Skip to content

pipelines

Classes

fastvideo.configs.pipelines.Cosmos25Config dataclass 

Cosmos25Config(model_path: str = '', pipeline_config_path: str | None = None, embedded_cfg_scale: float = 0.0, flow_shift: float = 5.0, flow_shift_sr: float | None = None, disable_autocast: bool = False, scheduler_step_in_fp32: bool = False, is_causal: bool = False, dit_config: DiTConfig = (lambda: Cosmos25VideoConfig(arch_config=Cosmos25ArchConfig(num_attention_heads=16, attention_head_dim=128, in_channels=16, out_channels=16, num_layers=28, patch_size=[1, 2, 2], max_size=[128, 240, 240], rope_scale=[1.0, 3.0, 3.0], text_embed_dim=1024, mlp_ratio=4.0, adaln_lora_dim=256, use_adaln_lora=True, concat_padding_mask=True, extra_pos_embed_type=None, use_crossattn_projection=True, rope_enable_fps_modulation=False, qk_norm='rms_norm')))(), dit_precision: str = 'bf16', upsampler_config: UpsamplerConfig = UpsamplerConfig(), upsampler_precision: str = 'fp32', vae_config: VAEConfig = Cosmos25VAEConfig(), vae_precision: str = 'bf16', vae_decode_precision: str | None = None, vae_tiling: bool = False, vae_sp: bool = False, image_encoder_config: EncoderConfig = EncoderConfig(), image_encoder_precision: str = 'fp32', text_encoder_configs: tuple[EncoderConfig, ...] = (lambda: (Reason1Config(arch_config=Reason1ArchConfig(embedding_concat_strategy='full_concat')),))(), text_encoder_precisions: tuple[str, ...] = (lambda: ('bf16',))(), preprocess_text_funcs: tuple[Callable[[str], str], ...] = (lambda: (_identity_preprocess_text,))(), postprocess_text_funcs: tuple[Callable[[BaseEncoderOutput], Tensor], ...] = (lambda: (reason1_postprocess_text,))(), dmd_denoising_steps: list[int] | None = None, ti2v_task: bool = False, lucy_edit_task: bool = False, boundary_ratio: float | None = None)

Bases: PipelineConfig

Configuration for Cosmos 2.5 (Predict2.5) video generation pipeline.

fastvideo.configs.pipelines.CosmosConfig dataclass 

CosmosConfig(model_path: str = '', pipeline_config_path: str | None = None, embedded_cfg_scale: int = 6, flow_shift: float = 1.0, flow_shift_sr: float | None = None, disable_autocast: bool = False, scheduler_step_in_fp32: bool = False, is_causal: bool = False, dit_config: DiTConfig = CosmosVideoConfig(), dit_precision: str = 'bf16', upsampler_config: UpsamplerConfig = UpsamplerConfig(), upsampler_precision: str = 'fp32', vae_config: VAEConfig = CosmosVAEConfig(), vae_precision: str = 'fp16', vae_decode_precision: str | None = None, vae_tiling: bool = True, vae_sp: bool = True, image_encoder_config: EncoderConfig = EncoderConfig(), image_encoder_precision: str = 'fp32', text_encoder_configs: tuple[EncoderConfig, ...] = (lambda: (T5LargeConfig(),))(), text_encoder_precisions: tuple[str, ...] = (lambda: ('bf16',))(), preprocess_text_funcs: tuple[Callable[[str], str], ...] = (lambda: (preprocess_text,))(), postprocess_text_funcs: tuple[Callable[[BaseEncoderOutput], Tensor], ...] = (lambda: (t5_large_postprocess_text,))(), dmd_denoising_steps: list[int] | None = None, ti2v_task: bool = False, lucy_edit_task: bool = False, boundary_ratio: float | None = None, conditioning_strategy: str = 'frame_replace', min_num_conditional_frames: int = 1, max_num_conditional_frames: int = 2, sigma_conditional: float = 0.0001, sigma_data: float = 1.0, state_ch: int = 16, state_t: int = 24, text_encoder_class: str = 'T5')

Bases: PipelineConfig

Configuration for Cosmos2 Video2World pipeline matching diffusers.

fastvideo.configs.pipelines.DreamXWorld5BARPipelineConfig dataclass 

DreamXWorld5BARPipelineConfig(model_path: str = '', pipeline_config_path: str | None = None, embedded_cfg_scale: float = 6.0, flow_shift: float | None = 5.0, flow_shift_sr: float | None = None, disable_autocast: bool = False, scheduler_step_in_fp32: bool = False, is_causal: bool = True, dit_config: DiTConfig = make_dreamx_world_5b_ar_dit_config(), dit_precision: str = 'bf16', upsampler_config: UpsamplerConfig = UpsamplerConfig(), upsampler_precision: str = 'fp32', vae_config: VAEConfig = make_dreamx_world_5b_cam_vae_config(), vae_precision: str = 'fp32', vae_decode_precision: str | None = 'bf16', vae_tiling: bool = False, vae_sp: bool = False, image_encoder_config: EncoderConfig = EncoderConfig(), image_encoder_precision: str = 'fp32', text_encoder_configs: tuple[EncoderConfig, ...] = (lambda: (make_dreamx_world_5b_cam_text_encoder_config(),))(), text_encoder_precisions: tuple[str, ...] = (lambda: ('bf16',))(), preprocess_text_funcs: tuple[Callable[[str], str], ...] = (lambda: (preprocess_text,))(), postprocess_text_funcs: tuple[Callable[[BaseEncoderOutput], Tensor], ...] = (lambda: (t5_postprocess_text,))(), dmd_denoising_steps: tuple[int, ...] = (1000, 750, 500, 250), ti2v_task: bool = True, lucy_edit_task: bool = False, boundary_ratio: float | None = None, expand_timesteps: bool = True, warp_denoising_step: bool = True, context_noise: float = 0.1, num_frames_per_block: int = 3, color_correction_strength: float = 1.0)

