Alexey Borsky
parent
a553de32db
commit
4adcaf026b
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@ -0,0 +1,143 @@
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import torch
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import torch.nn as nn
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import torch.functional as F
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# Define the model
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class FloweR(nn.Module):
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def __init__(self, input_size = (384, 384), window_size = 4):
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super(FloweR, self).__init__()
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self.input_size = input_size
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self.window_size = window_size
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#INPUT: 384 x 384 x 10 * 3
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### DOWNSCALE ###
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self.conv_block_1 = nn.Sequential(
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nn.Conv2d(3 * self.window_size, 128, kernel_size=3, stride=1, padding='same'),
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nn.ReLU(),
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) # 384 x 384 x 128
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self.conv_block_2 = nn.Sequential(
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nn.AvgPool2d(2),
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nn.Conv2d(128, 128, kernel_size=3, stride=1, padding='same'),
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nn.ReLU(),
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) # 192 x 192 x 128
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self.conv_block_3 = nn.Sequential(
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nn.AvgPool2d(2),
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nn.Conv2d(128, 128, kernel_size=3, stride=1, padding='same'),
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nn.ReLU(),
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) # 96 x 96 x 128
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self.conv_block_4 = nn.Sequential(
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nn.AvgPool2d(2),
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nn.Conv2d(128, 128, kernel_size=3, stride=1, padding='same'),
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nn.ReLU(),
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) # 48 x 48 x 128
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self.conv_block_5 = nn.Sequential(
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nn.AvgPool2d(2),
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nn.Conv2d(128, 128, kernel_size=3, stride=1, padding='same'),
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nn.ReLU(),
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) # 24 x 24 x 128
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self.conv_block_6 = nn.Sequential(
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nn.AvgPool2d(2),
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nn.Conv2d(128, 128, kernel_size=3, stride=1, padding='same'),
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nn.ReLU(),
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) # 12 x 12 x 128
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self.conv_block_7 = nn.Sequential(
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nn.AvgPool2d(2),
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nn.Conv2d(128, 128, kernel_size=3, stride=1, padding='same'),
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nn.ReLU(),
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) # 6 x 6 x 128
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self.conv_block_8 = nn.Sequential(
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nn.AvgPool2d(2),
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nn.Conv2d(128, 128, kernel_size=3, stride=1, padding='same'),
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nn.ReLU(),
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) # 3 x 3 x 128
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### UPSCALE ###
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self.conv_block_9 = nn.Sequential(
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nn.Upsample(scale_factor=2),
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nn.Conv2d(128, 128, kernel_size=3, stride=1, padding='same'),
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nn.ReLU(),
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) # 6 x 6 x 128
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self.conv_block_10 = nn.Sequential(
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nn.Upsample(scale_factor=2),
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nn.Conv2d(128, 128, kernel_size=3, stride=1, padding='same'),
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nn.ReLU(),
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) # 12 x 12 x 128
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self.conv_block_11 = nn.Sequential(
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nn.Upsample(scale_factor=2),
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nn.Conv2d(128, 128, kernel_size=3, stride=1, padding='same'),
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nn.ReLU(),
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) # 24 x 24 x 128
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self.conv_block_12 = nn.Sequential(
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nn.Upsample(scale_factor=2),
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nn.Conv2d(128, 128, kernel_size=3, stride=1, padding='same'),
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nn.ReLU(),
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) # 48 x 48 x 128
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self.conv_block_13 = nn.Sequential(
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nn.Upsample(scale_factor=2),
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nn.Conv2d(128, 128, kernel_size=3, stride=1, padding='same'),
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nn.ReLU(),
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) # 96 x 96 x 128
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self.conv_block_14 = nn.Sequential(
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nn.Upsample(scale_factor=2),
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nn.Conv2d(128, 128, kernel_size=3, stride=1, padding='same'),
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nn.ReLU(),
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) # 192 x 192 x 128
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self.conv_block_15 = nn.Sequential(
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nn.Upsample(scale_factor=2),
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nn.Conv2d(128, 128, kernel_size=3, stride=1, padding='same'),
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nn.ReLU(),
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) # 384 x 384 x 128
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self.conv_block_16 = nn.Conv2d(128, 3, kernel_size=3, stride=1, padding='same')
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def forward(self, x):
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if x.size(1) != self.window_size:
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raise Exception(f'Shape of the input is not compatable. There should be exactly {self.window_size} frames in an input video.')
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# batch, frames, height, width, colors
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in_x = x.permute((0, 1, 4, 2, 3))
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# batch, frames, colors, height, width
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in_x = in_x.reshape(-1, self.window_size * 3, self.input_size[0], self.input_size[1])
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### DOWNSCALE ###
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block_1_out = self.conv_block_1(in_x) # 384 x 384 x 128
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block_2_out = self.conv_block_2(block_1_out) # 192 x 192 x 128
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block_3_out = self.conv_block_3(block_2_out) # 96 x 96 x 128
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block_4_out = self.conv_block_4(block_3_out) # 48 x 48 x 128
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block_5_out = self.conv_block_5(block_4_out) # 24 x 24 x 128
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block_6_out = self.conv_block_6(block_5_out) # 12 x 12 x 128
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block_7_out = self.conv_block_7(block_6_out) # 6 x 6 x 128
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block_8_out = self.conv_block_8(block_7_out) # 3 x 3 x 128
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### UPSCALE ###
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block_9_out = block_7_out + self.conv_block_9(block_8_out) # 6 x 6 x 128
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block_10_out = block_6_out + self.conv_block_10(block_9_out) # 12 x 12 x 128
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block_11_out = block_5_out + self.conv_block_11(block_10_out) # 24 x 24 x 128
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block_12_out = block_4_out + self.conv_block_12(block_11_out) # 48 x 48 x 128
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block_13_out = block_3_out + self.conv_block_13(block_12_out) # 96 x 96 x 128
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block_14_out = block_2_out + self.conv_block_14(block_13_out) # 192 x 192 x 128
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block_15_out = block_1_out + self.conv_block_15(block_14_out) # 384 x 384 x 128
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block_16_out = self.conv_block_16(block_15_out) # 384 x 384 x (2 + 1)
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out = block_16_out.reshape(-1, 3, self.input_size[0], self.input_size[1])
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# batch, colors, height, width
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out = out.permute((0, 2, 3, 1))
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# batch, height, width, colors
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return out
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After Width: | Height: | Size: 631 KiB |
17
readme.md
17
readme.md
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@ -1,6 +1,10 @@
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# SD-CN-Animation
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This project allows you to automate video stylization task using StableDiffusion and ControlNet. It also allows you to generate completely new videos from text at any resolution and length in contrast to other current text2video methods using any Stable Diffusion model as a backbone, including custom ones. It uses '[RAFT](https://github.com/princeton-vl/RAFT)' optical flow estimation algorithm to keep the animation stable and create an occlusion mask that is used to generate the next frame. In text to video mode it relies on 'FloweR' method (work in progress) that predicts optical flow from the previous frames.
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sd-cn-animation ui preview
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### Video to Video Examples:
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</table>
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<table class="center">
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@ -46,11 +50,10 @@ Examples presented are generated at 1024x576 resolution using the 'realisticVisi
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All examples you can see here are originally generated at 512x512 resolution using the 'sd-v1-5-inpainting' model as a base. They were downsized and compressed for better loading speed. You can see them in their original quality in the 'examples' folder. Actual prompts used were stated in the following format: "RAW photo, {subject}, 8k uhd, dslr, soft lighting, high quality, film grain, Fujifilm XT3", only the 'subject' part is described in the table above.
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## Installing the extension
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To install the extension go to 'Extensions' tab in [Automatic1111 web-ui](https://github.com/AUTOMATIC1111/stable-diffusion-webui), then go to 'Install from URL' tab. In 'URL for extension's git repository' field inter the path to this repository, i.e. 'https://github.com/volotat/SD-CN-Animation.git'. Leave 'Local directory name' field empty. Then just press 'Install' button. Download RAFT 'raft-things.pth' model from here: [Google Drive](https://drive.google.com/drive/folders/1sWDsfuZ3Up38EUQt7-JDTT1HcGHuJgvT) and place it into 'stable-diffusion-webui/models/RAFT/' folder. Restart web-ui, new 'SD-CN-Animation' tab should appear. All generated video will be saved into 'stable-diffusion-webui/outputs/sd-cn-animation' folder.
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## Last version changes: v0.6
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* Complete rewrite of the project to make it possible to install as an Automatic1111/Web-ui extension.
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* Added flow normalization before resizing it, so the magnitude of the flow computed correctly at the different resolution.
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* Less ghosting and color drift in vid2vid mode
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* Added "warped styled frame fix" at vid2vid mode that removes duplicates from the parts of the image that cannot be relocated from the optical flow.
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To install the extension go to 'Extensions' tab in [Automatic1111 web-ui](https://github.com/AUTOMATIC1111/stable-diffusion-webui), then go to 'Install from URL' tab. In 'URL for extension's git repository' field inter the path to this repository, i.e. 'https://github.com/volotat/SD-CN-Animation.git'. Leave 'Local directory name' field empty. Then just press 'Install' button. Restart web-ui, new 'SD-CN-Animation' tab should appear. All generated video will be saved into 'stable-diffusion-webui/outputs/sd-cn-animation' folder.
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## Last version changes: v0.7
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* Text to Video mode added to the extension
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* 'Generate' button is now automatically disabled while the video is generated
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* Added 'Interrupt' button that allows to stop video generation process
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* Now all necessary models are automatically downloaded. No need for manual preparation.
