Input video required
", visible=False ) end_button = gr.Button(value="โ Cancel Current Job", interactive=True, visible=False) with gr.Tab("Queue"): with gr.Row(): with gr.Column(): with gr.Row() as queue_controls_row: refresh_button = gr.Button("๐ Refresh Queue") load_queue_button = gr.Button("โถ๏ธ Resume Queue") queue_export_button = gr.Button("๐ฆ Export Queue") clear_complete_button = gr.Button("๐งน Clear Completed Jobs", variant="secondary") clear_queue_button = gr.Button("โ Cancel Queued Jobs", variant="stop") with gr.Row(): import_queue_file = gr.File( label="Import Queue", file_types=[".json", ".zip"], type="filepath", visible=True, elem_classes="short-import-box" ) with gr.Row(visible=False) as confirm_cancel_row: gr.Markdown("### Are you sure you want to cancel all pending jobs?") confirm_cancel_yes_btn = gr.Button("โ Yes, Cancel All", variant="stop") confirm_cancel_no_btn = gr.Button("โฉ๏ธ No, Go Back") with gr.Row(): queue_status = gr.DataFrame( headers=["Job ID", "Type", "Status", "Created", "Started", "Completed", "Elapsed", "Preview"], datatype=["str", "str", "str", "str", "str", "str", "str", "html"], label="Job Queue" ) with gr.Accordion("Queue Documentation", open=False): gr.Markdown(""" ## Queue Tab Guide This tab is for managing your generation jobs. - **Refresh Queue**: Update the job list. - **Cancel Queue**: Stop all pending jobs. - **Clear Complete**: Remove finished, failed, or cancelled jobs from the list. - **Load Queue**: Load jobs from the default `queue.json`. - **Export Queue**: Save the current job list and its images to a zip file. - **Import Queue**: Load a queue from a `.json` or `.zip` file. """) # --- Event Handlers for Queue Tab --- # Function to clear all jobs in the queue def clear_all_jobs(): try: cancelled_count = job_queue.clear_queue() print(f"Cleared {cancelled_count} jobs from the queue") return update_stats() except Exception as e: import traceback print(f"Error in clear_all_jobs: {e}") traceback.print_exc() return [], "" # Function to clear completed and cancelled jobs def clear_completed_jobs(): try: removed_count = job_queue.clear_completed_jobs() print(f"Removed {removed_count} completed/cancelled jobs from the queue") return update_stats() except Exception as e: import traceback print(f"Error in clear_completed_jobs: {e}") traceback.print_exc() return [], "" # Function to load queue from queue.json def load_queue_from_json(): try: loaded_count = job_queue.load_queue_from_json() print(f"Loaded {loaded_count} jobs from queue.json") return update_stats() except Exception as e: import traceback print(f"Error loading queue from JSON: {e}") traceback.print_exc() return [], "" # Function to import queue from a custom JSON file def import_queue_from_file(file_path): if not file_path: return update_stats() try: loaded_count = job_queue.load_queue_from_json(file_path) print(f"Loaded {loaded_count} jobs from {file_path}") return update_stats() except Exception as e: import traceback print(f"Error importing queue from file: {e}") traceback.print_exc() return [], "" # Function to export queue to a zip file def export_queue_to_zip(): try: zip_path = job_queue.export_queue_to_zip() if zip_path and os.path.exists(zip_path): print(f"Queue exported to {zip_path}") else: print("Failed to export queue to zip") return update_stats() except Exception as e: import traceback print(f"Error exporting queue to zip: {e}") traceback.print_exc() return [], "" # --- Connect Buttons --- refresh_button.click(fn=update_stats, inputs=[], outputs=[queue_status, queue_stats_display]) # Confirmation logic for Cancel Queue def show_cancel_confirmation(): return gr.update(visible=False), gr.update(visible=True) def hide_cancel_confirmation(): return gr.update(visible=True), gr.update(visible=False) def confirmed_clear_all_jobs(): qs_data, qs_text = clear_all_jobs() return qs_data, qs_text, gr.update(visible=True), gr.update(visible=False) clear_queue_button.click(fn=show_cancel_confirmation, inputs=None, outputs=[queue_controls_row, confirm_cancel_row]) confirm_cancel_no_btn.click(fn=hide_cancel_confirmation, inputs=None, outputs=[queue_controls_row, confirm_cancel_row]) confirm_cancel_yes_btn.click(fn=confirmed_clear_all_jobs, inputs=None, outputs=[queue_status, queue_stats_display, queue_controls_row, confirm_cancel_row]) clear_complete_button.click(fn=clear_completed_jobs, inputs=[], outputs=[queue_status, queue_stats_display]) queue_export_button.click(fn=export_queue_to_zip, inputs=[], outputs=[queue_status, queue_stats_display]) # Create a container for thumbnails (kept for potential future use, though not displayed in DataFrame) with gr.Row(): thumbnail_container = gr.Column() thumbnail_container.elem_classes = ["thumbnail-container"] # Add CSS for thumbnails with gr.Tab("Outputs", id="outputs_tab"): # Ensure 'id' is present for tab switching outputDirectory_video = settings.get("output_dir", settings.default_settings['output_dir']) outputDirectory_metadata = settings.get("metadata_dir", settings.default_settings['metadata_dir']) def get_gallery_items(): items = [] for f in os.listdir(outputDirectory_metadata): if f.endswith(".png"): prefix = os.path.splitext(f)[0] latest_video = get_latest_video_version(prefix) if latest_video: video_path = os.path.join(outputDirectory_video, latest_video) mtime = os.path.getmtime(video_path) preview_path = os.path.join(outputDirectory_metadata, f) items.append((preview_path, prefix, mtime)) items.sort(key=lambda x: x[2], reverse=True) return [(i[0], i[1]) for i in items] def get_latest_video_version(prefix): max_number = -1 selected_file = None for f in os.listdir(outputDirectory_video): if f.startswith(prefix + "_") and f.endswith(".mp4"): # Skip files that include "combined" in their name if "combined" in f: continue try: num = int(f.replace(prefix + "_", '').replace(".mp4", '')) if num > max_number: max_number = num selected_file = f except ValueError: # Ignore files that do not have a valid number in their name continue return selected_file # load_video_and_info_from_prefix now also returns button visibility def load_video_and_info_from_prefix(prefix): video_file = get_latest_video_version(prefix) json_path = os.path.join(outputDirectory_metadata, prefix) + ".json" if not video_file or not os.path.exists(os.path.join(outputDirectory_video, video_file)) or not os.path.exists(json_path): # If video or info not found, button should be hidden return None, "Video or JSON not found.", gr.update(visible=False) video_path = os.path.join(outputDirectory_video, video_file) info_content = {"description": "no info"} if os.path.exists(json_path): with open(json_path, "r", encoding="utf-8") as f: info_content = json.load(f) # If video and info found, button should be visible return video_path, json.dumps(info_content, indent=2, ensure_ascii=False), gr.update(visible=True) gallery_items_state = gr.State(get_gallery_items()) selected_original_video_path_state = gr.State(None) # Holds the ORIGINAL, UNPROCESSED path with gr.Row(): with gr.Column(scale=2): thumbs = gr.Gallery( # value=[i[0] for i in get_gallery_items()], columns=[4], allow_preview=False, object_fit="cover", height="auto" ) refresh_button = gr.Button("๐ Update Gallery") with gr.Column(scale=5): video_out = gr.Video(sources=[], autoplay=True, loop=True, visible=False) with gr.Column(scale=1): info_out = gr.Textbox(label="Generation info", visible=False) send_to_toolbox_btn = gr.Button("โก๏ธ Send to Post-processing", visible=False) # Added new send_to_toolbox_btn def refresh_gallery(): new_items = get_gallery_items() return gr.update(value=[i[0] for i in new_items]), new_items refresh_button.click(fn=refresh_gallery, outputs=[thumbs, gallery_items_state]) # MODIFIED: on_select now handles visibility of the new button def on_select(evt: gr.SelectData, gallery_items): if evt.index is None or not gallery_items or evt.index >= len(gallery_items): return gr.update(visible=False), gr.update(visible=False), gr.update(visible=False), None prefix = gallery_items[evt.index][1] # original_video_path is e.g., "outputs/my_actual_video.mp4" original_video_path, info_string, button_visibility_update = load_video_and_info_from_prefix(prefix) # Determine visibility for video and info based on whether video_path was found video_out_update = gr.update(value=original_video_path, visible=bool(original_video_path)) info_out_update = gr.update(value=info_string, visible=bool(original_video_path)) # IMPORTANT: Store the ORIGINAL, UNPROCESSED path in the gr.State return video_out_update, info_out_update, button_visibility_update, original_video_path thumbs.select( fn=on_select, inputs=[gallery_items_state], outputs=[video_out, info_out, send_to_toolbox_btn, selected_original_video_path_state] # Output original path to State ) with gr.Tab("Post-processing", id="toolbox_tab"): # Call the function from toolbox_app.py to build the Toolbox UI # The toolbox_ui_layout (e.g., a gr.Column) is automatically placed here. toolbox_ui_layout, tb_target_video_input = tb_create_video_toolbox_ui() with gr.Tab("Settings"): with gr.Row(): with gr.Column(): save_metadata = gr.Checkbox( label="Save Metadata", info="Save to JSON file", value=settings.get("save_metadata", 6), ) gpu_memory_preservation = gr.Slider( label="Memory Buffer for Stability (VRAM GB)", minimum=1, maximum=128, step=0.1, value=settings.get("gpu_memory_preservation", 6), info="Increase reserve if you see computer freezes, stagnant generation, or super slow sampling steps (try 1G at a time).\ Otherwise smaller buffer is faster. Some models and lora need more buffer than others. \ (5.5 - 8.5 is a common range)" ) mp4_crf = gr.Slider( label="MP4 Compression", minimum=0, maximum=100, step=1, value=settings.get("mp4_crf", 16), info="Lower means better quality. 0 is uncompressed. Change to 16 if you get black outputs." ) clean_up_videos = gr.Checkbox( label="Clean up video files", value=settings.get("clean_up_videos", True), info="If checked, only the final video will be kept after generation." ) auto_cleanup_on_startup = gr.Checkbox( label="Automatically clean up temp folders on startup", value=settings.get("auto_cleanup_on_startup", False), info="If checked, temporary files (inc. post-processing) will be cleaned up when the application starts." ) latents_display_top = gr.Checkbox( label="Display Next Latents across top of interface", value=get_latents_display_top(), info="If checked, the Next Latents preview will be displayed across the top of the interface instead of in the right column." ) # gr.Markdown("---") # gr.Markdown("### Startup Settings") gr.Markdown("") # Initial values for startup preset dropdown # Ensure settings and load_presets are available in this scope initial_startup_model_val = settings.get("startup_model_type", "None") initial_startup_presets_choices_val = [] initial_startup_preset_value_val = None if initial_startup_model_val and initial_startup_model_val != "None": # load_presets is defined further down in create_interface initial_startup_presets_choices_val = load_presets(initial_startup_model_val) saved_preset_for_initial_model_val = settings.get("startup_preset_name") if saved_preset_for_initial_model_val in initial_startup_presets_choices_val: initial_startup_preset_value_val = saved_preset_for_initial_model_val startup_model_type_dropdown = gr.Dropdown( label="Startup Model Type", choices=["None"] + [choice[0] for choice in model_type.choices if choice[0] != "XY Plot"], # model_type is the Radio on Generate tab value=initial_startup_model_val, info="Select a model type to load on startup. 'None' to disable." ) startup_preset_name_dropdown = gr.Dropdown( label="Startup Preset", choices=initial_startup_presets_choices_val, value=initial_startup_preset_value_val, info="Select a preset for the startup model. Updates when Startup Model Type changes.", interactive=True # Must be interactive to be updated by another component ) with gr.Accordion("System Prompt", open=False): with gr.Row(equal_height=True): # New Row to contain checkbox and reset button override_system_prompt = gr.Checkbox( label="Override System Prompt", value=settings.get("override_system_prompt", False), info="If checked, the system prompt template below will be used instead of the default one.", scale=1 # Give checkbox some scale ) reset_system_prompt_btn = gr.Button( "๐ Reset", scale=0 ) system_prompt_template = gr.Textbox( label="System Prompt Template", value=settings.get("system_prompt_template", "{\"template\": \"<|start_header_id|>system<|end_header_id|>\\n\\nDescribe the video by detailing the following aspects: 1. The main content and theme of the video.2. The color, shape, size, texture, quantity, text, and spatial relationships of the objects.3. Actions, events, behaviors temporal relationships, physical movement changes of the objects.4. background environment, light, style and atmosphere.5. camera angles, movements, and transitions used in the video:<|eot_id|><|start_header_id|>user<|end_header_id|>\\n\\n{}<|eot_id|>\", \"crop_start\": 95}"), lines=10, info="System prompt template used for video generation. Must be a valid JSON or Python dictionary string with 'template' and 'crop_start' keys. Example: {\"template\": \"your template here\", \"crop_start\": 95}" ) # The reset_system_prompt_btn is now defined above within the Row # --- Settings Tab Event Handlers --- output_dir = gr.Textbox( label="Output Directory", value=settings.get("output_dir"), placeholder="Path to save generated videos" ) metadata_dir = gr.Textbox( label="Metadata Directory", value=settings.get("metadata_dir"), placeholder="Path to save metadata files" ) lora_dir = gr.Textbox( label="LoRA Directory", value=settings.get("lora_dir"), placeholder="Path to LoRA models" ) gradio_temp_dir = gr.Textbox(label="Gradio Temporary Directory", value=settings.get("gradio_temp_dir")) auto_save = gr.Checkbox( label="Auto-save settings", value=settings.get("auto_save_settings", True) ) # Add Gradio Theme Dropdown gradio_themes = ["default", "base", "soft", "glass", "mono", "origin", "citrus", "monochrome", "ocean", "NoCrypt/miku", "earneleh/paris", "gstaff/xkcd"] theme_dropdown = gr.Dropdown( label="Theme", choices=gradio_themes, value=settings.get("gradio_theme", "default"), info="Select the Gradio UI theme. Requires restart." ) save_btn = gr.Button("๐พ Save Settings") cleanup_btn = gr.Button("๐๏ธ Clean Up Temporary Files") status = gr.HTML("") cleanup_output = gr.Textbox(label="Cleanup Status", interactive=False) def save_settings(save_metadata, gpu_memory_preservation, mp4_crf, clean_up_videos, auto_cleanup_on_startup_val, latents_display_top_val, override_system_prompt_value, system_prompt_template_value, output_dir, metadata_dir, lora_dir, gradio_temp_dir, auto_save, selected_theme, startup_model_type_val, startup_preset_name_val): """Handles the manual 'Save Settings' button click.""" # This function is for the manual save button. # It collects all current UI values and saves them. # The auto-save logic is handled by individual .change() and .blur() handlers # calling settings.set(). # First, update the settings object with all current values from the UI try: # Save the system prompt template as is, without trying to parse it # The hunyuan.py file will handle parsing it when needed processed_template = system_prompt_template_value settings.save_settings( save_metadata=save_metadata, gpu_memory_preservation=gpu_memory_preservation, mp4_crf=mp4_crf, clean_up_videos=clean_up_videos, auto_cleanup_on_startup=auto_cleanup_on_startup_val, # ADDED latents_display_top=latents_display_top_val, # NEW: Added latents display position setting override_system_prompt=override_system_prompt_value, system_prompt_template=processed_template, output_dir=output_dir, metadata_dir=metadata_dir, lora_dir=lora_dir, gradio_temp_dir=gradio_temp_dir, auto_save_settings=auto_save, gradio_theme=selected_theme, startup_model_type=startup_model_type_val, startup_preset_name=startup_preset_name_val ) # settings.save_settings() is called inside settings.save_settings if auto_save is true, # but for the manual button, we ensure it saves regardless of the auto_save flag's previous state. # The call above to settings.save_settings already handles writing to disk. return "Settings saved successfully! Restart required for theme change.
