Machine Learning | とあるかえるのつぶやき

Docker Composeが途中で止まってしまった原因は不明だが、まずは動かしてみたいというところでDevContainer使ってVSCode上のターミナルから試行錯誤してみようとと。

参考はこちら
Stable Diffusionをローカルマシンで実行する(VSCodeとDevcontainerを使用)
https://zenn.dev/hayatok/articles/6141a9a46e4f48

DevContainer準備

VSCodeで適当なフォルダを開き、Dockerfileを作成します

FROM nvcr.io/nvidia/cuda:11.7.1-cudnn8-runtime-ubuntu20.04

ARG DEBIAN_FRONTEND=noninteractive
ENV TZ=Asia/Tokyo

RUN apt-get update && apt-get install -y wget git git-lfs libglib2.0-0 libsm6 libxrender1 libxext-dev

RUN wget https://repo.anaconda.com/miniconda/Miniconda3-latest-Linux-x86_64.sh && \
    sh Miniconda3-latest-Linux-x86_64.sh -b -p /opt/miniconda3 && \
    rm -r Miniconda3-latest-Linux-x86_64.sh

ENV PATH /opt/miniconda3/bin:$PATH

RUN git clone https://github.com/CompVis/stable-diffusion && \
    cd stable-diffusion && \
    conda init bash && \
    conda env create -f environment.yaml && \
    echo "conda activate ldm" >> ~/.bashrc

VSCode のコマンドパレットから「Remote-Containers: Add Development Container Configuration Files…」を選択し、作成したDockerfileを指定します。

devcontainer.jsonが作成されるので、編集します

// For format details, see https://aka.ms/devcontainer.json. For config options, see the README at:
// https://github.com/microsoft/vscode-dev-containers/tree/v0.245.0/containers/docker-existing-dockerfile
{
	"name": "Existing Dockerfile",

	// Sets the run context to one level up instead of the .devcontainer folder.
	"context": "..",

	// Update the 'dockerFile' property if you aren't using the standard 'Dockerfile' filename.
	"dockerFile": "../Dockerfile",

	// 追記ここから。GPU利用可のコンテナ起動オプション
	"runArgs":[
		"--gpus",
		"all"
	],
	// 追記ここまで。
	"customizations": {
		"vscode": {
			"extensions": [
				"ms-python.python"
			]
		}
	}

	// Use 'forwardPorts' to make a list of ports inside the container available locally.
	// "forwardPorts": [],

	// Uncomment the next line to run commands after the container is created - for example installing curl.
	// "postCreateCommand": "apt-get update && apt-get install -y curl",

	// Uncomment when using a ptrace-based debugger like C++, Go, and Rust
	// "runArgs": [ "--cap-add=SYS_PTRACE", "--security-opt", "seccomp=unconfined" ],

	// Uncomment to use the Docker CLI from inside the container. See https://aka.ms/vscode-remote/samples/docker-from-docker.
	// "mounts": [ "source=/var/run/docker.sock,target=/var/run/docker.sock,type=bind" ],

	// Uncomment to connect as a non-root user if you've added one. See https://aka.ms/vscode-remote/containers/non-root.
	// "remoteUser": "vscode"
}

コマンドパレットから「Remote-Containers: Rebuild and Reopen in Container」を選択することで、コンテナ内でVSCodeが起動する形となります。
VSCode の左下がこの状態ですね

Modelの配置

前回取得した Hugging Face のモデルを、Stable Diffusionがモデルのデフォルトパスとして指定している”models/ldm/stable-diffusion-v1/model.ckpt”に配備します。
このあたりは、”txt2image.py”を読んでいくとなんのパラメータ指定が出来るのかがわ借ります。
別のパスを指定する場合は —ckpt オプションで指定可能となっています

