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| data: | |
| data_dir: [data/data_public/controlnet_data] | |
| image_size: 1024 | |
| caption_proportion: | |
| prompt: 1 | |
| external_caption_suffixes: [] | |
| external_clipscore_suffixes: [] | |
| clip_thr_temperature: 0.1 | |
| clip_thr: 25.0 | |
| load_text_feat: false | |
| load_vae_feat: false | |
| transform: default_train | |
| type: SanaWebDatasetMSControl | |
| sort_dataset: false | |
| # model config | |
| model: | |
| model: SanaMSControlNet_600M_P1_D28 | |
| image_size: 1024 | |
| mixed_precision: fp16 | |
| fp32_attention: true | |
| load_from: hf://Efficient-Large-Model/Sana_600M_1024px/checkpoint/Sana_600M_1024px.pth | |
| resume_from: | |
| aspect_ratio_type: ASPECT_RATIO_1024 | |
| multi_scale: true | |
| attn_type: linear | |
| ffn_type: glumbconv | |
| mlp_acts: | |
| - silu | |
| - silu | |
| - | |
| mlp_ratio: 2.5 | |
| use_pe: false | |
| qk_norm: false | |
| class_dropout_prob: 0.1 | |
| # VAE setting | |
| vae: | |
| vae_type: AutoencoderDC | |
| vae_pretrained: mit-han-lab/dc-ae-f32c32-sana-1.0-diffusers | |
| scale_factor: 0.41407 | |
| vae_latent_dim: 32 | |
| vae_downsample_rate: 32 | |
| sample_posterior: true | |
| # text encoder | |
| text_encoder: | |
| text_encoder_name: gemma-2-2b-it | |
| y_norm: true | |
| y_norm_scale_factor: 0.01 | |
| model_max_length: 300 | |
| # CHI | |
| chi_prompt: | |
| - 'Given a user prompt, generate an "Enhanced prompt" that provides detailed visual descriptions suitable for image generation. Evaluate the level of detail in the user prompt:' | |
| - '- If the prompt is simple, focus on adding specifics about colors, shapes, sizes, textures, and spatial relationships to create vivid and concrete scenes.' | |
| - '- If the prompt is already detailed, refine and enhance the existing details slightly without overcomplicating.' | |
| - 'Here are examples of how to transform or refine prompts:' | |
| - '- User Prompt: A cat sleeping -> Enhanced: A small, fluffy white cat curled up in a round shape, sleeping peacefully on a warm sunny windowsill, surrounded by pots of blooming red flowers.' | |
| - '- User Prompt: A busy city street -> Enhanced: A bustling city street scene at dusk, featuring glowing street lamps, a diverse crowd of people in colorful clothing, and a double-decker bus passing by towering glass skyscrapers.' | |
| - 'Please generate only the enhanced description for the prompt below and avoid including any additional commentary or evaluations:' | |
| - 'User Prompt: ' | |
| # Sana schedule Flow | |
| scheduler: | |
| predict_v: true | |
| noise_schedule: linear_flow | |
| pred_sigma: false | |
| flow_shift: 4.0 | |
| # logit-normal timestep | |
| weighting_scheme: logit_normal | |
| logit_mean: 0.0 | |
| logit_std: 1.0 | |
| vis_sampler: flow_dpm-solver | |
| # training setting | |
| train: | |
| num_workers: 10 | |
| seed: 1 | |
| train_batch_size: 16 | |
| num_epochs: 100 | |
| gradient_accumulation_steps: 1 | |
| grad_checkpointing: true | |
| gradient_clip: 0.1 | |
| optimizer: | |
| betas: | |
| - 0.9 | |
| - 0.999 | |
| - 0.9999 | |
| eps: | |
| - 1.0e-30 | |
| - 1.0e-16 | |
| lr: 0.0001 | |
| type: CAMEWrapper | |
| weight_decay: 0.0 | |
| lr_schedule: constant | |
| lr_schedule_args: | |
| num_warmup_steps: 30 | |
| local_save_vis: true # if save log image locally | |
| visualize: true | |
| eval_sampling_steps: 500 | |
| log_interval: 20 | |
| save_model_epochs: 5 | |
| save_model_steps: 500 | |
| work_dir: output/debug | |
| online_metric: false | |
| eval_metric_step: 2000 | |
| online_metric_dir: metric_helper | |
| controlnet: | |
| control_signal_type: "scribble" | |