Update app.py

app.py

@@ -1,3 +1,4 @@
+import space 
 import gradio as gr
 import numpy as np
 import PIL.Image
@@ -5,10 +6,9 @@ from PIL import Image
 import random
 from diffusers import ControlNetModel, StableDiffusionXLPipeline, AutoencoderKL
 from diffusers import DDIMScheduler, EulerAncestralDiscreteScheduler
-import cv2
 import torch
 import os
-import uuid  # UUIDを使用するために追加
+import uuid
 
 device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
 
@@ -41,16 +41,10 @@ def infer(prompt, negative_prompt, seed, randomize_seed, width, height, guidance
         generator=generator
     ).images[0]
 
-    # UUIDを使用して一意のファイル名を生成
-    unique_filename = f"output_image_{uuid.uuid4().hex}.png"
-    output_image.save(unique_filename, format="PNG")
+    # PNGに変換する
+    output_image = output_image.convert("RGBA")
 
-    return unique_filename  # ファイルパスを返す
-
-def cleanup(filename):
-    if os.path.exists(filename):
-        os.remove(filename)
-        print(f"Deleted temporary file: {filename}")
+    return output_image  # 画像オブジェクトを直接返す
 
 css = """
 #col-container {
@@ -77,7 +71,8 @@ with gr.Blocks(css=css) as demo:
 
             run_button = gr.Button("Run", scale=0)
 
-        result = gr.Image(label="Result", show_label=False, type="filepath")
+        # show_download_button=True でPNG形式でダウンロードできるようにする
+        result = gr.Image(label="Result", show_label=False, type="pil", elem_id="output_image", show_download_button=True)
         
         with gr.Accordion("Advanced Settings", open=False):
 
@@ -136,10 +131,6 @@ with gr.Blocks(css=css) as demo:
         fn=infer,
         inputs=[prompt, negative_prompt, seed, randomize_seed, width, height, guidance_scale, num_inference_steps],
         outputs=[result]
-    ).then(
-        fn=cleanup,
-        inputs=[result],
-        outputs=[]
     )
 
 demo.queue().launch()