao and Liang Lin}, 65 | year={2023}, 66 | eprint={2305.13840}, 67 | archivePrefix={arXiv}, 68 | primaryClass={cs.CV} 69 | } 70 | ``` 71 | 72 | # Acknowledgement 73 | This repository borrows heavily from [Diffusers](https://github.com/huggingface/diffusers), [ControlNet](https://github.com/lllyasviel/ControlNet), [Tune-A-Video](https://github.com/showlab/Tune-A-Video), thanks for open-sourcing! This work was done in Bytedance, thanks for the cooperators! 74 | 75 | 76 | # Future Plan 77 | - support segmentation(mask) generation. 78 | - video lora/dreambooth. 79 | - optical flow enhancement. 80 | - Any other methods to improve the model. It's also welcomed to contribute any applications based on our models, feel free to contact me and propose a PR. -------------------------------------------------------------------------------- /model/video_diffusion/models/unet_3d_blocks_control.py: -------------------------------------------------------------------------------- 1 | # Copyright 2023 Bytedance Ltd. and/or its affiliates 2 | 3 | # Licensed under the Apache License, Version 2.0 (the "License"); 4 | # you may not use this file except in compliance with the License. 5 | # You may obtain a copy of the License at 6 | 7 | # http://www.apache.org/licenses/LICENSE-2.0 8 | 9 | # Unless required by applicable law or agreed to in writing, software 10 | # distributed under the License is distributed on an "AS IS" BASIS, 11 | # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. 12 | # See the License for the specific language governing permissions and 13 | # limitations under the License. 14 | 15 | import torch 16 | from torch import nn 17 | from .attention import SpatioTemporalTransformerModel 18 | from .resnet import DownsamplePseudo3D, ResnetBlockPseudo3D, UpsamplePseudo3D 19 | import glob 20 | import json 21 | from dataclasses import dataclass 22 | from typing import List, Optional, Tuple, Union 23 | import torch 24 | import torch.nn as nn 25 | import torch.utils.checkpoint 26 | from diffusers.configuration_utils import ConfigMixin, register_to_config 27 | from diffusers.models.modeling_utils import ModelMixin 28 | from diffusers.utils import BaseOutput, logging 29 | from diffusers.models.embeddings import TimestepEmbedding, Timesteps 30 | from .unet_3d_blocks import ( 31 | CrossAttnDownBlockPseudo3D, 32 | CrossAttnUpBlockPseudo3D, 33 | DownBlockPseudo3D, 34 | UNetMidBlockPseudo3DCrossAttn, 35 | UpBlockPseudo3D, 36 | get_down_block, 37 | get_up_block, 38 | ) 39 | from .resnet import PseudoConv3d 40 | from diffusers.models.cross_attention import AttnProcessor 41 | from typing import Dict 42 | 43 | 44 | 45 | def set_zero_parameters(module): 46 | for p in module.parameters(): 47 | p.detach().zero_() 48 | return module 49 | 50 | # ControlNet: Zero Convolution 51 | def zero_conv(channels): 52 | return set_zero_parameters(PseudoConv3d(channels, channels, 1, padding=0)) 53 | 54 | class ControlNetInputHintBlock(nn.Module): 55 | def __init__(self, hint_channels: int = 3, channels: int = 320): 56 | super().__init__() 57 | # Layer configurations are from reference implementation. 58 | self.input_hint_block = nn.Sequential( 59 | PseudoConv3d(hint_channels, 16, 3, padding=1), 60 | nn.SiLU(), 61 | PseudoConv3d(16, 16, 3, padding=1), 62 | nn.SiLU(), 63 | PseudoConv3d(16, 32, 3, padding=1, stride=2), 64 | nn.SiLU(), 65 | PseudoConv3d(32, 32, 3, padding=1), 66 | nn.SiLU(), 67 | PseudoConv3d(32, 96, 3, padding=1, stride=2), 68 | nn.SiLU(), 69 | PseudoConv3d(96, 96, 3, padding=1), 70 | nn.SiLU(), 71 | PseudoConv3d(96, 256, 3, padding=1, stride=2), 72 | nn.SiLU(), 73 | set_zero_parameters(PseudoConv3d(256, channels, 3, padding=1)), 74 | ) 75 | def forward(self, hint: torch.Tensor): 76 | return self.input_hint_block(hint) 77 | 78 | 79 | class ControlNetPseudoZeroConv3dBlock(nn.Module): 80 | def __init__( 81 | self, 82 | block_out_channels: Tuple[int] = (320, 640, 1280, 1280), 83 | down_block_types: Tuple[str] = ( 84 | "CrossAttnDownBlockPseudo3D", 85 | "CrossAttnDownBlockPseudo3D", 86 | "CrossAttnDownBlockPseudo3D", 87 | "DownBlockPseudo3D", 88 | ), 89 | layers_per_block: int = 2, 90 | ): 91 | super().__init__() 92 | self.input_zero_conv = zero_conv(block_out_channels[0]) 93 | zero_convs = [] 94 | for i, down_block_type in enumerate(down_block_types): 95 | output_channel = block_out_channels[i] 96 | is_final_block = i == len(block_out_channels) - 1 97 | for _ in range(layers_per_block): 98 | zero_convs.append(zero_conv(output_channel)) 99 | if not is_final_block: 100 | zero_convs.append(zero_conv(output_channel)) 101 | self.zero_convs = nn.ModuleList(zero_convs) 102 | self.mid_zero_conv = zero_conv(block_out_channels[-1]) 103 | 104 | def forward( 105 | self, 106 | down_block_res_samples: List[torch.Tensor], 107 | mid_block_sample: torch.Tensor, 108 | ) -> List[torch.Tensor]: 109 | outputs = [] 110 | outputs.append(self.input_zero_conv(down_block_res_samples[0])) 111 | for res_sample, zero_conv in zip(down_block_res_samples[1:], self.zero_convs): 112 | outputs.append(zero_conv(res_sample)) 113 | outputs.append(self.mid_zero_conv(mid_block_sample)) 114 | return outputs 115 | 116 | 117 | -------------------------------------------------------------------------------- /model/annotator/hed/__init__.py: -------------------------------------------------------------------------------- 1 | import numpy as np 2 | import cv2 3 | import os 4 | import torch 5 | from einops import rearrange 6 | 7 | 8 | class HEDNetwork(torch.nn.Module): 9 | def __init__(self, model_path): 10 | super().__init__() 11 | 12 | self.netVggOne = torch.nn.Sequential( 13 | torch.nn.Conv2d(in_channels=3, out_channels=64, kernel_size=3, stride=1, padding=1), 14 | torch.nn.ReLU(inplace=False), 15 | torch.nn.Conv2d(in_channels=64, out_channels=64, kernel_size=3, stride=1, padding=1), 16 | torch.nn.ReLU(inplace=False) 17 | ) 18 | 19 | self.netVggTwo = torch.nn.Sequential( 20 | torch.nn.MaxPool2d(kernel_size=2, stride=2), 21 | torch.nn.Conv2d(in_channels=64, out_channels=128, kernel_size=3, stride=1, padding=1), 22 | torch.nn.ReLU(inplace=False), 23 | torch.nn.Conv2d(in_channels=128, out_channels=128, kernel_size=3, stride=1, padding=1), 24 | torch.nn.ReLU(inplace=False) 25 | ) 26 | 27 | self.netVggThr = torch.nn.Sequential( 28 | torch.nn.MaxPool2d(kernel_size=2, stride=2), 29 | torch.nn.Conv2d(in_channels=128, out_channels=256, kernel_size=3, stride=1, padding=1), 30 | torch.nn.ReLU(inplace=False), 31 | torch.nn.Conv2d(in_channels=256, out_channels=256, kernel_size=3, stride=1, padding=1), 32 | torch.nn.ReLU(inplace=False), 33 | torch.nn.Conv2d(in_channels=256, out_channels=256, kernel_size=3, stride=1, padding=1), 34 | torch.nn.ReLU(inplace=False) 35 | ) 36 | 37 | self.netVggFou = torch.nn.Sequential( 38 | torch.nn.MaxPool2d(kernel_size=2, stride=2), 39 | torch.nn.Conv2d(in_channels=256, out_channels=512, kernel_size=3, stride=1, padding=1), 40 | torch.nn.ReLU(inplace=False), 41 | torch.nn.Conv2d(in_channels=512, out_channels=512, kernel_size=3, stride=1, padding=1), 42 | torch.nn.ReLU(inplace=False), 43 | torch.nn.Conv2d(in_channels=512, out_channels=512, kernel_size=3, stride=1, padding=1), 44 | torch.nn.ReLU(inplace=False) 45 | ) 46 | 47 | self.netVggFiv = torch.nn.Sequential( 48 | torch.nn.MaxPool2d(kernel_size=2, stride=2), 49 | torch.nn.Conv2d(in_channels=512, out_channels=512, kernel_size=3, stride=1, padding=1), 50 | torch.nn.ReLU(inplace=False), 51 | torch.nn.Conv2d(in_channels=512, out_channels=512, kernel_size=3, stride=1, padding=1), 52 | torch.nn.ReLU(inplace=False), 53 | torch.nn.Conv2d(in_channels=512, out_channels=512, kernel_size=3, stride=1, padding=1), 54 | torch.nn.ReLU(inplace=False) 55 | ) 56 | 57 | self.netScoreOne = torch.nn.Conv2d(in_channels=64, out_channels=1, kernel_size=1, stride=1, padding=0) 58 | self.netScoreTwo = torch.nn.Conv2d(in_channels=128, out_channels=1, kernel_size=1, stride=1, padding=0) 59 | self.netScoreThr = torch.nn.Conv2d(in_channels=256, out_channels=1, kernel_size=1, stride=1, padding=0) 60 | self.netScoreFou = torch.nn.Conv2d(in_channels=512, out_channels=1, kernel_size=1, stride=1, padding=0) 61 | self.netScoreFiv = torch.nn.Conv2d(in_channels=512, out_channels=1, kernel_size=1, stride=1, padding=0) 62 | 63 | self.netCombine = torch.nn.Sequential( 64 | torch.nn.Conv2d(in_channels=5, out_channels=1, kernel_size=1, stride=1, padding=0), 65 | torch.nn.Sigmoid() 66 | ) 67 | 68 | self.load_state_dict({strKey.replace('module', 'net'): tenWeight for strKey, tenWeight in torch.load(model_path).items()}) 69 | 70 | def forward(self, tenInput): 71 | tenInput = tenInput * 255.0 72 | tenInput = tenInput - torch.tensor(data=[104.00698793, 116.66876762, 122.67891434], dtype=tenInput.dtype, device=tenInput.device).view(1, 3, 1, 1) 73 | 74 | tenVggOne = self.netVggOne(tenInput) 75 | tenVggTwo = self.netVggTwo(tenVggOne) 76 | tenVggThr = self.netVggThr(tenVggTwo) 77 | tenVggFou = self.netVggFou(tenVggThr) 78 | tenVggFiv = self.netVggFiv(tenVggFou) 79 | 80 | tenScoreOne = self.netScoreOne(tenVggOne) 81 | tenScoreTwo = self.netScoreTwo(tenVggTwo) 82 | tenScoreThr = self.netScoreThr(tenVggThr) 83 | tenScoreFou = self.netScoreFou(tenVggFou) 84 | tenScoreFiv = self.netScoreFiv(tenVggFiv) 85 | 86 | tenScoreOne = torch.nn.functional.interpolate(input=tenScoreOne, size=(tenInput.shape[2], tenInput.shape[3]), mode='bilinear', align_corners=False) 87 | tenScoreTwo = torch.nn.functional.interpolate(input=tenScoreTwo, size=(tenInput.shape[2], tenInput.shape[3]), mode='bilinear', align_corners=False) 88 | tenScoreThr = torch.nn.functional.interpolate(input=tenScoreThr, size=(tenInput.shape[2], tenInput.shape[3]), mode='bilinear', align_corners=False) 89 | tenScoreFou = torch.nn.functional.interpolate(input=tenScoreFou, size=(tenInput.shape[2], tenInput.shape[3]), mode='bilinear', align_corners=False) 90 | tenScoreFiv = torch.nn.functional.interpolate(input=tenScoreFiv, size=(tenInput.shape[2], tenInput.shape[3]), mode='bilinear', align_corners=False) 91 | 92 | return self.netCombine(torch.cat([ tenScoreOne, tenScoreTwo, tenScoreThr, tenScoreFou, tenScoreFiv ], 1)) 93 | 94 | 95 | class HEDdetector: 96 | def __init__(self, network ): 97 | self.netNetwork = network 98 | 99 | def __call__(self, input_image): 100 | if isinstance(input_image, torch.Tensor): 101 | # 输入的就是 b c h w的tensor 范围是-1~1，需要转换为0～1 102 | input_image = (input_image + 1) / 2 103 | input_image = input_image.float().cuda() 104 | edge = self.netNetwork(input_image) # 范围也是0～1, 不用转了直接用 105 | return edge 106 | else: 107 | assert input_image.ndim == 3 108 | input_image = input_image[:, :, ::-1].copy() 109 | with torch.no_grad(): 110 | image_hed = torch.from_numpy(input_image).float().cuda() 111 | image_hed = image_hed / 255.0 112 | image_hed = rearrange(image_hed, 'h w c -> 1 c h w') 113 | edge = self.netNetwork(image_hed)[0] 114 | edge = (edge.cpu().numpy() * 255.0).clip(0, 255).astype(np.uint8) 115 | return edge[0] 116 | 117 | 118 | def nms(x, t, s): 119 | x = cv2.GaussianBlur(x.astype(np.float32), (0, 0), s) 120 | 121 | f1 = np.array([[0, 0, 0], [1, 1, 1], [0, 0, 0]], dtype=np.uint8) 122 | f2 = np.array([[0, 1, 0], [0, 1, 0], [0, 1, 0]], dtype=np.uint8) 123 | f3 = np.array([[1, 0, 0], [0, 1, 0], [0, 0, 1]], dtype=np.uint8) 124 | f4 = np.array([[0, 0, 1], [0, 1, 0], [1, 0, 0]], dtype=np.uint8) 125 | 126 | y = np.zeros_like(x) 127 | 128 | for f in [f1, f2, f3, f4]: 129 | np.putmask(y, cv2.dilate(x, kernel=f) == x, x) 130 | 131 | z = np.zeros_like(y, dtype=np.uint8) 132 | z[y > t] = 255 133 | return z 134 | -------------------------------------------------------------------------------- /inference.py: -------------------------------------------------------------------------------- 1 | # Copyright 2023 Bytedance Ltd. and/or its affiliates 2 | 3 | # Licensed under the Apache License, Version 2.0 (the "License"); 4 | # you may not use this file except in compliance with the License. 5 | # You may obtain a copy of the License at 6 | 7 | # http://www.apache.org/licenses/LICENSE-2.0 8 | 9 | # Unless required by applicable law or agreed to in writing, software 10 | # distributed under the License is distributed on an "AS IS" BASIS, 11 | # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. 12 | # See the License for the specific language governing permissions and 13 | # limitations under the License. 14 | 15 | from model.video_diffusion.models.controlnet3d import ControlNet3DModel 16 | from model.video_diffusion.models.unet_3d_condition import UNetPseudo3DConditionModel 17 | from model.video_diffusion.pipelines.pipeline_stable_diffusion_controlnet3d import Controlnet3DStableDiffusionPipeline 18 | from transformers import DPTForDepthEstimation 19 | from model.annotator.hed import HEDNetwork 20 | import torch 21 | import os 22 | from einops import rearrange,repeat 23 | import imageio 24 | import numpy as np 25 | import cv2 26 | import torch.nn.functional as F 27 | from PIL import Image 28 | import argparse 29 | 30 | 31 | parser = argparse.ArgumentParser() 32 | parser.add_argument('--control_mode', type=str, default='depth', help='support: hed, canny, depth') 33 | parser.add_argument('--inference_step', type=int, default=20, help='denoising steps for inference') 34 | parser.add_argument('--guidance_scale', type=float, default=7.5, help='guidance scale') 35 | parser.add_argument('--seed', type=int, default=1, help='seed') 36 | parser.add_argument('--num_sample_frames', type=int, default=8, help='total frames to inference') 37 | parser.add_argument('--each_sample_frame', type=int, default=8, help='auto-regressive generation for each iteration') 38 | parser.add_argument('--sampling_rate', type=int, default=3, help='skip sampling from input video') 39 | parser.add_argument('--height', type=int, default=512, help='ouput height') 40 | parser.add_argument('--width', type=int, default=512, help='ouput width') 41 | parser.add_argument('--video_scale', type=float, default=1.5, help='video smoothness scale') 42 | parser.add_argument('--init_noise_thres', type=float, default=0.1, help='thres for res noise init') 43 | parser.add_argument('--input_video',type=str, default='bear.mp4') 44 | parser.add_argument('--prompt',type=str, default="a bear walking through stars, artstation") 45 | 46 | args = parser.parse_args() 47 | 48 | control_mode = args.control_mode 49 | num_inference_steps = args.inference_step 50 | guidance_scale = args.guidance_scale 51 | seed = args.seed 52 | num_sample_frames = args.num_sample_frames 53 | sampling_rate = args.sampling_rate 54 | h, w = args.height, args.width 55 | video_scale = args.video_scale 56 | init_noise_thres = args.init_noise_thres 57 | video_path = args.input_video 58 | testing_prompt = [args.prompt] 59 | each_sample_frame = args.each_sample_frame 60 | 61 | 62 | control_net_path = f"wf-genius/controlavideo-{control_mode}" 63 | unet = UNetPseudo3DConditionModel.from_pretrained(control_net_path, 64 | torch_dtype = torch.float16, 65 | subfolder='unet', 66 | ).to("cuda") 67 | controlnet = ControlNet3DModel.from_pretrained(control_net_path, 68 | torch_dtype = torch.float16, 69 | subfolder='controlnet', 70 | ).to("cuda") 71 | 72 | if control_mode == 'depth': 73 | annotator_model = DPTForDepthEstimation.from_pretrained("Intel/dpt-hybrid-midas").to("cuda") 74 | elif control_mode == 'canny': 75 | annotator_model = None 76 | elif control_mode == 'hed': 77 | # firstly download from https://huggingface.co/wf-genius/controlavideo-hed/resolve/main/hed-network.pth 78 | annotator_model = HEDNetwork('hed-network.pth').to("cuda") 79 | 80 | video_controlnet_pipe = Controlnet3DStableDiffusionPipeline.from_pretrained(control_net_path, unet=unet, 81 | controlnet=controlnet, annotator_model=annotator_model, 82 | torch_dtype = torch.float16, 83 | ).to("cuda") 84 | 85 | # get video 86 | np_frames, fps_vid = Controlnet3DStableDiffusionPipeline.get_frames_preprocess(video_path, num_frames=num_sample_frames, sampling_rate=sampling_rate, return_np=True) 87 | if control_mode == 'depth': 88 | frames = torch.from_numpy(np_frames).div(255) * 2 - 1 89 | frames = rearrange(frames, "f h w c -> c f h w").unsqueeze(0) 90 | frames = rearrange(frames, 'b c f h w -> (b f) c h w') 91 | control_maps = video_controlnet_pipe.get_depth_map(frames, h, w, return_standard_norm=False) # (b f) 1 h w 92 | elif control_mode == 'canny': 93 | control_maps = np.stack([cv2.Canny(inp, 100, 200) for inp in np_frames]) 94 | control_maps = repeat(control_maps, 'f h w -> f c h w',c=1) 95 | control_maps = torch.from_numpy(control_maps).div(255) # 0~1 96 | elif control_mode == 'hed': 97 | control_maps = np.stack([video_controlnet_pipe.get_hed_map(inp) for inp in np_frames]) 98 | control_maps = repeat(control_maps, 'f h w -> f c h w',c=1) 99 | control_maps = torch.from_numpy(control_maps).div(255) # 0~1 100 | control_maps = control_maps.to(dtype=controlnet.dtype, device=controlnet.device) 101 | control_maps = F.interpolate(control_maps, size=(h,w), mode='bilinear', align_corners=False) 102 | control_maps = rearrange(control_maps, "(b f) c h w -> b c f h w", f=num_sample_frames) 103 | if control_maps.shape[1] == 1: 104 | control_maps = repeat(control_maps, 'b c f h w -> b (n c) f h w', n=3) 105 | 106 | frames = torch.from_numpy(np_frames).div(255) 107 | frames = rearrange(frames, 'f h w c -> f c h w') 108 | v2v_input_frames = torch.nn.functional.interpolate( 109 | frames, 110 | size=(h, w), 111 | mode="bicubic", 112 | antialias=True, 113 | ) 114 | v2v_input_frames = rearrange(v2v_input_frames, '(b f) c h w -> b c f h w ', f=num_sample_frames) 115 | 116 | 117 | out = [] 118 | for i in range(num_sample_frames//each_sample_frame): 119 | out1 = video_controlnet_pipe( 120 | # controlnet_hint= control_maps[:,:,:each_sample_frame,:,:], 121 | # images= v2v_input_frames[:,:,:each_sample_frame,:,:], 122 | controlnet_hint=control_maps[:,:,i*each_sample_frame-1:(i+1)*each_sample_frame-1,:,:] if i>0 else control_maps[:,:,:each_sample_frame,:,:], 123 | images=v2v_input_frames[:,:,i*each_sample_frame-1:(i+1)*each_sample_frame-1,:,:] if i>0 else v2v_input_frames[:,:,:each_sample_frame,:,:], 124 | first_frame_output=out[-1] if i>0 else None, 125 | prompt=testing_prompt, 126 | num_inference_steps=num_inference_steps, 127 | width=w, 128 | height=h, 129 | guidance_scale=guidance_scale, 130 | generator=[torch.Generator(device="cuda").manual_seed(seed)], 131 | video_scale = video_scale, # per-frame as negative (>= 1 or set 0) 132 | init_noise_by_residual_thres = init_noise_thres, # residual-based init. larger thres ==> more smooth. 133 | controlnet_conditioning_scale=1.0, 134 | fix_first_frame=True, 135 | in_domain=True, # whether to use the video model to generate the first frame. 136 | ) 137 | out1 = out1.images[0][1:] # drop the first frame 138 | out.extend(out1) 139 | 140 | imageio.mimsave('demo.gif', out, fps=8) 141 | # import IPython 142 | # from IPython.display import Image 143 | # Image(filename='demo.gif') -------------------------------------------------------------------------------- /model/video_diffusion/models/attention.py: -------------------------------------------------------------------------------- 1 | # Copyright 2023 Bytedance Ltd. and/or its affiliates 2 | 3 | # Licensed under the Apache License, Version 2.0 (the "License"); 4 | # you may not use this file except in compliance with the License. 5 | # You may obtain a copy of the License at 6 | 7 | # http://www.apache.org/licenses/LICENSE-2.0 8 | 9 | # Unless required by applicable law or agreed to in writing, software 10 | # distributed under the License is distributed on an "AS IS" BASIS, 11 | # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. 12 | # See the License for the specific language governing permissions and 13 | # limitations under the License. 14 | 15 | from dataclasses import dataclass 16 | from typing import Optional 17 | 18 | import torch 19 | from torch import nn 20 | 21 | from diffusers.configuration_utils import ConfigMixin, register_to_config 22 | from diffusers.models.modeling_utils import ModelMixin 23 | from diffusers.models.attention import FeedForward, CrossAttention, AdaLayerNorm 24 | from diffusers.utils import BaseOutput 25 | from diffusers.utils.import_utils import is_xformers_available 26 | from diffusers.models.cross_attention import XFormersCrossAttnProcessor 27 | from einops import rearrange 28 | 29 | 30 | @dataclass 31 | class SpatioTemporalTransformerModelOutput(BaseOutput): 32 | """torch.FloatTensor of shape [batch x channel x frames x height x width]""" 33 | 34 | sample: torch.FloatTensor 35 | 36 | 37 | if is_xformers_available(): 38 | import xformers 39 | import xformers.ops 40 | else: 41 | xformers = None 42 | 43 | 44 | class SpatioTemporalTransformerModel(ModelMixin, ConfigMixin): 45 | @register_to_config 46 | def __init__( 47 | self, 48 | num_attention_heads: int = 16, 49 | attention_head_dim: int = 88, 50 | in_channels: Optional[int] = None, 51 | num_layers: int = 1, 52 | dropout: float = 0.0, 53 | norm_num_groups: int = 32, 54 | cross_attention_dim: Optional[int] = None, 55 | attention_bias: bool = False, 56 | activation_fn: str = "geglu", 57 | num_embeds_ada_norm: Optional[int] = None, 58 | use_linear_projection: bool = False, 59 | only_cross_attention: bool = False, 60 | upcast_attention: bool = False, 61 | **transformer_kwargs, 62 | ): 63 | super().__init__() 64 | self.use_linear_projection = use_linear_projection 65 | self.num_attention_heads = num_attention_heads 66 | self.attention_head_dim = attention_head_dim 67 | inner_dim = num_attention_heads * attention_head_dim 68 | 69 | # Define input layers 70 | self.in_channels = in_channels 71 | 72 | self.norm = torch.nn.GroupNorm( 73 | num_groups=norm_num_groups, num_channels=in_channels, eps=1e-6, affine=True 74 | ) 75 | if use_linear_projection: 76 | self.proj_in = nn.Linear(in_channels, inner_dim) 77 | else: 78 | self.proj_in = nn.Conv2d(in_channels, inner_dim, kernel_size=1, stride=1, padding=0) 79 | 80 | # Define transformers blocks 81 | self.transformer_blocks = nn.ModuleList( 82 | [ 83 | SpatioTemporalTransformerBlock( 84 | inner_dim, 85 | num_attention_heads, 86 | attention_head_dim, 87 | dropout=dropout, 88 | cross_attention_dim=cross_attention_dim, 89 | activation_fn=activation_fn, 90 | num_embeds_ada_norm=num_embeds_ada_norm, 91 | attention_bias=attention_bias, 92 | only_cross_attention=only_cross_attention, 93 | upcast_attention=upcast_attention, 94 | **transformer_kwargs, 95 | ) 96 | for d in range(num_layers) 97 | ] 98 | ) 99 | 100 | # Define output layers 101 | if use_linear_projection: 102 | self.proj_out = nn.Linear(in_channels, inner_dim) 103 | else: 104 | self.proj_out = nn.Conv2d(inner_dim, in_channels, kernel_size=1, stride=1, padding=0) 105 | 106 | def forward( 107 | self, hidden_states, encoder_hidden_states=None, timestep=None, return_dict: bool = True 108 | ): 109 | # 1. Input 110 | clip_length = None 111 | is_video = hidden_states.ndim == 5 112 | if is_video: 113 | clip_length = hidden_states.shape[2] 114 | hidden_states = rearrange(hidden_states, "b c f h w -> (b f) c h w") 115 | encoder_hidden_states = encoder_hidden_states.repeat_interleave(clip_length, 0) 116 | 117 | *_, h, w = hidden_states.shape 118 | residual = hidden_states 119 | 120 | hidden_states = self.norm(hidden_states) 121 | if not self.use_linear_projection: 122 | hidden_states = self.proj_in(hidden_states) 123 | hidden_states = rearrange(hidden_states, "b c h w -> b (h w) c") 124 | else: 125 | hidden_states = rearrange(hidden_states, "b c h w -> b (h w) c") 126 | hidden_states = self.proj_in(hidden_states) 127 | 128 | # 2. Blocks 129 | for block in self.transformer_blocks: 130 | hidden_states = block( 131 | hidden_states, 132 | encoder_hidden_states=encoder_hidden_states, 133 | timestep=timestep, 134 | clip_length=clip_length, 135 | ) 136 | 137 | # 3. Output 138 | if not self.use_linear_projection: 139 | hidden_states = rearrange(hidden_states, "b (h w) c -> b c h w", h=h, w=w).contiguous() 140 | hidden_states = self.proj_out(hidden_states) 141 | else: 142 | hidden_states = self.proj_out(hidden_states) 143 | hidden_states = rearrange(hidden_states, "b (h w) c -> b c h w", h=h, w=w).contiguous() 144 | 145 | output = hidden_states + residual 146 | if is_video: 147 | output = rearrange(output, "(b f) c h w -> b c f h w", f=clip_length) 148 | 149 | if not return_dict: 150 | return (output,) 151 | 152 | return SpatioTemporalTransformerModelOutput(sample=output) 153 | 154 | 155 | class SpatioTemporalTransformerBlock(nn.Module): 156 | def __init__( 157 | self, 158 | dim: int, 159 | num_attention_heads: int, 160 | attention_head_dim: int, 161 | dropout=0.0, 162 | cross_attention_dim: Optional[int] = None, 163 | activation_fn: str = "geglu", 164 | num_embeds_ada_norm: Optional[int] = None, 165 | attention_bias: bool = False, 166 | only_cross_attention: bool = False, 167 | upcast_attention: bool = False, 168 | use_sparse_causal_attention: bool = False, 169 | use_full_sparse_causal_attention: bool = True, 170 | temporal_attention_position: str = "after_feedforward", 171 | use_gamma = False, 172 | ): 173 | super().