404 Client Error: unet Not Found

[Anashel-RPG]

This prevent my training to continue.

404 Client Error: unet Not Found for url: https://huggingface.co/black-forest-labs/FLUX.1-dev/resolve/main/unet/config.json

Any idea? There is no config or unet foler on FLUX.1

[mhirki]

Make sure you have FLUX=true in config.env. Flux has a transformer in place of a unet. You just need to specify correct model architecture.

[Anashel-RPG]

Oh thank you so much for the quick reply!

I did set FLUX=true in 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=false
# 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.
MODEL_NAME="black-forest-labs/FLUX.1-dev"
export FLUX=True

# 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="black-forest-labs/FLUX.1-dev"
# 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="false"
# 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="ethnographic photography of teddy bear at a picnic"
export VALIDATION_GUIDANCE=7.5
# 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=2
# 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="--lora_rank=4"
## 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

[Anashel-RPG]

Progress!! Thank you for your help, once I manage to set this on runpod, I will save it as a template so people can spin one fully functional.

ERROR: Entry Not Found for url: https://huggingface.co/black-forest-labs/FLUX.1-dev/resolve/main/unet/config.json.

2024-08-11 14:14:26,545 [INFO] (ArgsParser) VAE Model: madebyollin/sdxl-vae-fp16-fix
2024-08-11 14:14:26,545 [INFO] (ArgsParser) Default VAE Cache location:
2024-08-11 14:14:26,545 [INFO] (ArgsParser) Text Cache location: cache
2024-08-11 14:14:26,545 [WARNING] (ArgsParser) Gradient accumulation steps are enabled, but gradient precision is set to 'unmodified'. This may lead to numeric instability. Consider setting --gradient_precision=fp32.
2024-08-11 14:14:26,555 [INFO] (__main__) Enabling tf32 precision boost for NVIDIA devices due to --allow_tf32.
2024-08-11 14:14:26,555 [INFO] (__main__) Load tokenizers
The tokenizer class you load from this checkpoint is not the same type as the class this function is called from. It may result in unexpected tokenization.
The tokenizer class you load from this checkpoint is 'T5Tokenizer'.
The class this function is called from is 'CLIPTokenizer'.
2024-08-11 14:14:26,708 [WARNING] (__main__) Could not load secondary tokenizer (OpenCLIP-G/14). Cannot continue: expected str, bytes or os.PathLike object, not NoneType
2024-08-11 14:14:26,712 [INFO] (__main__) Load CLIP text encoder..
2024-08-11 14:14:26,802 [INFO] (__main__) Loading CLIP text encoder from black-forest-labs/FLUX.1-dev/text_encoder..
2024-08-11 14:14:26,896 [INFO] (__main__) Load VAE: madebyollin/sdxl-vae-fp16-fix
2024-08-11 14:14:26,928 [WARNING] (__main__) Couldn't load VAE with default path. Trying without a subfolder..
2024-08-11 14:14:27,020 [INFO] (__main__) Moving text encoder to GPU.
2024-08-11 14:14:27,270 [INFO] (__main__) Loading VAE onto accelerator, converting from torch.float32 to torch.bfloat16
2024-08-11 14:14:27,584 [INFO] (DataBackendFactory) Loading data backend config from config/multidatabackend.json
2024-08-11 14:14:27,584 [INFO] (DataBackendFactory) Configuring text embed backend: text-embeds
2024-08-11 14:14:27,585 [INFO] (TextEmbeddingCache) (Rank: 0) (id=text-embeds) Listing all text embed cache entries
2024-08-11 14:14:27,586 [INFO] (DataBackendFactory) Pre-computing null embedding
2024-08-11 14:14:32,592 [INFO] (DataBackendFactory) Completed loading text embed services.
2024-08-11 14:14:32,592 [INFO] (DataBackendFactory) Configuring data backend: pseudo-camera-10k-flux
2024-08-11 14:14:32,593 [INFO] (DataBackendFactory) (id=pseudo-camera-10k-flux) Loading bucket manager.
2024-08-11 14:14:32,593 [INFO] (JsonMetadataBackend) Checking for cache file: datasets/pseudo-camera-10k/train/aspect_ratio_bucket_indices.json
2024-08-11 14:14:32,594 [INFO] (JsonMetadataBackend) Pulling cache file from storage
2024-08-11 14:14:32,594 [INFO] (DataBackendFactory) (id=pseudo-camera-10k-flux) Refreshing aspect buckets on main process.
2024-08-11 14:14:32,594 [INFO] (BaseMetadataBackend) Discovering new files...
2024-08-11 14:14:32,595 [INFO] (BaseMetadataBackend) Compressed 25 existing files from 1.
