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/home/Ubuntu/Downloads/Unmodel/ACE_plus/./examples/exp_example/20250602092700/std_log.txt +scepter [WARNING] 2025-06-02 09:27:02,412 [File: import_utils.py Function: import_module at line 325] ('DATASETS', 'ACEPlusDataset') not found in ast index file +scepter [INFO] 2025-06-02 09:27:02,413 [File: ace_plus_dataset.py Function: read_data_list at line 151] subject has 4 samples. +scepter [INFO] 2025-06-02 09:27:02,414 [File: registry.py Function: __init__ at line 185] Built dataloader with len 9223372036854775807 +scepter [WARNING] 2025-06-02 09:27:02,414 [File: import_utils.py Function: import_module at line 325] ('DATASETS', 'ACEPlusDataset') not found in ast index file +scepter [INFO] 2025-06-02 09:27:02,414 [File: ace_plus_dataset.py Function: read_data_list at line 151] subject has 4 samples. +scepter [INFO] 2025-06-02 09:27:02,415 [File: registry.py Function: __init__ at line 185] Built dataloader with len 4 +scepter [INFO] 2025-06-02 09:28:16,963 [File: flux.py Function: 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('base_model.model.single_blocks.35.modulation.lin.lora_A.0_SwiftLoRA.weight', torch.Size([64, 3072])), ('base_model.model.single_blocks.35.modulation.lin.lora_B.0_SwiftLoRA.weight', torch.Size([9216, 64])), ('base_model.model.single_blocks.36.linear1.lora_A.0_SwiftLoRA.weight', torch.Size([64, 3072])), ('base_model.model.single_blocks.36.linear1.lora_B.0_SwiftLoRA.weight', torch.Size([21504, 64])), ('base_model.model.single_blocks.36.linear2.lora_A.0_SwiftLoRA.weight', torch.Size([64, 15360])), ('base_model.model.single_blocks.36.linear2.lora_B.0_SwiftLoRA.weight', torch.Size([3072, 64])), ('base_model.model.single_blocks.36.modulation.lin.lora_A.0_SwiftLoRA.weight', torch.Size([64, 3072])), ('base_model.model.single_blocks.36.modulation.lin.lora_B.0_SwiftLoRA.weight', torch.Size([9216, 64])), ('base_model.model.single_blocks.37.linear1.lora_A.0_SwiftLoRA.weight', torch.Size([64, 3072])), ('base_model.model.single_blocks.37.linear1.lora_B.0_SwiftLoRA.weight', torch.Size([21504, 64])), ('base_model.model.single_blocks.37.linear2.lora_A.0_SwiftLoRA.weight', torch.Size([64, 15360])), ('base_model.model.single_blocks.37.linear2.lora_B.0_SwiftLoRA.weight', torch.Size([3072, 64])), ('base_model.model.single_blocks.37.modulation.lin.lora_A.0_SwiftLoRA.weight', torch.Size([64, 3072])), ('base_model.model.single_blocks.37.modulation.lin.lora_B.0_SwiftLoRA.weight', torch.Size([9216, 64]))] +scepter [INFO] 2025-06-02 09:28:30,450 [File: diffusion_solver.py Function: print_model_params_status at line 996] Load trainable params 306315264 / 17178094051 = 1.78%, train part: {'model.double_blocks': 171835392, 'model.single_blocks': 134479872}. +scepter [INFO] 2025-06-02 09:28:30,450 [File: diffusion_solver.py Function: print_model_params_status at line 1000] Load frozen params 16871778787 / 17178094051 = 98.22%, frozen part: {'model': 11902587968, 'first_stage_model': 83819683, 'cond_stage_model': 4885371136}. +scepter [INFO] 2025-06-02 09:29:07,140 [File: diffusion_solver.py Function: set_up at line 230] SwiftModel( + (base_model): LatentDiffusionACEPlus LatentDiffusionACEPlus( + (model): FluxMRModiACEPlus FluxMRModiACEPlus( + (pe_embedder): EmbedND() + (img_in): Linear(in_features=448, out_features=3072, bias=True) + (time_in): MLPEmbedder( + (in_layer): Linear(in_features=256, out_features=3072, bias=True) + (silu): SiLU() + (out_layer): Linear(in_features=3072, out_features=3072, bias=True) + ) + (vector_in): MLPEmbedder( + (in_layer): Linear(in_features=768, out_features=3072, bias=True) + (silu): SiLU() + (out_layer): Linear(in_features=3072, out_features=3072, bias=True) + ) + (guidance_in): MLPEmbedder( + (in_layer): Linear(in_features=256, out_features=3072, bias=True) + (silu): SiLU() + (out_layer): Linear(in_features=3072, out_features=3072, bias=True) + ) + (txt_in): Linear(in_features=4096, out_features=3072, bias=True) + (double_blocks): ModuleList( + (0-18): 19 x DoubleStreamBlock( + (img_mod): Modulation( + (lin): lora.Linear( + (base_layer): Linear(in_features=3072, out_features=18432, bias=True) + (lora_dropout): ModuleDict( + (0_SwiftLoRA): Identity() + ) + (lora_A): ModuleDict( + (0_SwiftLoRA): Linear(in_features=3072, out_features=64, bias=False) + ) + (lora_B): ModuleDict( + (0_SwiftLoRA): Linear(in_features=64, out_features=18432, bias=False) + ) + (lora_embedding_A): ParameterDict() + (lora_embedding_B): ParameterDict() + (lora_magnitude_vector): ModuleDict() + ) + ) + (img_norm1): LayerNorm((3072,), eps=1e-06, elementwise_affine=False) + (img_attn): SelfAttention( + (qkv): lora.Linear( + (base_layer): Linear(in_features=3072, out_features=9216, bias=True) + (lora_dropout): ModuleDict( + (0_SwiftLoRA): Identity() + ) + (lora_A): ModuleDict( + (0_SwiftLoRA): Linear(in_features=3072, out_features=64, bias=False) + ) + (lora_B): ModuleDict( + (0_SwiftLoRA): Linear(in_features=64, out_features=9216, bias=False) + ) + (lora_embedding_A): ParameterDict() + (lora_embedding_B): ParameterDict() + (lora_magnitude_vector): ModuleDict() + ) + (norm): QKNorm( + (query_norm): RMSNorm() + (key_norm): RMSNorm() + ) + (proj): lora.Linear( + (base_layer): Linear(in_features=3072, out_features=3072, bias=True) + (lora_dropout): ModuleDict( + (0_SwiftLoRA): Identity() + ) + (lora_A): ModuleDict( + (0_SwiftLoRA): Linear(in_features=3072, out_features=64, bias=False) + ) + (lora_B): ModuleDict( + (0_SwiftLoRA): Linear(in_features=64, out_features=3072, bias=False) + ) + (lora_embedding_A): ParameterDict() + (lora_embedding_B): ParameterDict() + (lora_magnitude_vector): ModuleDict() + ) + ) + (img_norm2): LayerNorm((3072,), eps=1e-06, elementwise_affine=False) + (img_mlp): Sequential( + (0): lora.Linear( + (base_layer): Linear(in_features=3072, out_features=12288, bias=True) + (lora_dropout): ModuleDict( + (0_SwiftLoRA): Identity() + ) + (lora_A): ModuleDict( + (0_SwiftLoRA): Linear(in_features=3072, out_features=64, bias=False) + ) + (lora_B): ModuleDict( + (0_SwiftLoRA): Linear(in_features=64, out_features=12288, bias=False) + ) + (lora_embedding_A): ParameterDict() + (lora_embedding_B): ParameterDict() + (lora_magnitude_vector): ModuleDict() + ) + (1): GELU(approximate='tanh') + (2): lora.Linear( + (base_layer): Linear(in_features=12288, out_features=3072, bias=True) + (lora_dropout): ModuleDict( + (0_SwiftLoRA): Identity() + ) + (lora_A): ModuleDict( + (0_SwiftLoRA): Linear(in_features=12288, out_features=64, bias=False) + ) + (lora_B): ModuleDict( + (0_SwiftLoRA): Linear(in_features=64, out_features=3072, bias=False) + ) + (lora_embedding_A): ParameterDict() + (lora_embedding_B): ParameterDict() + (lora_magnitude_vector): ModuleDict() + ) + ) + (txt_mod): Modulation( + (lin): lora.Linear( + (base_layer): Linear(in_features=3072, out_features=18432, bias=True) + (lora_dropout): ModuleDict( + (0_SwiftLoRA): Identity() + ) + (lora_A): ModuleDict( + (0_SwiftLoRA): Linear(in_features=3072, out_features=64, bias=False) + ) + (lora_B): ModuleDict( + (0_SwiftLoRA): Linear(in_features=64, out_features=18432, bias=False) + ) + (lora_embedding_A): ParameterDict() + (lora_embedding_B): ParameterDict() + (lora_magnitude_vector): ModuleDict() + ) + ) + (txt_norm1): LayerNorm((3072,), eps=1e-06, elementwise_affine=False) + (txt_attn): SelfAttention( + (qkv): lora.Linear( + (base_layer): Linear(in_features=3072, out_features=9216, bias=True) + (lora_dropout): ModuleDict( + (0_SwiftLoRA): Identity() + ) + (lora_A): ModuleDict( + (0_SwiftLoRA): Linear(in_features=3072, out_features=64, bias=False) + ) + (lora_B): ModuleDict( + (0_SwiftLoRA): Linear(in_features=64, out_features=9216, bias=False) + ) + (lora_embedding_A): ParameterDict() + (lora_embedding_B): ParameterDict() + (lora_magnitude_vector): ModuleDict() + ) + (norm): QKNorm( + (query_norm): RMSNorm() + (key_norm): RMSNorm() + ) + (proj): lora.Linear( + (base_layer): Linear(in_features=3072, out_features=3072, bias=True) + (lora_dropout): ModuleDict( + (0_SwiftLoRA): Identity() + ) + (lora_A): ModuleDict( + (0_SwiftLoRA): Linear(in_features=3072, out_features=64, bias=False) + ) + (lora_B): ModuleDict( + (0_SwiftLoRA): Linear(in_features=64, out_features=3072, bias=False) + ) + (lora_embedding_A): ParameterDict() + (lora_embedding_B): ParameterDict() + (lora_magnitude_vector): ModuleDict() + ) + ) + (txt_norm2): LayerNorm((3072,), eps=1e-06, elementwise_affine=False) + (txt_mlp): Sequential( + (0): lora.Linear( + (base_layer): Linear(in_features=3072, out_features=12288, bias=True) + (lora_dropout): ModuleDict( + (0_SwiftLoRA): Identity() + ) + (lora_A): ModuleDict( + (0_SwiftLoRA): Linear(in_features=3072, out_features=64, bias=False) + ) + (lora_B): ModuleDict( + (0_SwiftLoRA): Linear(in_features=64, out_features=12288, bias=False) + ) + (lora_embedding_A): ParameterDict() + (lora_embedding_B): ParameterDict() + (lora_magnitude_vector): ModuleDict() + ) + (1): GELU(approximate='tanh') + (2): lora.Linear( + (base_layer): Linear(in_features=12288, out_features=3072, bias=True) + (lora_dropout): ModuleDict( + (0_SwiftLoRA): Identity() + ) + (lora_A): ModuleDict( + (0_SwiftLoRA): Linear(in_features=12288, out_features=64, bias=False) + ) + (lora_B): ModuleDict( + (0_SwiftLoRA): Linear(in_features=64, out_features=3072, bias=False) + ) + (lora_embedding_A): ParameterDict() + (lora_embedding_B): ParameterDict() + (lora_magnitude_vector): ModuleDict() + ) + ) + ) + ) + (single_blocks): ModuleList( + (0-37): 38 x SingleStreamBlock( + (linear1): lora.Linear( + (base_layer): Linear(in_features=3072, out_features=21504, bias=True) + (lora_dropout): ModuleDict( + (0_SwiftLoRA): Identity() + ) + (lora_A): ModuleDict( + (0_SwiftLoRA): Linear(in_features=3072, out_features=64, bias=False) + ) + (lora_B): ModuleDict( + (0_SwiftLoRA): Linear(in_features=64, out_features=21504, bias=False) + ) + (lora_embedding_A): ParameterDict() + (lora_embedding_B): ParameterDict() + (lora_magnitude_vector): ModuleDict() + ) + (linear2): lora.Linear( + (base_layer): Linear(in_features=15360, out_features=3072, bias=True) + (lora_dropout): ModuleDict( + (0_SwiftLoRA): Identity() + ) + (lora_A): ModuleDict( + (0_SwiftLoRA): Linear(in_features=15360, out_features=64, bias=False) + ) + (lora_B): ModuleDict( + (0_SwiftLoRA): Linear(in_features=64, out_features=3072, bias=False) + ) + (lora_embedding_A): ParameterDict() + (lora_embedding_B): ParameterDict() + (lora_magnitude_vector): ModuleDict() + ) + (norm): QKNorm( + (query_norm): RMSNorm() + (key_norm): RMSNorm() + ) + (pre_norm): LayerNorm((3072,), eps=1e-06, elementwise_affine=False) + (mlp_act): GELU(approximate='tanh') + (modulation): Modulation( + (lin): lora.Linear( + (base_layer): Linear(in_features=3072, out_features=9216, bias=True) + (lora_dropout): ModuleDict( + (0_SwiftLoRA): Identity() + ) + (lora_A): ModuleDict( + (0_SwiftLoRA): Linear(in_features=3072, out_features=64, bias=False) + ) + (lora_B): ModuleDict( + (0_SwiftLoRA): Linear(in_features=64, out_features=9216, bias=False) + ) + (lora_embedding_A): ParameterDict() + (lora_embedding_B): ParameterDict() + (lora_magnitude_vector): ModuleDict() + ) + ) + ) + ) + (final_layer): LastLayer( + (norm_final): LayerNorm((3072,), eps=1e-06, elementwise_affine=False) + (linear): Linear(in_features=3072, out_features=64, bias=True) + (adaLN_modulation): Sequential( + (0): SiLU() + (1): Linear(in_features=3072, out_features=6144, bias=True) + ) + ) + ) + (first_stage_model): AutoencoderKLFlux