実行！

参考にさせていただいたページに従って、test.pyを用意し、実行します

(ldm) root@21feb17171f4:/workspaces/StableDiffusion2/stable-diffusion# python test.py
Traceback (most recent call last):
  File "test.py", line 6, in <module>
    pipe = StableDiffusionPipeline.from_pretrained(
  File "/opt/miniconda3/envs/ldm/lib/python3.8/site-packages/diffusers/pipeline_utils.py", line 154, in from_pretrained
    cached_folder = snapshot_download(
  File "/opt/miniconda3/envs/ldm/lib/python3.8/site-packages/huggingface_hub/utils/_deprecation.py", line 93, in inner_f
    return f(*args, **kwargs)
  File "/opt/miniconda3/envs/ldm/lib/python3.8/site-packages/huggingface_hub/_snapshot_download.py", line 168, in snapshot_download
    repo_info = _api.repo_info(
  File "/opt/miniconda3/envs/ldm/lib/python3.8/site-packages/huggingface_hub/hf_api.py", line 1454, in repo_info
    return self.model_info(
  File "/opt/miniconda3/envs/ldm/lib/python3.8/site-packages/huggingface_hub/hf_api.py", line 1276, in model_info
    _raise_for_status(r)
  File "/opt/miniconda3/envs/ldm/lib/python3.8/site-packages/huggingface_hub/utils/_errors.py", line 169, in _raise_for_status
    raise e
  File "/opt/miniconda3/envs/ldm/lib/python3.8/site-packages/huggingface_hub/utils/_errors.py", line 131, in _raise_for_status
    response.raise_for_status()
  File "/opt/miniconda3/envs/ldm/lib/python3.8/site-packages/requests/models.py", line 1021, in raise_for_status
    raise HTTPError(http_error_msg, response=self)
requests.exceptions.HTTPError: 403 Client Error: Forbidden for url: https://huggingface.co/api/models/CompVis/stable-diffusion-v1-4/revision/fp16 (Request ID: PQ6M8N2Lators-j6XdN6V)

Access to model CompVis/stable-diffusion-v1-4 is restricted and you are not in the authorized list. Visit https://huggingface.co/CompVis/stable-diffusion-v1-4 to ask for access.
(ldm) root@21feb17171f4:/workspaces/StableDiffusion2/stable-diffusion# python test.py
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0it [00:01, ?it/s]
Traceback (most recent call last):
  File "test.py", line 15, in <module>
    image = pipe(prompt)["sample"][0]  
  File "/opt/miniconda3/envs/ldm/lib/python3.8/site-packages/torch/autograd/grad_mode.py", line 27, in decorate_context
    return func(*args, **kwargs)
  File "/opt/miniconda3/envs/ldm/lib/python3.8/site-packages/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion.py", line 137, in __call__
    noise_pred = self.unet(latent_model_input, t, encoder_hidden_states=text_embeddings)["sample"]
  File "/opt/miniconda3/envs/ldm/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1110, in _call_impl
    return forward_call(*input, **kwargs)
  File "/opt/miniconda3/envs/ldm/lib/python3.8/site-packages/diffusers/models/unet_2d_condition.py", line 150, in forward
    sample, res_samples = downsample_block(
  File "/opt/miniconda3/envs/ldm/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1110, in _call_impl
    return forward_call(*input, **kwargs)
  File "/opt/miniconda3/envs/ldm/lib/python3.8/site-packages/diffusers/models/unet_blocks.py", line 505, in forward
    hidden_states = attn(hidden_states, context=encoder_hidden_states)
  File "/opt/miniconda3/envs/ldm/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1110, in _call_impl
    return forward_call(*input, **kwargs)
  File "/opt/miniconda3/envs/ldm/lib/python3.8/site-packages/diffusers/models/attention.py", line 168, in forward
    x = block(x, context=context)
  File "/opt/miniconda3/envs/ldm/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1110, in _call_impl
    return forward_call(*input, **kwargs)
  File "/opt/miniconda3/envs/ldm/lib/python3.8/site-packages/diffusers/models/attention.py", line 196, in forward
    x = self.attn1(self.norm1(x)) + x
  File "/opt/miniconda3/envs/ldm/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1110, in _call_impl
    return forward_call(*input, **kwargs)
  File "/opt/miniconda3/envs/ldm/lib/python3.8/site-packages/diffusers/models/attention.py", line 245, in forward
    sim = torch.einsum("b i d, b j d -> b i j", q, k) * self.scale
RuntimeError: CUDA out of memory. Tried to allocate 512.00 MiB (GPU 0; 4.00 GiB total capacity; 3.13 GiB already allocated; 0 bytes free; 3.13 GiB reserved in total by PyTorch) If reserved memory is >> allocated memory try setting max_split_size_mb to avoid fragmentation.  See documentation for Memory Management and PYTORCH_CUDA_ALLOC_CONF

