Update app.py

app.py

@@ -13,24 +13,18 @@ pipe = StableCascadeCombinedPipeline.from_pretrained("stabilityai/stable-cascade
 pipe.enable_xformers_memory_efficient_attention()
 pipe = pipe.to(device)
 torch.cuda.empty_cache()
-upscaler = StableDiffusionLatentUpscalePipeline.from_pretrained("stabilityai/sd-x2-latent-upscaler", torch_dtype=torch.float16, use_safetensors=True)
-upscaler.enable_xformers_memory_efficient_attention()
-upscaler = upscaler.to(device)
+
 
 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()
     image = pipe(prompt=Prompt, negative_prompt=negative_prompt, num_inference_steps=15, prior_num_inference_steps=steps, prior_guidance_scale=scale, width=width, height=height).images[0]
-    if upscale == 'Yes':
-        upscaled = upscaler(prompt=Prompt, negative_prompt=negative_prompt, image=image, num_inference_steps=5, guidance_scale=0).images[0]
-        return upscaled
-    else:
-        return 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'),
-                               gr.Slider(512, 1280, 768, step=128, label='Height'),
-                               gr.Slider(512, 1280, 768, step=128, label='Width'),
+                               gr.Slider(512, 2048, 768, step=128, label='Height'),
+                               gr.Slider(512, 2048, 768, step=128, label='Width'),
                                gr.Slider(1, maximum=15, value=5, step=.25, label='Guidance Scale'), 
                                gr.Slider(5, maximum=100, value=50, step=5, label='Number of Iterations'), 
                                gr.Slider(minimum=0, step=1, maximum=9999999999999999, randomize=True, label='Seed: 0 is Random'),