Bases: DreamXWorld5BCamPipelineConfig

Pipeline config for DreamX-World-5B autoregressive forcing.

fastvideo.configs.pipelines.DreamXWorld5BCamPipelineConfig dataclass 

DreamXWorld5BCamPipelineConfig(model_path: str = '', pipeline_config_path: str | None = None, embedded_cfg_scale: float = 6.0, flow_shift: float | None = 3.0, flow_shift_sr: float | None = None, disable_autocast: bool = False, scheduler_step_in_fp32: bool = False, is_causal: bool = False, dit_config: DiTConfig = make_dreamx_world_5b_cam_dit_config(), dit_precision: str = 'bf16', upsampler_config: UpsamplerConfig = UpsamplerConfig(), upsampler_precision: str = 'fp32', vae_config: VAEConfig = make_dreamx_world_5b_cam_vae_config(), vae_precision: str = 'fp32', vae_decode_precision: str | None = 'bf16', vae_tiling: bool = False, vae_sp: bool = False, image_encoder_config: EncoderConfig = EncoderConfig(), image_encoder_precision: str = 'fp32', text_encoder_configs: tuple[EncoderConfig, ...] = (lambda: (make_dreamx_world_5b_cam_text_encoder_config(),))(), text_encoder_precisions: tuple[str, ...] = (lambda: ('bf16',))(), preprocess_text_funcs: tuple[Callable[[str], str], ...] = (lambda: (preprocess_text,))(), postprocess_text_funcs: tuple[Callable[[BaseEncoderOutput], Tensor], ...] = (lambda: (t5_postprocess_text,))(), dmd_denoising_steps: list[int] | None = None, ti2v_task: bool = True, lucy_edit_task: bool = False, boundary_ratio: float | None = None, expand_timesteps: bool = True)

Bases: PipelineConfig

Pipeline config for the first-scope DreamX-World-5B-Cam mode.

fastvideo.configs.pipelines.FastHunyuanConfig dataclass 

FastHunyuanConfig(model_path: str = '', pipeline_config_path: str | None = None, embedded_cfg_scale: int = 6, flow_shift: int = 17, flow_shift_sr: float | None = None, disable_autocast: bool = False, scheduler_step_in_fp32: bool = False, is_causal: bool = False, dit_config: DiTConfig = HunyuanVideoConfig(), dit_precision: str = 'bf16', upsampler_config: UpsamplerConfig = UpsamplerConfig(), upsampler_precision: str = 'fp32', vae_config: VAEConfig = HunyuanVAEConfig(), vae_precision: str = 'fp16', vae_decode_precision: str | None = None, vae_tiling: bool = True, vae_sp: bool = True, image_encoder_config: EncoderConfig = EncoderConfig(), image_encoder_precision: str = 'fp32', text_encoder_configs: tuple[EncoderConfig, ...] = (lambda: (LlamaConfig(), CLIPTextConfig()))(), text_encoder_precisions: tuple[str, ...] = (lambda: ('fp16', 'fp16'))(), preprocess_text_funcs: tuple[Callable[[str], str], ...] = (lambda: (llama_preprocess_text, clip_preprocess_text))(), postprocess_text_funcs: tuple[Callable[[BaseEncoderOutput], tensor], ...] = (lambda: (llama_postprocess_text, clip_postprocess_text))(), dmd_denoising_steps: list[int] | None = None, ti2v_task: bool = False, lucy_edit_task: bool = False, boundary_ratio: float | None = None)

Bases: HunyuanConfig

Configuration specifically optimized for FastHunyuan weights.

fastvideo.configs.pipelines.HYWorldConfig dataclass 

HYWorldConfig(model_path: str = '', pipeline_config_path: str | None = None, embedded_cfg_scale: float = 6.0, flow_shift: int = 5, flow_shift_sr: float | None = None, disable_autocast: bool = False, scheduler_step_in_fp32: bool = False, is_causal: bool = False, dit_config: DiTConfig = HYWorldConfig(), dit_precision: str = 'bf16', upsampler_config: UpsamplerConfig = UpsamplerConfig(), upsampler_precision: str = 'fp32', vae_config: VAEConfig = Hunyuan15VAEConfig(), vae_precision: str = 'fp16', vae_decode_precision: str | None = None, vae_tiling: bool = True, vae_sp: bool = True, image_encoder_config: EncoderConfig = SiglipVisionConfig(), image_encoder_precision: str = 'fp16', text_encoder_configs: tuple[EncoderConfig, ...] = (lambda: (Qwen2_5_VLConfig(), T5Config()))(), text_encoder_precisions: tuple[str, ...] = (lambda: ('fp16', 'fp32'))(), preprocess_text_funcs: tuple[Callable[[Any], Any], ...] = (lambda: (qwen_preprocess_text, byt5_preprocess_text))(), postprocess_text_funcs: tuple[Callable[..., Any], ...] = (lambda: (qwen_postprocess_text, byt5_postprocess_text))(), dmd_denoising_steps: list[int] | None = None, ti2v_task: bool = False, lucy_edit_task: bool = False, boundary_ratio: float | None = None, text_encoder_crop_start: int = PROMPT_TEMPLATE_TOKEN_LENGTH, text_encoder_max_lengths: tuple[int, ...] = (lambda: (1000 + PROMPT_TEMPLATE_TOKEN_LENGTH, 256))())

