Create app.py

app.py

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+import gradio as gr
+from diffusers import DiffusionPipeline
+import torch
+import spaces
+
+# Load the model
+# The model will be downloaded and cached the first time the app runs.
+pipe = DiffusionPipeline.from_pretrained(
+    "Spestly/OdysseyXL_V2.5", 
+    torch_dtype=torch.float16, 
+    variant="fp16",
+    use_safetensors=True
+)
+# Move the pipeline to the GPU
+pipe.to("cuda")
+
+@spaces.GPU
+def generate_image(prompt, negative_prompt, guidance_scale, num_inference_steps):
+    """
+    Generates an image from a text prompt using the OdysseyXL V2.5 model.
+
+    Args:
+        prompt (str): The text prompt to generate the image from.
+        negative_prompt (str): The negative text prompt.
+        guidance_scale (float): The guidance scale for the generation.
+        num_inference_steps (int): The number of inference steps.
+
+    Returns:
+        PIL.Image.Image: The generated image.
+    """
+    image = pipe(
+        prompt=prompt, 
+        negative_prompt=negative_prompt,
+        guidance_scale=guidance_scale,
+        num_inference_steps=num_inference_steps
+    ).images[0]
+    return image
+
+# --- Gradio Interface ---
+
+with gr.Blocks(css="style.css") as demo:
+    gr.Markdown("# 🎨 OdysseyXL V2.5 Image Generation")
+    gr.Markdown("A Gradio UI for the [Spestly/OdysseyXL V2.5](https://huggingface.co/Spestly/OdysseyXL%20V2.5) SDXL model, optimized for ZeroGPU.")
+    
+    with gr.Row():
+        with gr.Column(scale=2):
+            prompt = gr.Textbox(
+                label="Prompt", 
+                show_label=False, 
+                max_lines=2, 
+                placeholder="Enter your prompt",
+                container=False
+            )
+            negative_prompt = gr.Textbox(
+                label="Negative Prompt",
+                placeholder="Enter a negative prompt"
+            )
+            with gr.Row():
+                guidance_scale = gr.Slider(
+                    label="Guidance Scale", 
+                    minimum=0, 
+                    maximum=20, 
+                    step=0.1, 
+                    value=7.5
+                )
+                num_inference_steps = gr.Slider(
+                    label="Inference Steps",
+                    minimum=10,
+                    maximum=100,
+                    step=1,
+                    value=30
+                )
+            run_button = gr.Button("Generate Image", variant="primary")
+        with gr.Column(scale=1):
+            image_output = gr.Image(label="Generated Image", show_label=False)
+
+    gr.Examples(
+        examples=[
+            ["A futuristic cityscape, vibrant neon colors, ultra-realistic, 8K", "blurry, low quality", 7.5, 30],
+            ["A majestic lion with a crown of stars, cosmic background, fantasy art", "cartoon, sketch", 8.0, 40],
+            ["An enchanted forest at night, glowing mushrooms, fireflies, mystical atmosphere", "daytime, bright", 7.0, 35],
+            ["A delicious-looking gourmet burger on a wooden table, hyperrealistic food photography", "messy, unappetizing", 7.5, 25]
+        ],
+        inputs=[prompt, negative_prompt, guidance_scale, num_inference_steps],
+        outputs=image_output,
+        fn=generate_image,
+        cache_examples=True,
+    )
+
+    run_button.click(
+        fn=generate_image, 
+        inputs=[prompt, negative_prompt, guidance_scale, num_inference_steps],
+        outputs=image_output
+    )
+
+if __name__ == "__main__":
+    demo.launch()