Update app.py

app.py

@@ -6,14 +6,11 @@ from PIL import Image
 from diffusers import DiffusionPipeline 
 from huggingface_hub import login
 import os
-from diffusers.models import AutoencoderKL
 
 login(token=os.environ.get('HF_KEY'))
 
 device = "cuda" if torch.cuda.is_available() else "cpu"
 
-vae = AutoencoderKL.from_pretrained("stabilityai/sdxl-vae")
-
 pipe = DiffusionPipeline.from_pretrained("stabilityai/stable-diffusion-xl-base-0.9", use_safetensors=True, vae=vae)
 pipe = pipe.to(device)
 pipe.unet = torch.compile(pipe.unet, mode="reduce-overhead", fullgraph=True)
@@ -25,7 +22,7 @@ refiner.unet = torch.compile(refiner.unet, mode="reduce-overhead", fullgraph=Tru
 def genie (prompt, negative_prompt, scale, steps, seed):
      generator = torch.Generator(device=device).manual_seed(seed)
      int_image = pipe(prompt, negative_prompt=negative_prompt, num_inference_steps=steps, guidance_scale=scale, num_images_per_prompt=1, generator=generator, width=768, height=768, output_type="latent").images 
-     image = refiner(prompt=prompt, image=int_image).images[0]
+     image = refiner(prompt=prompt, negative_prompt=negative_prompt, image=int_image).images[0]
      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.'), gr.Slider(1, 15, 10), gr.Slider(25, maximum=50, value=25, step=1), gr.Slider(minimum=1, step=1, maximum=999999999999999999, randomize=True)], outputs='image', title="Stable Diffusion XL .9 CPU", description="SDXL .9 CPU. <b>WARNING:</b> Extremely Slow. 65s/Iteration. Expect 25-50mins an image for 25-50 iterations respectively.", article = "Code Monkey: <a href=\"https://huggingface.co/Manjushri\">Manjushri</a>").launch(debug=True, max_threads=80)