Create ace_plus_fft_lora.yaml
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mcj
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ENV:
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BACKEND: nccl
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SEED: 1999
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SOLVER:
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# NAME DESCRIPTION: TYPE: default: 'LatentUfitSolver'
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NAME: FormalACEPlusSolver
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# MAX_STEPS DESCRIPTION: The total steps for training. TYPE: int default: 100000
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MAX_STEPS: 100000
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# USE_AMP DESCRIPTION: Use amp to surpport mix precision or not, default is False. TYPE: bool default: False
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USE_AMP: True
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# DTYPE DESCRIPTION: The precision for training. TYPE: str default: 'float32'
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DTYPE: bfloat16
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ENABLE_GRADSCALER: False
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# USE_FAIRSCALE DESCRIPTION: Use fairscale as the backend of ddp, default False. TYPE: bool default: False
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USE_FAIRSCALE: False
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USE_ORIG_PARAMS: True
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USE_FSDP: True # lora use ddp(USE_FSDP=False), else use fsdp(USE_FSDP=True)
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# LOAD_MODEL_ONLY DESCRIPTION: Only load the model rather than the optimizer and schedule, default is False. TYPE: bool default: False
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LOAD_MODEL_ONLY: False
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# RESUME_FROM DESCRIPTION: Resume from some state of training! TYPE: str default: ''
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RESUME_FROM:
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# WORK_DIR DESCRIPTION: Save dir of the training log or model. TYPE: str default: ''
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WORK_DIR: ./examples/exp_example/
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# LOG_FILE DESCRIPTION: Save log path. TYPE: str default: ''
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LOG_FILE: std_log.txt
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# LOG_TRAIN_NUM DESCRIPTION: The number samples used to log in training phase. TYPE: int default: -1
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LOG_TRAIN_NUM: 16
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# FSDP_REDUCE_DTYPE DESCRIPTION: The dtype of reduce in FSDP. TYPE: str default: 'float16'
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FSDP_REDUCE_DTYPE: float32
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# FSDP_BUFFER_DTYPE DESCRIPTION: The dtype of buffer in FSDP. TYPE: str default: 'float16'
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FSDP_BUFFER_DTYPE: float32
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# FSDP_SHARD_MODULES DESCRIPTION: The modules to be sharded in FSDP. TYPE: list default: ['model']
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FSDP_SHARD_MODULES:
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- MODULE: 'model.model'
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FSDP_GROUP: [ 'single_blocks', 'double_blocks']
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- MODULE: 'cond_stage_model.t5_model.hf_module.encoder'
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FSDP_GROUP: [ 'block' ] #
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SAVE_MODULES: [ 'model'] #
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TRAIN_MODULES: ['model']
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#
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FILE_SYSTEM:
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- NAME: HuggingfaceFs
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TEMP_DIR: ./cache
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- NAME: ModelscopeFs
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TEMP_DIR: ./cache
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#
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MODEL:
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NAME: LatentDiffusionACEPlus
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PARAMETERIZATION: rf
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TIMESTEPS: 1000
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GUIDE_SCALE: 1.0
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PRETRAINED_MODEL:
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IGNORE_KEYS: [ ]
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USE_EMA: False
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EVAL_EMA: False
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SIZE_FACTOR: 8
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DIFFUSION:
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NAME: DiffusionFluxRF
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PREDICTION_TYPE: raw
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NOISE_NORM: True
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# NOISE_SCHEDULER DESCRIPTION: TYPE: default: ''
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NOISE_SCHEDULER:
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NAME: FlowMatchFluxShiftScheduler
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SHIFT: False
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PRE_T_SAMPLE: True
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PRE_T_SAMPLE_FOLD: 1
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SIGMOID_SCALE: 1
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BASE_SHIFT: 0.5
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MAX_SHIFT: 1.15
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SAMPLER_SCHEDULER:
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NAME: FlowMatchFluxShiftScheduler
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SHIFT: True
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PRE_T_SAMPLE: False
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SIGMOID_SCALE: 1
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BASE_SHIFT: 0.5
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MAX_SHIFT: 1.15
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#
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DIFFUSION_MODEL:
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# NAME DESCRIPTION: TYPE: default: 'Flux'
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NAME: FluxMRACEPlus
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PRETRAINED_MODEL: ${ACE_PLUS_FFT_MODEL}
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# IN_CHANNELS DESCRIPTION: model's input channels. TYPE: int default: 64
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IN_CHANNELS: 448
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# OUT_CHANNELS DESCRIPTION: model's input channels. TYPE: int default: 64
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OUT_CHANNELS: 64
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# HIDDEN_SIZE DESCRIPTION: model's hidden size. TYPE: int default: 1024
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HIDDEN_SIZE: 3072
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REDUX_DIM: 1152
