# Copyright (C) 2023 Deforum LLC # # This program is free software: you can redistribute it and/or modify # it under the terms of the GNU Affero General Public License as published by # the Free Software Foundation, version 3 of the License. # # This program is distributed in the hope that it will be useful, # but WITHOUT ANY WARRANTY; without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the # GNU General Public License for more details. # # You should have received a copy of the GNU Affero General Public License # along with this program. If not, see . # Contact the authors: https://deforum.github.io/ import gradio as gr import modules.paths as ph from .general_utils import get_os from .upscaling import process_ncnn_upscale_vid_upload_logic from .video_audio_utilities import extract_number, get_quick_vid_info, get_ffmpeg_params from .frame_interpolation import process_interp_vid_upload_logic, process_interp_pics_upload_logic, gradio_f_interp_get_fps_and_fcount from .vid2depth import process_depth_vid_upload_logic f_models_path = ph.models_path + '/Deforum' def handle_change_functions(l_vars): l_vars['override_settings_with_file'].change(fn=hide_if_false, inputs=l_vars['override_settings_with_file'], outputs=l_vars['custom_settings_file']) l_vars['sampler'].change(fn=show_when_ddim, inputs=l_vars['sampler'], outputs=l_vars['enable_ddim_eta_scheduling']) l_vars['sampler'].change(fn=show_when_ancestral_samplers, inputs=l_vars['sampler'], outputs=l_vars['enable_ancestral_eta_scheduling']) l_vars['enable_ancestral_eta_scheduling'].change(fn=hide_if_false, inputs=l_vars['enable_ancestral_eta_scheduling'], outputs=l_vars['ancestral_eta_schedule']) l_vars['enable_ddim_eta_scheduling'].change(fn=hide_if_false, inputs=l_vars['enable_ddim_eta_scheduling'], outputs=l_vars['ddim_eta_schedule']) l_vars['animation_mode'].change(fn=change_max_frames_visibility, inputs=l_vars['animation_mode'], outputs=l_vars['max_frames']) diffusion_cadence_outputs = [l_vars['diffusion_cadence'], l_vars['guided_images_accord'], l_vars['optical_flow_cadence_row'], l_vars['cadence_flow_factor_schedule'], l_vars['optical_flow_redo_generation'], l_vars['redo_flow_factor_schedule'], l_vars['diffusion_redo']] for output in diffusion_cadence_outputs: l_vars['animation_mode'].change(fn=change_diffusion_cadence_visibility, inputs=l_vars['animation_mode'], outputs=output) three_d_related_outputs = [l_vars['only_3d_motion_column'], l_vars['depth_warp_row_1'], l_vars['depth_warp_row_2'], l_vars['depth_warp_row_3'], l_vars['depth_warp_row_4'], l_vars['depth_warp_row_5'], l_vars['depth_warp_row_6'], l_vars['depth_warp_row_7']] for output in three_d_related_outputs: l_vars['animation_mode'].change(fn=disble_3d_related_stuff, inputs=l_vars['animation_mode'], outputs=output) pers_flip_outputs = [l_vars['per_f_th_row'], l_vars['per_f_ph_row'], l_vars['per_f_ga_row'], l_vars['per_f_f_row']] for output in pers_flip_outputs: l_vars['enable_perspective_flip'].change(fn=hide_if_false, inputs=l_vars['enable_perspective_flip'], outputs=output) l_vars['animation_mode'].change(fn=per_flip_handle, inputs=[l_vars['animation_mode'], l_vars['enable_perspective_flip']], outputs=output) l_vars['animation_mode'].change(fn=only_show_in_non_3d_mode, inputs=l_vars['animation_mode'], outputs=l_vars['depth_warp_msg_html']) l_vars['animation_mode'].change(fn=enable_2d_related_stuff, inputs=l_vars['animation_mode'], outputs=l_vars['only_2d_motion_column']) l_vars['animation_mode'].change(fn=disable_by_interpolation, inputs=l_vars['animation_mode'], outputs=l_vars['color_force_grayscale']) l_vars['animation_mode'].change(fn=disable_by_interpolation, inputs=l_vars['animation_mode'], outputs=l_vars['noise_tab_column']) l_vars['animation_mode'].change(fn=disable_pers_flip_accord, inputs=l_vars['animation_mode'], outputs=l_vars['enable_per_f_row']) l_vars['animation_mode'].change(fn=disable_pers_flip_accord, inputs=l_vars['animation_mode'], outputs=l_vars['both_anim_mode_motion_params_column']) l_vars['aspect_ratio_use_old_formula'].change(fn=hide_if_true, inputs=l_vars['aspect_ratio_use_old_formula'], outputs=l_vars['aspect_ratio_schedule']) l_vars['animation_mode'].change(fn=show_hybrid_html_msg, inputs=l_vars['animation_mode'], outputs=l_vars['hybrid_msg_html']) l_vars['animation_mode'].change(fn=change_hybrid_tab_status, inputs=l_vars['animation_mode'], outputs=l_vars['hybrid_sch_accord']) l_vars['animation_mode'].change(fn=change_hybrid_tab_status, inputs=l_vars['animation_mode'], outputs=l_vars['hybrid_settings_accord']) l_vars['animation_mode'].change(fn=change_hybrid_tab_status, inputs=l_vars['animation_mode'], outputs=l_vars['humans_masking_accord']) l_vars['optical_flow_redo_generation'].change(fn=hide_if_none, inputs=l_vars['optical_flow_redo_generation'], outputs=l_vars['redo_flow_factor_schedule_column']) l_vars['optical_flow_cadence'].change(fn=hide_if_none, inputs=l_vars['optical_flow_cadence'], outputs=l_vars['cadence_flow_factor_schedule_column']) l_vars['seed_behavior'].change(fn=change_seed_iter_visibility, inputs=l_vars['seed_behavior'], outputs=l_vars['seed_iter_N_row']) l_vars['seed_behavior'].change(fn=change_seed_schedule_visibility, inputs=l_vars['seed_behavior'], outputs=l_vars['seed_schedule_row']) l_vars['color_coherence'].change(fn=change_color_coherence_video_every_N_frames_visibility, inputs=l_vars['color_coherence'], outputs=l_vars['color_coherence_video_every_N_frames_row']) l_vars['color_coherence'].change(fn=change_color_coherence_image_path_visibility, inputs=l_vars['color_coherence'], outputs=l_vars['color_coherence_image_path_row']) l_vars['noise_type'].change(fn=change_perlin_visibility, inputs=l_vars['noise_type'], outputs=l_vars['perlin_row']) l_vars['diffusion_cadence'].change(fn=hide_optical_flow_cadence, inputs=l_vars['diffusion_cadence'], outputs=l_vars['optical_flow_cadence_row']) l_vars['depth_algorithm'].change(fn=legacy_3d_mode, inputs=l_vars['depth_algorithm'], outputs=l_vars['midas_weight']) l_vars['depth_algorithm'].change(fn=show_leres_html_msg, inputs=l_vars['depth_algorithm'], outputs=l_vars['leres_license_msg']) l_vars['fps'].change(fn=change_gif_button_visibility, inputs=l_vars['fps'], outputs=l_vars['make_gif']) l_vars['r_upscale_model'].change(fn=update_r_upscale_factor, inputs=l_vars['r_upscale_model'], outputs=l_vars['r_upscale_factor']) l_vars['ncnn_upscale_model'].change(fn=update_r_upscale_factor, inputs=l_vars['ncnn_upscale_model'], outputs=l_vars['ncnn_upscale_factor']) l_vars['ncnn_upscale_model'].change(update_upscale_out_res_by_model_name, inputs=[l_vars['ncnn_upscale_in_vid_res'], l_vars['ncnn_upscale_model']], outputs=l_vars['ncnn_upscale_out_vid_res']) l_vars['ncnn_upscale_factor'].change(update_upscale_out_res, inputs=[l_vars['ncnn_upscale_in_vid_res'], l_vars['ncnn_upscale_factor']], outputs=l_vars['ncnn_upscale_out_vid_res']) l_vars['vid_to_upscale_chosen_file'].change(vid_upscale_gradio_update_stats, inputs=[l_vars['vid_to_upscale_chosen_file'], l_vars['ncnn_upscale_factor']], outputs=[l_vars['ncnn_upscale_in_vid_fps_ui_window'], l_vars['ncnn_upscale_in_vid_frame_count_window'], l_vars['ncnn_upscale_in_vid_res'], l_vars['ncnn_upscale_out_vid_res']]) l_vars['hybrid_comp_mask_type'].change(fn=hide_if_none, inputs=l_vars['hybrid_comp_mask_type'], outputs=l_vars['hybrid_comp_mask_row']) hybrid_motion_outputs = [l_vars['hybrid_flow_method'], l_vars['hybrid_flow_factor_schedule'], l_vars['hybrid_flow_consistency'], l_vars['hybrid_consistency_blur'], l_vars['hybrid_motion_use_prev_img']] for output in hybrid_motion_outputs: l_vars['hybrid_motion'].change(fn=disable_by_non_optical_flow, inputs=l_vars['hybrid_motion'], outputs=output) l_vars['hybrid_flow_consistency'].change(fn=hide_if_false, inputs=l_vars['hybrid_flow_consistency'], outputs=l_vars['hybrid_consistency_blur']) l_vars['hybrid_composite'].change(fn=disable_by_hybrid_composite_dynamic, inputs=[l_vars['hybrid_composite'], l_vars['hybrid_comp_mask_type']], outputs=l_vars['hybrid_comp_mask_row']) hybrid_composite_outputs = [l_vars['humans_masking_accord'], l_vars['hybrid_sch_accord'], l_vars['hybrid_comp_mask_type'], l_vars['hybrid_use_first_frame_as_init_image'], l_vars['hybrid_use_init_image']] for output in hybrid_composite_outputs: l_vars['hybrid_composite'].change(fn=hide_if_false, inputs=l_vars['hybrid_composite'], outputs=output) hybrid_comp_mask_type_outputs = [l_vars['hybrid_comp_mask_blend_alpha_schedule_row'], l_vars['hybrid_comp_mask_contrast_schedule_row'], l_vars['hybrid_comp_mask_auto_contrast_cutoff_high_schedule_row'], l_vars['hybrid_comp_mask_auto_contrast_cutoff_low_schedule_row']] for output in hybrid_comp_mask_type_outputs: l_vars['hybrid_comp_mask_type'].change(fn=hide_if_none, inputs=l_vars['hybrid_comp_mask_type'], outputs=output) # End of hybrid related skip_video_creation_outputs = [l_vars['fps_out_format_row'], l_vars['soundtrack_row'], l_vars['store_frames_in_ram'], l_vars['make_gif'], l_vars['r_upscale_row'], l_vars['delete_imgs'], l_vars['delete_input_frames']] for output in skip_video_creation_outputs: l_vars['skip_video_creation'].change(fn=change_visibility_from_skip_video, inputs=l_vars['skip_video_creation'], outputs=output) l_vars['frame_interpolation_slow_mo_enabled'].change(fn=hide_if_false, inputs=l_vars['frame_interpolation_slow_mo_enabled'], outputs=l_vars['frame_interp_slow_mo_amount_column']) l_vars['frame_interpolation_engine'].change(fn=change_interp_x_max_limit, inputs=[l_vars['frame_interpolation_engine'], l_vars['frame_interpolation_x_amount']], outputs=l_vars['frame_interpolation_x_amount']) # Populate the FPS and FCount values as soon as a video is uploaded to the FileUploadBox (vid_to_interpolate_chosen_file) l_vars['vid_to_interpolate_chosen_file'].change(gradio_f_interp_get_fps_and_fcount, inputs=[l_vars['vid_to_interpolate_chosen_file'], l_vars['frame_interpolation_x_amount'], l_vars['frame_interpolation_slow_mo_enabled'], l_vars['frame_interpolation_slow_mo_amount']], outputs=[l_vars['in_vid_fps_ui_window'], l_vars['in_vid_frame_count_window'], l_vars['out_interp_vid_estimated_fps']]) l_vars['vid_to_interpolate_chosen_file'].change(fn=hide_interp_stats, inputs=[l_vars['vid_to_interpolate_chosen_file']], outputs=[l_vars['interp_live_stats_row']]) interp_hide_list = [l_vars['frame_interpolation_slow_mo_enabled'], l_vars['frame_interpolation_keep_imgs'], l_vars['frame_interpolation_use_upscaled'], l_vars['frame_interp_amounts_row'], l_vars['interp_existing_video_row']] for output in interp_hide_list: l_vars['frame_interpolation_engine'].change(fn=hide_interp_by_interp_status, inputs=l_vars['frame_interpolation_engine'], outputs=output) # START gradio-to-frame-interoplation/ upscaling functions def upload_vid_to_interpolate(file, engine, x_am, sl_enabled, sl_am, keep_imgs, in_vid_fps): # print msg and do nothing if vid not uploaded or interp_x not provided if not file or engine == 'None': return print("Please