import os import cv2 import torch import gradio as gr import torchvision import warnings import numpy as np from PIL import Image, ImageSequence from moviepy.editor import VideoFileClip import imageio from diffusers import ( TextToVideoSDPipeline, AutoencoderKL, DDPMScheduler, DDIMScheduler, UNet3DConditionModel, ) from transformers import CLIPTokenizer, CLIPTextModel from diffusers.utils import export_to_video from typing import List from text2vid_torch2 import TextToVideoSDPipelineModded from invert_utils import ddim_inversion as dd_inversion from gifs_filter import filter import subprocess import spaces def load_frames(image: Image, mode='RGBA'): return np.array([np.array(frame.convert(mode)) for frame in ImageSequence.Iterator(image)]) def run_setup(): try: # Step 1: Install Git LFS subprocess.run(["git", "lfs", "install"], check=True) # Step 2: Clone the repository repo_url = "https://huggingface.co/Hmrishav/t2v_sketch-lora" subprocess.run(["git", "clone", repo_url], check=True) # Step 3: Move the checkpoint file source = "t2v_sketch-lora/checkpoint-2500" destination = "./checkpoint-2500/" os.rename(source, destination) print("Setup completed successfully!") except subprocess.CalledProcessError as e: print(f"Error during setup: {e}") except FileNotFoundError as e: print(f"File operation error: {e}") except Exception as e: print(f"Unexpected error: {e}") # Automatically run setup during app initialization run_setup() def save_gif(frames, path): imageio.mimsave( path, [frame.astype(np.uint8) for frame in frames], format="GIF", duration=1 / 10, loop=0 # 0 means infinite loop ) def load_image(imgname, target_size=None): pil_img = Image.open(imgname).convert('RGB') if target_size: if isinstance(target_size, int): target_size = (target_size, target_size) pil_img = pil_img.resize(target_size, Image.Resampling.LANCZOS) return torchvision.transforms.ToTensor()(pil_img).unsqueeze(0) def prepare_latents(pipe, x_aug): with torch.cuda.amp.autocast(): batch_size, num_frames, channels, height, width = x_aug.shape x_aug = x_aug.reshape(batch_size * num_frames, channels, height, width) latents = pipe.vae.encode(x_aug).latent_dist.sample() latents = latents.view(batch_size, num_frames, -1, latents.shape[2], latents.shape[3]) latents = latents.permute(0, 2, 1, 3, 4) return pipe.vae.config.scaling_factor * latents @torch.no_grad() def invert(pipe, inv, load_name, device="cuda", dtype=torch.bfloat16): input_img = [load_image(load_name, 256).to(device, dtype=dtype).unsqueeze(1)] * 5 input_img = torch.cat(input_img, dim=1) latents = prepare_latents(pipe, input_img).to(torch.bfloat16) inv.set_timesteps(25) id_latents = dd_inversion(pipe, inv, video_latent=latents, num_inv_steps=25, prompt="")[-1].to(dtype) return torch.mean(id_latents, dim=2, keepdim=True) def load_primary_models(pretrained_model_path): return ( DDPMScheduler.from_config(pretrained_model_path, subfolder="scheduler"), CLIPTokenizer.from_pretrained(pretrained_model_path, subfolder="tokenizer"), CLIPTextModel.from_pretrained(pretrained_model_path, subfolder="text_encoder"), AutoencoderKL.from_pretrained(pretrained_model_path, subfolder="vae"), UNet3DConditionModel.from_pretrained(pretrained_model_path, subfolder="unet"), ) def initialize_pipeline(model: str, device: str = "cuda"): with warnings.catch_warnings(): warnings.simplefilter("ignore") scheduler, tokenizer, text_encoder, vae, unet = load_primary_models(model) pipe = TextToVideoSDPipeline.from_pretrained( pretrained_model_name_or_path="damo-vilab/text-to-video-ms-1.7b", scheduler=scheduler, tokenizer=tokenizer, text_encoder=text_encoder.to(device=device, dtype=torch.bfloat16), vae=vae.to(device=device, dtype=torch.bfloat16), unet=unet.to(device=device, dtype=torch.bfloat16), ) pipe.scheduler = DDIMScheduler.from_config(pipe.scheduler.config) return pipe, pipe.scheduler # Initialize the models LORA_CHECKPOINT = "checkpoint-2500" os.environ["TORCH_CUDNN_V8_API_ENABLED"] = "1" device = 'cuda' if torch.cuda.is_available() else 