magi_human_pipeline

MagiHuman base text-to-AV pipeline.

Top-level composition for the daVinci-MagiHuman base model. Wires:

InputValidationStage -> TextEncodingStage (T5-Gemma)
    -> MagiHumanLatentPreparationStage
    -> MagiHumanDenoisingStage
    -> DecodingStage (Wan 2.2 TI2V-5B VAE decode for video)
    -> MagiHumanAudioDecodingStage (Stable Audio Open 1.0 VAE decode)

The base checkpoint is a joint audio-visual generator; both the video and audio paths run in the denoising loop and both are decoded.

load_modules is overridden so the four cross-variant shared components (text_encoder, tokenizer, audio_vae, video vae) lazy-load from their canonical upstream HF repos at first build time instead of being bundled inside every converted MagiHuman variant. This keeps each variant's converted repo at ~5-30 GB (transformer + scheduler + model_index.json) instead of ~30-55 GB, and lets all variants share the same ~25 GB of cached upstream weights.

`fastvideo.pipelines.basic.magi_human.magi_human_pipeline.T5GemmaEncoderConfig`

T5GemmaEncoderConfig(arch_config: TextEncoderArchConfig = T5GemmaEncoderArchConfig(), prefix: str = 't5gemma', quant_config: QuantizationConfig | None = None, lora_config: Any | None = None, is_chat_model: bool = False, treat_empty_as_dot: bool = False, *, _resolved_attention_backend: AttentionBackendEnum | None = None, chat_template_enable_thinking: bool = False)

`fastvideo.pipelines.basic.magi_human.magi_human_pipeline.OobleckVAEConfig`

OobleckVAEConfig(arch_config: VAEArchConfig = OobleckVAEArchConfig(), load_encoder: bool = True, load_decoder: bool = True, tile_sample_min_height: int = 256, tile_sample_min_width: int = 256, tile_sample_min_num_frames: int = 16, tile_sample_stride_height: int = 192, tile_sample_stride_width: int = 192, tile_sample_stride_num_frames: int = 12, blend_num_frames: int = 0, use_tiling: bool = False, use_temporal_tiling: bool = False, use_parallel_tiling: bool = False, use_temporal_scaling_frames: bool = True, pretrained_path: str = 'stabilityai/stable-audio-open-1.0', pretrained_subfolder: str = 'vae', pretrained_dtype: str = 'float16', *, _resolved_attention_backend: AttentionBackendEnum | None = None)

FastVideo VAE config wrapping the Oobleck arch.

Audio VAEs don't use the temporal/spatial tiling defaults that the base VAEConfig is shaped for (those exist for video VAEs); they are retained but irrelevant for audio.

`fastvideo.pipelines.basic.magi_human.magi_human_pipeline.FastVideoArgs`

FastVideoArgs(model_path: str, mode: ExecutionMode = ExecutionMode.INFERENCE, workload_type: WorkloadType = WorkloadType.T2V, distributed_executor_backend: str = 'mp', enable_streaming_ipc_queues: bool = False, ray_placement_group: PlacementGroup | None = None, ray_runtime_env: RuntimeEnv | None = None, inference_mode: bool = True, trust_remote_code: bool = False, revision: str | None = None, num_gpus: int = 1, tp_size: int = -1, sp_size: int = -1, hsdp_replicate_dim: int = 1, hsdp_shard_dim: int = -1, dist_timeout: int | None = None, pipeline_config: PipelineConfig = PipelineConfig(), preprocess_config: PreprocessConfig | None = None, lora_path: str | None = None, lora_nickname: str = 'default', lora_strength: float = 1.0, lora_target_modules: list[str] | None = None, output_type: str = 'pil', attention_backend: str | None = None, dit_cpu_offload: bool = True, use_fsdp_inference: bool = False, dit_layerwise_offload: bool = True, text_encoder_cpu_offload: bool = True, image_encoder_cpu_offload: bool = True, vae_cpu_offload: bool = True, pin_cpu_memory: bool = True, h3_sequential_load: bool | None = None, h3_encoder_split: bool | None = None, h3_encoder_nodes: int = -1, h3_encoder_workers: int = 0, video_decode_backend: str = 'h3-vae', taeh3_checkpoint: str | None = None, taeh3_chunk_size: int = 5, h3_vae_nvfp4_decoder: str | None = None, lazy_module_load: bool | None = None, vae_parallel_decode: bool = False, vae_parallel_encode: bool = False, vae_parallel_decode_strategy: str | None = None, enable_torch_compile: bool = False, regional_compile: bool = False, enable_torch_compile_text_encoder: bool = False, enable_torch_compile_vae: bool = False, enable_torch_compile_audio_vae: bool = False, torch_compile_kwargs: dict[str, Any] = dict(), torch_compile_kwargs_dit: dict[str, Any] = dict(), torch_compile_kwargs_text_encoder: dict[str, Any] = dict(), torch_compile_kwargs_vae: dict[str, Any] = dict(), torch_compile_kwargs_audio_vae: dict[str, Any] = dict(), inference_torch_compile: bool = False, h3_vsa_fp4: dict[str, Any] = dict(), disable_autocast: bool = False, VSA_sparsity: float | None = None, VSA_tile_size: int | None = None, moba_config_path: str | None = None, moba_config: dict[str, Any] = dict(), master_port: int | None = None, enable_stage_verification: bool = True, prompt_txt: str | None = None, ltx2_vae_tiling: bool | None = None, ltx2_vae_spatial_tile_size_in_pixels: int | None = None, ltx2_vae_spatial_tile_overlap_in_pixels: int | None = None, ltx2_vae_temporal_tile_size_in_frames: int | None = None, ltx2_vae_temporal_tile_overlap_in_frames: int | None = None, ltx2_initial_latent_path: str | None = None, ltx2_audio_latent_path: str | None = None, refine_enabled: bool | None = None, refine_upsampler_path: str | None = None, refine_transformer_path: str | None = None, refine_lora_path: str | None = None, refine_num_inference_steps: int | None = None, refine_guidance_scale: float | None = None, refine_add_noise: bool | None = None, refine_noise_path: str | None = None, refine_audio_noise_path: str | None = None, ltx2_refine_enabled: bool = False, ltx2_refine_upsampler_path: str | None = None, ltx2_refine_transformer_path: str | None = None, ltx2_refine_lora_path: str | None = None, ltx2_refine_num_inference_steps: int = 3, ltx2_refine_guidance_scale: float = 1.0, ltx2_refine_add_noise: bool = True, ltx2_refine_noise_path: str | None = None, ltx2_refine_audio_noise_path: str | None = None, ltx2_legacy_native_noise_order: bool = False, ltx2_use_distilled_sigmas: bool = True, model_paths: dict[str, str] = dict(), model_loaded: dict[str, bool] = (lambda: {'transformer': True, 'vae': True, 'upsampler': True})(), override_text_encoder_safetensors: str | None = None, override_text_encoder_quant: QuantizationMethods = None, transformer_quant: Any | None = None, override_transformer_cls_name: str | None = None, init_weights_from_safetensors: str = '', init_weights_from_safetensors_2: str = '', override_pipeline_cls_name: str | None = None, boundary_ratio: float | None = 0.875)

