Update app.py

app.py

@@ -1,32 +1,124 @@
+import os
+from huggingface_hub import snapshot_download
+
+os.environ["HF_HUB_ENABLE_HF_TRANSFER"] = "0"
+REVISION = "ceaf371f01ef66192264811b390bccad475a4f02"
+
+LOCAL_FLORENCE = snapshot_download(
+    repo_id="microsoft/Florence-2-base",
+    revision=REVISION
+)
+
+LOCAL_TURBOX = snapshot_download(
+    repo_id="tensorart/stable-diffusion-3.5-large-TurboX"
+)
+
+LOCAL_FLORENCE_DIR = snapshot_download(
+    repo_id="microsoft/Florence-2-base",
+    revision=REVISION,
+    local_files_only=False
+)
+
+import sys, types, importlib.machinery, importlib
+
+# flash_attn 무효화 처리
+spec = importlib.machinery.ModuleSpec('flash_attn', loader=None)
+mod = types.ModuleType('flash_attn')
+mod.__spec__ = spec
+sys.modules['flash_attn'] = mod
+
+import huggingface_hub as _hf_hub
+_hf_hub.cached_download = _hf_hub.hf_hub_download
+
 import gradio as gr
 import torch
 import random
 from PIL import Image
 from transformers import AutoProcessor, AutoModelForCausalLM
-from diffusers import DiffusionPipeline, FlowMatchEulerDiscreteScheduler
+from transformers import (
+    CLIPTextModel,
+    CLIPTokenizer,
+    CLIPFeatureExtractor,
+)
+import diffusers
+from diffusers import StableDiffusionPipeline
+from diffusers import DiffusionPipeline
+from diffusers import EulerDiscreteScheduler as FlowMatchEulerDiscreteScheduler
+from diffusers import UNet2DConditionModel 
 
-# Florence-2 로드
-device = "cuda" if torch.cuda.is_available() else "cpu"
-florence_model = AutoModelForCausalLM.from_pretrained('microsoft/Florence-2-base', trust_remote_code=True).to(device).eval()
-florence_processor = AutoProcessor.from_pretrained('microsoft/Florence-2-base', trust_remote_code=True)
+# flash attention 관련 import 우회
+import transformers.utils.import_utils as _import_utils
+from transformers.utils import is_flash_attn_2_available
+_import_utils._is_package_available = lambda pkg: False
+_import_utils.is_flash_attn_2_available = lambda: False
+
+hf_utils = importlib.import_module('transformers.utils')
+hf_utils.is_flash_attn_2_available = lambda *a, **k: False
+hf_utils.is_flash_attn_greater_or_equal_2_10 = lambda *a, **k: False
+
+mask_utils = importlib.import_module("transformers.modeling_attn_mask_utils")
+for fn in ("_prepare_4d_attention_mask_for_sdpa", "_prepare_4d_causal_attention_mask_for_sdpa"):
+    if not hasattr(mask_utils, fn):
+        setattr(mask_utils, fn, lambda *a, **k: None)
+
+cfg_mod = importlib.import_module("transformers.configuration_utils")
+_PrC = cfg_mod.PretrainedConfig
+_orig_getattr = _PrC.__getattribute__
+def _getattr(self, name):
+    if name == "_attn_implementation":
+        return "sdpa"
+    return _orig_getattr(self, name)
+_PrC.__getattribute__ = _getattr
 
-# Stable Diffusion TurboX 로드
 model_repo = "tensorart/stable-diffusion-3.5-large-TurboX"
+device = "cuda" if torch.cuda.is_available() else "cpu"
+
+scheduler = FlowMatchEulerDiscreteScheduler.from_pretrained(
+    model_repo,
+    subfolder="scheduler",
+    torch_dtype=torch.float16,
+)
+
+text_encoder = CLIPTextModel.from_pretrained(
+    model_repo, subfolder="text_encoder", torch_dtype=torch.float16
+)
+tokenizer = CLIPTokenizer.from_pretrained(model_repo, subfolder="tokenizer")
+feature_extractor = CLIPFeatureExtractor.from_pretrained("runwayml/stable-diffusion-v1-5", subfolder="feature_extractor")
+
+unet = UNet2DConditionModel.from_pretrained(
+    model_repo, 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()
+florence_processor = AutoProcessor.from_pretrained(
+    LOCAL_FLORENCE, trust_remote_code=True
+)
+
+diffusers.StableDiffusion3Pipeline = StableDiffusionPipeline
 pipe = DiffusionPipeline.from_pretrained(
     model_repo,
-    torch_dtype=torch.float16 if torch.cuda.is_available() else torch.float32
+    torch_dtype=torch.float16,
+    trust_remote_code=True,
+    safety_checker=None,
+    feature_extractor=None
+)
+pipe = pipe.to("cuda")
+
+pipe.scheduler = FlowMatchEulerDiscreteScheduler.from_pretrained(
+    model_repo, subfolder="scheduler", local_files_only=True,
+    trust_remote_code=True, shift=5
 )
-pipe.scheduler = FlowMatchEulerDiscreteScheduler.from_pretrained(model_repo, subfolder="scheduler", shift=5)
-pipe = pipe.to(device)
 
 MAX_SEED = 2**31 - 1
 
 def pseudo_translate_to_korean_style(en_prompt: str) -> str:
-    # 번역 없이 스타일 적용
-    return f"이 장면은 {en_prompt} 장면입니다. 밝고 귀여운 카툰 스타일로 그려주세요. 디지털 일러스트 느낌으로 묘사해 주세요."
+    return f"Cartoon styled {en_prompt} handsome or pretty people"
 
 def generate_prompt(image):
-    """이미지 → 영어 설명 → 한국어 프롬프트 스타일로 변환"""
     if not isinstance(image, Image.Image):
         image = Image.fromarray(image)
 
@@ -44,19 +136,15 @@ def generate_prompt(image):
         image_size=(image.width, image.height)
     )
     prompt_en = parsed_answer["<MORE_DETAILED_CAPTION>"]
-
-    # 번역기 없이 스타일 적용
     cartoon_prompt = pseudo_translate_to_korean_style(prompt_en)
     return cartoon_prompt
 
 def generate_image(prompt, seed=42, randomize_seed=False):
-    """텍스트 프롬프트 → 이미지 생성"""
     if randomize_seed:
         seed = random.randint(0, MAX_SEED)
     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,
@@ -65,10 +153,9 @@ def generate_image(prompt, seed=42, randomize_seed=False):
     ).images[0]
     return image, seed
 
-# Gradio UI 구성
+# Gradio UI
 with gr.Blocks() as demo:
     gr.Markdown("# 🖼 이미지 → 설명 생성 → 카툰 이미지 자동 생성기")
-    
     gr.Markdown("**📌 사용법 안내 (한국어)**\n"
                 "- 왼쪽에 이미지를 업로드하세요.\n"
                 "- AI가 영어 설명을 만들고, 내부에서 한국어 스타일 프롬프트로 재구성합니다.\n"
@@ -78,7 +165,6 @@ with gr.Blocks() as demo:
         with gr.Column():
             input_img = gr.Image(label="🎨 원본 이미지 업로드")
             run_button = gr.Button("✨ 생성 시작")
-
         with gr.Column():
             prompt_out = gr.Textbox(label="📝 스타일 적용된 프롬프트", lines=3, show_copy_button=True)
             output_img = gr.Image(label="🎉 생성된 이미지")