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modelscope-scepter/scepter/methods/studio/self_train/stable_diffusion/sd21_pro.yaml
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2024-04-22 16:19:03 +08:00

286 lines
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YAML

ENV:
BACKEND: nccl
META:
VERSION: 'SD2.1'
DESCRIPTION: "Stable Diffusion v2.1"
IS_DEFAULT: False
IS_SHARE: True
INFERENCE_PARAS:
INFERENCE_BATCH_SIZE: 1
INFERENCE_PREFIX: ""
DEFAULT_SAMPLER: "ddim"
DEFAULT_SAMPLE_STEPS: 40
INFERENCE_N_PROMPT: ""
RESOLUTION: [768, 768]
PARAS:
-
TRAIN_BATCH_SIZE: 2
TRAIN_PREFIX: ""
TRAIN_N_PROMPT: ""
RESOLUTION: [768, 768]
MEMORY: 29000
EPOCHS: 50
SAVE_INTERVAL: 25
EPSEC: 0.818
LEARNING_RATE: 0.0001
IS_DEFAULT: False
TUNER: FULL
-
TRAIN_BATCH_SIZE: 2
TRAIN_PREFIX: ""
TRAIN_N_PROMPT: ""
RESOLUTION: [768, 768]
MEMORY: 29000
EPOCHS: 50
SAVE_INTERVAL: 25
EPSEC: 0.818
LEARNING_RATE: 0.0001
IS_DEFAULT: False
TUNER: LORA
-
TRAIN_BATCH_SIZE: 2
TRAIN_PREFIX: ""
TRAIN_N_PROMPT: ""
RESOLUTION: [768, 768]
MEMORY: 29000
EPOCHS: 200
SAVE_INTERVAL: 25
EPSEC: 0.818
LEARNING_RATE: 0.0001
IS_DEFAULT: True
TUNER: SCE
TUNERS:
LORA:
-
NAME: SwiftLoRA
R: 256
LORA_ALPHA: 256
LORA_DROPOUT: 0.0
BIAS: "none"
TARGET_MODULES: "model.*(to_q|to_k|to_v|to_out.0|net.0.proj|net.2)$"
SCE:
-
NAME: SwiftSCETuning
DIMS: [1280, 1280, 1280, 1280, 1280, 640, 640, 640, 320, 320, 320, 320]
DOWN_RATIO: 1.0
TARGET_MODULES: model.lsc_identity\.\d+$
TUNER_MODE: identity
SOLVER:
NAME: LatentDiffusionSolver
RESUME_FROM:
LOAD_MODEL_ONLY: True
USE_FSDP: False
SHARDING_STRATEGY:
USE_AMP: True
DTYPE: float16
CHANNELS_LAST: True
MAX_STEPS: 1000
MAX_EPOCHS: -1
NUM_FOLDS: 1
ACCU_STEP: 1
EVAL_INTERVAL: -1
#
WORK_DIR:
LOG_FILE: std_log.txt
#
FILE_SYSTEM:
NAME: "ModelscopeFs"
TEMP_DIR: "./cache/data"
#
FREEZE:
#
TUNER:
#
MODEL:
NAME: LatentDiffusion
PARAMETERIZATION: v
TIMESTEPS: 1000
MIN_SNR_GAMMA:
ZERO_TERMINAL_SNR: False
PRETRAINED_MODEL: ms://AI-ModelScope/stable-diffusion-2-1@v2-1_768-ema-pruned.safetensors
IGNORE_KEYS: [ ]
SCALE_FACTOR: 0.18215
SIZE_FACTOR: 8
# DEFAULT_N_PROMPT: 'lowres, error, worst quality, low quality, jpeg artifacts, ugly, duplicate, morbid, mutilated, out of frame, extra fingers, mutated hands, poorly drawn hands, poorly drawn face, mutation, deformed, blurry, dehydrated, bad anatomy, bad proportions, extra limbs, cloned face, disfigured, gross proportions, malformed limbs, missing arms, missing legs, extra arms, extra legs, fused fingers, too many fingers, long neck, username, watermark, signature'
DEFAULT_N_PROMPT:
SCHEDULE_ARGS:
"NAME": "scaled_linear"
"BETA_MIN": 0.00085
"BETA_MAX": 0.012
USE_EMA: False
#
DIFFUSION_MODEL:
NAME: DiffusionUNet
IN_CHANNELS: 4
OUT_CHANNELS: 4
MODEL_CHANNELS: 320
NUM_HEADS_CHANNELS: 64
NUM_RES_BLOCKS: 2
ATTENTION_RESOLUTIONS: [ 4, 2, 1 ]
CHANNEL_MULT: [ 1, 2, 4, 4 ]
