problem about synthgen

[kd-scki3011]

What's the problem, is this normal?The program keeps generating images in synth/images and synth/glyphs, and then keeps showing 'not a valid generation', is it something I haven't set up?

[Question406]

Hi, this is not an error. The code prints this error to indicate that some generations are not valid. All invalid generation is not included in the result folder.

[kd-scki3011]

Hi, this is not an error. The code prints this error to indicate that some generations are not valid. All invalid generation is not included in the result folder.

thanks!

[kd-scki3011]

Hi, this is not an error. The code prints this error to indicate that some generations are not valid. All invalid generation is not included in the result folder.

Hello, excuse me. I would like to ask if the pictures produced during my training are all like this, and the progress is still at about 29%, may I ask where I set it wrong?

this is where I put runwayml/stable-diffusion-inpainting in

[kd-scki3011]

Hi, this is not an error. The code prints this error to indicate that some generations are not valid. All invalid generation is not included in the result folder.

Hello, excuse me. I would like to ask if the pictures produced during my training are all like this, and the progress is still at about 29%, may I ask where I set it wrong? this is where I put runwayml/stable-diffusion-inpainting in

this is my config:

train on combination of all datas

data:
target: "src.trainers.WrappedDataModule"
batch_size: 1
scene_data: ocr-dataset/
synth_data: ocr-dataset/synth/
train:
size: 256
max_num: 2000000 # diffly choose this number
augconf:
synth:
center: 0.1
pad: false
scene:
expand_mask:
center_mask: 0.6
additional_mask: 0.4
crop:
mask_image_ratio: 15
rotate:
cat_prob: [1, 0, 0]
angle_list: [-15, -30, -45, -60, -90, 15, 30, 45, 60, 90]
rotate_range: 90

dataconfs:
  ArT:
    type: scene
    label_path: ArT/train_labels.json
    image_dir: ArT/train_images/

  #COCO:
  #  type: scene
  #  label_path: ocr-dataset/COCO/cocotext.v2.json
  #  image_dir: ocr-dataset/COCO/train2014/

  #TextOCR:
  #  type: scene
  #  label_path: TextOCR/TextOCR_0.1_train.json
  #  image_dir: TextOCR/train_images/

  #Synthtiger:
  #  type: synth
  #  label_path: ocr-dataset/synth/data.csv
  #  image_dir: ocr-dataset/synth/
  #  style_mode: same-same
  #  use_textbbox: false
  #  style_dropout: [0.5, 0.5]
  #  rand_mask_text: true

validation:
size: 256
# max_num: 6400 # diffly choose this number
augconf:
synth:
center: 1.
pad: false
scene:
expand_mask:
center_mask: 0.
additional_mask: 0.
crop:
mask_image_ratio: 30
rotate:
cat_prob: [1, 0, 0]
angle_list: [-15, -30, -45, -60, -90, 15, 30, 45, 60, 90]
rotate_range: 90

dataconfs:
  #ArT:
  #  type: scene
  #  label_path: ArT/train_labels.json
  #  image_dir: ArT/train_images/

  #COCO:
  #  type: scene
  #  label_path: ocr-dataset/COCO/cocotext.v2.json
  #  image_dir: ocr-dataset/COCO/train2014/

  TextOCR:
    type: scene
    label_path: TextOCR/TextOCR_0.1_val.json
    image_dir: TextOCR/train_images/

model:
source: raw
target: "src.trainers.CharInpaintModelWrapper"
pretrained_model_path: runwayml/stable-diffusion-inpainting
loss_type: MaskMSELoss
loss_alpha: 5
base_learning_rate: 5.0e-5
precision: 16
weight_decay: 0.0
adam_epsilon: 1.0e-8
freeze_char_embedder: false
optimize_vae: false
vae:

tokenizer:
model_max_length: 20
char_tokenizer:
pretrained_path: checkpoints/chartokenizer
pad_token: " "
unk_token: " "
model_max_length: 20
char_embedder:
vocab_size: 95 # by default
embedding_dim: 32
max_length: 20
padding_idx: 0
attention_head_dim: 2
unet:
attention_head_dim: { "text": 8, "char": 2 }
cross_attention_dim: { "text": 768, "char": 32 }
noise_scheduler: diffusers.DDIMScheduler

lightning:
logger:
callbacks:
checkpoint_callback:
params:
save_top_k: -1
image_logger:
target: "src.trainers.CharInpaintImageLogger"
params:
# train_batch_frequency: 2400
# valid_batch_frequency: 500
train_batch_frequency: 2
valid_batch_frequency: 2
disable_wandb: true
generation_kwargs:
num_inference_steps: 30
num_sample_per_image: 3
guidance_scale: 7.5
seed: 42

# NOTE: Download pretrained ABINet model from https://github.com/FangShancheng/ABINet.git and
#       put model checkpoints in checkpoints/abinet to use this callback
# ocracc_logger:
#   target: "src.trainers.OCRAccLogger"
#   params:
#     train_eval_conf:
#       size: 256
#       augconf: ${data.validation.augconf}
#       max_num: 5
#       dataconfs:
#         TextOCR:
#           type: scene
#           label_path: ${data.scene_data}/TextOCR/TextOCR_0.1_train.json
#           image_dir: ${data.scene_data}/TextOCR/train_images/
#           len_counter:
#             eachnum: 10

#     val_eval_conf:
#       size: 256
#       augconf: ${data.validation.augconf}
#       max_num: 5
#       dataconfs:
#         TextOCR:
#           type: scene
#           label_path: ${data.scene_data}/TextOCR/TextOCR_0.1_val.json
#           image_dir: ${data.scene_data}/TextOCR/train_images/
#           max_num: 1000
#     base_log_dir: ${base_log_dir}/ocrlogs # will be set in code

trainer:
accelerator: gpu
devices: [0]
strategy: ddp
amp_backend: native
log_every_n_steps: 16 # this is global step
precision: 16
max_epochs: 15
check_val_every_n_epoch: 1
accumulate_grad_batches: 8
gradient_clip_val: 3.
gradient_clip_algorithm: norm
benchmark: true