`fastvideo.pipelines.basic.magi_human.magi_human_pipeline.FastVideoArgs.is_output_rank`

is_output_rank: bool

Whether this process may materialize user-facing outputs.

This is runtime state assigned by an SPMD executor, not a public configuration field, so dataclass serialization and typed config parsing deliberately ignore it.

`fastvideo.pipelines.basic.magi_human.magi_human_pipeline.FastVideoArgs.add_cli_args`

add_cli_args(parser: FlexibleArgumentParser) -> FlexibleArgumentParser

`fastvideo.pipelines.basic.magi_human.magi_human_pipeline.FastVideoArgs.from_cli_args`

from_cli_args(args: argparse.Namespace) -> FastVideoArgs

`fastvideo.pipelines.basic.magi_human.magi_human_pipeline.FastVideoArgs.from_kwargs`

from_kwargs(**kwargs: Any) -> FastVideoArgs

`fastvideo.pipelines.basic.magi_human.magi_human_pipeline.FastVideoArgs.check_fastvideo_args`

check_fastvideo_args() -> None

Validate inference arguments for consistency

`fastvideo.pipelines.basic.magi_human.magi_human_pipeline.FastVideoArgs.finalize_device_offload_policy`

finalize_device_offload_policy(device_id: int = 0) -> bool

Apply device-local memory policy, then resolve incompatible modes.

`fastvideo.pipelines.basic.magi_human.magi_human_pipeline.FastVideoArgs.disable_offload_on_unified_memory`

disable_offload_on_unified_memory(device_id: int = 0, *, offload_flag: str | None = None) -> bool

Disable host offload after a worker has selected its device.

CUDA's unified-memory probe reads runtime device properties and may initialize a CUDA context. Callers must therefore use this only inside a device-owning process, after selecting and binding device_id. Returning the classification lets direct component-loader callers apply the same policy to explicit per-call overrides. When offload_flag is given, the return value says whether this policy covers that component role.

`fastvideo.pipelines.basic.magi_human.magi_human_pipeline.init_logger`

init_logger(name: str) -> _FastvideoLogger

The main purpose of this function is to ensure that loggers are retrieved in such a way that we can be sure the root fastvideo logger has already been configured.

`fastvideo.pipelines.basic.magi_human.magi_human_pipeline.T5GemmaEncoderModel`

T5GemmaEncoderModel(config: TextEncoderConfig)

Thin wrapper over HuggingFace's T5GemmaEncoderModel.

On first forward, the wrapper lazily instantiates the upstream encoder from t5gemma_model_path (defaulting to google/t5gemma-9b-9b-ul2). Afterwards, forward returns a BaseEncoderOutput with last_hidden_state = [B, L, 3584] matching MagiHuman's context.half() output.

`fastvideo.pipelines.basic.magi_human.magi_human_pipeline.T5GemmaEncoderModel.named_parameters`

named_parameters(prefix: str = '', recurse: bool = True)

`fastvideo.pipelines.basic.magi_human.magi_human_pipeline.T5GemmaEncoderModel.forward`

forward(input_ids: torch.Tensor | None = None, position_ids: torch.Tensor | None = None, attention_mask: torch.Tensor | None = None, inputs_embeds: torch.Tensor | None = None, output_hidden_states: bool | None = None, **kwargs) -> BaseEncoderOutput

`fastvideo.pipelines.basic.magi_human.magi_human_pipeline.T5GemmaEncoderModel.load_weights`

load_weights(weights: Iterable[tuple[str, torch.Tensor]]) -> set[str]

`fastvideo.pipelines.basic.magi_human.magi_human_pipeline.SAAudioVAEModel`

SAAudioVAEModel(config: OobleckVAEConfig)

Pipeline-glue lazy loader around OobleckVAE.

`fastvideo.pipelines.basic.magi_human.magi_human_pipeline.SAAudioVAEModel.named_parameters`

named_parameters(prefix: str = '', recurse: bool = True)

`fastvideo.pipelines.basic.magi_human.magi_human_pipeline.SAAudioVAEModel.decode`

decode(latent: torch.Tensor) -> torch.Tensor

Decode an audio latent ([B, C_latent, L]) -> waveform ([B, audio_channels, samples]).

`fastvideo.pipelines.basic.magi_human.magi_human_pipeline.SAAudioVAEModel.encode`

encode(waveform: torch.Tensor, sample_posterior: bool = False) -> torch.Tensor

Encode [B, C_audio, samples] -> latent [B, C_latent, L].

sample_posterior=False (default): deterministic mean. sample_posterior=True: stochastic sample (mean + softplus(scale) * randn).

