Update app.py

app.py

@@ -51,165 +51,6 @@ def genie (Model, Prompt, negative_prompt, height, width, scale, steps, seed, re
                torch.cuda.empty_cache()
                return image
     
-    if Model == "Anime":
-        anime = DiffusionPipeline.from_pretrained("circulus/canvers-anime-v3.9.1", torch_dtype=torch.float16, safety_checker=None) if torch.cuda.is_available() else DiffusionPipeline.from_pretrained("circulus/canvers-anime-v3.8.1")
-        anime.enable_xformers_memory_efficient_attention()
-        anime = anime.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 = anime(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 = anime(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 = anime(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 == "Disney":
-        disney = DiffusionPipeline.from_pretrained("circulus/canvers-disney-v3.9.1", torch_dtype=torch.float16, safety_checker=None) if torch.cuda.is_available() else DiffusionPipeline.from_pretrained("circulus/canvers-disney-v3.8.1")
-        disney.enable_xformers_memory_efficient_attention()
-        disney = disney.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 = disney(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 = disney(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 = disney(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 == "StoryBook":
-        story = DiffusionPipeline.from_pretrained("circulus/canvers-story-v3.9.1", torch_dtype=torch.float16, safety_checker=None) if torch.cuda.is_available() else DiffusionPipeline.from_pretrained("circulus/canvers-story-v3.8.1")
-        story.enable_xformers_memory_efficient_attention()
-        story = story.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 = story(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 = story(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 = story(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 == "SemiReal":
-        semi = DiffusionPipeline.from_pretrained("circulus/canvers-semi-v3.8.1", torch_dtype=torch.float16, safety_checker=None) if torch.cuda.is_available() else DiffusionPipeline.from_pretrained("circulus/canvers-semi-v3.8.1")
-        semi.enable_xformers_memory_efficient_attention()
-        semi = semi.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()
-            image = semi(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=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 = semi(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 = semi(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")
         animagine.enable_xformers_memory_efficient_attention()
@@ -349,44 +190,10 @@ def genie (Model, Prompt, negative_prompt, height, width, scale, steps, seed, re
                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 == 'SDXL-Turbo':
-        torch.cuda.empty_cache()
-        pipe = DiffusionPipeline.from_pretrained("stabilityai/sdxl-turbo", torch_dtype=torch.float16, variant="fp16")
-        pipe.enable_xformers_memory_efficient_attention()
-        pipe = pipe.to(device)
-        image = pipe(prompt=Prompt, num_inference_steps=1, guidance_scale=0.0).images[0]
-        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()
-            refined = refiner(Prompt, negative_prompt=negative_prompt, image=image, denoising_start=high_noise_frac).images[0]
-            torch.cuda.empty_cache()
-            if upscale == 'Yes':
-                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=refined, num_inference_steps=5, guidance_scale=0).images[0]
-                torch.cuda.empty_cache()
-                return upscaled
-            else:     
-                return refined
-        
-        if upscale == "Yes":
-            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=5, 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
     
-gr.Interface(fn=genie, inputs=[gr.Radio(['PhotoReal', 'Anime', 'Disney', 'StoryBook', 'SemiReal', 'Animagine XL 3.0', 'SDXL 1.0', 'FusionXL', 'SDXL-Turbo'], value='PhotoReal', label='Choose Model'),
+gr.Interface(fn=genie, inputs=[gr.Radio(['PhotoReal', 'Animagine XL 3.0', 'SDXL 1.0', 'FusionXL',], 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'),
@@ -398,6 +205,6 @@ gr.Interface(fn=genie, inputs=[gr.Radio(['PhotoReal', 'Anime', 'Disney', 'StoryB
                                gr.Slider(minimum=.9, maximum=.99, value=.95, step=.01, label='Refiner Denoise Start %'),
                                gr.Radio(["Yes", "No"], label = 'SD X2 Latent Upscaler?', value="No")],
              outputs=gr.Image(label='Generated Image'), 
-             title="Manju Dream Booth V1.9 with SDXL 1.0 Refiner and SD X2 Latent Upscaler - GPU", 
+             title="Manju Dream Booth V2.0 with SDXL 1.0 Refiner and SD X2 Latent Upscaler - 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)