'cpu' dtype = torch.bfloat16 pipe_inversion, inv = initialize_pipeline(LORA_CHECKPOINT, device) pipe = TextToVideoSDPipelineModded.from_pretrained( pretrained_model_name_or_path="damo-vilab/text-to-video-ms-1.7b", scheduler=pipe_inversion.scheduler, tokenizer=pipe_inversion.tokenizer, text_encoder=pipe_inversion.text_encoder, vae=pipe_inversion.vae, unet=pipe_inversion.unet, ).to(device) @spaces.GPU(duration=100) @torch.no_grad() def process_video(num_frames, num_seeds, generator, exp_dir, load_name, caption, lambda_): pipe_inversion.to(device) id_latents = invert(pipe_inversion, inv, load_name).to(device, dtype=dtype) latents = id_latents.repeat(num_seeds, 1, 1, 1, 1) generator = [torch.Generator(device="cuda").manual_seed(i) for i in range(num_seeds)] video_frames = pipe( prompt=caption, negative_prompt="", num_frames=num_frames, num_inference_steps=25, inv_latents=latents, guidance_scale=9, generator=generator, lambda_=lambda_, ).frames gifs = [] for seed in range(num_seeds): vid_name = f"{exp_dir}/mp4_logs/vid_{os.path.basename(load_name)[:-4]}-rand{seed}.mp4" gif_name = f"{exp_dir}/gif_logs/vid_{os.path.basename(load_name)[:-4]}-rand{seed}.gif" os.makedirs(os.path.dirname(vid_name), exist_ok=True) os.makedirs(os.path.dirname(gif_name), exist_ok=True) video_path = export_to_video(video_frames[seed], output_video_path=vid_name) VideoFileClip(vid_name).write_gif(gif_name) with Image.open(gif_name) as im: frames = load_frames(im) frames_collect = np.empty((0, 1024, 1024), int) for frame in frames: frame = cv2.resize(frame, (1024, 1024))[:, :, :3] frame = cv2.cvtColor(255 - frame, cv2.COLOR_RGB2GRAY) _, frame = cv2.threshold(255 - frame, 0, 255, cv2.THRESH_BINARY + cv2.THRESH_OTSU) frames_collect = np.append(frames_collect, [frame], axis=0) save_gif(frames_collect, gif_name) gifs.append(gif_name) return gifs def generate_output(image, prompt: str, num_seeds: int = 3, lambda_value: float = 0.5) -> List[str]: """Main function to generate output GIFs""" exp_dir = "static/app_tmp" os.makedirs(exp_dir, exist_ok=True) # Save the input image temporarily temp_image_path = os.path.join(exp_dir, "temp_input.png") image.save(temp_image_path) # Generate the GIFs generated_gifs = process_video( num_frames=10, num_seeds=num_seeds, generator=None, exp_dir=exp_dir, load_name=temp_image_path, caption=prompt, lambda_=1 - lambda_value ) # Apply filtering (assuming filter function is imported) filtered_gifs = filter(generated_gifs, temp_image_path) return filtered_gifs def create_gradio_interface(): with gr.Blocks(css=""" .container { max-width: 1200px; margin: 0 auto; padding: 20px; } .example-gallery { margin: 20px 0; padding: 20px; background: #f7f7f7; border-radius: 8px; } .selected-example { margin: 20px 0; padding: 20px; background: #ffffff; border-radius: 8px; } .controls-section { background: #ffffff; padding: 20px; margin: 20px 0; border-radius: 8px; } .output-gallery { min-height: 500px; margin: 20px 0; padding: 20px; background: #f7f7f7; border-radius: 8px; } .example-item { border-radius: 8px; overflow: hidden; box-shadow: 0 2px 4px rgba(0,0,0,0.1); transition: transform 0.2s; cursor: pointer; } .example-item:hover { transform: scale(1.05); } /* Prevent gallery images from expanding */ .gallery-image { height: 200px !important; width: 200px !important; object-fit: cover !important; } .generate-btn { width: 100%; margin-top: 1rem; } .generate-btn:disabled { opacity: 0.7; cursor: not-allowed; } """) as demo: gr.Markdown( """
