Create app.py

app.py

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+import gradio as gr
+import torch
+from diffusers import StableDiffusionPipeline, ControlNetModel
+from safetensors.torch import load_file
+
+# Initialize the pipeline with CPU
+model_id = "runwayml/stable-diffusion-v1-5"
+pipe = StableDiffusionPipeline.from_pretrained(
+    model_id,
+    torch_dtype=torch.float32,
+)
+
+# Load your ControlNet LoRA
+lora_path = "naonauno/40k-half-sd15"
+pipe.load_lora_weights(lora_path)
+
+# Merge LoRA weights
+pipe.unet.load_attn_procs(lora_state_dict)
+
+def generate_image(prompt, negative_prompt, guidance_scale, steps):
+    with torch.no_grad():
+        image = pipe(
+            prompt=prompt,
+            negative_prompt=negative_prompt,
+            num_inference_steps=steps,
+            guidance_scale=guidance_scale,
+        ).images[0]
+    return image
+
+# Create the Gradio interface
+with gr.Blocks() as demo:
+    with gr.Row():
+        with gr.Column():
+            prompt = gr.Textbox(label="Prompt")
+            negative_prompt = gr.Textbox(label="Negative Prompt")
+            guidance_scale = gr.Slider(minimum=1, maximum=20, value=7.5, label="Guidance Scale")
+            steps = gr.Slider(minimum=1, maximum=100, value=50, label="Steps")
+            generate = gr.Button("Generate")
+        
+        with gr.Column():
+            result = gr.Image(label="Generated Image")
+    
+    generate.click(
+        fn=generate_image,
+        inputs=[prompt, negative_prompt, guidance_scale, steps],
+        outputs=result
+    )
+
+demo.launch()