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Added ui sopport for multi control samples and datasets. Added qwen image edit 5209 to the ui
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105
config/examples/train_lora_qwen_image_edit_2509_32gb.yaml
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105
config/examples/train_lora_qwen_image_edit_2509_32gb.yaml
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---
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job: extension
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config:
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# this name will be the folder and filename name
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name: "my_first_qwen_image_edit_2509_lora_v1"
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process:
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- type: 'diffusion_trainer'
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# root folder to save training sessions/samples/weights
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training_folder: "output"
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# uncomment to see performance stats in the terminal every N steps
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# performance_log_every: 1000
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device: cuda:0
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network:
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type: "lora"
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linear: 16
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linear_alpha: 16
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save:
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dtype: float16 # precision to save
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save_every: 250 # save every this many steps
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max_step_saves_to_keep: 4 # how many intermittent saves to keep
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datasets:
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# datasets are a folder of images. captions need to be txt files with the same name as the image
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# for instance image2.jpg and image2.txt. Only jpg, jpeg, and png are supported currently
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# images will automatically be resized and bucketed into the resolution specified
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# on windows, escape back slashes with another backslash so
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# "C:\\path\\to\\images\\folder"
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- folder_path: "/path/to/images/folder"
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# can do up to 3 control image folders, file names must match target file names, but aspect/size can be different
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control_path:
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- "/path/to/control/images/folder1"
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- "/path/to/control/images/folder2"
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- "/path/to/control/images/folder3"
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caption_ext: "txt"
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# default_caption: "a person" # if caching text embeddings, if you don't have captions, this will get cached
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caption_dropout_rate: 0.05 # will drop out the caption 5% of time
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resolution: [ 512, 768, 1024 ] # qwen image enjoys multiple resolutions
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# a trigger word that can be cached with the text embeddings
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# trigger_word: "optional trigger word"
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train:
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batch_size: 1
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# caching text embeddings is required for 32GB
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cache_text_embeddings: true
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# unload_text_encoder: true
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steps: 3000 # total number of steps to train 500 - 4000 is a good range
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gradient_accumulation: 1
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timestep_type: "weighted"
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train_unet: true
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train_text_encoder: false # probably won't work with qwen image
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gradient_checkpointing: true # need the on unless you have a ton of vram
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noise_scheduler: "flowmatch" # for training only
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optimizer: "adamw8bit"
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lr: 1e-4
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# uncomment this to skip the pre training sample
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# skip_first_sample: true
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# uncomment to completely disable sampling
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# disable_sampling: true
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dtype: bf16
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model:
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# huggingface model name or path
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name_or_path: "Qwen/Qwen-Image-Edit-2509"
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arch: "qwen_image_edit_plus"
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quantize: true
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# to use the ARA use the | pipe to point to hf path, or a local path if you have one.
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# 3bit is required for 32GB
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qtype: "uint3|ostris/accuracy_recovery_adapters/qwen_image_edit_2509_torchao_uint3.safetensors"
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quantize_te: true
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qtype_te: "qfloat8"
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low_vram: true
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sample:
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sampler: "flowmatch" # must match train.noise_scheduler
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sample_every: 250 # sample every this many steps
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width: 1024
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height: 1024
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# you can provide up to 3 control images here
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samples:
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- prompt: "Do whatever with Image1 and Image2"
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ctrl_img_1: "/path/to/image1.png"
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ctrl_img_2: "/path/to/image2.png"
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# ctrl_img_3: "/path/to/image3.png"
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- prompt: "Do whatever with Image1 and Image2"
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ctrl_img_1: "/path/to/image1.png"
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ctrl_img_2: "/path/to/image2.png"
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# ctrl_img_3: "/path/to/image3.png"
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- prompt: "Do whatever with Image1 and Image2"
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ctrl_img_1: "/path/to/image1.png"
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ctrl_img_2: "/path/to/image2.png"
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# ctrl_img_3: "/path/to/image3.png"
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- prompt: "Do whatever with Image1 and Image2"
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ctrl_img_1: "/path/to/image1.png"
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ctrl_img_2: "/path/to/image2.png"
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# ctrl_img_3: "/path/to/image3.png"
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- prompt: "Do whatever with Image1 and Image2"
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ctrl_img_1: "/path/to/image1.png"
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ctrl_img_2: "/path/to/image2.png"
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# ctrl_img_3: "/path/to/image3.png"
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neg: ""
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seed: 42
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walk_seed: true
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guidance_scale: 3
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sample_steps: 25
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# you can add any additional meta info here. [name] is replaced with config name at top
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meta:
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name: "[name]"
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version: '1.0'
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