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ace_plus_diffuser_infer +IS_DEFAULT: True +USE_DYNAMIC_MODEL: False +INFERENCE_TYPE: ACE_DIFFUSER_PLUS +DEFAULT_PARAS: + PARAS: + # + INPUT: + INPUT_IMAGE: + INPUT_MASK: + TASK: + PROMPT: "" + OUTPUT_HEIGHT: 1024 + OUTPUT_WIDTH: 1024 + SAMPLER: flow_euler + SAMPLE_STEPS: 28 + GUIDE_SCALE: 50 + SEED: 42 + MAX_SEQ_LENGTH: 4096 + OUTPUT: + LATENT: + IMAGES: + SEED: +MODEL: + PRETRAINED_MODEL: ${FLUX_FILL_PATH} \ No newline at end of file diff --git a/ACE_plus/config/ace_plus_fft.yaml b/ACE_plus/config/ace_plus_fft.yaml new file mode 100644 index 0000000000000000000000000000000000000000..889c92d6fcc7c4bf450e58f0a5327ee615aeec4d --- /dev/null +++ b/ACE_plus/config/ace_plus_fft.yaml @@ -0,0 +1,192 @@ +NAME: ACEInference +DTYPE: bfloat16 +VERSION: fft +IS_DEFAULT: True +MAX_SEQ_LEN: 4096 +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: ${ACE_PLUS_FFT_MODEL} + # 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: flash_attn + + # + FIRST_STAGE_MODEL: + NAME: AutoencoderKLFlux + EMBED_DIM: 16 + PRETRAINED_MODEL: ${FLUX_FILL_PATH}/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: T5PlusClipFluxEmbedder + # T5_MODEL DESCRIPTION: TYPE: default: '' + T5_MODEL: + # NAME DESCRIPTION: TYPE: default: 'HFEmbedder' + NAME: HFEmbedder + # 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: ${FLUX_FILL_PATH}/text_encoder_2/ + # 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: ${FLUX_FILL_PATH}/tokenizer_2/ + 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: HFEmbedder + # 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: ${FLUX_FILL_PATH}/text_encoder/ + # 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: ${FLUX_FILL_PATH}/tokenizer/ + # 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 + +PREPROCESSOR: + - TYPE: repainting + REPAINTING_SCALE: 1.0 + ANNOTATOR: + - TYPE: no_preprocess + REPAINTING_SCALE: 0.0 + ANNOTATOR: + - TYPE: mosaic_repainting + REPAINTING_SCALE: 0.0 + ANNOTATOR: + NAME: ColorAnnotator + RATIO: 64 + - TYPE: contour_repainting + REPAINTING_SCALE: 0.0 + ANNOTATOR: + NAME: InfoDrawContourAnnotator + INPUT_NC: 3 + OUTPUT_NC: 1 + N_RESIDUAL_BLOCKS: 3 + SIGMOID: True + PRETRAINED_MODEL: "ms://iic/scepter_annotator@annotator/ckpts/informative_drawing_contour_style.pth" + - TYPE: depth_repainting + REPAINTING_SCALE: 0.0 + ANNOTATOR: + NAME: MidasDetector + PRETRAINED_MODEL: "ms://iic/scepter_annotator@annotator/ckpts/dpt_hybrid-midas-501f0c75.pt" + - TYPE: recolorizing + REPAINTING_SCALE: 0.0 + ANNOTATOR: + NAME: GrayAnnotator + +SAMPLE_ARGS: + SAMPLE_STEPS: 28 + SAMPLER: flow_euler + SEED: 42 + IMAGE_SIZE: [ 1024, 1024 ] + GUIDE_SCALE: 50 \ No newline at end of file diff --git a/ACE_plus/data/eval.csv b/ACE_plus/data/eval.csv new file mode 100644 index 0000000000000000000000000000000000000000..eed34472bb190b993ac55d9798620be4e5e2583b --- /dev/null +++ b/ACE_plus/data/eval.csv @@ -0,0 +1,12 @@ +/home/Ubuntu/Downloads/Unmodel/Training_Data/Venusperson_1.jpg#;#/home/Ubuntu/Downloads/Unmodel/Training_Data/Venusperson_1-Mask.jpg#;#/home/Ubuntu/Downloads/Unmodel/Training_Data/Venusperson_1-Garment.jpg#;#/home/Ubuntu/Downloads/Unmodel/Training_Data/Venusperson_1-Garment.jpg#;#The Woman wears this outfit consisting of a Sleeveless top.The sleeveless top in a light purple color. 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b/ACE_plus/examples/exp_example/20250624073511/checkpoints/ldm_step-100/README.md @@ -0,0 +1,10 @@ +## Training procedure + +### Framework versions + + +- SWIFT 3.4.0 +### Base model information + + +- BaseModel Class LatentDiffusionACEPlus diff --git a/ACE_plus/examples/exp_example/20250624073511/checkpoints/ldm_step-100/configuration.json b/ACE_plus/examples/exp_example/20250624073511/checkpoints/ldm_step-100/configuration.json new file mode 100644 index 0000000000000000000000000000000000000000..9e26dfeeb6e641a33dae4961196235bdb965b21b --- /dev/null +++ b/ACE_plus/examples/exp_example/20250624073511/checkpoints/ldm_step-100/configuration.json @@ -0,0 +1 @@ +{} \ No newline at end of file diff --git a/ACE_plus/examples/exp_example/20250624073511/checkpoints/ldm_step-150/0_SwiftLoRA/adapter_config.json b/ACE_plus/examples/exp_example/20250624073511/checkpoints/ldm_step-150/0_SwiftLoRA/adapter_config.json new file mode 100644 index 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('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] 2025-06-24 07:36:42,703 [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] 2025-06-24 07:36:42,703 [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] 2025-06-24 07:37:18,925 [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] 2025-06-24 07:37:18,962 [File: log.py Function: before_solve at line 260] Tensorboard: save to ./examples/exp_example/20250624073511/tensorboard +scepter [INFO] 2025-06-24 07:43:30,805 [File: log.py Function: _print_iter_log at line 71] Stage [train] iter: [20/100000], data_time: 12.7230(12.7230), time: 18.5911(18.5911), loss: 0.1549(0.1549), throughput: 23664/day, all_throughput: 20, pg0_lr: 0.001000, scale: 1.000000, [8mins 17secs 0.02%(28days 18hours 39mins 9secs)] +scepter [INFO] 2025-06-24 07:49:40,079 [File: log.py Function: _print_iter_log at line 71] Stage [train] iter: [40/100000], data_time: 12.8183(12.7707), time: 18.4638(18.5274), loss: 0.1357(0.1453), throughput: 23698/day, all_throughput: 40, pg0_lr: 0.001000, scale: 1.000000, [14mins 26secs 0.04%(25days 1hours 35mins 41secs)] +scepter [INFO] 2025-06-24 07:52:42,409 [File: checkpoint.py Function: after_iter at line 109] Saving checkpoint after 50 steps +scepter [INFO] 2025-06-24 07:55:48,532 [File: log.py Function: _print_iter_log at line 71] Stage [train] iter: [60/100000], data_time: 12.7975(12.7796), time: 18.4226(18.4925), loss: 0.1088(0.1332), throughput: 23757/day, all_throughput: 60, pg0_lr: 0.001000, scale: 1.000000, [20mins 35secs 0.06%(23days 19hours 27mins 38secs)] +scepter [INFO] 2025-06-24 08:01:55,863 [File: log.py Function: _print_iter_log at line 71] Stage [train] iter: [80/100000], data_time: 12.5829(12.7304), time: 18.3665(18.4610), loss: 0.1000(0.1249), throughput: 23724/day, all_throughput: 80, pg0_lr: 0.001000, scale: 1.000000, [26mins 42secs 0.08%(23days 3hours 57mins 11secs)] +scepter [INFO] 2025-06-24 08:08:02,295 [File: log.py Function: _print_iter_log at line 71] Stage [train] iter: [100/100000], data_time: 12.4769(12.6797), time: 18.3217(18.4331), loss: 0.0889(0.1177), throughput: 23748/day, all_throughput: 100, pg0_lr: 0.001000, scale: 1.000000, [32mins 48secs 0.10%(22days 18hours 21mins 29secs)] +scepter [INFO] 2025-06-24 08:08:02,296 [File: checkpoint.py Function: after_iter at line 109] Saving checkpoint after 100 steps +scepter [INFO] 2025-06-24 08:14:11,503 [File: log.py Function: _print_iter_log at line 71] Stage [train] iter: [120/100000], data_time: 12.8457(12.7074), time: 18.4603(18.4377), loss: 0.0917(0.1134), throughput: 23766/day, all_throughput: 120, pg0_lr: 0.001000, scale: 1.000000, [38mins 58secs 0.12%(22days 12hours 34mins 10secs)] +scepter [INFO] 2025-06-24 08:20:09,138 [File: log.py Function: _print_iter_log at line 71] Stage [train] iter: [140/100000], data_time: 12.2640(12.6440), time: 17.8818(18.3583), loss: 0.1563(0.1195), throughput: 23784/day, all_throughput: 140, pg0_lr: 0.001000, scale: 1.000000, [44mins 55secs 0.14%(22days 6hours 6mins 45secs)] +scepter [INFO] 2025-06-24 08:23:12,656 [File: checkpoint.py Function: after_iter at line 109] Saving checkpoint after 150 steps +scepter [INFO] 2025-06-24 08:26:15,711 [File: log.py Function: _print_iter_log at line 71] Stage [train] iter: [160/100000], data_time: 12.6226(12.6414), time: 18.3286(18.3545), loss: 0.1930(0.1287), throughput: 23818/day, all_throughput: 160, pg0_lr: 0.001000, scale: 1.000000, [51mins 2secs 0.16%(22days 2hours 47mins 39secs)] diff --git a/ACE_plus/examples/exp_example/20250624073511/train.yaml b/ACE_plus/examples/exp_example/20250624073511/train.yaml new file mode 100644 index 0000000000000000000000000000000000000000..9c8f141686d91b64513fe45a530c356a9f311e72 --- /dev/null +++ b/ACE_plus/examples/exp_example/20250624073511/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 -Vijay + D: 16 + PIN_MEMORY: True + BATCH_SIZE: 1 + NUM_WORKERS: 4 + + TRAIN_HOOKS: + - 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The background is white."]]} \ No newline at end of file diff --git a/ACE_plus/inference/__init__.py b/ACE_plus/inference/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..973feb7848e5accfddc0a935d4d1ca4f4cf7a03c --- /dev/null +++ b/ACE_plus/inference/__init__.py @@ -0,0 +1,2 @@ +from .ace_plus_diffusers import ACEPlusDiffuserInference +from .ace_plus_inference import ACEInference \ No newline at end of file diff --git a/ACE_plus/inference/ace_plus_diffusers.py b/ACE_plus/inference/ace_plus_diffusers.py new file mode 100644 index 0000000000000000000000000000000000000000..868f1765c8ac3ed42e4b617bc250873186c81e18 --- /dev/null +++ b/ACE_plus/inference/ace_plus_diffusers.py @@ -0,0 +1,121 @@ +# -*- coding: utf-8 -*- +# Copyright (c) Alibaba, Inc. and its affiliates. +import random +from collections import OrderedDict + +import torch, os +from diffusers import FluxFillPipeline +from scepter.modules.utils.config import Config +from scepter.modules.utils.distribute import we +from scepter.modules.utils.file_system import FS +from scepter.modules.utils.logger import get_logger +from transformers import T5TokenizerFast +from .utils import ACEPlusImageProcessor + +class ACEPlusDiffuserInference(): + def __init__(self, logger=None): + if logger is None: + logger = get_logger(name='ace_plus') + self.logger = logger + self.input = {} + + def load_default(self, cfg): + if cfg is not None: + self.input_cfg = {k.lower(): v for k, v in cfg.INPUT.items()} + self.input = {k.lower(): dict(v).get('DEFAULT', None) if isinstance(v, (dict, OrderedDict, Config)) else v for k, v in cfg.INPUT.items()} + self.output = {k.lower(): v for k, v in cfg.OUTPUT.items()} + + def init_from_cfg(self, cfg): + self.max_seq_len = cfg.get("MAX_SEQ_LEN", 4096) + self.image_processor = ACEPlusImageProcessor(max_seq_len=self.max_seq_len) + + local_folder = FS.get_dir_to_local_dir(cfg.MODEL.PRETRAINED_MODEL) + + self.pipe = FluxFillPipeline.from_pretrained(local_folder, torch_dtype=torch.bfloat16).to(we.device_id) + + tokenizer_2 = T5TokenizerFast.from_pretrained(os.path.join(local_folder, "tokenizer_2"), + additional_special_tokens=["{image}"]) + self.pipe.tokenizer_2 = tokenizer_2 + self.load_default(cfg.DEFAULT_PARAS) + + def prepare_input(self, + image, + mask, + batch_size=1, + dtype = torch.bfloat16, + num_images_per_prompt=1, + height=512, + width=512, + generator=None): + num_channels_latents = self.pipe.vae.config.latent_channels + # import pdb;pdb.set_trace() + mask, masked_image_latents = self.pipe.prepare_mask_latents( + mask.unsqueeze(0), + image.unsqueeze(0).to(we.device_id, dtype = dtype), + batch_size, + num_channels_latents, + num_images_per_prompt, + height, + width, + dtype, + we.device_id, + generator, + ) + # import pdb;pdb.set_trace() + masked_image_latents = torch.cat((masked_image_latents, mask), dim=-1) + return masked_image_latents + + @torch.no_grad() + def __call__(self, + reference_image=None, + edit_image=None, + edit_mask=None, + prompt='', + task=None, + output_height=1024, + output_width=1024, + sampler='flow_euler', + sample_steps=28, + guide_scale=50, + lora_path=None, + seed=-1, + tar_index=0, + align=0, + repainting_scale=0, + **kwargs): + if isinstance(prompt, str): + prompt = [prompt] + seed = seed if seed >= 0 else random.randint(0, 2 ** 32 - 1) + # edit_image, edit_mask, change_image, content_image, out_h, out_w, slice_w + image, mask, _, _, out_h, out_w, slice_w = self.image_processor.preprocess(reference_image, edit_image, edit_mask, + width = output_width, + height = output_height, + repainting_scale = repainting_scale) + h, w = image.shape[1:] + generator = torch.Generator("cpu").manual_seed(seed) + masked_image_latents = self.prepare_input(image, mask, + batch_size=len(prompt) , height=h, width=w, generator = generator) + + if lora_path is not None: + with FS.get_from(lora_path) as local_path: + self.pipe.load_lora_weights(local_path) + + + + image = self.pipe( + prompt=prompt, + masked_image_latents=masked_image_latents, + height=h, + width=w, + guidance_scale=guide_scale, + num_inference_steps=sample_steps, + max_sequence_length=512, + generator=generator + ).images[0] + if lora_path is not None: + self.pipe.unload_lora_weights() + return self.image_processor.postprocess(image, slice_w, out_w, out_h), seed + + +if __name__ == '__main__': + pass \ No newline at end of file diff --git a/ACE_plus/inference/ace_plus_inference.py b/ACE_plus/inference/ace_plus_inference.py new file mode 100644 index 0000000000000000000000000000000000000000..f1e7b755a22cce320b2a120e6a87b65cce5644d4 --- /dev/null +++ b/ACE_plus/inference/ace_plus_inference.py @@ -0,0 +1,83 @@ +# -*- coding: utf-8 -*- +# Copyright (c) Alibaba, Inc. and its affiliates. +import random +from collections import OrderedDict + +import torch, numpy as np +from PIL import Image +from scepter.modules.model.registry import MODELS +from scepter.modules.utils.config import Config +from scepter.modules.utils.distribute import we +from .registry import BaseInference, INFERENCES +from .utils import ACEPlusImageProcessor + +@INFERENCES.register_class() +class ACEInference(BaseInference): + ''' + reuse the ldm code + ''' + def __init__(self, cfg, logger=None): + super().__init__(cfg, logger) + self.pipe = MODELS.build(cfg.MODEL, logger=self.logger).eval().to(we.device_id) + self.image_processor = ACEPlusImageProcessor(max_seq_len=cfg.MAX_SEQ_LEN) + self.input = {k.lower(): dict(v).get('DEFAULT', None) if isinstance(v, (dict, OrderedDict, Config)) else v for + k, v in cfg.SAMPLE_ARGS.items()} + self.dtype = getattr(torch, cfg.get("DTYPE", "bfloat16")) + @torch.no_grad() + def __call__(self, + reference_image=None, + edit_image=None, + edit_mask=None, + prompt='', + edit_type=None, + output_height=1024, + output_width=1024, + sampler='flow_euler', + sample_steps=28, + guide_scale=50, + lora_path=None, + seed=-1, + repainting_scale=0, + use_change=False, + keep_pixels=False, + keep_pixels_rate=0.8, + **kwargs): + # convert the input info to the input of ldm. + if isinstance(prompt, str): + prompt = [prompt] + seed = seed if seed >= 0 else random.randint(0, 2 ** 24 - 1) + image, mask, change_image, content_image, out_h, out_w, slice_w = self.image_processor.preprocess(reference_image, edit_image, edit_mask, + height=output_height, width=output_width, + repainting_scale=repainting_scale, + keep_pixels=keep_pixels, + keep_pixels_rate=keep_pixels_rate, + use_change = use_change) + change_image = [None] if change_image is None else [change_image.to(we.device_id)] + image, mask = [image.to(we.device_id)], [mask.to(we.device_id)] + + (src_image_list, src_mask_list, modify_image_list, + edit_id, prompt) = [image], [mask], [change_image], [[0]], [prompt] + + with torch.amp.autocast(enabled=True, dtype=self.dtype, device_type='cuda'): + out_image = self.pipe( + src_image_list=src_image_list, + modify_image_list= modify_image_list, + src_mask_list=src_mask_list, + edit_id=edit_id, + image=image, + image_mask=mask, + prompt=prompt, + sampler='flow_euler', + sample_steps=sample_steps, + seed=seed, + guide_scale=guide_scale, + show_process=True, + ) + imgs = [x_i['reconstruct_image'].float().permute(1, 2, 0).cpu().numpy() + for x_i in out_image + ] + imgs = [Image.fromarray((img * 255).astype(np.uint8)) for img in imgs] + edit_image = Image.fromarray((torch.clamp(image[0] / 2 + 0.5, min=0.0, max=1.0)*255).float().permute(1, 2, 0).cpu().numpy().astype(np.uint8)) + change_image = Image.fromarray((torch.clamp(change_image[0] / 2 + 0.5, min=0.0, max=1.0)*255).float().permute(1, 2, 0).cpu().numpy().astype(np.uint8)) + mask = Image.fromarray((mask[0] * 255).squeeze(0).cpu().numpy().astype(np.uint8)) + return self.image_processor.postprocess(imgs[0], slice_w, out_w, out_h), edit_image, change_image, mask, seed diff --git a/ACE_plus/inference/registry.py b/ACE_plus/inference/registry.py new file mode 100644 index 0000000000000000000000000000000000000000..61eb4c8faf23c7e1a9b28c85a26b1578decec0b9 --- /dev/null +++ b/ACE_plus/inference/registry.py @@ -0,0 +1,228 @@ +# -*- coding: utf-8 -*- +# Copyright (c) Alibaba, Inc. and its affiliates. + +import torch +from PIL.Image import Image +from collections import OrderedDict +from scepter.modules.utils.distribute import we +from scepter.modules.utils.config import Config +from scepter.modules.utils.logger import get_logger +from scepter.studio.utils.env import get_available_memory +from scepter.modules.model.registry import MODELS, BACKBONES, EMBEDDERS +from scepter.modules.utils.registry import Registry, build_from_config +def get_model(model_tuple): + assert 'model' in model_tuple + return model_tuple['model'] + +class BaseInference(): + ''' + support to load the components dynamicly. + create and load model when run this model at the first time. + ''' + def __init__(self, cfg, logger=None): + if logger is None: + logger = get_logger(name='scepter') + self.logger = logger + self.name = cfg.NAME + + def init_from_modules(self, modules): + for k, v in modules.items(): + self.__setattr__(k, v) + + def infer_model(self, cfg, module_paras=None): + module = { + 'model': None, + 'cfg': cfg, + 'device': 'offline', + 'name': cfg.NAME, + 'function_info': {}, + 'paras': {} + } + if module_paras is None: + return module + function_info = {} + paras = { + k.lower(): v + for k, v in module_paras.get('PARAS', {}).items() + } + for function in module_paras.get('FUNCTION', []): + input_dict = {} + for inp in function.get('INPUT', []): + if inp.lower() in self.input: + input_dict[inp.lower()] = self.input[inp.lower()] + function_info[function.NAME] = { + 'dtype': function.get('DTYPE', 'float32'), + 'input': input_dict + } + module['paras'] = paras + module['function_info'] = function_info + return module + + def init_from_ckpt(self, path, model, ignore_keys=list()): + if path.endswith('safetensors'): + from safetensors.torch import load_file as load_safetensors + sd = load_safetensors(path) + else: + sd = torch.load(path, map_location='cpu', weights_only=True) + + new_sd = OrderedDict() + for k, v in sd.items(): + ignored = False + for ik in ignore_keys: + if ik in k: + if we.rank == 0: + self.logger.info( + 'Ignore key {} from state_dict.'