from modules.shared import cmd_opts from modules.processing import get_fixed_seed from modules.ui_components import FormRow import modules.shared as sh import modules.paths as ph import os from .frame_interpolation import set_interp_out_fps, gradio_f_interp_get_fps_and_fcount, process_interp_vid_upload_logic, process_interp_pics_upload_logic from .upscaling import process_ncnn_upscale_vid_upload_logic from .vid2depth import process_depth_vid_upload_logic from .video_audio_utilities import find_ffmpeg_binary, ffmpeg_stitch_video, direct_stitch_vid_from_frames, get_quick_vid_info, extract_number from .gradio_funcs import * from .general_utils import get_os, get_deforum_version from .deforum_controlnet import setup_controlnet_ui, controlnet_component_names, controlnet_infotext # controlnet_component_names, setup_controlnet_ui import tempfile def Root(): device = sh.device models_path = ph.models_path + '/Deforum' half_precision = not cmd_opts.no_half mask_preset_names = ['everywhere','video_mask'] p = None frames_cache = [] initial_seed = None initial_info = None first_frame = None outpath_samples = "" animation_prompts = None color_corrections = None initial_clipskip = None current_user_os = get_os() tmp_deforum_run_duplicated_folder = os.path.join(tempfile.gettempdir(), 'tmp_run_deforum') return locals() def DeforumAnimArgs(): animation_mode = '2D' # ['None', '2D', '3D', 'Video Input', 'Interpolation'] max_frames = 120 border = 'replicate' # ['wrap', 'replicate'] angle = "0:(0)" zoom = "0:(1.0025+0.002*sin(1.25*3.14*t/30))" translation_x = "0:(0)" translation_y = "0:(0)" translation_z = "0:(1.75)" transform_center_x = "0:(0.5)" transform_center_y = "0:(0.5)" rotation_3d_x = "0:(0)" rotation_3d_y = "0:(0)" rotation_3d_z = "0:(0)" enable_perspective_flip = False perspective_flip_theta = "0:(0)" perspective_flip_phi = "0:(0)" perspective_flip_gamma = "0:(0)" perspective_flip_fv = "0:(53)" noise_schedule = "0: (0.065)" strength_schedule = "0: (0.65)" contrast_schedule = "0: (1.0)" cfg_scale_schedule = "0: (7)" enable_steps_scheduling = False steps_schedule = "0: (25)" fov_schedule = "0: (70)" aspect_ratio_schedule = "0: (1)" near_schedule = "0: (200)" far_schedule = "0: (10000)" seed_schedule = '0:(s), 1:(-1), "max_f-2":(-1), "max_f-1":(s)' pix2pix_img_cfg_scale = "1.5" pix2pix_img_cfg_scale_schedule = "0:(1.5)" enable_subseed_scheduling = False subseed_schedule = "0:(1)" subseed_strength_schedule = "0:(0)" # Sampler Scheduling enable_sampler_scheduling = False sampler_schedule = '0: ("Euler a")' # Composable mask scheduling use_noise_mask = False mask_schedule = '0: ("{video_mask}")' noise_mask_schedule = '0: ("{video_mask}")' # Checkpoint Scheduling enable_checkpoint_scheduling = False checkpoint_schedule = '0: ("model1.ckpt"), 100: ("model2.ckpt")' # CLIP skip Scheduling enable_clipskip_scheduling = False clipskip_schedule = '0: (2)' # Noise Multiplier Scheduling enable_noise_multiplier_scheduling = True noise_multiplier_schedule = '0: (1.05)' # Anti-blur amount_schedule = "0: (0.1)" kernel_schedule = "0: (5)" sigma_schedule = "0: (1.0)" threshold_schedule = "0: (0.0)" # Hybrid video hybrid_comp_alpha_schedule = "0:(1)" hybrid_comp_mask_blend_alpha_schedule = "0:(0.5)" hybrid_comp_mask_contrast_schedule = "0:(1)" hybrid_comp_mask_auto_contrast_cutoff_high_schedule = "0:(100)" hybrid_comp_mask_auto_contrast_cutoff_low_schedule = "0:(0)" #Coherence color_coherence = 'LAB' # ['None', 'HSV', 'LAB', 'RGB', 'Video Input', 'Image'] color_coherence_image_path = "" color_coherence_video_every_N_frames = 1 color_force_grayscale = False diffusion_cadence = '2' #['1','2','3','4','5','6','7','8'] optical_flow_cadence = False optical_flow_redo_generation = False #**Noise settings:** noise_type = 'perlin' # ['uniform', 'perlin'] # Perlin params perlin_w = 8 perlin_h = 8 perlin_octaves = 4 perlin_persistence = 0.5 #**3D Depth Warping:** use_depth_warping = True midas_weight = 0.2 padding_mode = 'border' # ['border', 'reflection', 'zeros'] sampling_mode = 'bicubic' # ['bicubic', 'bilinear', 'nearest'] save_depth_maps = False #**Video Input:** video_init_path ='https://deforum.github.io/a1/V1.mp4' extract_nth_frame = 1 extract_from_frame = 0 extract_to_frame = -1 # minus 1 for unlimited frames overwrite_extracted_frames = True use_mask_video = False video_mask_path ='https://deforum.github.io/a1/VM1.mp4' #**Hybrid Video for 2D/3D Animation Mode:** hybrid_generate_inputframes = False hybrid_generate_human_masks = "None" #['None','PNGs','Video', 'Both'] hybrid_use_first_frame_as_init_image = True hybrid_motion = "None" #['None','Optical Flow','Perspective','Affine'] hybrid_motion_use_prev_img = False hybrid_flow_method = "Farneback" #['DIS Medium','Farneback'] hybrid_composite = False hybrid_comp_mask_type = "None" #['None', 'Depth', 'Video Depth', 'Blend', 'Difference'] hybrid_comp_mask_inverse = False hybrid_comp_mask_equalize = "None" # ['None','Before','After','Both'] hybrid_comp_mask_auto_contrast = False hybrid_comp_save_extra_frames = False #**Resume Animation:** resume_from_timestring = False resume_timestring = "20230129210106" return locals() def DeforumAnimPrompts(): return r"""{ "0": "tiny cute swamp bunny, highly detailed, intricate, ultra hd, sharp photo, crepuscular rays, in focus, by tomasz alen kopera", "30": "anthropomorphic clean cat, surrounded by fractals, epic angle and pose, symmetrical, 3d, depth of field, ruan jia and fenghua zhong", "60": "a beautiful coconut --neg photo, realistic", "90": "a beautiful durian, trending on Artstation" } """ def DeforumArgs(): #**Image Settings** W = 512 # H = 512 # W, H = map(lambda x: x - x % 64, (W, H)) # resize to integer multiple of 64 #**Webui stuff** tiling = False restore_faces = False seed_enable_extras = False subseed = -1 subseed_strength = 0 seed_resize_from_w = 0 seed_resize_from_h = 0 #**Sampling Settings** seed = -1 # sampler = 'euler_ancestral' # ["klms","dpm2","dpm2_ancestral","heun","euler","euler_ancestral","plms", "ddim"] steps = 25 # scale = 7 # ddim_eta = 0.0 # dynamic_threshold = None static_threshold = None #**Save & Display Settings** save_samples = True save_settings = True display_samples = True save_sample_per_step = False show_sample_per_step = False #**Prompt Settings** prompt_weighting = False normalize_prompt_weights = True log_weighted_subprompts = False #**Batch Settings** n_batch = 1 # batch_name = "Deforum" filename_format = "{timestring}_{index}_{prompt}.png" # ["{timestring}_{index}_{seed}.png","{timestring}_{index}_{prompt}.png"] seed_behavior = "iter" # ["iter","fixed","random","ladder","alternate","schedule"] seed_iter_N = 1 outdir = "" #**Init Settings** use_init = False strength = 0.8 strength_0_no_init = True # Set the strength to 0 automatically when no init image is used init_image = "https://deforum.github.io/a1/I1.png" # Whiter areas of the mask are areas that change more use_mask = False use_alpha_as_mask = False # use the alpha channel of the init image as the mask mask_file = "https://deforum.github.io/a1/M1.jpg" invert_mask = False # Adjust mask image, 1.0 is no adjustment. Should be positive numbers. mask_contrast_adjust = 1.0 mask_brightness_adjust = 1.0 # Overlay the masked image at the end of the generation so it does not get degraded by encoding and decoding overlay_mask = True # Blur edges of final overlay mask, if used. Minimum = 0 (no blur) mask_overlay_blur = 4 fill = 1 #MASKARGSEXPANSION Todo : Rename and convert to same formatting as used in img2img masked content full_res_mask = True full_res_mask_padding = 4 reroll_blank_frames = 'reroll' # reroll, interrupt, or ignore n_samples = 1 # doesnt do anything precision = 'autocast' C = 4 f = 8 prompt = "" timestring = "" init_latent = None init_sample = None init_c = None mask_image = None noise_mask = None seed_internal = 0 return locals() def keyframeExamples(): return '''{ "0": "https://deforum.github.io/a1/Gi1.png", "max_f/4-5": "https://deforum.github.io/a1/Gi2.png", "max_f/2-10": "https://deforum.github.io/a1/Gi3.png", "3*max_f/4-15": "https://deforum.github.io/a1/Gi4.jpg", "max_f-20": "https://deforum.github.io/a1/Gi1.png" }''' def LoopArgs(): use_looper = False init_images = keyframeExamples() image_strength_schedule = "0:(0.75)" blendFactorMax = "0:(0.35)" blendFactorSlope = "0:(0.25)" tweening_frames_schedule = "0:(20)" color_correction_factor = "0:(0.075)" return locals() def ParseqArgs(): parseq_manifest = None parseq_use_deltas = True return locals() def DeforumOutputArgs(): skip_video_creation = False fps = 15 make_gif = False delete_imgs = False # True will delete all imgs after a successful mp4 creation image_path = "C:/SD/20230124234916_%09d.png" mp4_path = "testvidmanualsettings.mp4" ffmpeg_location = find_ffmpeg_binary() ffmpeg_crf = '17' ffmpeg_preset = 'slow' add_soundtrack = 'None' # ["File","Init Video"] soundtrack_path = "https://deforum.github.io/a1/A1.mp3" # End-Run upscaling r_upscale_video = False r_upscale_factor = 'x2' # ['2x', 'x3', 'x4'] r_upscale_model = 'realesr-animevideov3' # 'realesr-animevideov3' (default of realesrgan engine, does 2-4x), the rest do only 4x: 'realesrgan-x4plus', 'realesrgan-x4plus-anime' r_upscale_keep_imgs = True render_steps = False path_name_modifier = "x0_pred" #["x0_pred","x"] store_frames_in_ram = False #**Interpolate Video Settings** frame_interpolation_engine = "None" # ["None", "RIFE v4.6", "FILM"] frame_interpolation_x_amount = 2 # [2 to 1000 depends on the engine] frame_interpolation_slow_mo_enabled = False frame_interpolation_slow_mo_amount = 2 #[2 to 10] frame_interpolation_keep_imgs = False return locals() import gradio as gr import time from types import SimpleNamespace i1_store_backup = f"

