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import gradio as gr | |
import torch | |
#from torch import autocast // only for GPU | |
from PIL import Image | |
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("start generating") | |
YOUR_TOKEN=MY_SECRET_TOKEN | |
device="cpu" | |
pipe = StableDiffusionPipeline.from_pretrained("AkiKagura/mkgen-diffusion", use_auth_token=YOUR_TOKEN) | |
pipe.safety_checker = empty_checker | |
pipe.to(device) | |
gallery = gr.Gallery(label="Generated images", show_label=False, elem_id="gallery").style(grid=[1], height="auto") | |
def infer(prompt, guide, steps, seed, img_w, img_h): | |
generator = torch.Generator('cpu').manual_seed(seed) | |
#image = pipe(prompt, init_image=init_image)["sample"][0] | |
images_list = pipe([prompt] * 1, guidance_scale=guide, num_inference_steps=steps, width=img_w, height=img_h) #TODO | |
images = [] | |
for i, image in enumerate(images_list["images"]): | |
images.append(image) | |
return images | |
print("okay") | |
title="Marco Generation" | |
description="Use 'mkmk woman' to get Marco pics. <br />Warning: Slow process... about 10 min inference time." | |
gr.Interface(fn=infer, inputs=["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='Width', minimum = 512, maximum = 768, step = 256, value = 512), | |
gr.Slider(label='Height', minimum = 512, maximum = 768, step = 256, value = 512)], outputs=gallery,title=title,description=description).queue(max_size=100).launch(enable_queue=True) | |