__init__() 174 | self.only_cross_attention = only_cross_attention 175 | self.use_ada_layer_norm = num_embeds_ada_norm is not None 176 | self.use_sparse_causal_attention = use_sparse_causal_attention 177 | self.use_full_sparse_causal_attention = use_full_sparse_causal_attention 178 | self.use_gamma = use_gamma 179 | 180 | self.temporal_attention_position = temporal_attention_position 181 | temporal_attention_positions = ["after_spatial", "after_cross", "after_feedforward"] 182 | if temporal_attention_position not in temporal_attention_positions: 183 | raise ValueError( 184 | f"`temporal_attention_position` must be one of {temporal_attention_positions}" 185 | ) 186 | 187 | # 1. Spatial-Attn 188 | if use_sparse_causal_attention: 189 | spatial_attention = SparseCausalAttention 190 | elif use_full_sparse_causal_attention: 191 | spatial_attention = SparseCausalFullAttention 192 | else: 193 | spatial_attention = CrossAttention 194 | 195 | self.attn1 = spatial_attention( 196 | query_dim=dim, 197 | heads=num_attention_heads, 198 | dim_head=attention_head_dim, 199 | dropout=dropout, 200 | bias=attention_bias, 201 | cross_attention_dim=cross_attention_dim if only_cross_attention else None, 202 | upcast_attention=upcast_attention, 203 | processor=XFormersCrossAttnProcessor(), 204 | ) # is a self-attention 205 | self.norm1 = ( 206 | AdaLayerNorm(dim, num_embeds_ada_norm) if self.use_ada_layer_norm else nn.LayerNorm(dim) 207 | ) 208 | if use_gamma: 209 | self.attn1_gamma = nn.Parameter(torch.ones(dim)) 210 | 211 | # 2. Cross-Attn 212 | if cross_attention_dim is not None: 213 | self.attn2 = CrossAttention( 214 | query_dim=dim, 215 | cross_attention_dim=cross_attention_dim, 216 | heads=num_attention_heads, 217 | dim_head=attention_head_dim, 218 | dropout=dropout, 219 | bias=attention_bias, 220 | upcast_attention=upcast_attention, 221 | processor=XFormersCrossAttnProcessor(), 222 | ) # is self-attn if encoder_hidden_states is none 223 | self.norm2 = ( 224 | AdaLayerNorm(dim, num_embeds_ada_norm) if self.use_ada_layer_norm else nn.LayerNorm(dim) 225 | ) 226 | if use_gamma: 227 | self.attn2_gamma = nn.Parameter(torch.ones(dim)) 228 | else: 229 | self.attn2 = None 230 | self.norm2 = None 231 | 232 | # 3. Temporal-Attn 233 | self.attn_temporal = CrossAttention( 234 | query_dim=dim, 235 | heads=num_attention_heads, 236 | dim_head=attention_head_dim, 237 | dropout=dropout, 238 | bias=attention_bias, 239 | upcast_attention=upcast_attention, 240 | processor=XFormersCrossAttnProcessor() 241 | ) 242 | zero_module(self.attn_temporal) # 默认参数置0 243 | 244 | self.norm_temporal = ( 245 | AdaLayerNorm(dim, num_embeds_ada_norm) if self.use_ada_layer_norm else nn.LayerNorm(dim) 246 | ) 247 | 248 | # 4. Feed-forward 249 | self.ff = FeedForward(dim, dropout=dropout, activation_fn=activation_fn) 250 | self.norm3 = nn.LayerNorm(dim) 251 | if use_gamma: 252 | self.ff_gamma = nn.Parameter(torch.ones(dim)) 253 | 254 | 255 | def forward( 256 | self, 257 | hidden_states, 258 | encoder_hidden_states=None, 259 | timestep=None, 260 | attention_mask=None, 261 | clip_length=None, 262 | ): 263 | # 1. Self-Attention 264 | norm_hidden_states = ( 265 | self.norm1(hidden_states, timestep) if self.use_ada_layer_norm else self.norm1(hidden_states) 266 | ) 267 | 268 | kwargs = dict( 269 | hidden_states=norm_hidden_states, 270 | attention_mask=attention_mask, 271 | ) 272 | if self.only_cross_attention: 273 | kwargs.update(encoder_hidden_states=encoder_hidden_states) 274 | if self.use_sparse_causal_attention or self.use_full_sparse_causal_attention: 275 | kwargs.update(clip_length=clip_length) 276 | 277 | if self.use_gamma: 278 | hidden_states = hidden_states + self.attn1(**kwargs) * self.attn1_gamma # NOTE gamma 279 | else: 280 | hidden_states = hidden_states + self.attn1(**kwargs) 281 | 282 | 283 | if clip_length is not None and self.temporal_attention_position == "after_spatial": 284 | hidden_states = self.apply_temporal_attention(hidden_states, timestep, clip_length) 285 | 286 | if self.attn2 is not None: 287 | # 2. Cross-Attention 288 | norm_hidden_states = ( 289 | self.norm2(hidden_states, timestep) 290 | if self.use_ada_layer_norm 291 | else self.norm2(hidden_states) 292 | ) 293 | if self.use_gamma: 294 | hidden_states = ( 295 | self.attn2( 296 | norm_hidden_states, 297 | encoder_hidden_states=encoder_hidden_states, 298 | attention_mask=attention_mask, 299 | ) * self.attn2_gamma 300 | + hidden_states 301 | ) 302 | else: 303 | hidden_states = ( 304 | self.attn2( 305 | norm_hidden_states, 306 | encoder_hidden_states=encoder_hidden_states, 307 | attention_mask=attention_mask, 308 | ) 309 | + hidden_states 310 | ) 311 | 312 | if clip_length is not None and self.temporal_attention_position == "after_cross": 313 | hidden_states = self.apply_temporal_attention(hidden_states, timestep, clip_length) 314 | 315 | # 3. Feed-forward 316 | if self.use_gamma: 317 | hidden_states = self.ff(self.norm3(hidden_states)) * self.ff_gamma + hidden_states 318 | else: 319 | hidden_states = self.ff(self.norm3(hidden_states)) + hidden_states 320 | 321 | if clip_length is not None and self.temporal_attention_position == "after_feedforward": 322 | hidden_states = self.apply_temporal_attention(hidden_states, timestep, clip_length) 323 | 324 | return hidden_states 325 | 326 | def apply_temporal_attention(self, hidden_states, timestep, clip_length): 327 | d = hidden_states.shape[1] 328 | hidden_states = rearrange(hidden_states, "(b f) d c -> (b d) f c", f=clip_length) 329 | norm_hidden_states = ( 330 | self.norm_temporal(hidden_states, timestep) 331 | if self.use_ada_layer_norm 332 | else self.norm_temporal(hidden_states) 333 | ) 334 | hidden_states = self.attn_temporal(norm_hidden_states) + hidden_states 335 | hidden_states = rearrange(hidden_states, "(b d) f c -> (b f) d c", d=d) 336 | return hidden_states 337 | 338 | 339 | class SparseCausalAttention(CrossAttention): 340 | def forward( 341 | self, 342 | hidden_states, 343 | encoder_hidden_states=None, 344 | attention_mask=None, 345 | clip_length: int = None, 346 | ): 347 | if ( 348 | self.added_kv_proj_dim is not None 349 | or encoder_hidden_states is not None 350 | or attention_mask is not None 351 | ): 352 | raise NotImplementedError 353 | 354 | if self.group_norm is not None: 355 | hidden_states = self.group_norm(hidden_states.transpose(1, 2)).transpose(1, 2) 356 | 357 | query = self.to_q(hidden_states) 358 | dim = query.shape[-1] 359 | query = self.head_to_batch_dim(query) # 64 4096 40 360 | 361 | key = self.to_k(hidden_states) 362 | value = self.to_v(hidden_states) 363 | 364 | if clip_length is not None and clip_length > 1: 365 | # spatial temporal 366 | prev_frame_index = torch.arange(clip_length) - 1 367 | prev_frame_index[0] = 0 368 | key = rearrange(key, "(b f) d c -> b f d c", f=clip_length) 369 | key = torch.cat([key[:, [0] * clip_length], key[:, prev_frame_index]], dim=2) 370 | key = rearrange(key, "b f d c -> (b f) d c", f=clip_length) 371 | 372 | value = rearrange(value, "(b f) d c -> b f d c", f=clip_length) 373 | value = torch.cat([value[:, [0] * clip_length], value[:, prev_frame_index]], dim=2) 374 | value = rearrange(value, "b f d c -> (b f) d c", f=clip_length) 375 | 376 | 377 | key = self.head_to_batch_dim(key) 378 | value = self.head_to_batch_dim(value) 379 | # use xfromers by default~ 380 | hidden_states = xformers.ops.memory_efficient_attention( 381 | query, key, value, attn_bias=attention_mask, op=None 382 | ) 383 | hidden_states = hidden_states.to(query.dtype) 384 | hidden_states = self.batch_to_head_dim(hidden_states) 385 | 386 | # linear proj 387 | hidden_states = self.to_out[0](hidden_states) 388 | 389 | # dropout 390 | hidden_states = self.to_out[1](hidden_states) 391 | return hidden_states 392 | 393 | def zero_module(module): 394 | for p in module.parameters(): 395 | nn.init.zeros_(p) 396 | return module 397 | 398 | 399 | class SparseCausalFullAttention(CrossAttention): 400 | def forward( 401 | self, 402 | hidden_states, 403 | encoder_hidden_states=None, 404 | attention_mask=None, 405 | clip_length: int = None, 406 | ): 407 | if ( 408 | self.added_kv_proj_dim is not None 409 | or encoder_hidden_states is not None 410 | or attention_mask is not None 411 | ): 412 | raise NotImplementedError 413 | 414 | if self.group_norm is not None: 415 | hidden_states = self.group_norm(hidden_states.transpose(1, 2)).transpose(1, 2) 416 | 417 | query = self.to_q(hidden_states) 418 | dim = query.shape[-1] 419 | query = self.head_to_batch_dim(query) # 64 4096 40 420 | 421 | key = self.to_k(hidden_states) 422 | value = self.to_v(hidden_states) 423 | 424 | if clip_length is not None and clip_length > 1: 425 | # 和所有帧做 spatial temporal attention 426 | key = rearrange(key, "(b f) d c -> b f d c", f=clip_length) 427 | # cat full frames 428 | key = torch.cat([key[:, [iii] * clip_length] for iii in range(clip_length) ], dim=2) # concat第一帧+第i帧。以此为key, value。而非自己这一帧。 429 | key = rearrange(key, "b f d c -> (b f) d c", f=clip_length) 430 | 431 | value = rearrange(value, "(b f) d c -> b f d c", f=clip_length) 432 | value = torch.cat([value[:, [iii] * clip_length] for iii in range(clip_length) ], dim=2) # concat第一帧+第i帧。以此为key, value。而非自己这一帧。 433 | value = rearrange(value, "b f d c -> (b f) d c", f=clip_length) 434 | 435 | key = self.head_to_batch_dim(key) 436 | value = self.head_to_batch_dim(value) 437 | # use xfromers by default~ 438 | hidden_states = xformers.ops.memory_efficient_attention( 439 | query, key, value, attn_bias=attention_mask, op=None 440 | ) 441 | hidden_states = hidden_states.to(query.dtype) 442 | hidden_states = self.batch_to_head_dim(hidden_states) 443 | 444 | # linear proj 445 | hidden_states = self.to_out[0](hidden_states) 446 | 447 | # dropout 448 | hidden_states = self.to_out[1](hidden_states) 449 | return hidden_states 450 | 451 | def zero_module(module): 452 | for p in module.parameters(): 453 | nn.init.zeros_(p) 454 | return module -------------------------------------------------------------------------------- /model/video_diffusion/models/resnet.py: -------------------------------------------------------------------------------- 1 | # Copyright 2023 Bytedance Ltd. and/or its affiliates 2 | 3 | # Licensed under the Apache License, Version 2.0 (the "License"); 4 | # you may not use this file except in compliance with the License. 5 | # You may obtain a copy of the License at 6 | 7 | # http://www.apache.org/licenses/LICENSE-2.0 8 | 9 | # Unless required by applicable law or agreed to in writing, software 10 | # distributed under the License is distributed on an "AS IS" BASIS, 11 | # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. 12 | # See the License for the specific language governing permissions and 13 | # limitations under the License. 14 | 15 | from functools import partial 16 | 17 | import torch 18 | import torch.nn as nn 19 | import torch.nn.functional as F 20 | 21 | from einops import rearrange 22 | 23 | 24 | class PseudoConv3d(nn.Conv2d): 25 | def __init__(self, in_channels, out_channels, kernel_size, temporal_kernel_size=None, **kwargs): 26 | super().__init__( 27 | in_channels=in_channels, 28 | out_channels=out_channels, 29 | kernel_size=kernel_size, 30 | **kwargs, 31 | ) 32 | if temporal_kernel_size is None: 33 | temporal_kernel_size = kernel_size 34 | 35 | self.conv_temporal = ( 36 | nn.Conv1d( 37 | out_channels, 38 | out_channels, 39 | kernel_size=temporal_kernel_size, 40 | padding=temporal_kernel_size // 2, 41 | ) 42 | if kernel_size > 1 43 | else None 44 | ) 45 | 46 | if self.conv_temporal is not None: 47 | nn.init.dirac_(self.conv_temporal.weight.data) # initialized to be identity 48 | nn.init.zeros_(self.conv_temporal.bias.data) 49 | 50 | def forward(self, x): 51 | b = x.shape[0] 52 | 53 | is_video = x.ndim == 5 54 | if is_video: 55 | x = rearrange(x, "b c f h w -> (b f) c h w") 56 | 57 | x = super().forward(x) 58 | 59 | if is_video: 60 | x = rearrange(x, "(b f) c h w -> b c f h w", b=b) 61 | 62 | if self.conv_temporal is None or not is_video: 63 | return x 64 | 65 | *_, h, w = x.shape 66 | 67 | x = rearrange(x, "b c f h w -> (b h w) c f") 68 | 69 | x = self.conv_temporal(x) # 加入空间1D的时序卷积。channel不变。（建模时序信息） 70 | 71 | x = rearrange(x, "(b h w) c f -> b c f h w", h=h, w=w) 72 | 73 | return x 74 | 75 | 76 | class UpsamplePseudo3D(nn.Module): 77 | """ 78 | An upsampling layer with an optional convolution. 79 | 80 | Parameters: 81 | channels: channels in the inputs and outputs. 82 | use_conv: a bool determining if a convolution is applied. 83 | use_conv_transpose: 84 | out_channels: 85 | """ 86 | 87 | def __init__( 88 | self, channels, use_conv=False, use_conv_transpose=False, out_channels=None, name="conv" 89 | ): 90 | super().__init__() 91 | self.channels = channels 92 | self.out_channels = out_channels or channels 93 | self.use_conv = use_conv 94 | self.use_conv_transpose = use_conv_transpose 95 | self.name = name 96 | 97 | conv = None 98 | if use_conv_transpose: 99 | raise NotImplementedError 100 | conv = nn.ConvTranspose2d(channels, self.out_channels, 4, 2, 1) 101 | elif use_conv: 102 | conv = PseudoConv3d(self.channels, self.out_channels, 3, padding=1) 103 | 104 | # TODO(Suraj, Patrick) - clean up after weight dicts are correctly renamed 105 | if name == "conv": 106 | self.conv = conv 107 | else: 108 | self.Conv2d_0 = conv 109 | 110 | def forward(self, hidden_states, output_size=None): 111 | assert hidden_states.shape[1] == self.channels 112 | 113 | # Cast to float32 to as 'upsample_nearest2d_out_frame' op does not support bfloat16 114 | # TODO(Suraj): Remove this cast once the issue is fixed in PyTorch 115 | # https://github.com/pytorch/pytorch/issues/86679 116 | dtype = hidden_states.dtype 117 | if dtype == torch.bfloat16: 118 | hidden_states = hidden_states.to(torch.float32) 119 | 120 | # upsample_nearest_nhwc fails with large batch sizes. see https://github.com/huggingface/diffusers/issues/984 121 | if hidden_states.shape[0] >= 64: 122 | hidden_states = hidden_states.contiguous() 123 | 124 | b = hidden_states.shape[0] 125 | is_video = hidden_states.ndim == 5 126 | if is_video: 127 | hidden_states = rearrange(hidden_states, "b c f h w -> (b f) c h w") 128 | 129 | # if `output_size` is passed we force the interpolation output 130 | # size and do not make use of `scale_factor=2` 131 | if output_size is None: 132 | # 先插值再用conv 133 | hidden_states = F.interpolate(hidden_states, scale_factor=2.0, mode="nearest") 134 | else: 135 | hidden_states = F.interpolate(hidden_states, size=output_size, mode="nearest") 136 | 137 | # If the input is bfloat16, we cast back to bfloat16 138 | if dtype == torch.bfloat16: 139 | hidden_states = hidden_states.to(dtype) 140 | 141 | if is_video: 142 | hidden_states = rearrange(hidden_states, "(b f) c h w -> b c f h w", b=b) 143 | 144 | if self.use_conv: 145 | if self.name == "conv": 146 | hidden_states = self.conv(hidden_states) 147 | else: 148 | hidden_states = self.Conv2d_0(hidden_states) 149 | 150 | return hidden_states 151 | 152 | 153 | class DownsamplePseudo3D(nn.Module): 154 | """ 155 | A downsampling layer with an optional convolution. 156 | 157 | Parameters: 158 | channels: channels in the inputs and outputs. 159 | use_conv: a bool determining if a convolution is applied. 160 | out_channels: 161 | padding: 162 | """ 163 | 164 | def __init__(self, channels, use_conv=False, out_channels=None, padding=1, name="conv"): 165 | super().__init__() 166 | self.channels = channels 167 | self.out_channels = out_channels or channels 168 | self.use_conv = use_conv 169 | self.padding = padding 170 | stride = 2 171 | self.name = name 172 | 173 | if use_conv: 174 | conv = PseudoConv3d(self.channels, self.out_channels, 3, stride=stride, padding=padding) 175 | else: 176 | assert self.channels == self.out_channels 177 | conv = nn.AvgPool2d(kernel_size=stride, stride=stride) 178 | 179 | if name == "conv": 180 | self.Conv2d_0 = conv 181 | self.conv = conv 182 | elif name == "Conv2d_0": 183 | self.conv = conv 184 | else: 185 | self.conv = conv 186 | 187 | def forward(self, hidden_states): 188 | assert hidden_states.shape[1] == self.channels 189 | if self.use_conv and self.padding == 0: 190 | pad = (0, 1, 0, 1) 191 | hidden_states = F.pad(hidden_states, pad, mode="constant", value=0) 192 | 193 | assert hidden_states.shape[1] == self.channels 194 | if self.use_conv: 195 | hidden_states = self.conv(hidden_states) 196 | else: 197 | b = hidden_states.shape[0] 198 | is_video = hidden_states.ndim == 5 199 | if is_video: 200 | hidden_states = rearrange(hidden_states, "b c f h w -> (b f) c h w") 201 | hidden_states = self.conv(hidden_states) 202 | if is_video: 203 | hidden_states = rearrange(hidden_states, "(b f) c h w -> b c f h w", b=b) 204 | 205 | return hidden_states 206 | 207 | 208 | class ResnetBlockPseudo3D(nn.Module): 209 | def __init__( 210 | self, 211 | *, 212 | in_channels, 213 | out_channels=None, 214 | conv_shortcut=False, 215 | dropout=0.0, 216 | temb_channels=512, 217 | groups=32, 218 | groups_out=None, 219 | pre_norm=True, 220 | eps=1e-6, 221 | non_linearity="swish", 222 | time_embedding_norm="default", 223 | kernel=None, 224 | output_scale_factor=1.0, 225 | use_in_shortcut=None, 226 | up=False, 227 | down=False, 228 | ): 229 | super().__init__() 230 | self.pre_norm = pre_norm 231 | self.pre_norm = True 232 | self.in_channels = in_channels 233 | out_channels = in_channels if out_channels is None else out_channels 234 | self.out_channels = out_channels 235 | self.use_conv_shortcut = conv_shortcut 236 | self.time_embedding_norm = time_embedding_norm 237 | self.up = up 238 | self.down = down 239 | self.output_scale_factor = output_scale_factor 240 | 241 | if groups_out is None: 242 | groups_out = groups 243 | 244 | self.norm1 = torch.nn.GroupNorm( 245 | num_groups=groups, num_channels=in_channels, eps=eps, affine=True 246 | ) 247 | 248 | self.conv1 = PseudoConv3d(in_channels, out_channels, kernel_size=3, stride=1, padding=1) 249 | 250 | if temb_channels is not None: 251 | if self.time_embedding_norm == "default": 252 | time_emb_proj_out_channels = out_channels 253 | elif self.time_embedding_norm == "scale_shift": 254 | time_emb_proj_out_channels = out_channels * 2 255 | else: 256 | raise ValueError(f"unknown time_embedding_norm : {self.time_embedding_norm} ") 257 | 258 | self.time_emb_proj = torch.nn.Linear(temb_channels, time_emb_proj_out_channels) 259 | else: 260 | self.time_emb_proj = None 261 | 262 | self.norm2 = torch.nn.GroupNorm( 263 | num_groups=groups_out, num_channels=out_channels, eps=eps, affine=True 264 | ) 265 | self.dropout = torch.nn.Dropout(dropout) 266 | self.conv2 = PseudoConv3d(out_channels, out_channels, kernel_size=3, stride=1, padding=1) 267 | 268 | if non_linearity == "swish": 269 | self.nonlinearity = lambda x: F.silu(x) 270 | elif non_linearity == "mish": 271 | self.nonlinearity = Mish() 272 | elif non_linearity == "silu": 273 | self.nonlinearity = nn.SiLU() 274 | 275 | self.upsample = self.downsample = None 276 | if self.up: 277 | if kernel == "fir": 278 | fir_kernel = (1, 3, 3, 1) 279 | self.upsample = lambda x: upsample_2d(x, kernel=fir_kernel) 280 | elif kernel == "sde_vp": 281 | self.upsample = partial(F.interpolate, scale_factor=2.0, mode="nearest") 282 | else: 283 | self.upsample = UpsamplePseudo3D(in_channels, use_conv=False) 284 | elif self.down: 285 | if kernel == "fir": 286 | fir_kernel = (1, 3, 3, 1) 287 | self.downsample = lambda x: downsample_2d(x, kernel=fir_kernel) 288 | elif kernel == "sde_vp": 289 | self.downsample = partial(F.avg_pool2d, kernel_size=2, stride=2) 290 | else: 291 | self.downsample = DownsamplePseudo3D(in_channels, use_conv=False, padding=1, name="op") 292 | 293 | self.use_in_shortcut = ( 294 | self.in_channels != self.out_channels if use_in_shortcut is None else use_in_shortcut 295 | ) 296 | 297 | self.conv_shortcut = None 298 | if self.use_in_shortcut: 299 | self.conv_shortcut = PseudoConv3d( 300 | in_channels, out_channels, kernel_size=1, stride=1, padding=0 301 | ) 302 | 303 | def forward(self, input_tensor, temb): 304 | hidden_states = input_tensor 305 | 306 | hidden_states = self.norm1(hidden_states) 307 | hidden_states = self.nonlinearity(hidden_states) 308 | 309 | if self.upsample is not None: 310 | # upsample_nearest_nhwc fails with large batch sizes. see https://github.com/huggingface/diffusers/issues/984 311 | if hidden_states.shape[0] >= 64: 312 | input_tensor = input_tensor.contiguous() 313 | hidden_states = hidden_states.contiguous() 314 | input_tensor = self.upsample(input_tensor) 315 | hidden_states = self.upsample(hidden_states) 316 | elif self.downsample is not None: 317 | input_tensor = self.downsample(input_tensor) 318 | hidden_states = self.downsample(hidden_states) 319 | 320 | hidden_states = self.conv1(hidden_states) 321 | 322 | if temb is not None: 323 | temb = self.time_emb_proj(self.nonlinearity(temb))[:, :, None, None] 324 | 325 | if temb is not None and self.time_embedding_norm == "default": 326 | is_video = hidden_states.ndim == 5 327 | if is_video: 328 | b, c, f, h, w = hidden_states.shape 329 | hidden_states = rearrange(hidden_states, "b c f h w -> (b f) c h w") 330 | temb = temb.repeat_interleave(f, 0) 331 | 332 | hidden_states = hidden_states + temb 333 | 334 | if is_video: 335 | hidden_states = rearrange(hidden_states, "(b f) c h w -> b c f h w", b=b) 336 | 337 | hidden_states = self.norm2(hidden_states) 338 | 339 | if temb is not None and self.time_embedding_norm == "scale_shift": 340 | is_video = hidden_states.ndim == 5 341 | if is_video: 342 | b, c, f, h, w = hidden_states.shape 343 | hidden_states = rearrange(hidden_states, "b c f h w -> (b f) c h w") 344 | temb = temb.repeat_interleave(f, 0) 345 | 346 | scale, shift = torch.chunk(temb, 2, dim=1) 347 | hidden_states = hidden_states * (1 + scale) + shift 348 | 349 | if is_video: 350 | hidden_states = rearrange(hidden_states, "(b f) c h w -> b c f h w", b=b) 351 | 352 | hidden_states = self.nonlinearity(hidden_states) 353 | 354 | hidden_states = self.dropout(hidden_states) 355 | hidden_states = self.conv2(hidden_states) 356 | 357 | if self.conv_shortcut is not None: 358 | input_tensor = self.conv_shortcut(input_tensor) 359 | 360 | output_tensor = (input_tensor + hidden_states) / self.output_scale_factor 361 | 362 | return output_tensor 363 | 364 | 365 | class Mish(torch.nn.Module): 366 | def forward(self, hidden_states): 367 | return hidden_states * torch.tanh(torch.nn.functional.softplus(hidden_states)) 368 | 369 | 370 | def upsample_2d(hidden_states, kernel=None, factor=2, gain=1): 371 | r"""Upsample2D a batch of 2D images with the given filter. 372 | Accepts a batch of 2D images of the shape `[N, C, H, W]` or `[N, H, W, C]` and upsamples each image with the given 373 | filter. The filter is normalized so that if the input pixels are constant, they will be scaled by the specified 374 | `gain`. Pixels outside the image are assumed to be zero, and the filter is padded with zeros so that its shape is 375 | a: multiple of the upsampling factor. 376 | 377 | Args: 378 | hidden_states: Input tensor of the shape `[N, C, H, W]` or `[N, H, W, C]`. 379 | kernel: FIR filter of the shape `[firH, firW]` or `[firN]` 380 | (separable). The default is `[1] * factor`, which corresponds to nearest-neighbor upsampling. 381 | factor: Integer upsampling factor (default: 2). 382 | gain: Scaling factor for signal magnitude (default: 1.0). 383 | 384 | Returns: 385 | output: Tensor of the shape `[N, C, H * factor, W * factor]` 386 | """ 387 | assert isinstance(factor, int) and factor >= 1 388 | if kernel is None: 389 | kernel = [1] * factor 390 | 391 | kernel = torch.tensor(kernel, dtype=torch.float32) 392 | if kernel.ndim == 1: 393 | kernel = torch.outer(kernel, kernel) 394 | kernel /= torch.sum(kernel) 395 | 396 | kernel = kernel * (gain * (factor**2)) 397 | pad_value = kernel.shape[0] - factor 398 | output = upfirdn2d_native( 399 | hidden_states, 400 | kernel.to(device=hidden_states.device), 401 | up=factor, 402 | pad=((pad_value + 1) // 2 + factor - 1, pad_value // 2), 403 | ) 404 | return output 405 | 406 | 407 | def downsample_2d(hidden_states, kernel=None, factor=2, gain=1): 408 | r"""Downsample2D a batch of 2D images with the given filter. 409 | Accepts a batch of 2D images of the shape `[N, C, H, W]` or `[N, H, W, C]` and downsamples each image with the 410 | given filter. The filter is normalized so that if the input pixels are constant, they will be scaled by the 411 | specified `gain`. Pixels outside the image are assumed to be zero, and the filter is padded with zeros so that its 412 | shape is a multiple of the downsampling factor. 413 | 414 | Args: 415 | hidden_states: Input tensor of the shape `[N, C, H, W]` or `[N, H, W, C]`. 416 | kernel: FIR filter of the shape `[firH, firW]` or `[firN]` 417 | (separable). The default is `[1] * factor`, which corresponds to average pooling. 418 | factor: Integer downsampling factor (default: 2). 419 | gain: Scaling factor for signal magnitude (default: 1.0). 