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import gradio as gr |
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import torch |
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import numpy as np |
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import modin.pandas as pd |
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from PIL import Image |
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from diffusers import DiffusionPipeline, StableDiffusionLatentUpscalePipeline |
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device = "cuda" if torch.cuda.is_available() else "cpu" |
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pipe = DiffusionPipeline.from_pretrained("prompthero/openjourney-v4", safety_checker=None) |
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upscaler = StableDiffusionLatentUpscalePipeline.from_pretrained("stabilityai/sd-x2-latent-upscaler", safety_checker=None) |
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upscaler = upscaler.to(device) |
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pipe = pipe.to(device) |
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def genie (Prompt, scale, steps, seed): |
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generator = torch.Generator(device=device).manual_seed(seed) |
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low_res_latents = pipe(Prompt, num_inference_steps=steps, guidance_scale=scale, generator=generator, output_type="latent").images |
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upscaled_image = upscaler(prompt='', image=low_res_latents, num_inference_steps=5, guidance_scale=0, generator=generator).images[0] |
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return upscaled_image |
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gr.Interface(fn=genie, inputs=[gr.Textbox(label='What you want the AI to generate. 77 Token Limit.'), |
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gr.Slider(1, maximum=15, value=10, step=.25), |
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gr.Slider(1, maximum=50, value=25, step=1), |
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gr.Slider(minimum=1, step=1, maximum=987654321, randomize=True)], |
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outputs = 'image', |
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title = 'OpenJourney V4 with SD 2.1 2X Upscaler - CPU', |
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description = "OJ V4 CPU. <b>WARNING:</b> Extremely Slow. 35s/Iteration. Expect 8-16mins an image for 15-30 iterations respectively. 50 iterations takes ~28mins.", |
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article = "Code Monkey: <a href=\"https://huggingface.co./Manjushri\">Manjushri</a>").launch(debug=True, max_threads=True) |
