import gradio as gr import numpy as np import random import torch from PIL import Image import os from huggingface_hub import hf_hub_download from pathlib import Path import sys # Add src directory to Python path sys.path.append(os.path.dirname(os.path.dirname(os.path.abspath(__file__)))) from src import model_loader from src import pipeline from src.config import Config, DeviceConfig from transformers import CLIPTokenizer # Create data directory if it doesn't exist data_dir = Path("data") data_dir.mkdir(exist_ok=True) # Model configuration MODEL_REPO = "stable-diffusion-v1-5/stable-diffusion-v1-5" MODEL_FILENAME = "v1-5-pruned-emaonly.ckpt" model_file = data_dir / MODEL_FILENAME # Download model if it doesn't exist if not model_file.exists(): print(f"Downloading model from {MODEL_REPO}...") model_file = hf_hub_download( repo_id=MODEL_REPO, filename=MODEL_FILENAME, local_dir=data_dir, local_dir_use_symlinks=False ) print("Model downloaded successfully!") # Device configuration device = "cuda" if torch.cuda.is_available() else "cpu" print(f"Using device: {device}") # Initialize configuration config = Config( device=DeviceConfig(device=device), tokenizer=CLIPTokenizer.from_pretrained("openai/clip-vit-base-patch32") ) # Load models config.models = model_loader.load_models(str(model_file), device) MAX_SEED = np.iinfo(np.int32).max MAX_IMAGE_SIZE = 1024 def txt2img( prompt, negative_prompt, seed, randomize_seed, width, height, guidance_scale, num_inference_steps, progress=gr.Progress(track_tqdm=True), ): if randomize_seed: seed = random.randint(0, MAX_SEED) # Update config with user settings config.seed = seed config.diffusion.cfg_scale = guidance_scale config.diffusion.n_inference_steps = num_inference_steps config.model.width = width config.model.height = height # Generate image output_image = pipeline.generate( prompt=prompt, uncond_prompt=negative_prompt, input_image=None, config=config ) # Convert numpy array to PIL Image image = Image.fromarray(output_image) return image, seed def img2img( prompt, negative_prompt, seed, randomize_seed, width, height, guidance_scale, num_inference_steps, input_image, strength, progress=gr.Progress(track_tqdm=True), ): try: if randomize_seed: seed = random.randint(0, MAX_SEED) if input_image is None: return None, seed # Update config with user settings config.seed = seed config.diffusion.cfg_scale = guidance_scale config.diffusion.n_inference_steps = num_inference_steps config.model.width = width config.model.height = height config.diffusion.strength = strength # Generate image output_image = pipeline.generate( prompt=prompt, uncond_prompt=negative_prompt, input_image=input_image, config=config ) # Convert numpy array to PIL Image image = Image.fromarray(output_image) return image, seed except Exception as e: print(f"Error in img2img: {str(e)}") gr.Warning(f"Error: {str(e)}") return None, seed def inpaint( prompt, negative_prompt, seed, randomize_seed, width, height, guidance_scale, num_inference_steps, input_image, mask_image, strength, progress=gr.Progress(track_tqdm=True), ): try: if randomize_seed: seed = random.randint(0, MAX_SEED) if input_image is None or mask_image is None: gr.Warning("Both input image and mask are required for inpainting") return None, seed # Ensure mask is in the right format if mask_image.mode != "L": mask_image = mask_image.convert("L") # Update config with user settings config.seed = seed config.diffusion.cfg_scale = guidance_scale config.diffusion.n_inference_steps = num_inference_steps config.model.width = width config.model.height = height config.diffusion.strength = strength # Generate image with mask output_image = pipeline.generate( prompt=prompt, uncond_prompt=negative_prompt, input_image=input_image, mask_image=mask_image, config=config ) # Convert numpy array to PIL Image image = Image.fromarray(output_image) return image, seed except Exception as e: print(f"Error in inpainting: {str(e)}") gr.Warning(f"Error: {str(e)}") return None, seed examples = [ "A ultra sharp photorealtici painting of a futuristic cityscape at night with neon lights and flying cars", "A serene mountain landscape at sunset with snow-capped peaks and a clear lake reflection", "A detailed portrait of a cyberpunk character with glowing neon implants and holographic tattoos", ] css = """ #col-container { margin: 0 auto; max-width: 640px; } .tabs { margin-top: 10px; margin-bottom: 10px; } .disclaimer { font-size: 0.8em; color: #666; margin-top: 20px; } """ with gr.Blocks(css=css) as demo: with gr.Column(elem_id="col-container"): gr.Markdown(" # LiteDiffusion") with gr.Tabs(elem_classes="tabs") as tabs: with gr.TabItem("Text-to-Image"): txt2img_prompt = gr.Text( label="Prompt", max_lines=1, placeholder="Enter your prompt", ) txt2img_run = gr.Button("Generate", variant="primary") txt2img_result = gr.Image(label="Result") with gr.TabItem("Image-to-Image"): img2img_prompt = gr.Text( label="Prompt", max_lines=1, placeholder="Enter your prompt", ) with gr.Row(): with gr.Column(scale=1): input_image = gr.Image(label="Input Image", type="pil") strength_slider = gr.Slider( label="Strength", minimum=0.0, maximum=1.0, step=0.01, value=0.8, ) img2img_run = gr.Button("Generate", variant="primary") with gr.Column(scale=1): img2img_result = gr.Image(label="Result") with gr.TabItem("Inpainting"): inpaint_prompt = gr.Text( label="Prompt", max_lines=1, placeholder="Enter your prompt", ) with gr.Row(): with gr.Column(scale=1): inpaint_image = gr.Image(label="Input Image", type="pil") inpaint_mask = gr.Image(label="Mask (White areas will be inpainted)", type="pil") inpaint_strength = gr.Slider( label="Strength", minimum=0.0, maximum=1.0, step=0.01, value=0.8, ) inpaint_run = gr.Button("Generate", variant="primary") with gr.Column(scale=1): inpaint_result = gr.Image(label="Result") with gr.Accordion("Advanced Settings", open=False): negative_prompt = gr.Text( label="Negative prompt", max_lines=1, placeholder="Enter a negative prompt", ) seed = gr.Slider( label="Seed", minimum=0, maximum=MAX_SEED, step=1, value=42, ) randomize_seed = gr.Checkbox(label="Randomize seed", value=True) with gr.Row(): width = gr.Slider( label="Width", minimum=256, maximum=MAX_IMAGE_SIZE, step=32, value=512, ) height = gr.Slider( label="Height", minimum=256, maximum=MAX_IMAGE_SIZE, step=32, value=512, ) with gr.Row(): guidance_scale = gr.Slider( label="Guidance scale", minimum=0.0, maximum=10.0, step=0.1, value=7.5, ) num_inference_steps = gr.Slider( label="Number of inference steps", minimum=1, maximum=50, step=1, value=50, ) gr.Markdown( "By using LiteDiffusion, you agree to the terms in our [disclaimer](disclaimer.md).", elem_classes="disclaimer" ) # Example prompts for text to image gr.Examples(examples=examples, inputs=[txt2img_prompt]) # Text-to-Image generation txt2img_run.click( fn=txt2img, inputs=[ txt2img_prompt, negative_prompt, seed, randomize_seed, width, height, guidance_scale, num_inference_steps, ], outputs=[txt2img_result, seed], ) # Image-to-Image generation img2img_run.click( fn=img2img, inputs=[ img2img_prompt, negative_prompt, seed, randomize_seed, width, height, guidance_scale, num_inference_steps, input_image, strength_slider, ], outputs=[img2img_result, seed], ) # Inpainting inpaint_run.click( fn=inpaint, inputs=[ inpaint_prompt, negative_prompt, seed, randomize_seed, width, height, guidance_scale, num_inference_steps, inpaint_image, inpaint_mask, inpaint_strength, ], outputs=[inpaint_result, seed], ) if __name__ == "__main__": demo.launch()