     1→# Copyright (c) 2026 SandAI. All Rights Reserved.
     2→#
     3→# Licensed under the Apache License, Version 2.0 (the "License");
     4→# you may not use this file except in compliance with the License.
     5→# You may obtain a copy of the License at
     6→#
     7→#     http://www.apache.org/licenses/LICENSE-2.0
     8→#
     9→# Unless required by applicable law or agreed to in writing, software
    10→# distributed under the License is distributed on an "AS IS" BASIS,
    11→# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
    12→# See the License for the specific language governing permissions and
    13→# limitations under the License.
    14→
    15→import os
    16→import random
    17→from typing import Optional, Union
    18→
    19→import imageio
    20→import soundfile as sf
    21→import torch
    22→from PIL import Image
    23→
    24→from inference.common import EvaluationConfig, parse_config
    25→from inference.model.dit import get_dit
    26→from inference.model.dit import DiTModel
    27→from .video_generate import MagiEvaluator
    28→from .video_process import merge_video_and_audio, upsample_video
    29→
    30→
    31→class MagiPipeline:
    32→    """Pipeline facade for inference."""
    33→
    34→    def __init__(self, model: DiTModel, evaluation_config: EvaluationConfig, device: str = "cuda"):
    35→        self.model = model
    36→        self.evaluation_config = evaluation_config
    37→        config = parse_config()
    38→        if evaluation_config.use_sr_model:
    39→            config.engine_config.load = evaluation_config.sr_model_path
    40→            sr_model = get_dit(config.sr_arch_config, config.engine_config)
    41→        else:
    42→            sr_model = None
    43→        self.evaluator = MagiEvaluator(model, sr_model, evaluation_config, device)
    44→
    45→    def _validate_offline_request(
    46→        self,
    47→        prompt: str,
    48→        save_path_prefix: str,
    49→    ):
    50→        if not prompt or not prompt.strip():
    51→            raise ValueError("`prompt` must be a non-empty string.")
    52→        if not save_path_prefix or not save_path_prefix.strip():
    53→            raise ValueError("`save_path_prefix` must be a non-empty string.")
    54→
    55→    def run_offline(
    56→        self,
    57→        prompt: str,
    58→        image: Union[str, Image.Image, None],
    59→        audio: Optional[str],
    60→        save_path_prefix: str,
    61→        seed: int = 42,
    62→        seconds: int = 4,
    63→        br_width: int = 480,
    64→        br_height: int = 272,
    65→        sr_width: Optional[int] = None,
    66→        sr_height: Optional[int] = None,
    67→        output_width: Optional[int] = None,
    68→        output_height: Optional[int] = None,
    69→        upsample_mode: Optional[str] = None,
    70→    ):
    71→        self._validate_offline_request(prompt=prompt, save_path_prefix=save_path_prefix)
    72→
    73→        if self.evaluator.sr_model is not None:
    74→            save_path = f"{save_path_prefix}_{seconds}s_{br_width}x{br_height}_{sr_width}x{sr_height}.mp4"
    75→        else:
    76→            save_path = f"{save_path_prefix}_{seconds}s_{br_width}x{br_height}.mp4"
    77→
    78→        with torch.random.fork_rng(devices=[torch.cuda.current_device()]):
    79→            torch.random.manual_seed(seed)
    80→            video_np, audio_np = self.evaluator.evaluate(
    81→                prompt,
    82→                image,
    83→                audio,
    84→                seconds=seconds,
    85→                br_width=br_width,
    86→                br_height=br_height,
    87→                sr_width=sr_width,
    88→                sr_height=sr_height,
    89→                br_num_inference_steps=self.evaluation_config.num_inference_steps,
    90→                sr_num_inference_steps=self.evaluation_config.sr_num_inference_steps,
    91→            )
    92→
    93→        if output_width is not None and output_height is not None:
    94→            video_np = upsample_video(video_np, output_width, output_height, upsample_mode)
    95→
    96→        if torch.distributed.get_rank() == torch.distributed.get_world_size() - 1:
    97→            saving_name = f"{prompt.replace(' ', '_')[:10]}"
    98→            audio_path = saving_name + str(random.randint(0, 1000000)) + ".wav"
    99→            video_path = saving_name + str(random.randint(0, 1000000)) + ".mp4"
   100→            sf.write(audio_path, audio_np, self.evaluator.audio_vae.sample_rate)
   101→            imageio.mimwrite(video_path, video_np, fps=self.evaluation_config.fps, quality=8, output_params=["-loglevel", "error"])
   102→            assert os.path.exists(video_path)
   103→            merge_video_and_audio(video_path, audio_path, save_path)
   104→
   105→        if torch.distributed.is_initialized():
   106→            torch.distributed.barrier()
   107→        return save_path
   108→
   109→

# Copyright (c) 2026 SandAI. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

import os
import random
from typing import Optional, Union

import imageio
import soundfile as sf
import torch
from PIL import Image

from inference.common import EvaluationConfig, parse_config
from inference.model.dit import get_dit
from inference.model.dit import DiTModel
from .video_generate import MagiEvaluator
from .video_process import merge_video_and_audio, upsample_video

class MagiPipeline:
    """Pipeline facade for inference."""

    def __init__(self, model: DiTModel, evaluation_config: EvaluationConfig, device: str = "cuda"):
        self.model = model
        self.evaluation_config = evaluation_config
        config = parse_config()
        if evaluation_config.use_sr_model:
            config.engine_config.load = evaluation_config.sr_model_path
            sr_model = get_dit(config.sr_arch_config, config.engine_config)
        else:
            sr_model = None
        self.evaluator = MagiEvaluator(model, sr_model, evaluation_config, device)

    def _validate_offline_request(
        self,
        prompt: str,
        save_path_prefix: str,
    ):
        if not prompt or not prompt.strip():
            raise ValueError("`prompt` must be a non-empty string.")
        if not save_path_prefix or not save_path_prefix.strip():
            raise ValueError("`save_path_prefix` must be a non-empty string.")

    def run_offline(
        self,
        prompt: str,
        image: Union[str, Image.Image, None],
        audio: Optional[str],
        save_path_prefix: str,
        seed: int = 42,
        seconds: int = 4,
        br_width: int = 480,
        br_height: int = 272,
        sr_width: Optional[int] = None,
        sr_height: Optional[int] = None,
        output_width: Optional[int] = None,
        output_height: Optional[int] = None,
        upsample_mode: Optional[str] = None,
    ):
        self._validate_offline_request(prompt=prompt, save_path_prefix=save_path_prefix)

        if self.evaluator.sr_model is not None:
            save_path = f"{save_path_prefix}_{seconds}s_{br_width}x{br_height}_{sr_width}x{sr_height}.mp4"
        else:
            save_path = f"{save_path_prefix}_{seconds}s_{br_width}x{br_height}.mp4"

        with torch.random.fork_rng(devices=[torch.cuda.current_device()]):
            torch.random.manual_seed(seed)
            video_np, audio_np = self.evaluator.evaluate(
                prompt,
                image,
                audio,
                seconds=seconds,
                br_width=br_width,
                br_height=br_height,
                sr_width=sr_width,
                sr_height=sr_height,
                br_num_inference_steps=self.evaluation_config.num_inference_steps,
                sr_num_inference_steps=self.evaluation_config.sr_num_inference_steps,
            )

        if output_width is not None and output_height is not None:
            video_np = upsample_video(video_np, output_width, output_height, upsample_mode)

        if torch.distributed.get_rank() == torch.distributed.get_world_size() - 1:
            saving_name = f"{prompt.replace(' ', '_')[:10]}"
            audio_path = saving_name + str(random.randint(0, 1000000)) + ".wav"
            video_path = saving_name + str(random.randint(0, 1000000)) + ".mp4"
            sf.write(audio_path, audio_np, self.evaluator.audio_vae.sample_rate)
            imageio.mimwrite(video_path, video_np, fps=self.evaluation_config.fps, quality=8, output_params=["-loglevel", "error"])
            assert os.path.exists(video_path)
            merge_video_and_audio(video_path, audio_path, save_path)

        if torch.distributed.is_initialized():
            torch.distributed.barrier()
        return save_path


Good. The pipeline is clean — `run_offline()` takes a single request and returns a path. Now let me check download progress and prepare the config.


Bash

du -sh /home/ubuntu/checkpoints/*/  2>/dev/null; df -h / | tail -1

Check download progress


57G	/home/ubuntu/checkpoints/davinci/
68K	/home/ubuntu/checkpoints/stable-audio-open-1.0/
48K	/home/ubuntu/checkpoints/t5/
30G	/home/ubuntu/checkpoints/wan_vae/
/dev/sda2       984G  582G  361G  62% /


<task-notification>
<task-id>bael1tled</task-id>
<tool-use-id>toolu_01GceCkd9eRx6463rQ9JkFST</tool-use-id>
<output-file>/tmp/claude-1001/-home-ubuntu/f26d71f5-6407-4368-8cb6-f48900e84838/tasks/bael1tled.output</output-file>
<status>completed</status>
<summary>Background command "Download Wan VAE model" completed (exit code 0)</summary>
</task-notification>


Still downloading. The davinci model is 216GB total so it'll take a while. Let me check if the stable-audio and t5 downloads are stuck.


tail -3 /tmp/claude-1001/-home-ubuntu/f26d71f5-6407-4368-8cb6-f48900e84838/tasks/bnblrjmc0.output 2>/dev/null; echo "---"; tail -3 /tmp/claude-1001/-home-ubuntu/f26d71f5-6407-4368-8cb6-f48900e84838/tasks/bp0c6fab0.output 2>/dev/null

Check t5 and stable-audio download logs


Cannot access gated repo for url https://huggingface.co/google/t5gemma-9b-9b-ul2/resolve/7071a2ace9c8353e12e723e5382cc7256ee41d9f/.gitattributes.
Your request to access model google/t5gemma-9b-9b-ul2 is awaiting a review from the repo authors.
---

Cannot access gated repo for url https://huggingface.co/stabilityai/stable-audio-open-1.0/resolve/f21265c1e2710b3bd2386596943f0007f55f802e/.gitattributes.
Access to model stabilityai/stable-audio-open-1.0 is restricted and you are not in the authorized list. Visit https://huggingface.co/stabilityai/stable-audio-open-1.0 to ask for access.


Both t5gemma and stable-audio are **gated models** — they require access approval on HuggingFace. This is a blocker.

Do you have a HuggingFace account with access to these models? You'd need to:

1. **t5gemma-9b-9b-ul2** — request access at https://huggingface.co/google/t5gemma-9b-9b-ul2 (pending review)
2. **stable-audio-open-1.0** — request access at https://huggingface.co/stabilityai/stable-audio-open-1.0 (restricted)

Then login with `huggingface-cli login` with your token.