`fastvideo.pipelines.basic.magi_human.magi_human_pipeline.FlowUniPCMultistepScheduler`

FlowUniPCMultistepScheduler(num_train_timesteps: int = 1000, solver_order: int = 2, prediction_type: str = 'flow_prediction', shift: float | None = 1.0, use_dynamic_shifting = False, thresholding: bool = False, dynamic_thresholding_ratio: float = 0.995, sample_max_value: float = 1.0, predict_x0: bool = True, solver_type: str = 'bh2', lower_order_final: bool = True, disable_corrector: tuple = (), solver_p: SchedulerMixin = None, timestep_spacing: str = 'linspace', steps_offset: int = 0, final_sigmas_type: str | None = 'zero', **kwargs)

UniPCMultistepScheduler is a training-free framework designed for the fast sampling of diffusion models.

This model inherits from [SchedulerMixin] and [ConfigMixin]. Check the superclass documentation for the generic methods the library implements for all schedulers such as loading and saving.

Parameters

num_train_timesteps: `int`, defaults to 1000: The number of diffusion steps to train the model.
solver_order: `int`, default `2`: The UniPC order which can be any positive integer. The effective order of accuracy is solver_order + 1 due to the UniC. It is recommended to use solver_order=2 for guided sampling, and solver_order=3 for unconditional sampling.
prediction_type: `str`, defaults to "flow_prediction": Prediction type of the scheduler function; must be flow_prediction for this scheduler, which predicts the flow of the diffusion process.
thresholding: `bool`, defaults to `False`: Whether to use the "dynamic thresholding" method. This is unsuitable for latent-space diffusion models such as Stable Diffusion.
dynamic_thresholding_ratio: `float`, defaults to 0.995: The ratio for the dynamic thresholding method. Valid only when thresholding=True.
sample_max_value: `float`, defaults to 1.0: The threshold value for dynamic thresholding. Valid only when thresholding=True and predict_x0=True.
predict_x0: `bool`, defaults to `True`: Whether to use the updating algorithm on the predicted x0.
solver_type: `str`, default `bh2`: Solver type for UniPC. It is recommended to use bh1 for unconditional sampling when steps < 10, and bh2 otherwise.
lower_order_final: `bool`, default `True`: Whether to use lower-order solvers in the final steps. Only valid for < 15 inference steps. This can stabilize the sampling of DPMSolver for steps < 15, especially for steps <= 10.
disable_corrector: `list`, default `[]`: Decides which step to disable the corrector to mitigate the misalignment between epsilon_theta(x_t, c) and epsilon_theta(x_t^c, c) which can influence convergence for a large guidance scale. Corrector is usually disabled during the first few steps.
solver_p: `SchedulerMixin`, default `None`: Any other scheduler that if specified, the algorithm becomes solver_p + UniC.
use_karras_sigmas: `bool`, *optional*, defaults to `False`: Whether to use Karras sigmas for step sizes in the noise schedule during the sampling process. If True, the sigmas are determined according to a sequence of noise levels {σi}.
use_exponential_sigmas: `bool`, *optional*, defaults to `False`: Whether to use exponential sigmas for step sizes in the noise schedule during the sampling process.
timestep_spacing: `str`, defaults to `"linspace"`: The way the timesteps should be scaled. Refer to Table 2 of the Common Diffusion Noise Schedules and Sample Steps are Flawed for more information.
steps_offset: `int`, defaults to 0: An offset added to the inference steps, as required by some model families.
final_sigmas_type: `str`, defaults to `"zero"`: The final sigma value for the noise schedule during the sampling process. If "sigma_min", the final sigma is the same as the last sigma in the training schedule. If zero, the final sigma is set to 0.

`fastvideo.pipelines.basic.magi_human.magi_human_pipeline.FlowUniPCMultistepScheduler.step_index`

step_index

The index counter for current timestep. It will increase 1 after each scheduler step.

`fastvideo.pipelines.basic.magi_human.magi_human_pipeline.FlowUniPCMultistepScheduler.begin_index`

begin_index

The index for the first timestep. It should be set from pipeline with set_begin_index method.

`fastvideo.pipelines.basic.magi_human.magi_human_pipeline.FlowUniPCMultistepScheduler.set_shift`

set_shift(shift: float) -> None

`fastvideo.pipelines.basic.magi_human.magi_human_pipeline.FlowUniPCMultistepScheduler.set_begin_index`

set_begin_index(begin_index: int = 0)

Sets the begin index for the scheduler. This function should be run from pipeline before the inference.

Parameters

begin_index: `int`: The begin index for the scheduler.

`fastvideo.pipelines.basic.magi_human.magi_human_pipeline.FlowUniPCMultistepScheduler.set_timesteps`

set_timesteps(num_inference_steps: int | None = None, device: str | torch.device = None, sigmas: list[float] | None = None, mu: float | None | None = None, shift: float | None | None = None, use_karras_sigmas: bool | None = None, use_kerras_sigma: bool | None = None)

Sets the discrete timesteps used for the diffusion chain (to be run before inference). Args: num_inference_steps (int): Total number of the spacing of the time steps. device (str or torch.device, optional): The device to which the timesteps should be moved to. If None, the timesteps are not moved.

`fastvideo.pipelines.basic.magi_human.magi_human_pipeline.FlowUniPCMultistepScheduler.time_shift`

time_shift(mu: float, sigma: float, t: torch.Tensor)

`fastvideo.pipelines.basic.magi_human.magi_human_pipeline.FlowUniPCMultistepScheduler.convert_model_output`

convert_model_output(model_output: torch.Tensor, *args, sample: torch.Tensor = None, **kwargs) -> torch.Tensor

Convert the model output to the corresponding type the UniPC algorithm needs.

