import os import time import sys import threading import argparse from mmgp import offload, safetensors2, profile_type try: import triton except ImportError: pass from pathlib import Path from datetime import datetime import gradio as gr import random import json import wan from wan.configs import MAX_AREA_CONFIGS, WAN_CONFIGS, SUPPORTED_SIZES, VACE_SIZE_CONFIGS from wan.utils.utils import cache_video from wan.modules.attention import get_attention_modes, get_supported_attention_modes import torch import gc import traceback import math import typing import asyncio import inspect from wan.utils import prompt_parser import base64 import io from PIL import Image import zipfile import tempfile import atexit import shutil import glob from tqdm import tqdm import requests global_queue_ref = [] AUTOSAVE_FILENAME = "queue.zip" PROMPT_VARS_MAX = 10 target_mmgp_version = "3.4.2" from importlib.metadata import version mmgp_version = version("mmgp") if mmgp_version != target_mmgp_version: print(f"Incorrect version of mmgp ({mmgp_version}), version {target_mmgp_version} is needed. Please upgrade with the command 'pip install -r requirements.txt'") exit() lock = threading.Lock() current_task_id = None task_id = 0 def download_ffmpeg(): if os.name != 'nt': return exes = ['ffmpeg.exe', 'ffprobe.exe', 'ffplay.exe'] if all(os.path.exists(e) for e in exes): return api_url = 'https://api.github.com/repos/GyanD/codexffmpeg/releases/latest' r = requests.get(api_url, headers={'Accept': 'application/vnd.github+json'}) assets = r.json().get('assets', []) zip_asset = next((a for a in assets if 'essentials_build.zip' in a['name']), None) if not zip_asset: return zip_url = zip_asset['browser_download_url'] zip_name = zip_asset['name'] with requests.get(zip_url, stream=True) as resp: total = int(resp.headers.get('Content-Length', 0)) with open(zip_name, 'wb') as f, tqdm(total=total, unit='B', unit_scale=True) as pbar: for chunk in resp.iter_content(chunk_size=8192): f.write(chunk) pbar.update(len(chunk)) with zipfile.ZipFile(zip_name) as z: for f in z.namelist(): if f.endswith(tuple(exes)) and '/bin/' in f: z.extract(f) os.rename(f, os.path.basename(f)) os.remove(zip_name) def format_time(seconds): if seconds < 60: return f"{seconds:.1f}s" elif seconds < 3600: minutes = seconds / 60 return f"{minutes:.1f}m" else: hours = int(seconds // 3600) minutes = int((seconds % 3600) // 60) return f"{hours}h {minutes}m" def pil_to_base64_uri(pil_image, format="png", quality=75): if pil_image is None: return None if isinstance(pil_image, str): from wan.utils.utils import get_video_frame pil_image = get_video_frame(pil_image, 0) buffer = io.BytesIO() try: img_to_save = pil_image if format.lower() == 'jpeg' and pil_image.mode == 'RGBA': img_to_save = pil_image.convert('RGB') elif format.lower() == 'png' and pil_image.mode not in ['RGB', 'RGBA', 'L', 'P']: img_to_save = pil_image.convert('RGBA') elif pil_image.mode == 'P': img_to_save = pil_image.convert('RGBA' if 'transparency' in pil_image.info else 'RGB') if format.lower() == 'jpeg': img_to_save.save(buffer, format=format, quality=quality) else: img_to_save.save(buffer, format=format) img_bytes = buffer.getvalue() encoded_string = base64.b64encode(img_bytes).decode("utf-8") return f"data:image/{format.lower()};base64,{encoded_string}" except Exception as e: print(f"Error converting PIL to base64: {e}") return None def is_integer(n): try: float(n) except ValueError: return False else: return float(n).is_integer() def process_prompt_and_add_tasks(state, model_choice): if state.get("validate_success",0) != 1: return state["validate_success"] = 0 model_filename = state["model_filename"] model_type = get_model_type(model_filename) inputs = state.get(model_type, None) if model_choice != model_type or inputs ==None: raise gr.Error("Webform can not be used as the App has been restarted since the form was displayed. Please refresh the page") inputs["state"] = state inputs.pop("lset_name") if inputs == None: gr.Warning("Internal state error: Could not retrieve inputs for the model.") gen = get_gen_info(state) queue = gen.get("queue", []) return get_queue_table(queue) prompt = inputs["prompt"] if len(prompt) ==0: gr.Info("Prompt cannot be empty.") gen = get_gen_info(state) queue = gen.get("queue", []) return get_queue_table(queue) prompt, errors = prompt_parser.process_template(prompt) if len(errors) > 0: gr.Info("Error processing prompt template: " + errors) return inputs["model_filename"] = model_filename prompts = prompt.replace("\r", "").split("\n") prompts = [prompt.strip() for prompt in prompts if len(prompt.strip())>0 and not prompt.startswith("#")] if len(prompts) ==0: gr.Info("Prompt cannot be empty.") gen = get_gen_info(state) queue = gen.get("queue", []) return get_queue_table(queue) resolution = inputs["resolution"] width, height = resolution.split("x") width, height = int(width), int(height) # if test_class_i2v(model_filename): # if "480p" in model_filename and not "Fun" in model_filename and width * height > 848*480: # gr.Info("You must use the 720P image to video model to generate videos with a resolution equivalent to 720P") # return # resolution = str(width) + "*" + str(height) # if resolution not in ['720*1280', '1280*720', '480*832', '832*480']: # gr.Info(f"Resolution {resolution} not supported by image 2 video") # return if "1.3B" in model_filename and width * height > 848*480 and any( model in model_filename for model in ["image2video", "text2video"] ): gr.Info("You must use the 14B model to generate videos with a resolution equivalent to 720P") return if "diffusion_forcing" in model_filename or "Vace" in model_filename: video_length = inputs["video_length"] sliding_window_size = inputs["sliding_window_size"] if video_length > sliding_window_size: gr.Info(f"The Number of Frames to generate ({video_length}) is greater than the Sliding Window Size ({sliding_window_size}) , multiple Windows will be generated") if "phantom" in model_filename: image_refs = inputs["image_refs"] if isinstance(image_refs, list): image_refs = [ convert_image(tup[0]) for tup in image_refs ] os.environ["U2NET_HOME"] = os.path.join(os.getcwd(), "ckpts", "rembg") from wan.utils.utils import resize_and_remove_background image_refs = resize_and_remove_background(image_refs, width, height, inputs["remove_background_image_ref"] ==1, fit_into_canvas= True) if len(prompts) > 0: prompts = ["\n".join(prompts)] for single_prompt in prompts: extra_inputs = { "prompt" : single_prompt, "image_refs": image_refs, } inputs.update(extra_inputs) add_video_task(**inputs) elif "diffusion_forcing" in model_filename: image_start = inputs["image_start"] video_source = inputs["video_source"] keep_frames_video_source = inputs["keep_frames_video_source"] image_prompt_type = inputs["image_prompt_type"] if len(keep_frames_video_source) > 0: if not is_integer(keep_frames_video_source): gr.Info("The number of frames to keep must be an integer") return if "V" in image_prompt_type: if video_source == None or len(video_source) == 0: gr.Info("You must provide a Video to continue") return image_start = None if "S" in image_prompt_type: if image_start == None : gr.Info("You must provide a Start Image") return if len(image_start) > 1: gr.Info("Only one Start Image is supported for the moment") return if isinstance(image_start[0][0], str) : gr.Info("Start Image should be an Image") return image_start = [ convert_image(tup[0]) for tup in image_start ] video_source = None if "T" in image_prompt_type: image_start = None video_source = None if len(prompts) > 0: prompts = ["\n".join(prompts)] for single_prompt in prompts: extra_inputs = { "prompt" : single_prompt, "image_start" : image_start, "video_source" : video_source, } inputs.update(extra_inputs) add_video_task(**inputs) elif "recam" in model_filename: video_source = inputs["video_source"] if video_source == None: gr.Info("You must provide a Source Video") return frames = get_resampled_video(video_source, 0, 81, 16) if len(frames)<81: gr.Info("Recammaster source video should be at least 81 frames once the resampling at 16 fps has been done") return for single_prompt in prompts: extra_inputs = { "prompt" : single_prompt, "video_source" : video_source, } inputs.update(extra_inputs) add_video_task(**inputs) elif "Vace" in model_filename: video_prompt_type = inputs["video_prompt_type"] image_refs = inputs["image_refs"] video_guide = inputs["video_guide"] video_mask = inputs["video_mask"] if "1.3B" in model_filename : resolution_reformated = str(height) + "*" + str(width) if not resolution_reformated in VACE_SIZE_CONFIGS: res = (" and ").join(VACE_SIZE_CONFIGS.keys()) gr.Info(f"Video Resolution for Vace model is not supported. Only {res} resolutions are allowed.") return if "I" in video_prompt_type: if image_refs == None: gr.Info("You must provide at least one Refererence Image") return else: image_refs = None if "V" in video_prompt_type: if video_guide == None: gr.Info("You must provide a Control Video") return else: video_guide = None if "M" in video_prompt_type: if video_mask == None: gr.Info("You must provide a Video Mask ") return else: video_mask = None if "O" in video_prompt_type : keep_frames_video_guide= inputs["keep_frames_video_guide"] video_length = inputs["video_length"] if len(keep_frames_video_guide) ==0: gr.Info(f"Warning : you have asked to reuse all the frames of the control Video in the Alternate Video Ending it. Please make sure the number of frames of the control Video is lower than the total number of frames to generate otherwise it won't make a difference.") # elif keep_frames >= video_length: # gr.Info(f"The number of frames in the control Video to reuse ({keep_frames_video_guide}) in Alternate Video Ending can not be bigger than the total number of frames ({video_length}) to generate.") # return elif "V" in video_prompt_type: keep_frames_video_guide= inputs["keep_frames_video_guide"] video_length = inputs["video_length"] _, error = parse_keep_frames_video_guide(keep_frames_video_guide, video_length) if len(error) > 0: gr.Info(f"Invalid Keep Frames property: {error}") return if isinstance(image_refs, list): image_refs = [ convert_image(tup[0]) for tup in image_refs ] os.environ["U2NET_HOME"] = os.path.join(os.getcwd(), "ckpts", "rembg") from wan.utils.utils import resize_and_remove_background image_refs = resize_and_remove_background(image_refs, width, height, inputs["remove_background_image_ref"] ==1) if len(prompts) > 0: prompts = ["\n".join(prompts)] for single_prompt in prompts: extra_inputs = { "prompt" : single_prompt, "image_refs": image_refs, "video_guide" : video_guide, "video_mask" : video_mask , } inputs.update(extra_inputs) add_video_task(**inputs) elif test_class_i2v(model_filename) : image_prompt_type = inputs["image_prompt_type"] image_start = inputs["image_start"] image_end = inputs["image_end"] if image_start == None or isinstance(image_start, list) and len(image_start) == 0: return if not "E" in image_prompt_type: image_end = None if not isinstance(image_start, list): image_start = [image_start] if not all( not isinstance(img[0], str) for img in image_start) : gr.Info("Start Image should be an Image") return image_start = [ convert_image(tup[0]) for tup in image_start ] if image_end != None: if not isinstance(image_end , list): image_end = [image_end] if not all( not isinstance(img[0], str) for img in image_end) : gr.Info("End Image should be an Image") return image_end = [ convert_image(tup[0]) for tup in image_end ] if len(image_start) != len(image_end): gr.Info("The number of start and end images should be the same ") return if inputs["multi_images_gen_type"] == 0: new_prompts = [] new_image_start = [] new_image_end = [] for i in range(len(prompts) * len(image_start) ): new_prompts.append( prompts[ i % len(prompts)] ) new_image_start.append(image_start[i // len(prompts)] ) if image_end != None: new_image_end.append(image_end[i // len(prompts)] ) prompts = new_prompts image_start = new_image_start if image_end != None: image_end = new_image_end else: if len(prompts) >= len(image_start): if len(prompts) % len(image_start) != 0: raise gr.Error("If there are more text prompts than input images the number of text prompts should be dividable by the number of images") rep = len(prompts) // len(image_start) new_image_start = [] new_image_end = [] for i, _ in enumerate(prompts): new_image_start.append(image_start[i//rep] ) if image_end != None: new_image_end.append(image_end[i//rep] ) image_start = new_image_start if image_end != None: image_end = new_image_end else: if len(image_start) % len(prompts) !=0: raise gr.Error("If there are more input images than text prompts the number of images should be dividable by the number of text prompts") rep = len(image_start) // len(prompts) new_prompts = [] for i, _ in enumerate(image_start): new_prompts.append( prompts[ i//rep] ) prompts = new_prompts if image_start == None: image_start = [None] * len(prompts) if image_end == None: image_end = [None] * len(prompts) for single_prompt, start, end in zip(prompts, image_start, image_end) : extra_inputs = { "prompt" : single_prompt, "image_start": start, "image_end" : end, } inputs.update(extra_inputs) add_video_task(**inputs) else: for single_prompt in prompts : extra_inputs = { "prompt" : single_prompt, } inputs.update(extra_inputs) add_video_task(**inputs) gen = get_gen_info(state) gen["prompts_max"] = len(prompts) + gen.get("prompts_max",0) state["validate_success"] = 1 queue= gen.get("queue", []) return update_queue_data(queue) def get_preview_images(inputs): inputs_to_query = ["image_start", "image_end", "video_guide", "image_refs","video_mask", "video_source"] start_image_data = None end_image_data = None for name in inputs_to_query: image= inputs.get(name, None) if image != None: image= [image] if not isinstance(image, list) else image if start_image_data == None: start_image_data = image else: end_image_data = image break return start_image_data, end_image_data def add_video_task(**inputs): global task_id state = inputs["state"] gen = get_gen_info(state) queue = gen["queue"] task_id += 1 current_task_id = task_id start_image_data, end_image_data = get_preview_images(inputs) queue.append({ "id": current_task_id, "params": inputs.copy(), "repeats": inputs["repeat_generation"], "length": inputs["video_length"], "steps": inputs["num_inference_steps"], "prompt": inputs["prompt"], "start_image_data": start_image_data, "end_image_data": end_image_data, "start_image_data_base64": [pil_to_base64_uri(img, format="jpeg", quality=70) for img in start_image_data] if start_image_data != None else None, "end_image_data_base64": [pil_to_base64_uri(img, format="jpeg", quality=70) for img in end_image_data] if end_image_data != None else None }) return update_queue_data(queue) def move_up(queue, selected_indices): if not selected_indices or len(selected_indices) == 0: return update_queue_data(queue) idx = selected_indices[0] if isinstance(idx, list): idx = idx[0] idx = int(idx) with lock: if idx > 0: idx += 1 queue[idx], queue[idx-1] = queue[idx-1], queue[idx] return update_queue_data(queue) def move_down(queue, selected_indices): if not selected_indices or len(selected_indices) == 0: return update_queue_data(queue) idx = selected_indices[0] if isinstance(idx, list): idx = idx[0] idx = int(idx) with lock: idx += 1 if idx < len(queue)-1: queue[idx], queue[idx+1] = queue[idx+1], queue[idx] return update_queue_data(queue) def remove_task(queue, selected_indices): if not selected_indices or len(selected_indices) == 0: return update_queue_data(queue) idx = selected_indices[0] if isinstance(idx, list): idx = idx[0] idx = int(idx) + 1 with lock: if idx < len(queue): if idx == 0: wan_model._interrupt = True del queue[idx] return update_queue_data(queue) def update_global_queue_ref(queue): global global_queue_ref with lock: global_queue_ref = queue[:] def save_queue_action(state): gen = get_gen_info(state) queue = gen.get("queue", []) if not queue or len(queue) <=1 : gr.Info("Queue is empty. Nothing to save.") return "" zip_buffer = io.BytesIO() with tempfile.TemporaryDirectory() as tmpdir: queue_manifest = [] file_paths_in_zip = {} for task_index, task in enumerate(queue): if task is None or not isinstance(task, dict) or task.get('id') is None: continue params_copy = task.get('params', {}).copy() task_id_s = task.get('id', f"task_{task_index}") image_keys = ["image_start", "image_end", "image_refs"] video_keys = ["video_guide", "video_mask", "video_source", "audio_guide"] for key in image_keys: images_pil = params_copy.get(key) if images_pil is None: continue is_originally_list = isinstance(images_pil, list) if not is_originally_list: images_pil = [images_pil] image_filenames_for_json = [] for img_index, pil_image in enumerate(images_pil): if not isinstance(pil_image, Image.Image): print(f"Warning: Expected PIL Image for key '{key}' in task {task_id_s}, got {type(pil_image)}. Skipping image.") continue img_id = id(pil_image) if img_id in file_paths_in_zip: image_filenames_for_json.append(file_paths_in_zip[img_id]) continue img_filename_in_zip = f"task{task_id_s}_{key}_{img_index}.png" img_save_path = os.path.join(tmpdir, img_filename_in_zip) try: pil_image.save(img_save_path, "PNG") image_filenames_for_json.append(img_filename_in_zip) file_paths_in_zip[img_id] = img_filename_in_zip print(f"Saved image: {img_filename_in_zip}") except Exception as e: print(f"Error saving image {img_filename_in_zip} for task {task_id_s}: {e}") if image_filenames_for_json: params_copy[key] = image_filenames_for_json if is_originally_list else image_filenames_for_json[0] else: pass # params_copy.pop(key, None) #cant pop otherwise crash during reload for key in video_keys: video_path_orig = params_copy.get(key) if video_path_orig is None or not isinstance(video_path_orig, str): continue if video_path_orig in file_paths_in_zip: params_copy[key] = file_paths_in_zip[video_path_orig] continue if not os.path.isfile(video_path_orig): print(f"Warning: Video file not found for key '{key}' in task {task_id_s}: {video_path_orig}. Skipping video.") params_copy.pop(key, None) continue _, extension = os.path.splitext(video_path_orig) vid_filename_in_zip = f"task{task_id_s}_{key}{extension if extension else '.mp4'}" vid_save_path = os.path.join(tmpdir, vid_filename_in_zip) try: shutil.copy2(video_path_orig, vid_save_path) params_copy[key] = vid_filename_in_zip file_paths_in_zip[video_path_orig] = vid_filename_in_zip print(f"Copied video: {video_path_orig} -> {vid_filename_in_zip}") except Exception as e: print(f"Error copying video {video_path_orig} to {vid_filename_in_zip} for task {task_id_s}: {e}") params_copy.pop(key, None) params_copy.pop('state', None) params_copy.pop('start_image_data_base64', None) params_copy.pop('end_image_data_base64', None) params_copy.pop('start_image_data', None) params_copy.pop('end_image_data', None) task.pop('start_image_data', None) task.pop('end_image_data', None) manifest_entry = { "id": task.get('id'), "params": params_copy, } manifest_entry = {k: v for k, v in manifest_entry.items() if v is not None} queue_manifest.append(manifest_entry) manifest_path = os.path.join(tmpdir, "queue.json") try: with open(manifest_path, 'w', encoding='utf-8') as f: json.dump(queue_manifest, f, indent=4) except Exception as e: print(f"Error writing queue.json: {e}") gr.Warning("Failed to create queue manifest.") return None try: with zipfile.ZipFile(zip_buffer, 'w', zipfile.ZIP_DEFLATED) as zf: zf.write(manifest_path, arcname="queue.json") for file_id, saved_file_rel_path in file_paths_in_zip.items(): saved_file_abs_path = os.path.join(tmpdir, saved_file_rel_path) if os.path.exists(saved_file_abs_path): zf.write(saved_file_abs_path, arcname=saved_file_rel_path) print(f"Adding to zip: {saved_file_rel_path}") else: print(f"Warning: File {saved_file_rel_path} (ID: {file_id}) not found during zipping.") zip_buffer.seek(0) zip_binary_content = zip_buffer.getvalue() zip_base64 = base64.b64encode(zip_binary_content).decode('utf-8') print(f"Queue successfully prepared as base64 string ({len(zip_base64)} chars).") return zip_base64 except Exception as e: print(f"Error creating zip file in memory: {e}") gr.Warning("Failed to create zip data for download.") return None finally: zip_buffer.close() def load_queue_action(filepath, state, evt:gr.EventData): global task_id gen = get_gen_info(state) original_queue = gen.get("queue", []) delete_autoqueue_file = False if evt.target == None: if original_queue or not Path(AUTOSAVE_FILENAME).is_file(): return print(f"Autoloading queue from {AUTOSAVE_FILENAME}...") filename = AUTOSAVE_FILENAME delete_autoqueue_file = True else: if not filepath or not hasattr(filepath, 'name') or not Path(filepath.name).is_file(): print("[load_queue_action] Warning: No valid file selected or file not found.") return update_queue_data(original_queue) filename = filepath.name save_path_base = server_config.get("save_path", "outputs") loaded_cache_dir = os.path.join(save_path_base, "_loaded_queue_cache") newly_loaded_queue = [] max_id_in_file = 0 error_message = "" local_queue_copy_for_global_ref = None try: print(f"[load_queue_action] Attempting to load queue from: {filename}") os.makedirs(loaded_cache_dir, exist_ok=True) print(f"[load_queue_action] Using cache directory: {loaded_cache_dir}") with tempfile.TemporaryDirectory() as tmpdir: with zipfile.ZipFile(filename, 'r') as zf: if "queue.json" not in zf.namelist(): raise ValueError("queue.json not found in zip file") print(f"[load_queue_action] Extracting {filename} to {tmpdir}") zf.extractall(tmpdir) print(f"[load_queue_action] Extraction complete.") manifest_path = os.path.join(tmpdir, "queue.json") print(f"[load_queue_action] Reading manifest: {manifest_path}") with open(manifest_path, 'r', encoding='utf-8') as f: loaded_manifest = json.load(f) print(f"[load_queue_action] Manifest loaded. Processing {len(loaded_manifest)} tasks.") for task_index, task_data in enumerate(loaded_manifest): if task_data is None or not isinstance(task_data, dict): print(f"[load_queue_action] Skipping invalid task data at index {task_index}") continue params = task_data.get('params', {}) task_id_loaded = task_data.get('id', 0) max_id_in_file = max(max_id_in_file, task_id_loaded) params['state'] = state image_keys = ["image_start", "image_end", "image_refs"] video_keys = ["video_guide", "video_mask", "video_source", "audio_guide"] loaded_pil_images = {} loaded_video_paths = {} for key in image_keys: image_filenames = params.get(key) if image_filenames is None: continue is_list = isinstance(image_filenames, list) if not is_list: image_filenames = [image_filenames] loaded_pils = [] for img_filename_in_zip in image_filenames: if not isinstance(img_filename_in_zip, str): print(f"[load_queue_action] Warning: Non-string filename found for image key '{key}'. Skipping.") continue img_load_path = os.path.join(tmpdir, img_filename_in_zip) if not os.path.exists(img_load_path): print(f"[load_queue_action] Image file not found in extracted data: {img_load_path}. Skipping.") continue try: pil_image = Image.open(img_load_path) pil_image.load() converted_image = convert_image(pil_image) loaded_pils.append(converted_image) pil_image.close() print(f"Loaded image: {img_filename_in_zip} for key {key}") except Exception as img_e: print(f"[load_queue_action] Error loading image {img_filename_in_zip}: {img_e}") if loaded_pils: params[key] = loaded_pils if is_list else loaded_pils[0] loaded_pil_images[key] = params[key] else: params.pop(key, None) for key in video_keys: video_filename_in_zip = params.get(key) if video_filename_in_zip is None or not isinstance(video_filename_in_zip, str): continue video_load_path = os.path.join(tmpdir, video_filename_in_zip) if not os.path.exists(video_load_path): print(f"[load_queue_action] Video file not found in extracted data: {video_load_path}. Skipping.") params.pop(key, None) continue persistent_video_path = os.path.join(loaded_cache_dir, video_filename_in_zip) try: shutil.copy2(video_load_path, persistent_video_path) params[key] = persistent_video_path loaded_video_paths[key] = persistent_video_path print(f"Loaded video: {video_filename_in_zip} -> {persistent_video_path}") except Exception as vid_e: print(f"[load_queue_action] Error copying video {video_filename_in_zip} to cache: {vid_e}") params.pop(key, None) primary_preview_pil_list, secondary_preview_pil_list = get_preview_images(params) start_b64 = [pil_to_base64_uri(primary_preview_pil_list[0], format="jpeg", quality=70)] if isinstance(primary_preview_pil_list, list) and primary_preview_pil_list else None end_b64 = [pil_to_base64_uri(secondary_preview_pil_list[0], format="jpeg", quality=70)] if isinstance(secondary_preview_pil_list, list) and secondary_preview_pil_list else None top_level_start_image = params.get("image_start") or params.get("image_refs") top_level_end_image = params.get("image_end") runtime_task = { "id": task_id_loaded, "params": params.copy(), "repeats": params.get('repeat_generation', 1), "length": params.get('video_length'), "steps": params.get('num_inference_steps'), "prompt": params.get('prompt'), "start_image_data": top_level_start_image, "end_image_data": top_level_end_image, "start_image_data_base64": start_b64, "end_image_data_base64": end_b64, } newly_loaded_queue.append(runtime_task) print(f"[load_queue_action] Reconstructed task {task_index+1}/{len(loaded_manifest)}, ID: {task_id_loaded}") with lock: print("[load_queue_action] Acquiring lock to update state...") gen["queue"] = newly_loaded_queue[:] local_queue_copy_for_global_ref = gen["queue"][:] current_max_id_in_new_queue = max([t['id'] for t in newly_loaded_queue if 'id' in t] + [0]) if current_max_id_in_new_queue >= task_id: new_task_id = current_max_id_in_new_queue + 1 print(f"[load_queue_action] Updating global task_id from {task_id} to {new_task_id}") task_id = new_task_id else: print(f"[load_queue_action] Global task_id ({task_id}) is > max in file ({current_max_id_in_new_queue}). Not changing task_id.") gen["prompts_max"] = len(newly_loaded_queue) print("[load_queue_action] State update complete. Releasing lock.") if local_queue_copy_for_global_ref is not None: print("[load_queue_action] Updating global queue reference...") update_global_queue_ref(local_queue_copy_for_global_ref) else: print("[load_queue_action] Warning: Skipping global ref update as local copy is None.") print(f"[load_queue_action] Queue load successful. Returning DataFrame update for {len(newly_loaded_queue)} tasks.") return update_queue_data(newly_loaded_queue) except (ValueError, zipfile.BadZipFile, FileNotFoundError, Exception) as e: error_message = f"Error during queue load: {e}" print(f"[load_queue_action] Caught error: {error_message}") traceback.print_exc() gr.Warning(f"Failed to load queue: {error_message[:200]}") print("[load_queue_action] Load failed. Returning DataFrame update for original queue.") return update_queue_data(original_queue) finally: if delete_autoqueue_file: if os.path.isfile(filename): os.remove(filename) print(f"Clear Queue: Deleted autosave file '{filename}'.") if filepath and hasattr(filepath, 'name') and filepath.name and os.path.exists(filepath.name): if tempfile.gettempdir() in os.path.abspath(filepath.name): try: os.remove(filepath.name) print(f"[load_queue_action] Removed temporary upload file: {filepath.name}") except OSError as e: print(f"[load_queue_action] Info: Could not remove temp file {filepath.name}: {e}") else: print(f"[load_queue_action] Info: Did not remove non-temporary file: {filepath.name}") def clear_queue_action(state): gen = get_gen_info(state) queue = gen.get("queue", []) aborted_current = False cleared_pending = False with lock: if "in_progress" in gen and gen["in_progress"]: print("Clear Queue: Signalling abort for in-progress task.") gen["abort"] = True gen["extra_orders"] = 0 if wan_model is not None: wan_model._interrupt = True aborted_current = True if queue: if len(queue) > 1 or (len(queue) == 1 and queue[0] is not None and queue[0].get('id') is not None): print(f"Clear Queue: Clearing {len(queue)} tasks from queue.") queue.clear() cleared_pending = True else: pass if aborted_current or cleared_pending: gen["prompts_max"] = 0 if cleared_pending: try: if os.path.isfile(AUTOSAVE_FILENAME): os.remove(AUTOSAVE_FILENAME) print(f"Clear Queue: Deleted autosave file '{AUTOSAVE_FILENAME}'.") except OSError as e: print(f"Clear Queue: Error deleting autosave file '{AUTOSAVE_FILENAME}': {e}") gr.Warning(f"Could not delete the autosave file '{AUTOSAVE_FILENAME}'. You may need to remove it manually.") if aborted_current and cleared_pending: gr.Info("Queue cleared and current generation aborted.") elif aborted_current: gr.Info("Current generation aborted.") elif cleared_pending: gr.Info("Queue cleared.") else: gr.Info("Queue is already empty or only contains the active task (which wasn't aborted now).") return update_queue_data([]) def quit_application(): print("Save and Quit requested...") autosave_queue() import signal os.kill(os.getpid(), signal.SIGINT) def start_quit_process(): return 5, gr.update(visible=False), gr.update(visible=True) def cancel_quit_process(): return -1, gr.update(visible=True), gr.update(visible=False) def show_countdown_info_from_state(current_value: int): if current_value > 0: gr.Info(f"Quitting in {current_value}...") return current_value - 1 return current_value def autosave_queue(): global global_queue_ref if not global_queue_ref: print("Autosave: Queue is empty, nothing to save.") return print(f"Autosaving queue ({len(global_queue_ref)} items) to {AUTOSAVE_FILENAME}...") temp_state_for_save = {"gen": {"queue": global_queue_ref}} zip_file_path = None try: def _save_queue_to_file(queue_to_save, output_filename): if not queue_to_save: return None with tempfile.TemporaryDirectory() as tmpdir: queue_manifest = [] file_paths_in_zip = {} for task_index, task in enumerate(queue_to_save): if task is None or not isinstance(task, dict) or task.get('id') is None: continue params_copy = task.get('params', {}).copy() task_id_s = task.get('id', f"task_{task_index}") image_keys = ["image_start", "image_end", "image_refs"] video_keys = ["video_guide", "video_mask", "video_source", "audio_guide"] for key in image_keys: images_pil = params_copy.get(key) if images_pil is None: continue is_list = isinstance(images_pil, list) if not is_list: images_pil = [images_pil] image_filenames_for_json = [] for img_index, pil_image in enumerate(images_pil): if not isinstance(pil_image, Image.Image): continue img_id = id(pil_image) if img_id in file_paths_in_zip: image_filenames_for_json.append(file_paths_in_zip[img_id]) continue img_filename_in_zip = f"task{task_id_s}_{key}_{img_index}.png" img_save_path = os.path.join(tmpdir, img_filename_in_zip) try: pil_image.save(img_save_path, "PNG") image_filenames_for_json.append(img_filename_in_zip) file_paths_in_zip[img_id] = img_filename_in_zip except Exception as e: print(f"Autosave error saving image {img_filename_in_zip}: {e}") if image_filenames_for_json: params_copy[key] = image_filenames_for_json if is_list else image_filenames_for_json[0] else: params_copy.pop(key, None) for key in video_keys: video_path_orig = params_copy.get(key) if video_path_orig is None or not isinstance(video_path_orig, str): continue if video_path_orig in file_paths_in_zip: params_copy[key] = file_paths_in_zip[video_path_orig] continue if not os.path.isfile(video_path_orig): print(f"Warning (Autosave): Video file not found for key '{key}' in task {task_id_s}: {video_path_orig}. Skipping.") params_copy.pop(key, None) continue _, extension = os.path.splitext(video_path_orig) vid_filename_in_zip = f"task{task_id_s}_{key}{extension if extension else '.mp4'}" vid_save_path = os.path.join(tmpdir, vid_filename_in_zip) try: shutil.copy2(video_path_orig, vid_save_path) params_copy[key] = vid_filename_in_zip file_paths_in_zip[video_path_orig] = vid_filename_in_zip except Exception as e: print(f"Error (Autosave) copying video {video_path_orig} to {vid_filename_in_zip} for task {task_id_s}: {e}") params_copy.pop(key, None) params_copy.pop('state', None) params_copy.pop('start_image_data_base64', None) params_copy.pop('end_image_data_base64', None) params_copy.pop('start_image_data', None) params_copy.pop('end_image_data', None) manifest_entry = { "id": task.get('id'), "params": params_copy, } manifest_entry = {k: v for k, v in manifest_entry.items() if v is not None} queue_manifest.append(manifest_entry) manifest_path = os.path.join(tmpdir, "queue.json") with open(manifest_path, 'w', encoding='utf-8') as f: json.dump(queue_manifest, f, indent=4) with zipfile.ZipFile(output_filename, 'w', zipfile.ZIP_DEFLATED) as zf: zf.write(manifest_path, arcname="queue.json") for saved_file_rel_path in file_paths_in_zip.values(): saved_file_abs_path = os.path.join(tmpdir, saved_file_rel_path) if os.path.exists(saved_file_abs_path): zf.write(saved_file_abs_path, arcname=saved_file_rel_path) else: print(f"Warning (Autosave): File {saved_file_rel_path} not found during zipping.") return output_filename return None saved_path = _save_queue_to_file(global_queue_ref, AUTOSAVE_FILENAME) if saved_path: print(f"Queue autosaved successfully to {saved_path}") else: print("Autosave failed.") except Exception as e: print(f"Error during autosave: {e}") traceback.print_exc() def finalize_generation_with_state(current_state): if not isinstance(current_state, dict) or 'gen' not in current_state: return gr.update(), gr.update(interactive=True), gr.update(visible=True), gr.update(visible=False), gr.update(visible=False), gr.update(visible=False, value=""), gr.update(), current_state gallery_update, abort_btn_update, gen_btn_update, add_queue_btn_update, current_gen_col_update, gen_info_update = finalize_generation(current_state) accordion_update = gr.Accordion(open=False) if len(get_gen_info(current_state).get("queue", [])) <= 1 else gr.update() return gallery_update, abort_btn_update, gen_btn_update, add_queue_btn_update, current_gen_col_update, gen_info_update, accordion_update, current_state def get_queue_table(queue): data = [] if len(queue) == 1: return data for i, item in enumerate(queue): if i==0: continue truncated_prompt = (item['prompt'][:97] + '...') if len(item['prompt']) > 100 else item['prompt'] full_prompt = item['prompt'].replace('"', '"') prompt_cell = f'{truncated_prompt}' start_img_uri =item.get('start_image_data_base64') start_img_uri = start_img_uri[0] if start_img_uri !=None else None end_img_uri = item.get('end_image_data_base64') end_img_uri = end_img_uri[0] if end_img_uri !=None else None thumbnail_size = "50px" num_steps = item.get('steps') length = item.get('length') start_img_md = "" end_img_md = "" if start_img_uri: start_img_md = f'Start' if end_img_uri: end_img_md = f'End' data.append([item.get('repeats', "1"), prompt_cell, length, num_steps, start_img_md, end_img_md, "↑", "↓", "✖" ]) return data def update_queue_data(queue): update_global_queue_ref(queue) data = get_queue_table(queue) if len(data) == 0: return gr.DataFrame(visible=False) else: return gr.DataFrame(value=data, visible= True) def create_html_progress_bar(percentage=0.0, text="Idle", is_idle=True): bar_class = "progress-bar-custom idle" if is_idle else "progress-bar-custom" bar_text_html = f'
{text}
' html = f"""
{bar_text_html}
""" return html def update_generation_status(html_content): if(html_content): return gr.update(value=html_content) def _parse_args(): parser = argparse.ArgumentParser( description="Generate a video from a text prompt or image using Gradio") parser.add_argument( "--quantize-transformer", action="store_true", help="On the fly 'transformer' quantization" ) parser.add_argument( "--share", action="store_true", help="Create a shared URL to access webserver remotely" ) parser.add_argument( "--lock-config", action="store_true", help="Prevent modifying the configuration from the web interface" ) parser.add_argument( "--lock-model", action="store_true", help="Prevent switch models" ) parser.add_argument( "--preload", type=str, default="0", help="Megabytes of the diffusion model to preload in VRAM" ) parser.add_argument( "--multiple-images", action="store_true", help="Allow inputting multiple images with image to video" ) parser.add_argument( "--lora-dir-i2v", type=str, default="", help="Path to a directory that contains Loras for i2v" ) parser.add_argument( "--lora-dir", type=str, default="", help="Path to a directory that contains Loras" ) parser.add_argument( "--check-loras", action="store_true", help="Filter Loras that are not valid" ) parser.add_argument( "--lora-preset", type=str, default="", help="Lora preset to preload" ) parser.add_argument( "--settings", type=str, default="settings", help="Path to settings folder" ) # parser.add_argument( # "--lora-preset-i2v", # type=str, # default="", # help="Lora preset to preload for i2v" # ) parser.add_argument( "--profile", type=str, default=-1, help="Profile No" ) parser.add_argument( "--verbose", type=str, default=1, help="Verbose level" ) parser.add_argument( "--steps", type=int, default=0, help="default denoising steps" ) parser.add_argument( "--frames", type=int, default=0, help="default number of frames" ) parser.add_argument( "--seed", type=int, default=-1, help="default generation seed" ) parser.add_argument( "--advanced", action="store_true", help="Access advanced options by default" ) parser.add_argument( "--fp16", action="store_true", help="For using fp16 transformer model" ) parser.add_argument( "--server-port", type=str, default=0, help="Server port" ) parser.add_argument( "--theme", type=str, default="", help="set UI Theme" ) parser.add_argument( "--perc-reserved-mem-max", type=float, default=0, help="% of RAM allocated to Reserved RAM" ) parser.add_argument( "--server-name", type=str, default="", help="Server name" ) parser.add_argument( "--gpu", type=str, default="", help="Default GPU Device" ) parser.add_argument( "--open-browser", action="store_true", help="open browser" ) parser.add_argument( "--t2v", action="store_true", help="text to video mode" ) parser.add_argument( "--i2v", action="store_true", help="image to video mode" ) parser.add_argument( "--t2v-14B", action="store_true", help="text to video mode 14B model" ) parser.add_argument( "--t2v-1-3B", action="store_true", help="text to video mode 1.3B model" ) parser.add_argument( "--vace-1-3B", action="store_true", help="Vace ControlNet 1.3B model" ) parser.add_argument( "--i2v-1-3B", action="store_true", help="Fun InP image to video mode 1.3B model" ) parser.add_argument( "--i2v-14B", action="store_true", help="image to video mode 14B model" ) parser.add_argument( "--compile", action="store_true", help="Enable pytorch compilation" ) parser.add_argument( "--listen", action="store_true", help="Server accessible on local network" ) # parser.add_argument( # "--fast", # action="store_true", # help="use Fast model" # ) # parser.add_argument( # "--fastest", # action="store_true", # help="activate the best config" # ) parser.add_argument( "--attention", type=str, default="", help="attention mode" ) parser.add_argument( "--vae-config", type=str, default="", help="vae config mode" ) args = parser.parse_args() return args def get_lora_dir(model_filename): lora_dir =args.lora_dir i2v = test_class_i2v(model_filename) if i2v and len(lora_dir)==0: lora_dir =args.lora_dir_i2v if len(lora_dir) > 0: return lora_dir root_lora_dir = "loras_i2v" if i2v else "loras" if "1.3B" in model_filename : lora_dir_1_3B = os.path.join(root_lora_dir, "1.3B") if os.path.isdir(lora_dir_1_3B ): return lora_dir_1_3B else: lora_dir_14B = os.path.join(root_lora_dir, "14B") if os.path.isdir(lora_dir_14B ): return lora_dir_14B return root_lora_dir attention_modes_installed = get_attention_modes() attention_modes_supported = get_supported_attention_modes() args = _parse_args() args.flow_reverse = True processing_device = args.gpu if len(processing_device) == 0: processing_device ="cuda" # torch.backends.cuda.matmul.allow_fp16_accumulation = True lock_ui_attention = False lock_ui_transformer = False lock_ui_compile = False preload =int(args.preload) force_profile_no = int(args.profile) verbose_level = int(args.verbose) quantizeTransformer = args.quantize_transformer check_loras = args.check_loras ==1 advanced = args.advanced text_encoder_choices = ["ckpts/models_t5_umt5-xxl-enc-bf16.safetensors", "ckpts/models_t5_umt5-xxl-enc-quanto_int8.safetensors"] server_config_filename = "wgp_config.json" if not os.path.isdir("settings"): os.mkdir("settings") if os.path.isfile("t2v_settings.json"): for f in glob.glob(os.path.join(".", "*_settings.json*")): target_file = os.path.join("settings", Path(f).parts[-1] ) shutil.move(f, target_file) if not os.path.isfile(server_config_filename) and os.path.isfile("gradio_config.json"): shutil.move("gradio_config.json", server_config_filename) if not Path(server_config_filename).is_file(): server_config = {"attention_mode" : "auto", "transformer_types": [], "transformer_quantization": "int8", "text_encoder_filename" : text_encoder_choices[1], "save_path": "outputs", #os.path.join(os.getcwd(), "compile" : "", "metadata_type": "metadata", "default_ui": "t2v", "boost" : 1, "clear_file_list" : 5, "vae_config": 0, "profile" : profile_type.LowRAM_LowVRAM, "preload_model_policy": [], "UI_theme": "default" } with open(server_config_filename, "w", encoding="utf-8") as writer: writer.write(json.dumps(server_config)) else: with open(server_config_filename, "r", encoding="utf-8") as reader: text = reader.read() server_config = json.loads(text) # Deprecated models for path in ["wan2.1_Vace_1.3B_preview_bf16.safetensors", "sky_reels2_diffusion_forcing_1.3B_bf16.safetensors","sky_reels2_diffusion_forcing_720p_14B_bf16.safetensors", "sky_reels2_diffusion_forcing_720p_14B_quanto_int8.safetensors", "sky_reels2_diffusion_forcing_720p_14B_quanto_fp16_int8.safetensors", "wan2.1_image2video_480p_14B_bf16.safetensors", "wan2.1_image2video_480p_14B_quanto_int8.safetensors", "wan2.1_image2video_720p_14B_quanto_int8.safetensors", "wan2.1_image2video_720p_14B_quanto_fp16_int8.safetensors", "wan2.1_image2video_720p_14B_bf16.safetensors" ]: if Path(os.path.join("ckpts" , path)).is_file(): os.remove( os.path.join("ckpts" , path)) transformer_choices_t2v=["ckpts/wan2.1_text2video_1.3B_bf16.safetensors", "ckpts/wan2.1_text2video_14B_bf16.safetensors", "ckpts/wan2.1_text2video_14B_quanto_int8.safetensors", "ckpts/wan2.1_Vace_1.3B_preview_mbf16.safetensors", "ckpts/wan2.1_recammaster_1.3B_bf16.safetensors", "ckpts/sky_reels2_diffusion_forcing_1.3B_mbf16.safetensors", "ckpts/sky_reels2_diffusion_forcing_14B_bf16.safetensors", "ckpts/sky_reels2_diffusion_forcing_14B_quanto_int8.safetensors", "ckpts/sky_reels2_diffusion_forcing_720p_14B_mbf16.safetensors","ckpts/sky_reels2_diffusion_forcing_720p_14B_quanto_mbf16_int8.safetensors", "ckpts/wan2_1_phantom_1.3B_mbf16.safetensors"] transformer_choices_i2v=["ckpts/wan2.1_image2video_480p_14B_mbf16.safetensors", "ckpts/wan2.1_image2video_480p_14B_quanto_mbf16_int8.safetensors", "ckpts/wan2.1_image2video_720p_14B_mbf16.safetensors", "ckpts/wan2.1_image2video_720p_14B_quanto_mbf16_int8.safetensors", "ckpts/wan2.1_Fun_InP_1.3B_bf16.safetensors", "ckpts/wan2.1_Fun_InP_14B_bf16.safetensors", "ckpts/wan2.1_Fun_InP_14B_quanto_int8.safetensors", "ckpts/wan2.1_FLF2V_720p_14B_bf16.safetensors", "ckpts/wan2.1_FLF2V_720p_14B_quanto_int8.safetensors", "ckpts/wan2.1_fantasy_speaking_14B_bf16.safetensors"] transformer_choices = transformer_choices_t2v + transformer_choices_i2v def get_dependent_models(model_filename, quantization ): if "fantasy" in model_filename: return [get_model_filename("i2v_720p", quantization)] else: return [] model_types = [ "t2v_1.3B", "vace_1.3B", "fun_inp_1.3B", "t2v", "i2v", "i2v_720p", "fun_inp", "recam_1.3B", "flf2v_720p", "sky_df_1.3B", "sky_df_14B", "sky_df_720p_14B", "phantom_1.3B", "fantasy"] model_signatures = {"t2v": "text2video_14B", "t2v_1.3B" : "text2video_1.3B", "fun_inp_1.3B" : "Fun_InP_1.3B", "fun_inp" : "Fun_InP_14B", "i2v" : "image2video_480p", "i2v_720p" : "image2video_720p" , "vace_1.3B" : "Vace_1.3B", "recam_1.3B": "recammaster_1.3B", "flf2v_720p" : "FLF2V_720p", "sky_df_1.3B" : "sky_reels2_diffusion_forcing_1.3B", "sky_df_14B" : "sky_reels2_diffusion_forcing_14B", "sky_df_720p_14B" : "sky_reels2_diffusion_forcing_720p_14B", "phantom_1.3B" : "phantom_1.3B", "fantasy" : "fantasy" } def