Update app.py

app.py

@@ -300,10 +300,49 @@ def genie (Model, Prompt, negative_prompt, height, width, scale, steps, seed, re
                 
                image = sdxl(Prompt, negative_prompt=negative_prompt, height=height, width=width, num_inference_steps=steps, guidance_scale=scale).images[0]
                torch.cuda.empty_cache()
-                      
+        
+    if model == 'Test':
+        
+        pipe = DiffusionPipeline.from_pretrained("circulus/canvers-fusionXL-v1", torch_dtype=torch.float16, safety_checker=None) if torch.cuda.is_available() else DiffusionPipeline.from_pretrained("circulus/canvers-real-v3.8.1")
+        pipe.enable_xformers_memory_efficient_attention()
+        pipe = pipe.to(device)
+        torch.cuda.empty_cache()
+        if refine == "Yes":
+            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()
+            int_image = pipe(Prompt, negative_prompt=negative_prompt, height=height, width=width, num_inference_steps=steps, guidance_scale=scale).images
+            image = refiner(Prompt, negative_prompt=negative_prompt, image=int_image, denoising_start=high_noise_frac).images[0]
+            torch.cuda.empty_cache()
+            if upscale == "Yes":
+                refiner = DiffusionPipeline.from_pretrained("stabilityai/sd-x2-latent-upscaler", torch_dtype=torch.float16, use_safetensors=True)
+                refiner.enable_xformers_memory_efficient_attention()
+                refiner = refiner.to(device)
+                torch.cuda.empty_cache()
+                upscaled = refiner(prompt=Prompt, negative_prompt=negative_prompt, image=image, num_inference_steps=15, guidance_scale=0).images[0]
+                torch.cuda.empty_cache()
+                return upscaled
+            else:
+                return image
+        else:
+            if upscale == "Yes":
+                image = pipe(Prompt, negative_prompt=negative_prompt, height=height, width=width, num_inference_steps=steps, guidance_scale=scale).images[0]
+                upscaler = DiffusionPipeline.from_pretrained("stabilityai/sd-x2-latent-upscaler", torch_dtype=torch.float16, use_safetensors=True)
+                upscaler.enable_xformers_memory_efficient_attention()
+                upscaler = upscaler.to(device)
+                torch.cuda.empty_cache()
+                upscaled = upscaler(prompt=Prompt, negative_prompt=negative_prompt, image=image, num_inference_steps=15, guidance_scale=0).images[0]
+                torch.cuda.empty_cache()
+                return upscaled
+            else:
+               image = pipe(Prompt, negative_prompt=negative_prompt, height=height, width=width, num_inference_steps=steps, guidance_scale=scale).images[0]
+               torch.cuda.empty_cache()
+               return image
+        
     return image
     
-gr.Interface(fn=genie, inputs=[gr.Radio(['PhotoReal', 'Anime', 'Disney', 'StoryBook', 'SemiReal', 'Animagine XL 3.0', 'SDXL 1.0'], value='PhotoReal', label='Choose Model'),
+gr.Interface(fn=genie, inputs=[gr.Radio(['PhotoReal', 'Anime', 'Disney', 'StoryBook', 'SemiReal', 'Animagine XL 3.0', 'SDXL 1.0', 'Test'], value='PhotoReal', label='Choose Model'),
                                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, 1024, 768, step=128, label='Height'),