エラーになってしまった。要するにメモリが足りないんだと。
そこでメモリの状態を調べるコマンドを打ってみる

(ldm) root@21feb17171f4:/workspaces/StableDiffusion2/stable-diffusion# nvidia-smi
Tue Sep  6 14:52:34 2022       
+-----------------------------------------------------------------------------+
| NVIDIA-SMI 515.65.01    Driver Version: 516.94       CUDA Version: 11.7     |
|-------------------------------+----------------------+----------------------+
| GPU  Name        Persistence-M| Bus-Id        Disp.A | Volatile Uncorr. ECC |
| Fan  Temp  Perf  Pwr:Usage/Cap|         Memory-Usage | GPU-Util  Compute M. |
|                               |                      |               MIG M. |
|===============================+======================+======================|
|   0  NVIDIA GeForce ...  On   | 00000000:01:00.0 Off |                  N/A |
| N/A   56C    P3    13W /  N/A |      0MiB /  4096MiB |      0%      Default |
|                               |                      |                  N/A |
+-------------------------------+----------------------+----------------------+
                                                                               
+-----------------------------------------------------------------------------+
| Processes:                                                                  |
|  GPU   GI   CI        PID   Type   Process name                  GPU Memory |
|        ID   ID                                                   Usage      |
|=============================================================================|
|  No running processes found                                                 |
+-----------------------------------------------------------------------------+

むぅ。そもそもいくつあればOKなのかがいまいちわからん。
エラーメッセージに出てきた「PYTORCH_CUDA_ALLOC_CONF」あたりを見ていくと何とかパラメータ指定でうまく行けないかな？という気もするけど、多くの人がStable Diffusionをそのまま使うのではなく、メモリ消費を抑えたバージョンを使っているという事を見聞きしているので、そちらを使うことにする

(ldm) root@21feb17171f4:/workspaces/StableDiffusion2# mkdir basujindal
(ldm) root@21feb17171f4:/workspaces/StableDiffusion2# ls
Dockerfile  basujindal  stable-diffusion
(ldm) root@21feb17171f4:/workspaces/StableDiffusion2# cd basujindal/
(ldm) root@21feb17171f4:/workspaces/StableDiffusion2/basujindal# git clone https://github.com/basujindal/stable-diffusion.git

