Update app.py

app.py

@@ -50,9 +50,6 @@ pipe = StableDiffusionXLPipeline(
     force_zeros_for_empty_prompt=False
 ).to(device)
 
-#pipe = pipe.to(device)
-#pipe.enable_model_cpu_offload()
-
 if hasattr(pipe.unet, 'encoder_hid_proj'):
     pipe.unet.text_encoder_hid_proj = pipe.unet.encoder_hid_proj
 
@@ -61,8 +58,33 @@ pipe.load_ip_adapter(f'{root_dir}/weights/Kolors-IP-Adapter-Plus', subfolder="",
 MAX_SEED = np.iinfo(np.int32).max
 MAX_IMAGE_SIZE = 1024
 
+# ----------------------------------------------
+# 수정된 부분: infer 함수 내에서 hidden_prompt를 앞에 추가
+# ----------------------------------------------
 @spaces.GPU(duration=80)
-def infer(prompt, ip_adapter_image, ip_adapter_scale=0.5, negative_prompt="", seed=100, randomize_seed=False, width=1024, height=1024, guidance_scale=5.0, num_inference_steps=50, progress=gr.Progress(track_tqdm=True)):
+def infer(
+    user_prompt,
+    ip_adapter_image,
+    ip_adapter_scale=0.5,
+    negative_prompt="",
+    seed=100,
+    randomize_seed=False,
+    width=1024,
+    height=1024,
+    guidance_scale=5.0,
+    num_inference_steps=50,
+    progress=gr.Progress(track_tqdm=True)
+):
+    # 숨겨진(기본/필수) 프롬프트
+    hidden_prompt = (
+        "Studio Ghibli animation style, featuring whimsical characters with expressive eyes "
+        "and fluid movements. Lush, detailed natural environments with ethereal lighting "
+        "and soft color palettes of blues, greens, and warm earth tones."
+    )
+
+    # 실제로 파이프라인에 전달할 최종 프롬프트
+    prompt = f"{hidden_prompt}, {user_prompt}"
+
     if randomize_seed:
         seed = random.randint(0, MAX_SEED)
     
@@ -87,13 +109,29 @@ def infer(prompt, ip_adapter_image, ip_adapter_scale=0.5, negative_prompt="", se
     return image, seed
 
 examples = [
-    ["Studio Ghibli animation style, featuring whimsical characters with expressive eyes and fluid movements. Lush, detailed natural environments with ethereal lighting and soft color palettes of blues, greens, and warm earth tones. "aged baby"", "gh1.jpg", 0.5],
-    ["Studio Ghibli animation style, featuring whimsical characters with expressive eyes and fluid movements. Lush, detailed natural environments with ethereal lighting and soft color palettes of blues, greens, and warm earth tones. "aged baby"", "gh2.jpg", 0.5],
-    ["Studio Ghibli animation style, featuring whimsical characters with expressive eyes and fluid movements. Lush, detailed natural environments with ethereal lighting and soft color palettes of blues, greens, and warm earth tones. "aged baby"", "gh3.webp", 0.5],
-    ["Studio Ghibli animation style, featuring whimsical characters with expressive eyes and fluid movements. Lush, detailed natural environments with ethereal lighting and soft color palettes of blues, greens, and warm earth tones. "aged baby"", "gh4.webp", 0.5],
+    [
+        "Studio Ghibli animation style, featuring whimsical characters with expressive eyes and fluid movements. Lush, detailed natural environments with ethereal lighting and soft color palettes of blues, greens, and warm earth tones. \"aged baby\"",
+        "gh1.jpg",
+        0.5
+    ],
+    [
+        "Studio Ghibli animation style, featuring whimsical characters with expressive eyes and fluid movements. Lush, detailed natural environments with ethereal lighting and soft color palettes of blues, greens, and warm earth tones. \"aged baby\"",
+        "gh2.jpg",
+        0.5
+    ],
+    [
+        "Studio Ghibli animation style, featuring whimsical characters with expressive eyes and fluid movements. Lush, detailed natural environments with ethereal lighting and soft color palettes of blues, greens, and warm earth tones. \"aged baby\"",
+        "gh3.webp",
+        0.5
+    ],
+    [
+        "Studio Ghibli animation style, featuring whimsical characters with expressive eyes and fluid movements. Lush, detailed natural environments with ethereal lighting and soft color palettes of blues, greens, and warm earth tones. \"aged baby\"",
+        "gh4.webp",
+        0.5
+    ],
 ]
 
-css="""
+css = """
 #col-container {
     margin: 0 auto;
     max-width: 720px;
@@ -105,6 +143,7 @@ css="""
     height: 100%
 }
 """
+
 with gr.Blocks(css=css) as demo:
     with gr.Column(elem_id="col-container"):
         gr.Markdown(f"""
@@ -138,7 +177,13 @@ with gr.Blocks(css=css) as demo:
             negative_prompt = gr.Text(
                 label="Negative prompt",
                 max_lines=1,
-                placeholder="Copy(worst quality, low quality:1.4), bad anatomy, bad hands, text, error, missing fingers, extra digit, fewer digits, cropped, worst quality, low quality, normal quality, jpeg artifacts, signature, watermark, username, blurry, artist name, (deformed iris, deformed pupils:1.2), (semi-realistic, cgi, 3d, render:1.1), amateur, (poorly drawn hands, poorly drawn face:1.2)",
+                placeholder=(
+                    "Copy(worst quality, low quality:1.4), bad anatomy, bad hands, text, error, "
+                    "missing fingers, extra digit, fewer digits, cropped, worst quality, low quality, "
+                    "normal quality, jpeg artifacts, signature, watermark, username, blurry, "
+                    "artist name, (deformed iris, deformed pupils:1.2), (semi-realistic, cgi, "
+                    "3d, render:1.1), amateur, (poorly drawn hands, poorly drawn face:1.2)"
+                ),
             )
             seed = gr.Slider(
                 label="Seed",
@@ -179,7 +224,6 @@ with gr.Blocks(css=css) as demo:
                     value=50,
                 )
             
-        
         gr.Examples(
             examples=examples,
             fn=infer,
@@ -191,8 +235,19 @@ with gr.Blocks(css=css) as demo:
     gr.on(
         triggers=[run_button.click, prompt.submit],
         fn=infer,
-        inputs=[prompt, ip_adapter_image, ip_adapter_scale, negative_prompt, seed, randomize_seed, width, height, guidance_scale, num_inference_steps],
+        inputs=[
+            prompt,
+            ip_adapter_image,
+            ip_adapter_scale,
+            negative_prompt,
+            seed,
+            randomize_seed,
+            width,
+            height,
+            guidance_scale,
+            num_inference_steps
+        ],
         outputs=[result, seed]
     )
 
-demo.queue().launch()
+demo.queue().launch()