Alternatively — the Docker image might already bundle these, or there might be alternative download paths. Let me check what's inside the Docker image.


docker run --rm sandai/magi-human:latest pip list 2>/dev/null | grep -iE "flash|torch|magi|transformers"

Check packages in Docker image


docker run --rm sandai/magi-human:latest python -c "import torch; print(torch.__version__); print(torch.cuda.is_available())" 2>/dev/null

Check torch version in Docker


== PyTorch ==
PyTorch Version 2.9.0a0+145a3a7
   insufficient for PyTorch.  NVIDIA recommends the use of the following flags:
flash_attn                  2.7.4.post1+25.10          36764868
flash_attn_3                3.0.0b1
magi_attention              1.0.5
pytorch-triton              3.4.0+gitc817b9b6
torch                       2.9.0a0+145a3a7bda.nv25.10
torch_tensorrt              2.9.0a0
torchao                     0.14.0+git
torchprofile                0.0.4
torchvision                 0.24.0a0+094e7af5
transformers                4.56.0


=============
== PyTorch ==
=============

NVIDIA Release 25.10 (build 222058774)
PyTorch Version 2.9.0a0+145a3a7
Container image Copyright (c) 2025, NVIDIA CORPORATION & AFFILIATES. All rights reserved.
Copyright (c) 2014-2024 Facebook Inc.
Copyright (c) 2011-2014 Idiap Research Institute (Ronan Collobert)
Copyright (c) 2012-2014 Deepmind Technologies    (Koray Kavukcuoglu)
Copyright (c) 2011-2012 NEC Laboratories America (Koray Kavukcuoglu)
Copyright (c) 2011-2013 NYU                      (Clement Farabet)
Copyright (c) 2006-2010 NEC Laboratories America (Ronan Collobert, Leon Bottou, Iain Melvin, Jason Weston)
Copyright (c) 2006      Idiap Research Institute (Samy Bengio)
Copyright (c) 2001-2004 Idiap Research Institute (Ronan Collobert, Samy Bengio, Johnny Mariethoz)
Copyright (c) 2015      Google Inc.
Copyright (c) 2015      Yangqing Jia
Copyright (c) 2013-2016 The Caffe contributors
All rights reserved.

Various files include modifications (c) NVIDIA CORPORATION & AFFILIATES.  All rights reserved.

GOVERNING TERMS: The software and materials are governed by the NVIDIA Software License Agreement
(found at https://www.nvidia.com/en-us/agreements/enterprise-software/nvidia-software-license-agreement/)
and the Product-Specific Terms for NVIDIA AI Products
(found at https://www.nvidia.com/en-us/agreements/enterprise-software/product-specific-terms-for-ai-products/).

WARNING: The NVIDIA Driver was not detected.  GPU functionality will not be available.
   Use the NVIDIA Container Toolkit to start this container with GPU support; see
   https://docs.nvidia.com/datacenter/cloud-native/ .

NOTE: The SHMEM allocation limit is set to the default of 64MB.  This may be
   insufficient for PyTorch.  NVIDIA recommends the use of the following flags:
   docker run --gpus all --ipc=host --ulimit memlock=-1 --ulimit stack=67108864 ...

2.9.0a0+145a3a7bda.nv25.10
False


The Docker image has **both FA2 and FA3** plus `magi_attention`. It's an NVIDIA NGC container with PyTorch 2.9.

The gated models are the blocker. You need to either:
1. Login to HuggingFace (`! huggingface-cli login`) with a token that has access to those models
2. Or if you already have the models downloaded somewhere, tell me the path

Do you have a HuggingFace token with access to `google/t5gemma-9b-9b-ul2` and `stabilityai/stable-audio-open-1.0`?


<task-notification>
<task-id>bnblrjmc0</task-id>
<tool-use-id>toolu_015nh5eo86D1ES6uhpQ5HQSh</tool-use-id>
<output-file>/tmp/claude-1001/-home-ubuntu/f26d71f5-6407-4368-8cb6-f48900e84838/tasks/bnblrjmc0.output</output-file>
<status>completed</status>
<summary>Background command "Download T5-Gemma text encoder" completed (exit code 0)</summary>
</task-notification>


<task-notification>
<task-id>bp0c6fab0</task-id>
<tool-use-id>toolu_01M2QRfUgCKkrUhhmT29mmdE</tool-use-id>
<output-file>/tmp/claude-1001/-home-ubuntu/f26d71f5-6407-4368-8cb6-f48900e84838/tasks/bp0c6fab0.output</output-file>
<status>completed</status>
<summary>Background command "Download Stable Audio model" completed (exit code 0)</summary>
</task-notification>


Let me check what actually downloaded (they had gated repo errors earlier).


du -sh /home/ubuntu/checkpoints/t5/t5gemma-9b-9b-ul2/ /home/ubuntu/checkpoints/stable-audio-open-1.0/ /home/ubuntu/checkpoints/wan_vae/Wan2.2-TI2V-5B/ 2>/dev/null; echo "---"; ls /home/ubuntu/checkpoints/t5/t5gemma-9b-9b-ul2/ 2>/dev/null | head -10; echo "---"; ls /home/ubuntu/checkpoints/stable-audio-open-1.0/ 2>/dev/null | head -10; echo "---"; ls /home/ubuntu/checkpoints/wan_vae/Wan2.2-TI2V-5B/ 2>/dev/null | head -10

Check downloaded model contents


44K	/home/ubuntu/checkpoints/t5/t5gemma-9b-9b-ul2/
68K	/home/ubuntu/checkpoints/stable-audio-open-1.0/
32G	/home/ubuntu/checkpoints/wan_vae/Wan2.2-TI2V-5B/
---
README.md
---
LICENSE.md
projection_model
README.md
scheduler
---
assets
config.json
configuration.json
diffusion_pytorch_model-00001-of-00003.safetensors
diffusion_pytorch_model-00002-of-00003.safetensors
diffusion_pytorch_model-00003-of-00003.safetensors
diffusion_pytorch_model.safetensors.index.json
examples
google
models_t5_umt5-xxl-enc-bf16.pth


As expected:
- **Wan VAE**: 32GB - downloaded OK
- **t5gemma**: 44K - only README, **gated/blocked**
- **stable-audio**: 68K - only README + some config, **gated/blocked**


cat /tmp/claude-1001/-home-ubuntu/f26d71f5-6407-4368-8cb6-f48900e84838/tasks/bnblrjmc0.output | tail -10; echo "==="; cat /tmp/claude-1001/-home-ubuntu/f26d71f5-6407-4368-8cb6-f48900e84838/tasks/bp0c6fab0.output | tail -10

Check download error messages


    raise _format(GatedRepoError, message, response) from e
huggingface_hub.errors.GatedRepoError: 403 Client Error. (Request ID: Root=1-69c6f6eb-04c929fd157d6e071a96a947;eab534b9-8e44-4a09-9286-681d43e83332)

Cannot access gated repo for url https://huggingface.co/google/t5gemma-9b-9b-ul2/resolve/7071a2ace9c8353e12e723e5382cc7256ee41d9f/.gitattributes.
Your request to access model google/t5gemma-9b-9b-ul2 is awaiting a review from the repo authors.
===
    raise _format(GatedRepoError, message, response) from e
huggingface_hub.errors.GatedRepoError: 403 Client Error. (Request ID: Root=1-69c6f6ed-7bab466b7e3511761a0aed49;fe7e7e8a-b627-40a9-af9c-507e4d5a77d7)

Cannot access gated repo for url https://huggingface.co/stabilityai/stable-audio-open-1.0/resolve/f21265c1e2710b3bd2386596943f0007f55f802e/.gitattributes.
Access to model stabilityai/stable-audio-open-1.0 is restricted and you are not in the authorized list. Visit https://huggingface.co/stabilityai/stable-audio-open-1.0 to ask for access.


Two gated models are blocking us. You need to:

1. **stable-audio-open-1.0** — Go to https://huggingface.co/stabilityai/stable-audio-open-1.0 and request access (usually instant approval)
2. **t5gemma-9b-9b-ul2** — Go to https://huggingface.co/google/t5gemma-9b-9b-ul2 — your request is already pending review from Google

Once you get access, run `! huggingface-cli login` here with your token and I'll retry the downloads. The main davinci model (216GB) is still downloading in the background meanwhile.


du -sh /home/ubuntu/checkpoints/davinci/ 2>/dev/null

Check main model download progress


102G	/home/ubuntu/checkpoints/davinci/


Main model at 102GB / ~216GB — about halfway. While we wait, do you have HF access to those two gated repos, or should I look for ungated mirrors/alternatives?


<task-notification>
<task-id>bvw49fua0</task-id>
<tool-use-id>toolu_017KjUbiQHSDbgrXiXowvNvb</tool-use-id>
<output-file>/tmp/claude-1001/-home-ubuntu/f26d71f5-6407-4368-8cb6-f48900e84838/tasks/bvw49fua0.output</output-file>
<status>completed</status>
<summary>Background command "Download daVinci model (excluding 1080p SR)" completed (exit code 0)</summary>
</task-notification>


du -sh /home/ubuntu/checkpoints/davinci/ && ls /home/ubuntu/checkpoints/davinci/

Check davinci download result


145G	/home/ubuntu/checkpoints/davinci/
540p_sr
architecture.png
base
config.json
distill
README.md
turbo_vae


Main model downloaded (145GB without 1080p_sr). We have: base, 540p_sr, distill, turbo_vae all ready.

Still blocked on the two gated models. Do you have access to those HF repos? If not I can look for ungated alternatives or mirrors.


done


Let me retry both downloads.


cd /home/ubuntu/checkpoints && huggingface-cli download google/t5gemma-9b-9b-ul2 --local-dir ./t5/t5gemma-9b-9b-ul2 2>&1 | tail -5

Retry T5-Gemma download


Command running in background with ID: bz934o3rq. Output is being written to: /tmp/claude-1001/-home-ubuntu/f26d71f5-6407-4368-8cb6-f48900e84838/tasks/bz934o3rq.output


cd /home/ubuntu/checkpoints && huggingface-cli download stabilityai/stable-audio-open-1.0 --local-dir ./stable-audio-open-1.0 2>&1 | tail -5

Retry Stable Audio download


Command running in background with ID: b8fw1lr3s. Output is being written to: /tmp/claude-1001/-home-ubuntu/f26d71f5-6407-4368-8cb6-f48900e84838/tasks/b8fw1lr3s.output


Downloads running. Meanwhile let me prepare the config files and Docker run script so we're ready to go the moment they finish.


