Delete app.py

app.py

@@ -1,184 +0,0 @@
-import gradio as gr
-import numpy as np
-import random
-import torch
-from PIL import Image
-import model_loader
-import pipeline
-from transformers import CLIPTokenizer
-from config import Config, DeviceConfig
-import os
-from huggingface_hub import hf_hub_download
-from pathlib import Path
-
-# Create data directory if it doesn't exist
-data_dir = Path("data")
-data_dir.mkdir(exist_ok=True)
-
-# Model configuration
-MODEL_REPO = "stabilityai/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 with SE blocks enabled
-config.models = model_loader.load_models(str(model_file), device, use_se=True)
-
-MAX_SEED = np.iinfo(np.int32).max
-MAX_IMAGE_SIZE = 1024
-
-def infer(
-    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,
-        config=config
-    )
-    
-    # Convert numpy array to PIL Image
-    image = Image.fromarray(output_image)
-    
-    return image, 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;
-}
-"""
-
-with gr.Blocks(css=css) as demo:
-    with gr.Column(elem_id="col-container"):
-        gr.Markdown(" # Custom Diffusion Model Text-to-Image Generator")
-
-        with gr.Row():
-            prompt = gr.Text(
-                label="Prompt",
-                show_label=False,
-                max_lines=1,
-                placeholder="Enter your prompt",
-                container=False,
-            )
-
-            run_button = gr.Button("Run", scale=0, variant="primary")
-
-        result = gr.Image(label="Result", show_label=False)
-
-        with gr.Accordion("Advanced Settings", open=False):
-            negative_prompt = gr.Text(
-                label="Negative prompt",
-                max_lines=1,
-                placeholder="Enter a negative prompt",
-                visible=False,
-            )
-
-            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.Examples(examples=examples, inputs=[prompt])
-        
-    gr.on(
-        triggers=[run_button.click, prompt.submit],
-        fn=infer,
-        inputs=[
-            prompt,
-            negative_prompt,
-            seed,
-            randomize_seed,
-            width,
-            height,
-            guidance_scale,
-            num_inference_steps,
-        ],
-        outputs=[result, seed],
-    )
-
-if __name__ == "__main__":
-    demo.launch()