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      "title": "目前的视频生成模型都是首尾帧+prompt，是否拥有多轮对话方式(openai chat 的方式)生成视频的，可以参考多张图/视频",
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      "title": "https://tongyi.aliyun.com/wan/ 这个体验网站不可用",
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      "title": "Wan2.2-TI2V中的ref_conv缺少权重和偏置",
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      "body": "设置是这样的，想让传入的图片当作生成**视频的背景**\n\nvideo = pipe(\n      prompt=\"A person wearing a helmet and red shorts is riding a bicycle on a paved road next to a grassy area. Realistic video, natural lighting, stable camera, medium shot, eye-level.\",\n    negative_prompt=\"色调艳丽，过曝，静态，细节模糊不清，字幕，风格，作品，画作，画面，静止，整体发灰，最差质量，低质量，JPEG压缩残留，丑陋的，残缺的，多余的手指，画得不好的手部，画得不好的脸部，畸形的，毁容的，形态畸形的肢体，手指融合，静止不动的画面，杂乱的背景，三条腿，背景人很多，倒着走\",\n    seed=0, tiled=True,\n    height=384, width=640,\n    reference_image=image, #这里\n    num_frames=81,\n)\n\n但是运行报错，显示\nTraceback (most recent call last):\n  File \"/root/shared-nvme/hf-cache/DiffSynth-Studio/examples/wanvideo/model_inference/Wan2.2-TI2V-5B.py\", line 10, in <module>\n    pipe = WanVideoPipeline.from_pretrained(\n           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n  File \"/root/shared-nvme/hf-cache/DiffSynth-Studio/diffsynth/pipelines/wan_video.py\", line 129, in from_pretrained\n    model_pool = pipe.download_and_load_models(model_configs, vram_limit) #多模型池   支持不同参数模型、不同版本VAE的切换\n                 ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n  File \"/root/shared-nvme/hf-cache/DiffSynth-Studio/diffsynth/diffusion/base_pipeline.py\", line 289, in download_and_load_models\n    model_pool.auto_load_model(\n  File \"/root/shared-nvme/hf-cache/DiffSynth-Studio/diffsynth/models/model_loader.py\", line 70, in auto_load_model\n    model = self.load_model_file(config, path, vram_config, vram_limit=vram_limit)\n            ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n  File \"/root/shared-nvme/hf-cache/DiffSynth-Studio/diffsynth/models/model_loader.py\", line 40, in load_model_file\n    model = load_model(\n            ^^^^^^^^^^^\n  File \"/root/shared-nvme/hf-cache/DiffSynth-Studio/diffsynth/core/loader/model.py\", line 49, in load_model\n    model.load_state_dict(state_dict, assign=True)\n  File \"/root/shared-nvme/miniconda3/envs/wan/lib/python3.12/site-packages/torch/nn/modules/module.py\", line 2635, in load_state_dict\n    raise RuntimeError(\nRuntimeError: Error(s) in loading state_dict for WanModel:\n        Missing key(s) in state_dict: \"ref_conv.weight\", \"ref_conv.bias\". \n\n缺少ref_conv的权重和偏置，这是哪里出了问题？？",
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      "number": 306,
      "title": "Animate-14B 在 A800 上的计算效率基准",
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      "body": "我在A800上测试了Animate-14B模型替换模式下的视频生成速率，在480P、30fps、20次抽样，其他默认参数的情况下，单次抽样的速率在 10.76s/it 波动，15秒的视频总耗时大概在 10.76s/it * 20 * ceil(15*30/77) = 21.5分钟。\n\n是否有官方基准指标参考下？",
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      "title": "Image Address Not Affecting Video Creation in Wan-AI/Wan2.2-I2V-A14B Model",
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      "body": " It appears that the model is not utilizing the provided image addresses when generating videos. Instead, it solely relies on the prompt for video creation.\n\nThe image address provided is direct and does not trigger any CORS (Cross-Origin Resource Sharing) errors.\n\n\n",
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      "title": "ISSUE DECODING IMAGES.",
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      "body": "I am facing problem generating images.\n\nI was able to generate images through wan 2.2 when it was launch but haven't used it long time.\n\nTo check above workflow, I ran it again. It was generated 1 image with upscaling as well but, for next, it was stopping at the decode phase. \n\nthis is the workflow: https://www.reddit.com/r/comfyui/comments/1mlvwh1/wan_22_text2image_custom_workflow_v2/?sort=new\n\nTried old workflow as well, same issue. it is reconnecting every time when goes to decode node.\n\nclearly it has generated images in ksampler, but while decoding it is still not creating final image.\n\nI am using Q6, RTX 4090 24GB, wan 2.2 / wan 2.1 also. but same issue.\n\nIs there any issue with coding or cache?\n\nTo resolve this issue, I updated entire comfyui and python dependencies. But still not working.\n\n<img width=\"320\" height=\"102\" alt=\"Image\" src=\"https://github.com/user-attachments/assets/7462060b-9c4f-42b9-9062-74b0adc3d57f\" />\n\n<img width=\"603\" height=\"617\" alt=\"Image\" src=\"https://github.com/user-attachments/assets/e74db2f0-0a24-4c3c-bceb-8da076404872\" />\n\n<img width=\"447\" height=\"693\" alt=\"Image\" src=\"https://github.com/user-attachments/assets/e1e888d0-7032-43ac-80b3-58d77bdf9c31\" />",
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      "body": "# Thank you so much Alibaba team for this great open source model. I did massive R&D to prepare this tutorial and presets for end users to directly train locally.\n\n# Wan 2.2 Complete Training Tutorial - Text to Image, Text to Video, Image to Video, Windows & Cloud\n\n## Full tutorial link > https://www.youtube.com/watch?v=ocEkhAsPOs4\n\n[![Wan 2.2 Complete Training Tutorial - Text to Image, Text to Video, Image to Video, Windows & Cloud](https://img.youtube.com/vi/ocEkhAsPOs4/sddefault.jpg)](https://www.youtube.com/watch?v=ocEkhAsPOs4 \"Wan 2.2 Complete Training Tutorial - Text to Image, Text to Video, Image to Video, Windows & Cloud\")\n\nWan 2.2 training is now so easy. I have done over 64 different unique Wan 2.2 trainings to prepare the very best working training configurations for you. The configurations are fully working locally with as low as 6 GB GPUs. So you will be able to train your awesome Wan 2.2 image or video generation LoRAs on your Windows computer with easiness. Moreover, I have shown how to train on cloud platforms RunPod and Massed Compute so even if you have no GPU or you want faster training, you can train on cloud for very cheap prices fully privately.\n\n📂 Resources & Links:\n\nDownload the One-Click Installer & Configs: [ [https://www.patreon.com/posts/Musubi-Tuner-Trainer-App-Configs-137551634](https://www.patreon.com/posts/Musubi-Tuner-Trainer-App-Configs-137551634) ]\n\nQwen Image Model Training Tutorial (Prerequisite): [ [https://youtu.be/DPX3eBTuO_Y](https://youtu.be/DPX3eBTuO_Y) ]\n\nSwarmUI & ComfyUI Setup Guide for Windows: [ [https://youtu.be/c3gEoAyL2IE](https://youtu.be/c3gEoAyL2IE) ]\n\nSwarmUI Installer and Model Downloader : [ [https://www.patreon.com/posts/SwarmUI-Install-Download-Models-114517862](https://www.patreon.com/posts/SwarmUI-Install-Download-Models-114517862) ]\n\nComfyUI Installer : [ [https://www.patreon.com/posts/ComfyUI-Installers-105023709](https://www.patreon.com/posts/ComfyUI-Installers-105023709) ]\n\nSwarmUI & ComfyUI Setup Guide for RunPod & Massed Compute: [ [https://youtu.be/bBxgtVD3ek4](https://youtu.be/bBxgtVD3ek4) ]\n\nUpload / Download Big Files Guide for RunPod & Massed Compute: [ [https://youtu.be/X5WVZ0NMaTg](https://youtu.be/X5WVZ0NMaTg) ]\n\n⏱️ Video Chapters:\n\n[00:00:00](https://youtu.be/ocEkhAsPOs4?t=0) Introduction to Wan 2.2 Training & Capabilities\n- [00:00:56](https://youtu.be/ocEkhAsPOs4?t=56) Installing & Updating Musubi Tuner Locally\n- [00:02:20](https://youtu.be/ocEkhAsPOs4?t=140) Explanation of Optimized Presets & Research Logic\n- [00:04:00](https://youtu.be/ocEkhAsPOs4?t=240) Differences Between T2I, T2V, and I2V Configs\n- [00:05:36](https://youtu.be/ocEkhAsPOs4?t=336) Extracting Files & Running Update Batch File\n- [00:06:14](https://youtu.be/ocEkhAsPOs4?t=374) Downloading Wan 2.2 Training Models via Script\n- [00:07:30](https://youtu.be/ocEkhAsPOs4?t=450) Loading Configs: Selecting GPU & VRAM Options\n- [00:09:33](https://youtu.be/ocEkhAsPOs4?t=573) Using nvitop to Monitor RAM & VRAM Usage\n- [00:10:28](https://youtu.be/ocEkhAsPOs4?t=628) Preparing Image Dataset & Trigger Words\n- [00:11:17](https://youtu.be/ocEkhAsPOs4?t=677) Generating Dataset Config & Resolution Logic\n- [00:12:55](https://youtu.be/ocEkhAsPOs4?t=775) Calculating Epochs & Checkpoint Save Frequency\n- [00:13:40](https://youtu.be/ocEkhAsPOs4?t=820) Troubleshooting: Fixing Missing VAE Path Error\n- [00:15:12](https://youtu.be/ocEkhAsPOs4?t=912) VRAM Cache Behavior & Training Speed Analysis\n- [00:15:51](https://youtu.be/ocEkhAsPOs4?t=951) Trade-offs: Learning Rate vs Resolution vs Epochs\n- [00:16:29](https://youtu.be/ocEkhAsPOs4?t=989) Installing SwarmUI & Updating ComfyUI Backend\n- [00:18:13](https://youtu.be/ocEkhAsPOs4?t=1093) Importing Latest Presets into SwarmUI\n- [00:19:25](https://youtu.be/ocEkhAsPOs4?t=1165) Downloading Inference Models via Script\n- [00:20:33](https://youtu.be/ocEkhAsPOs4?t=1233) Generating Images with Trained Low Noise LoRA\n- [00:22:22](https://youtu.be/ocEkhAsPOs4?t=1342) Upscaling Workflow for High-Fidelity Results\n- [00:24:15](https://youtu.be/ocEkhAsPOs4?t=1455) Increasing Base Resolution to 1280x1280\n- [00:27:26](https://youtu.be/ocEkhAsPOs4?t=1646) Text-to-Video Generation with Lightning LoRA\n- [00:30:12](https://youtu.be/ocEkhAsPOs4?t=1812) Image-to-Video Generation Workflow & Settings\n- [00:31:35](https://youtu.be/ocEkhAsPOs4?t=1895) Restarting Backend to Clear VRAM for Model Switching\n- [00:33:45](https://youtu.be/ocEkhAsPOs4?t=2025) Fixing RAM Crashes with Cache-None Argument\n- [00:35:13](https://youtu.be/ocEkhAsPOs4?t=2113) Dual Model (High & Low Noise) Training Setup\n- [00:36:54](https://youtu.be/ocEkhAsPOs4?t=2214) Preparing Hybrid Datasets (Images + Videos)\n- [00:37:40](https://youtu.be/ocEkhAsPOs4?t=2260) Manually Editing Dataset TOML for Resolution Control\n- [00:39:53](https://youtu.be/ocEkhAsPOs4?t=2393) Setting High Noise Model Paths for Dual Training\n- [00:41:50](https://youtu.be/ocEkhAsPOs4?t=2510) Optimization: Block Swap vs CPU Offload\n- [00:43:10](https://youtu.be/ocEkhAsPOs4?t=2590) Generating Video with Dual-Model Trained LoRA\n- [00:45:35](https://youtu.be/ocEkhAsPOs4?t=2735) Massed Compute: Server Setup & Coupon Code\n- [00:47:00](https://youtu.be/ocEkhAsPOs4?t=2820) Connecting via ThinLinc & File Transfer Methods\n- [00:49:12](https://youtu.be/ocEkhAsPOs4?t=2952) Massed Compute: Fast UV Installation & Downloads\n- [00:50:27](https://youtu.be/ocEkhAsPOs4?t=3027) Loading Configurations on Massed Compute\n- [00:52:18](https://youtu.be/ocEkhAsPOs4?t=3138) Troubleshooting: Fixing Config Version Error\n- [00:53:20](https://youtu.be/ocEkhAsPOs4?t=3200) Dual Model Training Speed Analysis on Cloud\n- [00:55:40](https://youtu.be/ocEkhAsPOs4?t=3340) RunPod: Selecting the Correct Template & GPU\n- [00:57:45](https://youtu.be/ocEkhAsPOs4?t=3465) RunPod: Uploading Files & Extracting Archive\n- [00:58:38](https://youtu.be/ocEkhAsPOs4?t=3518) RunPod: Terminal Installation & Model Downloads\n- [01:00:26](https://youtu.be/ocEkhAsPOs4?t=3626) RunPod: Correct Pathing Syntax & Backslash Fix\n- [01:01:28](https://youtu.be/ocEkhAsPOs4?t=3688) Setting Dataset Paths on RunPod\n- [01:03:34](https://youtu.be/ocEkhAsPOs4?t=3814) Installing nvitop on RunPod Terminal\n- [01:03:54](https://youtu.be/ocEkhAsPOs4?t=3834) Speed Hack: Disabling Numpy Memory Mapping\n- [01:06:00](https://youtu.be/ocEkhAsPOs4?t=3960) Terminating Instances & Final Remarks\n\n\n![Image](https://github.com/user-attachments/assets/1f305387-52f3-4679-8f6d-7f5eb58f76e2)\n![Image](https://github.com/user-attachments/assets/2e342898-e308-4bea-9cae-7daa6ab3c243)\n![Image](https://github.com/user-attachments/assets/5534e0f1-b59d-4d32-951c-73fb70e1c18f)\n<img width=\"2435\" height=\"1298\" alt=\"Image\" src=\"https://github.com/user-attachments/assets/6115e150-8e51-409b-bf70-f95f9e4be108\" />\n![Image](https://github.com/user-attachments/assets/f786f0f1-d714-4b7a-b01d-f1c8221fc4a9)\n<img width=\"893\" height=\"1200\" alt=\"Image\" src=\"https://github.com/user-attachments/assets/5efaf588-5853-431f-ac41-5f9edc93ba8c\" />\n![Image](https://github.com/user-attachments/assets/1341077c-2f40-4d17-935d-5996a2b31d7c)\n![Image](https://github.com/user-attachments/assets/07477334-4400-4656-adc0-dbc36077c1a4)\n![Image](https://github.com/user-attachments/assets/2a798ef1-250d-4239-989a-defd671646b0)\n![Image](https://github.com/user-attachments/assets/8272aedb-876c-4bce-9208-c8ccb2528c69)\n![Image](https://github.com/user-attachments/assets/867d51a3-02c1-476d-b987-b179831a19dd)\n![Image](https://github.com/user-attachments/assets/6eca296a-36e8-4926-acf5-779230acce3e)\n![Image](https://github.com/user-attachments/assets/3dc8b3a9-9144-4396-af16-8a49b06d61d7)\n![Image](https://github.com/user-attachments/assets/8e833d42-af09-425f-9806-b2cf54bd16f3)\n![Image](https://github.com/user-attachments/assets/bfa19197-b69b-415d-a444-796c0db415e7)\n![Image](https://github.com/user-attachments/assets/26aba28f-bdb4-4197-b71c-58b437dee3ef)\n![Image](https://github.com/user-attachments/assets/3020fe7d-df1f-4c62-8307-a278ace57a1a)\n![Image](https://github.com/user-attachments/assets/adedc80d-a48d-4cee-b3b8-6c8c5f80a421)\n![Image](https://github.com/user-attachments/assets/038ed2e5-6c7c-4c88-af27-1790265ff3d4)\n![Image](https://github.com/user-attachments/assets/9d8727fa-856b-4026-a5d8-55dc3bc9db99)\n![Image](https://github.com/user-attachments/assets/b2ff8e71-61c9-4588-818b-b267eb251a59)\n![Image](https://github.com/user-attachments/assets/b5ecc6e2-642e-424f-95e7-86b6bebd5366)\n![Image](https://github.com/user-attachments/assets/7d0ee718-23b9-4319-aff9-2f18a0232aff)\n![Image](https://github.com/user-attachments/assets/6d0bdf7e-cd54-40ab-8965-f55598ec4c45)\n![Image](https://github.com/user-attachments/assets/c0f42cd7-fe91-4eda-9881-adfc4dda01b9)\n\n",
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      "number": 300,
      "title": "wan animate replace mode preprocess error",
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      "body": "Hi, i just meet this error as following:\n```\n2025-12-19 10:14:01,392 - INFO - [PID:574672] - propagate in video:  99%|█████████▊| 135/137 [00:27<00:00,  4.83it/s]\n2025-12-19 10:14:01,393 - INFO - [PID:574672] - propagate in video:  99%|█████████▉| 136/137 [00:27<00:00,  4.82it/s]\n2025-12-19 10:14:01,394 - INFO - [PID:574672] - propagate in video: 100%|██████████| 137/137 [00:27<00:00,  4.89it/s]\n2025-12-19 10:14:03,820 - INFO - [PID:574672] - Traceback (most recent call last):\n2025-12-19 10:14:03,821 - INFO - [PID:574672] - File \"Wan2.2/./wan/modules/animate/preprocess/preprocess_data.py\", line 115, in <module>\n2025-12-19 10:14:03,821 - INFO - [PID:574672] - process_pipeline(video_path=args.video_path,\n2025-12-19 10:14:03,821 - INFO - [PID:574672] - File \"Wan2.2/wan/modules/animate/preprocess/process_pipepline.py\", line 111, in __call__\n2025-12-19 10:14:03,821 - INFO - [PID:574672] - each_aug_mask = get_aug_mask(each_mask, w_len=w_len, h_len=h_len)\n2025-12-19 10:14:03,821 - INFO - [PID:574672] - File \"Wan2.2/wan/modules/animate/preprocess/utils.py\", line 26, in get_aug_mask\n2025-12-19 10:14:03,821 - INFO - [PID:574672] - body_bbox = get_mask_boxes(body_mask)\n2025-12-19 10:14:03,821 - INFO - [PID:574672] - File \"Wan2.2/wan/modules/animate/preprocess/utils.py\", line 17, in get_mask_boxes\n2025-12-19 10:14:03,821 - INFO - [PID:574672] - x_min = x_coords.min()\n2025-12-19 10:14:03,821 - INFO - [PID:574672] - File \"uv_envs/wan/lib/python3.10/site-packages/numpy/core/_methods.py\", line 45, in _amin\n2025-12-19 10:14:03,821 - INFO - [PID:574672] - return umr_minimum(a, axis, None, out, keepdims, initial, where)\n2025-12-19 10:14:03,821 - INFO - [PID:574672] - ValueError: zero-size array to reduction operation minimum which has no identity\n2025-12-19 10:14:07,297 - ERROR - [PID:574672] - ❌ 任务失败 (返回码 1)\n2025-12-19 10:14:07,297 - ERROR - [PID:574672] - ❌ [1/5] 失败: 一批_image_004_主体_1_video_004_视频\n2025-12-19 10:14:07,298 - INFO - [PID:574672] - 📌 [2/5] 处理中... 图片: 005_主体_4, 视频: 005_视频\n```\nI never seen it before, it works in other videos and images, is that model did not get the mask? Thanks!",
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      "title": "What is the minimum requirement for GPU to deploy this?",
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      "number": 297,
      "title": "如何解决文字失真问题呢 ？",
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      "created_at": "2025-12-16T06:41:53Z",
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      "body": "我在图生视频的场景中，经常会遇到文字变形，sku变形等问题，请问有没有什么办法能通过后训练来解决这个问题？ ",
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      "title": "单卡4090报错CUDA out of memory，添加了--t5_cpu也没用",
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      "created_at": "2025-12-11T10:15:20Z",
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      "body": "这是我的命令python generate.py --task ti2v-5B --size 1280*704 --ckpt_dir /home/2tdisk/working/AI/model/Wan2.2-TI2V-5B --offload_model True --convert_model_dtype  --t5_cpu --frame_num 9 --prompt \"Two anthropomorphic cats in comfy boxing gear and bright gloves fight intensely on a spotlighted stage\"\n\n\n这时报错信息：\n\n(/home/2tdisk/env/anaconda-env/python3.10) johnzyx@johnzyx-System-Product-Name:~/working/pythonprojects/Wan2.2$ python generate.py --task ti2v-5B --size 1280*704 --ckpt_dir /home/2tdisk/working/AI/model/Wan2.2-TI2V-5B --offload_model True --convert_model_dtype  --t5_cpu --frame_num 9 --prompt \"Two anthropomorphic cats in comfy boxing gear and bright gloves fight intensely on a spotlighted stage\"\n[2025-12-11 17:49:00,550] INFO: Generation job args: Namespace(task='ti2v-5B', size='1280*704', frame_num=9, ckpt_dir='/home/2tdisk/working/AI/model/Wan2.2-TI2V-5B', offload_model=True, ulysses_size=1, t5_fsdp=False, t5_cpu=True, dit_fsdp=False, save_file=None, prompt='Two anthropomorphic cats in comfy boxing gear and bright gloves fight intensely on a spotlighted stage', use_prompt_extend=False, prompt_extend_method='local_qwen', prompt_extend_model=None, prompt_extend_target_lang='zh', base_seed=2646853457180984931, image=None, sample_solver='unipc', sample_steps=50, sample_shift=5.0, sample_guide_scale=5.0, convert_model_dtype=True, src_root_path=None, refert_num=77, replace_flag=False, use_relighting_lora=False, num_clip=None, audio=None, enable_tts=False, tts_prompt_audio=None, tts_prompt_text=None, tts_text=None, pose_video=None, start_from_ref=False, infer_frames=80)\n[2025-12-11 17:49:00,550] INFO: Generation model config: {'__name__': 'Config: Wan TI2V 5B', 't5_model': 'umt5_xxl', 't5_dtype': torch.bfloat16, 'text_len': 512, 'param_dtype': torch.bfloat16, 'num_train_timesteps': 1000, 'sample_fps': 24, 'sample_neg_prompt': '色调艳丽，过曝，静态，细节模糊不清，字幕，风格，作品，画作，画面，静止，整体发灰，最差质量，低质，形态畸形的肢体，手指融合，静止不动的画面，杂乱的背景，三条腿，背景人很多，倒着走', 'frame_num': 121, 't5_checkpoint': 'models_t5_umt5-xxl-enc-bf16.pth', 't5_tokenizer': 'google/pth', 'vae_stride': (4, 16, 16), 'patch_size': (1, 2, 2), 'dim': 3072, 'ffn_dim': 14336, 'freq_dim': 256, 'num_heads': 24, 'num_layers': 30, 'window_size': (-1, -1), 'qk_norm': True, 'cross_attn_norm': True, 'eps': 1e-06, 'sample_shift': 5.0, 'sample_steps': 50, 'sample_guide_scale': 5.0}\n[2025-12-11 17:49:00,550] INFO: Input prompt: Two anthropomorphic cats in comfy boxing gear and bright gloves fight intensely on a spotlighted stage\n[2025-12-11 17:49:00,550] INFO: Creating WanTI2V pipeline.\n[2025-12-11 17:49:30,888] INFO: loading /home/2tdisk/working/AI/model/Wan2.2-TI2V-5B/models_t5_umt5-xxl-enc-bf16.pth\n[2025-12-11 17:49:35,617] INFO: loading /home/2tdisk/working/AI/model/Wan2.2-TI2V-5B/Wan2.2_VAE.pth\n[2025-12-11 17:49:36,768] INFO: Creating WanModel from /home/2tdisk/working/AI/model/Wan2.2-TI2V-5B\nLoading checkpoint shards: 100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 3/3 [00:00<00:00, 129.09it/s]\n[2025-12-11 17:49:38,336] INFO: Generating video ...\n100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 50/50 [00:30<00:00,  1.66it/s]\nTraceback (most recent call last):\n  File \"/home/johnzyx/working/pythonprojects/Wan2.2/generate.py\", line 575, in <module>\n    generate(args)\n  File \"/home/johnzyx/working/pythonprojects/Wan2.2/generate.py\", line 443, in generate\n    video = wan_ti2v.generate(\n  File \"/home/johnzyx/working/pythonprojects/Wan2.2/wan/textimage2video.py\", line 227, in generate\n    return self.t2v(\n  File \"/home/johnzyx/working/pythonprojects/Wan2.2/wan/textimage2video.py\", line 401, in t2v\n    videos = self.vae.decode(x0)\n  File \"/home/johnzyx/working/pythonprojects/Wan2.2/wan/modules/vae2_2.py\", line 1043, in decode\n    return [\n  File \"/home/johnzyx/working/pythonprojects/Wan2.2/wan/modules/vae2_2.py\", line 1044, in <listcomp>\n    self.model.decode(u.unsqueeze(0),\n  File \"/home/johnzyx/working/pythonprojects/Wan2.2/wan/modules/vae2_2.py\", line 831, in decode\n    out_ = self.decoder(\n  File \"/home/2tdisk/env/anaconda-env/python3.10/lib/python3.10/site-packages/torch/nn/modules/module.py\", line 1751, in _wrapped_call_impl\n    return self._call_impl(*args, **kwargs)\n  File \"/home/2tdisk/env/anaconda-env/python3.10/lib/python3.10/site-packages/torch/nn/modules/module.py\", line 1762, in _call_impl\n    return forward_call(*args, **kwargs)\n  File \"/home/johnzyx/working/pythonprojects/Wan2.2/wan/modules/vae2_2.py\", line 700, in forward\n    x = layer(x, feat_cache, feat_idx, first_chunk)\n  File \"/home/2tdisk/env/anaconda-env/python3.10/lib/python3.10/site-packages/torch/nn/modules/module.py\", line 1751, in _wrapped_call_impl\n    return self._call_impl(*args, **kwargs)\n  File \"/home/2tdisk/env/anaconda-env/python3.10/lib/python3.10/site-packages/torch/nn/modules/module.py\", line 1762, in _call_impl\n    return forward_call(*args, **kwargs)\n  File \"/home/johnzyx/working/pythonprojects/Wan2.2/wan/modules/vae2_2.py\", line 492, in forward\n    x_main = module(x_main, feat_cache, feat_idx)\n  File \"/home/2tdisk/env/anaconda-env/python3.10/lib/python3.10/site-packages/torch/nn/modules/module.py\", line 1751, in _wrapped_call_impl\n    return self._call_impl(*args, **kwargs)\n  File \"/home/2tdisk/env/anaconda-env/python3.10/lib/python3.10/site-packages/torch/nn/modules/module.py\", line 1762, in _call_impl\n    return forward_call(*args, **kwargs)\n  File \"/home/johnzyx/working/pythonprojects/Wan2.2/wan/modules/vae2_2.py\", line 230, in forward\n    x = layer(x, feat_cache[idx])\n  File \"/home/2tdisk/env/anaconda-env/python3.10/lib/python3.10/site-packages/torch/nn/modules/module.py\", line 1751, in _wrapped_call_impl\n    return self._call_impl(*args, **kwargs)\n  File \"/home/2tdisk/env/anaconda-env/python3.10/lib/python3.10/site-packages/torch/nn/modules/module.py\", line 1762, in _call_impl\n    return forward_call(*args, **kwargs)\n  File \"/home/johnzyx/working/pythonprojects/Wan2.2/wan/modules/vae2_2.py\", line 40, in forward\n    x = F.pad(x, padding)\n  File \"/home/2tdisk/env/anaconda-env/python3.10/lib/python3.10/site-packages/torch/nn/functional.py\", line 5209, in pad\n    return torch._C._nn.pad(input, pad, mode, value)\ntorch.OutOfMemoryError: CUDA out of memory. Tried to allocate 2.60 GiB. GPU 0 has a total capacity of 22.02 GiB of which 2.04 GiB is free. Including non-PyTorch memory, this process has 19.32 GiB memory in use. Of the allocated memory 15.98 GiB is allocated by PyTorch, and 2.85 GiB is reserved by PyTorch but unallocated. If reserved but unallocated memory is large try setting PYTORCH_CUDA_ALLOC_CONF=expandable_segments:True to avoid fragmentation.  See documentation for Memory Management  (https://pytorch.org/docs/stable/notes/cuda.html#environment-variables)\n",
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      "id": 3717341678,
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      "number": 294,
      "title": "Wan2.2 14B Animate 生成的视频，视频打开全是黑色的背景，啥都没有，求大神指点",
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      "id": 3704438854,
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      "number": 293,
      "title": "4x3090也报torch.OutOfMemoryError: CUDA out of memory",
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      "created_at": "2025-12-08T03:01:18Z",
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      "body": "task='i2v-A14B'\n4x3090也报torch.OutOfMemoryError: CUDA out of memory\n有什么可以调整参数的地方能降一降显存需求吗？",
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      "number": 292,
      "title": "二次方程等式有误",
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      "body": "wan/speech2video.py文件夹下的函数 def get_size_less_than_area体中，\n\nmin_scale = (-b + math.sqrt(b**2 - 2 * a * c)) / (\n                2 * a)\n\n# Calculate scale boundaries using quadratic equation\nmin_scale = (-b + math.sqrt(b**2 - 2 * a * c)) / (2 * a)  # Scale when maximum padding is applied\n\n应该是：\nmin_scale = (-b + math.sqrt(b**2 - 4* a * c)) / (2 * a)  # Scale when maximum padding is applied\n\n 2 * a * c  -->  4* a * c\n\n以上错误由网页端qwen千问发现，大模型为qwen3-max",
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      "id": 3685239708,
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      "number": 291,
      "title": "Wan 2.5 weights? Will be open-sourced?",
      "user": {
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      "created_at": "2025-12-02T10:45:30Z",
      "updated_at": "2025-12-17T06:04:47Z",
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      "body": "Dear Respected Support. \n\nThank you so much for a great input. I want to ask that is there any plan to release the weights of wan 2.5? Or they will not be open-sourced?\n\nPlease guide .. ",
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      "number": 290,
      "title": "How to convert diffusers model to wan2.2 format",
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      "created_at": "2025-12-02T09:17:31Z",
      "updated_at": "2025-12-02T09:17:31Z",
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      "body": "Great work!\n\nI see `convert_wan_to_diffusers.py` in diffusers repo，but no `convert_diffusers_to_wan.py`. Do you have plan to upload a offical convert scripts?",
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      "id": 3684397669,
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      "number": 289,
      "title": "求教混合精度训练和推理",
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      "created_at": "2025-12-02T07:03:16Z",
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      "body": "感谢开源，想要学习一下，DiT based model，如何保持训练和推理时 混合精度推理的效果呢？ 之前尝试CosyVoice3 的DiT-flow-matching ，其训练时可以bf16 混合训练，但是推理之前将模型转换为 Tensorrt fp16，就会数值溢出，生成Nan。 看到Wan2.2 项目中有使用T5模块做DiT 建模，其中设计了 fp16 clamp。请问这个设计的话对模型训练的最终效果有多大负面影响吗？ 另外进行TensorRT 推理时是否可以保持效果？谢谢！",
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      "body": "麻烦给出Wan-Animate预处理模型地址\n```\n/path/to/your/ckpt_path/\n    ├── det/\n    │ └── yolov10m.onnx\n    ├── pose2d/\n    │ └── vitpose_h_wholebody.onnx\n    ├── sam2/\n    │ └── sam2_hiera_large.pt\n```",
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      "number": 285,
      "title": "为什么运行wan2.2的视频生成工作流执行到13%就中断，文件位置都放对啦，可以运行，但运行wan2.2的视频就会中断comfyui的链接，显存也没爆",
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      "body": "<img width=\"1920\" height=\"1047\" alt=\"Image\" src=\"https://github.com/user-attachments/assets/d7490378-1763-4842-bfa2-72c6b18b34dc\" />\n\n<img width=\"1903\" height=\"1137\" alt=\"Image\" src=\"https://github.com/user-attachments/assets/5dae9c3c-5c73-4856-8c2c-b3bce8516a67\" />",