get_model_type(model_filename): for model_type, signature in model_signatures.items(): if signature in model_filename: return model_type raise Exception("Unknown model:" + model_filename) def test_class_i2v(model_filename): return "image2video" in model_filename or "Fun_InP" in model_filename or "FLF2V" in model_filename or "fantasy" in model_filename def get_model_name(model_filename, description_container = [""]): if "Fun" in model_filename: model_name = "Fun InP image2video" model_name += " 14B" if "14B" in model_filename else " 1.3B" description = "The Fun model is an alternative image 2 video that supports out the box End Image fixing (contrary to the original Wan image 2 video model). The 1.3B adds also image 2 to video capability to the 1.3B model." elif "Vace" in model_filename: model_name = "Vace ControlNet" model_name += " 14B" if "14B" in model_filename else " 1.3B" description = "The Vace ControlNet model is a powerful model that allows you to control the content of the generated video based of additional custom data : pose or depth video, images or objects you want to see in the video." elif "image" in model_filename: model_name = "Wan2.1 image2video" model_name += " 720p" if "720p" in model_filename else " 480p" if "720p" in model_filename: description = "The standard Wan Image 2 Video specialized to generate 720p images. It also offers Start and End Image support (End Image is not supported in the original model but seems to work well)" else: description = "The standard Wan Image 2 Video specialized to generate 480p images. It also offers Start and End Image support (End Image is not supported in the original model but seems to work well)" elif "recam" in model_filename: model_name = "ReCamMaster" model_name += " 14B" if "14B" in model_filename else " 1.3B" description = "The Recam Master in theory should allow you to replay a video by applying a different camera movement. The model supports only video that are at least 81 frames long (any frame beyond will be ignored)" elif "FLF2V" in model_filename: model_name = "Wan2.1 FLF2V" model_name += " 720p" if "720p" in model_filename else " 480p" description = "The First Last Frame 2 Video model is the official model Image 2 Video model that support Start and End frames." elif "sky_reels2_diffusion_forcing" in model_filename: model_name = "SkyReels2 Diffusion Forcing" if "720p" in model_filename : model_name += " 720p" elif not "1.3B" in model_filename : model_name += " 540p" model_name += " 14B" if "14B" in model_filename else " 1.3B" description = "The SkyReels 2 Diffusion Forcing model has been designed to generate very long videos that exceeds the usual 5s limit. You can also use this model to extend any existing video." elif "phantom" in model_filename: model_name = "Wan2.1 Phantom" model_name += " 14B" if "14B" in model_filename else " 1.3B" description = "The Phantom model is specialized to transfer people or objects of your choice into a generated Video. It produces very nices results when used at 720p." elif "fantasy" in model_filename: model_name = "Wan2.1 Fantasy Speaking 720p" model_name += " 14B" if "14B" in model_filename else " 1.3B" description = "The Fantasy Speaking model corresponds to the original Wan image 2 video model combined with the Fantasy Speaking extension to process an audio Input." else: model_name = "Wan2.1 text2video" model_name += " 14B" if "14B" in model_filename else " 1.3B" description = "The original Wan Text 2 Video model. Most other models have been built on top of it" description_container[0] = description return model_name def get_model_filename(model_type, quantization): signature = model_signatures[model_type] choices = [ name for name in transformer_choices if signature in name] if len(quantization) == 0: quantization = "bf16" if len(choices) <= 1: return choices[0] sub_choices = [ name for name in choices if quantization in name] if len(sub_choices) > 0: return sub_choices[0] else: return choices[0] def get_settings_file_name(model_filename): return os.path.join(args.settings, get_model_type(model_filename) + "_settings.json") def get_default_settings(filename): def get_default_prompt(i2v): if i2v: return "Several giant wooly mammoths approach treading through a snowy meadow, their long wooly fur lightly blows in the wind as they walk, snow covered trees and dramatic snow capped mountains in the distance, mid afternoon light with wispy clouds and a sun high in the distance creates a warm glow, the low camera view is stunning capturing the large furry mammal with beautiful photography, depth of field." else: return "A large orange octopus is seen resting on the bottom of the ocean floor, blending in with the sandy and rocky terrain. Its tentacles are spread out around its body, and its eyes are closed. The octopus is unaware of a king crab that is crawling towards it from behind a rock, its claws raised and ready to attack. The crab is brown and spiny, with long legs and antennae. The scene is captured from a wide angle, showing the vastness and depth of the ocean. The water is clear and blue, with rays of sunlight filtering through. The shot is sharp and crisp, with a high dynamic range. The octopus and the crab are in focus, while the background is slightly blurred, creating a depth of field effect." i2v = test_class_i2v(filename) defaults_filename = get_settings_file_name(filename) if not Path(defaults_filename).is_file(): ui_defaults = { "prompt": get_default_prompt(i2v), "resolution": "1280x720" if "720p" in filename else "832x480", "video_length": 81, "num_inference_steps": 30, "seed": -1, "repeat_generation": 1, "multi_images_gen_type": 0, "guidance_scale": 5.0, "audio_guidance_scale": 5.0, "flow_shift": get_default_flow(filename, i2v), "negative_prompt": "", "activated_loras": [], "loras_multipliers": "", "tea_cache": 0.0, "tea_cache_start_step_perc": 0, "RIFLEx_setting": 0, "slg_switch": 0, "slg_layers": [9], "slg_start_perc": 10, "slg_end_perc": 90 } if get_model_type(filename) in ("sky_df_1.3B", "sky_df_14B", "sky_df_720p_14B"): ui_defaults.update({ "guidance_scale": 6.0, "flow_shift": 8, "sliding_window_discard_last_frames" : 0, "resolution": "1280x720" if "720p" in filename else "960x544", "sliding_window_size" : 121 if "720p" in filename else 97, "RIFLEx_setting": 2, "guidance_scale": 6, "flow_shift": 8, }) if get_model_type(filename) in ("phantom_1.3B"): ui_defaults.update({ "guidance_scale": 7.5, "flow_shift": 5, "resolution": "1280x720" }) with open(defaults_filename, "w", encoding="utf-8") as f: json.dump(ui_defaults, f, indent=4) else: with open(defaults_filename, "r", encoding="utf-8") as f: ui_defaults = json.load(f) prompts = ui_defaults.get("prompts", "") if len(prompts) > 0: ui_defaults["prompt"] = prompts image_prompt_type = ui_defaults.get("image_prompt_type", None) if image_prompt_type !=None and not isinstance(image_prompt_type, str): ui_defaults["image_prompt_type"] = "S" if image_prompt_type == 0 else "SE" default_seed = args.seed if default_seed > -1: ui_defaults["seed"] = default_seed default_number_frames = args.frames if default_number_frames > 0: ui_defaults["video_length"] = default_number_frames default_number_steps = args.steps if default_number_steps > 0: ui_defaults["num_inference_steps"] = default_number_steps return ui_defaults transformer_types = server_config.get("transformer_types", []) transformer_type = transformer_types[0] if len(transformer_types) > 0 else model_types[0] transformer_quantization =server_config.get("transformer_quantization", "int8") transformer_filename = get_model_filename(transformer_type, transformer_quantization) text_encoder_filename = server_config["text_encoder_filename"] attention_mode = server_config["attention_mode"] if len(args.attention)> 0: if args.attention in ["auto", "sdpa", "sage", "sage2", "flash", "xformers"]: attention_mode = args.attention lock_ui_attention = True else: raise Exception(f"Unknown attention mode '{args.attention}'") profile = force_profile_no if force_profile_no >=0 else server_config["profile"] compile = server_config.get("compile", "") boost = server_config.get("boost", 1) vae_config = server_config.get("vae_config", 0) if len(args.vae_config) > 0: vae_config = int(args.vae_config) reload_needed = False default_ui = server_config.get("default_ui", "t2v") save_path = server_config.get("save_path", os.path.join(os.getcwd(), "gradio_outputs")) preload_model_policy = server_config.get("preload_model_policy", []) if args.t2v_14B or args.t2v: transformer_filename = get_model_filename("t2v", transformer_quantization) if args.i2v_14B or args.i2v: transformer_filename = get_model_filename("i2v", transformer_quantization) if args.t2v_1_3B: transformer_filename = get_model_filename("t2v_1.3B", transformer_quantization) if args.i2v_1_3B: transformer_filename = get_model_filename("fun_inp_1.3B", transformer_quantization) if args.vace_1_3B: transformer_filename = get_model_filename("vace_1.3B", transformer_quantization) only_allow_edit_in_advanced = False lora_preselected_preset = args.lora_preset lora_preset_model = transformer_filename if args.compile: #args.fastest or compile="transformer" lock_ui_compile = True model_filename = "" #attention_mode="sage" #attention_mode="sage2" #attention_mode="flash" #attention_mode="sdpa" #attention_mode="xformers" # compile = "transformer" def preprocess_loras(sd): if wan_model == None: return sd model_filename = wan_model._model_file_name first = next(iter(sd), None) if first == None: return sd if first.startswith("lora_unet_"): new_sd = {} print("Converting Lora Safetensors format to Lora Diffusers format") alphas = {} repl_list = ["cross_attn", "self_attn", "ffn"] src_list = ["_" + k + "_" for k in repl_list] tgt_list = ["." + k + "." for k in repl_list] for k,v in sd.items(): k = k.replace("lora_unet_blocks_","diffusion_model.blocks.") for s,t in zip(src_list, tgt_list): k = k.replace(s,t) k = k.replace("lora_up","lora_B") k = k.replace("lora_down","lora_A") if "alpha" in k: alphas[k] = v else: new_sd[k] = v new_alphas = {} for k,v in new_sd.items(): if "lora_B" in k: dim = v.shape[1] elif "lora_A" in k: dim = v.shape[0] else: continue alpha_key = k[:-len("lora_X.weight")] +"alpha" if alpha_key in alphas: scale = alphas[alpha_key] / dim new_alphas[alpha_key] = scale else: print(f"Lora alpha'{alpha_key}' is missing") new_sd.update(new_alphas) sd = new_sd if "text2video" in model_filename: new_sd = {} # convert loras for i2v to t2v for k,v in sd.items(): if any(layer in k for layer in ["cross_attn.k_img", "cross_attn.v_img"]): continue new_sd[k] = v sd = new_sd return sd def download_models(transformer_filename, text_encoder_filename): def computeList(filename): pos = filename.rfind("/") filename = filename[pos+1:] return [filename] from huggingface_hub import hf_hub_download, snapshot_download repoId = "DeepBeepMeep/Wan2.1" sourceFolderList = ["xlm-roberta-large", "pose", "depth", "mask", "wav2vec", "" ] fileList = [ [], [],[], ["sam_vit_h_4b8939_fp16.safetensors"], ["config.json", "feature_extractor_config.json", "model.safetensors", "preprocessor_config.json", "special_tokens_map.json", "tokenizer_config.json", "vocab.json"], ["Wan2.1_VAE.safetensors", "models_clip_open-clip-xlm-roberta-large-vit-huge-14-bf16.safetensors", "flownet.pkl", "fantasy_proj_model.safetensors" ] + computeList(text_encoder_filename) + computeList(transformer_filename) ] targetRoot = "ckpts/" for sourceFolder, files in zip(sourceFolderList,fileList ): if len(files)==0: if not Path(targetRoot + sourceFolder).exists(): snapshot_download(repo_id=repoId, allow_patterns=sourceFolder +"/*", local_dir= targetRoot) else: for onefile in files: if len(sourceFolder) > 0: if not os.path.isfile(targetRoot + sourceFolder + "/" + onefile ): hf_hub_download(repo_id=repoId, filename=onefile, local_dir = targetRoot, subfolder=sourceFolder) else: if not os.path.isfile(targetRoot + onefile ): hf_hub_download(repo_id=repoId, filename=onefile, local_dir = targetRoot) offload.default_verboseLevel = verbose_level to_remove = ["models_clip_open-clip-xlm-roberta-large-vit-huge-14.pth", "Wan2.1_VAE.pth"] for file_name in to_remove: file_name = os.path.join("ckpts",file_name) if os.path.isfile(file_name): try: os.remove(file_name) except: pass download_models(transformer_filename, text_encoder_filename) def sanitize_file_name(file_name, rep =""): return file_name.replace("/",rep).replace("\\",rep).replace(":",rep).replace("|",rep).replace("?",rep).replace("<",rep).replace(">",rep).replace("\"",rep).replace("\n",rep).replace("\r",rep) def extract_preset(model_filename, lset_name, loras): loras_choices = [] loras_choices_files = [] loras_mult_choices = "" prompt ="" full_prompt ="" lset_name = sanitize_file_name(lset_name) lora_dir = get_lora_dir(model_filename) if not lset_name.endswith(".lset"): lset_name_filename = os.path.join(lora_dir, lset_name + ".lset" ) else: lset_name_filename = os.path.join(lora_dir, lset_name ) error = "" if not os.path.isfile(lset_name_filename): error = f"Preset '{lset_name}' not found " else: missing_loras = [] with open(lset_name_filename, "r", encoding="utf-8") as reader: text = reader.read() lset = json.loads(text) loras_choices_files = lset["loras"] for lora_file in loras_choices_files: choice = os.path.join(lora_dir, lora_file) if choice not in loras: missing_loras.append(lora_file) else: loras_choice_no = loras.index(choice) loras_choices.append(str(loras_choice_no)) if len(missing_loras) > 0: error = f"Unable to apply Lora preset '{lset_name} because the following Loras files are missing or invalid: {missing_loras}" loras_mult_choices = lset["loras_mult"] prompt = lset.get("prompt", "") full_prompt = lset.get("full_prompt", False) return loras_choices, loras_mult_choices, prompt, full_prompt, error def setup_loras(model_filename, transformer, lora_dir, lora_preselected_preset, split_linear_modules_map = None): loras =[] loras_names = [] default_loras_choices = [] default_loras_multis_str = "" loras_presets = [] default_lora_preset = "" default_lora_preset_prompt = "" from pathlib import Path lora_dir = get_lora_dir(model_filename) if lora_dir != None : if not os.path.isdir(lora_dir): raise Exception("--lora-dir should be a path to a directory that contains Loras") if lora_dir != None: dir_loras = glob.glob( os.path.join(lora_dir , "*.sft") ) + glob.glob( os.path.join(lora_dir , "*.safetensors") ) dir_loras.sort() loras += [element for element in dir_loras if element not in loras ] dir_presets = glob.glob( os.path.join(lora_dir , "*.lset") ) dir_presets.sort() loras_presets = [ Path(Path(file_path).parts[-1]).stem for file_path in dir_presets] if transformer !=None: loras = offload.load_loras_into_model(transformer, loras, activate_all_loras=False, check_only= True, preprocess_sd=preprocess_loras, split_linear_modules_map = split_linear_modules_map) #lora_multiplier, if len(loras) > 0: loras_names = [ Path(lora).stem for lora in loras ] if len(lora_preselected_preset) > 0: if not os.path.isfile(os.path.join(lora_dir, lora_preselected_preset + ".lset")): raise Exception(f"Unknown preset '{lora_preselected_preset}'") default_lora_preset = lora_preselected_preset default_loras_choices, default_loras_multis_str, default_lora_preset_prompt, _ , error = extract_preset(model_filename, default_lora_preset, loras) if len(error) > 0: print(error[:200]) return loras, loras_names, loras_presets, default_loras_choices, default_loras_multis_str, default_lora_preset_prompt, default_lora_preset def load_t2v_model(model_filename, quantizeTransformer = False, dtype = torch.bfloat16, VAE_dtype = torch.float32, mixed_precision_transformer = False): cfg = WAN_CONFIGS['t2v-14B'] filename = model_filename[-1] # cfg = WAN_CONFIGS['t2v-1.3B'] print(f"Loading '{filename}' model...") if get_model_type(filename) in ("sky_df_1.3B", "sky_df_14B", "sky_df_720p_14B"): model_factory = wan.DTT2V else: model_factory = wan.WanT2V wan_model = model_factory( config=cfg, checkpoint_dir="ckpts", model_filename=model_filename, text_encoder_filename= text_encoder_filename, quantizeTransformer = quantizeTransformer, dtype = dtype, VAE_dtype = VAE_dtype, mixed_precision_transformer = mixed_precision_transformer ) pipe = {"transformer": wan_model.model, "text_encoder" : wan_model.text_encoder.model, "vae": wan_model.vae.model } return wan_model, pipe def load_i2v_model(model_filename, quantizeTransformer = False, dtype = torch.bfloat16, VAE_dtype = torch.float32, mixed_precision_transformer = False): filename = model_filename[-1] print(f"Loading '{filename}' model...") cfg = WAN_CONFIGS['i2v-14B'] wan_model = wan.WanI2V( config=cfg, checkpoint_dir="ckpts", model_filename=model_filename, text_encoder_filename=text_encoder_filename, quantizeTransformer = quantizeTransformer, dtype = dtype, VAE_dtype = VAE_dtype, mixed_precision_transformer = mixed_precision_transformer ) pipe = {"transformer": wan_model.model, "text_encoder" : wan_model.text_encoder.model, "text_encoder_2": wan_model.clip.model, "vae": wan_model.vae.model } # return wan_model, pipe def load_models(model_filename): global transformer_filename perc_reserved_mem_max = args.perc_reserved_mem_max major, minor = torch.cuda.get_device_capability(args.gpu if len(args.gpu) > 0 else None) if major < 8: print("Switching to f16 model as GPU architecture doesn't support bf16") default_dtype = torch.float16 else: default_dtype = torch.float16 if args.fp16 else torch.bfloat16 model_filelist = get_dependent_models(model_filename, quantization= transformer_quantization) + [model_filename] updated_model_filename = [] for filename in model_filelist: if default_dtype == torch.float16 : if "quanto_int8" in filename: filename = filename.replace("quanto_int8", "quanto_fp16_int8") elif "quanto_mbf16_int8": filename = filename.replace("quanto_mbf16_int8", "quanto_mfp16_int8") updated_model_filename.append(filename) download_models(filename, text_encoder_filename) model_filelist = updated_model_filename VAE_dtype = torch.float16 if server_config.get("vae_precision","16") == "16" else torch.float mixed_precision_transformer = server_config.get("mixed_precision","0") == "1" transformer_filename = None new_transformer_filename = model_filelist[-1] if test_class_i2v(new_transformer_filename): wan_model, pipe = load_i2v_model(model_filelist, quantizeTransformer = quantizeTransformer, dtype = default_dtype, VAE_dtype = VAE_dtype, mixed_precision_transformer = mixed_precision_transformer) else: wan_model, pipe = load_t2v_model(model_filelist, quantizeTransformer = quantizeTransformer, dtype = default_dtype, VAE_dtype = VAE_dtype, mixed_precision_transformer = mixed_precision_transformer) wan_model._model_file_name = new_transformer_filename kwargs = { "extraModelsToQuantize": None} if profile == 2 or profile == 4: kwargs["budgets"] = { "transformer" : 100 if preload == 0 else preload, "text_encoder" : 100, "*" : 1000 } # if profile == 4: # kwargs["partialPinning"] = True elif profile == 3: kwargs["budgets"] = { "*" : "70%" } offloadobj = offload.profile(pipe, profile_no= profile, compile = compile, quantizeTransformer = quantizeTransformer, loras = "transformer", coTenantsMap= {}, perc_reserved_mem_max = perc_reserved_mem_max , convertWeightsFloatTo = default_dtype, **kwargs) if len(args.gpu) > 0: torch.set_default_device(args.gpu) transformer_filename = new_transformer_filename return wan_model, offloadobj, pipe["transformer"] if not "P" in preload_model_policy: wan_model, offloadobj, transformer = None, None, None reload_needed = True else: wan_model, offloadobj, transformer = load_models(transformer_filename) if check_loras: setup_loras(model_filename, transformer, get_lora_dir(transformer_filename), "", None) exit() del transformer gen_in_progress = False def get_auto_attention(): for attn in ["sage2","sage","sdpa"]: if attn in attention_modes_supported: return attn return "sdpa" def get_default_flow(filename, i2v): return 7.0 if "480p" in filename and i2v else 5.0 def generate_header(model_filename, compile, attention_mode): description_container = [""] get_model_name(model_filename, description_container) description = description_container[0] header = "
" + description + "
" header += "
Attention mode " + (attention_mode if attention_mode!="auto" else "auto/" + get_auto_attention() ) if attention_mode not in attention_modes_installed: header += " -NOT INSTALLED-" elif attention_mode not in attention_modes_supported: header += " -NOT SUPPORTED-" header += "" if compile: header += ", Pytorch compilation ON" if "int8" in model_filename: header += ", Quantization Int8" header += "
" return header def apply_changes( state, transformer_types_choices, text_encoder_choice, VAE_precision_choice, mixed_precision_choice, save_path_choice, attention_choice, compile_choice, profile_choice, vae_config_choice, metadata_choice, quantization_choice, boost_choice = 1, clear_file_list = 0, preload_model_policy_choice = 1, UI_theme_choice = "default" ): if args.lock_config: return if gen_in_progress: return "
Unable to change config when a generation is in progress
