Update app.py

app.py

@@ -19,6 +19,7 @@ from funcs import (
     get_latent_z,
     save_videos
 )
+from transformers import pipeline
 
 def download_model():
     REPO_ID = 'Doubiiu/DynamiCrafter_1024'
@@ -35,15 +36,23 @@ ckpt_path='checkpoints/dynamicrafter_1024_v1/model.ckpt'
 config_file='configs/inference_1024_v1.0.yaml'
 config = OmegaConf.load(config_file)
 model_config = config.pop("model", OmegaConf.create())
-model_config['params']['unet_config']['params']['use_checkpoint']=True  # Checkpoint 사용하여 메모리 사용 최적화
+model_config['params']['unet_config']['params']['use_checkpoint']=True
 model = instantiate_from_config(model_config)
 assert os.path.exists(ckpt_path), "Error: checkpoint Not Found!"
 model = load_model_checkpoint(model, ckpt_path)
 model.eval()
 model = model.cuda()
 
-@spaces.GPU(duration=300, gpu_type="h100")  # H100 GPU 사용 지정
+# 번역 모델 로드
+translator = pipeline("translation", model="Helsinki-NLP/opus-mt-ko-en")
+
+@spaces.GPU(duration=300, gpu_type="h100")
 def infer(image, prompt, steps=50, cfg_scale=7.5, eta=1.0, fs=3, seed=123):
+    # 한글 입력 확인 및 번역
+    if any('\u3131' <= char <= '\u318E' or '\uAC00' <= char <= '\uD7A3' for char in prompt):
+        translated = translator(prompt, max_length=512)
+        prompt = translated[0]['translation_text']
+    
     resolution = (576, 1024)
     save_fps = 8
     seed_everything(seed)
@@ -51,7 +60,7 @@ def infer(image, prompt, steps=50, cfg_scale=7.5, eta=1.0, fs=3, seed=123):
         transforms.Resize(min(resolution)),
         transforms.CenterCrop(resolution),
         ])
-    torch.cuda.empty_cache()  # GPU 캐시 메모리 정리
+    torch.cuda.empty_cache()
     print('start:', prompt, time.strftime('%Y-%m-%d %H:%M:%S', time.localtime(time.time())))
     start = time.time()
     if steps > 60:
@@ -63,20 +72,18 @@ def infer(image, prompt, steps=50, cfg_scale=7.5, eta=1.0, fs=3, seed=123):
     h, w = resolution[0] // 8, resolution[1] // 8
     noise_shape = [batch_size, channels, frames, h, w]
 
-    # 텍스트 조건 생성
-    with torch.no_grad(), torch.cuda.amp.autocast():  # 메모리 사용량 감소 및 연산 속도 개선
+    with torch.no_grad(), torch.cuda.amp.autocast():
         text_emb = model.get_learned_conditioning([prompt])
         
-        # 이미지 조건 생성
         img_tensor = torch.from_numpy(image).permute(2, 0, 1).float().to(model.device)
         img_tensor = (img_tensor / 255. - 0.5) * 2
-        image_tensor_resized = transform(img_tensor) #3,256,256
-        videos = image_tensor_resized.unsqueeze(0) # bchw
+        image_tensor_resized = transform(img_tensor)
+        videos = image_tensor_resized.unsqueeze(0)
         
-        z = get_latent_z(model, videos.unsqueeze(2)) #bc,1,hw
+        z = get_latent_z(model, videos.unsqueeze(2))
         img_tensor_repeat = repeat(z, 'b c t h w -> b c (repeat t) h w', repeat=frames)
     
-        cond_images = model.embedder(img_tensor.unsqueeze(0)) ## blc
+        cond_images = model.embedder(img_tensor.unsqueeze(0))
         img_emb = model.image_proj_model(cond_images)
     
         imtext_cond = torch.cat([text_emb, img_emb], dim=1)
@@ -84,15 +91,12 @@ def infer(image, prompt, steps=50, cfg_scale=7.5, eta=1.0, fs=3, seed=123):
         fs = torch.tensor([fs], dtype=torch.long, device=model.device)
         cond = {"c_crossattn": [imtext_cond], "fs": fs, "c_concat": [img_tensor_repeat]}
         
-        # 추론
         batch_samples = batch_ddim_sampling(model, cond, noise_shape, n_samples=1, ddim_steps=steps, ddim_eta=eta, cfg_scale=cfg_scale)
-        # b,samples,c,t,h,w
     
         video_path = './output.mp4'
         save_videos(batch_samples, './', filenames=['output'], fps=save_fps)
     return video_path
 
-
 i2v_examples = [
     ['prompts/1024/astronaut04.png', 'a man in an astronaut suit playing a guitar', 30, 7.5, 1.0, 6, 123],
     ['prompts/1024/bloom01.png', 'time-lapse of a blooming flower with leaves and a stem', 30, 7.5, 1.0, 10, 123],
@@ -102,9 +106,6 @@ i2v_examples = [
     ['prompts/1024/firework03.png', 'fireworks display', 30, 7.5, 1.0, 10, 123],
 ]
 
-
-
-
 css = """#input_img {max-width: 1024px !important} #output_vid {max-width: 1024px; max-height: 576px}"""
 
 with gr.Blocks(analytics_enabled=False, css=css) as dynamicrafter_iface:
@@ -116,7 +117,7 @@ with gr.Blocks(analytics_enabled=False, css=css) as dynamicrafter_iface:
                     with gr.Row():
                         i2v_input_image = gr.Image(label="Input Image",elem_id="input_img")
                     with gr.Row():
-                        i2v_input_text = gr.Text(label='Prompts')
+                        i2v_input_text = gr.Text(label='Prompts (한글 입력 가능)')
                     with gr.Row():
                         i2v_seed = gr.Slider(label='Random Seed', minimum=0, maximum=10000, step=1, value=123)
                         i2v_eta = gr.Slider(minimum=0.0, maximum=1.0, step=0.1, label='ETA', value=1.0, elem_id="i2v_eta")
@@ -125,7 +126,6 @@ with gr.Blocks(analytics_enabled=False, css=css) as dynamicrafter_iface:
                         i2v_steps = gr.Slider(minimum=1, maximum=50, step=1, elem_id="i2v_steps", label="Sampling steps", value=50)
                         i2v_motion = gr.Slider(minimum=5, maximum=20, step=1, elem_id="i2v_motion", label="FPS", value=10)
                     i2v_end_btn = gr.Button("Generate")
-                # with gr.Tab(label='Result'):
                 with gr.Row():
                     i2v_output_video = gr.Video(label="Generated Video",elem_id="output_vid",autoplay=True,show_share_button=True)
 
@@ -140,6 +140,4 @@ with gr.Blocks(analytics_enabled=False, css=css) as dynamicrafter_iface:
                         fn = infer
         )
 
-# 포트 7890 설정, 대기열 활성화, API 활성화
-dynamicrafter_iface.launch()
-
+dynamicrafter_iface.launch(server_port=7890, server_name="0.0.0.0", share=True)