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      "id": 3650818268,
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      "number": 284,
      "title": "wan2.2 Computational Efficiency on Different GPUs",
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      "body": "https://github.com/Wan-Video/Wan2.2\n\nFor T2V case,  we know that the inference time is 2735.7s  at A100.  What's the frame number?  81frames ?\n\nThanks.",
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      "number": 283,
      "title": "OutOfMemoryError: CUDA out of memory Animate-14B",
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      "body": "[2025-11-20 21:19:21,501] INFO: Generation job args: Namespace(task='animate-14B', size='1280*720', frame_num=77, ckpt_dir='./Wan2.2-Animate-14B', offload_model=True, ulysses_size=1, t5_fsdp=False, t5_cpu=True, dit_fsdp=False, save_file=None, prompt='视频中的人在做动作', use_prompt_extend=False, prompt_extend_method='local_qwen', prompt_extend_model=None, prompt_extend_target_lang='zh', base_seed=3912865311457787091, image=None, sample_solver='unipc', sample_steps=20, sample_shift=5.0, sample_guide_scale=1.0, convert_model_dtype=True, src_root_path='./replacement_output/process_results', refert_num=1, replace_flag=True, use_relighting_lora=True, num_clip=None, audio=None, enable_tts=False, tts_prompt_audio=None, tts_prompt_text=None, tts_text=None, pose_video=None, start_from_ref=False, infer_frames=80)\n[2025-11-20 21:19:21,501] INFO: Generation model config: {'__name__': 'Config: Wan animate 14B', 't5_model': 'umt5_xxl', 't5_dtype': torch.bfloat16, 'text_len': 512, 'param_dtype': torch.bfloat16, 'num_train_timesteps': 1000, 'sample_fps': 30, 'sample_neg_prompt': '色调艳丽，过曝，静态，细节模糊不清，字幕，风格，作品，画作，画面，静止，整体发灰，最差质量，低质量，JPEG压缩残留，丑陋的，残缺的，多余的手指，画得不好的手部，画得不好的脸部，畸形的，毁容的，形态畸形的肢体，手指融合，静止不动的画面，杂乱的背景，三条腿，背景人很多，倒着走', 'frame_num': 77, 't5_checkpoint': 'models_t5_umt5-xxl-enc-bf16.pth', 't5_tokenizer': 'google/umt5-xxl', 'clip_checkpoint': 'models_clip_open-clip-xlm-roberta-large-vit-huge-14.pth', 'clip_tokenizer': 'xlm-roberta-large', 'lora_checkpoint': 'relighting_lora.ckpt', 'vae_checkpoint': 'Wan2.1_VAE.pth', 'vae_stride': (4, 8, 8), 'patch_size': (1, 2, 2), 'dim': 5120, 'ffn_dim': 13824, 'freq_dim': 256, 'num_heads': 40, 'num_layers': 40, 'window_size': (-1, -1), 'qk_norm': True, 'cross_attn_norm': True, 'eps': 1e-06, 'use_face_encoder': True, 'motion_encoder_dim': 512, 'sample_shift': 5.0, 'sample_steps': 20, 'sample_guide_scale': 1.0, 'prompt': '视频中的人在做动作'}\n[2025-11-20 21:19:21,502] INFO: Input prompt: 视频中的人在做动作\n[2025-11-20 21:19:21,502] INFO: Creating Wan-Animate pipeline.\n[2025-11-20 21:20:37,088] INFO: loading ./Wan2.2-Animate-14B/models_t5_umt5-xxl-enc-bf16.pth\n[2025-11-20 21:20:45,665] INFO: loading ./Wan2.2-Animate-14B/models_clip_open-clip-xlm-roberta-large-vit-huge-14.pth\n[2025-11-20 21:20:52,849] INFO: loading ./Wan2.2-Animate-14B/Wan2.1_VAE.pth\n[2025-11-20 21:20:53,660] INFO: Creating WanAnimate from ./Wan2.2-Animate-14B\nLoading checkpoint shards: 100%|████████████████████████████████████████████████████████| 4/4 [00:43<00:00, 10.76s/it]\n[2025-11-20 21:21:37,671] INFO: Loading Relighting Lora.\n[2025-11-20 21:21:51,071] INFO: Generating video ...\n[2025-11-20 22:19:53,554] INFO: real frames: 1142 target frames: 1217\nTraceback (most recent call last):\n  File \"/home/roland/Sites/Wan2.2/generate.py\", line 575, in <module>\n    generate(args)\n  File \"/home/roland/Sites/Wan2.2/generate.py\", line 471, in generate\n    video = wan_animate.generate(\n            ^^^^^^^^^^^^^^^^^^^^^\n  File \"/home/roland/Sites/Wan2.2/wan/animate.py\", line 564, in generate\n    chunk_latents = self.vae.encode([chunk.to(torch.bfloat16)])[0]\n                    ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n  File \"/home/roland/Sites/Wan2.2/wan/modules/vae2_1.py\", line 653, in encode\n    self.model.encode(u.unsqueeze(0), self.scale).float().squeeze(0)\n    ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n  File \"/home/roland/Sites/Wan2.2/wan/modules/vae2_1.py\", line 530, in encode\n    out_ = self.encoder(\n           ^^^^^^^^^^^^^\n  File \"/home/roland/Sites/Wan2.2/.venv/lib/python3.12/site-packages/torch/nn/modules/module.py\", line 1736, in _wrapped_call_impl\n    return self._call_impl(*args, **kwargs)\n           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n  File \"/home/roland/Sites/Wan2.2/.venv/lib/python3.12/site-packages/torch/nn/modules/module.py\", line 1747, in _call_impl\n    return forward_call(*args, **kwargs)\n           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n  File \"/home/roland/Sites/Wan2.2/wan/modules/vae2_1.py\", line 338, in forward\n    x = layer(x, feat_cache, feat_idx)\n        ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n  File \"/home/roland/Sites/Wan2.2/.venv/lib/python3.12/site-packages/torch/nn/modules/module.py\", line 1736, in _wrapped_call_impl\n    return self._call_impl(*args, **kwargs)\n           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n  File \"/home/roland/Sites/Wan2.2/.venv/lib/python3.12/site-packages/torch/nn/modules/module.py\", line 1747, in _call_impl\n    return forward_call(*args, **kwargs)\n           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n  File \"/home/roland/Sites/Wan2.2/wan/modules/vae2_1.py\", line 215, in forward\n    x = layer(x, feat_cache[idx])\n        ^^^^^^^^^^^^^^^^^^^^^^^^^\n  File \"/home/roland/Sites/Wan2.2/.venv/lib/python3.12/site-packages/torch/nn/modules/module.py\", line 1736, in _wrapped_call_impl\n    return self._call_impl(*args, **kwargs)\n           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n  File \"/home/roland/Sites/Wan2.2/.venv/lib/python3.12/site-packages/torch/nn/modules/module.py\", line 1747, in _call_impl\n    return forward_call(*args, **kwargs)\n           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n  File \"/home/roland/Sites/Wan2.2/wan/modules/vae2_1.py\", line 34, in forward\n    x = F.pad(x, padding)\n        ^^^^^^^^^^^^^^^^^\n  File \"/home/roland/Sites/Wan2.2/.venv/lib/python3.12/site-packages/torch/nn/functional.py\", line 5096, in pad\n    return torch._C._nn.pad(input, pad, mode, value)\n           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\ntorch.OutOfMemoryError: CUDA out of memory. Tried to allocate 1.99 GiB. GPU 0 has a total capacity of 47.54 GiB of which 1.79 GiB is free. Including non-PyTorch memory, this process has 45.73 GiB memory in use. Of the allocated memory 45.32 GiB is allocated by PyTorch, and 103.44 MiB is reserved by PyTorch but unallocated. If reserved but unallocated memory is large try setting PYTORCH_CUDA_ALLOC_CONF=expandable_segments:True to avoid fragmentation.  See documentation for Memory Management  (https://pytorch.org/docs/stable/notes/cuda.html#environment-variables)\n\n\nIs it possible to run this on a A6000 GPU 48 GB VRAM? \n\npython 3.12.3\ntorch 2.5.1+cu121 (CUDA 12.1, CUDA available True)\nflash-attn 2.8.3\ntransformers 4.51.3\npeft 0.17.0\nonnxruntime-gpu 1.23.2\nGPU: NVIDIA RTX A6000 (driver 535.274.02, ~48 GB)",
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      "id": 3646344079,
      "node_id": "I_kwDOPT-eoM7ZVsuP",
      "number": 282,
      "title": "May i ask you how much time it costs to generate video?",
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      "created_at": "2025-11-20T09:31:40Z",
      "updated_at": "2025-12-06T07:57:56Z",
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      "body": "I use single 4090 to generate \n\npython generate.py --task ti2v-5B --size 1280*704 --ckpt_dir ../Models/Wan2.2-TI2V-5B --offload_model True --convert_model_dtype --t5_cpu --prompt \"Two anthropomorphic cats in comfy boxing gear and bright gloves fight intensely on a spotlighted stage\" \n\n\n[2025-11-20 16:41:29,023] INFO: Input prompt: Two anthropomorphic cats in comfy boxing gear and bright gloves fight intensely on a spotlighted stage\n[2025-11-20 16:41:29,023] INFO: Creating WanTI2V pipeline.\n[2025-11-20 16:42:41,146] INFO: loading ../Models/Wan2.2-TI2V-5B/models_t5_umt5-xxl-enc-bf16.pth\n[2025-11-20 16:43:27,652] INFO: loading ../Models/Wan2.2-TI2V-5B/Wan2.2_VAE.pth\n[2025-11-20 16:43:38,051] INFO: Creating WanModel from ../Models/Wan2.2-TI2V-5B\nLoading checkpoint shards: 100%|███████████████████████████████████████████████████████████████████████████████████| 3/3 [00:00<00:00,  4.00it/s]\n[2025-11-20 16:44:49,164] INFO: Generating video ...\n\n\nHowever, an hour has been used ,there is no other message , is it normal ?\n\nGPU util is zero and memory usage is 3GB",
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      "title": "微调训练框架选择",
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      "created_at": "2025-11-18T03:19:21Z",
      "updated_at": "2025-11-21T11:09:13Z",
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      "body": "请问如果想微调wan2.2-animate-move 有没有训练框架推荐 感谢 ",
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      "body": "https://github.com/Wan-Video/Wan2.2/blob/main/wan/animate.py#L586\n\n<img width=\"791\" height=\"393\" alt=\"Image\" src=\"https://github.com/user-attachments/assets/997cf8cc-e182-46cd-be54-19469b216649\" />\n",
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      "title": "请问amd7900显卡可以运行Wan2.2吗",
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      "id": 3629350593,
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      "number": 278,
      "title": "Libraries not mentioned in requirements.txt",
      "user": {
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      "body": "The tools and libraries that are used by the model are not mentioned in the requirements.txt file .\nHence while inferencing the model I getting errors like\n`\nFile \"/workspace/Wan2.2/wan/modules/s2v/audio_encoder.py\", line 4, in <module>\n    import librosa\nModuleNotFoundError: No module named 'librosa'\n`\n",
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      "title": "क्या तुम भी किसी का दिमाग पढ़ना चाहते हो?तो तुम्हें सिर्फ यह तीन बातों का ध्यान रखना चाहिए",
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      "body": "#111 ",
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      "number": 273,
      "title": "wan 2.2 cause nan in latents",
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      "created_at": "2025-11-07T12:08:27Z",
      "updated_at": "2025-11-25T09:35:40Z",
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      "body": "here is the source code:\nimport asyncio, aiohttp.web, math, pathlib, torch, builtins, transformers, diffusers, av, io, numpy, uvloop, concurrent, multiprocessing, os, zipfile, sys\nfrom modelscope.hub.snapshot_download import snapshot_download\nfrom modelscope.hub.file_download import model_file_download\n#import sd_embed.embedding_funcs\n\nmodel_id = 'Wan-AI/Wan2.2-T2V-A14B-Diffusers'\nmodel_dir = snapshot_download(model_id)\nonload_device = torch.device('cuda')\npipe = diffusers.WanPipeline.from_pretrained(model_dir, vae=diffusers.AutoencoderKLWan.from_pretrained(model_dir, subfolder='vae', torch_dtype=torch.float32).to(onload_device),\ndtype=torch.bfloat16, low_cpu_mem_usage=True,\nquantization_config=diffusers.PipelineQuantizationConfig(quant_backend='bitsandbytes_4bit', quant_kwargs={'load_in_4bit':True, 'bnb_4bit_quant_type':'nf4', 'bnb_4bit_compute_dtype':torch.bfloat16}, components_to_quantize=['transformer', 'transformer_2']))\ndiffusers.hooks.apply_group_offloading(pipe.text_encoder, onload_device=onload_device, offload_type='leaf_level', use_stream=True, record_stream=True, non_blocking=True, low_cpu_mem_usage=True)\npipe.transformer.enable_group_offload(onload_device=onload_device, offload_type='leaf_level', use_stream=True, record_stream=True, non_blocking=True, low_cpu_mem_usage=True)\npipe.transformer.enable_layerwise_casting(storage_dtype=torch.float8_e4m3fn, compute_dtype=torch.bfloat16)\npipe.transformer_2.enable_group_offload(onload_device=onload_device, offload_type='leaf_level', use_stream=True, record_stream=True, non_blocking=True, low_cpu_mem_usage=True)\npipe.transformer_2.enable_layerwise_casting(storage_dtype=torch.float8_e4m3fn, compute_dtype=torch.bfloat16)\npipe.vae.enable_tiling()\npipe.vae.enable_slicing()\n\ndef decode_tensors(pipe, step, timestep, callback_kwargs):\nlatents = callback_kwargs[\"latents\"]\nprint(step, latents)\nreturn callback_kwargs\n\nwith zipfile.ZipFile('output.zip', 'w') as output:\nbuffer = io.BytesIO()\nwith av.open(buffer, mode='w', format='mp4') as writer:\nstream = writer.add_stream('h264', rate=8)\nstream.width = 1280\nstream.height = 720\nstream.pix_fmt = 'yuv420p'\n#prompt_embeds, prompt_neg_embeds = sd_embed.embedding_funcs.get_weighted_text_embeddings_sd15(pipe, prompt=prompt, neg_prompt=negative)\nfor frame in pipe(prompt=prompt, negative_prompt=negative, width=stream.width, height=stream.height, num_frames=81, num_inference_steps=5, callback_on_step_end=decode_tensors).frames[0]: writer.mux(stream.encode(av.VideoFrame.from_ndarray((frame * 255).astype(numpy.uint8))))\nwriter.mux(stream.encode())\noutput.writestr('output.mp4', buffer.getvalue())\n\nprint(step, latents) all the latents are nan please help to solve the problem.\n\nubuntu22.04-cuda12.4.0-py311-torch2.8.0, diffusers 0.35.2",
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      "id": 3599223686,
      "node_id": "I_kwDOPT-eoM7Wh8uG",
      "number": 272,
      "title": "Wan 2.2 微调：noisy reference latent对应的loss如何计算？求助help",
      "user": {
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      "created_at": "2025-11-07T08:18:08Z",
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      "body": "Hi Wan team,\nI’m trying to fine-tune Wan-2.2-Animate and would like to clarify how the loss is calculated for the two “extra” latents:\n```\nnoisy reference latents\nnoisy temporal latents\n```\nFrom the inference code I see that the denoising network receives three kinds of noisy latents:\n```\nnoisy video latents\nnoisy reference latents\nnoisy temporal latents\n```\nThe reference latents is also denoised step-by-step, so their value changes during sampling.\nThis implies that you must have computed a loss on both the reference and the temporal latent during training. Could you share the details?\nDid you simply apply the same flow-matching loss to all three tensors and sum/average them?\n\nFinally, I would like to ask: in current image/video generation models such as Flux-Kontext, Stand-in, Phantom, and RealisDance-DiT, the noisy latents never contain the reference image, and reference is not supervised by any loss during training. What was the motivation behind Wan-2.2-Animate’s decision to add noise to the reference latents.",
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      "id": 3594844677,
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      "number": 271,
      "title": "wan-s2v drop_first_motion的作用",
      "user": {
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      "created_at": "2025-11-06T09:11:28Z",
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      "body": "看代码的时候有个疑问，当drop_first_motion=True and r==0的时候，为什么要去掉前三帧呀？\n\n<img width=\"600\" height=\"562\" alt=\"Image\" src=\"https://github.com/user-attachments/assets/b738d77c-5b1b-4986-be26-30ed3ec350c9\" />",
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      "id": 3594392673,
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      "number": 270,
      "title": "wan2.2各渠道的版本及分辨率问题",
      "user": {
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      "created_at": "2025-11-06T06:57:46Z",
      "updated_at": "2025-11-06T06:57:46Z",
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      "body": "在计划使用wan2.2模型推理时，发现各个来源渠道的支持内容都不一样，进而不清楚该选用哪种能力。有大佬能帮忙看看，确认下背后的逻辑吗？以及各个模型的关联\n\n<img width=\"1754\" height=\"761\" alt=\"Image\" src=\"https://github.com/user-attachments/assets/dc24716c-3cc7-407c-8fb9-695435c41c33\" />",
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      "number": 269,
      "title": "CUDA OOM for 193frames of I2V using 4 H100. 图生视频 193 帧，4 张 H100爆显存",
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      "created_at": "2025-10-31T16:59:31Z",
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      "body": "为什么4 个h100-80gb跑 193 帧的视频都会爆显存？\n\nCUDA OOM for 193frames of I2V using 4 H100.\n14B 模型，设置如下。\n```\ntorchrun --nproc_per_node=4 generate.py --task i2v-A14B --size 720*1280 \\\n    --ckpt_dir ./models/Wan2.2-I2V-A14B --offload_model True --convert_model_dtype \\\n    --image /home/ubuntu/data/image/man_spin-0002.png \\\n    --prompt \"A man sits on a chair and holds a camera in his hand. He is spinning on this turnable chair.\" \\\n    --frame_num 193 \\\n    --offload_model True --convert_model_dtype \\\n    --dit_fsdp --t5_fsdp --ulysses_size 4 \\\n``` \n\n刚启动就如下的显存占用，然后跑一半显示爆显存。\n<img width=\"758\" height=\"209\" alt=\"Image\" src=\"https://github.com/user-attachments/assets/f5c2e961-1b51-4f36-a8ad-ae4723b7680d\" />",
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      "title": "Why do we need tpl_pose_metas_new list in get_retarget_pose function",
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      "body": "I am struggling to get an idea if the model will perfectly run on which gpu with what configuration.\n",
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      "body": "Hi,\n\nI saw somewhere between 2-30s is supported, but I wonder if 5 minute animate for avatar replacement is feasible?\n\nHow does compute requirements scale, is it just more time or does memory also skyrocket?\n\nThanks",
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      "title": "Teozinho",
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      "body": "Faz um vídeo de uma mulher loira com olhos cor de mel anando de moto numa tarde e verão ",
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      "body": "This is a placeholder issue for the Wan-Video/Wan2.2 repository. Please provide more details about your bug report, feature request, or task. Helpful information includes:\n- The type of issue (bug, feature request, task, etc.)\n- A detailed description of the problem or request\n- Steps to reproduce (if applicable)\n- Expected behavior\n- Relevant files, screenshots, or code snippets\n\nOnce you provide the requested information, the issue can be updated to include all necessary context.",
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      "body": "Preserve 100% a identidade do personagem da imagem de referência:\n— Rosto: formato oval, testa alta com entradas discretas, sorriso largo com dentes à mostra, óculos retangulares pretos, orelhas discretas.\n— Pele: tom médio claro (mesmo tom da referência).\n— Corpo: proporção adulta, tronco curto, braços fortes, mãos grandes.\n— Roupa: camisa polo vermelha com gola e abotoamento escuros; logotipo Geninho em branco no peito esquerdo (mesma fonte/símbolo); calça azul-marinho; cinto marrom; bolsos/bolsas de ferramentas nas laterais; botas marrons.\n— Proibições: não alterar cor do uniforme, não adicionar capacete, barba, gorro, bigode, tatuagens, adesivos novos ou logotipos diferentes; não mudar formato dos óculos.\n\nCena: corredor de loja de materiais de construção, prateleiras desfocadas ao fundo, luz quente e comercial.\nAção: Geninho segura um incinerador/lança-promoções (prop de marketing, preto e laranja). Ao acionar, surge efeito de chama VFX controlada acima do bocal, sem queimar nada real. Etiquetas/flâmulas flutuantes com símbolo % e textos “OFERTA”, “QUEIMA DE PREÇOS”, “QUEIMA HOJE” surgem e brilham (glow).\nCâmera: plano americano com leve dolly-in; depois close no bocal e nas etiquetas; fim em plano mais aberto com o corredor.\nLook & segurança: contraste alto, bloom suave no VFX, partículas douradas; nenhum objeto pega fogo de verdade; chamas são apenas efeito digital publicitário.\n\n<img width=\"1024\" height=\"1024\" alt=\"Image\" src=\"https://github.com/user-attachments/assets/080b96ee-8c17-44ac-b45f-e438418b3f8d\" />",
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      "title": "Nachrichten",
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      "body": "Erstelle einen Trailer für eine Nachrichtensendung an Anfang ein Polizeiwagen in Thailand mit Blaulicht, dann die Polizisten einen Mann verfolgen und thailändische Strand, wo ein Schnellboot lang fährt und die Polizei dahinter",
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      "title": "Porsche is driving in the mountains",
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      "body": "Black Porsche \n",
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      "title": "KeyError: 'frames_tracked_per_obj' #219",
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      "body": "python ./wan/modules/animate/preprocess/preprocess_data.py     --ckpt_path ./Wan2.2-Animate-14B/process_checkpoint     --video_path ./examples/wan_animate/replace/video.mp4     --refer_path ./examples/wan_animate/replace/image.jpeg     --save_path ./examples/wan_animate/replace/process_results     --resolution_area 1280 720     --iterations 3     --k 7     --w_len 1     --h_len 1     --replace_flag\n{'ckpt_path': './Wan2.2-Animate-14B/process_checkpoint', 'video_path': './examples/wan_animate/replace/video.mp4', 'refer_path': './examples/wan_animate/replace/image.jpeg', 'save_path': './examples/wan_animate/replace/process_results', 'resolution_area': [1280, 720], 'fps': 30, 'replace_flag': True, 'retarget_flag': False, 'use_flux': False, 'iterations': 3, 'k': 7, 'w_len': 1, 'h_len': 1}\nframe_num: 106\nvideo_fps: 30.0\nfps: 30\ntarget_num: 106\n2025-10-17 08:20:44.839 | INFO     | process_pipepline:__call__:67 - Processing pose meta\n2025-10-17 08:20:51.165 | INFO     | process_pipepline:__call__:82 - Processing reference image: ./examples/wan_animate/replace/image.jpeg\n2025-10-17 08:20:51.209 | INFO     | process_pipepline:__call__:89 - Processing template video: ./examples/wan_animate/replace/video.mp4\nvideo frame: 100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 106/106 [00:02<00:00, 39.27it/s]\nTraceback (most recent call last):\n  File \"/home/ubuntu/Wan2.2/./wan/modules/animate/preprocess/preprocess_data.py\", line 109, in <module>\n    process_pipeline(video_path=args.video_path, \n  File \"/home/ubuntu/Wan2.2/wan/modules/animate/preprocess/process_pipepline.py\", line 97, in __call__\n    masks = self.get_mask(frames, 400, tpl_pose_metas)\n  File \"/home/ubuntu/Wan2.2/wan/modules/animate/preprocess/process_pipepline.py\", line 319, in get_mask\n    self.predictor.reset_state(inference_state)\n  File \"/home/ubuntu/miniconda3/envs/wan/lib/python3.10/site-packages/torch/utils/_contextlib.py\", line 116, in decorate_context\n    return func(*args, **kwargs)\n  File \"/home/ubuntu/Wan2.2/sam2/sam2/sam2_video_predictor.py\", line 678, in reset_state\n    self._reset_tracking_results(inference_state)\n  File \"/home/ubuntu/Wan2.2/sam2/sam2/sam2_video_predictor.py\", line 701, in _reset_tracking_results\n    for v in inference_state[\"frames_tracked_per_obj\"].values():\nKeyError: 'frames_tracked_per_obj'",
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      "title": "Does this model support distributed multi-machine operation?",
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      "body": "torchrun is only for single-machine use; if it cannot run in a distributed manner and utilize GPUs across multiple machines, video generation will still be very, very slow.",
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      "id": 3521478968,
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      "number": 237,
      "title": "Inquiry About Sudden Drop in Animation Quality for WAN2.2 Model in freepik",
      "user": {
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      "created_at": "2025-10-16T11:32:04Z",
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      "body": "I am writing to ask about an issue I recently experienced with the WAN2.2 animation model in freepik platform. Until very recently, this model was generating excellent quality animations for my images. However, there has been a sudden and significant drop in the quality of animated results—images now appear much worse than before.\n\nCould you please clarify if there has been an update or change made to the WAN2.2 model? Is there a current issue affecting output quality, or should I expect this to be fixed soon?\n\nI want to clarify that I am not encountering any error messages—the WAN2.2 model is running and producing results every time. However, the problem is that, starting suddenly two days ago, the animation quality became very poor. This drop in quality is consistent and happens with every image I try.\n\nI have tested various animation prompts, multiple different images, and used different browsers (including incognito/private mode), but the issue remains unchanged across all attempts.\n\nThis is surprising because since I joined Freepik, the WAN2.2 model has always produced excellent results and worked perfectly. The sudden change two days ago is very unusual, and I am unable to identify any reason for it.\n\n\nThank you for your support. I look forward to your response, as this feature is vital for my workflow.",
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      "body": "Thanks for your great work. I want to know what is the base model of wan-animate? wan2.1 or wan2.2. I can't find the seperately low and high noise model in wan-animate?",
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      "title": "wan2.2 i2v 14B，图片生成视频，是以输入图片作为首帧吗？实际生成的视频发现首帧与输入图片不完全一样",
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      "title": "wan animate生成效果（pose控制）出现画面模糊和背景异常变化问题",
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      "body": "大家有没有遇到，wan animate生成效果（pose控制）出现画面模糊和背景异常变化问题？还是哪里参数没有设置好。",
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      "title": "Wan2.2-I2V-A14B 图生视频的帧宽高与输入图片的宽高ratio不一致",
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      "body": "请教一下，图生视频的帧宽高与输入图片的宽高ratio不一致，希望视频的帧宽高与输入图片的宽高一致，这个size的设置有什么特别注意的吗？感谢！",
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      "title": "[Bug] Potential bug in VAE",
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      "body": "This is the code in `vae_2_1.py` and this code looks confusing. If `feat_cache[idx] != 'Rep'`, the code is actually doing repeative padding while if `feat_cache[idx] == 'Rep'`, it is doing zero padding. Is this a bug?\n\n```python\nif cache_x.shape[2] < 2 and feat_cache[\n        idx] is not None and feat_cache[idx] != 'Rep':\n    # cache last frame of last two chunk\n    cache_x = torch.cat([\n        feat_cache[idx][:, :, -1, :, :].unsqueeze(2).to(\n            cache_x.device), cache_x\n    ],\n                        dim=2)\nif cache_x.shape[2] < 2 and feat_cache[\n        idx] is not None and feat_cache[idx] == 'Rep':\n    cache_x = torch.cat([\n        torch.zeros_like(cache_x).to(cache_x.device),\n        cache_x\n    ],\n                        dim=2)\n```",
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      "number": 228,
      "title": "Wan2.2-T2V-A14B模型无法接入Wan2.2-Lightning加速模型",
