fixed issue with running ControlNet
Alexey Borsky
parent
c522008065
commit
9a861d2a60
12
readme.md
12
readme.md
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@ -1,6 +1,5 @@
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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 inpainting 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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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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### Video to Video Examples:
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</table>
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@ -46,17 +45,12 @@ 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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*TODO*
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Download RAFT 'raft-things.pth' from here: https://drive.google.com/drive/folders/1sWDsfuZ3Up38EUQt7-JDTT1HcGHuJgvT and place it into 'stable-diffusion-webui/models/RAFT/' folder.
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All generated video will be saved into 'outputs/sd-cn-animation' folder.
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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'. 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 separate flag '-rb' for background removal process at the flow computation stage in the compute_flow.py script.
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* Added flow normalization before rescaling it, so the magnitude of the flow computed correctly at the different resolution.
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* Less ghosting and color change in vid2vid mode
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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 image duplicated from the parts of the image that cannot be relocated from the optical flow.
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@ -178,6 +178,7 @@ def on_ui_tabs():
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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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@ -132,10 +132,10 @@ def compute_diff_map(next_flow, prev_flow, prev_frame, cur_frame, prev_frame_sty
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diff_mask_org = np.abs(warped_frame.astype(np.float32) - cur_frame.astype(np.float32)) / 255
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diff_mask_org = diff_mask_org.max(axis = -1, keepdims=True)
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diff_mask_stl = np.abs(warped_frame_styled.astype(np.float32) - cur_frame.astype(np.float32)) / 255
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diff_mask_stl = diff_mask_stl.max(axis = -1, keepdims=True)
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#diff_mask_stl = np.abs(warped_frame_styled.astype(np.float32) - cur_frame.astype(np.float32)) / 255
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#diff_mask_stl = diff_mask_stl.max(axis = -1, keepdims=True)
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alpha_mask = np.maximum(occlusion_mask * 0.3, diff_mask_org * 4, diff_mask_stl * 2)
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alpha_mask = np.maximum(occlusion_mask * 0.3, diff_mask_org * 3) #, diff_mask_stl * 2
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alpha_mask = alpha_mask.repeat(3, axis = -1)
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#alpha_mask_blured = cv2.dilate(alpha_mask, np.ones((5, 5), np.float32))
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@ -24,7 +24,7 @@ from modules import devices, sd_samplers, img2img
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from modules import shared, sd_hijack, lowvram
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from modules.generation_parameters_copypaste import create_override_settings_dict
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from modules.processing import Processed, StableDiffusionProcessingImg2Img, process_images
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from modules.shared import opts, devices
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from modules.shared import opts, devices, state
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import modules.shared as shared
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import modules.processing as processing
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from modules.ui import plaintext_to_html
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@ -50,6 +50,7 @@ class sdcn_anim_tmp:
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curr_frame = None
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prev_frame = None
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prev_frame_styled = None
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prev_frame_alpha_mask = None
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fps = None
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total_frames = None
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prepared_frames = None
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@ -92,7 +93,7 @@ def get_device():
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return device
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def args_to_dict(*args): # converts list of argumets into dictionary for better handling of it
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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', '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']
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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']
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# set default values for params that were not specified
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args_dict = {
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@ -114,6 +115,7 @@ def args_to_dict(*args): # converts list of argumets into dictionary for better
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'cfg_scale': 5.5,
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'image_cfg_scale': 1.5,
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'denoising_strength': 0.75,
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'fix_frame_strength': 0.15,
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'seed': -1,
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'subseed': -1,
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'subseed_strength': 0,
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@ -138,6 +140,8 @@ def args_to_dict(*args): # converts list of argumets into dictionary for better
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args_dict['script_inputs'] = args[len(args_list):]
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return args_dict, args
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# TODO: Refactor all the code below
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def start_process(*args):
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args_dict, args_list = args_to_dict(*args)
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@ -163,9 +167,9 @@ def start_process(*args):
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sdcn_anim_tmp.prepared_prev_flows = np.zeros((10, args_dict['height'], args_dict['width'], 2))
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sdcn_anim_tmp.prepared_frames[0] = curr_frame
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#args_dict['init_img'] = cur_frame
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args_list[5] = Image.fromarray(curr_frame)
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processed_frames, _, _, _ = modules.img2img.img2img(*args_list) #img2img(args_dict)
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args_dict['init_img'] = Image.fromarray(curr_frame)
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#args_list[5] = Image.fromarray(curr_frame)
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processed_frames, _, _, _ = img2img(args_dict)
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processed_frame = np.array(processed_frames[0])
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processed_frame = skimage.exposure.match_histograms(processed_frame, curr_frame, multichannel=False, channel_axis=-1)
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processed_frame = np.clip(processed_frame, 0, 255).astype(np.uint8)
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@ -177,8 +181,6 @@ def start_process(*args):
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sdcn_anim_tmp.prev_frame_styled = processed_frame.copy()
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yield get_cur_stat(), sdcn_anim_tmp.curr_frame, None, None, processed_frame, ''
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# TODO: SOLVE PROBLEM with wrong prev frame on the start on new processing iterations
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for step in range((sdcn_anim_tmp.total_frames-1) * 2):
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args_dict, args_list = args_to_dict(*args)
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@ -229,14 +231,20 @@ def start_process(*args):
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prev_flow = sdcn_anim_tmp.prepared_prev_flows[cn]
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# process current frame
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args_list[5] = Image.fromarray(curr_frame)
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args_list[24] = -1
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processed_frames, _, _, _ = modules.img2img.img2img(*args_list)
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args_dict['init_img'] = Image.fromarray(curr_frame)
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args_dict['seed'] = -1
