Delete inference_webui.py

inference_webui.py

@@ -1,225 +0,0 @@
-import random
-import os
-import uuid
-from datetime import datetime
-import gradio as gr
-import numpy as np
-import spaces
-import torch
-from diffusers import DiffusionPipeline
-from PIL import Image
-
-# Create permanent storage directory
-SAVE_DIR = "saved_images"  # Gradio will handle the persistence
-if not os.path.exists(SAVE_DIR):
-    os.makedirs(SAVE_DIR, exist_ok=True)
-
-# Load the default image
-DEFAULT_IMAGE_PATH = "cover1.webp"
-
-device = "cuda" if torch.cuda.is_available() else "cpu"
-repo_id = "black-forest-labs/FLUX.1-dev"
-adapter_id = "alvdansen/pola-photo-flux"
-
-pipeline = DiffusionPipeline.from_pretrained(repo_id, torch_dtype=torch.bfloat16)
-pipeline.load_lora_weights(adapter_id)
-pipeline = pipeline.to(device)
-
-MAX_SEED = np.iinfo(np.int32).max
-MAX_IMAGE_SIZE = 1024
-
-def save_generated_image(image, prompt):
-    # Generate unique filename with timestamp
-    timestamp = datetime.now().strftime("%Y%m%d_%H%M%S")
-    unique_id = str(uuid.uuid4())[:8]
-    filename = f"{timestamp}_{unique_id}.png"
-    filepath = os.path.join(SAVE_DIR, filename)
-    
-    # Save the image
-    image.save(filepath)
-    
-    # Save metadata
-    metadata_file = os.path.join(SAVE_DIR, "metadata.txt")
-    with open(metadata_file, "a", encoding="utf-8") as f:
-        f.write(f"{filename}|{prompt}|{timestamp}\n")
-    
-    return filepath
-
-def load_generated_images():
-    if not os.path.exists(SAVE_DIR):
-        return []
-    
-    # Load all images from the directory
-    image_files = [os.path.join(SAVE_DIR, f) for f in os.listdir(SAVE_DIR) 
-                  if f.endswith(('.png', '.jpg', '.jpeg', '.webp'))]
-    # Sort by creation time (newest first)
-    image_files.sort(key=lambda x: os.path.getctime(x), reverse=True)
-    return image_files
-
-def load_predefined_images():
-    # Return empty list since we're not using predefined images
-    return []
-
-@spaces.GPU(duration=120)
-def inference(
-    prompt: str,
-    seed: int,
-    randomize_seed: bool,
-    width: int,
-    height: int,
-    guidance_scale: float,
-    num_inference_steps: int,
-    lora_scale: float,
-    progress: gr.Progress = gr.Progress(track_tqdm=True),
-):
-    if randomize_seed:
-        seed = random.randint(0, MAX_SEED)
-    generator = torch.Generator(device=device).manual_seed(seed)
-    
-    image = pipeline(
-        prompt=prompt,
-        guidance_scale=guidance_scale,
-        num_inference_steps=num_inference_steps,
-        width=width,
-        height=height,
-        generator=generator,
-        joint_attention_kwargs={"scale": lora_scale},
-    ).images[0]
-    
-    # Save the generated image
-    filepath = save_generated_image(image, prompt)
-    
-    # Return the image, seed, and updated gallery
-    return image, seed, load_generated_images()
-
-
-examples = [
-   "polaroid style, a woman with long blonde hair wearing big round hippie sunglasses with a slight smile, white oversized fur coat, black dress, early evening in the city, polaroid style [trigger]"
-]
-
-css = """
-footer {
-    visibility: hidden;
-}
-"""
-
-with gr.Blocks(theme=gr.themes.Soft(), css=css, analytics_enabled=False) as demo:
-    gr.HTML('<div class="title"> Polaroid style Image Generation </div>')
-    
-
-    with gr.Tabs() as tabs:
-        with gr.Tab("Generation"):
-            with gr.Column(elem_id="col-container"):
-                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)
-
-                # Modified to include the default image
-                result = gr.Image(
-                    label="Result",
-                    show_label=False,
-                    value=DEFAULT_IMAGE_PATH  # Set the default image
-                )
-
-                with gr.Accordion("Advanced Settings", open=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=1024,
-                        )
-                        height = gr.Slider(
-                            label="Height",
-                            minimum=256,
-                            maximum=MAX_IMAGE_SIZE,
-                            step=32,
-                            value=768,
-                        )
-
-                    with gr.Row():
-                        guidance_scale = gr.Slider(
-                            label="Guidance scale",
-                            minimum=0.0,
-                            maximum=10.0,
-                            step=0.1,
-                            value=3.5,
-                        )
-                        num_inference_steps = gr.Slider(
-                            label="Number of inference steps",
-                            minimum=1,
-                            maximum=50,
-                            step=1,
-                            value=30,
-                        )
-                        lora_scale = gr.Slider(
-                            label="LoRA scale",
-                            minimum=0.0,
-                            maximum=1.0,
-                            step=0.1,
-                            value=1.0,
-                        )
-
-                gr.Examples(
-                    examples=examples,
-                    inputs=[prompt],
-                    outputs=[result, seed],
-                )
-
-        with gr.Tab("Gallery"):
-            gallery_header = gr.Markdown("### Generated Images Gallery")
-            generated_gallery = gr.Gallery(
-                label="Generated Images",
-                columns=6,
-                show_label=False,
-                value=load_generated_images(),
-                elem_id="generated_gallery",
-                height="auto"
-            )
-            refresh_btn = gr.Button("🔄 Refresh Gallery")
-
-
-    # Event handlers
-    def refresh_gallery():
-        return load_generated_images()
-
-    refresh_btn.click(
-        fn=refresh_gallery,
-        inputs=None,
-        outputs=generated_gallery,
-    )
-
-    gr.on(
-        triggers=[run_button.click, prompt.submit],
-        fn=inference,
-        inputs=[
-            prompt,
-            seed,
-            randomize_seed,
-            width,
-            height,
-            guidance_scale,
-            num_inference_steps,
-            lora_scale,
-        ],
-        outputs=[result, seed, generated_gallery],
-    )
-
-demo.queue()
-demo.launch()