Deforum extension for auto1111 — version 2.3b | Git commit: {get_deforum_version()}

" i1_store = i1_store_backup mask_fill_choices=['fill', 'original', 'latent noise', 'latent nothing'] def setup_deforum_setting_dictionary(self, is_img2img, is_extension = True): d = SimpleNamespace(**DeforumArgs()) #default args da = SimpleNamespace(**DeforumAnimArgs()) #default anim args dp = SimpleNamespace(**ParseqArgs()) #default parseq ars dv = SimpleNamespace(**DeforumOutputArgs()) #default video args dr = SimpleNamespace(**Root()) # ROOT args dloopArgs = SimpleNamespace(**LoopArgs()) if not is_extension: with gr.Row(variant='compact'): btn = gr.Button("Click here after the generation to show the video") with gr.Row(variant='compact'): i1 = gr.HTML(i1_store, elem_id='deforum_header') else: btn = i1 = gr.HTML("") # MAIN (TOP) EXTENSION INFO ACCORD with gr.Accordion("Info, Links and Help", open=False, elem_id='main_top_info_accord'): gr.HTML("""Made by deforum.github.io, port for AUTOMATIC1111's webui maintained by kabachuha""") gr.HTML("""FOR HELP CLICK HERE
  • The code for this extension: here.
  • Join the official Deforum Discord to share your creations and suggestions.
  • Official Deforum Wiki: here.
  • Anime-inclined great guide (by FizzleDorf) with lots of examples: here.
  • For advanced keyframing with Math functions, see here.
  • Alternatively, use sd-parseq as a UI to define your animation schedules (see the Parseq section in the Init tab).
  • framesync.xyz is also a good option, it makes compact math formulae for Deforum keyframes by selecting various waveforms.
  • The other site allows for making keyframes using interactive splines and Bezier curves (select Disco output format).
  • If you want to use Width/Height which are not multiples of 64, please change noise_type to 'Uniform', in Keyframes --> Noise.
    • If you liked this extension, please give it a star on GitHub! 😊""") if not is_extension: def show_vid(): return { i1: gr.update(value=i1_store, visible=True) } btn.click( show_vid, [], [i1] ) with gr.Blocks(): with gr.Tabs(): # RUN TAB with gr.TabItem('Run'): from modules.sd_samplers import samplers_for_img2img with gr.Row(variant='compact'): sampler = gr.Dropdown(label="Sampler", choices=[x.name for x in samplers_for_img2img], value=samplers_for_img2img[0].name, type="value", elem_id="sampler", interactive=True) steps = gr.Slider(label="Steps", minimum=0, maximum=200, step=1, value=d.steps, interactive=True) with gr.Row(variant='compact'): W = gr.Slider(label="Width", minimum=64, maximum=2048, step=64, value=d.W, interactive=True) H = gr.Slider(label="Height", minimum=64, maximum=2048, step=64, value=d.H, interactive=True) with gr.Row(variant='compact'): seed = gr.Number(label="Seed", value=d.seed, interactive=True, precision=0) batch_name = gr.Textbox(label="Batch name", lines=1, interactive=True, value = d.batch_name) with gr.Accordion('Restore Faces, Tiling & more', open=False) as run_more_settings_accord: with gr.Row(variant='compact'): restore_faces = gr.Checkbox(label='Restore Faces', value=d.restore_faces) tiling = gr.Checkbox(label='Tiling', value=False) ddim_eta = gr.Number(label="DDIM Eta", value=d.ddim_eta, interactive=True) with gr.Row(variant='compact') as pix2pix_img_cfg_scale_row: pix2pix_img_cfg_scale_schedule = gr.Textbox(label="Pix2Pix img CFG schedule", value=da.pix2pix_img_cfg_scale_schedule, interactive=True) # RUN FROM SETTING FILE ACCORD with gr.Accordion('Resume & Run from file', open=False): with gr.Tab('Run from Settings file'): with gr.Row(variant='compact'): override_settings_with_file = gr.Checkbox(label="Override settings", value=False, interactive=True, elem_id='override_settings') custom_settings_file = gr.Textbox(label="Custom settings file", lines=1, interactive=True, elem_id='custom_settings_file') # RESUME ANIMATION ACCORD with gr.Tab('Resume Animation'): with gr.Row(variant='compact'): resume_from_timestring = gr.Checkbox(label="Resume from timestring", value=da.resume_from_timestring, interactive=True) resume_timestring = gr.Textbox(label="Resume timestring", lines=1, value = da.resume_timestring, interactive=True) # KEYFRAMES TAB with gr.TabItem('Keyframes'): #TODO make a some sort of the original dictionary parsing with gr.Row(variant='compact'): with gr.Column(scale=2): animation_mode = gr.Radio(['2D', '3D', 'Interpolation', 'Video Input'], label="Animation mode", value=da.animation_mode, elem_id="animation_mode") with gr.Column(scale=1, min_width=180): border = gr.Radio(['replicate', 'wrap'], label="Border", value=da.border, elem_id="border") with gr.Row(variant='compact'): diffusion_cadence = gr.Slider(label="Cadence", minimum=1, maximum=50, step=1, value=da.diffusion_cadence, interactive=True) max_frames = gr.Number(label="Max frames", lines=1, value = da.max_frames, interactive=True, precision=0) # GUIDED IMAGES ACCORD with gr.Accordion('Guided Images', open=False, elem_id='guided_images_accord') as guided_images_accord: # GUIDED IMAGES INFO ACCORD with gr.Accordion('*READ ME before you use this mode!*', open=False): gr.HTML("""You can use this as a guided image tool or as a looper depending on your settings in the keyframe images field. Set the keyframes and the images that you want to show up. Note: the number of frames between each keyframe should be greater than the tweening frames.""") # In later versions this should be also in the strength schedule, but for now you need to set it. gr.HTML("""Prerequisites and Important Info: """) gr.HTML("""Looping recommendations: """) with gr.Row(variant='compact'): use_looper = gr.Checkbox(label="Enable guided images mode", value=dloopArgs.use_looper, interactive=True) with gr.Row(variant='compact'): init_images = gr.Textbox(label="Images to use for keyframe guidance", lines=9, value = keyframeExamples(), interactive=True) # GUIDED IMAGES SCHEDULES ACCORD with gr.Accordion('Guided images schedules', open=False): with gr.Row(variant='compact'): image_strength_schedule = gr.Textbox(label="Image strength schedule", lines=1, value = dloopArgs.image_strength_schedule, interactive=True) with gr.Row(variant='compact'): blendFactorMax = gr.Textbox(label="Blend factor max", lines=1, value = dloopArgs.blendFactorMax, interactive=True) with gr.Row(variant='compact'): blendFactorSlope = gr.Textbox(label="Blend factor slope", lines=1, value = dloopArgs.blendFactorSlope, interactive=True) with gr.Row(variant='compact'): tweening_frames_schedule = gr.Textbox(label="Tweening frames schedule", lines=1, value = dloopArgs.tweening_frames_schedule, interactive=True) with gr.Row(variant='compact'): color_correction_factor = gr.Textbox(label="Color correction factor", lines=1, value = dloopArgs.color_correction_factor, interactive=True) # EXTA SCHEDULES TABS with gr.Tabs(elem_id='extra_schedules'): with gr.TabItem('Strength'): with gr.Row(variant='compact'): strength_schedule = gr.Textbox(label="Strength schedule", lines=1, value = da.strength_schedule, interactive=True) with gr.TabItem('CFG'): with gr.Row(variant='compact'): cfg_scale_schedule = gr.Textbox(label="CFG scale schedule", lines=1, value = da.cfg_scale_schedule, interactive=True) with