適当なフォルダを作って、リポジトリからclone

(ldm) root@21feb17171f4:/workspaces/StableDiffusion2/basujindal# cd stable-diffusion/
(ldm) root@21feb17171f4:/workspaces/StableDiffusion2/basujindal/stable-diffusion# python optimizedSD/optimized_txt2img.py --prompt "a photograph of an astronaut riding a horse" --H 512 --W 512 --seed 27 --n_iter 2 --n_samples 10 --ddim_steps 50
Global seed set to 27
Loading model from models/ldm/stable-diffusion-v1/model.ckpt
Traceback (most recent call last):
  File "optimizedSD/optimized_txt2img.py", line 184, in <module>
    sd = load_model_from_config(f"{ckpt}")
  File "optimizedSD/optimized_txt2img.py", line 29, in load_model_from_config
    pl_sd = torch.load(ckpt, map_location="cpu")
  File "/opt/miniconda3/envs/ldm/lib/python3.8/site-packages/torch/serialization.py", line 699, in load
    with _open_file_like(f, 'rb') as opened_file:
  File "/opt/miniconda3/envs/ldm/lib/python3.8/site-packages/torch/serialization.py", line 231, in _open_file_like
    return _open_file(name_or_buffer, mode)
  File "/opt/miniconda3/envs/ldm/lib/python3.8/site-packages/torch/serialization.py", line 212, in __init__
    super(_open_file, self).__init__(open(name, mode))
FileNotFoundError: [Errno 2] No such file or directory: 'models/ldm/stable-diffusion-v1/model.ckpt'

おっと、モデルを新たに置き直さないと行けない。
サクッとコピーして、再度実行する

(ldm) root@21feb17171f4:/workspaces/StableDiffusion2/basujindal/stable-diffusion# python optimizedSD/optimized_txt2img.py --prompt "a photograph of an astronaut riding a horse" --H 512 --W 512 --seed 27 --n_iter 2 --n_samples 10 --ddim_steps 50
Global seed set to 27
Loading model from models/ldm/stable-diffusion-v1/model.ckpt
Global Step: 470000
Traceback (most recent call last):
  File "optimizedSD/optimized_txt2img.py", line 204, in <module>
    model = instantiate_from_config(config.modelUNet)
  File "/stable-diffusion/ldm/util.py", line 85, in instantiate_from_config
    return get_obj_from_str(config["target"])(**config.get("params", dict()))
  File "/stable-diffusion/ldm/util.py", line 93, in get_obj_from_str
    return getattr(importlib.import_module(module, package=None), cls)
  File "/opt/miniconda3/envs/ldm/lib/python3.8/importlib/__init__.py", line 127, in import_module
    return _bootstrap._gcd_import(name[level:], package, level)
  File "<frozen importlib._bootstrap>", line 1014, in _gcd_import
  File "<frozen importlib._bootstrap>", line 991, in _find_and_load
  File "<frozen importlib._bootstrap>", line 961, in _find_and_load_unlocked
  File "<frozen importlib._bootstrap>", line 219, in _call_with_frames_removed
  File "<frozen importlib._bootstrap>", line 1014, in _gcd_import
  File "<frozen importlib._bootstrap>", line 991, in _find_and_load
  File "<frozen importlib._bootstrap>", line 973, in _find_and_load_unlocked
ModuleNotFoundError: No module named 'optimizedSD'

あれ、何だ？optimizedSDがない？

調べてみると、新しくディレクトリ作って作業しているので、PythonのEnvを作り直す必要があるそうだ

(ldm) root@21feb17171f4:/workspaces/StableDiffusion2/basujindal/stable-diffusion# conda deactivate       
(base) root@21feb17171f4:/workspaces/StableDiffusion2/basujindal/stable-diffusion# conda remove -n ldm --all
(base) root@21feb17171f4:/workspaces/StableDiffusion2/basujindal/stable-diffusion# conda env create -f environment.yaml 
(base) root@21feb17171f4:/workspaces/StableDiffusion2/basujindal/stable-diffusion# conda activate ldm

よし、気を取り直して実行だ

(ldm) root@21feb17171f4:/workspaces/StableDiffusion2/basujindal/stable-diffusion# python optimizedSD/optimized_txt2img.py --prompt "a photograph of an astronaut riding a horse" --H 512 --W 512 --seed 27 --n_iter 2 --n_samples 10 --ddim_steps 50
Global seed set to 27
Loading model from models/ldm/stable-diffusion-v1/model.ckpt
Global Step: 470000
UNet: Running in eps-prediction mode
CondStage: Running in eps-prediction mode
Downloading: "https://github.com/DagnyT/hardnet/raw/master/pretrained/train_liberty_with_aug/checkpoint_liberty_with_aug.pth" to /root/.cache/torch/hub/checkpoints/checkpoint_liberty_with_aug.pth
100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 5.10M/5.10M [00:00<00:00, 21.2MB/s]
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Killed