" except Exception as e: return f"Error saving settings: {str(e)}
" def handle_individual_setting_change(key, value, setting_name_for_ui): """Called by .change() and .submit() events of individual setting components.""" if key == "auto_save_settings": # For the "auto_save_settings" checkbox itself: # 1. Update its value directly in the settings object in memory. # This bypasses the conditional save logic within settings.set() for this specific action. settings.settings[key] = value # 2. Force a save of all settings to disk. This will be correct because either: # - auto_save_settings is turning True: so all changes already in memory need to be saved now. # - auto_save_settings turning False from True: prior changes already saved so only auto_save_settings will be saved. settings.save_settings() # 3. Provide feedback. if value is True: return f"'{setting_name_for_ui}' setting is now ON and saved.
" else: return f"'{setting_name_for_ui}' setting is now OFF and saved.
" else: # For all other settings: # Let settings.set() handle the auto-save logic based on the current "auto_save_settings" value. settings.set(key, value) # settings.set() will call save_settings() if auto_save is True if settings.get("auto_save_settings"): # Check the current state of auto_save return f"'{setting_name_for_ui}' setting auto-saved.
" else: return f"'{setting_name_for_ui}' setting changed (auto-save is off, click 'Save Settings').
" # REMOVE `cleanup_temp_folder` from the `inputs` list save_btn.click( fn=save_settings, inputs=[save_metadata, gpu_memory_preservation, mp4_crf, clean_up_videos, auto_cleanup_on_startup, latents_display_top, override_system_prompt, system_prompt_template, output_dir, metadata_dir, lora_dir, gradio_temp_dir, auto_save, theme_dropdown, startup_model_type_dropdown, startup_preset_name_dropdown], outputs=[status] ).then( # NEW: Update latents display layout after manual save fn=create_latents_layout_update, inputs=None, outputs=[top_preview_row, preview_image] ) def reset_system_prompt_template_value(): return settings.default_settings["system_prompt_template"], False reset_system_prompt_btn.click( fn=reset_system_prompt_template_value, outputs=[system_prompt_template, override_system_prompt] ).then( # Trigger auto-save for the reset values if auto-save is on lambda val_template, val_override: handle_individual_setting_change("system_prompt_template", val_template, "System Prompt Template") or handle_individual_setting_change("override_system_prompt", val_override, "Override System Prompt"), inputs=[system_prompt_template, override_system_prompt], outputs=[status]) def manual_cleanup_handler(): """UI handler for the manual cleanup button.""" # This directly calls the toolbox_processor's cleanup method and returns the summary string. summary = tb_processor.tb_clear_temporary_files() return summary cleanup_btn.click( fn=manual_cleanup_handler, inputs=None, outputs=[cleanup_output] ) # Add .change handlers for auto-saving individual settings save_metadata.change(lambda v: handle_individual_setting_change("save_metadata", v, "Save Metadata"), inputs=[save_metadata], outputs=[status]) gpu_memory_preservation.change(lambda v: handle_individual_setting_change("gpu_memory_preservation", v, "GPU Memory Preservation"), inputs=[gpu_memory_preservation], outputs=[status]) mp4_crf.change(lambda v: handle_individual_setting_change("mp4_crf", v, "MP4 Compression"), inputs=[mp4_crf], outputs=[status]) clean_up_videos.change(lambda v: handle_individual_setting_change("clean_up_videos", v, "Clean Up Videos"), inputs=[clean_up_videos], outputs=[status]) # NEW: auto-cleanup temp files on startup checkbox auto_cleanup_on_startup.change(lambda v: handle_individual_setting_change("auto_cleanup_on_startup", v, "Auto Cleanup on Startup"), inputs=[auto_cleanup_on_startup], outputs=[status]) # NEW: latents display position setting latents_display_top.change(lambda v: handle_individual_setting_change("latents_display_top", v, "Latents Display Position"), inputs=[latents_display_top], outputs=[status]) # Connect the latents display setting to layout updates def update_latents_display_layout_from_checkbox(display_top): """Update layout when checkbox changes - uses the checkbox value directly""" if display_top: return ( gr.update(visible=True), # top_preview_row gr.update(visible=False, value=None) # preview_image (right column) ) else: return ( gr.update(visible=False), # top_preview_row gr.update(visible=True) # preview_image (right column) ) latents_display_top.change( fn=update_latents_display_layout_from_checkbox, inputs=[latents_display_top], outputs=[top_preview_row, preview_image] ) override_system_prompt.change(lambda v: handle_individual_setting_change("override_system_prompt", v, "Override System Prompt"), inputs=[override_system_prompt], outputs=[status]) # Using .blur for text changes so they are processed after the user finishes, not on every keystroke system_prompt_template.blur(lambda v: handle_individual_setting_change("system_prompt_template", v, "System Prompt Template"), inputs=[system_prompt_template], outputs=[status]) # reset_system_prompt_btn # is handled separately above, on click # Using .blur for text changes so they are processed after the user finishes, not on every keystroke output_dir.blur(lambda v: handle_individual_setting_change("output_dir", v, "Output Directory"), inputs=[output_dir], outputs=[status]) metadata_dir.blur(lambda v: handle_individual_setting_change("metadata_dir", v, "Metadata Directory"), inputs=[metadata_dir], outputs=[status]) lora_dir.blur(lambda v: handle_individual_setting_change("lora_dir", v, "LoRA Directory"), inputs=[lora_dir], outputs=[status]) gradio_temp_dir.blur(lambda v: handle_individual_setting_change("gradio_temp_dir", v, "Gradio Temporary Directory"), inputs=[gradio_temp_dir], outputs=[status]) auto_save.change(lambda v: handle_individual_setting_change("auto_save_settings", v, "Auto-save Settings"), inputs=[auto_save], outputs=[status]) theme_dropdown.change(lambda v: handle_individual_setting_change("gradio_theme", v, "Theme"), inputs=[theme_dropdown], outputs=[status]) # Event handlers for startup settings def update_startup_preset_dropdown_choices(selected_startup_model_type_from_ui): if not selected_startup_model_type_from_ui or selected_startup_model_type_from_ui == "None": return gr.update(choices=[], value=None) loaded_presets_for_model = load_presets(selected_startup_model_type_from_ui) # Get the preset name that was saved for the *previous* model type current_saved_startup_preset = settings.get("startup_preset_name") # Default to None value_to_select = None # If the previously saved preset name exists for the new model, select it if current_saved_startup_preset and current_saved_startup_preset in loaded_presets_for_model: value_to_select = current_saved_startup_preset return gr.update(choices=loaded_presets_for_model, value=value_to_select) startup_model_type_dropdown.change( fn=lambda v: handle_individual_setting_change("startup_model_type", v, "Startup Model Type"), inputs=[startup_model_type_dropdown], outputs=[status] ).then( # Chain the update to the preset dropdown fn=update_startup_preset_dropdown_choices, inputs=[startup_model_type_dropdown], outputs=[startup_preset_name_dropdown]) startup_preset_name_dropdown.change(lambda v: handle_individual_setting_change("startup_preset_name", v, "Startup Preset Name"), inputs=[startup_preset_name_dropdown], outputs=[status]) # --- Event Handlers and Connections (Now correctly indented) --- # --- Connect Monitoring --- # Auto-check for current job on page load and job change def check_for_current_job(): # This function will be called when the interface loads # It will check if there's a current job in the queue and update the UI with job_queue.lock: current_job = job_queue.current_job if current_job: # Return all the necessary information to update the preview windows job_id = current_job.id result = current_job.result