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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.", | |
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