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# StableCascadeUNet

Model UNet dari [pipeline Stable Cascade](../pipelines/stable_cascade).

## StableCascadeUNet[[diffusers.models.StableCascadeUNet]]

#### diffusers.models.StableCascadeUNet[[diffusers.models.StableCascadeUNet]]

```python
diffusers.models.StableCascadeUNet(in_channels: int = 16, out_channels: int = 16, timestep_ratio_embedding_dim: int = 64, patch_size: int = 1, conditioning_dim: int = 2048, block_out_channels: tuple[int, ...] = (2048, 2048), num_attention_heads: tuple[int, ...] = (32, 32), down_num_layers_per_block: tuple[int, ...] = (8, 24), up_num_layers_per_block: tuple[int, ...] = (24, 8), down_blocks_repeat_mappers: tuple[int] | None = (1, 1), up_blocks_repeat_mappers: tuple[int] | None = (1, 1), block_types_per_layer: tuple[tuple[str]] = (('SDCascadeResBlock', 'SDCascadeTimestepBlock', 'SDCascadeAttnBlock'), ('SDCascadeResBlock', 'SDCascadeTimestepBlock', 'SDCascadeAttnBlock')), clip_text_in_channels: int | None = None, clip_text_pooled_in_channels = 1280, clip_image_in_channels: int | None = None, clip_seq = 4, effnet_in_channels: int | None = None, pixel_mapper_in_channels: int | None = None, kernel_size = 3, dropout: float | tuple[float] = (0.1, 0.1), self_attn: bool | tuple[bool] = True, timestep_conditioning_type: tuple[str, ...] = ('sca', 'crp'), switch_level: tuple[bool] | None = None)
```

[Sumber](https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/unets/unet_stable_cascade.py#L136)

#### forward[[diffusers.models.StableCascadeUNet.forward]]

```python
forward(sample, timestep_ratio, clip_text_pooled, clip_text = None, clip_img = None, effnet = None, pixels = None, sca = None, crp = None, return_dict = True)
```

[Sumber](https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/unets/unet_stable_cascade.py#L538)

**Parameter:**

sample (`torch.Tensor`) : Sampel input berisik.

timestep_ratio (`torch.Tensor`) : Rasio timestep yang digunakan untuk menghitung embedding timestep.
clip_text_pooled (`torch.Tensor`) : Embedding teks CLIP yang dipool.
clip_text (`torch.Tensor`, *opsional*) : Embedding teks CLIP tingkat urutan.

clip_img (`torch.Tensor`, *opsional*) : embedding gambar CLIP.
effnet (`torch.Tensor`, *opsional*) : Peta fitur EfficientNet yang digunakan sebagai kondisioning tambahan.
pixels (`torch.Tensor`, *opsional*) : Tensor kondisioning tingkat piksel. Jika `None`, tensor berisi nol akan digunakan.
sca (`torch.Tensor`, *opsional*) : Nilai kondisioning `sca` opsional yang digunakan untuk membangun embedding timestep.
crp (`torch.Tensor`, *opsional*) : Nilai kondisioning `crp` opsional yang digunakan untuk membangun embedding timestep.
return_dict (`bool`, *opsional*, defaultnya `True`) : Apakah akan mengembalikan `StableCascadeUNetOutput` alih-alih tuple biasa.

**Mengembalikan:**

Jika `return_dict` bernilai True, sebuah `StableCascadeUNetOutput` akan dikembalikan, selain itu sebuah `tuple` di mana elemen pertama adalah tensor sampel.

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	# StableCascadeUNet

	Model UNet dari [pipeline Stable Cascade](../pipelines/stable_cascade).

	## StableCascadeUNet[[diffusers.models.StableCascadeUNet]]

	#### diffusers.models.StableCascadeUNet[[diffusers.models.StableCascadeUNet]]

	```python
	diffusers.models.StableCascadeUNet(in_channels: int = 16, out_channels: int = 16, timestep_ratio_embedding_dim: int = 64, patch_size: int = 1, conditioning_dim: int = 2048, block_out_channels: tuple[int, ...] = (2048, 2048), num_attention_heads: tuple[int, ...] = (32, 32), down_num_layers_per_block: tuple[int, ...] = (8, 24), up_num_layers_per_block: tuple[int, ...] = (24, 8), down_blocks_repeat_mappers: tuple[int] \| None = (1, 1), up_blocks_repeat_mappers: tuple[int] \| None = (1, 1), block_types_per_layer: tuple[tuple[str]] = (('SDCascadeResBlock', 'SDCascadeTimestepBlock', 'SDCascadeAttnBlock'), ('SDCascadeResBlock', 'SDCascadeTimestepBlock', 'SDCascadeAttnBlock')), clip_text_in_channels: int \| None = None, clip_text_pooled_in_channels = 1280, clip_image_in_channels: int \| None = None, clip_seq = 4, effnet_in_channels: int \| None = None, pixel_mapper_in_channels: int \| None = None, kernel_size = 3, dropout: float \| tuple[float] = (0.1, 0.1), self_attn: bool \| tuple[bool] = True, timestep_conditioning_type: tuple[str, ...] = ('sca', 'crp'), switch_level: tuple[bool] \| None = None)
	```

	[Sumber](https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/unets/unet_stable_cascade.py#L136)

	#### forward[[diffusers.models.StableCascadeUNet.forward]]

	```python
	forward(sample, timestep_ratio, clip_text_pooled, clip_text = None, clip_img = None, effnet = None, pixels = None, sca = None, crp = None, return_dict = True)
	```

	[Sumber](https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/unets/unet_stable_cascade.py#L538)

	Parameter:

	sample (`torch.Tensor`) : Sampel input berisik.

	timestep_ratio (`torch.Tensor`) : Rasio timestep yang digunakan untuk menghitung embedding timestep.

	clip_text_pooled (`torch.Tensor`) : Embedding teks CLIP yang dipool.

	clip_text (`torch.Tensor`, opsional) : Embedding teks CLIP tingkat urutan.

	clip_img (`torch.Tensor`, opsional) : embedding gambar CLIP.

	effnet (`torch.Tensor`, opsional) : Peta fitur EfficientNet yang digunakan sebagai kondisioning tambahan.

	pixels (`torch.Tensor`, opsional) : Tensor kondisioning tingkat piksel. Jika `None`, tensor berisi nol akan digunakan.

	sca (`torch.Tensor`, opsional) : Nilai kondisioning `sca` opsional yang digunakan untuk membangun embedding timestep.

	crp (`torch.Tensor`, opsional) : Nilai kondisioning `crp` opsional yang digunakan untuk membangun embedding timestep.

	return_dict (`bool`, opsional, defaultnya `True`) : Apakah akan mengembalikan `StableCascadeUNetOutput` alih-alih tuple biasa.

	Mengembalikan:

	Jika `return_dict` bernilai True, sebuah `StableCascadeUNetOutput` akan dikembalikan, selain itu sebuah `tuple` di mana elemen pertama adalah tensor sampel.