Bases: Hunyuan15T2V480PConfig

Base configuration for HYWorld pipeline architecture.

fastvideo.configs.pipelines.Hunyuan15T2V480PConfig dataclass 

Hunyuan15T2V480PConfig(model_path: str = '', pipeline_config_path: str | None = None, embedded_cfg_scale: float = 6.0, flow_shift: int = 5, flow_shift_sr: float | None = None, disable_autocast: bool = False, scheduler_step_in_fp32: bool = False, is_causal: bool = False, dit_config: DiTConfig = HunyuanVideo15Config(), dit_precision: str = 'bf16', upsampler_config: UpsamplerConfig = UpsamplerConfig(), upsampler_precision: str = 'fp32', vae_config: VAEConfig = Hunyuan15VAEConfig(), vae_precision: str = 'fp16', vae_decode_precision: str | None = None, vae_tiling: bool = True, vae_sp: bool = True, image_encoder_config: EncoderConfig = EncoderConfig(), image_encoder_precision: str = 'fp32', text_encoder_configs: tuple[EncoderConfig, ...] = (lambda: (Qwen2_5_VLConfig(), T5Config()))(), text_encoder_precisions: tuple[str, ...] = (lambda: ('bf16', 'fp32'))(), preprocess_text_funcs: tuple[Callable[[Any], Any], ...] = (lambda: (qwen_preprocess_text, byt5_preprocess_text))(), postprocess_text_funcs: tuple[Callable[..., Any], ...] = (lambda: (qwen_postprocess_text, byt5_postprocess_text))(), dmd_denoising_steps: list[int] | None = None, ti2v_task: bool = False, lucy_edit_task: bool = False, boundary_ratio: float | None = None, text_encoder_crop_start: int = PROMPT_TEMPLATE_TOKEN_LENGTH, text_encoder_max_lengths: tuple[int, ...] = (lambda: (1000 + PROMPT_TEMPLATE_TOKEN_LENGTH, 256))())

Bases: PipelineConfig

Base configuration for HunYuan pipeline architecture.

fastvideo.configs.pipelines.Hunyuan15T2V720PConfig dataclass 

Hunyuan15T2V720PConfig(model_path: str = '', pipeline_config_path: str | None = None, embedded_cfg_scale: float = 6.0, flow_shift: int = 9, flow_shift_sr: float | None = None, disable_autocast: bool = False, scheduler_step_in_fp32: bool = False, is_causal: bool = False, dit_config: DiTConfig = HunyuanVideo15Config(), dit_precision: str = 'bf16', upsampler_config: UpsamplerConfig = UpsamplerConfig(), upsampler_precision: str = 'fp32', vae_config: VAEConfig = Hunyuan15VAEConfig(), vae_precision: str = 'fp16', vae_decode_precision: str | None = None, vae_tiling: bool = True, vae_sp: bool = True, image_encoder_config: EncoderConfig = EncoderConfig(), image_encoder_precision: str = 'fp32', text_encoder_configs: tuple[EncoderConfig, ...] = (lambda: (Qwen2_5_VLConfig(), T5Config()))(), text_encoder_precisions: tuple[str, ...] = (lambda: ('bf16', 'fp32'))(), preprocess_text_funcs: tuple[Callable[[Any], Any], ...] = (lambda: (qwen_preprocess_text, byt5_preprocess_text))(), postprocess_text_funcs: tuple[Callable[..., Any], ...] = (lambda: (qwen_postprocess_text, byt5_postprocess_text))(), dmd_denoising_steps: list[int] | None = None, ti2v_task: bool = False, lucy_edit_task: bool = False, boundary_ratio: float | None = None, text_encoder_crop_start: int = PROMPT_TEMPLATE_TOKEN_LENGTH, text_encoder_max_lengths: tuple[int, ...] = (lambda: (1000 + PROMPT_TEMPLATE_TOKEN_LENGTH, 256))())

Bases: Hunyuan15T2V480PConfig

Base configuration for HunYuan pipeline architecture.

fastvideo.configs.pipelines.HunyuanConfig dataclass 

HunyuanConfig(model_path: str = '', pipeline_config_path: str | None = None, embedded_cfg_scale: int = 6, flow_shift: int = 7, flow_shift_sr: float | None = None, disable_autocast: bool = False, scheduler_step_in_fp32: bool = False, is_causal: bool = False, dit_config: DiTConfig = HunyuanVideoConfig(), dit_precision: str = 'bf16', upsampler_config: UpsamplerConfig = UpsamplerConfig(), upsampler_precision: str = 'fp32', vae_config: VAEConfig = HunyuanVAEConfig(), vae_precision: str = 'fp16', vae_decode_precision: str | None = None, vae_tiling: bool = True, vae_sp: bool = True, image_encoder_config: EncoderConfig = EncoderConfig(), image_encoder_precision: str = 'fp32', text_encoder_configs: tuple[EncoderConfig, ...] = (lambda: (LlamaConfig(), CLIPTextConfig()))(), text_encoder_precisions: tuple[str, ...] = (lambda: ('fp16', 'fp16'))(), preprocess_text_funcs: tuple[Callable[[str], str], ...] = (lambda: (llama_preprocess_text, clip_preprocess_text))(), postprocess_text_funcs: tuple[Callable[[BaseEncoderOutput], tensor], ...] = (lambda: (llama_postprocess_text, clip_postprocess_text))(), dmd_denoising_steps: list[int] | None = None, ti2v_task: bool = False, lucy_edit_task: bool = False, boundary_ratio: float | None = None)

