Create app.py

app.py

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+import os
+import torch
+import gradio as gr
+from diffusers import FluxPipeline
+import json
+from huggingface_hub import hf_hub_download
+import time
+
+# Constants
+MODEL_ID = "black-forest-labs/FLUX.1-dev"  # Base model
+YOUR_LORA = "anuraj-sisyphus/avatar-loras"  # Your LoRA model
+DEFAULT_PROMPT = "a portrait of a person with realistic details, high quality"
+DEFAULT_NEG_PROMPT = "low quality, blurry, distorted, deformed features"
+
+# Create a list of available LoRAs
+# You can expand this with other compatible LoRAs if desired
+LORAS = [
+    {
+        "name": "Avatar LoRAs",
+        "repo_id": "anuraj-sisyphus/avatar-loras",
+        "filename": "SLAY1MNSHA.safetensors",  # Update this with the actual filename
+        "base_model": "FLUX.1-dev"
+    }
+]
+
+# Initialize the pipeline
+@torch.inference_mode()
+def load_model():
+    pipe = FluxPipeline.from_pretrained(
+        MODEL_ID,
+        torch_dtype=torch.bfloat16 if torch.cuda.is_available() else torch.float32
+    )
+    
+    if torch.cuda.is_available():
+        pipe = pipe.to("cuda")
+    
+    return pipe
+
+# Generate image function
+def generate_image(
+    prompt,
+    negative_prompt,
+    lora_selection,
+    lora_scale=0.8,
+    guidance_scale=5.0,
+    steps=30,
+    width=1024,
+    height=1024,
+    seed=None
+):
+    # Load model if not already loaded
+    global pipe
+    if "pipe" not in globals():
+        pipe = load_model()
+    
+    # Set the seed for reproducibility
+    if seed is None or seed == 0:
+        seed = int(time.time()) % 100000
+    generator = torch.Generator(device="cuda" if torch.cuda.is_available() else "cpu").manual_seed(seed)
+    
+    # Find the selected LoRA details
+    selected_lora = None
+    for lora in LORAS:
+        if lora["name"] == lora_selection:
+            selected_lora = lora
+            break
+    
+    if selected_lora:
+        # Unload any previous LoRA
+        try:
+            pipe.unload_lora_weights()
+        except:
+            pass
+            
+        # Load the selected LoRA
+        pipe.load_lora_weights(
+            selected_lora["repo_id"],
+            weight_name=selected_lora.get("filename", None)
+        )
+        
+        # Set the LoRA scale
+        pipe.fuse_lora(lora_scale=lora_scale)
+    
+    # Generate the image
+    image = pipe(
+        prompt=prompt,
+        negative_prompt=negative_prompt,
+        guidance_scale=guidance_scale,
+        num_inference_steps=steps,
+        width=width,
+        height=height,
+        generator=generator
+    ).images[0]
+    
+    return image, seed
+
+# Create the Gradio interface
+with gr.Blocks(title="Avatar LoRAs Explorer") as demo:
+    gr.Markdown("# Avatar LoRAs Explorer")
+    gr.Markdown("Generate images using the Avatar LoRAs model. Adjust settings to customize your results.")
+    
+    with gr.Row():
+        with gr.Column(scale=2):
+            prompt = gr.Textbox(
+                label="Prompt",
+                placeholder="Enter your prompt here...",
+                value=DEFAULT_PROMPT,
+                lines=3
+            )
+            negative_prompt = gr.Textbox(
+                label="Negative Prompt",
+                placeholder="Enter what you don't want to see...",
+                value=DEFAULT_NEG_PROMPT,
+                lines=2
+            )
+            
+            with gr.Row():
+                lora_selection = gr.Dropdown(
+                    label="Select LoRA Model",
+                    choices=[lora["name"] for lora in LORAS],
+                    value=LORAS[0]["name"]
+                )
+                lora_scale = gr.Slider(
+                    label="LoRA Scale",
+                    minimum=0.0,
+                    maximum=1.5,
+                    step=0.05,
+                    value=0.8
+                )
+            
+            with gr.Row():
+                guidance_scale = gr.Slider(
+                    label="Guidance Scale",
+                    minimum=1.0,
+                    maximum=15.0,
+                    step=0.5,
+                    value=5.0
+                )
+                steps = gr.Slider(
+                    label="Steps",
+                    minimum=10,
+                    maximum=100,
+                    step=1,
+                    value=30
+                )
+            
+            with gr.Row():
+                width = gr.Slider(
+                    label="Width",
+                    minimum=512,
+                    maximum=1536,
+                    step=64,
+                    value=1024
+                )
+                height = gr.Slider(
+                    label="Height",
+                    minimum=512,
+                    maximum=1536,
+                    step=64,
+                    value=1024
+                )
+            
+            seed = gr.Number(
+                label="Seed (0 for random)",
+                value=0,
+                precision=0
+            )
+            
+            generate_button = gr.Button("Generate Image", variant="primary")
+        
+        with gr.Column(scale=2):
+            output_image = gr.Image(label="Generated Image", type="pil")
+            used_seed = gr.Number(label="Used Seed", value=0, precision=0)
+    
+    # Setup the button click event
+    generate_button.click(
+        fn=generate_image,
+        inputs=[
+            prompt,
+            negative_prompt,
+            lora_selection,
+            lora_scale,
+            guidance_scale,
+            steps,
+            width,
+            height,
+            seed
+        ],
+        outputs=[output_image, used_seed]
+    )
+    
+    # Add examples if you have any
+    gr.Examples(
+        examples=[
+            [
+                "a portrait photo of a person with blue eyes",
+                DEFAULT_NEG_PROMPT,
+                LORAS[0]["name"],
+                0.8,
+                5.0,
+                30,
+                1024,
+                1024,
+                42
+            ]
+        ],
+        inputs=[
+            prompt,
+            negative_prompt,
+            lora_selection,
+            lora_scale,
+            guidance_scale,
+            steps,
+            width,
+            height,
+            seed
+        ],
+        outputs=[output_image, used_seed]
+    )
+
+# Launch the app
+demo.launch()