| 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("SG161222/RealVisXL_V1.0", 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("SG161222/RealVisXL_V1.0", 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='Positive Promt. 77 Token Limit.'), | |
| gr.Textbox(label='Negative Prompt.'), | |
| 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=("Realistic Vision XL V1.0 Demo for",<a href="https://huggingface.co/SG161222/RealVisXL_V1.0">Realistic Vision XL V1</a>, Stable Diffusion model by <a href="https://huggingface.co/SG161222/"><abbr title="SG1611222">Eugene</abbr></a><br><br>CPU or GPU. Currently running on <b>CPU</b><br><br>) | |
| article = "Please use the prompt template below to get an example of the desired generation results:<br><b>Positive prompt:</b> dark shot, photo of cute 24 y.o blonde woman, perfect eyes, skin moles, short hair, looks at viewer, cinematic shot, hard shadows<br><b>Negative prompt:</b> (worst quality, low quality, illustration, 3d, 2d, painting, cartoons, sketch), open mouth <br> CFG scale: 7, Seed: 4271781772<br><br> <b>WARNING:</b> Be patient, as generation is Slow.<br>65s/Iteration. Expected Generation Time is 25-50mins an image for 25-50 iterations respectively. This model is capable of producing mild NSFW images" | |
| .launch(debug=True, max_threads=80) |