Update app.py

Testing upscaler

app.py

@@ -11,30 +11,48 @@ from diffusers.models import AutoencoderKL
 login(token=os.environ.get('HF_KEY'))
 
 device = "cuda" if torch.cuda.is_available() else "cpu"
-torch.cuda.max_memory_allocated(device=device)
+torch.cuda.max_memory_allocated(device='cuda')
 vae = AutoencoderKL.from_pretrained("stabilityai/sdxl-vae", torch_dtype=torch.float16)
 torch.cuda.empty_cache()
 
-def genie (prompt, negative_prompt, scale, steps, seed):
+def genie (prompt, negative_prompt, scale, steps, seed, upscale):
+    torch.cuda.max_memory_allocated(device='cuda')
     pipe = DiffusionPipeline.from_pretrained("stabilityai/stable-diffusion-xl-base-0.9", torch_dtype=torch.float16, variant="fp16", use_safetensors=True, vae=vae)
     pipe = pipe.to(device)
-    #pipe.enable_xformers_memory_efficient_attention()
+    pipe.enable_xformers_memory_efficient_attention()
     torch.cuda.empty_cache()
     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).images
     torch.cuda.empty_cache()
-    pipe = DiffusionPipeline.from_pretrained("stabilityai/stable-diffusion-xl-refiner-0.9", torch_dtype=torch.float16, variant="fp16", use_safetensors=True, vae=vae)
-    pipe = pipe.to(device)
-    #pipe.enable_xformers_memory_efficient_attention()
-    torch.cuda.empty_cache()
-    image = pipe(prompt=prompt, image=int_image).images[0]
-    return image
+    if upscaler == 'Yes':
+        torch.cuda.max_memory_allocated(device='cuda')
+        pipe = DiffusionPipeline.from_pretrained("stabilityai/stable-diffusion-xl-refiner-0.9", torch_dtype=torch.float16, variant="fp16", use_safetensors=True, vae=vae)
+        pipe = pipe.to(device)
+        pipe.enable_xformers_memory_efficient_attention()
+        image = pipe(prompt=prompt, image=int_image).images[0]
+        torch.cuda.empty_cache()
+        torch.cuda.max_memory_allocated(device='cuda')
+        pipe = DiffusionPipeline.from_pretrained("stabilityai/sd-x2-latent-upscaler", torch_dtype=torch.float16, use_safetensors=True)
+        pipe.to("cuda")
+        pipe.enable_xformers_memory_efficient_attention()
+        upscaled = pipe(prompt=prompt, negative_prompt=negative_prompt, image=image, num_inference_steps=5, guidance_scale=0).images[0]
+        torch.cuda.empty_cache()
+    else:
+        torch.cuda.empty_cache()
+        torch.cuda.max_memory_allocated(device=device)
+        pipe = DiffusionPipeline.from_pretrained("stabilityai/stable-diffusion-xl-refiner-0.9", torch_dtype=torch.float16, variant="fp16", use_safetensors=True, vae=vae)
+        pipe = pipe.to(device)
+        pipe.enable_xformers_memory_efficient_attention()
+        image = pipe(prompt=prompt, image=int_image).images[0]
+        torch.cuda.empty_cache()
+    return (image, upscaled)
    
 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=100, value=50, step=1), 
-    gr.Slider(minimum=1, step=1, maximum=999999999999999999, randomize=True)], 
-    outputs='image', 
+    gr.Slider(minimum=1, step=1, maximum=999999999999999999, randomize=True),
+    gr.Radio(['Yes', 'No'], label='Upscale?')], 
+    outputs=['image', 'image'],
     title="Stable Diffusion XL 0.9 GPU", 
     description="SDXL 0.9 GPU. <b>WARNING:</b> Capable of producing NSFW images.", 
     article = "Code Monkey: <a href=\"https://huggingface.co/Manjushri\">Manjushri</a>").launch(debug=True, max_threads=80)