Update app.py

app.py

@@ -5,41 +5,16 @@ import torch
 
 st.title('Anime Character Generator')
 
-pipe = DiffusionPipeline.from_pretrained("stabilityai/stable-diffusion-xl-base-1.0")
-
-# load both base & refiner
-base = DiffusionPipeline.from_pretrained(
-    "stabilityai/stable-diffusion-xl-base-1.0", torch_dtype=torch.float16, variant="fp16", use_safetensors=True
-)
-base.enable_model_cpu_offload()
-refiner = DiffusionPipeline.from_pretrained(
-    "stabilityai/stable-diffusion-xl-refiner-1.0",
-    text_encoder_2=base.text_encoder_2,
-    vae=base.vae,
-    torch_dtype=torch.float16,
-    use_safetensors=True,
-    variant="fp16",
-)
-refiner.enable_model_cpu_offload()
+from diffusers import DiffusionPipeline
+import torch
 
-# Define how many steps and what % of steps to be run on each experts (80/20) here
-n_steps = 40
-high_noise_frac = 0.8
+pipe = DiffusionPipeline.from_pretrained("stabilityai/stable-diffusion-xl-base-1.0", torch_dtype=torch.float16, use_safetensors=True, variant="fp16")
+pipe.unet = torch.compile(pipe.unet, mode="reduce-overhead", fullgraph=True)
+pipe.enable_model_cpu_offload()
 
 prompt = st.text_input('Enter a prompt for your Anime character: ')
 
-# run both experts
-image = base(
-    prompt=prompt,
-    num_inference_steps=n_steps,
-    denoising_end=high_noise_frac,
-    output_type="latent",
-).images
-image = refiner(
-    prompt=prompt,
-    num_inference_steps=n_steps,
-    denoising_start=high_noise_frac,
-    image=image,
-).images[0]
+# run iamge
+images = pipe(prompt=prompt).images[0]
 
 st.image(image)