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sys.path.extend([scripts_path_fix])
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import gradio as gr
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import modules
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from types import SimpleNamespace
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from modules import script_callbacks, shared
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@ -26,8 +27,7 @@ import modules.scripts as scripts
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from modules.sd_samplers import samplers_for_img2img
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from modules.ui import setup_progressbar, create_sampler_and_steps_selection, ordered_ui_categories, create_output_panel
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from vid2vid import *
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from core import vid2vid, txt2vid, utils
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def V2VArgs():
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seed = -1
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def inputs_ui():
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v2v_args = SimpleNamespace(**V2VArgs())
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t2v_args = SimpleNamespace(**T2VArgs())
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with gr.Tab('vid2vid') as tab_vid2vid:
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with gr.Row():
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gr.HTML('Put your video here')
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with gr.Row():
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vid2vid_file = gr.File(label="Input video", interactive=True, file_count="single", file_types=["video"], elem_id="vid_to_vid_chosen_file")
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#init_img = gr.Image(label="Image for img2img", elem_id="img2img_image", show_label=False, source="upload", interactive=True, type="pil", image_mode="RGBA")
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#with gr.Row():
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# gr.HTML('Alternative: enter the relative (to the webui) path to the file')
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#with gr.Row():
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# vid2vid_frames_path = gr.Textbox(label="Input video path", interactive=True, elem_id="vid_to_vid_chosen_path", placeholder='Enter your video path here, or upload in the box above ^')
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with gr.Tabs():
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sdcn_process_mode = gr.State(value='vid2vid')
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width, height, prompt, n_prompt, cfg_scale, seed, processing_strength, fix_frame_strength = setup_common_values('vid2vid', v2v_args)
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with gr.Tab('vid2vid') as tab_vid2vid:
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with gr.Row():
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gr.HTML('Put your video here')
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with gr.Row():
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v2v_file = gr.File(label="Input video", interactive=True, file_count="single", file_types=["video"], elem_id="vid_to_vid_chosen_file")
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#init_img = gr.Image(label="Image for img2img", elem_id="img2img_image", show_label=False, source="upload", interactive=True, type="pil", image_mode="RGBA")
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#with gr.Row():
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# gr.HTML('Alternative: enter the relative (to the webui) path to the file')
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#with gr.Row():
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# vid2vid_frames_path = gr.Textbox(label="Input video path", interactive=True, elem_id="vid_to_vid_chosen_path", placeholder='Enter your video path here, or upload in the box above ^')
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with FormRow(elem_id=f"sampler_selection_v2v"):
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sampler_index = gr.Dropdown(label='Sampling method', elem_id=f"v2v_sampling", choices=[x.name for x in samplers_for_img2img], value=samplers_for_img2img[0].name, type="index")
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steps = gr.Slider(minimum=1, maximum=150, step=1, elem_id=f"v2v_steps", label="Sampling steps", value=15)
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v2v_width, v2v_height, v2v_prompt, v2v_n_prompt, v2v_cfg_scale, v2v_seed, v2v_processing_strength, v2v_fix_frame_strength = setup_common_values('vid2vid', v2v_args)
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with FormRow(elem_id="vid2vid_override_settings_row") as row:
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override_settings = create_override_settings_dropdown("vid2vid", row)
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with FormRow(elem_id=f"sampler_selection_v2v"):
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v2v_sampler_index = gr.Dropdown(label='Sampling method', elem_id=f"v2v_sampling", choices=[x.name for x in samplers_for_img2img], value=samplers_for_img2img[0].name, type="index")
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v2v_steps = gr.Slider(minimum=1, maximum=150, step=1, elem_id=f"v2v_steps", label="Sampling steps", value=15)
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with FormGroup(elem_id=f"script_container"):
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custom_inputs = scripts.scripts_img2img.setup_ui()
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#with gr.Row():
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# strength = gr.Slider(label="denoising strength", value=d.strength, minimum=0, maximum=1, step=0.05, interactive=True)
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# vid2vid_startFrame=gr.Number(label='vid2vid start frame',value=d.vid2vid_startFrame)
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with FormRow(elem_id="vid2vid_override_settings_row") as row:
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v2v_override_settings = create_override_settings_dropdown("vid2vid", row)
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with FormGroup(elem_id=f"script_container"):
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v2v_custom_inputs = scripts.scripts_img2img.setup_ui()
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#with gr.Row():
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# strength = gr.Slider(label="denoising strength", value=d.strength, minimum=0, maximum=1, step=0.05, interactive=True)
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# vid2vid_startFrame=gr.Number(label='vid2vid start frame',value=d.vid2vid_startFrame)
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with gr.Tab('txt2vid') as tab_txt2vid:
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t2v_width, t2v_height, t2v_prompt, t2v_n_prompt, t2v_cfg_scale, t2v_seed, t2v_processing_strength, t2v_fix_frame_strength = setup_common_values('txt2vid', t2v_args)
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with gr.Row():
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t2v_length = gr.Slider(label='Length (in frames)', minimum=10, maximum=2048, step=10, value=40, interactive=True)
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t2v_fps = gr.Slider(label='Video FPS', minimum=4, maximum=64, step=4, value=12, interactive=True)
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with gr.Tab('txt2vid') as tab_txt2vid:
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gr.Markdown('Work in progress...')
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# width, height, prompt, n_prompt, steps, cfg_scale, seed, processing_strength, fix_frame_strength = setup_common_values('txt2vid', t2v_args)
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#with gr.Tab('settings') as tab_setts:
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# gr.Markdown('Work in progress...')
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tab_vid2vid.select(fn=lambda: 'vid2vid', inputs=[], outputs=[sdcn_process_mode])
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tab_txt2vid.select(fn=lambda: 'txt2vid', inputs=[], outputs=[sdcn_process_mode])
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return locals()
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def process(*args):
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if args[0] == 'vid2vid':
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yield from vid2vid.start_process(*args)
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elif args[0] == 'txt2vid':
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yield from txt2vid.start_process(*args)
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else:
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raise Exception(f"Unsupported processing mode: '{args[0]}'")
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def stop_process(*args):
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utils.shared.is_interrupted = True
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return gr.Button.update(interactive=False)
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def on_ui_tabs():
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modules.scripts.scripts_current = modules.scripts.scripts_img2img
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modules.scripts.scripts_img2img.initialize_scripts(is_img2img=True)
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@ -118,11 +136,8 @@ def on_ui_tabs():
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components = {}
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#dv = SimpleNamespace(**T2VOutputArgs())
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with gr.Row(elem_id='v2v-core').style(equal_height=False, variant='compact'):
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with gr.Row(elem_id='sdcn-core').style(equal_height=False, variant='compact'):
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with gr.Column(scale=1, variant='panel'):
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with gr.Row(variant='compact'):
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run_button = gr.Button('Generate', elem_id=f"sdcn_anim_generate", variant='primary')
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with gr.Tabs():
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components = inputs_ui()
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@ -139,6 +154,10 @@ def on_ui_tabs():
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# custom_inputs = scripts.scripts_img2img.setup_ui()
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with gr.Column(scale=1, variant='compact'):
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with gr.Row(variant='compact'):
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run_button = gr.Button('Generate', elem_id=f"sdcn_anim_generate", variant='primary')
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stop_button = gr.Button('Interrupt', elem_id=f"sdcn_anim_interrupt", variant='primary', interactive=False)
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with gr.Column(variant="panel"):
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sp_progress = gr.HTML(elem_id="sp_progress", value="")
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sp_progress.update()
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@ -157,52 +176,9 @@ def on_ui_tabs():
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with gr.Row(variant='compact'):
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dummy_component = gr.Label(visible=False)
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# Define parameters for the action methods. Not all of them are included yet
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method_inputs = [
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dummy_component, # send None for task_id
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dummy_component, # mode
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components['prompt'], # prompt
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components['n_prompt'], # negative_prompt
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dummy_component, # prompt_styles
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components['vid2vid_file'], # input_video
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dummy_component, # sketch
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dummy_component, # init_img_with_mask
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dummy_component, # inpaint_color_sketch
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dummy_component, # inpaint_color_sketch_orig
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dummy_component, # init_img_inpaint
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dummy_component, # init_mask_inpaint
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components['steps'], # steps
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components['sampler_index'], # sampler_index
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dummy_component, # mask_blur
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dummy_component, # mask_alpha
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dummy_component, # inpainting_fill
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dummy_component, # restore_faces
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dummy_component, # tiling
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dummy_component, # n_iter
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dummy_component, # batch_size
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components['cfg_scale'], # cfg_scale
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dummy_component, # image_cfg_scale
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components['processing_strength'], # denoising_strength
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components['fix_frame_strength'], # fix_frame_strength
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components['seed'], # seed
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dummy_component, # subseed
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dummy_component, # subseed_strength
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dummy_component, # seed_resize_from_h
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dummy_component, # seed_resize_from_w
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dummy_component, # seed_enable_extras
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components['height'], # height
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components['width'], # width
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dummy_component, # resize_mode
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dummy_component, # inpaint_full_res
|
||||
dummy_component, # inpaint_full_res_padding
|
||||
dummy_component, # inpainting_mask_invert
|
||||
dummy_component, # img2img_batch_input_dir
|
||||
dummy_component, # img2img_batch_output_dir
|
||||
dummy_component, # img2img_batch_inpaint_mask_dir
|
||||
components['override_settings'], # override_settings_texts
|
||||
] + components['custom_inputs']
