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# HiDream training is still highly experimental. The settings here will take ~35.2GB of vram to train.
# It is not possible to train on a single 24GB card yet, but I am working on it. If you have more VRAM
# I highly recommend first disabling quantization on the model itself if you can. You can leave the TEs quantized.
# HiDream has a mixture of experts that may take special training considerations that I do not
# have implemented properly. The current implementation seems to work well for LoRA training, but
# may not be effective for longer training runs. The implementation could change in future updates
# so your results may vary when this happens.
---
job: extension
config:
# this name will be the folder and filename name
name: "my_first_hidream_lora_v1"
process:
- type: 'sd_trainer'
# root folder to save training sessions/samples/weights
training_folder: "output"
# uncomment to see performance stats in the terminal every N steps
# performance_log_every: 1000
device: cuda:0
# if a trigger word is specified, it will be added to captions of training data if it does not already exist
# alternatively, in your captions you can add [trigger] and it will be replaced with the trigger word
# trigger_word: "p3r5on"
network:
type: "lora"
linear: 32
linear_alpha: 32
network_kwargs:
# it is probably best to ignore the mixture of experts since only 2 are active each block. It works activating it, but I wouldnt.
# proper training of it is not fully implemented
ignore_if_contains:
- "ff_i.experts"
- "ff_i.gate"
save:
dtype: bfloat16 # precision to save
save_every: 250 # save every this many steps
max_step_saves_to_keep: 4 # how many intermittent saves to keep
datasets:
# datasets are a folder of images. captions need to be txt files with the same name as the image
# for instance image2.jpg and image2.txt. Only jpg, jpeg, and png are supported currently
# images will automatically be resized and bucketed into the resolution specified
# on windows, escape back slashes with another backslash so
# "C:\\path\\to\\images\\folder"
- folder_path: "/path/to/images/folder"
caption_ext: "txt"
caption_dropout_rate: 0.05 # will drop out the caption 5% of time
resolution: [ 512, 768, 1024 ] # hidream enjoys multiple resolutions
train:
batch_size: 1
steps: 3000 # total number of steps to train 500 - 4000 is a good range
gradient_accumulation_steps: 1
train_unet: true
train_text_encoder: false # wont work with hidream
gradient_checkpointing: true # need the on unless you have a ton of vram
noise_scheduler: "flowmatch" # for training only
timestep_type: shift # sigmoid, shift, linear
optimizer: "adamw8bit"
lr: 2e-4
# uncomment this to skip the pre training sample
# skip_first_sample: true
# uncomment to completely disable sampling
# disable_sampling: true
# uncomment to use new vell curved weighting. Experimental but may produce better results
# linear_timesteps: true
# ema will smooth out learning, but could slow it down. Defaults off
ema_config:
use_ema: false
ema_decay: 0.99
# will probably need this if gpu supports it for hidream, other dtypes may not work correctly
dtype: bf16
model:
# the transformer will get grabbed from this hf repo
# warning ONLY train on Full. The dev and fast models are distilled and will break
name_or_path: "HiDream-ai/HiDream-I1-Full"
# the extras will be grabbed from this hf repo. (text encoder, vae)
extras_name_or_path: "HiDream-ai/HiDream-I1-Full"
arch: "hidream"
# both need to be quantized to train on 48GB currently
quantize: true
quantize_te: true
model_kwargs:
# llama is a gated model, It defaults to unsloth version, but you can set the llama path here
llama_model_path: "unsloth/Meta-Llama-3.1-8B-Instruct"
sample:
sampler: "flowmatch" # must match train.noise_scheduler
sample_every: 250 # sample every this many steps
width: 1024
height: 1024
prompts:
# you can add [trigger] to the prompts here and it will be replaced with the trigger word
# - "[trigger] holding a sign that says 'I LOVE PROMPTS!'"\
- "woman with red hair, playing chess at the park, bomb going off in the background"
- "a woman holding a coffee cup, in a beanie, sitting at a cafe"
- "a horse is a DJ at a night club, fish eye lens, smoke machine, lazer lights, holding a martini"
- "a man showing off his cool new t shirt at the beach, a shark is jumping out of the water in the background"
- "a bear building a log cabin in the snow covered mountains"
- "woman playing the guitar, on stage, singing a song, laser lights, punk rocker"
- "hipster man with a beard, building a chair, in a wood shop"
- "photo of a man, white background, medium shot, modeling clothing, studio lighting, white backdrop"
- "a man holding a sign that says, 'this is a sign'"
- "a bulldog, in a post apocalyptic world, with a shotgun, in a leather jacket, in a desert, with a motorcycle"
neg: ""
seed: 42
walk_seed: true
guidance_scale: 4
sample_steps: 25
# you can add any additional meta info here. [name] is replaced with config name at top
meta:
name: "[name]"
version: '1.0'
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