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      "body": "Wan2.2-Lightning的readme中提到了调用方法如下，有--lora_dir参数：\ntorchrun --nproc_per_node=8 generate.py --task t2v-A14B --size \"1280*720\" --ckpt_dir ./Wan2.2-T2V-A14B --lora_dir ./Wan2.2-Lightning/Wan2.2-T2V-A14B-4steps-lora-rank64-Seko-V1 --dit_fsdp --t5_fsdp --ulysses_size 8 --base_seed 42 --prompt_file examples/prompt_list.txt\n\n但是本仓库的\n\n[generate.py](https://github.com/user-attachments/files/22882258/generate.py)脚本没有接收该参数。\n请问是还没有开发完成吗？",
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      "id": 3508421628,
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      "number": 227,
      "title": "关于import flash_attn_interface",
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      "body": "### wan/modules/attention.py\n\n```\ntry:\n    import flash_attn_interface\n    FLASH_ATTN_3_AVAILABLE = True\nexcept ModuleNotFoundError:\n    FLASH_ATTN_3_AVAILABLE = False\n```\n\n请问这个`flash_attn_interface`的安装方法以及版本，谢谢！",
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      "title": "wan 2.2的“ 文‘生视频，能否加一个语音声音模块，直接驱动视频产生同步的口型",
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      "created_at": "2025-10-12T07:02:15Z",
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      "body": "在wan2.2的文生视频的过程中，导入音频格式的语音“直接驱动”画面人物同步口型，而不是先生成画面再进行口型匹配。\n\n具体来说，是希望找到一种方法或技术，能够在文生视频工作流中，将语音信息与视频生成过程深度融合，使得生成的视频人物能够根据输入的语音实时、自然地做出相应匹配的说话“口型”。",
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      "number": 224,
      "title": "Saurav singh",
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      "body": "- Video",
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      "body": "1. ### ### ",
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      "id": 3502063746,
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      "number": 221,
      "title": "Wan 2.2 Animate: Question about loss computation on noisy reference and temporal latents.",
      "user": {
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      "body": "Hi Wan team,\nI’m trying to fine-tune Wan-2.2-Animate and would like to clarify how the loss is calculated for the two “extra” latents:\n\nnoisy reference latents\nnoisy temporal latents\nFrom the inference code I see that the denoising network receives three kinds of noisy latents:\n\nnoisy video latents\nnoisy reference latents\nnoisy temporal latents\nThe reference latents is also denoised step-by-step, so their value changes during sampling.\nThis implies that you must have computed a loss on both the reference and the temporal latent during training. Could you share the details?\nDid you simply apply the same flow-matching loss to all three tensors and sum/average them?\n\nFinally, I would like to ask: in current image/video generation models such as Flux-Kontext, Stand-in, Phantom, and RealisDance-DiT, the noisy latents never contain the reference image, and reference is not supervised by any loss during training. What was the motivation behind Wan-2.2-Animate’s decision to add noise to the reference latents.",
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      "id": 3501691088,
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      "number": 220,
      "title": "wan2.2-animate-14B 效果特别差",
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      "created_at": "2025-10-10T07:22:52Z",
      "updated_at": "2025-12-15T02:00:02Z",
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      "body": "跑出来的视频和官方结果差太多了\n模糊性和伪影特别多，一致性也不好\n有一样的吗\n",
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      "title": "KeyError: 'frames_tracked_per_obj'",
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      "created_at": "2025-10-09T09:47:41Z",
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      "body": "(venv_2) root@llmdevelop-46-74c75987bd-zlvmv:/data/try/Wan2.2# python ./wan/modules/animate/preprocess/preprocess_data.py \\\n>     --ckpt_path ./Wan2.2-Animate-14B/process_checkpoint \\\n>     --video_path ./examples/wan_animate/replace/video.mp4 \\\n>     --refer_path ./examples/wan_animate/replace/image.jpeg \\\n>     --save_path ./examples/wan_animate/replace/process_results \\\n>     --resolution_area 1280 720 \\\n>     --iterations 3 \\\n>     --k 7 \\\n>     --w_len 1 \\\n>     --h_len 1 \\\n>     --replace_flag\n\n\n\n{'ckpt_path': './Wan2.2-Animate-14B/process_checkpoint', 'video_path': './examples/wan_animate/replace/video.mp4', 'refer_path': './examples/wan_animate/replace/image.jpeg', 'save_path': './examples/wan_animate/replace/process_results', 'resolution_area': [1280, 720], 'fps': 30, 'replace_flag': True, 'retarget_flag': False, 'use_flux': False, 'iterations': 3, 'k': 7, 'w_len': 1, 'h_len': 1}\n/data/try/Wan2.2/venv_2/lib/python3.10/site-packages/onnxruntime/capi/onnxruntime_inference_collection.py:123: UserWarning: Specified provider 'CUDAExecutionProvider' is not in available provider names.Available providers: 'AzureExecutionProvider, CPUExecutionProvider'\n  warnings.warn(\nframe_num: 164\nvideo_fps: 24.0\nfps: 30\ntarget_num: 205\n2025-10-09 05:42:17.053 | INFO     | process_pipepline:__call__:67 - Processing pose meta\n2025-10-09 05:43:28.828 | INFO     | process_pipepline:__call__:82 - Processing reference image: ./examples/wan_animate/replace/image.jpeg\n2025-10-09 05:43:28.901 | INFO     | process_pipepline:__call__:89 - Processing template video: ./examples/wan_animate/replace/video.mp4\nvideo frame: 100%|██████████████████████████| 205/205 [00:07<00:00, 27.23it/s]\nTraceback (most recent call last):\n  File \"/data/try/Wan2.2/./wan/modules/animate/preprocess/preprocess_data.py\", line 109, in <module>\n    process_pipeline(video_path=args.video_path, \n  File \"/data/try/Wan2.2/wan/modules/animate/preprocess/process_pipepline.py\", line 97, in __call__\n    masks = self.get_mask(frames, 400, tpl_pose_metas)\n  File \"/data/try/Wan2.2/wan/modules/animate/preprocess/process_pipepline.py\", line 319, in get_mask\n    self.predictor.reset_state(inference_state)\n  File \"/data/try/Wan2.2/venv_2/lib/python3.10/site-packages/torch/utils/_contextlib.py\", line 120, in decorate_context\n    return func(*args, **kwargs)\n  File \"/data/try/Wan2.2/venv_2/lib/python3.10/site-packages/sam2/sam2_video_predictor.py\", line 678, in reset_state\n    self._reset_tracking_results(inference_state)\n  File \"/data/try/Wan2.2/venv_2/lib/python3.10/site-packages/sam2/sam2_video_predictor.py\", line 701, in _reset_tracking_results\n    for v in inference_state[\"frames_tracked_per_obj\"].values():\nKeyError: 'frames_tracked_per_obj'\n\n\n-------------------\n(venv_2) root@llmdevelop-46-74c75987bd-zlvmv:/data/try/Wan2.2# pip list\nPackage                  Version\n------------------------ -----------\naccelerate               1.10.1\naiohappyeyeballs         2.6.1\naiohttp                  3.13.0\naiosignal                1.4.0\nantlr4-python3-runtime   4.9.3\nasync-timeout            5.0.1\nattrs                    25.4.0\naudioread                3.0.1\nbeautifulsoup4           4.14.2\ncertifi                  2025.10.5\ncffi                     2.0.0\ncharset-normalizer       3.4.3\ncoloredlogs              15.0.1\nconformer                0.3.2\ncontourpy                1.3.2\ncryptography             46.0.2\ncycler                   0.12.1\ndashscope                1.24.6\ndecorator                5.2.1\ndecord                   0.6.0\ndiffusers                0.35.1\neasydict                 1.13\neinops                   0.8.1\nfilelock                 3.20.0\nflatbuffers              25.9.23\nfonttools                4.60.1\nfrozenlist               1.8.0\nfsspec                   2025.9.0\nftfy                     6.3.1\ngdown                    5.2.0\ngitdb                    4.0.12\nGitPython                3.1.45\nhf-xet                   1.1.10\nhuggingface-hub          0.35.3\nhumanfriendly            10.0\nhydra-core               1.3.2\nHyperPyYAML              1.2.2\nidna                     3.10\nimageio                  2.37.0\nimageio-ffmpeg           0.6.0\nimportlib_metadata       8.7.0\ninflect                  7.5.0\niopath                   0.1.10\nJinja2                   3.1.6\njoblib                   1.5.2\nkaldifst                 1.7.17\nkiwisolver               1.4.9\nlazy_loader              0.4\nlibrosa                  0.11.0\nlightning                2.5.5\nlightning-utilities      0.15.2\nllvmlite                 0.45.1\nloguru                   0.7.3\nmarkdown-it-py           4.0.0\nMarkupSafe               3.0.3\nmatplotlib               3.10.7\nmdurl                    0.1.2\nmodelscope               1.30.0\nmore-itertools           10.8.0\nmoviepy                  2.2.1\nmpmath                   1.3.0\nmsgpack                  1.1.2\nmultidict                6.7.0\nnetworkx                 3.4.2\nnumba                    0.62.1\nnumpy                    1.26.4\nnvidia-cublas-cu12       12.8.4.1\nnvidia-cuda-cupti-cu12   12.8.90\nnvidia-cuda-nvrtc-cu12   12.8.93\nnvidia-cuda-runtime-cu12 12.8.90\nnvidia-cudnn-cu12        9.10.2.21\nnvidia-cufft-cu12        11.3.3.83\nnvidia-cufile-cu12       1.13.1.3\nnvidia-curand-cu12       10.3.9.90\nnvidia-cusolver-cu12     11.7.3.90\nnvidia-cusparse-cu12     12.5.8.93\nnvidia-cusparselt-cu12   0.7.1\nnvidia-nccl-cu12         2.27.3\nnvidia-nvjitlink-cu12    12.8.93\nnvidia-nvtx-cu12         12.8.90\nomegaconf                2.3.0\nonnxruntime              1.23.1\nopenai-whisper           20250625\nopencv-python            4.11.0.86\npackaging                25.0\npillow                   11.3.0\npip                      23.0.1\nplatformdirs             4.5.0\npooch                    1.8.2\nportalocker              3.2.0\nproglog                  0.1.12\npropcache                0.4.1\nprotobuf                 6.32.1\npsutil                   7.1.0\npyarrow                  21.0.0\npycparser                2.23\nPygments                 2.19.2\npyparsing                3.2.5\nPySocks                  1.7.1\npython-dateutil          2.9.0.post0\npython-dotenv            1.1.1\npytorch-lightning        2.5.5\npyworld                  0.3.5\nPyYAML                   6.0.3\nregex                    2025.9.18\nrequests                 2.32.5\nrich                     14.1.0\nruamel.yaml              0.18.15\nruamel.yaml.clib         0.2.14\nsafetensors              0.6.2\nsam2                     1.1.0\nscikit-learn             1.7.2\nscipy                    1.15.3\nsentencepiece            0.2.1\nsetuptools               65.5.0\nsix                      1.17.0\nsmmap                    5.0.2\nsoundfile                0.13.1\nsoupsieve                2.8\nsoxr                     1.0.0\nsympy                    1.14.0\nthreadpoolctl            3.6.0\ntiktoken                 0.12.0\ntokenizers               0.21.4\ntorch                    2.8.0\ntorchmetrics             1.8.2\ntorchvision              0.23.0\ntqdm                     4.67.1\ntransformers             4.51.3\ntriton                   3.4.0\ntypeguard                4.4.4\ntyping_extensions        4.15.0\nurllib3                  2.5.0\nwcwidth                  0.2.14\nwebsocket-client         1.9.0\nwetext                   0.1.0\nwget                     3.2\nyarl                     1.22.0\nzipp                     3.23.0\n\n",
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      "id": 3490080002,
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      "number": 216,
      "title": "S2V模型生成的视频，下方总会出现马赛克一样的横幅",
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      "created_at": "2025-10-07T06:07:06Z",
      "updated_at": "2025-10-09T03:21:23Z",
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      "body": "S2V模型生成的视频，下方总会出现马赛克一样的横幅\nThe videos generated by the S2V model always have a mosaic-like banner appearing at the bottom.\n\n<img width=\"1262\" height=\"1791\" alt=\"Image\" src=\"https://github.com/user-attachments/assets/a9df54f5-0bcf-41d1-8c47-3f9f840551db\" />\n\n我使用的是ComfyUI官方模版中的s2v工作流，\nI am using the s2v workflow from the official ComfyUI template.\nS2V 14B bf16模型+4步lora\nS2V 14B bf16 model + 4-step LORA\n\n<img width=\"3492\" height=\"1547\" alt=\"Image\" src=\"https://github.com/user-attachments/assets/dc708c6d-645d-4efc-8c6d-171c822f7a11\" />\n\nhttps://github.com/user-attachments/assets/75fcb5de-c01b-4e81-9228-50b0579776fe",
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      "id": 3481679201,
      "node_id": "I_kwDOPT-eoM7PhjVh",
      "number": 214,
      "title": "Community Work: Ovi — Human-centric Video+Audio Generation (video branch initialized from Wan2.2)",
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      "comments": 4,
      "created_at": "2025-10-03T16:16:04Z",
      "updated_at": "2025-10-04T09:07:40Z",
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      "body": "Hi Wan team! Thanks a ton for releasing Wan2.2 🙏 it’s been super inspiring for us.\n\nWe just open-sourced Ovi, an audio-video generation model built on top of Wan2.2’s TI2V backbone. On top of that, we trained our 5B audio branch + 1B fusion branch from scratch using our high-quality internal video/audio datasets. Ovi can do synced speech, multi-person dialogue, music, and sound effects.\n\nLinks here:\n\nGitHub: https://github.com/character-ai/Ovi\n\nPaper: https://arxiv.org/abs/2510.01284\n\nWebsite demo: https://character-ai.github.io/Ovi\n\nWould be awesome if you could add Ovi under the Community Works section 🙌",
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      "title": "thanks",
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      "id": 3469401521,
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      "number": 212,
      "title": "malaikat mikail dengan tugasnya",
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      "body": "Malaikat Mikail adalah salah satu malaikat utama yang diutus Allah. Tugas utamanya adalah mengatur rezeki bagi seluruh makhluk, seperti menurunkan hujan, menyuburkan tumbuhan, dan menjaga keseimbangan alam. Mikail digambarkan sebagai malaikat yang penuh kasih sayang terhadap manusia dan selalu mendoakan kebaikan bagi orang-orang beriman. Dalam Islam, ia bekerja bersama malaikat lain sesuai perintah Allah tanpa pernah lelah atau membangkang. Karena jasanya, umat manusia dapat merasakan kenikmatan hidup sehari-hari, mulai dari makanan yang tumbuh di bumi hingga hujan yang menyegarkan.",
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      "id": 3467411475,
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      "number": 211,
      "title": "Poor generation quality when using diffusers",
      "user": {
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      "created_at": "2025-09-30T03:48:30Z",
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      "body": "I encountered a problem with video generation using the Wan2.2-TI2V-5B-Diffusers model with diffusers. The results show poor quality. Below are the code, environment details, and the results. \n\n## Code\n```\nimport sys\nimport time\nimport torch\nfrom diffusers.utils import export_to_video\nfrom diffusers import AutoencoderKLWan, WanPipeline\nfrom diffusers.schedulers.scheduling_unipc_multistep import UniPCMultistepScheduler\n\nmodel_id = \"Wan-AI/Wan2.2-TI2V-5B-Diffusers\"\n\nvae = AutoencoderKLWan.from_pretrained(model_id, subfolder=\"vae\", torch_dtype=torch.bfloat16)\n\nflow_shift = 5.0 \npipe = WanPipeline.from_pretrained(model_id, vae=vae, torch_dtype=torch.bfloat16)\npipe.scheduler.config.flow_shift = flow_shift\nscheduler = UniPCMultistepScheduler.from_config(pipe.scheduler.config)\npipe.scheduler = scheduler\npipe.to(\"cuda\")\n\nprompt = \"A cat and a dog baking a cake together in a kitchen. The cat is carefully measuring flour, while the dog is stirring the batter with a wooden spoon. The kitchen is cozy, with sunlight streaming through the window.\"\nnegative_prompt = \"Bright tones, overexposed, static, blurred details, subtitles, style, works, paintings, images, static, overall gray, worst quality, low quality, JPEG compression residue, ugly, incomplete, extra fingers, poorly drawn hands, poorly drawn faces, deformed, disfigured, misshapen limbs, fused fingers, still picture, messy background, three legs, many people in the background, walking backwards\"\n\noutput = pipe(\n     prompt=prompt,\n     negative_prompt=negative_prompt,\n     height=704,\n     width=1280,\n     num_frames=17,\n     guidance_scale=5.0,\n    ).frames[0]\nexport_to_video(output, \"output.mp4\", fps=16)\n```\n\n## Environment\n```\ntorch==2.4.1+cu121\ndiffusers==0.35.1\ntransformers==4.49.0\n```\n\n## Result\n\nhttps://github.com/user-attachments/assets/73002f52-ef54-46d8-b45a-32e4823f3377",
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      "id": 3466133878,
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      "number": 209,
      "title": "ModuleNotFoundError: No module named 'moviepy'",
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      "body": "## Description\n\nThe `requirements_animate.txt` file is missing the `moviepy` dependency, which causes a `ModuleNotFoundError` when running the animation preprocessing script.\n\n## Error Message\nModuleNotFoundError: No module named 'moviepy'\n\n## Steps to Reproduce\n\n1. Install dependencies from `requirements_animate.txt`:\n```bash\npip install -r requirements_animate.txt\n```\n\n2. Run the preprocessing script:\n```bash\npython ./wan/modules/animate/preprocess/preprocess_data.py \\\n       --ckpt_path ./Wan2.2-Animate-14B/process_checkpoint \\\n       --video_path ./examples/wan_animate/animate/video.mp4 \\\n       --refer_path ./examples/wan_animate/animate/image.jpeg \\\n       --save_path ./examples/wan_animate/animate/process_results \\\n       --resolution_area 1280 720 \\\n       --retarget_flag \\\n       --use_flux\n```",
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      "id": 3465404759,
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      "number": 208,
      "title": "Wan 2.2 Animate API???",
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      "created_at": "2025-09-29T14:56:57Z",
      "updated_at": "2025-12-03T15:29:08Z",
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      "body": "Hi, out of curiosity does Wan official provide API for Wan 2.2 Animate??",
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      "id": 3464013891,
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      "number": 207,
      "title": "wan2.2 s2v生成的跳舞视频这么抽象，是什么原因？",
      "user": {
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      "created_at": "2025-09-29T09:17:44Z",
      "updated_at": "2025-10-10T07:24:26Z",
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      "body": "生成视频：\n\nhttps://github.com/user-attachments/assets/6fbb1089-f941-41c3-9dc5-0f82f3720d2a\n生成pose视频的原视频：\n\nhttps://github.com/user-attachments/assets/df2d04c2-866f-4e5c-ab37-188ce418ce88\npose视频：\n\nhttps://github.com/user-attachments/assets/8f265a91-d2ec-4f3f-a7c2-08ec1fe195e0",
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      "id": 3463875475,
      "node_id": "I_kwDOPT-eoM7OdouT",
      "number": 206,
      "title": "Wan-Animate 长视频，在20多秒就会，严重变形",
      "user": {
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      "created_at": "2025-09-29T08:43:51Z",
      "updated_at": "2025-12-12T09:15:41Z",
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      "body": "Wan-Animate 长视频，共计5分钟的视频，跑了2天的时间，但是在25秒左右开始就会，严重变形，色彩和颜色都出现了很大的情况\n\n\n由于文件太大，无法上传，目前 视频的对比度 严重下降，并且视频种的任务，严重变形",
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      "title": "wan2.2 animate的输入图像比例都要求16:9嘛？",
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      "created_at": "2025-09-29T02:14:04Z",
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      "body": "命令中的resolution_area是不是也得是16：9比例？\n\n<img width=\"689\" height=\"280\" alt=\"Image\" src=\"https://github.com/user-attachments/assets/867133a9-46d1-455d-b730-7dd61a0a2cce\" />",
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      "title": "replacement任务处理超过超过30s的视频出现漂移问题",
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      "created_at": "2025-09-28T14:31:14Z",
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      "body": "用官方文档处理replacement任务的时候会出现视频越到后面颜色越深画面也越来越模糊的问题，我尝试去掉了use_relighting_lora选项，结果出来的视频颜色倒没有变深了，但是整个视频会很晃，想问下这个是什么原因呢？是素材的问题还是说目前处理长视频的replacement任务还存在缺陷",
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      "title": "Wan 2.2 Animate: Question about loss computation on noisy reference and temporal latents.",
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      "created_at": "2025-09-28T09:52:27Z",
      "updated_at": "2025-09-28T10:20:59Z",
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      "body": "Hi Wan team,\nI’m trying to fine-tune Wan-2.2-Animate and would like to clarify how the loss is calculated for the two “extra” latents:\n- noisy reference latents\n- noisy temporal latents\n\nFrom the inference code I see that the denoising network receives three kinds of noisy latents:\n- noisy video latents\n- noisy reference latents\n- noisy temporal latents\n\nThe reference latents is also denoised step-by-step, so their value changes during sampling.\nThis implies that you must have computed a loss on both the reference and the temporal latent during training. Could you share the details?\nDid you simply apply the same flow-matching loss to all three tensors and sum/average them?\n\nFinally, I would like to ask: in current image/video generation models such as Flux-Kontext, Stand-in, Phantom, and RealisDance-DiT, the noisy latents never contain the reference image, and reference is not supervised by any loss during training. What was the motivation behind Wan-2.2-Animate’s decision to add noise to the reference latents.",
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      "id": 3461408734,
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      "number": 199,
      "title": "SAM2Base has no attribute init_state_v2",
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      "body": "I see that `process_pipeline.py` has this function `self.predictor.init_state_v2(frames=each_frames)` but no such function is supported by SAM2 from where did you guys installed SAM2 because in the requirements file i see `git+https://github.com/facebookresearch/sam2.git@0e78a118995e66bb27d78518c4bd9a3e95b4e266#egg=SAM-2 ` which is not helpful.",
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      "id": 3461011976,
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      "number": 196,
      "title": "inference i2v-A14B on 2*A100-80G OOM",
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      "body": "这个问题很奇怪哦，单卡运行不会报错，两卡就会报错OOM\n\n单卡命令：\npython generate.py --task i2v-A14B --size 1280*720 --ckpt_dir ./Wan2.2-I2V-A14B-BF16 --offload_model True --convert_model_dtype --image examples/i2v_input.JPG --prompt\n\n双卡命令：\ntorchrun --nproc_per_node=2 generate.py --task i2v-A14B --size 1280*720 --offload_model True --convert_model_dtype --t5_cpu --ckpt_dir ./Wan2.2-I2V-A14B-BF16 --image examples/i2v_input.JPG --dit_fsdp --t5_fsdp --ulysses_size 2 --prompt",
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      "id": 3460190069,
      "node_id": "I_kwDOPT-eoM7OPk91",
      "number": 195,
      "title": "Wan2.5 循环视频支持",
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      "body": "一个视频可能需要首帧与尾帧相同，目前单纯通过单图+提示词是很难做到的。尾帧可以做到相似但很难做到完全相同。我看即梦有多帧功能来实现，wan目前还没有看到这个功能。首尾帧与多帧还是不一样的，多帧是一个提示词，多帧拆分为多个镜头",
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      "id": 3460185000,
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      "number": 194,
      "title": "Wan2.5 多人时嘴型混乱",
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      "created_at": "2025-09-27T11:39:56Z",
      "updated_at": "2025-09-27T18:11:43Z",
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      "body": "图生视频与文生视频都能遇到。多人时嘴型可能出现混乱，不该说的也张嘴说。",
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      "number": 193,
      "title": "Wan2.5多图像输入",
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      "created_at": "2025-09-27T11:38:10Z",
      "updated_at": "2025-09-27T11:38:10Z",
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      "body": "目前因为支持10秒的时长，可能需要拆分为多个镜头。每个镜头里面的人物又可能是不一样的，因此多图像输入感觉是刚需，不知道正式版会不会支持。我试着做一个短片也遇到这个问题，图生视频的话单图是不够的。",
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      "id": 3458068881,
      "node_id": "I_kwDOPT-eoM7OHfGR",
      "number": 190,
      "title": "Wan Animate: face was covered by hair",
      "user": {
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      "body": "用中文说啦，发现Wan Animate有两个问题\n\n1. 脸被头发挡住了（尝试了两次，一样问题），请看视频对比\n2. 音画不同步，以下视频没有加声音，实际生成的视频比原来的视频时间要长点，导致音画不同步，（帧率24，滑动窗口77）。越长的视频差异越明显。\n\nhttps://github.com/user-attachments/assets/bc7fbe33-ada7-43fb-bb6f-463fa1fd7d79",
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      "id": 3453463444,
      "node_id": "I_kwDOPT-eoM7N16uU",
      "number": 189,
      "title": "Wan2.2 Animate",
      "user": {
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      "comments": 3,
      "created_at": "2025-09-25T12:48:49Z",
      "updated_at": "2025-12-27T04:03:47Z",
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      "body": "Hi there,\n\nI'm trying to do preprocessing with the following command:\n\n`python ./wan/modules/animate/preprocess/preprocess_data.py --ckpt_path /mnt/dev-s1/Wan2.2-Animate-14B/process_checkpoint --video_path ./examples/wan_animate/animate/video.mp4 --refer_path ./examples/wan_animate/animate/image.jpeg --save_path ./examples/wan_animate/animate/process_results --resolution_area 1280 720 --retarget_flag --use_flux`\n\nHowever, I'm getting the error below:\n\n{'ckpt_path': '/mnt/dev-s1/Wan2.2-Animate-14B/process_checkpoint', 'video_path': './examples/wan_animate/animate/video.mp4', 'refer_path': './examples/wan_animate/animate/image.jpeg', 'save_path': './examples/wan_animate/animate/process_results', 'resolution_area': [1280, 720], 'fps': 30, 'replace_flag': False, 'retarget_flag': True, 'use_flux': True, 'iterations': 3, 'k': 7, 'w_len': 1, 'h_len': 1}\nTraceback (most recent call last):\n  File \"/home/wrusr/Wan2.2/./wan/modules/animate/preprocess/preprocess_data.py\", line 107, in <module>\n    process_pipeline = ProcessPipeline(det_checkpoint_path=det_checkpoint_path, pose2d_checkpoint_path=pose2d_checkpoint_path, sam_checkpoint_path=sam2_checkpoint_path, flux_kontext_path=flux_kontext_path)\n  File \"/home/wrusr/Wan2.2/wan/modules/animate/preprocess/process_pipepline.py\", line 30, in __init__\n    self.pose2d = Pose2d(checkpoint=pose2d_checkpoint_path, detector_checkpoint=det_checkpoint_path)\n  File \"/home/wrusr/Wan2.2/wan/modules/animate/preprocess/pose2d.py\", line 354, in __init__\n    self.model = ViTPose(checkpoint, device)\n  File \"/home/wrusr/Wan2.2/wan/modules/animate/preprocess/pose2d.py\", line 311, in __init__\n    super(ViTPose, self).__init__(checkpoint, device=device)\n  File \"/home/wrusr/Wan2.2/wan/modules/animate/preprocess/pose2d.py\", line 36, in __init__\n    self.session = onnxruntime.InferenceSession(checkpoint,\n  File \"/home/wrusr/miniconda3/envs/wan2_2_animate_env/lib/python3.10/site-packages/onnxruntime/capi/onnxruntime_inference_collection.py\", line 472, in __init__\n    self._create_inference_session(providers, provider_options, disabled_optimizers)\n  File \"/home/wrusr/miniconda3/envs/wan2_2_animate_env/lib/python3.10/site-packages/onnxruntime/capi/onnxruntime_inference_collection.py\", line 561, in _create_inference_session\n    sess.initialize_session(providers, provider_options, disabled_optimizers)\nonnxruntime.capi.onnxruntime_pybind11_state.Fail: [ONNXRuntimeError] : 1 : FAIL : Deserialize tensor backbone.blocks.16.norm1.bias failed.tensorprotoutils.cc:1091 GetExtDataFromTensorProto External initializer: backbone.blocks.16.norm1.bias offset: 0 size to read: 5120 given file_length: 129 are out of bounds or can not be read in full.\n\n\nCan you please help me this error?",