いきなり殺された。。。

stable_diffusion.openvino を WSL2 で実行したけど”Killed” されちゃった人へ
https://zenn.dev/suzuki5080/articles/1438d52377b9df

調べてみると、これも結局はメモリが不足しているために発生しているという可能性が高い。
そして、WSL2に対する割当メモリを変えることで、この問題は解消する可能性が！

[wsl2]
memory=12GB

.wslconfigファイルを配備して、再起動。
そして再チャレンジ

(ldm) root@21feb17171f4:/workspaces/StableDiffusion2/basujindal/stable-diffusion# python optimizedSD/optimized_txt2img.py --prompt "a photograph of an astronaut riding a horse" --H 512 --W 512 --seed 27 --n_iter 2 --n_samples 10 --ddim_steps 50
Global seed set to 27
Loading model from models/ldm/stable-diffusion-v1/model.ckpt
Global Step: 470000
UNet: Running in eps-prediction mode
CondStage: Running in eps-prediction mode
FirstStage: Running in eps-prediction mode
making attention of type 'vanilla' with 512 in_channels
Working with z of shape (1, 4, 32, 32) = 4096 dimensions.
making attention of type 'vanilla' with 512 in_channels
Sampling:   0%|                                                                                                                             | 0/2 [00:00<?, ?it/sseeds used =  [27, 28, 29, 30, 31, 32, 33, 34, 35, 36]                                                                                       | 0/1 [00:00<?, ?it/s]
Data shape for PLMS sampling is [10, 4, 64, 64]
Running PLMS Sampling with 50 timesteps
PLMS Sampler: 100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████| 50/50 [06:46<00:00,  8.13s/it]
torch.Size([10, 4, 64, 64])
saving images 100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████| 50/50 [06:46<00:00,  8.38s/it]
memory_final =  7.72352
data: 100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 1/1 [07:04<00:00, 424.21s/it]
Sampling:  50%|██████████████████████████████████████████████████████████                                                          | 1/2 [07:04<07:04, 424.21s/itseeds used =  [37, 38, 39, 40, 41, 42, 43, 44, 45, 46]                                                                                       | 0/1 [00:00<?, ?it/s]
Data shape for PLMS sampling is [10, 4, 64, 64]
Running PLMS Sampling with 50 timesteps
PLMS Sampler: 100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████| 50/50 [07:19<00:00,  8.79s/it]
torch.Size([10, 4, 64, 64])
saving images 100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████| 50/50 [07:19<00:00,  8.62s/it]
memory_final =  6.833664
data: 100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 1/1 [07:27<00:00, 447.21s/it]
Sampling: 100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 2/2 [14:31<00:00, 435.71s/it]
Samples finished in 15.12 minutes and exported to outputs/txt2img-samples/a_photograph_of_an_astronaut_riding_a_horse
 Seeds used = 27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46

できた！できたよ、ママン！

ようやく、なんとかStable Diffusionを動かすことが出来る環境を作ることができました。
Pythonに関する知識不足もさることながら、メモリ周りで苦労しますね。

せっかく環境を作ることができたので、もう少し触って遊んでみようと思っています

とあるかえるのつぶやき

「Machine Learning」カテゴリーアーカイブ

続・画像生成AIを触ってみた

DevContainer準備

Modelの配置

実行！

機械学習をもう少し勉強したかった

気がつけば11月

機械学習入門を読んだ。。。？

2022年11月
月	火	水	木	金	土	日
	1	2	3	4	5	6
7	8	9	10	11	12	13
14	15	16	17	18	19	20
21	22	23	24	25	26	27
28	29	30