--- job: extension config: # this name will be the folder and filename name name: "my_first_qwen_image_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 words will not work when caching text embeddings # trigger_word: "p3r5on" network: type: "lora" linear: 16 linear_alpha: 16 save: dtype: float16 # 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" # default_caption: "a person" # if caching text embeddings, if you dont have captions, this will get cached caption_dropout_rate: 0.05 # will drop out the caption 5% of time shuffle_tokens: false # shuffle caption order, split by commas cache_latents_to_disk: true # leave this true unless you have a large dataset # if you OOM, 1024 may be too much, but should work resolution: [ 512, 768, 1024 ] # qwen image enjoys multiple resolutions train: batch_size: 1 # caching text embeddings is required for 24GB cache_text_embeddings: true steps: 2000 # total number of steps to train 500 - 4000 is a good range gradient_accumulation: 1 train_unet: true train_text_encoder: false # probably won't work with qwen image gradient_checkpointing: true # need the on unless you have a ton of vram noise_scheduler: "flowmatch" # for training only optimizer: "adamw8bit" lr: 1e-4 # uncomment this to skip the pre training sample # skip_first_sample: true # uncomment to completely disable sampling # disable_sampling: true dtype: bf16 model: # huggingface model name or path name_or_path: "Qwen/Qwen-Image" arch: "qwen_image" quantize: true # qtype_te: "qfloat8" Default float8 qquantization # to use the ARA use the | pipe to point to hf path, or a local path if you have one. # 3bit is required for 24GB qtype: "uint3|ostris/accuracy_recovery_adapters/qwen_image_torchao_uint3.safetensors" quantize_te: true qtype_te: "qfloat8" low_vram: true 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: 3 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'