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      "title": "[Wan2.2 Animate] 本地部署跑的效果和官方Demo效果差距较大",
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      "created_at": "2025-09-25T08:15:16Z",
      "updated_at": "2025-12-09T06:59:30Z",
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      "body": "RT，本地部署的效果和官方Demo(ModelScope)上的效果差距比较明显。\n想问下\n1. 官方Demo在模型上和开源的模型一样的吗，还是有内部优化版本。\n2. 如果Demo和开源用的模型是一样的，前处理或后处理流程和开源方案有什么不同呢。\n\n以下是我本地部署和官方效果的对比：\n官方效果：\n<video width=\"480\" height=\"720\" src=\"https://github.com/user-attachments/assets/a3444059-4518-4da7-81c8-d2561fb25646\" />\n\n我跑出来的：\n<video width=\"480\" height=\"720\" src=\"https://github.com/user-attachments/assets/df0af021-4a92-4dea-a126-8ba9deca7c35\" />\n\n人物的动作流畅度、脸部动作都有很大的差异。\n\n以下是我的输入参数。\n输入视频：\n<video width=\"480\" height=\"720\" src=\"https://github.com/user-attachments/assets/867ec892-6ace-4526-93e6-7c89d017ab79\" />\n\n参考图：\n<img width=\"720\" height=\"1280\" alt=\"Image\" src=\"https://github.com/user-attachments/assets/483a2f43-8bd2-486d-a210-b57b30783223\" />\n\n以下是我的预处理结果\n[process_results.zip](https://github.com/user-attachments/files/22531748/process_results.zip)\n\n以下是我的预处理命令\n`\npython ./wan/modules/animate/preprocess/preprocess_data.py \\\n    --ckpt_path /path/to/Wan2.2-Animate-14B/process_checkpoint \\\n    --video_path /path/to/input/video \\\n    --refer_path /path/to/ref/img \\\n    --save_path /path/to/output \\\n    --resolution_area 720 1280 \\\n    --retarget_flag \\\n    --use_flux\n`\n\n以下是我的推理命令\n`\npython -m torch.distributed.run --nnodes 1 --nproc_per_node 8 generate.py --size \"720*1280\" --task animate-14B --ckpt_dir /path/to/Wan2.2-Animate-14B/ --src_root_path /path/to/preprocess/output --refert_num 1 --save_file /path/to/output/video --dit_fsdp --t5_fsdp --ulysses_size 8\n`\n\n使用的机器为H20*8卡。",
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      "id": 3450012067,
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      "number": 186,
      "title": "wan 2.5 音质太差了",
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      "created_at": "2025-09-24T15:50:24Z",
      "updated_at": "2025-10-20T18:32:45Z",
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      "body": "恰好腾讯之前开源了HunyuanVideo-Foley，建议结合一下。目前测试下来音乐明显参考了图像元素，这点是非常好的，但就是音质太差了，有时候全是噪音，没有一点可以听的成分。",
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      "number": 185,
      "title": "Add Pusa for Community Works?",
      "user": {
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      "created_at": "2025-09-24T15:05:00Z",
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      "body": "Hi, thanks for your great project! \n\nWe have developed [Pusa](https://github.com/Yaofang-Liu/Pusa-VidGen) for both Wan2.1-T2V and Wan2.2-T2V. Could you add it to Community Works? Many thanks!\n\nBasically, Pusa proposes a novel paradigm that leverages vectorized timestep adaptation and an ultra-efficient finetuning process to enable fine-grained temporal control within a unified video diffusion framework, such as I2V, start-end frames, video extension, and so on, while still holding strong T2V capability.",
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      "id": 3449696386,
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      "number": 184,
      "title": "Is WAN 2.5 going to be open source?",
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      "created_at": "2025-09-24T14:29:40Z",
      "updated_at": "2025-11-17T02:25:00Z",
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      "body": "I see 2.5 is announced and available on places like wavespeed. Will it be open source?\nhttps://wavespeed.ai/models/alibaba/wan-2.5/text-to-video",
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      "title": "animate preprocess problem",
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      "created_at": "2025-09-24T11:33:26Z",
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      "body": "Thanks for your excellent work! I have a problem about the wan animate preprocessing code. \n\nIt looks like the frames passed to pose2d are in RGB format, since asnumpy() return RGB format.\n\nhttps://github.com/Wan-Video/Wan2.2/blob/a64d5b25af052a24e8e1bc23aa7af3ee130b1d84/wan/modules/animate/preprocess/process_pipepline.py#L63-L70\n\nWhile The load_images in Pose2d convert the RGB format to BGR format, which is different from the processing result of str. I am confused which format is desired.\n\nhttps://github.com/Wan-Video/Wan2.2/blob/a64d5b25af052a24e8e1bc23aa7af3ee130b1d84/wan/modules/animate/preprocess/pose2d.py#L357-L413",
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      "number": 182,
      "title": "wan 2.2 animate的animate模式，开源与官方的差距只是前后处理吗？",
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      "created_at": "2025-09-24T10:35:44Z",
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      "body": "如题。\n本地跑的结果总是不如官网的稳定。开源的区别只是retargeting这个过程吗？求解答",
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      "id": 3448620755,
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      "number": 181,
      "title": "Thank you Alibaba for deceiving and using the open source community",
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      "created_at": "2025-09-24T09:50:49Z",
      "updated_at": "2025-12-01T18:34:39Z",
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      "body": "Thank you very much for doing what you just did, wan goes closed source finally, you used community as an asset to advance your paid product while at the same time deceiving them with \"Open source freedom\", your intentions was never to support opensource community, was just a testing opportunity, most people promoted your test models for nothing thinking wan will remain open source, now it's clear what was it's all about, it's not you instantly turn into bad guys just when you decided to close source, it's about mal-intentions which are now exposed. \n\nNow imagine what would have happened to wan if they never open-sourced it from start, result, no one will know wan today, right? How many people know about WAN 1.0 or even WAN 2.0? None, because they were never open sourced. So it was all marketing strategy. \n\nI had high hopes, that we will get something close to veo 3 from Alibaba and open source, now i get it, it was never their intentions to open source something when they finally reach a desired state for their so called open-source sota (paid Product). \n\nNow i know every single product from Alibaba today that is open sourced is actually a \"test model\", and that includes recent \"Qwen-Image-Edit\" model namely \"2509\", which will go closed sourced after it reaches a desired state.\n\nWan 2.5 will never be open-sourced, and if it did some how it will be a complete crap, and will be just another marketing strategy. \n\nIn my opinion, do not play such a stupid move, make WAN an open source community project funded by community to keep it's opensource development going, i can guarantee you there are many people willing to pay and keep wan project going and opensource, since you already succeeded in building a strong community around it. \n\nThanks.",
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      "number": 175,
      "title": "generate.py 支持量化参数么",
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      "body": "现在两个5090 64G 在推理时会显存不足",
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      "number": 174,
      "title": "wan2.2 animate是不是做了加速了，跟relighting lora有关嘛？官方网站的20s视频生成只要5-6min",
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      "body": "https://www.modelscope.cn/studios/Wan-AI/Wan2.2-Animate",
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      "body": "For the `i2v-A14B`, at least 80G of video memory is required for a single GPU to run. When I used four graphics cards with 40G of video memory for multi-GPU inference, an error was still reported: `out of memmory`. Will tensor parallelism be supported in the future?",
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      "number": 172,
      "title": "Wan2.2 Long Video - Color Difference",
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      "body": "Hi I encountered same issue that I used previous video's last frame as input in new short video. However, color often shifted a little bit and it didn't match with original color althought I used Color Match. Any advices for me ?\n\nhttps://github.com/user-attachments/assets/eac5d57b-1071-4a6e-8bfb-67191d5f309e\n\n",
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      "title": "update to new qwen image edit model",
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      "body": "please use your qwen new image edit model to repose",
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      "title": "cannot import name 'FluxKontextPipeline' from 'diffusers' (D:\\Wan2.2-main\\wan\\modules\\animate\\preprocess\\diffusers\\__init__.py)",
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      "body": "预处理时报错",
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      "id": 3439240920,
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      "number": 167,
      "title": "wan animate该借鉴借鉴Lucy Edit",
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      "created_at": "2025-09-22T05:22:41Z",
      "updated_at": "2025-09-25T07:18:06Z",
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      "body": "Lucy Edit真正实现了语意的控制编辑，这才是真正的智能。本来以为wan animate也是这样的，可惜，像角色替换，Lucy edit只是一段话而已，简单快速又实用。",
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      "id": 3438475357,
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      "number": 166,
      "title": "【solution】installing latest flash-attn",
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      "created_at": "2025-09-21T18:12:33Z",
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      "body": "Hi ALL,\nThis is the solution for installing the latest version of flash-attn(2.8.3) for WSL Ubuntu 24.04(tested), but I think this method is pretty general, so you can follow this to other OS.\n\n**Envs**\ncuda 12.8\npytorch 2.8\npython 3.10\n\n**Requirements**\n1. Before building the whl for yourself, you have to install [NVCC](https://developer.nvidia.com/cuda-toolkit-archive)\n2. `nano ~/.bashrc` to edit the PATH at the end of the file\n4. add this`export PATH=/usr/local/cuda-{your_version}/bin${PATH:+:${PATH}}`\n5. and this `export LD_LIBRARY_PATH=/usr/local/cuda-{your_version}/lib64${LD_LIBRARY_PATH:+:${LD_LIBRARY_PATH}}`\n6. and then `source ~/.bashrc` to refresh the PATH\n**Steps**\n```bash\nconda activate {your_env_name}\nsudo apt update\nsudo apt install build-essential # install the essential build tools for Ubuntu\npip install ninja packaging\ngit clone https://github.com/Dao-AILab/flash-attention.git\ncd flash-attention\nMAX_JOBS={cpu_core_numbers, 16 works for me} pip install . --no-build-isolation\n```\n\nDone, it only takes me for less than 30s, it may vary depend on your CPU speed.\nEnjoy it~",
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      "id": 3438293465,
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      "number": 164,
      "title": "wan animate就是个pose controlnet",
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      "body": "本来以为都是大模型的能力会自动替换，最终用comfyui实现却发现所有的都是用的类似图片的controlnet去控制表情与姿态。完全就是视频的controlnet。还需要用其他模型去画遮罩抠图，提取表情、姿态还有人物。最终成品的质量严重依赖于抠图的好坏，虽然结果看起来还可以，但是确实与预期有差距。\n\n期待3天后的wan2.5能带来新的能力吧~",
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      "id": 3438198642,
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      "number": 163,
      "title": "Feature Request: Support Multiple Reference Images for I2V/TI2V (similar to `src_ref_images` in Wan2.1)",
      "user": {
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      "created_at": "2025-09-21T12:18:31Z",
      "updated_at": "2025-12-26T10:19:20Z",
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      "body": "I noticed that in Wan2.1 there was an option `--src_ref_images` which allowed conditioning on multiple reference images. This was very useful for scenarios like providing front/back/side orthographic views of an object model or combining several design sketches into a single coherent video generation.\n\nBut in Wan2.2, It seems that the current `generate.py` CLI only supports a single `--image` input for I2V/TI2V.  So is there any plan to reintroduce `--src_ref_images` or a similar multi-image conditioning feature in Wan2.2?  ",
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      "number": 162,
      "title": "What is the technical difference between Wan2.1 and Wan2.2?",
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      "created_at": "2025-09-20T15:09:04Z",
      "updated_at": "2025-09-21T08:47:15Z",
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      "body": "I would like to check the technical report for Wan2.2, but it leads me to Wan2.1's report. Could you provide a link for it?",
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      "id": 3436941876,
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      "number": 160,
      "title": "A Confirmation",
      "user": {
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      "comments": 4,
      "created_at": "2025-09-20T08:59:01Z",
      "updated_at": "2025-09-22T05:52:03Z",
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      "body": "Hi,\n    Thanks for your hard work and support for community. I want to know if these news are still valid that \"Wan 2.5\" is still coming? because some are saying it was \"Wan-Animate\". Please tell if \"Wan-2.5\" will come, because if it's coming then it's a very good news. Please confirm if the news of upcoming \"Wan-2.5\" is authentic? \n\nThank you.\n",
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      "id": 3436819615,
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      "number": 159,
      "title": "animate支持多卡吗?",
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      "created_at": "2025-09-20T07:07:38Z",
      "updated_at": "2025-10-13T02:05:14Z",
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      "body": "之前看的很多sd模型,好像即使多卡,也会有最低内存限制, 这个animate支持8k 4090跑吗?",
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      "node_id": "I_kwDOPT-eoM7M0BLD",
      "number": 157,
      "title": "sam-2 compatiblity error for replace mode",
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      "created_at": "2025-09-19T23:23:39Z",
      "updated_at": "2025-09-23T21:50:06Z",
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      "body": "```\nvideo frame: 100%|████████████████████████████████████████████████████████████████████████| 205/205 [00:04<00:00, 47.13it/s]\nTraceback (most recent call last):\n  File \"/root/Wan-Animate/Wan2.2/wan/modules/animate/preprocess/preprocess_data.py\", line 109, in <module>\n    process_pipeline(video_path=args.video_path,\n  File \"/root/Wan-Animate/Wan2.2/wan/modules/animate/preprocess/process_pipepline.py\", line 97, in call\n    masks = self.get_mask(frames, 400, tpl_pose_metas)\n  File \"/root/Wan-Animate/Wan2.2/wan/modules/animate/preprocess/process_pipepline.py\", line 319, in get_mask\n    self.predictor.reset_state(inference_state)\n  File \"/root/Wan-Animate/.venv/lib/python3.10/site-packages/torch/utils/_contextlib.py\", line 116, in decorate_context\n    return func(*args, kwargs)\n  File \"/root/Wan-Animate/segment-anything-2/sam2/sam2_video_predictor.py\", line 678, in reset_state\n    self._reset_tracking_results(inference_state)\n  File \"/root/Wan-Animate/segment-anything-2/sam2/sam2_video_predictor.py\", line 701, in resettracking_results\n    for v in inference_state[\"frames_tracked_per_obj\"].values():\nKeyError: 'frames_tracked_per_obj'\n```\n\nstuck at above error when running command for replacement mode - \n\npython wan/modules/animate/preprocess/preprocess_data.py \\\n    --ckpt_path Wan2.2-Animate-14B/process_checkpoint \\\n    --video_path examples/wan_animate/replace/video.mp4 \\\n    --refer_path examples/wan_animate/replace/image.jpeg \\\n    --save_path examples/wan_animate/replace/process_results \\\n    --resolution_area 1280 720 \\\n    --iterations 3 \\\n    --k 7 \\\n    --w_len 1 \\\n    --h_len 1 \\\n    --replace_flag",
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      "id": 3434637634,
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      "number": 153,
      "title": "hello,能够像wan2.2 fun control那样挂载ltx2v lighting加速lora吗？",
      "user": {
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    {
      "url": "https://api.github.com/repos/Wan-Video/Wan2.2/issues/150",
      "id": 3433551185,
      "node_id": "I_kwDOPT-eoM7Mp9VR",
      "number": 150,
      "title": "wan2.2 animate是否支持多人",
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      "created_at": "2025-09-19T09:06:21Z",
      "updated_at": "2025-10-01T07:08:13Z",
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      "body": "我在用hugging face spaces生成相关的视频中，使用mix模型推理，当我的人物超过1个人的时候会报错\n`Task failed: The input image has multi human bodies. Please upload other image with single person. Code: InvalidImage.MultiHuman TaskId: a9bb0451-6e79-4d6a-9658-55446beb02ad`\n\n是否会支持多人",
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    {
      "url": "https://api.github.com/repos/Wan-Video/Wan2.2/issues/149",
      "id": 3433332697,
      "node_id": "I_kwDOPT-eoM7MpH_Z",
      "number": 149,
      "title": "Where is Official Discord Server??",
      "user": {
        "login": "KihongK",
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      "created_at": "2025-09-19T08:01:07Z",
      "updated_at": "2025-09-19T08:01:07Z",
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      "body": "- Wan1 -  https://discord.gg/WB3vupDS \n- Wan2 - https://discord.gg/r72FKG7v\n\nI think Wan1 is official server\nand Wan2 is Scam Server \n\nAm i Right??\n\nplease check this page\nhttps://alidocs.dingtalk.com/i/nodes/R1zknDm0WR6XzZ4Lt9yGG3qeWBQEx5rG?utm_scene=team_space\n\nDiscord QR Link",
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    {
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      "id": 3424174216,
      "node_id": "I_kwDOPT-eoM7MGMCI",
      "number": 143,
      "title": "Pixel jittering and flickering effects on the output video",
      "user": {
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      "created_at": "2025-09-17T00:55:05Z",
      "updated_at": "2025-09-17T00:55:05Z",
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      "body": "Hello Authors,\n\nThank you for the great work! The quality is truly amazing.\n\nHowever, similar to #43, I also encounter some pixel-level artifacts using the ti2v model. It seems like the model is trying to generate some \"flickering\" or \"pixel\" jittering effects when animating the image, which in general is what we want when doing i2v, t2v or ti2v. Do you think what might be the cause of this?\n\nThank you.",
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      "id": 3424062827,
      "node_id": "I_kwDOPT-eoM7MFw1r",
      "number": 142,
      "title": "Issues with batch of start images - continious generation using wan2.2 A14B I2V",
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      "created_at": "2025-09-16T23:46:31Z",
      "updated_at": "2025-09-16T23:46:31Z",
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      "body": "Hey,\nI've been experimenting with the model to make generations continious and consistent between multiple runs. But it hasn't been perfect so far. The basic First Last frame approach isnt too bad but it is slightly noticable even with a temporal blend applied;\n\n- Do you think it is possible for wan2.2 A14B I2V models to have latent consistency in any way as it is done in SI2V?\n- If there is a way is it possible for you to guide us to make that possible?\n- Is there something that you came across in your tests that can overcome these issues?\n\n\nYou can ignore the speed changes that are caused by lightx2v but I've tried all with native 10 + 10 workflows as well.\n\nTemporal blend (last frame blended with previous frames to preserve motion, unfortunately not always functional):\n\nhttps://github.com/user-attachments/assets/66f4c4bb-7464-493c-b785-53886fe0b7c6\n\n\n\nGenerating latent with input images + gray, then encoding them with mask layer, groups of 4, 0 for images, 1 for empty frames. masked conditioning, empty latent;\n\nhttps://github.com/user-attachments/assets/2af296e7-6ce5-4b41-a930-223f99361b3c\n\n\nSame with 0 buffer mask frames;\n\nhttps://github.com/user-attachments/assets/ad4cc6dd-c955-4adc-a925-a07c746ba88c\n\n\nTorch.cat a previously generated latent with an empty latent on dimension 2, masked conditioning with mask layer, groups of 4, 0 for images, 1 for empty frames.;\n\nhttps://github.com/user-attachments/assets/b2d21f0a-230e-4c43-b57f-c32e6adf3446\n\n",
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      "id": 3421939681,
      "node_id": "I_kwDOPT-eoM7L9qfh",
      "number": 141,
      "title": "unflatten error",
      "user": {
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      "created_at": "2025-09-16T11:51:51Z",
      "updated_at": "2026-01-08T08:08:52Z",
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      "body": "how to solve this problem\n\nTraceback (most recent call last):\n  File \"/mnt/hdfs/user/zengbohan/code/video_gen/Wan2.2/generate.py\", line 522, in <module>\n    generate(args)\n  File \"/mnt/hdfs/user/zengbohan/code/video_gen/Wan2.2/generate.py\", line 415, in generate\n    video = wan_ti2v.generate(\n            ^^^^^^^^^^^^^^^^^^\n  File \"/mnt/hdfs/user/zengbohan/code/video_gen/Wan2.2/wan/textimage2video.py\", line 227, in generate\n    return self.t2v(\n           ^^^^^^^^^\n  File \"/mnt/hdfs/user/zengbohan/code/video_gen/Wan2.2/wan/textimage2video.py\", line 380, in t2v\n    noise_pred_cond = self.model(\n                      ^^^^^^^^^^^\n  File \"/usr/local/lib/python3.11/dist-packages/torch/nn/modules/module.py\", line 1736, in _wrapped_call_impl\n    return self._call_impl(*args, **kwargs)\n           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n  File \"/usr/local/lib/python3.11/dist-packages/torch/nn/modules/module.py\", line 1747, in _call_impl\n    return forward_call(*args, **kwargs)\n           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n  File \"/mnt/hdfs/user/zengbohan/code/video_gen/Wan2.2/wan/modules/model.py\", line 490, in forward\n    x = block(x, **kwargs)\n        ^^^^^^^^^^^^^^^^^^\n  File \"/usr/local/lib/python3.11/dist-packages/torch/nn/modules/module.py\", line 1736, in _wrapped_call_impl\n    return self._call_impl(*args, **kwargs)\n           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n  File \"/usr/local/lib/python3.11/dist-packages/torch/nn/modules/module.py\", line 1747, in _call_impl\n    return forward_call(*args, **kwargs)\n           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n  File \"/mnt/hdfs/user/zengbohan/code/video_gen/Wan2.2/wan/modules/model.py\", line 243, in forward\n    y = self.self_attn(\n        ^^^^^^^^^^^^^^^\n  File \"/usr/local/lib/python3.11/dist-packages/torch/nn/modules/module.py\", line 1736, in _wrapped_call_impl\n    return self._call_impl(*args, **kwargs)\n           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n  File \"/usr/local/lib/python3.11/dist-packages/torch/nn/modules/module.py\", line 1747, in _call_impl\n    return forward_call(*args, **kwargs)\n           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n  File \"/mnt/hdfs/user/zengbohan/code/video_gen/Wan2.2/wan/modules/model.py\", line 145, in forward\n    x = flash_attention(\n        ^^^^^^^^^^^^^^^^\n  File \"/mnt/hdfs/user/zengbohan/code/video_gen/Wan2.2/wan/modules/attention.py\", line 110, in flash_attention\n    deterministic=deterministic)[0].unflatten(0, (b, lq))\n                                    ^^^^^^^^^^^^^^^^^^^^^\n  File \"/usr/local/lib/python3.11/dist-packages/torch/_tensor.py\", line 1376, in unflatten\n    return super().unflatten(dim, sizes)\n           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\nRuntimeError: unflatten: Provided sizes [1, 27280] don't multiply up to the size of dim 0 (24) in the input tensor",
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      "id": 3421056298,
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      "number": 140,
      "title": "Diffusers version of Wan2.2-S2V",
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      "created_at": "2025-09-16T08:21:56Z",
      "updated_at": "2025-09-16T08:36:12Z",
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      "body": "Hi, when will the diffusers version of Wan2.2-S2V be released?",
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      "title": "[BUG/ISSUE] Wan 2.2 I2V 14B - Image-to-Video Output Pixelated & Oversaturated (Stained Glass Effect)",
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      "created_at": "2025-09-16T00:36:59Z",
      "updated_at": "2025-10-25T01:19:41Z",
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      "body": "https://github.com/user-attachments/assets/13c2428f-f0b9-4ef1-9bb7-88a4c0f0101e\n\nHi all,\n\nI'm running into a persistent issue with the Wan 2.2 I2V 14B model in ComfyUI. 2.1 works fine for me. Whenever I generate image-to-video outputs, the resulting frames are heavily pixelated, over-saturated, and have a strange stained-glass or mosaic filter effect—almost like an extreme artistic filter is being applied.\n\n🔧 My Setup:\n\nGPU: RTX 3090\n\nMotherboard: AMD4\n\nComfyUI Version: 1.23.4\n\nVAE Used: wan_2.1_vae.safetensors\n\nCLIP: umt5_xxl_fp8_e4m3fn_scaled.safetensors\n\nUNETs: wan2.2_i2v_high_noise_14B_fp8_scaled and wan2.2_i2v_low_noise_14B_fp8_scaled\n\nSampler: Euler\n\nSchedulers: Normal\n\nSteps: 10\n\nCFG: 5\n\nResolution: 480x832 (vertical 9:16 as recommended)\n\nFPS: 16\n\nLength: 17 frames\n\nI've verified the resolutions and frame length align with recommended specs. The model loads fine and renders video output, but the visual result is nowhere near expected quality—definitely not photorealistic, and doesn’t preserve facial features even with strong prompts focused on identity and clarity.\n\n🧪 What I Tried:\n\nChanging the CFG and sampling steps, and samplers\n\n\nAdjusting positive and negative prompts\n\nRunning on different resolutions\n\nStill getting the same odd \"stained glass\" or \"pixelated oil painting\" look.\n\n Has anyone else run into this?\n\n Would appreciate any insights or fixes others have found.",
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      "number": 137,
      "title": "Training loss",
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      "created_at": "2025-09-15T01:21:13Z",
