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| import gradio as gr | |
| import torch | |
| import modin.pandas as pd | |
| import numpy as np | |
| from diffusers import DiffusionPipeline | |
| device = "cuda" if torch.cuda.is_available() else "cpu" | |
| if torch.cuda.is_available(): | |
| torch.cuda.max_memory_allocated(device=device) | |
| torch.cuda.empty_cache() | |
| pipe = DiffusionPipeline.from_pretrained("stabilityai/sdxl-turbo", torch_dtype=torch.float16, variant="fp16", use_safetensors=True) | |
| pipe.enable_xformers_memory_efficient_attention() | |
| pipe = pipe.to(device) | |
| torch.cuda.empty_cache() | |
| else: | |
| pipe = DiffusionPipeline.from_pretrained("stabilityai/sdxl-turbo", use_safetensors=True) | |
| pipe = pipe.to(device) | |
| def genie (prompt, steps, seed): | |
| generator = np.random.seed(0) if seed == 0 else torch.manual_seed(seed) | |
| int_image = pipe(prompt=prompt, generator=generator, num_inference_steps=steps, guidance_scale=0.0).images[0] | |
| return int_image | |
| gr.Interface(fn=genie, inputs=[gr.Textbox(label='What you want the AI to generate. 77 Token Limit.'), | |
| gr.Slider(1, maximum=5, value=2, step=1, label='Number of Iterations'), | |
| gr.Slider(minimum=0, step=1, maximum=999999999999999999, randomize=True), | |
| ], | |
| outputs='image', | |
| title="Stable Diffusion Turbo CPU or GPU", | |
| description="SDXL Turbo CPU or GPU. Currently running on CPU. <br><br><b>WARNING: This model is capable of producing NSFW (Softcore) images.</b>", | |
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