|
||||
# Define parameters for the action methods.
|
||||
method_inputs = [components[name] for name in utils.get_component_names()] + components['v2v_custom_inputs']
|
||||
|
||||
method_outputs = [
|
||||
sp_progress,
|
||||
|
|
@ -211,15 +187,23 @@ def on_ui_tabs():
|
|||
img_preview_prev_warp,
|
||||
img_preview_processed,
|
||||
html_log,
|
||||
run_button,
|
||||
stop_button
|
||||
]
|
||||
|
||||
run_button.click(
|
||||
fn=start_process, #wrap_gradio_gpu_call(start_process, extra_outputs=[None, '', '']),
|
||||
fn=process, #wrap_gradio_gpu_call(start_process, extra_outputs=[None, '', '']),
|
||||
inputs=method_inputs,
|
||||
outputs=method_outputs,
|
||||
show_progress=True,
|
||||
)
|
||||
|
||||
stop_button.click(
|
||||
fn=stop_process,
|
||||
outputs=[stop_button],
|
||||
show_progress=False
|
||||
)
|
||||
|
||||
modules.scripts.scripts_current = None
|
||||
|
||||
# define queue - required for generators
|
||||
|
|
|
|||
|
|
@ -42,6 +42,15 @@ def RAFT_clear_memory():
|
|||
|
||||
def RAFT_estimate_flow(frame1, frame2, device='cuda', subtract_background=True):
|
||||
global RAFT_model
|
||||
|
||||
model_path = ph.models_path + '/RAFT/raft-things.pth'
|
||||
remote_model_path = 'https://drive.google.com/uc?id=1MqDajR89k-xLV0HIrmJ0k-n8ZpG6_suM'
|
||||
|
||||
if not os.path.isfile(model_path):
|
||||
from basicsr.utils.download_util import load_file_from_url
|
||||
os.makedirs(os.path.dirname(model_path), exist_ok=True)
|
||||
load_file_from_url(remote_model_path, file_name=model_path)
|
||||
|
||||
if RAFT_model is None:
|
||||
args = argparse.Namespace(**{
|
||||
'model': ph.models_path + '/RAFT/raft-things.pth',
|
||||
|
|
@ -0,0 +1,150 @@
|
|||
import sys, os
|
||||
basedirs = [os.getcwd()]
|
||||
|
||||
for basedir in basedirs:
|
||||
paths_to_ensure = [
|
||||
basedir,
|
||||
basedir + '/extensions/sd-cn-animation/scripts',
|
||||
basedir + '/extensions/SD-CN-Animation/scripts'
|
||||
]
|
||||
|
||||
for scripts_path_fix in paths_to_ensure:
|
||||
if not scripts_path_fix in sys.path:
|
||||
sys.path.extend([scripts_path_fix])
|
||||
|
||||
import torch
|
||||
import gc
|
||||
import numpy as np
|
||||
from PIL import Image
|
||||
|
||||
import modules.paths as ph
|
||||
from modules.shared import devices
|
||||
|
||||
from core import utils, flow_utils
|
||||
from FloweR.model import FloweR
|
||||
|
||||
import skimage
|
||||
import datetime
|
||||
import cv2
|
||||
import gradio as gr
|
||||
|
||||
FloweR_model = None
|
||||
DEVICE = 'cuda'
|
||||
def FloweR_clear_memory():
|
||||
global FloweR_model
|
||||
del FloweR_model
|
||||
gc.collect()
|
||||
torch.cuda.empty_cache()
|
||||
FloweR_model = None
|
||||
|
||||
def FloweR_load_model(w, h):
|
||||
global DEVICE, FloweR_model
|
||||
model_path = ph.models_path + '/FloweR/FloweR_0.1.1.pth'
|
||||
remote_model_path = 'https://drive.google.com/uc?id=1K7gXUosgxU729_l-osl1HBU5xqyLsALv'
|
||||
|
||||
if not os.path.isfile(model_path):
|
||||
from basicsr.utils.download_util import load_file_from_url
|
||||
os.makedirs(os.path.dirname(model_path), exist_ok=True)
|
||||
load_file_from_url(remote_model_path, file_name=model_path)
|
||||
|
||||
FloweR_model = FloweR(input_size = (h, w))
|
||||
FloweR_model.load_state_dict(torch.load(model_path))
|
||||
# Move the model to the device
|
||||
DEVICE = devices.get_optimal_device()
|
||||
FloweR_model = FloweR_model.to(DEVICE)
|
||||
|
||||
def start_process(*args):
|
||||
args_dict = utils.args_to_dict(*args)
|
||||
args_dict = utils.get_mode_args('t2v', args_dict)
|
||||
|
||||
#utils.set_CNs_input_image(args_dict, Image.fromarray(curr_frame))
|
||||
processed_frames, _, _, _ = utils.txt2img(args_dict)
|
||||
processed_frame = np.array(processed_frames[0])
|
||||
processed_frame = np.clip(processed_frame, 0, 255).astype(np.uint8)
|
||||
init_frame = processed_frame.copy()
|
||||
|
||||
# Create an output video file with the same fps, width, and height as the input video
|
||||
output_video_name = f'outputs/sd-cn-animation/txt2vid/{datetime.datetime.now().strftime("%Y-%m-%d_%H-%M-%S")}.mp4'
|
||||
os.makedirs(os.path.dirname(output_video_name), exist_ok=True)
|
||||
output_video = cv2.VideoWriter(output_video_name, cv2.VideoWriter_fourcc(*'mp4v'), args_dict['fps'], (args_dict['width'], args_dict['height']))
|
||||
output_video.write(cv2.cvtColor(processed_frame, cv2.COLOR_RGB2BGR))
|
||||
|
||||
stat = f"Frame: 1 / {args_dict['length']}"
|
||||
utils.shared.is_interrupted = False
|
||||
yield stat, init_frame, None, None, processed_frame, '', gr.Button.update(interactive=False), gr.Button.update(interactive=True)
|
||||
|
||||
FloweR_load_model(args_dict['width'], args_dict['height'])
|
||||
|
||||
clip_frames = np.zeros((4, args_dict['height'], args_dict['width'], 3), dtype=np.uint8)
|
||||
|
||||
prev_frame = init_frame
|
||||
try:
|
||||
for ind in range(args_dict['length']):
|
||||
if utils.shared.is_interrupted: break
|
||||
|
||||
args_dict = utils.args_to_dict(*args)
|
||||
args_dict = utils.get_mode_args('t2v', args_dict)
|
||||
|
||||
clip_frames = np.roll(clip_frames, -1, axis=0)
|
||||
clip_frames[-1] = prev_frame
|
||||
clip_frames_torch = flow_utils.frames_norm(torch.from_numpy(clip_frames).to(DEVICE, dtype=torch.float32))
|
||||
|
||||
with torch.no_grad():
|
||||
pred_data = FloweR_model(clip_frames_torch.unsqueeze(0))[0]
|
||||
|
||||
pred_flow = flow_utils.flow_renorm(pred_data[...,:2]).cpu().numpy()
|
||||
pred_occl = flow_utils.occl_renorm(pred_data[...,2:3]).cpu().numpy().repeat(3, axis = -1)
|
||||
|
||||
pred_flow = pred_flow / (1 + np.linalg.norm(pred_flow, axis=-1, keepdims=True) * 0.05)
|
||||
pred_flow = cv2.GaussianBlur(pred_flow, (31,31), 1, cv2.BORDER_REFLECT_101)
|
||||
|
||||
|
||||
pred_occl = cv2.GaussianBlur(pred_occl, (21,21), 2, cv2.BORDER_REFLECT_101)
|
||||
pred_occl = (np.abs(pred_occl / 255) ** 1.5) * 255
|
||||
pred_occl = np.clip(pred_occl * 25, 0, 255).astype(np.uint8)
|
||||
|
||||
flow_map = pred_flow.copy()
|
||||
flow_map[:,:,0] += np.arange(args_dict['width'])
|
||||
flow_map[:,:,1] += np.arange(args_dict['height'])[:,np.newaxis]
|
||||
|
||||
warped_frame = cv2.remap(prev_frame, flow_map, None, cv2.INTER_CUBIC, borderMode = cv2.BORDER_REFLECT_101)
|
||||
|
||||
curr_frame = warped_frame.copy()
|
||||
|
||||
|
||||
args_dict['init_img'] = Image.fromarray(curr_frame)
|
||||
args_dict['mask_img'] = Image.fromarray(pred_occl)
|
||||
|
||||
#utils.set_CNs_input_image(args_dict, Image.fromarray(curr_frame))
|
||||
processed_frames, _, _, _ = utils.img2img(args_dict)
|
||||
processed_frame = np.array(processed_frames[0])
|
||||
processed_frame = skimage.exposure.match_histograms(processed_frame, init_frame, multichannel=False, channel_axis=-1)
|
||||
processed_frame = np.clip(processed_frame, 0, 255).astype(np.uint8)
|
||||
|
||||
args_dict['mode'] = 0
|
||||
args_dict['init_img'] = Image.fromarray(processed_frame)
|
||||
args_dict['mask_img'] = None
|
||||
args_dict['denoising_strength'] = args_dict['fix_frame_strength']
|
||||
|
||||
#utils.set_CNs_input_image(args_dict, Image.fromarray(curr_frame))
|
||||
processed_frames, _, _, _ = utils.img2img(args_dict)
|
||||
processed_frame = np.array(processed_frames[0])
|
||||
processed_frame = skimage.exposure.match_histograms(processed_frame, init_frame, multichannel=False, channel_axis=-1)
|
||||
processed_frame = np.clip(processed_frame, 0, 255).astype(np.uint8)
|
||||
|
||||
output_video.write(cv2.cvtColor(processed_frame, cv2.COLOR_RGB2BGR))
|
||||
prev_frame = processed_frame.copy()
|
||||
|
||||
stat = f"Frame: {ind + 2} / {args_dict['length']}"
|
||||
yield stat, curr_frame, pred_occl, warped_frame, processed_frame, '', gr.Button.update(interactive=False), gr.Button.update(interactive=True)
|
||||
except: pass
|
||||
|
||||
output_video.release()
|
||||
FloweR_clear_memory()
|
||||
|
||||
curr_frame = gr.Image.update()
|
||||
occlusion_mask = gr.Image.update()
|
||||
warped_styled_frame_ = gr.Image.update()
|
||||
processed_frame = gr.Image.update()
|
||||
|
||||
yield 'done', curr_frame, occlusion_mask, warped_styled_frame_, processed_frame, '', gr.Button.update(interactive=True), gr.Button.update(interactive=False)
|
||||
|
|
@ -0,0 +1,370 @@
|
|||
class shared:
|
||||
is_interrupted = False
|
||||
|
||||
def get_component_names():
|
||||
components_list = [
|
||||
'sdcn_process_mode',
|
||||
'v2v_file', 'v2v_width', 'v2v_height', 'v2v_prompt', 'v2v_n_prompt', 'v2v_cfg_scale', 'v2v_seed', 'v2v_processing_strength', 'v2v_fix_frame_strength',
|
||||
'v2v_sampler_index', 'v2v_steps', 'v2v_override_settings',
|
||||
't2v_width', 't2v_height', 't2v_prompt', 't2v_n_prompt', 't2v_cfg_scale', 't2v_seed', 't2v_processing_strength', 't2v_fix_frame_strength',
|
||||
't2v_length', 't2v_fps'
|
||||
]
|
||||
|
||||
return components_list
|
||||
|
||||
def args_to_dict(*args): # converts list of argumets into dictionary for better handling of it
|
||||
args_list = get_component_names()
|
||||
|
||||
# set default values for params that were not specified
|
||||
args_dict = {
|
||||
# video to video params
|
||||
'v2v_mode': 0,
|
||||
'v2v_prompt': '',
|
||||
'v2v_n_prompt': '',
|
||||
'v2v_prompt_styles': [],
|
||||
'v2v_init_video': None, # Always required
|
||||
|
||||
'v2v_steps': 15,
|
||||
'v2v_sampler_index': 0, # 'Euler a'
|
||||
'v2v_mask_blur': 0,
|
||||
|
||||
'v2v_inpainting_fill': 1, # original
|
||||
'v2v_restore_faces': False,
|
||||
'v2v_tiling': False,
|
||||
'v2v_n_iter': 1,
|
||||
'v2v_batch_size': 1,
|
||||
'v2v_cfg_scale': 5.5,
|
||||
'v2v_image_cfg_scale': 1.5,
|
||||
'v2v_denoising_strength': 0.75,
|
||||
'v2v_fix_frame_strength': 0.15,
|
||||
'v2v_seed': -1,
|
||||
'v2v_subseed': -1,
|
||||
'v2v_subseed_strength': 0,
|
||||
'v2v_seed_resize_from_h': 512,
|
||||
'v2v_seed_resize_from_w': 512,
|
||||
'v2v_seed_enable_extras': False,
|
||||
'v2v_height': 512,
|
||||
'v2v_width': 512,
|
||||
'v2v_resize_mode': 1,
|
||||
'v2v_inpaint_full_res': True,
|
||||
'v2v_inpaint_full_res_padding': 0,
|
||||
'v2v_inpainting_mask_invert': False,
|
||||
|
||||
# text to video params
|
||||
't2v_mode': 4,
|
||||
't2v_prompt': '',
|
||||
't2v_n_prompt': '',
|
||||
't2v_prompt_styles': [],
|
||||
't2v_init_img': None,
|
||||
't2v_mask_img': None,
|
||||
|
||||
't2v_steps': 15,
|
||||
't2v_sampler_index': 0, # 'Euler a'
|
||||
't2v_mask_blur': 0,
|
||||
|
||||
't2v_inpainting_fill': 1, # original
|
||||
't2v_restore_faces': False,
|
||||
't2v_tiling': False,
|
||||
't2v_n_iter': 1,
|
||||
't2v_batch_size': 1,
|
||||
't2v_cfg_scale': 5.5,
|
||||
't2v_image_cfg_scale': 1.5,
|
||||
't2v_denoising_strength': 0.75,
|
||||
't2v_fix_frame_strength': 0.15,
|
||||
't2v_seed': -1,
|
||||
't2v_subseed': -1,
|
||||
't2v_subseed_strength': 0,
|
||||
't2v_seed_resize_from_h': 512,
|
||||
't2v_seed_resize_from_w': 512,
|
||||
't2v_seed_enable_extras': False,
|
||||
't2v_height': 512,
|
||||
't2v_width': 512,
|
||||
't2v_resize_mode': 1,
|
||||
't2v_inpaint_full_res': True,
|
||||
't2v_inpaint_full_res_padding': 0,
|
||||
't2v_inpainting_mask_invert': False,
|
||||
|
||||
't2v_override_settings': [],
|
||||
't2v_script_inputs': [0],
|
||||
|
||||
't2v_fps': 12,
|
||||
}
|
||||
|
||||
args = list(args)
|
||||
|
||||
for i in range(len(args_list)):
|
||||
if (args[i] is None) and (args_list[i] in args_dict):
|
||||
args[i] = args_dict[args_list[i]]
|
||||
else:
|
||||
args_dict[args_list[i]] = args[i]
|
||||
|
||||
args_dict['v2v_script_inputs'] = args[len(args_list):]
|
||||
return args_dict
|
||||
|
||||
def get_mode_args(mode, args_dict):
|
||||
mode_args_dict = {}
|
||||
for key, value in args_dict.items():
|
||||
if key[:3] == mode:
|
||||
mode_args_dict[key[4:]] = value
|
||||
|
||||
return mode_args_dict
|
||||
|
||||
def set_CNs_input_image(args_dict, image):
|
||||
for script_input in args_dict['script_inputs']:
|
||||
if type(script_input).__name__ == 'UiControlNetUnit':
|
||||
script_input.batch_images = [image]
|
||||
|
||||
|
||||
import numpy as np
|
||||
from PIL import Image, ImageOps, ImageFilter, ImageEnhance, ImageChops
|
||||
from types import SimpleNamespace
|
||||
|
||||
from modules.generation_parameters_copypaste import create_override_settings_dict
|
||||
from modules.processing import Processed, StableDiffusionProcessingImg2Img, StableDiffusionProcessingTxt2Img, process_images
|
||||
import modules.processing as processing
|
||||
from modules.ui import plaintext_to_html
|
||||
import modules.images as images
|
||||
import modules.scripts
|
||||
from modules.shared import opts, devices, state
|
||||
from modules import devices, sd_samplers, img2img
|
||||
from modules import shared, sd_hijack, lowvram
|
||||
|
||||
# TODO: Refactor all the code below
|
||||
|
||||
def process_img(p, input_img, output_dir, inpaint_mask_dir, args):
|
||||
processing.fix_seed(p)
|
||||
|
||||
#images = shared.listfiles(input_dir)
|
||||
images = [input_img]
|
||||
|
||||
is_inpaint_batch = False
|
||||
#if inpaint_mask_dir:
|
||||
# inpaint_masks = shared.listfiles(inpaint_mask_dir)
|
||||
# is_inpaint_batch = len(inpaint_masks) > 0
|
||||
#if is_inpaint_batch:
