Delete app.py

app.py

@@ -1,319 +0,0 @@
-import gradio as gr
-import torch
-import numpy as np
-import modin.pandas as pd
-from PIL import Image
-from diffusers import DiffusionPipeline, StableDiffusionLatentUpscalePipeline
-
-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]
-            
-                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()
-               
-            
-    return image
-    
-gr.Interface(fn=genie, inputs=[gr.Radio(['PhotoReal', 'Anime', 'Disney', 'StoryBook', 'SemiReal', 'Animagine XL 3.0', 'SDXL 1.0'], 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(25, maximum=100, value=50, 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 %'),
-                               gr.Radio(["Yes", "No"], label = 'SD X2 Latent Upscaler?', value="No")],
-             outputs=gr.Image(label='Generated Image'), 
-             title="Manju Dream Booth V1.7 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>BTC: bc1qzdm9j73mj8ucwwtsjx4x4ylyfvr6kp7svzjn84 <br>BTC2: 3LWRoKYx6bCLnUrKEdnPo3FCSPQUSFDjFP <br>DOGE: DK6LRc4gfefdCTRk9xPD239N31jh9GjKez <br>SHIB (BEP20): 0xbE8f2f3B71DFEB84E5F7E3aae1909d60658aB891 <br>PayPal: https://www.paypal.me/ManjushriBodhisattva <br>ETH: 0xbE8f2f3B71DFEB84E5F7E3aae1909d60658aB891 <br><br>Code Monkey: <a href=\"https://huggingface.co/Manjushri\">Manjushri</a>").launch(debug=True, max_threads=80)