Bases: PipelineConfig

Base configuration for HunYuan pipeline architecture.

fastvideo.configs.pipelines.HunyuanGameCraftPipelineConfig dataclass 

HunyuanGameCraftPipelineConfig(model_path: str = '', pipeline_config_path: str | None = None, embedded_cfg_scale: float = 6.0, flow_shift: int = 5, flow_shift_sr: float | None = None, disable_autocast: bool = False, scheduler_step_in_fp32: bool = False, is_causal: bool = False, dit_config: DiTConfig = HunyuanGameCraftConfig(), dit_precision: str = 'bf16', upsampler_config: UpsamplerConfig = UpsamplerConfig(), upsampler_precision: str = 'fp32', vae_config: VAEConfig = GameCraftVAEConfig(), vae_precision: str = 'fp16', vae_decode_precision: str | None = None, vae_tiling: bool = True, vae_sp: bool = True, image_encoder_config: EncoderConfig = EncoderConfig(), image_encoder_precision: str = 'fp32', text_encoder_configs: tuple[EncoderConfig, ...] = (lambda: (LlamaConfig(), CLIPTextConfig()))(), text_encoder_precisions: tuple[str, ...] = (lambda: ('fp16', 'fp16'))(), preprocess_text_funcs: tuple[Callable[[str], str], ...] = (lambda: (llama_preprocess_text, clip_preprocess_text))(), postprocess_text_funcs: tuple[Callable[[BaseEncoderOutput], Tensor], ...] = (lambda: (llama_postprocess_text, clip_postprocess_text))(), dmd_denoising_steps: list[int] | None = None, ti2v_task: bool = False, lucy_edit_task: bool = False, boundary_ratio: float | None = None)

Bases: PipelineConfig

Configuration for HunyuanGameCraft pipeline.

Inherits text encoding from HunyuanVideo but uses: - GameCraft DiT with CameraNet - Same VAE (HunyuanVAE) - Same text encoders (LLaMA + CLIP)

fastvideo.configs.pipelines.Kandinsky5DMDConfig dataclass 

Kandinsky5DMDConfig(model_path: str = '', pipeline_config_path: str | None = None, embedded_cfg_scale: float = 6.0, flow_shift: float | None = 5.0, flow_shift_sr: float | None = None, disable_autocast: bool = False, scheduler_step_in_fp32: bool = False, is_causal: bool = False, dit_config: DiTConfig = Kandinsky5VideoConfig(), dit_precision: str = 'bf16', upsampler_config: UpsamplerConfig = UpsamplerConfig(), upsampler_precision: str = 'fp32', vae_config: VAEConfig = HunyuanVAEConfig(), vae_precision: str = 'bf16', vae_decode_precision: str | None = None, vae_tiling: bool = True, vae_sp: bool = True, image_encoder_config: EncoderConfig = EncoderConfig(), image_encoder_precision: str = 'fp32', text_encoder_configs: tuple[EncoderConfig, ...] = (lambda: (Reason1Config(), CLIPTextConfig()))(), text_encoder_precisions: tuple[str, ...] = (lambda: ('bf16', 'bf16'))(), preprocess_text_funcs: tuple[Callable[[str], Any], ...] = (lambda: (kandinsky5_qwen_preprocess_text, preprocess_text))(), postprocess_text_funcs: tuple[Callable[..., Any], ...] = (lambda: (kandinsky5_qwen_postprocess_text, kandinsky5_clip_postprocess_text))(), dmd_denoising_steps: list[int] | None = (lambda: [1000, 750, 500, 250])(), ti2v_task: bool = False, lucy_edit_task: bool = False, boundary_ratio: float | None = None, text_encoder_max_lengths: tuple[int, ...] = (lambda: (KANDINSKY5_PROMPT_TEMPLATE_ENCODE_START_IDX + 512, 77))())

Bases: Kandinsky5T2VConfig

Kandinsky-5.0 DMD (few-step distilled) text-to-video pipeline configuration.

Checkpoints exported by fastvideo.train.entrypoint.dcp_to_diffusers copy their base T2V checkpoint's model_index.json unchanged, so _class_name still says the base T2V pipeline and the registry (fastvideo/registry.py) cannot auto-detect a DMD export -- pass this config together with override_pipeline_cls_name="Kandinsky5DMDPipeline" explicitly to VideoGenerator.from_pretrained/from_config (see examples/train/configs/fine_tuning/kandinsky5/README.md). Without it, Kandinsky5T2VConfig's dmd_denoising_steps=None makes Kandinsky5DmdDenoisingStage raise immediately.