2024-08-11 14:14:32,595 [INFO] (BaseMetadataBackend) No new files discovered. Doing nothing.
2024-08-11 14:14:32,595 [INFO] (BaseMetadataBackend) Statistics: {'total_processed': 0, 'skipped': {'already_exists': 25, 'metadata_missing': 0, 'not_found': 0, 'too_small': 0, 'other': 0}}
2024-08-11 14:14:32,596 [WARNING] (DataBackendFactory) Key crop_aspect_buckets not found in the current backend config, using the existing value 'None'.
2024-08-11 14:14:32,596 [INFO] (DataBackendFactory) Configured backend: {'id': 'pseudo-camera-10k-flux', 'config':{'crop': True, 'crop_aspect': 'square', 'crop_aspect_buckets': None, 'crop_style': 'center', 'disable_validation':False, 'resolution': 1.0, 'resolution_type': 'area', 'caption_strategy': 'filename', 'instance_data_dir': 'datasets/pseudo-camera-10k/train', 'maximum_image_size': 1.0, 'target_downsample_size': 1.0, 'config_version': 1}, 'dataset_type': 'image', 'data_backend': <helpers.data_backend.local.LocalDataBackend object at 0x766d8c62ec80>, 'instance_data_dir': 'datasets/pseudo-camera-10k/train', 'metadata_backend': <helpers.metadata.backends.json.JsonMetadataBackend object at 0x766d8c62f6a0>}
(Rank: 0)  | Bucket     | Image Count (per-GPU)
------------------------------
(Rank: 0)  | 1.0        | 24
2024-08-11 14:14:32,597 [INFO] (DataBackendFactory) (id=pseudo-camera-10k-flux) Collecting captions.
Loading captions:   0%|                                                                               | 0/25 [00:002024-08-11 14:14:32,598 [INFO] (DataBackendFactory) (id=pseudo-camera-10k-flux) Initialise text embed pre-computation using the filename caption strategy. We have 25 captions to process.
2024-08-11 14:14:32,599 [INFO] (DataBackendFactory) (id=pseudo-camera-10k-flux) Completed processing 25 captions.
2024-08-11 14:14:32,600 [INFO] (DataBackendFactory) (id=pseudo-camera-10k-flux) Creating VAE latent cache.
2024-08-11 14:14:32,600 [INFO] (DataBackendFactory) (id=pseudo-camera-10k-flux) Discovering cache objects..
2024-08-11 14:14:32,601 [INFO] (DataBackendFactory) Configured backend: {'id': 'pseudo-camera-10k-flux', 'config':{'crop': True, 'crop_aspect': 'square', 'crop_aspect_buckets': None, 'crop_style': 'center', 'disable_validation':False, 'resolution': 1.0, 'resolution_type': 'area', 'caption_strategy': 'filename', 'instance_data_dir': 'datasets/pseudo-camera-10k/train', 'maximum_image_size': 1.0, 'target_downsample_size': 1.0, 'config_version': 1, 'hash_filenames': True}, 'dataset_type': 'image', 'data_backend': <helpers.data_backend.local.LocalDataBackend object at 0x766d8c62ec80>, 'instance_data_dir': 'datasets/pseudo-camera-10k/train', 'metadata_backend': <helpers.metadata.backends.json.JsonMetadataBackend object at 0x766d8c62f6a0>, 'train_dataset': <helpers.multiaspect.dataset.MultiAspectDataset object at 0x766d8ebeeec0>, 'sampler': <helpers.multiaspect.sampler.MultiAspectSampler object at 0x766d8c46d090>, 'train_dataloader': <torch.utils.data.dataloader.DataLoader object at 0x766d8c46d120>, 'text_embed_cache': <helpers.caching.text_embeds.TextEmbeddingCache object at 0x766d8eb0aa70>, 'vaecache': <helpers.caching.vae.VAECache object at 0x766d8c62fa00>}
2024-08-11 14:14:32,975 [INFO] (validation) Precomputing the negative prompt embed for validations.
2024-08-11 14:14:33,082 [INFO] (__main__) Unloading text encoders, as they are not being trained.
2024-08-11 14:14:33,357 [INFO] (__main__) After nuking text encoders from orbit, we freed 0.24 GB of VRAM. The real memories were the friends we trained a model on along the way.
2024-08-11 14:14:33,357 [INFO] (__main__) Loading U-net..
black-forest-labs/FLUX.1-dev does not appear to have a file named config.json.
Traceback (most recent call last):
  File "/workspace/SimpleTuner/.venv/lib/python3.10/site-packages/huggingface_hub/utils/_errors.py", line 304, in hf_raise_for_status
    response.raise_for_status()
  File "/workspace/SimpleTuner/.venv/lib/python3.10/site-packages/requests/models.py", line 1024, in raise_for_status
    raise HTTPError(http_error_msg, response=self)
requests.exceptions.HTTPError: 404 Client Error: Not Found for url: https://huggingface.co/black-forest-labs/FLUX.1-dev/resolve/main/unet/config.json