", gr.update(), gr.update() global offloadobj, wan_model, server_config, loras, loras_names, default_loras_choices, default_loras_multis_str, default_lora_preset_prompt, default_lora_preset, loras_presets server_config = {"attention_mode" : attention_choice, "transformer_types": transformer_types_choices, "text_encoder_filename" : text_encoder_choices[text_encoder_choice], "save_path" : save_path_choice, "compile" : compile_choice, "profile" : profile_choice, "vae_config" : vae_config_choice, "vae_precision" : VAE_precision_choice, "mixed_precision" : mixed_precision_choice, "metadata_type": metadata_choice, "transformer_quantization" : quantization_choice, "boost" : boost_choice, "clear_file_list" : clear_file_list, "preload_model_policy" : preload_model_policy_choice, "UI_theme" : UI_theme_choice } if Path(server_config_filename).is_file(): with open(server_config_filename, "r", encoding="utf-8") as reader: text = reader.read() old_server_config = json.loads(text) if lock_ui_transformer: server_config["transformer_filename"] = old_server_config["transformer_filename"] if lock_ui_attention: server_config["attention_mode"] = old_server_config["attention_mode"] if lock_ui_compile: server_config["compile"] = old_server_config["compile"] with open(server_config_filename, "w", encoding="utf-8") as writer: writer.write(json.dumps(server_config)) changes = [] for k, v in server_config.items(): v_old = old_server_config.get(k, None) if v != v_old: changes.append(k) global attention_mode, profile, compile, text_encoder_filename, vae_config, boost, lora_dir, reload_needed, preload_model_policy, transformer_quantization, transformer_types attention_mode = server_config["attention_mode"] profile = server_config["profile"] compile = server_config["compile"] text_encoder_filename = server_config["text_encoder_filename"] vae_config = server_config["vae_config"] boost = server_config["boost"] preload_model_policy = server_config["preload_model_policy"] transformer_quantization = server_config["transformer_quantization"] transformer_types = server_config["transformer_types"] if all(change in ["attention_mode", "vae_config", "boost", "save_path", "metadata_type", "clear_file_list"] for change in changes ): model_choice = gr.Dropdown() else: reload_needed = True model_choice = generate_dropdown_model_list() header = generate_header(transformer_filename, compile=compile, attention_mode= attention_mode) return "
The new configuration has been succesfully applied
", header, model_choice from moviepy.editor import ImageSequenceClip import numpy as np def save_video(final_frames, output_path, fps=24): assert final_frames.ndim == 4 and final_frames.shape[3] == 3, f"invalid shape: {final_frames} (need t h w c)" if final_frames.dtype != np.uint8: final_frames = (final_frames * 255).astype(np.uint8) ImageSequenceClip(list(final_frames), fps=fps).write_videofile(output_path, verbose= False, logger = None) def get_gen_info(state): cache = state.get("gen", None) if cache == None: cache = dict() state["gen"] = cache return cache def build_callback(state, pipe, send_cmd, status, num_inference_steps): gen = get_gen_info(state) gen["num_inference_steps"] = num_inference_steps def callback(step_idx, latent, force_refresh, read_state = False, override_num_inference_steps = -1): refresh_id = gen.get("refresh", -1) if force_refresh or step_idx >= 0: pass else: refresh_id = gen.get("refresh", -1) if refresh_id < 0: return UI_refresh = state.get("refresh", 0) if UI_refresh >= refresh_id: return if override_num_inference_steps > 0: gen["num_inference_steps"] = override_num_inference_steps num_inference_steps = gen.get("num_inference_steps", 0) status = gen["progress_status"] state["refresh"] = refresh_id if read_state: phase, step_idx = gen["progress_phase"] else: step_idx += 1 if gen.get("abort", False): # pipe._interrupt = True phase = " - Aborting" elif step_idx == num_inference_steps: phase = " - VAE Decoding" else: phase = " - Denoising" gen["progress_phase"] = (phase, step_idx) status_msg = status + phase if step_idx >= 0: progress_args = [(step_idx , num_inference_steps) , status_msg , num_inference_steps] else: progress_args = [0, status_msg] # progress(*progress_args) send_cmd("progress", progress_args) if latent != None: send_cmd("preview", latent.to("cpu", non_blocking=True)) # gen["progress_args"] = progress_args return callback def abort_generation(state): gen = get_gen_info(state) if "in_progress" in gen and wan_model != None: wan_model._interrupt= True msg = "Processing Request to abort Current Generation" gen["status"] = msg gr.Info(msg) return gr.Button(interactive= False) else: return gr.Button(interactive= True) def refresh_gallery(state): #, msg gen = get_gen_info(state) # gen["last_msg"] = msg file_list = gen.get("file_list", None) choice = gen.get("selected",0) in_progress = "in_progress" in gen if in_progress: if gen.get("last_selected", True): choice = max(len(file_list) - 1,0) queue = gen.get("queue", []) abort_interactive = not gen.get("abort", False) if not in_progress or len(queue) == 0: return gr.Gallery(selected_index=choice, value = file_list), gr.HTML("", visible= False), gr.Button(visible=True), gr.Button(visible=False), gr.Row(visible=False), update_queue_data(queue), gr.Button(interactive= abort_interactive), gr.Button(visible= False) else: task = queue[0] start_img_md = "" end_img_md = "" prompt = task["prompt"] params = task["params"] model_filename = params["model_filename"] onemorewindow_visible = "Vace" in model_filename or "diffusion_forcing" in model_filename if "\n" in prompt : prompts = prompt.split("\n") window_no= gen.get("window_no",1) if window_no > len(prompts): window_no = len(prompts) window_no -= 1 prompts[window_no]="" + prompts[window_no] + "" prompt = "
".join(prompts) start_img_uri = task.get('start_image_data_base64') start_img_uri = start_img_uri[0] if start_img_uri !=None else None end_img_uri = task.get('end_image_data_base64') end_img_uri = end_img_uri[0] if end_img_uri !=None else None thumbnail_size = "100px" if start_img_uri: start_img_md = f'Start' if end_img_uri: end_img_md = f'End' label = f"Prompt of Video being Generated" html = "" if start_img_md != "": html += "" if end_img_md != "": html += "" html += "
" + prompt + "" + start_img_md + "" + end_img_md + "
" html_output = gr.HTML(html, visible= True) return gr.Gallery(selected_index=choice, value = file_list), html_output, gr.Button(visible=False), gr.Button(visible=True), gr.Row(visible=True), update_queue_data(queue), gr.Button(interactive= abort_interactive), gr.Button(visible= onemorewindow_visible) def finalize_generation(state): gen = get_gen_info(state) choice = gen.get("selected",0) if "in_progress" in gen: del gen["in_progress"] if gen.get("last_selected", True): file_list = gen.get("file_list", []) choice = len(file_list) - 1 gen["extra_orders"] = 0 time.sleep(0.2) global gen_in_progress gen_in_progress = False return gr.Gallery(selected_index=choice), gr.Button(interactive= True), gr.Button(visible= True), gr.Button(visible= False), gr.Column(visible= False), gr.HTML(visible= False, value="") def select_video(state , event_data: gr.EventData): data= event_data._data gen = get_gen_info(state) if data!=None: choice = data.get("index",0) file_list = gen.get("file_list", []) gen["last_selected"] = (choice + 1) >= len(file_list) gen["selected"] = choice return def expand_slist(slist, num_inference_steps ): new_slist= [] inc = len(slist) / num_inference_steps pos = 0 for i in range(num_inference_steps): new_slist.append(slist[ int(pos)]) pos += inc return new_slist def convert_image(image): from PIL import ImageOps from typing import cast image = image.convert('RGB') return cast(Image, ImageOps.exif_transpose(image)) def get_resampled_video(video_in, start_frame, max_frames, target_fps): from wan.utils.utils import resample import decord decord.bridge.set_bridge('torch') reader = decord.VideoReader(video_in) fps = reader.get_avg_fps() frame_nos = resample(fps, len(reader), max_target_frames_count= max_frames, target_fps=target_fps, start_target_frame= start_frame) frames_list = reader.get_batch(frame_nos) return frames_list def preprocess_video(process_type, height, width, video_in, max_frames, start_frame=0, fit_canvas = False, target_fps = 16): frames_list = get_resampled_video(video_in, start_frame, max_frames, target_fps) if len(frames_list) == 0: return None frame_height, frame_width, _ = frames_list[0].shape if fit_canvas : scale1 = min(height / frame_height, width / frame_width) scale2 = min(height / frame_width, width / frame_height) scale = max(scale1, scale2) else: scale = ((height * width ) / (frame_height * frame_width))**(1/2) new_height = (int(frame_height * scale) // 16) * 16 new_width = (int(frame_width * scale) // 16) * 16 # if fit_canvas : # new_height = height # new_width = width processed_frames_list = [] for frame in frames_list: frame = Image.fromarray(np.clip(frame.cpu().numpy(), 0, 255).astype(np.uint8)) frame = frame.resize((new_width,new_height), resample=Image.Resampling.LANCZOS) processed_frames_list.append(frame) if process_type=="pose": from preprocessing.dwpose.pose import PoseBodyFaceVideoAnnotator cfg_dict = { "DETECTION_MODEL": "ckpts/pose/yolox_l.onnx", "POSE_MODEL": "ckpts/pose/dw-ll_ucoco_384.onnx", "RESIZE_SIZE": 1024 } anno_ins = PoseBodyFaceVideoAnnotator(cfg_dict) elif process_type=="depth": from preprocessing.midas.depth import DepthVideoAnnotator cfg_dict = { "PRETRAINED_MODEL": "ckpts/depth/dpt_hybrid-midas-501f0c75.pt" } anno_ins = DepthVideoAnnotator(cfg_dict) elif process_type=="gray": from preprocessing.gray import GrayVideoAnnotator cfg_dict = {} anno_ins = GrayVideoAnnotator(cfg_dict) else: anno_ins = None if anno_ins == None: np_frames = [np.array(frame) for frame in processed_frames_list] else: np_frames = anno_ins.forward(processed_frames_list) # from preprocessing.dwpose.pose import save_one_video # save_one_video("test.mp4", np_frames, fps=8, quality=8, macro_block_size=None) torch_frames = [] for np_frame in np_frames: torch_frame = torch.from_numpy(np_frame) torch_frames.append(torch_frame) return torch.stack(torch_frames) def parse_keep_frames_video_guide(keep_frames, video_length): def absolute(n): if n==0: return 0 elif n < 0: return max(0, video_length + n) else: return min(n-1, video_length-1) if len(keep_frames) == 0: return [True] *video_length, "" frames =[False] *video_length error = "" sections = keep_frames.split(" ") for section in sections: section = section.strip() if ":" in section: parts = section.split(":") if not is_integer(parts[0]): error =f"Invalid integer {parts[0]}" break start_range = absolute(int(parts[0])) if not is_integer(parts[1]): error =f"Invalid integer {parts[1]}" break end_range = absolute(int(parts[1])) for i in range(start_range, end_range + 1): frames[i] = True else: if not is_integer(section): error =f"Invalid integer {section}" break index = absolute(int(section)) frames[index] = True if len(error ) > 0: return [], error for i in range(len(frames)-1, 0, -1): if frames[i]: break frames= frames[0: i+1] return frames, error def generate_video( task_id, send_cmd, prompt, negative_prompt, resolution, video_length, seed, num_inference_steps, guidance_scale, audio_guidance_scale, flow_shift, embedded_guidance_scale, repeat_generation, multi_images_gen_type, tea_cache_setting, tea_cache_start_step_perc, activated_loras, loras_multipliers, image_prompt_type, image_start, image_end, model_mode, video_source, keep_frames_video_source, video_prompt_type, image_refs, video_guide, keep_frames_video_guide, video_mask, audio_guide, sliding_window_size, sliding_window_overlap, sliding_window_overlap_noise, sliding_window_discard_last_frames, remove_background_image_ref, temporal_upsampling, spatial_upsampling, RIFLEx_setting, slg_switch, slg_layers, slg_start_perc, slg_end_perc, cfg_star_switch, cfg_zero_step, state, model_filename ): global wan_model, offloadobj, reload_needed gen = get_gen_info(state) file_list = gen["file_list"] prompt_no = gen["prompt_no"] # if wan_model == None: # gr.Info("Unable to generate a Video while a new configuration is being applied.") # return if "P" in preload_model_policy and not "U" in preload_model_policy: while wan_model == None: time.sleep(1) if model_filename != transformer_filename or reload_needed: wan_model = None if offloadobj is not None: offloadobj.release() offloadobj = None gc.collect() send_cmd("status", f"Loading model {get_model_name(model_filename)}...") wan_model, offloadobj, trans = load_models(model_filename) send_cmd("status", "Model loaded") reload_needed= False if attention_mode == "auto": attn = get_auto_attention() elif attention_mode in attention_modes_supported: attn = attention_mode else: send_cmd("info", f"You have selected attention mode '{attention_mode}'. However it is not installed or supported on your system. You should either install it or switch to the default 'sdpa' attention.") send_cmd("exit") return width, height = resolution.split("x") width, height = int(width), int(height) resolution_reformated = str(height) + "*" + str(width) if slg_switch == 0: slg_layers = None offload.shared_state["_attention"] = attn # VAE Tiling device_mem_capacity = torch.cuda.get_device_properties(0).total_memory / 1048576 if vae_config == 0: if server_config.get("vae_precision", "16") == "32": device_mem_capacity = device_mem_capacity / 2 if device_mem_capacity >= 24000: use_vae_config = 1 elif device_mem_capacity >= 8000: use_vae_config = 2 else: use_vae_config = 3 else: use_vae_config = vae_config if use_vae_config == 1: VAE_tile_size = 0 elif use_vae_config == 2: VAE_tile_size = 256 else: VAE_tile_size = 128 trans = wan_model.model temp_filename = None loras = state["loras"] if len(loras) > 0: def is_float(element: any) -> bool: if element is None: return False try: float(element) return True except ValueError: return False list_mult_choices_nums = [] if len(loras_multipliers) > 0: loras_mult_choices_list = loras_multipliers.replace("\r", "").split("\n") loras_mult_choices_list = [multi for multi in loras_mult_choices_list if len(multi)>0 and not multi.startswith("#")] loras_multipliers = " ".join(loras_mult_choices_list) list_mult_choices_str = loras_multipliers.split(" ") for i, mult in enumerate(list_mult_choices_str): mult = mult.strip() if "," in mult: multlist = mult.split(",") slist = [] for smult in multlist: if not is_float(smult): raise gr.Error(f"Lora sub value no {i+1} ({smult}) in Multiplier definition '{multlist}' is invalid") slist.append(float(smult)) slist = expand_slist(slist, num_inference_steps ) list_mult_choices_nums.append(slist) else: if not is_float(mult): raise gr.Error(f"Lora Multiplier no {i+1} ({mult}) is invalid") list_mult_choices_nums.append(float(mult)) if len(list_mult_choices_nums ) < len(activated_loras): list_mult_choices_nums += [1.0] * ( len(activated_loras) - len(list_mult_choices_nums ) ) loras_selected = [ lora for lora in loras if os.path.basename(lora) in activated_loras] pinnedLora = profile !=5 #False # # # offload.load_loras_into_model(trans, loras_selected, list_mult_choices_nums, activate_all_loras=True, preprocess_sd=preprocess_loras, pinnedLora=pinnedLora, split_linear_modules_map = None) errors = trans._loras_errors if len(errors) > 0: error_files = [msg for _ , msg in errors] raise gr.Error("Error while loading Loras: " + ", ".join(error_files)) seed = None if seed == -1 else seed # negative_prompt = "" # not applicable in the inference image2video = test_class_i2v(model_filename) enable_RIFLEx = RIFLEx_setting == 0 and video_length > (6* 16) or RIFLEx_setting == 1 # VAE Tiling device_mem_capacity = torch.cuda.get_device_properties(None).total_memory / 1048576 diffusion_forcing = "diffusion_forcing" in model_filename vace = "Vace" in model_filename if diffusion_forcing: fps = 24 elif audio_guide != None: fps = 23 else: fps = 16 joint_pass = boost ==1 #and profile != 1 and profile != 3 # TeaCache trans.enable_teacache = tea_cache_setting > 0 if trans.enable_teacache: trans.teacache_multiplier = tea_cache_setting trans.rel_l1_thresh = 0 trans.teacache_start_step = int(tea_cache_start_step_perc*num_inference_steps/100) if image2video: if '720p' in model_filename: trans.coefficients = [-114.36346466, 65.26524496, -18.82220707, 4.91518089, -0.23412683] else: trans.coefficients = [-3.02331670e+02, 2.23948934e+02, -5.25463970e+01, 5.87348440e+00, -2.01973289e-01] else: if '1.3B' in model_filename: trans.coefficients = [2.39676752e+03, -1.31110545e+03, 2.01331979e+02, -8.29855975e+00, 1.37887774e-01] elif '14B' in model_filename: trans.coefficients = [-5784.54975374, 5449.50911966, -1811.16591783, 256.27178429, -13.02252404] else: raise gr.Error("Teacache not supported for this model") source_video = None target_camera = None if "recam" in model_filename: source_video = preprocess_video("", width=width, height=height,video_in=video_source, max_frames= video_length, start_frame = 0, fit_canvas= True) target_camera = model_mode audio_proj_split = None audio_scale = None audio_context_lens = None if audio_guide != None: from fantasytalking.infer import parse_audio import librosa duration = librosa.get_duration(path=audio_guide) video_length = min(int(fps * duration // 4) * 4 + 5, video_length) audio_proj_split, audio_context_lens = parse_audio(audio_guide, num_frames= video_length, fps= fps, device= processing_device ) audio_scale = 1.0 import random if seed == None or seed <0: seed = random.randint(0, 999999999) global save_path os.makedirs(save_path, exist_ok=True) abort = False gc.collect() torch.cuda.empty_cache() wan_model._interrupt = False gen["abort"] = False gen["prompt"] = prompt repeat_no = 0 extra_generation = 0 initial_total_windows = 0 max_frames_to_generate = video_length phantom = "phantom" in model_filename if diffusion_forcing or vace: reuse_frames = min(sliding_window_size - 4, sliding_window_overlap) else: reuse_frames = 0 if diffusion_forcing and source_video != None: video_length += sliding_window_overlap sliding_window = ("Vace" in model_filename or diffusion_forcing) and video_length > sliding_window_size if sliding_window: discard_last_frames = sliding_window_discard_last_frames left_after_first_window = video_length - sliding_window_size + discard_last_frames initial_total_windows= 1 + math.ceil(left_after_first_window / (sliding_window_size - discard_last_frames - reuse_frames)) video_length = sliding_window_size prompts = prompt.split("\n") prompts = [part for part in prompts if len(prompt)>0] else: initial_total_windows = 1 first_window_video_length = video_length gen["sliding_window"] = sliding_window while not abort: extra_generation += gen.get("extra_orders",0) gen["extra_orders"] = 0 total_generation = repeat_generation + extra_generation gen["total_generation"] = total_generation if repeat_no >= total_generation: break repeat_no +=1 gen["repeat_no"] = repeat_no src_video, src_mask, src_ref_images = None, None, None prefix_video = None prefix_video_frames_count = 0 frames_already_processed = None pre_video_guide = None window_no = 0 extra_windows = 0 guide_start_frame = 0 video_length = first_window_video_length gen["extra_windows"] = 0 start_time = time.time() while not abort: if sliding_window: prompt = prompts[window_no] if window_no < len(prompts) else prompts[-1] extra_windows += gen.get("extra_windows",0) if extra_windows > 0: video_length = sliding_window_size gen["extra_windows"] = 0 total_windows = initial_total_windows + extra_windows gen["total_windows"] = total_windows if window_no >= total_windows: break window_no += 1 gen["window_no"] = window_no if phantom: src_ref_images = image_refs.copy() if image_refs != None else None elif diffusion_forcing: if video_source != None and len(video_source) > 0 and window_no == 1: keep_frames_video_source= 1000 if len(keep_frames_video_source) ==0 else int(keep_frames_video_source) prefix_video = preprocess_video(None, width=width, height=height,video_in=video_source, max_frames= keep_frames_video_source , start_frame = 0, fit_canvas= True, target_fps = fps) prefix_video = prefix_video .permute(3, 0, 1, 2) prefix_video = prefix_video .float().div_(127.5).sub_(1.) # c, f, h, w prefix_video_frames_count = prefix_video.shape[1] pre_video_guide = prefix_video[:, -reuse_frames:] elif vace: # video_prompt_type = video_prompt_type +"G" image_refs_copy = image_refs.copy() if image_refs != None else None # required since prepare_source do inplace modifications video_guide_copy = video_guide video_mask_copy = video_mask if any(process in video_prompt_type for process in ("P", "D", "G")) : prompts_max = gen["prompts_max"] status = get_latest_status(state) preprocess_type = None if "P" in video_prompt_type : progress_args = [0, status + " - Extracting Open Pose Information"] preprocess_type = "pose" elif "D" in video_prompt_type : progress_args = [0, status + " - Extracting Depth Information"] preprocess_type = "depth" elif "G" in video_prompt_type : progress_args = [0, status + " - Extracting Gray Level Information"] preprocess_type = "gray" if preprocess_type != None : send_cmd("progress", progress_args) video_guide_copy = preprocess_video(preprocess_type, width=width, height=height,video_in=video_guide, max_frames= video_length if window_no == 1 else video_length - reuse_frames, start_frame = guide_start_frame, fit_canvas = True, target_fps = fps) keep_frames_parsed, error = parse_keep_frames_video_guide(keep_frames_video_guide, max_frames_to_generate) if len(error) > 0: raise gr.Error(f"invalid keep frames {keep_frames_video_guide}") keep_frames_parsed = keep_frames_parsed[guide_start_frame: guide_start_frame + video_length] if window_no == 1: image_size = VACE_SIZE_CONFIGS[resolution_reformated] # default frame dimensions until it is set by video_src (if there is any) src_video, src_mask, src_ref_images = wan_model.prepare_source([video_guide_copy], [video_mask_copy ], [image_refs_copy], video_length, image_size = image_size, device ="cpu", original_video= "O" in video_prompt_type, keep_frames=keep_frames_parsed, start_frame = guide_start_frame, pre_src_video = [pre_video_guide] ) if window_no == 1 and src_video != None and len(src_video) > 0: image_size = src_video[0].shape[-2:] prompts_max = gen["prompts_max"] status = get_latest_status(state) gen["progress_status"] = status gen["progress_phase"] = (" - Encoding Prompt", -1 ) callback = build_callback(state, trans, send_cmd, status, num_inference_steps) progress_args = [0, status + " - Encoding Prompt"] send_cmd("progress", progress_args) # samples = torch.empty( (1,2)) #for testing # if False: try: if trans.enable_teacache: trans.teacache_counter = 0 trans.num_steps = num_inference_steps trans.teacache_skipped_steps = 0 trans.previous_residual = None if image2video: samples = wan_model.generate( prompt, image_start, image_end if image_end != None else None, frame_num=(video_length // 4)* 4 + 1, # max_area=MAX_AREA_CONFIGS[resolution_reformated], height = height, width = width, shift=flow_shift, sampling_steps=num_inference_steps, guide_scale=guidance_scale, n_prompt=negative_prompt, seed=seed, callback=callback, enable_RIFLEx = enable_RIFLEx, VAE_tile_size = VAE_tile_size, joint_pass = joint_pass, slg_layers = slg_layers, slg_start = slg_start_perc/100, slg_end = slg_end_perc/100, cfg_star_switch = cfg_star_switch, cfg_zero_step = cfg_zero_step, add_frames_for_end_image = "image2video" in model_filename, audio_cfg_scale= audio_guidance_scale, audio_proj= audio_proj_split, audio_scale= audio_scale, audio_context_lens= audio_context_lens ) elif diffusion_forcing: samples = wan_model.generate( prompt = prompt, negative_prompt = negative_prompt, image = image_start, input_video= pre_video_guide, height = height, width = width, seed = seed, num_frames = (video_length // 4)* 4 + 1, #377 num_inference_steps = num_inference_steps, shift = flow_shift, guidance_scale= guidance_scale, callback= callback, VAE_tile_size = VAE_tile_size, joint_pass = joint_pass, slg_layers = slg_layers, slg_start = slg_start_perc/100, slg_end = slg_end_perc/100, addnoise_condition = sliding_window_overlap_noise, ar_step = model_mode, #5 causal_block_size = 5, causal_attention = True, fps = fps, ) else: samples = wan_model.generate( prompt, input_frames = src_video, input_ref_images= src_ref_images, input_masks = src_mask, source_video= source_video, target_camera= target_camera, frame_num=(video_length // 4)* 4 + 1, size=(width, height), shift=flow_shift, sampling_steps=num_inference_steps, guide_scale=guidance_scale, n_prompt=negative_prompt, seed=seed, offload_model=False, callback=callback, enable_RIFLEx = enable_RIFLEx, VAE_tile_size = VAE_tile_size, joint_pass = joint_pass, slg_layers = slg_layers, slg_start = slg_start_perc/100, slg_end = slg_end_perc/100, cfg_star_switch = cfg_star_switch, cfg_zero_step = cfg_zero_step, overlapped_latents = 0 if reuse_frames == 0 or window_no == 1 else ((reuse_frames - 1) // 4 + 1), overlap_noise = sliding_window_overlap_noise, vace = vace ) except Exception as e: if temp_filename!