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| from diffusers import StableDiffusionPipeline, EulerDiscreteScheduler, StableDiffusionImg2ImgPipeline | |
| import torch | |
| import gradio as gr | |
| from io import BytesIO | |
| from PIL import Image | |
| import numpy as np | |
| #from trans import LanguageTrans | |
| import requests | |
| import http.client | |
| import random | |
| import json | |
| model_id = "stabilityai/stable-diffusion-2" | |
| # Use the Euler scheduler here instead | |
| scheduler = EulerDiscreteScheduler.from_pretrained(model_id, subfolder="scheduler") | |
| device = "cuda" if torch.cuda.is_available() else "cpu" | |
| def is_contains_chinese(strs): | |
| for _char in strs: | |
| if '\u4e00' <= _char <='\u9fa5': | |
| return True | |
| return False | |
| def trans_youdao(sentence): | |
| """有道翻译""" | |
| content = sentence | |
| data = { | |
| "i": content, | |
| "from": "AUTO", | |
| "to": "AUTO", | |
| "smartresult": "dict", | |
| "client": "fanyideskweb", | |
| "doctype": "json", | |
| "version": "2.1", | |
| "keyfrom": "fanyi.web", | |
| "action": "FY_BY_REALTIME", | |
| "typoResult": "false" | |
| } | |
| response = requests.post("http://fanyi.youdao.com/translate?smartresult=dict&smartresult=rule", | |
| data=data).json() | |
| resp = response["translateResult"][0][0]["tgt"] | |
| #print('{}'.format(resp)) | |
| #print('{}'.format(resp)) | |
| return(resp) | |
| #return resp | |
| scale=7.5 | |
| steps=100 | |
| #i=0 | |
| #examples = [["An adventurer is approached by a mysterious stranger in the tavern for a new quest."],[]] | |
| def gen(prompt, input_image, strength, scale, steps): | |
| if is_contains_chinese(prompt): | |
| prompt = trans_youdao(prompt) | |
| print(prompt) | |
| #print(input_image) | |
| if input_image is None: | |
| pipe = StableDiffusionPipeline.from_pretrained(model_id, scheduler=scheduler, revision="fp16", torch_dtype=torch.float16) | |
| pipe = pipe.to(device) | |
| pipe.enable_attention_slicing() | |
| image = pipe(prompt=prompt, height=768, width=768,guidance_scale=scale, num_inference_steps=steps).images | |
| else: | |
| pipe = StableDiffusionImg2ImgPipeline.from_pretrained(model_id, scheduler=scheduler, revision="fp16", torch_dtype=torch.float16) | |
| input_image=Image.fromarray(np.uint8(input_image)) | |
| input_image = input_image.resize((768, 768)) | |
| pipe = pipe.to(device) | |
| pipe.enable_attention_slicing() | |
| image = pipe(prompt=prompt, init_image=input_image, strength=strength, guidance_scale=scale, num_inference_steps=steps).images | |
| return image[0] | |
| gr.Interface(fn=gen, inputs=['text', 'image', gr.Slider(0, 1, 0.75),gr.Slider(1, 10, 7.5), gr.Slider(1, maximum=100, value=50, step=1)], outputs='image', title="Bilingual Stable Diffusion 2.0", description="SD 2.0. <b>Welcome:</b> My implementation of SD2 both text and image to image, with both Chinese and English support.", article = "<b>Example</b>:a fabulous mountain view from the window of a luxury hotel with the sun rising in the sky <br> <b>Example</b>: 站在山上看远方的两个人<br> Code Ape: <a href=\"https://huggingface.co/qianli\">千里马</a>").launch(share=True) | |