131 lines
3.5 KiB
YAML
131 lines
3.5 KiB
YAML
name: "HunyuanDiT flowmatching; VAE: 4096 token length; ImageEncoder: DINO-v2 Large; ImageSize: 518"
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# training successfully on 8 x H20 with 98G Memory
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training:
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steps: 10_0000_0000
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use_amp: true
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amp_type: "bf16"
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base_lr: 1e-5
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gradient_clip_val: 1.0
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gradient_clip_algorithm: "norm"
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every_n_train_steps: 2000 # 5000
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val_check_interval: 200 # 4096
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# val_check_interval must be smaller than every_n_train_steps!!!
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limit_val_batches: 16
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dataset:
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target: hy3dshape.data.dit_asl.AlignedShapeLatentModule
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params:
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#! Base setting
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batch_size: 4
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num_workers: 8
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val_num_workers: 4
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# Data
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train_data_list: tools/mini_trainset/preprocessed
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val_data_list: tools/mini_trainset/preprocessed
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#! Image loading
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cond_stage_key: "image"
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image_size: 518
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mean: &mean [0.5, 0.5, 0.5]
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std: &std [0.5, 0.5, 0.5]
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#! Point cloud sampling
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pc_size: &pc_size 81920
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pc_sharpedge_size: &pc_sharpedge_size 0
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sharpedge_label: &sharpedge_label true
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return_normal: true
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#! Augmentation
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padding: true
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model:
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target: hy3dshape.models.diffusion.flow_matching_sit.Diffuser
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params:
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first_stage_key: "surface"
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cond_stage_key: "image"
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scale_by_std: false
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z_scale_factor: &z_scale_factor 1.0039506158752403
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torch_compile: false
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# ema_config:
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# ema_model: LitEma
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# ema_decay: 0.999
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# ema_inference: false
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first_stage_config:
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target: hy3dshape.models.autoencoders.ShapeVAE
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from_pretrained: tencent/Hunyuan3D-2.1
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cond_stage_config:
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target: hy3dshape.models.conditioner.SingleImageEncoder
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params:
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drop_ratio: 0.1
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main_image_encoder:
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type: DinoImageEncoder
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kwargs:
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version: 'facebook/dinov2-large'
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image_size: 518
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use_cls_token: true
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denoiser_cfg:
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target: hy3dshape.models.denoisers.hunyuandit.HunYuanDiTPlain
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from_pretrained: tencent/Hunyuan3D-2.1
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scheduler_cfg:
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transport:
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target: hy3dshape.models.diffusion.transport.create_transport
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params:
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path_type: Linear
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prediction: velocity
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sampler:
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target: hy3dshape.models.diffusion.transport.Sampler
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params: {}
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ode_params:
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sampling_method: euler # dopri5 ...
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num_steps: &num_steps 50
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optimizer_cfg:
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optimizer:
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target: torch.optim.AdamW
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params:
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betas: [0.9, 0.99]
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eps: 1.e-6
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weight_decay: 1.e-2
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scheduler:
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target: hy3dshape.utils.trainings.lr_scheduler.LambdaWarmUpCosineFactorScheduler
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params:
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warm_up_steps: 50 # 5000
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f_start: 1.e-6
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f_min: 1.e-3
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f_max: 1.0
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pipeline_cfg:
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target: hy3dshape.pipelines.Hunyuan3DDiTFlowMatchingPipeline
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image_processor_cfg:
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target: hy3dshape.preprocessors.ImageProcessorV2
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params: {}
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callbacks:
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logger:
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target: hy3dshape.utils.trainings.mesh_log_callback.ImageConditionalASLDiffuserLogger
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params:
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step_frequency: 1000 # 10000
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num_samples: 1
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sample_times: 1
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mean: *mean
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std: *std
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bounds: [-1.01, -1.01, -1.01, 1.01, 1.01, 1.01]
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octree_depth: 8
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num_chunks: 50000
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mc_level: 0.0
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file_loggers:
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target: hy3dshape.utils.trainings.mesh_log_callback.ImageConditionalFixASLDiffuserLogger
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params:
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step_frequency: 500 # 5000
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test_data_path: "tools/mini_testset/images.json"
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