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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 #, StableDiffusion3Pipeline | |
| 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): | |
| 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-v3.9.1") | |
| pipe.enable_xformers_memory_efficient_attention() | |
| pipe = pipe.to(device) | |
| torch.cuda.empty_cache() | |
| 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 == "Animagine XL 4": | |
| animagine = DiffusionPipeline.from_pretrained("cagliostrolab/animagine-xl-4.0", torch_dtype=torch.float16, safety_checker=None) if torch.cuda.is_available() else DiffusionPipeline.from_pretrained("cagliostrolab/animagine-xl-4.0") | |
| animagine.enable_xformers_memory_efficient_attention() | |
| animagine = animagine.to(device) | |
| torch.cuda.empty_cache() | |
| 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 | |
| return image | |
| gr.Interface(fn=genie, inputs=[gr.Radio(['PhotoReal', 'Animagine XL 4',], 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(3, maximum=12, value=5, step=.25, label='Guidance Scale', info="5-7 for PhotoReal and 7-10 for Animagine"), | |
| gr.Slider(25, maximum=50, value=25, step=25, label='Number of Iterations'), | |
| gr.Slider(minimum=0, step=1, maximum=9999999999999999, randomize=True, label='Seed: 0 is Random'), | |
| ], | |
| outputs=gr.Image(label='Generated Image'), | |
| title="Manju Dream Booth V2.5 - 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: DL5qRkGCzB2ENBKfEhHarvKm1qas3wyHx7<br><br>Code Monkey: <a href=\"https://huggingface.co/Manjushri\">Manjushri</a>").launch(debug=True, max_threads=80) |