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Fine Tune Stable Diffusion¶
Fine tuning Stable Diffusion on Pokemon, for more details see the Lambda Labs examples repo.
We recommend using a multi-GPU machine, for example an instance from Lambda GPU Cloud. If running on Colab this notebook is likely to need a GPU with >16GB of VRAM and a runtime with high RAM, which will almost certainly need Colab Pro or Pro+. (If you get errors suchs as Killed or CUDA out of memory then one of these is not sufficient)
In [2]:
!git clone https://github.com/justinpinkney/stable-diffusion.git
%cd stable-diffusion
!pip install --upgrade pip
!pip install -r requirements.txt
!pip install --upgrade keras # on lambda stack we need to upgrade keras
#!pip uninstall -y torchtext # on colab we need to remove torchtext
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Building wheels for collected packages: albumentations, pudb, fire, antlr4-python3-runtime, test-tube, imgaug, urwid, validators, ffmpy, python-multipart
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Created wheel for albumentations: filename=albumentations-0.4.3-py3-none-any.whl size=60766 sha256=05eadca09805575f3074ec44f877ba2879dcd3e6da2704396d8a47032fc14ec8
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Successfully built albumentations pudb fire antlr4-python3-runtime test-tube imgaug urwid validators ffmpy python-multipart
Installing collected packages: wcwidth, urwid, tokenizers, rfc3986, pydub, monotonic, ffmpy, einops, commonmark, braceexpand, antlr4-python3-runtime, xxhash, webdataset, watchdog, validators, urllib3, uc-micro-py, tzdata, toml, taming-transformers, smmap, semver, rich, regex, python-multipart, pympler, pyDeprecate, pycryptodome, pyarrow, pudb, protobuf, orjson, opencv-python-headless, opencv-python, omegaconf, multidict, mdurl, latent-diffusion, imageio-ffmpeg, imageio, h11, ftfy, fsspec, frozenlist, fire, dill, bcrypt, backports.zoneinfo, backoff, async-timeout, yarl, uvicorn, torchmetrics, torch-fidelity, starlette, pytz-deprecation-shim, pydeck, paramiko, multiprocess, markdown-it-py, linkify-it-py, kornia, imgaug, httpcore, gitdb, clip, aiosignal, tzlocal, test-tube, responses, mdit-py-plugins, huggingface-hub, httpx, gitpython, fastapi, analytics-python, altair, albumentations, aiohttp, transformers, streamlit, diffusers, pytorch-lightning, gradio, datasets
Running setup.py develop for taming-transformers
Running setup.py develop for latent-diffusion
Running setup.py develop for clip
Successfully installed aiohttp-3.8.3 aiosignal-1.2.0 albumentations-0.4.3 altair-4.2.0 analytics-python-1.4.0 antlr4-python3-runtime-4.8 async-timeout-4.0.2 backoff-1.10.0 backports.zoneinfo-0.2.1 bcrypt-4.0.0 braceexpand-0.1.7 clip commonmark-0.9.1 datasets-2.4.0 diffusers-0.3.0 dill-0.3.5.1 einops-0.3.0 fastapi-0.85.0 ffmpy-0.3.0 fire-0.4.0 frozenlist-1.3.1 fsspec-2022.8.2 ftfy-6.1.1 gitdb-4.0.9 gitpython-3.1.28 gradio-3.1.4 h11-0.12.0 httpcore-0.15.0 httpx-0.23.0 huggingface-hub-0.10.0 imageio-2.9.0 imageio-ffmpeg-0.4.2 imgaug-0.2.6 kornia-0.6.0 