bghira
commented
3 months ago try the latest main branch, i've pushed a fix for that.
Closed TheMindExpansionNetwork closed 3 months ago
bghira
commented
3 months ago try the latest main branch, i've pushed a fix for that.
TheMindExpansionNetwork
commented
3 months ago try the latest main branch, i've pushed a fix for that.
you are amazing let me go ahead and give it another try now
thank you
TheMindExpansionNetwork
commented
3 months ago 2024-08-10 01:15:07,102 [ERROR] (main) No images were discovered by the bucket manager in the dataset: SL-Dataset-Selfie-01., traceback: Traceback (most recent call last): File "/workspace/SimpleTuner/train.py", line 670, in main configure_multi_databackend( File "/workspace/SimpleTuner/helpers/data_backend/factory.py", line 771, in configure_multi_databackend raise Exception( Exception: No images were discovered by the bucket manager in the dataset: SL-Dataset-Selfie-01.
(.venv) root@ad462c1a43c6:/workspace/SimpleTuner#
getting closer but getting this
I just have a batch of 30 photos is that enough
bghira
commented
3 months ago you see this:
2024-08-10 01:15:07,101 [ERROR] (BaseMetadataBackend) Bucket 1.0 has no images after trimming because 30 images are not enough to satisfy an effective batch size of 40. Lower your batch size, increase repeat count, or increase data pool size.
means your batch size is just a tad too high. you can do what it suggests, and see if it improves the situation.
TheMindExpansionNetwork
commented
3 months ago export TRAIN_BATCH_SIZE=10
export GRADIENT_ACCUMULATION_STEPS=4
export VAE_BATCH_SIZE=4
is this what I am changing
Sorry or is it best if I shorten data set say 15
Thanks for the help
TheMindExpansionNetwork
commented
3 months ago export TRAIN_BATCH_SIZE=10
ok sweet trained what above to 5 seems to be working.
this is an awesome tool thanks for this been doing training since joe penna notebook this is amazing seems to be groovy but i notice it is taking this long
is it normal cuz how many images prob be best to make smaller any suggestion on good number
bghira
commented
3 months ago that's a great speed to have
bghira
commented
3 months ago i suggest starting a discussion about the training settings where others can come along to share experience. my experience mostly revolves around extremely large training runs on millions of samples.
TheMindExpansionNetwork
commented
3 months ago that's a great speed to have
ok sweet just making sure
i suggest starting a discussion about the training settings where others can come along to share experience. my experience mostly revolves around extremely large training runs on millions of samples.
Ok not a problem I understand.
just wanting to ensure I'm on the correct track you know
bghira
commented
3 months ago
Hello you all really want to be able to tune an SD3 model I should have everything situated but I get this error
local variable 'update_flux_schedule_to_fast' referenced before assignment Traceback (most recent call last): File "/workspace/SimpleTuner/train.py", line 2776, in
main()
File "/workspace/SimpleTuner/train.py", line 496, in the main
update_flux_schedule_to_fast(
UnboundLocalError: local variable 'update_flux_schedule_to_fast' referenced before assignment
i am on runpod
Below is my config
Configure these values.
'lora' or 'full'
lora - train a small network for a character or style, or both. quite versatile.
full - requires lots of vram, trains very slowly, needs a lot of data and concepts.
export MODEL_TYPE='lora'
SDXL is trained by default, but you will need to enable one of these options for anything else.
Set this to 'true' if you are training a Stable Diffusion 3 checkpoint.
Use MODEL_NAME="stabilityai/stable-diffusion-3-medium-diffusers"
export STABLE_DIFFUSION_3=true
Similarly, this is to train PixArt Sigma (1K or 2K) models.
Use MODEL_NAME="PixArt-alpha/PixArt-Sigma-XL-2-1024-MS"
export PIXART_SIGMA=false
For old Stable Diffusion 1.x/2.x models, you'll enable this.
Use MODEL_NAME="stabilityai/stable-diffusion-2-1"
export STABLE_DIFFUSION_LEGACY=false
For Kwai-Kolors, enable KOLORS.
Use MODEL_NAME="kwai-kolors/kolors-diffusers"
export KOLORS=false
For Flux, if you have 8 GPUs and DeepSpeed configured.
