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| 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() |