Parameters

model_output: `torch.Tensor`: The direct output from the learned diffusion model.
timestep: `int`: The current discrete timestep in the diffusion chain.
sample: `torch.Tensor`: A current instance of a sample created by the diffusion process.

Returns

torch.Tensor: torch.Tensor: The converted model output.

`fastvideo.pipelines.basic.magi_human.magi_human_pipeline.FlowUniPCMultistepScheduler.multistep_uni_p_bh_update`

multistep_uni_p_bh_update(model_output: torch.Tensor, *args, sample: torch.Tensor = None, order: int | None = None, **kwargs) -> torch.Tensor

One step for the UniP (B(h) version). Alternatively, self.solver_p is used if is specified.

Parameters

model_output: `torch.Tensor`: The direct output from the learned diffusion model at the current timestep.
prev_timestep: `int`: The previous discrete timestep in the diffusion chain.
sample: `torch.Tensor`: A current instance of a sample created by the diffusion process.
order: `int`: The order of UniP at this timestep (corresponds to the p in UniPC-p).

Returns

torch.Tensor: torch.Tensor: The sample tensor at the previous timestep.

`fastvideo.pipelines.basic.magi_human.magi_human_pipeline.FlowUniPCMultistepScheduler.multistep_uni_c_bh_update`

multistep_uni_c_bh_update(this_model_output: torch.Tensor, *args, last_sample: torch.Tensor = None, this_sample: torch.Tensor = None, order: int | None = None, **kwargs) -> torch.Tensor

One step for the UniC (B(h) version).

Parameters

this_model_output: `torch.Tensor`: The model outputs at x_t.
this_timestep: `int`: The current timestep t.
last_sample: `torch.Tensor`: The generated sample before the last predictor x_{t-1}.
this_sample: `torch.Tensor`: The generated sample after the last predictor x_{t}.
order: `int`: The p of UniC-p at this step. The effective order of accuracy should be order + 1.

Returns

torch.Tensor: torch.Tensor: The corrected sample tensor at the current timestep.

`fastvideo.pipelines.basic.magi_human.magi_human_pipeline.FlowUniPCMultistepScheduler.index_for_timestep`

index_for_timestep(timestep, schedule_timesteps = None) -> int

`fastvideo.pipelines.basic.magi_human.magi_human_pipeline.FlowUniPCMultistepScheduler.step`

step(model_output: torch.Tensor, timestep: int | torch.Tensor, sample: torch.Tensor, return_dict: bool = True, generator = None) -> SchedulerOutput | tuple

Predict the sample from the previous timestep by reversing the SDE. This function propagates the sample with the multistep UniPC.

Parameters

model_output: `torch.Tensor`: The direct output from learned diffusion model.
timestep: `int`: The current discrete timestep in the diffusion chain.
sample: `torch.Tensor`: A current instance of a sample created by the diffusion process.
return_dict: `bool`: Whether or not to return a [~schedulers.scheduling_utils.SchedulerOutput] or tuple.

Returns

SchedulerOutput | tuple: [~schedulers.scheduling_utils.SchedulerOutput] or tuple: If return_dict is True, [~schedulers.scheduling_utils.SchedulerOutput] is returned, otherwise a tuple is returned where the first element is the sample tensor.

`fastvideo.pipelines.basic.magi_human.magi_human_pipeline.FlowUniPCMultistepScheduler.scale_model_input`

scale_model_input(sample: torch.Tensor, *args, **kwargs) -> torch.Tensor

Ensures interchangeability with schedulers that need to scale the denoising model input depending on the current timestep.

Parameters

sample: `torch.Tensor`: The input sample.

Returns

torch.Tensor: torch.Tensor: A scaled input sample.

`fastvideo.pipelines.basic.magi_human.magi_human_pipeline.FlowUniPCMultistepScheduler.add_noise`

add_noise(original_samples: torch.Tensor, noise: torch.Tensor, timesteps: torch.IntTensor) -> torch.Tensor

`fastvideo.pipelines.basic.magi_human.magi_human_pipeline.MagiHumanAudioDecodingStage`

MagiHumanAudioDecodingStage(audio_vae, time_stretching: float = _UPSTREAM_AUDIO_TIME_STRETCH)

Decode batch.audio_latents to a waveform using Stable Audio's VAE.

The VAE is loaded lazily by SAAudioVAEModel.sa_audio_vae_model — the first call triggers a snapshot_download (requires HF token + accepted terms on stabilityai/stable-audio-open-1.0).

`fastvideo.pipelines.basic.magi_human.magi_human_pipeline.MagiHumanAudioDecodingStage.verify_input`

verify_input(batch, fastvideo_args)

`fastvideo.pipelines.basic.magi_human.magi_human_pipeline.MagiHumanAudioDecodingStage.verify_output`

verify_output(batch, fastvideo_args)

`fastvideo.pipelines.basic.magi_human.magi_human_pipeline.MagiHumanAudioDecodingStage.forward`

forward(batch: ForwardBatch, fastvideo_args: FastVideoArgs) -> ForwardBatch

`fastvideo.pipelines.basic.magi_human.magi_human_pipeline.MagiHumanDenoisingStage`

MagiHumanDenoisingStage(transformer, scheduler, patch_size: tuple[int, int, int] = (1, 2, 2), video_in_channels: int = 192, audio_in_channels: int = 64, video_txt_guidance_scale: float = 5.0, audio_txt_guidance_scale: float = 5.0, cfg_number: int = 2, coords_style: str = 'v2', video_guidance_high_t_threshold: int = 500, video_guidance_low_t_value: float = 2.0)

UniPC-flow joint denoising with CFG=2 over (video, audio) latents.