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      "body": "你好，想请教下Wan-S2V训练中除了基本的diffusion loss 之外，有引入唇形同步损失以及身份ID 损失吗？",
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      "title": "wan2.2 i2v model been trained on the data from the first and last frames? I found that it can support the generation of the first and last frames.",
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      "number": 134,
      "title": "s2v, 8*5090 generate done, but still oom finally",
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      "created_at": "2025-09-11T16:02:47Z",
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      "body": "torchrun --nproc_per_node=8 generate.py --task s2v-14B --size 1024*704 --ckpt_dir ./Wan2.2-S2V-14B/ --dit_fsdp --t5_fsdp --ulysses_size 8 --prompt \"Summer beach vacation style, a white cat wearing sunglasses sits on a surfboard.\" --image \"examples/i2v_input.JPG\" --audio \"examples/talk.wav\" --offload_model True\nW0911 23:21:56.300000 302916 site-packages/torch/distributed/run.py:774] \nW0911 23:21:56.300000 302916 site-packages/torch/distributed/run.py:774] *****************************************\nW0911 23:21:56.300000 302916 site-packages/torch/distributed/run.py:774] Setting OMP_NUM_THREADS environment variable for each process to be 1 in default, to avoid your system being overloaded, please further tune the variable for optimal performance in your application as needed. \nW0911 23:21:56.300000 302916 site-packages/torch/distributed/run.py:774] *****************************************\n[2025-09-11 23:22:04,485] INFO: Generation job args: Namespace(task='s2v-14B', size='1024*704', frame_num=81, ckpt_dir='./Wan2.2-S2V-14B/', offload_model=True, ulysses_size=8, t5_fsdp=True, t5_cpu=False, dit_fsdp=True, save_file=None, prompt='Summer beach vacation style, a white cat wearing sunglasses sits on a surfboard.', use_prompt_extend=False, prompt_extend_method='local_qwen', prompt_extend_model=None, prompt_extend_target_lang='zh', base_seed=4323574886398545916, image='examples/i2v_input.JPG', sample_solver='unipc', sample_steps=40, sample_shift=3, sample_guide_scale=4.5, convert_model_dtype=False, num_clip=None, audio='examples/talk.wav', enable_tts=False, tts_prompt_audio=None, tts_prompt_text=None, tts_text=None, pose_video=None, start_from_ref=False, infer_frames=80)\n[2025-09-11 23:22:04,485] INFO: Generation model config: {'__name__': 'Config: Wan S2V 14B', 't5_model': 'umt5_xxl', 't5_dtype': torch.bfloat16, 'text_len': 512, 'param_dtype': torch.bfloat16, 'num_train_timesteps': 1000, 'sample_fps': 16, 'sample_neg_prompt': '画面模糊，最差质量，画面模糊，细节模糊不清，情绪激动剧烈，手快速抖动，字幕，丑陋的，残缺的，多余的手指，画得不好的手部，画得不好的脸部，畸形的，毁容的，形态畸形的肢体，手指融合，静止不动的画面，杂乱的背景，三条腿，背景人很多，倒着走', 'frame_num': 81, 't5_checkpoint': 'models_t5_umt5-xxl-enc-bf16.pth', 't5_tokenizer': 'google/umt5-xxl', 'vae_checkpoint': 'Wan2.1_VAE.pth', 'vae_stride': (4, 8, 8), 'wav2vec': 'wav2vec2-large-xlsr-53-english', 'num_heads': 40, 'transformer': {'__name__': 'Config: Transformer config for WanModel_S2V', 'patch_size': (1, 2, 2), 'dim': 5120, 'ffn_dim': 13824, 'freq_dim': 256, 'num_heads': 40, 'num_layers': 40, 'window_size': (-1, -1), 'qk_norm': True, 'cross_attn_norm': True, 'eps': 1e-06, 'enable_adain': True, 'adain_mode': 'attn_norm', 'audio_inject_layers': [0, 4, 8, 12, 16, 20, 24, 27, 30, 33, 36, 39], 'zero_init': True, 'zero_timestep': True, 'enable_motioner': False, 'add_last_motion': True, 'trainable_token': False, 'enable_tsm': False, 'enable_framepack': True, 'framepack_drop_mode': 'padd', 'audio_dim': 1024, 'motion_frames': 73, 'cond_dim': 16}, 'drop_first_motion': True, 'sample_shift': 3, 'sample_steps': 40, 'sample_guide_scale': 4.5}\n[2025-09-11 23:22:05,577] INFO: Input prompt: Summer beach vacation style, a white cat wearing sunglasses sits on a surfboard.\n[2025-09-11 23:22:05,593] INFO: Input image: examples/i2v_input.JPG\n[2025-09-11 23:22:05,593] INFO: Creating WanS2V pipeline.\n[2025-09-11 23:23:00,964] INFO: loading ./Wan2.2-S2V-14B/models_t5_umt5-xxl-enc-bf16.pth\n[2025-09-11 23:23:13,252] INFO: loading ./Wan2.2-S2V-14B/Wan2.1_VAE.pth\n[2025-09-11 23:23:13,686] INFO: Creating WanModel from ./Wan2.2-S2V-14B/\nLoading checkpoint shards: 100%|██████████████████████████████████████████████████████████████████████████████████████████████████████| 4/4 [00:00<00:00, 57.44it/s]\nLoading checkpoint shards: 100%|██████████████████████████████████████████████████████████████████████████████████████████████████████| 4/4 [00:00<00:00, 57.03it/s]\nLoading checkpoint shards: 100%|██████████████████████████████████████████████████████████████████████████████████████████████████████| 4/4 [00:00<00:00, 58.45it/s]\nLoading checkpoint shards: 100%|██████████████████████████████████████████████████████████████████████████████████████████████████████| 4/4 [00:00<00:00, 57.60it/s]\nLoading checkpoint shards: 100%|██████████████████████████████████████████████████████████████████████████████████████████████████████| 4/4 [00:00<00:00, 58.09it/s]\nLoading checkpoint shards: 100%|██████████████████████████████████████████████████████████████████████████████████████████████████████| 4/4 [00:00<00:00, 58.35it/s]\nLoading checkpoint shards: 100%|██████████████████████████████████████████████████████████████████████████████████████████████████████| 4/4 [00:00<00:00, 58.45it/s]\nLoading checkpoint shards: 100%|██████████████████████████████████████████████████████████████████████████████████████████████████████| 4/4 [00:00<00:00, 59.18it/s]\n[2025-09-11 23:23:34,146] INFO: Generating video ...\n100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 40/40 [13:33<00:00, 20.34s/it]\n100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 40/40 [13:33<00:00, 20.34s/it]\n100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 40/40 [13:33<00:00, 20.34s/it]\n100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 40/40 [13:33<00:00, 20.34s/it]\n100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 40/40 [13:33<00:00, 20.34s/it]\n100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 40/40 [13:33<00:00, 20.34s/it]\n100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 40/40 [13:33<00:00, 20.34s/it]\n100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 40/40 [13:33<00:00, 20.34s/it]\n[rank7]: Traceback (most recent call last):\n[rank7]:   File \"/home/zwz/work/code/Wan2.2/generate.py\", line 522, in <module>\n[rank7]:     generate(args)\n[rank7]:   File \"/home/zwz/work/code/Wan2.2/generate.py\", line 441, in generate\n[rank7]:     video = wan_s2v.generate(\n[rank7]:             ^^^^^^^^^^^^^^^^^\n[rank7]:   File \"/home/zwz/work/code/Wan2.2/wan/speech2video.py\", line 653, in generate\n[rank7]:     image = torch.stack(self.vae.decode(decode_latents))\n[rank7]:                         ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n[rank7]:   File \"/home/zwz/work/code/Wan2.2/wan/modules/vae2_1.py\", line 660, in decode\n[rank7]:     self.model.decode(u.unsqueeze(0),\n[rank7]:   File \"/home/zwz/work/code/Wan2.2/wan/modules/vae2_1.py\", line 562, in decode\n[rank7]:     out_ = self.decoder(\n[rank7]:            ^^^^^^^^^^^^^\n[rank7]:   File \"/home/zwz/miniconda3/envs/wan/lib/python3.12/site-packages/torch/nn/modules/module.py\", line 1773, in _wrapped_call_impl\n[rank7]:     return self._call_impl(*args, **kwargs)\n[rank7]:            ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n[rank7]:   File \"/home/zwz/miniconda3/envs/wan/lib/python3.12/site-packages/torch/nn/modules/module.py\", line 1784, in _call_impl\n[rank7]:     return forward_call(*args, **kwargs)\n[rank7]:            ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n[rank7]:   File \"/home/zwz/work/code/Wan2.2/wan/modules/vae2_1.py\", line 451, in forward\n[rank7]:     x = layer(x, feat_cache, feat_idx)\n[rank7]:         ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n[rank7]:   File \"/home/zwz/miniconda3/envs/wan/lib/python3.12/site-packages/torch/nn/modules/module.py\", line 1773, in _wrapped_call_impl\n[rank7]:     return self._call_impl(*args, **kwargs)\n[rank7]:            ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n[rank7]:   File \"/home/zwz/miniconda3/envs/wan/lib/python3.12/site-packages/torch/nn/modules/module.py\", line 1784, in _call_impl\n[rank7]:     return forward_call(*args, **kwargs)\n[rank7]:            ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n[rank7]:   File \"/home/zwz/work/code/Wan2.2/wan/modules/vae2_1.py\", line 215, in forward\n[rank7]:     x = layer(x, feat_cache[idx])\n[rank7]:         ^^^^^^^^^^^^^^^^^^^^^^^^^\n[rank7]:   File \"/home/zwz/miniconda3/envs/wan/lib/python3.12/site-packages/torch/nn/modules/module.py\", line 1773, in _wrapped_call_impl\n[rank7]:     return self._call_impl(*args, **kwargs)\n[rank7]:            ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n[rank7]:   File \"/home/zwz/miniconda3/envs/wan/lib/python3.12/site-packages/torch/nn/modules/module.py\", line 1784, in _call_impl\n[rank7]:     return forward_call(*args, **kwargs)\n[rank7]:            ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n[rank7]:   File \"/home/zwz/work/code/Wan2.2/wan/modules/vae2_1.py\", line 34, in forward\n[rank7]:     x = F.pad(x, padding)\n[rank7]:         ^^^^^^^^^^^^^^^^^\n[rank7]:   File \"/home/zwz/miniconda3/envs/wan/lib/python3.12/site-packages/torch/nn/functional.py\", line 5290, in pad\n[rank7]:     return torch._C._nn.pad(input, pad, mode, value)\n[rank7]:            ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n[rank7]: torch.OutOfMemoryError: CUDA out of memory. Tried to allocate 1.46 GiB. GPU 7 has a total capacity of 31.36 GiB of which 428.44 MiB is free. Including non-PyTorch memory, this process has 30.92 GiB memory in use. Of the allocated memory 24.58 GiB is allocated by PyTorch, and 5.45 GiB is reserved by PyTorch but unallocated. If reserved but unallocated memory is large try setting PYTORCH_CUDA_ALLOC_CONF=expandable_segments:True to avoid fragmentation.  See documentation for Memory Management  (https://pytorch.org/docs/stable/notes/cuda.html#environment-variables)\nW0911 23:37:38.491000 302916 site-packages/torch/distributed/elastic/multiprocessing/api.py:900] Sending process 303068 closing signal SIGTERM\nW0911 23:37:38.493000 302916 site-packages/torch/distributed/elastic/multiprocessing/api.py:900] Sending process 303069 closing signal SIGTERM\nW0911 23:37:38.493000 302916 site-packages/torch/distributed/elastic/multiprocessing/api.py:900] Sending process 303070 closing signal SIGTERM\nW0911 23:37:38.494000 302916 site-packages/torch/distributed/elastic/multiprocessing/api.py:900] Sending process 303071 closing signal SIGTERM\nW0911 23:37:38.494000 302916 site-packages/torch/distributed/elastic/multiprocessing/api.py:900] Sending process 303072 closing signal SIGTERM\nW0911 23:37:38.495000 302916 site-packages/torch/distributed/elastic/multiprocessing/api.py:900] Sending process 303073 closing signal SIGTERM\nW0911 23:37:38.495000 302916 site-packages/torch/distributed/elastic/multiprocessing/api.py:900] Sending process 303074 closing signal SIGTERM\nE0911 23:37:40.642000 302916 site-packages/torch/distributed/elastic/multiprocessing/api.py:874] failed (exitcode: 1) local_rank: 7 (pid: 303075) of binary: /home/zwz/miniconda3/envs/wan/bin/python3.12\nTraceback (most recent call last):\n  File \"/home/zwz/miniconda3/envs/wan/bin/torchrun\", line 7, in <module>\n    sys.exit(main())\n             ^^^^^^\n  File \"/home/zwz/miniconda3/envs/wan/lib/python3.12/site-packages/torch/distributed/elastic/multiprocessing/errors/__init__.py\", line 357, in wrapper\n    return f(*args, **kwargs)\n           ^^^^^^^^^^^^^^^^^^\n  File \"/home/zwz/miniconda3/envs/wan/lib/python3.12/site-packages/torch/distributed/run.py\", line 901, in main\n    run(args)\n  File \"/home/zwz/miniconda3/envs/wan/lib/python3.12/site-packages/torch/distributed/run.py\", line 892, in run\n    elastic_launch(\n  File \"/home/zwz/miniconda3/envs/wan/lib/python3.12/site-packages/torch/distributed/launcher/api.py\", line 143, in __call__\n    return launch_agent(self._config, self._entrypoint, list(args))\n           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n  File \"/home/zwz/miniconda3/envs/wan/lib/python3.12/site-packages/torch/distributed/launcher/api.py\", line 277, in launch_agent\n    raise ChildFailedError(\ntorch.distributed.elastic.multiprocessing.errors.ChildFailedError: \n============================================================\ngenerate.py FAILED\n------------------------------------------------------------\nFailures:\n  <NO_OTHER_FAILURES>\n------------------------------------------------------------\nRoot Cause (first observed failure):\n[0]:\n  time      : 2025-09-11_23:37:38\n  host      : zwz-AI\n  rank      : 7 (local_rank: 7)\n  exitcode  : 1 (pid: 303075)\n  error_file: <N/A>\n  traceback : To enable traceback see: https://pytorch.org/docs/stable/elastic/errors.html\n============================================================",
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      "number": 133,
      "title": "different VRAM GPU devices get different quality, does it make sense?",
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      "body": "I use same workflow and same seed to execute the ComfyUI workflow to generate video, by 14b I2V models with lightx2v lora.\nwhen deployed in 16GB 4060, it can works but get dizzy image quality, the motion is blurred. But when I deployed in 4090 24 GB GPU, the video quality was good.\nI dont know why that happens. all the parameters are the same. \n\nBelow is my workflow , it's the official 14B video gen workflow from the ComfyUI \n\n<img width=\"2868\" height=\"1360\" alt=\"Image\" src=\"https://github.com/user-attachments/assets/af22d47a-0320-41b2-9864-e7a3ddee2ea0\" />",
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      "id": 3405451285,
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      "number": 132,
      "title": "package not loading in ComfyUI portable",
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      "created_at": "2025-09-11T08:36:16Z",
      "updated_at": "2025-09-11T08:36:50Z",
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      "body": "getting the message:\n```FileNotFoundError: [Errno 2] No such file or directory: 'D:\\\\ComfyUI\\\\ComfyUI_windows_portable2\\\\ComfyUI\\\\custom_nodes\\\\Wan2.2\\\\__init__.py'```\n\nso, usual this file is also per default in the pack, but not here in your repo?\n\nthanks for help..",
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      "id": 3400635317,
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      "number": 128,
      "title": "Wan2.2-T2V-A14B  - Portrait video",
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      "created_at": "2025-09-10T02:24:48Z",
      "updated_at": "2025-09-10T02:24:48Z",
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      "body": "I'm using `size=720*1280` to create portrait videos but I'm still getting only landscape videos.\n\nIs it possible to create portrait videos using Wan2.2-T2V-A14B?",
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      "id": 3391496819,
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      "number": 125,
      "title": "[Bug] RuntimeError in VAE tiled decoding for Wan2.2-diffusers when enable_tiling() is used",
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      "created_at": "2025-09-07T10:51:31Z",
      "updated_at": "2025-09-11T10:08:55Z",
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      "body": "## Describe the bug\nWhen running inference with the Wan2.2 diffusers model , enabling tiled VAE decoding via vae.enable_tiling() results in a RuntimeError. The error is a tensor dimension mismatch that occurs deep within the VAE decoder's forward pass. The issue does not occur when tiling is disabled.\n\nThe root cause appears to be that the first_chunk parameter is not being correctly propagated when the tiled_decode method is called, leading to a shape mismatch in a residual connection within an UpDecoderBlock2D.\nThis is official inference code with vae.enable_tiling():\n<img width=\"1163\" height=\"703\" alt=\"Image\" src=\"https://github.com/user-attachments/assets/75840c61-cca0-4792-bb7d-f4f18082734d\" />\nThis is the logs:\n\n<img width=\"1015\" height=\"423\" alt=\"Image\" src=\"https://github.com/user-attachments/assets/a37125d7-77d4-411e-a589-e73faac115ea\" />\n\n## Analysis\nI have investigated the source code and believe the issue is located in diffusers/models/autoencoders/autoencoder_kl_wan.py.\nIn the _decode method, around line 1208, the code checks if tiling should be used:\n```\n# line 1207-1208 in autoencoder_kl_wan.py\nif self.use_tiling and (width > tile_latent_min_width or height > tile_latent_min_height):\n    return self.tiled_decode(z, return_dict=return_dict)\n```\nThis call to self.tiled_decode does not pass the first_chunk=True argument. This seems to cause an issue later in the UpDecoderBlock2D.forward method (around line 713), which contains a residual connection:\n```\n# L713 in autoencoder_kl_wan.py\nx = x + self.avg_shortcut(x_copy, first_chunk=first_chunk)\n```\nThe RuntimeError happens here because first_chunk is False, and the shapes of x and the output of self.avg_shortcut do not match.\n\n## Questions:\nIs this a bug in the tiled decoding path for this specific VAE?\nAs a temporary workaround, would modifying line 1208 to self.tiled_decode(z, return_dict=return_dict, first_chunk=True) be a safe fix, or could this introduce other side effects?\nThank you for looking into this!",
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      "id": 3389643939,
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      "number": 123,
      "title": "Why is Wan2.2 much faster than Wan2.1?",
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      "created_at": "2025-09-06T08:23:16Z",
      "updated_at": "2025-09-06T08:23:50Z",
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      "body": "According to the published \"Computational Efficiency on Different GPUs\", Wan2.2 takes much less time than Wan2.1 for video generation. \n\n<img width=\"2826\" height=\"1232\" alt=\"Image\" src=\"https://github.com/user-attachments/assets/3611336d-2d08-4f90-9d55-9c826d43ec4c\" />\n\n\nWhy is Wan2.2 much faster than Wan2.1?\n\n",
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      "number": 122,
      "title": "Та улыбаюсь я улыбаюсь - шо, не видно?",
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      "body": "[dj-quads-grows-up.mp3](https://github.com/user-attachments/files/22179778/dj-quads-grows-up.mp3)\n\n![Image](https://github.com/user-attachments/assets/3b8bd1d9-3305-4c00-ab60-a9bfe76efe02)\n\n",
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      "id": 3383449886,
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      "number": 118,
      "title": "RuntimeError: CUDA error: too many resources requested for launch on V100",
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      "created_at": "2025-09-04T12:01:26Z",
      "updated_at": "2025-09-11T01:15:21Z",
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      "body": "Encountered following error when running on V100, does any one have same issue or any suggestions?\n\n> RuntimeError: CUDA error: too many resources requested for launch\n> CUDA kernel errors might be asynchronously reported at some other API call, so the stacktrace below might be incorrect.\n> For debugging consider passing CUDA_LAUNCH_BLOCKING=1\n> Compile with `TORCH_USE_CUDA_DSA` to enable device-side assertions.",
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      "title": "Sign language integration and understanding",
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      "created_at": "2025-09-04T08:28:40Z",
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      "body": "I want to ask if this is possible to add the sign language for the pose generation with text to pose and later with image ..image to pose >> to video generation?",
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      "number": 115,
      "title": "[教程]我在huggingface的space部署了Wan2.2-S2V-14B并且成功运行，有需要的可以参考",
      "user": {
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      "created_at": "2025-09-04T07:53:22Z",
      "updated_at": "2025-09-29T16:33:35Z",
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      "body": "huggingface space https://huggingface.co/spaces/immango/Wan2.2-S2V-14B\n这个space仅仅是为了演示快速部署代码，没有选择GPU部署，所以无法正常运行。\n如果想要完整运行，可以参考Files或者复制这个space，最低选择 Nvidia 1xL40S 48G VRAM。\n\n在我自己的space运行时，编译启动和加载模型大概10分钟，生成5秒的视频需要20分钟左右。\n如果你有更大的显存，可以在界面-高级参数-取消勾选 “模型卸载”, 取消后48G VRAM会提示OOM，可能需要选择更大的VRAM。",
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      "body": "Man asks woman ‘what do you for living?’ Then he pointing mic towards the woman and replies ‘I have my own shoe brand Jessica rich, I think you should buy some if you have a girlfriend’\n\n![Image](https://github.com/user-attachments/assets/be632e4f-5c54-460b-b762-9c9808114753)\n\n[Jessica audio .mp3](https://github.com/user-attachments/files/22109279/Jessica.audio.mp3)",
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      "id": 3375992485,
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      "number": 112,
      "title": "How to train wan 2.2 S2V  for other languages for lipsync",
      "user": {
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      "created_at": "2025-09-02T13:12:41Z",
      "updated_at": "2025-09-24T03:29:14Z",
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      "body": "create a guide for how to finetune or LORA training for perfecting the S2V model for other languages",
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      "id": 3370487733,
      "node_id": "I_kwDOPT-eoM7I5Y-1",
      "number": 111,
      "title": "W11 installation fail",
      "user": {
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      "created_at": "2025-08-31T16:52:25Z",
      "updated_at": "2025-11-18T21:59:34Z",
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      "body": "I can't complete installation\n\n`PS H:\\Wan2.2> pip install -r requirements.txt\nCollecting torch>=2.4.0 (from -r requirements.txt (line 1))\n  Using cached torch-2.8.0-cp313-cp313-win_amd64.whl.metadata (30 kB)\nCollecting torchvision>=0.19.0 (from -r requirements.txt (line 2))\n  Using cached torchvision-0.23.0-cp313-cp313-win_amd64.whl.metadata (6.1 kB)\nCollecting opencv-python>=4.9.0.80 (from -r requirements.txt (line 3))\n  Using cached opencv_python-4.12.0.88-cp37-abi3-win_amd64.whl.metadata (19 kB)\nCollecting diffusers>=0.31.0 (from -r requirements.txt (line 4))\n  Using cached diffusers-0.35.1-py3-none-any.whl.metadata (20 kB)\nCollecting transformers>=4.49.0 (from -r requirements.txt (line 5))\n  Using cached transformers-4.56.0-py3-none-any.whl.metadata (40 kB)\nCollecting tokenizers>=0.20.3 (from -r requirements.txt (line 6))\n  Using cached tokenizers-0.22.0-cp39-abi3-win_amd64.whl.metadata (6.9 kB)\nCollecting accelerate>=1.1.1 (from -r requirements.txt (line 7))\n  Using cached accelerate-1.10.1-py3-none-any.whl.metadata (19 kB)\nRequirement already satisfied: tqdm in c:\\python313\\lib\\site-packages (from -r requirements.txt (line 8)) (4.67.1)\nCollecting easydict (from -r requirements.txt (line 10))\n  Using cached easydict-1.13-py3-none-any.whl.metadata (4.2 kB)\nCollecting ftfy (from -r requirements.txt (line 11))\n  Using cached ftfy-6.3.1-py3-none-any.whl.metadata (7.3 kB)\nCollecting dashscope (from -r requirements.txt (line 12))\n  Using cached dashscope-1.24.2-py3-none-any.whl.metadata (7.1 kB)\nCollecting imageio-ffmpeg (from -r requirements.txt (line 13))\n  Using cached imageio_ffmpeg-0.6.0-py3-none-win_amd64.whl.metadata (1.5 kB)\nCollecting flash_attn (from -r requirements.txt (line 14))\n  Using cached flash_attn-2.8.3.tar.gz (8.4 MB)\n  Preparing metadata (setup.py) ... error\n  error: subprocess-exited-with-error\n\n  × python setup.py egg_info did not run successfully.\n  │ exit code: 1\n  ╰─> [15 lines of output]\n      Traceback (most recent call last):\n        File \"<string>\", line 2, in <module>\n          exec(compile('''\n          ~~~~^^^^^^^^^^^^\n          # This is <pip-setuptools-caller> -- a caller that pip uses to run setup.py\n          ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n          ...<32 lines>...\n          exec(compile(setup_py_code, filename, \"exec\"))\n          ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n          ''' % ('C:\\\\Users\\\\Gareth\\\\AppData\\\\Local\\\\Temp\\\\pip-install-_3jljyjw\\\\flash-attn_b3d2ac920f2f44cfa6ef4989fb739b67\\\\setup.py',), \"<pip-setuptools-caller>\", \"exec\"))\n          ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n        File \"<pip-setuptools-caller>\", line 35, in <module>\n        File \"C:\\Users\\Gareth\\AppData\\Local\\Temp\\pip-install-_3jljyjw\\flash-attn_b3d2ac920f2f44cfa6ef4989fb739b67\\setup.py\", line 22, in <module>\n          import torch\n      ModuleNotFoundError: No module named 'torch'\n      [end of output]\n\n  note: This error originates from a subprocess, and is likely not a problem with pip.\n\n[notice] A new release of pip is available: 25.1.1 -> 25.2\n[notice] To update, run: python.exe -m pip install --upgrade pip\nerror: metadata-generation-failed\n\n× Encountered error while generating package metadata.\n╰─> See above for output.`",
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      "id": 3370271927,
      "node_id": "I_kwDOPT-eoM7I4kS3",
      "number": 110,
      "title": "WanGP app : WAN 2.2 models support with only 8 GB of VRAM",
      "user": {
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      "created_at": "2025-08-31T13:39:07Z",
      "updated_at": "2025-11-25T13:43:01Z",
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      "body": "First let me thank the Wan teams for their generative models which are by far the best !\n\nWanGP supports WAN 2.1 and WAN 2.2 models (from Vace to Infinitetalk. It is highly VRAM optimized and will run on low end PCs. It includes many tools (Mmaudio, mask generator for Vace, uncensored prompt enhancer, Qwen image generator, …). \nCheck it here : \nhttps://github.com/deepbeepmeep/Wan2GP\n\n@ruihang-chu please add a link on the homepage so that more people can access your great work. ",
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      "id": 3369315890,
      "node_id": "I_kwDOPT-eoM7I064y",
      "number": 109,
      "title": "Does the resolution of the reference image need to be the same as the size parameter?",
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      "created_at": "2025-08-30T17:14:03Z",
      "updated_at": "2025-08-30T17:14:03Z",
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      "body": "for example\n```\npython generate.py  \\\n--task s2v-14B \\\n--size 832*480 \\\n--ckpt_dir ./Wan2.2-S2V-14B/ \\\n--offload_model True \\\n--convert_model_dtype \\\n--prompt \"A girl is singing.\"  \\\n--image \"examples/2.png\" \\\n--audio \"examples/SeoulCity_20s.wav\"\n```\nDoes the image `examples/2.png` need to have a resolution of 832x480 if the `--size` is set to 832x480?\n",
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      "id": 3366334337,
      "node_id": "I_kwDOPT-eoM7Ipi-B",
      "number": 108,
