Update api/ltx_server.py

api/ltx_server.py

@@ -60,9 +60,9 @@ def _query_gpu_processes_via_nvidiasmi(device_index: int) -> List[Dict]:
         parts = [p.strip() for p in line.split(",")]
         if len(parts) >= 3:
             try:
-                pid = int(parts[^20_0])
-                name = parts[^20_1]
-                used_mb = int(parts[^20_2])
+                pid = int(parts[0])
+                name = parts[1]
+                used_mb = int(parts[2])
                 user = "unknown"
                 try:
                     import psutil
@@ -351,7 +351,7 @@ class VideoService:
                                  padding_values, progress_callback=None):
         pad_left, pad_right, pad_top, pad_bottom = padding_values
         with imageio.get_writer(output_video_path, fps=frame_rate, codec="libx264", quality=8) as writer:
-            T = latents.shape[^20_2]
+            T = latents.shape[2]
             for i in range(T):
                 latent_chw = latents[0, :, i].to(self.device)
                 with torch.autocast(device_type="cuda", dtype=self.runtime_autocast_dtype) if self.device == "cuda" else contextlib.nullcontext():
@@ -362,13 +362,13 @@ class VideoService:
                         pixel_bchw = self.pipeline.vae.decode(latent_chw.unsqueeze(0))
                     else:
                         raise RuntimeError("Pipeline não expõe decode_latents nem vae.decode para decodificar latentes.")
-                pixel_chw = pixel_bchw[^20_0]
+                pixel_chw = pixel_bchw[0]
                 if pixel_chw.min() < 0:
                     pixel_chw = (pixel_chw.clamp(-1, 1) + 1.0) / 2.0
                 else:
                     pixel_chw = pixel_chw.clamp(0, 1)
-                H = pixel_chw.shape[^20_1]
-                W = pixel_chw.shape[^20_2]
+                H = pixel_chw.shape[1]
+                W = pixel_chw.shape[2]
                 h_end = H - pad_bottom if pad_bottom > 0 else H
                 w_end = W - pad_right if pad_right > 0 else W
                 pixel_chw = pixel_chw[:, pad_top:h_end, pad_left:w_end]
@@ -566,7 +566,7 @@ class VideoService:
                 result_tensor = result_tensor[:, :, :actual_num_frames, pad_top:slice_h_end, pad_left:slice_w_end]
                 log_tensor_info(result_tensor, "Tensor Final (Após Pós-processamento, Antes de Salvar)")
                 with imageio.get_writer(output_video_path, fps=call_kwargs["frame_rate"], codec="libx264", quality=8) as writer:
-                    T = result_tensor.shape[^20_2]
+                    T = result_tensor.shape[2]
                     for i in range(T):
                         frame_chw = result_tensor[0, :, i]
                         frame_hwc_u8 = (frame_chw.permute(1, 2, 0)