diffusers/main/en/api/models/transformer_joyimage_edit_plus.md

JoyImageEditPlusTransformer3DModel

The model can be loaded with the following code snippet.

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 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 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.