CONV_RESAMPLE: True
DIMS: 2
USE_CHECKPOINT: False
USE_SCALE_SHIFT_NORM: False
RESBLOCK_UPDOWN: False
USE_SPATIAL_TRANSFORMER: True
TRANSFORMER_DEPTH: 1
CONTEXT_DIM: 1024
DISABLE_MIDDLE_SELF_ATTN: False
USE_LINEAR_IN_TRANSFORMER: True
PRETRAINED_MODEL:
#
FIRST_STAGE_MODEL:
NAME: AutoencoderKL
EMBED_DIM: 4
PRETRAINED_MODEL:
IGNORE_KEYS: [ ]
BATCH_SIZE: 4
#
ENCODER:
NAME: Encoder
CH: 128
OUT_CH: 3
NUM_RES_BLOCKS: 2
IN_CHANNELS: 3
ATTN_RESOLUTIONS: [ ]
CH_MULT: [ 1, 2, 4, 4 ]
Z_CHANNELS: 4
DOUBLE_Z: True
DROPOUT: 0.0
RESAMP_WITH_CONV: True
#
DECODER:
NAME: Decoder
CH: 128
OUT_CH: 3
NUM_RES_BLOCKS: 2
IN_CHANNELS: 3
ATTN_RESOLUTIONS: [ ]
CH_MULT: [ 1, 2, 4, 4 ]
Z_CHANNELS: 4
DROPOUT: 0.0
RESAMP_WITH_CONV: True
GIVE_PRE_END: False
TANH_OUT: False
#
TOKENIZER:
NAME: OpenClipTokenizer
LENGTH: 77
#
COND_STAGE_MODEL:
NAME: FrozenOpenCLIPEmbedder
ARCH: ViT-H-14
PRETRAINED_MODEL:
USE_GRAD: False
LAYER: penultimate
#
LOSS:
NAME: ReconstructLoss
LOSS_TYPE: l2
#
SAMPLE_ARGS:
SAMPLER: ddim
SAMPLE_STEPS: 50
SEED: 2023
GUIDE_SCALE: 7.5
GUIDE_RESCALE:
DISCRETIZATION: trailing
RUN_TRAIN_N: False
#
OPTIMIZER:
NAME: AdamW
LEARNING_RATE: 0.064
BETAS: [ 0.9, 0.999 ]
EPS: 1e-8
WEIGHT_DECAY: 1e-2
AMSGRAD: False
#
TRAIN_DATA:
NAME: ImageTextPairMSDataset
MODE: train
MS_DATASET_NAME: style_custom_dataset
MS_DATASET_NAMESPACE: damo
MS_DATASET_SUBNAME: 3D
PROMPT_PREFIX: ""
MS_DATASET_SPLIT: train
MS_REMAP_KEYS: { 'Image:FILE': 'Target:FILE' }
REPLACE_STYLE: False
PIN_MEMORY: True
BATCH_SIZE: 1
NUM_WORKERS: 4
SAMPLER:
NAME: LoopSampler
TRANSFORMS:
- NAME: LoadImageFromFile
RGB_ORDER: RGB
BACKEND: pillow
- NAME: FlexibleResize
INTERPOLATION: bilinear
SIZE: [ 768, 768 ]
INPUT_KEY: [ 'img' ]
OUTPUT_KEY: [ 'img' ]
BACKEND: pillow
- NAME: FlexibleCenterCrop
SIZE: [ 768, 768 ]
INPUT_KEY: [ 'img' ]
OUTPUT_KEY: [ 'img' ]
BACKEND: pillow
- NAME: ImageToTensor
INPUT_KEY: [ 'img' ]
OUTPUT_KEY: [ 'img' ]
BACKEND: pillow
- NAME: Normalize
MEAN: [ 0.5, 0.5, 0.5 ]
STD: [ 0.5, 0.5, 0.5 ]
INPUT_KEY: [ 'img' ]
OUTPUT_KEY: [ 'image' ]
BACKEND: torchvision
- NAME: Select
KEYS: [ 'image', 'prompt' ]
META_KEYS: [ 'data_key' ]
#
EVAL_DATA:
NAME: Text2ImageDataset
MODE: eval
PROMPT_FILE:
PROMPT_DATA: [ "a boy wearing a jacket", "a dog running on the lawn" ]
IMAGE_SIZE: [ 768, 768 ]
FIELDS: [ "prompt" ]
DELIMITER: '#;#'
PROMPT_PREFIX: ''
PIN_MEMORY: True
BATCH_SIZE: 1
NUM_WORKERS: 4
TRANSFORMS:
- NAME: Select
KEYS: [ 'index', 'prompt' ]
META_KEYS: [ 'image_size' ]
#
TRAIN_HOOKS:
-
NAME: BackwardHook
PRIORITY: 0
-
NAME: LogHook
LOG_INTERVAL: 10
SHOW_GPU_MEM: True
-
NAME: TensorboardLogHook
-
NAME: CheckpointHook
INTERVAL: 10000
PRIORITY: 200
SAVE_LAST: True
SAVE_NAME_PREFIX: 'step'
DISABLE_SNAPSHOT: True
#
EVAL_HOOKS:
-
NAME: ProbeDataHook
PROB_INTERVAL: 100
SAVE_LAST: True
SAVE_NAME_PREFIX: 'step'
SAVE_PROBE_PREFIX: 'image'