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import gradio as gr |
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import 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 gradio as gr |
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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 datasets import load_dataset |
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from diffusers import StableDiffusionImg2ImgPipeline |
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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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device = "cpu" |
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pipe = StableDiffusionPipeline.from_pretrained(model_id, torch_dtype=torch.float32) |
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pipe.scheduler = DPMSolverMultistepScheduler.from_config(pipe.scheduler.config) |
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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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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, init_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( |
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fn=infer, |
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inputs=[ |
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gr.inputs.Image(type="filepath", label="Raw Image. Must Be .png"), |
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gr.Textbox(label='Prompt Input Text. 77 Token (Keyword or Symbol) Maximum'), |
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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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], |
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outputs='image', |
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title="Stable Diffusion 2.1 Image to Image Pipeline on CPU", |
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description="For more information on Stable Diffusion 2.1 see https://github.com/Stability-AI/stablediffusion" |
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).launch() |
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