gr.Row(variant='compact'): enable_clipskip_scheduling = gr.Checkbox(label="Enable CLIP skip scheduling", value=da.enable_clipskip_scheduling, interactive=True) with gr.Row(variant='compact'): clipskip_schedule = gr.Textbox(label="CLIP skip schedule", lines=1, value = da.clipskip_schedule, interactive=True) with gr.TabItem('Seed') as a3: with gr.Row(variant='compact'): seed_behavior = gr.Radio(['iter', 'fixed', 'random', 'ladder', 'alternate', 'schedule'], label="Seed behavior", value=d.seed_behavior, elem_id="seed_behavior") with gr.Row(variant='compact') as seed_iter_N_row: seed_iter_N = gr.Number(label="Seed iter N", value=d.seed_iter_N, interactive=True, precision=0) with gr.Row(visible=False) as seed_schedule_row: seed_schedule = gr.Textbox(label="Seed schedule", lines=1, value = da.seed_schedule, interactive=True) with gr.TabItem('SubSeed', open=False) as subseed_sch_tab: with gr.Row(variant='compact'): enable_subseed_scheduling = gr.Checkbox(label="Enable Subseed scheduling", value=da.enable_subseed_scheduling, interactive=True) subseed_schedule = gr.Textbox(label="Subseed schedule", lines=1, value = da.subseed_schedule, interactive=True) subseed_strength_schedule = gr.Textbox(label="Subseed strength schedule", lines=1, value = da.subseed_strength_schedule, interactive=True) with gr.Row(variant='compact'): seed_resize_from_w = gr.Slider(minimum=0, maximum=2048, step=64, label="Resize seed from width", value=0) seed_resize_from_h = gr.Slider(minimum=0, maximum=2048, step=64, label="Resize seed from height", value=0) # Steps Scheduling with gr.TabItem('Step') as a13: with gr.Row(variant='compact'): enable_steps_scheduling = gr.Checkbox(label="Enable steps scheduling", value=da.enable_steps_scheduling, interactive=True) with gr.Row(variant='compact'): steps_schedule = gr.Textbox(label="Steps schedule", lines=1, value = da.steps_schedule, interactive=True) # Sampler Scheduling with gr.TabItem('Sampler') as a14: with gr.Row(variant='compact'): enable_sampler_scheduling = gr.Checkbox(label="Enable sampler scheduling", value=da.enable_sampler_scheduling, interactive=True) with gr.Row(variant='compact'): sampler_schedule = gr.Textbox(label="Sampler schedule", lines=1, value = da.sampler_schedule, interactive=True) # Checkpoint Scheduling with gr.TabItem('Checkpoint') as a15: with gr.Row(variant='compact'): enable_checkpoint_scheduling = gr.Checkbox(label="Enable checkpoint scheduling", value=da.enable_checkpoint_scheduling, interactive=True) with gr.Row(variant='compact'): checkpoint_schedule = gr.Textbox(label="Checkpoint schedule", lines=1, value = da.checkpoint_schedule, interactive=True) # MOTION INNER TAB with gr.Tabs(elem_id='motion_noise_etc'): with gr.TabItem('Motion') as motion_tab: with gr.Column(visible=True) as only_2d_motion_column: with gr.Row(variant='compact'): zoom = gr.Textbox(label="Zoom", lines=1, value = da.zoom, interactive=True) with gr.Row(variant='compact'): angle = gr.Textbox(label="Angle", lines=1, value = da.angle, interactive=True) with gr.Row(variant='compact'): transform_center_x = gr.Textbox(label="Transform Center X", lines=1, value = da.transform_center_x, interactive=True) with gr.Row(variant='compact'): transform_center_y = gr.Textbox(label="Transform Center Y", lines=1, value = da.transform_center_y, interactive=True) with gr.Column(visible=True) as both_anim_mode_motion_params_column: with gr.Row(variant='compact'): translation_x = gr.Textbox(label="Translation X", lines=1, value = da.translation_x, interactive=True) with gr.Row(variant='compact'): translation_y = gr.Textbox(label="Translation Y", lines=1, value = da.translation_y, interactive=True) with gr.Column(visible=False) as only_3d_motion_column: with gr.Row(variant='compact'): translation_z = gr.Textbox(label="Translation Z", lines=1, value = da.translation_z, interactive=True) with gr.Row(variant='compact'): rotation_3d_x = gr.Textbox(label="Rotation 3D X", lines=1, value = da.rotation_3d_x, interactive=True) with gr.Row(variant='compact'): rotation_3d_y = gr.Textbox(label="Rotation 3D Y", lines=1, value = da.rotation_3d_y, interactive=True) with gr.Row(variant='compact'): rotation_3d_z = gr.Textbox(label="Rotation 3D Z", lines=1, value = da.rotation_3d_z, interactive=True) # 3D DEPTH & FOV ACCORD with gr.Accordion('Depth Warping & FOV', visible=False, open=False) as depth_3d_warping_accord: with gr.TabItem('Depth Warping'): with gr.Row(variant='compact'): use_depth_warping = gr.Checkbox(label="Use depth warping", value=da.use_depth_warping, interactive=True) midas_weight = gr.Number(label="MiDaS weight", value=da.midas_weight, interactive=True) with gr.Row(variant='compact'): padding_mode = gr.Radio(['border', 'reflection', 'zeros'], label="Padding mode", value=da.padding_mode, elem_id="padding_mode") sampling_mode = gr.Radio(['bicubic', 'bilinear', 'nearest'], label="Sampling mode", value=da.sampling_mode, elem_id="sampling_mode") with gr.TabItem('Field Of View', visible=False, open=False) as fov_accord: with gr.Row(variant='compact'): fov_schedule = gr.Textbox(label="FOV schedule", lines=1, value = da.fov_schedule, interactive=True) with gr.Row(variant='compact'): aspect_ratio_schedule = gr.Textbox(label="Aspect Ratio schedule", lines=1, value = da.aspect_ratio_schedule, interactive=True) with gr.Row(variant='compact'): near_schedule = gr.Textbox(label="Near schedule", lines=1, value = da.near_schedule, interactive=True) with gr.Row(variant='compact'): far_schedule = gr.Textbox(label="Far schedule", lines=1, value = da.far_schedule, interactive=True) # PERSPECTIVE FLIP ACCORD with gr.Accordion('Perspective Flip', open=False) as perspective_flip_accord: with gr.Row(variant='compact'): enable_perspective_flip = gr.Checkbox(label="Enable perspective flip", value=da.enable_perspective_flip, interactive=True) with gr.Row(variant='compact'): perspective_flip_theta = gr.Textbox(label="Perspective flip theta", lines=1, value = da.perspective_flip_theta, interactive=True) with gr.Row(variant='compact'): perspective_flip_phi = gr.Textbox(label="Perspective flip phi", lines=1, value = da.perspective_flip_phi, interactive=True) with gr.Row(variant='compact'): perspective_flip_gamma = gr.Textbox(label="Perspective flip gamma", lines=1, value = da.perspective_flip_gamma, interactive=True) with gr.Row(variant='compact'): perspective_flip_fv = gr.Textbox(label="Perspective flip fv", lines=1, value = da.perspective_flip_fv, interactive=True) # NOISE INNER TAB with gr.TabItem('Noise'): with gr.Column() as noise_tab_column: with gr.Row(variant='compact'): noise_type = gr.Radio(['uniform', 'perlin'], label="Noise type", value=da.noise_type, elem_id="noise_type") with gr.Row(variant='compact'): noise_schedule = gr.Textbox(label="Noise schedule", lines=1, value = da.noise_schedule, interactive=True) with gr.Row(variant='compact') as perlin_row: with gr.Column(min_width=220): perlin_octaves = gr.Slider(label="Perlin octaves", minimum=1, maximum=7, value=da.perlin_octaves, step=1, interactive=True) with gr.Column(min_width=220): perlin_persistence = gr.Slider(label="Perlin persistence", minimum=0, maximum=1, value=da.perlin_persistence, step=0.02, interactive=True) with gr.Row(variant='compact'): enable_noise_multiplier_scheduling = gr.Checkbox(label="Enable noise multiplier scheduling", value=da.enable_noise_multiplier_scheduling, interactive=True) with gr.Row(variant='compact'): noise_multiplier_schedule = gr.Textbox(label="Noise multiplier schedule", lines=1, value = da.noise_multiplier_schedule, interactive=True) # COHERENCE INNER TAB with gr.TabItem('Coherence', open=False) as coherence_accord: with gr.Row(variant='compact'): color_coherence = gr.Dropdown(label="Color coherence", choices=['None', 'HSV', 'LAB', 'RGB', 'Video Input', 'Image'], value=da.color_coherence, type="value", elem_id="color_coherence", interactive=True) color_force_grayscale = gr.Checkbox(label="Color force Grayscale", value=da.color_force_grayscale, interactive=True) with gr.Row(visible=False) as color_coherence_image_path_row: color_coherence_image_path = gr.Textbox(label="Color coherence image path", lines=1, value=da.color_coherence_image_path, interactive=True) with gr.Row(visible=False) as color_coherence_video_every_N_frames_row: color_coherence_video_every_N_frames = gr.Number(label="Color coherence video every N frames", value=1, interactive=True) with gr.Row(variant='compact'): contrast_schedule = gr.Textbox(label="Contrast schedule", lines=1, value = da.contrast_schedule, interactive=True) optical_flow_cadence = gr.Checkbox(label="Optical flow cadence", value=False, visible=False, interactive=True, elem_id='optical_flow_cadence') optical_flow_redo_generation = gr.Checkbox(label="Optical flow redo generation", value=False, visible=True, interactive=True, elem_id='optical_flow_redo_generation') with gr.Row(variant='compact'): # what to do with blank frames (they may result from glitches or the NSFW filter being turned on): reroll with +1 seed, interrupt the animation generation, or do nothing reroll_blank_frames = gr.Radio(['reroll', 'interrupt', 'ignore'], label="Reroll blank frames", value=d.reroll_blank_frames, elem_id="reroll_blank_frames") # ANTI BLUR INNER TAB with gr.TabItem('Anti Blur', open=False, elem_id='anti_blur_accord') as anti_blur_tab: with gr.Row(variant='compact'): amount_schedule = gr.Textbox(label="Amount schedule", lines=1, value = da.amount_schedule, interactive=True) with gr.Row(variant='compact'): kernel_schedule = gr.Textbox(label="Kernel schedule", lines=1, value = da.kernel_schedule, interactive=True) with gr.Row(variant='compact'): sigma_schedule = gr.Textbox(label="Sigma schedule", lines=1, value = da.sigma_schedule, interactive=True) with gr.Row(variant='compact'): threshold_schedule = gr.Textbox(label="Threshold schedule", lines=1, value = da.threshold_schedule, interactive=True) # PROMPTS TAB with gr.TabItem('Prompts'): # PROMPTS INFO ACCORD with gr.Accordion(label='*Important* notes on Prompts', elem_id='prompts_info_accord', open=False, visible=True) as prompts_info_accord: gr.HTML(""" """) with gr.Row(variant='compact'): animation_prompts = gr.Textbox(label="Prompts", lines=8, interactive=True, value = DeforumAnimPrompts()) with gr.Row(variant='compact'): animation_prompts_positive = gr.Textbox(label="Prompts positive", lines=1, interactive=True, value = "") with gr.Row(variant='compact'): animation_prompts_negative = gr.Textbox(label="Prompts negative", lines=1, interactive=True, value = "") # COMPOSABLE MASK SCHEDULING ACCORD with gr.Accordion('Composable Mask scheduling', open=False): gr.HTML(""" """) with gr.Row(variant='compact'): mask_schedule = gr.Textbox(label="Mask schedule", lines=1, value = da.mask_schedule, interactive=True) with gr.Row(variant='compact'): use_noise_mask = gr.Checkbox(label="Use noise mask", value=da.use_noise_mask, interactive=True) with gr.Row(variant='compact'): noise_mask_schedule = gr.Textbox(label="Noise mask schedule", lines=1, value = da.noise_mask_schedule, interactive=True) # INIT MAIN TAB with gr.TabItem('Init'): # IMAGE INIT INNER-TAB with gr.Tab('Image Init'): with gr.Row(variant='compact'): with gr.Column(min_width=150): use_init = gr.Checkbox(label="Use init", value=d.use_init, interactive=True, visible=True) with gr.Column(min_width=150): strength_0_no_init = gr.Checkbox(label="Strength 0 no init", value=d.strength_0_no_init, interactive=True) with gr.Column(min_width=170): strength = gr.Slider(label="Strength", minimum=0, maximum=1, step=0.01, value=d.strength, interactive=True) with gr.Row(variant='compact'): init_image = gr.Textbox(label="Init image", lines=1, interactive=True, value = d.init_image) # VIDEO INIT INNER-TAB with gr.Tab('Video Init'): with gr.Row(variant='compact'): video_init_path = gr.Textbox(label="Video init path", lines=1, value = da.video_init_path, interactive=True) with gr.Row(variant='compact'): extract_from_frame = gr.Number(label="Extract from frame", value=da.extract_from_frame, interactive=True, precision=0) extract_to_frame = gr.Number(label="Extract to frame", value=da.extract_to_frame, interactive=True, precision=0) extract_nth_frame = gr.Number(label="Extract nth frame", value=da.extract_nth_frame, interactive=True, precision=0) overwrite_extracted_frames = gr.Checkbox(label="Overwrite extracted frames", value=False, interactive=True) use_mask_video = gr.Checkbox(label="Use mask video", value=False, interactive=True) with gr.Row(variant='compact'): video_mask_path = gr.Textbox(label="Video mask path", lines=1, value = da.video_mask_path, interactive=True) # MASK INIT INNER-TAB with gr.Tab('Mask Init'): with gr.Row(variant='compact'): use_mask = gr.Checkbox(label="Use mask", value=d.use_mask, interactive=True) use_alpha_as_mask = gr.Checkbox(label="Use alpha as mask", value=d.use_alpha_as_mask, interactive=True) invert_mask = gr.Checkbox(label="Invert mask", value=d.invert_mask, interactive=True) overlay_mask = gr.Checkbox(label="Overlay mask", value=d.overlay_mask, interactive=True) with gr.Row(variant='compact'): mask_file = gr.Textbox(label="Mask file", lines=1, interactive=True, value = d.mask_file) with gr.Row(variant='compact'): mask_overlay_blur = gr.Slider(label="Mask overlay blur", minimum=0, maximum=64, step=1, value=d.mask_overlay_blur, interactive=True) with gr.Row(variant='compact'): choice = mask_fill_choices[d.fill] fill = gr.Radio(label='Mask fill', choices=mask_fill_choices, value=choice, type="index") with gr.Row(variant='compact'): full_res_mask = gr.Checkbox(label="Full res mask", value=d.full_res_mask, interactive=True) full_res_mask_padding = gr.Slider(minimum=0, maximum=512, step=1, label="Full res mask padding", value=d.full_res_mask_padding, interactive=True) with gr.Row(variant='compact'): with gr.Column(min_width=240): mask_contrast_adjust = gr.Number(label="Mask contrast adjust", value=d.mask_contrast_adjust, interactive=True) with gr.Column(min_width=250): mask_brightness_adjust = gr.Number(label="Mask brightness adjust", value=d.mask_brightness_adjust, interactive=True) # PARSEQ ACCORD with gr.Accordion('Parseq', open=False): gr.HTML(""" Use an sd-parseq manifest for your animation (leave blank to ignore).

    Note that parseq overrides:

    Parseq does not override:

    """) with gr.Row(variant='compact'): parseq_manifest = gr.Textbox(label="Parseq Manifest (JSON or URL)", lines=4, value = dp.parseq_manifest, interactive=True) with gr.Row(variant='compact'): parseq_use_deltas = gr.Checkbox(label="Use delta values for movement parameters", value=dp.parseq_use_deltas, interactive=True) def show_hybrid_html_msg(choice): if choice not in ['2D','3D']: return gr.update(visible=True) else: return gr.update(visible=False) def change_hybrid_tab_status(choice): if choice in ['2D','3D']: return gr.update(visible=True) else: return gr.update(visible=False) # CONTROLNET TAB with gr.TabItem('ControlNet'): gr.HTML(controlnet_infotext()) controlnet_dict = setup_controlnet_ui() # HYBRID VIDEO TAB with gr.TabItem('Hybrid Video'): # this html only shows when not in 2d/3d mode hybrid_msg_html = gr.HTML(value='Please, change animation mode to 2D or 3D to enable Hybrid Mode',visible=False, elem_id='hybrid_msg_html') # HYBRID INFO ACCORD with gr.Accordion("Info & Help", open=False): hybrid_html = "

    Hybrid Video Compositing in 2D/3D Modeby reallybigname

    " hybrid_html += "" hybrid_html += "Hybrid Video Schedules" hybrid_html += "" hybrid_html += "Click Here for more info/ a Guide." gr.HTML(hybrid_html) # HYBRID SETTINGS ACCORD with gr.Accordion("Hybrid Settings", open=True) as hybrid_settings_accord: with gr.Row(variant='compact'): with gr.Column(min_width=340): with gr.Row(variant='compact'): hybrid_generate_inputframes = gr.Checkbox(label="Generate inputframes", value=False, interactive=True) hybrid_composite = gr.Checkbox(label="Hybrid composite", value=False, interactive=True) with gr.Column(min_width=340) as hybrid_2nd_column: with gr.Row(variant='compact'): hybrid_use_first_frame_as_init_image = gr.Checkbox(label="First frame as init image", value=da.hybrid_use_first_frame_as_init_image, interactive=True, visible=False) hybrid_motion_use_prev_img = gr.Checkbox(label="Motion use prev img", value=False, interactive=True, visible=False) with gr.Row(variant='compact') as hybrid_flow_row: with gr.Column(variant='compact'): with gr.Row(variant='compact'): hybrid_motion = gr.Radio(['None', 'Optical Flow', 'Perspective', 'Affine'], label="Hybrid motion", value=da.hybrid_motion, elem_id="hybrid_motion") with gr.Column(variant='compact'): with gr.Row(variant='compact'): with gr.Column(scale=1): hybrid_flow_method = gr.Radio(['DIS Medium', 'Farneback'], label="Flow method", value=da.hybrid_flow_method, elem_id="hybrid_flow_method", visible=False) hybrid_comp_mask_type = gr.Radio(['None', 'Depth', 'Video Depth', 'Blend', 'Difference'], label="Comp