preview = current_job.progress_data.get('preview') if current_job.progress_data else None desc = current_job.progress_data.get('desc', '') if current_job.progress_data else '' html = current_job.progress_data.get('html', '') if current_job.progress_data else '' # Also trigger the monitor_job function to start monitoring this job print(f"Auto-check found current job {job_id}, triggering monitor_job") return job_id, result, preview, preview, desc, html return None, None, None, None, '', '' # Auto-check for current job on page load and handle handoff between jobs. def check_for_current_job_and_monitor(): # This function is now the key to the handoff. # It finds the current job and returns its ID, which will trigger the monitor. job_id, result, preview, top_preview, desc, html = check_for_current_job() # We also need to get fresh stats at the same time. queue_status_data, queue_stats_text = update_stats() # Return everything needed to update the UI atomically. return job_id, result, preview, top_preview, desc, html, queue_status_data, queue_stats_text # Connect the main process function (wrapper for adding to queue) def process_with_queue_update(model_type_arg, *args): # Call update_stats to get both queue_status_data and queue_stats_text queue_status_data, queue_stats_text = update_stats() # MODIFIED # Extract all arguments (ensure order matches inputs lists) # The order here MUST match the order in the `ips` list. # RT_BORG: Global settings gpu_memory_preservation, mp4_crf, save_metadata removed from direct args. (input_image_arg, input_video_arg, end_frame_image_original_arg, end_frame_strength_original_arg, prompt_text_arg, n_prompt_arg, seed_arg, # the seed value randomize_seed_arg, # the boolean value of the checkbox total_second_length_arg, latent_window_size_arg, steps_arg, cfg_arg, gs_arg, rs_arg, cache_type_arg, teacache_num_steps_arg, teacache_rel_l1_thresh_arg, magcache_threshold_arg, magcache_max_consecutive_skips_arg, magcache_retention_ratio_arg, blend_sections_arg, latent_type_arg, clean_up_videos_arg, # UI checkbox from Generate tab selected_loras_arg, resolutionW_arg, resolutionH_arg, combine_with_source_arg, num_cleaned_frames_arg, lora_names_states_arg, # This is from lora_names_states (gr.State) *lora_slider_values_tuple # Remaining args are LoRA slider values ) = args # DO NOT parse the prompt here. Parsing happens once in the worker. # Determine the model type to send to the backend backend_model_type = model_type_arg # model_type_arg is the UI selection if model_type_arg == "Video with Endframe": backend_model_type = "Video" # The backend "Video" model_type handles with and without endframe # Use the appropriate input based on model type is_ui_video_model = is_video_model(model_type_arg) input_data = input_video_arg if is_ui_video_model else input_image_arg # Define actual end_frame params to pass to backend actual_end_frame_image_for_backend = None actual_end_frame_strength_for_backend = 1.0 # Default strength if model_type_arg == "Original with Endframe" or model_type_arg == "F1 with Endframe" or model_type_arg == "Video with Endframe": actual_end_frame_image_for_backend = end_frame_image_original_arg actual_end_frame_strength_for_backend = end_frame_strength_original_arg # Get the input video path for Video model input_image_path = None if is_ui_video_model and input_video_arg is not None: # For Video models, input_video contains the path to the video file input_image_path = input_video_arg # Use the current seed value as is for this job # Call the process function with all arguments # Pass the backend_model_type and the ORIGINAL prompt_text string to the backend process function result = process_fn(backend_model_type, input_data, actual_end_frame_image_for_backend, actual_end_frame_strength_for_backend, prompt_text_arg, n_prompt_arg, seed_arg, total_second_length_arg, latent_window_size_arg, steps_arg, cfg_arg, gs_arg, rs_arg, cache_type_arg == 'TeaCache', teacache_num_steps_arg, teacache_rel_l1_thresh_arg, cache_type_arg == 'MagCache', magcache_threshold_arg, magcache_max_consecutive_skips_arg, magcache_retention_ratio_arg, blend_sections_arg, latent_type_arg, clean_up_videos_arg, # clean_up_videos_arg is from UI selected_loras_arg, resolutionW_arg, resolutionH_arg, input_image_path, combine_with_source_arg, num_cleaned_frames_arg, lora_names_states_arg, *lora_slider_values_tuple ) # If randomize_seed is checked, generate a new random seed for the next job new_seed_value = None if randomize_seed_arg: new_seed_value = random.randint(0, 21474) print(f"Generated new seed for next job: {new_seed_value}") # Create the button update for start_button WITHOUT interactive=True. # The interactivity will be set by update_start_button_state later in the chain. start_button_update_after_add = gr.update(value="๐ Add to Queue") # If a job ID was created, automatically start monitoring it and update queue if result and result[1]: # Check if job_id exists in results job_id = result[1] # queue_status_data = update_queue_status_fn() # OLD: update_stats now called earlier # Call update_stats again AFTER the job is added to get the freshest stats queue_status_data, queue_stats_text = update_stats() # Add the new seed value to the results if randomize is checked if new_seed_value is not None: # Use result[6] directly for end_button to preserve its value. Add gr.update() for video_input_required_message. return [result[0], job_id, result[2], result[3], result[4], start_button_update_after_add, result[6], queue_status_data, queue_stats_text, new_seed_value, gr.update()] else: # Use result[6] directly for end_button to preserve its value. Add gr.update() for video_input_required_message. return [result[0], job_id, result[2], result[3], result[4], start_button_update_after_add, result[6], queue_status_data, queue_stats_text, gr.update(), gr.update()] # If no job ID was created, still return the new seed if randomize is checked # Also, ensure we return the latest stats even if no job was created (e.g., error during param validation) queue_status_data, queue_stats_text = update_stats() if new_seed_value is not None: # Make sure to preserve the end_button update from result[6] return [result[0], result[1], result[2], result[3], result[4], start_button_update_after_add, result[6], queue_status_data, queue_stats_text, new_seed_value, gr.update()] else: # Make sure to preserve the end_button update from result[6] return [result[0], result[1], result[2], result[3], result[4], start_button_update_after_add, result[6], queue_status_data, queue_stats_text, gr.update(), gr.update()] # Custom end process function that ensures the queue is updated and changes button text def end_process_with_update(): _ = end_process_fn() # Call the original end_process_fn # Now, get fresh stats for both queue table and toolbar queue_status_data, queue_stats_text = update_stats() # Don't try to get the new job ID immediately after cancellation # The monitor_job function will handle the transition to the next job # Change the cancel button text to "Cancelling..." and make it non-interactive # This ensures the button stays in this state until the job is fully cancelled return queue_status_data, queue_stats_text, gr.update(value="Cancelling...", interactive=False), gr.update(value=None) # MODIFIED handle_send_video_to_toolbox: def handle_send_video_to_toolbox(original_path_from_state): # Input is now the original path from gr.State print(f"Sending selected Outputs' video to Post-processing: {original_path_from_state}") if original_path_from_state and isinstance(original_path_from_state, str) and os.path.exists(original_path_from_state): # tb_target_video_input will now process the ORIGINAL path (e.g., "outputs/my_actual_video.mp4"). return gr.update(value=original_path_from_state), gr.update(selected="toolbox_tab") else: print(f"No valid video path (from State) found to send. Path: {original_path_from_state}") return