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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) |