Use MODEL_NAME="black-forest-labs/FLUX.1-dev"
export FLUX=false
ControlNet model training is only supported when MODEL_TYPE='full'
See this document for more information: https://github.com/bghira/SimpleTuner/blob/main/documentation/CONTROLNET.md
DeepFloyd, PixArt, and SD3 do not currently support ControlNet model training.
export CONTROLNET=false
DoRA enhances the training style of LoRA, but it will run more slowly at the same rank.
See: https://arxiv.org/abs/2402.09353
See: https://github.com/huggingface/peft/pull/1474
export USE_DORA=false
BitFit freeze strategy for the u-net causes everything but the biases to be frozen.
This may help retain the full model's underlying capabilities. LoRA is currently not tested/known to work.
if [[ "$MODEL_TYPE" == "full" ]]; then
When training a full model, we will rely on BitFit to keep the u-net intact.
export USE_BITFIT=true
elif [[ "$MODEL_TYPE" == "lora" ]]; then
LoRA can not use BitFit.
export USE_BITFIT=false
elif [[ "$MODEL_TYPE" == "deepfloyd-full" ]]; then
export USE_BITFIT=true
fi
Restart where we left off. Change this to "checkpoint-1234" to start from a specific checkpoint.
export RESUME_CHECKPOINT="latest"
How often to checkpoint. Depending on your learning rate, you may wish to change this.
For the default settings with 10 gradient accumulations, more frequent checkpoints might be preferable at first.
export CHECKPOINTING_STEPS=150
This is how many checkpoints we will keep. Two is safe, but three is safer.
export CHECKPOINTING_LIMIT=2
This is decided as a relatively conservative 'constant' learning rate.
Adjust higher or lower depending on how burnt your model becomes.
export LEARNING_RATE=8e-7 #@param {type:"number"}
Using a Huggingface Hub model:
export MODEL_NAME="stabilityai/stable-diffusion-3-medium-diffusers"
Using a local path to a huggingface hub model or saved checkpoint:
export MODEL_NAME="/datasets/models/pipeline"
Make DEBUG_EXTRA_ARGS empty to disable wandb.
export DEBUG_EXTRA_ARGS="--report_to=wandb" export TRACKER_PROJECT_NAME="${MODEL_TYPE}-training" export TRACKER_RUN_NAME="simpletuner-sdxl"
Max number of steps OR epochs can be used. Not both.
export MAX_NUM_STEPS=30000
Will likely overtrain, but that's fine.
export NUM_EPOCHS=0
A convenient prefix for all of your training paths.
These may be absolute or relative paths. Here, we are using relative paths.
The output will just be in a folder called "output/models" by default.
export DATALOADER_CONFIG="config/multidatabackend.json" export OUTPUT_DIR="output/models"
Set this to "true" to push your model to Hugging Face Hub.
export PUSH_TO_HUB="false"
If PUSH_TO_HUB and PUSH_CHECKPOINTS are both enabled, every saved checkpoint will be pushed to Hugging Face Hub.
export PUSH_CHECKPOINTS="true"
This will be the model name for your final hub upload, eg. "yourusername/yourmodelname"
It defaults to the wandb project name, but you can override this here.
export HUB_MODEL_NAME=$TRACKER_PROJECT_NAME
By default, images will be resized so their SMALLER EDGE is 1024 pixels, maintaining aspect ratio.
Setting this value to 768px might result in more reasonable training data sizes for SDXL.
export RESOLUTION=1024
If you want to have the training data resized by pixel area (Megapixels) rather than edge length,
set this value to "area" instead of "pixel", and uncomment the next RESOLUTION declaration.
export RESOLUTION_TYPE="pixel"
export RESOLUTION=1 # 1.0 Megapixel training sizes
If RESOLUTION_TYPE="pixel", the minimum resolution specifies the smaller edge length, measured in pixels. Recommended: 1024.
If RESOLUTION_TYPE="area", the minimum resolution specifies the total image area, measured in megapixels. Recommended: 1.
export MINIMUM_RESOLUTION=$RESOLUTION
How many decimals to round aspect buckets to.
export ASPECT_BUCKET_ROUNDING=2
Use this to append an instance prompt to each caption, used for adding trigger words.
