Create app.py

app.py

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+import gradio as gr
+from gradio_client import Client
+
+# Initialize the Hugging Face client with the specific model
+client = Client("ByteDance/Hyper-FLUX-8Steps-LoRA")
+
+def generate_image(prompt, height, width, steps, scale, seed):
+    """
+    Function to generate an image based on the provided prompt and parameters.
+    
+    Args:
+        prompt (str): The text prompt to generate the image.
+        height (int): The height of the generated image.
+        width (int): The width of the generated image.
+        steps (int): Number of inference steps.
+        scale (float): Guidance scale for the image generation.
+        seed (int): Seed for random number generator to ensure reproducibility.
+    
+    Returns:
+        Image: Generated image based on the prompt and parameters.
+    """
+    try:
+        # Call the predict method of the client with provided parameters
+        result = client.predict(
+            height=height,
+            width=width,
+            steps=steps,
+            scales=scale,
+            prompt=prompt,
+            seed=seed,
+            api_name="/process_image"
+        )
+        return result
+    except Exception as e:
+        return f"An error occurred: {e}"
+
+# Define the input components
+prompt_input = gr.inputs.Textbox(
+    lines=2, 
+    placeholder="Enter your prompt here...", 
+    label="Prompt"
+)
+
+height_input = gr.inputs.Slider(
+    minimum=256, 
+    maximum=2048, 
+    step=64, 
+    default=1024, 
+    label="Image Height"
+)
+
+width_input = gr.inputs.Slider(
+    minimum=256, 
+    maximum=2048, 
+    step=64, 
+    default=1024, 
+    label="Image Width"
+)
+
+steps_input = gr.inputs.Slider(
+    minimum=1, 
+    maximum=50, 
+    step=1, 
+    default=8, 
+    label="Inference Steps"
+)
+
+scale_input = gr.inputs.Slider(
+    minimum=1.0, 
+    maximum=10.0, 
+    step=0.1, 
+    default=3.5, 
+    label="Guidance Scale"
+)
+
+seed_input = gr.inputs.Number(
+    default=3413, 
+    label="Seed", 
+    precision=0
+)
+
+# Define the output component
+image_output = gr.outputs.Image(label="Generated Image")
+
+# Create the Gradio interface
+iface = gr.Interface(
+    fn=generate_image,
+    inputs=[prompt_input, height_input, width_input, steps_input, scale_input, seed_input],
+    outputs=image_output,
+    title="Hyper-FLUX-8Steps-LoRA Image Generator",
+    description="Generate images from text prompts using the Hyper-FLUX-8Steps-LoRA model.",
+    examples=[
+        ["A serene landscape with mountains and a river", 1024, 1024, 8, 3.5, 42],
+        ["A futuristic city skyline at sunset", 1024, 1024, 8, 3.5, 1234],
+        ["An abstract painting with vibrant colors", 1024, 1024, 8, 3.5, 5678],
+    ],
+    allow_flagging="never"
+)
+
+# Launch the interface
+if __name__ == "__main__":
+    iface.launch()