.format(k)) + ignored = True + break + if not ignored: + new_sd[k] = v + + missing, unexpected = model.load_state_dict(new_sd, strict=False) + if we.rank == 0: + self.logger.info( + f'Restored from {path} with {len(missing)} missing and {len(unexpected)} unexpected keys' + ) + if len(missing) > 0: + self.logger.info(f'Missing Keys:\n {missing}') + if len(unexpected) > 0: + self.logger.info(f'\nUnexpected Keys:\n {unexpected}') + + def load(self, module): + if module['device'] == 'offline': + from scepter.modules.utils.import_utils import LazyImportModule + if (LazyImportModule.get_module_type(('MODELS', module['cfg'].NAME)) or + module['cfg'].NAME in MODELS.class_map): + model = MODELS.build(module['cfg'], logger=self.logger).eval() + elif (LazyImportModule.get_module_type(('BACKBONES', module['cfg'].NAME)) or + module['cfg'].NAME in BACKBONES.class_map): + model = BACKBONES.build(module['cfg'], + logger=self.logger).eval() + elif (LazyImportModule.get_module_type(('EMBEDDERS', module['cfg'].NAME)) or + module['cfg'].NAME in EMBEDDERS.class_map): + model = EMBEDDERS.build(module['cfg'], + logger=self.logger).eval() + else: + raise NotImplementedError + if 'DTYPE' in module['cfg'] and module['cfg']['DTYPE'] is not None: + model = model.to(getattr(torch, module['cfg'].DTYPE)) + if module['cfg'].get('RELOAD_MODEL', None): + self.init_from_ckpt(module['cfg'].RELOAD_MODEL, model) + module['model'] = model + module['device'] = 'cpu' + if module['device'] == 'cpu': + module['device'] = we.device_id + module['model'] = module['model'].to(we.device_id) + return module + + def unload(self, module): + if module is None: + return module + mem = get_available_memory() + free_mem = int(mem['available'] / (1024**2)) + total_mem = int(mem['total'] / (1024**2)) + if free_mem < 0.5 * total_mem: + if module['model'] is not None: + module['model'] = module['model'].to('cpu') + del module['model'] + module['model'] = None + module['device'] = 'offline' + print('delete module') + else: + if module['model'] is not None: + module['model'] = module['model'].to('cpu') + module['device'] = 'cpu' + else: + module['device'] = 'offline' + if torch.cuda.is_available(): + torch.cuda.empty_cache() + torch.cuda.ipc_collect() + return module + + def dynamic_load(self, module=None, name=''): + self.logger.info('Loading {} model'.format(name)) + if name == 'all': + for subname in self.loaded_model_name: + self.loaded_model[subname] = self.dynamic_load( + getattr(self, subname), subname) + elif name in self.loaded_model_name: + if name in self.loaded_model: + if module['cfg'] != self.loaded_model[name]['cfg']: + self.unload(self.loaded_model[name]) + module = self.load(module) + self.loaded_model[name] = module + return module + elif module['device'] == 'cpu' or module['device'] == 'offline': + module = self.load(module) + return module + else: + return module + else: + module = self.load(module) + self.loaded_model[name] = module + return module + else: + return self.load(module) + + def dynamic_unload(self, module=None, name='', skip_loaded=False): + self.logger.info('Unloading {} model'.format(name)) + if name == 'all': + for name, module in self.loaded_model.items(): + module = self.unload(self.loaded_model[name]) + self.loaded_model[name] = module + elif name in self.loaded_model_name: + if name in self.loaded_model: + if not skip_loaded: + module = self.unload(self.loaded_model[name]) + self.loaded_model[name] = module + else: + self.unload(module) + else: + self.unload(module) + + def load_default(self, cfg): + module_paras = {} + if cfg is not None: + self.paras = cfg.PARAS + self.input_cfg = {k.lower(): v for k, v in cfg.INPUT.items()} + self.input = {k.lower(): dict(v).get('DEFAULT', None) if isinstance(v, (dict, OrderedDict, Config)) else v for k, v in cfg.INPUT.items()} + self.output = {k.lower(): v for k, v in cfg.OUTPUT.items()} + module_paras = cfg.MODULES_PARAS + return module_paras + + def load_image(self, image, num_samples=1): + if isinstance(image, torch.Tensor): + pass + elif isinstance(image, Image): + pass + elif isinstance(image, Image): + pass + + def get_function_info(self, module, function_name=None): + all_function = module['function_info'] + if function_name in all_function: + return function_name, all_function[function_name]['dtype'] + if function_name is None and len(all_function) == 1: + for k, v in all_function.items(): + return k, v['dtype'] + + @torch.no_grad() + def __call__(self, + input, + **kwargs): + return + +def build_inference(cfg, registry, logger=None, *args, **kwargs): + """ After build model, load pretrained model if exists key `pretrain`. + + pretrain (str, dict): Describes how to load pretrained model. + str, treat pretrain as model path; + dict: should contains key `path`, and other parameters token by function load_pretrained(); + """ + if not isinstance(cfg, Config): + raise TypeError(f'Config must be type dict, got {type(cfg)}') + model = build_from_config(cfg, registry, logger=logger, *args, **kwargs) + return model + +# reigister cls for diffusion. +INFERENCES = Registry('INFERENCE', build_func=build_inference) diff --git a/ACE_plus/inference/utils.py b/ACE_plus/inference/utils.py new file mode 100644 index 0000000000000000000000000000000000000000..14bc93e712aca932122acc67e22b44a02a2daf56 --- /dev/null +++ b/ACE_plus/inference/utils.py @@ -0,0 +1,132 @@ +# -*- coding: utf-8 -*- +# Copyright (c) Alibaba, Inc. and its affiliates. +import math + +import torch +import torchvision.transforms as T +import numpy as np +from scepter.modules.annotator.registry import ANNOTATORS +from scepter.modules.utils.config import Config +from PIL import Image + + +def edit_preprocess(processor, device, edit_image, edit_mask): + if edit_image is None or processor is None: + return edit_image + processor = Config(cfg_dict=processor, load=False) + processor = ANNOTATORS.build(processor).to(device) + new_edit_image = processor(np.asarray(edit_image)) + processor = processor.to("cpu") + del processor + new_edit_image = Image.fromarray(new_edit_image) + return Image.composite(new_edit_image, edit_image, edit_mask) + +class ACEPlusImageProcessor(): + def __init__(self, max_aspect_ratio=4, d=16, max_seq_len=2048): + self.max_aspect_ratio = max_aspect_ratio + self.d = d + self.max_seq_len = max_seq_len + self.transforms = T.Compose([ + T.ToTensor(), + T.Normalize(mean=[0.5, 0.5, 0.5], std=[0.5, 0.5, 0.5]) + ]) + + def image_check(self, image): + if image is None: + return image + # preprocess + W, H = image.size + if H / W > self.max_aspect_ratio: + image = T.CenterCrop([int(self.max_aspect_ratio * W), W])(image) + elif W / H > self.max_aspect_ratio: + image = T.CenterCrop([H, int(self.max_aspect_ratio * H)])(image) + return self.transforms(image) + + + def preprocess(self, + reference_image=None, + edit_image=None, + edit_mask=None, + height=1024, + width=1024, + repainting_scale = 1.0, + keep_pixels = False, + keep_pixels_rate = 0.8, + use_change = False): + reference_image = self.image_check(reference_image) + edit_image = self.image_check(edit_image) + # for reference generation + if edit_image is None: + edit_image = torch.zeros([3, height, width]) + edit_mask = torch.ones([1, height, width]) + else: + if edit_mask is None: + _, eH, eW = edit_image.shape + edit_mask = np.ones((eH, eW)) + else: + edit_mask = np.asarray(edit_mask) + edit_mask = np.where(edit_mask > 128, 1, 0) + edit_mask = edit_mask.astype( + np.float32) if np.any(edit_mask) else np.ones_like(edit_mask).astype( + np.float32) + edit_mask = torch.tensor(edit_mask).unsqueeze(0) + + edit_image = edit_image * (1 - edit_mask * repainting_scale) + + + out_h, out_w = edit_image.shape[-2:] + + assert edit_mask is not None + if reference_image is not None: + _, H, W = reference_image.shape + _, eH, eW = edit_image.shape + if not keep_pixels: + # align height with edit_image + scale = eH / H + tH, tW = eH, int(W * scale) + reference_image = T.Resize((tH, tW), interpolation=T.InterpolationMode.BILINEAR, antialias=True)( + reference_image) + else: + # padding + if H >= keep_pixels_rate * eH: + tH = int(eH * keep_pixels_rate) + scale = tH/H + tW = int(W * scale) + reference_image = T.Resize((tH, tW), interpolation=T.InterpolationMode.BILINEAR, antialias=True)( + reference_image) + rH, rW = reference_image.shape[-2:] + delta_w = 0 + delta_h = eH - rH + padding = (delta_w // 2, delta_h // 2, delta_w - (delta_w // 2), delta_h - (delta_h // 2)) + reference_image = T.Pad(padding, fill=0, padding_mode="constant")(reference_image) + edit_image = torch.cat([reference_image, edit_image], dim=-1) + edit_mask = torch.cat([torch.zeros([1, reference_image.shape[1], reference_image.shape[2]]), edit_mask], dim=-1) + slice_w = reference_image.shape[-1] + else: + slice_w = 0 + + H, W = edit_image.shape[-2:] + scale = min(1.0, math.sqrt(self.max_seq_len / ((H / self.d) * (W / self.d)))) + rH = int(H * scale) // self.d * self.d # ensure divisible by self.d + rW = int(W * scale) // self.d * self.d + slice_w = int(slice_w * scale) // self.d * self.d + + edit_image = T.Resize((rH, rW), interpolation=T.InterpolationMode.NEAREST_EXACT, antialias=True)(edit_image) + edit_mask = T.Resize((rH, rW), interpolation=T.InterpolationMode.NEAREST_EXACT, antialias=True)(edit_mask) + content_image = edit_image + if use_change: + change_image = edit_image * edit_mask + edit_image = edit_image * (1 - edit_mask) + else: + change_image = None + return edit_image, edit_mask, change_image, content_image, out_h, out_w, slice_w + + + def postprocess(self, image, slice_w, out_w, out_h): + w, h = image.size + if slice_w > 0: + output_image = image.crop((slice_w + 30, 0, w, h)) + output_image = output_image.resize((out_w, out_h)) + else: + output_image = image + return output_image diff --git a/ACE_plus/models/model_zoo.yaml b/ACE_plus/models/model_zoo.yaml new file mode 100644 index 0000000000000000000000000000000000000000..1c1044f098787f81ac8acb0d7c9222ac2d2a1a4d --- /dev/null +++ b/ACE_plus/models/model_zoo.yaml @@ -0,0 +1,28 @@ +MODEL: + PORTRAIT: + MODEL_PATH: ${PORTRAIT_MODEL_PATH} + SUBJECT: + MODEL_PATH: ${SUBJECT_MODEL_PATH} + LOCAL_EDITING: + MODEL_PATH: ${LOCAL_MODEL_PATH} + REPAINTING_SCALE: 0.5 + PREPROCESSOR: + - NAME: CannyAnnotator + TYPE: canny_repainting + LOW_THRESHOLD: 100 + HIGH_THRESHOLD: 200 + - NAME: ColorAnnotator + TYPE: mosaic_repainting + RATIO: 64 + - NAME: InfoDrawContourAnnotator + TYPE: contour_repainting + INPUT_NC: 3 + OUTPUT_NC: 1 + N_RESIDUAL_BLOCKS: 3 + SIGMOID: True + PRETRAINED_MODEL: "ms://iic/scepter_annotator@annotator/ckpts/informative_drawing_contour_style.pth" + - NAME: MidasDetector + PRETRAINED_MODEL: "ms://iic/scepter_annotator@annotator/ckpts/dpt_hybrid-midas-501f0c75.pt" + TYPE: depth_repainting + - NAME: GrayAnnotator + TYPE: recolorizing \ No newline at end of file diff --git a/ACE_plus/modules/__init__.py b/ACE_plus/modules/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..00b169693a4b1080024d430f7e6718199e08731e --- /dev/null +++ b/ACE_plus/modules/__init__.py @@ -0,0 +1,6 @@ +from .flux import FluxMRACEPlus, FluxMRModiACEPlus +from .ace_plus_dataset import ACEPlusDataset +from .ace_plus_ldm import LatentDiffusionACEPlus +from .ace_plus_solver import FormalACEPlusSolver +from .embedder import ACEHFEmbedder, T5ACEPlusClipFluxEmbedder +from .checkpoint import ACECheckpointHook, ACEBackwardHook \ No newline at end of file diff --git a/ACE_plus/modules/ace_plus_dataset.py b/ACE_plus/modules/ace_plus_dataset.py new file mode 100644 index 0000000000000000000000000000000000000000..88305ff0a385c4bc990d6d4756b2e5e4710c4c1d --- /dev/null +++ b/ACE_plus/modules/ace_plus_dataset.py @@ -0,0 +1,279 @@ +# -*- coding: utf-8 -*- +# Copyright (c) Alibaba, Inc. and its affiliates. +import math +import re, io +import numpy as np +import random, torch +from PIL import Image +import torchvision.transforms as T +from collections import defaultdict +from scepter.modules.data.dataset.registry import DATASETS +from scepter.modules.data.dataset.base_dataset import BaseDataset +from scepter.modules.transform.io import pillow_convert +from scepter.modules.utils.directory import osp_path +from scepter.modules.utils.file_system import FS +from torchvision.transforms import InterpolationMode +def load_image(prefix, img_path, cvt_type=None): + if img_path is None or img_path == '': + return None + img_path = osp_path(prefix, img_path) + with FS.get_object(img_path) as image_bytes: + image = Image.open(io.BytesIO(image_bytes)) + if cvt_type is not None: + image = pillow_convert(image, cvt_type) + return image +def transform_image(image, std = 0.5, mean = 0.5): + return (image.permute(2, 0, 1)/255. - mean)/std +def transform_mask(mask): + return mask.unsqueeze(0)/255. + +def ensure_src_align_target_h_mode(src_image, size, image_id, interpolation=InterpolationMode.BILINEAR): + # padding mode + H, W = size + ret_image = [] + for one_id in image_id: + edit_image = src_image[one_id] + tH, tW = H, W + ret_image.append(T.Resize((tH, tW), interpolation=interpolation, antialias=True)(edit_image)) + return ret_image + +def ensure_src_align_target_padding_mode(src_image, size, image_id, size_h = [], interpolation=InterpolationMode.BILINEAR): + # padding mode + H, W = size + + ret_data = [] + ret_h = [] + for idx, one_id in enumerate(image_id): + if len(size_h) < 1: + rH = random.randint(int(H / 3), int(H)) + else: + rH = size_h[idx] + ret_h.append(rH) + edit_image = src_image[one_id] + _, eH, eW = edit_image.shape + scale = rH/eH + tH, tW = rH, int(eW * scale) + edit_image = T.Resize((tH, tW), interpolation=interpolation, antialias=True)(edit_image) + # padding + delta_w = 0 + delta_h = H - tH + padding = (delta_w // 2, delta_h // 2, delta_w - (delta_w // 2), delta_h - (delta_h // 2)) + ret_data.append(T.Pad(padding, fill=0, padding_mode="constant")(edit_image).float()) + return ret_data, ret_h + +def ensure_limit_sequence(image, max_seq_len = 4096, d = 16, interpolation=InterpolationMode.BILINEAR): + # resize image for max_seq_len, while keep the aspect ratio + H, W = image.shape[-2:] + scale = min(1.0, math.sqrt(max_seq_len / ((H / d) * (W / d)))) + rH = int(H * scale) // d * d # ensure divisible by self.d + rW = int(W * scale) // d * d + # print(f"{H} {W} -> {rH} {rW}") + image = T.Resize((rH, rW), interpolation=interpolation, antialias=True)(image) + return image + +@DATASETS.register_class() +class ACEPlusDataset(BaseDataset): + para_dict = { + "DELIMITER": { + "value": "#;#", + "description": "The delimiter for records of data list." + }, + "FIELDS": { + "value": ["data_type", "edit_image", "edit_mask", "ref_image", "target_image", "prompt"], + "description": "The fields for every record." + }, + "PATH_PREFIX": { + "value": "", + "description": "The path prefix for every input image." + }, + "EDIT_TYPE_LIST": { + "value": [], + "description": "The edit type list to be trained for data list." + }, + "MAX_SEQ_LEN": { + "value": 4096, + "description": "The max sequence length for input image." + }, + "D": { + "value": 16, + "description": "Patch size for resized image." + } + } + para_dict.update(BaseDataset.para_dict) + def __init__(self, cfg, logger=None): + super().__init__(cfg, logger=logger) + delimiter = cfg.get("DELIMITER", "#;#") + fields = cfg.get("FIELDS", []) + prefix = cfg.get("PATH_PREFIX", "") + edit_type_list = cfg.get("EDIT_TYPE_LIST", []) + self.modify_mode = cfg.get("MODIFY_MODE", True) + self.max_seq_len = cfg.get("MAX_SEQ_LEN", 4096) + self.repaiting_scale = cfg.get("REPAINTING_SCALE", 0.5) + self.d = cfg.get("D", 16) + prompt_file = cfg.DATA_LIST + self.items = self.read_data_list(delimiter, + fields, + prefix, + edit_type_list, + prompt_file) + random.shuffle(self.items) + use_num = int(cfg.get('USE_NUM', -1)) + if use_num > 0: + self.items = self.items[:use_num] + def read_data_list(self, delimiter, + fields, + prefix, + edit_type_list, + prompt_file): + with FS.get_object(prompt_file) as local_data: + rows = local_data.decode('utf-8').strip().split('\n') + items = list() + dtype_level_num = {} + for i, row in enumerate(rows): + item = {"prefix": prefix} + for key, val in zip(fields, row.split(delimiter)): + item[key] = val + edit_type = item["data_type"] + if len(edit_type_list) > 0: + for re_pattern in edit_type_list: + if re.match(re_pattern, edit_type): + items.append(item) + if edit_type not in dtype_level_num: + dtype_level_num[edit_type] = 0 + dtype_level_num[edit_type] += 1 + break + else: + items.append(item) + if edit_type not in dtype_level_num: + dtype_level_num[edit_type] = 0 + dtype_level_num[edit_type] += 1 + for edit_type in dtype_level_num: + self.logger.info(f"{edit_type} has {dtype_level_num[edit_type]} samples.") + return items + def __len__(self): + return len(self.items) + + def __getitem__(self, index): + item = self._get(index) + return self.pipeline(item) + + def _get(self, index): + # normalize + sample_id = index%len(self) + index = self.items[index%len(self)] + prefix = index.get("prefix", "") + edit_image = index.get("edit_image", "") + edit_mask = index.get("edit_mask", "") + ref_image = index.get("ref_image", "") + target_image = index.get("target_image", "") + prompt = index.get("prompt", "") + + edit_image = load_image(prefix, edit_image, cvt_type="RGB") if edit_image != "" else None + edit_mask = load_image(prefix, edit_mask, cvt_type="L") if edit_mask != "" else None + ref_image = load_image(prefix, ref_image, cvt_type="RGB") if ref_image != "" else None + target_image = load_image(prefix, target_image, cvt_type="RGB") if target_image != "" else None + assert target_image is not None + + edit_id, ref_id, src_image_list, src_mask_list = [], [], [], [] + # parse editing image + if edit_image is None: + edit_image = Image.new("RGB", target_image.size, (255, 255, 255)) + edit_mask = Image.new("L", edit_image.size, 255) + elif edit_mask is None: + edit_mask = Image.new("L", edit_image.size, 255) + src_image_list.append(edit_image) + edit_id.append(0) + src_mask_list.append(edit_mask) + # parse reference image + if ref_image is not None: + src_image_list.append(ref_image) + ref_id.append(1) + src_mask_list.append(Image.new("L", ref_image.size, 0)) + + image = transform_image(torch.tensor(np.array(target_image).astype(np.float32))) + if edit_mask is not None: + image_mask = transform_mask(torch.tensor(np.array(edit_mask).astype(np.float32))) + else: + image_mask = Image.new("L", target_image.size, 255) + image_mask = transform_mask(torch.tensor(np.array(image_mask).astype(np.float32))) + + + src_image_list = [transform_image(torch.tensor(np.array(im).astype(np.float32))) for