modify app input and output

app.py

@@ -12,30 +12,36 @@ pipe = StableDiffusionPipeline.from_single_file(
     use_safetensors=True,
 )
 
-num_images_per_prompt = 4
+num_images_per_prompt = 2
 prompt = 'Modern Elegance:Explore the seamless blend of sleek lines, minimalist aesthetics, and cutting-edge design in modern interior decor'
 
-def inference():
+negative_prompt = '(nsfw:1.3),(Nude:1.3),(Naked:1.3),low quality, blurry, bad anatomy, worst quality, text, watermark, normal quality, ugly, signature, lowres, deformed, disfigured, cropped, jpeg artifacts, error, \
+                    mutation, logo, watermark,text, logo,contact, error, blurry, cropped, username, artist name, (worst quality, low quality:1.4),monochrome,'
+def inference(prompt):
     image = pipe(
         prompt=prompt, 
-        negative_prompt="watermark, logo, symbol, word, phrase, human, noisy, blurry, deformed, ugly, NSFW, low quality, worst quality, monochrome, illustration, sketch, grayscale, backlight, messy, blurry",
-        num_inference_steps=30, 
+        negative_prompt=negative_prompt,
+        num_inference_steps=20, 
         # cross_attention_kwargs={"scale": lora_scale}, 
         generator=torch.manual_seed(0),
-        width=768,
-        height=768,
+        width=512,
+        height=512,
         guidance_scale=7.0,
         use_karras_sigmas=True,
         num_images_per_prompt=num_images_per_prompt,  # 如果是 4, image 就是四张图片组成的 List
     ).images
     return image
-demo = gr.Interface(
-    fn=inference,
-    inputs=gr.Image(height=512, width=512, label="参考图片"),
-    outputs=gr.Gallery(label="可能满足你需求的室内设计图：", 
+
+ref_image = gr.Image(height=512, width=512, label="参考图片")
+prompt = gr.Textbox(placeholder="输入你对室内设计的要求，以便 AI 能够更好地满足你的需求。", label="要求")
+result_image = gr.Gallery(label="可能满足你需求的室内设计图：", 
                                   columns=3, 
                                   height="auto", 
                                   object_fit="contain")
+demo = gr.Interface(
+    fn=inference,
+    inputs=prompt,
+    outputs=result_image
 )
 
 demo.launch()