Upload app.py

app.py

@@ -0,0 +1,369 @@
+import gradio as gr
+import torch
+import numpy as np
+import modin.pandas as pd
+from PIL import Image
+from diffusers import DiffusionPipeline, StableDiffusionLatentUpscalePipeline
+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()
+
+def genie (Model, Prompt, negative_prompt, height, width, scale, steps, seed, refine, high_noise_frac, upscale):
+    generator = np.random.seed(0) if seed == 0 else torch.manual_seed(seed)
+       
+    if Model == "PhotoReal":
+        pipe = DiffusionPipeline.from_pretrained("circulus/canvers-real-v3.8.1", 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
+    
+    if Model == "Anime":
+        anime = DiffusionPipeline.from_pretrained("circulus/canvers-anime-v3.8.1", 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-v3.8.1", 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-v3.8.1", 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()
+        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()
+            
+            if upscale == "Yes":
+                animagine = DiffusionPipeline.from_pretrained("stabilityai/sd-x2-latent-upscaler", torch_dtype=torch.float16, use_safetensors=True)
+                animagine.enable_xformers_memory_efficient_attention()
+                animagine = animagine.to(device)
+                torch.cuda.empty_cache()
+                upscaled = animagine(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 = animagine(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 = 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":
+        
+        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()
+            
+            if upscale == "Yes":
+                sdxl = DiffusionPipeline.from_pretrained("stabilityai/sd-x2-latent-upscaler", torch_dtype=torch.float16, use_safetensors=True)
+                sdxl.enable_xformers_memory_efficient_attention()
+                sdxl = sdxl.to(device)
+                torch.cuda.empty_cache()
+                upscaled = sdxl(prompt=Prompt, negative_prompt=negative_prompt, image=refined, num_inference_steps=15, guidance_scale=0).images[0]
+                torch.cuda.empty_cache()
+                return upscaled
+            else:
+                return refined
+        else:
+            if upscale == "Yes":
+                image = sdxl(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)
+                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 = 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 == '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()
+            if upscale == "Yes":
+                torch.cuda.empty_cache()
+                torch.cuda.max_memory_allocated(device=device)
+                pipe = DiffusionPipeline.from_pretrained("stabilityai/sd-x2-latent-upscaler", torch_dtype=torch.float16, use_safetensors=True)
+                pipe.enable_xformers_memory_efficient_attention()
+                pipe = pipe.to(device)
+                torch.cuda.empty_cache()
+                upscaled = pipe(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]
+                torch.cuda.empty_cache()
+                torch.cuda.max_memory_allocated(device=device)
+
+                pipe = DiffusionPipeline.from_pretrained("stabilityai/sd-x2-latent-upscaler", torch_dtype=torch.float16, use_safetensors=True)
+                pipe.enable_xformers_memory_efficient_attention()
+                pipe = pipe.to(device)
+                torch.cuda.empty_cache()
+                upscaled = pipe(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', '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, 1536, 768, step=128, label='Height'),
+                               gr.Slider(512, 1536, 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(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 %'),
+                               gr.Radio(["Yes", "No"], label = 'SD X2 Latent Upscaler?', value="No")],
+             outputs=gr.Image(label='Generated Image'), 
+             title="Manju Dream Booth V1.8 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)