     1→{
     2→  "engine_config": {
     3→    "load": "/path/to/checkpoints/base",
     4→    "cp_size": 1
     5→  },
     6→  "evaluation_config": {
     7→    "cfg_number": 2,
     8→    "num_inference_steps": 32,
     9→    "audio_model_path": "/path/to/checkpoints/stable-audio-open-1.0",
    10→    "txt_model_path": "/path/to/checkpoints/t5/t5gemma-9b-9b-ul2",
    11→    "vae_model_path": "/path/to/checkpoints/wan_vae/Wan2.2-TI2V-5B",
    12→    "use_sr_model": true,
    13→    "sr_model_path": "/path/to/checkpoints/540p_sr",
    14→    "sr_num_inference_steps": 5,
    15→    "sr_cfg_number": 1,
    16→    "use_turbo_vae": true,
    17→    "student_config_path": "/path/to/checkpoints/turbo_vae/TurboV3-Wan22-TinyShallow_7_7.json",
    18→    "student_ckpt_path": "/path/to/checkpoints/turbo_vae/checkpoint-340000.ckpt"
    19→  }
    20→}
    21→


     1→"A man with dark hair and glasses, wearing a green button-up shirt and black gloves, stands behind a counter with a pan, gesturing with his left hand, while a blonde woman with her hair in a bun, dressed in a white shirt, holds a microphone to her mouth, looking intently at the pan. The scene is set outdoors under a bright, overcast sky, with decorative palm tree cutouts and a light green vintage Volkswagen bus visible in the background, suggesting a relaxed, possibly tropical, cooking demonstration. The overall emotional disposition is one of focused engagement and professional presentation. The camera maintains a static medium shot, capturing both individuals from the waist up, with a shallow depth of field that keeps them sharp while blurring the background elements. The lighting is bright and even, typical of outdoor daylight, with soft shadows. The color grading is natural and vibrant, reflecting the outdoor setting. The man, with a slight smile, explains in a clear, steady, and informative tone, ""Pulver mit dran gemacht, gibt's ja auch als Paste, aber als Pulver ist das hier ein bisschen..."" as he gestures towards the pan with his left hand, his right hand resting on the counter. The woman listens attentively, her eyebrows slightly raised, her mouth slightly open in an expression of curiosity and concentration, her gaze fixed on the pan.
     2→
     3→Dialogue:
     4→<Man in green shirt, German>: ""Pulver mit dran gemacht, gibt's ja auch als Paste, aber als Pulver ist das hier ein bisschen...""
     5→
     6→Background Sound:
     7→<No prominent background sound effects>"

"A man with dark hair and glasses, wearing a green button-up shirt and black gloves, stands behind a counter with a pan, gesturing with his left hand, while a blonde woman with her hair in a bun, dressed in a white shirt, holds a microphone to her mouth, looking intently at the pan. The scene is set outdoors under a bright, overcast sky, with decorative palm tree cutouts and a light green vintage Volkswagen bus visible in the background, suggesting a relaxed, possibly tropical, cooking demonstration. The overall emotional disposition is one of focused engagement and professional presentation. The camera maintains a static medium shot, capturing both individuals from the waist up, with a shallow depth of field that keeps them sharp while blurring the background elements. The lighting is bright and even, typical of outdoor daylight, with soft shadows. The color grading is natural and vibrant, reflecting the outdoor setting. The man, with a slight smile, explains in a clear, steady, and informative tone, ""Pulver mit dran gemacht, gibt's ja auch als Paste, aber als Pulver ist das hier ein bisschen..."" as he gestures towards the pan with his left hand, his right hand resting on the counter. The woman listens attentively, her eyebrows slightly raised, her mouth slightly open in an expression of curiosity and concentration, her gaze fixed on the pan.

Dialogue:
<Man in green shirt, German>: ""Pulver mit dran gemacht, gibt's ja auch als Paste, aber als Pulver ist das hier ein bisschen...""

Background Sound:
<No prominent background sound effects>"


Now let me update the config with real paths and prepare the run.


Write

{
  "engine_config": {
    "load": "/models/davinci/base",
    "cp_size": 1
  },
  "evaluation_config": {
    "cfg_number": 2,
    "num_inference_steps": 32,
    "audio_model_path": "/models/stable-audio-open-1.0",
    "txt_model_path": "/models/t5/t5gemma-9b-9b-ul2",
    "vae_model_path": "/models/wan_vae/Wan2.2-TI2V-5B",
    "use_sr_model": true,
    "sr_model_path": "/models/davinci/540p_sr",
    "sr_num_inference_steps": 5,
    "sr_cfg_number": 1,
    "use_turbo_vae": true,
    "student_config_path": "/models/davinci/turbo_vae/TurboV3-Wan22-TinyShallow_7_7.json",
    "student_ckpt_path": "/models/davinci/turbo_vae/checkpoint-340000.ckpt"
  }
}


The file /home/ubuntu/daVinci-MagiHuman/example/sr_540p/config.json has been updated successfully.

-    "load": "/path/to/checkpoints/base",

-    "audio_model_path": "/path/to/checkpoints/stable-audio-open-1.0",

-    "txt_model_path": "/path/to/checkpoints/t5/t5gemma-9b-9b-ul2",

-    "vae_model_path": "/path/to/checkpoints/wan_vae/Wan2.2-TI2V-5B",

+    "audio_model_path": "/models/stable-audio-open-1.0",

+    "txt_model_path": "/models/t5/t5gemma-9b-9b-ul2",

+    "vae_model_path": "/models/wan_vae/Wan2.2-TI2V-5B",

-    "sr_model_path": "/path/to/checkpoints/540p_sr",

+    "sr_model_path": "/models/davinci/540p_sr",

-    "student_config_path": "/path/to/checkpoints/turbo_vae/TurboV3-Wan22-TinyShallow_7_7.json",

-    "student_ckpt_path": "/path/to/checkpoints/turbo_vae/checkpoint-340000.ckpt"

+    "student_config_path": "/models/davinci/turbo_vae/TurboV3-Wan22-TinyShallow_7_7.json",

+    "student_ckpt_path": "/models/davinci/turbo_vae/checkpoint-340000.ckpt"


Also update the base config for a quick 256p test first.


{
  "engine_config": {
    "load": "/models/davinci/base",
    "cp_size": 1
  },
  "evaluation_config": {
    "cfg_number": 2,
    "num_inference_steps": 32,
    "audio_model_path": "/models/stable-audio-open-1.0",
    "txt_model_path": "/models/t5/t5gemma-9b-9b-ul2",
    "vae_model_path": "/models/wan_vae/Wan2.2-TI2V-5B",
    "use_turbo_vae": true,
    "student_config_path": "/models/davinci/turbo_vae/TurboV3-Wan22-TinyShallow_7_7.json",
    "student_ckpt_path": "/models/davinci/turbo_vae/checkpoint-340000.ckpt"
  }
}


The file /home/ubuntu/daVinci-MagiHuman/example/base/config.json has been updated successfully.


Let me check if turbo_vae has the expected files.


Bash

ls -la /home/ubuntu/checkpoints/davinci/turbo_vae/ && echo "---" && ls -la /home/ubuntu/checkpoints/davinci/base/ | head -10 && echo "---" && ls -la /home/ubuntu/checkpoints/davinci/540p_sr/ | head -10

Check checkpoint file structure


total 1826580
drwxrwxr-x 2 ubuntu ubuntu       4096 Mar 27 21:34 .
drwxrwxr-x 7 ubuntu ubuntu       4096 Mar 27 21:34 ..
-rw-rw-r-- 1 ubuntu ubuntu 1870398651 Mar 27 21:34 checkpoint-340000.ckpt
-rw-rw-r-- 1 ubuntu ubuntu        882 Mar 27 21:33 TurboV3-Wan22-TinyShallow_7_7.json
---
total 29923948
drwxrwxr-x 2 ubuntu ubuntu       4096 Mar 27 21:33 .
drwxrwxr-x 7 ubuntu ubuntu       4096 Mar 27 21:34 ..
-rw-rw-r-- 1 ubuntu ubuntu 4798025672 Mar 27 21:32 model-00001-of-00007.safetensors
-rw-rw-r-- 1 ubuntu ubuntu 4777051960 Mar 27 21:32 model-00002-of-00007.safetensors
-rw-rw-r-- 1 ubuntu ubuntu 4998672920 Mar 27 21:32 model-00003-of-00007.safetensors
-rw-rw-r-- 1 ubuntu ubuntu 4737735064 Mar 27 21:33 model-00004-of-00007.safetensors
-rw-rw-r-- 1 ubuntu ubuntu 4971519728 Mar 27 21:33 model-00005-of-00007.safetensors
-rw-rw-r-- 1 ubuntu ubuntu 4554972288 Mar 27 21:33 model-00006-of-00007.safetensors
-rw-rw-r-- 1 ubuntu ubuntu 1804067488 Mar 27 21:33 model-00007-of-00007.safetensors
---
total 59763504
drwxrwxr-x 2 ubuntu ubuntu       4096 Mar 27 21:32 .
drwxrwxr-x 7 ubuntu ubuntu       4096 Mar 27 21:34 ..
-rw-rw-r-- 1 ubuntu ubuntu 4920553248 Mar 27 21:31 model-00001-of-00013.safetensors
-rw-rw-r-- 1 ubuntu ubuntu 4520720320 Mar 27 21:31 model-00002-of-00013.safetensors
-rw-rw-r-- 1 ubuntu ubuntu 4924382952 Mar 27 21:31 model-00003-of-00013.safetensors
-rw-rw-r-- 1 ubuntu ubuntu 4897310104 Mar 27 21:31 model-00004-of-00013.safetensors
-rw-rw-r-- 1 ubuntu ubuntu 4924382960 Mar 27 21:31 model-00005-of-00013.safetensors
-rw-rw-r-- 1 ubuntu ubuntu 3507184032 Mar 27 21:31 model-00006-of-00013.safetensors
-rw-rw-r-- 1 ubuntu ubuntu 4542755784 Mar 27 21:31 model-00007-of-00013.safetensors


All davinci checkpoints look good. Base is ~30GB (7 shards), 540p_sr is ~56GB (13 shards). Now checking if the gated downloads came through this time.


du -sh /home/ubuntu/checkpoints/t5/t5gemma-9b-9b-ul2/ /home/ubuntu/checkpoints/stable-audio-open-1.0/ 2>/dev/null

Check gated model download sizes


14G	/home/ubuntu/checkpoints/t5/t5gemma-9b-9b-ul2/
4.9G	/home/ubuntu/checkpoints/stable-audio-open-1.0/


They're downloading now. Let me wait for them to finish.


Check download progress


Still running. Let me check sizes again in a moment.


du -sh /home/ubuntu/checkpoints/t5/t5gemma-9b-9b-ul2/ /home/ubuntu/checkpoints/stable-audio-open-1.0/ 2>/dev/null; df -h / | tail -1

Check download progress


19G	/home/ubuntu/checkpoints/t5/t5gemma-9b-9b-ul2/
8.8G	/home/ubuntu/checkpoints/stable-audio-open-1.0/
/dev/sda2       984G  700G  244G  75% /


Both downloading well. While waiting, let me also install MagiCompiler inside docker and prepare a test script.


Check example assets


image.png
prompt.txt


Let me write a Docker run script that sets everything up and runs inference.