# [FlipSketch: Flipping Static Drawings to Text-Guided Sketch Animations](https://hmrishavbandy.github.io/flipsketch-web/) ## [Hmrishav Bandyopadhyay](https://hmrishavbandy.github.io/) . [Yi-Zhe Song](https://personalpages.surrey.ac.uk/y.song/)
""" ) with gr.Tabs() as tabs: # First tab: Examples (Secure) with gr.Tab("Examples"): gr.Markdown("## Step 1 👉       Select a sketch from the gallery of sketches") examples_dir = "static/examples" if os.path.exists(examples_dir): example_images = [] for example in os.listdir(examples_dir): if example.endswith(('.png', '.jpg', '.jpeg')): example_path = os.path.join(examples_dir, example) example_images.append(Image.open(example_path)) example_selection = gr.Gallery( example_images, label="Sketch Gallery", elem_classes="example-gallery", columns=4, rows=2, height="auto", allow_preview=False, # Disable preview expansion show_share_button=False, interactive=False, selected_index=None # Don't pre-select any image ) gr.Markdown("## Step 2 👉       Describe the motion you want to generate") with gr.Group(elem_classes="selected-example"): with gr.Row(): selected_example = gr.Image( type="pil", label="Selected Sketch", scale=1, interactive=False, show_download_button=False, height=300 # Fixed height for consistency ) with gr.Column(scale=2): example_prompt = gr.Textbox( label="Prompt", placeholder="Describe the motion...", lines=3 ) with gr.Row(): example_num_seeds = gr.Slider( minimum=1, maximum=10, value=5, step=1, label="Seeds" ) example_lambda = gr.Slider( minimum=0, maximum=1, value=0.5, step=0.1, label="Motion Strength" ) example_generate_btn = gr.Button( "Generate Animation", variant="primary", elem_classes="generate-btn", interactive=True, ) gr.Markdown("## Result 👉       Generated Animations ❤️") example_gallery = gr.Gallery( label="Results", elem_classes="output-gallery", columns=3, rows=2, height="auto", allow_preview=False, # Disable preview expansion show_share_button=False, object_fit="cover", preview=False ) # Second tab: Upload with gr.Tab("Upload Your Sketch"): with gr.Group(elem_classes="selected-example"): with gr.Row(): upload_image = gr.Image( type="pil", label="Upload Your Sketch", scale=1, height=300, # Fixed height for consistency show_download_button=False, sources=["upload"], ) with gr.Column(scale=2): upload_prompt = gr.Textbox( label="Prompt", placeholder="Describe what you want to generate...", lines=3 ) with gr.Row(): upload_num_seeds = gr.Slider( minimum=1, maximum=10, value=5, step=1, label="Number of Variations" ) upload_lambda = gr.Slider( minimum=0, maximum=1, value=0.5, step=0.1, label="Motion Strength" ) upload_generate_btn = gr.Button( "Generate Animation", variant="primary", elem_classes="generate-btn", size="lg", interactive=True, ) gr.Markdown("## Result 👉       Generated Animations ❤️") upload_gallery = gr.Gallery( label="Results", elem_classes="output-gallery", columns=3, rows=2, height="auto", allow_preview=False, # Disable preview expansion show_share_button=False, object_fit="cover", preview=False ) # Event handlers def select_example(evt: gr.SelectData): prompts = {'sketch1.png': 'The camel walks slowly', 'sketch2.png': 'The wine in the wine glass sways from side to side', 'sketch3.png': 'The squirrel is eating a nut', 'sketch4.png': 'The surfer surfs on the waves', 'sketch5.png': 'A galloping horse', 'sketch6.png': 'The cat walks forward', 'sketch7.png': 'The eagle flies in the sky', 'sketch8.png': 'The flower is blooming slowly', 'sketch9.png': 'The reindeer looks around', 'sketch10.png': 'The cloud floats in the sky', 'sketch11.png': 'The jazz saxophonist performs on stage with a rhythmic sway, his upper body sways subtly to the rhythm of the music.', 'sketch12.png': 'The biker rides on the road',} if evt.index < len(example_images): example_img = example_images[evt.index] prompt_text = prompts.get(os.path.basename(example_img.filename), "") return [ example_img, prompt_text ] return [None, ""] example_selection.select( select_example, None, [selected_example, example_prompt] ) example_generate_btn.click( fn=generate_output, inputs=[ selected_example, example_prompt, example_num_seeds, example_lambda ], outputs=example_gallery ) upload_generate_btn.click( fn=generate_output, inputs=[ upload_image, upload_prompt, upload_num_seeds, upload_lambda ], outputs=upload_gallery ) return demo # Launch the app if __name__ == "__main__": demo = create_gradio_interface() demo.launch( server_name="0.0.0.0", server_port=7860, show_api=False )