The above exception was the direct cause of the following exception:

Traceback (most recent call last):
  File "/workspace/SimpleTuner/.venv/lib/python3.10/site-packages/diffusers/configuration_utils.py", line 379, in load_config
    config_file = hf_hub_download(
  File "/workspace/SimpleTuner/.venv/lib/python3.10/site-packages/huggingface_hub/utils/_deprecation.py", line 101, in inner_f
    return f(*args, **kwargs)
  File "/workspace/SimpleTuner/.venv/lib/python3.10/site-packages/huggingface_hub/utils/_validators.py", line 114,in _inner_fn
    return fn(*args, **kwargs)
  File "/workspace/SimpleTuner/.venv/lib/python3.10/site-packages/huggingface_hub/file_download.py", line 1240, inhf_hub_download
    return _hf_hub_download_to_cache_dir(
  File "/workspace/SimpleTuner/.venv/lib/python3.10/site-packages/huggingface_hub/file_download.py", line 1303, in_hf_hub_download_to_cache_dir
    (url_to_download, etag, commit_hash, expected_size, head_call_error) = _get_metadata_or_catch_error(
  File "/workspace/SimpleTuner/.venv/lib/python3.10/site-packages/huggingface_hub/file_download.py", line 1751, in_get_metadata_or_catch_error
    metadata = get_hf_file_metadata(
  File "/workspace/SimpleTuner/.venv/lib/python3.10/site-packages/huggingface_hub/utils/_validators.py", line 114,in _inner_fn
    return fn(*args, **kwargs)
  File "/workspace/SimpleTuner/.venv/lib/python3.10/site-packages/huggingface_hub/file_download.py", line 1673, inget_hf_file_metadata
    r = _request_wrapper(
  File "/workspace/SimpleTuner/.venv/lib/python3.10/site-packages/huggingface_hub/file_download.py", line 376, in _request_wrapper
    response = _request_wrapper(
  File "/workspace/SimpleTuner/.venv/lib/python3.10/site-packages/huggingface_hub/file_download.py", line 400, in _request_wrapper
    hf_raise_for_status(response)
  File "/workspace/SimpleTuner/.venv/lib/python3.10/site-packages/huggingface_hub/utils/_errors.py", line 315, in hf_raise_for_status
    raise EntryNotFoundError(message, response) from e
huggingface_hub.utils._errors.EntryNotFoundError: 404 Client Error. (Request ID: Root=1-66b8c749-5a24a9cb1390b52f449c1431;8b183eed-f81a-4db9-bdeb-65f089844343)

Entry Not Found for url: https://huggingface.co/black-forest-labs/FLUX.1-dev/resolve/main/unet/config.json.

During handling of the above exception, another exception occurred:

Traceback (most recent call last):
  File "/workspace/SimpleTuner/train.py", line 2776, in <module>
    main()
  File "/workspace/SimpleTuner/train.py", line 821, in main
    unet = UNet2DConditionModel.from_pretrained(
  File "/workspace/SimpleTuner/.venv/lib/python3.10/site-packages/huggingface_hub/utils/_validators.py", line 114,in _inner_fn
    return fn(*args, **kwargs)
  File "/workspace/SimpleTuner/.venv/lib/python3.10/site-packages/diffusers/models/modeling_utils.py", line 612, in from_pretrained
    config, unused_kwargs, commit_hash = cls.load_config(
  File "/workspace/SimpleTuner/.venv/lib/python3.10/site-packages/huggingface_hub/utils/_validators.py", line 114,in _inner_fn
    return fn(*args, **kwargs)
  File "/workspace/SimpleTuner/.venv/lib/python3.10/site-packages/diffusers/configuration_utils.py", line 406, in load_config
    raise EnvironmentError(
OSError: black-forest-labs/FLUX.1-dev does not appear to have a file named config.json.

[bghira]

make sure you're following the quickstart guide for Flux. it requires FLUX=true to be set

[Anashel-RPG]

My configuration is still the same, with FLUX=true set. I followed the advice to change FLUX=True to FLUX=true. I carefully executed all the steps as explained in the tutorial. :)

[bghira]

when you see this:

The tokenizer class you load from this checkpoint is not the same type as the class this function is called from. It may result in unexpected tokenization.

it's happening because it is receiving some argument other than --flux to tell it what model is being trained. maybe PIXART_SIGMA=true or KOLORS=true or something?

