Create app.py

app.py

@@ -0,0 +1,39 @@
+import gradio as gr
+from diffusers import StableDiffusionPipeline
+import torch
+import spaces
+
+@spaces.GPU
+def generate_image(prompt, num_inference_steps, guidance_scale):
+    # Load the base model
+    base_model_id = "black-forest-labs/FLUX.1-dev"
+    pipe = StableDiffusionPipeline.from_pretrained(base_model_id, torch_dtype=torch.float16)
+    
+    # Load the LoRA weights
+    pipe.unet.load_attn_procs("MegaTronX/SuicideGirl-FLUX")
+    
+    pipe = pipe.to("cuda")
+    
+    # Generate the image
+    image = pipe(
+        prompt,
+        num_inference_steps=num_inference_steps,
+        guidance_scale=guidance_scale
+    ).images[0]
+    
+    return image
+
+# Create the Gradio interface
+iface = gr.Interface(
+    fn=generate_image,
+    inputs=[
+        gr.Textbox(label="Prompt"),
+        gr.Slider(minimum=1, maximum=100, value=50, label="Number of Inference Steps"),
+        gr.Slider(minimum=1, maximum=20, value=7.5, label="Guidance Scale")
+    ],
+    outputs=gr.Image(label="Generated Image"),
+    title="Image Generation with Custom LoRA",
+    description="Generate images using a custom LoRA model trained on Flux Dev."
+)
+
+iface.launch()