Update app.py

app.py

@@ -10,112 +10,42 @@ device = 'cuda' if torch.cuda.is_available() else 'cpu'
 torch.cuda.max_memory_allocated(device=device)
 torch.cuda.empty_cache()
 
-def genie (Model, Prompt, negative_prompt, height, width, scale, steps, seed, refine, high_noise_frac, upscale):
+def genie (Model, Prompt, negative_prompt, height, width, scale, steps, seed):
     generator = np.random.seed(0) if seed == 0 else torch.manual_seed(seed)
        
     if Model == "PhotoReal":
-        pipe = DiffusionPipeline.from_pretrained("circulus/canvers-real-v3.9.1", torch_dtype=torch.float16, safety_checker=None) if torch.cuda.is_available() else DiffusionPipeline.from_pretrained("circulus/canvers-real-v3.8.1")
+        pipe = DiffusionPipeline.from_pretrained("circulus/canvers-real-v3.9.1", torch_dtype=torch.float16, safety_checker=None) if torch.cuda.is_available() else DiffusionPipeline.from_pretrained("circulus/canvers-real-v3.9.1")
         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()
-            return image
-        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
     
-    if Model == "Animagine XL 3.0":
-        animagine = DiffusionPipeline.from_pretrained("cagliostrolab/animagine-xl-3.0", torch_dtype=torch.float16, safety_checker=None) if torch.cuda.is_available() else DiffusionPipeline.from_pretrained("cagliostrolab/animagine-xl-3.0")
+        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
+    
+    if Model == "Animagine XL 4":
+        animagine = DiffusionPipeline.from_pretrained("cagliostrolab/animagine-xl-4.0", torch_dtype=torch.float16, safety_checker=None) if torch.cuda.is_available() else DiffusionPipeline.from_pretrained("cagliostrolab/animagine-xl-4.0")
         animagine.enable_xformers_memory_efficient_attention()
         animagine = animagine.to(device)
         torch.cuda.empty_cache()
-        if refine == "Yes":
-            torch.cuda.empty_cache()
-            torch.cuda.max_memory_allocated(device=device)
-            int_image = animagine(Prompt, negative_prompt=negative_prompt, height=height, width=width, num_inference_steps=steps, guidance_scale=scale, output_type="latent").images
-            torch.cuda.empty_cache()
-            animagine = 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")
-            animagine.enable_xformers_memory_efficient_attention()
-            animagine = animagine.to(device)
-            torch.cuda.empty_cache()
-            image = animagine(Prompt, negative_prompt=negative_prompt, image=int_image, denoising_start=high_noise_frac).images[0]
-            torch.cuda.empty_cache()
-            return image    
-        else:
-            image = animagine(Prompt, negative_prompt=negative_prompt, height=height, width=width, num_inference_steps=steps, guidance_scale=scale).images[0]
-            torch.cuda.empty_cache()
-            return image
-
-    if Model == "SDXL 1.0":
         