mask type", value=da.hybrid_comp_mask_type, elem_id="hybrid_comp_mask_type", visible=False) with gr.Row(visible=False, variant='compact') as hybrid_comp_mask_row: hybrid_comp_mask_equalize = gr.Radio(['None', 'Before', 'After', 'Both'], label="Comp mask equalize", value=da.hybrid_comp_mask_equalize, elem_id="hybrid_comp_mask_equalize") with gr.Column(variant='compact'): hybrid_comp_mask_auto_contrast = gr.Checkbox(label="Comp mask auto contrast", value=False, interactive=True) hybrid_comp_mask_inverse = gr.Checkbox(label="Comp mask inverse", value=False, interactive=True) with gr.Row(variant='compact'): hybrid_comp_save_extra_frames = gr.Checkbox(label="Comp save extra frames", value=False, interactive=True) # HYBRID SCHEDULES ACCORD with gr.Accordion("Hybrid Schedules", open=False, visible=False) as hybrid_sch_accord: with gr.Row(variant='compact') as hybrid_comp_alpha_schedule_row: hybrid_comp_alpha_schedule = gr.Textbox(label="Comp alpha schedule", lines=1, value = da.hybrid_comp_alpha_schedule, interactive=True) with gr.Row(variant='compact', visible=False) as hybrid_comp_mask_blend_alpha_schedule_row: hybrid_comp_mask_blend_alpha_schedule = gr.Textbox(label="Comp mask blend alpha schedule", lines=1, value = da.hybrid_comp_mask_blend_alpha_schedule, interactive=True, elem_id="hybridelemtest") with gr.Row(variant='compact', visible=False) as hybrid_comp_mask_contrast_schedule_row: hybrid_comp_mask_contrast_schedule = gr.Textbox(label="Comp mask contrast schedule", lines=1, value = da.hybrid_comp_mask_contrast_schedule, interactive=True) with gr.Row(variant='compact', visible=False) as hybrid_comp_mask_auto_contrast_cutoff_high_schedule_row : hybrid_comp_mask_auto_contrast_cutoff_high_schedule = gr.Textbox(label="Comp mask auto contrast cutoff high schedule", lines=1, value = da.hybrid_comp_mask_auto_contrast_cutoff_high_schedule, interactive=True) with gr.Row(variant='compact', visible=False) as hybrid_comp_mask_auto_contrast_cutoff_low_schedule_row: hybrid_comp_mask_auto_contrast_cutoff_low_schedule = gr.Textbox(label="Comp mask auto contrast cutoff low schedule", lines=1, value = da.hybrid_comp_mask_auto_contrast_cutoff_low_schedule, interactive=True) # HUMANS MASKING ACCORD with gr.Accordion("Humans Masking", open=False, visible=False) as humans_masking_accord: with gr.Row(variant='compact'): hybrid_generate_human_masks = gr.Radio(['None', 'PNGs', 'Video', 'Both'], label="Generate human masks", value=da.hybrid_generate_human_masks, elem_id="hybrid_generate_human_masks") # OUTPUT TAB with gr.TabItem('Output', elem_id='output_tab'): # VID OUTPUT ACCORD with gr.Accordion('Video Output Settings', open=True): with gr.Row(variant='compact') as fps_out_format_row: fps = gr.Slider(label="FPS", value=dv.fps, minimum=1, maximum=240, step=1) # NOT VISIBLE AS OF 11-02-23 moving to ffmpeg-only! output_format = gr.Dropdown(visible=False, label="Output format", choices=['FFMPEG mp4'], value='FFMPEG mp4', type="value", elem_id="output_format", interactive=True) with gr.Column(variant='compact'): with gr.Row(variant='compact') as soundtrack_row: add_soundtrack = gr.Radio(['None', 'File', 'Init Video'], label="Add soundtrack", value=dv.add_soundtrack) soundtrack_path = gr.Textbox(label="Soundtrack path", lines=1, interactive=True, value = dv.soundtrack_path) with gr.Row(variant='compact'): skip_video_creation = gr.Checkbox(label="Skip video creation", value=dv.skip_video_creation, interactive=True) delete_imgs = gr.Checkbox(label="Delete Imgs", value=dv.delete_imgs, interactive=True) store_frames_in_ram = gr.Checkbox(label="Store frames in ram", value=dv.store_frames_in_ram, interactive=True) save_depth_maps = gr.Checkbox(label="Save depth maps", value=da.save_depth_maps, interactive=True) # the following param only shows for windows and linux users! make_gif = gr.Checkbox(label="Make GIF", value=dv.make_gif, interactive=True) with gr.Row(equal_height=True, variant='compact', visible=True) as r_upscale_row: r_upscale_video = gr.Checkbox(label="Upscale", value=dv.r_upscale_video, interactive=True) r_upscale_model = gr.Dropdown(label="Upscale model", choices=['realesr-animevideov3', 'realesrgan-x4plus', 'realesrgan-x4plus-anime'], interactive=True, value = dv.r_upscale_model, type="value") r_upscale_factor = gr.Dropdown(choices=['x2', 'x3', 'x4'], label="Upscale factor", interactive=True, value=dv.r_upscale_factor, type="value") r_upscale_keep_imgs = gr.Checkbox(label="Keep Imgs", value=dv.r_upscale_keep_imgs, interactive=True) with gr.Accordion('FFmpeg settings', visible=True, open=False) as ffmpeg_quality_accordion: with gr.Row(equal_height=True, variant='compact', visible=True) as ffmpeg_set_row: ffmpeg_crf = gr.Slider(minimum=0, maximum=51, step=1, label="CRF", value=dv.ffmpeg_crf, interactive=True) ffmpeg_preset = gr.Dropdown(label="Preset", choices=['veryslow', 'slower', 'slow', 'medium', 'fast', 'faster', 'veryfast', 'superfast', 'ultrafast'], interactive=True, value = dv.ffmpeg_preset, type="value") with gr.Row(equal_height=True, variant='compact', visible=True) as ffmpeg_location_row: ffmpeg_location = gr.Textbox(label="Location", lines=1, interactive=True, value = dv.ffmpeg_location) # FRAME INTERPOLATION TAB with gr.Tab('Frame Interpolation') as frame_interp_tab: with gr.Accordion('Important notes and Help', open=False): gr.HTML(""" Use RIFE / FILM Frame Interpolation to smooth out, slow-mo (or both) any video.

    Supported engines:

    Important notes:

    """) with gr.Column(variant='compact'): with gr.Row(variant='compact'): # Interpolation Engine frame_interpolation_engine = gr.Dropdown(label="Engine", choices=['None','RIFE v4.6','FILM'], value=dv.frame_interpolation_engine, type="value", elem_id="frame_interpolation_engine", interactive=True) frame_interpolation_slow_mo_enabled = gr.Checkbox(label="Slow Mo", elem_id="frame_interpolation_slow_mo_enabled", value=dv.frame_interpolation_slow_mo_enabled, interactive=True, visible=False) # If this is set to True, we keep all of the interpolated frames in a folder. Default is False - means we delete them at the end of the run frame_interpolation_keep_imgs = gr.Checkbox(label="Keep Imgs", elem_id="frame_interpolation_keep_imgs", value=dv.frame_interpolation_keep_imgs, interactive=True, visible=False) with gr.Row(variant='compact', visible=False) as frame_interp_amounts_row: with gr.Column(min_width=180) as frame_interp_x_amount_column: # How many times to interpolate (interp X) frame_interpolation_x_amount = gr.Slider(minimum=2, maximum=10, step=1, label="Interp X", value=dv.frame_interpolation_x_amount, interactive=True) with gr.Column(min_width=180, visible=False) as frame_interp_slow_mo_amount_column: # Interp Slow-Mo (setting final output fps, not really doing anything direclty with RIFE/FILM) frame_interpolation_slow_mo_amount = gr.Slider(minimum=2, maximum=10, step=1, label="Slow-Mo X", value=dv.frame_interpolation_x_amount, interactive=True) with gr.Row(visible=False) as interp_existing_video_row: # Intrpolate any existing video from the connected PC with gr.Accordion('Interpolate existing Video/ Images', open=False) as interp_existing_video_accord: with gr.Row(variant='compact') as interpolate_upload_files_row: # A drag-n-drop UI box to which the user uploads a *single* (at this stage) video vid_to_interpolate_chosen_file = gr.File(label="Video to Interpolate", interactive=True, file_count="single", file_types=["video"], elem_id="vid_to_interpolate_chosen_file") # A drag-n-drop UI box to which the user uploads a pictures to interpolate pics_to_interpolate_chosen_file = gr.File(label="Pics to Interpolate", interactive=True, file_count="multiple", file_types=["image"], elem_id="pics_to_interpolate_chosen_file") with gr.Row(variant='compact', visible=False) as interp_live_stats_row: # Non interactive textbox showing uploaded input vid total Frame Count in_vid_frame_count_window = gr.Textbox(label="In Frame Count", lines=1, interactive=False, value='---') # Non interactive textbox showing uploaded input vid FPS in_vid_fps_ui_window = gr.Textbox(label="In FPS", lines=1, interactive=False, value='---') # Non interactive textbox showing expected output interpolated video FPS out_interp_vid_estimated_fps = gr.Textbox(label="Interpolated Vid FPS", value='---') with gr.Row(variant='compact') as interp_buttons_row: # This is the actual button that's pressed to initiate the interpolation: interpolate_button = gr.Button(value="*Interpolate