= None and os.path.isfile(temp_filename): os.remove(temp_filename) offload.last_offload_obj.unload_all() offload.unload_loras_from_model(trans) # if compile: # cache_size = torch._dynamo.config.cache_size_limit # torch.compiler.reset() # torch._dynamo.config.cache_size_limit = cache_size gc.collect() torch.cuda.empty_cache() s = str(e) keyword_list = {"CUDA out of memory" : "VRAM", "Tried to allocate":"VRAM", "CUDA error: out of memory": "RAM", "CUDA error: too many resources requested": "RAM"} crash_type = "" for keyword, tp in keyword_list.items(): if keyword in s: crash_type = tp break state["prompt"] = "" if crash_type == "VRAM": new_error = "The generation of the video has encountered an error: it is likely that you have unsufficient VRAM and you should therefore reduce the video resolution or its number of frames." elif crash_type == "RAM": new_error = "The generation of the video has encountered an error: it is likely that you have unsufficient RAM and / or Reserved RAM allocation should be reduced using 'perc_reserved_mem_max' or using a different Profile." else: new_error = gr.Error(f"The generation of the video has encountered an error, please check your terminal for more information. '{s}'") tb = traceback.format_exc().split('\n')[:-1] print('\n'.join(tb)) send_cmd("error", new_error) return finally: trans.previous_residual = None if trans.enable_teacache: print(f"Teacache Skipped Steps:{trans.teacache_skipped_steps}/{trans.num_steps}" ) if samples != None: samples = samples.to("cpu") offload.last_offload_obj.unload_all() gc.collect() torch.cuda.empty_cache() if samples == None: abort = True state["prompt"] = "" send_cmd("output") else: sample = samples.cpu() if True: # for testing torch.save(sample, "output.pt") else: sample =torch.load("output.pt") if sliding_window : guide_start_frame += video_length if discard_last_frames > 0: sample = sample[: , :-discard_last_frames] guide_start_frame -= discard_last_frames if reuse_frames == 0: pre_video_guide = sample[:,9999 :] else: # noise_factor = 200/ 1000 # pre_video_guide = sample[:, -reuse_frames:] * (1.0 - noise_factor) + torch.randn_like(sample[:, -reuse_frames:] ) * noise_factor pre_video_guide = sample[:, -reuse_frames:] if prefix_video != None: if reuse_frames == 0: sample = torch.cat([ prefix_video[:, :], sample], dim = 1) else: sample = torch.cat([ prefix_video[:, :-reuse_frames], sample], dim = 1) prefix_video = None if sliding_window and window_no > 1: if reuse_frames == 0: sample = sample[: , :] else: sample = sample[: , reuse_frames:] guide_start_frame -= reuse_frames time_flag = datetime.fromtimestamp(time.time()).strftime("%Y-%m-%d-%Hh%Mm%Ss") if os.name == 'nt': file_name = f"{time_flag}_seed{seed}_{sanitize_file_name(prompt[:50]).strip()}.mp4" else: file_name = f"{time_flag}_seed{seed}_{sanitize_file_name(prompt[:100]).strip()}.mp4" video_path = os.path.join(save_path, file_name) exp = 0 if len(temporal_upsampling) > 0 or len(spatial_upsampling) > 0: progress_args = [(num_inference_steps , num_inference_steps) , status + " - Upsampling" , num_inference_steps] send_cmd("progress", progress_args) if temporal_upsampling == "rife2": exp = 1 elif temporal_upsampling == "rife4": exp = 2 output_fps = fps if exp > 0: from rife.inference import temporal_interpolation if sliding_window and window_no > 1: sample = torch.cat([frames_already_processed[:, -2:-1], sample], dim=1) sample = temporal_interpolation( os.path.join("ckpts", "flownet.pkl"), sample, exp, device=processing_device) sample = sample[:, 1:] else: sample = temporal_interpolation( os.path.join("ckpts", "flownet.pkl"), sample, exp, device=processing_device) output_fps = output_fps * 2**exp if len(spatial_upsampling) > 0: from wan.utils.utils import resize_lanczos # need multithreading or to do lanczos with cuda if spatial_upsampling == "lanczos1.5": scale = 1.5 else: scale = 2 sample = (sample + 1) / 2 h, w = sample.shape[-2:] h *= scale w *= scale h = int(h) w = int(w) new_frames =[] for i in range( sample.shape[1] ): frame = sample[:, i] frame = resize_lanczos(frame, h, w) frame = frame.unsqueeze(1) new_frames.append(frame) sample = torch.cat(new_frames, dim=1) new_frames = None sample = sample * 2 - 1 if sliding_window : if frames_already_processed == None: frames_already_processed = sample else: sample = torch.cat([frames_already_processed, sample], dim=1) frames_already_processed = sample if audio_guide == None: cache_video( tensor=sample[None], save_file=video_path, fps=output_fps, nrow=1, normalize=True, value_range=(-1, 1)) else: save_path_tmp = video_path[:-4] + "_tmp.mp4" cache_video( tensor=sample[None], save_file=save_path_tmp, fps=output_fps, nrow=1, normalize=True, value_range=(-1, 1)) final_command = [ "ffmpeg", "-y", "-i", save_path_tmp, "-i", audio_guide, "-c:v", "libx264", "-c:a", "aac", "-shortest", "-loglevel", "warning", "-nostats", video_path, ] import subprocess subprocess.run(final_command, check=True) os.remove(save_path_tmp) end_time = time.time() inputs = get_function_arguments(generate_video, locals()) inputs.pop("send_cmd") inputs.pop("task_id") configs = prepare_inputs_dict("metadata", inputs) configs["generation_time"] = round(end_time-start_time) metadata_choice = server_config.get("metadata_type","metadata") if metadata_choice == "json": with open(video_path.replace('.mp4', '.json'), 'w') as f: json.dump(configs, f, indent=4) elif metadata_choice == "metadata": from mutagen.mp4 import MP4 file = MP4(video_path) file.tags['©cmt'] = [json.dumps(configs)] file.save() print(f"New video saved to Path: "+video_path) file_list.append(video_path) send_cmd("output") if sliding_window : if max_frames_to_generate > 0 and extra_windows == 0: current_length = sample.shape[1] if (current_length - prefix_video_frames_count)>= max_frames_to_generate: break video_length = min(sliding_window_size, ((max_frames_to_generate - (current_length - prefix_video_frames_count) + reuse_frames + discard_last_frames) // 4) * 4 + 1 ) seed += 1 if temp_filename!= None and os.path.isfile(temp_filename): os.remove(temp_filename) offload.unload_loras_from_model(trans) def prepare_generate_video(state): if state.get("validate_success",0) != 1: return gr.Button(visible= True), gr.Button(visible= False), gr.Column(visible= False) else: return gr.Button(visible= False), gr.Button(visible= True), gr.Column(visible= True) def generate_preview(latents): import einops latent_channels = 16 latent_dimensions = 3 latents = latents.unsqueeze(0) latent_rgb_factors = [ [-0.1299, -0.1692, 0.2932], [ 0.0671, 0.0406, 0.0442], [ 0.3568, 0.2548, 0.1747], [ 0.0372, 0.2344, 0.1420], [ 0.0313, 0.0189, -0.0328], [ 0.0296, -0.0956, -0.0665], [-0.3477, -0.4059, -0.2925], [ 0.0166, 0.1902, 0.1975], [-0.0412, 0.0267, -0.1364], [-0.1293, 0.0740, 0.1636], [ 0.0680, 0.3019, 0.1128], [ 0.0032, 0.0581, 0.0639], [-0.1251, 0.0927, 0.1699], [ 0.0060, -0.0633, 0.0005], [ 0.3477, 0.2275, 0.2950], [ 0.1984, 0.0913, 0.1861] ] # credits for the rgb factors to ComfyUI ? latent_rgb_factors_bias = [-0.1835, -0.0868, -0.3360] latent_rgb_factors_bias = [0.0259, -0.0192, -0.0761] nb_latents = latents.shape[2] latents_to_preview = 4 latents_to_preview = min(nb_latents, latents_to_preview) skip_latent = nb_latents / latents_to_preview latent_no = 0 selected_latents = [] while latent_no < nb_latents: selected_latents.append( latents[:, : , int(latent_no): int(latent_no)+1]) latent_no += skip_latent latents = torch.cat(selected_latents, dim = 2) weight = torch.tensor(latent_rgb_factors, device=latents.device, dtype=latents.dtype).transpose(0, 1)[:, :, None, None, None] bias = torch.tensor(latent_rgb_factors_bias, device=latents.device, dtype=latents.dtype) images = torch.nn.functional.conv3d(latents, weight, bias=bias, stride=1, padding=0, dilation=1, groups=1) images = images.clamp(0.0, 1.0) images = (images * 255.0).detach().cpu().numpy().clip(0, 255).astype(np.uint8) images = einops.rearrange(images, 'b c t h w -> (b h) (t w) c') h, w, _ = images.shape scale = 200 / h images= Image.fromarray(images) images = images.resize(( int(w*scale),int(h*scale)), resample=Image.Resampling.BILINEAR) return images def process_tasks(state): from wan.utils.thread_utils import AsyncStream, async_run gen = get_gen_info(state) queue = gen.get("queue", []) progress = None if len(queue) == 0: gen["status_display"] = False return gen = get_gen_info(state) clear_file_list = server_config.get("clear_file_list", 0) file_list = gen.get("file_list", []) if clear_file_list > 0: file_list_current_size = len(file_list) keep_file_from = max(file_list_current_size - clear_file_list, 0) files_removed = keep_file_from choice = gen.get("selected",0) choice = max(choice- files_removed, 0) file_list = file_list[ keep_file_from: ] else: file_list = [] choice = 0 gen["selected"] = choice gen["file_list"] = file_list start_time = time.time() global gen_in_progress gen_in_progress = True gen["in_progress"] = True gen["preview"] = None gen["status"] = "Generating Video" yield time.time(), time.time() prompt_no = 0 while len(queue) > 0: prompt_no += 1 gen["prompt_no"] = prompt_no task = queue[0] task_id = task["id"] params = task['params'] com_stream = AsyncStream() send_cmd = com_stream.output_queue.push def generate_video_error_handler(): try: generate_video(task_id, send_cmd, **params) except Exception as e: tb = traceback.format_exc().split('\n')[:-1] print('\n'.join(tb)) send_cmd("error",str(e)) finally: send_cmd("exit", None) async_run(generate_video_error_handler) while True: cmd, data = com_stream.output_queue.next() if cmd == "exit": break elif cmd == "info": gr.Info(data) elif cmd == "error": queue.clear() gen["prompts_max"] = 0 gen["prompt"] = "" gen["status_display"] = False raise gr.Error(data, print_exception= False) elif cmd == "status": gen["status"] = data elif cmd == "output": gen["preview"] = None yield time.time() , time.time() elif cmd == "progress": gen["progress_args"] = data # progress(*data) elif cmd == "preview": preview= None if data== None else generate_preview(data) gen["preview"] = preview yield time.time() , gr.Text() else: raise Exception(f"unknown command {cmd}") abort = gen.get("abort", False) if abort: gen["abort"] = False status = "Video Generation Aborted", "Video Generation Aborted" yield gr.Text(), gr.Text() gen["status"] = status queue[:] = [item for item in queue if item['id'] != task['id']] update_global_queue_ref(queue) gen["prompts_max"] = 0 gen["prompt"] = "" end_time = time.time() if abort: status = f"Video generation was aborted. Total Generation Time: {end_time-start_time:.1f}s" else: status = f"Total Generation Time: {end_time-start_time:.1f}s" gen["status"] = status gen["status_display"] = False def get_generation_status(prompt_no, prompts_max, repeat_no, repeat_max, window_no, total_windows): if prompts_max == 1: if repeat_max == 1: status = "Video" else: status = f"Sample {repeat_no}/{repeat_max}" else: if repeat_max == 1: status = f"Prompt {prompt_no}/{prompts_max}" else: status = f"Prompt {prompt_no}/{prompts_max}, Sample {repeat_no}/{repeat_max}" if total_windows > 1: status += f", Sliding Window {window_no}/{total_windows}" return status refresh_id = 0 def get_new_refresh_id(): global refresh_id refresh_id += 1 return refresh_id def get_latest_status(state): gen = get_gen_info(state) prompt_no = gen["prompt_no"] prompts_max = gen.get("prompts_max",0) total_generation = gen.get("total_generation", 1) repeat_no = gen.get("repeat_no",0) total_generation += gen.get("extra_orders", 0) total_windows = gen.get("total_windows", 0) total_windows += gen.get("extra_windows", 0) window_no = gen.get("window_no", 0) status = get_generation_status(prompt_no, prompts_max, repeat_no, total_generation, window_no, total_windows) return status def update_status(state): gen = get_gen_info(state) gen["progress_status"] = get_latest_status(state) gen["refresh"] = get_new_refresh_id() def one_more_sample(state): gen = get_gen_info(state) extra_orders = gen.get("extra_orders", 0) extra_orders += 1 gen["extra_orders"] = extra_orders in_progress = gen.get("in_progress", False) if not in_progress : return state total_generation = gen.get("total_generation", 0) + extra_orders gen["progress_status"] = get_latest_status(state) gen["refresh"] = get_new_refresh_id() gr.Info(f"An extra sample generation is planned for a total of {total_generation} videos for this prompt") return state def one_more_window(state): gen = get_gen_info(state) extra_windows = gen.get("extra_windows", 0) extra_windows += 1 gen["extra_windows"]= extra_windows in_progress = gen.get("in_progress", False) if not in_progress : return state total_windows = gen.get("total_windows", 0) + extra_windows gen["progress_status"] = get_latest_status(state) gen["refresh"] = get_new_refresh_id() gr.Info(f"An extra window generation is planned for a total of {total_windows} videos for this sample") return state def get_new_preset_msg(advanced = True): if advanced: return "Enter here a Name for a Lora Preset or Choose one in the List" else: return "Choose a Lora Preset in this List to Apply a Special Effect" def validate_delete_lset(lset_name): if len(lset_name) == 0 or lset_name == get_new_preset_msg(True) or lset_name == get_new_preset_msg(False): gr.Info(f"Choose a Preset to delete") return gr.Button(visible= True), gr.Checkbox(visible= True), gr.Button(visible= True), gr.Button(visible= True), gr.Button(visible= False), gr.Button(visible= False) else: return gr.Button(visible= False), gr.Checkbox(visible= False), gr.Button(visible= False), gr.Button(visible= False), gr.Button(visible= True), gr.Button(visible= True) def validate_save_lset(lset_name): if len(lset_name) == 0 or lset_name == get_new_preset_msg(True) or lset_name == get_new_preset_msg(False): gr.Info("Please enter a name for the preset") return gr.Button(visible= True), gr.Checkbox(visible= True), gr.Button(visible= True), gr.Button(visible= True), gr.Button(visible= False), gr.Button(visible= False),gr.Checkbox(visible= False) else: return gr.Button(visible= False), gr.Button(visible= False), gr.Button(visible= False), gr.Button(visible= False), gr.Button(visible= True), gr.Button(visible= True),gr.Checkbox(visible= True) def cancel_lset(): return gr.Button(visible= True), gr.Button(visible= True), gr.Button(visible= True), gr.Button(visible= True), gr.Button(visible= False), gr.Button(visible= False), gr.Button(visible= False), gr.Checkbox(visible= False) def save_lset(state, lset_name, loras_choices, loras_mult_choices, prompt, save_lset_prompt_cbox): loras_presets = state["loras_presets"] loras = state["loras"] if state.get("validate_success",0) == 0: pass if len(lset_name) == 0 or lset_name == get_new_preset_msg(True) or lset_name == get_new_preset_msg(False): gr.Info("Please enter a name for the preset") lset_choices =[("Please enter a name for a Lora Preset","")] else: lset_name = sanitize_file_name(lset_name) loras_choices_files = [ Path(loras[int(choice_no)]).parts[-1] for choice_no in loras_choices ] lset = {"loras" : loras_choices_files, "loras_mult" : loras_mult_choices} if save_lset_prompt_cbox!=1: prompts = prompt.replace("\r", "").split("\n") prompts = [prompt for prompt in prompts if len(prompt)> 0 and prompt.startswith("#")] prompt = "\n".join(prompts) if len(prompt) > 0: lset["prompt"] = prompt lset["full_prompt"] = save_lset_prompt_cbox ==1 lset_name_filename = lset_name + ".lset" full_lset_name_filename = os.path.join(get_lora_dir(state["model_filename"]), lset_name_filename) with open(full_lset_name_filename, "w", encoding="utf-8") as writer: writer.write(json.dumps(lset, indent=4)) if lset_name in loras_presets: gr.Info(f"Lora Preset '{lset_name}' has been updated") else: gr.Info(f"Lora Preset '{lset_name}' has been created") loras_presets.append(Path(Path(lset_name_filename).parts[-1]).stem ) lset_choices = [ ( preset, preset) for preset in loras_presets ] lset_choices.append( (get_new_preset_msg(), "")) state["loras_presets"] = loras_presets return gr.Dropdown(choices=lset_choices, value= lset_name), gr.Button(visible= True), gr.Button(visible= True), gr.Button(visible= True), gr.Button(visible= True), gr.Button(visible= False), gr.Button(visible= False), gr.Checkbox(visible= False) def delete_lset(state, lset_name): loras_presets = state["loras_presets"] lset_name_filename = os.path.join( get_lora_dir(state["model_filename"]), sanitize_file_name(lset_name) + ".lset" ) if len(lset_name) > 0 and lset_name != get_new_preset_msg(True) and lset_name != get_new_preset_msg(False): if not os.path.isfile(lset_name_filename): raise gr.Error(f"Preset '{lset_name}' not found ") os.remove(lset_name_filename) pos = loras_presets.index(lset_name) gr.Info(f"Lora Preset '{lset_name}' has been deleted") loras_presets.remove(lset_name) else: pos = len(loras_presets) gr.Info(f"Choose a Preset to delete") state["loras_presets"] = loras_presets lset_choices = [ (preset, preset) for preset in loras_presets] lset_choices.append((get_new_preset_msg(), "")) return gr.Dropdown(choices=lset_choices, value= lset_choices[pos][1]), gr.Button(visible= True), gr.Button(visible= True), gr.Button(visible= True), gr.Button(visible= True), gr.Button(visible= False), gr.Checkbox(visible= False) def refresh_lora_list(state, lset_name, loras_choices): loras_names = state["loras_names"] prev_lora_names_selected = [ loras_names[int(i)] for i in loras_choices] model_filename= state["model_filename"] loras, loras_names, loras_presets, _, _, _, _ = setup_loras(model_filename, None, get_lora_dir(model_filename), lora_preselected_preset, None) state["loras"] = loras state["loras_names"] = loras_names state["loras_presets"] = loras_presets gc.collect() new_loras_choices = [ (loras_name, str(i)) for i,loras_name in enumerate(loras_names)] new_loras_dict = { loras_name: str(i) for i,loras_name in enumerate(loras_names) } lora_names_selected = [] for lora in prev_lora_names_selected: lora_id = new_loras_dict.get(lora, None) if lora_id!= None: lora_names_selected.append(lora_id) lset_choices = [ (preset, preset) for preset in loras_presets] lset_choices.append((get_new_preset_msg( state["advanced"]), "")) if lset_name in loras_presets: pos = loras_presets.index(lset_name) else: pos = len(loras_presets) lset_name ="" if wan_model != None: errors = getattr(wan_model.model, "_loras_errors", "") if errors !