+        image = animagine(Prompt, negative_prompt=negative_prompt, height=height, width=width, num_inference_steps=steps, guidance_scale=scale).images[0]
         torch.cuda.empty_cache()
-        torch.cuda.max_memory_allocated(device=device)
-        sdxl = DiffusionPipeline.from_pretrained("stabilityai/stable-diffusion-xl-base-1.0", torch_dtype=torch.float16, variant="fp16", use_safetensors=True)
-        sdxl.enable_xformers_memory_efficient_attention()
-        sdxl = sdxl.to(device)   
-        torch.cuda.empty_cache()
-    
-        if refine == "Yes":
-            torch.cuda.max_memory_allocated(device=device)
-            torch.cuda.empty_cache()
-            image = sdxl(Prompt, negative_prompt=negative_prompt, height=height, width=width, num_inference_steps=steps, guidance_scale=scale, output_type="latent").images
-            torch.cuda.empty_cache()
-            sdxl = 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")
-            sdxl.enable_xformers_memory_efficient_attention()
-            sdxl = sdxl.to(device)
-            torch.cuda.empty_cache()
-            refined = sdxl(Prompt, negative_prompt=negative_prompt, image=image, denoising_start=high_noise_frac).images[0]
-            torch.cuda.empty_cache()
-            return refined
-        else:   
-            image = sdxl(Prompt, negative_prompt=negative_prompt, height=height, width=width, num_inference_steps=steps, guidance_scale=scale).images[0]
-            torch.cuda.empty_cache()
-            return image
-        
-    if Model == 'FusionXL':
-        torch.cuda.empty_cache()
-        torch.cuda.max_memory_allocated(device=device)
-        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":
-            torch.cuda.empty_cache()
-            torch.cuda.max_memory_allocated(device=device)
-            int_image = pipe(Prompt, negative_prompt=negative_prompt, height=height, width=width, num_inference_steps=steps, guidance_scale=scale, output_type="latent").images
-            pipe = 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")
-            pipe.enable_xformers_memory_efficient_attention()
-            pipe = pipe.to(device)
-            torch.cuda.empty_cache()
-            image = pipe(Prompt, negative_prompt=negative_prompt, image=int_image, denoising_start=high_noise_frac).images[0]
-            torch.cuda.empty_cache()
-            return image
-        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
 
+    
     return image
     
-gr.Interface(fn=genie, inputs=[gr.Radio(['PhotoReal', 'Animagine XL 3.0', 'SDXL 1.0', 'FusionXL',], value='PhotoReal', label='Choose Model'),
+gr.Interface(fn=genie, inputs=[gr.Radio(['PhotoReal', 'Animagine XL 4',], 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'),
                                gr.Slider(512, 1024, 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(3, maximum=12, value=5, step=.25, label='Guidance Scale', info="5-7 for PhotoReal and 7-10 for Animagine"), 
+                               gr.Slider(25, maximum=50, value=25, step=25, label='Number of Iterations'), 
                                gr.Slider(minimum=0, step=1, maximum=9999999999999999, randomize=True, label='Seed: 0 is Random'), 
-                               gr.Radio(["Yes", "No"], label='SDXL 1.0 Refiner: Use if the Image has too much Noise', value='No'),
-                               gr.Slider(minimum=.9, maximum=.99, value=.95, step=.01, label='Refiner Denoise Start %')],
+                               ],
              outputs=gr.Image(label='Generated Image'), 
-             title="Manju Dream Booth V2.1 with SDXL 1.0 Refiner - GPU", 
+             title="Manju Dream Booth V2.5 - GPU", 
              description="<br><br><b/>Warning: This Demo is capable of producing NSFW content.", 
-             article = "If You Enjoyed this Demo and would like to Donate, you can send any amount to any of these Wallets. <br><br>SHIB (BEP20): 0xbE8f2f3B71DFEB84E5F7E3aae1909d60658aB891 <br>PayPal: https://www.paypal.me/ManjushriBodhisattva <br>ETH: 0xbE8f2f3B71DFEB84E5F7E3aae1909d60658aB891 <br>DOGE: D9QdVPtcU1EFH8jDC8jhU9uBcSTqUiA8h6<br><br>Code Monkey: <a href=\"https://huggingface.co/Manjushri\">Manjushri</a>").launch(debug=True, max_threads=80)
+             article = "If You Enjoyed this Demo and would like to Donate, you can send any amount to any of these Wallets. <br><br>SHIB (BEP20): 0xbE8f2f3B71DFEB84E5F7E3aae1909d60658aB891 <br>PayPal: https://www.paypal.me/ManjushriBodhisattva <br>ETH: 0xbE8f2f3B71DFEB84E5F7E3aae1909d60658aB891 <br>DOGE: DL5qRkGCzB2ENBKfEhHarvKm1qas3wyHx7<br><br>Code Monkey: <a href=\"https://huggingface.co/Manjushri\">Manjushri</a>").launch(debug=True, max_threads=80)