Buckets:
| # Lapisan normalisasi | |
| Lapisan normalisasi yang dikustomisasi untuk mendukung berbagai model di 🤗 Diffusers. | |
| ## AdaLayerNorm[[diffusers.models.normalization.AdaLayerNorm]] | |
| #### diffusers.models.normalization.AdaLayerNorm[[diffusers.models.normalization.AdaLayerNorm]] | |
| ```python | |
| diffusers.models.normalization.AdaLayerNorm(embedding_dim: int, num_embeddings: int | None = None, output_dim: int | None = None, norm_elementwise_affine: bool = False, norm_eps: float = 1e-05, chunk_dim: int = 0) | |
| ``` | |
| [Sumber](https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/normalization.py#L27) | |
| **Parameter:** | |
| embedding_dim (`int`) : Ukuran setiap vektor embedding. | |
| num_embeddings (`int`, *opsional*) : Ukuran dari kamus embedding. | |
| output_dim (`int`, *opsional*) -- | |
| norm_elementwise_affine (`bool`, defaultnya `False) -- | |
| norm_eps (`bool`, defaultnya `False`) -- | |
| chunk_dim (`int`, defaultnya `0`) -- | |
| Lapisan norm dimodifikasi untuk mengintegrasikan embedding timestep. | |
| ## AdaLayerNormZero[[diffusers.models.normalization.AdaLayerNormZero]] | |
| #### diffusers.models.normalization.AdaLayerNormZero[[diffusers.models.normalization.AdaLayerNormZero]] | |
| ```python | |
| diffusers.models.normalization.AdaLayerNormZero(embedding_dim: int, num_embeddings: int | None = None, norm_type = 'layer_norm', bias = True) | |
| ``` | |
| [Sumber](https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/normalization.py#L130) | |
| **Parameter:** | |
| embedding_dim (`int`) : Ukuran setiap vektor embedding. | |
| num_embeddings (`int`) : Ukuran kamus embeddings. | |
| Norm layer adaptive layer norm zero (adaLN-Zero). | |
| ## AdaLayerNormSingle[[diffusers.models.normalization.AdaLayerNormSingle]] | |
| #### diffusers.models.normalization.AdaLayerNormSingle[[diffusers.models.normalization.AdaLayerNormSingle]] | |
| ```python | |
| diffusers.models.normalization.AdaLayerNormSingle(embedding_dim: int, use_additional_conditions: bool = False) | |
| ``` | |
| [Sumber](https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/normalization.py#L235) | |
| **Parameter:** | |
| embedding_dim (`int`) : Ukuran setiap vektor embedding. | |
| use_additional_conditions (`bool`) : Untuk menggunakan kondisi tambahan untuk normalisasi atau tidak. | |
| Norm layer adaptive layer norm single (adaLN-single). | |
| Seperti yang diusulkan dalam PixArt-Alpha (lihat: https://huggingface.co/papers/2310.00426; Bagian 2.3). | |
| ## AdaGroupNorm[[diffusers.models.normalization.AdaGroupNorm]] | |
| #### diffusers.models.normalization.AdaGroupNorm[[diffusers.models.normalization.AdaGroupNorm]] | |
| ```python | |
| diffusers.models.normalization.AdaGroupNorm(embedding_dim: int, out_dim: int, num_groups: int, act_fn: str | None = None, eps: float = 1e-05) | |
| ``` | |
| [Sumber](https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/normalization.py#L269) | |
| **Parameter:** | |
| embedding_dim (`int`) : Ukuran setiap vektor embedding. | |
| num_embeddings (`int`) : Ukuran kamus embeddings. | |
| num_groups (`int`) : Jumlah grup untuk memisahkan channel. | |
| act_fn (`str`, *opsional*, defaultnya `None`) : Fungsi aktivasi yang akan digunakan. | |
| eps (`float`, *opsional*, defaultnya `1e-5`) : Nilai epsilon yang digunakan untuk stabilitas numerik. | |
| Lapisan GroupNorm yang dimodifikasi untuk mengintegrasikan embedding timestep. | |
| ## AdaLayerNormContinuous[[diffusers.models.normalization.AdaLayerNormContinuous]] | |
| #### diffusers.models.normalization.AdaLayerNormContinuous[[diffusers.models.normalization.AdaLayerNormContinuous]] | |
| ```python | |
