File size: 9,636 Bytes
07d5247 058e9d8 6d70521 058e9d8 01807fb 9d9e3ec ed724f7 3b633b6 3837c03 3459d34 b4bce2e 07d5247 fa37ad2 6b2dfd8 2c339b8 1d66cba fa37ad2 b4bce2e fa37ad2 b4bce2e 0389115 573943b 1d66cba fa37ad2 b4bce2e fa37ad2 b4bce2e fd0860d 0521099 1d66cba 573943b b4bce2e 573943b b4bce2e 0521099 2c339b8 1d66cba 0521099 4824429 2c339b8 4824429 80ba029 81dec84 d6c9a76 058e9d8 81dec84 fa37ad2 a2749d1 b32943f b1f7543 ed810ef 70733c7 ebec2b0 d6c9a76 81dec84 26d38a8 81dec84 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 |
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'
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()
def genie (Model, Prompt, negative_prompt, height, width, scale, steps, seed, upscale, high_noise_frac):
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 = pipe.to(device)
pipe.enable_xformers_memory_efficient_attention()
torch.cuda.empty_cache()
if upscale == "Yes":
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()
return image
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 = anime.to(device)
anime.enable_xformers_memory_efficient_attention()
torch.cuda.empty_cache()
if upscale == "Yes":
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()
return image
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 = disney.to(device)
disney.enable_xformers_memory_efficient_attention()
torch.cuda.empty_cache()
if upscale == "Yes":
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()
return image
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 = story.to(device)
story.enable_xformers_memory_efficient_attention()
torch.cuda.empty_cache()
if upscale == "Yes":
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()
return image
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 = semi.to(device)
semi.enable_xformers_memory_efficient_attention()
torch.cuda.empty_cache()
if upscale == "Yes":
int_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=int_image, denoising_start=high_noise_frac).images[0]
torch.cuda.empty_cache()
return image
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 = animagine.to(device)
animagine.enable_xformers_memory_efficient_attention()
torch.cuda.empty_cache()
if upscale == "Yes":
int_image = animagine(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()
return image
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":
sdxl = DiffusionPipeline.from_pretrained("stabilityai/stable-diffusion-xl-base-1.0", torch_dtype=torch.float16, variant="fp16", use_safetensors=True)
sdxl = sdxl.to(device)
sdxl.enable_xformers_memory_efficient_attention()
torch.cuda.empty_cache()
if upscale == "Yes":
int_image = sdxl(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()
return image
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=9, 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 %')],
outputs=gr.Image(label='Generated Image'),
title="Manju Dream Booth V1.5 with SDXL 1.0 Refiner - 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>Code Monkey: <a href=\"https://huggingface.co/Manjushri\">Manjushri</a>").launch(debug=True, max_threads=80) |