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="

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.

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