gr.update(), gr.update() send_to_toolbox_btn.click( fn=handle_send_video_to_toolbox, inputs=[selected_original_video_path_state], # INPUT IS NOW THE gr.State holding the ORIGINAL path outputs=[ tb_target_video_input, # This is tb_input_video_component from toolbox_app.py main_tabs_component ] ) # --- Inputs Lists --- # --- Inputs for all models --- ips = [ input_image, # Corresponds to input_image_arg input_video, # Corresponds to input_video_arg end_frame_image_original, # Corresponds to end_frame_image_original_arg end_frame_strength_original,# Corresponds to end_frame_strength_original_arg prompt, # Corresponds to prompt_text_arg n_prompt, # Corresponds to n_prompt_arg seed, # Corresponds to seed_arg randomize_seed, # Corresponds to randomize_seed_arg total_second_length, # Corresponds to total_second_length_arg latent_window_size, # Corresponds to latent_window_size_arg steps, # Corresponds to steps_arg cfg, # Corresponds to cfg_arg gs, # Corresponds to gs_arg rs, # Corresponds to rs_arg cache_type, # Corresponds to cache_type_arg teacache_num_steps, # Corresponds to teacache_num_steps_arg teacache_rel_l1_thresh, # Corresponds to teacache_rel_l1_thresh_arg magcache_threshold, # Corresponds to magcache_threshold_arg magcache_max_consecutive_skips, # Corresponds to magcache_max_consecutive_skips_arg magcache_retention_ratio, # Corresponds to magcache_retention_ratio_arg blend_sections, # Corresponds to blend_sections_arg latent_type, # Corresponds to latent_type_arg clean_up_videos, # Corresponds to clean_up_videos_arg (UI checkbox) lora_selector, # Corresponds to selected_loras_arg resolutionW, # Corresponds to resolutionW_arg resolutionH, # Corresponds to resolutionH_arg combine_with_source, # Corresponds to combine_with_source_arg num_cleaned_frames, # Corresponds to num_cleaned_frames_arg lora_names_states # Corresponds to lora_names_states_arg ] # Add LoRA sliders to the input list ips.extend([lora_sliders[lora] for lora in lora_names]) # --- Connect Buttons --- def handle_start_button(selected_model, *args): # For other model types, use the regular process function return process_with_queue_update(selected_model, *args) def handle_batch_add_to_queue(*args): # The last argument will be the list of files from batch_input_images batch_files = args[-1] if not batch_files or not isinstance(batch_files, list): print("No batch images provided.") return print(f"Starting batch processing for {len(batch_files)} images.") # Reconstruct the arguments for the single process function, excluding the batch files list single_job_args = list(args[:-1]) # The first argument to process_with_queue_update is model_type model_type_arg = single_job_args.pop(0) # Keep track of the seed current_seed = single_job_args[6] # seed is the 7th element in the ips list randomize_seed_arg = single_job_args[7] # randomize_seed is the 8th for image_path in batch_files: # --- FIX IS HERE --- # Load the image from the path into a NumPy array try: pil_image = Image.open(image_path).convert("RGB") numpy_image = np.array(pil_image) except Exception as e: print(f"Error loading batch image {image_path}: {e}. Skipping.") continue # --- END OF FIX --- # Replace the single input_image argument with the loaded NumPy image current_job_args = single_job_args[:] current_job_args[0] = numpy_image # Use the loaded numpy_image current_job_args[6] = current_seed # Set the seed for the current job # Call the original processing function with the modified arguments process_with_queue_update(model_type_arg, *current_job_args) # If randomize seed is checked, generate a new one for the next image if randomize_seed_arg: current_seed = random.randint(0, 21474) print("Batch processing complete. All jobs added to the queue.") # Validation ensures the start button is only enabled when appropriate def update_start_button_state(*args): """ Validation fails if a video model is selected and no input video is provided. Updates the start button interactivity and validation message visibility. Handles variable inputs from different Gradio event chains. """ # The required values are the last two arguments provided by the Gradio event if len(args) >= 2: selected_model = args[-2] input_video_value = args[-1] else: # Fallback or error handling if not enough arguments are received # This might happen if the event is triggered in an unexpected way print(f"Warning: update_start_button_state received {len(args)} args, expected at least 2.") # Default to a safe state (button disabled) return gr.Button(value="โ Error", interactive=False), gr.update(visible=True) video_provided = input_video_value is not None if is_video_model(selected_model) and not video_provided: # Video model selected, but no video provided return gr.Button(value="โ Missing Video", interactive=False), gr.update(visible=True) else: # Either not a video model, or video model selected and video provided return gr.update(value="๐ Add to Queue", interactive=True), gr.update(visible=False) # Function to update button state before processing def update_button_before_processing(selected_model, *args): # First update the button to show "Adding..." and disable it # Also return current stats so they don't get blanked out during the "Adding..." phase qs_data, qs_text = update_stats() return gr.update(), gr.update(), gr.update(), gr.update(), gr.update(), gr.update(), gr.update(value="โณ Adding...", interactive=False), gr.update(), qs_data, qs_text, gr.update(), gr.update() # Added update for video_input_required_message # Connect the start button to first update its state start_button.click( fn=update_button_before_processing, inputs=[model_type] + ips, outputs=[result_video, current_job_id, preview_image, top_preview_image, progress_desc, progress_bar, start_button, end_button, queue_status, queue_stats_display, seed, video_input_required_message] ).then( # Then process the job fn=handle_start_button, inputs=[model_type] + ips, outputs=[result_video, current_job_id, preview_image, progress_desc, progress_bar, start_button, end_button, queue_status, queue_stats_display, seed, video_input_required_message] # Added video_input_required_message ).then( # Ensure validation is re-checked after job processing completes fn=update_start_button_state, inputs=[model_type, input_video], # Current values of model_type and input_video outputs=[start_button, video_input_required_message] ) def show_batch_gallery(files): return gr.update(value=files, visible=True) if files else gr.update(visible=False) batch_input_images.change( fn=show_batch_gallery, inputs=[batch_input_images], outputs=[batch_input_gallery] ) # We need to gather all the same inputs as the single 'Add to Queue' button, plus the new file input batch_ips = [model_type] + ips + [batch_input_images] add_batch_to_queue_btn.click( fn=handle_batch_add_to_queue, inputs=batch_ips, outputs=None # No direct output updates from this button ).then( fn=update_stats, # Refresh the queue stats in the UI inputs=None, outputs=[queue_status, queue_stats_display] ).then( # This new block checks for a running job and updates the monitor UI fn=check_for_current_job, inputs=None, outputs=[current_job_id, result_video, preview_image, top_preview_image, progress_desc, progress_bar] ).then( # NEW: Update latents display layout after loading queue to ensure correct visibility fn=create_latents_layout_update, inputs=None, outputs=[top_preview_row, preview_image] ) # --- START OF REFACTORED XY PLOT EVENT WIRING --- # Get the process button from the created components xy_plot_process_btn = xy_plot_components["process_btn"] # Prepare the process function with its static dependencies (job_queue, settings) fn_xy_process_with_deps = functools.partial(xy_plot_process, job_queue, settings) # Construct the full list of inputs for the click handler in the correct order c = xy_plot_components xy_plot_input_components = [ c["model_type"], c["input_image"], c["end_frame_image_original"], c["end_frame_strength_original"], c["latent_type"], c["prompt"], c["blend_sections"], c["steps"], c["total_second_length"], resolutionW, resolutionH, # The