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Manju-Dream-Booth-GPU / app.py

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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
	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 == "Anime":
	anime = DiffusionPipeline.from_pretrained("circulus/canvers-anime-v3.9.1", 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-v3.9.1", 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-v3.9.1", 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=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
	if Model == 'SDXL-Turbo':
	torch.cuda.empty_cache()
	pipe = DiffusionPipeline.from_pretrained("stabilityai/sdxl-turbo", torch_dtype=torch.float16, variant="fp16")
	pipe.enable_xformers_memory_efficient_attention()
	pipe = pipe.to(device)
	image = pipe(prompt=Prompt, num_inference_steps=1, guidance_scale=0.0).images[0]
	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()
	refined = refiner(Prompt, negative_prompt=negative_prompt, image=image, denoising_start=high_noise_frac).images[0]
	torch.cuda.empty_cache()
	if upscale == 'Yes':
	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=refined, num_inference_steps=5, guidance_scale=0).images[0]
	torch.cuda.empty_cache()
	return upscaled
	else:
	return refined

	if upscale == "Yes":
	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

	gr.Interface(fn=genie, inputs=[gr.Radio(['PhotoReal', 'Anime', 'Disney', 'StoryBook', 'SemiReal', 'Animagine XL 3.0', 'SDXL 1.0', 'FusionXL', 'SDXL-Turbo'], 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 V1.9 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)