Diffusers documentation
JoyImageEditTransformer3DModel
You are viewing main version, which requires installation from source. If you'd like
regular pip install, checkout the latest stable version (v0.38.0).
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
class diffusers.JoyImageEditTransformer3DModel
< source >( patch_size: list = [1, 2, 2] in_channels: int = 16 out_channels: int | None = None hidden_size: int = 3072 num_attention_heads: int = 24 text_dim: int = 4096 mlp_width_ratio: float = 4.0 num_layers: int = 20 rope_dim_list: list = [16, 56, 56] rope_type: str = 'rope' theta: int = 256 )
JoyImage Transformer model for image generation / editing.
Dual-stream DiT architecture with WAN-style conditioning embeddings and custom rotary position embeddings.
Transformer2DModelOutput
class diffusers.models.modeling_outputs.Transformer2DModelOutput
< source >( sample: torch.Tensor )
Parameters
- sample (
torch.Tensorof 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 theencoder_hidden_statesinput. If discrete, returns probability distributions for the unnoised latent pixels.
The output of Transformer2DModel.