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Runtime error
| import gradio as gr | |
| import torch | |
| #from torch import autocast // only for GPU | |
| from PIL import Image | |
| import numpy as np | |
| from io import BytesIO | |
| import os | |
| MY_SECRET_TOKEN=os.environ.get('HF_TOKEN_SD') | |
| #from diffusers import StableDiffusionPipeline | |
| from diffusers import StableDiffusionImg2ImgPipeline | |
| def empty_checker(images, **kwargs): return images, False | |
| print("hello") | |
| YOUR_TOKEN=MY_SECRET_TOKEN | |
| device="cpu" | |
| img_pipe = StableDiffusionImg2ImgPipeline.from_pretrained("AkiKagura/mkgen-diffusion", use_auth_token=YOUR_TOKEN) | |
| img_pipe.to(device) | |
| source_img = gr.Image(source="canvas", type="filepath", tool='color-sketch', label="new gradio color sketch") | |
| gallery = gr.Gallery(label="Generated images", show_label=False, elem_id="gallery").style(grid=[1], height="auto") | |
| def resize(value,img): | |
| #baseheight = value | |
| img = Image.open(img) | |
| #hpercent = (baseheight/float(img.size[1])) | |
| #wsize = int((float(img.size[0])*float(hpercent))) | |
| #img = img.resize((wsize,baseheight), Image.Resampling.LANCZOS) | |
| img = img.resize((value,value), Image.Resampling.LANCZOS) | |
| return img | |
| def infer(source_img, prompt, guide, steps, seed, strength): | |
| source_image = resize(512, source_img) | |
| source_image.save('source.png') | |
| images_list = img_pipe([prompt] * 1, init_image=source_image, strength=strength, guidance_scale=guide, num_inference_steps=steps) | |
| images = [] | |
| for i, image in enumerate(images_list["images"]): | |
| images.append(image) | |
| return images | |
| print("done") | |
| title="Marco Generation Sketch" | |
| description="<p style='text-align: center;'>Draw and use 'mkmk woman' to get Marco pics. <br />Warning: Slow process... about 10 min inference time.</p>" | |
| custom_css = "style.css" | |
| gr.Interface(fn=infer, inputs=[source_img, | |
| "text", | |
| gr.Slider(2, 15, value = 7, label = 'Guidence Scale'), | |
| gr.Slider(10, 50, value = 25, step = 1, label = 'Number of Iterations'), | |
| gr.Slider(label = "Seed", minimum = 0, maximum = 2147483647, step = 1, randomize = True), | |
| gr.Slider(label='Strength', minimum = 0, maximum = 1, step = .05, value = .75)], outputs=gallery,title=title,description=description,css=custom_css).queue(max_size=100).launch(enable_queue=True) | |