      "title": "flash attn error",
      "user": {
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        "id": 55529956,
        "node_id": "MDQ6VXNlcjU1NTI5OTU2",
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      "comments": 3,
      "created_at": "2025-08-29T10:22:54Z",
      "updated_at": "2025-12-30T03:56:41Z",
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      "body": "I followed the installation instruction and when I try to inference, here is the error (flash attn installation error) I got: \n```\nFile \"/root/miniconda3/envs/wan2.2/lib/python3.10/site-packages/flash_attn/__init__.py\", line 3, in <module>\n    from flash_attn.flash_attn_interface import (\n  File \"/root/miniconda3/envs/wan2.2/lib/python3.10/site-packages/flash_attn/flash_attn_interface.py\", line 15, in <module>\n    import flash_attn_2_cuda as flash_attn_gpu\nImportError: /root/miniconda3/envs/wan2.2/lib/python3.10/site-packages/flash_attn_2_cuda.cpython-310-x86_64-linux-gnu.so: undefined symbol: _ZN3c104cuda9SetDeviceEa\n```\nAnybody has any clue?",
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      "id": 3365275385,
      "node_id": "I_kwDOPT-eoM7Ilgb5",
      "number": 105,
      "title": "已经安装了ffmpeg，还是报错",
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      "created_at": "2025-08-29T03:00:46Z",
      "updated_at": "2025-08-29T03:00:46Z",
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      "body": "\n100%|██████████| 40/40 [05:07<00:00,  7.68s/it]\n100%|██████████| 40/40 [05:07<00:00,  7.68s/it]\n\n\n100%|██████████| 40/40 [05:07<00:00,  7.68s/it]\n\n100%|██████████| 40/40 [05:07<00:00,  7.68s/it]\n\n100%|██████████| 40/40 [05:07<00:00,  7.68s/it]\n[2025-08-29 10:48:58,023] INFO: Saving generated video to s2v-14B_1024*704_8_A_person_clasps_both_hands_tightly_in_front_of_the_20250829_104858.mp4\n[2025-08-29 10:49:03,555] INFO: Start merging video and audio...\n[2025-08-29 10:49:03,557] ERROR: merge_video_audio failed with error: [Errno 2] No such file or directory: 'ffmpeg'\n[2025-08-29 10:49:04,677] INFO: Finished.",
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      "number": 104,
      "title": "有在24G上运行的办法吗？",
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      "created_at": "2025-08-29T02:46:04Z",
      "updated_at": "2025-08-29T02:46:35Z",
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      "body": "消费级的显卡完全没法跑",
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      "title": "大佬，有计划出wan2.2 s2v 5b的模型嘛？14b infer太慢了，训练也不好训",
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      "title": "A intersting example when combine Wan S-V with VibeVoice",
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      "created_at": "2025-08-28T07:25:03Z",
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      "body": "I make a interesting visual podcasting example by using wan S2V model and our VibeVoice model, see\nhttps://github.com/microsoft/VibeVoice?tab=readme-ov-file#-demo-examples\n\nThanks for your great work, and maybe it's also a good case to show S2V's capability.",
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      "id": 3359038617,
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      "number": 100,
      "title": "FA3 Compatibility Issues",
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      "created_at": "2025-08-27T11:40:03Z",
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      "body": "The FA3 model fails to run, returning a shape incompatibility error like torch.Size([5, 128]).\nThe issue arises in torch.cat(outputs, dim=gather_dim) where gather_dim=2 exceeds the tensor's dimension range. This indicates the input tensor has fewer than 3 dimensions.\nMy base environment is\nPython 11\nCUDA 12.4\nPyTorch 2.5.1+cu124",
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      "number": 99,
      "title": "Great work for Wan2.2-S2V-14B ! Error to run demo",
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      "created_at": "2025-08-27T03:30:45Z",
      "updated_at": "2025-08-28T12:59:53Z",
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      "body": "mple_solver='unipc', sample_steps=40, sample_shift=3, sample_guide_scale=4.5, convert_model_dtype=True, num_clip=None, audio='examples/talk.wav', pose_video=None, start_from_ref=False, infer_frames=80)\n[2025-08-27 11:17:18,599] INFO: Generation model config: {'__name__': 'Config: Wan S2V 14B', 't5_model': 'umt5_xxl', 't5_dtype': torch.bfloat16, 'text_len': 512, 'param_dtype': torch.bfloat16, 'num_train_timesteps': 1000, 'sample_fps': 16, 'sample_neg_prompt': '画面模糊，最差质量，画面模糊，细节模糊不清，情绪激动剧烈，手快速抖动，字幕，丑陋的，残缺的，多余的手指，画得不好的手部，画得不好的脸部，畸形的，毁容的，形态畸形的肢体，手指融合，静止不动的画面，杂乱的背景，三条腿，背景人很多，倒着走', 'frame_num': 81, 't5_checkpoint': 'models_t5_umt5-xxl-enc-bf16.pth', 't5_tokenizer': 'google/umt5-xxl', 'vae_checkpoint': 'Wan2.1_VAE.pth', 'vae_stride': (4, 8, 8), 'wav2vec': 'wav2vec2-large-xlsr-53-english', 'num_heads': 40, 'transformer': {'__name__': 'Config: Transformer config for WanModel_S2V', 'patch_size': (1, 2, 2), 'dim': 5120, 'ffn_dim': 13824, 'freq_dim': 256, 'num_heads': 40, 'num_layers': 40, 'window_size': (-1, -1), 'qk_norm': True, 'cross_attn_norm': True, 'eps': 1e-06, 'enable_adain': True, 'adain_mode': 'attn_norm', 'audio_inject_layers': [0, 4, 8, 12, 16, 20, 24, 27, 30, 33, 36, 39], 'zero_init': True, 'zero_timestep': True, 'enable_motioner': False, 'add_last_motion': True, 'trainable_token': False, 'enable_tsm': False, 'enable_framepack': True, 'framepack_drop_mode': 'padd', 'audio_dim': 1024, 'motion_frames': 73, 'cond_dim': 16}, 'drop_first_motion': True, 'sample_shift': 3, 'sample_steps': 40, 'sample_guide_scale': 4.5}\n[2025-08-27 11:17:18,599] INFO: Input prompt: Summer beach vacation style, a white cat wearing sunglasses sits on a surfboard.\n[2025-08-27 11:17:18,612] INFO: Input image: examples/i2v_input.JPG\n[2025-08-27 11:17:18,612] INFO: Creating WanS2V pipeline.\n[2025-08-27 11:17:48,447] INFO: loading ./Wan2.2-S2V-14B/models_t5_umt5-xxl-enc-bf16.pth\n[2025-08-27 11:17:52,919] INFO: loading ./Wan2.2-S2V-14B/Wan2.1_VAE.pth\n[2025-08-27 11:17:53,126] INFO: Creating WanModel from ./Wan2.2-S2V-14B/\nLoading checkpoint shards: 100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 4/4 [00:03<00:00,  1.04it/s]\n[2025-08-27 11:17:57,171] INFO: Generating video ...\nTraceback (most recent call last):\n  File \"/root/miniconda3/lib/python3.12/site-packages/transformers/feature_extraction_utils.py\", line 189, in convert_to_tensors\n    tensor = as_tensor(value)\n             ^^^^^^^^^^^^^^^^\n  File \"/root/miniconda3/lib/python3.12/site-packages/transformers/feature_extraction_utils.py\", line 142, in as_tensor\n    return torch.from_numpy(value)\n           ^^^^^^^^^^^^^^^^^^^^^^^\nTypeError: expected np.ndarray (got numpy.ndarray)\n\nDuring handling of the above exception, another exception occurred:\n\nTraceback (most recent call last):\n  File \"Wan2.2/generate.py\", line 485, in <module>\n    generate(args)\n  File \"/root/autodl-tmp/Wan2.2/generate.py\", line 411, in generate\n    video = wan_s2v.generate(\n            ^^^^^^^^^^^^^^^^^\n  File \"Wan2.2/wan/speech2video.py\", line 481, in generate\n    audio_emb, nr = self.encode_audio(audio_path, infer_frames=infer_frames)\n                    ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n  File \"Wan2.2/wan/speech2video.py\", line 284, in encode_audio\n    z = self.audio_encoder.extract_audio_feat(\n        ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n  File \"Wan2.2/wan/modules/s2v/audio_encoder.py\", line 72, in extract_audio_feat\n    input_values = self.processor(\n                   ^^^^^^^^^^^^^^^\n  File \"/root/miniconda3/lib/python3.12/site-packages/transformers/models/wav2vec2/processing_wav2vec2.py\", line 119, in __call__\n    inputs = self.feature_extractor(audio, **output_kwargs[\"audio_kwargs\"])\n             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n  File \"/root/miniconda3/lib/python3.12/site-packages/transformers/models/wav2vec2/feature_extraction_wav2vec2.py\", line 238, in __call__\n    padded_inputs = padded_inputs.convert_to_tensors(return_tensors)\n                    ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n  File \"/root/miniconda3/lib/python3.12/site-packages/transformers/feature_extraction_utils.py\", line 195, in convert_to_tensors\n    raise ValueError(\nValueError: Unable to create tensor, you should probably activate padding with 'padding=True' to have batched tensors with the same length.",
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      "id": 3357870798,
      "node_id": "I_kwDOPT-eoM7IJQrO",
      "number": 98,
      "title": "Any wan discussion group? please join in",
      "user": {
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      "created_at": "2025-08-27T03:28:01Z",
      "updated_at": "2025-09-05T09:06:40Z",
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      "body": "Thanks to the author for the great contribution!\nWhile working on the implementation, I ran into some issues and was wondering if there’s an existing group I could join for discussion.\nIf the authors don’t plan to create one, I’ve set up a group myself — anyone interested is very welcome to join!\n\n\n![Image](https://github.com/user-attachments/assets/4a59567c-07c2-42ef-8f55-66af3e7bedf2)",
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      "id": 3355400180,
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      "number": 96,
      "title": "模型加载速度缓慢",
      "user": {
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      "comments": 2,
      "created_at": "2025-08-26T12:14:23Z",
      "updated_at": "2025-09-15T09:24:51Z",
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      "body": "您好，我使用4090 48G显卡进行推理（5B的模型），模型加载的非常缓慢，请问有什么缓解的方法吗",
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      "id": 3353896517,
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      "number": 94,
      "title": "模型中文提示词理解能力问题！生成结果，无法去除不想要的元素",
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      "created_at": "2025-08-26T03:09:03Z",
      "updated_at": "2025-08-29T07:40:15Z",
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      "body": "各位工程师好！\n我在comfyui中使用Wan2.2 14B文本生成视频工作流模版，生成结果总是有元素遮挡在人物前。\n\nhttps://github.com/user-attachments/assets/26cde7c1-e5b3-4d58-847a-f7dfa069e731\n\n我的正向提示词：一个20岁中国性感女人，身穿长裙，半透明服装, 微笑, 撩头发, 桃花眼, 室外, 晴天, 日光, 中近景, 中心构图, 干净的单人镜头, 镜头位置保持不动，构图稳定，画面无明显抖动。, 写实风格\n负面提示词：色调艳丽，过曝，静态，细节模糊不清，字幕，风格，作品，画作，画面，静止，整体发灰，最差质量，低质量，JPEG压缩残留，丑陋的，残缺的，多余的手指，画得不好的手部，画得不好的脸部，畸形的，毁容的，形态畸形的肢体，手指融合，静止不动的画面，杂乱的背景，三条腿，背景人很多，倒着走，裸露，NSFW，铁丝网，网，头纱，面罩，雨伞，伞，\n\n<img width=\"2373\" height=\"1158\" alt=\"Image\" src=\"https://github.com/user-attachments/assets/ecb1903e-f46e-4603-8117-603896237254\" />\n提示词多次修改调整，任然有很大的概率出现，添加负面提示用来排除没有效果。\n\n是不是在模型的设定中，前景必须有元素遮挡？\n\n<img width=\"2079\" height=\"1071\" alt=\"Image\" src=\"https://github.com/user-attachments/assets/8357d738-c716-472a-bb0a-9da751158553\" />\n\nhttps://github.com/user-attachments/assets/821011f9-64eb-4ff2-a871-6d5eba996ab4",
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      "number": 92,
      "title": "install flash-attn error",
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      "created_at": "2025-08-22T02:08:05Z",
      "updated_at": "2025-09-24T17:45:12Z",
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      "body": "as you say, when i run pip install -r requirements.txt，flash-attn could not be installed. after that when i finish all packages installation, i still cannot install flash-attn.\n\nhow can i solve this problem!!!\n\nneed help!,thx",
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      "number": 91,
      "title": "Running in colab",
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      "comments": 3,
      "created_at": "2025-08-20T17:33:44Z",
      "updated_at": "2025-11-10T23:56:30Z",
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      "body": "I don't have an environment that has the hardware to run this, so I thought I would try running it in Colab with an A100. It didn't work, throwing an error about Flash attention (there is a separate github issue for this, basically saying the versions across CUA, flash attention and torch don't seem to fit together at the moment).\n\nBut I wonder if this would work to begin with. Is a single A100 good enough to run it without having to wait for a video to be generated for a day? Has anyone tried this?",
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      "id": 3337912282,
      "node_id": "I_kwDOPT-eoM7G9H_a",
      "number": 90,
      "title": "CUDA OOM in TI2V-5B with RTX 3090 24GB",
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      "created_at": "2025-08-20T12:18:38Z",
      "updated_at": "2025-09-22T20:53:08Z",
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      "body": "I tried to run the model using a single 3090 GPU, but it reported an OOM error. I don't know where I might have gone wrong.\n\nThe commands I used are the examples given in README.md.\n\n```sh\npython generate.py \\\n    --task ti2v-5B \\\n    --size 1280*704 \\\n    --ckpt_dir ./Wan2.2-TI2V-5B \\\n    --offload_model True \\\n    --convert_model_dtype \\\n    --t5_cpu \\\n    --prompt \"Two anthropomorphic cats in comfy boxing gear and bright gloves fight intensely on a spotlighted stage\"\n```\n\nHowever，in the decode stage，the program errors out\n\n```\nFile \"/home/user/workspace/Text2ID/Wan2.2/.venv/lib/python3.10/site-packages/torch/nn/\nfunctional.py\", line 4552, in pad           \n    return torch._C._nn.pad(input, pad, mode, value)\ntorch.OutOfMemoryError: CUDA out of memory. Tried to allocate 2.60 GiB. GPU 0 has a total\n capacity of 23.68 GiB of which 892.75 MiB is free. Including non-PyTorch memory, this pr\nocess has 22.80 GiB memory in use.\n```\n\nShould I adjust relevant settings (e.g., config or PyTorch environment variables) to avoid this situation?\n\nAlso, after using the `--t5_cpu` option, the program takes an extremely long time (about half an hour, I guess) without any output before diffusion, with GPU memory usage remaining around 3GB and GPU usage is 0%.\n\nI use uv to install the environment, and its configuration file is as follows\n```toml\n[build-system]\nrequires = [\"setuptools>=61.0\"]\nbuild-backend = \"setuptools.build_meta\"\n\n[project]\nname = \"wan\"\nversion = \"2.2.0\"\ndescription = \"Wan: Open and Advanced Large-Scale Video Generative Models\"\nauthors = [\n    { name = \"Wan Team\", email = \"wan.ai@alibabacloud.com\" }\n]\nlicense = { file = \"LICENSE.txt\" }\nreadme = \"README.md\"\nrequires-python = \">=3.10,<4.0\"\ndependencies = [\n    \"torch==2.4.0\",\n    \"torchvision==0.19.0\",\n    \"opencv-python>=4.9.0.80\",\n    \"diffusers>=0.31.0\",\n    \"transformers>=4.49.0\",\n    \"tokenizers>=0.20.3\",\n    \"accelerate>=1.1.1\",\n    \"markupsafe<3.0.0\",\n    \"tqdm\",\n    \"imageio\",\n    \"easydict\",\n    \"ftfy\",\n    \"dashscope\",\n    \"imageio-ffmpeg\",\n    \"numpy>=1.23.5,<2\",\n    \"setuptools\",\n    \"wheel\",\n    \"ninja\",\n]\n\n[project.optional-dependencies]\ndev = [\n    \"pytest\",\n    \"black\",\n    \"flake8\",\n    \"isort\",\n    \"mypy\",\n    \"huggingface-hub[cli]\"\n]\n\n[project.urls]\nhomepage = \"https://wanxai.com\"\ndocumentation = \"https://github.com/Wan-Video/Wan2.2\"\nrepository = \"https://github.com/Wan-Video/Wan2.2\"\nhuggingface = \"https://huggingface.co/Wan-AI/\"\nmodelscope = \"https://modelscope.cn/organization/Wan-AI\"\ndiscord = \"https://discord.gg/p5XbdQV7\"\n\n[tool.setuptools]\npackages = [\"wan\"]\n\n[tool.setuptools.package-data]\n\"wan\" = [\"**/*.py\"]\n\n[tool.black]\nline-length = 88\n\n[tool.isort]\nprofile = \"black\"\n\n[tool.mypy]\nstrict = true\n\n[[tool.uv.index]]\nurl = \"https://download.pytorch.org/whl/cu124\"\n```\nAnd then, install `flash-attn` using `MAX_JOBS=4 uv pip install \"flash-attn==2.7.3\" --no-build-isolation`.\n\nThe complete error message is\n```\nTraceback (most recent call last):                                                       \n  File \"/home/user/workspace/Text2ID/Wan2.2/generate.py\", line 411, in <module>         \n    generate(args)                                                                       \n  File \"/home/user/workspace/Text2ID/Wan2.2/generate.py\", line 344, in generate         \n    video = wan_ti2v.generate(                                                           \n  File \"/home/user/workspace/Text2ID/Wan2.2/wan/textimage2video.py\", line 227, in genera\nte                                                                                       \n    return self.t2v(                                                                     \n  File \"/home/user/workspace/Text2ID/Wan2.2/wan/textimage2video.py\", line 401, in t2v   \n    videos = self.vae.decode(x0)                                                         \n  File \"/home/user/workspace/Text2ID/Wan2.2/wan/modules/vae2_2.py\", line 1043, in decode\n    return [                                                                             \n  File \"/home/user/workspace/Text2ID/Wan2.2/wan/modules/vae2_2.py\", line 1044, in <listc\nomp>                                                                                     \n    self.model.decode(u.unsqueeze(0),                                                    \n  File \"/home/user/workspace/Text2ID/Wan2.2/wan/modules/vae2_2.py\", line 831, in decode \n    out_ = self.decoder(                                                                 \n  File \"/home/user/workspace/Text2ID/Wan2.2/.venv/lib/python3.10/site-packages/torch/nn/\nmodules/module.py\", line 1553, in _wrapped_call_impl                                     \n    return self._call_impl(*args, **kwargs)                                              \n  File \"/home/user/workspace/Text2ID/Wan2.2/.venv/lib/python3.10/site-packages/torch/nn/\nmodules/module.py\", line 1562, in _call_impl                                             \n    return forward_call(*args, **kwargs)                                                 \n  File \"/home/user/workspace/Text2ID/Wan2.2/wan/modules/vae2_2.py\", line 700, in forward\nx = layer(x, feat_cache, feat_idx, first_chunk)\n  File \"/home/user/workspace/Text2ID/Wan2.2/.venv/lib/python3.10/site-packages/torch/nn/\nmodules/module.py\", line 1553, in _wrapped_call_impl                                     \n    return self._call_impl(*args, **kwargs)\n  File \"/home/user/workspace/Text2ID/Wan2.2/.venv/lib/python3.10/site-packages/torch/nn/\nmodules/module.py\", line 1562, in _call_impl\n    return forward_call(*args, **kwargs)\n  File \"/home/user/workspace/Text2ID/Wan2.2/wan/modules/vae2_2.py\", line 492, in forward\n    x_main = module(x_main, feat_cache, feat_idx)\n  File \"/home/user/workspace/Text2ID/Wan2.2/.venv/lib/python3.10/site-packages/torch/nn/\nmodules/module.py\", line 1553, in _wrapped_call_impl                                     \n    return self._call_impl(*args, **kwargs)\n  File \"/home/user/workspace/Text2ID/Wan2.2/.venv/lib/python3.10/site-packages/torch/nn/\nmodules/module.py\", line 1562, in _call_impl\n    return forward_call(*args, **kwargs)\n  File \"/home/user/workspace/Text2ID/Wan2.2/wan/modules/vae2_2.py\", line 230, in forward\n    x = layer(x, feat_cache[idx])\n  File \"/home/user/workspace/Text2ID/Wan2.2/.venv/lib/python3.10/site-packages/torch/nn/\nmodules/module.py\", line 1553, in _wrapped_call_impl                                     \n    return self._call_impl(*args, **kwargs)\n  File \"/home/user/workspace/Text2ID/Wan2.2/.venv/lib/python3.10/site-packages/torch/nn/\nmodules/module.py\", line 1562, in _call_impl\n    return forward_call(*args, **kwargs)\nFile \"/home/user/workspace/Text2ID/Wan2.2/wan/modules/vae2_2.py\", line 40, in forward\n    x = F.pad(x, padding)\n  File \"/home/user/workspace/Text2ID/Wan2.2/.venv/lib/python3.10/site-packages/torch/nn/\nfunctional.py\", line 4552, in pad           \n    return torch._C._nn.pad(input, pad, mode, value)\ntorch.OutOfMemoryError: CUDA out of memory. Tried to allocate 2.60 GiB. GPU 0 has a total\n capacity of 23.68 GiB of which 892.75 MiB is free. Including non-PyTorch memory, this pr\nocess has 22.80 GiB memory in use. Of the allocated memory 18.43 GiB is allocated by PyTo\nrch, and 4.03 GiB is reserved by PyTorch but unallocated. If reserved but unallocated mem\nory is large try setting PYTORCH_CUDA_ALLOC_CONF=expandable_segments:True to avoid fragme\nntation.  See documentation for Memory Management  (https://pytorch.org/docs/stable/notes\n/cuda.html#environment-variables)\n```",
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    {
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      "id": 3333298456,
      "node_id": "I_kwDOPT-eoM7GrhkY",
      "number": 89,
      "title": "request new freature for FLF2V model..",
      "user": {
        "login": "tangjm24",
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      "comments": 5,
      "created_at": "2025-08-19T07:46:47Z",
      "updated_at": "2025-10-08T08:07:23Z",
      "closed_at": null,
      "author_association": "NONE",
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      "body": "Thank you for open-sourcing wan2.2!\nThe FLF2V (First-Last-Frame to Video) model in wan2.1 was extremely useful for generating smooth transitions between frames.\nI’d like to ask if wan2.2 will also support or release a similar FLF2V model in the future?",
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    {
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      "id": 3332764598,
      "node_id": "I_kwDOPT-eoM7GpfO2",
      "number": 88,
      "title": "Inference Time",
      "user": {
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      "created_at": "2025-08-19T03:34:22Z",
      "updated_at": "2025-08-20T20:31:45Z",
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      "body": "I’m using an H800 GPU for inference with the following command. The generated video is 5 seconds long, but the runtime is 1600 seconds, which is significantly slower than the single-H800 performance mentioned in the official documentation.\n\npython generate.py --task i2v-A14B --size 1280*720 --ckpt_dir ./Wan2.2-I2V-A14B --offload_model True --convert_model_dtype --image examples/i2v_input.JPG --prompt \"Summer beach vacation style, a white cat wearing sunglasses sits on a surfboard. The fluffy-furred feline gazes directly at the camera with a relaxed expression. Blurred beach scenery forms the background featuring crystal-clear waters, distant green hills, and a blue sky dotted with white clouds. The cat assumes a naturally relaxed posture, as if savoring the sea breeze and warm sunlight. A close-up shot highlights the feline's intricate details and the refreshing atmosphere of the seaside.\"",
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      "id": 3324992681,
      "node_id": "I_kwDOPT-eoM7GL1yp",
      "number": 87,
      "title": "4*A100(40G) is enough for Wan2.2-I2V-A14B ?",
      "user": {
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      "created_at": "2025-08-15T10:49:55Z",
      "updated_at": "2026-01-07T16:22:30Z",
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      "body": "thank for your great amazing work! I have problem in Multi-GPU inference.\nrun : \n```\nsrun --jobid=$SLURM_JOB_ID \\\n     --nodes=$NNODES \\\n     --ntasks=$NNODES \\\n     --ntasks-per-node=1 \\\n     --gpus-per-task=$NGPUS_PER_NODE \\\n     --export=ALL \\\n     torchrun \\\n        --nnodes=$NNODES \\\n        --nproc_per_node=$NGPUS_PER_NODE \\\n        --rdzv_backend=c10d \\\n        --rdzv_endpoint=\"$MASTER_ADDR:$MASTER_PORT\" \\\n        --rdzv_id=\"$RZDV_ID\" \\\n        \"$GENERATE_PY\" \\\n          --task i2v-A14B \\\n          --size 832*480 \\\n          --ckpt_dir ./Wan2.2-I2V-A14B \\\n          --image examples/i2v_input.JPG \\\n          --dit_fsdp \\\n          --t5_fsdp \\\n          --t5_cpu \\\n          --offload_model  True \\\n          --ulysses_size 4 \\\n          --prompt \"Summer beach vacation style, a white cat wearing sunglasses sits on a surfboard. The fluffy-furred feline gazes directly at the camera with a relaxed expression. Blurred beach scenery forms the background featuring crystal-clear waters, distant green hills, and a blue sky dotted with white clouds. The cat assumes a naturally relaxed posture, as if savoring the sea breeze and warm sunlight. A close-up shot highlights the feline's intricate details and the refreshing atmosphere of the seaside.