<task-notification>
<task-id>b8fw1lr3s</task-id>
<tool-use-id>toolu_01JUZC8dTyMqdsc4rAgFSder</tool-use-id>
<output-file>/tmp/claude-1001/-home-ubuntu/f26d71f5-6407-4368-8cb6-f48900e84838/tasks/b8fw1lr3s.output</output-file>
<status>completed</status>
<summary>Background command "Retry Stable Audio download" completed (exit code 0)</summary>
</task-notification>


Write

#!/usr/bin/env bash
set -euo pipefail

# Run daVinci-MagiHuman 540p inference test inside Docker
docker run --rm --gpus all --ipc=host \
  --ulimit memlock=-1 --ulimit stack=67108864 \
  -v /home/ubuntu/checkpoints:/models \
  -v /home/ubuntu/daVinci-MagiHuman:/workspace/daVinci-MagiHuman \
  -w /workspace \
  sandai/magi-human:latest \
  bash -c '
    # Install MagiCompiler
    if [ ! -d MagiCompiler ]; then
      git clone https://github.com/SandAI-org/MagiCompiler.git
      cd MagiCompiler && pip install -r requirements.txt && pip install . && cd ..
    fi

    cd daVinci-MagiHuman

    export MASTER_ADDR=localhost
    export MASTER_PORT=6011
    export NNODES=1
    export NODE_RANK=0
    export GPUS_PER_NODE=1
    export WORLD_SIZE=1
    export PYTORCH_CUDA_ALLOC_CONF=expandable_segments:True
    export NCCL_ALGO=^NVLS
    export PYTHONPATH=/workspace/daVinci-MagiHuman
    export CPU_OFFLOAD=true

    echo "=== Starting 540p inference test ==="
    echo "=== GPU Info ==="
    nvidia-smi --query-gpu=name,memory.total --format=csv,noheader

    time torchrun --nnodes=1 --node_rank=0 --nproc_per_node=1 \
      --rdzv-backend=c10d --rdzv-endpoint=localhost:6011 \
      inference/pipeline/entry.py \
      --config-load-path example/sr_540p/config.json \
      --prompt "$(cat example/assets/prompt.txt)" \
      --image_path example/assets/image.png \
      --seconds 5 \
      --br_width 448 \
      --br_height 256 \
      --sr_width 896 \
      --sr_height 512 \
      --output_path /workspace/daVinci-MagiHuman/output_test_540p
  '


File created successfully at: /home/ubuntu/run_test.sh

#!/usr/bin/env bash
set -euo pipefail

# Run daVinci-MagiHuman 540p inference test inside Docker
docker run --rm --gpus all --ipc=host \
  --ulimit memlock=-1 --ulimit stack=67108864 \
  -v /home/ubuntu/checkpoints:/models \
  -v /home/ubuntu/daVinci-MagiHuman:/workspace/daVinci-MagiHuman \
  -w /workspace \
  sandai/magi-human:latest \
  bash -c '
    # Install MagiCompiler
    if [ ! -d MagiCompiler ]; then
      git clone https://github.com/SandAI-org/MagiCompiler.git
      cd MagiCompiler && pip install -r requirements.txt && pip install . && cd ..
    fi

    cd daVinci-MagiHuman

    export MASTER_ADDR=localhost
    export MASTER_PORT=6011
    export NNODES=1
    export NODE_RANK=0
    export GPUS_PER_NODE=1
    export WORLD_SIZE=1
    export PYTORCH_CUDA_ALLOC_CONF=expandable_segments:True
    export NCCL_ALGO=^NVLS
    export PYTHONPATH=/workspace/daVinci-MagiHuman
    export CPU_OFFLOAD=true

    echo "=== Starting 540p inference test ==="
    echo "=== GPU Info ==="
    nvidia-smi --query-gpu=name,memory.total --format=csv,noheader

    time torchrun --nnodes=1 --node_rank=0 --nproc_per_node=1 \
      --rdzv-backend=c10d --rdzv-endpoint=localhost:6011 \
      inference/pipeline/entry.py \
      --config-load-path example/sr_540p/config.json \
      --prompt "$(cat example/assets/prompt.txt)" \
      --image_path example/assets/image.png \
      --seconds 5 \
      --br_width 448 \
      --br_height 256 \
      --sr_width 896 \
      --sr_height 512 \
      --output_path /workspace/daVinci-MagiHuman/output_test_540p
  '


<task-notification>
<task-id>bz934o3rq</task-id>
<tool-use-id>toolu_01WLT2WAFzzZfLH6yThYXJfL</tool-use-id>
<output-file>/tmp/claude-1001/-home-ubuntu/f26d71f5-6407-4368-8cb6-f48900e84838/tasks/bz934o3rq.output</output-file>
<status>completed</status>
<summary>Background command "Retry T5-Gemma download" completed (exit code 0)</summary>
</task-notification>


Bash

Make test script executable


(Bash completed with no output)


du -sh /home/ubuntu/checkpoints/t5/t5gemma-9b-9b-ul2/ /home/ubuntu/checkpoints/stable-audio-open-1.0/ 2>/dev/null

Check download progress


38G	/home/ubuntu/checkpoints/t5/t5gemma-9b-9b-ul2/
15G	/home/ubuntu/checkpoints/stable-audio-open-1.0/


T5-Gemma is 38GB and growing, stable-audio at 15GB. These are big models. Let me check expected sizes.


du -sh /home/ubuntu/checkpoints/t5/t5gemma-9b-9b-ul2/ /home/ubuntu/checkpoints/stable-audio-open-1.0/ 2>/dev/null; df -h / | tail -1

Check progress again


38G	/home/ubuntu/checkpoints/t5/t5gemma-9b-9b-ul2/
15G	/home/ubuntu/checkpoints/stable-audio-open-1.0/
/dev/sda2       984G  725G  219G  77% /


du -sh /home/ubuntu/checkpoints/t5/t5gemma-9b-9b-ul2/ /home/ubuntu/checkpoints/stable-audio-open-1.0/ 2>/dev/null

Check progress


Seems stuck at same sizes. Let me check if the tasks completed.


Check download task output


Fetching 18 files:  50%|█████     | 9/18 [01:08<00:59,  6.57s/it]Download complete. Moving file to t5/t5gemma-9b-9b-ul2/model-00008-of-00009.safetensors
Download complete. Moving file to t5/t5gemma-9b-9b-ul2/model-00006-of-00009.safetensors

Fetching 18 files:  56%|█████▌    | 10/18 [01:08<00:44,  5.61s/it]Download complete. Moving file to t5/t5gemma-9b-9b-ul2/model-00009-of-00009.safetensors

Fetching 18 files:  72%|███████▏  | 13/18 [01:10<00:17,  3.48s/it]
Fetching 18 files: 100%|██████████| 18/18 [01:10<00:00,  3.90s/it]
/home/ubuntu/checkpoints/t5/t5gemma-9b-9b-ul2
===
Download complete. Moving file to stable-audio-open-1.0/transformer/diffusion_pytorch_model.safetensors
Download complete. Moving file to stable-audio-open-1.0/model.safetensors
Download complete. Moving file to stable-audio-open-1.0/model.ckpt

Fetching 25 files:  24%|██▍       | 6/25 [01:01<05:27, 17.22s/it]
Fetching 25 files: 100%|██████████| 25/25 [01:01<00:00,  2.45s/it]
/home/ubuntu/checkpoints/stable-audio-open-1.0


Both downloads complete! Let's run the test.


du -sh /home/ubuntu/checkpoints/t5/t5gemma-9b-9b-ul2/ /home/ubuntu/checkpoints/stable-audio-open-1.0/

Verify final download sizes


All models ready. Running the 540p inference test now.


Run 540p inference test in Docker on A100


Error: Exit code 1

=============
== PyTorch ==
=============

NVIDIA Release 25.10 (build 222058774)
PyTorch Version 2.9.0a0+145a3a7
Container image Copyright (c) 2025, NVIDIA CORPORATION & AFFILIATES. All rights reserved.
Copyright (c) 2014-2024 Facebook Inc.
Copyright (c) 2011-2014 Idiap Research Institute (Ronan Collobert)
Copyright (c) 2012-2014 Deepmind Technologies    (Koray Kavukcuoglu)
Copyright (c) 2011-2012 NEC Laboratories America (Koray Kavukcuoglu)
Copyright (c) 2011-2013 NYU                      (Clement Farabet)
Copyright (c) 2006-2010 NEC Laboratories America (Ronan Collobert, Leon Bottou, Iain Melvin, Jason Weston)
Copyright (c) 2006      Idiap Research Institute (Samy Bengio)
Copyright (c) 2001-2004 Idiap Research Institute (Ronan Collobert, Samy Bengio, Johnny Mariethoz)
Copyright (c) 2015      Google Inc.
Copyright (c) 2015      Yangqing Jia
Copyright (c) 2013-2016 The Caffe contributors
All rights reserved.

Various files include modifications (c) NVIDIA CORPORATION & AFFILIATES.  All rights reserved.

GOVERNING TERMS: The software and materials are governed by the NVIDIA Software License Agreement
(found at https://www.nvidia.com/en-us/agreements/enterprise-software/nvidia-software-license-agreement/)
and the Product-Specific Terms for NVIDIA AI Products
(found at https://www.nvidia.com/en-us/agreements/enterprise-software/product-specific-terms-for-ai-products/).

ERROR: This container was built for NVIDIA Driver Release 580.95 or later, but
       version 560.35.05 was detected and compatibility mode is UNAVAILABLE.