fastvideo.configs.pipelines.Kandinsky5I2VConfig dataclass 

Kandinsky5I2VConfig(model_path: str = '', pipeline_config_path: str | None = None, embedded_cfg_scale: float = 6.0, flow_shift: float | None = 5.0, flow_shift_sr: float | None = None, disable_autocast: bool = False, scheduler_step_in_fp32: bool = False, is_causal: bool = False, dit_config: DiTConfig = Kandinsky5VideoConfig(), dit_precision: str = 'bf16', upsampler_config: UpsamplerConfig = UpsamplerConfig(), upsampler_precision: str = 'fp32', vae_config: VAEConfig = HunyuanVAEConfig(), vae_precision: str = 'bf16', vae_decode_precision: str | None = None, vae_tiling: bool = True, vae_sp: bool = True, image_encoder_config: EncoderConfig = EncoderConfig(), image_encoder_precision: str = 'fp32', text_encoder_configs: tuple[EncoderConfig, ...] = (lambda: (Reason1Config(), CLIPTextConfig()))(), text_encoder_precisions: tuple[str, ...] = (lambda: ('bf16', 'bf16'))(), preprocess_text_funcs: tuple[Callable[[str], Any], ...] = (lambda: (kandinsky5_qwen_preprocess_text, preprocess_text))(), postprocess_text_funcs: tuple[Callable[..., Any], ...] = (lambda: (kandinsky5_qwen_postprocess_text, kandinsky5_clip_postprocess_text))(), dmd_denoising_steps: list[int] | None = None, ti2v_task: bool = False, lucy_edit_task: bool = False, boundary_ratio: float | None = None, text_encoder_max_lengths: tuple[int, ...] = (lambda: (KANDINSKY5_PROMPT_TEMPLATE_ENCODE_START_IDX + 512, 77))())

Bases: Kandinsky5T2VConfig

Kandinsky-5.0 image-to-video pipeline configuration.

fastvideo.configs.pipelines.Kandinsky5T2VConfig dataclass 

Kandinsky5T2VConfig(model_path: str = '', pipeline_config_path: str | None = None, embedded_cfg_scale: float = 6.0, flow_shift: float | None = 5.0, flow_shift_sr: float | None = None, disable_autocast: bool = False, scheduler_step_in_fp32: bool = False, is_causal: bool = False, dit_config: DiTConfig = Kandinsky5VideoConfig(), dit_precision: str = 'bf16', upsampler_config: UpsamplerConfig = UpsamplerConfig(), upsampler_precision: str = 'fp32', vae_config: VAEConfig = HunyuanVAEConfig(), vae_precision: str = 'bf16', vae_decode_precision: str | None = None, vae_tiling: bool = True, vae_sp: bool = True, image_encoder_config: EncoderConfig = EncoderConfig(), image_encoder_precision: str = 'fp32', text_encoder_configs: tuple[EncoderConfig, ...] = (lambda: (Reason1Config(), CLIPTextConfig()))(), text_encoder_precisions: tuple[str, ...] = (lambda: ('bf16', 'bf16'))(), preprocess_text_funcs: tuple[Callable[[str], Any], ...] = (lambda: (kandinsky5_qwen_preprocess_text, preprocess_text))(), postprocess_text_funcs: tuple[Callable[..., Any], ...] = (lambda: (kandinsky5_qwen_postprocess_text, kandinsky5_clip_postprocess_text))(), dmd_denoising_steps: list[int] | None = None, ti2v_task: bool = False, lucy_edit_task: bool = False, boundary_ratio: float | None = None, text_encoder_max_lengths: tuple[int, ...] = (lambda: (KANDINSKY5_PROMPT_TEMPLATE_ENCODE_START_IDX + 512, 77))())

Bases: PipelineConfig

Kandinsky-5.0 Lite text-to-video pipeline configuration.

fastvideo.configs.pipelines.LTX2T2VConfig dataclass 

LTX2T2VConfig(model_path: str = '', pipeline_config_path: str | None = None, embedded_cfg_scale: float = 6.0, flow_shift: float | None = None, flow_shift_sr: float | None = None, disable_autocast: bool = False, scheduler_step_in_fp32: bool = False, is_causal: bool = False, dit_config: DiTConfig = LTX2VideoConfig(), dit_precision: str = 'bf16', upsampler_config: UpsamplerConfig = UpsamplerConfig(), upsampler_precision: str = 'fp32', vae_config: VAEConfig = LTX2VAEConfig(), vae_precision: str = 'bf16', vae_decode_precision: str | None = None, vae_tiling: bool = True, vae_sp: bool = False, image_encoder_config: EncoderConfig = EncoderConfig(), image_encoder_precision: str = 'fp32', text_encoder_configs: tuple[EncoderConfig, ...] = (lambda: (LTX2GemmaConfig(),))(), text_encoder_precisions: tuple[str, ...] = (lambda: ('bf16',))(), preprocess_text_funcs: tuple[Callable[[str], str], ...] = (lambda: (preprocess_text,))(), postprocess_text_funcs: tuple[Callable[[BaseEncoderOutput], Tensor], ...] = (lambda: (ltx2_postprocess_text,))(), dmd_denoising_steps: list[int] | None = None, ti2v_task: bool = False, lucy_edit_task: bool = False, boundary_ratio: float | None = None, audio_decoder_config: ModelConfig = LTX2AudioDecoderConfig(), vocoder_config: ModelConfig = LTX2VocoderConfig(), audio_decoder_precision: str = 'bf16', vocoder_precision: str = 'bf16')

Bases: PipelineConfig

Configuration for LTX-2 T2V pipeline.