upload a video and set a proper value for 'Interp X'. Can't interpolate x0 times :)") f_location, f_crf, f_preset = get_ffmpeg_params() process_interp_vid_upload_logic(file, engine, x_am, sl_enabled, sl_am, keep_imgs, f_location, f_crf, f_preset, in_vid_fps, f_models_path, file.orig_name) def upload_pics_to_interpolate(pic_list, engine, x_am, sl_enabled, sl_am, keep_imgs, fps, add_audio, audio_track): from PIL import Image if pic_list is None or len(pic_list) < 2: return print("Please upload at least 2 pics for interpolation.") f_location, f_crf, f_preset = get_ffmpeg_params() # make sure all uploaded pics have the same resolution pic_sizes = [Image.open(picture_path.name).size for picture_path in pic_list] if len(set(pic_sizes)) != 1: return print("All uploaded pics need to be of the same Width and Height / resolution.") resolution = pic_sizes[0] process_interp_pics_upload_logic(pic_list, engine, x_am, sl_enabled, sl_am, keep_imgs, f_location, f_crf, f_preset, fps, f_models_path, resolution, add_audio, audio_track) def ncnn_upload_vid_to_upscale(vid_path, in_vid_fps, in_vid_res, out_vid_res, upscale_model, upscale_factor, keep_imgs): if vid_path is None: print("Please upload a video :)") return f_location, f_crf, f_preset = get_ffmpeg_params() current_user = get_os() process_ncnn_upscale_vid_upload_logic(vid_path, in_vid_fps, in_vid_res, out_vid_res, f_models_path, upscale_model, upscale_factor, keep_imgs, f_location, f_crf, f_preset, current_user) def upload_vid_to_depth(vid_to_depth_chosen_file, mode, thresholding, threshold_value, threshold_value_max, adapt_block_size, adapt_c, invert, end_blur, midas_weight_vid2depth, depth_keep_imgs): # print msg and do nothing if vid not uploaded if not vid_to_depth_chosen_file: return print("Please upload a video :()") f_location, f_crf, f_preset = get_ffmpeg_params() process_depth_vid_upload_logic(vid_to_depth_chosen_file, mode, thresholding, threshold_value, threshold_value_max, adapt_block_size, adapt_c, invert, end_blur, midas_weight_vid2depth, vid_to_depth_chosen_file.orig_name, depth_keep_imgs, f_location, f_crf, f_preset, f_models_path) # END gradio-to-frame-interoplation/ upscaling functions def change_visibility_from_skip_video(choice): return gr.update(visible=False) if choice else gr.update(visible=True) def update_r_upscale_factor(choice): return gr.update(value='x4', choices=['x4']) if choice != 'realesr-animevideov3' else gr.update(value='x2', choices=['x2', 'x3', 'x4']) def change_perlin_visibility(choice): return gr.update(visible=choice == "perlin") def legacy_3d_mode(choice): return gr.update(visible=choice.lower() in ["midas+adabins (old)", 'zoe+adabins (old)']) def change_color_coherence_image_path_visibility(choice): return gr.update(visible=choice == "Image") def change_color_coherence_video_every_N_frames_visibility(choice): return gr.update(visible=choice == "Video Input") def change_seed_iter_visibility(choice): return gr.update(visible=choice == "iter") def change_seed_schedule_visibility(choice): return gr.update(visible=choice == "schedule") def disable_pers_flip_accord(choice): return gr.update(visible=True) if choice in ['2D', '3D'] else gr.update(visible=False) def per_flip_handle(anim_mode, per_f_enabled): if anim_mode in ['2D', '3D'] and per_f_enabled: return gr.update(visible=True) return gr.update(visible=False) def change_max_frames_visibility(choice): return gr.update(visible=choice != "Video Input") def change_diffusion_cadence_visibility(choice): return gr.update(visible=choice not in ['Video Input', 'Interpolation']) def disble_3d_related_stuff(choice): return gr.update(visible=False) if choice != '3D' else gr.update(visible=True) def only_show_in_non_3d_mode(choice): return gr.update(visible=False) if choice == '3D' else gr.update(visible=True) def enable_2d_related_stuff(choice): return gr.update(visible=True) if choice == '2D' else gr.update(visible=False) def disable_by_interpolation(choice): return gr.update(visible=False) if choice in ['Interpolation'] else gr.update(visible=True) def disable_by_video_input(choice): return gr.update(visible=False) if choice in ['Video Input'] else gr.update(visible=True) def hide_if_none(choice): return gr.update(visible=choice != "None") def change_gif_button_visibility(choice): if choice is None or choice == "": return gr.update(visible=True) return gr.update(visible=False, value=False) if int(choice) > 30 else gr.update(visible=True) def hide_if_false(choice): return gr.update(visible=True) if choice else gr.update(visible=False) def hide_if_true(choice): return gr.update(visible=False) if choice else gr.update(visible=True) def disable_by_hybrid_composite_dynamic(choice, comp_mask_type): if choice in ['Normal', 'Before Motion', 'After Generation']: if comp_mask_type != 'None': return gr.update(visible=True) return gr.update(visible=False) def disable_by_non_optical_flow(choice): return gr.update(visible=False) if choice != 'Optical Flow' else gr.update(visible=True) # Upscaling Gradio UI related funcs def vid_upscale_gradio_update_stats(vid_path, upscale_factor): if not vid_path: return '---', '---', '---', '---' factor = extract_number(upscale_factor) fps, fcount, resolution = get_quick_vid_info(vid_path.name) in_res_str = f"{resolution[0]}*{resolution[1]}" out_res_str = f"{resolution[0] * factor}*{resolution[1] * factor}" return fps, fcount, in_res_str, out_res_str def update_upscale_out_res(in_res, upscale_factor): if not in_res: return '---' factor = extract_number(upscale_factor) w, h = [int(x) * factor for x in in_res.split('*')] return f"{w}*{h}" def update_upscale_out_res_by_model_name(in_res, upscale_model_name): if not upscale_model_name or in_res == '---': return '---' factor = 2 if upscale_model_name == 'realesr-animevideov3' else 4 return f"{int(in_res.split('*')[0]) * factor}*{int(in_res.split('*')[1]) * factor}" def hide_optical_flow_cadence(cadence_value): return gr.update(visible=True) if cadence_value > 1 else gr.update(visible=False) def hide_interp_by_interp_status(choice): return gr.update(visible=False) if choice == 'None' else gr.update(visible=True) def change_interp_x_max_limit(engine_name, current_value): if engine_name == 'FILM': return gr.update(maximum=300) elif current_value > 10: return gr.update(maximum=10, value=2) return gr.update(maximum=10) def hide_interp_stats(choice): return gr.update(visible=True) if choice is not None else gr.update(visible=False) def show_hybrid_html_msg(choice): return gr.update(visible=True) if choice not in ['2D', '3D'] else gr.update(visible=False) def change_hybrid_tab_status(choice): return gr.update(visible=True) if choice in ['2D', '3D'] else gr.update(visible=False) def show_leres_html_msg(choice): return gr.update(visible=True) if choice.lower() == 'leres' else gr.update(visible=False) def show_when_ddim(sampler_name): return gr.update(visible=True) if sampler_name.lower() == 'ddim' else gr.update(visible=False) def show_when_ancestral_samplers(sampler_name): return gr.update(visible=True) if sampler_name.lower() in ['euler a', 'dpm++ 2s a', 'dpm2 a', 'dpm2 a karras', 'dpm++ 2s a karras'] else gr.update(visible=False) def change_css(checkbox_status): if checkbox_status: display = "block" else: display = "none" html_template = f''' ''' return html_template