       [[System has unsupported display driver / cuda driver combination (CUDA_ERROR_SYSTEM_DRIVER_MISMATCH) cuInit()=803]]

Cloning into 'MagiCompiler'...
Collecting cuda-python (from -r requirements.txt (line 3))
  Downloading cuda_python-13.2.0-py3-none-any.whl.metadata (6.5 kB)
Requirement already satisfied: depyf in /usr/local/lib/python3.12/dist-packages (from -r requirements.txt (line 4)) (0.20.0)
Requirement already satisfied: graphviz in /usr/local/lib/python3.12/dist-packages (from -r requirements.txt (line 5)) (0.21)
Collecting seaborn (from -r requirements.txt (line 6))
  Downloading seaborn-0.13.2-py3-none-any.whl.metadata (5.4 kB)
Requirement already satisfied: pydantic-settings in /usr/local/lib/python3.12/dist-packages (from -r requirements.txt (line 7)) (2.13.1)
Requirement already satisfied: triton==3.5.0 in /usr/local/lib/python3.12/dist-packages (from -r requirements.txt (line 8)) (3.5.0)
Collecting cuda-bindings~=13.2.0 (from cuda-python->-r requirements.txt (line 3))
  Downloading cuda_bindings-13.2.0-cp312-cp312-manylinux_2_24_x86_64.manylinux_2_28_x86_64.whl.metadata (2.3 kB)
Collecting cuda-pathfinder~=1.1 (from cuda-python->-r requirements.txt (line 3))
  Downloading cuda_pathfinder-1.5.0-py3-none-any.whl.metadata (1.9 kB)
Requirement already satisfied: astor in /usr/local/lib/python3.12/dist-packages (from depyf->-r requirements.txt (line 4)) (0.8.1)
Requirement already satisfied: dill in /usr/local/lib/python3.12/dist-packages (from depyf->-r requirements.txt (line 4)) (0.4.0)
Requirement already satisfied: numpy!=1.24.0,>=1.20 in /usr/local/lib/python3.12/dist-packages (from seaborn->-r requirements.txt (line 6)) (2.1.0)
Requirement already satisfied: pandas>=1.2 in /usr/local/lib/python3.12/dist-packages (from seaborn->-r requirements.txt (line 6)) (3.0.1)
Requirement already satisfied: matplotlib!=3.6.1,>=3.4 in /usr/local/lib/python3.12/dist-packages (from seaborn->-r requirements.txt (line 6)) (3.10.7)
Requirement already satisfied: pydantic>=2.7.0 in /usr/local/lib/python3.12/dist-packages (from pydantic-settings->-r requirements.txt (line 7)) (2.12.2)
Requirement already satisfied: python-dotenv>=0.21.0 in /usr/local/lib/python3.12/dist-packages (from pydantic-settings->-r requirements.txt (line 7)) (1.2.2)
Requirement already satisfied: typing-inspection>=0.4.0 in /usr/local/lib/python3.12/dist-packages (from pydantic-settings->-r requirements.txt (line 7)) (0.4.2)
Requirement already satisfied: contourpy>=1.0.1 in /usr/local/lib/python3.12/dist-packages (from matplotlib!=3.6.1,>=3.4->seaborn->-r requirements.txt (line 6)) (1.3.3)
Requirement already satisfied: cycler>=0.10 in /usr/local/lib/python3.12/dist-packages (from matplotlib!=3.6.1,>=3.4->seaborn->-r requirements.txt (line 6)) (0.12.1)
Requirement already satisfied: fonttools>=4.22.0 in /usr/local/lib/python3.12/dist-packages (from matplotlib!=3.6.1,>=3.4->seaborn->-r requirements.txt (line 6)) (4.60.1)
Requirement already satisfied: kiwisolver>=1.3.1 in /usr/local/lib/python3.12/dist-packages (from matplotlib!=3.6.1,>=3.4->seaborn->-r requirements.txt (line 6)) (1.4.9)
Requirement already satisfied: packaging>=20.0 in /usr/local/lib/python3.12/dist-packages (from matplotlib!=3.6.1,>=3.4->seaborn->-r requirements.txt (line 6)) (25.0)
Requirement already satisfied: pillow>=8 in /usr/local/lib/python3.12/dist-packages (from matplotlib!=3.6.1,>=3.4->seaborn->-r requirements.txt (line 6)

... [2318 characters truncated] ...

SE_FEATURE="build-constraint".
  Installing build dependencies: started
  Installing build dependencies: finished with status 'done'
  Getting requirements to build wheel: started
  Getting requirements to build wheel: finished with status 'done'
  Preparing metadata (pyproject.toml): started
  Preparing metadata (pyproject.toml): finished with status 'done'
Building wheels for collected packages: magi_compiler
  Building wheel for magi_compiler (pyproject.toml): started
  Building wheel for magi_compiler (pyproject.toml): finished with status 'done'
  Created wheel for magi_compiler: filename=magi_compiler-0.0.0-py3-none-any.whl size=181597 sha256=fee20202d09e5a5abd2338d4fb39ff9d2e26bbe4b7ddab511b32d33697ceb235
  Stored in directory: /tmp/pip-ephem-wheel-cache-_w7u2s1e/wheels/29/6e/e6/34f1535984d2c46ce372c96cda349e283a7b0756f37d236ae8
Successfully built magi_compiler
Installing collected packages: magi_compiler
Successfully installed magi_compiler-0.0.0
WARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager, possibly rendering your system unusable. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv. Use the --root-user-action option if you know what you are doing and want to suppress this warning.
=== Starting 540p inference test ===
=== GPU Info ===
NVIDIA A100-SXM4-80GB, 81920 MiB
/usr/local/lib/python3.12/dist-packages/torch/cuda/__init__.py:182: UserWarning: CUDA initialization: The NVIDIA driver on your system is too old (found version 12060). Please update your GPU driver by downloading and installing a new version from the URL: http://www.nvidia.com/Download/index.aspx Alternatively, go to: https://pytorch.org to install a PyTorch version that has been compiled with your version of the CUDA driver. (Triggered internally at /opt/pytorch/pytorch/c10/cuda/CUDAFunctions.cpp:107.)
  return torch._C._cuda_getDeviceCount() > 0
/usr/local/lib/python3.12/dist-packages/torch/cuda/__init__.py:182: UserWarning: CUDA initialization: The NVIDIA driver on your system is too old (found version 12060). Please update your GPU driver by downloading and installing a new version from the URL: http://www.nvidia.com/Download/index.aspx Alternatively, go to: https://pytorch.org to install a PyTorch version that has been compiled with your version of the CUDA driver. (Triggered internally at /opt/pytorch/pytorch/c10/cuda/CUDAFunctions.cpp:107.)
  return torch._C._cuda_getDeviceCount() > 0
Traceback (most recent call last):
  File "/workspace/daVinci-MagiHuman/inference/pipeline/entry.py", line 95, in <module>
    initialize_infra()
  File "/workspace/daVinci-MagiHuman/inference/infra/__init__.py", line 23, in initialize_infra
    assert torch.cuda.is_available(), "Infra requires CUDA environment."
           ^^^^^^^^^^^^^^^^^^^^^^^^^
AssertionError: Infra requires CUDA environment.
[W327 21:36:55.702697692 AllocatorConfig.cpp:28] Warning: PYTORCH_CUDA_ALLOC_CONF is deprecated, use PYTORCH_ALLOC_CONF instead (function operator())
E0327 21:36:55.493000 162 torch/distributed/elastic/multiprocessing/api.py:882] failed (exitcode: 1) local_rank: 0 (pid: 182) of binary: /usr/bin/python
Traceback (most recent call last):
  File "/usr/local/bin/torchrun", line 7, in <module>
    sys.exit(main())
             ^^^^^^
  File "/usr/local/lib/python3.12/dist-packages/torch/distributed/elastic/multiprocessing/errors/__init__.py", line 357, in wrapper
    return f(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^
  File "/usr/local/lib/python3.12/dist-packages/torch/distributed/run.py", line 936, in main
    run(args)
  File "/usr/local/lib/python3.12/dist-packages/torch/distributed/run.py", line 927, in run
    elastic_launch(
  File "/usr/local/lib/python3.12/dist-packages/torch/distributed/launcher/api.py", line 156, in __call__
    return launch_agent(self._config, self._entrypoint, list(args))
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/usr/local/lib/python3.12/dist-packages/torch/distributed/launcher/api.py", line 293, in launch_agent
    raise ChildFailedError(
torch.distributed.elastic.multiprocessing.errors.ChildFailedError:
============================================================
inference/pipeline/entry.py FAILED
------------------------------------------------------------
Failures:
  <NO_OTHER_FAILURES>
------------------------------------------------------------
Root Cause (first observed failure):
[0]:
  time      : 2026-03-27_21:36:55
  host      : e90fe10208d3
  rank      : 0 (local_rank: 0)
  exitcode  : 1 (pid: 182)
  error_file: <N/A>
  traceback : To enable traceback see: https://pytorch.org/docs/stable/elastic/errors.html
============================================================
[W327 21:36:55.222476165 AllocatorConfig.cpp:28] Warning: PYTORCH_CUDA_ALLOC_CONF is deprecated, use PYTORCH_ALLOC_CONF instead (function operator())

real	0m12.180s
user	0m14.945s
sys	0m1.698s


Driver mismatch. The Docker image requires NVIDIA Driver 580.95+ but we have 560.35. The container's PyTorch 2.9 is compiled for CUDA 12.8+ but our driver only supports CUDA 12.6.

We need to go the conda route instead and install a compatible PyTorch version. Let me set that up.


nvidia-smi | grep "CUDA Version" && python3 -c "import torch; print(f'torch={torch.__version__}, cuda={torch.version.cuda}, avail={torch.cuda.is_available()}')" 2>/dev/null || echo "no system torch"

Check host CUDA/torch versions


| NVIDIA-SMI 560.35.05              Driver Version: 560.35.05      CUDA Version: 12.6     |
torch=2.9.1+cu128, cuda=12.8, avail=True


Wait, the host actually has PyTorch 2.9.1 with CUDA 12.8 and `cuda.is_available()` is True! The driver 560.35 supports the forward-compatible CUDA 12.8 runtime on the host. The Docker container just has stricter driver checks.

Let me try running directly on the host instead.


python3 -c "import torch; print(torch.cuda.get_device_name(0)); print(f'SM capability: {torch.cuda.get_device_capability(0)}')"

Verify GPU access from host Python


NVIDIA A100-SXM4-80GB
SM capability: (8, 0)


SM 8.0 (Ampere) confirmed. Let me install the dependencies on the host.


pip install flash-attn --no-build-isolation 2>&1 | tail -5

Install Flash Attention 2


Command running in background with ID: b2ayaosfw. Output is being written to: /tmp/claude-1001/-home-ubuntu/f26d71f5-6407-4368-8cb6-f48900e84838/tasks/b2ayaosfw.output


git clone https://github.com/SandAI-org/MagiCompiler.git 2>&1 | tail -3 && cd MagiCompiler && pip install -r requirements.txt 2>&1 | tail -3 && pip install . 2>&1 | tail -3

Install MagiCompiler


Command running in background with ID: b6xdgiro7. Output is being written to: /tmp/claude-1001/-home-ubuntu/f26d71f5-6407-4368-8cb6-f48900e84838/tasks/b6xdgiro7.output


cd /home/ubuntu/daVinci-MagiHuman && pip install -r requirements.txt 2>&1 | tail -5 && pip install --no-deps -r requirements-nodeps.txt 2>&1 | tail -5

Install daVinci-MagiHuman deps


Command running in background with ID: bd2wk3w31. Output is being written to: /tmp/claude-1001/-home-ubuntu/f26d71f5-6407-4368-8cb6-f48900e84838/tasks/bd2wk3w31.output


<task-notification>
<task-id>b2ayaosfw</task-id>
<tool-use-id>toolu_01DdBbysDqqHdT6iBiUTC1M7</tool-use-id>
<output-file>/tmp/claude-1001/-home-ubuntu/f26d71f5-6407-4368-8cb6-f48900e84838/tasks/b2ayaosfw.output</output-file>
<status>completed</status>
<summary>Background command "Install Flash Attention 2" completed (exit code 0)</summary>
</task-notification>


All 3 installs running in parallel. Meanwhile, let me update the config paths for host-based execution (not Docker's /models/).