420 | 421 | Returns: 422 | output: Tensor of the shape `[N, C, H // factor, W // factor]` 423 | """ 424 | 425 | assert isinstance(factor, int) and factor >= 1 426 | if kernel is None: 427 | kernel = [1] * factor 428 | 429 | kernel = torch.tensor(kernel, dtype=torch.float32) 430 | if kernel.ndim == 1: 431 | kernel = torch.outer(kernel, kernel) 432 | kernel /= torch.sum(kernel) 433 | 434 | kernel = kernel * gain 435 | pad_value = kernel.shape[0] - factor 436 | output = upfirdn2d_native( 437 | hidden_states, 438 | kernel.to(device=hidden_states.device), 439 | down=factor, 440 | pad=((pad_value + 1) // 2, pad_value // 2), 441 | ) 442 | return output 443 | 444 | 445 | def upfirdn2d_native(tensor, kernel, up=1, down=1, pad=(0, 0)): 446 | up_x = up_y = up 447 | down_x = down_y = down 448 | pad_x0 = pad_y0 = pad[0] 449 | pad_x1 = pad_y1 = pad[1] 450 | 451 | _, channel, in_h, in_w = tensor.shape 452 | tensor = tensor.reshape(-1, in_h, in_w, 1) 453 | 454 | _, in_h, in_w, minor = tensor.shape 455 | kernel_h, kernel_w = kernel.shape 456 | 457 | out = tensor.view(-1, in_h, 1, in_w, 1, minor) 458 | out = F.pad(out, [0, 0, 0, up_x - 1, 0, 0, 0, up_y - 1]) 459 | out = out.view(-1, in_h * up_y, in_w * up_x, minor) 460 | 461 | out = F.pad(out, [0, 0, max(pad_x0, 0), max(pad_x1, 0), max(pad_y0, 0), max(pad_y1, 0)]) 462 | out = out.to(tensor.device) # Move back to mps if necessary 463 | out = out[ 464 | :, 465 | max(-pad_y0, 0) : out.shape[1] - max(-pad_y1, 0), 466 | max(-pad_x0, 0) : out.shape[2] - max(-pad_x1, 0), 467 | :, 468 | ] 469 | 470 | out = out.permute(0, 3, 1, 2) 471 | out = out.reshape([-1, 1, in_h * up_y + pad_y0 + pad_y1, in_w * up_x + pad_x0 + pad_x1]) 472 | w = torch.flip(kernel, [0, 1]).view(1, 1, kernel_h, kernel_w) 473 | out = F.conv2d(out, w) 474 | out = out.reshape( 475 | -1, 476 | minor, 477 | in_h * up_y + pad_y0 + pad_y1 - kernel_h + 1, 478 | in_w * up_x + pad_x0 + pad_x1 - kernel_w + 1, 479 | ) 480 | out = out.permute(0, 2, 3, 1) 481 | out = out[:, ::down_y, ::down_x, :] 482 | 483 | out_h = (in_h * up_y + pad_y0 + pad_y1 - kernel_h) // down_y + 1 484 | out_w = (in_w * up_x + pad_x0 + pad_x1 - kernel_w) // down_x + 1 485 | 486 | return out.view(-1, channel, out_h, out_w) 487 | -------------------------------------------------------------------------------- /model/video_diffusion/pipelines/pipeline_stable_diffusion_controlnet3d.py: -------------------------------------------------------------------------------- 1 | # Copyright 2023 Bytedance Ltd. and/or its affiliates 2 | 3 | # Licensed under the Apache License, Version 2.0 (the "License"); 4 | # you may not use this file except in compliance with the License. 5 | # You may obtain a copy of the License at 6 | 7 | # http://www.apache.org/licenses/LICENSE-2.0 8 | 9 | # Unless required by applicable law or agreed to in writing, software 10 | # distributed under the License is distributed on an "AS IS" BASIS, 11 | # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. 12 | # See the License for the specific language governing permissions and 13 | # limitations under the License. 14 | 15 | 16 | from .pipeline_st_stable_diffusion import SpatioTemporalStableDiffusionPipeline 17 | from typing import Callable, List, Optional, Union 18 | from diffusers.schedulers import ( 19 | DDIMScheduler, 20 | DPMSolverMultistepScheduler, 21 | EulerAncestralDiscreteScheduler, 22 | EulerDiscreteScheduler, 23 | LMSDiscreteScheduler, 24 | PNDMScheduler, 25 | ) 26 | from transformers import DPTForDepthEstimation 27 | from transformers import CLIPTextModel, CLIPTokenizer 28 | from diffusers.pipelines.stable_diffusion import StableDiffusionPipelineOutput 29 | from diffusers import AutoencoderKL, DDIMScheduler, DDPMScheduler 30 | import torch 31 | from einops import rearrange, repeat 32 | import decord 33 | import cv2 34 | import random 35 | import numpy as np 36 | from ..models.unet_3d_condition import UNetPseudo3DConditionModel 37 | from ..models.controlnet3d import ControlNet3DModel 38 | 39 | 40 | class Controlnet3DStableDiffusionPipeline(SpatioTemporalStableDiffusionPipeline): 41 | def __init__( 42 | self, 43 | vae: AutoencoderKL, 44 | text_encoder: CLIPTextModel, 45 | tokenizer: CLIPTokenizer, 46 | unet: UNetPseudo3DConditionModel, 47 | controlnet: ControlNet3DModel, 48 | scheduler: Union[ 49 | DDIMScheduler, 50 | PNDMScheduler, 51 | LMSDiscreteScheduler, 52 | EulerDiscreteScheduler, 53 | EulerAncestralDiscreteScheduler, 54 | DPMSolverMultistepScheduler, 55 | ], 56 | annotator_model=None, 57 | 58 | ): 59 | super().__init__(vae, text_encoder, tokenizer, unet, scheduler) 60 | 61 | self.annotator_model = annotator_model 62 | self.controlnet = controlnet 63 | self.unet = unet 64 | self.vae = vae 65 | self.tokenizer = tokenizer 66 | self.text_encoder = text_encoder 67 | self.scheduler = scheduler 68 | self.register_modules( 69 | vae=vae, 70 | text_encoder=text_encoder, 71 | tokenizer=tokenizer, 72 | unet=unet, 73 | controlnet=controlnet, 74 | scheduler=scheduler, 75 | ) 76 | 77 | @staticmethod 78 | def get_frames_preprocess(data_path, num_frames=24, sampling_rate=1, begin_indice=0, return_np=False): 79 | vr = decord.VideoReader(data_path,) 80 | n_images = len(vr) 81 | fps_vid = round(vr.get_avg_fps()) 82 | frame_indices = [begin_indice + i*sampling_rate for i in range(num_frames)] # 随机取n帧 83 | 84 | 85 | while n_images <= frame_indices[-1]: 86 | # 超过视频长度，采样率减小直至不超过。 87 | sampling_rate -= 1 88 | if sampling_rate == 0: 89 | # NOTE 边界检查 90 | return None, None 91 | frame_indices = [i*sampling_rate for i in range(num_frames)] 92 | frames = vr.get_batch(frame_indices).asnumpy() 93 | 94 | if return_np: 95 | return frames, fps_vid 96 | 97 | frames = torch.from_numpy(frames).div(255) * 2 - 1 98 | frames = rearrange(frames, "f h w c -> c f h w").unsqueeze(0) 99 | return frames, fps_vid 100 | 101 | @torch.no_grad() 102 | def get_canny_edge_map(self, frames, ): 103 | # (b f) c h w" 104 | # from tensor to numpy 105 | inputs = frames.cpu().numpy() 106 | inputs = rearrange(inputs, 'f c h w -> f h w c') 107 | # inputs from [-1, 1] to [0, 255] 108 | inputs = (inputs + 1) * 127.5 109 | inputs = inputs.astype(np.uint8) 110 | lower_threshold = 100 111 | higher_threshold = 200 112 | edge_images = np.stack([cv2.Canny(inp, lower_threshold, higher_threshold) for inp in inputs]) 113 | # from numpy to tensors 114 | edge_images = torch.from_numpy(edge_images).unsqueeze(1) # f, 1, h, w 115 | edge_images = edge_images.div(255)*2 - 1 116 | # print(torch.max(out_images), torch.min(out_images), out_images.dtype) 117 | return edge_images.to(dtype= self.controlnet.dtype, device=self.controlnet.device) 118 | 119 | @torch.no_grad() 120 | def get_depth_map(self, frames, height, width, return_standard_norm=False ): 121 | """ 122 | frames should be like: (f c h w), you may turn b f c h w -> (b f) c h w first 123 | """ 124 | h,w = height, width 125 | inputs = torch.nn.functional.interpolate( 126 | frames, 127 | size=(384, 384), 128 | mode="bicubic", 129 | antialias=True, 130 | ) 131 | # 转类型和设备 132 | inputs = inputs.to(dtype= self.annotator_model.dtype, device=self.annotator_model.device) 133 | 134 | outputs = self.annotator_model(inputs) 135 | predicted_depths = outputs.predicted_depth 136 | 137 | # interpolate to original size 138 | predictions = torch.nn.functional.interpolate( 139 | predicted_depths.unsqueeze(1), 140 | size=(h, w), 141 | mode="bicubic", 142 | ) 143 | 144 | # normalize output 145 | if return_standard_norm: 146 | depth_min = torch.amin(predictions, dim=[1, 2, 3], keepdim=True) 147 | depth_max = torch.amax(predictions, dim=[1, 2, 3], keepdim=True) 148 | predictions = 2.0 * (predictions - depth_min) / (depth_max - depth_min) - 1.0 149 | else: 150 | predictions -= torch.min(predictions) 151 | predictions /= torch.max(predictions) 152 | 153 | return predictions 154 | 155 | 156 | @torch.no_grad() 157 | def get_hed_map(self, frames,): 158 | if isinstance(frames, torch.Tensor): 159 | # 输入的就是 b c h w的tensor 范围是-1~1，需要转换为0～1 160 | frames = (frames + 1) / 2 161 | #rgb转bgr 162 | bgr_frames = frames.clone() 163 | bgr_frames[:, 0, :, :] = frames[:, 2, :, :] 164 | bgr_frames[:, 2, :, :] = frames[:, 0, :, :] 165 | 166 | edge = self.annotator_model(bgr_frames) # 范围也是0～1 167 | return edge 168 | else: 169 | assert frames.ndim == 3 170 | frames = frames[:, :, ::-1].copy() 171 | with torch.no_grad(): 172 | image_hed = torch.from_numpy(frames).to(next(self.annotator_model.parameters()).device, dtype=next(self.annotator_model.parameters()).dtype ) 173 | image_hed = image_hed / 255.0 174 | image_hed = rearrange(image_hed, 'h w c -> 1 c h w') 175 | edge = self.annotator_model(image_hed)[0] 176 | edge = (edge.cpu().numpy() * 255.0).clip(0, 255).astype(np.uint8) 177 | return edge[0] 178 | 179 | @torch.no_grad() 180 | def get_pose_map(self, frames,): 181 | if isinstance(frames, torch.Tensor): 182 | # 输入的就是 b c h w的tensor 范围是-1~1，需要转换为0～1 183 | frames = (frames + 1) / 2 184 | np_frames = frames.cpu().numpy() * 255 185 | np_frames = np.array(np_frames, dtype=np.uint8) 186 | np_frames = rearrange(np_frames, 'f c h w-> f h w c') 187 | poses = np.stack([self.annotator_model(inp) for inp in np_frames]) 188 | else: 189 | poses = self.annotator_model(frames) 190 | return poses 191 | 192 | def get_timesteps(self, num_inference_steps, strength,): 193 | # get the original timestep using init_timestep 194 | init_timestep = min(int(num_inference_steps * strength), num_inference_steps) 195 | 196 | t_start = max(num_inference_steps - init_timestep, 0) 197 | timesteps = self.scheduler.timesteps[t_start:] 198 | 199 | return timesteps, num_inference_steps - t_start 200 | 201 | @torch.no_grad() 202 | def __call__( 203 | self, 204 | prompt: Union[str, List[str]], 205 | controlnet_hint = None, 206 | fps_labels = None, 207 | height: Optional[int] = None, 208 | width: Optional[int] = None, 209 | num_inference_steps: int = 50, 210 | clip_length: int = 8, # NOTE clip_length和images的帧数一致。 211 | guidance_scale: float = 7.5, 212 | negative_prompt: Optional[Union[str, List[str]]] = None, 213 | num_images_per_prompt: Optional[int] = 1, 214 | eta: float = 0.0, 215 | generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None, 216 | latents: Optional[torch.FloatTensor] = None, 217 | output_type: Optional[str] = "pil", 218 | return_dict: bool = True, 219 | callback: Optional[Callable[[int, int, torch.FloatTensor], None]] = None, 220 | callback_steps: Optional[int] = 1, 221 | cross_attention_kwargs = None, 222 | video_scale: float = 0.0, 223 | controlnet_conditioning_scale: float = 1.0, 224 | fix_first_frame=True, 225 | first_frame_output = None , # 也可以允许挑好图后传入。 226 | first_frame_output_latent = None, 227 | first_frame_control_hint = None, # 维持第一帧 228 | add_first_frame_by_concat = False, 229 | controlhint_in_uncond = False, 230 | init_same_noise_per_frame=False, 231 | init_noise_by_residual_thres=0.0, 232 | images=None, 233 | in_domain=False, # 是否调用视频模型生成图片 234 | residual_control_steps=1, 235 | first_frame_ddim_strength=1.0, 236 | return_last_latent = False, 237 | ): 238 | ''' 239 | add origin video frames to get depth maps 240 | ''' 241 | 242 | if fix_first_frame and first_frame_output is None and first_frame_output_latent is None: 243 | first_frame_output = self.__call__( 244 | prompt=prompt, 245 | controlnet_hint=controlnet_hint[:,:,0,:,:] if not in_domain else controlnet_hint[:,:,0:1,:,:], 246 | # b c f h w 247 | num_inference_steps=20, 248 | width=width, 249 | height=height, 250 | guidance_scale=guidance_scale, 251 | num_images_per_prompt=1, 252 | generator=generator, 253 | fix_first_frame=False, 254 | controlhint_in_uncond=controlhint_in_uncond, 255 | ).images[0] 256 | 257 | 258 | if first_frame_output is not None: 259 | if isinstance(first_frame_output, list): 260 | first_frame_output = first_frame_output[0] 261 | first_frame_output = torch.from_numpy(np.array(first_frame_output)).div(255) * 2 - 1 262 | first_frame_output = rearrange(first_frame_output, "h w c -> c h w").unsqueeze(0) # FIXME 目前不允许多个batch 先设置为1 263 | first_frame_output = first_frame_output.to(dtype= self.vae.dtype, device=self.vae.device) 264 | 265 | first_frame_output_latent = self.vae.encode(first_frame_output).latent_dist.sample() 266 | first_frame_output_latent = first_frame_output_latent * 0.18215 267 | # 0. Default height and width to unet 268 | height = height or self.unet.config.sample_size * self.vae_scale_factor 269 | width = width or self.unet.config.sample_size * self.vae_scale_factor 270 | 271 | # 1. Check inputs. Raise error if not correct 272 | self.check_inputs(prompt, height, width, callback_steps) 273 | 274 | # 2. Define call parameters 275 | batch_size = 1 if isinstance(prompt, str) else len(prompt) 276 | device = self._execution_device 277 | # here `guidance_scale` is defined analog to the guidance weight `w` of equation (2) 278 | # of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1` 279 | # corresponds to doing no classifier free guidance. 280 | do_classifier_free_guidance = guidance_scale > 5.0 281 | 282 | # 3. Encode input prompt 283 | text_embeddings = self._encode_prompt( 284 | prompt, device, num_images_per_prompt, do_classifier_free_guidance, negative_prompt 285 | ) 286 | 287 | # 4. Prepare timesteps 288 | self.scheduler.set_timesteps(num_inference_steps, device=device) 289 | timesteps = self.scheduler.timesteps 290 | 291 | # 5. Prepare latent variables 292 | num_channels_latents = self.unet.in_channels 293 | if controlnet_hint is not None: 294 | if len(controlnet_hint.shape) == 5: 295 | clip_length = controlnet_hint.shape[2] 296 | else: 297 | clip_length = 0 298 | 299 | latents = self.prepare_latents( 300 | batch_size * num_images_per_prompt, 301 | num_channels_latents, 302 | clip_length, 303 | height, 304 | width, 305 | text_embeddings.dtype, 306 | device, 307 | generator, 308 | latents, 309 | ) 310 | latents_dtype = latents.dtype 311 | 312 | 313 | if len(latents.shape) == 5 and init_same_noise_per_frame: 314 | latents[:,:,1:,:,:] = latents[:,:,0:1,:,:] 315 | 316 | if len(latents.shape) == 5 and init_noise_by_residual_thres > 0.0 and images is not None: 317 | 318 | images = images.to(device=device, dtype=latents_dtype) # b c f h w 319 | image_residual = torch.abs(images[:,:,1:,:,:] - images[:,:,:-1,:,:]) 320 | images = rearrange(images, "b c f h w -> (b f) c h w") 321 | 322 | # norm residual 323 | image_residual = image_residual / torch.max(image_residual) 324 | 325 | image_residual = rearrange(image_residual, "b c f h w -> (b f) c h w") 326 | image_residual = torch.nn.functional.interpolate( 327 | image_residual, 328 | size=(latents.shape[-2], latents.shape[-1]), 329 | mode='bilinear') 330 | image_residual = torch.mean(image_residual, dim=1) 331 | 332 | image_residual_mask = (image_residual > init_noise_by_residual_thres).float() 333 | image_residual_mask = repeat(image_residual_mask, '(b f) h w -> b f h w', b=batch_size) 334 | image_residual_mask = repeat(image_residual_mask, 'b f h w -> b c f h w', c=latents.shape[1]) 335 | 336 | # 6. Prepare extra step kwargs. TODO: Logic should ideally just be moved out of the pipeline 337 | extra_step_kwargs = self.prepare_extra_step_kwargs(generator, eta) 338 | 339 | # 7. Denoising loop 340 | num_warmup_steps = len(timesteps) - num_inference_steps * self.scheduler.order 341 | with self.progress_bar(total=num_inference_steps) as progress_bar: 342 | if fix_first_frame: 343 | if add_first_frame_by_concat: 344 | if len(first_frame_output_latent.shape) == 4: 345 | latents = torch.cat([first_frame_output_latent.unsqueeze(2), latents], dim=2) 346 | else: 347 | latents = torch.cat([first_frame_output_latent, latents], dim=2) 348 | if first_frame_control_hint is not None: 349 | controlnet_hint = torch.cat([first_frame_control_hint, controlnet_hint], dim=2) 350 | else: 351 | controlnet_hint = torch.cat([controlnet_hint[:,:,0:1 ,:,:], controlnet_hint], dim=2) 352 | 353 | if controlhint_in_uncond: 354 | controlnet_hint = torch.cat([controlnet_hint] * 2) if do_classifier_free_guidance else controlnet_hint 355 | for i, t in enumerate(timesteps): 356 | # expand the latents if we are doing classifier free guidance 357 | if i 0.0 and images is not None : 358 | if first_frame_ddim_strength < 1.0 and i == 0 : 359 | # NOTE DDIM to get the first noise 360 | first_frame_output_latent_DDIM = first_frame_output_latent.clone() 361 | full_noise_timestep, _ = self.get_timesteps(num_inference_steps, strength=first_frame_ddim_strength) 362 | latent_timestep = full_noise_timestep[:1].repeat(batch_size * num_images_per_prompt) 363 | first_frame_output_latent_DDIM = self.scheduler.add_noise(first_frame_output_latent_DDIM, latents[:,:,0,:,:], latent_timestep) 364 | latents[:,:,0,:,:]=first_frame_output_latent_DDIM 365 | begin_frame = 1 366 | for n_frame in range(begin_frame, latents.shape[2]): 367 | latents[:,:, n_frame, :, :] = \ 368 | (latents[:,:, n_frame, :, :] - latents[:,:, n_frame-1, :, :]) \ 369 | * image_residual_mask[:,:, n_frame-1, :, :] + \ 370 | latents[:,:, n_frame-1, :, :] 371 | if fix_first_frame: 372 | latents[:,:,0 ,:,:] = first_frame_output_latent 373 | 374 | latent_model_input = torch.cat([latents] * 2) if do_classifier_free_guidance else latents 375 | latent_model_input = self.scheduler.scale_model_input(latent_model_input, t) 376 | if controlnet_hint is not None: 377 | down_block_res_samples, mid_block_res_sample = self.controlnet( 378 | latent_model_input, 379 | t, 380 | encoder_hidden_states=text_embeddings, 381 | controlnet_cond=controlnet_hint, 382 | return_dict=False, 383 | ) 384 | down_block_res_samples = [ 385 | down_block_res_sample * controlnet_conditioning_scale 386 | for down_block_res_sample in down_block_res_samples 387 | ] 388 | mid_block_res_sample *= controlnet_conditioning_scale 389 | 390 | noise_pred = self.unet( 391 | latent_model_input, 392 | t, 393 | encoder_hidden_states=text_embeddings, 394 | cross_attention_kwargs=cross_attention_kwargs, 395 | down_block_additional_residuals=down_block_res_samples, 396 | mid_block_additional_residual=mid_block_res_sample, 397 | ).sample.to(dtype=latents_dtype) 398 | else: 399 | # predict the noise residual 400 | noise_pred = self.unet( 401 | latent_model_input, 402 | t, 403 | encoder_hidden_states=text_embeddings, 404 | ).sample.to(dtype=latents_dtype) 405 | 406 | if video_scale > 0 and controlnet_hint is not None: 407 | bsz = latents.shape[0] 408 | f = latents.shape[2] 409 | # 逐帧预测 410 | latent_model_input_single_frame = rearrange(latent_model_input, 'b c f h w -> (b f) c h w') 411 | text_embeddings_single_frame = torch.cat([text_embeddings] * f, dim=0) 412 | control_maps_single_frame = rearrange(controlnet_hint, 'b c f h w -> (b f) c h w') 413 | latent_model_input_single_frame = latent_model_input_single_frame.chunk(2, dim=0)[0] 414 | text_embeddings_single_frame = text_embeddings_single_frame.chunk(2, dim=0)[0] 415 | if controlhint_in_uncond: 416 | control_maps_single_frame = control_maps_single_frame.chunk(2, dim=0)[0] 417 | 418 | down_block_res_samples_single_frame, mid_block_res_sample_single_frame = self.controlnet( 419 | latent_model_input_single_frame, 420 | t, 421 | encoder_hidden_states=text_embeddings_single_frame, 422 | controlnet_cond=control_maps_single_frame, 423 | return_dict=False, 424 | ) 425 | down_block_res_samples_single_frame = [ 426 | down_block_res_sample_single_frame * controlnet_conditioning_scale 427 | for down_block_res_sample_single_frame in down_block_res_samples_single_frame 428 | ] 429 | mid_block_res_sample_single_frame *= controlnet_conditioning_scale 430 | 431 | noise_pred_single_frame_uncond = self.unet( 432 | latent_model_input_single_frame, 433 | t, 434 | encoder_hidden_states = text_embeddings_single_frame, 435 | down_block_additional_residuals=down_block_res_samples_single_frame, 436 | mid_block_additional_residual=mid_block_res_sample_single_frame, 437 | ).sample 438 | noise_pred_single_frame_uncond = rearrange(noise_pred_single_frame_uncond, '(b f) c h w -> b c f h w', f=f) 439 | # perform guidance 440 | if do_classifier_free_guidance: 441 | if video_scale > 0 and controlnet_hint is not None: 442 | noise_pred_uncond, noise_pred_text = noise_pred.chunk(2) 443 | noise_pred = noise_pred_single_frame_uncond + video_scale * ( 444 | noise_pred_uncond - noise_pred_single_frame_uncond 445 | ) + guidance_scale * ( 446 | noise_pred_text - noise_pred_uncond 447 | ) 448 | else: 449 | noise_pred_uncond, noise_pred_text = noise_pred.chunk(2) 450 | noise_pred = noise_pred_uncond + guidance_scale * ( 451 | noise_pred_text - noise_pred_uncond 452 | ) 453 | 454 | # compute the previous noisy sample x_t -> x_t-1 455 | latents = self.scheduler.step(noise_pred, t, latents, **extra_step_kwargs).prev_sample 456 | 457 | # call the callback, if provided 458 | if i == len(timesteps) - 1 or ( 459 | (i + 1) > num_warmup_steps and (i + 1) % self.scheduler.order == 0 460 | ): 461 | progress_bar.update() 462 | if callback is not None and i % callback_steps == 0: 463 | callback(i, t, latents) 464 | # 8. Post-processing 465 | image = self.decode_latents(latents) 466 | if add_first_frame_by_concat: 467 | image = image[:,1:,:,:,:] 468 | 469 | # 9. Run safety checker 470 | has_nsfw_concept = None 471 | # 10. Convert to PIL 472 | if output_type == "pil": 473 | image = self.numpy_to_pil(image) 474 | 475 | if not return_dict: 476 | return (image, has_nsfw_concept) 477 | 478 | if return_last_latent: 479 | last_latent = latents[:,:,-1,:,:] 480 | return StableDiffusionPipelineOutput(images=image, nsfw_content_detected=has_nsfw_concept), last_latent 481 | else: 482 | return StableDiffusionPipelineOutput(images=image, nsfw_content_detected=has_nsfw_concept) 483 | -------------------------------------------------------------------------------- /model/video_diffusion/models/unet_3d_blocks.py: -------------------------------------------------------------------------------- 1 | # Copyright 2023 Bytedance Ltd. and/or its affiliates 2 | 3 | # Licensed under the Apache License, Version 2.0 (the "License"); 4 | # you may not use this file except in compliance with the License. 5 | # You may obtain a copy of the License at 6 | 7 | # http://www.apache.org/licenses/LICENSE-2.0 8 | 9 | # Unless required by applicable law or agreed to in writing, software 10 | # distributed under the License is distributed on an "AS IS" BASIS, 11 | # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. 12 | # See the License for the specific language governing permissions and 13 | # limitations under the License. 14 | 15 | import torch 16 | from torch import nn 17 | 18 | from .attention import SpatioTemporalTransformerModel 19 | from .resnet import DownsamplePseudo3D, ResnetBlockPseudo3D, UpsamplePseudo3D 20 | 21 | 22 | def get_down_block( 23 | down_block_type, 24 | num_layers, 25 | in_channels, 26 | out_channels, 27 | temb_channels, 28 | add_downsample, 29 | resnet_eps, 30 | resnet_act_fn, 31 | attn_num_head_channels, 32 | resnet_groups=None, 33 | cross_attention_dim=None, 34 | downsample_padding=None, 35 | dual_cross_attention=False, 36 | use_linear_projection=False, 37 | only_cross_attention=False, 38 | upcast_attention=False, 39 | resnet_time_scale_shift="default", 40 | ): 41 | down_block_type = down_block_type[7:] if down_block_type.startswith("UNetRes") else down_block_type 42 | if down_block_type == "DownBlockPseudo3D": 43 | return DownBlockPseudo3D( 44 | num_layers=num_layers, 45 | in_channels=in_channels, 46 | out_channels=out_channels, 47 | temb_channels=temb_channels, 48 | add_downsample=add_downsample, 49 | resnet_eps=resnet_eps, 50 | resnet_act_fn=resnet_act_fn, 51 | resnet_groups=resnet_groups, 52 | downsample_padding=downsample_padding, 53 | resnet_time_scale_shift=resnet_time_scale_shift, 54 | ) 55 | elif down_block_type == "CrossAttnDownBlockPseudo3D": 56 | if cross_attention_dim is None: 57 | raise ValueError("cross_attention_dim must be specified for CrossAttnDownBlockPseudo3D") 58 | return CrossAttnDownBlockPseudo3D( 59 | num_layers=num_layers, 60 | in_channels=in_channels, 61 | out_channels=out_channels, 62 | temb_channels=temb_channels, 63 | add_downsample=add_downsample, 64 | resnet_eps=resnet_eps, 65 | resnet_act_fn=resnet_act_fn, 66 | resnet_groups=resnet_groups, 67 | downsample_padding=downsample_padding, 68 | cross_attention_dim=cross_attention_dim, 69 | attn_num_head_channels=attn_num_head_channels, 70 | dual_cross_attention=dual_cross_attention, 71 | use_linear_projection=use_linear_projection, 72 | only_cross_attention=only_cross_attention, 73 | upcast_attention=upcast_attention, 74 | resnet_time_scale_shift=resnet_time_scale_shift, 75 | ) 76 | raise ValueError(f"{down_block_type} does not exist.") 