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app.py
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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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import numpy as np
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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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#import os
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#login(token=os.environ.get('HF_KEY'))
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import torch
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from diffusers import StableDiffusionPipeline, DPMSolverMultistepScheduler
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model_id = "stabilityai/stable-diffusion-2-1"
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# Use the DPMSolverMultistepScheduler (DPM-Solver++) scheduler here instead
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pipe = StableDiffusionPipeline.from_pretrained(model_id, torch_dtype=torch.float16)
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pipe.scheduler = DPMSolverMultistepScheduler.from_config(pipe.scheduler.config)
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pipe = pipe.to("cuda")
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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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source_image = resize(768, source_img)
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source_image.save('source.png')
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image = pipe(prompt, negative_prompt=negative_prompt, image=source_image, 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="upload", type="filepath", label="Raw Image. Must Be .png"), 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', title = "Stable Diffusion XL 1.0 Image to Image Pipeline CPU", 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", article = "Code Monkey: <a href=\"https://huggingface.co/Manjushri\">Manjushri</a>").queue(max_size=5).launch()
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