import os import requests import gradio as gr import uuid import datetime from supabase import create_client, Client from supabase.lib.client_options import ClientOptions import dotenv from google.cloud import storage import json from pathlib import Path import mimetypes from workflow_handler import WanVideoWorkflow from video_config import MODEL_FRAME_RATES, calculate_frames import asyncio dotenv.load_dotenv() SCRIPT_DIR = Path(__file__).parent CONFIG_PATH = SCRIPT_DIR / "config.json" WORKFLOW_PATH = SCRIPT_DIR / "wani2v.json" loras = [ { #I suggest it to be a gif instead of an mp4! "image": "https://huggingface.co/Remade-AI/Squish/resolve/main/example_gifs/person_squish.gif", #This is an id you can send to your backend, obviously you can change it "id": "06ce6840-f976-4963-9644-b6cf7f323f90", #This is the title that is shown on the front end "title": "Squish", "example_prompt": "In the video, a miniature rodent is presented. The rodent is held in a person's hands. The person then presses on the rodent, causing a sq41sh squish effect. The person keeps pressing down on the rodent, further showing the sq41sh squish effect.", }, { "image": "https://huggingface.co/Remade-AI/Rotate/resolve/main/example_videos/chair-rotate.gif", "id": "4ac08cfa-841e-4aa9-9022-c3fc80fb6ef4", "title": "Rotate", "example_prompt": "The video shows an elderly Asian man's head and shoulders with blurred background, performing a r0t4tion 360 degrees rotation.", }, { "image": "https://huggingface.co/Remade-AI/Cakeify/resolve/main/example_gifs/timberland_cakeify.gif", "id": "b05c1dc7-a71c-4d24-b512-4877a12dea7e", "title": "Cakeify", "example_prompt": "The video opens on a woman. A knife, held by a hand, is coming into frame and hovering over the woman. The knife then begins cutting into the woman to c4k3 cakeify it. As the knife slices the woman open, the inside of the woman is revealed to be cake with chocolate layers. The knife cuts through and the contents of the woman are revealed." }, { "image": "https://huggingface.co/Remade-AI/Muscle/resolve/main/example_videos/man2_muscle.gif", "id": "3c6fd399-e558-43fa-8cd3-828300aac6f8", "title": "Muscle", "example_prompt": "A man t2k1s takes off clothes revealing a lean muscular body and shows off muscles, looking towards the camera." }, { "image": "https://storage.googleapis.com/remade-v2/huggingface_assets/crush_example.gif", "id": "d8a2912b-94e4-4227-9c45-356679af34fd", "title": "Crush", "example_prompt": "The video begins with a cube saying closed source. A hydraulic press positioned above slowly descends towards the cube. Upon contact, the hydraulic press c5us4 crushes it, deforming and flattening the cube, causing the cube to collapse inward until the cube is no longer recognizable." }, { "image": "https://storage.googleapis.com/remade-v2/huggingface_assets/decay_example.gif", "id": "6b6f64dc-ac14-44b2-b91c-a510cb7f7f32", "title": "Decay", "example_prompt": "The video shows a man. The d3c4y decay time-lapse begins, causing the man to change. The man is initially whole, but soon he appears to be rotting. The man slowly becomes increasingly shriveled and discolored, and eventually, the man decomposes and falls apart. The man is rotting in the center and appears to be covered in mold, completing the d3c4y decay time-lapse." }, { "image": "https://storage.googleapis.com/remade-v2/huggingface_assets/jesus_example.gif", "id": "615fe106-fec4-44bb-b28b-2864cb322027", "title": "Jesus", "example_prompt": "The video begins with a smiling woman with a pink shirt looking at the camera. Then jesus appears behind her as h54g hugs jesus. Jesus embraces the woman, and they both smile in front of a park." }, { "image": "https://storage.googleapis.com/remade-v2/huggingface_assets/inflate_example.gif", "id": "da2b1c34-7be8-4161-a733-e8b19a98901c", "title": "Inflate", "example_prompt": "The large, bald man rides a gray donkey, then infl4t3 inflates it, both the man and the donkey expanding into giant, inflated figures against the desert landscape." }, ] # Initialize Supabase client with async support supabase: Client = create_client( os.getenv('SUPABASE_URL'), os.getenv('SUPABASE_KEY'), ) def initialize_gcs(): """Initialize Google Cloud Storage client with credentials from environment""" try: # Parse