77 | 78 | 79 | def get_up_block( 80 | up_block_type, 81 | num_layers, 82 | in_channels, 83 | out_channels, 84 | prev_output_channel, 85 | temb_channels, 86 | add_upsample, 87 | resnet_eps, 88 | resnet_act_fn, 89 | attn_num_head_channels, 90 | resnet_groups=None, 91 | cross_attention_dim=None, 92 | dual_cross_attention=False, 93 | use_linear_projection=False, 94 | only_cross_attention=False, 95 | upcast_attention=False, 96 | resnet_time_scale_shift="default", 97 | ): 98 | up_block_type = up_block_type[7:] if up_block_type.startswith("UNetRes") else up_block_type 99 | if up_block_type == "UpBlockPseudo3D": 100 | return UpBlockPseudo3D( 101 | num_layers=num_layers, 102 | in_channels=in_channels, 103 | out_channels=out_channels, 104 | prev_output_channel=prev_output_channel, 105 | temb_channels=temb_channels, 106 | add_upsample=add_upsample, 107 | resnet_eps=resnet_eps, 108 | resnet_act_fn=resnet_act_fn, 109 | resnet_groups=resnet_groups, 110 | resnet_time_scale_shift=resnet_time_scale_shift, 111 | ) 112 | elif up_block_type == "CrossAttnUpBlockPseudo3D": 113 | if cross_attention_dim is None: 114 | raise ValueError("cross_attention_dim must be specified for CrossAttnUpBlockPseudo3D") 115 | return CrossAttnUpBlockPseudo3D( 116 | num_layers=num_layers, 117 | in_channels=in_channels, 118 | out_channels=out_channels, 119 | prev_output_channel=prev_output_channel, 120 | temb_channels=temb_channels, 121 | add_upsample=add_upsample, 122 | resnet_eps=resnet_eps, 123 | resnet_act_fn=resnet_act_fn, 124 | resnet_groups=resnet_groups, 125 | cross_attention_dim=cross_attention_dim, 126 | attn_num_head_channels=attn_num_head_channels, 127 | dual_cross_attention=dual_cross_attention, 128 | use_linear_projection=use_linear_projection, 129 | only_cross_attention=only_cross_attention, 130 | upcast_attention=upcast_attention, 131 | resnet_time_scale_shift=resnet_time_scale_shift, 132 | ) 133 | raise ValueError(f"{up_block_type} does not exist.") 134 | 135 | 136 | class UNetMidBlockPseudo3DCrossAttn(nn.Module): 137 | def __init__( 138 | self, 139 | in_channels: int, 140 | temb_channels: int, 141 | dropout: float = 0.0, 142 | num_layers: int = 1, 143 | resnet_eps: float = 1e-6, 144 | resnet_time_scale_shift: str = "default", 145 | resnet_act_fn: str = "swish", 146 | resnet_groups: int = 32, 147 | resnet_pre_norm: bool = True, 148 | attn_num_head_channels=1, 149 | output_scale_factor=1.0, 150 | cross_attention_dim=1280, 151 | dual_cross_attention=False, 152 | use_linear_projection=False, 153 | upcast_attention=False, 154 | ): 155 | super().__init__() 156 | 157 | self.has_cross_attention = True 158 | self.attn_num_head_channels = attn_num_head_channels 159 | resnet_groups = resnet_groups if resnet_groups is not None else min(in_channels // 4, 32) 160 | 161 | # there is always at least one resnet 162 | resnets = [ 163 | ResnetBlockPseudo3D( 164 | in_channels=in_channels, 165 | out_channels=in_channels, 166 | temb_channels=temb_channels, 167 | eps=resnet_eps, 168 | groups=resnet_groups, 169 | dropout=dropout, 170 | time_embedding_norm=resnet_time_scale_shift, 171 | non_linearity=resnet_act_fn, 172 | output_scale_factor=output_scale_factor, 173 | pre_norm=resnet_pre_norm, 174 | ) 175 | ] 176 | attentions = [] 177 | 178 | for _ in range(num_layers): 179 | if dual_cross_attention: 180 | raise NotImplementedError 181 | attentions.append( 182 | SpatioTemporalTransformerModel( 183 | attn_num_head_channels, 184 | in_channels // attn_num_head_channels, 185 | in_channels=in_channels, 186 | num_layers=1, 187 | cross_attention_dim=cross_attention_dim, 188 | norm_num_groups=resnet_groups, 189 | use_linear_projection=use_linear_projection, 190 | upcast_attention=upcast_attention, 191 | ) 192 | ) 193 | resnets.append( 194 | ResnetBlockPseudo3D( 195 | in_channels=in_channels, 196 | out_channels=in_channels, 197 | temb_channels=temb_channels, 198 | eps=resnet_eps, 199 | groups=resnet_groups, 200 | dropout=dropout, 201 | time_embedding_norm=resnet_time_scale_shift, 202 | non_linearity=resnet_act_fn, 203 | output_scale_factor=output_scale_factor, 204 | pre_norm=resnet_pre_norm, 205 | ) 206 | ) 207 | 208 | self.attentions = nn.ModuleList(attentions) 209 | self.resnets = nn.ModuleList(resnets) 210 | 211 | def forward(self, hidden_states, temb=None, encoder_hidden_states=None, attention_mask=None): 212 | # TODO(Patrick, William) - attention_mask is currently not used. Implement once used 213 | hidden_states = self.resnets[0](hidden_states, temb) 214 | for attn, resnet in zip(self.attentions, self.resnets[1:]): 215 | hidden_states = attn(hidden_states, encoder_hidden_states=encoder_hidden_states).sample 216 | hidden_states = resnet(hidden_states, temb) 217 | 218 | return hidden_states 219 | 220 | 221 | class CrossAttnDownBlockPseudo3D(nn.Module): 222 | def __init__( 223 | self, 224 | in_channels: int, 225 | out_channels: int, 226 | temb_channels: int, 227 | dropout: float = 0.0, 228 | num_layers: int = 1, 229 | resnet_eps: float = 1e-6, 230 | resnet_time_scale_shift: str = "default", 231 | resnet_act_fn: str = "swish", 232 | resnet_groups: int = 32, 233 | resnet_pre_norm: bool = True, 234 | attn_num_head_channels=1, 235 | cross_attention_dim=1280, 236 | output_scale_factor=1.0, 237 | downsample_padding=1, 238 | add_downsample=True, 239 | dual_cross_attention=False, 240 | use_linear_projection=False, 241 | only_cross_attention=False, 242 | upcast_attention=False, 243 | ): 244 | super().__init__() 245 | resnets = [] 246 | attentions = [] 247 | 248 | self.has_cross_attention = True 249 | self.attn_num_head_channels = attn_num_head_channels 250 | 251 | for i in range(num_layers): 252 | in_channels = in_channels if i == 0 else out_channels 253 | resnets.append( 254 | ResnetBlockPseudo3D( 255 | in_channels=in_channels, 256 | out_channels=out_channels, 257 | temb_channels=temb_channels, 258 | eps=resnet_eps, 259 | groups=resnet_groups, 260 | dropout=dropout, 261 | time_embedding_norm=resnet_time_scale_shift, 262 | non_linearity=resnet_act_fn, 263 | output_scale_factor=output_scale_factor, 264 | pre_norm=resnet_pre_norm, 265 | ) 266 | ) 267 | if dual_cross_attention: 268 | raise NotImplementedError 269 | attentions.append( 270 | SpatioTemporalTransformerModel( 271 | attn_num_head_channels, 272 | out_channels // attn_num_head_channels, 273 | in_channels=out_channels, 274 | num_layers=1, 275 | cross_attention_dim=cross_attention_dim, 276 | norm_num_groups=resnet_groups, 277 | use_linear_projection=use_linear_projection, 278 | only_cross_attention=only_cross_attention, 279 | upcast_attention=upcast_attention, 280 | ) 281 | ) 282 | self.attentions = nn.ModuleList(attentions) 283 | self.resnets = nn.ModuleList(resnets) 284 | 285 | if add_downsample: 286 | self.downsamplers = nn.ModuleList( 287 | [ 288 | DownsamplePseudo3D( 289 | out_channels, 290 | use_conv=True, 291 | out_channels=out_channels, 292 | padding=downsample_padding, 293 | name="op", 294 | ) 295 | ] 296 | ) 297 | else: 298 | self.downsamplers = None 299 | 300 | self.gradient_checkpointing = False 301 | 302 | def forward(self, hidden_states, temb=None, encoder_hidden_states=None, attention_mask=None): 303 | # TODO(Patrick, William) - attention mask is not used 304 | output_states = () 305 | 306 | for resnet, attn in zip(self.resnets, self.attentions): 307 | if self.training and self.gradient_checkpointing: 308 | 309 | def create_custom_forward(module, return_dict=None): 310 | def custom_forward(*inputs): 311 | if return_dict is not None: 312 | return module(*inputs, return_dict=return_dict) 313 | else: 314 | return module(*inputs) 315 | 316 | return custom_forward 317 | 318 | hidden_states = torch.utils.checkpoint.checkpoint( 319 | create_custom_forward(resnet), hidden_states, temb 320 | ) 321 | hidden_states = torch.utils.checkpoint.checkpoint( 322 | create_custom_forward(attn, return_dict=False), 323 | hidden_states, 324 | encoder_hidden_states, 325 | )[0] 326 | else: 327 | hidden_states = resnet(hidden_states, temb) 328 | hidden_states = attn(hidden_states, encoder_hidden_states=encoder_hidden_states).sample 329 | 330 | output_states += (hidden_states,) 331 | 332 | if self.downsamplers is not None: 333 | for downsampler in self.downsamplers: 334 | hidden_states = downsampler(hidden_states) 335 | 336 | output_states += (hidden_states,) 337 | 338 | return hidden_states, output_states 339 | 340 | 341 | class DownBlockPseudo3D(nn.Module): 342 | def __init__( 343 | self, 344 | in_channels: int, 345 | out_channels: int, 346 | temb_channels: int, 347 | dropout: float = 0.0, 348 | num_layers: int = 1, 349 | resnet_eps: float = 1e-6, 350 | resnet_time_scale_shift: str = "default", 351 | resnet_act_fn: str = "swish", 352 | resnet_groups: int = 32, 353 | resnet_pre_norm: bool = True, 354 | output_scale_factor=1.0, 355 | add_downsample=True, 356 | downsample_padding=1, 357 | ): 358 | super().__init__() 359 | resnets = [] 360 | 361 | for i in range(num_layers): 362 | in_channels = in_channels if i == 0 else out_channels 363 | resnets.append( 364 | ResnetBlockPseudo3D( 365 | in_channels=in_channels, 366 | out_channels=out_channels, 367 | temb_channels=temb_channels, 368 | eps=resnet_eps, 369 | groups=resnet_groups, 370 | dropout=dropout, 371 | time_embedding_norm=resnet_time_scale_shift, 372 | non_linearity=resnet_act_fn, 373 | output_scale_factor=output_scale_factor, 374 | pre_norm=resnet_pre_norm, 375 | ) 376 | ) 377 | 378 | self.resnets = nn.ModuleList(resnets) 379 | 380 | if add_downsample: 381 | self.downsamplers = nn.ModuleList( 382 | [ 383 | DownsamplePseudo3D( 384 | out_channels, 385 | use_conv=True, 386 | out_channels=out_channels, 387 | padding=downsample_padding, 388 | name="op", 389 | ) 390 | ] 391 | ) 392 | else: 393 | self.downsamplers = None 394 | 395 | self.gradient_checkpointing = False 396 | 397 | def forward(self, hidden_states, temb=None): 398 | output_states = () 399 | 400 | for resnet in self.resnets: 401 | if self.training and self.gradient_checkpointing: 402 | 403 | def create_custom_forward(module): 404 | def custom_forward(*inputs): 405 | return module(*inputs) 406 | 407 | return custom_forward 408 | 409 | hidden_states = torch.utils.checkpoint.checkpoint( 410 | create_custom_forward(resnet), hidden_states, temb 411 | ) 412 | else: 413 | hidden_states = resnet(hidden_states, temb) 414 | 415 | output_states += (hidden_states,) 416 | 417 | if self.downsamplers is not None: 418 | for downsampler in self.downsamplers: 419 | hidden_states = downsampler(hidden_states) 420 | 421 | output_states += (hidden_states,) 422 | 423 | return hidden_states, output_states 424 | 425 | 426 | class CrossAttnUpBlockPseudo3D(nn.Module): 427 | def __init__( 428 | self, 429 | in_channels: int, 430 | out_channels: int, 431 | prev_output_channel: int, 432 | temb_channels: int, 433 | dropout: float = 0.0, 434 | num_layers: int = 1, 435 | resnet_eps: float = 1e-6, 436 | resnet_time_scale_shift: str = "default", 437 | resnet_act_fn: str = "swish", 438 | resnet_groups: int = 32, 439 | resnet_pre_norm: bool = True, 440 | attn_num_head_channels=1, 441 | cross_attention_dim=1280, 442 | output_scale_factor=1.0, 443 | add_upsample=True, 444 | dual_cross_attention=False, 445 | use_linear_projection=False, 446 | only_cross_attention=False, 447 | upcast_attention=False, 448 | ): 449 | super().__init__() 450 | resnets = [] 451 | attentions = [] 452 | 453 | self.has_cross_attention = True 454 | self.attn_num_head_channels = attn_num_head_channels 455 | 456 | for i in range(num_layers): 457 | res_skip_channels = in_channels if (i == num_layers - 1) else out_channels 458 | resnet_in_channels = prev_output_channel if i == 0 else out_channels 459 | 460 | resnets.append( 461 | ResnetBlockPseudo3D( 462 | in_channels=resnet_in_channels + res_skip_channels, 463 | out_channels=out_channels, 464 | temb_channels=temb_channels, 465 | eps=resnet_eps, 466 | groups=resnet_groups, 467 | dropout=dropout, 468 | time_embedding_norm=resnet_time_scale_shift, 469 | non_linearity=resnet_act_fn, 470 | output_scale_factor=output_scale_factor, 471 | pre_norm=resnet_pre_norm, 472 | ) 473 | ) 474 | if dual_cross_attention: 475 | raise NotImplementedError 476 | attentions.append( 477 | SpatioTemporalTransformerModel( 478 | attn_num_head_channels, 479 | out_channels // attn_num_head_channels, 480 | in_channels=out_channels, 481 | num_layers=1, 482 | cross_attention_dim=cross_attention_dim, 483 | norm_num_groups=resnet_groups, 484 | use_linear_projection=use_linear_projection, 485 | only_cross_attention=only_cross_attention, 486 | upcast_attention=upcast_attention, 487 | ) 488 | ) 489 | self.attentions = nn.ModuleList(attentions) 490 | self.resnets = nn.ModuleList(resnets) 491 | 492 | if add_upsample: 493 | self.upsamplers = nn.ModuleList( 494 | [UpsamplePseudo3D(out_channels, use_conv=True, out_channels=out_channels)] 495 | ) 496 | else: 497 | self.upsamplers = None 498 | 499 | self.gradient_checkpointing = False 500 | 501 | def forward( 502 | self, 503 | hidden_states, 504 | res_hidden_states_tuple, 505 | temb=None, 506 | encoder_hidden_states=None, 507 | upsample_size=None, 508 | attention_mask=None, 509 | ): 510 | # TODO(Patrick, William) - attention mask is not used 511 | for resnet, attn in zip(self.resnets, self.attentions): 512 | # pop res hidden states 513 | res_hidden_states = res_hidden_states_tuple[-1] 514 | res_hidden_states_tuple = res_hidden_states_tuple[:-1] 515 | hidden_states = torch.cat([hidden_states, res_hidden_states], dim=1) 516 | 517 | if self.training and self.gradient_checkpointing: 518 | 519 | def create_custom_forward(module, return_dict=None): 520 | def custom_forward(*inputs): 521 | if return_dict is not None: 522 | return module(*inputs, return_dict=return_dict) 523 | else: 524 | return module(*inputs) 525 | 526 | return custom_forward 527 | 528 | hidden_states = torch.utils.checkpoint.checkpoint( 529 | create_custom_forward(resnet), hidden_states, temb 530 | ) 531 | hidden_states = torch.utils.checkpoint.checkpoint( 532 | create_custom_forward(attn, return_dict=False), 533 | hidden_states, 534 | encoder_hidden_states, 535 | )[0] 536 | else: 537 | hidden_states = resnet(hidden_states, temb) 538 | hidden_states = attn(hidden_states, encoder_hidden_states=encoder_hidden_states).sample 539 | 540 | if self.upsamplers is not None: 541 | for upsampler in self.upsamplers: 542 | hidden_states = upsampler(hidden_states, upsample_size) 543 | 544 | return hidden_states 545 | 546 | 547 | class UpBlockPseudo3D(nn.Module): 548 | def __init__( 549 | self, 550 | in_channels: int, 551 | prev_output_channel: int, 552 | out_channels: int, 553 | temb_channels: int, 554 | dropout: float = 0.0, 555 | num_layers: int = 1, 556 | resnet_eps: float = 1e-6, 557 | resnet_time_scale_shift: str = "default", 558 | resnet_act_fn: str = "swish", 559 | resnet_groups: int = 32, 560 | resnet_pre_norm: bool = True, 561 | output_scale_factor=1.0, 562 | add_upsample=True, 563 | ): 564 | super().__init__() 565 | resnets = [] 566 | 567 | for i in range(num_layers): 568 | res_skip_channels = in_channels if (i == num_layers - 1) else out_channels 569 | resnet_in_channels = prev_output_channel if i == 0 else out_channels 570 | 571 | resnets.append( 572 | ResnetBlockPseudo3D( 573 | in_channels=resnet_in_channels + res_skip_channels, 574 | out_channels=out_channels, 575 | temb_channels=temb_channels, 576 | eps=resnet_eps, 577 | groups=resnet_groups, 578 | dropout=dropout, 579 | time_embedding_norm=resnet_time_scale_shift, 580 | non_linearity=resnet_act_fn, 581 | output_scale_factor=output_scale_factor, 582 | pre_norm=resnet_pre_norm, 583 | ) 584 | ) 585 | 586 | self.resnets = nn.ModuleList(resnets) 587 | 588 | if add_upsample: 589 | self.upsamplers = nn.ModuleList( 590 | [UpsamplePseudo3D(out_channels, use_conv=True, out_channels=out_channels)] 591 | ) 592 | else: 593 | self.upsamplers = None 594 | 595 | self.gradient_checkpointing = False 596 | 597 | def forward(self, hidden_states, res_hidden_states_tuple, temb=None, upsample_size=None): 598 | for resnet in self.resnets: 599 | # pop res hidden states 600 | res_hidden_states = res_hidden_states_tuple[-1] 601 | res_hidden_states_tuple = res_hidden_states_tuple[:-1] 602 | hidden_states = torch.cat([hidden_states, res_hidden_states], dim=1) 603 | 604 | if self.training and self.gradient_checkpointing: 605 | 606 | def create_custom_forward(module): 607 | def custom_forward(*inputs): 608 | return module(*inputs) 609 | 610 | return custom_forward 611 | 612 | hidden_states = torch.utils.checkpoint.checkpoint( 613 | create_custom_forward(resnet), hidden_states, temb 614 | ) 615 | else: 616 | hidden_states = resnet(hidden_states, temb) 617 | 618 | if self.upsamplers is not None: 619 | for upsampler in self.upsamplers: 620 | hidden_states = upsampler(hidden_states, upsample_size) 621 | 622 | return hidden_states 623 | -------------------------------------------------------------------------------- /model/video_diffusion/models/unet_3d_condition.py: -------------------------------------------------------------------------------- 1 | # Copyright 2023 Bytedance Ltd. and/or its affiliates 2 | 3 | # Licensed under the Apache License, Version 2.0 (the "License"); 4 | # you may not use this file except in compliance with the License. 5 | # You may obtain a copy of the License at 6 | 7 | # http://www.apache.org/licenses/LICENSE-2.0 8 | 9 | # Unless required by applicable law or agreed to in writing, software 10 | # distributed under the License is distributed on an "AS IS" BASIS, 11 | # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. 12 | # See the License for the specific language governing permissions and 13 | # limitations under the License. 14 | 15 | import os 16 | import glob 17 | import json 18 | from dataclasses import dataclass 19 | from typing import List, Optional, Tuple, Union 20 | 21 | import torch 22 | import torch.nn as nn 23 | import torch.utils.checkpoint 24 | 25 | from diffusers.configuration_utils import ConfigMixin, register_to_config 26 | from diffusers.models.modeling_utils import ModelMixin 27 | from diffusers.utils import BaseOutput, logging 28 | from diffusers.models.embeddings import TimestepEmbedding, Timesteps 29 | from .unet_3d_blocks import ( 30 | CrossAttnDownBlockPseudo3D, 31 | CrossAttnUpBlockPseudo3D, 32 | DownBlockPseudo3D, 33 | UNetMidBlockPseudo3DCrossAttn, 34 | UpBlockPseudo3D, 35 | get_down_block, 36 | get_up_block, 37 | ) 38 | from .resnet import PseudoConv3d 39 | from diffusers.models.cross_attention import AttnProcessor 40 | from typing import Dict 41 | 42 | logger = logging.get_logger(__name__) # pylint: disable=invalid-name 43 | 44 | 45 | @dataclass 46 | class UNetPseudo3DConditionOutput(BaseOutput): 47 | sample: torch.FloatTensor 48 | 49 | 50 | class UNetPseudo3DConditionModel(ModelMixin, ConfigMixin): 51 | """ 52 | 这里把原来2D Unet的 2D卷积全换成新定义的PseudoConv3d。并且定义了从2D卷积继承的模型参数。 53 | """ 54 | _supports_gradient_checkpointing = True 55 | 56 | @register_to_config 57 | def __init__( 58 | self, 59 | sample_size: Optional[int] = None, 60 | in_channels: int = 4, 61 | out_channels: int = 4, 62 | center_input_sample: bool = False, 63 | flip_sin_to_cos: bool = True, 64 | freq_shift: int = 0, 65 | down_block_types: Tuple[str] = ( 66 | "CrossAttnDownBlockPseudo3D", 67 | "CrossAttnDownBlockPseudo3D", 68 | "CrossAttnDownBlockPseudo3D", 69 | "DownBlockPseudo3D", 70 | ), 71 | mid_block_type: str = "UNetMidBlockPseudo3DCrossAttn", 72 | up_block_types: Tuple[str] = ( 73 | "UpBlockPseudo3D", 74 | "CrossAttnUpBlockPseudo3D", 75 | "CrossAttnUpBlockPseudo3D", 76 | "CrossAttnUpBlockPseudo3D", 77 | ), 78 | only_cross_attention: Union[bool, Tuple[bool]] = False, 79 | block_out_channels: Tuple[int] = (320, 640, 1280, 1280), 80 | layers_per_block: int = 2, 81 | downsample_padding: int = 1, 82 | mid_block_scale_factor: float = 1, 83 | act_fn: str = "silu", 84 | norm_num_groups: int = 32, 85 | norm_eps: float = 1e-5, 86 | cross_attention_dim: int = 1280, 87 | attention_head_dim: Union[int, Tuple[int]] = 8, 88 | dual_cross_attention: bool = False, 89 | use_linear_projection: bool = False, 90 | fps_embed_type: Optional[str] = None, 91 | num_fps_embeds: Optional[int] = None, 92 | upcast_attention: bool = False, 93 | resnet_time_scale_shift: str = "default", 94 | num_class_embeds=None, 95 | 96 | ): 97 | super().__init__() 98 | 99 | 100 | self.sample_size = sample_size 101 | time_embed_dim = block_out_channels[0] * 4 102 | 103 | # input 104 | self.conv_in = PseudoConv3d(in_channels, block_out_channels[0], kernel_size=3, padding=(1, 1)) 105 | 106 | # time 107 | self.time_proj = Timesteps(block_out_channels[0], flip_sin_to_cos, freq_shift) 108 | timestep_input_dim = block_out_channels[0] 109 | 110 | self.time_embedding = TimestepEmbedding(timestep_input_dim, time_embed_dim) 111 | 112 | # class embedding 113 | if fps_embed_type is None and num_fps_embeds is not None: 114 | self.fps_embedding = nn.Embedding(num_fps_embeds, time_embed_dim) 115 | elif fps_embed_type == "timestep": 116 | self.fps_embedding = TimestepEmbedding(timestep_input_dim, time_embed_dim) 117 | elif fps_embed_type == "identity": 118 | self.fps_embedding = nn.Identity(time_embed_dim, time_embed_dim) 119 | else: 120 | self.fps_embedding = None 121 | 122 | self.down_blocks = nn.ModuleList([]) 123 | self.mid_block = None 124 | self.up_blocks = nn.ModuleList([]) 125 | 126 | if isinstance(only_cross_attention, bool): 127 | only_cross_attention = [only_cross_attention] * len(down_block_types) 128 | 129 | if isinstance(attention_head_dim, int): 130 | attention_head_dim = (attention_head_dim,) * len(down_block_types) 131 | 132 | # down 133 | output_channel = block_out_channels[0] 134 | for i, down_block_type in enumerate(down_block_types): 135 | input_channel = output_channel 136 | output_channel = block_out_channels[i] 137 | is_final_block = i == len(block_out_channels) - 1 138 | 139 | down_block = get_down_block( 140 | down_block_type, 141 | num_layers=layers_per_block, 142 | in_channels=input_channel, 143 | out_channels=output_channel, 144 | temb_channels=time_embed_dim, 145 | add_downsample=not is_final_block, 146 | resnet_eps=norm_eps, 147 | resnet_act_fn=act_fn, 148 | resnet_groups=norm_num_groups, 149 | cross_attention_dim=cross_attention_dim, 150 | attn_num_head_channels=attention_head_dim[i], 151 | downsample_padding=downsample_padding, 152 | dual_cross_attention=dual_cross_attention, 153 | use_linear_projection=use_linear_projection, 154 | only_cross_attention=only_cross_attention[i], 155 | upcast_attention=upcast_attention, 156 | resnet_time_scale_shift=resnet_time_scale_shift, 157 | ) 158 | self.down_blocks.append(down_block) 159 | 160 | # mid 161 | if mid_block_type == "UNetMidBlockPseudo3DCrossAttn": 162 | self.mid_block = UNetMidBlockPseudo3DCrossAttn( 163 | in_channels=block_out_channels[-1], 164 | temb_channels=time_embed_dim, 165 | resnet_eps=norm_eps, 166 | resnet_act_fn=act_fn, 167 | output_scale_factor=mid_block_scale_factor, 168 | resnet_time_scale_shift=resnet_time_scale_shift, 169 | cross_attention_dim=cross_attention_dim, 170 | attn_num_head_channels=attention_head_dim[-1], 171 | resnet_groups=norm_num_groups, 172 | dual_cross_attention=dual_cross_attention, 173 | use_linear_projection=use_linear_projection, 174 | upcast_attention=upcast_attention, 175 | ) 176 | else: 177 | raise ValueError(f"unknown mid_block_type : {mid_block_type}") 178 | 179 | # count how many layers upsample the images 180 | self.num_upsamplers = 0 181 | 182 | # up 183 | reversed_block_out_channels = list(reversed(block_out_channels)) 184 | reversed_attention_head_dim = list(reversed(attention_head_dim)) 185 | only_cross_attention = list(reversed(only_cross_attention)) 186 | output_channel = reversed_block_out_channels[0] 187 | for i, up_block_type in enumerate(up_block_types): 188 | is_final_block = i == len(block_out_channels) - 1 189 | 190 | prev_output_channel = output_channel 191 | output_channel = reversed_block_out_channels[i] 192 | input_channel = reversed_block_out_channels[min(i + 1, len(block_out_channels) - 1)] 193 | 194 | # add upsample block for all BUT final layer 195 | if not is_final_block: 196 | add_upsample = True 197 | self.num_upsamplers += 1 198 | else: 199 | add_upsample = False 200 | 201 | up_block = get_up_block( 202 | up_block_type, 203 | num_layers=layers_per_block + 1, 204 | in_channels=input_channel, 205 | out_channels=output_channel, 206 | prev_output_channel=prev_output_channel, 207 | temb_channels=time_embed_dim, 208 | add_upsample=add_upsample, 209 | resnet_eps=norm_eps, 210 | resnet_act_fn=act_fn, 211 | resnet_groups=norm_num_groups, 212 | cross_attention_dim=cross_attention_dim, 213 | attn_num_head_channels=reversed_attention_head_dim[i], 214 | dual_cross_attention=dual_cross_attention, 215 | use_linear_projection=use_linear_projection, 216 | only_cross_attention=only_cross_attention[i], 217 | upcast_attention=upcast_attention, 218 | resnet_time_scale_shift=resnet_time_scale_shift, 219 | ) 220 | self.up_blocks.append(up_block) 221 | prev_output_channel = output_channel 222 | 223 | # out 224 | self.conv_norm_out = nn.GroupNorm( 225 | num_channels=block_out_channels[0], num_groups=norm_num_groups, eps=norm_eps 226 | ) 227 | self.conv_act = nn.SiLU() 228 | self.conv_out = PseudoConv3d(block_out_channels[0], out_channels, kernel_size=3, padding=1) 229 | @property 230 | def attn_processors(self) -> Dict[str, AttnProcessor]: 231 | r""" 232 | Returns: 233 | `dict` of attention processors: A dictionary containing all attention processors used in the model with 234 | indexed by its weight name. 235 | """ 236 | # set recursively 237 | processors = {} 238 | 239 | def fn_recursive_add_processors(name: str, module: torch.nn.Module, processors: Dict[str, AttnProcessor]): 240 | if hasattr(module, "set_processor"): 241 | processors[f"{name}.processor"] = module.processor 242 | 243 | for sub_name, child in module.named_children(): 244 | fn_recursive_add_processors(f"{name}.