latent-diffusion linkify-it-py-1.0.3 markdown-it-py-2.1.0 mdit-py-plugins-0.3.1 mdurl-0.1.2 monotonic-1.6 multidict-6.0.2 multiprocess-0.70.13 omegaconf-2.1.1 opencv-python-4.5.5.64 opencv-python-headless-4.6.0.66 orjson-3.8.0 paramiko-2.11.0 protobuf-3.20.3 pudb-2019.2 pyDeprecate-0.3.1 pyarrow-9.0.0 pycryptodome-3.15.0 pydeck-0.8.0b3 pydub-0.25.1 pympler-1.0.1 python-multipart-0.0.5 pytorch-lightning-1.4.2 pytz-deprecation-shim-0.1.0.post0 regex-2022.9.13 responses-0.18.0 rfc3986-1.5.0 rich-12.6.0 semver-2.13.0 smmap-5.0.0 starlette-0.20.4 streamlit-1.13.0 taming-transformers test-tube-0.7.5 tokenizers-0.12.1 toml-0.10.2 torch-fidelity-0.3.0 torchmetrics-0.6.0 transformers-4.22.2 tzdata-2022.4 tzlocal-4.2 uc-micro-py-1.0.1 urllib3-1.26.12 urwid-2.1.2 uvicorn-0.18.3 validators-0.20.0 watchdog-2.1.9 wcwidth-0.2.5 webdataset-0.2.5 xxhash-3.0.0 yarl-1.8.1
Defaulting to user installation because normal site-packages is not writeable
Requirement already satisfied: keras in /usr/lib/python3/dist-packages (2.9.0)
Collecting keras
Downloading keras-2.10.0-py2.py3-none-any.whl (1.7 MB)
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Installing collected packages: keras
Successfully installed keras-2.10.0
In [3]:
!nvidia-smi
Fri Oct 7 07:47:59 2022
+-----------------------------------------------------------------------------+
| NVIDIA-SMI 510.47.03 Driver Version: 510.47.03 CUDA Version: 11.6 |
|-------------------------------+----------------------+----------------------+
| 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 A100-SXM... On | 00000000:06:00.0 Off | 0 |
| N/A 33C P0 46W / 400W | 0MiB / 40960MiB | 0% Default |
| | | Disabled |
+-------------------------------+----------------------+----------------------+
+-----------------------------------------------------------------------------+
| Processes: |
| GPU GI CI PID Type Process name GPU Memory |
| ID ID Usage |
|=============================================================================|
| No running processes found |
+-----------------------------------------------------------------------------+
In [8]:
# Check the dataset
# from datasets import load_dataset
# ds = load_dataset("lambdalabs/pokemon-blip-captions", split="train")
# sample = ds[0]
# display(sample["image"].resize((256, 256)))
# print(sample["text"])
from paris_dataloader import ParisDataset
ds = ParisDataset("../VLoD/", 512, 512, None, [])
# sample = ds[0]
--------------------------------------------------------------------------- NotImplementedError Traceback (most recent call last) <ipython-input-8-df3cd5287312> in <module> 9 ds = ParisDataset("../VLoD/", 512, 512, None, []) 10 ---> 11 sample = ds[0] /usr/lib/python3/dist-packages/torch/utils/data/dataset.py in __getitem__(self, index) 66 67 def __getitem__(self, index) -> T_co: ---> 68 raise NotImplementedError 69 70 def __add__(self, other: 'Dataset[T_co]') -> 'ConcatDataset[T_co]': NotImplementedError:
To get the weights you need to you'll need to go to the model card, read the license and tick the checkbox if you agree.