=None and len(errors) > 0: error_files = [path for path, _ in errors] gr.Info("Error while refreshing Lora List, invalid Lora files: " + ", ".join(error_files)) else: gr.Info("Lora List has been refreshed") return gr.Dropdown(choices=lset_choices, value= lset_choices[pos][1]), gr.Dropdown(choices=new_loras_choices, value= lora_names_selected) def apply_lset(state, wizard_prompt_activated, lset_name, loras_choices, loras_mult_choices, prompt): state["apply_success"] = 0 if len(lset_name) == 0 or lset_name== get_new_preset_msg(True) or lset_name== get_new_preset_msg(False): gr.Info("Please choose a preset in the list or create one") else: loras = state["loras"] loras_choices, loras_mult_choices, preset_prompt, full_prompt, error = extract_preset(state["model_filename"], lset_name, loras) if len(error) > 0: gr.Info(error) else: if full_prompt: prompt = preset_prompt elif len(preset_prompt) > 0: prompts = prompt.replace("\r", "").split("\n") prompts = [prompt for prompt in prompts if len(prompt)>0 and not prompt.startswith("#")] prompt = "\n".join(prompts) prompt = preset_prompt + '\n' + prompt gr.Info(f"Lora Preset '{lset_name}' has been applied") state["apply_success"] = 1 wizard_prompt_activated = "on" return wizard_prompt_activated, loras_choices, loras_mult_choices, prompt def extract_prompt_from_wizard(state, variables_names, prompt, wizard_prompt, allow_null_values, *args): prompts = wizard_prompt.replace("\r" ,"").split("\n") new_prompts = [] macro_already_written = False for prompt in prompts: if not macro_already_written and not prompt.startswith("#") and "{" in prompt and "}" in prompt: variables = variables_names.split("\n") values = args[:len(variables)] macro = "! " for i, (variable, value) in enumerate(zip(variables, values)): if len(value) == 0 and not allow_null_values: return prompt, "You need to provide a value for '" + variable + "'" sub_values= [ "\"" + sub_value + "\"" for sub_value in value.split("\n") ] value = ",".join(sub_values) if i>0: macro += " : " macro += "{" + variable + "}"+ f"={value}" if len(variables) > 0: macro_already_written = True new_prompts.append(macro) new_prompts.append(prompt) else: new_prompts.append(prompt) prompt = "\n".join(new_prompts) return prompt, "" def validate_wizard_prompt(state, wizard_prompt_activated, wizard_variables_names, prompt, wizard_prompt, *args): state["validate_success"] = 0 if wizard_prompt_activated != "on": state["validate_success"] = 1 return prompt prompt, errors = extract_prompt_from_wizard(state, wizard_variables_names, prompt, wizard_prompt, False, *args) if len(errors) > 0: gr.Info(errors) return prompt state["validate_success"] = 1 return prompt def fill_prompt_from_wizard(state, wizard_prompt_activated, wizard_variables_names, prompt, wizard_prompt, *args): if wizard_prompt_activated == "on": prompt, errors = extract_prompt_from_wizard(state, wizard_variables_names, prompt, wizard_prompt, True, *args) if len(errors) > 0: gr.Info(errors) wizard_prompt_activated = "off" return wizard_prompt_activated, "", gr.Textbox(visible= True, value =prompt) , gr.Textbox(visible= False), gr.Column(visible = True), *[gr.Column(visible = False)] * 2, *[gr.Textbox(visible= False)] * PROMPT_VARS_MAX def extract_wizard_prompt(prompt): variables = [] values = {} prompts = prompt.replace("\r" ,"").split("\n") if sum(prompt.startswith("!") for prompt in prompts) > 1: return "", variables, values, "Prompt is too complex for basic Prompt editor, switching to Advanced Prompt" new_prompts = [] errors = "" for prompt in prompts: if prompt.startswith("!"): variables, errors = prompt_parser.extract_variable_names(prompt) if len(errors) > 0: return "", variables, values, "Error parsing Prompt templace: " + errors if len(variables) > PROMPT_VARS_MAX: return "", variables, values, "Prompt is too complex for basic Prompt editor, switching to Advanced Prompt" values, errors = prompt_parser.extract_variable_values(prompt) if len(errors) > 0: return "", variables, values, "Error parsing Prompt templace: " + errors else: variables_extra, errors = prompt_parser.extract_variable_names(prompt) if len(errors) > 0: return "", variables, values, "Error parsing Prompt templace: " + errors variables += variables_extra variables = [var for pos, var in enumerate(variables) if var not in variables[:pos]] if len(variables) > PROMPT_VARS_MAX: return "", variables, values, "Prompt is too complex for basic Prompt editor, switching to Advanced Prompt" new_prompts.append(prompt) wizard_prompt = "\n".join(new_prompts) return wizard_prompt, variables, values, errors def fill_wizard_prompt(state, wizard_prompt_activated, prompt, wizard_prompt): def get_hidden_textboxes(num = PROMPT_VARS_MAX ): return [gr.Textbox(value="", visible=False)] * num hidden_column = gr.Column(visible = False) visible_column = gr.Column(visible = True) wizard_prompt_activated = "off" if state["advanced"] or state.get("apply_success") != 1: return wizard_prompt_activated, gr.Text(), prompt, wizard_prompt, gr.Column(), gr.Column(), hidden_column, *get_hidden_textboxes() prompt_parts= [] wizard_prompt, variables, values, errors = extract_wizard_prompt(prompt) if len(errors) > 0: gr.Info( errors ) return wizard_prompt_activated, "", gr.Textbox(prompt, visible=True), gr.Textbox(wizard_prompt, visible=False), visible_column, *[hidden_column] * 2, *get_hidden_textboxes() for variable in variables: value = values.get(variable, "") prompt_parts.append(gr.Textbox( placeholder=variable, info= variable, visible= True, value= "\n".join(value) )) any_macro = len(variables) > 0 prompt_parts += get_hidden_textboxes(PROMPT_VARS_MAX-len(prompt_parts)) variables_names= "\n".join(variables) wizard_prompt_activated = "on" return wizard_prompt_activated, variables_names, gr.Textbox(prompt, visible = False), gr.Textbox(wizard_prompt, visible = True), hidden_column, visible_column, visible_column if any_macro else hidden_column, *prompt_parts def switch_prompt_type(state, wizard_prompt_activated_var, wizard_variables_names, prompt, wizard_prompt, *prompt_vars): if state["advanced"]: return fill_prompt_from_wizard(state, wizard_prompt_activated_var, wizard_variables_names, prompt, wizard_prompt, *prompt_vars) else: state["apply_success"] = 1 return fill_wizard_prompt(state, wizard_prompt_activated_var, prompt, wizard_prompt) visible= False def switch_advanced(state, new_advanced, lset_name): state["advanced"] = new_advanced loras_presets = state["loras_presets"] lset_choices = [ (preset, preset) for preset in loras_presets] lset_choices.append((get_new_preset_msg(new_advanced), "")) if lset_name== get_new_preset_msg(True) or lset_name== get_new_preset_msg(False) or lset_name=="": lset_name = get_new_preset_msg(new_advanced) if only_allow_edit_in_advanced: return gr.Row(visible=new_advanced), gr.Row(visible=new_advanced), gr.Button(visible=new_advanced), gr.Row(visible= not new_advanced), gr.Dropdown(choices=lset_choices, value= lset_name) else: return gr.Row(visible=new_advanced), gr.Row(visible=True), gr.Button(visible=True), gr.Row(visible= False), gr.Dropdown(choices=lset_choices, value= lset_name) def prepare_inputs_dict(target, inputs ): state = inputs.pop("state") loras = state["loras"] if "loras_choices" in inputs: loras_choices = inputs.pop("loras_choices") inputs.pop("model_filename", None) activated_loras = [Path( loras[int(no)]).parts[-1] for no in loras_choices ] inputs["activated_loras"] = activated_loras if target == "state": return inputs unsaved_params = ["image_start", "image_end", "image_refs", "video_guide", "video_source", "video_mask", "audio_guide", "embedded_guidance_scale"] for k in unsaved_params: inputs.pop(k) model_filename = state["model_filename"] inputs["type"] = "Wan2.1GP by DeepBeepMeep - " + get_model_name(model_filename) if target == "settings": return inputs if not test_class_i2v(model_filename): inputs.pop("image_prompt_type") if not "recam" in model_filename or not "diffusion_forcing" in model_filename: inputs.pop("model_mode") if not "Vace" in model_filename or not "phantom" in model_filename: unsaved_params = ["keep_frames_video_guide", "video_prompt_type", "remove_background_image_ref"] for k in unsaved_params: inputs.pop(k) if not "diffusion_forcing" in model_filename: unsaved_params = ["keep_frames_video_source"] for k in unsaved_params: inputs.pop(k) if not "Vace" in model_filename or "diffusion_forcing" in model_filename: unsaved_params = [ "sliding_window_size", "sliding_window_overlap", "sliding_window_overlap_noise", "sliding_window_discard_last_frames"] for k in unsaved_params: inputs.pop(k) if not "fantasy" in model_filename: inputs.pop("audio_guidance_scale") if target == "metadata": inputs = {k: v for k,v in inputs.items() if v != None } return inputs def get_function_arguments(func, locals): args_names = list(inspect.signature(func).parameters) kwargs = typing.OrderedDict() for k in args_names: kwargs[k] = locals[k] return kwargs def save_inputs( target, lset_name, prompt, negative_prompt, resolution, video_length, seed, num_inference_steps, guidance_scale, audio_guidance_scale, flow_shift, embedded_guidance_scale, repeat_generation, multi_images_gen_type, tea_cache_setting, tea_cache_start_step_perc, loras_choices, loras_multipliers, image_prompt_type, image_start, image_end, model_mode, video_source, keep_frames_video_source, video_prompt_type, image_refs, video_guide, keep_frames_video_guide, video_mask, audio_guide, sliding_window_size, sliding_window_overlap, sliding_window_overlap_noise, sliding_window_discard_last_frames, remove_background_image_ref, temporal_upsampling, spatial_upsampling, RIFLEx_setting, slg_switch, slg_layers, slg_start_perc, slg_end_perc, cfg_star_switch, cfg_zero_step, state, ): # if state.get("validate_success",0) != 1: # return model_filename = state["model_filename"] inputs = get_function_arguments(save_inputs, locals()) inputs.pop("target") cleaned_inputs = prepare_inputs_dict(target, inputs) if target == "settings": defaults_filename = get_settings_file_name(model_filename) with open(defaults_filename, "w", encoding="utf-8") as f: json.dump(cleaned_inputs, f, indent=4) gr.Info("New Default Settings saved") elif target == "state": state[get_model_type(model_filename)] = cleaned_inputs def download_loras(): from huggingface_hub import snapshot_download yield gr.Row(visible=True), "Please wait while the Loras are being downloaded", *[gr.Column(visible=False)] * 2 lora_dir = get_lora_dir(get_model_filename("i2v", transformer_quantization)) log_path = os.path.join(lora_dir, "log.txt") if not os.path.isfile(log_path): tmp_path = os.path.join(lora_dir, "tmp_lora_dowload") import glob snapshot_download(repo_id="DeepBeepMeep/Wan2.1", allow_patterns="loras_i2v/*", local_dir= tmp_path) for f in glob.glob(os.path.join(tmp_path, "loras_i2v", "*.*")): target_file = os.path.join(lora_dir, Path(f).parts[-1] ) if os.path.isfile(target_file): os.remove(f) else: shutil.move(f, lora_dir) try: os.remove(tmp_path) except: pass yield gr.Row(visible=True), "Loras have been completely downloaded", *[gr.Column(visible=True)] * 2 from datetime import datetime dt = datetime.today().strftime('%Y-%m-%d') with open( log_path, "w", encoding="utf-8") as writer: writer.write(f"Loras downloaded on the {dt} at {time.time()} on the {time.time()}") return def refresh_image_prompt_type(state, image_prompt_type): return gr.update(visible = "S" in image_prompt_type ), gr.update(visible = "E" in image_prompt_type ), gr.update(visible = "V" in image_prompt_type) , gr.update(visible = "V" in image_prompt_type ) def refresh_video_prompt_type(state, video_prompt_type): return gr.Gallery(visible = "I" in video_prompt_type), gr.Video(visible= "V" in video_prompt_type),gr.Video(visible= "M" in video_prompt_type ), gr.Text(visible= "V" in video_prompt_type) , gr.Checkbox(visible= "I" in video_prompt_type) def handle_celll_selection(state, evt: gr.SelectData): gen = get_gen_info(state) queue = gen.get("queue", []) if evt.index is None: return gr.update(), gr.update(), gr.update(visible=False) row_index, col_index = evt.index cell_value = None if col_index in [6, 7, 8]: if col_index == 6: cell_value = "↑" elif col_index == 7: cell_value = "↓" elif col_index == 8: cell_value = "✖" if col_index == 6: new_df_data = move_up(queue, [row_index]) return new_df_data, gr.update(), gr.update(visible=False) elif col_index == 7: new_df_data = move_down(queue, [row_index]) return new_df_data, gr.update(), gr.update(visible=False) elif col_index == 8: new_df_data = remove_task(queue, [row_index]) gen["prompts_max"] = gen.get("prompts_max",0) - 1 update_status(state) return new_df_data, gr.update(), gr.update(visible=False) start_img_col_idx = 4 end_img_col_idx = 5 image_data_to_show = None if col_index == start_img_col_idx: with lock: row_index += 1 if row_index < len(queue): image_data_to_show = queue[row_index].get('start_image_data') elif col_index == end_img_col_idx: with lock: row_index += 1 if row_index < len(queue): image_data_to_show = queue[row_index].get('end_image_data') if image_data_to_show: return gr.update(), gr.update(value=image_data_to_show[0]), gr.update(visible=True) else: return gr.update(), gr.update(), gr.update(visible=False) def change_model(state, model_choice): if model_choice == None: return model_filename = get_model_filename(model_choice, transformer_quantization) state["model_filename"] = model_filename header = generate_header(model_filename, compile=compile, attention_mode=attention_mode) return header def fill_inputs(state): model_filename = state["model_filename"] prefix = get_model_type(model_filename) ui_defaults = state.get(prefix, None) if ui_defaults == None: ui_defaults = get_default_settings(model_filename) return generate_video_tab(update_form = True, state_dict = state, ui_defaults = ui_defaults) def preload_model_when_switching(state): global reload_needed, wan_model, offloadobj if "S" in preload_model_policy: model_filename = state["model_filename"] if state["model_filename"] != transformer_filename: wan_model = None if offloadobj is not None: offloadobj.release() offloadobj = None gc.collect() yield f"Loading model {get_model_name(model_filename)}..." wan_model, offloadobj, _ = load_models(model_filename) yield f"Model loaded" reload_needed= False return return gr.Text() def unload_model_if_needed(state): global reload_needed, wan_model, offloadobj if "U" in preload_model_policy: if wan_model != None: wan_model = None if offloadobj is not None: offloadobj.release() offloadobj = None gc.collect() reload_needed= True def filter_letters(source_str, letters): ret = "" for letter in letters: if letter in source_str: ret += letter return ret def add_to_sequence(source_str, letters): ret = source_str for letter in letters: if not letter in source_str: ret += letter return ret def del_in_sequence(source_str, letters): ret = source_str for letter in letters: if letter in source_str: ret = ret.replace(letter, "") return ret def refresh_video_prompt_type_image_refs(video_prompt_type, video_prompt_type_image_refs): # video_prompt_type = add_to_sequence(video_prompt_type, "I") if video_prompt_type_image_refs else del_in_sequence(video_prompt_type, "I") video_prompt_type_image_refs = "I" in video_prompt_type_image_refs video_prompt_type = add_to_sequence(video_prompt_type, "I") if video_prompt_type_image_refs else del_in_sequence(video_prompt_type, "I") return video_prompt_type, gr.update(visible = video_prompt_type_image_refs),gr.update(visible = video_prompt_type_image_refs) def refresh_video_prompt_type_video_guide(video_prompt_type, video_prompt_type_video_guide): video_prompt_type = del_in_sequence(video_prompt_type, "ODPCMV") video_prompt_type = add_to_sequence(video_prompt_type, video_prompt_type_video_guide) visible = "V" in video_prompt_type return video_prompt_type, gr.update(visible = visible), gr.update(visible = visible), gr.update(visible= "M" in video_prompt_type ) def refresh_video_prompt_video_guide_trigger(video_prompt_type, video_prompt_type_video_guide): video_prompt_type_video_guide = video_prompt_type_video_guide.split("#")[0] video_prompt_type = del_in_sequence(video_prompt_type, "ODPCMV") video_prompt_type = add_to_sequence(video_prompt_type, video_prompt_type_video_guide) return video_prompt_type, video_prompt_type_video_guide, gr.update(visible= "V" in video_prompt_type ), gr.update(visible= "M" in video_prompt_type) , gr.update(visible= "V" in video_prompt_type ) def refresh_preview(state): gen = get_gen_info(state) preview = gen.get("preview", None) return preview def init_process_queue_if_any(state): gen = get_gen_info(state) if bool(gen.get("queue",[])): state["validate_success"] = 1 return gr.Button(visible=False), gr.Button(visible=True), gr.Column(visible=True) else: return gr.Button(visible=True), gr.Button(visible=False), gr.Column(visible=False) def generate_video_tab(update_form = False, state_dict = None, ui_defaults = None, model_choice = None, header = None, main = None): global inputs_names #, advanced if update_form: model_filename = state_dict["model_filename"] advanced_ui = state_dict["advanced"] else: model_filename = transformer_filename advanced_ui = advanced ui_defaults= get_default_settings(model_filename) state_dict = {} state_dict["model_filename"] = model_filename state_dict["advanced"] = advanced_ui gen = dict() gen["queue"] = [] state_dict["gen"] = gen preset_to_load = lora_preselected_preset if lora_preset_model == model_filename else "" loras, loras_names, loras_presets, default_loras_choices, default_loras_multis_str, default_lora_preset_prompt, default_lora_preset = setup_loras(model_filename, None, get_lora_dir(model_filename), preset_to_load, None) state_dict["loras"] = loras state_dict["loras_presets"] = loras_presets state_dict["loras_names"] = loras_names launch_prompt = "" launch_preset = "" launch_loras = [] launch_multis_str = "" if update_form: pass if len(default_lora_preset) > 0 and lora_preset_model == model_filename: launch_preset = default_lora_preset launch_prompt = default_lora_preset_prompt launch_loras = default_loras_choices launch_multis_str = default_loras_multis_str if len(launch_preset) == 0: launch_preset = ui_defaults.get("lset_name","") if len(launch_prompt) == 0: launch_prompt = ui_defaults.get("prompt","") if len(launch_loras) == 0: launch_multis_str = ui_defaults.get("loras_multipliers","") activated_loras = ui_defaults.get("activated_loras",[]) if len(activated_loras) > 0: lora_filenames = [os.path.basename(lora_path) for lora_path in loras] activated_indices = [] for lora_file in ui_defaults["activated_loras"]: try: idx = lora_filenames.index(lora_file) activated_indices.append(str(idx)) except ValueError: print(f"Warning: Lora file {lora_file} from config not found in loras directory") launch_loras = activated_indices with gr.Row(): with gr.Column(): with gr.Column(visible=False, elem_id="image-modal-container") as modal_container: with gr.Row(elem_id="image-modal-close-button-row"): close_modal_button = gr.Button("❌", size="sm") modal_image_display = gr.Image(label="Full Resolution Image", interactive=False, show_label=False) with gr.Row(visible= True): #len(loras)>0) as presets_column: lset_choices = [ (preset, preset) for preset in loras_presets ] + [(get_new_preset_msg(advanced_ui), "")] with gr.Column(scale=6): lset_name = gr.Dropdown(show_label=False, allow_custom_value= True, scale=5, filterable=True, choices= lset_choices, value=launch_preset) with gr.Column(scale=1): with gr.Row(height=17): apply_lset_btn = gr.Button("Apply Lora Preset", size="sm", min_width= 1) refresh_lora_btn = gr.Button("Refresh", size="sm", min_width= 1, visible=advanced_ui or not only_allow_edit_in_advanced) save_lset_prompt_drop= gr.Dropdown( choices=[ ("Save Prompt Comments Only", 0), ("Save Full Prompt", 1) ], show_label= False, container=False, value =1, visible= False ) with gr.Row(height=17, visible=False) as refresh2_row: refresh_lora_btn2 = gr.Button("Refresh", size="sm", min_width= 1) with gr.Row(height=17, visible=advanced_ui or not only_allow_edit_in_advanced) as preset_buttons_rows: confirm_save_lset_btn = gr.Button("Go Ahead Save it !", size="sm", min_width= 1, visible=False) confirm_delete_lset_btn = gr.Button("Go Ahead Delete it !", size="sm", min_width= 1, visible=False) save_lset_btn = gr.Button("Save", size="sm", min_width= 1) delete_lset_btn = gr.Button("Delete", size="sm", min_width= 1) cancel_lset_btn = gr.Button("Don't do it !", size="sm", min_width= 1 , visible=False) if not update_form: state = gr.State(state_dict) trigger_refresh_input_type = gr.Text(interactive= False, visible= False) diffusion_forcing = "diffusion_forcing" in model_filename recammaster = "recam" in model_filename vace = "Vace" in model_filename phantom = "phantom" in model_filename fantasy = "fantasy" in model_filename with gr.Column(visible= test_class_i2v(model_filename) or diffusion_forcing or recammaster) as image_prompt_column: if diffusion_forcing: image_prompt_type_value= ui_defaults.get("image_prompt_type","S") image_prompt_type = gr.Radio( [("Start Video with Image", "S"),("Continue Video", "V"),("Text Prompt Only", "T")], value =image_prompt_type_value, label="Location", show_label= False, visible= True, scale= 3) # image_start = gr.Image(label= "Image as a starting point for a new video", type ="pil",value= ui_defaults.get("image_start", None), visible= "S" in image_prompt_type_value ) image_start = gr.Gallery( label="Images as starting points for new videos", type ="pil", #file_types= "image", columns=[3], rows=[1], object_fit="contain", height="auto", selected_index=0, interactive= True, value= ui_defaults.get("image_start", None), visible= "S" in image_prompt_type_value) image_end = gr.Gallery(visible=False) video_source = gr.Video(label= "Video to Continue", visible= "V" in image_prompt_type_value, value= ui_defaults.get("video_source", None),) model_mode = gr.Dropdown( choices=[ ("Synchronous", 0), ("Asynchronous (better quality but around 50% extra steps added)", 5), ], value=ui_defaults.get("model_mode", 0), label="Generation Type", scale = 3, visible= True ) keep_frames_video_source = gr.Text(value=ui_defaults.get("keep_frames_video_source","") , visible= "V" in image_prompt_type_value, scale = 2, label= "Truncate Video beyond this number of Frames of Video (empty=Keep All)" ) elif recammaster: image_prompt_type = gr.Radio(visible= False) image_start = gr.Gallery(visible = False) image_end = gr.Gallery(visible=False) video_source = gr.Video(label= "Video Source", visible = True, value= ui_defaults.get("video_source", None),) model_mode = gr.Dropdown( choices=[ ("Pan Right", 1), ("Pan Left", 2), ("Tilt Up", 3), ("Tilt Down", 4), ("Zoom In", 5), ("Zoom Out", 6), ("Translate Up (with rotation)", 7), ("Translate Down (with rotation)", 8), ("Arc Left (with rotation)", 9), ("Arc Right (with rotation)", 10), ], value=ui_defaults.get("model_mode", 1), label="Camera Movement Type", scale = 3, visible= True ) keep_frames_video_source = gr.Text(visible=False) else: image_prompt_type_value= ui_defaults.get("image_prompt_type","S") image_prompt_type = gr.Radio( [("Use only a Start Image", "S"),("Use both a Start and an End Image", "SE")], value =image_prompt_type_value, label="Location", show_label= False, visible= True, scale= 3) image_start = gr.Gallery( label="Images as starting points for new videos", type ="pil", #file_types= "image", columns=[3], rows=[1], object_fit="contain", height="auto", selected_index=0, interactive= True, value= ui_defaults.get("image_start", None), visible= "S" in image_prompt_type_value) image_end = gr.Gallery( label="Images as ending points for new videos", type ="pil", #file_types= "image", columns=[3], rows=[1], object_fit="contain", height="auto", selected_index=0, interactive= True, visible="E" in image_prompt_type_value, value= ui_defaults.get("image_end", None)) video_source = gr.Video(visible=False) model_mode = gr.Dropdown(visible=False) keep_frames_video_source = gr.Text(visible=False) with gr.Column(visible= vace or phantom) as video_prompt_column: video_prompt_type_value= ui_defaults.get("video_prompt_type","") video_prompt_type = gr.Text(value= video_prompt_type_value, visible= False) with gr.Row(): if vace: video_prompt_type_video_guide = gr.Dropdown( choices=[ ("None", ""), ("Transfer Human Motion from the Control Video", "PV"), ("Transfer Depth from