{sub_name}", child, processors) 245 | 246 | return processors 247 | 248 | for name, module in self.named_children(): 249 | fn_recursive_add_processors(name, module, processors) 250 | 251 | return processors 252 | 253 | def set_attn_processor(self, processor: Union[AttnProcessor, Dict[str, AttnProcessor]]): 254 | r""" 255 | Parameters: 256 | `processor (`dict` of `AttnProcessor` or `AttnProcessor`): 257 | The instantiated processor class or a dictionary of processor classes that will be set as the processor 258 | of **all** `CrossAttention` layers. 259 | In case `processor` is a dict, the key needs to define the path to the corresponding cross attention processor. This is strongly recommended when setting trainablae attention processors.: 260 | 261 | """ 262 | count = len(self.attn_processors.keys()) 263 | 264 | if isinstance(processor, dict) and len(processor) != count: 265 | raise ValueError( 266 | f"A dict of processors was passed, but the number of processors {len(processor)} does not match the" 267 | f" number of attention layers: {count}. Please make sure to pass {count} processor classes." 268 | ) 269 | 270 | def fn_recursive_attn_processor(name: str, module: torch.nn.Module, processor): 271 | if hasattr(module, "set_processor"): 272 | if not isinstance(processor, dict): 273 | module.set_processor(processor) 274 | else: 275 | module.set_processor(processor.pop(f"{name}.processor")) 276 | 277 | for sub_name, child in module.named_children(): 278 | fn_recursive_attn_processor(f"{name}.{sub_name}", child, processor) 279 | 280 | for name, module in self.named_children(): 281 | fn_recursive_attn_processor(name, module, processor) 282 | 283 | 284 | def set_attention_slice(self, slice_size): 285 | r""" 286 | Enable sliced attention computation. 287 | 288 | When this option is enabled, the attention module will split the input tensor in slices, to compute attention 289 | in several steps. This is useful to save some memory in exchange for a small speed decrease. 290 | 291 | Args: 292 | slice_size (`str` or `int` or `list(int)`, *optional*, defaults to `"auto"`): 293 | When `"auto"`, halves the input to the attention heads, so attention will be computed in two steps. If 294 | `"max"`, maxium amount of memory will be saved by running only one slice at a time. If a number is 295 | provided, uses as many slices as `attention_head_dim // slice_size`. In this case, `attention_head_dim` 296 | must be a multiple of `slice_size`. 297 | """ 298 | sliceable_head_dims = [] 299 | 300 | def fn_recursive_retrieve_slicable_dims(module: torch.nn.Module): 301 | if hasattr(module, "set_attention_slice"): 302 | sliceable_head_dims.append(module.sliceable_head_dim) 303 | 304 | for child in module.children(): 305 | fn_recursive_retrieve_slicable_dims(child) 306 | 307 | # retrieve number of attention layers 308 | for module in self.children(): 309 | fn_recursive_retrieve_slicable_dims(module) 310 | 311 | num_slicable_layers = len(sliceable_head_dims) 312 | 313 | if slice_size == "auto": 314 | # half the attention head size is usually a good trade-off between 315 | # speed and memory 316 | slice_size = [dim // 2 for dim in sliceable_head_dims] 317 | elif slice_size == "max": 318 | # make smallest slice possible 319 | slice_size = num_slicable_layers * [1] 320 | 321 | slice_size = ( 322 | num_slicable_layers * [slice_size] if not isinstance(slice_size, list) else slice_size 323 | ) 324 | 325 | if len(slice_size) != len(sliceable_head_dims): 326 | raise ValueError( 327 | f"You have provided {len(slice_size)}, but {self.config} has {len(sliceable_head_dims)} different" 328 | f" attention layers. Make sure to match `len(slice_size)` to be {len(sliceable_head_dims)}." 329 | ) 330 | 331 | for i in range(len(slice_size)): 332 | size = slice_size[i] 333 | dim = sliceable_head_dims[i] 334 | if size is not None and size > dim: 335 | raise ValueError(f"size {size} has to be smaller or equal to {dim}.") 336 | 337 | # Recursively walk through all the children. 338 | # Any children which exposes the set_attention_slice method 339 | # gets the message 340 | def fn_recursive_set_attention_slice(module: torch.nn.Module, slice_size: List[int]): 341 | if hasattr(module, "set_attention_slice"): 342 | module.set_attention_slice(slice_size.pop()) 343 | 344 | for child in module.children(): 345 | fn_recursive_set_attention_slice(child, slice_size) 346 | 347 | reversed_slice_size = list(reversed(slice_size)) 348 | for module in self.children(): 349 | fn_recursive_set_attention_slice(module, reversed_slice_size) 350 | 351 | def _set_gradient_checkpointing(self, module, value=False): 352 | if isinstance( 353 | module, 354 | (CrossAttnDownBlockPseudo3D, DownBlockPseudo3D, CrossAttnUpBlockPseudo3D, UpBlockPseudo3D), 355 | ): 356 | module.gradient_checkpointing = value 357 | 358 | def forward( 359 | self, 360 | sample: torch.FloatTensor, 361 | timestep: Union[torch.Tensor, float, int], 362 | encoder_hidden_states: torch.Tensor, 363 | fps_labels: Optional[torch.Tensor] = None, 364 | attention_mask: Optional[torch.Tensor] = None, 365 | cross_attention_kwargs=None, 366 | down_block_additional_residuals: Optional[Tuple[torch.Tensor]] = None, 367 | mid_block_additional_residual: Optional[torch.Tensor] = None, 368 | return_dict: bool = True, 369 | ) -> Union[UNetPseudo3DConditionOutput, Tuple]: 370 | # By default samples have to be AT least a multiple of the overall upsampling factor. 371 | # The overall upsampling factor is equal to 2 ** (# num of upsampling layears). 372 | # However, the upsampling interpolation output size can be forced to fit any upsampling size 373 | # on the fly if necessary. 374 | default_overall_up_factor = 2**self.num_upsamplers 375 | 376 | # upsample size should be forwarded when sample is not a multiple of `default_overall_up_factor` 377 | forward_upsample_size = False 378 | upsample_size = None 379 | 380 | if any(s % default_overall_up_factor != 0 for s in sample.shape[-2:]): 381 | logger.info("Forward upsample size to force interpolation output size.") 382 | forward_upsample_size = True 383 | 384 | # prepare attention_mask 385 | if attention_mask is not None: 386 | attention_mask = (1 - attention_mask.to(sample.dtype)) * -10000.0 387 | attention_mask = attention_mask.unsqueeze(1) 388 | 389 | # 0. center input if necessary 390 | if self.config.center_input_sample: 391 | sample = 2 * sample - 1.0 392 | 393 | # 1. time 394 | timesteps = timestep 395 | if not torch.is_tensor(timesteps): 396 | # TODO: this requires sync between CPU and GPU. So try to pass timesteps as tensors if you can 397 | # This would be a good case for the `match` statement (Python 3.10+) 398 | is_mps = sample.device.type == "mps" 399 | if isinstance(timestep, float): 400 | dtype = torch.float32 if is_mps else torch.float64 401 | else: 402 | dtype = torch.int32 if is_mps else torch.int64 403 | timesteps = torch.tensor([timesteps], dtype=dtype, device=sample.device) 404 | elif len(timesteps.shape) == 0: 405 | timesteps = timesteps[None].to(sample.device) 406 | # broadcast to batch dimension in a way that's compatible with ONNX/Core ML 407 | timesteps = timesteps.expand(sample.shape[0]) 408 | 409 | t_emb = self.time_proj(timesteps) 410 | # timesteps does not contain any weights and will always return f32 tensors 411 | # but time_embedding might actually be running in fp16. so we need to cast here. 412 | # there might be better ways to encapsulate this. 413 | t_emb = t_emb.to(dtype=self.dtype) 414 | emb = self.time_embedding(t_emb) 415 | 416 | if self.fps_embedding is not None: 417 | if fps_labels is None: 418 | raise ValueError("fps_labels should be provided when num_fps_embeds > 0") 419 | 420 | if self.config.fps_embed_type == "timestep": 421 | fps_labels = self.time_proj(fps_labels) # 和timesteps共用，都是sin embedding？这里的weight不更新的。 422 | 423 | # 这里和上面timesteps does not contain any weights and will always return f32 tensors的bug一样。需要先cast过去，不然多机多卡就有问题了。 424 | fps_labels = fps_labels.to(dtype=self.dtype) 425 | class_emb = self.fps_embedding(fps_labels) 426 | 427 | emb = emb + class_emb 428 | 429 | # 2. pre-process 430 | sample = self.conv_in(sample) 431 | 432 | # 3. down 433 | down_block_res_samples = (sample,) 434 | for downsample_block in self.down_blocks: 435 | if hasattr(downsample_block, "has_cross_attention") and downsample_block.has_cross_attention: 436 | sample, res_samples = downsample_block( 437 | hidden_states=sample, 438 | temb=emb, 439 | encoder_hidden_states=encoder_hidden_states, 440 | attention_mask=attention_mask, 441 | ) 442 | else: 443 | sample, res_samples = downsample_block(hidden_states=sample, temb=emb) 444 | 445 | down_block_res_samples += res_samples 446 | 447 | if down_block_additional_residuals is not None: 448 | new_down_block_res_samples = () 449 | 450 | for down_block_res_sample, down_block_additional_residual in zip( 451 | down_block_res_samples, down_block_additional_residuals 452 | ): 453 | down_block_res_sample = down_block_res_sample + down_block_additional_residual 454 | new_down_block_res_samples += (down_block_res_sample,) 455 | 456 | down_block_res_samples = new_down_block_res_samples 457 | 458 | # 4. mid 459 | sample = self.mid_block( 460 | sample, emb, encoder_hidden_states=encoder_hidden_states, attention_mask=attention_mask 461 | ) 462 | if mid_block_additional_residual is not None: 463 | sample = sample + mid_block_additional_residual 464 | 465 | # 5. up 466 | for i, upsample_block in enumerate(self.up_blocks): 467 | is_final_block = i == len(self.up_blocks) - 1 468 | 469 | res_samples = down_block_res_samples[-len(upsample_block.resnets) :] 470 | down_block_res_samples = down_block_res_samples[: -len(upsample_block.resnets)] 471 | 472 | # if we have not reached the final block and need to forward the 473 | # upsample size, we do it here 474 | if not is_final_block and forward_upsample_size: 475 | upsample_size = down_block_res_samples[-1].shape[2:] 476 | 477 | if hasattr(upsample_block, "has_cross_attention") and upsample_block.has_cross_attention: 478 | sample = upsample_block( 479 | hidden_states=sample, 480 | temb=emb, 481 | res_hidden_states_tuple=res_samples, 482 | encoder_hidden_states=encoder_hidden_states, 483 | upsample_size=upsample_size, 484 | attention_mask=attention_mask, 485 | ) 486 | else: 487 | sample = upsample_block( 488 | hidden_states=sample, 489 | temb=emb, 490 | res_hidden_states_tuple=res_samples, 491 | upsample_size=upsample_size, 492 | ) 493 | # 6. post-process 494 | sample = self.conv_norm_out(sample) 495 | sample = self.conv_act(sample) 496 | sample = self.conv_out(sample) 497 | 498 | if not return_dict: 499 | return (sample,) 500 | 501 | return UNetPseudo3DConditionOutput(sample=sample) 502 | 503 | @classmethod 504 | def from_2d_model(cls, model_path, condition_on_fps=False): 505 | ''' 506 | load a 2d model and convert it to a pseudo 3d model 507 | ''' 508 | config_path = os.path.join(model_path, "config.json") 509 | if not os.path.isfile(config_path): 510 | raise RuntimeError(f"{config_path} does not exist") 511 | with open(config_path, "r") as f: 512 | config = json.load(f) 513 | 514 | config.pop("_class_name") 515 | config.pop("_diffusers_version") 516 | 517 | block_replacer = { 518 | "CrossAttnDownBlock2D": "CrossAttnDownBlockPseudo3D", 519 | "DownBlock2D": "DownBlockPseudo3D", 520 | "UpBlock2D": "UpBlockPseudo3D", 521 | "CrossAttnUpBlock2D": "CrossAttnUpBlockPseudo3D", 522 | } 523 | 524 | def convert_2d_to_3d_block(block): 525 | return block_replacer[block] if block in block_replacer else block 526 | 527 | config["down_block_types"] = [ 528 | convert_2d_to_3d_block(block) for block in config["down_block_types"] 529 | ] 530 | config["up_block_types"] = [convert_2d_to_3d_block(block) for block in config["up_block_types"]] 531 | 532 | if condition_on_fps: 533 | # config["num_fps_embeds"] = 60 # 这个在 trainable embeding时候才需要～ 534 | config["fps_embed_type"] = "timestep" # 和timestep保持一致的type。 535 | 536 | 537 | model = cls(**config) # 调用自身(init), 传入config参数全换成3d的setting 538 | 539 | state_dict_path_condidates = glob.glob(os.path.join(model_path, "*.bin")) 540 | if state_dict_path_condidates: 541 | state_dict = torch.load(state_dict_path_condidates[0], map_location="cpu") 542 | model.load_2d_state_dict(state_dict=state_dict) 543 | 544 | return model 545 | 546 | def load_2d_state_dict(self, state_dict, **kwargs): 547 | ''' 548 | 2D 部分的参数名完全不变。 549 | ''' 550 | state_dict_3d = self.state_dict() 551 | 552 | for k, v in state_dict.items(): 553 | if k not in state_dict_3d: 554 | raise KeyError(f"2d state_dict key {k} does not exist in 3d model") 555 | elif v.shape != state_dict_3d[k].shape: 556 | raise ValueError(f"state_dict shape mismatch, 2d {v.shape}, 3d {state_dict_3d[k].shape}") 557 | 558 | for k, v in state_dict_3d.items(): 559 | if "_temporal" in k: 560 | continue 561 | if "gamma" in k: 562 | continue 563 | 564 | if k not in state_dict: 565 | if "fps_embedding" in k: 566 | # 忽略检查fps_embedding 567 | continue 568 | raise KeyError(f"3d state_dict key {k} does not exist in 2d model") 569 | 570 | state_dict_3d.update(state_dict) 571 | self.load_state_dict(state_dict_3d, **kwargs) 572 | -------------------------------------------------------------------------------- /model/video_diffusion/models/controlnet3d.py: -------------------------------------------------------------------------------- 1 | # Copyright 2023 Bytedance Ltd. and/or its affiliates 2 | 3 | # Licensed under the Apache License, Version 2.0 (the "License"); 4 | # you may not use this file except in compliance with the License. 5 | # You may obtain a copy of the License at 6 | 7 | # http://www.apache.org/licenses/LICENSE-2.0 8 | 9 | # Unless required by applicable law or agreed to in writing, software 10 | # distributed under the License is distributed on an "AS IS" BASIS, 11 | # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. 12 | # See the License for the specific language governing permissions and 13 | # limitations under the License. 14 | 15 | 16 | from dataclasses import dataclass 17 | from typing import Any, Dict, List, Optional, Tuple, Union 18 | 19 | import torch 20 | from torch import nn 21 | from torch.nn import functional as F 22 | 23 | from diffusers.configuration_utils import ConfigMixin, register_to_config 24 | from diffusers.utils import BaseOutput, logging 25 | from diffusers.models.cross_attention import AttnProcessor 26 | from diffusers.models.embeddings import TimestepEmbedding, Timesteps 27 | from diffusers.models.modeling_utils import ModelMixin 28 | 29 | from .unet_3d_blocks import ( 30 | CrossAttnDownBlockPseudo3D, 31 | DownBlockPseudo3D, 32 | UNetMidBlockPseudo3DCrossAttn, 33 | get_down_block, 34 | ) 35 | from .resnet import PseudoConv3d 36 | from diffusers.models.cross_attention import AttnProcessor 37 | from typing import Dict 38 | from .unet_3d_blocks_control import ControlNetPseudoZeroConv3dBlock, ControlNetInputHintBlock 39 | import glob 40 | import os 41 | import json 42 | 43 | logger = logging.get_logger(__name__) # pylint: disable=invalid-name 44 | @dataclass 45 | class ControlNetOutput(BaseOutput): 46 | down_block_res_samples: Tuple[torch.Tensor] 47 | mid_block_res_sample: torch.Tensor 48 | 49 | 50 | class ControlNetConditioningEmbedding(nn.Module): 51 | """ 52 | Quoting from https://arxiv.org/abs/2302.05543: "Stable Diffusion uses a pre-processing method similar to VQ-GAN 53 | [11] to convert the entire dataset of 512 × 512 images into smaller 64 × 64 “latent images” for stabilized 54 | training. This requires ControlNets to convert image-based conditions to 64 × 64 feature space to match the 55 | convolution size. We use a tiny network E(·) of four convolution layers with 4 × 4 kernels and 2 × 2 strides 56 | (activated by ReLU, channels are 16, 32, 64, 128, initialized with Gaussian weights, trained jointly with the full 57 | model) to encode image-space conditions ... into feature maps ..." 58 | """ 59 | 60 | def __init__( 61 | self, 62 | conditioning_embedding_channels: int, 63 | conditioning_channels: int = 3, 64 | block_out_channels: Tuple[int] = (16, 32, 96, 256), 65 | ): 66 | super().__init__() 67 | 68 | self.conv_in = PseudoConv3d(conditioning_channels, block_out_channels[0], kernel_size=3, padding=1) 69 | 70 | self.blocks = nn.ModuleList([]) 71 | 72 | for i in range(len(block_out_channels) - 1): 73 | channel_in = block_out_channels[i] 74 | channel_out = block_out_channels[i + 1] 75 | self.blocks.append(PseudoConv3d(channel_in, channel_in, kernel_size=3, padding=1)) 76 | self.blocks.append(PseudoConv3d(channel_in, channel_out, kernel_size=3, padding=1, stride=2)) 77 | 78 | # self.conv_out = zero_module( 79 | # PseudoConv3d(block_out_channels[-1], conditioning_embedding_channels, kernel_size=3, padding=1) 80 | # ) 81 | self.conv_out = PseudoConv3d(block_out_channels[-1], conditioning_embedding_channels, kernel_size=3, padding=1) 82 | 83 | def forward(self, conditioning): 84 | embedding = self.conv_in(conditioning) 85 | embedding = F.silu(embedding) 86 | 87 | for block in self.blocks: 88 | embedding = block(embedding) 89 | embedding = F.silu(embedding) 90 | 91 | embedding = self.conv_out(embedding) 92 | 93 | return embedding 94 | 95 | 96 | class ControlNet3DModel(ModelMixin, ConfigMixin): 97 | _supports_gradient_checkpointing = True 98 | 99 | @register_to_config 100 | def __init__( 101 | self, 102 | in_channels: int = 4, 103 | flip_sin_to_cos: bool = True, 104 | freq_shift: int = 0, 105 | down_block_types: Tuple[str] = ( 106 | "CrossAttnDownBlockPseudo3D", 107 | "CrossAttnDownBlockPseudo3D", 108 | "CrossAttnDownBlockPseudo3D", 109 | "DownBlockPseudo3D", 110 | ), 111 | only_cross_attention: Union[bool, Tuple[bool]] = False, 112 | block_out_channels: Tuple[int] = (320, 640, 1280, 1280), 113 | layers_per_block: int = 2, 114 | downsample_padding: int = 1, 115 | mid_block_scale_factor: float = 1, 116 | act_fn: str = "silu", 117 | norm_num_groups: Optional[int] = 32, 118 | norm_eps: float = 1e-5, 119 | cross_attention_dim: int = 1280, 120 | attention_head_dim: Union[int, Tuple[int]] = 8, 121 | use_linear_projection: bool = False, 122 | class_embed_type: Optional[str] = None, 123 | num_class_embeds: Optional[int] = None, 124 | upcast_attention: bool = False, 125 | resnet_time_scale_shift: str = "default", 126 | projection_class_embeddings_input_dim: Optional[int] = None, 127 | controlnet_conditioning_channel_order: str = "rgb", 128 | conditioning_embedding_out_channels: Optional[Tuple[int]] = (16, 32, 96, 256), 129 | ): 130 | super().__init__() 131 | 132 | # Check inputs 133 | if len(block_out_channels) != len(down_block_types): 134 | raise ValueError( 135 | 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}." 136 | ) 137 | 138 | if not isinstance(only_cross_attention, bool) and len(only_cross_attention) != len(down_block_types): 139 | raise ValueError( 140 | 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}." 141 | ) 142 | 143 | if not isinstance(attention_head_dim, int) and len(attention_head_dim) != len(down_block_types): 144 | raise ValueError( 145 | 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}." 146 | ) 147 | 148 | # input 149 | conv_in_kernel = 3 150 | conv_in_padding = (conv_in_kernel - 1) // 2 151 | self.conv_in = PseudoConv3d( 152 | in_channels, block_out_channels[0], kernel_size=conv_in_kernel, padding=conv_in_padding 153 | ) 154 | 155 | # time 156 | time_embed_dim = block_out_channels[0] * 4 157 | 158 | self.time_proj = Timesteps(block_out_channels[0], flip_sin_to_cos, freq_shift) 159 | timestep_input_dim = block_out_channels[0] 160 | 161 | self.time_embedding = TimestepEmbedding( 162 | timestep_input_dim, 163 | time_embed_dim, 164 | act_fn=act_fn, 165 | ) 166 | 167 | # class embedding 168 | if class_embed_type is None and num_class_embeds is not None: 169 | self.class_embedding = nn.Embedding(num_class_embeds, time_embed_dim) 170 | elif class_embed_type == "timestep": 171 | self.class_embedding = TimestepEmbedding(timestep_input_dim, time_embed_dim) 172 | elif class_embed_type == "identity": 173 | self.class_embedding = nn.Identity(time_embed_dim, time_embed_dim) 174 | elif class_embed_type == "projection": 175 | if projection_class_embeddings_input_dim is None: 176 | raise ValueError( 177 | "`class_embed_type`: 'projection' requires `projection_class_embeddings_input_dim` be set" 178 | ) 179 | # The projection `class_embed_type` is the same as the timestep `class_embed_type` except 180 | # 1. the `class_labels` inputs are not first converted to sinusoidal embeddings 181 | # 2. it projects from an arbitrary input dimension. 182 | # 183 | # Note that `TimestepEmbedding` is quite general, being mainly linear layers and activations. 184 | # When used for embedding actual timesteps, the timesteps are first converted to sinusoidal embeddings. 185 | # As a result, `TimestepEmbedding` can be passed arbitrary vectors. 186 | self.class_embedding = TimestepEmbedding(projection_class_embeddings_input_dim, time_embed_dim) 187 | else: 188 | self.class_embedding = None 189 | 190 | # control net conditioning embedding 191 | self.controlnet_cond_embedding = ControlNetConditioningEmbedding( 192 | conditioning_embedding_channels=block_out_channels[0], 193 | block_out_channels=conditioning_embedding_out_channels, 194 | ) 195 | 196 | self.down_blocks = nn.ModuleList([]) 197 | self.controlnet_down_blocks = nn.ModuleList([]) 198 | 199 | if isinstance(only_cross_attention, bool): 200 | only_cross_attention = [only_cross_attention] * len(down_block_types) 201 | 202 | if isinstance(attention_head_dim, int): 203 | attention_head_dim = (attention_head_dim,) * len(down_block_types) 204 | 205 | # down 206 | output_channel = block_out_channels[0] 207 | 208 | controlnet_block = PseudoConv3d(output_channel, output_channel, kernel_size=1) 209 | # controlnet_block = zero_module(controlnet_block) 210 | self.controlnet_down_blocks.append(controlnet_block) 211 | 212 | for i, down_block_type in enumerate(down_block_types): 213 | input_channel = output_channel 214 | output_channel = block_out_channels[i] 215 | is_final_block = i == len(block_out_channels) - 1 216 | 217 | down_block = get_down_block( 218 | down_block_type, 219 | num_layers=layers_per_block, 220 | in_channels=input_channel, 221 | out_channels=output_channel, 222 | temb_channels=time_embed_dim, 223 | add_downsample=not is_final_block, 224 | resnet_eps=norm_eps, 225 | resnet_act_fn=act_fn, 226 | resnet_groups=norm_num_groups, 227 | cross_attention_dim=cross_attention_dim, 228 | attn_num_head_channels=attention_head_dim[i], 229 | downsample_padding=downsample_padding, 230 | use_linear_projection=use_linear_projection, 231 | only_cross_attention=only_cross_attention[i], 232 | upcast_attention=upcast_attention, 233 | resnet_time_scale_shift=resnet_time_scale_shift, 234 | ) 235 | self.down_blocks.append(down_block) 236 | 237 | for _ in range(layers_per_block): 238 | controlnet_block = PseudoConv3d(output_channel, output_channel, kernel_size=1) 239 | # controlnet_block = zero_module(controlnet_block) 240 | self.controlnet_down_blocks.append(controlnet_block) 241 | 242 | if not is_final_block: 243 | controlnet_block = PseudoConv3d(output_channel, output_channel, kernel_size=1) 244 | # controlnet_block = zero_module(controlnet_block) 245 | self.controlnet_down_blocks.append(controlnet_block) 246 | 247 | # mid 248 | mid_block_channel = block_out_channels[-1] 249 | 250 | controlnet_block = PseudoConv3d(mid_block_channel, mid_block_channel, kernel_size=1) 251 | # controlnet_block = zero_module(controlnet_block) 252 | self.controlnet_mid_block = controlnet_block 253 | 254 | self.mid_block = UNetMidBlockPseudo3DCrossAttn( 255 | in_channels=mid_block_channel, 256 | temb_channels=time_embed_dim, 257 | resnet_eps=norm_eps, 258 | resnet_act_fn=act_fn, 259 | output_scale_factor=mid_block_scale_factor, 260 | resnet_time_scale_shift=resnet_time_scale_shift, 261 | cross_attention_dim=cross_attention_dim, 262 | attn_num_head_channels=attention_head_dim[-1], 263 | resnet_groups=norm_num_groups, 264 | use_linear_projection=use_linear_projection, 265 | upcast_attention=upcast_attention, 266 | ) 267 | 268 | @property 269 | # Copied from diffusers.models.unet_2d_condition.UNet2DConditionModel.attn_processors 270 | def attn_processors(self) -> Dict[str, AttnProcessor]: 271 | r""" 272 | Returns: 273 | `dict` of attention processors: A dictionary containing all attention processors used in the model with 274 | indexed by its weight name. 275 | """ 276 | # set recursively 277 | processors = {} 278 | 279 | def fn_recursive_add_processors(name: str, module: torch.nn.Module, processors: Dict[str, AttnProcessor]): 280 | if hasattr(module, "set_processor"): 281 | processors[f"{name}.processor"] = module.processor 282 | 283 | for sub_name, child in module.named_children(): 284 | fn_recursive_add_processors(f"{name}.{sub_name}", child, processors) 285 | 286 | return processors 287 | 288 | for name, module in self.named_children(): 289 | fn_recursive_add_processors(name, module, processors) 290 | 291 | return processors 292 | 293 | # Copied from diffusers.models.unet_2d_condition.UNet2DConditionModel.set_attn_processor 294 | def set_attn_processor(self, processor: Union[AttnProcessor, Dict[str, AttnProcessor]]): 295 | r""" 296 | Parameters: 297 | `processor (`dict` of `AttnProcessor` or `AttnProcessor`): 298 | The instantiated processor class or a dictionary of processor classes that will be set as the processor 299 | of **all** `CrossAttention` layers. 