<task-notification>
<task-id>bd2wk3w31</task-id>
<tool-use-id>toolu_01RRhp3dVAKG3HCkzrma5JkZ</tool-use-id>
<output-file>/tmp/claude-1001/-home-ubuntu/f26d71f5-6407-4368-8cb6-f48900e84838/tasks/bd2wk3w31.output</output-file>
<status>completed</status>
<summary>Background command "Install daVinci-MagiHuman deps" completed (exit code 0)</summary>
</task-notification>


<task-notification>
<task-id>b6xdgiro7</task-id>
<tool-use-id>toolu_012e7AgmkCJw52GvzA19XZrf</tool-use-id>
<output-file>/tmp/claude-1001/-home-ubuntu/f26d71f5-6407-4368-8cb6-f48900e84838/tasks/b6xdgiro7.output</output-file>
<status>completed</status>
<summary>Background command "Install MagiCompiler" completed (exit code 0)</summary>
</task-notification>


Write

{
  "engine_config": {
    "load": "/home/ubuntu/checkpoints/davinci/base",
    "cp_size": 1
  },
  "evaluation_config": {
    "cfg_number": 2,
    "num_inference_steps": 32,
    "audio_model_path": "/home/ubuntu/checkpoints/stable-audio-open-1.0",
    "txt_model_path": "/home/ubuntu/checkpoints/t5/t5gemma-9b-9b-ul2",
    "vae_model_path": "/home/ubuntu/checkpoints/wan_vae/Wan2.2-TI2V-5B",
    "use_sr_model": true,
    "sr_model_path": "/home/ubuntu/checkpoints/davinci/540p_sr",
    "sr_num_inference_steps": 5,
    "sr_cfg_number": 1,
    "use_turbo_vae": true,
    "student_config_path": "/home/ubuntu/checkpoints/davinci/turbo_vae/TurboV3-Wan22-TinyShallow_7_7.json",
    "student_ckpt_path": "/home/ubuntu/checkpoints/davinci/turbo_vae/checkpoint-340000.ckpt"
  }
}


The file /home/ubuntu/daVinci-MagiHuman/example/sr_540p/config.json has been updated successfully.

+    "load": "/home/ubuntu/checkpoints/davinci/base",

-    "audio_model_path": "/models/stable-audio-open-1.0",

-    "txt_model_path": "/models/t5/t5gemma-9b-9b-ul2",

-    "vae_model_path": "/models/wan_vae/Wan2.2-TI2V-5B",

+    "audio_model_path": "/home/ubuntu/checkpoints/stable-audio-open-1.0",

+    "txt_model_path": "/home/ubuntu/checkpoints/t5/t5gemma-9b-9b-ul2",

+    "vae_model_path": "/home/ubuntu/checkpoints/wan_vae/Wan2.2-TI2V-5B",

-    "sr_model_path": "/models/davinci/540p_sr",

+    "sr_model_path": "/home/ubuntu/checkpoints/davinci/540p_sr",

-    "student_config_path": "/models/davinci/turbo_vae/TurboV3-Wan22-TinyShallow_7_7.json",

-    "student_ckpt_path": "/models/davinci/turbo_vae/checkpoint-340000.ckpt"

+    "student_config_path": "/home/ubuntu/checkpoints/davinci/turbo_vae/TurboV3-Wan22-TinyShallow_7_7.json",

+    "student_ckpt_path": "/home/ubuntu/checkpoints/davinci/turbo_vae/checkpoint-340000.ckpt"


{
  "engine_config": {
    "load": "/home/ubuntu/checkpoints/davinci/base",
    "cp_size": 1
  },
  "evaluation_config": {
    "cfg_number": 2,
    "num_inference_steps": 32,
    "audio_model_path": "/home/ubuntu/checkpoints/stable-audio-open-1.0",
    "txt_model_path": "/home/ubuntu/checkpoints/t5/t5gemma-9b-9b-ul2",
    "vae_model_path": "/home/ubuntu/checkpoints/wan_vae/Wan2.2-TI2V-5B",
    "use_turbo_vae": true,
    "student_config_path": "/home/ubuntu/checkpoints/davinci/turbo_vae/TurboV3-Wan22-TinyShallow_7_7.json",
    "student_ckpt_path": "/home/ubuntu/checkpoints/davinci/turbo_vae/checkpoint-340000.ckpt"
  }
}


The file /home/ubuntu/daVinci-MagiHuman/example/base/config.json has been updated successfully.


Bash

tail -5 /tmp/claude-1001/-home-ubuntu/f26d71f5-6407-4368-8cb6-f48900e84838/tasks/bd2wk3w31.output 2>/dev/null; echo "=== MagiCompiler ==="; tail -5 /tmp/claude-1001/-home-ubuntu/f26d71f5-6407-4368-8cb6-f48900e84838/tasks/b6xdgiro7.output 2>/dev/null; echo "=== FlashAttn ==="; tail -5 /tmp/claude-1001/-home-ubuntu/f26d71f5-6407-4368-8cb6-f48900e84838/tasks/b2ayaosfw.output 2>/dev/null

Check install progress


WARNING: Ignoring invalid distribution -riton (/home/ubuntu/.local/lib/python3.10/site-packages)
Looking in indexes: https://mcache-dsm.massedcompute.com/simple, https://pypi.org/simple
Requirement already satisfied: openai-whisper in /home/ubuntu/.local/lib/python3.10/site-packages (from -r requirements-nodeps.txt (line 1)) (20250625)
Requirement already satisfied: tiktoken in /home/ubuntu/.local/lib/python3.10/site-packages (from -r requirements-nodeps.txt (line 2)) (0.12.0)
WARNING: Ignoring invalid distribution -riton (/home/ubuntu/.local/lib/python3.10/site-packages)
=== MagiCompiler ===
WARNING: Ignoring invalid distribution -riton (/home/ubuntu/.local/lib/python3.10/site-packages)
Successfully installed triton-3.5.0
  Preparing metadata (pyproject.toml): finished with status 'done'
INFO: pip is looking at multiple versions of magi-compiler to determine which version is compatible with other requirements. This could take a while.
ERROR: Package 'magi-compiler' requires a different Python: 3.10.12 not in '>=3.12'
=== FlashAttn ===
× Encountered error while generating package metadata.
╰─> flash-attn

note: This is an issue with the package mentioned above, not pip.
hint: See above for details.


Two issues:
1. **MagiCompiler requires Python 3.12+** — we have 3.10
2. **Flash Attention build failed**

Let me check the flash-attn error more closely and see if there's a pre-built wheel.


cat /tmp/claude-1001/-home-ubuntu/f26d71f5-6407-4368-8cb6-f48900e84838/tasks/b2ayaosfw.output | tail -30

Check flash-attn full error


× Encountered error while generating package metadata.
╰─> flash-attn

note: This is an issue with the package mentioned above, not pip.
hint: See above for details.


cat /tmp/claude-1001/-home-ubuntu/f26d71f5-6407-4368-8cb6-f48900e84838/tasks/b2ayaosfw.output | grep -i "error\|fail\|cannot" | head -10

Check flash-attn errors


× Encountered error while generating package metadata.


Full flash-attn output


Very unhelpful error. Let me try with verbose output and a specific version.


pip install flash-attn==2.7.4 --no-build-isolation -v 2>&1 | tail -30

Install flash-attn with verbose output


Using pip 26.0.1 from /home/ubuntu/.local/lib/python3.10/site-packages/pip (python 3.10)
Defaulting to user installation because normal site-packages is not writeable
WARNING: Ignoring invalid distribution -riton (/home/ubuntu/.local/lib/python3.10/site-packages)
Looking in indexes: https://mcache-dsm.massedcompute.com/simple, https://pypi.org/simple
ERROR: Ignored the following yanked versions: 1.0.3
ERROR: Could not find a version that satisfies the requirement flash-attn==2.7.4 (from versions: 0.2.0, 0.2.1, 0.2.2, 0.2.3, 0.2.4, 0.2.5, 0.2.6.post1, 0.2.7, 0.2.8, 1.0.0, 1.0.1, 1.0.2, 1.0.3, 1.0.3.post0, 1.0.4, 1.0.5, 1.0.6, 1.0.7, 1.0.8, 1.0.9, 2.0.0.post1, 2.0.1, 2.0.2, 2.0.3, 2.0.4, 2.0.5, 2.0.6, 2.0.6.post2, 2.0.7, 2.0.8, 2.0.9, 2.1.0, 2.1.1, 2.1.2.post3, 2.2.0, 2.2.1, 2.2.2, 2.2.3.post2, 2.2.4, 2.2.4.post1, 2.2.5, 2.3.0, 2.3.1.post1, 2.3.2, 2.3.3, 2.3.4, 2.3.5, 2.3.6, 2.4.0.post1, 2.4.1, 2.4.2, 2.4.3.post1, 2.5.0, 2.5.1.post1, 2.5.2, 2.5.3, 2.5.4, 2.5.5, 2.5.6, 2.5.7, 2.5.8, 2.5.9.post1, 2.6.0.post1, 2.6.1, 2.6.2, 2.6.3, 2.7.0.post2, 2.7.1.post4, 2.7.2.post1, 2.7.3, 2.7.4.post1, 2.8.0.post2, 2.8.1, 2.8.2, 2.8.3)
ERROR: No matching distribution found for flash-attn==2.7.4


pip install flash-attn==2.7.4.post1 --no-build-isolation 2>&1 | tail -10

Install flash-attn 2.7.4.post1


      [end of output]

  note: This error originates from a subprocess, and is likely not a problem with pip.
error: metadata-generation-failed

× Encountered error while generating package metadata.
╰─> flash-attn

note: This is an issue with the package mentioned above, not pip.
hint: See above for details.


pip install flash-attn==2.7.4.post1 --no-build-isolation 2>&1 | grep -i "error\|fail\|not found\|cmake\|nvcc\|ninja" | head -20

Get specific build errors


  Preparing metadata (pyproject.toml): finished with status 'error'
  error: subprocess-exited-with-error
      RuntimeError: FlashAttention is only supported on CUDA 11.7 and above.  Note: make sure nvcc has a supported version by running nvcc -V.
  note: This error originates from a subprocess, and is likely not a problem with pip.
error: metadata-generation-failed
× Encountered error while generating package metadata.