fastvideo.configs.pipelines.LingBotVideoT2VConfig dataclass 

LingBotVideoT2VConfig(model_path: str = '', pipeline_config_path: str | None = None, embedded_cfg_scale: float | None = None, flow_shift: float | None = 3.0, flow_shift_sr: float | None = None, disable_autocast: bool = False, scheduler_step_in_fp32: bool = True, is_causal: bool = False, dit_config: DiTConfig = LingBotVideoConfig(), dit_precision: str = 'bf16', upsampler_config: UpsamplerConfig = UpsamplerConfig(), upsampler_precision: str = 'fp32', vae_config: VAEConfig = WanVAEConfig(), vae_precision: str = 'fp32', vae_decode_precision: str | None = 'fp32', vae_tiling: bool = False, vae_sp: bool = False, image_encoder_config: EncoderConfig = EncoderConfig(), image_encoder_precision: str = 'fp32', text_encoder_configs: tuple[EncoderConfig, ...] = (lambda: (LingBotVideoQwen3VLTextConfig(),))(), text_encoder_precisions: tuple[str, ...] = (lambda: ('bf16',))(), preprocess_text_funcs: tuple[Callable, ...] = (lambda: (preprocess_lingbot_video_prompt,))(), postprocess_text_funcs: tuple[Callable, ...] = (lambda: (postprocess_lingbot_video_text,))(), dmd_denoising_steps: list[int] | None = None, ti2v_task: bool = False, lucy_edit_task: bool = False, boundary_ratio: float | None = None)

Bases: PipelineConfig

Released Dense T2V component wiring and numerical precision policy.

Methods:

fastvideo.configs.pipelines.LingBotVideoT2VConfig.__post_init__ 
__post_init__() -> None

Load only the VAE decoder for the T2V workload.

Source code in fastvideo/configs/pipelines/lingbot_video.py 
70
71
72
73
def __post_init__(self) -> None:
    """Load only the VAE decoder for the T2V workload."""
    self.vae_config.load_encoder = False
    self.vae_config.load_decoder = True

fastvideo.configs.pipelines.LucyEditDevConfig dataclass 

LucyEditDevConfig(model_path: str = '', pipeline_config_path: str | None = None, embedded_cfg_scale: float = 6.0, flow_shift: float | None = 5.0, flow_shift_sr: float | None = None, disable_autocast: bool = False, scheduler_step_in_fp32: bool = False, is_causal: bool = False, dit_config: DiTConfig = (lambda: WanVideoConfig(arch_config=WanVideoArchConfig(num_attention_heads=24, in_channels=96, out_channels=48, ffn_dim=14336, num_layers=30)))(), dit_precision: str = 'bf16', upsampler_config: UpsamplerConfig = UpsamplerConfig(), upsampler_precision: str = 'fp32', vae_config: VAEConfig = (lambda: WanVAEConfig(arch_config=WanVAEArchConfig(base_dim=160, decoder_base_dim=256, z_dim=48, in_channels=12, out_channels=12, scale_factor_spatial=16, patch_size=2, is_residual=True, clip_output=False, latents_mean=(-0.2289, -0.0052, -0.1323, -0.2339, -0.2799, 0.0174, 0.1838, 0.1557, -0.1382, 0.0542, 0.2813, 0.0891, 0.157, -0.0098, 0.0375, -0.1825, -0.2246, -0.1207, -0.0698, 0.5109, 0.2665, -0.2108, -0.2158, 0.2502, -0.2055, -0.0322, 0.1109, 0.1567, -0.0729, 0.0899, -0.2799, -0.123, -0.0313, -0.1649, 0.0117, 0.0723, -0.2839, -0.2083, -0.052, 0.3748, 0.0152, 0.1957, 0.1433, -0.2944, 0.3573, -0.0548, -0.1681, -0.0667), latents_std=(0.4765, 1.0364, 0.4514, 1.1677, 0.5313, 0.499, 0.4818, 0.5013, 0.8158, 1.0344, 0.5894, 1.0901, 0.6885, 0.6165, 0.8454, 0.4978, 0.5759, 0.3523, 0.7135, 0.6804, 0.5833, 1.4146, 0.8986, 0.5659, 0.7069, 0.5338, 0.4889, 0.4917, 0.4069, 0.4999, 0.6866, 0.4093, 0.5709, 0.6065, 0.6415, 0.4944, 0.5726, 1.2042, 0.5458, 1.6887, 0.3971, 1.06, 0.3943, 0.5537, 0.5444, 0.4089, 0.7468, 0.7744))))(), vae_precision: str = 'fp32', vae_decode_precision: str = 'bf16', vae_tiling: bool = False, vae_sp: bool = False, image_encoder_config: EncoderConfig = EncoderConfig(), image_encoder_precision: str = 'fp32', text_encoder_configs: tuple[EncoderConfig, ...] = (lambda: (T5Config(),))(), text_encoder_precisions: tuple[str, ...] = (lambda: ('fp32',))(), preprocess_text_funcs: tuple[Callable[[str], str], ...] = (lambda: (preprocess_text,))(), postprocess_text_funcs: tuple[Callable[[BaseEncoderOutput], Tensor], ...] = (lambda: (t5_postprocess_text,))(), dmd_denoising_steps: list[int] | None = None, ti2v_task: bool = False, lucy_edit_task: bool = True, boundary_ratio: float | None = None, precision: str = 'bf16', warp_denoising_step: bool = True, expand_timesteps: bool = True)

Bases: Wan2_2_TI2V_5B_Config

Configuration for Decart Lucy Edit Dev video editing.