300 | In case `processor` is a dict, the key needs to define the path to the corresponding cross attention processor. This is strongly recommended when setting trainablae attention processors.: 301 | 302 | """ 303 | count = len(self.attn_processors.keys()) 304 | 305 | if isinstance(processor, dict) and len(processor) != count: 306 | raise ValueError( 307 | f"A dict of processors was passed, but the number of processors {len(processor)} does not match the" 308 | f" number of attention layers: {count}. Please make sure to pass {count} processor classes." 309 | ) 310 | 311 | def fn_recursive_attn_processor(name: str, module: torch.nn.Module, processor): 312 | if hasattr(module, "set_processor"): 313 | if not isinstance(processor, dict): 314 | module.set_processor(processor) 315 | else: 316 | module.set_processor(processor.pop(f"{name}.processor")) 317 | 318 | for sub_name, child in module.named_children(): 319 | fn_recursive_attn_processor(f"{name}.{sub_name}", child, processor) 320 | 321 | for name, module in self.named_children(): 322 | fn_recursive_attn_processor(name, module, processor) 323 | 324 | # Copied from diffusers.models.unet_2d_condition.UNet2DConditionModel.set_attention_slice 325 | def set_attention_slice(self, slice_size): 326 | r""" 327 | Enable sliced attention computation. 328 | 329 | When this option is enabled, the attention module will split the input tensor in slices, to compute attention 330 | in several steps. This is useful to save some memory in exchange for a small speed decrease. 331 | 332 | Args: 333 | slice_size (`str` or `int` or `list(int)`, *optional*, defaults to `"auto"`): 334 | When `"auto"`, halves the input to the attention heads, so attention will be computed in two steps. If 335 | `"max"`, maxium amount of memory will be saved by running only one slice at a time. If a number is 336 | provided, uses as many slices as `attention_head_dim // slice_size`. In this case, `attention_head_dim` 337 | must be a multiple of `slice_size`. 338 | """ 339 | sliceable_head_dims = [] 340 | 341 | def fn_recursive_retrieve_slicable_dims(module: torch.nn.Module): 342 | if hasattr(module, "set_attention_slice"): 343 | sliceable_head_dims.append(module.sliceable_head_dim) 344 | 345 | for child in module.children(): 346 | fn_recursive_retrieve_slicable_dims(child) 347 | 348 | # retrieve number of attention layers 349 | for module in self.children(): 350 | fn_recursive_retrieve_slicable_dims(module) 351 | 352 | num_slicable_layers = len(sliceable_head_dims) 353 | 354 | if slice_size == "auto": 355 | # half the attention head size is usually a good trade-off between 356 | # speed and memory 357 | slice_size = [dim // 2 for dim in sliceable_head_dims] 358 | elif slice_size == "max": 359 | # make smallest slice possible 360 | slice_size = num_slicable_layers * [1] 361 | 362 | slice_size = num_slicable_layers * [slice_size] if not isinstance(slice_size, list) else slice_size 363 | 364 | if len(slice_size) != len(sliceable_head_dims): 365 | raise ValueError( 366 | f"You have provided {len(slice_size)}, but {self.config} has {len(sliceable_head_dims)} different" 367 | f" attention layers. Make sure to match `len(slice_size)` to be {len(sliceable_head_dims)}." 368 | ) 369 | 370 | for i in range(len(slice_size)): 371 | size = slice_size[i] 372 | dim = sliceable_head_dims[i] 373 | if size is not None and size > dim: 374 | raise ValueError(f"size {size} has to be smaller or equal to {dim}.") 375 | 376 | # Recursively walk through all the children. 377 | # Any children which exposes the set_attention_slice method 378 | # gets the message 379 | def fn_recursive_set_attention_slice(module: torch.nn.Module, slice_size: List[int]): 380 | if hasattr(module, "set_attention_slice"): 381 | module.set_attention_slice(slice_size.pop()) 382 | 383 | for child in module.children(): 384 | fn_recursive_set_attention_slice(child, slice_size) 385 | 386 | reversed_slice_size = list(reversed(slice_size)) 387 | for module in self.children(): 388 | fn_recursive_set_attention_slice(module, reversed_slice_size) 389 | 390 | def _set_gradient_checkpointing(self, module, value=False): 391 | if isinstance(module, (CrossAttnDownBlockPseudo3D, DownBlockPseudo3D)): 392 | module.gradient_checkpointing = value 393 | 394 | def forward( 395 | self, 396 | sample: torch.FloatTensor, 397 | timestep: Union[torch.Tensor, float, int], 398 | encoder_hidden_states: torch.Tensor, 399 | controlnet_cond: torch.FloatTensor, 400 | class_labels: Optional[torch.Tensor] = None, 401 | timestep_cond: Optional[torch.Tensor] = None, 402 | attention_mask: Optional[torch.Tensor] = None, 403 | cross_attention_kwargs: Optional[Dict[str, Any]] = None, 404 | return_dict: bool = True, 405 | ) -> Union[ControlNetOutput, Tuple]: 406 | # check channel order 407 | channel_order = self.config.controlnet_conditioning_channel_order 408 | 409 | if channel_order == "rgb": 410 | # in rgb order by default 411 | ... 412 | elif channel_order == "bgr": 413 | controlnet_cond = torch.flip(controlnet_cond, dims=[1]) 414 | else: 415 | raise ValueError(f"unknown `controlnet_conditioning_channel_order`: {channel_order}") 416 | 417 | # prepare attention_mask 418 | if attention_mask is not None: 419 | attention_mask = (1 - attention_mask.to(sample.dtype)) * -10000.0 420 | attention_mask = attention_mask.unsqueeze(1) 421 | 422 | # 1. time 423 | timesteps = timestep 424 | if not torch.is_tensor(timesteps): 425 | # TODO: this requires sync between CPU and GPU. So try to pass timesteps as tensors if you can 426 | # This would be a good case for the `match` statement (Python 3.10+) 427 | is_mps = sample.device.type == "mps" 428 | if isinstance(timestep, float): 429 | dtype = torch.float32 if is_mps else torch.float64 430 | else: 431 | dtype = torch.int32 if is_mps else torch.int64 432 | timesteps = torch.tensor([timesteps], dtype=dtype, device=sample.device) 433 | elif len(timesteps.shape) == 0: 434 | timesteps = timesteps[None].to(sample.device) 435 | 436 | # broadcast to batch dimension in a way that's compatible with ONNX/Core ML 437 | timesteps = timesteps.expand(sample.shape[0]) 438 | 439 | t_emb = self.time_proj(timesteps) 440 | 441 | # timesteps does not contain any weights and will always return f32 tensors 442 | # but time_embedding might actually be running in fp16. so we need to cast here. 443 | # there might be better ways to encapsulate this. 444 | t_emb = t_emb.to(dtype=self.dtype) 445 | 446 | emb = self.time_embedding(t_emb, timestep_cond) 447 | 448 | if self.class_embedding is not None: 449 | if class_labels is None: 450 | raise ValueError("class_labels should be provided when num_class_embeds > 0") 451 | 452 | if self.config.class_embed_type == "timestep": 453 | class_labels = self.time_proj(class_labels) 454 | 455 | class_emb = self.class_embedding(class_labels).to(dtype=self.dtype) 456 | emb = emb + class_emb 457 | 458 | # 2. pre-process 459 | sample = self.conv_in(sample) 460 | 461 | controlnet_cond = self.controlnet_cond_embedding(controlnet_cond) 462 | # print(sample.shape, controlnet_cond.shape) 463 | 464 | sample += controlnet_cond 465 | # 3. down 466 | 467 | down_block_res_samples = (sample,) 468 | for downsample_block in self.down_blocks: 469 | if hasattr(downsample_block, "has_cross_attention") and downsample_block.has_cross_attention: 470 | sample, res_samples = downsample_block( 471 | hidden_states=sample, 472 | temb=emb, 473 | encoder_hidden_states=encoder_hidden_states, 474 | attention_mask=attention_mask, 475 | ) 476 | else: 477 | sample, res_samples = downsample_block(hidden_states=sample, temb=emb) 478 | 479 | down_block_res_samples += res_samples 480 | 481 | # 4. mid 482 | if self.mid_block is not None: 483 | sample = self.mid_block( 484 | sample, 485 | emb, 486 | encoder_hidden_states=encoder_hidden_states, 487 | attention_mask=attention_mask, 488 | ) 489 | 490 | # 5. Control net blocks 491 | 492 | controlnet_down_block_res_samples = () 493 | 494 | for down_block_res_sample, controlnet_block in zip(down_block_res_samples, self.controlnet_down_blocks): 495 | down_block_res_sample = controlnet_block(down_block_res_sample) 496 | controlnet_down_block_res_samples += (down_block_res_sample,) 497 | 498 | down_block_res_samples = controlnet_down_block_res_samples 499 | 500 | mid_block_res_sample = self.controlnet_mid_block(sample) 501 | 502 | if not return_dict: 503 | return (down_block_res_samples, mid_block_res_sample) 504 | 505 | return ControlNetOutput( 506 | down_block_res_samples=down_block_res_samples, mid_block_res_sample=mid_block_res_sample 507 | ) 508 | 509 | @classmethod 510 | def from_2d_model(cls, model_path, condition_on_fps=False, controlnet_hint_channels: Optional[int] = None,): 511 | ''' 512 | load a 2d model and convert it to a pseudo 3d model 513 | ''' 514 | config_path = os.path.join(model_path, "config.json") 515 | if not os.path.isfile(config_path): 516 | raise RuntimeError(f"{config_path} does not exist") 517 | with open(config_path, "r") as f: 518 | config = json.load(f) 519 | 520 | config.pop("_class_name") 521 | config.pop("_diffusers_version") 522 | 523 | block_replacer = { 524 | "CrossAttnDownBlock2D": "CrossAttnDownBlockPseudo3D", 525 | "DownBlock2D": "DownBlockPseudo3D", 526 | "UNetMidBlock2DCrossAttn": "UNetMidBlockPseudo3DCrossAttn", 527 | } 528 | 529 | def convert_2d_to_3d_block(block): 530 | return block_replacer[block] if block in block_replacer else block 531 | 532 | config["down_block_types"] = [ 533 | convert_2d_to_3d_block(block) for block in config["down_block_types"] 534 | ] 535 | 536 | if "mid_block_type" in config: 537 | config["mid_block_type"] = convert_2d_to_3d_block(config["mid_block_type"]) 538 | 539 | if condition_on_fps: 540 | config["fps_embed_type"] = "timestep" # 和timestep保持一致的type。 541 | 542 | if controlnet_hint_channels: 543 | config["controlnet_hint_channels"] = controlnet_hint_channels 544 | 545 | print(config) 546 | 547 | model = cls(**config) # 调用自身(init), 传入config参数全换成3d的setting 548 | state_dict_path_condidates = glob.glob(os.path.join(model_path, "*.bin")) 549 | if state_dict_path_condidates: 550 | state_dict = torch.load(state_dict_path_condidates[0], map_location="cpu") 551 | model.load_2d_state_dict(state_dict=state_dict) 552 | 553 | return model 554 | 555 | def load_2d_state_dict(self, state_dict, **kwargs): 556 | ''' 557 | 2D 部分的参数名完全不变。 558 | ''' 559 | state_dict_3d = self.state_dict() 560 | # print("diff params list:", list(set(state_dict_3d.keys()) - set(state_dict.keys()))) 561 | 562 | for k, v in state_dict.items(): 563 | if k not in state_dict_3d: 564 | raise KeyError(f"2d state_dict key {k} does not exist in 3d model") 565 | 566 | for k, v in state_dict_3d.items(): 567 | if "_temporal" in k: 568 | continue 569 | if "gamma" in k: 570 | continue 571 | if k not in state_dict: 572 | raise KeyError(f"3d state_dict key {k} does not exist in 2d model") 573 | state_dict_3d.update(state_dict) 574 | self.load_state_dict(state_dict_3d, strict=True, **kwargs) 575 | 576 | 577 | def zero_module(module): 578 | for p in module.parameters(): 579 | nn.init.zeros_(p) 580 | return module 581 | -------------------------------------------------------------------------------- /model/video_diffusion/pipelines/pipeline_st_stable_diffusion.py: -------------------------------------------------------------------------------- 1 | # Copyright 2023 Bytedance Ltd. and/or its affiliates 2 | 3 | # Licensed under the Apache License, Version 2.0 (the "License"); 4 | # you may not use this file except in compliance with the License. 5 | # You may obtain a copy of the License at 6 | 7 | # http://www.apache.org/licenses/LICENSE-2.0 8 | 9 | # Unless required by applicable law or agreed to in writing, software 10 | # distributed under the License is distributed on an "AS IS" BASIS, 11 | # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. 12 | # See the License for the specific language governing permissions and 13 | # limitations under the License. 14 | 15 | import inspect 16 | from typing import Callable, List, Optional, Union 17 | 18 | import torch 19 | from einops import rearrange 20 | 21 | from diffusers.utils import is_accelerate_available 22 | from packaging import version 23 | from transformers import CLIPTextModel, CLIPTokenizer 24 | 25 | from diffusers.configuration_utils import FrozenDict 26 | from diffusers.models import AutoencoderKL 27 | from diffusers.pipeline_utils import DiffusionPipeline 28 | from diffusers.schedulers import ( 29 | DDIMScheduler, 30 | DPMSolverMultistepScheduler, 31 | EulerAncestralDiscreteScheduler, 32 | EulerDiscreteScheduler, 33 | LMSDiscreteScheduler, 34 | PNDMScheduler, 35 | ) 36 | from diffusers.utils import deprecate, logging 37 | from diffusers.pipelines.stable_diffusion import StableDiffusionPipelineOutput 38 | 39 | from ..models.unet_3d_condition import UNetPseudo3DConditionModel 40 | import os, importlib 41 | 42 | logger = logging.get_logger(__name__) # pylint: disable=invalid-name 43 | 44 | 45 | class SpatioTemporalStableDiffusionPipeline(DiffusionPipeline): 46 | r""" 47 | Pipeline for text-to-video generation using Spatio-Temporal Stable Diffusion. 48 | 改变了unet的输入, unet换成3d unet, 其他部分完全和原来2D的一致。 49 | latents的变为 b,c,f,h,w 原来是 b,c,h,w。 50 | 要用VAE的decoder的时候, 把输入reshape 成 (b f) c h w 51 | """ 52 | _optional_components = [] 53 | 54 | def __init__( 55 | self, 56 | vae: AutoencoderKL, 57 | text_encoder: CLIPTextModel, 58 | tokenizer: CLIPTokenizer, 59 | unet: UNetPseudo3DConditionModel, 60 | scheduler: Union[ 61 | DDIMScheduler, 62 | PNDMScheduler, 63 | LMSDiscreteScheduler, 64 | EulerDiscreteScheduler, 65 | EulerAncestralDiscreteScheduler, 66 | DPMSolverMultistepScheduler, 67 | ], 68 | ): 69 | super().__init__() 70 | 71 | if hasattr(scheduler.config, "steps_offset") and scheduler.config.steps_offset != 1: 72 | deprecation_message = ( 73 | f"The configuration file of this scheduler: {scheduler} is outdated. `steps_offset`" 74 | f" should be set to 1 instead of {scheduler.config.steps_offset}. Please make sure " 75 | "to update the config accordingly as leaving `steps_offset` might led to incorrect results" 76 | " in future versions. If you have downloaded this checkpoint from the Hugging Face Hub," 77 | " it would be very nice if you could open a Pull request for the `scheduler/scheduler_config.json`" 78 | " file" 79 | ) 80 | deprecate("steps_offset!=1", "1.0.0", deprecation_message, standard_warn=False) 81 | new_config = dict(scheduler.config) 82 | new_config["steps_offset"] = 1 83 | scheduler._internal_dict = FrozenDict(new_config) 84 | 85 | if hasattr(scheduler.config, "clip_sample") and scheduler.config.clip_sample is True: 86 | deprecation_message = ( 87 | f"The configuration file of this scheduler: {scheduler} has not set the configuration `clip_sample`." 88 | " `clip_sample` should be set to False in the configuration file. Please make sure to update the" 89 | " config accordingly as not setting `clip_sample` in the config might lead to incorrect results in" 90 | " future versions. If you have downloaded this checkpoint from the Hugging Face Hub, it would be very" 91 | " nice if you could open a Pull request for the `scheduler/scheduler_config.json` file" 92 | ) 93 | deprecate("clip_sample not set", "1.0.0", deprecation_message, standard_warn=False) 94 | new_config = dict(scheduler.config) 95 | new_config["clip_sample"] = False 96 | scheduler._internal_dict = FrozenDict(new_config) 97 | 98 | is_unet_version_less_0_9_0 = hasattr(unet.config, "_diffusers_version") and version.parse( 99 | version.parse(unet.config._diffusers_version).base_version 100 | ) < version.parse("0.9.0.dev0") 101 | is_unet_sample_size_less_64 = ( 102 | hasattr(unet.config, "sample_size") and unet.config.sample_size < 64 103 | ) 104 | if is_unet_version_less_0_9_0 and is_unet_sample_size_less_64: 105 | deprecation_message = ( 106 | "The configuration file of the unet has set the default `sample_size` to smaller than" 107 | " 64 which seems highly unlikely. If your checkpoint is a fine-tuned version of any of the" 108 | " following: \n- CompVis/stable-diffusion-v1-4 \n- CompVis/stable-diffusion-v1-3 \n-" 109 | " CompVis/stable-diffusion-v1-2 \n- CompVis/stable-diffusion-v1-1 \n- runwayml/stable-diffusion-v1-5" 110 | " \n- runwayml/stable-diffusion-inpainting \n you should change 'sample_size' to 64 in the" 111 | " configuration file. Please make sure to update the config accordingly as leaving `sample_size=32`" 112 | " in the config might lead to incorrect results in future versions. If you have downloaded this" 113 | " checkpoint from the Hugging Face Hub, it would be very nice if you could open a Pull request for" 114 | " the `unet/config.json` file" 115 | ) 116 | deprecate("sample_size<64", "1.0.0", deprecation_message, standard_warn=False) 117 | new_config = dict(unet.config) 118 | new_config["sample_size"] = 64 119 | unet._internal_dict = FrozenDict(new_config) 120 | 121 | self.register_modules( 122 | vae=vae, 123 | text_encoder=text_encoder, 124 | tokenizer=tokenizer, 125 | unet=unet, 126 | scheduler=scheduler, 127 | ) 128 | self.vae_scale_factor = 2 ** (len(self.vae.config.block_out_channels) - 1) 129 | 130 | def enable_vae_slicing(self): 131 | r""" 132 | Enable sliced VAE decoding. 133 | 134 | When this option is enabled, the VAE will split the input tensor in slices to compute decoding in several 135 | steps. This is useful to save some memory and allow larger batch sizes. 136 | """ 137 | self.vae.enable_slicing() 138 | 139 | def disable_vae_slicing(self): 140 | r""" 141 | Disable sliced VAE decoding. If `enable_vae_slicing` was previously invoked, this method will go back to 142 | computing decoding in one step. 143 | """ 144 | self.vae.disable_slicing() 145 | 146 | def enable_sequential_cpu_offload(self, gpu_id=0): 147 | r""" 148 | Offloads all models to CPU using accelerate, significantly reducing memory usage. When called, unet, 149 | text_encoder, vae and safety checker have their state dicts saved to CPU and then are moved to a 150 | `torch.device('meta') and loaded to GPU only when their specific submodule has its `forward` method called. 151 | """ 152 | if is_accelerate_available(): 153 | from accelerate import cpu_offload 154 | else: 155 | raise ImportError("Please install accelerate via `pip install accelerate`") 156 | 157 | device = torch.device(f"cuda:{gpu_id}") 158 | 159 | for cpu_offloaded_model in [self.unet, self.text_encoder, self.vae]: 160 | if cpu_offloaded_model is not None: 161 | cpu_offload(cpu_offloaded_model, device) 162 | 163 | @property 164 | def _execution_device(self): 165 | r""" 166 | Returns the device on which the pipeline's models will be executed. After calling 167 | `pipeline.enable_sequential_cpu_offload()` the execution device can only be inferred from Accelerate's module 168 | hooks. 169 | """ 170 | if self.device != torch.device("meta") or not hasattr(self.unet, "_hf_hook"): 171 | return self.device 172 | for module in self.unet.modules(): 173 | if ( 174 | hasattr(module, "_hf_hook") 175 | and hasattr(module._hf_hook, "execution_device") 176 | and module._hf_hook.execution_device is not None 177 | ): 178 | return torch.device(module._hf_hook.execution_device) 179 | return self.device 180 | 181 | def _encode_prompt( 182 | self, prompt, device, num_images_per_prompt, do_classifier_free_guidance, negative_prompt 183 | ): 184 | r""" 185 | Encodes the prompt into text encoder hidden states. 186 | 187 | Args: 188 | prompt (`str` or `list(int)`): 189 | prompt to be encoded 190 | device: (`torch.device`): 191 | torch device 192 | num_images_per_prompt (`int`): 193 | number of images that should be generated per prompt 194 | do_classifier_free_guidance (`bool`): 195 | whether to use classifier free guidance or not 196 | negative_prompt (`str` or `List[str]`): 197 | The prompt or prompts not to guide the image generation. Ignored when not using guidance (i.e., ignored 198 | if `guidance_scale` is less than `1`). 199 | """ 200 | batch_size = len(prompt) if isinstance(prompt, list) else 1 201 | 202 | text_inputs = self.tokenizer( 203 | prompt, 204 | padding="max_length", 205 | max_length=self.tokenizer.model_max_length, 206 | truncation=True, 207 | return_tensors="pt", 208 | ) 209 | text_input_ids = text_inputs.input_ids 210 | untruncated_ids = self.tokenizer(prompt, padding="longest", return_tensors="pt").input_ids 211 | 212 | if untruncated_ids.shape[-1] >= text_input_ids.shape[-1] and not torch.equal( 213 | text_input_ids, untruncated_ids 214 | ): 215 | removed_text = self.tokenizer.batch_decode( 216 | untruncated_ids[:, self.tokenizer.model_max_length - 1 : -1] 217 | ) 218 | logger.warning( 219 | "The following part of your input was truncated because CLIP can only handle sequences up to" 220 | f" {self.tokenizer.model_max_length} tokens: {removed_text}" 221 | ) 222 | 223 | if ( 224 | hasattr(self.text_encoder.config, "use_attention_mask") 225 | and self.text_encoder.config.use_attention_mask 226 | ): 227 | attention_mask = text_inputs.attention_mask.to(device) 228 | else: 229 | attention_mask = None 230 | 231 | text_embeddings = self.text_encoder( 232 | text_input_ids.to(self.text_encoder.device), # FIXME 强制对齐device的位置 233 | attention_mask=attention_mask, 234 | ) 235 | text_embeddings = text_embeddings[0] 236 | 237 | # duplicate text embeddings for each generation per prompt, using mps friendly method 238 | bs_embed, seq_len, _ = text_embeddings.shape 239 | text_embeddings = text_embeddings.repeat(1, num_images_per_prompt, 1) 240 | text_embeddings = text_embeddings.view(bs_embed * num_images_per_prompt, seq_len, -1) 241 | 242 | # get unconditional embeddings for classifier free guidance 243 | if do_classifier_free_guidance: 244 | uncond_tokens: List[str] 245 | if negative_prompt is None: 246 | uncond_tokens = [""] * batch_size 247 | elif type(prompt) is not type(negative_prompt): 248 | raise TypeError( 249 | f"`negative_prompt` should be the same type to `prompt`, but got {type(negative_prompt)} !=" 250 | f" {type(prompt)}." 251 | ) 252 | elif isinstance(negative_prompt, str): 253 | uncond_tokens = [negative_prompt] 254 | elif batch_size != len(negative_prompt): 255 | raise ValueError( 256 | f"`negative_prompt`: {negative_prompt} has batch size {len(negative_prompt)}, but `prompt`:" 257 | f" {prompt} has batch size {batch_size}. Please make sure that passed `negative_prompt` matches" 258 | " the batch size of `prompt`." 259 | ) 260 | else: 261 | uncond_tokens = negative_prompt 262 | 263 | max_length = text_input_ids.shape[-1] 264 | uncond_input = self.tokenizer( 265 | uncond_tokens, 266 | padding="max_length", 267 | max_length=max_length, 268 | truncation=True, 269 | return_tensors="pt", 270 | ) 271 | 272 | if ( 273 | hasattr(self.text_encoder.config, "use_attention_mask") 274 | and self.text_encoder.config.use_attention_mask 275 | ): 276 | attention_mask = uncond_input.attention_mask.to(device) 277 | else: 278 | attention_mask = None 279 | 280 | uncond_embeddings = self.text_encoder( 281 | uncond_input.input_ids.to(self.text_encoder.device), # 同上，强制位置对齐。 282 | attention_mask=attention_mask, 283 | ) 284 | uncond_embeddings = uncond_embeddings[0] 285 | 286 | # duplicate unconditional embeddings for each generation per prompt, using mps friendly method 287 | seq_len = uncond_embeddings.shape[1] 288 | uncond_embeddings = uncond_embeddings.repeat(1, num_images_per_prompt, 1) 289 | uncond_embeddings = uncond_embeddings.view(batch_size * num_images_per_prompt, seq_len, -1) 290 | 291 | # For classifier free guidance, we need to do two forward passes. 292 | # Here we concatenate the unconditional and text embeddings into a single batch 293 | # to avoid doing two forward passes 294 | text_embeddings = torch.cat([uncond_embeddings, text_embeddings]) 295 | 296 | return text_embeddings 297 | 298 | def decode_latents(self, latents): 299 | b = latents.shape[0] 300 | latents = 1 / 0.18215 * latents 301 | 302 | is_video = len(latents.shape) == 5 303 | if is_video: 304 | latents = rearrange(latents, "b c f h w -> (b f) c h w") 305 | 306 | image = self.vae.decode(latents).sample 307 | image = (image / 2 + 0.5).clamp(0, 1) 308 | # we always cast to float32 as this does not cause significant overhead and is compatible with bfloa16 309 | 310 | image = image.cpu().float().numpy() 311 | if is_video: 312 | image = rearrange(image, "(b f) c h w -> b f h w c", b=b) 313 | else: 314 | image = rearrange(image, "b c h w -> b h w c") 315 | return image 316 | 317 | def prepare_extra_step_kwargs(self, generator, eta): 318 | # prepare extra kwargs for the scheduler step, since not all schedulers have the same signature 319 | # eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers. 320 | # eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502 321 | # and should be between [0, 1] 322 | 323 | accepts_eta = "eta" in set(inspect.signature(self.scheduler.step).parameters.keys()) 324 | extra_step_kwargs = {} 325 | if accepts_eta: 326 | extra_step_kwargs["eta"] = eta 327 | 328 | # check if the scheduler accepts generator 329 | accepts_generator = "generator" in set(inspect.signature(self.scheduler.step).parameters.keys()) 330 | if accepts_generator: 331 | extra_step_kwargs["generator"] = generator 332 | return extra_step_kwargs 333 | 334 | def check_inputs(self, prompt, height, width, callback_steps): 335 | if not isinstance(prompt, str) and not isinstance(prompt, list): 336 | raise ValueError(f"`prompt` has to be of type `str` or `list` but is {type(prompt)}") 337 | 338 | if height % 8 != 0 or width % 8 != 0: 339 | raise ValueError( 340 | f"`height` and `width` have to be divisible by 8 but are {height} and {width}." 