im in src_image_list] + src_mask_list = [transform_mask(torch.tensor(np.array(im).astype(np.float32))) for im in src_mask_list] + + # decide the repainting scale for the editing task + if len(ref_id) > 0: + repainting_scale = 1.0 + else: + repainting_scale = self.repaiting_scale + for e_i in edit_id: + src_image_list[e_i] = src_image_list[e_i] * (1 - repainting_scale * src_mask_list[e_i]) + size = image.shape[1:] + ref_image_list, ret_h = ensure_src_align_target_padding_mode(src_image_list, size, + image_id=ref_id, + interpolation=InterpolationMode.NEAREST_EXACT) + ref_mask_list, ret_h = ensure_src_align_target_padding_mode(src_mask_list, size, + size_h=ret_h, + image_id=ref_id, + interpolation=InterpolationMode.NEAREST_EXACT) + + edit_image_list = ensure_src_align_target_h_mode(src_image_list, size, + image_id=edit_id, + interpolation=InterpolationMode.NEAREST_EXACT) + edit_mask_list = ensure_src_align_target_h_mode(src_mask_list, size, + image_id=edit_id, + interpolation=InterpolationMode.NEAREST_EXACT) + + + + src_image_list = [torch.cat(ref_image_list + edit_image_list, dim=-1)] + src_mask_list = [torch.cat(ref_mask_list + edit_mask_list, dim=-1)] + image = torch.cat(ref_image_list + [image], dim=-1) + image_mask = torch.cat(ref_mask_list + [image_mask], dim=-1) + + # limit max sequence length + image = ensure_limit_sequence(image, max_seq_len = self.max_seq_len, + d = self.d, interpolation=InterpolationMode.NEAREST_EXACT) + image_mask = ensure_limit_sequence(image_mask, max_seq_len = self.max_seq_len, + d = self.d, interpolation=InterpolationMode.NEAREST_EXACT) + src_image_list = [ensure_limit_sequence(i, max_seq_len = self.max_seq_len, + d = self.d, interpolation=InterpolationMode.NEAREST_EXACT) for i in src_image_list] + src_mask_list = [ensure_limit_sequence(i, max_seq_len = self.max_seq_len, + d = self.d, interpolation=InterpolationMode.NEAREST_EXACT) for i in src_mask_list] + + if self.modify_mode: + # To be modified regions according to mask + modify_image_list = [ii * im for ii, im in zip(src_image_list, src_mask_list)] + # To be edited regions according to mask + src_image_list = [ii * (1 - im) for ii, im in zip(src_image_list, src_mask_list)] + else: + src_image_list = src_image_list + modify_image_list = src_image_list + + item = { + "src_image_list": src_image_list, + "src_mask_list": src_mask_list, + "modify_image_list": modify_image_list, + "image": image, + "image_mask": image_mask, + "edit_id": edit_id, + "ref_id": ref_id, + "prompt": prompt, + "edit_key": index["edit_key"] if "edit_key" in index else "", + "sample_id": sample_id + } + return item + + @staticmethod + def collate_fn(batch): + collect = defaultdict(list) + for sample in batch: + for k, v in sample.items(): + collect[k].append(v) + new_batch = dict() + for k, v in collect.items(): + if all([i is None for i in v]): + new_batch[k] = None + else: + new_batch[k] = v + return new_batch diff --git a/ACE_plus/modules/ace_plus_ldm.py b/ACE_plus/modules/ace_plus_ldm.py new file mode 100644 index 0000000000000000000000000000000000000000..68b461b199ea99f585d76b1aa8442bf34503e8bc --- /dev/null +++ b/ACE_plus/modules/ace_plus_ldm.py @@ -0,0 +1,451 @@ +# -*- coding: utf-8 -*- +# Copyright (c) Alibaba, Inc. and its affiliates. +import torch +import torch.nn.functional as F +import copy +import math +import random +from contextlib import nullcontext +from einops import rearrange +from scepter.modules.model.network.ldm import LatentDiffusion +from scepter.modules.model.registry import MODELS, DIFFUSIONS, BACKBONES, LOSSES, TOKENIZERS, EMBEDDERS +from scepter.modules.model.utils.basic_utils import check_list_of_list, to_device, pack_imagelist_into_tensor, \ + limit_batch_data, unpack_tensor_into_imagelist, count_params, disabled_train +from scepter.modules.utils.config import dict_to_yaml +from scepter.modules.utils.distribute import we + +@MODELS.register_class() +class LatentDiffusionACEPlus(LatentDiffusion): + para_dict = LatentDiffusion.para_dict + def __init__(self, cfg, logger=None): + super().__init__(cfg, logger=logger) + self.guide_scale = cfg.get('GUIDE_SCALE', 1.0) + + def init_params(self): + self.parameterization = self.cfg.get('PARAMETERIZATION', 'rf') + assert self.parameterization in [ + 'eps', 'x0', 'v', 'rf' + ], 'currently only supporting "eps" and "x0" and "v" and "rf"' + + diffusion_cfg = self.cfg.get("DIFFUSION", None) + assert diffusion_cfg is not None + if self.cfg.have("WORK_DIR"): + diffusion_cfg.WORK_DIR = self.cfg.WORK_DIR + self.diffusion = DIFFUSIONS.build(diffusion_cfg, logger=self.logger) + + self.pretrained_model = self.cfg.get('PRETRAINED_MODEL', None) + self.ignore_keys = self.cfg.get('IGNORE_KEYS', []) + + self.model_config = self.cfg.DIFFUSION_MODEL + self.first_stage_config = self.cfg.FIRST_STAGE_MODEL + self.cond_stage_config = self.cfg.COND_STAGE_MODEL + self.tokenizer_config = self.cfg.get('TOKENIZER', None) + self.loss_config = self.cfg.get('LOSS', None) + + self.scale_factor = self.cfg.get('SCALE_FACTOR', 0.18215) + self.size_factor = self.cfg.get('SIZE_FACTOR', 16) + self.default_n_prompt = self.cfg.get('DEFAULT_N_PROMPT', '') + self.default_n_prompt = '' if self.default_n_prompt is None else self.default_n_prompt + self.p_zero = self.cfg.get('P_ZERO', 0.0) + self.train_n_prompt = self.cfg.get('TRAIN_N_PROMPT', '') + if self.default_n_prompt is None: + self.default_n_prompt = '' + if self.train_n_prompt is None: + self.train_n_prompt = '' + self.use_ema = self.cfg.get('USE_EMA', False) + self.model_ema_config = self.cfg.get('DIFFUSION_MODEL_EMA', None) + + def construct_network(self): + # embedding_context = torch.device("meta") if self.model_config.get("PRETRAINED_MODEL", None) else nullcontext() + # with embedding_context: + self.model = BACKBONES.build(self.model_config, logger=self.logger).to(torch.bfloat16) + self.logger.info('all parameters:{}'.format(count_params(self.model))) + if self.use_ema: + if self.model_ema_config: + self.model_ema = BACKBONES.build(self.model_ema_config, + logger=self.logger) + else: + self.model_ema = copy.deepcopy(self.model) + self.model_ema = self.model_ema.eval() + for param in self.model_ema.parameters(): + param.requires_grad = False + if self.loss_config: + self.loss = LOSSES.build(self.loss_config, logger=self.logger) + if self.tokenizer_config is not None: + self.tokenizer = TOKENIZERS.build(self.tokenizer_config, + logger=self.logger) + if self.first_stage_config: + self.first_stage_model = MODELS.build(self.first_stage_config, + logger=self.logger) + self.first_stage_model = self.first_stage_model.eval() + self.first_stage_model.train = disabled_train + for param in self.first_stage_model.parameters(): + param.requires_grad = False + else: + self.first_stage_model = None + if self.tokenizer_config is not None: + self.cond_stage_config.KWARGS = { + 'vocab_size': self.tokenizer.vocab_size + } + if self.cond_stage_config == '__is_unconditional__': + print( + f'Training {self.__class__.__name__} as an unconditional model.' + ) + self.cond_stage_model = None + else: + model = EMBEDDERS.build(self.cond_stage_config, logger=self.logger) + self.cond_stage_model = model.eval().requires_grad_(False) + self.cond_stage_model.train = disabled_train + + @torch.no_grad() + def encode_first_stage(self, x, **kwargs): + def run_one_image(u): + zu = self.first_stage_model.encode(u) + if isinstance(zu, (tuple, list)): + zu = zu[0] + return zu + + z = [run_one_image(u.unsqueeze(0) if u.dim() == 3 else u) for u in x] + return z + + @torch.no_grad() + def decode_first_stage(self, z): + return [self.first_stage_model.decode(zu) for zu in z] + def noise_sample(self, num_samples, h, w, seed, dtype=torch.bfloat16): + noise = torch.randn( + num_samples, + 16, + # allow for packing + 2 * math.ceil(h / 16), + 2 * math.ceil(w / 16), + device=we.device_id, + dtype=dtype, + generator=torch.Generator(device=we.device_id).manual_seed(seed), + ) + return noise + def resize_func(self, x, size): + if x is None: return x + return F.interpolate(x.unsqueeze(0), size = size, mode='nearest-exact') + def parse_ref_and_edit(self, src_image, + modify_image, + src_image_mask, + text_embedding, + #text_mask, + edit_id): + edit_image = [] + modi_image = [] + edit_mask = [] + ref_image = [] + ref_mask = [] + ref_context = [] + ref_y = [] + ref_id = [] + txt = [] + txt_y = [] + for sample_id, (one_src, + one_modify, + one_src_mask, + one_text_embedding, + one_text_y, + # one_text_mask, + one_edit_id) in enumerate(zip(src_image, + modify_image, + src_image_mask, + text_embedding["context"], + text_embedding["y"], + #text_mask, + edit_id) + ): + ref_id.append([i for i in range(len(one_src))]) + if hasattr(self, "ref_cond_stage_model") and self.ref_cond_stage_model: + ref_image.append(self.ref_cond_stage_model.encode_list([((i + 1.0) / 2.0 * 255).type(torch.uint8) for i in one_src])) + else: + ref_image.append(one_src) + ref_mask.append(one_src_mask) + # process edit image & edit image mask + current_edit_image = to_device([one_src[i] for i in one_edit_id], strict=False) + current_edit_image = [v.squeeze(0) for v in self.encode_first_stage(current_edit_image)] + # process modi image + current_modify_image = to_device([one_modify[i] for i in one_edit_id], + strict=False) + current_modify_image = [ + v.squeeze(0) + for v in self.encode_first_stage(current_modify_image) + ] + current_edit_image_mask = to_device( + [one_src_mask[i] for i in one_edit_id], strict=False) + current_edit_image_mask = [ + self.reshape_func(m).squeeze(0) + for m in current_edit_image_mask + ] + + edit_image.append(current_edit_image) + modi_image.append(current_modify_image) + edit_mask.append(current_edit_image_mask) + ref_context.append(one_text_embedding[:len(ref_id[-1])]) + ref_y.append(one_text_y[:len(ref_id[-1])]) + if not sum(len(src_) for src_ in src_image) > 0: + ref_image = None + ref_context = None + ref_y = None + for sample_id, (one_text_embedding, one_text_y) in enumerate(zip(text_embedding["context"], + text_embedding["y"])): + txt.append(one_text_embedding[-1].squeeze(0)) + txt_y.append(one_text_y[-1]) + return { + "edit": edit_image, + 'modify': modi_image, + "edit_mask": edit_mask, + "edit_id": edit_id, + "ref_context": ref_context, + "ref_y": ref_y, + "context": txt, + "y": txt_y, + "ref_x": ref_image, + "ref_mask": ref_mask, + "ref_id": ref_id + } + + + def reshape_func(self, mask): + mask = mask.to(torch.bfloat16) + mask = mask.view((-1, mask.shape[-2], mask.shape[-1])) + mask = rearrange( + mask, + "c (h ph) (w pw) -> c (ph pw) h w", + ph=8, + pw=8, + ) + return mask + + def forward_train(self, + src_image_list=[], + modify_image_list=[], + src_mask_list=[], + edit_id=[], + image=None, + image_mask=None, + noise=None, + prompt=[], + **kwargs): + ''' + Args: + src_image: list of list of src_image + src_image_mask: list of list of src_image_mask + image: target image + image_mask: target image mask + noise: default is None, generate automaticly + ref_prompt: list of list of text + prompt: list of text + **kwargs: + Returns: + ''' + assert check_list_of_list(src_image_list) and check_list_of_list( + src_mask_list) + assert self.cond_stage_model is not None + + gc_seg = kwargs.pop("gc_seg", []) + gc_seg = int(gc_seg[0]) if len(gc_seg) > 0 else 0 + align = kwargs.pop("align", []) + prompt_ = [[pp] if isinstance(pp, str) else pp for pp in prompt] + if len(align) < 1: align = [0] * len(prompt_) + context = getattr(self.cond_stage_model, 'encode_list_of_list')(prompt_) + guide_scale = self.guide_scale + if guide_scale is not None: + guide_scale = torch.full((len(prompt_),), guide_scale, device=we.device_id) + else: + guide_scale = None + # image and image_mask + # print("is list of list", check_list_of_list(image)) + if check_list_of_list(image): + image = [to_device(ix) for ix in image] + x_start = [self.encode_first_stage(ix, **kwargs) for ix in image] + noise = [[torch.randn_like(ii) for ii in ix] for ix in x_start] + x_start = [torch.cat(ix, dim=-1) for ix in x_start] + noise = [torch.cat(ix, dim=-1) for ix in noise] + + noise, _ = pack_imagelist_into_tensor(noise) + + image_mask = [to_device(im, strict=False) for im in image_mask] + x_mask = [[self.reshape_func(i).squeeze(0) for i in im] if im is not None else [None] * len(ix) for ix, im in zip(image, image_mask)] + x_mask = [torch.cat(im, dim=-1) for im in x_mask] + else: + image = to_device(image) + x_start = self.encode_first_stage(image, **kwargs) + image_mask = to_device(image_mask, strict=False) + x_mask = [self.reshape_func(i).squeeze(0) for i in image_mask] if image_mask is not None else [None] * len( + image) + loss_mask, _ = pack_imagelist_into_tensor( + tuple(torch.ones_like(ix, dtype=torch.bool, device=ix.device) for ix in x_start)) + x_start, x_shapes = pack_imagelist_into_tensor(x_start) + context['x_shapes'] = x_shapes + context['align'] = align + # process image mask + + context['x_mask'] = x_mask + ref_edit_context = self.parse_ref_and_edit(src_image_list, modify_image_list, src_mask_list, context, edit_id) + context.update(ref_edit_context) + + teacher_context = copy.deepcopy(context) + teacher_context["context"] = torch.cat(teacher_context["context"], dim=0) + teacher_context["y"] = torch.cat(teacher_context["y"], dim=0) + loss = self.diffusion.loss(x_0=x_start, + model=self.model, + model_kwargs={"cond": context, + "gc_seg": gc_seg, + "guidance": guide_scale}, + noise=noise, + reduction='none', + **kwargs) + loss = loss[loss_mask].mean() + ret = {'loss': loss, 'probe_data': {'prompt': prompt}} + return ret + + @torch.no_grad() + def forward_test(self, + src_image_list=[], + modify_image_list=[], + src_mask_list=[], + edit_id=[], + image=None, + image_mask=None, + prompt=[], + sampler='flow_euler', + sample_steps=20, + seed=2023, + guide_scale=3.5, + guide_rescale=0.0, + show_process=False, + log_num=-1, + **kwargs): + outputs = self.forward_editing( + src_image_list=src_image_list, + src_mask_list=src_mask_list, + modify_image_list=modify_image_list, + edit_id=edit_id, + image=image, + image_mask=image_mask, + prompt=prompt, + sampler=sampler, + sample_steps=sample_steps, + seed=seed, + guide_scale=guide_scale, + guide_rescale=guide_rescale, + show_process=show_process, + log_num=log_num, + **kwargs + ) + return outputs + + @torch.no_grad() + def forward_editing(self, + src_image_list=[], + modify_image_list=None, + src_mask_list=[], + edit_id=[], + image=None, + image_mask=None, + prompt=[], + sampler='flow_euler', + sample_steps=20, + seed=2023, + guide_scale=3.5, + log_num=-1, + **kwargs + ): + # gc_seg is unused + prompt, image, image_mask, src_image, modify_image, src_image_mask, edit_id = limit_batch_data( + [prompt, image, image_mask, src_image_list, modify_image_list, src_mask_list, edit_id], log_num) + assert check_list_of_list(src_image) and check_list_of_list(src_image_mask) + assert self.cond_stage_model is not None + align = kwargs.pop("align", []) + prompt_ = [[pp] if isinstance(pp, str) else pp for pp in prompt] + if len(align) < 1: align = [0] * len(prompt_) + context = getattr(self.cond_stage_model, 'encode_list_of_list')(prompt_) + guide_scale = guide_scale or self.guide_scale + if guide_scale is not None: + guide_scale = torch.full((len(prompt),), guide_scale, device=we.device_id) + else: + guide_scale = None + # image and image_mask + seed = seed if seed >= 0 else random.randint(0, 2 ** 32 - 1) + if image is not None: + if check_list_of_list(image): + image = [torch.cat(ix, dim=-1) for ix in image] + image_mask = [torch.cat(im, dim=-1) for im in image_mask] + noise = [self.noise_sample(1, ix.shape[1], ix.shape[2], seed) for ix in image] + else: + height, width = kwargs.pop("height"), kwargs.pop("width") + noise = [self.noise_sample(1, height, width, seed) for _ in prompt] + noise, x_shapes = pack_imagelist_into_tensor(noise) + context['x_shapes'] = x_shapes + context['align'] = align + # process image mask + image_mask = to_device(image_mask, strict=False) + x_mask = [self.reshape_func(i).squeeze(0) for i in image_mask] + context['x_mask'] = x_mask + ref_edit_context = self.parse_ref_and_edit(src_image, modify_image, src_image_mask, context, edit_id) + context.update(ref_edit_context) + # UNet use input n_prompt + # model = self.model_ema if self.use_ema and self.eval_ema else self.model + # import pdb;pdb.set_trace() + model = self.model + embedding_context = model.no_sync if isinstance(model, torch.distributed.fsdp.FullyShardedDataParallel) \ + else nullcontext + with embedding_context(): + samples = self.diffusion.sample( + noise=noise, + sampler=sampler, + model=self.model, + model_kwargs={"cond": context, "guidance": guide_scale, "gc_seg": -1 + }, + steps=sample_steps, + show_progress=True, + guide_scale=guide_scale, + return_intermediate=None, + **kwargs).float() + samples = unpack_tensor_into_imagelist(samples, x_shapes) + with torch.autocast(device_type="cuda", dtype=torch.bfloat16): + x_samples = self.decode_first_stage(samples) + outputs = list() + for i in range(len(prompt)): + rec_img = torch.clamp((x_samples[i].float() + 1.0) / 2.0, min=0.0, max=1.0) + rec_img = rec_img.squeeze(0) + edit_imgs, modify_imgs, edit_img_masks = [], [], [] + if src_image is not None and src_image[i] is not None: + if src_image_mask[i] is None: + src_image_mask[i] = [None] * len(src_image[i]) + for edit_img, modify_img, edit_mask in zip(src_image[i], modify_image_list[i], src_image_mask[i]): + edit_img = torch.clamp((edit_img.float() + 1.0) / 2.0, min=0.0, max=1.0) + edit_imgs.append(edit_img.squeeze(0)) + modify_img = torch.clamp((modify_img.float() + 1.0) / 2.0, + min=0.0, + max=1.0) + modify_imgs.append(modify_img.squeeze(0)) + if edit_mask is None: + edit_mask = torch.ones_like(edit_img[[0], :, :]) + edit_img_masks.append(edit_mask) + one_tup = { + 'reconstruct_image': rec_img, + 'instruction': prompt[i], + 'edit_image': edit_imgs if len(edit_imgs) > 0 else None, + 'modify_image': modify_imgs if len(modify_imgs) > 0 else None, + 'edit_mask': edit_img_masks if len(edit_imgs) > 0 else None + } + if image is not None: + if image_mask is None: + image_mask = [None] * len(image) + ori_img = torch.clamp((image[i] + 1.0) / 2.0, min=0.0, max=1.0) + one_tup['target_image'] = ori_img.squeeze(0) + one_tup['target_mask'] = image_mask[i] if image_mask[i] is not None else torch.ones_like( + ori_img[[0], :, :]) + outputs.append(one_tup) + return outputs + + @staticmethod + def get_config_template(): + return dict_to_yaml('MODEL', + __class__.