`fastvideo.pipelines.basic.magi_human.magi_human_pipeline.MagiHumanDenoisingStage.verify_input`

verify_input(batch, fastvideo_args)

`fastvideo.pipelines.basic.magi_human.magi_human_pipeline.MagiHumanDenoisingStage.verify_output`

verify_output(batch, fastvideo_args)

`fastvideo.pipelines.basic.magi_human.magi_human_pipeline.MagiHumanDenoisingStage.forward`

forward(batch: ForwardBatch, fastvideo_args: FastVideoArgs) -> ForwardBatch

`fastvideo.pipelines.basic.magi_human.magi_human_pipeline.MagiHumanLatentPreparationStage`

MagiHumanLatentPreparationStage(vae_stride: tuple[int, int, int] = (4, 16, 16), z_dim: int = 48, patch_size: tuple[int, int, int] = (1, 2, 2), fps: int = 25, t5_gemma_target_length: int = 640, coords_style: Literal['v1', 'v2'] = 'v2', text_offset: int = 0, audio_in_channels: int = 64)

Prepare latents, coords, modality maps, and padded text embed.

`fastvideo.pipelines.basic.magi_human.magi_human_pipeline.MagiHumanLatentPreparationStage.verify_input`

verify_input(batch, fastvideo_args)

`fastvideo.pipelines.basic.magi_human.magi_human_pipeline.MagiHumanLatentPreparationStage.verify_output`

verify_output(batch, fastvideo_args)

`fastvideo.pipelines.basic.magi_human.magi_human_pipeline.MagiHumanLatentPreparationStage.forward`

forward(batch: ForwardBatch, fastvideo_args: FastVideoArgs) -> ForwardBatch

`fastvideo.pipelines.basic.magi_human.magi_human_pipeline.MagiHumanReferenceImageStage`

MagiHumanReferenceImageStage(vae: Any, vae_scale_factor: int = 16)

Encode a TI2V reference image into the first-frame video latent.

`fastvideo.pipelines.basic.magi_human.magi_human_pipeline.MagiHumanReferenceImageStage.verify_input`

verify_input(batch, fastvideo_args)

`fastvideo.pipelines.basic.magi_human.magi_human_pipeline.MagiHumanReferenceImageStage.verify_output`

verify_output(batch, fastvideo_args)

`fastvideo.pipelines.basic.magi_human.magi_human_pipeline.MagiHumanReferenceImageStage.forward`

forward(batch: ForwardBatch, fastvideo_args: FastVideoArgs) -> ForwardBatch

`fastvideo.pipelines.basic.magi_human.magi_human_pipeline.MagiHumanSRDenoisingStage`

MagiHumanSRDenoisingStage(transformer, scheduler, patch_size: tuple[int, int, int] = (1, 2, 2), video_in_channels: int = 192, audio_in_channels: int = 64, sr_num_inference_steps: int = 5, sr_video_txt_guidance_scale: float = 3.5, use_cfg_trick: bool = True, cfg_trick_start_frame: int = 13, cfg_trick_value: float = 2.0, cfg_number: int = 2, coords_style: str = 'v1')

Denoise only the SR video latent; audio passes through unchanged.

`fastvideo.pipelines.basic.magi_human.magi_human_pipeline.MagiHumanSRDenoisingStage.verify_input`

verify_input(batch, fastvideo_args)

`fastvideo.pipelines.basic.magi_human.magi_human_pipeline.MagiHumanSRDenoisingStage.verify_output`

verify_output(batch, fastvideo_args)

`fastvideo.pipelines.basic.magi_human.magi_human_pipeline.MagiHumanSRDenoisingStage.forward`

forward(batch: ForwardBatch, fastvideo_args: FastVideoArgs) -> ForwardBatch

`fastvideo.pipelines.basic.magi_human.magi_human_pipeline.MagiHumanSRLatentPreparationStage`

MagiHumanSRLatentPreparationStage(vae: Any, vae_stride: tuple[int, int, int] = (4, 16, 16), patch_size: tuple[int, int, int] = (1, 2, 2), noise_value: int = 220, sr_audio_noise_scale: float = 0.7, sr_height: int = 512, sr_width: int = 896, vae_scale_factor: int = 16)

Upsample base latents, add SR noise, and refresh SR conditioning.

`fastvideo.pipelines.basic.magi_human.magi_human_pipeline.MagiHumanSRLatentPreparationStage.verify_input`

verify_input(batch, fastvideo_args)

`fastvideo.pipelines.basic.magi_human.magi_human_pipeline.MagiHumanSRLatentPreparationStage.verify_output`

verify_output(batch, fastvideo_args)

`fastvideo.pipelines.basic.magi_human.magi_human_pipeline.MagiHumanSRLatentPreparationStage.forward`

forward(batch: ForwardBatch, fastvideo_args: FastVideoArgs) -> ForwardBatch

`fastvideo.pipelines.basic.magi_human.magi_human_pipeline.ComposedPipelineBase`

ComposedPipelineBase(model_path: str, fastvideo_args: FastVideoArgs | TrainingArgs, required_config_modules: list[str] | None = None, loaded_modules: dict[str, torch.nn.Module] | None = None)

Base class for pipelines composed of multiple stages.

This class provides the framework for creating pipelines by composing multiple stages together. Each stage is responsible for a specific part of the diffusion process, and the pipeline orchestrates the execution of these stages.

Initialize the pipeline. After init, the pipeline should be ready to use. The pipeline should be stateless and not hold any batch state.

`fastvideo.pipelines.basic.magi_human.magi_human_pipeline.ComposedPipelineBase.get_hf_download_component_dirs`

get_hf_download_component_dirs() -> tuple[str, ...] | None

Return component directories for an opt-in partial Hub download.

`fastvideo.pipelines.basic.magi_human.magi_human_pipeline.ComposedPipelineBase.get_hf_download_allow_patterns`

get_hf_download_allow_patterns() -> list[str] | None

Return Hub patterns for the manifest and selected components.