|
||||
# print(f"\nInpaint batch is enabled. {len(inpaint_masks)} masks found.")
|
||||
|
||||
#print(f"Will process {len(images)} images, creating {p.n_iter * p.batch_size} new images for each.")
|
||||
|
||||
save_normally = output_dir == ''
|
||||
|
||||
p.do_not_save_grid = True
|
||||
p.do_not_save_samples = not save_normally
|
||||
|
||||
state.job_count = len(images) * p.n_iter
|
||||
|
||||
generated_images = []
|
||||
for i, image in enumerate(images):
|
||||
state.job = f"{i+1} out of {len(images)}"
|
||||
if state.skipped:
|
||||
state.skipped = False
|
||||
|
||||
if state.interrupted:
|
||||
break
|
||||
|
||||
img = image #Image.open(image)
|
||||
# Use the EXIF orientation of photos taken by smartphones.
|
||||
img = ImageOps.exif_transpose(img)
|
||||
p.init_images = [img] * p.batch_size
|
||||
|
||||
#if is_inpaint_batch:
|
||||
# # try to find corresponding mask for an image using simple filename matching
|
||||
# mask_image_path = os.path.join(inpaint_mask_dir, os.path.basename(image))
|
||||
# # if not found use first one ("same mask for all images" use-case)
|
||||
# if not mask_image_path in inpaint_masks:
|
||||
# mask_image_path = inpaint_masks[0]
|
||||
# mask_image = Image.open(mask_image_path)
|
||||
# p.image_mask = mask_image
|
||||
|
||||
proc = modules.scripts.scripts_img2img.run(p, *args)
|
||||
if proc is None:
|
||||
proc = process_images(p)
|
||||
generated_images.append(proc.images[0])
|
||||
|
||||
#for n, processed_image in enumerate(proc.images):
|
||||
# filename = os.path.basename(image)
|
||||
|
||||
# if n > 0:
|
||||
# left, right = os.path.splitext(filename)
|
||||
# filename = f"{left}-{n}{right}"
|
||||
|
||||
# if not save_normally:
|
||||
# os.makedirs(output_dir, exist_ok=True)
|
||||
# if processed_image.mode == 'RGBA':
|
||||
# processed_image = processed_image.convert("RGB")
|
||||
# processed_image.save(os.path.join(output_dir, filename))
|
||||
|
||||
return generated_images
|
||||
|
||||
def img2img(args_dict):
|
||||
args = SimpleNamespace(**args_dict)
|
||||
override_settings = create_override_settings_dict(args.override_settings)
|
||||
|
||||
is_batch = args.mode == 5
|
||||
|
||||
if args.mode == 0: # img2img
|
||||
image = args.init_img.convert("RGB")
|
||||
mask = None
|
||||
elif args.mode == 1: # img2img sketch
|
||||
image = args.sketch.convert("RGB")
|
||||
mask = None
|
||||
elif args.mode == 2: # inpaint
|
||||
image, mask = args.init_img_with_mask["image"], args.init_img_with_mask["mask"]
|
||||
alpha_mask = ImageOps.invert(image.split()[-1]).convert('L').point(lambda x: 255 if x > 0 else 0, mode='1')
|
||||
mask = ImageChops.lighter(alpha_mask, mask.convert('L')).convert('L')
|
||||
image = image.convert("RGB")
|
||||
elif args.mode == 3: # inpaint sketch
|
||||
image = args.inpaint_color_sketch
|
||||
orig = args.inpaint_color_sketch_orig or args.inpaint_color_sketch
|
||||
pred = np.any(np.array(image) != np.array(orig), axis=-1)
|
||||
mask = Image.fromarray(pred.astype(np.uint8) * 255, "L")
|
||||
mask = ImageEnhance.Brightness(mask).enhance(1 - args.mask_alpha / 100)
|
||||
blur = ImageFilter.GaussianBlur(args.mask_blur)
|
||||
image = Image.composite(image.filter(blur), orig, mask.filter(blur))
|
||||
image = image.convert("RGB")
|
||||
elif args.mode == 4: # inpaint upload mask
|
||||
#image = args.init_img_inpaint
|
||||
#mask = args.init_mask_inpaint
|
||||
|
||||
image = args.init_img.convert("RGB")
|
||||
mask = args.mask_img.convert("L")
|
||||
else:
|
||||
image = None
|
||||
mask = None
|
||||
|
||||
# Use the EXIF orientation of photos taken by smartphones.
|
||||
if image is not None:
|
||||
image = ImageOps.exif_transpose(image)
|
||||
|
||||
assert 0. <= args.denoising_strength <= 1., 'can only work with strength in [0.0, 1.0]'
|
||||
|
||||
p = StableDiffusionProcessingImg2Img(
|
||||
sd_model=shared.sd_model,
|
||||
outpath_samples=opts.outdir_samples or opts.outdir_img2img_samples,
|
||||
outpath_grids=opts.outdir_grids or opts.outdir_img2img_grids,
|
||||
prompt=args.prompt,
|
||||
negative_prompt=args.n_prompt,
|
||||
styles=args.prompt_styles,
|
||||
seed=args.seed,
|
||||
subseed=args.subseed,
|
||||
subseed_strength=args.subseed_strength,
|
||||
seed_resize_from_h=args.seed_resize_from_h,
|
||||
seed_resize_from_w=args.seed_resize_from_w,
|
||||
seed_enable_extras=args.seed_enable_extras,
|
||||
sampler_name=sd_samplers.samplers_for_img2img[args.sampler_index].name,
|
||||
batch_size=args.batch_size,
|
||||
n_iter=args.n_iter,
|
||||
steps=args.steps,
|
||||
cfg_scale=args.cfg_scale,
|
||||
width=args.width,
|
||||
height=args.height,
|
||||
restore_faces=args.restore_faces,
|
||||
tiling=args.tiling,
|
||||
init_images=[image],
|
||||
mask=mask,
|
||||
mask_blur=args.mask_blur,
|
||||
inpainting_fill=args.inpainting_fill,
|
||||
resize_mode=args.resize_mode,
|
||||
denoising_strength=args.denoising_strength,
|
||||
image_cfg_scale=args.image_cfg_scale,
|
||||
inpaint_full_res=args.inpaint_full_res,
|
||||
inpaint_full_res_padding=args.inpaint_full_res_padding,
|
||||
inpainting_mask_invert=args.inpainting_mask_invert,
|
||||
override_settings=override_settings,
|
||||
)
|
||||
|
||||
p.scripts = modules.scripts.scripts_txt2img
|
||||
p.script_args = args.script_inputs
|
||||
|
||||
#if shared.cmd_opts.enable_console_prompts:
|
||||
# print(f"\nimg2img: {args.prompt}", file=shared.progress_print_out)
|
||||
|
||||
if mask:
|
||||
p.extra_generation_params["Mask blur"] = args.mask_blur
|
||||
|
||||
'''
|
||||
if is_batch:
|
||||
...
