Update app.py

app.py

@@ -19,12 +19,14 @@ pipe.scheduler = EulerDiscreteScheduler.from_config(pipe.scheduler.config, times
 def generate(prompt):
     image = pipe(prompt, num_inference_steps=1, guidance_scale=0).images[0]
     return image
-# Ensure using the same inference steps as the loaded model and CFG set to 0.
 
-# @spaces.GPU
-# def greet(prompt):
-#     image = pipe(prompt, num_inference_steps=1, guidance_scale=0).images[0].save("output.png")
-#     return image
 output_image = gr.Image(type="pil")
 demo = gr.Interface(fn=generate, inputs="text", outputs=output_image)
-demo.launch()
+
+if __name__ == "__main__":
+    unet = UNet2DConditionModel.from_config(base, subfolder="unet").to("cuda", torch.float16)
+    unet.load_state_dict(load_file(hf_hub_download(repo, ckpt), device="cuda"))
+    pipe = StableDiffusionXLPipeline.from_pretrained(base, unet=unet, torch_dtype=torch.float16, variant="fp16").to("cuda")
+    # Ensure sampler uses "trailing" timesteps and "sample" prediction type.
+    pipe.scheduler = EulerDiscreteScheduler.from_config(pipe.scheduler.config, timestep_spacing="trailing", prediction_type="sample")
+    demo.launch()