Update app.py

app.py

@@ -1,45 +1,51 @@
 import gradio as gr
-import modin.pandas as pd
 import torch
 import numpy as np
+import modin.pandas as pd
 from PIL import Image
-from diffusers import DiffusionPipeline
-from huggingface_hub import login
-#import os
-
-#login(token=os.environ.get('HF_KEY'))
-
-device = "cuda" if torch.cuda.is_available() else "cpu"
-pipe = DiffusionPipeline.from_pretrained("stabilityai/stable-diffusion-xl-base-1.0", torch_dtype=torch.float16) if torch.cuda.is_available() else DiffusionPipeline.from_pretrained("stabilityai/stable-diffusion-xl-refiner-1.0")
-pipe = pipe.to(device)
+from diffusers import DiffusionPipeline 
 
-def resize(value,img):
-    img = Image.open(img)
-    img = img.resize((value,value))
-    return img
 
-def infer(source_img, prompt, negative_prompt, guide, steps, seed, Strength):
-    generator = torch.Generator(device).manual_seed(seed)     
-    source_image = resize(768, source_img)
-    source_image.save('source.png')
-    image = pipe(prompt, negative_prompt=negative_prompt, image=source_image, strength=Strength, guidance_scale=guide, num_inference_steps=steps).images[0]
-    return image
+device = 'cuda' if torch.cuda.is_available() else 'cpu'
 
-gr.Interface(theme='ParityError/Anime', fn=genie, inputs=[gr.Textbox(label='Input field right under here(Prompt)'), 
-                               gr.Textbox(label='What You dont want (Negative Prompt)'),
-                               gr.Slider(512, 1024, 768, step=128, label='Height'),
-                               gr.Slider(512, 1024, 768, step=128, label='Width'),
-                               gr.Slider(1, maximum=15, value=10, step=.25), 
-                               gr.Slider(25, maximum=100, value=50, step=25), 
-                               gr.Slider(minimum=1, step=1, maximum=9999999999999999, randomize=True), 
-                              # gr.Radio(["Yes", "No"], label='Upscale?'),
-                               #gr.Textbox(label='Upscaler Prompt: Optional'),
-                               #gr.Textbox(label='Upscaler Negative Prompt: Both Optional And Experimental'),
-                               #gr.Slider(minimum=0, maximum=15, value=0, step=1, label='Upscale Guidance Scale'),
-                               #gr.Slider(minimum=5, maximum=25, value=5, step=5, label='Upscaler Iterations')
-             
-                              ],
-             outputs=gr.Image(label='Generated Image'), 
-             title="Dream Art (SD) ", 
-             description="<br> <h4> <div style='width:100%'> Info:Dream Art (SD) <br> This App is our favorite now and shows how Stable diffusion works i a good way !</h4> </div>", 
-             ).launch(debug=True, max_threads=True)
+if torch.cuda.is_available():
+    PYTORCH_CUDA_ALLOC_CONF={'max_split_size_mb': 6000}
+    torch.cuda.max_memory_allocated(device=device)
+    torch.cuda.empty_cache()
+    
+    pipe = DiffusionPipeline.from_pretrained("stabilityai/stable-diffusion-xl-base-1.0", torch_dtype=torch.float16, variant="fp16", use_safetensors=True)
+    pipe.enable_xformers_memory_efficient_attention()
+    pipe = pipe.to(device)
+    torch.cuda.empty_cache()
+ 
+else: 
+    pipe = DiffusionPipeline.from_pretrained("stabilityai/stable-diffusion-xl-base-1.0", use_safetensors=True)
+    pipe = pipe.to(device)
+    refiner = DiffusionPipeline.from_pretrained("stabilityai/stable-diffusion-xl-refiner-1.0", use_safetensors=True)
+    refiner = refiner.to(device)
+    
+def genie (prompt, negative_prompt, height, width, scale, steps, seed, upscaling):
+    generator = torch.Generator(device=device).manual_seed(seed)
+    int_image = pipe(prompt, negative_prompt=negative_prompt, num_inference_steps=steps, height=height, width=width, guidance_scale=scale, num_images_per_prompt=1, generator=generator, output_type="latent").images
+    if upscaling == 'Yes':
+        image = refiner(prompt=prompt, image=int_image).images[0]
+        upscaled = upscaler(prompt=prompt, negative_prompt=negative_prompt, image=image, num_inference_steps=5, guidance_scale=0).images[0]
+        torch.cuda.empty_cache()
+        return (image, upscaled)
+    else:
+        image = refiner(prompt=prompt, negative_prompt=negative_prompt, image=int_image).images[0]   
+        torch.cuda.empty_cache()
+    return (image, image)
+   
+gr.Interface(fn=genie, inputs=[gr.Textbox(label='What you want the AI to generate. 77 Token Limit. A Token is Any Word, Number, Symbol, or Punctuation. Everything Over 77 Will Be Truncated!'), 
+    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, 15, 10, step=.25, label='Guidance Scale: How Closely the AI follows the Prompt'), 
+    gr.Slider(25, maximum=100, value=50, step=25, label='Number of Iterations'), 
+    gr.Slider(minimum=1, step=1, maximum=999999999999999999, randomize=True, label='Seed'),
+    gr.Radio(['Yes', 'No'], label='Upscale?')], 
+    outputs=['image', 'image'],
+    title="Stable Diffusion XL 1.0 GPU", 
+    description="SDXL 1.0 GPU. <br><br><b>WARNING: Capable of producing NSFW (Softcore) images.</b>", 
+    article = "If You Enjoyed this Demo and would like to Donate, you can send to any of these Wallets. <br>BTC: bc1qzdm9j73mj8ucwwtsjx4x4ylyfvr6kp7svzjn84 <br>3LWRoKYx6bCLnUrKEdnPo3FCSPQUSFDjFP <br>DOGE: DK6LRc4gfefdCTRk9xPD239N31jh9GjKez <br>SHIB (BEP20): 0xbE8f2f3B71DFEB84E5F7E3aae1909d60658aB891 <br>PayPal: https://www.paypal.me/ManjushriBodhisattva <br>ETH: 0xbE8f2f3B71DFEB84E5F7E3aae1909d60658aB891 <br>Code Monkey: <a href=\"https://huggingface.co/Manjushri\">Manjushri</a>").launch(debug=True, max_threads=80)