AutoencoderKLFlux( + (encoder): Encoder Encoder( + (conv_in): Conv2d(3, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (down): ModuleList( + (0): Module( + (block): ModuleList( + (0-1): 2 x ResnetBlock( + (norm1): GroupNorm(32, 128, eps=1e-06, affine=True) + (conv1): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (norm2): GroupNorm(32, 128, eps=1e-06, affine=True) + (dropout): Dropout(p=0.0, inplace=False) + (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (attn): ModuleList() + (downsample): Downsample( + (conv): Conv2d(128, 128, kernel_size=(3, 3), stride=(2, 2)) + ) + ) + (1): Module( + (block): ModuleList( + (0): ResnetBlock( + (norm1): GroupNorm(32, 128, eps=1e-06, affine=True) + (conv1): Conv2d(128, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (norm2): GroupNorm(32, 256, eps=1e-06, affine=True) + (dropout): Dropout(p=0.0, inplace=False) + (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (nin_shortcut): Conv2d(128, 256, kernel_size=(1, 1), stride=(1, 1)) + ) + (1): ResnetBlock( + (norm1): GroupNorm(32, 256, eps=1e-06, affine=True) + (conv1): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (norm2): GroupNorm(32, 256, eps=1e-06, affine=True) + (dropout): Dropout(p=0.0, inplace=False) + (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (attn): ModuleList() + (downsample): Downsample( + (conv): Conv2d(256, 256, kernel_size=(3, 3), stride=(2, 2)) + ) + ) + (2): Module( + (block): ModuleList( + (0): ResnetBlock( + (norm1): GroupNorm(32, 256, eps=1e-06, affine=True) + (conv1): Conv2d(256, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (norm2): GroupNorm(32, 512, eps=1e-06, affine=True) + (dropout): Dropout(p=0.0, inplace=False) + (conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (nin_shortcut): Conv2d(256, 512, kernel_size=(1, 1), stride=(1, 1)) + ) + (1): ResnetBlock( + (norm1): GroupNorm(32, 512, eps=1e-06, affine=True) + (conv1): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (norm2): GroupNorm(32, 512, eps=1e-06, affine=True) + (dropout): Dropout(p=0.0, inplace=False) + (conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (attn): ModuleList() + (downsample): Downsample( + (conv): Conv2d(512, 512, kernel_size=(3, 3), stride=(2, 2)) + ) + ) + (3): Module( + (block): ModuleList( + (0-1): 2 x ResnetBlock( + (norm1): GroupNorm(32, 512, eps=1e-06, affine=True) + (conv1): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (norm2): GroupNorm(32, 512, eps=1e-06, affine=True) + (dropout): Dropout(p=0.0, inplace=False) + (conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (attn): ModuleList() + ) + ) + (mid): Module( + (block_1): ResnetBlock( + (norm1): GroupNorm(32, 512, eps=1e-06, affine=True) + (conv1): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (norm2): GroupNorm(32, 512, eps=1e-06, affine=True) + (dropout): Dropout(p=0.0, inplace=False) + (conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + (attn_1): MemoryEfficientAttention( + (norm): GroupNorm(32, 512, eps=1e-06, affine=True) + (q): Conv2d(512, 512, kernel_size=(1, 1), stride=(1, 1)) + (k): Conv2d(512, 512, kernel_size=(1, 1), stride=(1, 1)) + (v): Conv2d(512, 512, kernel_size=(1, 1), stride=(1, 1)) + (proj_out): Conv2d(512, 512, kernel_size=(1, 1), stride=(1, 1)) + ) + (block_2): ResnetBlock( + (norm1): GroupNorm(32, 512, eps=1e-06, affine=True) + (conv1): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (norm2): GroupNorm(32, 512, eps=1e-06, affine=True) + (dropout): Dropout(p=0.0, inplace=False) + (conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (norm_out): GroupNorm(32, 512, eps=1e-06, affine=True) + (conv_out): Conv2d(512, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + (decoder): Decoder Decoder( + (conv_in): Conv2d(16, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (mid): Module( + (block_1): ResnetBlock( + (norm1): GroupNorm(32, 512, eps=1e-06, affine=True) + (conv1): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (norm2): GroupNorm(32, 512, eps=1e-06, affine=True) + (dropout): Dropout(p=0.0, inplace=False) + (conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + (attn_1): MemoryEfficientAttention( + (norm): GroupNorm(32, 512, eps=1e-06, affine=True) + (q): Conv2d(512, 512, kernel_size=(1, 1), stride=(1, 1)) + (k): Conv2d(512, 512, kernel_size=(1, 1), stride=(1, 1)) + (v): Conv2d(512, 512, kernel_size=(1, 1), stride=(1, 1)) + (proj_out): Conv2d(512, 512, kernel_size=(1, 1), stride=(1, 1)) + ) + (block_2): ResnetBlock( + (norm1): GroupNorm(32, 512, eps=1e-06, affine=True) + (conv1): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (norm2): GroupNorm(32, 512, eps=1e-06, affine=True) + (dropout): Dropout(p=0.0, inplace=False) + (conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (up): ModuleList( + (0): Module( + (block): ModuleList( + (0): ResnetBlock( + (norm1): GroupNorm(32, 256, eps=1e-06, affine=True) + (conv1): Conv2d(256, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (norm2): GroupNorm(32, 