This has not been tested in SDXL.
export INSTANCE_PROMPT="lotr style "
If you also supply a user prompt library or
--use_prompt_library, this will be added to those lists.export VALIDATION_PROMPT="a realistic photo of a woman that is standing near the beach" export VALIDATION_GUIDANCE=3.0
You'll want to set this to 0.7 if you are training a terminal SNR model.
export VALIDATION_GUIDANCE_RESCALE=0.0
How frequently we will save and run a pipeline for validations.
export VALIDATION_STEPS=100 export VALIDATION_NUM_INFERENCE_STEPS=30 export VALIDATION_NEGATIVE_PROMPT="blurry, cropped, ugly" export VALIDATION_SEED=42 export VALIDATION_RESOLUTION=$RESOLUTION
Adjust this for your GPU memory size. This, and resolution, are the biggest VRAM killers.
export TRAIN_BATCH_SIZE=10
Accumulate your update gradient over many steps, to save VRAM while still having higher effective batch size:
effective batch size = ($TRAIN_BATCH_SIZE * $GRADIENT_ACCUMULATION_STEPS).
export GRADIENT_ACCUMULATION_STEPS=4
How many images to encode at once with the VAE. Can increase VRAM use.
export VAE_BATCH_SIZE=4
Use any standard scheduler type. constant, polynomial, constant_with_warmup
export LR_SCHEDULE="polynomial"
A warmup period allows the model and the EMA weights more importantly to familiarise itself with the current quanta.
For the cosine or sine type schedules, the warmup period defines the interval between peaks or valleys.
Use a sine schedule to simulate a warmup period, or a Cosine period to simulate a polynomial start.
export LR_WARMUP_STEPS=$((MAX_NUM_STEPS / 10))
export LR_WARMUP_STEPS=1000
Caption dropout probability. Set to 0.1 for 10% of captions dropped out. Set to 0 to disable.
You may wish to disable dropout if you want to limit your changes strictly to the prompts you show the model.
You may wish to increase the rate of dropout if you want to more broadly adopt your changes across the model.
export CAPTION_DROPOUT_PROBABILITY=0.1
export METADATA_UPDATE_INTERVAL=65
How many workers to use for VAE caching.
export MAX_WORKERS=32
Read and write batch sizes for VAE caching.
export READ_BATCH_SIZE=25 export WRITE_BATCH_SIZE=64
How many images to process at once (resize, crop, transform) during VAE caching.
export IMAGE_PROCESSING_BATCH_SIZE=32
When using large batch sizes, you'll need to increase the pool connection limit.
export AWS_MAX_POOL_CONNECTIONS=128
For very large systems, setting this can reduce CPU overhead of torch spawning an unnecessarily large number of threads.
export TORCH_NUM_THREADS=8
If this is set, any images that fail to open will be DELETED to avoid re-checking them every time.
export DELETE_ERRORED_IMAGES=0
If this is set, any images that are too small for the minimum resolution size will be DELETED.
export DELETE_SMALL_IMAGES=0
Bytedance recommends these be set to "trailing" so that inference and training behave in a more congruent manner.
To follow the original SDXL training strategy, use "leading" instead, though results are generally worse.
export TRAINING_SCHEDULER_TIMESTEP_SPACING="trailing" export INFERENCE_SCHEDULER_TIMESTEP_SPACING="trailing"
Removing this option or unsetting it uses vanilla training. Setting it reweights the loss by the position of the timestep in the noise schedule.
A value "5" is recommended by the researchers. A value of "20" is the least impact, and "1" is the most impact.
export MIN_SNR_GAMMA=5
Set this to an explicit value of "false" to disable Xformers. Probably required for AMD users.
export USE_XFORMERS=false
There's basically no reason to unset this. However, to disable it, use an explicit value of "false".
This will save a lot of memory consumption when enabled.
export USE_GRADIENT_CHECKPOINTING=true
Options below here may require a bit more complicated configuration, so they are not simple variables.
TF32 is great on Ampere or Ada, not sure about earlier generations.
export ALLOW_TF32=true
AdamW 8Bit is a robust and lightweight choice. Adafactor might reduce memory consumption, and Dadaptation is slow and experimental.
AdamW is the default optimizer, but it uses a lot of memory and is slower than AdamW8Bit or Adafactor.
NOTE: When training a quantised base model, you can't use adamw_bf16. Instead, try adafactor or adamw.
Choices: adamw, adamw8bit, adafactor, dadaptation, adamw_bf16
export OPTIMIZER="adamw_bf16"
EMA is a strong regularisation method that uses a lot of extra VRAM to hold two copies of the weights.
