Spaces:
Runtime error
Runtime error
File size: 14,370 Bytes
d8fcee4 8ec67ee d8fcee4 c462fe7 d8fcee4 2f00ac5 d8fcee4 2f00ac5 d8fcee4 b9c2637 d8fcee4 024d680 d8fcee4 b9c2637 d8fcee4 51d2448 d8fcee4 2f00ac5 d8fcee4 d8c3206 d8fcee4 2f00ac5 d8fcee4 b9c2637 d8fcee4 8024d12 e923469 d8c3206 8024d12 d8c3206 bb9a542 91ec6fc 4f26878 8024d12 d8c3206 a1c1647 8024d12 2f00ac5 8024d12 d8c3206 8024d12 d8c3206 a1c1647 8024d12 2f00ac5 8024d12 fdf21aa d8fcee4 fdf21aa d8fcee4 eedafde d8fcee4 a039a6a d8fcee4 fdf21aa d8fcee4 e7ae5d7 |
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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 |
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-v3.9.1", 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 == "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=5, 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=5, 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=5, 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=5, 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)
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=5, 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)
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=5, 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', '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, 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(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 V2.0 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) |