Update app.py

app.py

@@ -1,21 +1,82 @@
-from diffusers import StableDiffusionPipeline, EulerDiscreteScheduler
+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"
-device = "cuda" if torch.cuda.is_available() else "cpu"
+
 # Use the Euler scheduler here instead
 scheduler = EulerDiscreteScheduler.from_pretrained(model_id, subfolder="scheduler")
-pipe = StableDiffusionPipeline.from_pretrained(model_id, scheduler=scheduler, revision="fp16")
-pipe = pipe.to(device)
-#pipe.enable_attention_slicing()
-#prompt = "a young doctor talk with a patient in the cartoon style"
-#num_imgs=3
+
+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
-def gen(prompt, scale, steps):
-    image = pipe(prompt, height=768, width=768,guidance_scale=scale, num_inference_steps=steps).images
+#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', gr.Slider(1, 10, 7.5), gr.Slider(1, maximum=100, value=50, step=1)], outputs='image', title="Stable Diffusion 2.0 ZQL", description="SD 2.0. <b>WARNING:</b> My first implementation of SD2, and COOL.", article = "Code Ape: <a href=\"https://huggingface.co/qianli\">千里马</a>").launch()
+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)
+
+