341 | ) 342 | 343 | if (callback_steps is None) or ( 344 | callback_steps is not None and (not isinstance(callback_steps, int) or callback_steps <= 0) 345 | ): 346 | raise ValueError( 347 | f"`callback_steps` has to be a positive integer but is {callback_steps} of type" 348 | f" {type(callback_steps)}." 349 | ) 350 | 351 | def prepare_latents( 352 | self, 353 | batch_size, 354 | num_channels_latents, 355 | clip_length, 356 | height, 357 | width, 358 | dtype, 359 | device, 360 | generator, 361 | latents=None, 362 | ): 363 | if clip_length>0: 364 | shape = ( 365 | batch_size, 366 | num_channels_latents, 367 | clip_length, 368 | height // self.vae_scale_factor, 369 | width // self.vae_scale_factor, 370 | ) 371 | else: 372 | shape = ( 373 | batch_size, 374 | num_channels_latents, 375 | height // self.vae_scale_factor, 376 | width // self.vae_scale_factor, 377 | ) 378 | 379 | if isinstance(generator, list) and len(generator) != batch_size: 380 | raise ValueError( 381 | f"You have passed a list of generators of length {len(generator)}, but requested an effective batch" 382 | f" size of {batch_size}. Make sure the batch size matches the length of the generators." 383 | ) 384 | 385 | if latents is None: 386 | rand_device = "cpu" if device.type == "mps" else device 387 | 388 | if isinstance(generator, list): 389 | shape = (1,) + shape[1:] 390 | latents = [ 391 | torch.randn(shape, generator=generator[i], device=rand_device, dtype=dtype) 392 | for i in range(batch_size) 393 | ] 394 | latents = torch.cat(latents, dim=0).to(device) 395 | else: 396 | latents = torch.randn(shape, generator=generator, device=rand_device, dtype=dtype).to( 397 | device 398 | ) 399 | else: 400 | if latents.shape != shape: 401 | raise ValueError(f"Unexpected latents shape, got {latents.shape}, expected {shape}") 402 | latents = latents.to(device) 403 | 404 | # scale the initial noise by the standard deviation required by the scheduler 405 | latents = latents * self.scheduler.init_noise_sigma 406 | return latents 407 | 408 | @torch.no_grad() 409 | def __call__( 410 | self, 411 | prompt: Union[str, List[str]], 412 | height: Optional[int] = None, 413 | width: Optional[int] = None, 414 | fps_labels = None, 415 | num_inference_steps: int = 50, 416 | clip_length: int = 8, 417 | guidance_scale: float = 7.5, 418 | negative_prompt: Optional[Union[str, List[str]]] = None, 419 | num_images_per_prompt: Optional[int] = 1, 420 | eta: float = 0.0, 421 | generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None, 422 | latents: Optional[torch.FloatTensor] = None, 423 | output_type: Optional[str] = "pil", 424 | return_dict: bool = True, 425 | callback: Optional[Callable[[int, int, torch.FloatTensor], None]] = None, 426 | callback_steps: Optional[int] = 1, 427 | ): 428 | r""" 429 | Function invoked when calling the pipeline for generation. 430 | 431 | Args: 432 | prompt (`str` or `List[str]`): 433 | The prompt or prompts to guide the image generation. 434 | height (`int`, *optional*, defaults to self.unet.config.sample_size * self.vae_scale_factor): 435 | The height in pixels of the generated image. 436 | width (`int`, *optional*, defaults to self.unet.config.sample_size * self.vae_scale_factor): 437 | The width in pixels of the generated image. 438 | num_inference_steps (`int`, *optional*, defaults to 50): 439 | The number of denoising steps. More denoising steps usually lead to a higher quality image at the 440 | expense of slower inference. 441 | guidance_scale (`float`, *optional*, defaults to 7.5): 442 | Guidance scale as defined in [Classifier-Free Diffusion Guidance](https://arxiv.org/abs/2207.12598). 443 | `guidance_scale` is defined as `w` of equation 2. of [Imagen 444 | Paper](https://arxiv.org/pdf/2205.11487.pdf). Guidance scale is enabled by setting `guidance_scale > 445 | 1`. Higher guidance scale encourages to generate images that are closely linked to the text `prompt`, 446 | usually at the expense of lower image quality. 447 | negative_prompt (`str` or `List[str]`, *optional*): 448 | The prompt or prompts not to guide the image generation. Ignored when not using guidance (i.e., ignored 449 | if `guidance_scale` is less than `1`). 450 | num_images_per_prompt (`int`, *optional*, defaults to 1): 451 | The number of images to generate per prompt. 452 | eta (`float`, *optional*, defaults to 0.0): 453 | Corresponds to parameter eta (η) in the DDIM paper: https://arxiv.org/abs/2010.02502. Only applies to 454 | [`schedulers.DDIMScheduler`], will be ignored for others. 455 | generator (`torch.Generator`, *optional*): 456 | One or a list of [torch generator(s)](https://pytorch.org/docs/stable/generated/torch.Generator.html) 457 | to make generation deterministic. 458 | latents (`torch.FloatTensor`, *optional*): 459 | Pre-generated noisy latents, sampled from a Gaussian distribution, to be used as inputs for image 460 | generation. Can be used to tweak the same generation with different prompts. If not provided, a latents 461 | tensor will ge generated by sampling using the supplied random `generator`. 462 | output_type (`str`, *optional*, defaults to `"pil"`): 463 | The output format of the generate image. Choose between 464 | [PIL](https://pillow.readthedocs.io/en/stable/): `PIL.Image.Image` or `np.array`. 465 | return_dict (`bool`, *optional*, defaults to `True`): 466 | Whether or not to return a [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] instead of a 467 | plain tuple. 468 | callback (`Callable`, *optional*): 469 | A function that will be called every `callback_steps` steps during inference. The function will be 470 | called with the following arguments: `callback(step: int, timestep: int, latents: torch.FloatTensor)`. 471 | callback_steps (`int`, *optional*, defaults to 1): 472 | The frequency at which the `callback` function will be called. If not specified, the callback will be 473 | called at every step. 474 | 475 | Returns: 476 | [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] or `tuple`: 477 | [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] if `return_dict` is True, otherwise a `tuple. 478 | When returning a tuple, the first element is a list with the generated images, and the second element is a 479 | list of `bool`s denoting whether the corresponding generated image likely represents "not-safe-for-work" 480 | (nsfw) content, according to the `safety_checker`. 481 | """ 482 | # 0. Default height and width to unet 483 | height = height or self.unet.config.sample_size * self.vae_scale_factor 484 | width = width or self.unet.config.sample_size * self.vae_scale_factor 485 | 486 | # 1. Check inputs. Raise error if not correct 487 | self.check_inputs(prompt, height, width, callback_steps) 488 | 489 | # 2. Define call parameters 490 | batch_size = 1 if isinstance(prompt, str) else len(prompt) 491 | device = self._execution_device 492 | # here `guidance_scale` is defined analog to the guidance weight `w` of equation (2) 493 | # of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1` 494 | # corresponds to doing no classifier free guidance. 495 | do_classifier_free_guidance = guidance_scale > 1.0 496 | 497 | # 3. Encode input prompt 498 | text_embeddings = self._encode_prompt( 499 | prompt, device, num_images_per_prompt, do_classifier_free_guidance, negative_prompt 500 | ) 501 | 502 | # 4. Prepare timesteps 503 | self.scheduler.set_timesteps(num_inference_steps, device=device) 504 | timesteps = self.scheduler.timesteps 505 | 506 | # 5. Prepare latent variables 507 | num_channels_latents = self.unet.in_channels 508 | 509 | latents = self.prepare_latents( 510 | batch_size * num_images_per_prompt, 511 | num_channels_latents, 512 | clip_length, 513 | height, 514 | width, 515 | text_embeddings.dtype, 516 | device, 517 | generator, 518 | latents, 519 | ) 520 | latents_dtype = latents.dtype 521 | 522 | # 6. Prepare extra step kwargs. TODO: Logic should ideally just be moved out of the pipeline 523 | extra_step_kwargs = self.prepare_extra_step_kwargs(generator, eta) 524 | 525 | # 7. Denoising loop 526 | num_warmup_steps = len(timesteps) - num_inference_steps * self.scheduler.order 527 | with self.progress_bar(total=num_inference_steps) as progress_bar: 528 | for i, t in enumerate(timesteps): 529 | # expand the latents if we are doing classifier free guidance 530 | latent_model_input = torch.cat([latents] * 2) if do_classifier_free_guidance else latents 531 | latent_model_input = self.scheduler.scale_model_input(latent_model_input, t) 532 | # print(latent_model_input.shape, ) 533 | # predict the noise residual 534 | if fps_labels: 535 | if isinstance(fps_labels, list): 536 | fps_labels = torch.tensor(fps_labels).to(self.unet.device) 537 | # 控制帧率 538 | noise_pred = self.unet( 539 | latent_model_input, t, encoder_hidden_states=text_embeddings, fps_labels=fps_labels, 540 | ).sample.to(dtype=latents_dtype) 541 | else: 542 | noise_pred = self.unet( 543 | latent_model_input, t, encoder_hidden_states=text_embeddings 544 | ).sample.to(dtype=latents_dtype) 545 | 546 | # perform guidance 547 | if do_classifier_free_guidance: 548 | noise_pred_uncond, noise_pred_text = noise_pred.chunk(2) 549 | noise_pred = noise_pred_uncond + guidance_scale * ( 550 | noise_pred_text - noise_pred_uncond 551 | ) 552 | 553 | # compute the previous noisy sample x_t -> x_t-1 554 | latents = self.scheduler.step(noise_pred, t, latents, **extra_step_kwargs).prev_sample 555 | 556 | # call the callback, if provided 557 | if i == len(timesteps) - 1 or ( 558 | (i + 1) > num_warmup_steps and (i + 1) % self.scheduler.order == 0 559 | ): 560 | progress_bar.update() 561 | if callback is not None and i % callback_steps == 0: 562 | callback(i, t, latents) 563 | 564 | # 8. Post-processing 565 | image = self.decode_latents(latents) 566 | # image[:, 1:, :, :, :] = image[:, 1:, :, :, :] + image[:, 0:1, :, :, :] # 叠加残差 567 | 568 | # 9. Run safety checker 569 | has_nsfw_concept = None 570 | 571 | # 10. Convert to PIL 572 | if output_type == "pil": 573 | image = self.numpy_to_pil(image) 574 | 575 | if not return_dict: 576 | return (image, has_nsfw_concept) 577 | 578 | return StableDiffusionPipelineOutput(images=image, nsfw_content_detected=has_nsfw_concept) 579 | 580 | @staticmethod 581 | def numpy_to_pil(images): 582 | if len(images.shape)==5: 583 | pil_images = [] 584 | for sequence in images: 585 | pil_images.append(DiffusionPipeline.numpy_to_pil(sequence)) 586 | return pil_images 587 | else: 588 | return DiffusionPipeline.numpy_to_pil(images) 589 | 590 | 591 | # 改写一下 model_index.json的保存内容, Unet是新定义的，直接保存会导致读取的时候出错～ 592 | def to_json_string(self) -> str: 593 | from diffusers import __version__ 594 | import json 595 | import numpy as np 596 | 597 | config_dict = self._internal_dict if hasattr(self, "_internal_dict") else {} 598 | config_dict["_class_name"] = self.__class__.__name__ 599 | config_dict["_diffusers_version"] = __version__ 600 | 601 | def to_json_saveable(value): 602 | if isinstance(value, np.ndarray): 603 | value = value.tolist() 604 | return value 605 | 606 | config_dict = {k: to_json_saveable(v) for k, v in config_dict.items()} 607 | if 'unet' in config_dict: 608 | config_dict["unet"] = [ 609 | "diffusers", 610 | "UNet2DConditionModel" 611 | ] 612 | if 'controlnet' in config_dict: 613 | config_dict['controlnet'] = [ 614 | "diffusers", 615 | "UNet2DConditionModel" 616 | ] 617 | # 覆盖 618 | return json.dumps(config_dict, indent=2, sort_keys=True) + "\n" 619 | -------------------------------------------------------------------------------- /LICENSE: -------------------------------------------------------------------------------- 1 | GNU GENERAL PUBLIC LICENSE 2 | Version 3, 29 June 2007 3 | 4 | Copyright (C) 2007 Free Software Foundation, Inc. 5 | Everyone is permitted to copy and distribute verbatim copies 6 | of this license document, but changing it is not allowed. 7 | 8 | Preamble 9 | 10 | The GNU General Public License is a free, copyleft license for 11 | software and other kinds of works. 12 | 13 | The licenses for most software and other practical works are designed 14 | to take away your freedom to share and change the works. By contrast, 15 | the GNU General Public License is intended to guarantee your freedom to 16 | share and change all versions of a program--to make sure it remains free 17 | software for all its users. We, the Free Software Foundation, use the 18 | GNU General Public License for most of our software; it applies also to 19 | any other work released this way by its authors. You can apply it to 20 | your programs, too. 21 | 22 | When we speak of free software, we are referring to freedom, not 23 | price. Our General Public Licenses are designed to make sure that you 24 | have the freedom to distribute copies of free software (and charge for 25 | them if you wish), that you receive source code or can get it if you 26 | want it, that you can change the software or use pieces of it in new 27 | free programs, and that you know you can do these things. 28 | 29 | To protect your rights, we need to prevent others from denying you 30 | these rights or asking you to surrender the rights. Therefore, you have 31 | certain responsibilities if you distribute copies of the software, or if 32 | you modify it: responsibilities to respect the freedom of others. 33 | 34 | For example, if you distribute copies of such a program, whether 35 | gratis or for a fee, you must pass on to the recipients the same 36 | freedoms that you received. You must make sure that they, too, receive 37 | or can get the source code. And you must show them these terms so they 38 | know their rights. 39 | 40 | Developers that use the GNU GPL protect your rights with two steps: 41 | (1) assert copyright on the software, and (2) offer you this License 42 | giving you legal permission to copy, distribute and/or modify it. 43 | 44 | For the developers' and authors' protection, the GPL clearly explains 45 | that there is no warranty for this free software. For both users' and 46 | authors' sake, the GPL requires that modified versions be marked as 47 | changed, so that their problems will not be attributed erroneously to 48 | authors of previous versions. 49 | 50 | Some devices are designed to deny users access to install or run 51 | modified versions of the software inside them, although the manufacturer 52 | can do so. This is fundamentally incompatible with the aim of 53 | protecting users' freedom to change the software. The systematic 54 | pattern of such abuse occurs in the area of products for individuals to 55 | use, which is precisely where it is most unacceptable. Therefore, we 56 | have designed this version of the GPL to prohibit the practice for those 57 | products. If such problems arise substantially in other domains, we 58 | stand ready to extend this provision to those domains in future versions 59 | of the GPL, as needed to protect the freedom of users. 60 | 61 | Finally, every program is threatened constantly by software patents. 62 | States should not allow patents to restrict development and use of 63 | software on general-purpose computers, but in those that do, we wish to 64 | avoid the special danger that patents applied to a free program could 65 | make it effectively proprietary. To prevent this, the GPL assures that 66 | patents cannot be used to render the program non-free. 67 | 68 | The precise terms and conditions for copying, distribution and 69 | modification follow. 70 | 71 | TERMS AND CONDITIONS 72 | 73 | 0. Definitions. 74 | 75 | "This License" refers to version 3 of the GNU General Public License. 76 | 77 | "Copyright" also means copyright-like laws that apply to other kinds of 78 | works, such as semiconductor masks. 79 | 80 | "The Program" refers to any copyrightable work licensed under this 81 | License. Each licensee is addressed as "you". "Licensees" and 82 | "recipients" may be individuals or organizations. 83 | 84 | To "modify" a work means to copy from or adapt all or part of the work 85 | in a fashion requiring copyright permission, other than the making of an 86 | exact copy. The resulting work is called a "modified version" of the 87 | earlier work or a work "based on" the earlier work. 88 | 89 | A "covered work" means either the unmodified Program or a work based 90 | on the Program. 91 | 92 | To "propagate" a work means to do anything with it that, without 93 | permission, would make you directly or secondarily liable for 94 | infringement under applicable copyright law, except executing it on a 95 | computer or modifying a private copy. Propagation includes copying, 96 | distribution (with or without modification), making available to the 97 | public, and in some countries other activities as well. 98 | 99 | To "convey" a work means any kind of propagation that enables other 100 | parties to make or receive copies. Mere interaction with a user through 101 | a computer network, with no transfer of a copy, is not conveying. 102 | 103 | An interactive user interface displays "Appropriate Legal Notices" 104 | to the extent that it includes a convenient and prominently visible 105 | feature that (1) displays an appropriate copyright notice, and (2) 106 | tells the user that there is no warranty for the work (except to the 107 | extent that warranties are provided), that licensees may convey the 108 | work under this License, and how to view a copy of this License. If 109 | the interface presents a list of user commands or options, such as a 110 | menu, a prominent item in the list meets this criterion. 111 | 112 | 1. Source Code. 113 | 114 | The "source code" for a work means the preferred form of the work 115 | for making modifications to it. "Object code" means any non-source 116 | form of a work. 117 | 118 | A "Standard Interface" means an interface that either is an official 119 | standard defined by a recognized standards body, or, in the case of 120 | interfaces specified for a particular programming language, one that 121 | is widely used among developers working in that language. 122 | 123 | The "System Libraries" of an executable work include anything, other 124 | than the work as a whole, that (a) is included in the normal form of 125 | packaging a Major Component, but which is not part of that Major 126 | Component, and (b) serves only to enable use of the work with that 127 | Major Component, or to implement a Standard Interface for which an 128 | implementation is available to the public in source code form. A 129 | "Major Component", in this context, means a major essential component 130 | (kernel, window system, and so on) of the specific operating system 131 | (if any) on which the executable work runs, or a compiler used to 132 | produce the work, or an object code interpreter used to run it. 133 | 134 | The "Corresponding Source" for a work in object code form means all 135 | the source code needed to generate, install, and (for an executable 136 | work) run the object code and to modify the work, including scripts to 137 | control those activities. However, it does not include the work's 138 | System Libraries, or general-purpose tools or generally available free 139 | programs which are used unmodified in performing those activities but 140 | which are not part of the work. For example, Corresponding Source 141 | includes interface definition files associated with source files for 142 | the work, and the source code for shared libraries and dynamically 143 | linked subprograms that the work is specifically designed to require, 144 | such as by intimate data communication or control flow between those 145 | subprograms and other parts of the work. 146 | 147 | The Corresponding Source need not include anything that users 148 | can regenerate automatically from other parts of the Corresponding 149 | Source. 150 | 151 | The Corresponding Source for a work in source code form is that 152 | same work. 153 | 154 | 2. Basic Permissions. 155 | 156 | All rights granted under this License are granted for the term of 157 | copyright on the Program, and are irrevocable provided the stated 158 | conditions are met. This License explicitly affirms your unlimited 159 | permission to run the unmodified Program. The output from running a 160 | covered work is covered by this License only if the output, given its 161 | content, constitutes a covered work. This License acknowledges your 162 | rights of fair use or other equivalent, as provided by copyright law. 163 | 164 | You may make, run and propagate covered works that you do not 165 | convey, without conditions so long as your license otherwise remains 166 | in force. You may convey covered works to others for the sole purpose 167 | of having them make modifications exclusively for you, or provide you 168 | with facilities for running those works, provided that you comply with 169 | the terms of this License in conveying all material for which you do 170 | not control copyright. Those thus making or running the covered works 171 | for you must do so exclusively on your behalf, under your direction 172 | and control, on terms that prohibit them from making any copies of 173 | your copyrighted material outside their relationship with you. 174 | 175 | Conveying under any other circumstances is permitted solely under 176 | the conditions stated below. Sublicensing is not allowed; section 10 177 | makes it unnecessary. 178 | 179 | 3. Protecting Users' Legal Rights From Anti-Circumvention Law. 180 | 181 | No covered work shall be deemed part of an effective technological 182 | measure under any applicable law fulfilling obligations under article 183 | 11 of the WIPO copyright treaty adopted on 20 December 1996, or 184 | similar laws prohibiting or restricting circumvention of such 185 | measures. 186 | 187 | When you convey a covered work, you waive any legal power to forbid 188 | circumvention of technological measures to the extent such circumvention 189 | is effected by exercising rights under this License with respect to 190 | the covered work, and you disclaim any intention to limit operation or 191 | modification of the work as a means of enforcing, against the work's 192 | users, your or third parties' legal rights to forbid circumvention of 193 | technological measures. 194 | 195 | 4. Conveying Verbatim Copies. 196 | 197 | You may convey verbatim copies of the Program's source code as you 198 | receive it, in any medium, provided that you conspicuously and 199 | appropriately publish on each copy an appropriate copyright notice; 200 | keep intact all notices stating that this License and any 201 | non-permissive terms added in accord with section 7 apply to the code; 202 | keep intact all notices of the absence of any warranty; and give all 203 | recipients a copy of this License along with the Program. 204 | 205 | You may charge any price or no price for each copy that you convey, 206 | and you may offer support or warranty protection for a fee. 207 | 208 | 5. Conveying Modified Source Versions. 209 | 210 | You may convey a work based on the Program, or the modifications to 211 | produce it from the Program, in the form of source code under the 212 | terms of section 4, provided that you also meet all of these conditions: 213 | 214 | a) The work must carry prominent notices stating that you modified 215 | it, and giving a relevant date. 216 | 217 | b) The work must carry prominent notices stating that it is 218 | released under this License and any conditions added under section 219 | 7. This requirement modifies the requirement in section 4 to 220 | "keep intact all notices". 221 | 222 | c) You must license the entire work, as a whole, under this 223 | License to anyone who comes into possession of a copy. This 224 | License will therefore apply, along with any applicable section 7 225 | additional terms, to the whole of the work, and all its parts, 226 | regardless of how they are packaged. This License gives no 227 | permission to license the work in any other way, but it does not 228 | invalidate such permission if you have separately received it. 229 | 230 | d) If the work has interactive user interfaces, each must display 231 | Appropriate Legal Notices; however, if the Program has interactive 232 | interfaces that do not display Appropriate Legal Notices, your 233 | work need not make them do so. 234 | 235 | A compilation of a covered work with other separate and independent 236 | works, which are not by their nature extensions of the covered work, 237 | and which are not combined with it such as to form a larger program, 238 | in or on a volume of a storage or distribution medium, is called an 239 | "aggregate" if the compilation and its resulting copyright are not 240 | used to limit the access or legal rights of the compilation's users 241 | beyond what the individual works permit. Inclusion of a covered work 242 | in an aggregate does not cause this License to apply to the other 243 | parts of the aggregate. 