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# NUM_HEADS DESCRIPTION: number of heads in the transformer. TYPE: int default: 16
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NUM_HEADS: 24
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# AXES_DIM DESCRIPTION: dimensions of the axes of the positional encoding. TYPE: list default: [16, 56, 56]
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AXES_DIM: [ 16, 56, 56 ]
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# THETA DESCRIPTION: theta for positional encoding. TYPE: int default: 10000
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THETA: 10000
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# VEC_IN_DIM DESCRIPTION: dimension of the vector input. TYPE: int default: 768
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VEC_IN_DIM: 768
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# GUIDANCE_EMBED DESCRIPTION: whether to use guidance embedding. TYPE: bool default: False
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GUIDANCE_EMBED: True
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# CONTEXT_IN_DIM DESCRIPTION: dimension of the context input. TYPE: int default: 4096
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CONTEXT_IN_DIM: 4096
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# MLP_RATIO DESCRIPTION: ratio of mlp hidden size to hidden size. TYPE: float default: 4.0
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MLP_RATIO: 4.0
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# QKV_BIAS DESCRIPTION: whether to use bias in qkv projection. TYPE: bool default: True
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QKV_BIAS: True
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# DEPTH DESCRIPTION: number of transformer blocks. TYPE: int default: 19
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DEPTH: 19
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# DEPTH_SINGLE_BLOCKS DESCRIPTION: number of transformer blocks in the single stream block. TYPE: int default: 38
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DEPTH_SINGLE_BLOCKS: 38
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ATTN_BACKEND: flash_attn
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#
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FIRST_STAGE_MODEL:
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NAME: AutoencoderKLFlux
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EMBED_DIM: 16
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PRETRAINED_MODEL: ${FLUX_FILL_PATH}/ae.safetensors
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IGNORE_KEYS: [ ]
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BATCH_SIZE: 8
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USE_CONV: False
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SCALE_FACTOR: 0.3611
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SHIFT_FACTOR: 0.1159
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#
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ENCODER:
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NAME: Encoder
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CH: 128
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OUT_CH: 3
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NUM_RES_BLOCKS: 2
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IN_CHANNELS: 3
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ATTN_RESOLUTIONS: [ ]
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CH_MULT: [ 1, 2, 4, 4 ]
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Z_CHANNELS: 16
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DOUBLE_Z: True
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DROPOUT: 0.0
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RESAMP_WITH_CONV: True
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#
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DECODER:
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NAME: Decoder
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CH: 128
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OUT_CH: 3
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NUM_RES_BLOCKS: 2
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IN_CHANNELS: 3
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ATTN_RESOLUTIONS: [ ]
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CH_MULT: [ 1, 2, 4, 4 ]
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Z_CHANNELS: 16
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DROPOUT: 0.0
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RESAMP_WITH_CONV: True
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GIVE_PRE_END: False
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TANH_OUT: False
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#
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COND_STAGE_MODEL:
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# NAME DESCRIPTION: TYPE: default: 'T5PlusClipFluxEmbedder'
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NAME: T5ACEPlusClipFluxEmbedder
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# T5_MODEL DESCRIPTION: TYPE: default: ''
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T5_MODEL:
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# NAME DESCRIPTION: TYPE: default: 'HFEmbedder'
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NAME: ACEHFEmbedder
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# HF_MODEL_CLS DESCRIPTION: huggingface cls in transfomer TYPE: NoneType default: None
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HF_MODEL_CLS: T5EncoderModel
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# MODEL_PATH DESCRIPTION: model folder path TYPE: NoneType default: None
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MODEL_PATH: ${FLUX_FILL_PATH}/text_encoder_2/
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# HF_TOKENIZER_CLS DESCRIPTION: huggingface cls in transfomer TYPE: NoneType default: None
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HF_TOKENIZER_CLS: T5Tokenizer
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# TOKENIZER_PATH DESCRIPTION: tokenizer folder path TYPE: NoneType default: None
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TOKENIZER_PATH: ${FLUX_FILL_PATH}/tokenizer_2/
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ADDED_IDENTIFIER: [ '<img>','{image}', '{caption}', '{mask}', '{ref_image}', '{image1}', '{image2}', '{image3}', '{image4}', '{image5}', '{image6}', '{image7}', '{image8}', '{image9}' ]
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# MAX_LENGTH DESCRIPTION: max length of input TYPE: int default: 77
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MAX_LENGTH: 512
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# OUTPUT_KEY DESCRIPTION: output key TYPE: str default: 'last_hidden_state'
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OUTPUT_KEY: last_hidden_state
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# D_TYPE DESCRIPTION: dtype TYPE: str default: 'bfloat16'
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D_TYPE: bfloat16
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# BATCH_INFER DESCRIPTION: batch infer TYPE: bool default: False
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BATCH_INFER: False
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CLEAN: whitespace
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# CLIP_MODEL DESCRIPTION: TYPE: default: ''
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CLIP_MODEL:
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# NAME DESCRIPTION: TYPE: default: 'HFEmbedder'
