Update app.py

app.py

@@ -3,49 +3,40 @@ import torch
 import numpy as np
 import modin.pandas as pd
 from PIL import Image
-from diffusers import DiffusionPipeline #, StableDiffusion3Pipeline
+from diffusers import DiffusionPipeline, StableDiffusion3Pipeline
 from huggingface_hub import hf_hub_download
 
 device = 'cuda' if torch.cuda.is_available() else 'cpu'
 torch.cuda.max_memory_allocated(device=device)
 torch.cuda.empty_cache()
+#torch.cuda.max_memory_allocated(device=device)
+pipe = DiffusionPipeline.from_pretrained("circulus/canvers-fusionXL-v1", torch_dtype=torch.float16).to(device)
+pipe.enable_xformers_memory_efficient_attention()
+torch.cuda.empty_cache()
+
+#torch.cuda.max_memory_allocated(device=device)
+refiner = DiffusionPipeline.from_pretrained("stabilityai/stable-diffusion-xl-refiner-1.0", use_safetensors=True, torch_dtype=torch.float16, variant="fp16").to(device)
+refiner.enable_xformers_memory_efficient_attention()
+torch.cuda.empty_cache()
+
 
-def genie (Model, Prompt, negative_prompt, height, width, scale, steps, seed):
+def genie (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-v3.9.1")
-        pipe.enable_xformers_memory_efficient_attention()
-        pipe = pipe.to(device)
-        torch.cuda.empty_cache()
-    
-        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()
+    #generator=np.random.seed(0)
+    int_image = pipe(Prompt, negative_prompt=negative_prompt, height=height, width=width, num_inference_steps=steps, guidance_scale=scale, output_type="latent").images
+    image = refiner(Prompt, negative_prompt=negative_prompt, image=int_image, denoising_start=.99).images[0]
+    torch.cuda.empty_cache()
         
-        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
-
-    
     return image
     
-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.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, 1024, 768, step=128, label='Height'),
-                               gr.Slider(512, 1024, 768, step=128, label='Width'),
-                               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.Slider(512, 1536, 1024, step=128, label='Height'),
+                               gr.Slider(512, 1536, 1024, step=128, label='Width'),
+                               gr.Slider(.5, maximum=15, value=7, step=.25, label='Guidance Scale'), 
+                               gr.Slider(10, maximum=50, value=25, step=5, label='Number of Prior Iterations'),
+                               gr.Slider(minimum=0, step=1, maximum=9999999999999999, randomize=True, label='Seed: 0 is Random')],
              outputs=gr.Image(label='Generated Image'), 
-             title="Manju Dream Booth V2.5 - GPU", 
+             title="Manju Dream Booth V2.5 with Fusion XL - 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: DL5qRkGCzB2ENBKfEhHarvKm1qas3wyHx7<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: D9QdVPtcU1EFH8jDC8jhU9uBcSTqUiA8h6<br><br>Code Monkey: <a href=\"https://huggingface.co/Manjushri\">Manjushri</a>").launch(debug=True)