244 | 245 | 6. Conveying Non-Source Forms. 246 | 247 | You may convey a covered work in object code form under the terms 248 | of sections 4 and 5, provided that you also convey the 249 | machine-readable Corresponding Source under the terms of this License, 250 | in one of these ways: 251 | 252 | a) Convey the object code in, or embodied in, a physical product 253 | (including a physical distribution medium), accompanied by the 254 | Corresponding Source fixed on a durable physical medium 255 | customarily used for software interchange. 256 | 257 | b) Convey the object code in, or embodied in, a physical product 258 | (including a physical distribution medium), accompanied by a 259 | written offer, valid for at least three years and valid for as 260 | long as you offer spare parts or customer support for that product 261 | model, to give anyone who possesses the object code either (1) a 262 | copy of the Corresponding Source for all the software in the 263 | product that is covered by this License, on a durable physical 264 | medium customarily used for software interchange, for a price no 265 | more than your reasonable cost of physically performing this 266 | conveying of source, or (2) access to copy the 267 | Corresponding Source from a network server at no charge. 268 | 269 | c) Convey individual copies of the object code with a copy of the 270 | written offer to provide the Corresponding Source. This 271 | alternative is allowed only occasionally and noncommercially, and 272 | only if you received the object code with such an offer, in accord 273 | with subsection 6b. 274 | 275 | d) Convey the object code by offering access from a designated 276 | place (gratis or for a charge), and offer equivalent access to the 277 | Corresponding Source in the same way through the same place at no 278 | further charge. You need not require recipients to copy the 279 | Corresponding Source along with the object code. If the place to 280 | copy the object code is a network server, the Corresponding Source 281 | may be on a different server (operated by you or a third party) 282 | that supports equivalent copying facilities, provided you maintain 283 | clear directions next to the object code saying where to find the 284 | Corresponding Source. Regardless of what server hosts the 285 | Corresponding Source, you remain obligated to ensure that it is 286 | available for as long as needed to satisfy these requirements. 287 | 288 | e) Convey the object code using peer-to-peer transmission, provided 289 | you inform other peers where the object code and Corresponding 290 | Source of the work are being offered to the general public at no 291 | charge under subsection 6d. 292 | 293 | A separable portion of the object code, whose source code is excluded 294 | from the Corresponding Source as a System Library, need not be 295 | included in conveying the object code work. 296 | 297 | A "User Product" is either (1) a "consumer product", which means any 298 | tangible personal property which is normally used for personal, family, 299 | or household purposes, or (2) anything designed or sold for incorporation 300 | into a dwelling. In determining whether a product is a consumer product, 301 | doubtful cases shall be resolved in favor of coverage. For a particular 302 | product received by a particular user, "normally used" refers to a 303 | typical or common use of that class of product, regardless of the status 304 | of the particular user or of the way in which the particular user 305 | actually uses, or expects or is expected to use, the product. A product 306 | is a consumer product regardless of whether the product has substantial 307 | commercial, industrial or non-consumer uses, unless such uses represent 308 | the only significant mode of use of the product. 309 | 310 | "Installation Information" for a User Product means any methods, 311 | procedures, authorization keys, or other information required to install 312 | and execute modified versions of a covered work in that User Product from 313 | a modified version of its Corresponding Source. The information must 314 | suffice to ensure that the continued functioning of the modified object 315 | code is in no case prevented or interfered with solely because 316 | modification has been made. 317 | 318 | If you convey an object code work under this section in, or with, or 319 | specifically for use in, a User Product, and the conveying occurs as 320 | part of a transaction in which the right of possession and use of the 321 | User Product is transferred to the recipient in perpetuity or for a 322 | fixed term (regardless of how the transaction is characterized), the 323 | Corresponding Source conveyed under this section must be accompanied 324 | by the Installation Information. But this requirement does not apply 325 | if neither you nor any third party retains the ability to install 326 | modified object code on the User Product (for example, the work has 327 | been installed in ROM). 328 | 329 | The requirement to provide Installation Information does not include a 330 | requirement to continue to provide support service, warranty, or updates 331 | for a work that has been modified or installed by the recipient, or for 332 | the User Product in which it has been modified or installed. Access to a 333 | network may be denied when the modification itself materially and 334 | adversely affects the operation of the network or violates the rules and 335 | protocols for communication across the network. 336 | 337 | Corresponding Source conveyed, and Installation Information provided, 338 | in accord with this section must be in a format that is publicly 339 | documented (and with an implementation available to the public in 340 | source code form), and must require no special password or key for 341 | unpacking, reading or copying. 342 | 343 | 7. Additional Terms. 344 | 345 | "Additional permissions" are terms that supplement the terms of this 346 | License by making exceptions from one or more of its conditions. 347 | Additional permissions that are applicable to the entire Program shall 348 | be treated as though they were included in this License, to the extent 349 | that they are valid under applicable law. If additional permissions 350 | apply only to part of the Program, that part may be used separately 351 | under those permissions, but the entire Program remains governed by 352 | this License without regard to the additional permissions. 353 | 354 | When you convey a copy of a covered work, you may at your option 355 | remove any additional permissions from that copy, or from any part of 356 | it. (Additional permissions may be written to require their own 357 | removal in certain cases when you modify the work.) You may place 358 | additional permissions on material, added by you to a covered work, 359 | for which you have or can give appropriate copyright permission. 360 | 361 | Notwithstanding any other provision of this License, for material you 362 | add to a covered work, you may (if authorized by the copyright holders of 363 | that material) supplement the terms of this License with terms: 364 | 365 | a) Disclaiming warranty or limiting liability differently from the 366 | terms of sections 15 and 16 of this License; or 367 | 368 | b) Requiring preservation of specified reasonable legal notices or 369 | author attributions in that material or in the Appropriate Legal 370 | Notices displayed by works containing it; or 371 | 372 | c) Prohibiting misrepresentation of the origin of that material, or 373 | requiring that modified versions of such material be marked in 374 | reasonable ways as different from the original version; or 375 | 376 | d) Limiting the use for publicity purposes of names of licensors or 377 | authors of the material; or 378 | 379 | e) Declining to grant rights under trademark law for use of some 380 | trade names, trademarks, or service marks; or 381 | 382 | f) Requiring indemnification of licensors and authors of that 383 | material by anyone who conveys the material (or modified versions of 384 | it) with contractual assumptions of liability to the recipient, for 385 | any liability that these contractual assumptions directly impose on 386 | those licensors and authors. 387 | 388 | All other non-permissive additional terms are considered "further 389 | restrictions" within the meaning of section 10. If the Program as you 390 | received it, or any part of it, contains a notice stating that it is 391 | governed by this License along with a term that is a further 392 | restriction, you may remove that term. If a license document contains 393 | a further restriction but permits relicensing or conveying under this 394 | License, you may add to a covered work material governed by the terms 395 | of that license document, provided that the further restriction does 396 | not survive such relicensing or conveying. 397 | 398 | If you add terms to a covered work in accord with this section, you 399 | must place, in the relevant source files, a statement of the 400 | additional terms that apply to those files, or a notice indicating 401 | where to find the applicable terms. 402 | 403 | Additional terms, permissive or non-permissive, may be stated in the 404 | form of a separately written license, or stated as exceptions; 405 | the above requirements apply either way. 406 | 407 | 8. Termination. 408 | 409 | You may not propagate or modify a covered work except as expressly 410 | provided under this License. Any attempt otherwise to propagate or 411 | modify it is void, and will automatically terminate your rights under 412 | this License (including any patent licenses granted under the third 413 | paragraph of section 11). 414 | 415 | However, if you cease all violation of this License, then your 416 | license from a particular copyright holder is reinstated (a) 417 | provisionally, unless and until the copyright holder explicitly and 418 | finally terminates your license, and (b) permanently, if the copyright 419 | holder fails to notify you of the violation by some reasonable means 420 | prior to 60 days after the cessation. 421 | 422 | Moreover, your license from a particular copyright holder is 423 | reinstated permanently if the copyright holder notifies you of the 424 | violation by some reasonable means, this is the first time you have 425 | received notice of violation of this License (for any work) from that 426 | copyright holder, and you cure the violation prior to 30 days after 427 | your receipt of the notice. 428 | 429 | Termination of your rights under this section does not terminate the 430 | licenses of parties who have received copies or rights from you under 431 | this License. If your rights have been terminated and not permanently 432 | reinstated, you do not qualify to receive new licenses for the same 433 | material under section 10. 434 | 435 | 9. Acceptance Not Required for Having Copies. 436 | 437 | You are not required to accept this License in order to receive or 438 | run a copy of the Program. Ancillary propagation of a covered work 439 | occurring solely as a consequence of using peer-to-peer transmission 440 | to receive a copy likewise does not require acceptance. However, 441 | nothing other than this License grants you permission to propagate or 442 | modify any covered work. These actions infringe copyright if you do 443 | not accept this License. Therefore, by modifying or propagating a 444 | covered work, you indicate your acceptance of this License to do so. 445 | 446 | 10. Automatic Licensing of Downstream Recipients. 447 | 448 | Each time you convey a covered work, the recipient automatically 449 | receives a license from the original licensors, to run, modify and 450 | propagate that work, subject to this License. You are not responsible 451 | for enforcing compliance by third parties with this License. 452 | 453 | An "entity transaction" is a transaction transferring control of an 454 | organization, or substantially all assets of one, or subdividing an 455 | organization, or merging organizations. If propagation of a covered 456 | work results from an entity transaction, each party to that 457 | transaction who receives a copy of the work also receives whatever 458 | licenses to the work the party's predecessor in interest had or could 459 | give under the previous paragraph, plus a right to possession of the 460 | Corresponding Source of the work from the predecessor in interest, if 461 | the predecessor has it or can get it with reasonable efforts. 462 | 463 | You may not impose any further restrictions on the exercise of the 464 | rights granted or affirmed under this License. For example, you may 465 | not impose a license fee, royalty, or other charge for exercise of 466 | rights granted under this License, and you may not initiate litigation 467 | (including a cross-claim or counterclaim in a lawsuit) alleging that 468 | any patent claim is infringed by making, using, selling, offering for 469 | sale, or importing the Program or any portion of it. 470 | 471 | 11. Patents. 472 | 473 | A "contributor" is a copyright holder who authorizes use under this 474 | License of the Program or a work on which the Program is based. The 475 | work thus licensed is called the contributor's "contributor version". 476 | 477 | A contributor's "essential patent claims" are all patent claims 478 | owned or controlled by the contributor, whether already acquired or 479 | hereafter acquired, that would be infringed by some manner, permitted 480 | by this License, of making, using, or selling its contributor version, 481 | but do not include claims that would be infringed only as a 482 | consequence of further modification of the contributor version. For 483 | purposes of this definition, "control" includes the right to grant 484 | patent sublicenses in a manner consistent with the requirements of 485 | this License. 486 | 487 | Each contributor grants you a non-exclusive, worldwide, royalty-free 488 | patent license under the contributor's essential patent claims, to 489 | make, use, sell, offer for sale, import and otherwise run, modify and 490 | propagate the contents of its contributor version. 491 | 492 | In the following three paragraphs, a "patent license" is any express 493 | agreement or commitment, however denominated, not to enforce a patent 494 | (such as an express permission to practice a patent or covenant not to 495 | sue for patent infringement). To "grant" such a patent license to a 496 | party means to make such an agreement or commitment not to enforce a 497 | patent against the party. 498 | 499 | If you convey a covered work, knowingly relying on a patent license, 500 | and the Corresponding Source of the work is not available for anyone 501 | to copy, free of charge and under the terms of this License, through a 502 | publicly available network server or other readily accessible means, 503 | then you must either (1) cause the Corresponding Source to be so 504 | available, or (2) arrange to deprive yourself of the benefit of the 505 | patent license for this particular work, or (3) arrange, in a manner 506 | consistent with the requirements of this License, to extend the patent 507 | license to downstream recipients. "Knowingly relying" means you have 508 | actual knowledge that, but for the patent license, your conveying the 509 | covered work in a country, or your recipient's use of the covered work 510 | in a country, would infringe one or more identifiable patents in that 511 | country that you have reason to believe are valid. 512 | 513 | If, pursuant to or in connection with a single transaction or 514 | arrangement, you convey, or propagate by procuring conveyance of, a 515 | covered work, and grant a patent license to some of the parties 516 | receiving the covered work authorizing them to use, propagate, modify 517 | or convey a specific copy of the covered work, then the patent license 518 | you grant is automatically extended to all recipients of the covered 519 | work and works based on it. 520 | 521 | A patent license is "discriminatory" if it does not include within 522 | the scope of its coverage, prohibits the exercise of, or is 523 | conditioned on the non-exercise of one or more of the rights that are 524 | specifically granted under this License. You may not convey a covered 525 | work if you are a party to an arrangement with a third party that is 526 | in the business of distributing software, under which you make payment 527 | to the third party based on the extent of your activity of conveying 528 | the work, and under which the third party grants, to any of the 529 | parties who would receive the covered work from you, a discriminatory 530 | patent license (a) in connection with copies of the covered work 531 | conveyed by you (or copies made from those copies), or (b) primarily 532 | for and in connection with specific products or compilations that 533 | contain the covered work, unless you entered into that arrangement, 534 | or that patent license was granted, prior to 28 March 2007. 535 | 536 | Nothing in this License shall be construed as excluding or limiting 537 | any implied license or other defenses to infringement that may 538 | otherwise be available to you under applicable patent law. 539 | 540 | 12. No Surrender of Others' Freedom. 541 | 542 | If conditions are imposed on you (whether by court order, agreement or 543 | otherwise) that contradict the conditions of this License, they do not 544 | excuse you from the conditions of this License. If you cannot convey a 545 | covered work so as to satisfy simultaneously your obligations under this 546 | License and any other pertinent obligations, then as a consequence you may 547 | not convey it at all. For example, if you agree to terms that obligate you 548 | to collect a royalty for further conveying from those to whom you convey 549 | the Program, the only way you could satisfy both those terms and this 550 | License would be to refrain entirely from conveying the Program. 551 | 552 | 13. Use with the GNU Affero General Public License. 553 | 554 | Notwithstanding any other provision of this License, you have 555 | permission to link or combine any covered work with a work licensed 556 | under version 3 of the GNU Affero General Public License into a single 557 | combined work, and to convey the resulting work. The terms of this 558 | License will continue to apply to the part which is the covered work, 559 | but the special requirements of the GNU Affero General Public License, 560 | section 13, concerning interaction through a network will apply to the 561 | combination as such. 562 | 563 | 14. Revised Versions of this License. 564 | 565 | The Free Software Foundation may publish revised and/or new versions of 566 | the GNU General Public License from time to time. Such new versions will 567 | be similar in spirit to the present version, but may differ in detail to 568 | address new problems or concerns. 569 | 570 | Each version is given a distinguishing version number. If the 571 | Program specifies that a certain numbered version of the GNU General 572 | Public License "or any later version" applies to it, you have the 573 | option of following the terms and conditions either of that numbered 574 | version or of any later version published by the Free Software 575 | Foundation. If the Program does not specify a version number of the 576 | GNU General Public License, you may choose any version ever published 577 | by the Free Software Foundation. 578 | 579 | If the Program specifies that a proxy can decide which future 580 | versions of the GNU General Public License can be used, that proxy's 581 | public statement of acceptance of a version permanently authorizes you 582 | to choose that version for the Program. 583 | 584 | Later license versions may give you additional or different 585 | permissions. However, no additional obligations are imposed on any 586 | author or copyright holder as a result of your choosing to follow a 587 | later version. 588 | 589 | 15. Disclaimer of Warranty. 590 | 591 | THERE IS NO WARRANTY FOR THE PROGRAM, TO THE EXTENT PERMITTED BY 592 | APPLICABLE LAW. EXCEPT WHEN OTHERWISE STATED IN WRITING THE COPYRIGHT 593 | HOLDERS AND/OR OTHER PARTIES PROVIDE THE PROGRAM "AS IS" WITHOUT WARRANTY 594 | OF ANY KIND, EITHER EXPRESSED OR IMPLIED, INCLUDING, BUT NOT LIMITED TO, 595 | THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR 596 | PURPOSE. THE ENTIRE RISK AS TO THE QUALITY AND PERFORMANCE OF THE PROGRAM 597 | IS WITH YOU. SHOULD THE PROGRAM PROVE DEFECTIVE, YOU ASSUME THE COST OF 598 | ALL NECESSARY SERVICING, REPAIR OR CORRECTION. 599 | 600 | 16. Limitation of Liability. 601 | 602 | IN NO EVENT UNLESS REQUIRED BY APPLICABLE LAW OR AGREED TO IN WRITING 603 | WILL ANY COPYRIGHT HOLDER, OR ANY OTHER PARTY WHO MODIFIES AND/OR CONVEYS 604 | THE PROGRAM AS PERMITTED ABOVE, BE LIABLE TO YOU FOR DAMAGES, INCLUDING ANY 605 | GENERAL, SPECIAL, INCIDENTAL OR CONSEQUENTIAL DAMAGES ARISING OUT OF THE 606 | USE OR INABILITY TO USE THE PROGRAM (INCLUDING BUT NOT LIMITED TO LOSS OF 607 | DATA OR DATA BEING RENDERED INACCURATE OR LOSSES SUSTAINED BY YOU OR THIRD 608 | PARTIES OR A FAILURE OF THE PROGRAM TO OPERATE WITH ANY OTHER PROGRAMS), 609 | EVEN IF SUCH HOLDER OR OTHER PARTY HAS BEEN ADVISED OF THE POSSIBILITY OF 610 | SUCH DAMAGES. 611 | 612 | 17. Interpretation of Sections 15 and 16. 613 | 614 | If the disclaimer of warranty and limitation of liability provided 615 | above cannot be given local legal effect according to their terms, 616 | reviewing courts shall apply local law that most closely approximates 617 | an absolute waiver of all civil liability in connection with the 618 | Program, unless a warranty or assumption of liability accompanies a 619 | copy of the Program in return for a fee. 620 | 621 | END OF TERMS AND CONDITIONS 622 | 623 | How to Apply These Terms to Your New Programs 624 | 625 | If you develop a new program, and you want it to be of the greatest 626 | possible use to the public, the best way to achieve this is to make it 627 | free software which everyone can redistribute and change under these terms. 628 | 629 | To do so, attach the following notices to the program. It is safest 630 | to attach them to the start of each source file to most effectively 631 | state the exclusion of warranty; and each file should have at least 632 | the "copyright" line and a pointer to where the full notice is found. 633 | 634 | 635 | Copyright (C) 636 | 637 | This program is free software: you can redistribute it and/or modify 638 | it under the terms of the GNU General Public License as published by 639 | the Free Software Foundation, either version 3 of the License, or 640 | (at your option) any later version. 641 | 642 | This program is distributed in the hope that it will be useful, 643 | but WITHOUT ANY WARRANTY; without even the implied warranty of 644 | MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the 645 | GNU General Public License for more details. 646 | 647 | You should have received a copy of the GNU General Public License 648 | along with this program. If not, see . 649 | 650 | Also add information on how to contact you by electronic and paper mail. 651 | 652 | If the program does terminal interaction, make it output a short 653 | notice like this when it starts in an interactive mode: 654 | 655 | Copyright (C) 656 | This program comes with ABSOLUTELY NO WARRANTY; for details type `show w'. 657 | This is free software, and you are welcome to redistribute it 658 | under certain conditions; type `show c' for details. 659 | 660 | The hypothetical commands `show w' and `show c' should show the appropriate 661 | parts of the General Public License. Of course, your program's commands 662 | might be different; for a GUI interface, you would use an "about box". 663 | 664 | You should also get your employer (if you work as a programmer) or school, 665 | if any, to sign a "copyright disclaimer" for the program, if necessary. 666 | For more information on this, and how to apply and follow the GNU GPL, see 667 | . 668 | 669 | The GNU General Public License does not permit incorporating your program 670 | into proprietary programs. If your program is a subroutine library, you 671 | may consider it more useful to permit linking proprietary applications with 672 | the library. If this is what you want to do, use the GNU Lesser General 673 | Public License instead of this License. But first, please read 674 | . 675 | --------------------------------------------------------------------------------