\"\n```\n\nLoading checkpoint shards:   0%|          | 0/6 [00:00<?, ?it/s]\nLoading checkpoint shards:   0%|          | 0/6 [00:00<?, ?it/s]\nLoading checkpoint shards:  17%|█▋        | 1/6 [00:04<00:20,  4.08s/it]\nLoading checkpoint shards:  17%|█▋        | 1/6 [00:04<00:20,  4.08s/it]\nLoading checkpoint shards:  33%|███▎      | 2/6 [00:06<00:12,  3.09s/it]\nLoading checkpoint shards:  33%|███▎      | 2/6 [00:06<00:12,  3.09s/it]\nLoading checkpoint shards:  50%|█████     | 3/6 [00:10<00:10,  3.38s/it]\nLoading checkpoint shards:  50%|█████     | 3/6 [00:10<00:10,  3.38s/it]\nLoading checkpoint shards:  67%|██████▋   | 4/6 [00:12<00:06,  3.02s/it]\nLoading checkpoint shards:  67%|██████▋   | 4/6 [00:12<00:06,  3.02s/it]\nLoading checkpoint shards:  83%|████████▎ | 5/6 [00:15<00:02,  2.84s/it]\nLoading checkpoint shards:  83%|████████▎ | 5/6 [00:15<00:02,  2.84s/it]\nLoading 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\"/public/home/xyzhang/miniconda3/envs/skyreels_v2/lib/python3.10/site-packages/torch/distributed/fsdp/fully_sharded_data_parallel.py\", line 864, in forward\n[rank2]:     output = self._fsdp_wrapped_module(*args, **kwargs)\n[rank2]:   File \"/public/home/xyzhang/miniconda3/envs/skyreels_v2/lib/python3.10/site-packages/torch/nn/modules/module.py\", line 1736, in _wrapped_call_impl\n[rank2]:     return self._call_impl(*args, **kwargs)\n[rank2]:   File \"/public/home/xyzhang/miniconda3/envs/skyreels_v2/lib/python3.10/site-packages/torch/nn/modules/module.py\", line 1747, in _call_impl\n[rank2]:     return forward_call(*args, **kwargs)\n[rank2]:   File \"/public/home/xyzhang/Wan2.2/wan/distributed/sequence_parallel.py\", line 134, in sp_dit_forward\n[rank2]:     x = block(x, **kwargs)\n[rank2]:   File \"/public/home/xyzhang/miniconda3/envs/skyreels_v2/lib/python3.10/site-packages/torch/nn/modules/module.py\", line 1736, in _wrapped_call_impl\n[rank2]:     return self._call_impl(*args, **kwargs)\n[rank2]:   File \"/public/home/xyzhang/miniconda3/envs/skyreels_v2/lib/python3.10/site-packages/torch/nn/modules/module.py\", line 1747, in _call_impl\n[rank0]: Traceback (most recent call last):\n[rank0]:   File \"/public/home/xyzhang/Wan2.2/generate.py\", line 411, in <module>\n[rank0]:     generate(args)\n[rank0]:   File \"/public/home/xyzhang/Wan2.2/generate.py\", line 371, in generate\n[rank0]:     video = wan_i2v.generate(\n[rank0]:   File \"/public/home/xyzhang/Wan2.2/wan/image2video.py\", line 393, in generate\n[rank0]:     noise_pred_cond = model(\n[rank0]:   File \"/public/home/xyzhang/miniconda3/envs/skyreels_v2/lib/python3.10/site-packages/torch/nn/modules/module.py\", line 1736, in _wrapped_call_impl\n[rank0]:     return self._call_impl(*args, **kwargs)\n[rank0]:   File \"/public/home/xyzhang/miniconda3/envs/skyreels_v2/lib/python3.10/site-packages/torch/nn/modules/module.py\", line 1747, in _call_impl\n[rank0]:     return forward_call(*args, **kwargs)\n[rank0]:   File \"/public/home/xyzhang/miniconda3/envs/skyreels_v2/lib/python3.10/site-packages/torch/distributed/fsdp/fully_sharded_data_parallel.py\", line 864, in forward\n[rank2]:     return forward_call(*args, **kwargs)\n[rank2]:   File \"/public/home/xyzhang/miniconda3/envs/skyreels_v2/lib/python3.10/site-packages/torch/distributed/fsdp/fully_sharded_data_parallel.py\", line 864, in forward\n[rank2]:     output = self._fsdp_wrapped_module(*args, **kwargs)\n[rank2]:   File \"/public/home/xyzhang/miniconda3/envs/skyreels_v2/lib/python3.10/site-packages/torch/nn/modules/module.py\", line 1736, in _wrapped_call_impl\n[rank2]:     return self._call_impl(*args, **kwargs)\n[rank2]:   File \"/public/home/xyzhang/miniconda3/envs/skyreels_v2/lib/python3.10/site-packages/torch/nn/modules/module.py\", line 1747, in _call_impl\n[rank2]:     return forward_call(*args, **kwargs)\n[rank2]:   File \"/public/home/xyzhang/Wan2.2/wan/modules/model.py\", line 244, in forward\n[rank2]:     self.norm1(x).float() * (1 + e[1].squeeze(2)) + e[0].squeeze(2),\n[rank2]:   File \"/public/home/xyzhang/miniconda3/envs/skyreels_v2/lib/python3.10/site-packages/torch/nn/modules/module.py\", line 1736, in _wrapped_call_impl\n[rank0]:     output = self._fsdp_wrapped_module(*args, **kwargs)\n[rank0]:   File \"/public/home/xyzhang/miniconda3/envs/skyreels_v2/lib/python3.10/site-packages/torch/nn/modules/module.py\", line 1736, in _wrapped_call_impl\n[rank0]:     return self._call_impl(*args, **kwargs)\n[rank0]:   File \"/public/home/xyzhang/miniconda3/envs/skyreels_v2/lib/python3.10/site-packages/torch/nn/modules/module.py\", line 1747, in _call_impl\n[rank0]:     return forward_call(*args, **kwargs)\n[rank0]:   File \"/public/home/xyzhang/Wan2.2/wan/distributed/sequence_parallel.py\", line 134, in sp_dit_forward\n[rank0]:     x = block(x, **kwargs)\n[rank0]:   File \"/public/home/xyzhang/miniconda3/envs/skyreels_v2/lib/python3.10/site-packages/torch/nn/modules/module.py\", line 1736, in _wrapped_call_impl\n[rank0]:     return self._call_impl(*args, **kwargs)\n[rank0]:   File \"/public/home/xyzhang/miniconda3/envs/skyreels_v2/lib/python3.10/site-packages/torch/nn/modules/module.py\", line 1747, in _call_impl\n[rank2]:     return self._call_impl(*args, **kwargs)\n[rank2]:   File \"/public/home/xyzhang/miniconda3/envs/skyreels_v2/lib/python3.10/site-packages/torch/nn/modules/module.py\", line 1747, in _call_impl\n[rank2]:     return forward_call(*args, **kwargs)\n[rank2]:   File \"/public/home/xyzhang/Wan2.2/wan/modules/model.py\", line 98, in forward\n[rank2]:     return super().forward(x.float()).type_as(x)\n[rank2]:   File \"/public/home/xyzhang/miniconda3/envs/skyreels_v2/lib/python3.10/site-packages/torch/nn/modules/normalization.py\", line 217, in forward\n[rank2]:     return F.layer_norm(\n[rank2]:   File \"/public/home/xyzhang/miniconda3/envs/skyreels_v2/lib/python3.10/site-packages/torch/nn/functional.py\", line 2900, in layer_norm\n[rank2]:     return torch.layer_norm(\n[rank0]:     return forward_call(*args, **kwargs)\n[rank0]:   File \"/public/home/xyzhang/miniconda3/envs/skyreels_v2/lib/python3.10/site-packages/torch/distributed/fsdp/fully_sharded_data_parallel.py\", line 864, in forward\n[rank0]:     output = self._fsdp_wrapped_module(*args, **kwargs)\n[rank0]:   File \"/public/home/xyzhang/miniconda3/envs/skyreels_v2/lib/python3.10/site-packages/torch/nn/modules/module.py\", line 1736, in _wrapped_call_impl\n[rank0]:     return self._call_impl(*args, **kwargs)\n[rank0]:   File \"/public/home/xyzhang/miniconda3/envs/skyreels_v2/lib/python3.10/site-packages/torch/nn/modules/module.py\", line 1747, in _call_impl\n[rank0]:     return forward_call(*args, **kwargs)\n[rank0]:   File \"/public/home/xyzhang/Wan2.2/wan/modules/model.py\", line 244, in forward\n[rank0]:     self.norm1(x).float() * (1 + e[1].squeeze(2)) + e[0].squeeze(2),\n[rank0]:   File \"/public/home/xyzhang/miniconda3/envs/skyreels_v2/lib/python3.10/site-packages/torch/nn/modules/module.py\", line 1736, in _wrapped_call_impl\n[rank2]: torch.OutOfMemoryError: CUDA out of memory. Tried to allocate 158.00 MiB. GPU 0 has a total capacity of 39.39 GiB of which 32.31 MiB is free. Including non-PyTorch memory, this process has 39.35 GiB memory in use. Of the allocated memory 38.51 GiB is allocated by PyTorch, and 105.07 MiB is reserved by PyTorch but unallocated. If reserved but unallocated memory is large try setting PYTORCH_CUDA_ALLOC_CONF=expandable_segments:True to avoid fragmentation.  See documentation for Memory Management  (https://pytorch.org/docs/stable/notes/cuda.html#environment-variables)\n[rank3]: Traceback (most recent call last):\n[rank3]:   File \"/public/home/xyzhang/Wan2.2/generate.py\", line 411, in <module>\n[rank3]:     generate(args)\n[rank3]:   File \"/public/home/xyzhang/Wan2.2/generate.py\", line 371, in generate\n[rank3]:     video = wan_i2v.generate(\n[rank3]:   File \"/public/home/xyzhang/Wan2.2/wan/image2video.py\", line 393, in generate\n[rank3]:     noise_pred_cond = model(\n[rank0]:     return self._call_impl(*args, **kwargs)\n[rank0]:   File \"/public/home/xyzhang/miniconda3/envs/skyreels_v2/lib/python3.10/site-packages/torch/nn/modules/module.py\", line 1747, in _call_impl\n[rank0]:     return forward_call(*args, **kwargs)\n[rank0]:   File \"/public/home/xyzhang/Wan2.2/wan/modules/model.py\", line 98, in forward\n[rank0]:     return super().forward(x.float()).type_as(x)\n[rank0]:   File \"/public/home/xyzhang/miniconda3/envs/skyreels_v2/lib/python3.10/site-packages/torch/nn/modules/normalization.py\", line 217, in forward\n[rank0]:     return F.layer_norm(\n[rank0]:   File \"/public/home/xyzhang/miniconda3/envs/skyreels_v2/lib/python3.10/site-packages/torch/nn/functional.py\", line 2900, in layer_norm\n[rank0]:     return torch.layer_norm(\n[rank3]:   File \"/public/home/xyzhang/miniconda3/envs/skyreels_v2/lib/python3.10/site-packages/torch/nn/modules/module.py\", line 1736, in _wrapped_call_impl\n[rank3]:     return self._call_impl(*args, **kwargs)\n[rank3]:   File \"/public/home/xyzhang/miniconda3/envs/skyreels_v2/lib/python3.10/site-packages/torch/nn/modules/module.py\", line 1747, in _call_impl\n[rank3]:     return forward_call(*args, **kwargs)\n[rank3]:   File \"/public/home/xyzhang/miniconda3/envs/skyreels_v2/lib/python3.10/site-packages/torch/distributed/fsdp/fully_sharded_data_parallel.py\", line 864, in forward\n[rank3]:     output = self._fsdp_wrapped_module(*args, **kwargs)\n[rank3]:   File \"/public/home/xyzhang/miniconda3/envs/skyreels_v2/lib/python3.10/site-packages/torch/nn/modules/module.py\", line 1736, in _wrapped_call_impl\n[rank3]:     return self._call_impl(*args, **kwargs)\n[rank3]:   File \"/public/home/xyzhang/miniconda3/envs/skyreels_v2/lib/python3.10/site-packages/torch/nn/modules/module.py\", line 1747, in _call_impl\n[rank0]: torch.OutOfMemoryError: CUDA out of memory. Tried to allocate 158.00 MiB. GPU 0 has a total capacity of 39.39 GiB of which 32.31 MiB is free. Including non-PyTorch memory, this process has 39.35 GiB memory in use. Of the allocated memory 38.51 GiB is allocated by PyTorch, and 105.07 MiB is reserved by PyTorch but unallocated. If reserved but unallocated memory is large try setting PYTORCH_CUDA_ALLOC_CONF=expandable_segments:True to avoid fragmentation.  See documentation for Memory Management  (https://pytorch.org/docs/stable/notes/cuda.html#environment-variables)\n[rank1]: Traceback (most recent call last):\n[rank1]:   File \"/public/home/xyzhang/Wan2.2/generate.py\", line 411, in <module>\n[rank1]:     generate(args)\n[rank1]:   File \"/public/home/xyzhang/Wan2.2/generate.py\", line 371, in generate\n[rank1]:     video = wan_i2v.generate(\n[rank1]:   File \"/public/home/xyzhang/Wan2.2/wan/image2video.py\", line 393, in generate\n[rank1]:     noise_pred_cond = model(\n[rank3]:     return forward_call(*args, **kwargs)\n[rank3]:   File \"/public/home/xyzhang/Wan2.2/wan/distributed/sequence_parallel.py\", line 134, in sp_dit_forward\n[rank3]:     x = block(x, **kwargs)\n[rank3]:   File \"/public/home/xyzhang/miniconda3/envs/skyreels_v2/lib/python3.10/site-packages/torch/nn/modules/module.py\", line 1736, in _wrapped_call_impl\n[rank3]:     return self._call_impl(*args, **kwargs)\n[rank3]:   File \"/public/home/xyzhang/miniconda3/envs/skyreels_v2/lib/python3.10/site-packages/torch/nn/modules/module.py\", line 1747, in _call_impl\n[rank3]:     return forward_call(*args, **kwargs)\n[rank3]:   File \"/public/home/xyzhang/miniconda3/envs/skyreels_v2/lib/python3.10/site-packages/torch/distributed/fsdp/fully_sharded_data_parallel.py\", line 864, in forward\n[rank3]:     output = self._fsdp_wrapped_module(*args, **kwargs)\n[rank3]:   File \"/public/home/xyzhang/miniconda3/envs/skyreels_v2/lib/python3.10/site-packages/torch/nn/modules/module.py\", line 1736, in _wrapped_call_impl\n[rank1]:   File \"/public/home/xyzhang/miniconda3/envs/skyreels_v2/lib/python3.10/site-packages/torch/nn/modules/module.py\", line 1736, in _wrapped_call_impl\n[rank1]:     return self._call_impl(*args, **kwargs)\n[rank1]:   File \"/public/home/xyzhang/miniconda3/envs/skyreels_v2/lib/python3.10/site-packages/torch/nn/modules/module.py\", line 1747, in _call_impl\n[rank1]:     return forward_call(*args, **kwargs)\n[rank1]:   File \"/public/home/xyzhang/miniconda3/envs/skyreels_v2/lib/python3.10/site-packages/torch/distributed/fsdp/fully_sharded_data_parallel.py\", line 864, in forward\n[rank1]:     output = self._fsdp_wrapped_module(*args, **kwargs)\n[rank1]:   File \"/public/home/xyzhang/miniconda3/envs/skyreels_v2/lib/python3.10/site-packages/torch/nn/modules/module.py\", line 1736, in _wrapped_call_impl\n[rank1]:     return self._call_impl(*args, **kwargs)\n[rank1]:   File \"/public/home/xyzhang/miniconda3/envs/skyreels_v2/lib/python3.10/site-packages/torch/nn/modules/module.py\", line 1747, in _call_impl\n[rank3]:     return self._call_impl(*args, **kwargs)\n[rank3]:   File \"/public/home/xyzhang/miniconda3/envs/skyreels_v2/lib/python3.10/site-packages/torch/nn/modules/module.py\", line 1747, in _call_impl\n[rank3]:     return forward_call(*args, **kwargs)\n[rank3]:   File \"/public/home/xyzhang/Wan2.2/wan/modules/model.py\", line 244, in forward\n[rank3]:     self.norm1(x).float() * (1 + e[1].squeeze(2)) + e[0].squeeze(2),\n[rank3]:   File \"/public/home/xyzhang/miniconda3/envs/skyreels_v2/lib/python3.10/site-packages/torch/nn/modules/module.py\", line 1736, in _wrapped_call_impl\n[rank3]:     return self._call_impl(*args, **kwargs)\n[rank3]:   File \"/public/home/xyzhang/miniconda3/envs/skyreels_v2/lib/python3.10/site-packages/torch/nn/modules/module.py\", line 1747, in _call_impl\n[rank3]:     return forward_call(*args, **kwargs)\n[rank3]:   File \"/public/home/xyzhang/Wan2.2/wan/modules/model.py\", line 98, in forward\n[rank3]:     return super().forward(x.float()).type_as(x)\n[rank1]:     return forward_call(*args, **kwargs)\n[rank1]:   File \"/public/home/xyzhang/Wan2.2/wan/distributed/sequence_parallel.py\", line 134, in sp_dit_forward\n[rank1]:     x = block(x, **kwargs)\n[rank1]:   File \"/public/home/xyzhang/miniconda3/envs/skyreels_v2/lib/python3.10/site-packages/torch/nn/modules/module.py\", line 1736, in _wrapped_call_impl\n[rank1]:     return self._call_impl(*args, **kwargs)\n[rank1]:   File \"/public/home/xyzhang/miniconda3/envs/skyreels_v2/lib/python3.10/site-packages/torch/nn/modules/module.py\", line 1747, in _call_impl\n[rank1]:     return forward_call(*args, **kwargs)\n[rank1]:   File \"/public/home/xyzhang/miniconda3/envs/skyreels_v2/lib/python3.10/site-packages/torch/distributed/fsdp/fully_sharded_data_parallel.py\", line 864, in forward\n[rank1]:     output = self._fsdp_wrapped_module(*args, **kwargs)\n[rank1]:   File \"/public/home/xyzhang/miniconda3/envs/skyreels_v2/lib/python3.10/site-packages/torch/nn/modules/module.py\", line 1736, in _wrapped_call_impl\n[rank3]:   File \"/public/home/xyzhang/miniconda3/envs/skyreels_v2/lib/python3.10/site-packages/torch/nn/modules/normalization.py\", line 217, in forward\n[rank3]:     return F.layer_norm(\n[rank3]:   File \"/public/home/xyzhang/miniconda3/envs/skyreels_v2/lib/python3.10/site-packages/torch/nn/functional.py\", line 2900, in layer_norm\n[rank3]:     return torch.layer_norm(\n[rank3]: torch.OutOfMemoryError: CUDA out of memory. Tried to allocate 158.00 MiB. GPU 1 has a total capacity of 39.39 GiB of which 32.31 MiB is free. Including non-PyTorch memory, this process has 39.35 GiB memory in use. Of the allocated memory 38.51 GiB is allocated by PyTorch, and 105.07 MiB is reserved by PyTorch but unallocated. If reserved but unallocated memory is large try setting PYTORCH_CUDA_ALLOC_CONF=expandable_segments:True to avoid fragmentation.  See documentation for Memory Management  (https://pytorch.org/docs/stable/notes/cuda.html#environment-variables)\n[rank1]:     return self._call_impl(*args, **kwargs)\n[rank1]:   File \"/public/home/xyzhang/miniconda3/envs/skyreels_v2/lib/python3.10/site-packages/torch/nn/modules/module.py\", line 1747, in _call_impl\n[rank1]:     return forward_call(*args, **kwargs)\n[rank1]:   File \"/public/home/xyzhang/Wan2.2/wan/modules/model.py\", line 244, in forward\n[rank1]:     self.norm1(x).float() * (1 + e[1].squeeze(2)) + e[0].squeeze(2),\n[rank1]:   File \"/public/home/xyzhang/miniconda3/envs/skyreels_v2/lib/python3.10/site-packages/torch/nn/modules/module.py\", line 1736, in _wrapped_call_impl\n[rank1]:     return self._call_impl(*args, **kwargs)\n[rank1]:   File \"/public/home/xyzhang/miniconda3/envs/skyreels_v2/lib/python3.10/site-packages/torch/nn/modules/module.py\", line 1747, in _call_impl\n[rank1]:     return forward_call(*args, **kwargs)\n[rank1]:   File \"/public/home/xyzhang/Wan2.2/wan/modules/model.py\", line 98, in forward\n[rank1]:     return super().forward(x.float()).type_as(x)\n[rank1]:   File \"/public/home/xyzhang/miniconda3/envs/skyreels_v2/lib/python3.10/site-packages/torch/nn/modules/normalization.py\", line 217, in forward\n[rank1]:     return F.layer_norm(\n[rank1]:   File \"/public/home/xyzhang/miniconda3/envs/skyreels_v2/lib/python3.10/site-packages/torch/nn/functional.py\", line 2900, in layer_norm\n[rank1]:     return torch.layer_norm(\n[rank1]: torch.OutOfMemoryError: CUDA out of memory. Tried to allocate 158.00 MiB. GPU 1 has a total capacity of 39.39 GiB of which 32.31 MiB is free. Including non-PyTorch memory, this process has 39.35 GiB memory in use. Of the allocated memory 38.51 GiB is allocated by PyTorch, and 105.07 MiB is reserved by PyTorch but unallocated. If reserved but unallocated memory is large try setting PYTORCH_CUDA_ALLOC_CONF=expandable_segments:True to avoid fragmentation.  See documentation for Memory Management  (https://pytorch.org/docs/stable/notes/cuda.html#environment-variables)\n[rank0]:[W815 18:31:16.719474996 ProcessGroupNCCL.cpp:1250] Warning: WARNING: process group has NOT been destroyed before we destruct ProcessGroupNCCL. On normal program exit, the application should call destroy_process_group to ensure that any pending NCCL operations have finished in this process. In rare cases this process can exit before this point and block the progress of another member of the process group. This constraint has always been present,  but this warning has only been added since PyTorch 2.4 (function operator())\n[rank2]:[W815 18:31:16.603634774 ProcessGroupNCCL.cpp:1250] Warning: WARNING: process group has NOT been destroyed before we destruct ProcessGroupNCCL. On normal program exit, the application should call destroy_process_group to ensure that any pending NCCL operations have finished in this process. In rare cases this process can exit before this point and block the progress of another member of the process group. This constraint has always been present,  but this warning has only been added since PyTorch 2.4 (function operator())\nW0815 18:31:49.070000 187125 site-packages/torch/distributed/elastic/multiprocessing/api.py:897] Sending process 187129 closing signal SIGTERM\nW0815 18:31:49.069000 382388 site-packages/torch/distributed/elastic/multiprocessing/api.py:897] Sending process 382390 closing signal SIGTERM\nE0815 18:31:49.205000 187125 site-packages/torch/distributed/elastic/multiprocessing/api.py:869] failed (exitcode: 1) local_rank: 0 (pid: 187128) of binary: /public/home/xyzhang/miniconda3/envs/skyreels_v2/bin/python3.10\nE0815 18:31:49.201000 382388 site-packages/torch/distributed/elastic/multiprocessing/api.py:869] failed (exitcode: 1) local_rank: 1 (pid: 382391) of binary: /public/home/xyzhang/miniconda3/envs/skyreels_v2/bin/python3.10\nTraceback (most recent call last):\n  File \"/public/home/xyzhang/miniconda3/envs/skyreels_v2/bin/torchrun\", line 8, in <module>\nTraceback (most recent call last):\n  File \"/public/home/xyzhang/miniconda3/envs/skyreels_v2/bin/torchrun\", line 8, in <module>\n    sys.exit(main())\n  File \"/public/home/xyzhang/miniconda3/envs/skyreels_v2/lib/python3.10/site-packages/torch/distributed/elastic/multiprocessing/errors/__init__.py\", line 355, in wrapper\n    sys.exit(main())\n  File \"/public/home/xyzhang/miniconda3/envs/skyreels_v2/lib/python3.10/site-packages/torch/distributed/elastic/multiprocessing/errors/__init__.py\", line 355, in wrapper\n    return f(*args, **kwargs)\n  File \"/public/home/xyzhang/miniconda3/envs/skyreels_v2/lib/python3.10/site-packages/torch/distributed/run.py\", line 919, in main\n    return f(*args, **kwargs)\n  File \"/public/home/xyzhang/miniconda3/envs/skyreels_v2/lib/python3.10/site-packages/torch/distributed/run.py\", line 919, in main\n    run(args)\n  File \"/public/home/xyzhang/miniconda3/envs/skyreels_v2/lib/python3.10/site-packages/torch/distributed/run.py\", line 910, in run\n    run(args)\n  File \"/public/home/xyzhang/miniconda3/envs/skyreels_v2/lib/python3.10/site-packages/torch/distributed/run.py\", line 910, in run\n    elastic_launch(\n  File \"/public/home/xyzhang/miniconda3/envs/skyreels_v2/lib/python3.10/site-packages/torch/distributed/launcher/api.py\", line 138, in __call__\n    elastic_launch(\n  File \"/public/home/xyzhang/miniconda3/envs/skyreels_v2/lib/python3.10/site-packages/torch/distributed/launcher/api.py\", line 138, in __call__\n    return launch_agent(self._config, self._entrypoint, list(args))\n  File \"/public/home/xyzhang/miniconda3/envs/skyreels_v2/lib/python3.10/site-packages/torch/distributed/launcher/api.py\", line 269, in launch_agent\n    return launch_agent(self._config, self._entrypoint, list(args))\n  File \"/public/home/xyzhang/miniconda3/envs/skyreels_v2/lib/python3.10/site-packages/torch/distributed/launcher/api.py\", line 269, in launch_agent\n    raise ChildFailedError(\ntorch.distributed.elastic.multiprocessing.errors.ChildFailedError: \n============================================================\n/public/home/xyzhang/Wan2.2/generate.py FAILED\n\n\nI run Wan2.1-I2V-14B-480P in this environment on 4*A100 successful， but not in Wan2.2-I2V-A14B，maybe 4*A100 is not enough?\n\n",
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      "id": 3320582687,
      "node_id": "I_kwDOPT-eoM7F7BIf",
      "number": 86,
      "title": "question of timestep in generation",
      "user": {
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      "created_at": "2025-08-14T03:17:11Z",
      "updated_at": "2025-08-14T03:17:11Z",
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      "body": "https://github.com/Wan-Video/Wan2.2/blob/031a9be56cec91e86d140d3d3a74280fb05a9b1c/wan/textimage2video.py#L373\nDoes anyone know why it is extended to the token length here? Why not like wan2.1?",
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      "id": 3317392706,
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      "number": 85,
      "title": "ModelScope filenames are incorrect",
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      "created_at": "2025-08-13T08:16:23Z",
      "updated_at": "2025-08-13T10:44:27Z",
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      "body": "The `.safetensor` files from modelscope end in `-bf16` which causes an error because the code looks for the filename without it. The correct filenames come from hugging face.",
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      "id": 3316998180,
      "node_id": "I_kwDOPT-eoM7FtWAk",
      "number": 84,
      "title": "Environment Setup - Issues with Flash-Attn",
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      "comments": 2,
      "created_at": "2025-08-13T05:51:47Z",
      "updated_at": "2025-10-09T20:06:58Z",
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      "body": "Setting up this environment is far more complicated than the README explains. \n\nI'm using windows 11, perhaps I should try this on my linux distro.\n\n`requirements.txt` specifies `torch>=2.4.0` but `flash_attn` is only compatible up to `torch=2.5` and `python 3.12`, while the latest releases are `torch=2.8` and `python 3.13` and no version of `torch` is compatible with the latest release of `CUDA` which is `13.0`, so we have to install old versions of everything for this to work.\n\nEdit: Installing from these wheels worked: https://github.com/Dao-AILab/flash-attention/issues/1469\n\nCan the devs update the installation procedure, or am I the only one having this much trouble?",
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      "id": 3314877825,
      "node_id": "I_kwDOPT-eoM7FlQWB",
      "number": 83,
      "title": "What resolutions has WAN been trained on and what are the official optimized output resolutions?",
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      "created_at": "2025-08-12T15:45:25Z",
      "updated_at": "2025-08-12T15:45:25Z",
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      "body": "Presumably there are 480p 1:1 and 16:9 outputs and 720p 1:1 and 16:9 outputs??",
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      "id": 3310137715,
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      "number": 81,
      "title": "Could you add a possibility to create seamless looping video?",
      "user": {
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      "created_at": "2025-08-11T13:45:06Z",
      "updated_at": "2025-08-11T13:45:06Z",
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      "body": "Could you add a seamless looping video feature in a future update for the image-to-video function in WanVideo, where you can insert different image or images in between, but the start and end images would be the same to ensure the video matches perfectly for smooth playback?\nThanks a lot in advance",
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      "id": 3308738010,
      "node_id": "I_kwDOPT-eoM7FN1Xa",
      "number": 80,
      "title": "Tram",
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      "created_at": "2025-08-11T07:12:15Z",
      "updated_at": "2025-08-17T21:42:01Z",
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      "body": "Трамвай едет и весь заплетается красными розами ",
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      "id": 3308307351,
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      "number": 79,
      "title": "inference error",
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      "created_at": "2025-08-11T03:09:45Z",
      "updated_at": "2025-08-12T05:40:42Z",
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      "body": "`python generate.py --task i2v-A14B \\\n--size 480*832 --ckpt_dir ./Wan2.2-I2V-A14B-BF16 \\\n--offload_model True --convert_model_dtype \\\n--image \"data/human_imgs_0717/11371586364b1c11f39b39.jpg\" \\\n--frame_num 65 --sample_steps 50 \\\n--prompt \"The knitted character walks purposefully, arms swinging side to side.\"\n`\n`[2025-08-09 15:34:16,622] INFO: Generating video ...\n  0%|                                                                                                                                                     | 0/50 [00:00<?, ?it/s]tensor(32130) 26010\n  0%|                                                                                                                                                     | 0/50 [00:23<?, ?it/s]\nTraceback (most recent call last):\n  File \"Wan2.2/generate.py\", line 392, in <module>\n    generate(args)\n  File \"Wan2.2/generate.py\", line 354, in generate\n    video = wan_i2v.generate(args.prompt,\n  File \"Wan2.2/wan/image2video.py\", line 393, in generate\n    noise_pred_cond = model(\n  File \"/opt/nas/p/conda/envs/VACE-3/lib/python3.10/site-packages/torch/nn/modules/module.py\", line 1751, in _wrapped_call_impl\n    return self._call_impl(*args, **kwargs)\n  File \"/opt/nas/p/conda/envs/VACE-3/lib/python3.10/site-packages/torch/nn/modules/module.py\", line 1762, in _call_impl\n    return forward_call(*args, **kwargs)\n  File \"Wan2.2/wan/modules/model.py\", line 454, in forward\n    assert seq_lens.max() <= seq_len\nAssertionError\n` \n",
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      "id": 3307702063,
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      "title": "How to stop unwanted mouth movement in image2video animations?",
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      "body": "Hi,\n\nI’m using WAN 2.2’s image2video feature to animate characters. However, I consistently encounter unwanted mouth movements, where the character appears to be talking, even when the prompts explicitly instruct “no talking” or “mouth closed.”\n\nI’ve tried multiple prompt variations to prevent lip movement, but the mouth animation persists regardless.\n\nCould you please advise if there is a way to disable or control this automatic mouth animation? How can I keep the mouth still while animating other parts like the eyes, head, or body?\n\nThank you for your help!",
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      "title": "Wan vace 2.2 not on todo list ?",
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      "body": "I didn't saw wan VACE 2.2 on todo list, so wanted to ask from team qwen about if we will ever get VACE for 2.2 or not.",
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      "body": "I got it once, without prompting, but explosion, volcanoes blowing up, I cannot get it to shake.  Camera shake it ignores.  World shakes it ignores as well.\n\nIs there any reliable way to get it to do this and anything action related is DOA with this, and simulating it in post is rather a bad way to go, especially at how well Wan 2.2 pulled off that one world shake it did.",
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      "title": "one-click setup一键本地部署",
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      "body": "nice models! 为方便本地部署， https://learn2ask.net/, 支持Wan2.2一键本地docker部署，免去环境安装问题和手动下载模型。",
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      "title": "Are there any support for 3d camera trajectory condition input while generating the video?",
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      "body": "use 2 or 4 ranks to loads one experts, and other ranks load another experts\nWill there be any profit from this？",
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      "body": "Two 48GB 4090 servers, with a total of 96GB of video memory and 64GB of memory, surprisingly failed to run. The error message is that the memory is full and the process has been killed\n",