components from the main UI c["seed"], c["randomize_seed"], c["use_teacache"], c["teacache_num_steps"], c["teacache_rel_l1_thresh"], c["use_magcache"], c["magcache_threshold"], c["magcache_max_consecutive_skips"], c["magcache_retention_ratio"], c["latent_window_size"], c["cfg"], c["gs"], c["rs"], c["gpu_memory_preservation"], c["mp4_crf"], c["axis_x_switch"], c["axis_x_value_text"], c["axis_x_value_dropdown"], c["axis_y_switch"], c["axis_y_value_text"], c["axis_y_value_dropdown"], c["axis_z_switch"], c["axis_z_value_text"], c["axis_z_value_dropdown"], c["lora_selector"] ] # LoRA sliders are in a dictionary, so we add their values to the list xy_plot_input_components.extend(c["lora_sliders"].values()) # Wire the click handler for the XY Plot button xy_plot_process_btn.click( fn=fn_xy_process_with_deps, inputs=xy_plot_input_components, outputs=[xy_plot_status, xy_plot_output] ).then( fn=update_stats, inputs=None, outputs=[queue_status, queue_stats_display] ).then( fn=check_for_current_job, inputs=None, outputs=[current_job_id, result_video, preview_image, top_preview_image, progress_desc, progress_bar] ).then( # NEW: Update latents display layout after XY plot to ensure correct visibility fn=create_latents_layout_update, inputs=None, outputs=[top_preview_row, preview_image] ) # --- END OF REFACTORED XY PLOT EVENT WIRING --- # MODIFIED: on_model_type_change to handle new "XY Plot" option def on_model_type_change(selected_model): is_xy_plot = selected_model == "XY Plot" is_ui_video_model_flag = is_video_model(selected_model) shows_end_frame = selected_model in ["Original with Endframe", "Video with Endframe"] return ( gr.update(visible=not is_xy_plot), # standard_generation_group gr.update(visible=is_xy_plot), # xy_group gr.update(visible=not is_xy_plot and not is_ui_video_model_flag), # image_input_group gr.update(visible=not is_xy_plot and is_ui_video_model_flag), # video_input_group gr.update(visible=not is_xy_plot and shows_end_frame), # end_frame_group_original gr.update(visible=not is_xy_plot and shows_end_frame), # end_frame_slider_group gr.update(visible=not is_xy_plot), # start_button gr.update(visible=is_xy_plot) # xy_plot_process_btn ) # Model change listener model_type.change( fn=on_model_type_change, inputs=model_type, outputs=[ standard_generation_group, xy_group, image_input_group, video_input_group, end_frame_group_original, end_frame_slider_group, start_button, xy_plot_process_btn # This is the button returned from the dictionary ] ).then( # Also trigger validation after model type changes fn=update_start_button_state, inputs=[model_type, input_video], outputs=[start_button, video_input_required_message] ) # Connect input_video change to the validation function input_video.change( fn=update_start_button_state, inputs=[model_type, input_video], outputs=[start_button, video_input_required_message] ) # Also trigger validation when video is cleared input_video.clear( fn=update_start_button_state, inputs=[model_type, input_video], outputs=[start_button, video_input_required_message] ) # Auto-monitor the current job when job_id changes current_job_id.change( fn=monitor_fn, inputs=[current_job_id], outputs=[result_video, preview_image, top_preview_image, progress_desc, progress_bar, start_button, end_button] ).then( fn=update_stats, # When a monitor finishes, always update the stats. inputs=None, outputs=[queue_status, queue_stats_display] ).then( # re-validate button state fn=update_start_button_state, inputs=[model_type, input_video], outputs=[start_button, video_input_required_message] ).then( # NEW: Update latents display layout after monitoring to ensure correct visibility fn=create_latents_layout_update, inputs=None, outputs=[top_preview_row, preview_image] ) # The "end_button" (Cancel Job) is the trigger for the next job's monitor. # When a job is cancelled, we check for the next one. end_button.click( fn=end_process_with_update, outputs=[queue_status, queue_stats_display, end_button, current_job_id] ).then( fn=check_for_current_job_and_monitor, inputs=[], outputs=[current_job_id, result_video, preview_image, top_preview_image, progress_desc, progress_bar, queue_status, queue_stats_display] ).then( # NEW: Update latents display layout after job handoff to ensure correct visibility fn=create_latents_layout_update, inputs=None, outputs=[top_preview_row, preview_image] ) load_queue_button.click( fn=load_queue_from_json, inputs=[], outputs=[queue_status, queue_stats_display] ).then( # ADD THIS .then() CLAUSE fn=check_for_current_job, inputs=[], outputs=[current_job_id, result_video, preview_image, top_preview_image, progress_desc, progress_bar] ).then( # NEW: Update latents display layout after loading queue to ensure correct visibility fn=create_latents_layout_update, inputs=None, outputs=[top_preview_row, preview_image] ) import_queue_file.change( fn=import_queue_from_file, inputs=[import_queue_file], outputs=[queue_status, queue_stats_display] ).then( # ADD THIS .then() CLAUSE fn=check_for_current_job, inputs=[], outputs=[current_job_id, result_video, preview_image, top_preview_image, progress_desc, progress_bar] ).then( # NEW: Update latents display layout after importing queue to ensure correct visibility fn=create_latents_layout_update, inputs=None, outputs=[top_preview_row, preview_image] ) # --- Connect Queue Refresh --- # The update_stats function is now defined much earlier. # REMOVED: refresh_stats_btn.click - Toolbar refresh button is no longer needed # refresh_stats_btn.click( # fn=update_stats, # inputs=None, # outputs=[queue_status, queue_stats_display] # ) # Set up auto-refresh for queue status # Instead of using a timer with 'every' parameter, we'll use the queue refresh button # and rely on manual refreshes. The user can click the refresh button in the toolbar # to update the stats. # --- Connect LoRA UI --- # Function to update slider visibility based on selection def update_lora_sliders(selected_loras): updates = [] # Suppress dummy LoRA from workaround for the single lora bug. # Filter out the dummy LoRA for display purposes in the dropdown actual_selected_loras_for_display = [lora for lora in selected_loras if lora != DUMMY_LORA_NAME] updates.append(gr.update(value=actual_selected_loras_for_display)) # First update is for the dropdown itself # Need to handle potential missing keys if lora_names changes dynamically # lora_names is from the create_interface scope for lora_name_key in lora_names: # Iterate using lora_names to maintain order if lora_name_key == DUMMY_LORA_NAME: # Check for dummy LoRA updates.append(gr.update(visible=False)) else: # Visibility of sliders should be based on actual_selected_loras_for_display updates.append(gr.update(visible=(lora_name_key in actual_selected_loras_for_display))) return updates # This list will be correctly ordered # Connect the dropdown to the sliders lora_selector.change( fn=update_lora_sliders, inputs=[lora_selector], outputs=[lora_selector] + [lora_sliders[lora] for lora in lora_names if lora in lora_sliders] ) def apply_preset(preset_name, model_type): if not preset_name: # Create a list of empty updates matching the number of components return [gr.update()] * len(ui_components) with open(PRESET_FILE, 'r') as f: data = json.load(f) preset = data.get(model_type, {}).get(preset_name, {}) # Initialize updates for all components updates = {key: gr.update() for key in ui_components.keys()} # Update components based on the preset for key, value in preset.items(): if key in updates: updates[key] = gr.update(value=value) # Handle LoRA sliders specifically if 'lora_values' in preset and isinstance(preset['lora_values'], dict): lora_values_dict = preset['lora_values'] for lora_name, lora_value in lora_values_dict.items(): if lora_name in updates: updates[lora_name] = gr.update(value=lora_value) # Convert the dictionary of updates to a