| import gradio as gr | |
| import torch | |
| import modin.pandas as pd | |
| from diffusers import DiffusionPipeline | |
| device = "cuda" if torch.cuda.is_available() else "cpu" | |
| if torch.cuda.is_available(): | |
| PYTORCH_CUDA_ALLOC_CONF={'max_split_size_mb': 6000} | |
| torch.cuda.max_memory_allocated(device=device) | |
| torch.cuda.empty_cache() | |
| pipe = DiffusionPipeline.from_pretrained("stabilityai/stable-diffusion-xl-base-1.0", torch_dtype=torch.float16, variant="fp16", use_safetensors=True) | |
| pipe.enable_xformers_memory_efficient_attention() | |
| pipe = pipe.to(device) | |
| pipe.unet = torch.compile(pipe.unet, mode="reduce-overhead", fullgraph=True) | |
| torch.cuda.empty_cache() | |
| refiner = DiffusionPipeline.from_pretrained("stabilityai/stable-diffusion-xl-refiner-1.0", use_safetensors=True, torch_dtype=torch.float16, variant="fp16") | |
| refiner.enable_xformers_memory_efficient_attention() | |
| refiner.enable_sequential_cpu_offload() | |
| refiner.unet = torch.compile(refiner.unet, mode="reduce-overhead", fullgraph=True) | |
| else: | |
| pipe = DiffusionPipeline.from_pretrained("stabilityai/stable-diffusion-xl-base-1.0", use_safetensors=True) | |
| pipe = pipe.to(device) | |
| pipe.unet = torch.compile(pipe.unet, mode="reduce-overhead", fullgraph=True) | |
| refiner = DiffusionPipeline.from_pretrained("stabilityai/stable-diffusion-xl-refiner-1.0", use_safetensors=True) | |
| refiner = refiner.to(device) | |
| refiner.unet = torch.compile(refiner.unet, mode="reduce-overhead", fullgraph=True) | |
| def genie (prompt, negative_prompt, height, width, scale, steps, seed, prompt_2, negative_prompt_2, high_noise_frac): | |
| generator = np.random.seed(0) if seed == 0 else torch.manual_seed(seed) | |
| int_image = pipe(prompt, prompt_2=prompt_2, negative_prompt_2=negative_prompt_2, negative_prompt=negative_prompt, height=height, width=width, num_inference_steps=steps, guidance_scale=scale, num_images_per_prompt=1, generator=generator, output_type="latent").images | |
| image = refiner(prompt=prompt, prompt_2=prompt_2, negative_prompt=negative_prompt, negative_prompt_2=negative_prompt_2, image=int_image, denoising_start=high_noise_frac).images[0] | |
| return image | |
| gr.Interface(fn=genie, inputs=[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.'), | |
| gr.Slider(512, 1024, 768, step=128, label='Height'), | |
| gr.Slider(512, 1024, 768, step=128, label='Width'), | |
| gr.Slider(1, 15, 10, label='Guidance Scale'), | |
| gr.Slider(25, maximum=50, value=25, step=1, label='Number of Iterations'), | |
| gr.Slider(minimum=1, step=1, maximum=999999999999999999, randomize=True), | |
| gr.Textbox(label='Embedded Prompt'), | |
| gr.Textbox(label='Embedded Negative Prompt'), | |
| gr.Slider(minimum=.7, maximum=.99, value=.95, step=.01, label='Refiner Denoise Start %')], | |
| outputs='image', | |
| title="Stable Diffusion XL 1.0 CPU or GPU", | |
| description="SDXL 1.0 CPU or GPU. Currently running on CPU. <br><br><b>WARNING:</b> Extremely Slow. 65s/Iteration. Expect 25-50mins an image for 25-50 iterations respectively. This model is capable of producing NSFW (Softcore) images.", | |
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