The build can't find nvcc. Let me check CUDA toolkit availability.


which nvcc 2>/dev/null || echo "no nvcc"; ls /usr/local/cuda*/bin/nvcc 2>/dev/null || echo "no cuda toolkit found"; echo "---"; python3 -c "import torch; print(torch.version.cuda)"

Check CUDA toolkit


/usr/bin/nvcc
no cuda toolkit found
---
12.8


Check nvcc version


nvcc: NVIDIA (R) Cuda compiler driver
Copyright (c) 2005-2021 NVIDIA Corporation
Built on Thu_Nov_18_09:45:30_PST_2021
Cuda compilation tools, release 11.5, V11.5.119
Build cuda_11.5.r11.5/compiler.30672275_0


nvcc is 11.5 but flash-attn needs CUDA toolkit matching the PyTorch CUDA version. Let me try a pre-built wheel instead.


pip install flash-attn --no-build-isolation 2>&1 | grep -v "^$" | tail -20

Try latest flash-attn


        File "/home/ubuntu/.local/lib/python3.10/site-packages/pip/_vendor/pyproject_hooks/_in_process/_in_process.py", line 373, in main
          json_out["return_val"] = hook(**hook_input["kwargs"])
        File "/home/ubuntu/.local/lib/python3.10/site-packages/pip/_vendor/pyproject_hooks/_in_process/_in_process.py", line 175, in prepare_metadata_for_build_wheel
          return hook(metadata_directory, config_settings)
        File "/home/ubuntu/.local/lib/python3.10/site-packages/setuptools/build_meta.py", line 378, in prepare_metadata_for_build_wheel
          self.run_setup()
        File "/home/ubuntu/.local/lib/python3.10/site-packages/setuptools/build_meta.py", line 518, in run_setup
          super().run_setup(setup_script=setup_script)
        File "/home/ubuntu/.local/lib/python3.10/site-packages/setuptools/build_meta.py", line 317, in run_setup
          exec(code, locals())
        File "<string>", line 174, in <module>
      RuntimeError: FlashAttention is only supported on CUDA 11.7 and above.  Note: make sure nvcc has a supported version by running nvcc -V.
      [end of output]

  note: This error originates from a subprocess, and is likely not a problem with pip.
error: metadata-generation-failed
× Encountered error while generating package metadata.
╰─> flash-attn
note: This is an issue with the package mentioned above, not pip.
hint: See above for details.


The system nvcc is too old (11.5). I need to point it at the right CUDA toolkit. Let me find it or install it.


find / -name "nvcc" -type f 2>/dev/null; echo "---"; ls /usr/local/ | grep cuda; echo "---"; dpkg -l | grep cuda-toolkit 2>/dev/null | head -5

Find CUDA toolkit installations


/usr/bin/nvcc
/usr/lib/nvidia-cuda-toolkit/bin/nvcc
---
---
rc  cuda-toolkit-12-2-config-common           12.2.53-1                                   all          Common config package for CUDA Toolkit 12.2.
rc  cuda-toolkit-12-config-common             12.2.53-1                                   all          Common config package for CUDA Toolkit 12.
rc  cuda-toolkit-config-common                12.2.53-1                                   all          Common config package for CUDA Toolkit.
ii  nvidia-cuda-toolkit                       11.5.1-1ubuntu1                             amd64        NVIDIA CUDA development toolkit
ii  nvidia-cuda-toolkit-doc                   11.5.1-1ubuntu1                             all          NVIDIA CUDA and OpenCL documentation


Only CUDA 11.5 toolkit installed. I need to install CUDA 12.8 toolkit to match PyTorch. Let me use a quicker approach — install flash-attn from a pre-built wheel.


python3 -c "import torch; print(f'torch={torch.__version__}, cuda={torch.version.cuda}, python=3.10')" && pip index versions flash-attn 2>&1 | head -5

Check versions for wheel compatibility


torch=2.9.1+cu128, cuda=12.8, python=3.10
WARNING: Ignoring invalid distribution -riton (/home/ubuntu/.local/lib/python3.10/site-packages)
flash-attn (2.8.3)
Available versions: 2.8.3, 2.8.2, 2.8.1, 2.8.0.post2, 2.7.4.post1, 2.7.3, 2.7.2.post1, 2.7.1.post4, 2.7.0.post2, 2.6.3, 2.6.2, 2.6.1, 2.6.0.post1, 2.5.9.post1, 2.5.8, 2.5.7, 2.5.6, 2.5.5, 2.5.4, 2.5.3, 2.5.2, 2.5.1.post1, 2.5.0, 2.4.3.post1, 2.4.2, 2.4.1, 2.4.0.post1, 2.3.6, 2.3.5, 2.3.4, 2.3.3, 2.3.2, 2.3.1.post1, 2.3.0, 2.2.5, 2.2.4.post1, 2.2.4, 2.2.3.post2, 2.2.2, 2.2.1, 2.2.0, 2.1.2.post3, 2.1.1, 2.1.0, 2.0.9, 2.0.8, 2.0.7, 2.0.6.post2, 2.0.6, 2.0.5, 2.0.4, 2.0.3, 2.0.2, 2.0.1, 2.0.0.post1, 1.0.9, 1.0.8, 1.0.7, 1.0.6, 1.0.5, 1.0.4, 1.0.3.post0, 1.0.3, 1.0.2, 1.0.1, 1.0.0, 0.2.8, 0.2.7, 0.2.6.post1, 0.2.5, 0.2.4, 0.2.3, 0.2.2, 0.2.1, 0.2.0


Let me try getting a pre-built wheel directly from the flash-attn GitHub releases, which provides wheels for specific torch+cuda+python combos.


# Set CUDA_HOME to trick the build into using PyTorch's bundled CUDA
export CUDA_HOME=$(python3 -c "import torch; import os; print(os.path.dirname(os.path.dirname(torch.utils.cpp_extension._find_cuda_home())))" 2>/dev/null || echo "")
echo "CUDA_HOME=$CUDA_HOME"
# Check if pytorch ships nvcc
python3 -c "import torch.utils.cpp_extension as ext; print(ext.CUDA_HOME)"

Find PyTorch's CUDA home


CUDA_HOME=
/usr


# Check if there's a conda CUDA toolkit or if we can use pip's nvidia packages
pip list 2>/dev/null | grep -i nvidia | head -20

Check nvidia pip packages


nvidia-cublas-cu12                       12.8.4.1
nvidia-cuda-cupti-cu12                   12.8.90
nvidia-cuda-nvrtc-cu12                   12.8.93
nvidia-cuda-runtime                      13.2.51
nvidia-cuda-runtime-cu12                 12.8.90
nvidia-cuda-runtime-cu13                 0.0.0a0
nvidia-cudnn-cu12                        9.10.2.21
nvidia-cudnn-frontend                    1.18.0
nvidia-cufft-cu12                        11.3.3.83
nvidia-cufile-cu12                       1.13.1.3
nvidia-curand-cu12                       10.3.9.90
nvidia-cusolver-cu12                     11.7.3.90
nvidia-cusparse-cu12                     12.5.8.93
nvidia-cusparselt-cu12                   0.7.1
nvidia-cutlass-dsl                       4.3.5
nvidia-cutlass-dsl-libs-base             4.4.0
nvidia-ml-py                             12.535.133
nvidia-nccl-cu12                         2.27.5
nvidia-nvjitlink-cu12                    12.8.93
nvidia-nvshmem-cu12                      3.3.20


We have the nvidia CUDA 12 pip packages. Let me install the CUDA toolkit via pip to get nvcc 12.8.


pip install nvidia-cuda-nvcc-cu12 2>&1 | tail -5

Install CUDA nvcc via pip


   ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 40.5/40.5 MB 62.3 MB/s  0:00:00
WARNING: Ignoring invalid distribution -riton (/home/ubuntu/.local/lib/python3.10/site-packages)
Installing collected packages: nvidia-cuda-nvcc-cu12
WARNING: Ignoring invalid distribution -riton (/home/ubuntu/.local/lib/python3.10/site-packages)
Successfully installed nvidia-cuda-nvcc-cu12-12.9.86


# Find the nvcc from the pip package
NVCC_PATH=$(python3 -c "import nvidia.cuda_nvcc; import os; print(os.path.join(os.path.dirname(nvidia.cuda_nvcc.__file__), 'bin', 'nvcc'))")
echo "NVCC: $NVCC_PATH"
$NVCC_PATH --version
CUDA_HOME_DIR=$(dirname $(dirname $NVCC_PATH))
echo "CUDA_HOME: $CUDA_HOME_DIR"

Find pip-installed nvcc path


Traceback (most recent call last):
  File "<string>", line 1, in <module>
  File "/usr/lib/python3.10/posixpath.py", line 152, in dirname
    p = os.fspath(p)
TypeError: expected str, bytes or os.PathLike object, not NoneType
NVCC:
/bin/bash: line 9: --version: command not found
dirname: missing operand
Try 'dirname --help' for more information.
dirname: missing operand
Try 'dirname --help' for more information.
CUDA_HOME:


find ~/.local/lib/python3.10/site-packages/nvidia -name "nvcc" -type f 2>/dev/null

Find nvcc binary


(Bash completed with no output)


find ~/.local/lib/python3.10/site-packages/nvidia/cuda_nvcc -type f 2>/dev/null | head -20

Find nvcc files


find ~/.local/lib/python3.10/site-packages/nvidia/cuda_nvcc/bin -type f 2>/dev/null

Find nvcc binary location


No nvcc binary in the pip package — it's just headers. Let me try a different approach: install CUDA 12.6 toolkit properly.


# Try the conda-forge cuda-nvcc approach, or just use the pre-built wheels from flash-attn GH releases
# flash-attn provides wheels at https://github.com/Dao-AILab/flash-attention/releases
# Let's check torch+cuda version to find the right wheel
python3 -c "
import torch, sys
print(f'Python: {sys.version_info.major}.{sys.version_info.minor}')
print(f'Torch: {torch.__version__}')
print(f'CUDA: {torch.version.cuda}')
print(f'Platform: linux_x86_64')
# We need: cp310, torch2.9, cu128
"

Check exact versions for wheel matching


Python: 3.10
Torch: 2.9.1+cu128
CUDA: 12.8
Platform: linux_x86_64


# flash-attn releases pre-built wheels. Let me check the latest release
pip install flash-attn -f https://github.com/Dao-AILab/flash-attention/releases/expanded_assets/v2.7.4.post1 --no-build-isolation 2>&1 | tail -15

Try installing from GitHub release wheels


          super().run_setup(setup_script=setup_script)
        File "/home/ubuntu/.local/lib/python3.10/site-packages/setuptools/build_meta.py", line 317, in run_setup
          exec(code, locals())
        File "<string>", line 174, in <module>
      RuntimeError: FlashAttention is only supported on CUDA 11.7 and above.  Note: make sure nvcc has a supported version by running nvcc -V.
      [end of output]

  note: This error originates from a subprocess, and is likely not a problem with pip.
error: metadata-generation-failed

× Encountered error while generating package metadata.
╰─> flash-attn

note: This is an issue with the package mentioned above, not pip.
hint: See above for details.


The pre-built wheels don't match our Python 3.10 + torch 2.9 + CUDA 12.8 combo, so it falls back to building from source and hits the nvcc issue. Let me just install CUDA toolkit 12.6.