fastvideo.configs.pipelines.PipelineConfig dataclass 

PipelineConfig(model_path: str = '', pipeline_config_path: str | None = None, embedded_cfg_scale: float = 6.0, flow_shift: float | None = None, flow_shift_sr: float | None = None, disable_autocast: bool = False, scheduler_step_in_fp32: bool = False, is_causal: bool = False, dit_config: DiTConfig = DiTConfig(), dit_precision: str = 'bf16', upsampler_config: UpsamplerConfig = UpsamplerConfig(), upsampler_precision: str = 'fp32', vae_config: VAEConfig = VAEConfig(), vae_precision: str = 'fp32', vae_decode_precision: str | None = None, vae_tiling: bool = True, vae_sp: bool = True, image_encoder_config: EncoderConfig = EncoderConfig(), image_encoder_precision: str = 'fp32', text_encoder_configs: tuple[EncoderConfig, ...] = (lambda: (EncoderConfig(),))(), text_encoder_precisions: tuple[str, ...] = (lambda: ('fp32',))(), preprocess_text_funcs: tuple[Callable[[str], str], ...] = (lambda: (preprocess_text,))(), postprocess_text_funcs: tuple[Callable[[BaseEncoderOutput], tensor], ...] = (lambda: (postprocess_text,))(), dmd_denoising_steps: list[int] | None = None, ti2v_task: bool = False, lucy_edit_task: bool = False, boundary_ratio: float | None = None)

Base configuration for all pipeline architectures.

Methods:

fastvideo.configs.pipelines.PipelineConfig.from_kwargs classmethod 
from_kwargs(kwargs: dict[str, Any], config_cli_prefix: str = '') -> PipelineConfig

Load PipelineConfig from kwargs Dictionary. kwargs: dictionary of kwargs config_cli_prefix: prefix of CLI arguments for this PipelineConfig instance

Source code in fastvideo/configs/pipelines/base.py 
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
@classmethod
def from_kwargs(cls, kwargs: dict[str, Any], config_cli_prefix: str = "") -> "PipelineConfig":
    """
    Load PipelineConfig from kwargs Dictionary.
    kwargs: dictionary of kwargs
    config_cli_prefix: prefix of CLI arguments for this PipelineConfig instance
    """
    from fastvideo.registry import get_pipeline_config_cls_from_name

    prefix_with_dot = f"{config_cli_prefix}." if (config_cli_prefix.strip() != "") else ""
    model_path: str | None = kwargs.get(prefix_with_dot + 'model_path', None) or kwargs.get('model_path')
    pipeline_config_or_path: str | PipelineConfig | dict[str, Any] | None = kwargs.get(
        prefix_with_dot + 'pipeline_config', None) or kwargs.get('pipeline_config')
    if model_path is None:
        raise ValueError("model_path is required in kwargs")

    # 1. Get the pipeline config class from the registry
    pipeline_config_cls = get_pipeline_config_cls_from_name(model_path)

    # 2. Instantiate PipelineConfig
    if pipeline_config_cls is None:
        logger.warning("Couldn't find pipeline config for %s. Using the default pipeline config.", model_path)
        pipeline_config = cls()
    else:
        pipeline_config = pipeline_config_cls()

    # 3. Load PipelineConfig from a json file or a PipelineConfig object if provided
    if isinstance(pipeline_config_or_path, str):
        pipeline_config.load_from_json(pipeline_config_or_path)
        kwargs[prefix_with_dot + 'pipeline_config_path'] = pipeline_config_or_path
    elif isinstance(pipeline_config_or_path, PipelineConfig):
        pipeline_config = pipeline_config_or_path
    elif isinstance(pipeline_config_or_path, dict):
        pipeline_config.update_pipeline_config(pipeline_config_or_path)

    # 4. Update PipelineConfig from CLI arguments if provided
    kwargs[prefix_with_dot + 'model_path'] = model_path
    pipeline_config.update_config_from_dict(kwargs, config_cli_prefix)

    return pipeline_config
fastvideo.configs.pipelines.PipelineConfig.from_pretrained classmethod 
from_pretrained(model_path: str) -> PipelineConfig

use the pipeline class setting from model_path to match the pipeline config

Source code in fastvideo/configs/pipelines/base.py 
209
210
211
212
213
214
215
216
217
@classmethod
def from_pretrained(cls, model_path: str) -> "PipelineConfig":
    """
    use the pipeline class setting from model_path to match the pipeline config
    """
    from fastvideo.registry import get_pipeline_config_cls_from_name
    pipeline_config_cls = get_pipeline_config_cls_from_name(model_path)

    return cast(PipelineConfig, pipeline_config_cls(model_path=model_path))

fastvideo.configs.pipelines.WanI2V480PConfig dataclass 

WanI2V480PConfig(model_path: str = '', pipeline_config_path: str | None = None, embedded_cfg_scale: float = 6.0, flow_shift: float | None = 3.0, flow_shift_sr: float | None = None, disable_autocast: bool = False, scheduler_step_in_fp32: bool = False, is_causal: bool = False, dit_config: DiTConfig = WanVideoConfig(), dit_precision: str = 'bf16', upsampler_config: UpsamplerConfig = UpsamplerConfig(), upsampler_precision: str = 'fp32', vae_config: VAEConfig = WanVAEConfig(), vae_precision: str = 'fp32', vae_decode_precision: str = 'bf16', vae_tiling: bool = False, vae_sp: bool = False, image_encoder_config: EncoderConfig = CLIPVisionConfig(), image_encoder_precision: str = 'fp32', text_encoder_configs: tuple[EncoderConfig, ...] = (lambda: (T5Config(),))(), text_encoder_precisions: tuple[str, ...] = (lambda: ('fp32',))(), preprocess_text_funcs: tuple[Callable[[str], str], ...] = (lambda: (preprocess_text,))(), postprocess_text_funcs: tuple[Callable[[BaseEncoderOutput], Tensor], ...] = (lambda: (t5_postprocess_text,))(), dmd_denoising_steps: list[int] | None = None, ti2v_task: bool = False, lucy_edit_task: bool = False, boundary_ratio: float | None = None, precision: str = 'bf16', warp_denoising_step: bool = True)

