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StableCascadeUNet

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StableCascadeUNet

Model UNet dari pipeline Stable Cascade.

StableCascadeUNet

kelas diffusers.models.StableCascadeUNet

< >

( in_channels: int = 16out_channels: int = 16timestep_ratio_embedding_dim: int = 64patch_size: int = 1conditioning_dim: int = 2048block_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 = Noneclip_text_pooled_in_channels = 1280clip_image_in_channels: int | None = Noneclip_seq = 4effnet_in_channels: int | None = Nonepixel_mapper_in_channels: int | None = Nonekernel_size = 3dropout: float | tuple[float] = (0.1, 0.1)self_attn: bool | tuple[bool] = Truetimestep_conditioning_type: tuple[str, ...] = ('sca', 'crp')switch_level: tuple[bool] | None = None )

forward

< >

( sampletimestep_ratioclip_text_pooledclip_text = Noneclip_img = Noneeffnet = Nonepixels = Nonesca = Nonecrp = Nonereturn_dict = True )

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 telah di-pool.
  • clip_text (torch.Tensor, opsional) — Embedding teks CLIP pada level sekuens.
  • 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, default True) — Apakah akan mengembalikan StableCascadeUNetOutput alih-alih tuple biasa.
Update on GitHub

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