This is worthwhile on large training runs, but not so much for smaller training runs.
NOTE: EMA is not currently applied to LoRA.
export USE_EMA=false export EMA_DECAY=0.999
export TRAINER_EXTRA_ARGS="--base_model_precision=int8-quanto"
For offset noise training:
Not recommended for terminal SNR models.
export TRAINER_EXTRA_ARGS="${TRAINER_EXTRA_ARGS} --offset_noise --noise_offset=0.02"
For terminal SNR training:
export TRAINER_EXTRA_ARGS="${TRAINER_EXTRA_ARGS} --prediction_type=v_prediction --rescale_betas_zero_snr"
export TRAINER_EXTRA_ARGS="${TRAINER_EXTRA_ARGS} --training_scheduler_timestep_spacing=trailing --inference_scheduler_timestep_spacing=trailing"
You may benefit from directing training toward a specific weighted subset of timesteps.
In this example, we train the final 25% of the timestep schedule with a 3x bias.
export TRAINER_EXTRA_ARGS="${TRAINER_EXTRA_ARGS} --timestep_bias_strategy=later --timestep_bias_portion=0.25 --timestep_bias_multiplier=3"
In this example, we train the earliest 25% of the timestep schedule with a 5x bias.
export TRAINER_EXTRA_ARGS="${TRAINER_EXTRA_ARGS} --timestep_bias_strategy=earlier --timestep_bias_portion=0.25 --timestep_bias_multiplier=5"
Here, we designate that specifically, timesteps 200 to 500 should be prioritised.
export TRAINER_EXTRA_ARGS="${TRAINER_EXTRA_ARGS} --timestep_bias_strategy=range --timestep_bias_begin=200 --timestep_bias_end=500 --timestep_bias_multiplier=3"
For experimental min-SNR weighted loss training (5 is suggested value by the original researchers):
Not recommended for terminal SNR models.
export TRAINER_EXTRA_ARGS="${TRAINER_EXTRA_ARGS} --snr_gamma=5.0"
For Wasabi S3 filesystem backend (experimental)
export TRAINER_EXTRA_ARGS="${TRAINER_EXTRA_ARGS} --data_backend=aws --aws_bucket_name=test123"
export TRAINER_EXTRA_ARGS="${TRAINER_EXTRA_ARGS} --aws_endpoint_url=https://s3.wasabisys.com"
export TRAINER_EXTRA_ARGS="${TRAINER_EXTRA_ARGS} --aws_access_key=1234567890"
export TRAINER_EXTRA_ARGS="${TRAINER_EXTRA_ARGS} --aws_secret_access_key=0987654321"
Reproducible training. Set to -1 to disable.
export TRAINING_SEED=42
Mixed precision is the best. You honestly might need to YOLO it in fp16 mode for Google Colab type setups.
export MIXED_PRECISION="bf16" # Might not be supported on all GPUs. fp32 will be needed for others. export PURE_BF16=true
This has to be changed if you're training with multiple GPUs.
export TRAINING_NUM_PROCESSES=1 export TRAINING_NUM_MACHINES=1 export ACCELERATE_EXTRA_ARGS="" # --multi_gpu or other similar flags for huggingface accelerate
With Pytorch 2.1, you might have pretty good luck here.
If you're using aspect bucketing however, each resolution change will recompile. Seriously, just don't do it.
Well, then again... Pytorch 2.2 has support for dynamic shapes. Why not?
export TRAINING_DYNAMO_BACKEND='no' # or 'no' if you want to disable torch compile in case of performance issues or lack of support (eg. AMD)
export TOKENIZERS_PARALLELISM=false
Then this is backend
[ { "id": "mindexpander-slefie-01", "type": "local", "crop": true, "crop_aspect": "square", "crop_style": "center", "resolution": 1.0, "minimum_image_size": 0.5, "maximum_image_size": 1.0, "target_downsample_size": 1.0, "resolution_type": "area", "cache_dir_vae": "cache/vae/sd3/mindexpander-slefie-01", "instance_data_dir": "datasets/mindexpander-slefie-01", "disabled": false, "skip_file_discovery": "", "caption_strategy": "filename", "metadata_backend": "txt" }, { "id": "text-embeds", "type": "local", "dataset_type": "text_embeds", "default": true, "cache_dir": "cache/text/sd3/mindexpander-slefie-01", "disabled": false, "write_batch_size": 128 } ]