Update app.py

app.py

@@ -1,44 +1,47 @@
 import gradio as gr
-import spaces
 
-import torch
-from diffusers import FluxPipeline
-
-pipeline = FluxPipeline.from_pretrained("black-forest-labs/FLUX.1-dev", torch_dtype=torch.float16).to("cuda")
-#pipeline.enable_model_cpu_offload()
-
-@spaces.GPU(duration=70)
-def generate(prompt, negative_prompt, width, height, sample_steps):
-    return pipeline(prompt=f"{prompt}\nDO NOT INCLUDE {negative_prompt}", width=width, height=height, num_inference_steps=sample_steps, generator=torch.Generator("cpu").manual_seed(42), guidance_scale=7).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 a neon 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])
-
-def show_message():
-    return "# This is the legacy space, to access the app, [click here](https://huggingface.co/spaces/nroggendorff/flux-lora-tester)"
-
-iface = gr.Interface(fn=show_message, 
-                     inputs=None, 
-                     outputs="markdown")
-
-demo = interface if torch.cuda.is_available else iface
+d = "cuda" if torch.cuda.is_available else False
+
+if d:
+    import spaces
+    
+    import torch
+    from diffusers import FluxPipeline
+    
+    pipeline = FluxPipeline.from_pretrained("black-forest-labs/FLUX.1-dev", torch_dtype=torch.float16).to(d)
+    #pipeline.enable_model_cpu_offload()
+    
+    @spaces.GPU(duration=70)
+    def generate(prompt, negative_prompt, width, height, sample_steps):
+        return pipeline(prompt=f"{prompt}\nDO NOT INCLUDE {negative_prompt}", width=width, height=height, num_inference_steps=sample_steps, guidance_scale=7).images[0]
+    
+    with gr.Blocks() as demo:
+            with gr.Column():
+                with gr.Row():
+                    with gr.Column():
+                        prompt = gr.Textbox(label="Prompt", info="What do you want?", value="Keanu Reeves holding a neon 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])
+
+else:
+    def show_message():
+        return "# This is the legacy space, to access the app, [click here](https://huggingface.co/spaces/nroggendorff/flux-lora-tester)"
+    
+    demo = gr.Interface(fn=show_message, 
+                         inputs=None, 
+                         outputs="markdown")
 
 if __name__ == "__main__":
     demo.launch()