diffusers/pr_13751/en/api/models/transformer_joyimage.md

JoyImageEditTransformer3DModel

The model can be loaded with the following code snippet.

from diffusers import JoyImageEditTransformer3DModel

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

JoyImageEditTransformer3DModel[[diffusers.JoyImageEditTransformer3DModel]]

diffusers.JoyImageEditTransformer3DModel[[diffusers.JoyImageEditTransformer3DModel]]

Source

JoyImage Transformer model for image generation / editing.

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

forwarddiffusers.JoyImageEditTransformer3DModel.forwardhttps://github.com/huggingface/diffusers/blob/vr_13751/src/diffusers/models/transformers/transformer_joyimage.py#L522[{"name": "hidden_states", "val": ": Tensor"}, {"name": "timestep", "val": ": Tensor"}, {"name": "encoder_hidden_states", "val": ": Tensor = None"}, {"name": "return_dict", "val": ": bool = True"}]- 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.0

The JoyImageEditTransformer3DModel forward method.

Parameters:

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.

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

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

Source

The output of Transformer2DModel.

Parameters:

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.