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#args_list[5] = Image.fromarray(curr_frame)
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#args_list[24] = -1
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processed_frames, _, _, _ = img2img(args_dict)
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processed_frame = np.array(processed_frames[0])
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alpha_mask, warped_styled_frame = compute_diff_map(next_flow, prev_flow, prev_frame, curr_frame, sdcn_anim_tmp.prev_frame_styled)
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alpha_mask = np.clip(alpha_mask + 0.05, 0.05, 0.95)
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if sdcn_anim_tmp.process_counter > 0:
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alpha_mask = alpha_mask + sdcn_anim_tmp.prev_frame_alpha_mask * 0.5
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sdcn_anim_tmp.prev_frame_alpha_mask = alpha_mask
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# alpha_mask = np.clip(alpha_mask + 0.05, 0.05, 0.95)
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alpha_mask = np.clip(alpha_mask, 0, 1)
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fl_w, fl_h = prev_flow.shape[:2]
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prev_flow_n = prev_flow / np.array([fl_h,fl_w])
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@ -258,10 +266,13 @@ def start_process(*args):
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processed_frame = np.clip(processed_frame, 0, 255).astype(np.uint8)
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sdcn_anim_tmp.prev_frame_styled = processed_frame.copy()
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args_list[5] = Image.fromarray(processed_frame)
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args_list[23] = 0.15
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args_list[24] = 8888
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processed_frames, _, _, _ = modules.img2img.img2img(*args_list)
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args_dict['init_img'] = Image.fromarray(processed_frame)
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args_dict['denoising_strength'] = args_dict['fix_frame_strength']
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args_dict['seed'] = 8888
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#args_list[5] = Image.fromarray(processed_frame)
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#args_list[23] = 0.15
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#args_list[24] = 8888
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processed_frames, _, _, _ = img2img(args_dict)
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processed_frame = np.array(processed_frames[0])
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processed_frame = np.clip(processed_frame, 0, 255).astype(np.uint8)
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@ -287,7 +298,71 @@ def start_process(*args):
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return get_cur_stat(), curr_frame, occlusion_mask, warped_styled_frame, processed_frame, ''
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'''
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def process_img(p, input_img, output_dir, inpaint_mask_dir, args):
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processing.fix_seed(p)
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#images = shared.listfiles(input_dir)
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images = [input_img]
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is_inpaint_batch = False
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#if inpaint_mask_dir:
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# inpaint_masks = shared.listfiles(inpaint_mask_dir)
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# is_inpaint_batch = len(inpaint_masks) > 0
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#if is_inpaint_batch:
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# print(f"\nInpaint batch is enabled. {len(inpaint_masks)} masks found.")
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#print(f"Will process {len(images)} images, creating {p.n_iter * p.batch_size} new images for each.")
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save_normally = output_dir == ''
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p.do_not_save_grid = True
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p.do_not_save_samples = not save_normally
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state.job_count = len(images) * p.n_iter
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generated_images = []
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for i, image in enumerate(images):
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state.job = f"{i+1} out of {len(images)}"
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if state.skipped:
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state.skipped = False
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if state.interrupted:
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break
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img = image #Image.open(image)
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# Use the EXIF orientation of photos taken by smartphones.
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img = ImageOps.exif_transpose(img)
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p.init_images = [img] * p.batch_size
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#if is_inpaint_batch:
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# # try to find corresponding mask for an image using simple filename matching
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# mask_image_path = os.path.join(inpaint_mask_dir, os.path.basename(image))
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# # if not found use first one ("same mask for all images" use-case)
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# if not mask_image_path in inpaint_masks:
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# mask_image_path = inpaint_masks[0]
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# mask_image = Image.open(mask_image_path)
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# p.image_mask = mask_image
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proc = modules.scripts.scripts_img2img.run(p, *args)
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if proc is None:
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proc = process_images(p)
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generated_images.append(proc.images[0])
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#for n, processed_image in enumerate(proc.images):
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# filename = os.path.basename(image)
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# if n > 0:
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# left, right = os.path.splitext(filename)
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# filename = f"{left}-{n}{right}"
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# if not save_normally:
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# os.makedirs(output_dir, exist_ok=True)
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# if processed_image.mode == 'RGBA':
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# processed_image = processed_image.convert("RGB")
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# processed_image.save(os.path.join(output_dir, filename))
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return generated_images
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# 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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@ -376,6 +451,7 @@ def img2img(args_dict):
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if mask:
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p.extra_generation_params["Mask blur"] = args.mask_blur
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'''
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if is_batch:
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...
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# assert not shared.cmd_opts.hide_ui_dir_config, "Launched with --hide-ui-dir-config, batch img2img disabled"
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@ -385,7 +461,10 @@ def img2img(args_dict):
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processed = modules.scripts.scripts_img2img.run(p, *args.script_inputs)
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if processed is None:
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processed = process_images(p)
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'''
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generated_images = process_img(p, image, None, '', args.script_inputs)
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processed = Processed(p, [], p.seed, "")
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p.close()
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shared.total_tqdm.clear()
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@ -397,4 +476,4 @@ def img2img(args_dict):
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if opts.do_not_show_images:
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processed.images = []
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return processed.images[0] #, generation_info_js, plaintext_to_html(processed.info), plaintext_to_html(processed.comments)'''
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return generated_images, generation_info_js, plaintext_to_html(processed.info), plaintext_to_html(processed.comments)
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