__name__, + LatentDiffusionACEPlus.para_dict, + set_name=True) + diff --git a/ACE_plus/modules/ace_plus_solver.py b/ACE_plus/modules/ace_plus_solver.py new file mode 100644 index 0000000000000000000000000000000000000000..d09a40b8a489fbfbebea0da96a5b29f7cefd4a87 --- /dev/null +++ b/ACE_plus/modules/ace_plus_solver.py @@ -0,0 +1,181 @@ +# -*- coding: utf-8 -*- +# Copyright (c) Alibaba, Inc. and its affiliates. +import numpy as np +import torch +from scepter.modules.solver import LatentDiffusionSolver +from scepter.modules.solver.registry import SOLVERS +from scepter.modules.utils.data import transfer_data_to_cuda +from scepter.modules.utils.distribute import we +from scepter.modules.utils.probe import ProbeData +from tqdm import tqdm +@SOLVERS.register_class() +class FormalACEPlusSolver(LatentDiffusionSolver): + def __init__(self, cfg, logger=None): + super().__init__(cfg, logger=logger) + self.probe_prompt = cfg.get("PROBE_PROMPT", None) + self.probe_hw = cfg.get("PROBE_HW", []) + + @torch.no_grad() + def run_eval(self): + self.eval_mode() + self.before_all_iter(self.hooks_dict[self._mode]) + all_results = [] + for batch_idx, batch_data in tqdm( + enumerate(self.datas[self._mode].dataloader)): + self.before_iter(self.hooks_dict[self._mode]) + if self.sample_args: + batch_data.update(self.sample_args.get_lowercase_dict()) + with torch.autocast(device_type='cuda', + enabled=self.use_amp, + dtype=self.dtype): + results = self.run_step_eval(transfer_data_to_cuda(batch_data), + batch_idx, + step=self.total_iter, + rank=we.rank) + all_results.extend(results) + self.after_iter(self.hooks_dict[self._mode]) + log_data, log_label = self.save_results(all_results) + self.register_probe({'eval_label': log_label}) + self.register_probe({ + 'eval_image': + ProbeData(log_data, + is_image=True, + build_html=True, + build_label=log_label) + }) + self.after_all_iter(self.hooks_dict[self._mode]) + + @torch.no_grad() + def run_test(self): + self.test_mode() + self.before_all_iter(self.hooks_dict[self._mode]) + all_results = [] + for batch_idx, batch_data in tqdm( + enumerate(self.datas[self._mode].dataloader)): + self.before_iter(self.hooks_dict[self._mode]) + if self.sample_args: + batch_data.update(self.sample_args.get_lowercase_dict()) + with torch.autocast(device_type='cuda', + enabled=self.use_amp, + dtype=self.dtype): + results = self.run_step_eval(transfer_data_to_cuda(batch_data), + batch_idx, + step=self.total_iter, + rank=we.rank) + all_results.extend(results) + self.after_iter(self.hooks_dict[self._mode]) + log_data, log_label = self.save_results(all_results) + self.register_probe({'test_label': log_label}) + self.register_probe({ + 'test_image': + ProbeData(log_data, + is_image=True, + build_html=True, + build_label=log_label) + }) + + self.after_all_iter(self.hooks_dict[self._mode]) + + def run_step_val(self, batch_data, batch_idx=0, step=None, rank=None): + sample_id_list = batch_data['sample_id'] + loss_dict = {} + with torch.autocast(device_type='cuda', + enabled=self.use_amp, + dtype=self.dtype): + results = self.model.forward_train(**batch_data) + loss = results['loss'] + for sample_id in sample_id_list: + loss_dict[sample_id] = loss.detach().cpu().numpy() + return loss_dict + + def save_results(self, results): + log_data, log_label = [], [] + for result in results: + ret_images, ret_labels = [], [] + edit_image = result.get('edit_image', None) + modify_image = result.get('modify_image', None) + edit_mask = result.get('edit_mask', None) + if edit_image is not None: + for i, edit_img in enumerate(result['edit_image']): + if edit_img is None: + continue + ret_images.append((edit_img.permute(1, 2, 0).cpu().numpy() * 255).astype(np.uint8)) + ret_labels.append(f'edit_image{i}; ') + ret_images.append((modify_image[i].permute(1, 2, 0).cpu().numpy() * 255).astype(np.uint8)) + ret_labels.append(f'modify_image{i}; ') + if edit_mask is not None: + ret_images.append((edit_mask[i].permute(1, 2, 0).cpu().numpy() * 255).astype(np.uint8)) + ret_labels.append(f'edit_mask{i}; ') + + target_image = result.get('target_image', None) + target_mask = result.get('target_mask', None) + if target_image is not None: + ret_images.append((target_image.permute(1, 2, 0).cpu().numpy() * 255).astype(np.uint8)) + ret_labels.append(f'target_image; ') + if target_mask is not None: + ret_images.append((target_mask.permute(1, 2, 0).cpu().numpy() * 255).astype(np.uint8)) + ret_labels.append(f'target_mask; ') + teacher_image = result.get('image', None) + if teacher_image is not None: + ret_images.append((teacher_image.permute(1, 2, 0).cpu().numpy() * 255).astype(np.uint8)) + ret_labels.append(f"teacher_image") + reconstruct_image = result.get('reconstruct_image', None) + if reconstruct_image is not None: + ret_images.append((reconstruct_image.permute(1, 2, 0).cpu().numpy() * 255).astype(np.uint8)) + ret_labels.append(f"{result['instruction']}") + log_data.append(ret_images) + log_label.append(ret_labels) + return log_data, log_label + @property + def probe_data(self): + if not we.debug and self.mode == 'train': + batch_data = transfer_data_to_cuda(self.current_batch_data[self.mode]) + self.eval_mode() + with torch.autocast(device_type='cuda', + enabled=self.use_amp, + dtype=self.dtype): + batch_data['log_num'] = self.log_train_num + batch_data.update(self.sample_args.get_lowercase_dict()) + results = self.run_step_eval(batch_data) + self.train_mode() + log_data, log_label = self.save_results(results) + self.register_probe({ + 'train_image': + ProbeData(log_data, + is_image=True, + build_html=True, + build_label=log_label) + }) + self.register_probe({'train_label': log_label}) + if self.probe_prompt: + self.eval_mode() + all_results = [] + for prompt in self.probe_prompt: + with torch.autocast(device_type='cuda', + enabled=self.use_amp, + dtype=self.dtype): + batch_data = { + "prompt": [[prompt]], + "image": [torch.zeros(3, self.probe_hw[0], self.probe_hw[1])], + "image_mask": [torch.ones(1, self.probe_hw[0], self.probe_hw[1])], + "src_image_list": [[]], + "modify_image_list": [[]], + "src_mask_list": [[]], + "edit_id": [[]], + "height": self.probe_hw[0], + "width": self.probe_hw[1] + } + batch_data.update(self.sample_args.get_lowercase_dict()) + results = self.run_step_eval(batch_data) + all_results.extend(results) + self.train_mode() + log_data, log_label = self.save_results(all_results) + self.register_probe({ + 'probe_image': + ProbeData(log_data, + is_image=True, + build_html=True, + build_label=log_label) + }) + + return super(LatentDiffusionSolver, self).probe_data diff --git a/ACE_plus/modules/checkpoint.py b/ACE_plus/modules/checkpoint.py new file mode 100644 index 0000000000000000000000000000000000000000..8dcb4cfacec7721e6e96d9e331cf91034acc7d78 --- /dev/null +++ b/ACE_plus/modules/checkpoint.py @@ -0,0 +1,135 @@ +# -*- coding: utf-8 -*- +# Copyright (c) Alibaba, Inc. and its affiliates. +import os, torch +import os.path as osp +import warnings +from collections import OrderedDict +from safetensors.torch import save_file +from scepter.modules.solver.hooks import CheckpointHook, BackwardHook +from scepter.modules.solver.hooks.registry import HOOKS +from scepter.modules.utils.config import dict_to_yaml +from scepter.modules.utils.distribute import we +from scepter.modules.utils.file_system import FS + +_DEFAULT_CHECKPOINT_PRIORITY = 300 + +def convert_to_comfyui_lora(ori_sd, prefix = "lora_unet"): + new_ckpt = OrderedDict() + for k,v in ori_sd.items(): + new_k = k.replace(".lora_A.0_SwiftLoRA.", ".lora_down.").replace(".lora_B.0_SwiftLoRA.", ".lora_up.") + new_k = prefix + "_" + new_k.split(".lora")[0].replace("model.", "").replace(".", "_") + ".lora" + new_k.split(".lora")[1] + alpha_k = new_k.split(".lora")[0] + ".alpha" + new_ckpt[new_k] = v + if "lora_up" in new_k: + alpha = v.shape[-1] + elif "lora_down" in new_k: + alpha = v.shape[0] + new_ckpt[alpha_k] = torch.tensor(float(alpha)).to(v) + return new_ckpt + +@HOOKS.register_class() +class ACECheckpointHook(CheckpointHook): + """ Checkpoint resume or save hook. + Args: + interval (int): Save interval, by epoch. + save_best (bool): Save the best checkpoint by a metric key, default is False. + save_best_by (str): How to get the best the checkpoint by the metric key, default is ''. + + means the higher the best (default). + - means the lower the best. + E.g. +acc@1, -err@1, acc@5(same as +acc@5) + """ + + def __init__(self, cfg, logger=None): + super(ACECheckpointHook, self).__init__(cfg, logger=logger) + + def after_iter(self, solver): + super().after_iter(solver) + if solver.total_iter != 0 and ( + (solver.total_iter + 1) % self.interval == 0 + or solver.total_iter == solver.max_steps - 1): + from swift import SwiftModel + if isinstance(solver.model, SwiftModel) or ( + hasattr(solver.model, 'module') + and isinstance(solver.model.module, SwiftModel)): + save_path = osp.join( + solver.work_dir, + 'checkpoints/{}-{}'.format(self.save_name_prefix, + solver.total_iter + 1)) + if we.rank == 0: + tuner_model = os.path.join(save_path, '0_SwiftLoRA', 'adapter_model.bin') + save_model = os.path.join(save_path, '0_SwiftLoRA', 'comfyui_model.safetensors') + if FS.exists(tuner_model): + with FS.get_from(tuner_model) as local_file: + swift_lora_sd = torch.load(local_file, weights_only=True) + safetensor_lora_sd = convert_to_comfyui_lora(swift_lora_sd) + with FS.put_to(save_model) as local_file: + save_file(safetensor_lora_sd, local_file) + @staticmethod + def get_config_template(): + return dict_to_yaml('hook', + __class__.__name__, + ACECheckpointHook.para_dict, + set_name=True) + +@HOOKS.register_class() +class ACEBackwardHook(BackwardHook): + def grad_clip(self, optimizer): + for params_group in optimizer.param_groups: + train_params = [] + for param in params_group['params']: + if param.requires_grad: + train_params.append(param) + # print(len(train_params), self.gradient_clip) + torch.nn.utils.clip_grad_norm_(parameters=train_params, + max_norm=self.gradient_clip) + + def after_iter(self, solver): + if solver.optimizer is not None and solver.is_train_mode: + if solver.loss is None: + warnings.warn( + 'solver.loss should not be None in train mode, remember to call solver._reduce_scalar()!' + ) + return + if solver.scaler is not None: + solver.scaler.scale(solver.loss / + self.accumulate_step).backward() + self.current_step += 1 + # Suppose profiler run after backward, so we need to set backward_prev_step + # as the previous one step before the backward step + if self.current_step % self.accumulate_step == 0: + solver.scaler.unscale_(solver.optimizer) + if self.gradient_clip > 0: + self.grad_clip(solver.optimizer) + self.profile(solver) + solver.scaler.step(solver.optimizer) + solver.scaler.update() + solver.optimizer.zero_grad() + else: + (solver.loss / self.accumulate_step).backward() + self.current_step += 1 + # Suppose profiler run after backward, so we need to set backward_prev_step + # as the previous one step before the backward step + if self.current_step % self.accumulate_step == 0: + if self.gradient_clip > 0: + self.grad_clip(solver.optimizer) + self.profile(solver) + solver.optimizer.step() + solver.optimizer.zero_grad() + if solver.lr_scheduler: + if self.current_step % self.accumulate_step == 0: + solver.lr_scheduler.step() + if self.current_step % self.accumulate_step == 0: + setattr(solver, 'backward_step', True) + self.current_step = 0 + else: + setattr(solver, 'backward_step', False) + solver.loss = None + if self.empty_cache_step > 0 and solver.total_iter % self.empty_cache_step == 0: + torch.cuda.empty_cache() + + @staticmethod + def get_config_template(): + return dict_to_yaml('hook', + __class__.__name__, + ACEBackwardHook.para_dict, + set_name=True) diff --git a/ACE_plus/modules/embedder.py b/ACE_plus/modules/embedder.py new file mode 100644 index 0000000000000000000000000000000000000000..f01346d60ee557dec0b96d7c7f3425968c1546e6 --- /dev/null +++ b/ACE_plus/modules/embedder.py @@ -0,0 +1,219 @@ +# -*- coding: utf-8 -*- +# Copyright (c) Alibaba, Inc. and its affiliates. +# This file contains code that is adapted from +# https://github.com/black-forest-labs/flux.git +import warnings + +import torch +import torch.utils.dlpack +import transformers +from scepter.modules.model.embedder.base_embedder import BaseEmbedder +from scepter.modules.model.registry import EMBEDDERS +from scepter.modules.model.tokenizer.tokenizer_component import ( + basic_clean, canonicalize, whitespace_clean) +from scepter.modules.utils.config import dict_to_yaml +from scepter.modules.utils.file_system import FS + +try: + from transformers import AutoTokenizer, T5EncoderModel +except Exception as e: + warnings.warn( + f'Import transformers error, please deal with this problem: {e}') + +@EMBEDDERS.register_class() +class ACEHFEmbedder(BaseEmbedder): + para_dict = { + "HF_MODEL_CLS": { + "value": None, + "description": "huggingface cls in transfomer" + }, + "MODEL_PATH": { + "value": None, + "description": "model folder path" + }, + "HF_TOKENIZER_CLS": { + "value": None, + "description": "huggingface cls in transfomer" + }, + + "TOKENIZER_PATH": { + "value": None, + "description": "tokenizer folder path" + }, + "MAX_LENGTH": { + "value": 77, + "description": "max length of input" + }, + "OUTPUT_KEY": { + "value": "last_hidden_state", + "description": "output key" + }, + "D_TYPE": { + "value": "float", + "description": "dtype" + }, + "BATCH_INFER": { + "value": False, + "description": "batch infer" + } + } + para_dict.update(BaseEmbedder.para_dict) + def __init__(self, cfg, logger=None): + super().__init__(cfg, logger=logger) + hf_model_cls = cfg.get('HF_MODEL_CLS', None) + model_path = cfg.get("MODEL_PATH", None) + hf_tokenizer_cls = cfg.get('HF_TOKENIZER_CLS', None) + tokenizer_path = cfg.get('TOKENIZER_PATH', None) + self.max_length = cfg.get('MAX_LENGTH', 77) + self.output_key = cfg.get("OUTPUT_KEY", "last_hidden_state") + self.d_type = cfg.get("D_TYPE", "float") + self.clean = cfg.get("CLEAN", "whitespace") + self.batch_infer = cfg.get("BATCH_INFER", False) + self.added_identifier = cfg.get('ADDED_IDENTIFIER', None) + torch_dtype = getattr(torch, self.d_type) + + assert hf_model_cls is not None and hf_tokenizer_cls is not None + assert model_path is not None and tokenizer_path is not None + with FS.get_dir_to_local_dir(tokenizer_path, wait_finish=True) as local_path: + self.tokenizer = getattr(transformers, hf_tokenizer_cls).from_pretrained(local_path, + max_length = self.max_length, + torch_dtype = torch_dtype, + additional_special_tokens=self.added_identifier) + + with FS.get_dir_to_local_dir(model_path, wait_finish=True) as local_path: + self.hf_module = getattr(transformers, hf_model_cls).from_pretrained(local_path, torch_dtype = torch_dtype) + + + self.hf_module = self.hf_module.eval().requires_grad_(False) + + def forward(self, text: list[str], return_mask = False): + batch_encoding = self.tokenizer( + text, + truncation=True, + max_length=self.max_length, + return_length=False, + return_overflowing_tokens=False, + padding="max_length", + return_tensors="pt", + ) + + outputs = self.hf_module( + input_ids=batch_encoding["input_ids"].to(self.hf_module.device), + attention_mask=None, + output_hidden_states=False, + ) + if return_mask: + return outputs[self.output_key], batch_encoding['attention_mask'].to(self.hf_module.device) + else: + return outputs[self.output_key], None + + def encode(self, text, return_mask = False): + if isinstance(text, str): + text = [text] + if self.clean: + text = [self._clean(u) for u in text] + if not self.batch_infer: + cont, mask = [], [] + for tt in text: + one_cont, one_mask = self([tt], return_mask=return_mask) + cont.append(one_cont) + mask.append(one_mask) + if return_mask: + return torch.cat(cont, dim=0), torch.cat(mask, dim=0) + else: + return torch.cat(cont, dim=0) + else: + ret_data = self(text, return_mask = return_mask) + if return_mask: + return ret_data + else: + return ret_data[0] + + def encode_list(self, text_list, return_mask=True): + cont_list = [] + mask_list = [] + for pp in text_list: + cont = self.encode(pp, return_mask=return_mask) + cont_list.append(cont[0]) if return_mask else cont_list.append(cont) + mask_list.append(cont[1]) if return_mask else mask_list.append(None) + if return_mask: + return cont_list, mask_list + else: + return cont_list + + def encode_list_of_list(self, text_list, return_mask=True): + cont_list = [] + mask_list = [] + for pp in text_list: + cont = self.encode_list(pp, return_mask=return_mask) + cont_list.append(cont[0]) if return_mask else cont_list.append(cont) + mask_list.append(cont[1]) if return_mask else mask_list.append(None) + if return_mask: + return cont_list, mask_list + else: + return cont_list + + def _clean(self, text): + if self.clean == 'whitespace': + text = whitespace_clean(basic_clean(text)) + elif self.clean == 'lower': + text = whitespace_clean(basic_clean(text)).lower() + elif self.clean == 'canonicalize': + text = canonicalize(basic_clean(text)) + return text + @staticmethod + def get_config_template(): + return dict_to_yaml('EMBEDDER', + __class__.__name__, + ACEHFEmbedder.para_dict, + set_name=True) + +@EMBEDDERS.register_class() +class T5ACEPlusClipFluxEmbedder(BaseEmbedder): + """ + Uses the OpenCLIP transformer encoder for text + """ + para_dict = { + 'T5_MODEL': {}, + 'CLIP_MODEL': {} + } + + def __init__(self, cfg, logger=None): + super().__init__(cfg, logger=logger) + self.t5_model = EMBEDDERS.build(cfg.T5_MODEL, logger=logger) + self.clip_model = EMBEDDERS.build(cfg.CLIP_MODEL, logger=logger) + + def encode(self, text, return_mask = False): + t5_embeds = self.t5_model.encode(text, return_mask = return_mask) + clip_embeds = self.clip_model.encode(text, return_mask = return_mask) + # change embedding strategy here + return { + 'context': t5_embeds, + 'y': clip_embeds, + } + + def encode_list(self, text, return_mask = False): + t5_embeds = self.t5_model.encode_list(text, return_mask = return_mask) + clip_embeds = self.clip_model.encode_list(text, return_mask = return_mask) + # change embedding strategy here + return { + 'context': t5_embeds, + 'y': clip_embeds, + } + + def encode_list_of_list(self, text, return_mask = False): + t5_embeds = self.t5_model.encode_list_of_list(text, return_mask = return_mask) + clip_embeds = self.clip_model.encode_list_of_list(text, return_mask = return_mask) + # change embedding strategy here + return { + 'context': t5_embeds, + 'y': clip_embeds, + } + + + @staticmethod + def get_config_template(): + return dict_to_yaml('EMBEDDER', + __class__.