the Control Video", "DV"), ("Recolorize the Control Video", "CV"), # ("Alternate Video Ending", "OV"), ("Video contains Open Pose, Depth, Black & White, Inpainting ", "V"), ("Control Video and Mask video for stronger Inpainting ", "MV"), ], value=filter_letters(video_prompt_type_value, "ODPCMV"), label="Video to Video", scale = 3, visible= True ) else: video_prompt_type_video_guide = gr.Dropdown(visible= False) video_prompt_video_guide_trigger = gr.Text(visible=False, value="") video_prompt_type_image_refs = gr.Dropdown( choices=[ ("None", ""), ("Inject custom Faces / Objects", "I"), ], value="I" if "I" in video_prompt_type_value else "", label="Reference Images", scale = 2 ) # video_prompt_type_image_refs = gr.Checkbox(value="I" in video_prompt_type_value , label= "Use References Images (Faces, Objects) to customize New Video", scale =1 ) video_guide = gr.Video(label= "Control Video", visible= "V" in video_prompt_type_value, value= ui_defaults.get("video_guide", None),) keep_frames_video_guide = gr.Text(value=ui_defaults.get("keep_frames_video_guide","") , visible= "V" in video_prompt_type_value, scale = 2, label= "Frames to keep in Control Video (empty=All, 1=first, a:b for a range, space to separate values)" ) #, -1=last image_refs = gr.Gallery( label ="Reference Images", type ="pil", show_label= True, columns=[3], rows=[1], object_fit="contain", height="auto", selected_index=0, interactive= True, visible= "I" in video_prompt_type_value, value= ui_defaults.get("image_refs", None), ) # with gr.Row(): remove_background_image_ref = gr.Checkbox(value=ui_defaults.get("remove_background_image_ref",1), label= "Remove Background of Images References", visible= "I" in video_prompt_type_value, scale =1 ) video_mask = gr.Video(label= "Video Mask (for Inpainting or Outpaing, white pixels = Mask)", visible= "M" in video_prompt_type_value, value= ui_defaults.get("video_mask", None)) audio_guide = gr.Audio(value= ui_defaults.get("audio_guide", None), type="filepath", label="Voice to follow", show_download_button= True, visible= fantasy ) advanced_prompt = advanced_ui prompt_vars=[] if advanced_prompt: default_wizard_prompt, variables, values= None, None, None else: default_wizard_prompt, variables, values, errors = extract_wizard_prompt(launch_prompt) advanced_prompt = len(errors) > 0 with gr.Column(visible= advanced_prompt) as prompt_column_advanced: prompt = gr.Textbox( visible= advanced_prompt, label="Prompts (each new line of prompt will generate a new video, # lines = comments, ! lines = macros)", value=launch_prompt, lines=3) with gr.Column(visible=not advanced_prompt and len(variables) > 0) as prompt_column_wizard_vars: gr.Markdown("Please fill the following input fields to adapt automatically the Prompt:") wizard_prompt_activated = "off" wizard_variables = "" with gr.Row(): if not advanced_prompt: for variable in variables: value = values.get(variable, "") prompt_vars.append(gr.Textbox( placeholder=variable, min_width=80, show_label= False, info= variable, visible= True, value= "\n".join(value) )) wizard_prompt_activated = "on" if len(variables) > 0: wizard_variables = "\n".join(variables) for _ in range( PROMPT_VARS_MAX - len(prompt_vars)): prompt_vars.append(gr.Textbox(visible= False, min_width=80, show_label= False)) with gr.Column(not advanced_prompt) as prompt_column_wizard: wizard_prompt = gr.Textbox(visible = not advanced_prompt, label="Prompts (each new line of prompt will generate a new video, # lines = comments)", value=default_wizard_prompt, lines=3) wizard_prompt_activated_var = gr.Text(wizard_prompt_activated, visible= False) wizard_variables_var = gr.Text(wizard_variables, visible = False) with gr.Row(): if test_class_i2v(model_filename) and False: resolution = gr.Dropdown( choices=[ # 720p ("720p (same amount of pixels)", "1280x720"), ("480p (same amount of pixels)", "832x480"), ], value=ui_defaults.get("resolution","480p"), label="Resolution (video will have the same height / width ratio than the original image)" ) else: resolution = gr.Dropdown( choices=[ # 720p ("1280x720 (16:9, 720p)", "1280x720"), ("720x1280 (9:16, 720p)", "720x1280"), ("1024x1024 (4:3, 720p)", "1024x024"), ("832x1104 (3:4, 720p)", "832x1104"), ("1104x832 (3:4, 720p)", "1104x832"), ("960x960 (1:1, 720p)", "960x960"), # 480p ("960x544 (16:9, 540p)", "960x544"), ("544x960 (16:9, 540p)", "544x960"), ("832x480 (16:9, 480p)", "832x480"), ("480x832 (9:16, 480p)", "480x832"), ("832x624 (4:3, 480p)", "832x624"), ("624x832 (3:4, 480p)", "624x832"), ("720x720 (1:1, 480p)", "720x720"), ("512x512 (1:1, 480p)", "512x512"), ], value=ui_defaults.get("resolution","832x480"), label="Max Resolution (as it maybe less depending on video width / height ratio)" if test_class_i2v(model_filename) else "Resolution" ) with gr.Row(): if recammaster: video_length = gr.Slider(5, 193, value=ui_defaults.get("video_length", 81), step=4, label="Number of frames (16 = 1s), locked", interactive= False) elif diffusion_forcing: video_length = gr.Slider(17, 737, value=ui_defaults.get("video_length", 97), step=20, label="Number of frames (24 = 1s)", interactive= True) elif vace: video_length = gr.Slider(17, 737, value=ui_defaults.get("video_length", 81), step=4, label="Number of frames (16 = 1s)", interactive= True) elif fantasy: video_length = gr.Slider(5, 233, value=ui_defaults.get("video_length", 81), step=4, label="Number of frames (23 = 1s)", interactive= True) else: video_length = gr.Slider(5, 193, value=ui_defaults.get("video_length", 81), step=4, label="Number of frames (16 = 1s)", interactive= True) with gr.Row(): num_inference_steps = gr.Slider(1, 100, value=ui_defaults.get("num_inference_steps",30), step=1, label="Number of Inference Steps") show_advanced = gr.Checkbox(label="Advanced Mode", value=advanced_ui) with gr.Tabs(visible=advanced_ui) as advanced_row: # with gr.Row(visible=advanced_ui) as advanced_row: with gr.Tab("Generation"): with gr.Column(): seed = gr.Slider(-1, 999999999, value=ui_defaults["seed"], step=1, label="Seed (-1 for random)") with gr.Row(): repeat_generation = gr.Slider(1, 25.0, value=ui_defaults.get("repeat_generation",1), step=1, label="Default Number of Generated Videos per Prompt") multi_images_gen_type = gr.Dropdown( value=ui_defaults.get("multi_images_gen_type",0), choices=[ ("Generate every combination of images and texts", 0), ("Match images and text prompts", 1), ], visible= test_class_i2v(model_filename), label= "Multiple Images as Texts Prompts" ) with gr.Row(): guidance_scale = gr.Slider(1.0, 20.0, value=ui_defaults.get("guidance_scale",5), step=0.5, label="Guidance Scale", visible=True) audio_guidance_scale = gr.Slider(1.0, 20.0, value=ui_defaults.get("audio_guidance_scale",5), step=0.5, label="Audio Guidance", visible=fantasy) embedded_guidance_scale = gr.Slider(1.0, 20.0, value=6.0, step=0.5, label="Embedded Guidance Scale", visible=False) flow_shift = gr.Slider(0.0, 25.0, value=ui_defaults.get("flow_shift",3), step=0.1, label="Shift Scale") with gr.Row(): negative_prompt = gr.Textbox(label="Negative Prompt", value=ui_defaults.get("negative_prompt", "") ) with gr.Tab("Loras"): with gr.Column(visible = True): #as loras_column: gr.Markdown("Loras can be used to create special effects on the video by mentioning a trigger word in the Prompt. You can save Loras combinations in presets.") loras_choices = gr.Dropdown( choices=[ (lora_name, str(i) ) for i, lora_name in enumerate(loras_names) ], value= launch_loras, multiselect= True, label="Activated Loras" ) loras_multipliers = gr.Textbox(label="Loras Multipliers (1.0 by default) separated by space characters or carriage returns, line that starts with # are ignored", value=launch_multis_str) with gr.Row(): gr.Markdown("Tea Cache accelerates by skipping intelligently some steps, the more steps are skipped the lower the quality of the video (Tea Cache consumes also VRAM)") with gr.Tab("Speed"): with gr.Column(): gr.Markdown("Tea Cache accelerates the Video generation by skipping denoising steps. This may impact the quality") tea_cache_setting = gr.Dropdown( choices=[ ("Tea Cache Disabled", 0), ("around x1.5 speed up", 1.5), ("around x1.75 speed up", 1.75), ("around x2 speed up", 2.0), ("around x2.25 speed up", 2.25), ("around x2.5 speed up", 2.5), ], value=float(ui_defaults.get("tea_cache_setting",0)), visible=True, label="Tea Cache Global Acceleration" ) tea_cache_start_step_perc = gr.Slider(0, 100, value=ui_defaults.get("tea_cache_start_step_perc",0), step=1, label="Tea Cache starting moment in % of generation") with gr.Tab("Upsampling"): with gr.Column(): gr.Markdown("Upsampling - postprocessing that may improve fluidity and the size of the video") temporal_upsampling = gr.Dropdown( choices=[ ("Disabled", ""), ("Rife x2 (32 frames/s)", "rife2"), ("Rife x4 (64 frames/s)", "rife4"), ], value=ui_defaults.get("temporal_upsampling", ""), visible=True, scale = 1, label="Temporal Upsampling" ) spatial_upsampling = gr.Dropdown( choices=[ ("Disabled", ""), ("Lanczos x1.5", "lanczos1.5"), ("Lanczos x2.0", "lanczos2"), ], value=ui_defaults.get("spatial_upsampling", ""), visible=True, scale = 1, label="Spatial Upsampling" ) with gr.Tab("Quality"): with gr.Row(): gr.Markdown("Skip Layer Guidance (improves video quality)") with gr.Row(): slg_switch = gr.Dropdown( choices=[ ("OFF", 0), ("ON", 1), ], value=ui_defaults.get("slg_switch",0), visible=True, scale = 1, label="Skip Layer guidance" ) slg_layers = gr.Dropdown( choices=[ (str(i), i ) for i in range(40) ], value=ui_defaults.get("slg_layers", ["9"]), multiselect= True, label="Skip Layers", scale= 3 ) with gr.Row(): slg_start_perc = gr.Slider(0, 100, value=ui_defaults.get("slg_start_perc",10), step=1, label="Denoising Steps % start") slg_end_perc = gr.Slider(0, 100, value=ui_defaults.get("slg_end_perc",90), step=1, label="Denoising Steps % end") with gr.Row(): gr.Markdown("Experimental: Classifier-Free Guidance Zero Star, better adherence to Text Prompt") with gr.Row(): cfg_star_switch = gr.Dropdown( choices=[ ("OFF", 0), ("ON", 1), ], value=ui_defaults.get("cfg_star_switch",0), visible=True, scale = 1, label="CFG Star" ) with gr.Row(): cfg_zero_step = gr.Slider(-1, 39, value=ui_defaults.get("cfg_zero_step",-1), step=1, label="CFG Zero below this Layer (Extra Process)") with gr.Tab("Sliding Window", visible= "Vace" in model_filename or "diffusion_forcing" in model_filename) as sliding_window_tab: with gr.Column(): gr.Markdown("A Sliding Window allows you to generate video with a duration not limited by the Model") gr.Markdown("It is automatically turned on if the number of frames to generate is higher than the Window Size") if diffusion_forcing: sliding_window_size = gr.Slider(37, 137, value=ui_defaults.get("sliding_window_size", 97), step=20, label="Sliding Window Size (recommended to keep it at 97)") sliding_window_overlap = gr.Slider(17, 97, value=ui_defaults.get("sliding_window_overlap",17), step=20, label="Windows Frames Overlap (needed to maintain continuity between windows, a higher value will require more windows)") sliding_window_overlap_noise = gr.Slider(0, 100, value=ui_defaults.get("sliding_window_overlap_noise",20), step=1, label="Noise to be added to overlapped frames to reduce blur effect") sliding_window_discard_last_frames = gr.Slider(0, 20, value=ui_defaults.get("sliding_window_discard_last_frames", 0), step=4, visible = False) else: sliding_window_size = gr.Slider(5, 137, value=ui_defaults.get("sliding_window_size", 81), step=4, label="Sliding Window Size") sliding_window_overlap = gr.Slider(1, 97, value=ui_defaults.get("sliding_window_overlap",5), step=4, label="Windows Frames Overlap (needed to maintain continuity between windows, a higher value will require more windows)") sliding_window_overlap_noise = gr.Slider(0, 100, value=ui_defaults.get("sliding_window_overlap_noise",20), step=1, label="Noise to be added to overlapped frames to reduce blur effect") sliding_window_discard_last_frames = gr.Slider(0, 20, value=ui_defaults.get("sliding_window_discard_last_frames", 8), step=4, label="Discard Last Frames of a Window (that may have bad quality)", visible = True) with gr.Tab("Miscellaneous", visible= not "recam" in model_filename): gr.Markdown("With Riflex you can generate videos longer than 5s which is the default duration of videos used to train the model") RIFLEx_setting = gr.Dropdown( choices=[ ("Auto (ON if Video longer than 5s)", 0), ("Always ON", 1), ("Always OFF", 2), ], value=ui_defaults.get("RIFLEx_setting",0), label="RIFLEx positional embedding to generate long video" ) with gr.Row(): save_settings_btn = gr.Button("Set Settings as Default", visible = not args.lock_config) if not update_form: with gr.Column(): gen_status = gr.Text(interactive= False, label = "Status") status_trigger = gr.Text(interactive= False, visible=False) output = gr.Gallery( label="Generated videos", show_label=False, elem_id="gallery" , columns=[3], rows=[1], object_fit="contain", height=450, selected_index=0, interactive= False) output_trigger = gr.Text(interactive= False, visible=False) generate_btn = gr.Button("Generate") add_to_queue_btn = gr.Button("Add New Prompt To Queue", visible = False) with gr.Column(visible= False) as current_gen_column: with gr.Accordion("Preview", open=False) as queue_accordion: preview = gr.Image(label="Preview", height=200, show_label= False) preview_trigger = gr.Text(visible= False) gen_info = gr.HTML(visible=False, min_height=1) with gr.Row(): onemoresample_btn = gr.Button("One More Sample Please !") onemorewindow_btn = gr.Button("Extend this Sample Please !", visible = False) abort_btn = gr.Button("Abort") with gr.Accordion("Queue Management", open=False) as queue_accordion: with gr.Row( ): queue_df = gr.DataFrame( headers=["Qty","Prompt", "Length","Steps","", "", "", "", ""], datatype=[ "str","markdown","str", "markdown", "markdown", "markdown", "str", "str", "str"], column_widths= ["5%", None, "7%", "7%", "10%", "10%", "3%", "3%", "34"], interactive=False, col_count=(9, "fixed"), wrap=True, value=[], line_breaks= True, visible= True, elem_id="queue_df", max_height= 1000 ) with gr.Row(visible= True): queue_zip_base64_output = gr.Text(visible=False) save_queue_btn = gr.DownloadButton("Save Queue", size="sm") load_queue_btn = gr.UploadButton("Load Queue", file_types=[".zip"], size="sm") clear_queue_btn = gr.Button("Clear Queue", size="sm", variant="stop") quit_button = gr.Button("Save and Quit", size="sm", variant="secondary") with gr.Row(visible=False) as quit_confirmation_row: confirm_quit_button = gr.Button("Confirm", elem_id="comfirm_quit_btn_hidden", size="sm", variant="stop") cancel_quit_button = gr.Button("Cancel", size="sm", variant="secondary") hidden_force_quit_trigger = gr.Button("force_quit", visible=False, elem_id="force_quit_btn_hidden") hidden_countdown_state = gr.Number(value=-1, visible=False, elem_id="hidden_countdown_state_num") single_hidden_trigger_btn = gr.Button("trigger_countdown", visible=False, elem_id="trigger_info_single_btn") extra_inputs = prompt_vars + [wizard_prompt, wizard_variables_var, wizard_prompt_activated_var, video_prompt_column, image_prompt_column, prompt_column_advanced, prompt_column_wizard_vars, prompt_column_wizard, lset_name, advanced_row, sliding_window_tab, video_prompt_type_video_guide, video_prompt_type_image_refs] # show_advanced presets_column, if update_form: locals_dict = locals() gen_inputs = [state_dict if k=="state" else locals_dict[k] for k in inputs_names] + [state_dict] + extra_inputs return gen_inputs else: target_state = gr.Text(value = "state", interactive= False, visible= False) target_settings = gr.Text(value = "settings", interactive= False, visible= False) image_prompt_type.change(fn=refresh_image_prompt_type, inputs=[state, image_prompt_type], outputs=[image_start, image_end, video_source, keep_frames_video_source] ) video_prompt_video_guide_trigger.change(fn=refresh_video_prompt_video_guide_trigger, inputs=[video_prompt_type, video_prompt_video_guide_trigger], outputs=[video_prompt_type, video_prompt_type_video_guide, video_guide, video_mask, keep_frames_video_guide]) video_prompt_type_image_refs.input(fn=refresh_video_prompt_type_image_refs, inputs = [video_prompt_type, video_prompt_type_image_refs], outputs = [video_prompt_type, image_refs, remove_background_image_ref ]) video_prompt_type_video_guide.input(fn=refresh_video_prompt_type_video_guide, inputs = [video_prompt_type, video_prompt_type_video_guide], outputs = [video_prompt_type, video_guide, keep_frames_video_guide, video_mask]) show_advanced.change(fn=switch_advanced, inputs=[state, show_advanced, lset_name], outputs=[advanced_row, preset_buttons_rows, refresh_lora_btn, refresh2_row ,lset_name ]).then( fn=switch_prompt_type, inputs = [state, wizard_prompt_activated_var, wizard_variables_var, prompt, wizard_prompt, *prompt_vars], outputs = [wizard_prompt_activated_var, wizard_variables_var, prompt, wizard_prompt, prompt_column_advanced, prompt_column_wizard, prompt_column_wizard_vars, *prompt_vars]) queue_df.select( fn=handle_celll_selection, inputs=state, outputs=[queue_df, modal_image_display, modal_container]) save_lset_btn.click(validate_save_lset, inputs=[lset_name], outputs=[apply_lset_btn, refresh_lora_btn, delete_lset_btn, save_lset_btn,confirm_save_lset_btn, cancel_lset_btn, save_lset_prompt_drop]) confirm_save_lset_btn.click(fn=validate_wizard_prompt, inputs =[state, wizard_prompt_activated_var, wizard_variables_var, prompt, wizard_prompt, *prompt_vars] , outputs= [prompt]).then( save_lset, inputs=[state, lset_name, loras_choices, loras_multipliers, prompt, save_lset_prompt_drop], outputs=[lset_name, apply_lset_btn,refresh_lora_btn, delete_lset_btn, save_lset_btn, confirm_save_lset_btn, cancel_lset_btn, save_lset_prompt_drop]) delete_lset_btn.click(validate_delete_lset, inputs=[lset_name], outputs=[apply_lset_btn, refresh_lora_btn, delete_lset_btn, save_lset_btn,confirm_delete_lset_btn, cancel_lset_btn ]) confirm_delete_lset_btn.click(delete_lset, inputs=[state, lset_name], outputs=[lset_name, apply_lset_btn, refresh_lora_btn, delete_lset_btn, save_lset_btn,confirm_delete_lset_btn, cancel_lset_btn ]) cancel_lset_btn.click(cancel_lset, inputs=[], outputs=[apply_lset_btn, refresh_lora_btn, delete_lset_btn, save_lset_btn, confirm_delete_lset_btn,confirm_save_lset_btn, cancel_lset_btn,save_lset_prompt_drop ]) apply_lset_btn.click(apply_lset, inputs=[state, wizard_prompt_activated_var, lset_name,loras_choices, loras_multipliers, prompt], outputs=[wizard_prompt_activated_var, loras_choices, loras_multipliers, prompt]).then( fn = fill_wizard_prompt, inputs = [state, wizard_prompt_activated_var, prompt, wizard_prompt], outputs = [ wizard_prompt_activated_var, wizard_variables_var, prompt, wizard_prompt, prompt_column_advanced, prompt_column_wizard, prompt_column_wizard_vars, *prompt_vars] ) refresh_lora_btn.click(refresh_lora_list, inputs=[state, lset_name,loras_choices], outputs=[lset_name, loras_choices]) refresh_lora_btn2.click(refresh_lora_list, inputs=[state, lset_name,loras_choices], outputs=[lset_name, loras_choices]) output.select(select_video, state, None ) preview_trigger.change(refresh_preview, inputs= [state], outputs= [preview]) def refresh_status_async(state, progress=gr.Progress()): gen = get_gen_info(state) gen["progress"] = progress while True: progress_args= gen.get("progress_args", None) if progress_args != None: progress(*progress_args) gen["progress_args"] = None status= gen.get("status","") if status == None or len(status) > 0: yield status gen["status"]= "" if not gen.get("status_display", False): return time.sleep(0.5) def activate_status(state): if state.get("validate_success",0) != 1: return gen = get_gen_info(state) gen["status_display"] = True return time.time() status_trigger.change(refresh_status_async, inputs= [state] , outputs= [gen_status], show_progress_on= [gen_status]) output_trigger.change(refresh_gallery, inputs = [state], outputs = [output, gen_info, generate_btn, add_to_queue_btn, current_gen_column, queue_df, abort_btn, onemorewindow_btn]) abort_btn.click(abort_generation, [state], [ abort_btn] ) #.then(refresh_gallery, inputs = [state, gen_info], outputs = [output, gen_info, queue_df] ) onemoresample_btn.click(fn=one_more_sample,inputs=[state], outputs= [state]) onemorewindow_btn.click(fn=one_more_window,inputs=[state], outputs= [state]) inputs_names= list(inspect.signature(save_inputs).parameters)[1:-1] locals_dict = locals() gen_inputs = [locals_dict[k] for k in inputs_names] + [state] save_settings_btn.click( fn=validate_wizard_prompt, inputs =[state, wizard_prompt_activated_var, wizard_variables_var, prompt, wizard_prompt, *prompt_vars] , outputs= [prompt]).then( save_inputs, inputs =[target_settings] + gen_inputs, outputs = []) model_choice.change(fn=validate_wizard_prompt, inputs= [state, wizard_prompt_activated_var, wizard_variables_var, prompt, wizard_prompt, *prompt_vars] , outputs= [prompt] ).then(fn=save_inputs, inputs =[target_state] + gen_inputs, outputs= None ).then(fn= change_model, inputs=[state, model_choice], outputs= [header] ).then(fn= fill_inputs, inputs=[state], outputs=gen_inputs + extra_inputs ).then(fn= preload_model_when_switching, inputs=[state], outputs=[gen_status]) generate_btn.click(fn=validate_wizard_prompt, inputs= [state, wizard_prompt_activated_var, wizard_variables_var, prompt, wizard_prompt, *prompt_vars] , outputs= [prompt] ).then(fn=save_inputs, inputs =[target_state] + gen_inputs, outputs= None ).then(fn=process_prompt_and_add_tasks, inputs = [state, model_choice], outputs= queue_df ).then(fn=prepare_generate_video, inputs= [state], outputs= [generate_btn, add_to_queue_btn, current_gen_column] ).then(fn=activate_status, inputs= [state], outputs= [status_trigger], ).then(fn=process_tasks, inputs= [state], outputs= [preview_trigger, output_trigger], ).then(finalize_generation, inputs= [state], outputs= [output, abort_btn, generate_btn, add_to_queue_btn, current_gen_column, gen_info] ).then( fn=lambda s: gr.Accordion(open=False) if len(get_gen_info(s).get("queue", [])) <= 1 else gr.update(), inputs=[state], outputs=[queue_accordion] ).then(unload_model_if_needed, inputs= [state], outputs= [] ) gr.on(triggers=[load_queue_btn.upload, main.load], fn=load_queue_action, inputs=[load_queue_btn, state], outputs=[queue_df] ).then( fn=lambda