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, 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() 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 == "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() 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": torch.cuda.empty_cache() torch.cuda.max_memory_allocated(device=device) sdxl = StableDiffusion3Pipeline.from_pretrained("stabilityai/stable-diffusion-3-medium-diffusers", 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() return refined 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 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() 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 return image gr.Interface(fn=genie, inputs=[gr.Radio(['PhotoReal', 'Animagine XL 3.0', 'SDXL 1.0', 'FusionXL',], 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 %')], outputs=gr.Image(label='Generated Image'), title="Manju Dream Booth V2.1 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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Code Monkey: Manjushri").launch(debug=True, max_threads=80)