[bghira]

you got really close yesterday. the matmul error was solved by increasing lora rank. but then what happened? before it was loading Flux correctly. something changed

[kodxana]

you have
MODEL_NAME="black-forest-labs/FLUX.1-dev" twice hash the first one

[Anashel-RPG]

IT WORK!!! Thank you everyone for your help... :) I will make at template out of it for runpod. This is the final config file I made, added some clean up just to find myself more easily.

# SimpleTuner Configuration File
## Version 002 for RunPod

# Model Configuration
#######################
export MODEL_TYPE='lora'
export MODEL_NAME="black-forest-labs/FLUX.1-dev"
export FLUX=true

# Training Parameters
#######################
export LEARNING_RATE=1e-6
export MAX_NUM_STEPS=2000
export NUM_EPOCHS=0
export TRAIN_BATCH_SIZE=4
export GRADIENT_ACCUMULATION_STEPS=2
export VAE_BATCH_SIZE=4

# Checkpointing and Validation
###############################
export CHECKPOINTING_STEPS=500
export CHECKPOINTING_LIMIT=2
export RESUME_CHECKPOINT="latest"
export VALIDATION_STEPS=500
export VALIDATION_NUM_INFERENCE_STEPS=30
export VALIDATION_SEED=42

# Model Specific Settings
##########################
export STABLE_DIFFUSION_3=false
export PIXART_SIGMA=false
export STABLE_DIFFUSION_LEGACY=false
export KOLORS=false
export CONTROLNET=false
export USE_DORA=false

# Output and Logging
#####################
export OUTPUT_DIR="output/models"
export DEBUG_EXTRA_ARGS="--report_to=wandb"
export TRACKER_PROJECT_NAME="${MODEL_TYPE}-training"
export TRACKER_RUN_NAME="simpletuner-sdxl"

# Hugging Face Hub Integration
###############################
export PUSH_TO_HUB="false"
export PUSH_CHECKPOINTS="false"
export HUB_MODEL_NAME=$TRACKER_PROJECT_NAME

# Image Processing
###################
export RESOLUTION=1024
export RESOLUTION_TYPE="pixel"
export MINIMUM_RESOLUTION=$RESOLUTION

# Prompts and Captions
#######################
export VALIDATION_PROMPT="ethnographic photography of teddy bear at a picnic"
export VALIDATION_GUIDANCE=7.5
export VALIDATION_GUIDANCE_RESCALE=0.0
export VALIDATION_NEGATIVE_PROMPT="blurry, cropped, ugly"
export CAPTION_DROPOUT_PROBABILITY=0.1

# Learning Rate Schedule
#########################
export LR_SCHEDULE="polynomial"
export LR_WARMUP_STEPS=1000

# Performance Optimizations
############################
export MAX_WORKERS=32
export READ_BATCH_SIZE=25
export WRITE_BATCH_SIZE=64
export IMAGE_PROCESSING_BATCH_SIZE=35
export AWS_MAX_POOL_CONNECTIONS=128
export TORCH_NUM_THREADS=8

# Advanced Training Options
############################
export TRAINING_SCHEDULER_TIMESTEP_SPACING="trailing"
export INFERENCE_SCHEDULER_TIMESTEP_SPACING="trailing"
export MIN_SNR_GAMMA=5
export USE_XFORMERS=false
export USE_GRADIENT_CHECKPOINTING=true
export ALLOW_TF32=true
export OPTIMIZER="adamw_bf16"
export USE_EMA=false
export EMA_DECAY=0.999

# Additional Training Arguments
################################
export TRAINER_EXTRA_ARGS="--lora_rank=4"

# Reproducibility
##################
export TRAINING_SEED=42

# Precision Settings
#####################
export MIXED_PRECISION="bf16"
export PURE_BF16=true

# Distributed Training
#######################
export TRAINING_NUM_PROCESSES=1
export TRAINING_NUM_MACHINES=1
export ACCELERATE_EXTRA_ARGS=""

# Compilation Options
######################
export TRAINING_DYNAMO_BACKEND='no'
export TOKENIZERS_PARALLELISM=false

# Data Management
##################
export DELETE_ERRORED_IMAGES=0
export DELETE_SMALL_IMAGES=0
export DATALOADER_CONFIG="config/multidatabackend.json"
export METADATA_UPDATE_INTERVAL=65

Setting are low obviously as I just want to make some data set training test.