|
||||
# assert not shared.cmd_opts.hide_ui_dir_config, "Launched with --hide-ui-dir-config, batch img2img disabled"
|
||||
# process_batch(p, img2img_batch_input_dir, img2img_batch_output_dir, img2img_batch_inpaint_mask_dir, args.script_inputs)
|
||||
# processed = Processed(p, [], p.seed, "")
|
||||
else:
|
||||
processed = modules.scripts.scripts_img2img.run(p, *args.script_inputs)
|
||||
if processed is None:
|
||||
processed = process_images(p)
|
||||
'''
|
||||
|
||||
generated_images = process_img(p, image, None, '', args.script_inputs)
|
||||
processed = Processed(p, [], p.seed, "")
|
||||
p.close()
|
||||
|
||||
shared.total_tqdm.clear()
|
||||
|
||||
generation_info_js = processed.js()
|
||||
#if opts.samples_log_stdout:
|
||||
# print(generation_info_js)
|
||||
|
||||
#if opts.do_not_show_images:
|
||||
# processed.images = []
|
||||
|
||||
return generated_images, generation_info_js, plaintext_to_html(processed.info), plaintext_to_html(processed.comments)
|
||||
|
||||
def txt2img(args_dict):
|
||||
args = SimpleNamespace(**args_dict)
|
||||
override_settings = create_override_settings_dict(args.override_settings)
|
||||
|
||||
p = StableDiffusionProcessingTxt2Img(
|
||||
sd_model=shared.sd_model,
|
||||
outpath_samples=opts.outdir_samples or opts.outdir_txt2img_samples,
|
||||
outpath_grids=opts.outdir_grids or opts.outdir_txt2img_grids,
|
||||
prompt=args.prompt,
|
||||
styles=args.prompt_styles,
|
||||
negative_prompt=args.n_prompt,
|
||||
seed=args.seed,
|
||||
subseed=args.subseed,
|
||||
subseed_strength=args.subseed_strength,
|
||||
seed_resize_from_h=args.seed_resize_from_h,
|
||||
seed_resize_from_w=args.seed_resize_from_w,
|
||||
seed_enable_extras=args.seed_enable_extras,
|
||||
sampler_name=sd_samplers.samplers[args.sampler_index].name,
|
||||
batch_size=args.batch_size,
|
||||
n_iter=args.n_iter,
|
||||
steps=args.steps,
|
||||
cfg_scale=args.cfg_scale,
|
||||
width=args.width,
|
||||
height=args.height,
|
||||
restore_faces=args.restore_faces,
|
||||
tiling=args.tiling,
|
||||
#enable_hr=args.enable_hr,
|
||||
#denoising_strength=args.denoising_strength if enable_hr else None,
|
||||
#hr_scale=hr_scale,
|
||||
#hr_upscaler=hr_upscaler,
|
||||
#hr_second_pass_steps=hr_second_pass_steps,
|
||||
#hr_resize_x=hr_resize_x,
|
||||
#hr_resize_y=hr_resize_y,
|
||||
override_settings=override_settings,
|
||||
)
|
||||
|
||||
p.scripts = modules.scripts.scripts_txt2img
|
||||
p.script_args = args.script_inputs
|
||||
|
||||
#if cmd_opts.enable_console_prompts:
|
||||
# print(f"\ntxt2img: {prompt}", file=shared.progress_print_out)
|
||||
|
||||
processed = modules.scripts.scripts_txt2img.run(p, *args.script_inputs)
|
||||
|
||||
if processed is None:
|
||||
processed = process_images(p)
|
||||
|
||||
p.close()
|
||||
|
||||
shared.total_tqdm.clear()
|
||||
|
||||
generation_info_js = processed.js()
|
||||
#if opts.samples_log_stdout:
|
||||
# print(generation_info_js)
|
||||
|
||||
#if opts.do_not_show_images:
|
||||
# processed.images = []
|
||||
|
||||
return processed.images, generation_info_js, plaintext_to_html(processed.info), plaintext_to_html(processed.comments)
|
||||
|
|
@ -0,0 +1,252 @@
|
|||
import sys, os
|
||||
basedirs = [os.getcwd()]
|
||||
|
||||
for basedir in basedirs:
|
||||
paths_to_ensure = [
|
||||
basedir,
|
||||
basedir + '/extensions/sd-cn-animation/scripts',
|
||||
basedir + '/extensions/SD-CN-Animation/scripts'
|
||||
]
|
||||
|
||||
for scripts_path_fix in paths_to_ensure:
|
||||
if not scripts_path_fix in sys.path:
|
||||
sys.path.extend([scripts_path_fix])
|
||||
|
||||
import math
|
||||
import os
|
||||
import sys
|
||||
import traceback
|
||||
|
||||
import numpy as np
|
||||
from PIL import Image
|
||||
|
||||
from modules import devices, sd_samplers
|
||||
from modules import shared, sd_hijack, lowvram
|
||||
|
||||
from modules.shared import devices
|
||||
import modules.shared as shared
|
||||
|
||||
import gc
|
||||
import cv2
|
||||
import gradio as gr
|
||||
|
||||
import time
|
||||
import skimage
|
||||
import datetime
|
||||
|
||||
from core.flow_utils import RAFT_estimate_flow, RAFT_clear_memory, compute_diff_map
|
||||
from core import utils
|
||||
|
||||
class sdcn_anim_tmp:
|
||||
prepear_counter = 0
|
||||
process_counter = 0
|
||||
input_video = None
|
||||
output_video = None
|
||||
curr_frame = None
|
||||
prev_frame = None
|
||||
prev_frame_styled = None
|
||||
prev_frame_alpha_mask = None
|
||||
fps = None
|
||||
total_frames = None
|
||||
prepared_frames = None
|
||||
prepared_next_flows = None
|
||||
prepared_prev_flows = None
|
||||
frames_prepared = False
|
||||
|
||||
def read_frame_from_video():
|
||||
# Reading video file
|
||||
if sdcn_anim_tmp.input_video.isOpened():
|
||||
ret, cur_frame = sdcn_anim_tmp.input_video.read()
|
||||
if cur_frame is not None:
|
||||
cur_frame = cv2.cvtColor(cur_frame, cv2.COLOR_BGR2RGB)
|
||||
else:
|
||||
cur_frame = None
|
||||
sdcn_anim_tmp.input_video.release()
|
||||
|
||||
return cur_frame
|
||||
|
||||
def get_cur_stat():
|
||||
stat = f'Frames prepared: {sdcn_anim_tmp.prepear_counter + 1} / {sdcn_anim_tmp.total_frames}; '
|
||||
stat += f'Frames processed: {sdcn_anim_tmp.process_counter + 1} / {sdcn_anim_tmp.total_frames}; '
|
||||
return stat
|
||||
|
||||
def clear_memory_from_sd():
|
||||
if shared.sd_model is not None:
|
||||
sd_hijack.model_hijack.undo_hijack(shared.sd_model)
|
||||
try:
|
||||
lowvram.send_everything_to_cpu()
|
||||
except Exception as e:
|
||||
...
|
||||
del shared.sd_model
|
||||
shared.sd_model = None
|
||||
gc.collect()
|
||||
devices.torch_gc()
|
||||
|
||||
def start_process(*args):
|
||||
args_dict = utils.args_to_dict(*args)
|
||||
args_dict = utils.get_mode_args('v2v', args_dict)
|
||||
|
||||
sdcn_anim_tmp.process_counter = 0
|
||||
sdcn_anim_tmp.prepear_counter = 0
|
||||
|
||||
# Open the input video file
|
||||
sdcn_anim_tmp.input_video = cv2.VideoCapture(args_dict['file'].name)
|
||||
|
||||
# Get useful info from the source video
|
||||
sdcn_anim_tmp.fps = int(sdcn_anim_tmp.input_video.get(cv2.CAP_PROP_FPS))
|
||||
sdcn_anim_tmp.total_frames = int(sdcn_anim_tmp.input_video.get(cv2.CAP_PROP_FRAME_COUNT))
|
||||
|
||||
# Create an output video file with the same fps, width, and height as the input video
|
||||
output_video_name = f'outputs/sd-cn-animation/vid2vid/{datetime.datetime.now().strftime("%Y-%m-%d_%H-%M-%S")}.mp4'
|
||||
os.makedirs(os.path.dirname(output_video_name), exist_ok=True)
|
||||
sdcn_anim_tmp.output_video = cv2.VideoWriter(output_video_name, cv2.VideoWriter_fourcc(*'mp4v'), sdcn_anim_tmp.fps, (args_dict['width'], args_dict['height']))
|
||||
|
||||
curr_frame = read_frame_from_video()
|
||||
curr_frame = cv2.resize(curr_frame, (args_dict['width'], args_dict['height']))
|
||||
sdcn_anim_tmp.prepared_frames = np.zeros((11, args_dict['height'], args_dict['width'], 3), dtype=np.uint8)
|
||||
sdcn_anim_tmp.prepared_next_flows = np.zeros((10, args_dict['height'], args_dict['width'], 2))
|
||||
sdcn_anim_tmp.prepared_prev_flows = np.zeros((10, args_dict['height'], args_dict['width'], 2))
|
||||
sdcn_anim_tmp.prepared_frames[0] = curr_frame
|
||||
|
||||
args_dict['init_img'] = Image.fromarray(curr_frame)
|
||||
utils.set_CNs_input_image(args_dict, Image.fromarray(curr_frame))
|
||||
processed_frames, _, _, _ = utils.img2img(args_dict)
|
||||
processed_frame = np.array(processed_frames[0])
|
||||
processed_frame = skimage.exposure.match_histograms(processed_frame, curr_frame, multichannel=False, channel_axis=-1)
|
||||
processed_frame = np.clip(processed_frame, 0, 255).astype(np.uint8)
|
||||
#print('Processed frame ', 0)
|
||||
|
||||
sdcn_anim_tmp.curr_frame = curr_frame
|
||||
sdcn_anim_tmp.prev_frame = curr_frame.copy()
|
||||
sdcn_anim_tmp.prev_frame_styled = processed_frame.copy()
|
||||
utils.shared.is_interrupted = False
|
||||
yield get_cur_stat(), sdcn_anim_tmp.curr_frame, None, None, processed_frame, '', gr.Button.update(interactive=False), gr.Button.update(interactive=True)
|
||||
|
||||
try:
|
||||
for step in range((sdcn_anim_tmp.total_frames-1) * 2):
|
||||
if utils.shared.is_interrupted: break
|
||||
|
||||
args_dict = utils.args_to_dict(*args)
|
||||
args_dict = utils.get_mode_args('v2v', args_dict)
|
||||
|
||||
occlusion_mask = None
|
||||
prev_frame = None
|
||||
curr_frame = sdcn_anim_tmp.curr_frame
|
||||
warped_styled_frame_ = gr.Image.update()
|
||||
processed_frame = gr.Image.update()
|
||||
|
||||
prepare_steps = 10
|
||||
