MCPcopy Create free account

hub / github.com/JaydenLyh/Reward-Forcing / types & classes

Types & classes123 in github.com/JaydenLyh/Reward-Forcing

↓ 23 callersClassCausalConv3d
Causal 3d convolusion.
wan/modules/vae.py:17
↓ 18 callersClassMemBlock
demo_utils/taehv.py:25
↓ 14 callersClassWanDiffusionWrapper
utils/wan_wrapper.py:116
↓ 10 callersClassWanLayerNorm
wan/modules/model.py:89
↓ 9 callersClassRMS_norm
wan/modules/vae.py:39
↓ 9 callersClassWanTextEncoder
utils/wan_wrapper.py:14
↓ 8 callersClassAttentionBlock
Causal self-attention with a single head.
wan/modules/vae.py:223
↓ 8 callersClassWanVAEWrapper
utils/wan_wrapper.py:54
↓ 7 callersClassEMA_FSDP
utils/distributed.py:91
↓ 7 callersClassT5LayerNorm
wan/modules/t5.py:53
↓ 6 callersClassResidualBlock
wan/modules/vae.py:186
↓ 6 callersClassResidualBlock
demo_utils/vae.py:13
↓ 6 callersClassUpsample
wan/modules/vae.py:57
↓ 6 callersClassWanRMSNorm
wan/modules/model.py:70
↓ 5 callersClassLayerNorm
wan/modules/clip.py:47
↓ 5 callersClassPromptOutput
wan/utils/prompt_extend.py:101
↓ 5 callersClassShardingLMDBDataset
utils/dataset.py:72
↓ 4 callersClassFlowDPMSolverMultistepScheduler
`FlowDPMSolverMultistepScheduler` is a fast dedicated high-order solver for diffusion ODEs. This model inherits from [`SchedulerMixin`] and [
wan/utils/fm_solvers.py:69
↓ 4 callersClassFlowUniPCMultistepScheduler
`UniPCMultistepScheduler` is a training-free framework designed for the fast sampling of diffusion models. This model inherits from [`Schedu
wan/utils/fm_solvers_unipc.py:20
↓ 4 callersClassHuggingfaceTokenizer
wan/modules/tokenizers.py:37
↓ 4 callersClassT5RelativeEmbedding
wan/modules/t5.py:221
↓ 4 callersClassTextDataset
utils/dataset.py:12
↓ 3 callersClassT5Attention
wan/modules/t5.py:69
↓ 3 callersClassTGrow
demo_utils/taehv.py:48
↓ 3 callersClassTPool
demo_utils/taehv.py:37
↓ 2 callersClassCausVid
model/causvid.py:8
↓ 2 callersClassDashScopePromptExpander
wan/utils/prompt_extend.py:157
↓ 2 callersClassDataConfig
videoalign/data.py:16
↓ 2 callersClassFlowMatchScheduler
utils/scheduler.py:106
↓ 2 callersClassMLPProj
wan/modules/model.py:469
↓ 2 callersClassModelConfig
videoalign/utils.py:55
↓ 2 callersClassPEFTLoraConfig
videoalign/utils.py:34
↓ 2 callersClassQuickGELU
wan/modules/clip.py:41
↓ 2 callersClassQwenPromptExpander
wan/utils/prompt_extend.py:300
↓ 2 callersClassResample
wan/modules/vae.py:66
↓ 2 callersClassSelfForcingTrainingPipeline
pipeline/self_forcing_training.py:8
↓ 2 callersClassSiD
model/sid.py:8
↓ 2 callersClassT5EncoderModel
wan/modules/t5.py:472
↓ 2 callersClassT5FeedForward
wan/modules/t5.py:123
↓ 2 callersClassTrainingConfig
videoalign/utils.py:13
↓ 2 callersClassVideoVLMRewardInference
videoalign/wan_inference.py:33
↓ 2 callersClassWanVAE
wan/modules/vae.py:639
↓ 1 callersClassAttentionBlock
wan/modules/clip.py:112
↓ 1 callersClassAttentionBlock
wan/modules/xlm_roberta.py:49
↓ 1 callersClassAttentionPool
wan/modules/clip.py:156
↓ 1 callersClassCLIPModel
wan/modules/clip.py:501
↓ 1 callersClassCausalDiffusion
model/diffusion.py:8
↓ 1 callersClassCausalDiffusionInferencePipeline