In [5]:
!pip install huggingface_hub
from huggingface_hub import notebook_login
notebook_login()
Defaulting to user installation because normal site-packages is not writeable Requirement already satisfied: huggingface_hub in /home/ubuntu/.local/lib/python3.8/site-packages (0.10.0) Requirement already satisfied: packaging>=20.9 in /home/ubuntu/.local/lib/python3.8/site-packages (from huggingface_hub) (21.3) Requirement already satisfied: typing-extensions>=3.7.4.3 in /home/ubuntu/.local/lib/python3.8/site-packages (from huggingface_hub) (4.3.0) Requirement already satisfied: requests in /home/ubuntu/.local/lib/python3.8/site-packages (from huggingface_hub) (2.28.1) Requirement already satisfied: pyyaml>=5.1 in /usr/lib/python3/dist-packages (from huggingface_hub) (5.3.1) Requirement already satisfied: tqdm in /home/ubuntu/.local/lib/python3.8/site-packages (from huggingface_hub) (4.64.1) Requirement already satisfied: filelock in /usr/lib/python3/dist-packages (from huggingface_hub) (3.0.12) Requirement already satisfied: pyparsing!=3.0.5,>=2.0.2 in /usr/lib/python3/dist-packages (from packaging>=20.9->huggingface_hub) (2.4.6) Requirement already satisfied: urllib3<1.27,>=1.21.1 in /home/ubuntu/.local/lib/python3.8/site-packages (from requests->huggingface_hub) (1.26.12) Requirement already satisfied: certifi>=2017.4.17 in /usr/lib/python3/dist-packages (from requests->huggingface_hub) (2019.11.28) Requirement already satisfied: idna<4,>=2.5 in /usr/lib/python3/dist-packages (from requests->huggingface_hub) (2.8) Requirement already satisfied: charset-normalizer<3,>=2 in /home/ubuntu/.local/lib/python3.8/site-packages (from requests->huggingface_hub) (2.1.1)
VBox(children=(HTML(value='<center> <img\nsrc=https://huggingface.co/front/assets/huggingface_logo-noborder.sv…
In [6]:
from huggingface_hub import hf_hub_download
ckpt_path = hf_hub_download(repo_id="CompVis/stable-diffusion-v-1-4-original", filename="sd-v1-4-full-ema.ckpt", use_auth_token=True)
Downloading: 0%| | 0.00/7.70G [00:00<?, ?B/s]
In [14]:
ckpt_path
Out[14]:
'/home/ubuntu/.cache/huggingface/hub/models--CompVis--stable-diffusion-v-1-4-original/snapshots/0834a76f88354683d3f7ef271cadd28f4757a8cc/sd-v1-4-full-ema.ckpt'
Set your parameters below depending on your GPU setup, the settings below were used for training on a 2xA6000 machine, (the A6000 has 48GB of VRAM). On this set up good results are achieved in around 6 hours.
You can make up for using smaller batches or fewer gpus by accumulating batches:
total batch size = batach size * n gpus * accumulate batches
In [12]:
# 2xA6000:
BATCH_SIZE = 2
N_GPUS = 1
ACCUMULATE_BATCHES = 1
gpu_list = ",".join((str(x) for x in range(N_GPUS))) + ","
print(f"Using GPUs: {gpu_list}")
Using GPUs: 0,
In [11]:
# Run training
!(python main.py \
-t \
--base configs/stable-diffusion/paris.yaml \
--gpus "$gpu_list" \
--scale_lr False \
--num_nodes 1 \
--check_val_every_n_epoch 10 \
--finetune_from "$ckpt_path" \
data.params.batch_size="$BATCH_SIZE" \
lightning.trainer.accumulate_grad_batches="$ACCUMULATE_BATCHES" \
data.params.validation.params.n_gpus="$NUM_GPUS" \
)
The cache for model files in Transformers v4.22.0 has been updated. Migrating your old cache. This is a one-time only operation. You can interrupt this and resume the migration later on by calling `transformers.utils.move_cache()`.
Moving 0 files to the new cache system
0it [00:00, ?it/s]
usage: main.py [-h] [--finetune_from [FINETUNE_FROM]] [-n [NAME]]
[-r [RESUME]] [-b [base_config.yaml [base_config.yaml ...]]]
