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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("dreamlike-art/dreamlike-photoreal-2.0", torch_dtype=torch.float16, safety_checker=None) |
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upscaler = StableDiffusionLatentUpscalePipeline.from_pretrained("stabilityai/sd-x2-latent-upscaler", torch_dtype=torch.float16) |
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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, label='Prompt Guidance Scale:', interactive=True), |
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gr.Slider(1, maximum=100, value=50, step=1, label='Number of Iterations: 50 is typically fine.'), |
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gr.Slider(minimum=1, step=10, maximum=999999999999999999, randomize=True, interactive=True)], |
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outputs=gr.Image(label='512x512 Generated Image'), |
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title="PhotoReal V2 with SD x2 Upscaler - GPU", |
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description="<br><br><b/>Warning: This Demo is capable of producing NSFW content.", |
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article = "Code Monkey: <a href=\"https://huggingface.co/Manjushri\">Manjushri</a>").launch(debug=True, max_threads=True) |