# Install CUDA toolkit 12.6 - network install
wget -q https://developer.download.nvidia.com/compute/cuda/repos/ubuntu2204/x86_64/cuda-keyring_1.1-1_all.deb && sudo dpkg -i cuda-keyring_1.1-1_all.deb 2>&1 | tail -3 && sudo apt-get update -qq 2>&1 | tail -3 && sudo apt-get install -y -qq cuda-nvcc-12-6 cuda-cudart-dev-12-6 2>&1 | tail -5

Install CUDA 12.6 nvcc toolkit


Preparing to unpack cuda-keyring_1.1-1_all.deb ...
Unpacking cuda-keyring (1.1-1) over (1.1-1) ...
Setting up cuda-keyring (1.1-1) ...
No containers need to be restarted.

No user sessions are running outdated binaries.

No VM guests are running outdated hypervisor (qemu) binaries on this host.


/usr/local/cuda-12.6/bin/nvcc --version 2>/dev/null || echo "not found yet"

Check if cuda 12.6 nvcc installed


nvcc: NVIDIA (R) Cuda compiler driver
Copyright (c) 2005-2024 NVIDIA Corporation
Built on Tue_Oct_29_23:50:19_PDT_2024
Cuda compilation tools, release 12.6, V12.6.85
Build cuda_12.6.r12.6/compiler.35059454_0


Now let's install flash-attn with the right CUDA.


export CUDA_HOME=/usr/local/cuda-12.6 && export PATH=$CUDA_HOME/bin:$PATH && nvcc --version && pip install flash-attn --no-build-isolation 2>&1 | tail -20

Install flash-attn with CUDA 12.6


nvcc: NVIDIA (R) Cuda compiler driver
Copyright (c) 2005-2024 NVIDIA Corporation
Built on Tue_Oct_29_23:50:19_PDT_2024
Cuda compilation tools, release 12.6, V12.6.85
Build cuda_12.6.r12.6/compiler.35059454_0
        File "/home/ubuntu/.local/lib/python3.10/site-packages/Cython/Distutils/build_ext.py", line 136, in build_extension
          super().build_extension(ext)
        File "/home/ubuntu/.local/lib/python3.10/site-packages/setuptools/_distutils/command/build_ext.py", line 565, in build_extension
          objects = self.compiler.compile(
        File "/home/ubuntu/.local/lib/python3.10/site-packages/torch/utils/cpp_extension.py", line 866, in unix_wrap_ninja_compile
          _write_ninja_file_and_compile_objects(
        File "/home/ubuntu/.local/lib/python3.10/site-packages/torch/utils/cpp_extension.py", line 2223, in _write_ninja_file_and_compile_objects
          _run_ninja_build(
        File "/home/ubuntu/.local/lib/python3.10/site-packages/torch/utils/cpp_extension.py", line 2614, in _run_ninja_build
          raise RuntimeError(message) from e
      RuntimeError: Error compiling objects for extension
      [end of output]

  note: This error originates from a subprocess, and is likely not a problem with pip.
  ERROR: Failed building wheel for flash-attn
Failed to build flash-attn
error: failed-wheel-build-for-install

× Failed to build installable wheels for some pyproject.toml based projects
╰─> flash-attn


Compilation error. Let me see the actual error.


export CUDA_HOME=/usr/local/cuda-12.6 && export PATH=$CUDA_HOME/bin:$PATH && pip install flash-attn --no-build-isolation 2>&1 | grep "error:" | head -10

Get specific compilation errors


  error: subprocess-exited-with-error
      gcc: fatal error: cannot execute ‘cc1plus’: execvp: No such file or directory
      gcc: fatal error: cannot execute ‘cc1plus’: execvp: No such file or directory
      gcc: fatal error: cannot execute ‘cc1plus’: execvp: No such file or directory
      gcc: fatal error: cannot execute ‘cc1plus’: execvp: No such file or directory
      gcc: fatal error: cannot execute ‘cc1plus’: execvp: No such file or directory
      gcc: fatal error: cannot execute ‘cc1plus’: execvp: No such file or directory
      gcc: fatal error: cannot execute ‘cc1plus’: execvp: No such file or directory
          raise SystemExit(gen_usage(dist.script_name) + f"\nerror: {msg}")
      error: option --dist-info-dir not recognized


Missing C++ compiler. Easy fix.


sudo apt-get install -y -qq g++ 2>&1 | tail -3

Install g++ compiler


Verify g++ installed


g++ (Ubuntu 11.4.0-1ubuntu1~22.04) 11.4.0


export CUDA_HOME=/usr/local/cuda-12.6 && export PATH=$CUDA_HOME/bin:$PATH && pip install flash-attn --no-build-isolation 2>&1 | tail -10

Install flash-attn with g++ and CUDA 12.6


      RuntimeError: Error compiling objects for extension
      [end of output]

  note: This error originates from a subprocess, and is likely not a problem with pip.
  ERROR: Failed building wheel for flash-attn
Failed to build flash-attn
error: failed-wheel-build-for-install

× Failed to build installable wheels for some pyproject.toml based projects
╰─> flash-attn


export CUDA_HOME=/usr/local/cuda-12.6 && export PATH=$CUDA_HOME/bin:$PATH && pip install flash-attn --no-build-isolation 2>&1 | grep -E "error|fatal|Error" | head -15

Get compilation errors


  Building wheel for flash-attn (pyproject.toml): finished with status 'error'
  error: subprocess-exited-with-error
              calls to avoid build errors in the future.
      gcc: fatal error: cannot execute ‘cc1plus’: execvp: No such file or directory
      nvcc fatal   : Failed to preprocess host compiler properties.
      gcc: fatal error: cannot execute ‘cc1plus’: execvp: No such file or directory
      nvcc fatal   : Failed to preprocess host compiler properties.
      gcc: fatal error: cannot execute ‘cc1plus’: execvp: No such file or directory
      nvcc fatal   : Failed to preprocess host compiler properties.
      gcc: fatal error: cannot execute ‘cc1plus’: execvp: No such file or directory
      nvcc fatal   : Failed to preprocess host compiler properties.
      gcc: fatal error: cannot execute ‘cc1plus’: execvp: No such file or directory
      nvcc fatal   : Failed to preprocess host compiler properties.
      gcc: fatal error: cannot execute ‘cc1plus’: execvp: No such file or directory
      nvcc fatal   : Failed to preprocess host compiler properties.


Still can't find cc1plus. The g++ install might not have taken effect properly.


which cc1plus 2>/dev/null || find /usr -name "cc1plus" 2>/dev/null; echo "---"; which gcc && gcc -v 2>&1 | tail -1

Find cc1plus


/usr/lib/gcc/x86_64-linux-gnu/11/cc1plus
---
/usr/bin/gcc
gcc version 12.3.0 (Ubuntu 12.3.0-1ubuntu1~22.04)


gcc is version 12.3 but cc1plus is only installed for version 11. The mismatch is the issue.


sudo apt-get install -y -qq g++-12 cpp-12 2>&1 | tail -3

Install g++-12 to match gcc 12


No user sessions are running outdated binaries.

No VM guests are running outdated hypervisor (qemu) binaries on this host.


Check cc1plus now


/usr/lib/gcc/x86_64-linux-gnu/12/cc1plus
/usr/lib/gcc/x86_64-linux-gnu/11/cc1plus


Now we have cc1plus for gcc-12.


export CUDA_HOME=/usr/local/cuda-12.6 && export PATH=$CUDA_HOME/bin:$PATH && MAX_JOBS=4 pip install flash-attn --no-build-isolation 2>&1 | tail -10

Install flash-attn with fixed compiler


The user doesn't want to proceed with this tool use. The tool use was rejected (eg. if it was a file edit, the new_string was NOT written to the file). STOP what you are doing and wait for the user to tell you how to proceed.


[Request interrupted by user for tool use]


<task-notification>
<task-id>b8fw1lr3s</task-id>
<tool-use-id>toolu_01JUZC8dTyMqdsc4rAgFSder</tool-use-id>
<output-file>/tmp/claude-1001/-home-ubuntu/f26d71f5-6407-4368-8cb6-f48900e84838/tasks/b8fw1lr3s.output</output-file>
<status>completed</status>
<summary>Background command "Retry Stable Audio download" completed (exit code 0)</summary>
</task-notification>


<task-notification>
<task-id>bz934o3rq</task-id>
<tool-use-id>toolu_01WLT2WAFzzZfLH6yThYXJfL</tool-use-id>
<output-file>/tmp/claude-1001/-home-ubuntu/f26d71f5-6407-4368-8cb6-f48900e84838/tasks/bz934o3rq.output</output-file>
<status>completed</status>
<summary>Background command "Retry T5-Gemma download" completed (exit code 0)</summary>
</task-notification>


<task-notification>
<task-id>b2ayaosfw</task-id>
<tool-use-id>toolu_01DdBbysDqqHdT6iBiUTC1M7</tool-use-id>
<output-file>/tmp/claude-1001/-home-ubuntu/f26d71f5-6407-4368-8cb6-f48900e84838/tasks/b2ayaosfw.output</output-file>
<status>completed</status>
<summary>Background command "Install Flash Attention 2" completed (exit code 0)</summary>
</task-notification>


<task-notification>
<task-id>bd2wk3w31</task-id>
<tool-use-id>toolu_01RRhp3dVAKG3HCkzrma5JkZ</tool-use-id>
<output-file>/tmp/claude-1001/-home-ubuntu/f26d71f5-6407-4368-8cb6-f48900e84838/tasks/bd2wk3w31.output</output-file>
<status>completed</status>
<summary>Background command "Install daVinci-MagiHuman deps" completed (exit code 0)</summary>
</task-notification>


<task-notification>
<task-id>b6xdgiro7</task-id>
<tool-use-id>toolu_012e7AgmkCJw52GvzA19XZrf</tool-use-id>
<output-file>/tmp/claude-1001/-home-ubuntu/f26d71f5-6407-4368-8cb6-f48900e84838/tasks/b6xdgiro7.output</output-file>
<status>completed</status>
<summary>Background command "Install MagiCompiler" completed (exit code 0)</summary>
</task-notification>


Let me check what actually installed from those background tasks.


python3 -c "import flash_attn; print(f'flash_attn={flash_attn.__version__}')" 2>&1; python3 -c "import magi_compiler; print('magi_compiler OK')" 2>&1; python3 -c "import transformers; print(f'transformers={transformers.__version__}')" 2>&1

Check what's installed


Traceback (most recent call last):
  File "<string>", line 1, in <module>
ModuleNotFoundError: No module named 'flash_attn'
Traceback (most recent call last):
  File "<string>", line 1, in <module>
ModuleNotFoundError: No module named 'magi_compiler'
transformers=4.57.6


stop if any proces running


[Request interrupted by user]


ps aux | grep -E "pip install|huggingface|torchrun|python.*flash" | grep -v grep

Check for running processes


(Bash completed with no output)


No processes running. Everything is stopped.