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NAME: ACEHFEmbedder
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# HF_MODEL_CLS DESCRIPTION: huggingface cls in transfomer TYPE: NoneType default: None
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HF_MODEL_CLS: CLIPTextModel
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# MODEL_PATH DESCRIPTION: model folder path TYPE: NoneType default: None
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MODEL_PATH: ${FLUX_FILL_PATH}/text_encoder/
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# HF_TOKENIZER_CLS DESCRIPTION: huggingface cls in transfomer TYPE: NoneType default: None
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HF_TOKENIZER_CLS: CLIPTokenizer
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# TOKENIZER_PATH DESCRIPTION: tokenizer folder path TYPE: NoneType default: None
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TOKENIZER_PATH: ${FLUX_FILL_PATH}/tokenizer/
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# MAX_LENGTH DESCRIPTION: max length of input TYPE: int default: 77
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MAX_LENGTH: 77
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# OUTPUT_KEY DESCRIPTION: output key TYPE: str default: 'last_hidden_state'
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OUTPUT_KEY: pooler_output
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# D_TYPE DESCRIPTION: dtype TYPE: str default: 'bfloat16'
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D_TYPE: bfloat16
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# BATCH_INFER DESCRIPTION: batch infer TYPE: bool default: False
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BATCH_INFER: True
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CLEAN: whitespace
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TUNER:
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# THE LORA PARAMETERS
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- NAME: SwiftLoRA
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R: 64
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LORA_ALPHA: 64
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LORA_DROPOUT: 0.0
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BIAS: "none"
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TARGET_MODULES: "(model.double_blocks.*(.qkv|.img_mlp.0|.img_mlp.2|.txt_mlp.0|.txt_mlp.2|.proj|.img_mod.lin|.txt_mod.lin))|(model.single_blocks.*(.linear1|.linear2|.modulation.lin))$"
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#
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SAMPLE_ARGS:
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SAMPLE_STEPS: 28
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SAMPLER: flow_euler
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SEED: 42
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IMAGE_SIZE: [ 1024, 1024 ]
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GUIDE_SCALE: 50
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LR_SCHEDULER:
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NAME: StepAnnealingLR
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WARMUP_STEPS: 0
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TOTAL_STEPS: 100000
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DECAY_MODE: 'cosine'
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#
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OPTIMIZER:
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NAME: AdamW
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LEARNING_RATE: 1e-3
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BETAS: [ 0.9, 0.999 ]
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EPS: 1e-6
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WEIGHT_DECAY: 1e-2
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AMSGRAD: False
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#
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TRAIN_DATA:
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NAME: ACEPlusDataset
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MODE: train
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DATA_LIST: data/train.csv
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DELIMITER: "#;#"
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# input_image, input_mask, input_reference_image, target_image, instruction, task_type
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FIELDS: ["edit_image", "edit_mask", "ref_image", "target_image", "prompt", "data_type"]
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PATH_PREFIX: ""
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EDIT_TYPE_LIST: []
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MAX_SEQ_LEN: 2048
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D: 16
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PIN_MEMORY: True
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BATCH_SIZE: 1
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NUM_WORKERS: 4
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SAMPLER:
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NAME: LoopSampler
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EVAL_DATA:
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NAME: ACEPlusDataset
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MODE: eval
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DATA_LIST: data/train.csv
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DELIMITER: "#;#"
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# input_image, input_mask, input_reference_image, target_image, instruction, task_type
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FIELDS: [ "edit_image", "edit_mask", "ref_image", "target_image", "prompt", "data_type" ]
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PATH_PREFIX: ""
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EDIT_TYPE_LIST: [ ]
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MAX_SEQ_LEN: 2048
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D: 16
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PIN_MEMORY: True
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BATCH_SIZE: 1
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NUM_WORKERS: 4
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TRAIN_HOOKS:
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- NAME: ACEBackwardHook
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GRADIENT_CLIP: 1.0
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PRIORITY: 10
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- NAME: LogHook
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LOG_INTERVAL: 20
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- NAME: ACECheckpointHook
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INTERVAL: 250
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PRIORITY: 200
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DISABLE_SNAPSHOT: True
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- NAME: ProbeDataHook
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PROB_INTERVAL: 50
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PRIORITY: 0
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- NAME: TensorboardLogHook
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LOG_INTERVAL: 50
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EVAL_HOOKS:
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- NAME: ProbeDataHook
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PROB_INTERVAL: 50
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PRIORITY: 0
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