nohup: ignoring input Namespace(model='HYVideo-T/2-cfgdistill', latent_channels=16, precision='bf16', rope_theta=256, vae='884-16c-hy', vae_precision='fp16', vae_tiling=True, text_encoder='llm', text_encoder_precision='fp16', text_states_dim=4096, text_len=256, tokenizer='llm', prompt_template='dit-llm-encode', prompt_template_video='dit-llm-encode-video', hidden_state_skip_layer=2, apply_final_norm=False, text_encoder_2='clipL', text_encoder_precision_2='fp16', text_states_dim_2=768, tokenizer_2='clipL', text_len_2=77, denoise_type='flow', flow_shift=7.0, flow_reverse=True, flow_solver='euler', use_linear_quadratic_schedule=False, linear_schedule_end=25, model_base='ckpts', dit_weight='ckpts/hunyuan-video-t2v-720p/transformers/mp_rank_00_model_states.pt', model_resolution='540p', load_key='module', use_cpu_offload=False, batch_size=1, infer_steps=20, disable_autocast=False, save_path='./results', save_path_suffix='', name_suffix='', num_videos=1, video_size=[1280, 720], video_length=33, prompt='A cat walks on the grass, realistic style.', seed_type='auto', seed=42, neg_prompt=None, cfg_scale=1.0, embedded_cfg_scale=6.0, use_fp8=False, reproduce=False, ulysses_degree=1, ring_degree=1) 2026-02-02 14:09:46.064 | INFO | hyvideo.inference:from_pretrained:154 - Got text-to-video model root path: ckpts 2026-02-02 14:09:46.065 | INFO | hyvideo.inference:from_pretrained:189 - Building model... 2026-02-02 14:09:46.741 | INFO | hyvideo.inference:load_state_dict:340 - Loading torch model ckpts/hunyuan-video-t2v-720p/transformers/mp_rank_00_model_states.pt... /workspace/cicd/HunyuanVideo-t2v/hyvideo/inference.py:341: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature. state_dict = torch.load(model_path, map_location=lambda storage, loc: storage) 2026-02-02 14:10:02.963 | INFO | hyvideo.vae:load_vae:29 - Loading 3D VAE model (884-16c-hy) from: ./ckpts/hunyuan-video-t2v-720p/vae /workspace/cicd/HunyuanVideo-t2v/hyvideo/vae/__init__.py:39: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature. ckpt = torch.load(vae_ckpt, map_location=vae.device) 2026-02-02 14:10:05.461 | INFO | hyvideo.vae:load_vae:55 - VAE to dtype: torch.float16 2026-02-02 14:10:05.633 | INFO | hyvideo.text_encoder:load_text_encoder:28 - Loading text encoder model (llm) from: ./ckpts/text_encoder Using the `SDPA` attention implementation on multi-gpu setup with ROCM may lead to performance issues due to the FA backend. Disabling it to use alternative backends. Loading checkpoint shards: 0%| | 0/4 [00:00