`fastvideo.pipelines.basic.magi_human.magi_human_pipeline.ComposedPipelineBase.set_trainable`

set_trainable() -> None

`fastvideo.pipelines.basic.magi_human.magi_human_pipeline.ComposedPipelineBase.post_init`

post_init() -> None

`fastvideo.pipelines.basic.magi_human.magi_human_pipeline.ComposedPipelineBase.initialize_training_pipeline`

initialize_training_pipeline(training_args: TrainingArgs)

`fastvideo.pipelines.basic.magi_human.magi_human_pipeline.ComposedPipelineBase.initialize_validation_pipeline`

initialize_validation_pipeline(training_args: TrainingArgs)

`fastvideo.pipelines.basic.magi_human.magi_human_pipeline.ComposedPipelineBase.from_pretrained`

from_pretrained(model_path: str, device: str | None = None, torch_dtype: torch.dtype | None = None, pipeline_config: str | PipelineConfig | None = None, args: argparse.Namespace | None = None, required_config_modules: list[str] | None = None, loaded_modules: dict[str, torch.nn.Module] | None = None, **kwargs) -> ComposedPipelineBase

Load a pipeline from a pretrained model. loaded_modules: Optional[Dict[str, torch.nn.Module]] = None, If provided, loaded_modules will be used instead of loading from config/pretrained weights.

`fastvideo.pipelines.basic.magi_human.magi_human_pipeline.ComposedPipelineBase.get_module`

get_module(module_name: str, default_value: Any = None) -> Any

`fastvideo.pipelines.basic.magi_human.magi_human_pipeline.ComposedPipelineBase.add_module`

add_module(module_name: str, module: Any)

`fastvideo.pipelines.basic.magi_human.magi_human_pipeline.ComposedPipelineBase.required_config_modules`

required_config_modules: list[str]

List of modules that are required by the pipeline. The names should match the diffusers directory and model_index.json file. These modules will be loaded using the PipelineComponentLoader and made available in the modules dictionary. Access these modules using the get_module method.

class ConcretePipeline(ComposedPipelineBase): _required_config_modules = ["vae", "text_encoder", "transformer", "scheduler", "tokenizer"]

@property
def required_config_modules(self):
    return self._required_config_modules

`fastvideo.pipelines.basic.magi_human.magi_human_pipeline.ComposedPipelineBase.stages`

stages: list[PipelineStage]

List of stages in the pipeline.

`fastvideo.pipelines.basic.magi_human.magi_human_pipeline.ComposedPipelineBase.create_pipeline_stages`

create_pipeline_stages(fastvideo_args: FastVideoArgs)

Create the inference pipeline stages.

`fastvideo.pipelines.basic.magi_human.magi_human_pipeline.ComposedPipelineBase.create_training_stages`

create_training_stages(training_args: TrainingArgs)

Create the training pipeline stages.

`fastvideo.pipelines.basic.magi_human.magi_human_pipeline.ComposedPipelineBase.initialize_pipeline`

initialize_pipeline(fastvideo_args: FastVideoArgs)

Initialize the pipeline.

`fastvideo.pipelines.basic.magi_human.magi_human_pipeline.ComposedPipelineBase.load_modules`

load_modules(fastvideo_args: FastVideoArgs, loaded_modules: dict[str, torch.nn.Module] | None = None) -> dict[str, Any]

Load the modules from the config. loaded_modules: Optional[Dict[str, torch.nn.Module]] = None, If provided, loaded_modules will be used instead of loading from config/pretrained weights.

`fastvideo.pipelines.basic.magi_human.magi_human_pipeline.ComposedPipelineBase.add_stage`

add_stage(stage_name: str, stage: PipelineStage)

`fastvideo.pipelines.basic.magi_human.magi_human_pipeline.ComposedPipelineBase.forward`

forward(batch: ForwardBatch, fastvideo_args: FastVideoArgs) -> ForwardBatch

Generate a video or image using the pipeline.

Parameters

batch: ForwardBatch: The batch to generate from.
fastvideo_args: FastVideoArgs: The inference arguments.

Returns: ForwardBatch: The batch with the generated video or image.

`fastvideo.pipelines.basic.magi_human.magi_human_pipeline.ComposedPipelineBase.train`

train() -> None

`fastvideo.pipelines.basic.magi_human.magi_human_pipeline.ComposedPipelineBase.close`

close() -> None

`fastvideo.pipelines.basic.magi_human.magi_human_pipeline.DecodingStage`

DecodingStage(vae, pipeline = None)

Stage for decoding latent representations into pixel space.

This stage handles the decoding of latent representations into the final output format (e.g., pixel values).

`fastvideo.pipelines.basic.magi_human.magi_human_pipeline.DecodingStage.verify_input`

verify_input(batch: ForwardBatch, fastvideo_args: FastVideoArgs) -> VerificationResult

Verify decoding stage inputs.

`fastvideo.pipelines.basic.magi_human.magi_human_pipeline.DecodingStage.verify_output`

verify_output(batch: ForwardBatch, fastvideo_args: FastVideoArgs) -> VerificationResult

Verify decoding stage outputs.

`fastvideo.pipelines.basic.magi_human.magi_human_pipeline.DecodingStage.decode`

decode(latents: torch.Tensor, fastvideo_args: FastVideoArgs) -> torch.Tensor

Decode latent representations into pixel space using VAE.

