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import gradio as gr |
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import modin.pandas as pd |
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import torch |
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from PIL import Image |
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from diffusers import DiffusionPipeline |
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from huggingface_hub import login |
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device = "cuda" if torch.cuda.is_available() else "cpu" |
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pipe = DiffusionPipeline.from_pretrained("stabilityai/stable-diffusion-xl-refiner-1.0", torch_dtype=torch.float16) if torch.cuda.is_available() else DiffusionPipeline.from_pretrained("stabilityai/stable-diffusion-xl-refiner-1.0") |
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pipe = pipe.to(device) |
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def resize(value,img): |
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img = Image.open(img) |
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img = img.resize((value,value)) |
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return img |
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def infer(source_img, prompt, negative_prompt, guide, steps, seed, Strength): |
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generator = torch.Generator(device).manual_seed(seed) |
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src = resize(768, source_img) |
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image = pipe(prompt, negative_prompt=negative_prompt, image=src, strength=Strength, guidance_scale=guide, num_inference_steps=steps).images[0] |
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return image |
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gr.Interface(fn=infer, inputs=[gr.Image(source='canvas'), gr.Textbox(label = 'Prompt Input Text. 77 Token (Keyword or Symbol) Maximum'), gr.Textbox(label='What you Do Not want the AI to generate.'), |
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gr.Slider(2, 15, value = 7, label = 'Guidance Scale'), |
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gr.Slider(1, 25, value = 10, step = 1, label = 'Number of Iterations'), |
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gr.Slider(label = "Seed", minimum = 0, maximum = 987654321987654321, step = 1, randomize = True), |
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gr.Slider(label='Strength', minimum = 0, maximum = 1, step = .05, value = .5)], |
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outputs='image', |
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title = "Stable Diffusion XL 1.0 Doodle to Image CPU", |
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description = "For more information on Stable Diffusion XL 1.0 see https://huggingface.co/stabilityai/stable-diffusion-xl-refiner-1.0 <br><br>Upload an Image (<b>MUST Be .PNG and 512x512 or 768x768</b>) enter a Prompt, or let it just do its Thing, then click submit. 10 Iterations takes about ~900-1200 seconds currently. For more informationon about Stable Diffusion or Suggestions for prompts, keywords, artists or styles see https://github.com/Maks-s/sd-akashic", |
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article = "Code Monkey: <a href=\"https://huggingface.co/Manjushri\">Manjushri</a>").queue(max_size=5).launch() |