list in the correct order return [updates[key] for key in ui_components.keys()] def save_preset(preset_name, model_type, *args): if not preset_name: return gr.update() # Ensure the directory exists os.makedirs(os.path.dirname(PRESET_FILE), exist_ok=True) if not os.path.exists(PRESET_FILE): with open(PRESET_FILE, 'w') as f: json.dump({}, f) with open(PRESET_FILE, 'r') as f: data = json.load(f) if model_type not in data: data[model_type] = {} keys = list(ui_components.keys()) # Create a dictionary from the passed arguments args_dict = {keys[i]: args[i] for i in range(len(keys))} # Build the preset data from the arguments dictionary preset_data = {key: args_dict[key] for key in ui_components.keys() if key not in lora_sliders} # Handle LoRA values separately selected_loras = args_dict.get("lora_selector", []) lora_values = {} for lora_name in selected_loras: if lora_name in args_dict: lora_values[lora_name] = args_dict[lora_name] preset_data['lora_values'] = lora_values # Remove individual lora sliders from the top-level preset data for lora_name in lora_sliders: if lora_name in preset_data: del preset_data[lora_name] data[model_type][preset_name] = preset_data with open(PRESET_FILE, 'w') as f: json.dump(data, f, indent=2) return gr.update(choices=load_presets(model_type), value=preset_name) def delete_preset(preset_name, model_type): if not preset_name: return gr.update(), gr.update(visible=True), gr.update(visible=False) with open(PRESET_FILE, 'r') as f: data = json.load(f) if model_type in data and preset_name in data[model_type]: del data[model_type][preset_name] with open(PRESET_FILE, 'w') as f: json.dump(data, f, indent=2) return gr.update(choices=load_presets(model_type), value=None), gr.update(visible=True), gr.update(visible=False) # --- Connect Preset UI --- # Without this refresh, if you define a new preset for the Startup Model Type, and then try to select it in settings, it won't show up. def refresh_settings_tab_startup_presets_if_needed(generate_tab_model_type_value, settings_tab_startup_model_type_value): # generate_tab_model_type_value is the model for which a preset was just saved # settings_tab_startup_model_type_value is the current selection in the startup model dropdown on settings tab if generate_tab_model_type_value == settings_tab_startup_model_type_value and settings_tab_startup_model_type_value != "None": return update_startup_preset_dropdown_choices(settings_tab_startup_model_type_value) return gr.update() ui_components = { # Prompts "prompt": prompt, "n_prompt": n_prompt, "blend_sections": blend_sections, # Basic Params "steps": steps, "total_second_length": total_second_length, "resolutionW": resolutionW, "resolutionH": resolutionH, "seed": seed, "randomize_seed": randomize_seed, # Advanced Params "gs": gs, "cfg": cfg, "rs": rs, "latent_window_size": latent_window_size, # Cache type (Mag/Tea/None) "cache_type": cache_type, # TeaCache "teacache_num_steps": teacache_num_steps, "teacache_rel_l1_thresh": teacache_rel_l1_thresh, # MagCache "magcache_threshold": magcache_threshold, "magcache_max_consecutive_skips": magcache_max_consecutive_skips, "magcache_retention_ratio": magcache_retention_ratio, # Input Options "latent_type": latent_type, "end_frame_strength_original": end_frame_strength_original, # Video Specific "combine_with_source": combine_with_source, "num_cleaned_frames": num_cleaned_frames, # LoRAs "lora_selector": lora_selector, **lora_sliders } model_type.change( fn=lambda mt: (gr.update(choices=load_presets(mt)), gr.update(label=f"{mt} Presets")), inputs=[model_type], outputs=[preset_dropdown, preset_accordion] ) preset_dropdown.select( fn=apply_preset, inputs=[preset_dropdown, model_type], outputs=list(ui_components.values()) ).then( lambda name: name, inputs=[preset_dropdown], outputs=[preset_name_textbox] ) save_preset_button.click( fn=save_preset, inputs=[preset_name_textbox, model_type, *list(ui_components.values())], outputs=[preset_dropdown] # preset_dropdown is on Generate tab ).then( fn=refresh_settings_tab_startup_presets_if_needed, inputs=[model_type, startup_model_type_dropdown], # model_type (Generate tab), startup_model_type_dropdown (Settings tab) outputs=[startup_preset_name_dropdown] # startup_preset_name_dropdown (Settings tab) ) def show_delete_confirmation(): return gr.update(visible=False), gr.update(visible=True) def hide_delete_confirmation(): return gr.update(visible=True), gr.update(visible=False) delete_preset_button.click( fn=show_delete_confirmation, outputs=[save_preset_button, confirm_delete_row] ) confirm_delete_no_btn.click( fn=hide_delete_confirmation, outputs=[save_preset_button, confirm_delete_row] ) confirm_delete_yes_btn.click( fn=delete_preset, inputs=[preset_dropdown, model_type], outputs=[preset_dropdown, save_preset_button, confirm_delete_row] ) # --- Definition of apply_startup_settings (AFTER ui_components and apply_preset are defined) --- # This function needs access to `settings`, `model_type` (Generate tab Radio), # `preset_dropdown` (Generate tab Dropdown), `preset_name_textbox` (Generate tab Textbox), # `ui_components` (dict of all other UI elements), `load_presets`, and `apply_preset`. # All these are available in the scope of `create_interface`. def apply_startup_settings(): startup_model_val = settings.get("startup_model_type", "None") startup_preset_val = settings.get("startup_preset_name", None) # Default updates (no change) model_type_update = gr.update() preset_dropdown_update = gr.update() preset_name_textbox_update = gr.update() # ui_components is now defined ui_components_updates_list = [gr.update() for _ in ui_components] if startup_model_val and startup_model_val != "None": model_type_update = gr.update(value=startup_model_val) presets_for_startup_model = load_presets(startup_model_val) # load_presets is defined earlier preset_dropdown_update = gr.update(choices=presets_for_startup_model) preset_name_textbox_update = gr.update(value="") if startup_preset_val and startup_preset_val in presets_for_startup_model: preset_dropdown_update = gr.update(choices=presets_for_startup_model, value=startup_preset_val) preset_name_textbox_update = gr.update(value=startup_preset_val) # apply_preset is now defined ui_components_updates_list = apply_preset(startup_preset_val, startup_model_val) # NEW: Ensure latents_display_top checkbox reflects the current setting latents_display_top_update = gr.update(value=get_latents_display_top()) return tuple([model_type_update, preset_dropdown_update, preset_name_textbox_update] + ui_components_updates_list + [latents_display_top_update]) # --- Auto-refresh for Toolbar System Stats Monitor (Timer) --- main_toolbar_system_stats_timer = gr.Timer(2, active=True) main_toolbar_system_stats_timer.tick( fn=tb_get_formatted_toolbar_stats, # Function imported from toolbox_app.py inputs=None, outputs=[ # Target the Textbox components toolbar_ram_display_component, toolbar_vram_display_component, toolbar_gpu_display_component ] ) # --- Connect Metadata Loading --- # Function to load metadata from JSON file def load_metadata_from_json(json_path): # Define the total number of output components to handle errors gracefully num_outputs = 20 + len(lora_sliders) if not json_path: # Return empty updates for all components if no file is provided return [gr.update()] * num_outputs try: with open(json_path, 'r') as f: metadata = json.load(f) # Extract values from metadata with defaults prompt_val = metadata.get('prompt') n_prompt_val = metadata.get('negative_prompt') seed_val = metadata.get('seed') steps_val = metadata.get('steps') total_second_length_val = metadata.get('total_second_length') end_frame_strength_val = metadata.get('end_frame_strength') model_type_val = metadata.get('model_type') lora_weights = metadata.get('loras', {}) latent_window_size_val = metadata.get('latent_window_size') resolutionW_val = metadata.get('resolutionW') resolutionH_val = metadata.get('resolutionH') blend_sections_val = metadata.get('blend_sections') # Determine cache_type from metadata, with fallback for older formats cache_type_val = metadata.get('cache_type') if cache_type_val is None: use_magcache = metadata.get('use_magcache', False) use_teacache = metadata.get('use_teacache', False) if use_magcache: cache_type_val = "MagCache" elif use_teacache: cache_type_val = "TeaCache" else: cache_type_val = "None" magcache_threshold_val = metadata.get('magcache_threshold') magcache_max_consecutive_skips_val = metadata.get('magcache_max_consecutive_skips') magcache_retention_ratio_val = metadata.get('magcache_retention_ratio') teacache_num_steps_val = metadata.get('teacache_num_steps') teacache_rel_l1_thresh_val = metadata.get('teacache_rel_l1_thresh') latent_type_val = metadata.get('latent_type') combine_with_source_val = metadata.get('combine_with_source') # Get the names of the selected LoRAs from the metadata selected_lora_names = list(lora_weights.keys()) print(f"Loaded metadata from JSON: {json_path}") print(f"Model Type: {model_type_val}, Prompt: {prompt_val}, Seed: {seed_val}, LoRAs: {selected_lora_names}") # Create a list of UI updates updates = [ gr.update(value=prompt_val) if prompt_val is not None else gr.update(), gr.update(value=n_prompt_val) if n_prompt_val is not None else gr.update(), gr.update(value=seed_val) if seed_val is not None else gr.update(), gr.update(value=steps_val) if steps_val is not None else gr.update(), gr.update(value=total_second_length_val) if total_second_length_val is not None else gr.update(), gr.update(value=end_frame_strength_val) if end_frame_strength_val is not None else gr.update(), gr.update(value=model_type_val) if model_type_val else gr.update(), gr.update(value=selected_lora_names) if selected_lora_names else gr.update(), gr.update(value=latent_window_size_val) if latent_window_size_val is not None else gr.update(), gr.update(value=resolutionW_val) if resolutionW_val is not None else gr.update(), gr.update(value=resolutionH_val) if resolutionH_val is not None else gr.update(), gr.update(value=blend_sections_val) if blend_sections_val is not None else gr.update(), gr.update(value=cache_type_val), gr.update(value=magcache_threshold_val), gr.update(value=magcache_max_consecutive_skips_val), gr.update(value=magcache_retention_ratio_val), gr.update(value=teacache_num_steps_val) if teacache_num_steps_val is not None else gr.update(), gr.update(value=teacache_rel_l1_thresh_val) if teacache_rel_l1_thresh_val is not None else gr.update(), gr.update(value=latent_type_val) if latent_type_val else gr.update(), gr.update(value=combine_with_source_val) if combine_with_source_val else gr.update(), ] # Update LoRA sliders based on loaded weights for lora in lora_names: if lora in lora_weights: updates.append(gr.update(value=lora_weights[lora], visible=True)) else: # Hide sliders for LoRAs not in the metadata updates.append(gr.update(visible=False)) return updates except Exception as e: print(f"Error loading metadata: {e}") import traceback traceback.print_exc() # Return empty updates for all components on error return [gr.update()] * num_outputs # Connect JSON metadata loader for Original tab json_upload.change( fn=load_metadata_from_json, inputs=[json_upload], outputs=[ prompt, n_prompt, seed, steps, total_second_length, end_frame_strength_original, model_type, lora_selector, latent_window_size, resolutionW, resolutionH, blend_sections, cache_type, magcache_threshold, magcache_max_consecutive_skips, magcache_retention_ratio, teacache_num_steps, teacache_rel_l1_thresh, latent_type, combine_with_source ] + [lora_sliders[lora] for lora in lora_names] ) # --- Helper Functions (defined within create_interface scope if needed by handlers) --- # Function to get queue statistics def get_queue_stats(): try: # Get all jobs from the queue jobs = job_queue.get_all_jobs() # Count jobs by status status_counts = { "QUEUED": 0, "RUNNING": 0, "COMPLETED": 0, "FAILED": 0, "CANCELLED": 0 } for job in jobs: if hasattr(job, 'status'): status = str(job.status) # Use str() for safety if status in status_counts: status_counts[status] += 1 # Format the display text stats_text = f"Queue: {status_counts['QUEUED']} | Running: {status_counts['RUNNING']} | Completed: {status_counts['COMPLETED']} | Failed: {status_counts['FAILED']} | Cancelled: {status_counts['CANCELLED']}" return f"{stats_text}
" except Exception as e: print(f"Error getting queue stats: {e}") return "Error loading queue stats
" # Add footer with social links with gr.Row(elem_id="footer"): with gr.Column(scale=1): gr.HTML(f""" """) # Add CSS for footer # gr.HTML(""" # # """) # --- Function to update latents display layout on interface load --- def update_latents_layout_on_load(): """Update latents display layout based on saved setting when interface loads""" return create_latents_layout_update() # Connect the auto-check function to the interface load event block.load( fn=check_for_current_job_and_monitor, # Use the new combined function inputs=[], outputs=[current_job_id, result_video, preview_image, top_preview_image, progress_desc, progress_bar, queue_status, queue_stats_display] ).then( fn=apply_startup_settings, # apply_startup_settings is now defined inputs=None, outputs=[model_type, preset_dropdown, preset_name_textbox] + list(ui_components.values()) + [latents_display_top] # ui_components is now defined ).then( fn=update_start_button_state, # Ensure button state is correct after startup settings inputs=[model_type, input_video], outputs=[start_button, video_input_required_message] ).then( # NEW: Update latents display layout based on saved setting fn=create_latents_layout_update, inputs=None, outputs=[top_preview_row, preview_image] ) # --- Prompt Enhancer Connection --- def handle_enhance_prompt(current_prompt_text): """Calls the LLM enhancer and returns the updated text.""" if not current_prompt_text: return "" print("UI: Enhance button clicked. Sending prompt to enhancer.") enhanced_text = enhance_prompt(current_prompt_text) print(f"UI: Received enhanced prompt: {enhanced_text}") return gr.update(value=enhanced_text) enhance_prompt_btn.click( fn=handle_enhance_prompt, inputs=[prompt], outputs=[prompt] ) # --- Captioner Connection --- def handle_caption(input_image, prompt): """Calls the LLM enhancer and returns the updated text.""" if input_image is None: return prompt # Return current prompt if no image is provided caption_text = caption_image(input_image) print(f"UI: Received caption: {caption_text}") return gr.update(value=caption_text) caption_btn.click( fn=handle_caption, inputs=[input_image, prompt], outputs=[prompt] ) return block # --- Top-level Helper Functions (Used by Gradio callbacks, must be defined outside create_interface) --- def format_queue_status(jobs): """Format job data for display in the queue status table""" rows = [] for job in jobs: created = time.strftime('%H:%M:%S', time.localtime(job.created_at)) if job.created_at else "" started = time.strftime('%H:%M:%S', time.localtime(job.started_at)) if job.started_at else "" completed = time.strftime('%H:%M:%S', time.localtime(job.completed_at)) if job.completed_at else "" # Calculate elapsed time elapsed_time = "" if job.started_at: if job.completed_at: start_datetime = datetime.datetime.fromtimestamp(job.started_at) complete_datetime = datetime.datetime.fromtimestamp(job.completed_at) elapsed_seconds = (complete_datetime - start_datetime).total_seconds() elapsed_time = f"{elapsed_seconds:.2f}s" else: # For running jobs, calculate elapsed time from now start_datetime = datetime.datetime.fromtimestamp(job.started_at) current_datetime = datetime.datetime.now() elapsed_seconds = (current_datetime - start_datetime).total_seconds() elapsed_time = f"{elapsed_seconds:.2f}s (running)" # Get generation type from job data generation_type = getattr(job, 'generation_type', 'Original') # Get thumbnail from job data and format it as HTML for display thumbnail = getattr(job, 'thumbnail', None) thumbnail_html = f'