Parameters

latents: torch.Tensor: Input latent tensor with shape (batch, channels, frames, height_latents, width_latents)
fastvideo_args: FastVideoArgs: Configuration containing: - disable_autocast: Whether to disable automatic mixed precision (default: False) - pipeline_config.vae_precision: VAE computation precision ("fp32", "fp16", "bf16") - pipeline_config.vae_tiling: Whether to enable VAE tiling for memory efficiency

Returns

torch.Tensor: Decoded video tensor with shape (batch, channels, frames, height, width),
torch.Tensor: normalized to [0, 1] range and moved to CPU as float32

`fastvideo.pipelines.basic.magi_human.magi_human_pipeline.DecodingStage.streaming_decode`

streaming_decode(latents: torch.Tensor, fastvideo_args: FastVideoArgs, cache: list[torch.Tensor | None] | None = None, is_first_chunk: bool = False) -> tuple[torch.Tensor, list[torch.Tensor | None]]

Decode latent representations into pixel space using VAE with streaming cache.

Parameters

latents: torch.Tensor: Input latent tensor with shape (batch, channels, frames, height_latents, width_latents)
fastvideo_args: FastVideoArgs: Configuration object.
cache: list[torch.Tensor | None] | None: VAE cache from previous call, or None to initialize a new cache.
is_first_chunk: bool: Whether this is the first chunk.

Returns

tuple[torch.Tensor, list[torch.Tensor | None]]: A tuple of (decoded_frames, updated_cache).

`fastvideo.pipelines.basic.magi_human.magi_human_pipeline.DecodingStage.forward`

forward(batch: ForwardBatch, fastvideo_args: FastVideoArgs) -> ForwardBatch

Decode latent representations into pixel space.

This method processes the batch through the VAE decoder, converting latent representations to pixel-space video/images. It also optionally decodes trajectory latents for visualization purposes.

Parameters

batch: ForwardBatch: The current batch containing: - latents: Tensor to decode (batch, channels, frames, height_latents, width_latents) - return_trajectory_decoded (optional): Flag to decode trajectory latents - trajectory_latents (optional): Latents at different timesteps - trajectory_timesteps (optional): Corresponding timesteps
fastvideo_args: FastVideoArgs: Configuration containing: - output_type: "latent" to skip decoding, otherwise decode to pixels - vae_cpu_offload: Whether to offload VAE to CPU after decoding - model_loaded: Track VAE loading state - model_paths: Path to VAE model if loading needed

Returns

ForwardBatch: Modified batch with: - output: Decoded frames (batch, channels, frames, height, width) as CPU float32 - trajectory_decoded (if requested): List of decoded frames per timestep

`fastvideo.pipelines.basic.magi_human.magi_human_pipeline.InputValidationStage`

InputValidationStage()

Stage for validating and preparing inputs for diffusion pipelines.

This stage validates that all required inputs are present and properly formatted before proceeding with the diffusion process.

`fastvideo.pipelines.basic.magi_human.magi_human_pipeline.InputValidationStage.forward`

forward(batch: ForwardBatch, fastvideo_args: FastVideoArgs) -> ForwardBatch

Validate and prepare inputs.

Parameters

batch: ForwardBatch: The current batch information.
fastvideo_args: FastVideoArgs: The inference arguments.

Returns

ForwardBatch: The validated batch information.

`fastvideo.pipelines.basic.magi_human.magi_human_pipeline.InputValidationStage.verify_input`

verify_input(batch: ForwardBatch, fastvideo_args: FastVideoArgs) -> VerificationResult

Verify input validation stage inputs.

`fastvideo.pipelines.basic.magi_human.magi_human_pipeline.InputValidationStage.verify_output`

verify_output(batch: ForwardBatch, fastvideo_args: FastVideoArgs) -> VerificationResult

Verify input validation stage outputs.

`fastvideo.pipelines.basic.magi_human.magi_human_pipeline.TextEncodingStage`

TextEncodingStage(text_encoders, tokenizers)

Stage for encoding text prompts into embeddings for diffusion models.

This stage handles the encoding of text prompts into the embedding space expected by the diffusion model.

Initialize the prompt encoding stage.

Parameters

enable_logging: Whether to enable logging for this stage.
is_secondary: Whether this is a secondary text encoder.

`fastvideo.pipelines.basic.magi_human.magi_human_pipeline.TextEncodingStage.forward`

forward(batch: ForwardBatch, fastvideo_args: FastVideoArgs) -> ForwardBatch

Encode the prompt into text encoder hidden states.

Parameters

batch: ForwardBatch: The current batch information.
fastvideo_args: FastVideoArgs: The inference arguments.

Returns

ForwardBatch: The batch with encoded prompt embeddings.

`fastvideo.pipelines.basic.magi_human.magi_human_pipeline.TextEncodingStage.verify_input`

verify_input(batch: ForwardBatch, fastvideo_args: FastVideoArgs) -> VerificationResult

Verify text encoding stage inputs.

`fastvideo.pipelines.basic.magi_human.magi_human_pipeline.TextEncodingStage.encode_text`

encode_text(text: str | list[str], fastvideo_args: FastVideoArgs, encoder_index: int | list[int] | None = None, return_attention_mask: bool = False, return_type: str = 'list', device: torch.device | str | None = None, dtype: torch.dtype | None = None, max_length: int | Sequence[int | None] | None = None, truncation: bool | None = None, padding: bool | str | None = None)

Encode plain text using selected text encoder(s) and return embeddings.

Parameters

text: str | list[str]: A single string or a list of strings to encode.
fastvideo_args: FastVideoArgs: The inference arguments providing pipeline config, including tokenizer and encoder settings, preprocess and postprocess functions.
encoder_index: int | list[int] | None: Encoder selector by index. Accepts an int or list of ints.
return_attention_mask: bool: If True, also return attention masks for each selected encoder.
return_type: str: "list" (default) returns a list aligned with selection; "dict" returns a dict keyed by encoder index as a string; "stack" stacks along a new first dimension (requires matching shapes).
device: torch.device | str | None: Optional device override for inputs; defaults to local torch device.
dtype: torch.dtype | None: Optional dtype to cast returned embeddings to.
max_length: int | Sequence[int | None] | None: Optional per-call tokenizer override. Either one value applied to every selected encoder (existing behavior), or a sequence with one entry per encoder in encoder_index order -- None at a position falls back to that encoder's own configured default instead of the override.
truncation: bool | None: Optional per-call tokenizer override.
padding: bool | str | None: Optional per-call tokenizer override.

