import gradio as gr import os import subprocess import torch import gc from diffusers import AutoencoderKLCogVideoX, CogVideoXImageToVideoPipeline, CogVideoXTransformer3DModel from diffusers.utils import export_to_video, load_image from transformers import T5EncoderModel, T5Tokenizer from datetime import datetime import random from moviepy.editor import VideoFileClip import ffmpeg from huggingface_hub import hf_hub_download # Ensure 'checkpoint' directory exists os.makedirs("checkpoints", exist_ok=True) # Download LoRA weights hf_hub_download( repo_id="wenqsun/DimensionX", filename="orbit_left_lora_weights.safetensors", local_dir="checkpoints" ) hf_hub_download( repo_id="wenqsun/DimensionX", filename="orbit_up_lora_weights.safetensors", local_dir="checkpoints" ) # Load models in the global scope model_id = "THUDM/CogVideoX-5b-I2V" transformer = CogVideoXTransformer3DModel.from_pretrained(model_id, subfolder="transformer", torch_dtype=torch.float16).to("cpu") text_encoder = T5EncoderModel.from_pretrained(model_id, subfolder="text_encoder", torch_dtype=torch.float16).to("cpu") vae = AutoencoderKLCogVideoX.from_pretrained(model_id, subfolder="vae", torch_dtype=torch.float16).to("cpu") tokenizer = T5Tokenizer.from_pretrained(model_id, subfolder="tokenizer") pipe = CogVideoXImageToVideoPipeline.from_pretrained(model_id, tokenizer=tokenizer, text_encoder=text_encoder, transformer=transformer, vae=vae, torch_dtype=torch.float16) # Add this near the top after imports os.environ['PYTORCH_CUDA_ALLOC_CONF'] = 'expandable_segments:True' def calculate_resize_dimensions(width, height, max_width=1024): """Calculate new dimensions maintaining aspect ratio""" if width <= max_width: return width, height aspect_ratio = height / width new_width = max_width new_height = int(max_width * aspect_ratio) # Make height even number for video encoding new_height = new_height - (new_height % 2) return new_width, new_height def infer(image_path, prompt, orbit_type, progress=gr.Progress(track_tqdm=True)): # Move everything to CPU initially pipe.to("cpu") torch.cuda.empty_cache() # Load and get original image dimensions image = load_image(image_path) original_width, original_height = image.size print(f"IMAGE INPUT SIZE: {original_width} x {original_height}") # Calculate target dimensions maintaining aspect ratio target_width, target_height = calculate_resize_dimensions(original_width, original_height) print(f"TARGET SIZE: {target_width} x {target_height}") lora_path = "checkpoints/" weight_name = "orbit_left_lora_weights.safetensors" if orbit_type == "Left" else "orbit_up_lora_weights.safetensors" lora_rank = 256 adapter_timestamp = datetime.now().strftime("%Y%m%d_%H%M%S") # Load LoRA weights on CPU pipe.load_lora_weights(lora_path, weight_name=weight_name, adapter_name=f"adapter_{adapter_timestamp}") pipe.fuse_lora(lora_scale=1 / lora_rank) try: # Move to GPU just before inference pipe.to("cuda") torch.cuda.empty_cache() prompt = f"{prompt}. High quality, ultrarealistic detail and breath-taking movie-like camera shot." seed = random.randint(0, 2**8 - 1) with torch.inference_mode(): video = pipe( image, prompt, num_inference_steps=50, guidance_scale=7.0, use_dynamic_cfg=True, generator=torch.Generator(device="cpu").manual_seed(seed) ) finally: # Ensure cleanup happens even if inference fails pipe.to("cpu") pipe.unfuse_lora() pipe.unload_lora_weights() torch.cuda.empty_cache() gc.collect() # Generate initial output video timestamp = datetime.now().strftime("%Y%m%d_%H%M%S") temp_path = f"output_{timestamp}_temp.mp4" final_path = f"output_{timestamp}.mp4" # First export the original video export_to_video(video.frames[0], temp_path, fps=8) try: # Use ffmpeg via subprocess cmd = [ 'ffmpeg', '-i', temp_path, '-vf', f'scale={target_width}:{target_height}', '-c:v', 'libx264', '-preset', 'medium', '-crf', '23', '-y', # Overwrite output file if it exists final_path ] subprocess.run(cmd, check=True, capture_output=True) except subprocess.CalledProcessError as e: print(f"FFmpeg error: {e.stderr.decode()}") raise e finally: if os.path.exists(temp_path): os.remove(temp_path) return final_path # Set up Gradio UI with gr.Blocks(analytics_enabled=False) as demo: with gr.Column(elem_id="col-container"): gr.Markdown("# DimensionX") gr.Markdown("### Create Any 3D and 4D Scenes from a Single Image with Controllable Video Diffusion") gr.HTML("""
Duplicate this Space Follow me on HF
""") with gr.Row(): with gr.Column(): image_in = gr.Image(label="Image Input", type="filepath") prompt = gr.Textbox(label="Prompt") orbit_type = gr.Radio(label="Orbit type", choices=["Left", "Up"], value="Left", interactive=True) submit_btn = gr.Button("Submit") with gr.Column(): video_out = gr.Video(label="Video output") examples = gr.Examples( examples = [ [ "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/astronaut.jpg", "An astronaut hatching from an egg, on the surface of the moon, the darkness and depth of space realised in the background.", "Left", "./examples/output_astronaut_left.mp4" ], [ "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/astronaut.jpg", "An astronaut hatching from an egg, on the surface of the moon, the darkness and depth of space realised in the background.", "Up", "./examples/output_astronaut_up.mp4" ] ], inputs=[image_in, prompt, orbit_type, video_out] ) submit_btn.click( fn=infer, inputs=[image_in, prompt, orbit_type], outputs=[video_out] ) demo.queue().launch(show_error=True, show_api=False, ssr_mode=False)