Update app.py

app.py

@@ -1,154 +1,96 @@
 import gradio as gr
-import numpy as np
-import random
-
-# import spaces #[uncomment to use ZeroGPU]
-from diffusers import DiffusionPipeline
+from diffusers import StableDiffusionPipeline
 import torch
 
-device = "cuda" if torch.cuda.is_available() else "cpu"
-model_repo_id = "stabilityai/sdxl-turbo"  # Replace to the model you would like to use
-
-if torch.cuda.is_available():
-    torch_dtype = torch.float16
-else:
-    torch_dtype = torch.float32
-
-pipe = DiffusionPipeline.from_pretrained(model_repo_id, torch_dtype=torch_dtype)
-pipe = pipe.to(device)
-
-MAX_SEED = np.iinfo(np.int32).max
-MAX_IMAGE_SIZE = 1024
-
-
-# @spaces.GPU #[uncomment to use ZeroGPU]
-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)
-
-    generator = torch.Generator().manual_seed(seed)
-
-    image = pipe(
-        prompt=prompt,
-        negative_prompt=negative_prompt,
-        guidance_scale=guidance_scale,
-        num_inference_steps=num_inference_steps,
-        width=width,
-        height=height,
-        generator=generator,
-    ).images[0]
-
-    return image, seed
-
-
-examples = [
-    "Astronaut in a jungle, cold color palette, muted colors, detailed, 8k",
-    "An astronaut riding a green horse",
-    "A delicious ceviche cheesecake slice",
-]
-
-css = """
-#col-container {
-    margin: 0 auto;
-    max-width: 640px;
+# --- Configuration ---
+# Your Hugging Face repository ID where the LoRAs are stored
+HF_REPO_ID = "TonyRaju/GenImg" 
+BASE_MODEL_ID = "runwayml/stable-diffusion-v1-5"
+
+# --- Define Available LoRAs from your HF Repo ---
+# The key is the display name in the dropdown.
+# The value is the subfolder path inside your Hugging Face repository.
+AVAILABLE_LORAS = {
+    "None (Base Model)": None,
+    # --- Artists ---
+    "Artist: Vincent van Gogh": "artists/Vincent_van_Gogh",
+    "Artist: Claude Monet": "artists/Claude_Monet",
+    "Artist: Rembrandt": "artists/Rembrandt",
+    "Artist: Pablo Picasso": "artists/Pablo_Picasso",
+    # --- Styles ---
+    "Style: Impressionism": "styles/Impressionism",
+    "Style: Baroque": "styles/Baroque",
+    "Style: Cubism": "styles/Cubism",
+    "Style: Abstract Expressionism": "styles/Abstract_Expressionism",
+    "Style: Romanticism": "styles/Romanticism",
+    "Style: Realism": "styles/Realism",
+    "Style: Post Impressionism": "styles/Post_Impressionism",
 }
-"""
+print("✅ LoRA models from your Hugging Face repo are configured.")
 
-with gr.Blocks(css=css) as demo:
-    with gr.Column(elem_id="col-container"):
-        gr.Markdown(" # Text-to-Image Gradio Template")
-
-        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=0,
-            )
-
-            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,  # Replace with defaults that work for your model
-                )
-
-                height = gr.Slider(
-                    label="Height",
-                    minimum=256,
-                    maximum=MAX_IMAGE_SIZE,
-                    step=32,
-                    value=1024,  # Replace with defaults that work for your model
-                )
-
-            with gr.Row():
-                guidance_scale = gr.Slider(
-                    label="Guidance scale",
-                    minimum=0.0,
-                    maximum=10.0,
-                    step=0.1,
-                    value=0.0,  # Replace with defaults that work for your model
-                )
-
-                num_inference_steps = gr.Slider(
-                    label="Number of inference steps",
-                    minimum=1,
-                    maximum=50,
-                    step=1,
-                    value=2,  # Replace with defaults that work for your model
-                )
-
-        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()
+# --- Setup ---
+device = "cuda" if torch.cuda.is_available() else "cpu"
+dtype = torch.float16 if device == "cuda" else torch.float32
+print(f"Using device: {device}")
+
+# --- Load the Base Model ---
+# This will be cached in the Space for faster startups.
+print(f"🎨 Loading base model: {BASE_MODEL_ID}")
+pipe = StableDiffusionPipeline.from_pretrained(BASE_MODEL_ID, torch_dtype=dtype).to(device)
+if device == "cpu":
+    pipe.enable_attention_slicing()
+
+# --- The Core Generation Function ---
+def generate(prompt, quality, lora_choice):
+    """
+    Generates an image, dynamically loading the selected LoRA from the Hub.
+    """
+    # Unload any existing LoRA to reset to the base model
+    pipe.unload_lora_weights()
+    
+    lora_subfolder = AVAILABLE_LORAS.get(lora_choice)
+
+    if lora_subfolder:
+        print(f"✨ Downloading and applying LoRA: {lora_choice}")
+        try:
+            # Load LoRA directly from the Hugging Face Hub
+            pipe.load_lora_weights(HF_REPO_ID, subfolder=lora_subfolder)
+        except Exception as e:
+            print(f"❌ Failed to load LoRA from Hub '{HF_REPO_ID}/{lora_subfolder}': {e}")
+    else:
+        print("🎨 Using base model (no LoRA selected)")
+
+    steps = 25 if quality == "Fast" else 40
+    guidance_scale = 7.5
+    
+    print(f"🚀 Generating with prompt: '{prompt}'")
+    with torch.no_grad():
+        image = pipe(prompt, num_inference_steps=steps, guidance_scale=guidance_scale).images[0]
+        
+    return image
+
+# --- Build the Gradio UI ---
+title = f"🎨 Stable Diffusion Gallery from {HF_REPO_ID}"
+description = "Select a trained LoRA model from your Hugging Face repository to apply its style. The first time you select a LoRA, it may take a moment to download."
+
+demo = gr.Interface(
+    fn=generate,
+    inputs=[
+        gr.Textbox(label="Enter your prompt", placeholder="A beautiful painting of a fantasy landscape..."),
+        gr.Dropdown(["Fast", "High Quality"], value="Fast", label="Generation Quality"),
+        gr.Dropdown(
+            choices=list(AVAILABLE_LORAS.keys()),
+            value="None (Base Model)", 
+            label="Select a Trained LoRA Model"
+        )
+    ],
+    outputs=gr.Image(label="Generated Image"),
+    title=title,
+    description=description,
+    examples=[
+        ["A portrait of an astronaut, cinematic lighting, by vincent van gogh", "Fast", "Artist: Vincent van Gogh"],
+        ["A peaceful village in the mountains, impressionism style", "High Quality", "Style: Impressionism"],
+    ]
+)
+
+demo.launch()