128, eps=1e-06, affine=True) + (dropout): Dropout(p=0.0, inplace=False) + (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (nin_shortcut): Conv2d(256, 128, kernel_size=(1, 1), stride=(1, 1)) + ) + (1-2): 2 x ResnetBlock( + (norm1): GroupNorm(32, 128, eps=1e-06, affine=True) + (conv1): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (norm2): GroupNorm(32, 128, eps=1e-06, affine=True) + (dropout): Dropout(p=0.0, inplace=False) + (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (attn): ModuleList() + ) + (1): Module( + (block): ModuleList( + (0): ResnetBlock( + (norm1): GroupNorm(32, 512, eps=1e-06, affine=True) + (conv1): Conv2d(512, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (norm2): GroupNorm(32, 256, eps=1e-06, affine=True) + (dropout): Dropout(p=0.0, inplace=False) + (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (nin_shortcut): Conv2d(512, 256, kernel_size=(1, 1), stride=(1, 1)) + ) + (1-2): 2 x ResnetBlock( + (norm1): GroupNorm(32, 256, eps=1e-06, affine=True) + (conv1): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (norm2): GroupNorm(32, 256, eps=1e-06, affine=True) + (dropout): Dropout(p=0.0, inplace=False) + (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (attn): ModuleList() + (upsample): Upsample( + (conv): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (2-3): 2 x Module( + (block): ModuleList( + (0-2): 3 x ResnetBlock( + (norm1): GroupNorm(32, 512, eps=1e-06, affine=True) + (conv1): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (norm2): GroupNorm(32, 512, eps=1e-06, affine=True) + (dropout): Dropout(p=0.0, inplace=False) + (conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (attn): ModuleList() + (upsample): Upsample( + (conv): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + ) + (norm_out): GroupNorm(32, 128, eps=1e-06, affine=True) + (conv_out): Conv2d(128, 3, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + (conv1): Identity() + (conv2): Identity() + ) + (cond_stage_model): T5ACEPlusClipFluxEmbedder T5ACEPlusClipFluxEmbedder( + (t5_model): ACEHFEmbedder ACEHFEmbedder( + (hf_module): T5EncoderModel( + (shared): Embedding(32128, 4096) + (encoder): T5Stack( + (embed_tokens): Embedding(32128, 4096) + (block): ModuleList( + (0): T5Block( + (layer): ModuleList( + (0): T5LayerSelfAttention( + (SelfAttention): T5Attention( + (q): Linear(in_features=4096, out_features=4096, bias=False) + (k): Linear(in_features=4096, out_features=4096, bias=False) + (v): Linear(in_features=4096, out_features=4096, bias=False) + (o): Linear(in_features=4096, out_features=4096, bias=False) + (relative_attention_bias): Embedding(32, 64) + ) + (layer_norm): T5LayerNorm() + (dropout): Dropout(p=0.1, inplace=False) + ) + (1): T5LayerFF( + (DenseReluDense): T5DenseGatedActDense( + (wi_0): Linear(in_features=4096, out_features=10240, bias=False) + (wi_1): Linear(in_features=4096, out_features=10240, bias=False) + (wo): Linear(in_features=10240, out_features=4096, bias=False) + (dropout): Dropout(p=0.1, inplace=False) + (act): NewGELUActivation() + ) + (layer_norm): T5LayerNorm() + (dropout): Dropout(p=0.1, inplace=False) + ) + ) + ) + (1-23): 23 x T5Block( + (layer): ModuleList( + (0): T5LayerSelfAttention( + (SelfAttention): T5Attention( + (q): Linear(in_features=4096, out_features=4096, bias=False) + (k): Linear(in_features=4096, out_features=4096, bias=False) + (v): Linear(in_features=4096, out_features=4096, bias=False) + (o): Linear(in_features=4096, out_features=4096, bias=False) + ) + (layer_norm): T5LayerNorm() + (dropout): Dropout(p=0.1, inplace=False) + ) + (1): T5LayerFF( + (DenseReluDense): T5DenseGatedActDense( + (wi_0): Linear(in_features=4096, out_features=10240, bias=False) + (wi_1): Linear(in_features=4096, out_features=10240, bias=False) + (wo): Linear(in_features=10240, out_features=4096, bias=False) + (dropout): Dropout(p=0.1, inplace=False) + (act): NewGELUActivation() + ) + (layer_norm): T5LayerNorm() + (dropout): Dropout(p=0.1, inplace=False) + ) + ) + ) + ) + (final_layer_norm): T5LayerNorm() + (dropout): Dropout(p=0.1, inplace=False) + ) + ) + ) + (clip_model): ACEHFEmbedder ACEHFEmbedder( + (hf_module): CLIPTextModel( + (text_model): CLIPTextTransformer( + (embeddings): CLIPTextEmbeddings( + (token_embedding): Embedding(49408, 768) + (position_embedding): Embedding(77, 768) + ) + (encoder): CLIPEncoder( + (layers): ModuleList( + (0-11): 12 x CLIPEncoderLayer( + (self_attn): CLIPSdpaAttention( + (k_proj): Linear(in_features=768, out_features=768, bias=True) + (v_proj): Linear(in_features=768, out_features=768, bias=True) + (q_proj): Linear(in_features=768, out_features=768, bias=True) + (out_proj): Linear(in_features=768, out_features=768, bias=True) + ) + (layer_norm1): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + (mlp): CLIPMLP( + (activation_fn): QuickGELUActivation() + (fc1): Linear(in_features=768, out_features=3072, bias=True) + (fc2): Linear(in_features=3072, out_features=768, bias=True) + ) + (layer_norm2): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + ) + ) + ) + (final_layer_norm): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + ) + ) + ) + ) + ) +) +scepter [INFO] 2025-06-02 09:29:07,179 [File: log.py Function: before_solve at line 260] Tensorboard: save to ./examples/exp_example/20250602092700/tensorboard +scepter [INFO] 2025-06-02 09:51:38,590 [File: log.py Function: _print_iter_log at line 71] Stage [train] iter: [20/100000], data_time: 