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import gradio as gr
import torch
from PIL import Image
import numpy as np
from io import BytesIO
from diffusers import StableDiffusionImg2ImgPipeline, StableDiffusionPipeline, UNet2DConditionModel, AutoencoderKL
from transformers import BertTokenizer, BertModel
device="cuda"
model_id = "IDEA-CCNL/Taiyi-Stable-Diffusion-1B-Chinese-v0.1"
# pipe_img2img = StableDiffusionImg2ImgPipeline.from_pretrained(model_id, tokenizer=tokenizer, text_encoder=text_encoder, vae=vae, unet=unet).to(device)
pipe_text2img = StableDiffusionPipeline.from_pretrained(model_id).to(device)
def resize(w_val,l_val,img):
img = Image.open(img)
img = img.resize((w_val,l_val), Image.Resampling.LANCZOS)
return img
def infer(prompt, guide, steps, width, height):
image_list = pipe_text2img([prompt], guidance_scale=guide, num_inference_steps=steps, width=width, height=height)
images = []
for i, image in enumerate(image_list["sample"]):
images.append(image)
return image
gr.Interface(fn=infer, inputs=
[
# gr.Image(source="upload", type="filepath", label="ๅๅงๅพๅ"),
gr.Textbox(label = 'ๆ็คบ่ฏ(prompt)'),
gr.Slider(2, 15, value = 7, label = 'ๆๆฌๅผๅฏผๅผบๅบฆ'),
gr.Slider(10, 30, value = 20, step = 1, label = '่ฟญไปฃๆฌกๆฐ'),
gr.Slider(256, 768, value = 512, step = 64, label = 'ๅฎฝๅบฆ'),
gr.Slider(256, 768, value = 512, step = 64, label = '้ซๅบฆ'),
],outputs='image').queue(max_size=10).launch(enable_queue=True) |