Create app.py

app.py

@@ -0,0 +1,35 @@
+import gradio as gr
+import spaces
+
+import torch
+from diffusers import FluxPipeline
+
+pipeline = FluxPipeline.from_pretrained("black-forest-labs/FLUX.1-dev").to("cuda")
+pipeline.enable_model_cpu_offload()
+
+@spaces.GPU(duration=120)
+def generate(prompt, negative_prompt, width, height, sample_steps):
+    return pipeline(prompt=f"{prompt}\nDO NOT INCLUDE {negative_prompt} FOR ANY REASON", width=width, height=height, num_inference_steps=sample_steps, generator=torch.Generator("cpu").manual_seed(127)).images[0]
+
+with gr.Blocks() as interface:
+        with gr.Column():
+            with gr.Row():
+                with gr.Column():
+                    prompt = gr.Textbox(label="Prompt", info="What do you want?", value="Keanu Reeves holding an extravagant sign reading 'Hello, world!', 32k HDR, paparazzi", lines=4, interactive=True)
+                    negative_prompt = gr.Textbox(label="Negative Prompt", info="What do you want to exclude from the image?", value="ugly, low quality", lines=4, interactive=True)
+                with gr.Column():
+                    generate_button = gr.Button("Generate")
+                    output = gr.Image()
+            with gr.Row():
+                with gr.Accordion(label="Advanced Settings", open=False):
+                    with gr.Row():
+                        with gr.Column():
+                            width = gr.Slider(label="Width", info="The width in pixels of the generated image.", value=512, minimum=128, maximum=4096, step=64, interactive=True)
+                            height = gr.Slider(label="Height", info="The height in pixels of the generated image.", value=512, minimum=128, maximum=4096, step=64, interactive=True)
+                        with gr.Column():
+                            sampling_steps = gr.Slider(label="Sampling Steps", info="The number of denoising steps.", value=20, minimum=4, maximum=50, step=1, interactive=True)
+        
+        generate_button.click(fn=generate, inputs=[prompt, negative_prompt, width, height, sampling_steps], outputs=[output])
+
+if __name__ == "__main__":
+    interface.launch()