Diffusers documentation
ZImageTransformer2DModel
Get started
Pipelines
Adapters
Inference
Inference optimization
Hybrid Inference
Modular Diffusers
Training
Quantization
Model accelerators and hardware
Specific pipeline examples
Resources
API
Main Classes
Modular
Loaders
Models
OverviewAutoModel
ControlNets
Transformers
AllegroTransformer3DModelAuraFlowTransformer2DModelBriaFiboTransformer2DModelBriaTransformer2DModelChromaTransformer2DModelChronoEditTransformer3DModelCogVideoXTransformer3DModelCogView3PlusTransformer2DModelCogView4Transformer2DModelConsisIDTransformer3DModelCosmosTransformer3DModelDiTTransformer2DModelEasyAnimateTransformer3DModelFlux2Transformer2DModelFluxTransformer2DModelHiDreamImageTransformer2DModelHunyuanDiT2DModelHunyuanImageTransformer2DModelHunyuanVideo15Transformer3DModelHunyuanVideoTransformer3DModelLatteTransformer3DModelLTXVideoTransformer3DModelLumina2Transformer2DModelLuminaNextDiT2DModelMochiTransformer3DModelOmniGenTransformer2DModelOvisImageTransformer2DModelPixArtTransformer2DModelPriorTransformerQwenImageTransformer2DModelSanaTransformer2DModelSanaVideoTransformer3DModelSD3Transformer2DModelSkyReelsV2Transformer3DModelStableAudioDiTModelTransformer2DModelTransformerTemporalModelWanAnimateTransformer3DModelWanTransformer3DModelZImageTransformer2DModel
UNets
VAEs
Pipelines
Schedulers
Internal classes
You are viewing v0.36.0 version. A newer version v0.38.0 is available.
ZImageTransformer2DModel
A Transformer model for image-like data from Z-Image.
ZImageTransformer2DModel
class diffusers.ZImageTransformer2DModel
< source >( all_patch_size = (2,) all_f_patch_size = (1,) in_channels = 16 dim = 3840 n_layers = 30 n_refiner_layers = 2 n_heads = 30 n_kv_heads = 30 norm_eps = 1e-05 qk_norm = True cap_feat_dim = 2560 rope_theta = 256.0 t_scale = 1000.0 axes_dims = [32, 48, 48] axes_lens = [1024, 512, 512] )