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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 | |
| 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() | |
| 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 == "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 | |
| 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 %'), | |
| gr.Radio(["Yes", "No"], label = 'SD X2 Latent Upscaler?', value="No")], | |
| outputs=gr.Image(label='Generated Image'), | |
| title="Manju Dream Booth V2.0 with SDXL 1.0 Refiner and SD X2 Latent Upscaler - 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: D9QdVPtcU1EFH8jDC8jhU9uBcSTqUiA8h6<br><br>Code Monkey: <a href=\"https://huggingface.co/Manjushri\">Manjushri</a>").launch(debug=True, max_threads=80) |