__name__, + T5ACEPlusClipFluxEmbedder.para_dict, + set_name=True) \ No newline at end of file diff --git a/ACE_plus/modules/flux.py b/ACE_plus/modules/flux.py new file mode 100644 index 0000000000000000000000000000000000000000..3c57fbee1d58de02c66af44445e8895462ddd306 --- /dev/null +++ b/ACE_plus/modules/flux.py @@ -0,0 +1,812 @@ +# -*- coding: utf-8 -*- +# Copyright (c) Alibaba, Inc. and its affiliates. +# This file contains code that is adapted from +# https://github.com/black-forest-labs/flux.git +import math +import torch +from torch import Tensor, nn +from collections import OrderedDict +from functools import partial +from einops import rearrange, repeat +from scepter.modules.model.base_model import BaseModel +from scepter.modules.model.registry import BACKBONES +from scepter.modules.utils.config import dict_to_yaml +from scepter.modules.utils.distribute import we +from scepter.modules.utils.file_system import FS +from torch.utils.checkpoint import checkpoint_sequential +from torch.nn.utils.rnn import pad_sequence +from .layers import (DoubleStreamBlock, EmbedND, LastLayer, MLPEmbedder, + SingleStreamBlock, timestep_embedding) +@BACKBONES.register_class() +class Flux(BaseModel): + """ + Transformer backbone Diffusion model with RoPE. + """ + para_dict = { + 'IN_CHANNELS': { + 'value': 64, + 'description': "model's input channels." + }, + 'OUT_CHANNELS': { + 'value': 64, + 'description': "model's output channels." + }, + 'HIDDEN_SIZE': { + 'value': 1024, + 'description': "model's hidden size." + }, + 'NUM_HEADS': { + 'value': 16, + 'description': 'number of heads in the transformer.' + }, + 'AXES_DIM': { + 'value': [16, 56, 56], + 'description': 'dimensions of the axes of the positional encoding.' + }, + 'THETA': { + 'value': 10_000, + 'description': 'theta for positional encoding.' + }, + 'VEC_IN_DIM': { + 'value': 768, + 'description': 'dimension of the vector input.' + }, + 'GUIDANCE_EMBED': { + 'value': False, + 'description': 'whether to use guidance embedding.' + }, + 'CONTEXT_IN_DIM': { + 'value': 4096, + 'description': 'dimension of the context input.' + }, + 'MLP_RATIO': { + 'value': 4.0, + 'description': 'ratio of mlp hidden size to hidden size.' + }, + 'QKV_BIAS': { + 'value': True, + 'description': 'whether to use bias in qkv projection.' + }, + 'DEPTH': { + 'value': 19, + 'description': 'number of transformer blocks.' + }, + 'DEPTH_SINGLE_BLOCKS': { + 'value': + 38, + 'description': + 'number of transformer blocks in the single stream block.' + }, + 'USE_GRAD_CHECKPOINT': { + 'value': False, + 'description': 'whether to use gradient checkpointing.' + } + } + + def __init__(self, cfg, logger=None): + super().__init__(cfg, logger=logger) + self.in_channels = cfg.IN_CHANNELS + self.out_channels = cfg.get('OUT_CHANNELS', self.in_channels) + hidden_size = cfg.get('HIDDEN_SIZE', 1024) + num_heads = cfg.get('NUM_HEADS', 16) + axes_dim = cfg.AXES_DIM + theta = cfg.THETA + vec_in_dim = cfg.VEC_IN_DIM + self.guidance_embed = cfg.GUIDANCE_EMBED + context_in_dim = cfg.CONTEXT_IN_DIM + mlp_ratio = cfg.MLP_RATIO + qkv_bias = cfg.QKV_BIAS + depth = cfg.DEPTH + depth_single_blocks = cfg.DEPTH_SINGLE_BLOCKS + self.use_grad_checkpoint = cfg.get("USE_GRAD_CHECKPOINT", False) + self.attn_backend = cfg.get("ATTN_BACKEND", "pytorch") + self.cache_pretrain_model = cfg.get("CACHE_PRETRAIN_MODEL", False) + self.lora_model = cfg.get("DIFFUSERS_LORA_MODEL", None) + self.comfyui_lora_model = cfg.get("COMFYUI_LORA_MODEL", None) + self.swift_lora_model = cfg.get("SWIFT_LORA_MODEL", None) + self.blackforest_lora_model = cfg.get("BLACKFOREST_LORA_MODEL", None) + self.pretrain_adapter = cfg.get("PRETRAIN_ADAPTER", None) + + if hidden_size % num_heads != 0: + raise ValueError( + f"Hidden size {hidden_size} must be divisible by num_heads {num_heads}" + ) + pe_dim = hidden_size // num_heads + if sum(axes_dim) != pe_dim: + raise ValueError( + f"Got {axes_dim} but expected positional dim {pe_dim}") + self.hidden_size = hidden_size + self.num_heads = num_heads + self.pe_embedder = EmbedND(dim=pe_dim, theta=theta, axes_dim=axes_dim) + self.img_in = nn.Linear(self.in_channels, self.hidden_size, bias=True) + self.time_in = MLPEmbedder(in_dim=256, hidden_dim=self.hidden_size) + self.vector_in = MLPEmbedder(vec_in_dim, self.hidden_size) + self.guidance_in = (MLPEmbedder(in_dim=256, + hidden_dim=self.hidden_size) + if self.guidance_embed else nn.Identity()) + self.txt_in = nn.Linear(context_in_dim, self.hidden_size) + + self.double_blocks = nn.ModuleList( + [ + DoubleStreamBlock( + self.hidden_size, + self.num_heads, + mlp_ratio=mlp_ratio, + qkv_bias=qkv_bias, + backend=self.attn_backend + ) + for _ in range(depth) + ] + ) + + self.single_blocks = nn.ModuleList( + [ + SingleStreamBlock(self.hidden_size, self.num_heads, mlp_ratio=mlp_ratio, backend=self.attn_backend) + for _ in range(depth_single_blocks) + ] + ) + + self.final_layer = LastLayer(self.hidden_size, 1, self.out_channels) + + def prepare_input(self, x, context, y, x_shape=None): + # x.shape [6, 16, 16, 16] target is [6, 16, 768, 1360] + bs, c, h, w = x.shape + x = rearrange(x, "b c (h ph) (w pw) -> b (h w) (c ph pw)", ph=2, pw=2) + x_id = torch.zeros(h // 2, w // 2, 3) + x_id[..., 1] = x_id[..., 1] + torch.arange(h // 2)[:, None] + x_id[..., 2] = x_id[..., 2] + torch.arange(w // 2)[None, :] + x_ids = repeat(x_id, "h w c -> b (h w) c", b=bs) + txt_ids = torch.zeros(bs, context.shape[1], 3) + return x, x_ids.to(x), context.to(x), txt_ids.to(x), y.to(x), h, w + + def unpack(self, x: Tensor, height: int, width: int) -> Tensor: + return rearrange( + x, + "b (h w) (c ph pw) -> b c (h ph) (w pw)", + h=math.ceil(height/2), + w=math.ceil(width/2), + ph=2, + pw=2, + ) + + def merge_diffuser_lora(self, ori_sd, lora_sd, scale=1.0): + key_map = { + "single_blocks.{}.linear1.weight": {"key_list": [ + ["transformer.single_transformer_blocks.{}.attn.to_q.lora_A.weight", + "transformer.single_transformer_blocks.{}.attn.to_q.lora_B.weight", [0, 3072]], + ["transformer.single_transformer_blocks.{}.attn.to_k.lora_A.weight", + "transformer.single_transformer_blocks.{}.attn.to_k.lora_B.weight", [3072, 6144]], + ["transformer.single_transformer_blocks.{}.attn.to_v.lora_A.weight", + "transformer.single_transformer_blocks.{}.attn.to_v.lora_B.weight", [6144, 9216]], + ["transformer.single_transformer_blocks.{}.proj_mlp.lora_A.weight", + "transformer.single_transformer_blocks.{}.proj_mlp.lora_B.weight", [9216, 21504]] + ], "num": 38}, + "single_blocks.{}.modulation.lin.weight": {"key_list": [ + ["transformer.single_transformer_blocks.{}.norm.linear.lora_A.weight", + "transformer.single_transformer_blocks.{}.norm.linear.lora_B.weight", [0, 9216]], + ], "num": 38}, + "single_blocks.{}.linear2.weight": {"key_list": [ + ["transformer.single_transformer_blocks.{}.proj_out.lora_A.weight", + "transformer.single_transformer_blocks.{}.proj_out.lora_B.weight", [0, 3072]], + ], "num": 38}, + "double_blocks.{}.txt_attn.qkv.weight": {"key_list": [ + ["transformer.transformer_blocks.{}.attn.add_q_proj.lora_A.weight", + "transformer.transformer_blocks.{}.attn.add_q_proj.lora_B.weight", [0, 3072]], + ["transformer.transformer_blocks.{}.attn.add_k_proj.lora_A.weight", + "transformer.transformer_blocks.{}.attn.add_k_proj.lora_B.weight", [3072, 6144]], + ["transformer.transformer_blocks.{}.attn.add_v_proj.lora_A.weight", + "transformer.transformer_blocks.{}.attn.add_v_proj.lora_B.weight", [6144, 9216]], + ], "num": 19}, + "double_blocks.{}.img_attn.qkv.weight": {"key_list": [ + ["transformer.transformer_blocks.{}.attn.to_q.lora_A.weight", + "transformer.transformer_blocks.{}.attn.to_q.lora_B.weight", [0, 3072]], + ["transformer.transformer_blocks.{}.attn.to_k.lora_A.weight", + "transformer.transformer_blocks.{}.attn.to_k.lora_B.weight", [3072, 6144]], + ["transformer.transformer_blocks.{}.attn.to_v.lora_A.weight", + "transformer.transformer_blocks.{}.attn.to_v.lora_B.weight", [6144, 9216]], + ], "num": 19}, + "double_blocks.{}.img_attn.proj.weight": {"key_list": [ + ["transformer.transformer_blocks.{}.attn.to_out.0.lora_A.weight", + "transformer.transformer_blocks.{}.attn.to_out.0.lora_B.weight", [0, 3072]] + ], "num": 19}, + "double_blocks.{}.txt_attn.proj.weight": {"key_list": [ + ["transformer.transformer_blocks.{}.attn.to_add_out.lora_A.weight", + "transformer.transformer_blocks.{}.attn.to_add_out.lora_B.weight", [0, 3072]] + ], "num": 19}, + "double_blocks.{}.img_mlp.0.weight": {"key_list": [ + ["transformer.transformer_blocks.{}.ff.net.0.proj.lora_A.weight", + "transformer.transformer_blocks.{}.ff.net.0.proj.lora_B.weight", [0, 12288]] + ], "num": 19}, + "double_blocks.{}.img_mlp.2.weight": {"key_list": [ + ["transformer.transformer_blocks.{}.ff.net.2.lora_A.weight", + "transformer.transformer_blocks.{}.ff.net.2.lora_B.weight", [0, 3072]] + ], "num": 19}, + "double_blocks.{}.txt_mlp.0.weight": {"key_list": [ + ["transformer.transformer_blocks.{}.ff_context.net.0.proj.lora_A.weight", + "transformer.transformer_blocks.{}.ff_context.net.0.proj.lora_B.weight", [0, 12288]] + ], "num": 19}, + "double_blocks.{}.txt_mlp.2.weight": {"key_list": [ + ["transformer.transformer_blocks.{}.ff_context.net.2.lora_A.weight", + "transformer.transformer_blocks.{}.ff_context.net.2.lora_B.weight", [0, 3072]] + ], "num": 19}, + "double_blocks.{}.img_mod.lin.weight": {"key_list": [ + ["transformer.transformer_blocks.{}.norm1.linear.lora_A.weight", + "transformer.transformer_blocks.{}.norm1.linear.lora_B.weight", [0, 18432]] + ], "num": 19}, + "double_blocks.{}.txt_mod.lin.weight": {"key_list": [ + ["transformer.transformer_blocks.{}.norm1_context.linear.lora_A.weight", + "transformer.transformer_blocks.{}.norm1_context.linear.lora_B.weight", [0, 18432]] + ], "num": 19} + } + cover_lora_keys = set() + cover_ori_keys = set() + for k, v in key_map.items(): + key_list = v["key_list"] + block_num = v["num"] + for block_id in range(block_num): + for k_list in key_list: + if k_list[0].format(block_id) in lora_sd and k_list[1].format(block_id) in lora_sd: + cover_lora_keys.add(k_list[0].format(block_id)) + cover_lora_keys.add(k_list[1].format(block_id)) + current_weight = torch.matmul(lora_sd[k_list[0].format(block_id)].permute(1, 0), + lora_sd[k_list[1].format(block_id)].permute(1, 0)).permute(1, 0) + ori_sd[k.format(block_id)][k_list[2][0]:k_list[2][1], ...] += scale * current_weight + cover_ori_keys.add(k.format(block_id)) + # lora_sd.pop(k_list[0].format(block_id)) + # lora_sd.pop(k_list[1].format(block_id)) + self.logger.info(f"merge_blackforest_lora loads lora'parameters lora-paras: \n" + f"cover-{len(cover_lora_keys)} vs total {len(lora_sd)} \n" + f"cover ori-{len(cover_ori_keys)} vs total {len(ori_sd)}") + return ori_sd + + def merge_swift_lora(self, ori_sd, lora_sd, scale = 1.0): + have_lora_keys = {} + for k, v in lora_sd.items(): + k = k[len("model."):] if k.startswith("model.") else k + ori_key = k.split("lora")[0] + "weight" + if ori_key not in ori_sd: + raise f"{ori_key} should in the original statedict" + if ori_key not in have_lora_keys: + have_lora_keys[ori_key] = {} + if "lora_A" in k: + have_lora_keys[ori_key]["lora_A"] = v + elif "lora_B" in k: + have_lora_keys[ori_key]["lora_B"] = v + else: + raise NotImplementedError + self.logger.info(f"merge_swift_lora loads lora'parameters {len(have_lora_keys)}") + for key, v in have_lora_keys.items(): + current_weight = torch.matmul(v["lora_A"].permute(1, 0), v["lora_B"].permute(1, 0)).permute(1, 0) + ori_sd[key] += scale * current_weight + return ori_sd + + + def merge_blackforest_lora(self, ori_sd, lora_sd, scale = 1.0): + have_lora_keys = {} + cover_lora_keys = set() + cover_ori_keys = set() + for k, v in lora_sd.items(): + if "lora" in k: + ori_key = k.split("lora")[0] + "weight" + if ori_key not in ori_sd: + raise f"{ori_key} should in the original statedict" + if ori_key not in have_lora_keys: + have_lora_keys[ori_key] = {} + if "lora_A" in k: + have_lora_keys[ori_key]["lora_A"] = v + cover_lora_keys.add(k) + cover_ori_keys.add(ori_key) + elif "lora_B" in k: + have_lora_keys[ori_key]["lora_B"] = v + cover_lora_keys.add(k) + cover_ori_keys.add(ori_key) + else: + if k in ori_sd: + ori_sd[k] = v + cover_lora_keys.add(k) + cover_ori_keys.add(k) + else: + print("unsurpport keys: ", k) + self.logger.info(f"merge_blackforest_lora loads lora'parameters lora-paras: \n" + f"cover-{len(cover_lora_keys)} vs total {len(lora_sd)} \n" + f"cover ori-{len(cover_ori_keys)} vs total {len(ori_sd)}") + + for key, v in have_lora_keys.items(): + current_weight = torch.matmul(v["lora_A"].permute(1, 0), v["lora_B"].permute(1, 0)).permute(1, 0) + # print(key, ori_sd[key].shape, current_weight.shape) + ori_sd[key] += scale * current_weight + return ori_sd + + def merge_comfyui_lora(self, ori_sd, lora_sd, scale = 1.0): + ori_key_map = {key.replace("_", ".") : key for key in ori_sd.keys()} + parse_ckpt = OrderedDict() + for k, v in lora_sd.items(): + if "alpha" in k: + continue + k = k.replace("lora_unet_", "").replace("_", ".") + map_k = ori_key_map[k.split(".lora")[0] + ".weight"] + if map_k not in parse_ckpt: + parse_ckpt[map_k] = {} + if "lora.up" in k: + parse_ckpt[map_k]["lora_up"] = v + elif "lora.down" in k: + parse_ckpt[map_k]["lora_down"] = v + if self.cache_pretrain_model: + self.lora_dict[self.comfyui_lora_model] = {} + + for key, v in parse_ckpt.items(): + current_weight = torch.matmul(v["lora_down"].permute(1, 0), v["lora_up"].permute(1, 0)).permute(1, 0) + self.lora_dict[self.comfyui_lora_model] = current_weight + ori_sd[key] += scale * current_weight + return ori_sd + + def easy_lora_merge(self, ori_sd, lora_sd, scale = 1.0): + for key, v in lora_sd.items(): + ori_sd[key] += scale * v + return ori_sd + + def load_pretrained_model(self, pretrained_model, lora_scale = 1.0): + if next(self.parameters()).device.type == 'meta': + map_location = torch.device(we.device_id) + safe_device = we.device_id + else: + map_location = "cpu" + safe_device = "cpu" + + if pretrained_model is not None: + if not hasattr(self, "ckpt"): + with FS.get_from(pretrained_model, wait_finish=True) as local_model: + if local_model.endswith('safetensors'): + from safetensors.torch import load_file as load_safetensors + ckpt = load_safetensors(local_model, device=safe_device) + else: + ckpt = torch.load(local_model, map_location=map_location, weights_only=True) + if "state_dict" in ckpt: + ckpt = ckpt["state_dict"] + if "model" in ckpt: + ckpt = ckpt["model"]["model"] + if self.cache_pretrain_model: + self.ckpt = ckpt + self.lora_dict = {} + else: + ckpt = self.ckpt + + new_ckpt = OrderedDict() + for k, v in ckpt.items(): + if k in ("img_in.weight"): + model_p = self.state_dict()[k] + if v.shape != model_p.shape: + expanded_state_dict_weight = torch.zeros_like(model_p, device=v.device) + slices = tuple(slice(0, dim) for dim in v.shape) + expanded_state_dict_weight[slices] = v + new_ckpt[k] = expanded_state_dict_weight + else: + new_ckpt[k] = v + else: + new_ckpt[k] = v + + + if self.lora_model is not None: + with FS.get_from(self.lora_model, wait_finish=True) as local_model: + if local_model.endswith('safetensors'): + from safetensors.torch import load_file as load_safetensors + lora_sd = load_safetensors(local_model, device=safe_device) + else: + lora_sd = torch.load(local_model, map_location=map_location, weights_only=True) + new_ckpt = self.merge_diffuser_lora(new_ckpt, lora_sd, scale=lora_scale) + if self.swift_lora_model is not None: + if not isinstance(self.swift_lora_model, list): + self.swift_lora_model = [(self.swift_lora_model, 1.0)] + for lora_model in self.swift_lora_model: + if isinstance(lora_model, str): + lora_model = (lora_model, 1.0/len(self.swift_lora_model)) + print(lora_model) + self.logger.info(f"load swift lora model: {lora_model}") + with FS.get_from(lora_model[0], wait_finish=True) as local_model: + if local_model.endswith('safetensors'): + from safetensors.torch import load_file as load_safetensors + lora_sd = load_safetensors(local_model, device=safe_device) + else: + lora_sd = torch.load(local_model, map_location=map_location, weights_only=True) + new_ckpt = self.merge_swift_lora(new_ckpt, lora_sd, scale=lora_model[1]) + + if self.blackforest_lora_model is not None: + with FS.get_from(self.blackforest_lora_model, wait_finish=True) as local_model: + if local_model.endswith('safetensors'): + from safetensors.torch import load_file as load_safetensors + lora_sd = load_safetensors(local_model, device=safe_device) + else: + lora_sd = torch.load(local_model, map_location=map_location, weights_only=True) + new_ckpt = self.merge_blackforest_lora(new_ckpt, lora_sd, scale=lora_scale) + + if self.comfyui_lora_model is not None: + if hasattr(self, "current_lora") and self.current_lora == self.comfyui_lora_model: + return + if hasattr(self, "lora_dict") and self.comfyui_lora_model in self.lora_dict: + new_ckpt = self.easy_lora_merge(new_ckpt, self.lora_dict[self.comfyui_lora_model], scale=lora_scale) + else: + with FS.get_from(self.comfyui_lora_model, wait_finish=True) as local_model: + if local_model.endswith('safetensors'): + from safetensors.torch import load_file as load_safetensors + lora_sd = load_safetensors(local_model, device=safe_device) + else: + lora_sd = torch.load(local_model, map_location=map_location, weights_only=True) + new_ckpt = self.merge_comfyui_lora(new_ckpt, lora_sd, scale=lora_scale) + if self.comfyui_lora_model: + self.current_lora = self.comfyui_lora_model + + + adapter_ckpt = {} + if self.pretrain_adapter is not None: + with FS.get_from(self.pretrain_adapter, wait_finish=True) as local_adapter: + if local_adapter.endswith('safetensors'): + from safetensors.torch import load_file as load_safetensors + adapter_ckpt = load_safetensors(local_adapter, device=safe_device) + else: + adapter_ckpt = torch.load(local_adapter, map_location=map_location, weights_only=True) + new_ckpt.update(adapter_ckpt) + + missing, unexpected = self.load_state_dict(new_ckpt, strict=False, assign=True) + self.logger.info( + f'Restored from {pretrained_model} with {len(missing)} missing and {len(unexpected)} unexpected keys' + ) + if len(missing) > 0: + self.logger.info(f'Missing Keys:\n {missing}') + if len(unexpected) > 0: + self.logger.info(f'\nUnexpected Keys:\n {unexpected}') + + def forward( + self, + x: Tensor, + t: Tensor, + cond: dict = {}, + guidance: Tensor | None = None, + gc_seg: int = 0 + ) -> Tensor: + x, x_ids, txt, txt_ids, y, h, w = self.prepare_input(x, cond["context"], cond["y"]) + # running on sequences img + x = self.img_in(x) + vec = self.time_in(timestep_embedding(t, 256)) + if self.guidance_embed: + if guidance is None: + raise ValueError("Didn't get guidance strength for guidance distilled model.") + vec = vec + self.guidance_in(timestep_embedding(guidance, 256)) + vec = vec + self.vector_in(y) + txt = self.txt_in(txt) + ids = torch.cat((txt_ids, x_ids), dim=1) + pe = self.pe_embedder(ids) + kwargs = dict( + vec=vec, + pe=pe, + txt_length=txt.shape[1], + ) + x = torch.cat((txt, x), 1) + if self.use_grad_checkpoint and gc_seg >= 0: + x = checkpoint_sequential( + functions=[partial(block, **kwargs) for block in self.double_blocks], + segments=gc_seg if gc_seg > 0 else len(self.double_blocks), + input=x, + use_reentrant=False + ) + else: + for block in self.double_blocks: + x = block(x, **kwargs) + + kwargs = dict( + vec=vec, + pe=pe, + ) + + if self.use_grad_checkpoint and gc_seg >= 0: + x = checkpoint_sequential( + functions=[partial(block, **kwargs) for block in self.single_blocks], + segments=gc_seg if gc_seg > 0 else len(self.single_blocks), + input=x, + use_reentrant=False + ) + else: + for block in self.single_blocks: + x = block(x, **kwargs) + x = x[:, txt.shape[1] :, ...] + x = self.final_layer(x, vec) # (N, T, patch_size ** 2 * out_channels) 6 64 64 + x = self.unpack(x, h, w) + return x + + @staticmethod + def get_config_template(): + return dict_to_yaml('BACKBONE', + __class__.