Create app.py

app.py

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+import sys, types, importlib.machinery, importlib
+
+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
+try:
+    from diffusers import FlowMatchEulerDiscreteScheduler
+except ImportError:
+    from diffusers import EulerDiscreteScheduler as FlowMatchEulerDiscreteScheduler
+
+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
+
+REVISION = "ceaf371f01ef66192264811b390bccad475a4f02"
+
+# Florence-2 로드
+device = "cuda" if torch.cuda.is_available() else "cpu"
+florence_model = AutoModelForCausalLM.from_pretrained('microsoft/Florence-2-base', revision = REVISION, trust_remote_code=True, torch_dtype=torch.float16)
+florence_model.to("cpu")
+florence_model.eval()
+florence_processor = AutoProcessor.from_pretrained('microsoft/Florence-2-base', revision = REVISION, trust_remote_code=True)
+
+# Stable Diffusion TurboX 로드
+model_repo = "tensorart/stable-diffusion-3.5-large-TurboX"
+pipe = DiffusionPipeline.from_pretrained(
+    model_repo,
+    trust_remote_code=True,
+    torch_dtype=torch.float16 if torch.cuda.is_available() else torch.float32
+)
+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"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)
+
+    inputs = florence_processor(text="<MORE_DETAILED_CAPTION>", images=image, return_tensors="pt").to(device)
+    generated_ids = florence_model.generate(
+        input_ids=inputs["input_ids"],
+        pixel_values=inputs["pixel_values"],
+        max_new_tokens=512,
+        num_beams=3
+    )
+    generated_text = florence_processor.batch_decode(generated_ids, skip_special_tokens=False)[0]
+    parsed_answer = florence_processor.post_process_generation(
+        generated_text,
+        task="<MORE_DETAILED_CAPTION>",
+        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,
+        height=768,
+        generator=generator
+    ).images[0]
+    return image, seed
+
+# Gradio UI 구성
+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"
+                "- 오른쪽에 결과 이미지가 생성됩니다.")
+
+    with gr.Row():
+        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")
+
+    def full_process(img):
+        prompt = generate_prompt(img)
+        image, seed = generate_image(prompt, randomize_seed=True)
+        return prompt, image
+
+    run_button.click(fn=full_process, inputs=[input_img], outputs=[prompt_out, output_img])
+
+demo.launch()