Update app.py

app.py

@@ -72,6 +72,23 @@ _PrC.__getattribute__ = _getattr
 
 # Florence-2 로드
 device = "cuda" if torch.cuda.is_available() else "cpu"
+scheduler = EulerDiscreteScheduler.from_pretrained(
+    model_id,
+    subfolder="scheduler",
+    torch_dtype=torch.float16,
+)
+text_encoder      = CLIPTextModel.from_pretrained(
+    model_id, subfolder="text_encoder_1", torch_dtype=torch.float16
+)
+tokenizer         = CLIPTokenizer.from_pretrained(
+    model_id, subfolder="tokenizer_1"
+)
+feature_extractor = CLIPFeatureExtractor.from_pretrained(
+    model_id, subfolder="feature_extractor"
+)
+unet = UNet2DConditionModel.from_pretrained(
+    model_id, subfolder="unet", torch_dtype=torch.float16
+)
 florence_model = AutoModelForCausalLM.from_pretrained(LOCAL_FLORENCE, trust_remote_code=True, torch_dtype=torch.float16)
 florence_model.to("cpu")
 florence_model.eval()
@@ -83,14 +100,18 @@ diffusers.StableDiffusion3Pipeline = StableDiffusionPipeline
 
 model_repo = "tensorart/stable-diffusion-3.5-large-TurboX"
 
-pipe = diffusers.DiffusionPipeline.from_pretrained(
-    "tensorart/stable-diffusion-3.5-large-TurboX",
-    trust_remote_code=True,
-    torch_dtype=torch.float16 if torch.cuda.is_available() else torch.float32,
-)
+pipe = StableDiffusionPipeline(
+    text_encoder=text_encoder,
+    tokenizer=tokenizer,
+    feature_extractor=feature_extractor,
+    scheduler=scheduler,
+    unet=unet,
+    vae=vae,
+    safety_checker=safety_checker,
+    torch_dtype=torch.float16,
+).to(device)
 
 pipe.scheduler = FlowMatchEulerDiscreteScheduler.from_pretrained(model_repo, subfolder="scheduler", local_files_only=True, trust_remote_code = True, shift=5)
-pipe = pipe.to(device)
 
 MAX_SEED = 2**31 - 1
 
@@ -129,7 +150,6 @@ def generate_image(prompt, seed=42, randomize_seed=False):
     generator = torch.Generator().manual_seed(seed)
     image = pipe(
         prompt=prompt,
-        negative_prompt="왜곡된 손, 흐림, 이상한 얼굴",
         guidance_scale=1.5,
         num_inference_steps=8,
         width=768,