if sdcn_anim_tmp.process_counter % prepare_steps == 0 and not sdcn_anim_tmp.frames_prepared: # prepare next 10 frames for processing
|
||||
#clear_memory_from_sd()
|
||||
device = devices.get_optimal_device()
|
||||
|
||||
curr_frame = read_frame_from_video()
|
||||
if curr_frame is not None:
|
||||
curr_frame = cv2.resize(curr_frame, (args_dict['width'], args_dict['height']))
|
||||
prev_frame = sdcn_anim_tmp.prev_frame.copy()
|
||||
|
||||
next_flow, prev_flow, occlusion_mask, frame1_bg_removed, frame2_bg_removed = RAFT_estimate_flow(prev_frame, curr_frame, subtract_background=False, device=device)
|
||||
occlusion_mask = np.clip(occlusion_mask * 0.1 * 255, 0, 255).astype(np.uint8)
|
||||
|
||||
cn = sdcn_anim_tmp.prepear_counter % 10
|
||||
if sdcn_anim_tmp.prepear_counter % 10 == 0:
|
||||
sdcn_anim_tmp.prepared_frames[cn] = sdcn_anim_tmp.prev_frame
|
||||
sdcn_anim_tmp.prepared_frames[cn + 1] = curr_frame.copy()
|
||||
sdcn_anim_tmp.prepared_next_flows[cn] = next_flow.copy()
|
||||
sdcn_anim_tmp.prepared_prev_flows[cn] = prev_flow.copy()
|
||||
#print('Prepared frame ', cn+1)
|
||||
|
||||
sdcn_anim_tmp.prev_frame = curr_frame.copy()
|
||||
|
||||
sdcn_anim_tmp.prepear_counter += 1
|
||||
if sdcn_anim_tmp.prepear_counter % prepare_steps == 0 or \
|
||||
sdcn_anim_tmp.prepear_counter >= sdcn_anim_tmp.total_frames - 1 or \
|
||||
curr_frame is None:
|
||||
# Remove RAFT from memory
|
||||
RAFT_clear_memory()
|
||||
sdcn_anim_tmp.frames_prepared = True
|
||||
else:
|
||||
# process frame
|
||||
sdcn_anim_tmp.frames_prepared = False
|
||||
|
||||
cn = sdcn_anim_tmp.process_counter % 10
|
||||
curr_frame = sdcn_anim_tmp.prepared_frames[cn+1]
|
||||
prev_frame = sdcn_anim_tmp.prepared_frames[cn]
|
||||
next_flow = sdcn_anim_tmp.prepared_next_flows[cn]
|
||||
prev_flow = sdcn_anim_tmp.prepared_prev_flows[cn]
|
||||
|
||||
# process current frame
|
||||
args_dict['init_img'] = Image.fromarray(curr_frame)
|
||||
args_dict['seed'] = -1
|
||||
utils.set_CNs_input_image(args_dict, Image.fromarray(curr_frame))
|
||||
processed_frames, _, _, _ = utils.img2img(args_dict)
|
||||
processed_frame = np.array(processed_frames[0])
|
||||
|
||||
|
||||
alpha_mask, warped_styled_frame = compute_diff_map(next_flow, prev_flow, prev_frame, curr_frame, sdcn_anim_tmp.prev_frame_styled)
|
||||
warped_styled_frame_ = warped_styled_frame.copy()
|
||||
|
||||
if sdcn_anim_tmp.process_counter > 0:
|
||||
alpha_mask = alpha_mask + sdcn_anim_tmp.prev_frame_alpha_mask * 0.5
|
||||
sdcn_anim_tmp.prev_frame_alpha_mask = alpha_mask
|
||||
# alpha_mask = np.clip(alpha_mask + 0.05, 0.05, 0.95)
|
||||
alpha_mask = np.clip(alpha_mask, 0, 1)
|
||||
|
||||
fl_w, fl_h = prev_flow.shape[:2]
|
||||
prev_flow_n = prev_flow / np.array([fl_h,fl_w])
|
||||
flow_mask = np.clip(1 - np.linalg.norm(prev_flow_n, axis=-1)[...,None], 0, 1)
|
||||
|
||||
# fix warped styled frame from duplicated that occures on the places where flow is zero, but only because there is no place to get the color from
|
||||
warped_styled_frame = curr_frame.astype(float) * alpha_mask * flow_mask + warped_styled_frame.astype(float) * (1 - alpha_mask * flow_mask)
|
||||
|
||||
# This clipping at lower side required to fix small trailing issues that for some reason left outside of the bright part of the mask,
|
||||
# and at the higher part it making parts changed strongly to do it with less flickering.
|
||||
|
||||
occlusion_mask = np.clip(alpha_mask * 255, 0, 255).astype(np.uint8)
|
||||
|
||||
# normalizing the colors
|
||||
processed_frame = skimage.exposure.match_histograms(processed_frame, curr_frame, multichannel=False, channel_axis=-1)
|
||||
processed_frame = processed_frame.astype(float) * alpha_mask + warped_styled_frame.astype(float) * (1 - alpha_mask)
|
||||
|
||||
processed_frame = processed_frame * 0.9 + curr_frame * 0.1
|
||||
processed_frame = np.clip(processed_frame, 0, 255).astype(np.uint8)
|
||||
sdcn_anim_tmp.prev_frame_styled = processed_frame.copy()
|
||||
|
||||
args_dict['init_img'] = Image.fromarray(processed_frame)
|
||||
args_dict['denoising_strength'] = args_dict['fix_frame_strength']
|
||||
args_dict['seed'] = 8888
|
||||
utils.set_CNs_input_image(args_dict, Image.fromarray(curr_frame))
|
||||
processed_frames, _, _, _ = utils.img2img(args_dict)
|
||||
processed_frame = np.array(processed_frames[0])
|
||||
|
||||
processed_frame = np.clip(processed_frame, 0, 255).astype(np.uint8)
|
||||
warped_styled_frame_ = np.clip(warped_styled_frame_, 0, 255).astype(np.uint8)
|
||||
|
||||
|
||||
# Write the frame to the output video
|
||||
frame_out = np.clip(processed_frame, 0, 255).astype(np.uint8)
|
||||
frame_out = cv2.cvtColor(frame_out, cv2.COLOR_RGB2BGR)
|
||||
sdcn_anim_tmp.output_video.write(frame_out)
|
||||
|
||||
sdcn_anim_tmp.process_counter += 1
|
||||
if sdcn_anim_tmp.process_counter >= sdcn_anim_tmp.total_frames - 1:
|
||||
sdcn_anim_tmp.input_video.release()
|
||||
sdcn_anim_tmp.output_video.release()
|
||||
sdcn_anim_tmp.prev_frame = None
|
||||
|
||||
#print(f'\nEND OF STEP {step}, {sdcn_anim_tmp.prepear_counter}, {sdcn_anim_tmp.process_counter}')
|
||||
yield get_cur_stat(), curr_frame, occlusion_mask, warped_styled_frame_, processed_frame, '', gr.Button.update(interactive=False), gr.Button.update(interactive=True)
|
||||
except:
|
||||
pass
|
||||
|
||||
RAFT_clear_memory()
|
||||
|
||||
sdcn_anim_tmp.input_video.release()
|
||||
sdcn_anim_tmp.output_video.release()
|
||||
|
||||
curr_frame = gr.Image.update()
|
||||
occlusion_mask = gr.Image.update()
|
||||
warped_styled_frame_ = gr.Image.update()
|
||||
processed_frame = gr.Image.update()
|
||||
|
||||
yield get_cur_stat(), curr_frame, occlusion_mask, warped_styled_frame_, processed_frame, '', gr.Button.update(interactive=True), gr.Button.update(interactive=False)
|
||||
|
|
@ -1,476 +0,0 @@
|
|||
import sys, os
|
||||
basedirs = [os.getcwd()]
|
||||
|
||||
for basedir in basedirs:
|
||||
paths_to_ensure = [
|
||||
basedir,
|
||||
basedir + '/extensions/sd-cn-animation/scripts',
|
||||
basedir + '/extensions/SD-CN-Animation/scripts'
|
||||
]
|
||||
|
||||
for scripts_path_fix in paths_to_ensure:
|
||||
if not scripts_path_fix in sys.path:
|
||||
sys.path.extend([scripts_path_fix])
|
||||
|
||||
import math
|
||||
import os
|
||||
import sys
|
||||
import traceback
|
||||
|
||||
import numpy as np
|
||||
from PIL import Image, ImageOps, ImageFilter, ImageEnhance, ImageChops
|
||||
|
||||
from modules import devices, sd_samplers, img2img
|
||||
from modules import shared, sd_hijack, lowvram
|
||||
from modules.generation_parameters_copypaste import create_override_settings_dict
|
||||
from modules.processing import Processed, StableDiffusionProcessingImg2Img, process_images
|
||||
from modules.shared import opts, devices, state
|
||||
import modules.shared as shared
|
||||
import modules.processing as processing
|
||||
from modules.ui import plaintext_to_html
|
||||
import modules.images as images
|
||||
import modules.scripts
|
||||
|
||||
import gc
|
||||
import cv2
|
||||
import gradio as gr
|
||||
|
||||
import time
|
||||
import skimage
|
||||
import datetime
|
||||
|
||||
from flow_utils import RAFT_estimate_flow, RAFT_clear_memory, compute_diff_map
|
||||
from types import SimpleNamespace
|
||||
|
||||
class sdcn_anim_tmp:
|
||||
prepear_counter = 0
|
||||
process_counter = 0
|
||||
input_video = None
|
||||
output_video = None
|
||||
curr_frame = None
|
||||
prev_frame = None
|
||||
prev_frame_styled = None
|
||||
prev_frame_alpha_mask = None
|
||||
fps = None
|
||||
total_frames = None
|
||||
prepared_frames = None
|
||||
prepared_next_flows = None
|
||||
prepared_prev_flows = None
|
||||
frames_prepared = False
|
||||
|
||||
def read_frame_from_video():
|
||||
# Reading video file
|
||||
if sdcn_anim_tmp.input_video.isOpened():
|
||||
ret, cur_frame = sdcn_anim_tmp.input_video.read()
|
||||
if cur_frame is not None:
|
||||
cur_frame = cv2.cvtColor(cur_frame, cv2.COLOR_BGR2RGB)
|
||||
else:
|
||||
cur_frame = None
|
||||
sdcn_anim_tmp.input_video.release()
|
||||
|
||||
return cur_frame
|
||||
|
||||
def get_cur_stat():
|
||||
stat = f'Frames prepared: {sdcn_anim_tmp.prepear_counter + 1} / {sdcn_anim_tmp.total_frames}; '
|
||||
stat += f'Frames processed: {sdcn_anim_tmp.process_counter + 1} / {sdcn_anim_tmp.total_frames}; '
|
||||
return stat
|
||||
|
||||
def clear_memory_from_sd():
|
||||
if shared.sd_model is not None:
|
||||
sd_hijack.model_hijack.undo_hijack(shared.sd_model)
|
||||
try:
|
||||
lowvram.send_everything_to_cpu()
|
||||
except Exception as e:
|
||||
...