Bases: WanT2V480PConfig

Base configuration for Wan I2V 14B 480P pipeline architecture.

fastvideo.configs.pipelines.WanI2V720PConfig dataclass 

WanI2V720PConfig(model_path: str = '', pipeline_config_path: str | None = None, embedded_cfg_scale: float = 6.0, flow_shift: float | None = 5.0, flow_shift_sr: float | None = None, disable_autocast: bool = False, scheduler_step_in_fp32: bool = False, is_causal: bool = False, dit_config: DiTConfig = WanVideoConfig(), dit_precision: str = 'bf16', upsampler_config: UpsamplerConfig = UpsamplerConfig(), upsampler_precision: str = 'fp32', vae_config: VAEConfig = WanVAEConfig(), vae_precision: str = 'fp32', vae_decode_precision: str = 'bf16', vae_tiling: bool = False, vae_sp: bool = False, image_encoder_config: EncoderConfig = CLIPVisionConfig(), image_encoder_precision: str = 'fp32', text_encoder_configs: tuple[EncoderConfig, ...] = (lambda: (T5Config(),))(), text_encoder_precisions: tuple[str, ...] = (lambda: ('fp32',))(), preprocess_text_funcs: tuple[Callable[[str], str], ...] = (lambda: (preprocess_text,))(), postprocess_text_funcs: tuple[Callable[[BaseEncoderOutput], Tensor], ...] = (lambda: (t5_postprocess_text,))(), dmd_denoising_steps: list[int] | None = None, ti2v_task: bool = False, lucy_edit_task: bool = False, boundary_ratio: float | None = None, precision: str = 'bf16', warp_denoising_step: bool = True)

Bases: WanI2V480PConfig

Base configuration for Wan I2V 14B 720P pipeline architecture.

fastvideo.configs.pipelines.WanT2V480PConfig dataclass 

WanT2V480PConfig(model_path: str = '', pipeline_config_path: str | None = None, embedded_cfg_scale: float = 6.0, flow_shift: float | None = 3.0, flow_shift_sr: float | None = None, disable_autocast: bool = False, scheduler_step_in_fp32: bool = False, is_causal: bool = False, dit_config: DiTConfig = WanVideoConfig(), dit_precision: str = 'bf16', upsampler_config: UpsamplerConfig = UpsamplerConfig(), upsampler_precision: str = 'fp32', vae_config: VAEConfig = WanVAEConfig(), vae_precision: str = 'fp32', vae_decode_precision: str = 'bf16', vae_tiling: bool = False, vae_sp: bool = False, image_encoder_config: EncoderConfig = EncoderConfig(), image_encoder_precision: str = 'fp32', text_encoder_configs: tuple[EncoderConfig, ...] = (lambda: (T5Config(),))(), text_encoder_precisions: tuple[str, ...] = (lambda: ('fp32',))(), preprocess_text_funcs: tuple[Callable[[str], str], ...] = (lambda: (preprocess_text,))(), postprocess_text_funcs: tuple[Callable[[BaseEncoderOutput], Tensor], ...] = (lambda: (t5_postprocess_text,))(), dmd_denoising_steps: list[int] | None = None, ti2v_task: bool = False, lucy_edit_task: bool = False, boundary_ratio: float | None = None, precision: str = 'bf16', warp_denoising_step: bool = True)

Bases: PipelineConfig

Base configuration for Wan T2V 1.3B pipeline architecture.

fastvideo.configs.pipelines.WanT2V720PConfig dataclass 

WanT2V720PConfig(model_path: str = '', pipeline_config_path: str | None = None, embedded_cfg_scale: float = 6.0, flow_shift: float | None = 5.0, flow_shift_sr: float | None = None, disable_autocast: bool = False, scheduler_step_in_fp32: bool = False, is_causal: bool = False, dit_config: DiTConfig = WanVideoConfig(), dit_precision: str = 'bf16', upsampler_config: UpsamplerConfig = UpsamplerConfig(), upsampler_precision: str = 'fp32', vae_config: VAEConfig = WanVAEConfig(), vae_precision: str = 'fp32', vae_decode_precision: str = 'bf16', vae_tiling: bool = False, vae_sp: bool = False, image_encoder_config: EncoderConfig = EncoderConfig(), image_encoder_precision: str = 'fp32', text_encoder_configs: tuple[EncoderConfig, ...] = (lambda: (T5Config(),))(), text_encoder_precisions: tuple[str, ...] = (lambda: ('fp32',))(), preprocess_text_funcs: tuple[Callable[[str], str], ...] = (lambda: (preprocess_text,))(), postprocess_text_funcs: tuple[Callable[[BaseEncoderOutput], Tensor], ...] = (lambda: (t5_postprocess_text,))(), dmd_denoising_steps: list[int] | None = None, ti2v_task: bool = False, lucy_edit_task: bool = False, boundary_ratio: float | None = None, precision: str = 'bf16', warp_denoising_step: bool = True)

Bases: WanT2V480PConfig

Base configuration for Wan T2V 14B 720P pipeline architecture.