__name__, + Flux.para_dict, + set_name=True) +@BACKBONES.register_class() +class FluxMR(Flux): + def prepare_input(self, x, cond): + if isinstance(cond['context'], list): + context, y = torch.cat(cond["context"], dim=0).to(x), torch.cat(cond["y"], dim=0).to(x) + else: + context, y = cond['context'].to(x), cond['y'].to(x) + batch_frames, batch_frames_ids = [], [] + for ix, shape in zip(x, cond["x_shapes"]): + # unpack image from sequence + ix = ix[:, :shape[0] * shape[1]].view(-1, shape[0], shape[1]) + c, h, w = ix.shape + ix = rearrange(ix, "c (h ph) (w pw) -> (h w) (c ph pw)", ph=2, pw=2) + ix_id = torch.zeros(h // 2, w // 2, 3) + ix_id[..., 1] = ix_id[..., 1] + torch.arange(h // 2)[:, None] + ix_id[..., 2] = ix_id[..., 2] + torch.arange(w // 2)[None, :] + ix_id = rearrange(ix_id, "h w c -> (h w) c") + batch_frames.append([ix]) + batch_frames_ids.append([ix_id]) + + x_list, x_id_list, mask_x_list, x_seq_length = [], [], [], [] + for frames, frame_ids in zip(batch_frames, batch_frames_ids): + proj_frames = [] + for idx, one_frame in enumerate(frames): + one_frame = self.img_in(one_frame) + proj_frames.append(one_frame) + ix = torch.cat(proj_frames, dim=0) + if_id = torch.cat(frame_ids, dim=0) + x_list.append(ix) + x_id_list.append(if_id) + mask_x_list.append(torch.ones(ix.shape[0]).to(ix.device, non_blocking=True).bool()) + x_seq_length.append(ix.shape[0]) + x = pad_sequence(tuple(x_list), batch_first=True) + x_ids = pad_sequence(tuple(x_id_list), batch_first=True).to(x) # [b,pad_seq,2] pad (0.,0.) at dim2 + mask_x = pad_sequence(tuple(mask_x_list), batch_first=True) + + txt = self.txt_in(context) + txt_ids = torch.zeros(context.shape[0], context.shape[1], 3).to(x) + mask_txt = torch.ones(context.shape[0], context.shape[1]).to(x.device, non_blocking=True).bool() + + return x, x_ids, txt, txt_ids, y, mask_x, mask_txt, x_seq_length + + def unpack(self, x: Tensor, cond: dict = None, x_seq_length: list = None) -> Tensor: + x_list = [] + image_shapes = cond["x_shapes"] + for u, shape, seq_length in zip(x, image_shapes, x_seq_length): + height, width = shape + h, w = math.ceil(height / 2), math.ceil(width / 2) + u = rearrange( + u[seq_length-h*w:seq_length, ...], + "(h w) (c ph pw) -> (h ph w pw) c", + h=h, + w=w, + ph=2, + pw=2, + ) + x_list.append(u) + x = pad_sequence(tuple(x_list), batch_first=True).permute(0, 2, 1) + return x + + def forward( + self, + x: Tensor, + t: Tensor, + cond: dict = {}, + guidance: Tensor | None = None, + gc_seg: int = 0, + **kwargs + ) -> Tensor: + x, x_ids, txt, txt_ids, y, mask_x, mask_txt, seq_length_list = self.prepare_input(x, cond) + # running on sequences img + vec = self.time_in(timestep_embedding(t, 256)) + if self.guidance_embed and guidance[-1] >= 0: + if guidance is None: + raise ValueError("Didn't get guidance strength for guidance distilled model.") + vec = vec + self.guidance_in(timestep_embedding(guidance, 256)) + vec = vec + self.vector_in(y) + ids = torch.cat((txt_ids, x_ids), dim=1) + pe = self.pe_embedder(ids) + + mask_aside = torch.cat((mask_txt, mask_x), dim=1) + mask = mask_aside[:, None, :] * mask_aside[:, :, None] + + kwargs = dict( + vec=vec, + pe=pe, + mask=mask, + txt_length = txt.shape[1], + ) + x = torch.cat((txt, x), 1) + if self.use_grad_checkpoint and gc_seg >= 0: + x = checkpoint_sequential( + functions=[partial(block, **kwargs) for block in self.double_blocks], + segments=gc_seg if gc_seg > 0 else len(self.double_blocks), + input=x, + use_reentrant=False + ) + else: + for block in self.double_blocks: + x = block(x, **kwargs) + + kwargs = dict( + vec=vec, + pe=pe, + mask=mask, + ) + + if self.use_grad_checkpoint and gc_seg >= 0: + x = checkpoint_sequential( + functions=[partial(block, **kwargs) for block in self.single_blocks], + segments=gc_seg if gc_seg > 0 else len(self.single_blocks), + input=x, + use_reentrant=False + ) + else: + for block in self.single_blocks: + x = block(x, **kwargs) + x = x[:, txt.shape[1]:, ...] + x = self.final_layer(x, vec) # (N, T, patch_size ** 2 * out_channels) 6 64 64 + x = self.unpack(x, cond, seq_length_list) + return x + + @staticmethod + def get_config_template(): + return dict_to_yaml('MODEL', + __class__.__name__, + FluxMR.para_dict, + set_name=True) +@BACKBONES.register_class() +class FluxMRACEPlus(FluxMR): + def __init__(self, cfg, logger = None): + super().__init__(cfg, logger) + def prepare_input(self, x, cond): + context, y = cond["context"], cond["y"] + batch_frames, batch_frames_ids = [], [] + for ix, shape, imask, ie, ie_mask in zip(x, + cond['x_shapes'], + cond['x_mask'], + cond['edit'], + cond['edit_mask']): + # unpack image from sequence + ix = ix[:, :shape[0] * shape[1]].view(-1, shape[0], shape[1]) + imask = torch.ones_like( + ix[[0], :, :]) if imask is None else imask.squeeze(0) + if len(ie) > 0: + ie = [iie.squeeze(0) for iie in ie] + ie_mask = [ + torch.ones( + (ix.shape[0] * 4, ix.shape[1], + ix.shape[2])) if iime is None else iime.squeeze(0) + for iime in ie_mask + ] + ie = torch.cat(ie, dim=-1) + ie_mask = torch.cat(ie_mask, dim=-1) + else: + ie, ie_mask = torch.zeros_like(ix).to(x), torch.ones_like( + imask).to(x), + ix = torch.cat([ix, ie, ie_mask], dim=0) + c, h, w = ix.shape + ix = rearrange(ix, + 'c (h ph) (w pw) -> (h w) (c ph pw)', + ph=2, + pw=2) + ix_id = torch.zeros(h // 2, w // 2, 3) + ix_id[..., 1] = ix_id[..., 1] + torch.arange(h // 2)[:, None] + ix_id[..., 2] = ix_id[..., 2] + torch.arange(w // 2)[None, :] + ix_id = rearrange(ix_id, 'h w c -> (h w) c') + batch_frames.append([ix]) + batch_frames_ids.append([ix_id]) + x_list, x_id_list, mask_x_list, x_seq_length = [], [], [], [] + for frames, frame_ids in zip(batch_frames, batch_frames_ids): + proj_frames = [] + for idx, one_frame in enumerate(frames): + one_frame = self.img_in(one_frame) + proj_frames.append(one_frame) + ix = torch.cat(proj_frames, dim=0) + if_id = torch.cat(frame_ids, dim=0) + x_list.append(ix) + x_id_list.append(if_id) + mask_x_list.append(torch.ones(ix.shape[0]).to(ix.device, non_blocking=True).bool()) + x_seq_length.append(ix.shape[0]) + # if len(x_list) < 1: import pdb;pdb.set_trace() + x = pad_sequence(tuple(x_list), batch_first=True) + x_ids = pad_sequence(tuple(x_id_list), batch_first=True).to(x) # [b,pad_seq,2] pad (0.,0.) at dim2 + mask_x = pad_sequence(tuple(mask_x_list), batch_first=True) + if isinstance(context, list): + txt_list, mask_txt_list, y_list = [], [], [] + for sample_id, (ctx, yy) in enumerate(zip(context, y)): + txt_list.append(self.txt_in(ctx.to(x))) + mask_txt_list.append(torch.ones(txt_list[-1].shape[0]).to(ctx.device, non_blocking=True).bool()) + y_list.append(yy.to(x)) + txt = pad_sequence(tuple(txt_list), batch_first=True) + txt_ids = torch.zeros(txt.shape[0], txt.shape[1], 3).to(x) + mask_txt = pad_sequence(tuple(mask_txt_list), batch_first=True) + y = torch.cat(y_list, dim=0) + assert y.ndim == 2 and txt.ndim == 3 + else: + txt = self.txt_in(context) + txt_ids = torch.zeros(context.shape[0], context.shape[1], 3).to(x) + mask_txt = torch.ones(context.shape[0], context.shape[1]).to(x.device, non_blocking=True).bool() + return x, x_ids, txt, txt_ids, y, mask_x, mask_txt, x_seq_length + + @staticmethod + def get_config_template(): + return dict_to_yaml('MODEL', + __class__.__name__, + FluxMRACEPlus.para_dict, + set_name=True) + +@BACKBONES.register_class() +class FluxMRModiACEPlus(FluxMR): + def __init__(self, cfg, logger = None): + super().__init__(cfg, logger) + def prepare_input(self, x, cond): + context, y = cond["context"], cond["y"] + batch_frames, batch_frames_ids = [], [] + for ix, shape, imask, ie, im, ie_mask in zip(x, + cond['x_shapes'], + cond['x_mask'], + cond['edit'], + cond['modify'], + cond['edit_mask']): + # unpack image from sequence + ix = ix[:, :shape[0] * shape[1]].view(-1, shape[0], shape[1]) + imask = torch.ones_like( + ix[[0], :, :]) if imask is None else imask.squeeze(0) + if len(ie) > 0: + ie = [iie.squeeze(0) for iie in ie] + im = [iim.squeeze(0) for iim in im] + ie_mask = [ + torch.ones( + (ix.shape[0] * 4, ix.shape[1], + ix.shape[2])) if iime is None else iime.squeeze(0) + for iime in ie_mask + ] + im = torch.cat(im, dim=-1) + ie = torch.cat(ie, dim=-1) + ie_mask = torch.cat(ie_mask, dim=-1) + else: + ie, im, ie_mask = torch.zeros_like(ix).to(x), torch.zeros_like(ix).to(x), torch.ones_like( + imask).to(x), + ix = torch.cat([ix, ie, im, ie_mask], dim=0) + c, h, w = ix.shape + ix = rearrange(ix, + 'c (h ph) (w pw) -> (h w) (c ph pw)', + ph=2, + pw=2) + ix_id = torch.zeros(h // 2, w // 2, 3) + ix_id[..., 1] = ix_id[..., 1] + torch.arange(h // 2)[:, None] + ix_id[..., 2] = ix_id[..., 2] + torch.arange(w // 2)[None, :] + ix_id = rearrange(ix_id, 'h w c -> (h w) c') + batch_frames.append([ix]) + batch_frames_ids.append([ix_id]) + x_list, x_id_list, mask_x_list, x_seq_length = [], [], [], [] + for frames, frame_ids in zip(batch_frames, batch_frames_ids): + proj_frames = [] + for idx, one_frame in enumerate(frames): + one_frame = self.img_in(one_frame) + proj_frames.append(one_frame) + ix = torch.cat(proj_frames, dim=0) + if_id = torch.cat(frame_ids, dim=0) + x_list.append(ix) + x_id_list.append(if_id) + mask_x_list.append(torch.ones(ix.shape[0]).to(ix.device, non_blocking=True).bool()) + x_seq_length.append(ix.shape[0]) + # if len(x_list) < 1: import pdb;pdb.set_trace() + x = pad_sequence(tuple(x_list), batch_first=True) + x_ids = pad_sequence(tuple(x_id_list), batch_first=True).to(x) # [b,pad_seq,2] pad (0.,0.) at dim2 + mask_x = pad_sequence(tuple(mask_x_list), batch_first=True) + if isinstance(context, list): + txt_list, mask_txt_list, y_list = [], [], [] + for sample_id, (ctx, yy) in enumerate(zip(context, y)): + txt_list.append(self.txt_in(ctx.to(x))) + mask_txt_list.append(torch.ones(txt_list[-1].shape[0]).to(ctx.device, non_blocking=True).bool()) + y_list.append(yy.to(x)) + txt = pad_sequence(tuple(txt_list), batch_first=True) + txt_ids = torch.zeros(txt.shape[0], txt.shape[1], 3).to(x) + mask_txt = pad_sequence(tuple(mask_txt_list), batch_first=True) + y = torch.cat(y_list, dim=0) + assert y.ndim == 2 and txt.ndim == 3 + else: + txt = self.txt_in(context) + txt_ids = torch.zeros(context.shape[0], context.shape[1], 3).to(x) + mask_txt = torch.ones(context.shape[0], context.shape[1]).to(x.device, non_blocking=True).bool() + return x, x_ids, txt, txt_ids, y, mask_x, mask_txt, x_seq_length + + @staticmethod + def get_config_template(): + return dict_to_yaml('MODEL', + __class__.__name__, + FluxMRACEPlus.para_dict, + set_name=True) diff --git a/ACE_plus/modules/layers.py b/ACE_plus/modules/layers.py new file mode 100644 index 0000000000000000000000000000000000000000..348e7543580c674e5cf1428500731d98cbc53830 --- /dev/null +++ b/ACE_plus/modules/layers.py @@ -0,0 +1,521 @@ +# -*- coding: utf-8 -*- +# Copyright (c) Alibaba, Inc. and its affiliates. +# This file contains code that is adapted from +# https://github.com/black-forest-labs/flux.git +from __future__ import annotations + +import math +from dataclasses import dataclass +from torch import Tensor, nn +import torch +from einops import rearrange, repeat +from torch import Tensor +from torch.nn.utils.rnn import pad_sequence + +try: + from flash_attn import ( + flash_attn_varlen_func + ) + FLASHATTN_IS_AVAILABLE = True +except ImportError: + FLASHATTN_IS_AVAILABLE = False + flash_attn_varlen_func = None + +def attention(q: Tensor, k: Tensor, v: Tensor, pe: Tensor, mask: Tensor | None = None, backend = 'pytorch') -> Tensor: + q, k = apply_rope(q, k, pe) + if backend == 'pytorch': + if mask is not None and mask.dtype == torch.bool: + mask = torch.zeros_like(mask).to(q).masked_fill_(mask.logical_not(), -1e20) + x = torch.nn.functional.scaled_dot_product_attention(q, k, v, attn_mask=mask) + # x = torch.nan_to_num(x, nan=0.0, posinf=1e10, neginf=-1e10) + x = rearrange(x, "B H L D -> B L (H D)") + elif backend == 'flash_attn': + # q: (B, H, L, D) + # k: (B, H, S, D) now L = S + # v: (B, H, S, D) + b, h, lq, d = q.shape + _, _, lk, _ = k.shape + q = rearrange(q, "B H L D -> B L H D") + k = rearrange(k, "B H S D -> B S H D") + v = rearrange(v, "B H S D -> B S H D") + if mask is None: + q_lens = torch.tensor([lq] * b, dtype=torch.int32).to(q.device, non_blocking=True) + k_lens = torch.tensor([lk] * b, dtype=torch.int32).to(k.device, non_blocking=True) + else: + q_lens = torch.sum(mask[:, 0, :, 0], dim=1).int() + k_lens = torch.sum(mask[:, 0, 0, :], dim=1).int() + q = torch.cat([q_v[:q_l] for q_v, q_l in zip(q, q_lens)]) + k = torch.cat([k_v[:k_l] for k_v, k_l in zip(k, k_lens)]) + v = torch.cat([v_v[:v_l] for v_v, v_l in zip(v, k_lens)]) + cu_seqlens_q = torch.cat([q_lens.new_zeros([1]), q_lens]).cumsum(0, dtype=torch.int32) + cu_seqlens_k = torch.cat([k_lens.new_zeros([1]), k_lens]).cumsum(0, dtype=torch.int32) + max_seqlen_q = q_lens.max() + max_seqlen_k = k_lens.max() + + x = flash_attn_varlen_func( + q, + k, + v, + cu_seqlens_q=cu_seqlens_q, + cu_seqlens_k=cu_seqlens_k, + max_seqlen_q=max_seqlen_q, + max_seqlen_k=max_seqlen_k + ) + x_list = [x[cu_seqlens_q[i]:cu_seqlens_q[i+1]] for i in range(b)] + x = pad_sequence(tuple(x_list), batch_first=True) + x = rearrange(x, "B L H D -> B L (H D)") + else: + raise NotImplementedError + return x + + +def rope(pos: Tensor, dim: int, theta: int) -> Tensor: + assert dim % 2 == 0 + scale = torch.arange(0, dim, 2, dtype=torch.float64, device=pos.device) / dim + omega = 1.0 / (theta**scale) + out = torch.einsum("...n,d->...nd", pos, omega) + out = torch.stack([torch.cos(out), -torch.sin(out), torch.sin(out), torch.cos(out)], dim=-1) + out = rearrange(out, "b n d (i j) -> b n d i j", i=2, j=2) + return out.float() + + +def apply_rope(xq: Tensor, xk: Tensor, freqs_cis: Tensor) -> tuple[Tensor, Tensor]: + xq_ = xq.float().reshape(*xq.shape[:-1], -1, 1, 2) + xk_ = xk.float().reshape(*xk.shape[:-1], -1, 1, 2) + xq_out = freqs_cis[..., 0] * xq_[..., 0] + freqs_cis[..., 1] * xq_[..., 1] + xk_out = freqs_cis[..., 0] * xk_[..., 0] + freqs_cis[..., 1] * xk_[..., 1] + return xq_out.reshape(*xq.shape).type_as(xq), xk_out.reshape(*xk.shape).type_as(xk) + +class EmbedND(nn.Module): + def __init__(self, dim: int, theta: int, axes_dim: list[int]): + super().__init__() + self.dim = dim + self.theta = theta + self.axes_dim = axes_dim + + def forward(self, ids: Tensor) -> Tensor: + n_axes = ids.shape[-1] + emb = torch.cat( + [rope(ids[..., i], self.axes_dim[i], self.theta) for i in range(n_axes)], + dim=-3, + ) + + return emb.unsqueeze(1) + + +def timestep_embedding(t: Tensor, dim, max_period=10000, time_factor: float = 1000.0): + """ + Create sinusoidal timestep embeddings. + :param t: a 1-D Tensor of N indices, one per batch element. + These may be fractional. + :param dim: the dimension of the output. + :param max_period: controls the minimum frequency of the embeddings. + :return: an (N, D) Tensor of positional embeddings. + """ + t = time_factor * t + half = dim // 2 + freqs = torch.exp(-math.log(max_period) * torch.arange(start=0, end=half, dtype=torch.float32) / half).to( + t.device + ) + + args = t[:, None].float() * freqs[None] + embedding = torch.cat([torch.cos(args), torch.sin(args)], dim=-1) + if dim % 2: + embedding = torch.cat([embedding, torch.zeros_like(embedding[:, :1])], dim=-1) + if torch.is_floating_point(t): + embedding = embedding.to(t) + return embedding + + +class MLPEmbedder(nn.Module): + def __init__(self, in_dim: int, hidden_dim: int): + super().__init__() + self.in_layer = nn.Linear(in_dim, hidden_dim, bias=True) + self.silu = nn.SiLU() + self.out_layer = nn.Linear(hidden_dim, hidden_dim, bias=True) + + def forward(self, x: Tensor) -> Tensor: + return self.out_layer(self.silu(self.in_layer(x))) + + +class RMSNorm(torch.nn.Module): + def __init__(self, dim: int): + super().__init__() + self.scale = nn.Parameter(torch.ones(dim)) + + def forward(self, x: Tensor): + x_dtype = x.dtype + x = x.float() + rrms = torch.rsqrt(torch.mean(x**2, dim=-1, keepdim=True) + 1e-6) + return (x * rrms).to(dtype=x_dtype) * self.scale + + +class QKNorm(torch.nn.Module): + def __init__(self, dim: int): + super().__init__() + self.query_norm = RMSNorm(dim) + self.key_norm = RMSNorm(dim) + + def forward(self, q: Tensor, k: Tensor, v: Tensor) -> tuple[Tensor, Tensor]: + q = self.query_norm(q) + k = self.key_norm(k) + return q.to(v), k.to(v) + + +class SelfAttention(nn.Module): + def __init__(self, dim: int, num_heads: int = 8, qkv_bias: bool = False): + super().__init__() + self.num_heads = num_heads + head_dim = dim // num_heads + + self.qkv = nn.Linear(dim, dim * 3, bias=qkv_bias) + self.norm = QKNorm(head_dim) + self.proj = nn.Linear(dim, dim) + + def forward(self, x: Tensor, pe: Tensor, mask: Tensor | None = None) -> Tensor: + qkv = self.qkv(x) + q, k, v = rearrange(qkv, "B L (K H D) -> K B H L D", K=3, H=self.num_heads) + q, k = self.norm(q, k, v) + x = attention(q, k, v, pe=pe, mask=mask) + x = self.proj(x) + return x + +class CrossAttention(nn.Module): + def __init__(self, dim: int, context_dim: int, num_heads: int = 8, qkv_bias: bool = False): + super().