|
||||
del shared.sd_model
|
||||
shared.sd_model = None
|
||||
gc.collect()
|
||||
devices.torch_gc()
|
||||
|
||||
def get_device():
|
||||
device=devices.get_optimal_device()
|
||||
#print('device',device)
|
||||
return device
|
||||
|
||||
def args_to_dict(*args): # converts list of argumets into dictionary for better handling of it
|
||||
args_list = ['id_task', 'mode', 'prompt', 'negative_prompt', 'prompt_styles', 'init_video', 'sketch', 'init_img_with_mask', 'inpaint_color_sketch', 'inpaint_color_sketch_orig', 'init_img_inpaint', 'init_mask_inpaint', 'steps', 'sampler_index', 'mask_blur', 'mask_alpha', 'inpainting_fill', 'restore_faces', 'tiling', 'n_iter', 'batch_size', 'cfg_scale', 'image_cfg_scale', 'denoising_strength', 'fix_frame_strength', 'seed', 'subseed', 'subseed_strength', 'seed_resize_from_h', 'seed_resize_from_w', 'seed_enable_extras', 'height', 'width', 'resize_mode', 'inpaint_full_res', 'inpaint_full_res_padding', 'inpainting_mask_invert', 'img2img_batch_input_dir', 'img2img_batch_output_dir', 'img2img_batch_inpaint_mask_dir', 'override_settings_texts']
|
||||
|
||||
# set default values for params that were not specified
|
||||
args_dict = {
|
||||
'mode': 0,
|
||||
'prompt': '',
|
||||
'negative_prompt': '',
|
||||
'prompt_styles': [],
|
||||
'init_video': None, # Always required
|
||||
|
||||
'steps': 15,
|
||||
'sampler_index': 0, # 'Euler a'
|
||||
'mask_blur': 0,
|
||||
|
||||
'inpainting_fill': 1, # original
|
||||
'restore_faces': False,
|
||||
'tiling': False,
|
||||
'n_iter': 1,
|
||||
'batch_size': 1,
|
||||
'cfg_scale': 5.5,
|
||||
'image_cfg_scale': 1.5,
|
||||
'denoising_strength': 0.75,
|
||||
'fix_frame_strength': 0.15,
|
||||
'seed': -1,
|
||||
'subseed': -1,
|
||||
'subseed_strength': 0,
|
||||
'seed_resize_from_h': 512,
|
||||
'seed_resize_from_w': 512,
|
||||
'seed_enable_extras': False,
|
||||
'height': 512,
|
||||
'width': 512,
|
||||
'resize_mode': 1,
|
||||
'inpaint_full_res': True,
|
||||
'inpaint_full_res_padding': 0,
|
||||
}
|
||||
|
||||
args = list(args)
|
||||
|
||||
for i in range(len(args_list)):
|
||||
if (args[i] is None) and (args_list[i] in args_dict):
|
||||
args[i] = args_dict[args_list[i]]
|
||||
else:
|
||||
args_dict[args_list[i]] = args[i]
|
||||
|
||||
args_dict['script_inputs'] = args[len(args_list):]
|
||||
return args_dict, args
|
||||
|
||||
# TODO: Refactor all the code below
|
||||
|
||||
def start_process(*args):
|
||||
args_dict, args_list = args_to_dict(*args)
|
||||
|
||||
sdcn_anim_tmp.process_counter = 0
|
||||
sdcn_anim_tmp.prepear_counter = 0
|
||||
|
||||
# Open the input video file
|
||||
sdcn_anim_tmp.input_video = cv2.VideoCapture(args_dict['init_video'].name)
|
||||
|
||||
# Get useful info from the source video
|
||||
sdcn_anim_tmp.fps = int(sdcn_anim_tmp.input_video.get(cv2.CAP_PROP_FPS))
|
||||
sdcn_anim_tmp.total_frames = int(sdcn_anim_tmp.input_video.get(cv2.CAP_PROP_FRAME_COUNT))
|
||||
|
||||
# Create an output video file with the same fps, width, and height as the input video
|
||||
output_video_name = f'outputs/sd-cn-animation/vid2vid/{datetime.datetime.now().strftime("%Y-%m-%d_%H-%M-%S")}.mp4'
|
||||
os.makedirs(os.path.dirname(output_video_name), exist_ok=True)
|
||||
sdcn_anim_tmp.output_video = cv2.VideoWriter(output_video_name, cv2.VideoWriter_fourcc(*'mp4v'), sdcn_anim_tmp.fps, (args_dict['width'], args_dict['height']))
|
||||
|
||||
curr_frame = read_frame_from_video()
|
||||
curr_frame = cv2.resize(curr_frame, (args_dict['width'], args_dict['height']))
|
||||
sdcn_anim_tmp.prepared_frames = np.zeros((11, args_dict['height'], args_dict['width'], 3), dtype=np.uint8)
|
||||
sdcn_anim_tmp.prepared_next_flows = np.zeros((10, args_dict['height'], args_dict['width'], 2))
|
||||
sdcn_anim_tmp.prepared_prev_flows = np.zeros((10, args_dict['height'], args_dict['width'], 2))
|
||||
sdcn_anim_tmp.prepared_frames[0] = curr_frame
|
||||
|
||||
args_dict['init_img'] = Image.fromarray(curr_frame)
|
||||
#args_list[5] = Image.fromarray(curr_frame)
|
||||
processed_frames, _, _, _ = img2img(args_dict)
|
||||
processed_frame = np.array(processed_frames[0])
|
||||
processed_frame = skimage.exposure.match_histograms(processed_frame, curr_frame, multichannel=False, channel_axis=-1)
|
||||
processed_frame = np.clip(processed_frame, 0, 255).astype(np.uint8)
|
||||
#print('Processed frame ', 0)
|
||||
|
||||
|
||||
sdcn_anim_tmp.curr_frame = curr_frame
|
||||
sdcn_anim_tmp.prev_frame = curr_frame.copy()
|
||||
sdcn_anim_tmp.prev_frame_styled = processed_frame.copy()
|
||||
yield get_cur_stat(), sdcn_anim_tmp.curr_frame, None, None, processed_frame, ''
|
||||
|
||||
for step in range((sdcn_anim_tmp.total_frames-1) * 2):
|
||||
args_dict, args_list = args_to_dict(*args)
|
||||
|
||||
occlusion_mask = None
|
||||
prev_frame = None
|
||||
curr_frame = sdcn_anim_tmp.curr_frame
|
||||
warped_styled_frame = gr.Image.update()
|
||||
processed_frame = gr.Image.update()
|
||||
|
||||
prepare_steps = 10
|
||||
if sdcn_anim_tmp.process_counter % prepare_steps == 0 and not sdcn_anim_tmp.frames_prepared: # prepare next 10 frames for processing
|
||||
#clear_memory_from_sd()
|
||||
device = get_device()
|
||||
|
||||
curr_frame = read_frame_from_video()
|
||||
if curr_frame is not None:
|
||||
curr_frame = cv2.resize(curr_frame, (args_dict['width'], args_dict['height']))
|
||||
prev_frame = sdcn_anim_tmp.prev_frame.copy()
|
||||
|
||||
next_flow, prev_flow, occlusion_mask, frame1_bg_removed, frame2_bg_removed = RAFT_estimate_flow(prev_frame, curr_frame, subtract_background=False, device=device)
|
||||
occlusion_mask = np.clip(occlusion_mask * 0.1 * 255, 0, 255).astype(np.uint8)
|
||||
|
||||
cn = sdcn_anim_tmp.prepear_counter % 10
|
||||
if sdcn_anim_tmp.prepear_counter % 10 == 0:
|
||||
sdcn_anim_tmp.prepared_frames[cn] = sdcn_anim_tmp.prev_frame
|
||||
sdcn_anim_tmp.prepared_frames[cn + 1] = curr_frame.copy()
|
||||
sdcn_anim_tmp.prepared_next_flows[cn] = next_flow.copy()
|
||||
sdcn_anim_tmp.prepared_prev_flows[cn] = prev_flow.copy()
|
||||
#print('Prepared frame ', cn+1)
|
||||
|
||||
sdcn_anim_tmp.prev_frame = curr_frame.copy()
|
||||
|
||||
sdcn_anim_tmp.prepear_counter += 1
|
||||
if sdcn_anim_tmp.prepear_counter % prepare_steps == 0 or \
|
||||
sdcn_anim_tmp.prepear_counter >= sdcn_anim_tmp.total_frames - 1 or \
|
||||
curr_frame is None:
|
||||
# Remove RAFT from memory
|
||||
RAFT_clear_memory()
|
||||
sdcn_anim_tmp.frames_prepared = True
|
||||
else:
|
||||
# process frame
|
||||
sdcn_anim_tmp.frames_prepared = False
|
||||
|
||||
cn = sdcn_anim_tmp.process_counter % 10
|
||||
curr_frame = sdcn_anim_tmp.prepared_frames[cn+1]
|
||||
prev_frame = sdcn_anim_tmp.prepared_frames[cn]
|
||||
next_flow = sdcn_anim_tmp.prepared_next_flows[cn]
|
||||
prev_flow = sdcn_anim_tmp.prepared_prev_flows[cn]
|
||||
|
||||
# process current frame
|
||||
args_dict['init_img'] = Image.fromarray(curr_frame)
|
||||
args_dict['seed'] = -1
|
||||
#args_list[5] = Image.fromarray(curr_frame)
|
||||
#args_list[24] = -1
|
||||
processed_frames, _, _, _ = img2img(args_dict)
|
||||
processed_frame = np.array(processed_frames[0])
|
||||
|
||||
|
||||
alpha_mask, warped_styled_frame = compute_diff_map(next_flow, prev_flow, prev_frame, curr_frame, sdcn_anim_tmp.prev_frame_styled)
|
||||
if sdcn_anim_tmp.process_counter > 0:
|
||||
alpha_mask = alpha_mask + sdcn_anim_tmp.prev_frame_alpha_mask * 0.5
|
||||
sdcn_anim_tmp.prev_frame_alpha_mask = alpha_mask
|
||||
# alpha_mask = np.clip(alpha_mask + 0.05, 0.05, 0.95)
|
||||
alpha_mask = np.clip(alpha_mask, 0, 1)
|
||||
|
||||
fl_w, fl_h = prev_flow.shape[:2]
|
||||
prev_flow_n = prev_flow / np.array([fl_h,fl_w])
|
||||
flow_mask = np.clip(1 - np.linalg.norm(prev_flow_n, axis=-1)[...,None], 0, 1)
|
||||
|
||||
# fix warped styled frame from duplicated that occures on the places where flow is zero, but only because there is no place to get the color from
|
||||
warped_styled_frame = curr_frame.astype(float) * alpha_mask * flow_mask + warped_styled_frame.astype(float) * (1 - alpha_mask * flow_mask)
|
||||
|
||||
# This clipping at lower side required to fix small trailing issues that for some reason left outside of the bright part of the mask,
