# JoyImageEditPlusTransformer3DModel

The model can be loaded with the following code snippet.

```python
from diffusers import JoyImageEditPlusTransformer3DModel

transformer = JoyImageEditPlusTransformer3DModel.from_pretrained("jdopensource/JoyAI-Image-Edit-Plus-Diffusers", subfolder="transformer", torch_dtype=torch.bfloat16)
```

## JoyImageEditPlusTransformer3DModel[[diffusers.JoyImageEditPlusTransformer3DModel]]

- **patch_size** (`list`, defaults to `[1, 2, 2]`) --
  Patch size for patchifying the latent input along `(t, h, w)` dimensions.
- **in_channels** (`int`, defaults to `16`) --
  The number of channels in the input latent.
- **out_channels** (`int`, *optional*, defaults to `None`) --
  The number of channels in the output. If not specified, it defaults to `in_channels`.
- **hidden_size** (`int`, defaults to `3072`) --
  The dimensionality of the hidden representations.
- **num_attention_heads** (`int`, defaults to `24`) --
  The number of attention heads.
- **text_dim** (`int`, defaults to `4096`) --
  The dimensionality of the text encoder output.
- **mlp_width_ratio** (`float`, defaults to `4.0`) --
  The ratio of MLP hidden dimension to `hidden_size`.
- **num_layers** (`int`, defaults to `20`) --
  The number of double-stream transformer blocks.
- **rope_dim_list** (`list[int]`, defaults to `[16, 56, 56]`) --
  The dimensions for 3D rotary positional embeddings along `(t, h, w)`.
- **rope_type** (`str`, defaults to `"rope"`) --
  The type of rotary positional embedding.
- **theta** (`int`, defaults to `256`) --
  The base frequency for rotary embeddings.

JoyImage Edit Plus Transformer for multi-image editing.

Uses a patchify+padding approach where each reference image and the target noise are independently patchified and
concatenated into a flat patch sequence. Supports variable-resolution reference images.

Input format: `[B, max_patches, C, pt, ph, pw]` (6D padded patches).

- **hidden_states** -- [B, max_patches, C, pt, ph, pw] - patchified latent input.
- **timestep** -- [B] - diffusion timestep.
- **encoder_hidden_states** -- [B, L, D] - text encoder outputs.
- **encoder_hidden_states_mask** -- [B, L] - attention mask for text tokens.
- **shape_list** -- Per-sample list of (t, h, w) tuples for each component (target + references).
- **return_dict** -- Whether to return a dict or tuple.If `return_dict` is True, an [Transformer2DModelOutput](/docs/diffusers/main/en/api/models/sana_video_transformer3d#diffusers.models.modeling_outputs.Transformer2DModelOutput) is returned, otherwise a
`tuple` where the first element is the sample tensor.

## Transformer2DModelOutput[[diffusers.models.modeling_outputs.Transformer2DModelOutput]]

- **sample** (`torch.Tensor` of shape `(batch_size, num_channels, height, width)` or `(batch size, num_vector_embeds - 1, num_latent_pixels)` if [Transformer2DModel](/docs/diffusers/main/en/api/models/transformer2d#diffusers.Transformer2DModel) is discrete) --
  The hidden states output conditioned on the `encoder_hidden_states` input. If discrete, returns probability
  distributions for the unnoised latent pixels.

The output of [Transformer2DModel](/docs/diffusers/main/en/api/models/transformer2d#diffusers.Transformer2DModel).