__init__() + self.num_heads = num_heads + head_dim = dim // num_heads + self.q = nn.Linear(dim, dim, bias=qkv_bias) + self.kv = nn.Linear(dim, context_dim * 2, bias=qkv_bias) + self.norm = QKNorm(head_dim) + self.proj = nn.Linear(dim, dim) + + def forward(self, x: Tensor, context: Tensor, pe: Tensor, mask: Tensor | None = None) -> Tensor: + qkv = self.qkv(x) + q, k, v = rearrange(qkv, "B L (K H D) -> K B H L D", K=3, H=self.num_heads) + q, k = self.norm(q, k, v) + x = attention(q, k, v, pe=pe, mask=mask) + x = self.proj(x) + return x + + +@dataclass +class ModulationOut: + shift: Tensor + scale: Tensor + gate: Tensor + + +class Modulation(nn.Module): + def __init__(self, dim: int, double: bool): + super().__init__() + self.is_double = double + self.multiplier = 6 if double else 3 + self.lin = nn.Linear(dim, self.multiplier * dim, bias=True) + + def forward(self, vec: Tensor) -> tuple[ModulationOut, ModulationOut | None]: + out = self.lin(nn.functional.silu(vec))[:, None, :].chunk(self.multiplier, dim=-1) + + return ( + ModulationOut(*out[:3]), + ModulationOut(*out[3:]) if self.is_double else None, + ) + + +class DoubleStreamBlock(nn.Module): + def __init__(self, hidden_size: int, num_heads: int, mlp_ratio: float, qkv_bias: bool = False, backend = 'pytorch'): + super().__init__() + + mlp_hidden_dim = int(hidden_size * mlp_ratio) + self.num_heads = num_heads + self.hidden_size = hidden_size + self.img_mod = Modulation(hidden_size, double=True) + self.img_norm1 = nn.LayerNorm(hidden_size, elementwise_affine=False, eps=1e-6) + self.img_attn = SelfAttention(dim=hidden_size, num_heads=num_heads, qkv_bias=qkv_bias) + + self.img_norm2 = nn.LayerNorm(hidden_size, elementwise_affine=False, eps=1e-6) + self.img_mlp = nn.Sequential( + nn.Linear(hidden_size, mlp_hidden_dim, bias=True), + nn.GELU(approximate="tanh"), + nn.Linear(mlp_hidden_dim, hidden_size, bias=True), + ) + + self.backend = backend + + self.txt_mod = Modulation(hidden_size, double=True) + self.txt_norm1 = nn.LayerNorm(hidden_size, elementwise_affine=False, eps=1e-6) + self.txt_attn = SelfAttention(dim=hidden_size, num_heads=num_heads, qkv_bias=qkv_bias) + + self.txt_norm2 = nn.LayerNorm(hidden_size, elementwise_affine=False, eps=1e-6) + self.txt_mlp = nn.Sequential( + nn.Linear(hidden_size, mlp_hidden_dim, bias=True), + nn.GELU(approximate="tanh"), + nn.Linear(mlp_hidden_dim, hidden_size, bias=True), + ) + + + + + def forward(self, x: Tensor, vec: Tensor, pe: Tensor, mask: Tensor = None, txt_length = None): + img_mod1, img_mod2 = self.img_mod(vec) + txt_mod1, txt_mod2 = self.txt_mod(vec) + + txt, img = x[:, :txt_length], x[:, txt_length:] + + # prepare image for attention + img_modulated = self.img_norm1(img) + img_modulated = (1 + img_mod1.scale) * img_modulated + img_mod1.shift + img_qkv = self.img_attn.qkv(img_modulated) + img_q, img_k, img_v = rearrange(img_qkv, "B L (K H D) -> K B H L D", K=3, H=self.num_heads) + img_q, img_k = self.img_attn.norm(img_q, img_k, img_v) + # prepare txt for attention + txt_modulated = self.txt_norm1(txt) + txt_modulated = (1 + txt_mod1.scale) * txt_modulated + txt_mod1.shift + txt_qkv = self.txt_attn.qkv(txt_modulated) + txt_q, txt_k, txt_v = rearrange(txt_qkv, "B L (K H D) -> K B H L D", K=3, H=self.num_heads) + txt_q, txt_k = self.txt_attn.norm(txt_q, txt_k, txt_v) + + # run actual attention + q = torch.cat((txt_q, img_q), dim=2) + k = torch.cat((txt_k, img_k), dim=2) + v = torch.cat((txt_v, img_v), dim=2) + if mask is not None: + mask = repeat(mask, 'B L S-> B H L S', H=self.num_heads) + attn = attention(q, k, v, pe=pe, mask = mask, backend = self.backend) + txt_attn, img_attn = attn[:, : txt.shape[1]], attn[:, txt.shape[1] :] + + # calculate the img bloks + img = img + img_mod1.gate * self.img_attn.proj(img_attn) + img = img + img_mod2.gate * self.img_mlp((1 + img_mod2.scale) * self.img_norm2(img) + img_mod2.shift) + + # calculate the txt bloks + txt = txt + txt_mod1.gate * self.txt_attn.proj(txt_attn) + txt = txt + txt_mod2.gate * self.txt_mlp((1 + txt_mod2.scale) * self.txt_norm2(txt) + txt_mod2.shift) + x = torch.cat((txt, img), 1) + return x + + +class SingleStreamBlock(nn.Module): + """ + A DiT block with parallel linear layers as described in + https://arxiv.org/abs/2302.05442 and adapted modulation interface. + """ + + def __init__( + self, + hidden_size: int, + num_heads: int, + mlp_ratio: float = 4.0, + qk_scale: float | None = None, + backend='pytorch' + ): + super().__init__() + self.hidden_dim = hidden_size + self.num_heads = num_heads + head_dim = hidden_size // num_heads + self.scale = qk_scale or head_dim**-0.5 + + self.mlp_hidden_dim = int(hidden_size * mlp_ratio) + # qkv and mlp_in + self.linear1 = nn.Linear(hidden_size, hidden_size * 3 + self.mlp_hidden_dim) + # proj and mlp_out + self.linear2 = nn.Linear(hidden_size + self.mlp_hidden_dim, hidden_size) + + self.norm = QKNorm(head_dim) + + self.hidden_size = hidden_size + self.pre_norm = nn.LayerNorm(hidden_size, elementwise_affine=False, eps=1e-6) + + self.mlp_act = nn.GELU(approximate="tanh") + self.modulation = Modulation(hidden_size, double=False) + self.backend = backend + + def forward(self, x: Tensor, vec: Tensor, pe: Tensor, mask: Tensor = None) -> Tensor: + mod, _ = self.modulation(vec) + x_mod = (1 + mod.scale) * self.pre_norm(x) + mod.shift + qkv, mlp = torch.split(self.linear1(x_mod), [3 * self.hidden_size, self.mlp_hidden_dim], dim=-1) + + q, k, v = rearrange(qkv, "B L (K H D) -> K B H L D", K=3, H=self.num_heads) + q, k = self.norm(q, k, v) + if mask is not None: + mask = repeat(mask, 'B L S-> B H L S', H=self.num_heads) + # compute attention + attn = attention(q, k, v, pe=pe, mask = mask, backend=self.backend) + # compute activation in mlp stream, cat again and run second linear layer + output = self.linear2(torch.cat((attn, self.mlp_act(mlp)), 2)) + return x + mod.gate * output + + +class DoubleStreamBlockC(DoubleStreamBlock): + """ + A DiT block with parallel linear layers as described in + https://arxiv.org/abs/2302.05442 and adapted modulation interface. + """ + + def __init__(self, hidden_size: int, num_heads: int, mlp_ratio: float, + qkv_bias: bool = False, backend='pytorch', + abondon_cond = False): + super().__init__(hidden_size, num_heads, mlp_ratio, + qkv_bias, backend) + self.abondon_cond = abondon_cond + + def forward(self, x: Tensor, vec: Tensor, + pe: Tensor, mask: Tensor = None, + txt_length=None, + uncondi_length=None, + uncondi_pe = None, + mask_uncond = None): + # pad_sequence(tuple(x_list), batch_first=True) + if self.abondon_cond: + x = [ix[:u_l, :] for ix, u_l in zip(x, uncondi_length)] + x = pad_sequence(x, batch_first=True) + if not x.shape[1] == pe.shape[2]: + pe = uncondi_pe + mask = mask_uncond + # print("double stream block", x.shape, pe.shape) + x = super().forward(x, vec, pe, mask, txt_length) + return x + +class SingleStreamBlockC(SingleStreamBlock): + """ + A DiT block with parallel linear layers as described in + https://arxiv.org/abs/2302.05442 and adapted modulation interface. + """ + + def __init__(self, hidden_size: int, + num_heads: int, + mlp_ratio: float = 4.0, + qk_scale: float | None = None, + backend='pytorch', + abondon_cond = False): + super().__init__(hidden_size, num_heads, mlp_ratio, + qk_scale, backend) + self.abondon_cond = abondon_cond + + def forward(self, x: Tensor, vec: Tensor, pe: Tensor, mask: Tensor = None, + uncondi_length = None, uncondi_pe = None, mask_uncond = None) -> Tensor: + if self.abondon_cond: + x = [ix[:u_l, :] for ix, u_l in zip(x, uncondi_length)] + x = pad_sequence(x, batch_first=True) + if not x.shape[1] == pe.shape[2]: + pe = uncondi_pe + mask = mask_uncond + # print("single stream block", x.shape, pe.shape) + x = super().forward(x, vec, pe, mask) + return x + + +class DoubleStreamBlockD(DoubleStreamBlock): + """ + A DiT block with parallel linear layers as described in + https://arxiv.org/abs/2302.05442 and adapted modulation interface. + """ + + def __init__(self, hidden_size: int, num_heads: int, mlp_ratio: float, + qkv_bias: bool = False, backend='pytorch'): + super().__init__(hidden_size, num_heads, mlp_ratio, + qkv_bias, backend) + mlp_hidden_dim = int(hidden_size * mlp_ratio) + self.edit_mod = Modulation(hidden_size, double=True) + self.edit_norm1 = nn.LayerNorm(hidden_size, elementwise_affine=False, eps=1e-6) + self.edit_attn = SelfAttention(dim=hidden_size, num_heads=num_heads, qkv_bias=qkv_bias) + + self.edit_norm2 = nn.LayerNorm(hidden_size, elementwise_affine=False, eps=1e-6) + self.edit_mlp = nn.Sequential( + nn.Linear(hidden_size, mlp_hidden_dim, bias=True), + nn.GELU(approximate="tanh"), + nn.Linear(mlp_hidden_dim, hidden_size, bias=True), + ) + + def forward(self, x: Tensor, vec: Tensor, + pe: Tensor, mask: Tensor = None, + txt_length=None, + edit_length=None): + if edit_length is not None: + txt, edit, img = x[:, :txt_length], x[:, txt_length:txt_length + edit_length], x[:, txt_length + edit_length:] + else: + txt, img = x[:, :txt_length], x[:, txt_length:] + img_mod1, img_mod2 = self.img_mod(vec) + txt_mod1, txt_mod2 = self.txt_mod(vec) + # prepare image for attention + img_modulated = self.img_norm1(img) + img_modulated = (1 + img_mod1.scale) * img_modulated + img_mod1.shift + img_qkv = self.img_attn.qkv(img_modulated) + img_q, img_k, img_v = rearrange(img_qkv, "B L (K H D) -> K B H L D", K=3, H=self.num_heads) + img_q, img_k = self.img_attn.norm(img_q, img_k, img_v) + # prepare txt for attention + txt_modulated = self.txt_norm1(txt) + txt_modulated = (1 + txt_mod1.scale) * txt_modulated + txt_mod1.shift + txt_qkv = self.txt_attn.qkv(txt_modulated) + txt_q, txt_k, txt_v = rearrange(txt_qkv, "B L (K H D) -> K B H L D", K=3, H=self.num_heads) + txt_q, txt_k = self.txt_attn.norm(txt_q, txt_k, txt_v) + + if edit_length is not None: + edit_mod1, edit_mod2 = self.edit_mod(vec) + # prepare edit for attention + edit_modulated = self.edit_norm1(edit) + edit_modulated = (1 + edit_mod1.scale) * edit_modulated + edit_mod1.shift + edit_qkv = self.edit_attn.qkv(edit_modulated) + edit_q, edit_k, edit_v = rearrange(edit_qkv, "B L (K H D) -> K B H L D", K=3, H=self.num_heads) + edit_q, edit_k = self.edit_attn.norm(edit_q, edit_k, edit_v) + else: + edit_q, edit_k, edit_v = None, None, None + + + # run actual attention + q = torch.cat((txt_q,) + ((edit_q,) if edit_q is not None else ()) + (img_q,), dim=2) + k = torch.cat((txt_k,) + ((edit_k,) if edit_k is not None else ()) + (img_k,), dim=2) + v = torch.cat((txt_v,) + ((edit_v,) if edit_v is not None else ()) + (img_v,), dim=2) + if mask is not None: + mask = repeat(mask, 'B L S-> B H L S', H=self.num_heads) + attn = attention(q, k, v, pe=pe, mask=mask, backend=self.backend) + if edit_length is not None: + txt_attn, edit_attn, img_attn = attn[:, : txt_length], attn[:, txt_length:txt_length + edit_length ], attn[:, txt_length + edit_length:] + else: + txt_attn, img_attn = attn[:, : txt_length], attn[:, txt_length:] + + # calculate the img bloks + img = img + img_mod1.gate * self.img_attn.proj(img_attn) + img = img + img_mod2.gate * self.img_mlp((1 + img_mod2.scale) * self.img_norm2(img) + img_mod2.shift) + + # calculate the txt bloks + txt = txt + txt_mod1.gate * self.txt_attn.proj(txt_attn) + txt = txt + txt_mod2.gate * self.txt_mlp((1 + txt_mod2.scale) * self.txt_norm2(txt) + txt_mod2.shift) + + # calculate the img bloks + if edit_length is not None: + edit = edit + edit_mod1.gate * self.edit_attn.proj(edit_attn) + edit = edit + edit_mod2.gate * self.edit_mlp((1 + edit_mod2.scale) * self.edit_norm2(edit) + edit_mod2.shift) + x = torch.cat((txt, edit, img), 1) + else: + x = torch.cat((txt, img), 1) + return x + + +class LastLayer(nn.Module): + def __init__(self, hidden_size: int, patch_size: int, out_channels: int): + super().__init__() + self.norm_final = nn.LayerNorm(hidden_size, elementwise_affine=False, eps=1e-6) + self.linear = nn.Linear(hidden_size, patch_size * patch_size * out_channels, bias=True) + self.adaLN_modulation = nn.Sequential(nn.SiLU(), nn.Linear(hidden_size, 2 * hidden_size, bias=True)) + + def forward(self, x: Tensor, vec: Tensor) -> Tensor: + shift, scale = self.adaLN_modulation(vec).chunk(2, dim=1) + x = (1 + scale[:, None, :]) * self.norm_final(x) + shift[:, None, :] + x = self.linear(x) + return x + + +if __name__ == '__main__': + pe = EmbedND(dim=64, theta=10000, axes_dim=[16, 56, 56]) + + ix_id = torch.zeros(64 // 2, 64 // 2, 3) + ix_id[..., 1] = ix_id[..., 1] + torch.arange(64 // 2)[:, None] + ix_id[..., 2] = ix_id[..., 2] + torch.arange(64 // 2)[None, :] + ix_id = rearrange(ix_id, "h w c -> 1 (h w) c") + pos = torch.cat([ix_id, ix_id], dim = 1) + a = pe(pos) + + b = torch.cat([pe(ix_id), pe(ix_id)], dim = 2) + + print(a - b) \ No newline at end of file diff --git a/ACE_plus/train_config/ace_plus_fft.yaml b/ACE_plus/train_config/ace_plus_fft.yaml new file mode 100644 index 0000000000000000000000000000000000000000..b7f4b67cc03f870a466466b44395907a87ac48c8 --- /dev/null +++ b/ACE_plus/train_config/ace_plus_fft.yaml @@ -0,0 +1,296 @@ +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' + 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: ${ACE_PLUS_FFT_MODEL} + # 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: ${FLUX_FILL_PATH}/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: ${FLUX_FILL_PATH}/text_encoder_2/ + # 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: ${FLUX_FILL_PATH}/tokenizer_2/ + 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: ${FLUX_FILL_PATH}/text_encoder/ + # 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: ${FLUX_FILL_PATH}/tokenizer/ + # 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 + # + SAMPLE_ARGS: + SAMPLE_STEPS: 28 + SAMPLER: flow_euler + SEED: 42 + 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: 5e-5 + 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 + MODIFY_MODE: True + DELIMITER: "#;#" + # 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: 2048 + D: 16 + PIN_MEMORY: True + BATCH_SIZE: 1 + NUM_WORKERS: 0 + SAMPLER: + NAME: LoopSampler + + EVAL_DATA: + NAME: ACEPlusDataset + MODE: eval + DATA_LIST: data/train.csv + MODIFY_MODE: True + DELIMITER: "#;#" + # 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: 2048 + D: 16 + PIN_MEMORY: True + BATCH_SIZE: 1 + NUM_WORKERS: 4 + + TRAIN_HOOKS: + - NAME: ACEBackwardHook + GRADIENT_CLIP: 1.0 + PRIORITY: 10 + - NAME: LogHook + LOG_INTERVAL: 20 + - NAME: ACECheckpointHook + INTERVAL: 250 + PRIORITY: 200 + + - NAME: ValLossHook + VAL_INTERVAL: 250 + VAL_LIMITATION_SIZE: 1000000 + VAL_SEED: 42 + META_FIELD: [ 'edit_key' ] + PRIORITY: 5 + DATA: + NAME: ACEPlusDataset + MODE: eval + PIN_MEMORY: True + BATCH_SIZE: 1 + USE_NUM: -1 + NUM_WORKERS: 4 + DATA_LIST: data/train.csv + MODIFY_MODE: True + DELIMITER: "#;#" + # 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: 2048 + D: 16 + - NAME: ProbeDataHook + PROB_INTERVAL: 50 + PRIORITY: 0 + - NAME: TensorboardLogHook + LOG_INTERVAL: 50 + EVAL_HOOKS: + - NAME: ProbeDataHook + PROB_INTERVAL: 50 + PRIORITY: 0 \ No newline at end of file diff --git a/ACE_plus/train_config/ace_plus_fft_lora.yaml b/ACE_plus/train_config/ace_plus_fft_lora.yaml new file mode 100644 index 0000000000000000000000000000000000000000..9c8f141686d91b64513fe45a530c356a9f311e72 --- /dev/null +++ b/ACE_plus/train_config/ace_plus_fft_lora.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 -Vijay + D: 16 + PIN_MEMORY: True + BATCH_SIZE: 1 + NUM_WORKERS: 4 + + TRAIN_HOOKS: + - NAME: ACEBackwardHook + GRADIENT_CLIP: 1.0 + PRIORITY: 10 + - NAME: LogHook + LOG_INTERVAL: 20 + - NAME: ACECheckpointHook + INTERVAL: 50 + #INTERVAL: 250 --Vijay + PRIORITY: 200 + DISABLE_SNAPSHOT: True + - NAME: ProbeDataHook + PROB_INTERVAL: 10 + #PROB_INTERVAL: 50 -Vijay + PRIORITY: 0 + - NAME: TensorboardLogHook + LOG_INTERVAL: 50 + EVAL_HOOKS: + - NAME: ProbeDataHook + PROB_INTERVAL: 10 + #PROB_INTERVAL: 50 -Vijay + PRIORITY: 0 diff --git a/ACE_plus/train_config/ace_plus_lora.yaml b/ACE_plus/train_config/ace_plus_lora.yaml new file mode 100644 index 0000000000000000000000000000000000000000..f78b12c9aaf2143ec8f710ffc26b49ac50e4c988 --- /dev/null +++ b/ACE_plus/train_config/ace_plus_lora.yaml @@ -0,0 +1,282 @@ +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: FluxMRACEPlus + 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: 384 + # 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: [ 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: "#;#" + # 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: 2048 + 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: "#;#" + # 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: 2048 + D: 16 + PIN_MEMORY: True + BATCH_SIZE: 1 + NUM_WORKERS: 4 + + TRAIN_HOOKS: + - NAME: ACEBackwardHook + GRADIENT_CLIP: 1.0 + PRIORITY: 10 + - NAME: LogHook + LOG_INTERVAL: 20 + - NAME: ACECheckpointHook + INTERVAL: 10 + # INTERVAL: 250 + PRIORITY: 200 + DISABLE_SNAPSHOT: True + - NAME: ProbeDataHook + PROB_INTERVAL: 50 + PRIORITY: 0 + - NAME: TensorboardLogHook + LOG_INTERVAL: 50 + EVAL_HOOKS: + - NAME: ProbeDataHook + PROB_INTERVAL: 10 + #PROB_INTERVAL: 50 + PRIORITY: 0 diff --git a/ACE_plus/workflow/ComfyUI-ACE_Plus/__init__.py b/ACE_plus/workflow/ComfyUI-ACE_Plus/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..bc8e8363dc2fc7ca3cbd9b4ad25c759ddb5c90c4 --- /dev/null +++ b/ACE_plus/workflow/ComfyUI-ACE_Plus/__init__.py @@ -0,0 +1,17 @@ +# -*- coding: utf-8 -*- +# Copyright (c) Alibaba, Inc. and its affiliates. +from .ace_plus_fft_node import (ACEPlusFFTLoader, ACEPlusFFTConditioning, ACEPlusLoraConditioning, + AcePlusFFTProcessor, AcePlusLoraProcessor) + +NODE_MAPPINGS = { + 'ACEPlusLoader': ('ACEPlusFFTLoader~', ACEPlusFFTLoader), + 'ACEPlusConditioning': ('ACEPlusFFTConditioning~', ACEPlusFFTConditioning), + 'ACEPlusFFTProcessor': ('ACEPlusFFTProcessor~', AcePlusFFTProcessor), + 'ACEPlusLoraProcessor': ('ACEPlusLoraProcessor~', AcePlusLoraProcessor), + 'ACEPlusLoraConditioning': ('ACEPlusLoraConditioning~', ACEPlusLoraConditioning) +} + +NODE_CLASS_MAPPINGS = {k: v[1] for k, v in NODE_MAPPINGS.items()} +NODE_DISPLAY_NAME_MAPPINGS = {k: v[0] for k, v in NODE_MAPPINGS.items()} + +__all__ = ['NODE_CLASS_MAPPINGS', 'NODE_DISPLAY_NAME_MAPPINGS'] diff --git a/ACE_plus/workflow/ComfyUI-ACE_Plus/ace_plus_fft_node.py b/ACE_plus/workflow/ComfyUI-ACE_Plus/ace_plus_fft_node.py new file mode 100644 index 0000000000000000000000000000000000000000..1f40e3339123325cddd6f9020b298d0bcaa4a45f --- /dev/null +++ b/ACE_plus/workflow/ComfyUI-ACE_Plus/ace_plus_fft_node.py @@ -0,0 +1,585 @@ +# -*- coding: utf-8 -*- +# Copyright (c) Alibaba, Inc. and its affiliates. +import folder_paths, os +from comfy.supported_models import FluxInpaint, models +from nodes import UNETLoader +try: + from scepter.modules.utils.file_system import FS + from scepter.modules.annotator.registry import ANNOTATORS + from scepter.modules.utils.config import Config + + fs_list = [ + Config(cfg_dict={"NAME": "HuggingfaceFs", "TEMP_DIR": "./"}, load=False), + Config(cfg_dict={"NAME": "ModelscopeFs", "TEMP_DIR": "./"}, load=False), + Config(cfg_dict={"NAME": "HttpFs", "TEMP_DIR": "./"}, load=False), + Config(cfg_dict={"NAME": "LocalFs", "TEMP_DIR": "./"}, load=False) + ] + + for one_fs in fs_list: + FS.init_fs_client(one_fs) + SCEPTER = True +except: + SCEPTER = False + +class ACEPlus(FluxInpaint): + unet_config = { + "image_model": "flux", + "guidance_embed": True, + "in_channels": 112, + } + + +class ACEPlusFFTLoader(UNETLoader): + + def __init__(self): + pass + + @classmethod + def INPUT_TYPES(s): + return {"required": {"unet_name": (folder_paths.get_filename_list("diffusion_models"), ), + "weight_dtype": (["default", "fp8_e4m3fn", "fp8_e4m3fn_fast", "fp8_e5m2"],) + }} + RETURN_TYPES = ("MODEL",) + FUNCTION = "load_unet" + CATEGORY = "ComfyUI-ACE_Plus" + + def load_unet(self, unet_name, weight_dtype): + models.append(ACEPlus) + return super().load_unet(unet_name, weight_dtype) + + +import torch +import node_helpers + + +class ACEPlusFFTConditioning: + + def __init__(self): + pass + + @classmethod + def INPUT_TYPES(s): + return {"required": {"positive": ("CONDITIONING", ), + "negative": ("CONDITIONING", ), + "vae": ("VAE", ), + "ucpixels": ("IMAGE", ), + "cpixels": ("IMAGE", ), + "mask": ("MASK", ), + "noise_mask": ("BOOLEAN", {"default": True, "tooltip": "Add a noise mask to the latent " + "so sampling will only happen " + "within the mask. Might improve " + "results or completely break " + "things depending on the model."