Video*") interpolate_pics_button = gr.Button(value="*Interpolate Pics*") # Show a text about CLI outputs: gr.HTML("* check your CLI for outputs *", elem_id="below_interpolate_butts_msg") # TODO: CSS THIS TO CENTER OF ROW! # make the functin call when the interpolation button is clicked interpolate_button.click(upload_vid_to_interpolate,inputs=[vid_to_interpolate_chosen_file, frame_interpolation_engine, frame_interpolation_x_amount, frame_interpolation_slow_mo_enabled, frame_interpolation_slow_mo_amount, frame_interpolation_keep_imgs, ffmpeg_location, ffmpeg_crf, ffmpeg_preset, in_vid_fps_ui_window]) interpolate_pics_button.click(upload_pics_to_interpolate,inputs=[pics_to_interpolate_chosen_file, frame_interpolation_engine, frame_interpolation_x_amount, frame_interpolation_slow_mo_enabled, frame_interpolation_slow_mo_amount, frame_interpolation_keep_imgs, ffmpeg_location, ffmpeg_crf, ffmpeg_preset, fps, add_soundtrack, soundtrack_path]) # VIDEO UPSCALE TAB with gr.TabItem('Video Upscaling'): vid_to_upscale_chosen_file = gr.File(label="Video to Upscale", interactive=True, file_count="single", file_types=["video"], elem_id="vid_to_upscale_chosen_file") with gr.Column(): # NCNN UPSCALE TAB with gr.Row(variant='compact') as ncnn_upload_vid_stats_row: ncnn_upscale_in_vid_frame_count_window = gr.Textbox(label="In Frame Count", lines=1, interactive=False, value='---') # Non interactive textbox showing uploaded input vid Frame Count ncnn_upscale_in_vid_fps_ui_window = gr.Textbox(label="In FPS", lines=1, interactive=False, value='---') # Non interactive textbox showing uploaded input vid FPS ncnn_upscale_in_vid_res = gr.Textbox(label="In Res", lines=1, interactive=False, value='---') # Non interactive textbox showing uploaded input resolution ncnn_upscale_out_vid_res = gr.Textbox(label="Out Res", value='---') # Non interactive textbox showing expected output resolution with gr.Column(): with gr.Row(variant='compact', visible=True) as ncnn_actual_upscale_row: ncnn_upscale_model = gr.Dropdown(label="Upscale model", choices=['realesr-animevideov3', 'realesrgan-x4plus', 'realesrgan-x4plus-anime'], interactive=True, value = "realesr-animevideov3", type="value") ncnn_upscale_factor = gr.Dropdown(choices=['x2', 'x3', 'x4'], label="Upscale factor", interactive=True, value="x2", type="value") ncnn_upscale_keep_imgs = gr.Checkbox(label="Keep Imgs", value=True, interactive=True) # fix value ncnn_upscale_btn = gr.Button(value="*Upscale uploaded video*") ncnn_upscale_btn.click(ncnn_upload_vid_to_upscale,inputs=[vid_to_upscale_chosen_file, ncnn_upscale_in_vid_fps_ui_window, ncnn_upscale_in_vid_res, ncnn_upscale_out_vid_res, ncnn_upscale_model, ncnn_upscale_factor, ncnn_upscale_keep_imgs, ffmpeg_location, ffmpeg_crf, ffmpeg_preset]) with gr.Column(visible=False): # Upscale V1. Disabled 06-03-23 selected_tab = gr.State(value=0) with gr.Tabs(elem_id="extras_resize_mode"): with gr.TabItem('Scale by', elem_id="extras_scale_by_tab") as tab_scale_by: upscaling_resize = gr.Slider(minimum=1.0, maximum=8.0, step=0.05, label="Resize", value=2, elem_id="extras_upscaling_resize") with gr.TabItem('Scale to', elem_id="extras_scale_to_tab") as tab_scale_to: with FormRow(): upscaling_resize_w = gr.Slider(label="Width", minimum=1, maximum=7680, step=1, value=512, elem_id="extras_upscaling_resize_w") upscaling_resize_h = gr.Slider(label="Height", minimum=1, maximum=7680, step=1, value=512, elem_id="extras_upscaling_resize_h") upscaling_crop = gr.Checkbox(label='Crop to fit', value=True, elem_id="extras_upscaling_crop") with FormRow(): extras_upscaler_1 = gr.Dropdown(label='Upscaler 1', elem_id="extras_upscaler_1", choices=[x.name for x in sh.sd_upscalers], value=sh.sd_upscalers[3].name) extras_upscaler_2 = gr.Dropdown(label='Upscaler 2', elem_id="extras_upscaler_2", choices=[x.name for x in sh.sd_upscalers], value=sh.sd_upscalers[0].name) with FormRow(): with gr.Column(scale=3): extras_upscaler_2_visibility = gr.Slider(minimum=0.0, maximum=1.0, step=0.001, label="Upscaler 2 visibility", value=0.0, elem_id="extras_upscaler_2_visibility") with gr.Column(scale=1, min_width=80): upscale_keep_imgs = gr.Checkbox(label="Keep Imgs", elem_id="upscale_keep_imgs", value=True, interactive=True) tab_scale_by.select(fn=lambda: 0, inputs=[], outputs=[selected_tab]) tab_scale_to.select(fn=lambda: 1, inputs=[], outputs=[selected_tab]) # This is the actual button that's pressed to initiate the Upscaling: upscale_btn = gr.Button(value="*Upscale uploaded video*") # Show a text about CLI outputs: gr.HTML("* check your CLI for outputs") # make the function call when the UPSCALE button is clicked #upscale_btn.click(upload_vid_to_upscale,inputs=[vid_to_upscale_chosen_file, selected_tab, upscaling_resize, upscaling_resize_w, upscaling_resize_h, upscaling_crop, extras_upscaler_1, extras_upscaler_2, extras_upscaler_2_visibility, upscale_keep_imgs, ffmpeg_location, ffmpeg_crf, ffmpeg_preset]) # Vid2Depth TAB with gr.TabItem('Vid2depth'): vid_to_depth_chosen_file = gr.File(label="Video to get Depth from", interactive=True, file_count="single", file_types=["video"], elem_id="vid_to_depth_chosen_file") with gr.Row(variant='compact'): mode = gr.Dropdown(label='Mode', elem_id="mode", choices=['Depth (Midas/Adabins)', 'Anime Remove Background', 'Mixed', 'None (just grayscale)'], value='Depth (Midas/Adabins)') threshold_value = gr.Slider(label="Threshold Value Lower", value=127, minimum=0, maximum=255, step=1) threshold_value_max = gr.Slider(label="Threshold Value Upper", value=255, minimum=0, maximum=255, step=1) with gr.Row(variant='compact'): thresholding = gr.Radio(['None', 'Simple', 'Simple (Auto-value)', 'Adaptive (Mean)', 'Adaptive (Gaussian)'], label="Thresholding Mode", value='None') with gr.Row(variant='compact'): adapt_block_size = gr.Number(label="Block size", value=11) adapt_c = gr.Number(label="C", value=2) invert = gr.Checkbox(label='Closer is brighter', value=True, elem_id="invert") with gr.Row(variant='compact'): end_blur = gr.Slider(label="End blur width", value=0, minimum=0, maximum=255, step=1) midas_weight_vid2depth = gr.Slider(label="MiDaS weight (vid2depth)", value=da.midas_weight, minimum=0, maximum=1, step=0.05, interactive=True) depth_keep_imgs = gr.Checkbox(label='Keep Imgs', value=True, elem_id="depth_keep_imgs") with gr.Row(variant='compact'): # This is the actual button that's pressed to initiate the Upscaling: depth_btn = gr.Button(value="*Get depth from uploaded video*") with gr.Row(variant='compact'): # Show a text about CLI outputs: gr.HTML("* check your CLI for outputs") # make the function call when the UPSCALE button is clicked depth_btn.click(upload_vid_to_depth,inputs=[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, ffmpeg_location, ffmpeg_crf, ffmpeg_preset]) # STITCH FRAMES TO VID TAB with gr.TabItem('Frames to Video') as stitch_imgs_to_vid_row: with gr.Row(visible=False): path_name_modifier = gr.Dropdown(label="Path name modifier", choices=['x0_pred', 'x'], value=dv.path_name_modifier, type="value", elem_id="path_name_modifier", interactive=True, visible=False) gr.HTML("""

    Important Notes:

    """) with gr.Row(variant='compact'): image_path = gr.Textbox(label="Image path", lines=1, interactive=True, value = dv.image_path) ffmpeg_stitch_imgs_but = gr.Button(value="*Stitch frames to video*") ffmpeg_stitch_imgs_but.click(direct_stitch_vid_from_frames,inputs=[image_path, fps, ffmpeg_location, ffmpeg_crf, ffmpeg_preset, add_soundtrack, soundtrack_path]) # **OLD + NON ACTIVES AREA** with gr.Accordion(visible=False, label='INVISIBLE') as not_in_use_accordion: mp4_path = gr.Textbox(label="MP4 path", lines=1, interactive=True, value = dv.mp4_path) render_steps = gr.Checkbox(label="Render steps", value=dv.render_steps, interactive=True, visible=False) from_img2img_instead_of_link = gr.Checkbox(label="from_img2img_instead_of_link", value=False, interactive=False, visible=False) perlin_w = gr.Slider(label="Perlin W", minimum=0.1, maximum=16, step=0.1, value=da.perlin_w, interactive=True) perlin_h = gr.Slider(label="Perlin H", minimum=0.1, maximum=16, step=0.1, value=da.perlin_h, interactive=True) filename_format = gr.Textbox(label="Filename format", lines=1, interactive=True, value = d.filename_format, visible=False) save_settings = gr.Checkbox(label="save_settings", value=d.save_settings, interactive=True) save_samples = gr.Checkbox(label="save_samples", value=d.save_samples, interactive=True) display_samples = gr.Checkbox(label="display_samples", value=False, interactive=False) seed_enable_extras = gr.Checkbox(label="Enable subseed controls", value=False) n_batch = gr.Number(label="N Batch", value=d.n_batch, interactive=True, precision=0, visible=False) save_sample_per_step = gr.Checkbox(label="Save sample per step", value=d.save_sample_per_step, interactive=True) show_sample_per_step = gr.Checkbox(label="Show sample per step", value=d.show_sample_per_step, interactive=True) # Gradio's Change functions - hiding and renaming elements based on other elements fps.change(fn=change_gif_button_visibility, inputs=fps, outputs=make_gif) r_upscale_model.change(fn=update_r_upscale_factor, inputs=r_upscale_model, outputs=r_upscale_factor) ncnn_upscale_model.change(fn=update_r_upscale_factor, inputs=ncnn_upscale_model, outputs=ncnn_upscale_factor) ncnn_upscale_model.change(update_upscale_out_res_by_model_name, inputs=[ncnn_upscale_in_vid_res, ncnn_upscale_model], outputs=ncnn_upscale_out_vid_res) ncnn_upscale_factor.change(update_upscale_out_res, inputs=[ncnn_upscale_in_vid_res, ncnn_upscale_factor], outputs=ncnn_upscale_out_vid_res) vid_to_upscale_chosen_file.change(vid_upscale_gradio_update_stats,inputs=[vid_to_upscale_chosen_file, ncnn_upscale_factor],outputs=[ncnn_upscale_in_vid_fps_ui_window, ncnn_upscale_in_vid_frame_count_window, ncnn_upscale_in_vid_res, ncnn_upscale_out_vid_res]) animation_mode.change(fn=change_max_frames_visibility, inputs=animation_mode, outputs=max_frames) diffusion_cadence_outputs = [diffusion_cadence,guided_images_accord,optical_flow_cadence,optical_flow_redo_generation] for output in diffusion_cadence_outputs: animation_mode.change(fn=change_diffusion_cadence_visibility, inputs=animation_mode, outputs=output) three_d_related_outputs = [depth_3d_warping_accord,fov_accord,optical_flow_cadence,only_3d_motion_column] for output in three_d_related_outputs: animation_mode.change(fn=disble_3d_related_stuff, inputs=animation_mode, outputs=output) animation_mode.change(fn=enable_2d_related_stuff, inputs=animation_mode, outputs=only_2d_motion_column) animation_mode.change(fn=disable_by_interpolation, inputs=animation_mode, outputs=color_force_grayscale) animation_mode.change(fn=disable_by_interpolation, inputs=animation_mode, outputs=noise_tab_column) animation_mode.change(fn=disable_pers_flip_accord, inputs=animation_mode, outputs=perspective_flip_accord) animation_mode.change(fn=disable_pers_flip_accord, inputs=animation_mode, outputs=both_anim_mode_motion_params_column) #Hybrid related: animation_mode.change(fn=show_hybrid_html_msg, inputs=animation_mode, outputs=hybrid_msg_html) animation_mode.change(fn=change_hybrid_tab_status, inputs=animation_mode, outputs=hybrid_sch_accord) animation_mode.change(fn=change_hybrid_tab_status, inputs=animation_mode, outputs=hybrid_settings_accord) animation_mode.change(fn=change_hybrid_tab_status, inputs=animation_mode, outputs=humans_masking_accord) hybrid_comp_mask_type.change(fn=change_comp_mask_x_visibility, inputs=hybrid_comp_mask_type, outputs=hybrid_comp_mask_row) hybrid_motion.change(fn=disable_by_non_optical_flow, inputs=hybrid_motion, outputs=hybrid_flow_method) hybrid_motion.change(fn=disable_by_comp_mask, inputs=hybrid_motion, outputs=hybrid_motion_use_prev_img) hybrid_composite.change(fn=disable_by_hybrid_composite_dynamic, inputs=[hybrid_composite, hybrid_comp_mask_type], outputs=hybrid_comp_mask_row) hybrid_composite_outputs = [humans_masking_accord, hybrid_sch_accord, hybrid_comp_mask_type, hybrid_use_first_frame_as_init_image] for output in hybrid_composite_outputs: hybrid_composite.change(fn=disable_by_hybrid_composite, inputs=hybrid_composite, outputs=output) hybrid_comp_mask_type_outputs = [hybrid_comp_mask_blend_alpha_schedule_row, hybrid_comp_mask_contrast_schedule_row, hybrid_comp_mask_auto_contrast_cutoff_high_schedule_row, hybrid_comp_mask_auto_contrast_cutoff_low_schedule_row] for output in hybrid_comp_mask_type_outputs: hybrid_comp_mask_type.change(fn=disable_by_comp_mask, inputs=hybrid_comp_mask_type, outputs=output) # End of hybrid related seed_behavior.change(fn=change_seed_iter_visibility, inputs=seed_behavior, outputs=seed_iter_N_row) seed_behavior.change(fn=change_seed_schedule_visibility, inputs=seed_behavior, outputs=seed_schedule_row) color_coherence.change(fn=change_color_coherence_video_every_N_frames_visibility, inputs=color_coherence, outputs=color_coherence_video_every_N_frames_row) color_coherence.change(fn=change_color_coherence_image_path_visibility, inputs=color_coherence, outputs=color_coherence_image_path_row) noise_type.change(fn=change_perlin_visibility, inputs=noise_type, outputs=perlin_row) skip_video_creation_outputs = [fps_out_format_row, soundtrack_row, ffmpeg_quality_accordion, store_frames_in_ram, make_gif, r_upscale_row, delete_imgs] for output in skip_video_creation_outputs: skip_video_creation.change(fn=change_visibility_from_skip_video, inputs=skip_video_creation, outputs=output) frame_interpolation_slow_mo_enabled.change(fn=hide_slow_mo,inputs=frame_interpolation_slow_mo_enabled,outputs=frame_interp_slow_mo_amount_column) frame_interpolation_engine.change(fn=change_interp_x_max_limit,inputs=[frame_interpolation_engine,frame_interpolation_x_amount],outputs=frame_interpolation_x_amount) [change_fn.change(set_interp_out_fps, inputs=[frame_interpolation_x_amount, frame_interpolation_slow_mo_enabled, frame_interpolation_slow_mo_amount, in_vid_fps_ui_window], outputs=out_interp_vid_estimated_fps) for change_fn in [frame_interpolation_x_amount, frame_interpolation_slow_mo_amount, frame_interpolation_slow_mo_enabled]] # Populate the FPS and FCount values as soon as a video is uploaded to the FileUploadBox (vid_to_interpolate_chosen_file) vid_to_interpolate_chosen_file.change(gradio_f_interp_get_fps_and_fcount,inputs=[vid_to_interpolate_chosen_file, frame_interpolation_x_amount, frame_interpolation_slow_mo_enabled, frame_interpolation_slow_mo_amount],outputs=[in_vid_fps_ui_window,in_vid_frame_count_window, out_interp_vid_estimated_fps]) vid_to_interpolate_chosen_file.change(fn=hide_interp_stats,inputs=[vid_to_interpolate_chosen_file],outputs=[interp_live_stats_row]) interp_hide_list = [frame_interpolation_slow_mo_enabled,frame_interpolation_keep_imgs,frame_interp_amounts_row,interp_existing_video_row] for output in interp_hide_list: frame_interpolation_engine.change(fn=hide_interp_by_interp_status,inputs=frame_interpolation_engine,outputs=output) # END OF UI TABS stuff = locals() stuff = {**stuff, **controlnet_dict} stuff.pop('controlnet_dict') return stuff ### SETTINGS STORAGE UPDATE! 2023-01-27 ### To Reduce The Number Of Settings Overrides, ### They Are Being Passed As Dictionaries ### It Would Have Been Also Nice To Retrieve Them ### From Functions Like Deforumoutputargs(), ### But Over Time There Was Some Cross-Polination, ### So They Are Now Hardcoded As 'List'-Strings Below ### If you're adding a new setting, add it to one of the lists ### besides writing it in the setup functions above anim_args_names = str(r'''animation_mode, max_frames, border, angle, zoom, translation_x, translation_y, translation_z, transform_center_x, transform_center_y, rotation_3d_x, rotation_3d_y, rotation_3d_z, enable_perspective_flip, perspective_flip_theta, perspective_flip_phi, perspective_flip_gamma, perspective_flip_fv, noise_schedule, strength_schedule, contrast_schedule, cfg_scale_schedule, pix2pix_img_cfg_scale_schedule, enable_subseed_scheduling, subseed_schedule, subseed_strength_schedule, enable_steps_scheduling, steps_schedule, fov_schedule, aspect_ratio_schedule, near_schedule, far_schedule, seed_schedule, enable_sampler_scheduling, sampler_schedule, mask_schedule, use_noise_mask, noise_mask_schedule, enable_checkpoint_scheduling, checkpoint_schedule, enable_clipskip_scheduling, clipskip_schedule, enable_noise_multiplier_scheduling, noise_multiplier_schedule, kernel_schedule, sigma_schedule, amount_schedule, threshold_schedule, color_coherence, color_coherence_image_path, color_coherence_video_every_N_frames, color_force_grayscale, diffusion_cadence, optical_flow_cadence,optical_flow_redo_generation, noise_type, perlin_w, perlin_h, perlin_octaves, perlin_persistence, use_depth_warping, midas_weight, padding_mode, sampling_mode, save_depth_maps, video_init_path, extract_nth_frame, extract_from_frame, extract_to_frame, overwrite_extracted_frames, use_mask_video, video_mask_path, resume_from_timestring, resume_timestring''' ).replace("\n", "").replace("\r", "").replace(" ", "").split(',') hybrid_args_names = str(r'''hybrid_generate_inputframes, hybrid_generate_human_masks, hybrid_use_first_frame_as_init_image, hybrid_motion, hybrid_motion_use_prev_img, hybrid_flow_method, hybrid_composite, hybrid_comp_mask_type, hybrid_comp_mask_inverse, hybrid_comp_mask_equalize, hybrid_comp_mask_auto_contrast, hybrid_comp_save_extra_frames, hybrid_comp_alpha_schedule, hybrid_comp_mask_blend_alpha_schedule, hybrid_comp_mask_contrast_schedule, hybrid_comp_mask_auto_contrast_cutoff_high_schedule, hybrid_comp_mask_auto_contrast_cutoff_low_schedule''' ).replace("\n", "").replace("\r", "").replace(" ", "").split(',') args_names = str(r'''W, H, tiling, restore_faces, seed, sampler, seed_enable_extras, seed_resize_from_w, seed_resize_from_h, steps, ddim_eta, n_batch, save_settings, save_samples, display_samples, save_sample_per_step, show_sample_per_step, batch_name, filename_format, seed_behavior, seed_iter_N, use_init, from_img2img_instead_of_link, strength_0_no_init, strength, init_image, use_mask, use_alpha_as_mask, invert_mask, overlay_mask, mask_file, mask_contrast_adjust, mask_brightness_adjust, mask_overlay_blur, fill, full_res_mask, full_res_mask_padding, reroll_blank_frames''' ).replace("\n", "").replace("\r", "").replace(" ", "").split(',') video_args_names = str(r'''skip_video_creation, fps, make_gif, delete_imgs, output_format, ffmpeg_location, ffmpeg_crf, ffmpeg_preset, add_soundtrack, soundtrack_path, r_upscale_video, r_upscale_model, r_upscale_factor, r_upscale_keep_imgs, render_steps, path_name_modifier, image_path, mp4_path, store_frames_in_ram, frame_interpolation_engine, frame_interpolation_x_amount, frame_interpolation_slow_mo_enabled, frame_interpolation_slow_mo_amount, frame_interpolation_keep_imgs''' ).replace("\n", "").replace("\r", "").replace(" ", "").split(',') parseq_args_names = str(r'''parseq_manifest, parseq_use_deltas''' ).replace("\n", "").replace("\r", "").replace(" ", "").split(',') loop_args_names = str(r'''use_looper, init_images, image_strength_schedule, blendFactorMax, blendFactorSlope, tweening_frames_schedule, color_correction_factor''' ).replace("\n", "").replace("\r", "").replace(" ", "").split(',') def get_component_names(): return ['override_settings_with_file', 'custom_settings_file'] + anim_args_names +['animation_prompts', 'animation_prompts_positive', 'animation_prompts_negative'] + args_names + video_args_names + parseq_args_names + hybrid_args_names + loop_args_names + controlnet_component_names() def get_settings_component_names(): return [name for name in get_component_names()] #if name not in video_args_names] def setup_deforum_setting_ui(self, is_img2img, is_extension = True): ds = setup_deforum_setting_dictionary(self, is_img2img, is_extension) return [ds[name] for name in (['btn'] + get_component_names())] def pack_anim_args(args_dict): return {name: args_dict[name] for name in (anim_args_names + hybrid_args_names)} def pack_args(args_dict): args_dict = {name: args_dict[name] for name in args_names} args_dict['precision'] = 'autocast' args_dict['scale'] = 7 args_dict['subseed'] = -1 args_dict['subseed_strength'] = 0 args_dict['C'] = 4 args_dict['f'] = 8 args_dict['timestring'] = "" args_dict['init_latent'] = None args_dict['init_sample'] = None args_dict['init_c'] = None args_dict['noise_mask'] = None args_dict['seed_internal'] = 0 return args_dict def pack_video_args(args_dict): return {name: args_dict[name] for name in video_args_names} def pack_parseq_args(args_dict): return {name: args_dict[name] for name in parseq_args_names} def pack_loop_args(args_dict): return {name: args_dict[name] for name in loop_args_names} def pack_controlnet_args(args_dict): return {name: args_dict[name] for name in controlnet_component_names()} def process_args(args_dict_main): override_settings_with_file = args_dict_main['override_settings_with_file'] custom_settings_file = args_dict_main['custom_settings_file'] args_dict = pack_args(args_dict_main) anim_args_dict = pack_anim_args(args_dict_main) video_args_dict = pack_video_args(args_dict_main) parseq_args_dict = pack_parseq_args(args_dict_main) loop_args_dict = pack_loop_args(args_dict_main) controlnet_args_dict = pack_controlnet_args(args_dict_main) import json root = SimpleNamespace(**Root()) root.p = args_dict_main['p'] p = root.p root.animation_prompts = json.loads(args_dict_main['animation_prompts']) positive_prompts = args_dict_main['animation_prompts_positive'] negative_prompts = args_dict_main['animation_prompts_negative'] negative_prompts = negative_prompts.replace('--neg', '') # remove --neg from negative_prompts if recieved by mistake for key in root.animation_prompts: animationPromptCurr = root.animation_prompts[key] root.animation_prompts[key] = f"{positive_prompts} {animationPromptCurr} {'' if '--neg' in animationPromptCurr else '--neg'} {negative_prompts}" root.positive_prompts = positive_prompts root.negative_prompts = negative_prompts from deforum_helpers.settings import load_args if override_settings_with_file: load_args(args_dict, anim_args_dict, parseq_args_dict, loop_args_dict, controlnet_args_dict, custom_settings_file, root) if not os.path.exists(root.models_path): os.mkdir(root.models_path) args = SimpleNamespace(**args_dict) anim_args = SimpleNamespace(**anim_args_dict) video_args = SimpleNamespace(**video_args_dict) parseq_args = SimpleNamespace(**parseq_args_dict) loop_args = SimpleNamespace(**loop_args_dict) controlnet_args = SimpleNamespace(**controlnet_args_dict) p.width, p.height = map(lambda x: x - x % 64, (args.W, args.H)) p.steps = args.steps p.seed = args.seed p.sampler_name = args.sampler p.batch_size = args.n_batch p.tiling = args.tiling p.restore_faces = args.restore_faces p.seed_enable_extras = args.seed_enable_extras p.subseed = args.subseed p.subseed_strength = args.subseed_strength p.seed_resize_from_w = args.seed_resize_from_w p.seed_resize_from_h = args.seed_resize_from_h p.fill = args.fill p.ddim_eta = args.ddim_eta current_arg_list = [args, anim_args, video_args, parseq_args] args.outdir = os.path.join(p.outpath_samples, args.batch_name) root.outpath_samples = args.outdir args.outdir = os.path.join(os.getcwd(), args.outdir) if not os.path.exists(args.outdir): os.makedirs(args.outdir) args.seed = get_fixed_seed(args.seed) args.timestring = time.strftime('%Y%m%d%H%M%S') args.strength = max(0.0, min(1.0, args.strength)) if not args.use_init: args.init_image = None if anim_args.animation_mode == 'None': anim_args.max_frames = 1 elif anim_args.animation_mode == 'Video Input': args.use_init = True return root, args, anim_args, video_args, parseq_args, loop_args, controlnet_args def print_args(args): print("ARGS: /n") for key, value in args.__dict__.items(): print(f"{key}: {value}") # Local gradio-to-frame-interoplation function. *Needs* to stay here since we do Root() and use gradio elements directly, to be changed in the future def upload_vid_to_interpolate(file, engine, x_am, sl_enabled, sl_am, keep_imgs, f_location, f_crf, f_preset, 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 :)") root_params = Root() f_models_path = root_params['models_path'] 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, f_location, f_crf, f_preset, 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.") # make sure all uploaded pics have the same resolution pic_sizes = [Image.open(picture_path).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] root_params = Root() f_models_path = root_params['models_path'] 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 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, ffmpeg_location, ffmpeg_crf, ffmpeg_preset): # print msg and do nothing if vid not uploaded if not vid_to_depth_chosen_file: return print("Please upload a video :()") root_params = Root() f_models_path = root_params['models_path'] 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, ffmpeg_location, ffmpeg_crf, ffmpeg_preset, f_models_path) def ncnn_upload_vid_to_upscale(vid_path, in_vid_fps, in_vid_res, out_vid_res, upscale_model, upscale_factor, keep_imgs, f_location, f_crf, f_preset): if vid_path is None: print("Please upload a video :)") return root_params = Root() f_models_path = root_params['models_path'] current_user = root_params['current_user_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)