# JoyImageEditTransformer3DModel

The model can be loaded with the following code snippet.

```python
from diffusers import JoyImageEditTransformer3DModel

transformer = JoyImageEditTransformer3DModel.from_pretrained("jdopensource/JoyAI-Image-Edit-Diffusers", subfolder="transformer", torch_dtype=torch.bfloat16)
```

## JoyImageEditTransformer3DModel[[diffusers.JoyImageEditTransformer3DModel]]

JoyImage Transformer model for image generation / editing.

Dual-stream DiT architecture with WAN-style conditioning embeddings and custom rotary position embeddings.

- **hidden_states** (`torch.Tensor` of shape `(batch_size, num_channels, num_frames, height, width)` or `(batch_size, num_items, num_channels, num_frames, height, width)`) --
  Input `hidden_states`.
- **timestep** (`torch.LongTensor`) --
  Used to indicate denoising step.
- **encoder_hidden_states** (`torch.Tensor`, *optional*) --
  Conditional embeddings (embeddings computed from the input conditions such as prompts) to use.
- **return_dict** (`bool`, *optional*, defaults to `True`) --
  Whether or not to return a `~models.transformer_2d.Transformer2DModelOutput` instead of a plain
  tuple.

The [JoyImageEditTransformer3DModel](/docs/diffusers/main/en/api/models/transformer_joyimage#diffusers.JoyImageEditTransformer3DModel) forward method.

## 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).