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      "title": "Generative Video AI Web App: Text-to-Video, File Uploads, Demo",
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      "body": "Build a generative video AI web app with the following features:\n- Users enter text prompts to generate videos using AI\n- File uploads supported\n- Web interface for demonstration (not production)\n- Use https://huggingface.co/Wan-AI/Wan2.2-T2V-A14B-Diffusers as the backend model\n- Use React for the frontend\n- Include user authentication if possible\n\nThis issue is for the repository: Wan-Video/Wan2.2\n",
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      "number": 53,
      "title": "flash_attn版本不匹配",
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      "body": "torch2.7.1+cu128  默认的flash_attn为2.8.2，而运行wan要求的flash_attn不大于2.7.4，虽然可以编译安装低版本的，但有时更新其他组件时，flash_attn又变成2.8.2，运行wan时又需要手动安装不大于2.7.4的flash_attn,希望把flash_attn的兼容版本范围到2.8.2",
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      "number": 52,
      "title": "You can run A14B at full quality with a single 24GB GPU",
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      "body": "I've added DFloat11-compressed versions of Wan2.2 A14B:\n\n* **Text-to-Video:** [https://huggingface.co/DFloat11/Wan2.2-T2V-A14B-DF11](https://huggingface.co/DFloat11/Wan2.2-T2V-A14B-DF11)\n* **Image-to-Video:** [https://huggingface.co/DFloat11/Wan2.2-I2V-A14B-DF11](https://huggingface.co/DFloat11/Wan2.2-I2V-A14B-DF11)\n\nWith DFloat11 compression and offloading, these models can run on a single 24GB GPU. I've tested them on an A5000, and they work flawlessly.\n\nDFloat11 is a lossless compression framework that reduces model size by 30% while preserving bit-for-bit identical outputs. Learn more here: https://github.com/LeanModels/DFloat11.\n\nGive them a try, and let me know if you run into any issues!",
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      "title": "在RTX5090 32G*8上爆显存",
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      "body": "同样的命令在wan2.1 14B上不会爆显存，但在wan2.2 14B上就会爆显存，且降显存外，显卡利用率很低，\n比wan2.1还少了一个--ring_size 的参数\n\n运行命令\n\n`export PYTORCH_CUDA_ALLOC_CONF=max_split_size_mb:128\ntorchrun --nproc_per_node=8 generate.py \\\n--task t2v-A14B --ckpt_dir /root/wan2.2/Wan2.2-T2V-A14B \\\n--size 1280*720 --frame_num 81 \\\n--ulysses_size 8 \\\n--sample_steps 50 \\\n--sample_guide_scale 5.0 \\\n--offload_model true \\\n--t5_cpu --t5_fsdp --dit_fsdp \\\n--convert_model_dtype \\\n--prompt \"Two anthropomorphic cats in comfy boxing gear and bright gloves fight intensely on a spotlighted stage\"`\n\n显存报错时显卡状态\n[rank0]:   File \"/home/wan/lib/python3.12/site-packages/torch/nn/modules/linear.py\", line 125, in forward\n[rank0]:     return F.linear(input, self.weight, self.bias)\n[rank0]:            ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n[rank0]: torch.OutOfMemoryError: CUDA out of memory. Tried to allocate 8.65 GiB. GPU 0 has a total capacity of 31.37 GiB of which 8.65 GiB is free. Including non-PyTorch memory, this process has 22.71 GiB memory in use. Of the allocated memory 21.78 GiB is allocated by PyTorch, and 117.37 MiB is reserved by PyTorch but unallocated. If reserved but unallocated memory is large try setting PYTORCH_CUDA_ALLOC_CONF=expandable_segments:True to avoid fragmentation.  See documentation for Memory Management  (https://pytorch.org/docs/stable/notes/cuda.html#environment-variables)\n[rank0]:[W802 13:41:42.030667690 ProcessGroupNCCL.cpp:1479] Warning: WARNING: destroy_process_group() was not called before program exit, which can leak resources. For more info, please see https://pytorch.org/docs/stable/distributed.html#shutdown (function operator())\nW0802 13:41:44.010000 1732772 site-packages/torch/distributed/elastic/multiproce\nwan2.2\n<img width=\"333\" height=\"369\" alt=\"Image\" src=\"https://github.com/user-attachments/assets/f737f37e-8f46-4291-92ad-0bc679f53e29\" />\n\nwan2.1运行正常不会报错\n\n<img width=\"340\" height=\"377\" alt=\"Image\" src=\"https://github.com/user-attachments/assets/4983b101-b0ad-4edc-91b9-e3cf28964ed9\" />",
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      "number": 50,
      "title": "Is open source wan 2.2 27b a demo of original closed source?",
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      "body": "**Update: To all Users Q8, FP8, FP16 all versions of wan 2.2 opensource completely fail on the task below, upon further testing and running it at higher resolution like above 1280x720 it still fails, (wan 2.2 opensource) lacks all the ability to understand, any sort of \"text\" , \"textual-captions\", \"spatial prompting\" and their locations inside image, art direction is not possible with wan 2.2 open source i2v, it can just animate randomly based on your prompt and it will be a hit or miss, i don't know why official developers have to take this ability down from opensource version, when they have already given first, last frame feature which is cool too, but for now it's not possible with (wan2.2 opensource version), and there is no official confirmation about this feature either.**\n\nSo i watched a video few days back just after wan 2.2 open release, where a guy named \"guizang\" said wan 2.2 can do a veo3 style image instructions video and it follows instructions from image, so i tried in my comfyui environment after capturing the same screenshot from his video with instructions on it in first frame from original poster to check if what he is saying not fake, and ran it through wan 2.2 multiple times even with 40 steps, and it never get instructions from image correct and completely ignored them in all tries, but then i thought maybe that original poster's video is fake? So i got to original website from link in this repo of wan 2.2, and gave it same original first frame and same prompt, and in first 10 free credits it generated a video following the text instructions in image, fully following all the instructions even with that prompt below, but can't get it to work with open sourced version of wan 2.2? are models not same? i posted results below:\n\n<img width=\"1920\" height=\"1070\" alt=\"Image\" src=\"https://github.com/user-attachments/assets/474b979a-828c-43ae-9247-fc6964fb3636\" />\n\nPrompt: \"follow the instructions in first frame\".  \n\n\nresult from closed source wan 2.2:\n\nhttps://github.com/user-attachments/assets/71efb15a-50a3-4322-a6ce-21d42b48ac6c\n\nresults from open source wan 2.2:\n\nhttps://github.com/user-attachments/assets/c55cc242-8c01-49ea-bc56-05154dd77f2a\n\nhttps://github.com/user-attachments/assets/d16d73c3-5be7-4789-9cad-eedd98f46f4d\n\nUpdate another result from closed source wan 2.2 same prompt:\n\nhttps://github.com/user-attachments/assets/f963e4cc-8e34-430b-93e6-e836a186c0f4\n\nUpdate other results from open source wan 2.2 same prompt:\n\nhttps://github.com/user-attachments/assets/999987fa-e1d6-4721-a763-11930d728ff2\n\nhttps://github.com/user-attachments/assets/dfd7a793-e04e-49f3-b86d-f12781338b81\n",
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      "number": 49,
      "title": "Abuot Creating Perfect Looping Videos in WAN 2.2",
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      "created_at": "2025-08-01T12:16:04Z",
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      "body": "First, thank you for the new release! The improvements over 2.1 are noticeable and much appreciated.\n\nI was wondering if there’s currently an optimal setup in WAN 2.2 for producing seamless looping videos. Here’s a summary of my observations and challenges:\n\nPrevious Approach in WAN 2.1:\nGenerating a video clip, then adding empty frames for VACE to fill and bridge the starting and ending parts. While effective, this method is cumbersome, requiring multiple models and setups. Additionally, with the 81-frame generation limit, the VACE-filled frames (20–30) often disrupt the flow.\n\nAlternative Methods:\n\nUsing the Same Image for Start and End References:\nThis limits movement in the output and can still cause color differences, resulting in imperfect loops.\n\nCombining Two Clips (e.g., using the first frame of clip A as the last frame of clip B):\nThis approach yields inconsistent results due to mismatched movement, lighting, or physics between clips.\n\nCurrent Observations in WAN 2.2:\nThe “same image for start and end references” method seems to work better but still restricts movement, and color difference is still there (Cannot completely get rid of by using color match node). Adjusting the end frame strength (e.g., 0.1–0.3) improves movement but reduces loop consistency.\n\nWould love to hear thoughts or suggestions for achieving perfect loops in WAN 2.2. Thanks again for the incredible work!",
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      "number": 47,
      "title": "Question about training config",
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      "created_at": "2025-08-01T06:33:54Z",
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      "body": "Are the two experts of the 14B model trained separately?\n\nI noticed that the boundary setting is 0.9. If trained alone, does this mean that when training High-Noise Expert, only timesteps between 0.9 and 1 will be randomly selected?\n\nLooking forward to your reply.\n",
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      "body": "Is Tensor Parallel possible? Thanks. \n",
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      "number": 42,
      "title": "diffusers版本的T2V14B模型720*1280分辨率在A100-SXM-80GB上OOM",
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      "body": "如题，diffusers版本的T2V14B模型720*1280分辨率在A100-SXM-80GB上OOM了，具体是在decode部分。大家又碰见这个问题嘛，diffusers版本的怎么设置offload_model呢？",
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      "number": 39,
      "title": "[Inference issue] T2V/I2V Generation on H100 Takes Longer Than Reported",
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      "body": "I am unable to reproduce the reported inference speed for T2V and I2V generation using an H100 GPU. The generation time I observe is significantly slower than the claimed performance.\n\n```\npython generate.py  --task t2v-A14B --size 1280*720 --ckpt_dir ./Wan2.2-T2V-A14B --offload_model True --convert_model_dtype --prompt \"Two anthropomorphic cats in comfy boxing gear and bright gloves fight intensely on a spotlighted stage.\n```\n\nThis generate speed is follow this\n| Item | Time |\n| --- | --- |\n| reported | 1041 sec |\n| reproduce | 1600 sec |\n\nBut I use FA2 instead of FA3.",
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      "title": "CUDA out of memory on two NVIDIA 4090",
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      "created_at": "2025-07-30T07:28:33Z",
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      "body": "Hello, we used two NVIDIA 4090 for inference of the 5B model\npython generate.py \\\n    --task ti2v-5B \\\n    --size 1280*704 \\\n    --ckpt_dir Wan2.2-TI2V-5B \\\n    --offload_model True \\\n    --convert_model_dtype \\\n    --t5_cpu \\\n    --prompt \"A woman sitting in front of the table, and she will say hello two the camera.\"\n\nAfter the cpu loaded the model for 30 minutes, the gpu started inference. After 12 minutes of inference, OOM. I don't know how much memory is needed to run 5B.",
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      "title": "Wan 2.2 is a flop especially the 5B model",
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      "body": "Sorry but i tried all kind of prompts on the 5b models but it just does give not give quality is expected, too many deformations, either the model is badly trained or need some fixed dont get me started on the speed is extremely slow even thought it said it would be \"extremely fast for 720p model\" the vae is also a big problem it eats all my memory.\n\nPD: Some serious fixed needed. how many people (average people) can actually use the 14b model? if the small model flops is a big problem",
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      "number": 28,
      "title": "Wan2.2 T2V Report an error",
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      "body": "Wan2.2 T2V Report an error \n\nK采样器(高级)Given groups=1, weight of size 【5120, 36, 1, 2, 2】, expected input【1, 32, 21, 102, 44】 to have 36 channels, but got 32 channels instead",
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      "id": 3272761368,
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      "number": 27,
      "title": "[Feature] 多卡拆分加载以适配单卡显存不足",
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      "body": "我的机器为8*4090 24G，在使用\n`torchrun --nproc_per_node=8 generate.py --task i2v-A14B --size 1280*720 --ckpt_dir ./Wan2.2-I2V-A14B --image examples/i2v_input.JPG --dit_fsdp --t5_fsdp --ulysses_size 8 --prompt \"Summer beach vacation style, a white cat wearing sunglasses sits on a surfboard. The fluffy-furred feline gazes directly at the camera with a relaxed expression. Blurred beach scenery forms the background featuring crystal-clear waters, distant green hills, and a blue sky dotted with white clouds. The cat assumes a naturally relaxed posture, as if savoring the sea breeze and warm sunlight. A close-up shot highlights the feline's intricate details and the refreshing atmosphere of the seaside.\"\n`\n的示例指令推理时，出现oom\n请问目前是否支持拆分加载功能？如果支持，能否提供具体的配置方法或者示例脚本？\n如果暂不支持，是否有计划在后续版本中加入相关功能？\n感谢您的支持 期待回复",
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      "title": "Best 'num_frames' setting",
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      "body": "For Wan2.2, what is the best 'num_frames' setting in inference times? I guess it is '81' for T2V-A14B and I2V-A14B, and '121' for TI2V-5B?\n\nWhat if I input a large 'num_frames' like 161? Will the video quality degrade significantly?",
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      "title": "Phantom for 2.2",
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      "body": "Will there be a new version of the Phantom model based on 2.2 14B version? It was one of the most fun things to do with the older version, so I hope it makes a return at even better quality!",
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      "title": "Would it be possible to replace one of the MoE model into a LoRA?",
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      "body": "Maintaining two full 14 B-parameter models with identical architectures—one for high-noise and one for low-noise denoising—is very costly in terms of storage and memory loading. Would it be possible to convert one of the MoE models into a LoRA adapter of the other for distribution? I realize this would require applying the LoRA weights differently at each timestep, but it would be far more efficient for both storage and offloading.",
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      "body": "The discussion in the group and on Bilibili mainly reflected the following points (hot comments excerpts):\n(1) [Expectations of netizens] Originally, it was expected that Wan2.2 would have better effects and be compatible with Wan2.1's LoRA and VACE. There is also the same hardware configuration~\n\n(2) [Ecosystem compatibility]: Now, after the test results of the UP master on Bilibili:\nWan2.2's 14B workflow uses dual models and two samplers, and the previous Wan2.1 ecosystem seems to be incompatible.\nMany LoRA and VACE are not universal.\n\n(3) [Hardware requirements]: Now, it has higher requirements for graphics card hardware:\nGroup members who could barely play Wan2.1 14B before can no longer run Wan2.2 14B dual models.\n\n(4) [Generation speed]: Wan2.2 14B is slower than Wan2.1 14B, quite slow.\n\n(5) [Capability comparison]: Wan2.2 14B has enhanced camera movement and dynamics.\n\nSummary: Wan2.2 14B has improved camera movement and dynamics compared to Wan2.1 14B, but requires higher hardware and time costs and is not compatible with the Wan2.1 ecosystem.\n\nThe above are only some of the comments and test results of netizens, for reference only (not personal opinions).\n\n",
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      "id": 3271894576,
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      "number": 18,
      "title": "diffuser inference code wrong！",
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      "created_at": "2025-07-29T03:40:42Z",
      "updated_at": "2025-07-29T08:59:08Z",
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      "body": "When I run the following code from https://huggingface.co/Wan-AI/Wan2.2-T2V-A14B-Diffusers\n\n```\nimport torch\nimport numpy as np\nfrom diffusers import WanPipeline, AutoencoderKLWan\nfrom diffusers.utils import export_to_video, load_image\n\ndtype = torch.bfloat16\ndevice = \"cuda:2\"\nvae = AutoencoderKLWan.from_pretrained(\"Wan-AI/Wan2.2-T2V-A14B-Diffusers\", subfolder=\"vae\", torch_dtype=torch.float32)\npipe = WanPipeline.from_pretrained(\"Wan-AI/Wan2.2-T2V-A14B-Diffusers\", vae=vae, torch_dtype=dtype)\npipe.to(device)\n\nheight = 720\nwidth = 1280\n\nprompt = \"Two anthropomorphic cats in comfy boxing gear and bright gloves fight intensely on a spotlighted stage.\"\nnegative_prompt = \"色调艳丽，过曝，静态，细节模糊不清，字幕，风格，作品，画作，画面，静止，整体发灰，最差质量，低质量，JPEG压缩残留，丑陋的，残缺的，多余的手指，画得不好的手部，画得不好的脸部，畸形的，毁容的，形态畸形的肢体，手指融合，静止不动的画面，杂乱的背景，三条腿，背景人很多，倒着走\"\noutput = pipe(\n    prompt=prompt,\n    negative_prompt=negative_prompt,\n    height=height,\n    width=width,\n    num_frames=81,\n    guidance_scale=4.0,\n    guidance_scale_2=3.0,\n    num_inference_steps=40,\n).frames[0]\nexport_to_video(output, \"t2v_out.mp4\", fps=16)\n\n```\n\nI got the following error:\n\nTypeError: WanPipeline.__call__() got an unexpected keyword argument 'guidance_scale_2'",
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      "id": 3271877324,
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      "number": 17,
      "title": "are these lines hard code ?",
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      "body": "https://github.com/Wan-Video/Wan2.2/blob/4e24c3d54fc3f702fc9f7feace78dbfa5fc28d1c/wan/image2video.py#L282\n\nand \n\nL289 / L319\n\nlooks like should be calculated by `frame_num ` and vae stride ?",
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      "title": "请问如何使用论文中展示的\"Advanced Video Editing\"的功能？",
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      "id": 3271705003,
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      "number": 15,
      "title": "Training timestep distribution imbalance between experts",
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      "created_at": "2025-07-29T02:06:29Z",
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      "body": "Hi, I am trying to train Wan2.2, and I have a pipeline working, but using the regular logit-normal distribution with `m=0` and `s=1` described in [Scaling Rectified Flow Transformers for High-Resolution Image Synthesis](https://arxiv.org/abs/2403.03206) and the boundary described in [configs/wan_i2v_A14B.py](https://github.com/Wan-Video/Wan2.2/blob/main/wan/configs/wan_i2v_A14B.py) results in less than 6% of the training steps being allocated to the high-noise expert. Is this the correct distribution? Or is a different logit-normal distribution necessary?\n\nThank you!\n",
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      "title": "Question about MOE architecture",
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      "created_at": "2025-07-29T00:55:59Z",
      "updated_at": "2025-11-26T21:52:54Z",
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      "body": "Why not replace FFN with MOE like normal LLMs, but use two transformers？",
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      "title": "16/24 fps clarification",
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      "created_at": "2025-07-29T00:24:20Z",
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      "body": "Hi, should the new 2.2 14b i2v model be used at 16 or 24 fps? There seems to be some confusion since the new 2.2 5b model uses 24 fps.",
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      "id": 3271272518,
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      "number": 11,
      "title": "Finetuning Wan2.2 I2V model",
      "user": {
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      "created_at": "2025-07-28T20:41:52Z",
      "updated_at": "2025-09-24T03:29:07Z",
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      "body": "Hi, \nThanks for the amazing new model! \nI have a question about fine-tuning the Wan2.2 model with the \"two-step\" MOE architecture.\nIf I want to add a new condition feature, can I just inject the information into both two modules and finetuing the models at the same time?",
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      "title": "Does this model supports video generation using multiple keyframes? Or start/end frames?",
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      "number": 8,
      "title": "Is the 5B model distilled from the 14B model via video data generation?",
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      "body": "I am curious why 14B model does not use the new high compressed VAE. \n\nHow was the 5B model trained ? Is is something like that : \n- step 1:  train a image model with 5B , then image and video joint training \n- step 2:  distill video data  from the 14B model with designed prompts for motions \n- step 3: image-video joint training with the generated videos and real videos? ",
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      "body": "Does Wan 2.2 support most 2.1 loras? ",
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      "title": "Will 14B model with 2.2 VAE be released?",
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      "body": "https://github.com/Wan-Video/Wan2.2/blob/main/wan/configs/wan_t2v_A14B.py#L17\n\n```\n# vae\nt2v_A14B.vae_checkpoint = 'Wan2.1_VAE.pth'\nt2v_A14B.vae_stride = (4, 8, 8)\n```\n\nDiT + 2.2 VAE will be 3-5x faster than 2.1 VAE.",
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      "title": "Long video generate?",
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      "body": "Great work! What is the maximum duration supported for video generation?\n",
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      "body": "群快满了，加V拉：twitchdgg",
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      "body": "Hi!\n\nCan you elaborate a bit the training loop implementation? :)\n\nOf course, not the official one, which is impossible, but on how to tune a lora, for example, with diffusers?",
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      "body": "Are these new models working with the existing VACE or are new VACE models needed?",
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      "title": "fix: flash-attn-3 error",
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      "body": "Hi Wan Team!\r\n\r\nRecently, I use flash-attention-3 to accelerate inference, but I meet the error below:\r\n\r\n```text\r\n...\r\n[rank7]:   File \"/codespace/text-or-image-to-video-model/src/wan/modules/model.py\", line 243, in forward\r\n[rank7]:     y = self.self_attn(\r\n[rank7]:         ^^^^^^^^^^^^^^^\r\n[rank7]:   File \"/usr/local/lib/python3.12/dist-packages/torch/nn/modules/module.py\", line 1751, in _wrapped_call_impl\r\n[rank7]:     return self._call_impl(*args, **kwargs)\r\n[rank7]:            ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\r\n[rank7]:   File \"/usr/local/lib/python3.12/dist-packages/torch/nn/modules/module.py\", line 1762, in _call_impl\r\n[rank7]:     return forward_call(*args, **kwargs)\r\n[rank7]:            ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\r\n[rank7]:   File \"/codespace/text-or-image-to-video-model/src/wan/distributed/sequence_parallel.py\", line 165, in sp_attn_forward\r\n[rank7]:     x = distributed_attention(\r\n[rank7]:         ^^^^^^^^^^^^^^^^^^^^^^\r\n[rank7]:   File \"/codespace/text-or-image-to-video-model/src/wan/distributed/ulysses.py\", line 37, in distributed_attention\r\n[rank7]:     x = flash_attention(\r\n[rank7]:         ^^^^^^^^^^^^^^^^\r\n[rank7]:   File \"/codespace/text-or-image-to-video-model/src/wan/modules/attention.py\", line 110, in flash_attention\r\n[rank7]:     deterministic=deterministic)[0].unflatten(0, (b, lq))\r\n[rank7]:                                     ^^^^^^^^^^^^^^^^^^^^^\r\n[rank7]:   File \"/usr/local/lib/python3.12/dist-packages/torch/_tensor.py\", line 1432, in unflatten\r\n[rank7]:     return super().unflatten(dim, sizes)\r\n[rank7]:            ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\r\n[rank7]: RuntimeError: unflatten: Provided sizes [1, 75600] don't multiply up to the size of dim 0 (5) in the input tensor\r\n...\r\n```\r\n\r\nObviously, it's an attention error. I check the attention code in `wan/modules/attention.py`, and find a controversial code:\r\n\r\n```python\r\nx = flash_attn_interface.flash_attn_varlen_func(\r\n    q=q,\r\n    k=k,\r\n    v=v,\r\n    cu_seqlens_q=torch.cat([q_lens.new_zeros([1]), q_lens]).cumsum(\r\n        0, dtype=torch.int32).to(q.device, non_blocking=True),\r\n    cu_seqlens_k=torch.cat([k_lens.new_zeros([1]), k_lens]).cumsum(\r\n        0, dtype=torch.int32).to(q.device, non_blocking=True),\r\n    seqused_q=None,\r\n    seqused_k=None,\r\n    max_seqlen_q=lq,\r\n    max_seqlen_k=lk,\r\n    softmax_scale=softmax_scale,\r\n    causal=causal,\r\n    deterministic=deterministic)[0].unflatten(0, (b, lq))\r\n```\r\n\r\nI check the source code of flash-attention-3, the function `flash_attn_interface.flash_attn_varlen_func()` only return `tuple` under [the following condition](https://github.com/Dao-AILab/flash-attention/blob/main/flash_attn/flash_attn_interface.py#L968):\r\n\r\n```python\r\nreturn out if not return_softmax else (out, softmax_lse, S_dmask)\r\n```\r\n\r\n`return_softmax` only works under [the following condition](https://github.com/Dao-AILab/flash-attention/blob/main/flash_attn/flash_attn_interface.py#L950):\r\n\r\n```python\r\nreturn_softmax=return_softmax and dropout_p > 0,\r\n```\r\n\r\nHowever, these two parameters `return_softmax` and `dropout_p` are not included in your code.\r\n\r\nSo, we only need `flash_attn_interface.flash_attn_varlen_func()` return `out`, which is a `torch.Tensor`.\r\n\r\nIt works for me.",
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      "title": "[ROCM][AMD CDNA GPU]Advise wan2.2-14B users to tune frame-num value for better using HBM",
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      "body": "HI @yitongh  @kelseyee  @WanX-Video-1  \r\n\r\nGlad to see Wan2.2 models have been released. Thanks for your excellent work on Wan2.2. \r\n\r\nI have tested all the wan2.2 models on AMD CDNA3 device, and they can run successfully. I found the default frame number is only 81 , about 5 sec video clips, which is too short for AMD CDNA3 device. My test shows that up to 321 frames/ 20 sec /720P  video clips can be generated on a single CDNA3 device.\r\n\r\nThis PR is to advise users to tune frame number option on AMD CDNA device, to fully utilize HBM resource and generate longer video clips. \r\n\r\nThanks ",
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