s: (gr.update(visible=bool(get_gen_info(s).get("queue",[]))), gr.Accordion(open=True)) if bool(get_gen_info(s).get("queue",[])) else (gr.update(visible=False), gr.update()), inputs=[state], outputs=[current_gen_column, queue_accordion] ).then( fn=init_process_queue_if_any, inputs=[state], outputs=[generate_btn, add_to_queue_btn, current_gen_column, ] ).then(fn=activate_status, inputs= [state], outputs= [status_trigger], ).then( fn=process_tasks, inputs=[state], outputs=[preview_trigger, output_trigger], trigger_mode="once" ).then( fn=finalize_generation_with_state, inputs=[state], outputs=[output, abort_btn, generate_btn, add_to_queue_btn, current_gen_column, gen_info, queue_accordion, state], trigger_mode="always_last" ).then( unload_model_if_needed, inputs= [state], outputs= [] ) start_quit_timer_js, cancel_quit_timer_js, trigger_zip_download_js = get_timer_js() single_hidden_trigger_btn.click( fn=show_countdown_info_from_state, inputs=[hidden_countdown_state], outputs=[hidden_countdown_state] ) quit_button.click( fn=start_quit_process, inputs=[], outputs=[hidden_countdown_state, quit_button, quit_confirmation_row] ).then( fn=None, inputs=None, outputs=None, js=start_quit_timer_js ) confirm_quit_button.click( fn=quit_application, inputs=[], outputs=[] ).then( fn=None, inputs=None, outputs=None, js=cancel_quit_timer_js ) cancel_quit_button.click( fn=cancel_quit_process, inputs=[], outputs=[hidden_countdown_state, quit_button, quit_confirmation_row] ).then( fn=None, inputs=None, outputs=None, js=cancel_quit_timer_js ) hidden_force_quit_trigger.click( fn=quit_application, inputs=[], outputs=[] ) save_queue_btn.click( fn=save_queue_action, inputs=[state], outputs=[queue_zip_base64_output] ).then( fn=None, inputs=[queue_zip_base64_output], outputs=None, js=trigger_zip_download_js ) clear_queue_btn.click( fn=clear_queue_action, inputs=[state], outputs=[queue_df] ).then( fn=lambda: (gr.update(visible=False), gr.Accordion(open=False)), inputs=None, outputs=[current_gen_column, queue_accordion] ) add_to_queue_btn.click(fn=validate_wizard_prompt, inputs =[state, wizard_prompt_activated_var, wizard_variables_var, prompt, wizard_prompt, *prompt_vars] , outputs= [prompt] ).then(fn=save_inputs, inputs =[target_state] + gen_inputs, outputs= None ).then(fn=process_prompt_and_add_tasks, inputs = [state, model_choice], outputs=queue_df ).then( fn=lambda s: gr.Accordion(open=True) if len(get_gen_info(s).get("queue", [])) > 1 else gr.update(), inputs=[state], outputs=[queue_accordion] ).then( fn=update_status, inputs = [state], ) close_modal_button.click( lambda: gr.update(visible=False), inputs=[], outputs=[modal_container] ) return ( state, loras_choices, lset_name, state, video_guide, video_mask, video_prompt_video_guide_trigger ) def generate_download_tab(lset_name,loras_choices, state): with gr.Row(): with gr.Row(scale =2): gr.Markdown("WanGP's Lora Festival ! Press the following button to download i2v Remade_AI Loras collection (and bonuses Loras).") with gr.Row(scale =1): download_loras_btn = gr.Button("---> Let the Lora's Festival Start !", scale =1) with gr.Row(scale =1): gr.Markdown("") with gr.Row() as download_status_row: download_status = gr.Markdown() download_loras_btn.click(fn=download_loras, inputs=[], outputs=[download_status_row, download_status]).then(fn=refresh_lora_list, inputs=[state, lset_name,loras_choices], outputs=[lset_name, loras_choices]) def generate_configuration_tab(state, blocks, header, model_choice): gr.Markdown("Please click Apply Changes at the bottom so that the changes are effective. Some choices below may be locked if the app has been launched by specifying a config preset.") with gr.Column(): model_list = [] for model_type in model_types: choice = get_model_filename(model_type, transformer_quantization) model_list.append(choice) dropdown_choices = [ ( get_model_name(choice), get_model_type(choice) ) for choice in model_list] transformer_types_choices = gr.Dropdown( choices= dropdown_choices, value= transformer_types, label= "Selectable Wan Transformer Models (keep empty to get All of them)", scale= 2, multiselect= True ) quantization_choice = gr.Dropdown( choices=[ ("Scaled Int8 Quantization (recommended)", "int8"), ("16 bits (no quantization)", "bf16"), ], value= transformer_quantization, label="Wan Transformer Model Quantization Type (if available)", ) mixed_precision_choice = gr.Dropdown( choices=[ ("16 bits only, requires less VRAM", "0"), ("Mixed 16 / 32 bits, slightly more VRAM needed but better Quality", "1"), ], value= server_config.get("mixed_precision", "0"), label="Transformer Engine Calculation" ) index = text_encoder_choices.index(text_encoder_filename) index = 0 if index ==0 else index text_encoder_choice = gr.Dropdown( choices=[ ("UMT5 XXL 16 bits - unquantized text encoder, better quality uses more RAM", 0), ("UMT5 XXL quantized to 8 bits - quantized text encoder, slightly worse quality but uses less RAM", 1), ], value= index, label="Text Encoder model" ) VAE_precision_choice = gr.Dropdown( choices=[ ("16 bits, requires less VRAM and faster", "16"), ("32 bits, requires twice more VRAM and slower but recommended with Window Sliding", "32"), ], value= server_config.get("vae_precision", "16"), label="VAE Encoding / Decoding precision" ) save_path_choice = gr.Textbox( label="Output Folder for Generated Videos", value=server_config.get("save_path", save_path) ) def check(mode): if not mode in attention_modes_installed: return " (NOT INSTALLED)" elif not mode in attention_modes_supported: return " (NOT SUPPORTED)" else: return "" attention_choice = gr.Dropdown( choices=[ ("Auto : pick sage2 > sage > sdpa depending on what is installed", "auto"), ("Scale Dot Product Attention: default, always available", "sdpa"), ("Flash" + check("flash")+ ": good quality - requires additional install (usually complex to set up on Windows without WSL)", "flash"), ("Xformers" + check("xformers")+ ": good quality - requires additional install (usually complex, may consume less VRAM to set up on Windows without WSL)", "xformers"), ("Sage" + check("sage")+ ": 30% faster but slightly worse quality - requires additional install (usually complex to set up on Windows without WSL)", "sage"), ("Sage2" + check("sage2")+ ": 40% faster but slightly worse quality - requires additional install (usually complex to set up on Windows without WSL)", "sage2"), ], value= attention_mode, label="Attention Type", interactive= not lock_ui_attention ) gr.Markdown("Beware: when restarting the server or changing a resolution or video duration, the first step of generation for a duration / resolution may last a few minutes due to recompilation") compile_choice = gr.Dropdown( choices=[ ("ON: works only on Linux / WSL", "transformer"), ("OFF: no other choice if you have Windows without using WSL", "" ), ], value= compile, label="Compile Transformer (up to 50% faster and 30% more frames but requires Linux / WSL and Flash or Sage attention)", interactive= not lock_ui_compile ) vae_config_choice = gr.Dropdown( choices=[ ("Auto", 0), ("Disabled (faster but may require up to 22 GB of VRAM)", 1), ("256 x 256 : If at least 8 GB of VRAM", 2), ("128 x 128 : If at least 6 GB of VRAM", 3), ], value= vae_config, label="VAE Tiling - reduce the high VRAM requirements for VAE decoding and VAE encoding (if enabled it will be slower)" ) boost_choice = gr.Dropdown( choices=[ # ("Auto (ON if Video longer than 5s)", 0), ("ON", 1), ("OFF", 2), ], value=boost, label="Boost: Give a 10% speed speedup without losing quality at the cost of a litle VRAM (up to 1GB for max frames and resolution)" ) profile_choice = gr.Dropdown( choices=[ ("HighRAM_HighVRAM, profile 1: at least 48 GB of RAM and 24 GB of VRAM, the fastest for short videos a RTX 3090 / RTX 4090", 1), ("HighRAM_LowVRAM, profile 2 (Recommended): at least 48 GB of RAM and 12 GB of VRAM, the most versatile profile with high RAM, better suited for RTX 3070/3080/4070/4080 or for RTX 3090 / RTX 4090 with large pictures batches or long videos", 2), ("LowRAM_HighVRAM, profile 3: at least 32 GB of RAM and 24 GB of VRAM, adapted for RTX 3090 / RTX 4090 with limited RAM for good speed short video",3), ("LowRAM_LowVRAM, profile 4 (Default): at least 32 GB of RAM and 12 GB of VRAM, if you have little VRAM or want to generate longer videos",4), ("VerylowRAM_LowVRAM, profile 5: (Fail safe): at least 16 GB of RAM and 10 GB of VRAM, if you don't have much it won't be fast but maybe it will work",5) ], value= profile, label="Profile (for power users only, not needed to change it)" ) metadata_choice = gr.Dropdown( choices=[ ("Export JSON files", "json"), ("Add metadata to video", "metadata"), ("Neither", "none") ], value=server_config.get("metadata_type", "metadata"), label="Metadata Handling" ) preload_model_policy_choice = gr.CheckboxGroup([("Preload Model while Launching the App","P"), ("Preload Model while Switching Model", "S"), ("Unload Model when Queue is Done", "U")], value=server_config.get("preload_model_policy",[]), label="RAM Loading / Unloading Model Policy (in any case VRAM will be freed once the queue has been processed)" ) clear_file_list_choice = gr.Dropdown( choices=[ ("None", 0), ("Keep the last video", 1), ("Keep the last 5 videos", 5), ("Keep the last 10 videos", 10), ("Keep the last 20 videos", 20), ("Keep the last 30 videos", 30), ], value=server_config.get("clear_file_list", 5), label="Keep Previously Generated Videos when starting a Generation Batch" ) UI_theme_choice = gr.Dropdown( choices=[ ("Blue Sky", "default"), ("Classic Gradio", "gradio"), ], value=server_config.get("UI_theme_choice", "default"), label="User Interface Theme. You will need to restart the App the see new Theme." ) msg = gr.Markdown() apply_btn = gr.Button("Apply Changes") apply_btn.click( fn=apply_changes, inputs=[ state, transformer_types_choices, text_encoder_choice, VAE_precision_choice, mixed_precision_choice, save_path_choice, attention_choice, compile_choice, profile_choice, vae_config_choice, metadata_choice, quantization_choice, boost_choice, clear_file_list_choice, preload_model_policy_choice, UI_theme_choice ], outputs= [msg , header, model_choice] ) def generate_about_tab(): gr.Markdown("

WanGP - Wan 2.1 model for the GPU Poor by DeepBeepMeep (GitHub)

") gr.Markdown("Original Wan 2.1 Model by Alibaba (GitHub)") gr.Markdown("Many thanks to:") gr.Markdown("- Alibaba Wan team for the best open source video generator") gr.Markdown("- Alibaba Vace and Fun Teams for their incredible control net models") gr.Markdown("- Cocktail Peanuts : QA and simple installation via Pinokio.computer") gr.Markdown("- Tophness : created (former) multi tabs and queuing frameworks") gr.Markdown("- AmericanPresidentJimmyCarter : added original support for Skip Layer Guidance") gr.Markdown("- Remade_AI : for their awesome Loras collection") gr.Markdown("
Huge acknowlegments to these great open source projects used in WanGP:") gr.Markdown("- Rife: temporal upsampler (https://github.com/hzwer/ECCV2022-RIFE)") gr.Markdown("- DwPose: Open Pose extractor (https://github.com/IDEA-Research/DWPose)") gr.Markdown("- Midas: Depth extractor (https://github.com/isl-org/MiDaS") gr.Markdown("- Matanyone and SAM2: Mask Generation (https://github.com/pq-yang/MatAnyone) and (https://github.com/facebookresearch/sam2)") def generate_info_tab(): gr.Markdown("Welcome to WanGP a super fast and low VRAM AI Video Generator !") gr.Markdown("The VRAM requirements will depend greatly of the resolution and the duration of the video, for instance :") gr.Markdown("- 848 x 480 with a 14B model: 80 frames (5s) : 8 GB of VRAM") gr.Markdown("- 848 x 480 with the 1.3B model: 80 frames (5s) : 5 GB of VRAM") gr.Markdown("- 1280 x 720 with a 14B model: 80 frames (5s): 11 GB of VRAM") gr.Markdown("It is not recommmended to generate a video longer than 8s (128 frames) even if there is still some VRAM left as some artifacts may appear") gr.Markdown("Please note that if your turn on compilation, the first denoising step of the first video generation will be slow due to the compilation. Therefore all your tests should be done with compilation turned off.") def generate_dropdown_model_list(): dropdown_types= transformer_types if len(transformer_types) > 0 else model_types current_model_type = get_model_type(transformer_filename) if current_model_type not in dropdown_types: dropdown_types.append(current_model_type) model_list = [] for model_type in dropdown_types: choice = get_model_filename(model_type, transformer_quantization) model_list.append(choice) dropdown_choices = [ ( get_model_name(choice), get_model_type(choice) ) for choice in model_list] return gr.Dropdown( choices= dropdown_choices, value= current_model_type, show_label= False, scale= 2, elem_id="model_list", elem_classes="model_list_class", ) def select_tab(tab_state, evt:gr.SelectData): tab_video_mask_creator = 2 old_tab_no = tab_state.get("tab_no",0) new_tab_no = evt.index if old_tab_no == tab_video_mask_creator: vmc_event_handler(False) elif new_tab_no == tab_video_mask_creator: if gen_in_progress: gr.Info("Unable to access this Tab while a Generation is in Progress. Please come back later") tab_state["tab_no"] = 0 return gr.Tabs(selected="video_gen") else: vmc_event_handler(True) tab_state["tab_no"] = new_tab_no return gr.Tabs() def get_timer_js(): start_quit_timer_js = """ () => { function findAndClickGradioButton(elemId) { const gradioApp = document.querySelector('gradio-app') || document; const button = gradioApp.querySelector(`#${elemId}`); if (button) { button.click(); } } if (window.quitCountdownTimeoutId) clearTimeout(window.quitCountdownTimeoutId); let js_click_count = 0; const max_clicks = 5; function countdownStep() { if (js_click_count < max_clicks) { findAndClickGradioButton('trigger_info_single_btn'); js_click_count++; window.quitCountdownTimeoutId = setTimeout(countdownStep, 1000); } else { findAndClickGradioButton('force_quit_btn_hidden'); } } countdownStep(); } """ cancel_quit_timer_js = """ () => { if (window.quitCountdownTimeoutId) { clearTimeout(window.quitCountdownTimeoutId); window.quitCountdownTimeoutId = null; console.log("Quit countdown cancelled (single trigger)."); } } """ trigger_zip_download_js = """ (base64String) => { if (!base64String) { console.log("No base64 zip data received, skipping download."); return; } try { const byteCharacters = atob(base64String); const byteNumbers = new Array(byteCharacters.length); for (let i = 0; i < byteCharacters.length; i++) { byteNumbers[i] = byteCharacters.charCodeAt(i); } const byteArray = new Uint8Array(byteNumbers); const blob = new Blob([byteArray], { type: 'application/zip' }); const url = URL.createObjectURL(blob); const a = document.createElement('a'); a.style.display = 'none'; a.href = url; a.download = 'queue.zip'; document.body.appendChild(a); a.click(); window.URL.revokeObjectURL(url); document.body.removeChild(a); console.log("Zip download triggered."); } catch (e) { console.error("Error processing base64 data or triggering download:", e); } } """ return start_quit_timer_js, cancel_quit_timer_js, trigger_zip_download_js def create_demo(): global vmc_event_handler css = """ #model_list{ background-color:black; padding:1px} #model_list input { font-size:25px} .title-with-lines { display: flex; align-items: center; margin: 25px 0; } .line { flex-grow: 1; height: 1px; background-color: #333; } h2 { margin: 0 20px; white-space: nowrap; } .queue-item { border: 1px solid #ccc; padding: 10px; margin: 5px 0; border-radius: 5px; } .current { background: #f8f9fa; border-left: 4px solid #007bff; } .task-header { display: flex; justify-content: space-between; margin-bottom: 5px; } .progress-container { height: 10px; background: #e9ecef; border-radius: 5px; overflow: hidden; } .progress-bar { height: 100%; background: #007bff; transition: width 0.3s ease; } .task-details { display: flex; justify-content: space-between; font-size: 0.9em; color: #6c757d; margin-top: 5px; } .task-prompt { font-size: 0.8em; color: #868e96; margin-top: 5px; white-space: nowrap; overflow: hidden; text-overflow: ellipsis; } #queue_df th { pointer-events: none; text-align: center; vertical-align: middle; font-size:11px; } #xqueue_df table { width: 100%; overflow: hidden !important; } #xqueue_df::-webkit-scrollbar { display: none !important; } #xqueue_df { scrollbar-width: none !important; -ms-overflow-style: none !important; } .selection-button { display: none; } .cell-selected { --ring-color: none; } #queue_df th:nth-child(1), #queue_df td:nth-child(1) { width: 60px; text-align: center; vertical-align: middle; cursor: default !important; pointer-events: none; } #xqueue_df th:nth-child(2), #queue_df td:nth-child(2) { text-align: center; vertical-align: middle; white-space: normal; } #queue_df td:nth-child(2) { cursor: default !important; } #queue_df th:nth-child(3), #queue_df td:nth-child(3) { width: 60px; text-align: center; vertical-align: middle; cursor: default !important; pointer-events: none; } #queue_df th:nth-child(4), #queue_df td:nth-child(4) { width: 60px; text-align: center; white-space: nowrap; cursor: default !important; pointer-events: none; } #queue_df th:nth-child(5), #queue_df td:nth-child(7), #queue_df th:nth-child(6), #queue_df td:nth-child(8) { width: 60px; text-align: center; vertical-align: middle; } #queue_df td:nth-child(5) img, #queue_df td:nth-child(6) img { max-width: 50px; max-height: 50px; object-fit: contain; display: block; margin: auto; cursor: pointer; } #queue_df th:nth-child(7), #queue_df td:nth-child(9), #queue_df th:nth-child(8), #queue_df td:nth-child(10), #queue_df th:nth-child(9), #queue_df td:nth-child(11) { width: 20px; padding: 2px !important; cursor: pointer; text-align: center; font-weight: bold; vertical-align: middle; } #queue_df td:nth-child(5):hover, #queue_df td:nth-child(6):hover, #queue_df td:nth-child(7):hover, #queue_df td:nth-child(8):hover, #queue_df td:nth-child(9):hover { background-color: #e0e0e0; } #image-modal-container { position: fixed; top: 0; left: 0; width: 100%; height: 100%; background-color: rgba(0, 0, 0, 0.7); justify-content: center; align-items: center; z-index: 1000; padding: 20px; box-sizing: border-box; } #image-modal-container > div { background-color: white; padding: 15px; border-radius: 8px; max-width: 90%; max-height: 90%; overflow: auto; position: relative; display: flex; flex-direction: column; } #image-modal-container img { max-width: 100%; max-height: 80vh; object-fit: contain; margin-top: 10px; } #image-modal-close-button-row { display: flex; justify-content: flex-end; } #image-modal-close-button-row button { cursor: pointer; } .progress-container-custom { width: 100%; background-color: #e9ecef; border-radius: 0.375rem; overflow: hidden; height: 25px; position: relative; margin-top: 5px; margin-bottom: 5px; } .progress-bar-custom { height: 100%; background-color: #0d6efd; transition: width 0.3s ease-in-out; display: flex; align-items: center; justify-content: center; color: white; font-size: 0.9em; font-weight: bold; white-space: nowrap; overflow: hidden; } .progress-bar-custom.idle { background-color: #6c757d; } .progress-bar-text { position: absolute; top: 0; left: 0; width: 100%; height: 100%; display: flex; align-items: center; justify-content: center; color: white; mix-blend-mode: difference; font-size: 0.9em; font-weight: bold; white-space: nowrap; z-index: 2; pointer-events: none; } """ UI_theme = server_config.get("UI_theme", "default") UI_theme = args.theme if len(args.theme) > 0 else UI_theme if UI_theme == "gradio": theme = None else: theme = gr.themes.Soft(font=["Verdana"], primary_hue="sky", neutral_hue="slate", text_size="md") with gr.Blocks(css=css, theme=theme, title= "Wan2GP") as main: gr.Markdown("

WanGP v4.5 by DeepBeepMeep ") # (Updates)

") global model_list tab_state = gr.State({ "tab_no":0 }) with gr.Tabs(selected="video_gen", ) as main_tabs: with gr.Tab("Video Generator", id="video_gen"): with gr.Row(): if args.lock_model: gr.Markdown("

" + get_model_name(transformer_filename) + "

") model_choice = gr.Dropdown(visible=False, value= get_model_type(transformer_filename)) else: gr.Markdown("
") model_choice = generate_dropdown_model_list() gr.Markdown("
") with gr.Row(): header = gr.Markdown(generate_header(transformer_filename, compile, attention_mode), visible= True) with gr.Row(): ( state, loras_choices, lset_name, state, video_guide, video_mask, video_prompt_type_video_trigger ) = generate_video_tab(model_choice=model_choice, header=header, main = main) with gr.Tab("Informations", id="info"): generate_info_tab() with gr.Tab("Video Mask Creator", id="video_mask_creator") as video_mask_creator: from preprocessing.matanyone import app as matanyone_app vmc_event_handler = matanyone_app.get_vmc_event_handler() matanyone_app.display(main_tabs, model_choice, video_guide, video_mask, video_prompt_type_video_trigger) if not args.lock_config: with gr.Tab("Downloads", id="downloads") as downloads_tab: generate_download_tab(lset_name, loras_choices, state) with gr.Tab("Configuration", id="configuration"): generate_configuration_tab(state, main, header, model_choice) with gr.Tab("About"): generate_about_tab() main_tabs.select(fn=select_tab, inputs= [tab_state], outputs= main_tabs) return main if __name__ == "__main__": atexit.register(autosave_queue) download_ffmpeg() # threading.Thread(target=runner, daemon=True).start() os.environ["GRADIO_ANALYTICS_ENABLED"] = "False" server_port = int(args.server_port) if os.name == "nt": asyncio.set_event_loop_policy(asyncio.WindowsSelectorEventLoopPolicy()) if server_port == 0: server_port = int(os.getenv("SERVER_PORT", "7860")) server_name = args.server_name if args.listen: server_name = "0.0.0.0" if len(server_name) == 0: server_name = os.getenv("SERVER_NAME", "localhost") demo = create_demo() if args.open_browser: import webbrowser if server_name.startswith("http"): url = server_name else: url = "http://" + server_name webbrowser.open(url + ":" + str(server_port), new = 0, autoraise = True) demo.launch(server_name=server_name, server_port=server_port, share=args.share, allowed_paths=[save_path])