42.3201(42.3201), time: 67.5697(67.5697), loss: 0.2030(0.2030), throughput: 5349/day, all_throughput: 20, pg0_lr: 0.001000, scale: 1.000000, [24mins 36secs 0.02%(85days 9hours 50mins 19secs)] +scepter [INFO] 2025-06-02 10:13:55,895 [File: log.py Function: _print_iter_log at line 71] Stage [train] iter: [40/100000], data_time: 41.7579(42.0390), time: 66.8652(67.2175), loss: 0.1496(0.1763), throughput: 5343/day, all_throughput: 40, pg0_lr: 0.001000, scale: 1.000000, [46mins 53secs 0.04%(81days 9hours 1mins 35secs)] +scepter [INFO] 2025-06-02 10:25:05,166 [File: checkpoint.py Function: after_iter at line 109] Saving checkpoint after 50 steps +scepter [INFO] 2025-06-02 10:36:13,836 [File: log.py Function: _print_iter_log at line 71] Stage [train] iter: [60/100000], data_time: 41.7211(41.9331), time: 66.8971(67.1107), loss: 0.1511(0.1679), throughput: 5344/day, all_throughput: 60, pg0_lr: 0.001000, scale: 1.000000, [1hours 9mins 11secs 0.06%(80days 48mins 10secs)] +scepter [INFO] 2025-06-02 10:58:22,386 [File: log.py Function: _print_iter_log at line 71] Stage [train] iter: [80/100000], data_time: 41.4367(41.8090), time: 66.4275(66.9399), loss: 0.1644(0.1670), throughput: 5352/day, all_throughput: 80, pg0_lr: 0.001000, scale: 1.000000, [1hours 31mins 19secs 0.08%(79days 5hours 14mins 48secs)] +scepter [INFO] 2025-06-02 11:20:31,032 [File: log.py Function: _print_iter_log at line 71] Stage [train] iter: [100/100000], data_time: 41.3202(41.7112), time: 66.4323(66.8384), loss: 0.1641(0.1664), throughput: 5354/day, all_throughput: 100, pg0_lr: 0.001000, scale: 1.000000, [1hours 53mins 28secs 0.10%(78days 17hours 23mins 32secs)] +scepter [INFO] 2025-06-02 11:20:31,033 [File: checkpoint.py Function: after_iter at line 109] Saving checkpoint after 100 steps +scepter [INFO] 2025-06-02 11:42:39,626 [File: log.py Function: _print_iter_log at line 71] Stage [train] iter: [120/100000], data_time: 41.4255(41.6636), time: 66.4297(66.7703), loss: 0.1851(0.1695), throughput: 5355/day, all_throughput: 120, pg0_lr: 0.001000, scale: 1.000000, [2hours 15mins 37secs 0.12%(78days 9hours 21mins 15secs)] +scepter [INFO] 2025-06-02 12:04:51,591 [File: log.py Function: _print_iter_log at line 71] Stage [train] iter: [140/100000], data_time: 41.7225(41.6720), time: 66.5982(66.7457), loss: 0.2146(0.1760), throughput: 5357/day, all_throughput: 140, pg0_lr: 0.001000, scale: 1.000000, [2hours 37mins 49secs 0.14%(78days 4hours 10mins 30secs)] +scepter [INFO] 2025-06-02 12:15:54,666 [File: checkpoint.py Function: after_iter at line 109] Saving checkpoint after 150 steps +scepter [INFO] 2025-06-02 12:26:59,151 [File: log.py Function: _print_iter_log at line 71] Stage [train] iter: [160/100000], data_time: 41.3753(41.6349), time: 66.3780(66.6997), loss: 0.2634(0.1869), throughput: 5358/day, all_throughput: 160, pg0_lr: 0.001000, scale: 1.000000, [2hours 59mins 56secs 0.16%(77days 23hours 26mins 5secs)] +scepter [INFO] 2025-06-02 12:49:04,260 [File: log.py Function: _print_iter_log at line 71] Stage [train] iter: [180/100000], data_time: 41.2891(41.5965), time: 66.2555(66.6504), loss: 0.2875(0.1981), throughput: 5356/day, all_throughput: 180, pg0_lr: 0.001000, scale: 1.000000, [3hours 22mins 1secs 0.18%(77days 19hours 17mins 18secs)] +scepter [INFO] 2025-06-02 13:11:15,836 [File: log.py Function: _print_iter_log at line 71] Stage [train] iter: [200/100000], data_time: 41.4977(41.5866), time: 66.5788(66.6432), loss: 0.2561(0.2039), throughput: 5354/day, all_throughput: 200, pg0_lr: 0.001000, scale: 1.000000, [3hours 44mins 13secs 0.20%(77days 16hours 47mins 39secs)] +scepter [INFO] 2025-06-02 13:11:15,837 [File: checkpoint.py Function: after_iter at line 109] Saving checkpoint after 200 steps +scepter [INFO] 2025-06-02 13:33:26,818 [File: log.py Function: _print_iter_log at line 71] Stage [train] iter: [220/100000], data_time: 41.5564(41.5839), time: 66.5491(66.6346), loss: 0.2442(0.2076), throughput: 5358/day, all_throughput: 220, pg0_lr: 0.001000, scale: 1.000000, [4hours 6mins 24secs 0.22%(77days 14hours 36mins 40secs)] +scepter [INFO] 2025-06-02 13:55:36,662 [File: log.py Function: _print_iter_log at line 71] Stage [train] iter: [240/100000], data_time: 41.6124(41.5863), time: 66.4922(66.6228), loss: 0.2165(0.2083), throughput: 5359/day, all_throughput: 240, pg0_lr: 0.001000, scale: 1.000000, [4hours 28mins 34secs 0.24%(77days 12hours 35mins 57secs)] +scepter [INFO] 2025-06-02 14:06:40,396 [File: checkpoint.py Function: after_iter at line 109] Saving checkpoint after 250 steps +scepter [INFO] 2025-06-02 14:17:47,873 [File: log.py Function: _print_iter_log at line 71] Stage [train] iter: [260/100000], data_time: 41.6454(41.5908), time: 66.5605(66.6180), loss: 0.1728(0.2056), throughput: 5361/day, all_throughput: 260, pg0_lr: 0.001000, scale: 1.000000, [4hours 50mins 45secs 0.26%(77days 10hours 59mins 7secs)] +scepter [INFO] 2025-06-02 14:39:46,950 [File: log.py Function: _print_iter_log at line 71] Stage [train] iter: [280/100000], data_time: 41.1921(41.5623), time: 65.9539(66.5706), loss: 0.1977(0.2050), throughput: 5365/day, all_throughput: 280, pg0_lr: 0.001000, scale: 1.000000, [5hours 12mins 44secs 0.28%(77days 8hours 20mins 56secs)] +scepter [INFO] 2025-06-02 15:01:46,703 [File: log.py Function: _print_iter_log at line 71] Stage [train] iter: [300/100000], data_time: 41.1670(41.5360), time: 65.9876(66.5317), loss: 0.1706(0.2027), throughput: 5369/day, all_throughput: 300, pg0_lr: 0.001000, scale: 1.000000, [5hours 