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-v3.8.1", torch_dtype=torch.float16, safety_checker=None) if torch.cuda.is_available() else DiffusionPipeline.from_pretrained("circulus/canvers-real-v3.8.1") pipe.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-v3.8.1", torch_dtype=torch.float16, safety_checker=None) if torch.cuda.is_available() else DiffusionPipeline.from_pretrained("circulus/canvers-anime-v3.8.1") anime.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-v3.8.1", torch_dtype=torch.float16, safety_checker=None) if torch.cuda.is_available() else DiffusionPipeline.from_pretrained("circulus/canvers-disney-v3.8.1") disney.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-v3.8.1", torch_dtype=torch.float16, safety_checker=None) if torch.cuda.is_available() else DiffusionPipeline.from_pretrained("circulus/canvers-story-v3.8.1") story.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 V1.8 with SDXL 1.0 Refiner and SD X2 Latent Upscaler - 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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Code Monkey: Manjushri").launch(debug=True, max_threads=80)