pipeline/causal_diffusion_inference.py:10
↓ 1 callersClassCausalHead
wan/modules/causal_model.py:358
↓ 1 callersClassCausalInferencePipeline
pipeline/causal_inference.py:9
↓ 1 callersClassCausalWanAttentionBlock
wan/modules/causal_model.py:263
↓ 1 callersClassCausalWanSelfAttention
wan/modules/causal_model.py:59
↓ 1 callersClassClamp
demo_utils/taehv.py:20
↓ 1 callersClassDMD
model/dmd.py:9
↓ 1 callersClassDecoder3d
wan/modules/vae.py:369
↓ 1 callersClassEncoder3d
wan/modules/vae.py:265
↓ 1 callersClassGAN
model/gan.py:10
↓ 1 callersClassGELU
wan/modules/t5.py:46
↓ 1 callersClassGanAttentionBlock
wan/modules/model.py:357
↓ 1 callersClassHead
wan/modules/model.py:439
↓ 1 callersClassODERegression
model/ode_regression.py:9
↓ 1 callersClassODERegressionLMDBDataset
utils/dataset.py:37
↓ 1 callersClassPartialEmbeddingUpdateCallback
Callback to update the embedding of special tokens Only the special tokens are updated, the rest of the embeddings are kept fixed
videoalign/trainer.py:219
↓ 1 callersClassQWen2VLDataCollator
videoalign/data.py:131
↓ 1 callersClassReDMD
model/re_dmd.py:8
↓ 1 callersClassRegisterTokens
wan/modules/model.py:484
↓ 1 callersClassResample
demo_utils/vae_block3.py:9
↓ 1 callersClassResample
demo_utils/vae.py:51
↓ 1 callersClassSelfAttention
wan/modules/clip.py:53
↓ 1 callersClassSelfAttention
wan/modules/xlm_roberta.py:10
↓ 1 callersClassSwiGLU
wan/modules/clip.py:94
↓ 1 callersClassT5CrossAttention
wan/modules/t5.py:178
↓ 1 callersClassT5Decoder
wan/modules/t5.py:315
↓ 1 callersClassT5Encoder
wan/modules/t5.py:267
↓ 1 callersClassT5SelfAttention
wan/modules/t5.py:144
↓ 1 callersClassTAEHV
demo_utils/taehv.py:159
↓ 1 callersClassTextImagePairDataset
utils/dataset.py:127
↓ 1 callersClassVAECalibrator
demo_utils/vae_torch2trt.py:139
↓ 1 callersClassVAEDecoder3d
demo_utils/vae_block3.py:187
↓ 1 callersClassVAEDecoder3d
demo_utils/vae.py:199
↓ 1 callersClassVAEDecoderWrapperSingle
demo_utils/vae.py:151
↓ 1 callersClassVideoTensorReader
demo_utils/taehv.py:247
↓ 1 callersClassVideoTensorWriter
demo_utils/taehv.py:263
↓ 1 callersClassVideoVLMRewardTrainer
videoalign/trainer.py:245
↓ 1 callersClassVisionTransformer
wan/modules/clip.py:209
↓ 1 callersClassWanAttentionBlock
wan/modules/model.py:275
↓ 1 callersClassWanGanCrossAttention
wan/modules/model.py:197
↓ 1 callersClassWanSelfAttention
wan/modules/model.py:102
↓ 1 callersClassWanVAE_
wan/modules/vae.py:483
↓ 1 callersClassXLMRoberta
XLMRobertaModel with no pooler and no LM head.
wan/modules/xlm_roberta.py:76
↓ 1 callersClassXLMRobertaWithHead
wan/modules/clip.py:303
ClassBaseModel
model/base.py:12
ClassBaseRLModel
model/base.py:223
ClassBidirectionalDiffusionInferencePipeline
pipeline/bidirectional_diffusion_inference.py:10
ClassBidirectionalInferencePipeline
pipeline/bidirectional_inference.py:7
ClassCausalWanModel
r""" Wan diffusion backbone supporting both text-to-video and image-to-video.
wan/modules/causal_model.py:389
ClassDenoisingLoss
utils/loss.py:5
ClassDynamicSwapInstaller
demo_utils/memory.py:13
ClassFlowPredLoss
utils/loss.py:61
ClassNoisePredLoss
utils/loss.py:50
next →1–100 of 123, ranked by callers