[-t [TRAIN]] [--no-test [NO_TEST]] [-p PROJECT] [-d [DEBUG]]
[-s SEED] [-f POSTFIX] [-l LOGDIR] [--scale_lr [SCALE_LR]]
[--logger [LOGGER]]
[--checkpoint_callback [CHECKPOINT_CALLBACK]]
[--default_root_dir DEFAULT_ROOT_DIR]
[--gradient_clip_val GRADIENT_CLIP_VAL]
[--gradient_clip_algorithm GRADIENT_CLIP_ALGORITHM]
[--process_position PROCESS_POSITION] [--num_nodes NUM_NODES]
[--num_processes NUM_PROCESSES] [--devices DEVICES]
[--gpus GPUS] [--auto_select_gpus [AUTO_SELECT_GPUS]]
[--tpu_cores TPU_CORES] [--ipus IPUS]
[--log_gpu_memory LOG_GPU_MEMORY]
[--progress_bar_refresh_rate PROGRESS_BAR_REFRESH_RATE]
[--overfit_batches OVERFIT_BATCHES]
[--track_grad_norm TRACK_GRAD_NORM]
[--check_val_every_n_epoch CHECK_VAL_EVERY_N_EPOCH]
[--fast_dev_run [FAST_DEV_RUN]]
[--accumulate_grad_batches ACCUMULATE_GRAD_BATCHES]
[--max_epochs MAX_EPOCHS] [--min_epochs MIN_EPOCHS]
[--max_steps MAX_STEPS] [--min_steps MIN_STEPS]
[--max_time MAX_TIME]
[--limit_train_batches LIMIT_TRAIN_BATCHES]
[--limit_val_batches LIMIT_VAL_BATCHES]
[--limit_test_batches LIMIT_TEST_BATCHES]
[--limit_predict_batches LIMIT_PREDICT_BATCHES]
[--val_check_interval VAL_CHECK_INTERVAL]
[--flush_logs_every_n_steps FLUSH_LOGS_EVERY_N_STEPS]
[--log_every_n_steps LOG_EVERY_N_STEPS]
[--accelerator ACCELERATOR] [--sync_batchnorm [SYNC_BATCHNORM]]
[--precision PRECISION] [--weights_summary WEIGHTS_SUMMARY]
[--weights_save_path WEIGHTS_SAVE_PATH]
[--num_sanity_val_steps NUM_SANITY_VAL_STEPS]
[--truncated_bptt_steps TRUNCATED_BPTT_STEPS]
[--resume_from_checkpoint RESUME_FROM_CHECKPOINT]
[--profiler PROFILER] [--benchmark [BENCHMARK]]
[--deterministic [DETERMINISTIC]]
[--reload_dataloaders_every_n_epochs RELOAD_DATALOADERS_EVERY_N_EPOCHS]
[--reload_dataloaders_every_epoch [RELOAD_DATALOADERS_EVERY_EPOCH]]
[--auto_lr_find [AUTO_LR_FIND]]
[--replace_sampler_ddp [REPLACE_SAMPLER_DDP]]
[--terminate_on_nan [TERMINATE_ON_NAN]]
[--auto_scale_batch_size [AUTO_SCALE_BATCH_SIZE]]
[--prepare_data_per_node [PREPARE_DATA_PER_NODE]]
[--plugins PLUGINS] [--amp_backend AMP_BACKEND]
[--amp_level AMP_LEVEL]
[--distributed_backend DISTRIBUTED_BACKEND]
[--move_metrics_to_cpu [MOVE_METRICS_TO_CPU]]
[--multiple_trainloader_mode MULTIPLE_TRAINLOADER_MODE]
[--stochastic_weight_avg [STOCHASTIC_WEIGHT_AVG]]
main.py: error: argument --gpus: invalid _gpus_allowed_type value: ''
In [ ]:
# Run the model
!(python scripts/txt2img.py \
--prompt 'robotic cat with wings' \
--outdir 'outputs/generated_pokemon' \
--H 512 --W 512 \
--n_samples 4 \
--config 'configs/stable-diffusion/pokemon.yaml' \
--ckpt 'path/to/your/checkpoint')