Update app.py

app.py

@@ -3,74 +3,20 @@ import numpy as np
 import random
 import torch
 from diffusers import DiffusionPipeline
-import warnings
-import os
-from datetime import datetime
-import uuid
 
-# 경고 메시지 숨기기
-warnings.filterwarnings('ignore', category=UserWarning)
-
-# 저장 디렉토리 생성 
-SAVE_DIR = "saved_images"
-if not os.path.exists(SAVE_DIR):
-    os.makedirs(SAVE_DIR, exist_ok=True)
-
-# 장치 및 dtype 설정
-dtype = torch.float32 if torch.cuda.is_available() else torch.float32
+# 기본 설정
+dtype = torch.bfloat16
 device = "cuda" if torch.cuda.is_available() else "cpu"
 
 # 모델 로드
 pipe = DiffusionPipeline.from_pretrained(
-    "black-forest-labs/FLUX.1-schnell",
-    torch_dtype=dtype,
-    device_map="balanced" if torch.cuda.is_available() else None,
-    use_safetensors=True
+    "black-forest-labs/FLUX.1-schnell", 
+    torch_dtype=dtype
 ).to(device)
 
-# 메모리 최적화
-pipe.enable_attention_slicing()
-if device == "cpu":
-    pipe.enable_sequential_cpu_offload()
-
 MAX_SEED = np.iinfo(np.int32).max
 MAX_IMAGE_SIZE = 2048
 
-def generate_diagram(prompt, seed=42, randomize_seed=False, width=1024, height=1024, num_inference_steps=4):
-    """FLUX AI를 사용하여 다이어그램 생성"""
-    try:
-        if randomize_seed:
-            seed = random.randint(0, MAX_SEED)
-        generator = torch.Generator(device=device).manual_seed(seed)
-        
-        with torch.no_grad():
-            image = pipe(
-                prompt=prompt,
-                width=width,
-                height=height,
-                num_inference_steps=num_inference_steps,
-                generator=generator,
-                guidance_scale=0.0
-            ).images[0]
-            
-        # 이미지 저장
-        timestamp = datetime.now().strftime("%Y%m%d_%H%M%S")
-        unique_id = str(uuid.uuid4())[:8]
-        filename = f"diagram_{timestamp}_{unique_id}.png"
-        save_path = os.path.join(SAVE_DIR, filename)
-        image.save(save_path)
-        
-        return image, seed
-        
-    except Exception as e:
-        raise gr.Error(f"다이어그램 생성 중 오류 발생: {str(e)}")
-    finally:
-        if torch.cuda.is_available():
-            torch.cuda.empty_cache()
-
-
-
-
 # Enhanced examples with more detailed prompts and specific styling
 EXAMPLES = [
     {
@@ -272,33 +218,19 @@ GRADIO_EXAMPLES = [
     for example in EXAMPLES
 ]
 
-def generate_diagram(prompt, width=1024, height=1024):
-    """FLUX AI를 사용하여 다이어그램 생성"""
-    try:
-        # 시드 설정
+def infer(prompt, seed=42, randomize_seed=False, width=1024, height=1024, num_inference_steps=4, progress=gr.Progress(track_tqdm=True)):
+    if randomize_seed:
         seed = random.randint(0, MAX_SEED)
-        generator = torch.Generator(device=device).manual_seed(seed)
-        
-        # 이미지 생성
-        image = pipeline(
-            prompt=prompt,
-            width=width,
-            height=height,
-            num_inference_steps=4,
-            generator=generator,
-        ).images[0]
-        
-        # 이미지 저장
-        timestamp = datetime.now().strftime("%Y%m%d_%H%M%S")
-        unique_id = str(uuid.uuid4())[:8]
-        filename = f"diagram_{timestamp}_{unique_id}.png"
-        save_path = os.path.join(SAVE_DIR, filename)
-        image.save(save_path)
-        
-        return image
-        
-    except Exception as e:
-        raise gr.Error(f"다이어그램 생성 중 오류 발생: {str(e)}")
+    generator = torch.Generator().manual_seed(seed)
+    image = pipe(
+        prompt=prompt,
+        width=width,
+        height=height,
+        num_inference_steps=num_inference_steps,
+        generator=generator,
+        guidance_scale=0.0
+    ).images[0]
+    return image, seed
 
 # CSS 스타일
 css="""
@@ -311,36 +243,37 @@ css="""
 # Gradio 인터페이스 생성
 with gr.Blocks(css=css) as demo:
     with gr.Column(elem_id="col-container"):
-        gr.Markdown("""# FLUX 다이어그램 생성기
-        FLUX AI를 사용하여 아름다운 손그림 스타일의 다이어그램을 생성합니다
+        gr.Markdown("""# FLUX.1 [schnell]
+12B param rectified flow transformer distilled from [FLUX.1 [pro]](https://blackforestlabs.ai/) for 4 step generation
+[[blog](https://blackforestlabs.ai/announcing-black-forest-labs/)] [[model](https://huggingface.co/black-forest-labs/FLUX.1-schnell)]
         """)
         
         with gr.Row():
             prompt = gr.Text(
-                label="프롬프트",
+                label="Prompt",
                 show_label=False,
                 max_lines=1,
-                placeholder="다이어그램 구조를 입력하세요...",
+                placeholder="Enter your prompt",
                 container=False,
             )
-            run_button = gr.Button("생성", scale=0)
+            run_button = gr.Button("Run", scale=0)
         
-        result = gr.Image(label="생성된 다이어그램", show_label=False)
+        result = gr.Image(label="Result", show_label=False)
         
-        with gr.Accordion("고급 설정", open=False):
+        with gr.Accordion("Advanced Settings", open=False):
             seed = gr.Slider(
-                label="시드",
+                label="Seed",
                 minimum=0,
                 maximum=MAX_SEED,
                 step=1,
                 value=0,
             )
             
-            randomize_seed = gr.Checkbox(label="랜덤 시드", value=True)
+            randomize_seed = gr.Checkbox(label="Randomize seed", value=True)
             
             with gr.Row():
                 width = gr.Slider(
-                    label="너비",
+                    label="Width",
                     minimum=256,
                     maximum=MAX_IMAGE_SIZE,
                     step=32,
@@ -348,7 +281,7 @@ with gr.Blocks(css=css) as demo:
                 )
                 
                 height = gr.Slider(
-                    label="높이",
+                    label="Height",
                     minimum=256,
                     maximum=MAX_IMAGE_SIZE,
                     step=32,
@@ -356,26 +289,24 @@ with gr.Blocks(css=css) as demo:
                 )
             
             num_inference_steps = gr.Slider(
-                label="추론 단계 수",
+                label="Number of inference steps",
                 minimum=1,
                 maximum=50,
                 step=1,
                 value=4,
             )
         
-        # 예제 추가
         gr.Examples(
-            examples=EXAMPLES,  # 이전에 정의된 예제들 사용
-            fn=generate_diagram,
+            examples=GRADIO_EXAMPLES,
+            fn=infer,
             inputs=[prompt],
             outputs=[result, seed],
-            cache_examples=True
+            cache_examples="lazy"
         )
 
-    # 이벤트 핸들러
     gr.on(
         triggers=[run_button.click, prompt.submit],
-        fn=generate_diagram,
+        fn=infer,
         inputs=[prompt, seed, randomize_seed, width, height, num_inference_steps],
         outputs=[result, seed]
     )