}), + }} + + RETURN_TYPES = ("CONDITIONING", "CONDITIONING", "LATENT") + RETURN_NAMES = ("positive", "negative", "latent") + FUNCTION = "encode" + + CATEGORY = "ComfyUI-ACE_Plus" + + def encode(self, + positive, + negative, + vae, + ucpixels, + cpixels, + mask, + noise_mask=True): + x = (ucpixels.shape[1] // 8) * 8 + y = (ucpixels.shape[2] // 8) * 8 + mask = torch.nn.functional.interpolate(mask.reshape((-1, 1, mask.shape[-2], mask.shape[-1])), + size=(ucpixels.shape[1], ucpixels.shape[2]), mode="bilinear") + + orig_pixels = ucpixels + pixels = orig_pixels.clone() + if pixels.shape[1] != x or pixels.shape[2] != y: + x_offset = (pixels.shape[1] % 8) // 2 + y_offset = (pixels.shape[2] % 8) // 2 + pixels = pixels[:, x_offset:x + x_offset, y_offset:y + y_offset, :] + mask = mask[:, :, x_offset:x + x_offset, y_offset:y + y_offset] + + orig_c_pixels = cpixels + c_pixels = orig_c_pixels.clone() + if orig_c_pixels.shape[1] != x or orig_c_pixels.shape[2] != y: + x_offset = (orig_c_pixels.shape[1] % 8) // 2 + y_offset = (orig_c_pixels.shape[2] % 8) // 2 + c_pixels = orig_c_pixels[:, x_offset:x + x_offset, y_offset:y + y_offset, :] + + concat_latent = vae.encode(pixels) + orig_latent = vae.encode(orig_pixels) + c_concat_latent = vae.encode(c_pixels) + + out_latent = {"samples": orig_latent} + if noise_mask: + out_latent["noise_mask"] = mask + + out = [] + for conditioning in [positive, negative]: + c = node_helpers.conditioning_set_values(conditioning, { + "concat_latent_image": torch.cat([concat_latent, c_concat_latent], dim=1), + "concat_mask": mask}) + out.append(c) + + return (out[0], out[1], out_latent) + +class ACEPlusLoraConditioning: + + @classmethod + def INPUT_TYPES(s): + return {"required": {"positive": ("CONDITIONING",), + "negative": ("CONDITIONING",), + "vae": ("VAE",), + "pixels": ("IMAGE",), + "mask": ("MASK",), + "noise_mask": ("BOOLEAN", {"default": True, + "tooltip": "Add a noise mask to the latent so sampling will only happen within the mask. Might improve results or completely break things depending on the model."}), + }} + + RETURN_TYPES = ("CONDITIONING", "CONDITIONING", "LATENT") + RETURN_NAMES = ("positive", "negative", "latent") + FUNCTION = "encode" + + CATEGORY = "ComfyUI-ACE_Plus" + + def encode(self, positive, negative, pixels, vae, mask, noise_mask=True): + x = (pixels.shape[1] // 8) * 8 + y = (pixels.shape[2] // 8) * 8 + mask = torch.nn.functional.interpolate(mask.reshape((-1, 1, mask.shape[-2], mask.shape[-1])), + size=(pixels.shape[1], pixels.shape[2]), mode="bilinear") + + orig_pixels = pixels + pixels = orig_pixels.clone() + if pixels.shape[1] != x or pixels.shape[2] != y: + x_offset = (pixels.shape[1] % 8) // 2 + y_offset = (pixels.shape[2] % 8) // 2 + pixels = pixels[:, x_offset:x + x_offset, y_offset:y + y_offset, :] + mask = mask[:, :, x_offset:x + x_offset, y_offset:y + y_offset] + + concat_latent = vae.encode(pixels) + orig_latent = vae.encode(orig_pixels) + + out_latent = {} + + out_latent["samples"] = orig_latent + if noise_mask: + out_latent["noise_mask"] = mask + + out = [] + for conditioning in [positive, negative]: + c = node_helpers.conditioning_set_values(conditioning, {"concat_latent_image": concat_latent, + "concat_mask": mask}) + out.append(c) + return (out[0], out[1], out_latent) + + +import torch +import math +import os +import yaml +import torchvision.transforms as T +import numpy as np +from PIL import Image + + +class AcePlusFFTProcessor: + def __init__(self, + max_aspect_ratio=4, + d=16, + max_seq_len=1024): + self.max_aspect_ratio = max_aspect_ratio + self.max_seq_len = max_seq_len + self.d = d + current_dir = os.path.dirname(os.path.abspath(__file__)) + config_path = os.path.join(current_dir, 'config', 'ace_plus_fft_processor.yaml') + self.processor_cfg = self.load_yaml(config_path) + self.task_list = {} + for task in self.processor_cfg['PREPROCESSOR']: + self.task_list[task['TYPE']] = task + self.transforms = T.Compose([ + T.ToTensor(), + T.Normalize(mean=[0, 0, 0], std=[1.0, 1.0, 1.0]) + ]) + + CATEGORY = 'ComfyUI-ACE_Plus' + + @classmethod + def INPUT_TYPES(s): + return { + 'required': { + 'use_reference': ('BOOLEAN', {'default': True}), + 'height': ('INT', { + 'default': 1024, + 'min': 256, + 'max': 1436, + 'step': 16 + }), + 'width': ('INT', { + 'default': 1024, + 'min': 256, + 'max': 1436, + 'step': 16 + }), + 'task_type': (list(s().task_list.keys()),), + 'keep_pixels_rate': ('FLOAT', { + 'default': 0.8, + 'min': 0, + 'max': 1, + 'step': 0.01 + }), + 'max_seq_length': ('INT', { + 'default': 3072, + 'min': 1024, + 'max': 5120, + 'step': 0.01 + }), + }, + 'optional': { + 'reference_image': ('IMAGE',), + 'edit_image': ('IMAGE',), + 'edit_mask': ('MASK',), + + } + } + + OUTPUT_NODE = True + RETURN_TYPES = ('IMAGE', 'IMAGE', 'MASK', 'INT', 'INT', 'INT') + RETURN_NAMES = ('UC_IMAGE', 'C_IMAGE', 'MASK', 'OUT_H', 'OUT_W', 'SLICE_W') + FUNCTION = 'preprocess' + + def load_yaml(self, cfg_file): + with open(cfg_file, 'r') as f: + cfg = yaml.load(f.read(), Loader=yaml.SafeLoader) + return cfg + + def image_check(self, image): + if image is None: + return image + # preprocess + H, W = image.shape[1: 3] + image = image.permute(0, 3, 1, 2) + if H / W > self.max_aspect_ratio: + image[0] = T.CenterCrop([int(self.max_aspect_ratio * W), W])(image[0]) + elif W / H > self.max_aspect_ratio: + image[0] = T.CenterCrop([H, int(self.max_aspect_ratio * H)])(image[0]) + return image[0] + + def trans_pil_tensor(self, pil_image): + transform = T.Compose([ + T.ToTensor() + ]) + tensor_image = transform(pil_image) + return tensor_image + + def edit_preprocess(self, processor, device, edit_image, edit_mask): + + if edit_image is None or processor is None: + return edit_image + if not SCEPTER: + raise ImportError(f'Please install scepter to use edit processor {processor} by ' + f'runing "pip install scepter" in the conda env') + processor = Config(cfg_dict=processor, load=False) + processor = ANNOTATORS.build(processor).to(device) + edit_image = Image.fromarray(np.array(edit_image[0] * 255).astype(np.uint8)).convert('RGB') + new_edit_image = processor(np.asarray(edit_image)) + + del processor + new_edit_image = Image.fromarray(new_edit_image) + if edit_mask is not None: + edit_mask = np.where(edit_mask > 0.5, 1, 0) * 255 + edit_mask = Image.fromarray(np.array(edit_mask[0]).astype(np.uint8)).convert('L') + + if new_edit_image.size != edit_image.size: + edit_image = T.Resize((edit_image.size[1], edit_image.size[0]), + interpolation=T.InterpolationMode.BILINEAR, + antialias=True)(new_edit_image) + + image = Image.composite(new_edit_image, edit_image, edit_mask) + + return self.trans_pil_tensor(image).unsqueeze(0).permute(0, 2, 3, 1) + + def preprocess(self, + reference_image=None, + edit_image=None, + edit_mask=None, + use_reference=True, + task_type=None, + height=1024, + width=1024, + keep_pixels_rate=0.8, + max_seq_length=4096): + self.max_seq_len = max_seq_length + if not use_reference and edit_image is not None: + reference_image = None + if edit_mask is not None and edit_image is not None: + iH, iW = edit_image.shape[1:3] + mH, mW = edit_mask.shape[1:3] + if iH != mH or iW != mW: + edit_mask = torch.ones(edit_image.shape[:3]) + + if task_type != 'repainting': + repainting_scale = 0 + else: + repainting_scale = 1 + if task_type in self.task_list: + edit_image = self.edit_preprocess(self.task_list[task_type]['ANNOTATOR'], 0, + edit_image, edit_mask) + if reference_image is not None: + reference_image = self.image_check(reference_image) - 0.5 + if edit_image is not None: + edit_image = self.image_check(edit_image) - 0.5 + # for reference generation + if edit_image is None: + edit_image = torch.zeros([3, height, width]) + edit_mask = torch.ones([1, height, width]) + else: + if edit_mask is None: + _, eH, eW = edit_image.shape + edit_mask = np.ones((eH, eW)) + else: + edit_mask = np.asarray(edit_mask)[0] + edit_mask = np.where(edit_mask > 0.5, 1, 0) + edit_mask = edit_mask.astype( + np.float32) if np.any(edit_mask) else np.ones_like(edit_mask).astype( + np.float32) + edit_mask = torch.tensor(edit_mask).unsqueeze(0) + + edit_image = edit_image * (1 - edit_mask * repainting_scale) + + out_h, out_w = edit_image.shape[-2:] + + assert edit_mask is not None + if reference_image is not None: + _, H, W = reference_image.shape + _, eH, eW = edit_image.shape + if not True: + # align height with edit_image + scale = eH / H + tH, tW = eH, int(W * scale) + reference_image = T.Resize((tH, tW), interpolation=T.InterpolationMode.BILINEAR, antialias=True)( + reference_image) + else: + # padding + if H >= keep_pixels_rate * eH: + tH = int(eH * keep_pixels_rate) + scale = tH / H + tW = int(W * scale) + reference_image = T.Resize((tH, tW), interpolation=T.InterpolationMode.BILINEAR, antialias=True)( + reference_image) + rH, rW = reference_image.shape[-2:] + delta_w = 0 + delta_h = eH - rH + padding = (delta_w // 2, delta_h // 2, delta_w - (delta_w // 2), delta_h - (delta_h // 2)) + reference_image = T.Pad(padding, fill=0, padding_mode="constant")(reference_image) + edit_image = torch.cat([reference_image, edit_image], dim=-1) + edit_mask = torch.cat([torch.zeros([1, reference_image.shape[1], reference_image.shape[2]]), edit_mask], + dim=-1) + slice_w = reference_image.shape[-1] + else: + slice_w = 0 + + H, W = edit_image.shape[-2:] + scale = min(1.0, math.sqrt(self.max_seq_len / ((H / self.d) * (W / self.d)))) + rH = int(H * scale) // self.d * self.d + rW = int(W * scale) // self.d * self.d + slice_w = int(slice_w * scale) // self.d * self.d + + edit_image = T.Resize((rH, rW), interpolation=T.InterpolationMode.NEAREST_EXACT, antialias=True)(edit_image) + edit_mask = T.Resize((rH, rW), interpolation=T.InterpolationMode.NEAREST_EXACT, antialias=True)(edit_mask) + + change_image = edit_image * edit_mask + edit_image = edit_image * (1 - edit_mask) + edit_image = edit_image.unsqueeze(0).permute(0, 2, 3, 1) + change_image = change_image.unsqueeze(0).permute(0, 2, 3, 1) + slice_w = slice_w if slice_w > 30 else slice_w + 30 + + return edit_image + 0.5, change_image + 0.5, edit_mask, out_h, out_w, slice_w + +class AcePlusLoraProcessor: + def __init__(self, + max_aspect_ratio=4, + d=16, + max_seq_len=1024): + self.max_aspect_ratio = max_aspect_ratio + self.max_seq_len = max_seq_len + self.d = d + current_dir = os.path.dirname(os.path.abspath(__file__)) + config_path = os.path.join(current_dir, 'config', 'ace_plus_fft_processor.yaml') + self.processor_cfg = self.load_yaml(config_path) + self.task_list = {} + for task in self.processor_cfg['PREPROCESSOR']: + self.task_list[task['TYPE']] = task + self.transforms = T.Compose([ + T.ToTensor(), + T.Normalize(mean=[0, 0, 0], std=[1.0, 1.0, 1.0]) + ]) + + CATEGORY = 'ComfyUI-ACE_Plus' + + @classmethod + def INPUT_TYPES(s): + return { + 'required': { + 'use_reference': ('BOOLEAN', {'default': True}), + 'height': ('INT', { + 'default': 1024, + 'min': 256, + 'max': 1436, + 'step': 16 + }), + 'width': ('INT', { + 'default': 1024, + 'min': 256, + 'max': 1436, + 'step': 16 + }), + 'task_type': (list(s().task_list.keys()),), + 'max_seq_length': ('INT', { + 'default': 3072, + 'min': 1024, + 'max': 5120, + 'step': 0.01 + }), + }, + 'optional': { + 'reference_image': ('IMAGE',), + 'edit_image': ('IMAGE',), + 'edit_mask': ('MASK',), + + } + } + + OUTPUT_NODE = True + RETURN_TYPES = ('IMAGE', 'MASK', 'INT', 'INT', 'INT') + RETURN_NAMES = ('IMAGE', 'MASK', 'OUT_H', 'OUT_W', 'SLICE_W') + FUNCTION = 'preprocess' + + def load_yaml(self, cfg_file): + with open(cfg_file, 'r') as f: + cfg = yaml.load(f.read(), Loader=yaml.SafeLoader) + return cfg + + def image_check(self, image): + if image is None: + return image + # preprocess + H, W = image.shape[1: 3] + image = image.permute(0, 3, 1, 2) + if H / W > self.max_aspect_ratio: + image[0] = T.CenterCrop([int(self.max_aspect_ratio * W), W])(image[0]) + elif W / H > self.max_aspect_ratio: + image[0] = T.CenterCrop([H, int(self.max_aspect_ratio * H)])(image[0]) + return image[0] + + def trans_pil_tensor(self, pil_image): + transform = T.Compose([ + T.ToTensor() + ]) + tensor_image = transform(pil_image) + return tensor_image + + def edit_preprocess(self, processor, device, edit_image, edit_mask): + + if edit_image is None or processor is None: + return edit_image + if not SCEPTER: + raise ImportError(f'Please install scepter to use edit processor {processor} by ' + f'runing "pip install scepter" in the conda env') + processor = Config(cfg_dict=processor, load=False) + processor = ANNOTATORS.build(processor).to(device) + edit_image = Image.fromarray(np.array(edit_image[0] * 255).astype(np.uint8)).convert('RGB') + new_edit_image = processor(np.asarray(edit_image)) + + del processor + new_edit_image = Image.fromarray(new_edit_image) + if edit_mask is not None: + edit_mask = np.where(edit_mask > 0.5, 1, 0) * 255 + edit_mask = Image.fromarray(np.array(edit_mask[0]).astype(np.uint8)).convert('L') + + if new_edit_image.size != edit_image.size: + new_edit_image = T.Resize((edit_image.size[1], edit_image.size[0]), + interpolation=T.InterpolationMode.BILINEAR, + antialias=True)(new_edit_image) + + image = Image.composite(new_edit_image, edit_image, edit_mask) + + return self.trans_pil_tensor(image).unsqueeze(0).permute(0, 2, 3, 1) + + def preprocess(self, + reference_image=None, + edit_image=None, + edit_mask=None, + use_reference=True, + task_type=None, + height=1024, + width=1024, + max_seq_length=4096): + self.max_seq_len = max_seq_length + if not use_reference and edit_image is not None: + reference_image = None + if edit_mask is not None and edit_image is not None: + iH, iW = edit_image.shape[1:3] + mH, mW = edit_mask.shape[1:3] + if iH != mH or iW != mW: + edit_mask = torch.ones(edit_image.shape[:3]) + + if task_type != 'repainting': + repainting_scale = 0.0 + else: + repainting_scale = 1.0 + if task_type in self.task_list: + edit_image = self.edit_preprocess(self.task_list[task_type]['ANNOTATOR'], 0, + edit_image, edit_mask) + if reference_image is not None: + reference_image = self.image_check(reference_image) - 0.5 + if edit_image is not None: + edit_image = self.image_check(edit_image) - 0.5 + # for reference generation + if edit_image is None: + edit_image = torch.zeros([3, height, width]) + edit_mask = torch.ones([1, height, width]) + else: + if edit_mask is None: + _, eH, eW = edit_image.shape + edit_mask = np.ones((eH, eW)) + else: + edit_mask = np.asarray(edit_mask)[0] + edit_mask = np.where(edit_mask > 0.5, 1, 0) + edit_mask = edit_mask.astype( + np.float32) if np.any(edit_mask) else np.ones_like(edit_mask).astype( + np.float32) + edit_mask = torch.tensor(edit_mask).unsqueeze(0) + + edit_image = edit_image * (1 - edit_mask * repainting_scale) + + out_h, out_w = edit_image.shape[-2:] + + assert edit_mask is not None + if reference_image is not None: + _, H, W = reference_image.shape + _, eH, eW = edit_image.shape + # align height with edit_image + scale = eH / H + tH, tW = eH, int(W * scale) + reference_image = T.Resize((tH, tW), interpolation=T.InterpolationMode.BILINEAR, antialias=True)( + reference_image) + edit_image = torch.cat([reference_image, edit_image], dim=-1) + edit_mask = torch.cat([torch.zeros([1, reference_image.shape[1], reference_image.shape[2]]), edit_mask], + dim=-1) + slice_w = reference_image.shape[-1] + else: + slice_w = 0 + + H, W = edit_image.shape[-2:] + scale = min(1.0, math.sqrt(self.max_seq_len * 2 / ((H / self.d) * (W / self.d)))) + rH = int(H * scale) // self.d * self.d + rW = int(W * scale) // self.d * self.d + slice_w = int(slice_w * scale) // self.d * self.d + + edit_image = T.Resize((rH, rW), interpolation=T.InterpolationMode.NEAREST_EXACT, antialias=True)(edit_image) + edit_mask = T.Resize((rH, rW), interpolation=T.InterpolationMode.NEAREST_EXACT, antialias=True)(edit_mask) + + edit_image = edit_image.unsqueeze(0).permute(0, 2, 3, 1) + slice_w = slice_w if slice_w < 30 else slice_w + 30 + + return edit_image + 0.5, edit_mask, out_h, out_w, slice_w diff --git a/ACE_plus/workflow/ComfyUI-ACE_Plus/config/ace_plus_fft_processor.yaml b/ACE_plus/workflow/ComfyUI-ACE_Plus/config/ace_plus_fft_processor.yaml new file mode 100644 index 0000000000000000000000000000000000000000..5ed1e099a2ccf2ebdf85b8561ff56f3461c99ee2 --- /dev/null +++ b/ACE_plus/workflow/ComfyUI-ACE_Plus/config/ace_plus_fft_processor.yaml @@ -0,0 +1,25 @@ +PREPROCESSOR: + - TYPE: repainting + REPAINTING_SCALE: 1.0 + ANNOTATOR: + - TYPE: no_preprocess + REPAINTING_SCALE: 0.0 + ANNOTATOR: + - TYPE: contour_repainting + REPAINTING_SCALE: 0.0 + ANNOTATOR: + NAME: InfoDrawContourAnnotator + INPUT_NC: 3 + OUTPUT_NC: 1 + N_RESIDUAL_BLOCKS: 3 + SIGMOID: True + PRETRAINED_MODEL: "ms://iic/scepter_annotator@annotator/ckpts/informative_drawing_contour_style.pth" + - TYPE: depth_repainting + REPAINTING_SCALE: 0.0 + ANNOTATOR: + NAME: MidasDetector + PRETRAINED_MODEL: "ms://iic/scepter_annotator@annotator/ckpts/dpt_hybrid-midas-501f0c75.pt" + - TYPE: recolorizing 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