Update app.py

app.py

@@ -14,19 +14,19 @@ pipe.enable_xformers_memory_efficient_attention()
 pipe = pipe.to(device)
 torch.cuda.empty_cache()
  
-#refiner = DiffusionPipeline.from_pretrained("stabilityai/stable-diffusion-xl-refiner-1.0", use_safetensors=True, torch_dtype=torch.float16, variant="fp16") if torch.cuda.is_available() else DiffusionPipeline.from_pretrained("stabilityai/stable-diffusion-xl-refiner-1.0")
-#refiner.enable_xformers_memory_efficient_attention()
-#refiner = refiner.to(device)
-#torch.cuda.empty_cache()
+refiner = DiffusionPipeline.from_pretrained("stabilityai/stable-diffusion-xl-refiner-1.0", use_safetensors=True, torch_dtype=torch.float16, variant="fp16") if torch.cuda.is_available() else DiffusionPipeline.from_pretrained("stabilityai/stable-diffusion-xl-refiner-1.0")
+refiner.enable_xformers_memory_efficient_attention()
+refiner = refiner.to(device)
+torch.cuda.empty_cache()
  
 def genie (Prompt, negative_prompt, height, width, scale, steps, seed, upscale):
  generator = np.random.seed(0) if seed == 0 else torch.manual_seed(seed)
  torch.cuda.empty_cache()
- int_image = pipe(prompt=Prompt, negative_prompt=negative_prompt, num_inference_steps=steps, guidance_scale=scale, width=width, height=height, ).images[0] #output_type="latent"
+ int_image = pipe(prompt=Prompt, negative_prompt=negative_prompt, num_inference_steps=steps, guidance_scale=scale, width=width, height=height, output_type="latent").images #
+ torch.cuda.empty_cache()
+ image = refiner(Prompt, negative_prompt=negative_prompt, image=int_image, denoising_start=.99).images[0]
  torch.cuda.empty_cache()
- #image = refiner(Prompt, negative_prompt=negative_prompt, image=int_image, denoising_start=.99).images[0]
- #torch.cuda.empty_cache()
- return int_image
+ return image
  
 gr.Interface(fn=genie, inputs=[gr.Textbox(label='What you want the AI to generate. 77 Token Limit.'), 
  gr.Textbox(label='What you Do Not want the AI to generate. 77 Token Limit'),