|
||||
# and at the higher part it making parts changed strongly to do it with less flickering.
|
||||
|
||||
occlusion_mask = np.clip(alpha_mask * 255, 0, 255).astype(np.uint8)
|
||||
|
||||
# normalizing the colors
|
||||
processed_frame = skimage.exposure.match_histograms(processed_frame, curr_frame, multichannel=False, channel_axis=-1)
|
||||
processed_frame = processed_frame.astype(float) * alpha_mask + warped_styled_frame.astype(float) * (1 - alpha_mask)
|
||||
|
||||
processed_frame = processed_frame * 0.9 + curr_frame * 0.1
|
||||
processed_frame = np.clip(processed_frame, 0, 255).astype(np.uint8)
|
||||
sdcn_anim_tmp.prev_frame_styled = processed_frame.copy()
|
||||
|
||||
args_dict['init_img'] = Image.fromarray(processed_frame)
|
||||
args_dict['denoising_strength'] = args_dict['fix_frame_strength']
|
||||
args_dict['seed'] = 8888
|
||||
#args_list[5] = Image.fromarray(processed_frame)
|
||||
#args_list[23] = 0.15
|
||||
#args_list[24] = 8888
|
||||
processed_frames, _, _, _ = img2img(args_dict)
|
||||
processed_frame = np.array(processed_frames[0])
|
||||
|
||||
processed_frame = np.clip(processed_frame, 0, 255).astype(np.uint8)
|
||||
warped_styled_frame = np.clip(warped_styled_frame, 0, 255).astype(np.uint8)
|
||||
|
||||
|
||||
# Write the frame to the output video
|
||||
frame_out = np.clip(processed_frame, 0, 255).astype(np.uint8)
|
||||
frame_out = cv2.cvtColor(frame_out, cv2.COLOR_RGB2BGR)
|
||||
sdcn_anim_tmp.output_video.write(frame_out)
|
||||
|
||||
sdcn_anim_tmp.process_counter += 1
|
||||
if sdcn_anim_tmp.process_counter >= sdcn_anim_tmp.total_frames - 1:
|
||||
sdcn_anim_tmp.input_video.release()
|
||||
sdcn_anim_tmp.output_video.release()
|
||||
sdcn_anim_tmp.prev_frame = None
|
||||
|
||||
#print(f'\nEND OF STEP {step}, {sdcn_anim_tmp.prepear_counter}, {sdcn_anim_tmp.process_counter}')
|
||||
yield get_cur_stat(), curr_frame, occlusion_mask, warped_styled_frame, processed_frame, ''
|
||||
|
||||
#sdcn_anim_tmp.input_video.release()
|
||||
#sdcn_anim_tmp.output_video.release()
|
||||
|
||||
return get_cur_stat(), curr_frame, occlusion_mask, warped_styled_frame, processed_frame, ''
|
||||
|
||||
def process_img(p, input_img, output_dir, inpaint_mask_dir, args):
|
||||
processing.fix_seed(p)
|
||||
|
||||
#images = shared.listfiles(input_dir)
|
||||
images = [input_img]
|
||||
|
||||
is_inpaint_batch = False
|
||||
#if inpaint_mask_dir:
|
||||
# inpaint_masks = shared.listfiles(inpaint_mask_dir)
|
||||
# is_inpaint_batch = len(inpaint_masks) > 0
|
||||
#if is_inpaint_batch:
|
||||
# print(f"\nInpaint batch is enabled. {len(inpaint_masks)} masks found.")
|
||||
|
||||
#print(f"Will process {len(images)} images, creating {p.n_iter * p.batch_size} new images for each.")
|
||||
|
||||
save_normally = output_dir == ''
|
||||
|
||||
p.do_not_save_grid = True
|
||||
p.do_not_save_samples = not save_normally
|
||||
|
||||
state.job_count = len(images) * p.n_iter
|
||||
|
||||
generated_images = []
|
||||
for i, image in enumerate(images):
|
||||
state.job = f"{i+1} out of {len(images)}"
|
||||
if state.skipped:
|
||||
state.skipped = False
|
||||
|
||||
if state.interrupted:
|
||||
break
|
||||
|
||||
img = image #Image.open(image)
|
||||
# Use the EXIF orientation of photos taken by smartphones.
|
||||
img = ImageOps.exif_transpose(img)
|
||||
p.init_images = [img] * p.batch_size
|
||||
|
||||
#if is_inpaint_batch:
|
||||
# # try to find corresponding mask for an image using simple filename matching
|
||||
# mask_image_path = os.path.join(inpaint_mask_dir, os.path.basename(image))
|
||||
# # if not found use first one ("same mask for all images" use-case)
|
||||
# if not mask_image_path in inpaint_masks:
|
||||
# mask_image_path = inpaint_masks[0]
|
||||
# mask_image = Image.open(mask_image_path)
|
||||
# p.image_mask = mask_image
|
||||
|
||||
proc = modules.scripts.scripts_img2img.run(p, *args)
|
||||
if proc is None:
|
||||
proc = process_images(p)
|
||||
generated_images.append(proc.images[0])
|
||||
|
||||
#for n, processed_image in enumerate(proc.images):
|
||||
# filename = os.path.basename(image)
|
||||
|
||||
# if n > 0:
|
||||
# left, right = os.path.splitext(filename)
|
||||
# filename = f"{left}-{n}{right}"
|
||||
|
||||
# if not save_normally:
|
||||
# os.makedirs(output_dir, exist_ok=True)
|
||||
# if processed_image.mode == 'RGBA':
|
||||
# processed_image = processed_image.convert("RGB")
|
||||
# processed_image.save(os.path.join(output_dir, filename))
|
||||
|
||||
return generated_images
|
||||
|
||||
# id_task: str, mode: int, prompt: str, negative_prompt: str, prompt_styles: list, init_img, sketch, init_img_with_mask, inpaint_color_sketch, inpaint_color_sketch_orig, init_img_inpaint, init_mask_inpaint, steps: int, sampler_index: int, mask_blur: int, mask_alpha: float, inpainting_fill: int, restore_faces: bool, tiling: bool, n_iter: int, batch_size: int, cfg_scale: float, image_cfg_scale: float, denoising_strength: float, seed: int, subseed: int, subseed_strength: float, seed_resize_from_h: int, seed_resize_from_w: int, seed_enable_extras: bool, height: int, width: int, resize_mode: int, inpaint_full_res: bool, inpaint_full_res_padding: int, inpainting_mask_invert: int, img2img_batch_input_dir: str, img2img_batch_output_dir: str, img2img_batch_inpaint_mask_dir: str, override_settings_texts, *args
|
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def img2img(args_dict):
|
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args = SimpleNamespace(**args_dict)
|
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print('override_settings:', args.override_settings_texts)
|
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override_settings = create_override_settings_dict(args.override_settings_texts)
|
||||
|
||||
is_batch = args.mode == 5
|
||||
|
||||
if args.mode == 0: # img2img
|
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image = args.init_img.convert("RGB")
|
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mask = None
|
||||
elif args.mode == 1: # img2img sketch
|
||||
image = args.sketch.convert("RGB")
|
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mask = None
|
||||
elif args.mode == 2: # inpaint
|
||||
image, mask = args.init_img_with_mask["image"], args.init_img_with_mask["mask"]
|
||||
alpha_mask = ImageOps.invert(image.split()[-1]).convert('L').point(lambda x: 255 if x > 0 else 0, mode='1')
|
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mask = ImageChops.lighter(alpha_mask, mask.convert('L')).convert('L')
|
||||
image = image.convert("RGB")
|
||||
elif args.mode == 3: # inpaint sketch
|
||||
image = args.inpaint_color_sketch
|
||||
orig = args.inpaint_color_sketch_orig or args.inpaint_color_sketch
|
||||
pred = np.any(np.array(image) != np.array(orig), axis=-1)
|
||||
mask = Image.fromarray(pred.astype(np.uint8) * 255, "L")
|
||||
mask = ImageEnhance.Brightness(mask).enhance(1 - args.mask_alpha / 100)
|
||||
blur = ImageFilter.GaussianBlur(args.mask_blur)
|
||||
image = Image.composite(image.filter(blur), orig, mask.filter(blur))
|
||||
image = image.convert("RGB")
|
||||
elif args.mode == 4: # inpaint upload mask
|
||||
image = args.init_img_inpaint
|
||||
mask = args.init_mask_inpaint
|
||||
else:
|
||||
image = None
|
||||
mask = None
|
||||
|
||||
# Use the EXIF orientation of photos taken by smartphones.
|
||||
if image is not None:
|
||||
image = ImageOps.exif_transpose(image)
|
||||
|
||||
assert 0. <= args.denoising_strength <= 1., 'can only work with strength in [0.0, 1.0]'
|
||||
|
||||
p = StableDiffusionProcessingImg2Img(
|
||||
sd_model=shared.sd_model,
|
||||
outpath_samples=opts.outdir_samples or opts.outdir_img2img_samples,
|
||||
outpath_grids=opts.outdir_grids or opts.outdir_img2img_grids,
|
||||
prompt=args.prompt,
|
||||
negative_prompt=args.negative_prompt,
|
||||
styles=args.prompt_styles,
|
||||
seed=args.seed,
|
||||
subseed=args.subseed,
|
||||
subseed_strength=args.subseed_strength,
|
||||
seed_resize_from_h=args.seed_resize_from_h,
|
||||
seed_resize_from_w=args.seed_resize_from_w,
|
||||
seed_enable_extras=args.seed_enable_extras,
|
||||
sampler_name=sd_samplers.samplers_for_img2img[args.sampler_index].name,
|
||||
batch_size=args.batch_size,
|
||||
n_iter=args.n_iter,
|
||||
steps=args.steps,
|
||||
cfg_scale=args.cfg_scale,
|
||||
width=args.width,
|
||||
height=args.height,
|
||||
restore_faces=args.restore_faces,
|
||||
tiling=args.tiling,
|
||||
init_images=[image],
|
||||
mask=mask,
|
||||
mask_blur=args.mask_blur,
|
||||
inpainting_fill=args.inpainting_fill,
|
||||
resize_mode=args.resize_mode,
|
||||
denoising_strength=args.denoising_strength,
|
||||
image_cfg_scale=args.image_cfg_scale,
|
||||
inpaint_full_res=args.inpaint_full_res,
|
||||
inpaint_full_res_padding=args.inpaint_full_res_padding,
|
||||
inpainting_mask_invert=args.inpainting_mask_invert,
|
||||
override_settings=override_settings,
|
||||
)
|
||||
|
||||
p.scripts = modules.scripts.scripts_txt2img
|
||||
p.script_args = args.script_inputs
|
||||
|
||||
#if shared.cmd_opts.enable_console_prompts:
|
||||
# print(f"\nimg2img: {args.prompt}", file=shared.progress_print_out)
|
||||
|
||||
if mask:
|
||||
p.extra_generation_params["Mask blur"] = args.mask_blur
|
||||
|
||||
'''
|
||||
if is_batch:
|
||||
...
|
||||
# assert not shared.cmd_opts.hide_ui_dir_config, "Launched with --hide-ui-dir-config, batch img2img disabled"
|
||||
# process_batch(p, img2img_batch_input_dir, img2img_batch_output_dir, img2img_batch_inpaint_mask_dir, args.script_inputs)
|
||||
# processed = Processed(p, [], p.seed, "")
|
||||
else:
|
||||
processed = modules.scripts.scripts_img2img.run(p, *args.script_inputs)
|
||||
if processed is None:
|
||||
processed = process_images(p)
|
||||
'''
|
||||
|
||||
generated_images = process_img(p, image, None, '', args.script_inputs)
|
||||
processed = Processed(p, [], p.seed, "")
|
||||
p.close()
|
||||
|
||||
shared.total_tqdm.clear()
|
||||
|
||||
generation_info_js = processed.js()
|
||||
#if opts.samples_log_stdout:
|
||||
# print(generation_info_js)
|
||||
|
||||
#if opts.do_not_show_images:
|
||||
# processed.images = []
|
||||
|
||||
return generated_images, generation_info_js, plaintext_to_html(processed.info), plaintext_to_html(processed.comments)
|
||||
Loading…
Reference in New Issue