34mins 44secs 0.30%(77days 6hours 4mins 39secs)] +scepter [INFO] 2025-06-02 15:01:46,703 [File: checkpoint.py Function: after_iter at line 109] Saving checkpoint after 300 steps +scepter [INFO] 2025-06-02 15:23:47,457 [File: log.py Function: _print_iter_log at line 71] Stage [train] iter: [320/100000], data_time: 41.1280(41.5105), time: 66.0377(66.5008), loss: 0.1706(0.2007), throughput: 5370/day, all_throughput: 320, pg0_lr: 0.001000, scale: 1.000000, [5hours 56mins 45secs 0.32%(77days 4hours 7mins 51secs)] +scepter [INFO] 2025-06-02 15:45:53,102 [File: log.py Function: _print_iter_log at line 71] Stage [train] iter: [340/100000], data_time: 41.4600(41.5075), time: 66.2822(66.4880), loss: 0.1437(0.1974), throughput: 5374/day, all_throughput: 340, pg0_lr: 0.001000, scale: 1.000000, [6hours 18mins 50secs 0.34%(77days 2hours 46mins 6secs)] +scepter [INFO] 2025-06-02 15:56:57,492 [File: checkpoint.py Function: after_iter at line 109] Saving checkpoint after 350 steps +scepter [INFO] 2025-06-02 16:08:04,405 [File: log.py Function: _print_iter_log at line 71] Stage [train] iter: [360/100000], data_time: 41.6830(41.5173), time: 66.5652(66.4922), loss: 0.1532(0.1949), throughput: 5375/day, all_throughput: 360, pg0_lr: 0.001000, scale: 1.000000, [6hours 41mins 1secs 0.36%(77days 1hours 57mins 5secs)] +scepter [INFO] 2025-06-02 16:30:13,447 [File: log.py Function: _print_iter_log at line 71] Stage [train] iter: [380/100000], data_time: 41.4708(41.5148), time: 66.4521(66.4901), loss: 0.1476(0.1924), throughput: 5375/day, all_throughput: 380, pg0_lr: 0.001000, scale: 1.000000, [7hours 3mins 11secs 0.38%(77days 1hours 1mins 0secs)] +scepter [INFO] 2025-06-02 16:52:20,557 [File: log.py Function: _print_iter_log at line 71] Stage [train] iter: [400/100000], data_time: 41.3496(41.5065), time: 66.3555(66.4834), loss: 0.1394(0.1898), throughput: 5375/day, all_throughput: 400, pg0_lr: 0.001000, scale: 1.000000, [7hours 25mins 18secs 0.40%(77days 18secs)] +scepter [INFO] 2025-06-02 16:52:20,558 [File: checkpoint.py Function: after_iter at line 109] Saving checkpoint after 400 steps +scepter [INFO] 2025-06-02 17:14:31,006 [File: log.py Function: _print_iter_log at line 71] Stage [train] iter: [420/100000], data_time: 41.6155(41.5117), time: 66.5224(66.4853), loss: 0.1420(0.1875), throughput: 5375/day, all_throughput: 420, pg0_lr: 0.001000, scale: 1.000000, [7hours 47mins 28secs 0.42%(76days 23hours 16mins 28secs)] +scepter [INFO] 2025-06-02 17:36:43,227 [File: log.py Function: _print_iter_log at line 71] Stage [train] iter: [440/100000], data_time: 41.4885(41.5107), time: 66.6110(66.4910), loss: 0.1349(0.1851), throughput: 5374/day, all_throughput: 440, pg0_lr: 0.001000, scale: 1.000000, [8hours 9mins 40secs 0.44%(76days 22hours 41mins 17secs)] +scepter [INFO] 2025-06-02 17:47:48,583 [File: checkpoint.py Function: after_iter at line 109] Saving checkpoint after 450 steps +scepter [INFO] 2025-06-02 17:58:55,039 [File: log.py Function: _print_iter_log at line 71] Stage [train] iter: [460/100000], data_time: 41.6236(41.5156), time: 66.5906(66.4953), loss: 0.1423(0.1832), throughput: 5374/day, all_throughput: 460, pg0_lr: 0.001000, scale: 1.000000, [8hours 31mins 52secs 0.46%(76days 22hours 5mins 45secs)] +scepter [INFO] 2025-06-02 18:21:09,747 [File: log.py Function: _print_iter_log at line 71] Stage [train] iter: [480/100000], data_time: 41.6391(41.5207), time: 66.7354(66.5053), loss: 0.1559(0.1821), throughput: 5373/day, all_throughput: 480, pg0_lr: 0.001000, scale: 1.000000, [8hours 54mins 7secs 0.48%(76days 21hours 41mins 20secs)] +scepter [INFO] 2025-06-02 18:43:26,230 [File: log.py Function: _print_iter_log at line 71] Stage [train] iter: [500/100000], data_time: 41.8578(41.5342), time: 66.8242(66.5181), loss: 0.1370(0.1803), throughput: 5374/day, all_throughput: 500, pg0_lr: 0.001000, scale: 1.000000, [9hours 16mins 23secs 0.50%(76days 21hours 23mins 0secs)] +scepter [INFO] 2025-06-02 18:43:26,231 [File: checkpoint.py Function: after_iter at line 109] Saving checkpoint after 500 steps +scepter [INFO] 2025-06-02 19:05:33,274 [File: log.py Function: _print_iter_log at line 71] Stage [train] iter: [520/100000], data_time: 41.4453(41.5308), time: 66.3522(66.5117), loss: 0.2116(0.1815), throughput: 5375/day, all_throughput: 520, pg0_lr: 0.001000, scale: 1.000000, [9hours 38mins 30secs 0.52%(76days 20hours 34mins 15secs)] +scepter [INFO] 2025-06-02 19:27:53,538 [File: log.py Function: _print_iter_log at line 71] Stage [train] iter: [540/100000], data_time: 41.6550(41.5354), time: 67.0132(66.5303), loss: 0.1492(0.1803), throughput: 5372/day, all_throughput: 540, pg0_lr: 0.001000, scale: 1.000000, [10hours 51secs 0.54%(76days 20hours 28mins 3secs)] +scepter [INFO] 2025-06-02 19:39:01,432 [File: checkpoint.py Function: after_iter at line 109] Saving checkpoint after 550 steps +scepter [INFO] 2025-06-02 19:50:06,907 [File: log.py Function: _print_iter_log at line 71] Stage [train] iter: [560/100000], data_time: 41.6412(41.5392), time: 66.6685(66.5352), loss: 0.2414(0.1825), throughput: 5373/day, all_throughput: 560, pg0_lr: 0.001000, scale: 1.000000, [10hours 23mins 4secs 0.56%(76days 20hours 18secs)] +scepter [INFO] 2025-06-02 20:12:08,182 [File: log.py Function: _print_iter_log at line 71] Stage [train] iter: [580/100000], data_time: 41.2246(41.5283), time: 66.0638(66.5189), loss: 0.2961(0.1864), throughput: 5373/day, all_throughput: 580, pg0_lr: 0.001000, scale: 1.000000, [10hours 45mins 5secs 