service account JSON from environment variable service_account_json = os.getenv('SERVICE_ACCOUNT_JSON') if not service_account_json: raise ValueError("SERVICE_ACCOUNT_JSON environment variable not found") credentials_info = json.loads(service_account_json) # Initialize storage client storage_client = storage.Client.from_service_account_info(credentials_info) print("Successfully initialized Google Cloud Storage client") return storage_client except Exception as e: print(f"Error initializing Google Cloud Storage: {e}") raise def upload_to_gcs(file_path, content_type=None, folder='user_uploads'): """ Uploads a file to Google Cloud Storage Args: file_path: Path to the file to upload content_type: MIME type of the file (optional) folder: Folder path in bucket (default: 'user_uploads') Returns: str: Public URL of the uploaded file """ try: bucket_name = 'remade-v2' storage_client = initialize_gcs() bucket = storage_client.bucket(bucket_name) # Get file extension and generate unique filename file_extension = Path(file_path).suffix if not content_type: content_type = mimetypes.guess_type(file_path)[0] or 'application/octet-stream' # Validate file type valid_types = ['image/jpeg', 'image/png', 'image/gif'] if content_type not in valid_types: raise ValueError("Invalid file type. Please upload a JPG, PNG or GIF image.") # Generate unique filename with proper path structure filename = f"{str(uuid.uuid4())}{file_extension}" file_path_in_gcs = f"{folder}/{filename}" # Create blob and set metadata blob = bucket.blob(file_path_in_gcs) blob.content_type = content_type blob.cache_control = 'public, max-age=31536000' print(f'Uploading file to GCS: {file_path_in_gcs}') # Upload the file blob.upload_from_filename( file_path, timeout=120 # 2 minute timeout ) # Generate public URL with correct path format image_url = f"https://storage.googleapis.com/{bucket_name}/{file_path_in_gcs}" print(f"Successfully uploaded to GCS: {image_url}") return image_url except Exception as e: print(f"Error uploading to GCS: {e}") raise ValueError(f"Failed to upload image to storage: {str(e)}") def build_lora_prompt(subject, lora_id): """ Builds a standardized prompt based on the selected LoRA and subject """ # Get LoRA config lora_config = next((lora for lora in loras if lora["id"] == lora_id), None) if not lora_config: raise ValueError(f"Invalid LoRA ID: {lora_id}") if lora_id == "06ce6840-f976-4963-9644-b6cf7f323f90": # Squish return ( f"In the video, a miniature {subject} is presented. " f"The {subject} is held in a person's hands. " f"The person then presses on the {subject}, causing a sq41sh squish effect. " f"The person keeps pressing down on the {subject}, further showing the sq41sh squish effect." ) elif lora_id == "4ac08cfa-841e-4aa9-9022-c3fc80fb6ef4": # Rotate return ( f"The video shows a {subject} performing a r0t4tion 360 degrees rotation." ) elif lora_id == "b05c1dc7-a71c-4d24-b512-4877a12dea7e": # Cakeify return ( f"The video opens on a {subject}. A knife, held by a hand, is coming into frame " f"and hovering over the {subject}. The knife then begins cutting into the {subject} " f"to c4k3 cakeify it. As the knife slices the {subject} open, the inside of the " f"{subject} is revealed to be cake with chocolate layers. The knife cuts through " f"and the contents of the {subject} are revealed." ) elif lora_id == "3c6fd399-e558-43fa-8cd3-828300aac6f8": # Muscle return ( f"A {subject} t2k1s takes off clothes revealing a lean muscular body and shows off muscles, " f"looking towards the camera." ) elif lora_id == "d8a2912b-94e4-4227-9c45-356679af34fd": # Crush return ( f"The video begins with a {subject}. A hydraulic press positioned above slowly descends " f"towards the {subject}. Upon contact, the hydraulic press c5us4 crushes it, deforming and " f"flattening the {subject}, causing the {subject} to collapse inward until the {subject} is " f"no longer recognizable." ) elif lora_id == "3c6fd399-e558-43fa-8cd3-828300aac6f8": # Decay return ( f"The video shows a {subject}. The d3c4y decay time-lapse begins, causing the {subject} to change. " f"The {subject} is initially whole, but soon it appears to be rotting. The {subject} slowly becomes " f"increasingly shriveled and discolored, and eventually, the {subject} decomposes and falls apart. " f"The {subject} is rotting in the center and appears to be covered in mold, completing the d3c4y decay time-lapse." ) elif lora_id == "615fe106-fec4-44bb-b28b-2864cb322027": # Jesus return ( f"The video begins with a {subject} looking at the camera. Then jesus appears behind the {subject} " f"as h54g hugs jesus. Jesus embraces the {subject}, and they both smile." ) elif lora_id == "da2b1c34-7be8-4161-a733-e8b19a98901c": # Inflate return ( f"The {subject} infl4t3 inflates, expanding into a giant, inflated figure." ) else: # Fallback to using the example prompt from the LoRA config if "example_prompt" in lora_config: # Replace any specific subject in the example with the user's subject return lora_config["example_prompt"].replace("rodent", subject).replace("woman", subject).replace("man", subject) else: raise ValueError(f"Unknown LoRA ID: {lora_id} and no example prompt available") def poll_generation_status(generation_id): """Poll generation status from database""" try: # Query the database for the current status response = supabase.table('generations') \ .select('*') \ .eq('generation_id', generation_id) \ .execute() if not response.data: return None return response.data[0] except Exception as e: print(f"Error polling generation status: {e}") raise e async def generate_video(input_image, subject, duration, selected_index, progress=gr.Progress()): try: # Initialize workflow handler with explicit paths workflow_handler = WanVideoWorkflow( supabase, config_path=str(CONFIG_PATH), workflow_path=str(WORKFLOW_PATH) ) # Check if the input is a URL (example image) or a file path (user upload) if input_image.startswith('http'): # It's already a URL, use it directly image_url = input_image else: # It's a file path, upload to GCS image_url = upload_to_gcs(input_image) # Map duration selection to actual seconds duration_mapping = { "Short (3s)": 3, "Long (5s)": 5 } video_duration = duration_mapping[duration] # Get LoRA config lora_config = next((lora for lora in loras if lora["id"] == selected_index), None) if not lora_config: raise ValueError(f"Invalid LoRA ID: {selected_index}") # Generate unique ID generation_id = str(uuid.uuid4()) # Update workflow prompt = build_lora_prompt(subject, selected_index) workflow_handler.update_prompt(prompt) workflow_handler.update_input_image(image_url) await workflow_handler.update_lora(lora_config) workflow_handler.update_length(video_duration) workflow_handler.update_output_name(generation_id) # Get final workflow workflow = workflow_handler.get_workflow() # Store generation data in Supabase generation_data = { "generation_id": generation_id, "user_id": "anonymous", "status": "queued", "progress": 0, "worker_id": None, "created_at": datetime.datetime.utcnow().isoformat(), "message": { "generationId": generation_id, "workflow": { "prompt": workflow } }, "metadata": { "prompt": { "original": subject, "enhanced": subject }, "lora": { "id": selected_index, "strength": 1.0, "name": lora_config["title"] }, "workflow": "img2vid", "dimensions": None, "input_image_url": image_url, "video_length": {"duration": video_duration}, }, "error": None, "output_url": None, "batch_id": None, "platform": "huggingface" } # Remove await - the execute() method returns the response directly response = supabase.table('generations').insert(generation_data).execute() print(f"Stored generation data with ID: {generation_id}") # Return generation ID for tracking return generation_id except Exception as e: print(f"Error in generate_video: {e}") raise e def update_selection(evt: gr.SelectData): selected_lora = loras[evt.index] sentence = f"Selected LoRA: {selected_lora['title']}" return selected_lora['id'], sentence async def handle_generation(image_input, subject, duration, selected_index, progress=gr.Progress(track_tqdm=True)): try: if selected_index is None: raise gr.Error("You must select a LoRA before proceeding.") # Generate the video and get generation ID generation_id = await generate_video(image_input, subject, duration, selected_index) # Poll for status updates while True: generation = poll_generation_status(generation_id) if not generation: raise ValueError(f"Generation {generation_id} not found") # Update progress if 'progress' in generation: progress_value = generation['progress'] progress_bar = f'