Packages
diffuseR
auto_devicesbpe_tokenizerclear_vramclip_pooled_outputCLIPTokenizerddim_scheduler_createddim_scheduler_stepdecode_bpedownload_componentdownload_flux1download_flux2_kleindownload_ltx2download_modelencode_bpeencode_qwenencode_unigramencode_with_gemma3encode_with_qwen3encode_with_t5filename_from_promptflowmatch_calculate_shiftflowmatch_scale_noiseflowmatch_scheduler_createflowmatch_scheduler_stepflowmatch_set_timestepsflux_ada_layer_norm_continuousflux_ada_layer_norm_zeroflux_ada_layer_norm_zero_singleflux_apply_rotary_embflux_attentionflux_double_blockflux_is_quant_keyflux_load_pipelineflux_load_transformerflux_memory_profileflux_open_checkpointflux_open_quantizedflux_pack_latentsflux_pos_embedflux_prepare_latent_image_idsflux_quantizeflux_single_blockflux_transformerflux_unpack_latentsflux2_bn_normalizeflux2_double_blockflux2_empirical_muflux2_feed_forwardflux2_is_quant_keyflux2_load_pipelineflux2_modulationflux2_pack_latentsflux2_parallel_self_attentionflux2_patchify_latentsflux2_prepare_latent_idsflux2_prepare_text_idsflux2_single_blockflux2_transformerflux2_unpack_latents_with_idsflux2_unpatchify_latentsflux2_vae_decodergemma3_config_ltx2gemma3_text_modelgemma3_tokenizerimg2imgis_blackwell_gpulatents_to_videoload_decoder_safetensorsload_decoder_weightsload_flux2_vae_decoderload_gemma3_text_encoderload_model_componentload_pipelineload_qwen3_text_encoderload_t5_text_encoderload_text_encoder_safetensorsload_text_encoder_weightsload_text_encoder2_weightsload_to_gpuload_unet_sdxl_weightsload_unet_weightsltx23_ada_layer_norm_singleltx23_adain_filter_latentltx23_antialias_act1dltx23_apply_interleaved_rotary_embltx23_apply_split_rotary_embltx23_attentionltx23_audio_causal_conv2dltx23_audio_decoderltx23_audio_downsampleltx23_audio_encoderltx23_audio_mel_frontendltx23_audio_resnet_blockltx23_audio_upsampleltx23_audio_vaeltx23_causal_conv3dltx23_censusltx23_connector_transformer_1dltx23_denormalize_latentsltx23_distilled_sigmasltx23_downsample1dltx23_encode_audioltx23_encode_video_framesltx23_feed_forwardltx23_fp8_linearltx23_get_timestep_embeddingltx23_is_fp8_cast_keyltx23_kaiser_sinc_filter1dltx23_latent_upsamplerltx23_load_groupltx23_load_pipelineltx23_load_transformer_fp8ltx23_load_transformer_nf4ltx23_load_upsamplerltx23_map_audio_vae_keyltx23_map_connector_keyltx23_map_dit_keyltx23_map_vae_keyltx23_map_vocoder_keyltx23_mel_stftltx23_memory_profileltx23_nf4_dequantizeltx23_nf4_linearltx23_nf4_quantizeltx23_normalize_latentsltx23_open_checkpointltx23_open_fp8_checkpointltx23_per_channel_rms_normltx23_per_token_rms_normltx23_prepare_conditioned_latentsltx23_preprocess_framesltx23_quantize_fp8ltx23_quantize_nf4ltx23_read_audioltx23_read_tail_framesltx23_release_dequant_buffersltx23_rms_normltx23_rotary_pos_embedltx23_rotary_pos_embed_1dltx23_set_attn_chunkltx23_snake_betaltx23_split_keysltx23_stage2_distilled_sigmasltx23_text_connectorsltx23_tone_map_latentsltx23_transformerltx23_transformer_blockltx23_tune_gcltx23_upsample1dltx23_video_decoder3dltx23_video_down_block3dltx23_video_downsampler3dltx23_video_encoder3dltx23_video_mid_block3dltx23_video_resnet_block3dltx23_video_up_block3dltx23_video_upsampler3dltx23_video_vaeltx23_vocoderltx23_vocoder_resblockltx23_vocoder_with_bwemodels2devicesoffload_to_cpupost_quant_convpreprocess_imagequant_convqwen_bpe_tokenizerqwen3_encodersave_framessave_imagesave_videosave_video_ltx23scheduler_add_noisesdxl_memory_profilet5_encodertext_encoder_nativetext_encoder2_nativetokenize_gemma3txt2imgtxt2img_fluxtxt2img_flux2txt2img_sd21txt2img_sdxltxt2vid_ltx2unet_nativeunet_native_from_torchscriptunet_sdxl_nativeunet_sdxl_native_from_torchscriptunigram_tokenizervae_decoder_nativevocab_sizevram_reportwrite_wav
Adaptive layer norm single (adaLN-single)
Description
Embeds a timestep/sigma and projects it to a configurable number of modulation parameter vectors.
Usage
ltx23_ada_layer_norm_single(embedding_dim, num_mod_params = 6L)
Arguments
embedding_dim: Integer. Model dimension.num_mod_params: Integer. Number of modulation parameter vectors.
Value
Module whose forward returns
list(mod_params [N, num_mod_params * dim], embedded_timestep [N, dim]).