0.58%(76days 18hours 58mins 22secs)] +scepter [INFO] 2025-06-02 20:34:10,081 [File: log.py Function: _print_iter_log at line 71] Stage [train] iter: [600/100000], data_time: 41.2012(41.5174), time: 66.0950(66.5048), loss: 0.2136(0.1873), throughput: 5374/day, all_throughput: 600, pg0_lr: 0.001000, scale: 1.000000, [11hours 7mins 7secs 0.60%(76days 18hours 50secs)] +scepter [INFO] 2025-06-02 20:34:10,082 [File: checkpoint.py Function: after_iter at line 109] Saving checkpoint after 600 steps +scepter [INFO] 2025-06-02 20:56:21,375 [File: log.py Function: _print_iter_log at line 71] Stage [train] iter: [620/100000], data_time: 41.6078(41.5203), time: 66.5647(66.5067), loss: 0.1896(0.1874), throughput: 5375/day, all_throughput: 620, pg0_lr: 0.001000, scale: 1.000000, [11hours 29mins 18secs 0.62%(76days 17hours 30mins 41secs)] +scepter [INFO] 2025-06-02 21:18:27,057 [File: log.py Function: _print_iter_log at line 71] Stage [train] iter: [640/100000], data_time: 41.2849(41.5130), time: 66.2841(66.4998), loss: 0.1944(0.1876), throughput: 5376/day, all_throughput: 640, pg0_lr: 0.001000, scale: 1.000000, [11hours 51mins 24secs 0.64%(76days 16hours 46mins 31secs)] +scepter [INFO] 2025-06-02 21:29:27,002 [File: checkpoint.py Function: after_iter at line 109] Saving checkpoint after 650 steps +scepter [INFO] 2025-06-02 21:40:27,523 [File: log.py Function: _print_iter_log at line 71] Stage [train] iter: [660/100000], data_time: 41.1508(41.5020), time: 66.0233(66.4853), loss: 0.1546(0.1866), throughput: 5376/day, all_throughput: 660, pg0_lr: 0.001000, scale: 1.000000, [12hours 13mins 25secs 0.66%(76days 15hours 50mins 36secs)] diff --git a/ACE_plus/examples/exp_example/20250602092700/train.yaml b/ACE_plus/examples/exp_example/20250602092700/train.yaml new file mode 100644 index 0000000000000000000000000000000000000000..9c8f141686d91b64513fe45a530c356a9f311e72 --- /dev/null +++ b/ACE_plus/examples/exp_example/20250602092700/train.yaml @@ -0,0 +1,288 @@ +ENV: + BACKEND: nccl + SEED: 1999 +SOLVER: + # NAME DESCRIPTION: TYPE: default: 'LatentUfitSolver' + NAME: FormalACEPlusSolver + # MAX_STEPS DESCRIPTION: The total steps for training. TYPE: int default: 100000 + MAX_STEPS: 100000 + # USE_AMP DESCRIPTION: Use amp to surpport mix precision or not, default is False. TYPE: bool default: False + USE_AMP: True + # DTYPE DESCRIPTION: The precision for training. TYPE: str default: 'float32' + DTYPE: bfloat16 + ENABLE_GRADSCALER: False + # USE_FAIRSCALE DESCRIPTION: Use fairscale as the backend of ddp, default False. TYPE: bool default: False + USE_FAIRSCALE: False + USE_ORIG_PARAMS: True + USE_FSDP: True # lora use ddp(USE_FSDP=False), else use fsdp(USE_FSDP=True) + # LOAD_MODEL_ONLY DESCRIPTION: Only load the model rather than the optimizer and schedule, default is False. TYPE: bool default: False + LOAD_MODEL_ONLY: False + # RESUME_FROM DESCRIPTION: Resume from some state of training! TYPE: str default: '' + RESUME_FROM: + # WORK_DIR DESCRIPTION: Save dir of the training log or model. TYPE: str default: '' + WORK_DIR: ./examples/exp_example/ + # LOG_FILE DESCRIPTION: Save log path. TYPE: str default: '' + LOG_FILE: std_log.txt + # LOG_TRAIN_NUM DESCRIPTION: The number samples used to log in training phase. TYPE: int default: -1 + LOG_TRAIN_NUM: 16 + # FSDP_REDUCE_DTYPE DESCRIPTION: The dtype of reduce in FSDP. TYPE: str default: 'float16' + FSDP_REDUCE_DTYPE: float32 + # FSDP_BUFFER_DTYPE DESCRIPTION: The dtype of buffer in FSDP. TYPE: str default: 'float16' + FSDP_BUFFER_DTYPE: float32 + # FSDP_SHARD_MODULES DESCRIPTION: The modules to be sharded in FSDP. TYPE: list default: ['model'] + FSDP_SHARD_MODULES: + - MODULE: 'model.model' + FSDP_GROUP: [ 'single_blocks', 'double_blocks'] + - MODULE: 'cond_stage_model.t5_model.hf_module.encoder' + FSDP_GROUP: [ 'block' ] # + SAVE_MODULES: [ 'model'] # + TRAIN_MODULES: ['model'] + + # + FILE_SYSTEM: + - NAME: HuggingfaceFs + TEMP_DIR: ./cache + - NAME: ModelscopeFs + TEMP_DIR: ./cache + # + MODEL: + NAME: LatentDiffusionACEPlus + PARAMETERIZATION: rf + TIMESTEPS: 1000 + GUIDE_SCALE: 1.0 + PRETRAINED_MODEL: + IGNORE_KEYS: [ ] + USE_EMA: False + EVAL_EMA: False + SIZE_FACTOR: 8 + DIFFUSION: + NAME: DiffusionFluxRF + PREDICTION_TYPE: raw + NOISE_NORM: True + # NOISE_SCHEDULER DESCRIPTION: TYPE: default: '' + NOISE_SCHEDULER: + NAME: FlowMatchFluxShiftScheduler + SHIFT: False + PRE_T_SAMPLE: True + PRE_T_SAMPLE_FOLD: 1 + SIGMOID_SCALE: 1 + BASE_SHIFT: 0.5 + MAX_SHIFT: 1.15 + SAMPLER_SCHEDULER: + NAME: FlowMatchFluxShiftScheduler + SHIFT: True + PRE_T_SAMPLE: False + SIGMOID_SCALE: 1 + BASE_SHIFT: 0.5 + MAX_SHIFT: 1.15 + + # + DIFFUSION_MODEL: + # NAME DESCRIPTION: TYPE: default: 'Flux' + NAME: FluxMRModiACEPlus + PRETRAINED_MODEL: /home/Ubuntu/Downloads/Unmodel/Reference_models/flux1-fill-dev.safetensors + # IN_CHANNELS DESCRIPTION: model's input channels. TYPE: int default: 64 + IN_CHANNELS: 448 + # OUT_CHANNELS DESCRIPTION: model's input channels. TYPE: int default: 64 + OUT_CHANNELS: 64 + # HIDDEN_SIZE DESCRIPTION: model's hidden size. TYPE: int default: 1024 + HIDDEN_SIZE: 3072 + REDUX_DIM: 1152 + # NUM_HEADS DESCRIPTION: number of heads in the transformer. TYPE: int default: 16 + NUM_HEADS: 24 + # AXES_DIM DESCRIPTION: dimensions of the axes of the positional encoding. TYPE: list default: [16, 56, 56] + AXES_DIM: [ 16, 56, 56 ] + # THETA DESCRIPTION: theta for positional encoding. TYPE: int default: 10000 + THETA: 10000 + # VEC_IN_DIM DESCRIPTION: dimension