| diffusers.models.normalization.AdaLayerNormContinuous(embedding_dim: int, conditioning_embedding_dim: int, elementwise_affine = True, eps = 1e-05, bias = True, norm_type = 'layer_norm') | |
| ``` | |
| [Sumber](https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/normalization.py#L307) | |
| **Parameter:** | |
| embedding_dim (`int`) : Dimensi embedding yang digunakan selama proyeksi. | |
| conditioning_embedding_dim (`int`) : Dimensi dari kondisi input. | |
| elementwise_affine (`bool`, defaults to `True`) : Boolean flag to denote if affine transformation should be applied. | |
| eps (`float`, defaultnya 1e-5) : Faktor epsilon. | |
| bias (`bias`, defaultnya `True`) : Flag Boolean untuk menunjukkan apakah bias harus digunakan. | |
| norm_type (`str`, defaultnya `"layer_norm"`) : Lapisan normalisasi yang akan digunakan. Nilai yang didukung: "layer_norm", "rms_norm". | |
| Lapisan normalisasi adaptif dengan lapisan norm (layer_norm atau rms_norm). | |
| ## RMSNorm[[diffusers.models.normalization.RMSNorm]] | |
| #### diffusers.models.normalization.RMSNorm[[diffusers.models.normalization.RMSNorm]] | |
| ```python | |
| diffusers.models.normalization.RMSNorm(dim, eps: float, elementwise_affine: bool = True, bias: bool = False) | |
| ``` | |
| [Sumber](https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/normalization.py#L472) | |
| **Parameter:** | |
| dim (`int`) : Jumlah dimensi yang digunakan untuk `weights`. Hanya berlaku ketika `elementwise_affine` bernilai True. | |
| eps (`float`) : Nilai kecil yang digunakan saat menghitung kebalikan dari akar kuadrat. | |
| elementwise_affine (`bool`, defaults to `True`) : Boolean flag to denote if affine transformation should be applied. | |
| bias (`bool`, defaultnya False) : Jika juga melatih param `bias`. | |
| RMS Norm yang diperkenalkan dalam https://huggingface.co/papers/1910.07467 oleh Zhang dkk. | |
| ## GlobalResponseNorm[[diffusers.models.normalization.GlobalResponseNorm]] | |
| #### diffusers.models.normalization.GlobalResponseNorm[[diffusers.models.normalization.GlobalResponseNorm]] | |
| ```python | |
| diffusers.models.normalization.GlobalResponseNorm(dim) | |
| ``` | |
| [Sumber](https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/normalization.py#L563) | |
| **Parameter:** | |
| dim (`int`) : Jumlah dimensi yang digunakan untuk `gamma` dan `beta`. | |
| Normalisasi respons global yang diperkenalkan dalam ConvNeXt-v2 (https://huggingface.co/papers/2301.00808). | |
| ## LuminaLayerNormContinuous[[diffusers.models.normalization.LuminaLayerNormContinuous]] | |
| #### diffusers.models.normalization.LuminaLayerNormContinuous[[diffusers.models.normalization.LuminaLayerNormContinuous]] | |
| ```python | |
| diffusers.models.normalization.LuminaLayerNormContinuous(embedding_dim: int, conditioning_embedding_dim: int, elementwise_affine = True, eps = 1e-05, bias = True, norm_type = 'layer_norm', out_dim: int | None = None) | |
| ``` | |
| [Sumber](https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/normalization.py#L354) | |
| ## SD35AdaLayerNormZeroX[[diffusers.models.normalization.SD35AdaLayerNormZeroX]] | |
| #### diffusers.models.normalization.SD35AdaLayerNormZeroX[[diffusers.models.normalization.SD35AdaLayerNormZeroX]] | |
| ```python | |
| diffusers.models.normalization.SD35AdaLayerNormZeroX(embedding_dim: int, norm_type: str = 'layer_norm', bias: bool = True) | |
| ``` | |
| [Sumber](https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/normalization.py#L96) | |
| **Parameter:** | |
| embedding_dim (`int`) : Ukuran setiap vektor embedding. | |
| num_embeddings (`int`) : Ukuran kamus embeddings. | |
| Norm layer adaptive layer norm zero (AdaLN-Zero). | |
| ## AdaLayerNormZeroSingle[[diffusers.models.normalization.AdaLayerNormZeroSingle]] | |
