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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' | |