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app.py

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+from diffusers import DiffusionPipeline, DPMSolverMultistepScheduler
+import torch
+import gradio as gr
+import spaces
+
+
+lora_path = "OedoSoldier/detail-tweaker-lora"
+
+@spaces.GPU
+def generate_image(prompt, negative_prompt, num_inference_steps=50, guidance_scale=7.5,model="Real6.0"):
+    """
+    Generate an image using Stable Diffusion based on the input prompt
+    """
+        
+    if model == "Real5.0":
+        model_id = "SG161222/Realistic_Vision_V5.0_noVAE"
+        
+    elif model == "Real5.1":
+        model_id = "SG161222/Realistic_Vision_V5.1_noVAE"
+        
+    else:
+        model_id = "SG161222/Realistic_Vision_V6.0_B1_noVAE"
+        
+        
+    pipe = DiffusionPipeline.from_pretrained(model_id).to("cuda")
+    
+    if model == "Real6.0":
+        pipe.safety_checker = lambda images, **kwargs: (images, [False] * len(images))
+
+    pipe.load_lora_weights(lora_path)
+
+    pipe.scheduler = DPMSolverMultistepScheduler.from_config(
+        pipe.scheduler.config,
+        algorithm_type="dpmsolver++",
+        use_karras_sigmas=True
+    )
+
+    
+    # Generate the image
+    image = pipe(
+        prompt = prompt,
+        negative_prompt = negative_prompt,
+        cross_attention_kwargs = {"scale":1},
+        num_inference_steps = num_inference_steps,
+        guidance_scale = guidance_scale,
+        width = 960,
+        height = 960
+    ).images[0]
+    
+    return image
+
+# Create the Gradio interface
+with gr.Blocks() as demo:
+    gr.Markdown("# ProFaker ImageGen")
+    
+    with gr.Row():
+        with gr.Column():
+            # Input components
+            prompt = gr.Textbox(
+                label="Prompt",
+                placeholder="Enter your image description here...",
+                value="a photo of an astronaut riding a horse on mars"
+            )
+            negative_prompt = gr.Textbox(
+                label="Negative Prompt",
+                placeholder="Enter what you don't want in photo",
+            )
+            steps_slider = gr.Slider(
+                minimum=1,
+                maximum=100,
+                value=50,
+                step=1,
+                label="Number of Inference Steps"
+            )
+            guidance_slider = gr.Slider(
+                minimum=1,
+                maximum=20,
+                value=7.5,
+                step=0.5,
+                label="Guidance Scale"
+            )
+            model = gr.Dropdown(
+                choices=["Real6.0","Real5.1","Real5.0"],
+                value="Real6.0",
+                label="Model",
+            )
+            generate_button = gr.Button("Generate Image")
+        
+        with gr.Column():
+            # Output component
+            image_output = gr.Image(label="Generated Image")
+    
+    # Connect the interface to the generation function
+    generate_button.click(
+        fn=generate_image,
+        inputs=[prompt, negative_prompt, steps_slider, guidance_slider, model],
+        outputs=image_output
+    )
+    
+    gr.Markdown("""
+    ## Instructions
+    1. Enter your desired image description in the prompt field
+    2. Adjust the inference steps (higher = better quality but slower)
+    3. Adjust the guidance scale (higher = more prompt adherence)
+    4. Click 'Generate Image' and wait for the result
+    """)
+
+# Launch the interface
+if __name__ == "__main__":
+    demo.launch(share=True)

requirements.txt

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+spaces
+gradio
+diffusers
+transformers
+accelerate
+peft