of the vector input. TYPE: int default: 768 + VEC_IN_DIM: 768 + # GUIDANCE_EMBED DESCRIPTION: whether to use guidance embedding. TYPE: bool default: False + GUIDANCE_EMBED: True + # CONTEXT_IN_DIM DESCRIPTION: dimension of the context input. TYPE: int default: 4096 + CONTEXT_IN_DIM: 4096 + # MLP_RATIO DESCRIPTION: ratio of mlp hidden size to hidden size. TYPE: float default: 4.0 + MLP_RATIO: 4.0 + # QKV_BIAS DESCRIPTION: whether to use bias in qkv projection. TYPE: bool default: True + QKV_BIAS: True + # DEPTH DESCRIPTION: number of transformer blocks. TYPE: int default: 19 + DEPTH: 19 + # DEPTH_SINGLE_BLOCKS DESCRIPTION: number of transformer blocks in the single stream block. TYPE: int default: 38 + DEPTH_SINGLE_BLOCKS: 38 + # ATTN_BACKEND：setting 'flash_attn' to use flash_attn2, if the version of pytorch > 2.4.0, using 'pytorch' to use pytorch's implementation + ATTN_BACKEND: flash_attn + # USE_GRAD_CHECKPOINT: setting gc to true can decrease the memory usage. + USE_GRAD_CHECKPOINT: True + + # + FIRST_STAGE_MODEL: + NAME: AutoencoderKLFlux + EMBED_DIM: 16 + PRETRAINED_MODEL: /home/Ubuntu/Downloads/Unmodel/Reference_models/ae.safetensors + IGNORE_KEYS: [ ] + BATCH_SIZE: 8 + USE_CONV: False + SCALE_FACTOR: 0.3611 + SHIFT_FACTOR: 0.1159 + # + 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: 16 + 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: 16 + DROPOUT: 0.0 + RESAMP_WITH_CONV: True + GIVE_PRE_END: False + TANH_OUT: False + # + COND_STAGE_MODEL: + # NAME DESCRIPTION: TYPE: default: 'T5PlusClipFluxEmbedder' + NAME: T5ACEPlusClipFluxEmbedder + # T5_MODEL DESCRIPTION: TYPE: default: '' + T5_MODEL: + # NAME DESCRIPTION: TYPE: default: 'HFEmbedder' + NAME: ACEHFEmbedder + # HF_MODEL_CLS DESCRIPTION: huggingface cls in transfomer TYPE: NoneType default: None + HF_MODEL_CLS: T5EncoderModel + # MODEL_PATH DESCRIPTION: model folder path TYPE: NoneType default: None + MODEL_PATH: /home/Ubuntu/Downloads/Unmodel/Reference_models/t5_xxl/ + # HF_TOKENIZER_CLS DESCRIPTION: huggingface cls in transfomer TYPE: NoneType default: None + HF_TOKENIZER_CLS: T5Tokenizer + # TOKENIZER_PATH DESCRIPTION: tokenizer folder path TYPE: NoneType default: None + TOKENIZER_PATH: /home/Ubuntu/Downloads/Unmodel/Reference_models/t5_xxl/ + ADDED_IDENTIFIER: [ '','{image}', '{caption}', '{mask}', '{ref_image}', '{image1}', '{image2}', '{image3}', '{image4}', '{image5}', '{image6}', '{image7}', '{image8}', '{image9}' ] + # MAX_LENGTH DESCRIPTION: max length of input TYPE: int default: 77 + MAX_LENGTH: 512 + # OUTPUT_KEY DESCRIPTION: output key TYPE: str default: 'last_hidden_state' + OUTPUT_KEY: last_hidden_state + # D_TYPE DESCRIPTION: dtype TYPE: str default: 'bfloat16' + D_TYPE: bfloat16 + # BATCH_INFER DESCRIPTION: batch infer TYPE: bool default: False + BATCH_INFER: False + CLEAN: whitespace + # CLIP_MODEL DESCRIPTION: TYPE: default: '' + CLIP_MODEL: + # NAME DESCRIPTION: TYPE: default: 'HFEmbedder' + NAME: ACEHFEmbedder + # HF_MODEL_CLS DESCRIPTION: huggingface cls in transfomer TYPE: NoneType default: None + HF_MODEL_CLS: CLIPTextModel + # MODEL_PATH DESCRIPTION: model folder path TYPE: NoneType default: None + MODEL_PATH: /home/Ubuntu/Downloads/Unmodel/Reference_models/clip_l/ + # HF_TOKENIZER_CLS DESCRIPTION: huggingface cls in transfomer TYPE: NoneType default: None + HF_TOKENIZER_CLS: CLIPTokenizer + # TOKENIZER_PATH DESCRIPTION: tokenizer folder path TYPE: NoneType default: None + TOKENIZER_PATH: /home/Ubuntu/Downloads/Unmodel/Reference_models/clip_l/ + # MAX_LENGTH DESCRIPTION: max length of input TYPE: int default: 77 + MAX_LENGTH: 77 + # OUTPUT_KEY DESCRIPTION: output key TYPE: str default: 'last_hidden_state' + OUTPUT_KEY: pooler_output + # D_TYPE DESCRIPTION: dtype TYPE: str default: 'bfloat16' + D_TYPE: bfloat16 + # BATCH_INFER DESCRIPTION: batch infer TYPE: bool default: False + BATCH_INFER: True + CLEAN: whitespace + TUNER: + # THE LORA PARAMETERS + - NAME: SwiftLoRA + R: 64 + LORA_ALPHA: 64 + LORA_DROPOUT: 0.0 + BIAS: "none" + TARGET_MODULES: "(model.double_blocks.*(.qkv|.img_mlp.0|.img_mlp.2|.txt_mlp.0|.txt_mlp.2|.proj|.img_mod.lin|.txt_mod.lin))|(model.single_blocks.*(.linear1|.linear2|.modulation.lin))$" + # + SAMPLE_ARGS: + SAMPLE_STEPS: 28 + SAMPLER: flow_euler + SEED: 42 + IMAGE_SIZE: [ 2048, 2048 ] + #IMAGE_SIZE: [ 1024, 1024 ] + GUIDE_SCALE: 50 + + LR_SCHEDULER: + NAME: StepAnnealingLR + WARMUP_STEPS: 0 + TOTAL_STEPS: 100000 + DECAY_MODE: 'cosine' + # + OPTIMIZER: + NAME: AdamW + LEARNING_RATE: 1e-3 + BETAS: [ 0.9, 0.999 ] + EPS: 1e-6 + WEIGHT_DECAY: 1e-2 + AMSGRAD: False + # + TRAIN_DATA: + NAME: ACEPlusDataset + MODE: train + DATA_LIST: data/train.csv + DELIMITER: "#;#" + MODIFY_MODE: True + # input_image, input_mask, input_reference_image, target_image, instruction, task_type + FIELDS: ["edit_image", "edit_mask", "ref_image", "target_image", "prompt", "data_type"] + PATH_PREFIX: "" + EDIT_TYPE_LIST: [] + MAX_SEQ_LEN: 4096 + # MAX_SEQ_LEN: 2048 -Vijay + D: 16 + PIN_MEMORY: True + BATCH_SIZE: 1 + NUM_WORKERS: 4 + SAMPLER: + NAME: LoopSampler + + EVAL_DATA: + NAME: ACEPlusDataset + MODE: eval + DATA_LIST: data/train.csv + DELIMITER: "#;#" + MODIFY_MODE: True + # input_image, input_mask, input_reference_image, target_image, instruction, task_type + FIELDS: [ "edit_image", "edit_mask", "ref_image", "target_image", "prompt", "data_type" ] + PATH_PREFIX: "" + EDIT_TYPE_LIST: [ ] + MAX_SEQ_LEN: 4096 + # MAX_SEQ_LEN: 2048 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