| #### diffusers.models.normalization.AdaLayerNormZeroSingle[[diffusers.models.normalization.AdaLayerNormZeroSingle]] | |
| ```python | |
| diffusers.models.normalization.AdaLayerNormZeroSingle(embedding_dim: int, norm_type = 'layer_norm', bias = True) | |
| ``` | |
| [Sumber](https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/normalization.py#L173) | |
| **Parameter:** | |
| embedding_dim (`int`) : Ukuran setiap vektor embedding. | |
| num_embeddings (`int`) : Ukuran kamus embeddings. | |
| Norm layer adaptive layer norm zero (adaLN-Zero). | |
| ## LuminaRMSNormZero[[diffusers.models.normalization.LuminaRMSNormZero]] | |
| #### diffusers.models.normalization.LuminaRMSNormZero[[diffusers.models.normalization.LuminaRMSNormZero]] | |
| ```python | |
| diffusers.models.normalization.LuminaRMSNormZero(embedding_dim: int, norm_eps: float, norm_elementwise_affine: bool) | |
| ``` | |
| [Sumber](https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/normalization.py#L205) | |
| **Parameter:** | |
| embedding_dim (`int`) : Ukuran setiap vektor embedding. | |
| Norm layer adaptive RMS normalization zero. | |
| ## LpNorm[[diffusers.models.normalization.LpNorm]] | |
| #### diffusers.models.normalization.LpNorm[[diffusers.models.normalization.LpNorm]] | |
| ```python | |
| diffusers.models.normalization.LpNorm(p: int = 2, dim: int = -1, eps: float = 1e-12) | |
| ``` | |
| [Sumber](https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/normalization.py#L583) | |
| ## CogView3PlusAdaLayerNormZeroTextImage[[diffusers.models.normalization.CogView3PlusAdaLayerNormZeroTextImage]] | |
| #### diffusers.models.normalization.CogView3PlusAdaLayerNormZeroTextImage[[diffusers.models.normalization.CogView3PlusAdaLayerNormZeroTextImage]] | |
| ```python | |
| diffusers.models.normalization.CogView3PlusAdaLayerNormZeroTextImage(embedding_dim: int, dim: int) | |
| ``` | |
| [Sumber](https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/normalization.py#L403) | |
| **Parameter:** | |
| embedding_dim (`int`) : Ukuran setiap vektor embedding. | |
| num_embeddings (`int`) : Ukuran kamus embeddings. | |
| Norm layer adaptive layer norm zero (adaLN-Zero). | |
| ## CogVideoXLayerNormZero[[diffusers.models.normalization.CogVideoXLayerNormZero]] | |
| #### diffusers.models.normalization.CogVideoXLayerNormZero[[diffusers.models.normalization.CogVideoXLayerNormZero]] | |
| ```python | |
| diffusers.models.normalization.CogVideoXLayerNormZero(conditioning_dim: int, embedding_dim: int, elementwise_affine: bool = True, eps: float = 1e-05, bias: bool = True) | |
| ``` | |
| [Sumber](https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/normalization.py#L448) | |
| ## MochiRMSNormZero[[diffusers.models.transformers.transformer_mochi.MochiRMSNormZero]] | |
| #### diffusers.models.transformers.transformer_mochi.MochiRMSNormZero[[diffusers.models.transformers.transformer_mochi.MochiRMSNormZero]] | |
| ```python | |
| diffusers.models.transformers.transformer_mochi.MochiRMSNormZero(embedding_dim: int, hidden_dim: int, eps: float = 1e-05, elementwise_affine: bool = False) | |
| ``` | |
| [Sumber](https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/transformers/transformer_mochi.py#L88) | |
| **Parameter:** | |
| embedding_dim (`int`) : Ukuran setiap vektor embedding. | |
| Adaptive RMS Norm yang digunakan dalam Mochi. | |
| ## MochiRMSNorm[[diffusers.models.normalization.MochiRMSNorm]] | |
| #### diffusers.models.normalization.MochiRMSNorm[[diffusers.models.normalization.MochiRMSNorm]] | |
| ```python | |
| diffusers.models.normalization.MochiRMSNorm(dim, eps: float, elementwise_affine: bool = True) | |
| ``` | |
| [Sumber](https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/normalization.py#L535) | |
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