Add custom nodes, Civitai loras (LFS), and vast.ai setup script
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Includes 30 custom nodes committed directly, 7 Civitai-exclusive
loras stored via Git LFS, and a setup script that installs all
dependencies and downloads HuggingFace-hosted models on vast.ai.

Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
This commit is contained in:
2026-02-09 00:55:26 +00:00
parent 2b70ab9ad0
commit f09734b0ee
2274 changed files with 748556 additions and 3 deletions

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{
"architectures": [
"BertModel"
],
"attention_probs_dropout_prob": 0.1,
"hidden_act": "gelu",
"hidden_dropout_prob": 0.1,
"hidden_size": 768,
"initializer_range": 0.02,
"intermediate_size": 3072,
"layer_norm_eps": 1e-12,
"max_position_embeddings": 512,
"model_type": "bert",
"num_attention_heads": 12,
"num_hidden_layers": 12,
"pad_token_id": 0,
"type_vocab_size": 2,
"vocab_size": 30522,
"encoder_width": 768,
"add_cross_attention": true
}

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image_root: '/export/share/datasets/vision/coco/images/'
ann_root: 'annotation'
coco_gt_root: 'annotation/coco_gt'
# set pretrained as a file path or an url
pretrained: 'https://storage.googleapis.com/sfr-vision-language-research/BLIP/models/model_base_caption_capfilt_large.pth'
# size of vit model; base or large
vit: 'base'
vit_grad_ckpt: False
vit_ckpt_layer: 0
batch_size: 32
init_lr: 1e-5
# vit: 'large'
# vit_grad_ckpt: True
# vit_ckpt_layer: 5
# batch_size: 16
# init_lr: 2e-6
image_size: 384
# generation configs
max_length: 20
min_length: 5
num_beams: 3
prompt: 'a picture of '
# optimizer
weight_decay: 0.05
min_lr: 0
max_epoch: 5

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{
"architectures": [
"BertModel"
],
"attention_probs_dropout_prob": 0.1,
"hidden_act": "gelu",
"hidden_dropout_prob": 0.1,
"hidden_size": 768,
"initializer_range": 0.02,
"intermediate_size": 3072,
"layer_norm_eps": 1e-12,
"max_position_embeddings": 512,
"model_type": "bert",
"num_attention_heads": 12,
"num_hidden_layers": 12,
"pad_token_id": 0,
"type_vocab_size": 2,
"vocab_size": 30524,
"encoder_width": 768,
"add_cross_attention": true
}

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image_root: '/export/share/datasets/vision/NLVR2/'
ann_root: 'annotation'
# set pretrained as a file path or an url
pretrained: 'https://storage.googleapis.com/sfr-vision-language-research/BLIP/models/model_base_nlvr.pth'
#size of vit model; base or large
vit: 'base'
batch_size_train: 16
batch_size_test: 64
vit_grad_ckpt: False
vit_ckpt_layer: 0
max_epoch: 15
image_size: 384
# optimizer
weight_decay: 0.05
init_lr: 3e-5
min_lr: 0

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image_root: '/export/share/datasets/vision/nocaps/'
ann_root: 'annotation'
# set pretrained as a file path or an url
pretrained: 'https://storage.googleapis.com/sfr-vision-language-research/BLIP/models/model_base_caption_capfilt_large.pth'
vit: 'base'
batch_size: 32
image_size: 384
max_length: 20
min_length: 5
num_beams: 3
prompt: 'a picture of '

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train_file: ['/export/share/junnan-li/VL_pretrain/annotation/coco_karpathy_train.json',
'/export/share/junnan-li/VL_pretrain/annotation/vg_caption.json',
]
laion_path: ''
# size of vit model; base or large
vit: 'base'
vit_grad_ckpt: False
vit_ckpt_layer: 0
image_size: 224
batch_size: 75
queue_size: 57600
alpha: 0.4
# optimizer
weight_decay: 0.05
init_lr: 3e-4
min_lr: 1e-6
warmup_lr: 1e-6
lr_decay_rate: 0.9
max_epoch: 20
warmup_steps: 3000

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image_root: '/export/share/datasets/vision/coco/images/'
ann_root: 'annotation'
dataset: 'coco'
# set pretrained as a file path or an url
pretrained: 'https://storage.googleapis.com/sfr-vision-language-research/BLIP/models/model_base_retrieval_coco.pth'
# size of vit model; base or large
vit: 'base'
batch_size_train: 32
batch_size_test: 64
vit_grad_ckpt: True
vit_ckpt_layer: 4
init_lr: 1e-5
# vit: 'large'
# batch_size_train: 16
# batch_size_test: 32
# vit_grad_ckpt: True
# vit_ckpt_layer: 12
# init_lr: 5e-6
image_size: 384
queue_size: 57600
alpha: 0.4
k_test: 256
negative_all_rank: True
# optimizer
weight_decay: 0.05
min_lr: 0
max_epoch: 6

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image_root: '/export/share/datasets/vision/flickr30k/'
ann_root: 'annotation'
dataset: 'flickr'
# set pretrained as a file path or an url
pretrained: 'https://storage.googleapis.com/sfr-vision-language-research/BLIP/models/model_base_retrieval_flickr.pth'
# size of vit model; base or large
vit: 'base'
batch_size_train: 32
batch_size_test: 64
vit_grad_ckpt: True
vit_ckpt_layer: 4
init_lr: 1e-5
# vit: 'large'
# batch_size_train: 16
# batch_size_test: 32
# vit_grad_ckpt: True
# vit_ckpt_layer: 10
# init_lr: 5e-6
image_size: 384
queue_size: 57600
alpha: 0.4
k_test: 128
negative_all_rank: False
# optimizer
weight_decay: 0.05
min_lr: 0
max_epoch: 6

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video_root: '/export/share/dongxuli/data/msrvtt_retrieval/videos'
ann_root: 'annotation'
# set pretrained as a file path or an url
pretrained: 'https://storage.googleapis.com/sfr-vision-language-research/BLIP/models/model_base_retrieval_coco.pth'
# size of vit model; base or large
vit: 'base'
batch_size: 64
k_test: 128
image_size: 384
num_frm_test: 8

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vqa_root: '/export/share/datasets/vision/VQA/Images/mscoco/' #followed by train2014/
vg_root: '/export/share/datasets/vision/visual-genome/' #followed by image/
train_files: ['vqa_train','vqa_val','vg_qa']
ann_root: 'annotation'
# set pretrained as a file path or an url
pretrained: 'https://storage.googleapis.com/sfr-vision-language-research/BLIP/models/model_base_vqa_capfilt_large.pth'
# size of vit model; base or large
vit: 'base'
batch_size_train: 16
batch_size_test: 32
vit_grad_ckpt: False
vit_ckpt_layer: 0
init_lr: 2e-5
image_size: 480
k_test: 128
inference: 'rank'
# optimizer
weight_decay: 0.05
min_lr: 0
max_epoch: 10