Returns

Depending on return_type and return_attention_mask:

list: List[Tensor] or (List[Tensor], List[Tensor])

dict: Dict[str, Tensor] or (Dict[str, Tensor], Dict[str, Tensor])

stack: Tensor of shape [num_encoders, ...] or a tuple with stacked attention masks

`fastvideo.pipelines.basic.magi_human.magi_human_pipeline.TextEncodingStage.return_embeds`

return_embeds(embeds_list: list[torch.Tensor], attn_masks_list: list[torch.Tensor], return_type: str = 'list', return_attention_mask: bool = False, indices: list[int] | None = None) -> Any

`fastvideo.pipelines.basic.magi_human.magi_human_pipeline.TextEncodingStage.verify_output`

verify_output(batch: ForwardBatch, fastvideo_args: FastVideoArgs) -> VerificationResult

Verify text encoding stage outputs.

`fastvideo.pipelines.basic.magi_human.magi_human_pipeline.maybe_download_model`

maybe_download_model(model_name_or_path: str, local_dir: str | None = None, download: bool = True, revision: str | None = None, allow_patterns: list[str] | None = None) -> str

Check if the model path is a Hugging Face Hub model ID and download it if needed.

Supports an "umbrella" repo layout where a single HF repo holds multiple pipeline variants under sibling subfolders. If the input is shaped as org/repo/subfolder (i.e. a non-existent local path with 3+ slash- separated components and at least one segment that does not look like a posix-absolute path), treat the first two components as the HF repo id and the remainder as a subfolder; only the subfolder's blobs are downloaded, and the returned local path points inside that subfolder.

Parameters

model_name_or_path: str: Local path, Hugging Face Hub model ID, or org/repo/subfolder umbrella-repo reference.
local_dir: str | None: Local directory to save the model
download: bool: Whether to download the model from Hugging Face Hub
revision: str | None: Optional immutable Hub revision.
allow_patterns: list[str] | None: Optional Hub glob patterns limiting downloaded files. Local paths are returned unchanged. For umbrella references, the patterns are interpreted relative to the selected subfolder.

Returns

str: Local path to the model (or to the subfolder inside the snapshot).

`fastvideo.pipelines.basic.magi_human.magi_human_pipeline.resolve_hf_token`

resolve_hf_token() -> str | None

Return the first non-empty Hugging Face token from the environment.

Reads HF_TOKEN, then HUGGING_FACE_HUB_TOKEN, then the deprecated FastVideo-specific aliases HUGGINGFACE_HUB_TOKEN and HF_API_KEY, which log a deprecation warning. Does not mutate os.environ.

`fastvideo.pipelines.basic.magi_human.magi_human_pipeline.MagiHumanPipeline`

MagiHumanPipeline(model_path: str, fastvideo_args: FastVideoArgs | TrainingArgs, required_config_modules: list[str] | None = None, loaded_modules: dict[str, torch.nn.Module] | None = None)

Base MagiHuman text-to-AV pipeline (no LoRA, no distill, no SR).

Source

class MagiHumanPipeline(ComposedPipelineBase):
    """Base MagiHuman text-to-AV pipeline (no LoRA, no distill, no SR)."""
_required_config_modules = &#91;
    &quot;text_encoder&quot;,
    &quot;tokenizer&quot;,
    &quot;vae&quot;,
    &quot;transformer&quot;,
    &quot;scheduler&quot;,
    &quot;audio_vae&quot;,
&#93;

def load_modules(
    self,
    fastvideo_args: FastVideoArgs,
    loaded_modules: dict&#91;str, Any&#93; | None = None,
) -&gt; dict&#91;str, Any&#93;:
    &quot;&quot;&quot;Load the variant-specific transformer + scheduler from the
    converted MagiHuman repo and lazy-load the four cross-variant
    shared components from their canonical upstream HF repos:

      * text_encoder, tokenizer -&gt; ``google/t5gemma-9b-9b-ul2``
        (gated, requires HF token with accepted terms of use)
      * audio_vae -&gt; ``stabilityai/stable-audio-open-1.0`` (gated)
      * vae -&gt; ``Wan-AI/Wan2.2-TI2V-5B-Diffusers``

    Backwards-compatible with bundled converted repos: if any of
    these subfolders is present locally and listed in
    ``model_index.json``, the standard component loader picks it up
    via super(). Otherwise the loader is told to skip the entry and
    we lazy-load it here.
    &quot;&quot;&quot;
    # T5-Gemma is gated: expose `HF_API_KEY` as `HF_TOKEN` if needed.
    _ensure_hf_token_env()

    # Resolve to a local cache path so we can inspect
    # model_index.json before invoking super(). `maybe_download_model`
    # is idempotent for local paths; super() repeats the call cheaply
    # via `_load_config`.
    local_path = maybe_download_model(self.model_path)

    # Identify which cross-variant shared keys are bundled in the
    # converted repo (declared in model_index.json with a non-null
    # spec) versus absent (the umbrella scheme). Bundled keys stay
    # in `required_config_modules` and are loaded normally by super()
    # from `&lt;model_path&gt;/&lt;key&gt;/`. Absent keys are temporarily
    # dropped so super() does not fail the &quot;every required entry
    # must appear in model_index.json&quot; check, then lazy-loaded
    # below.
    model_index: dict&#91;str, Any&#93; = &#123;&#125;
    try:
        with open(Path(local_path) / &quot;model_index.json&quot;) as f:
            model_index = json.load(f)
    except (FileNotFoundError, json.JSONDecodeError):
        pass

    def _is_bundled(key: str) -&gt; bool:
        spec = model_index.get(key)
        return (isinstance(spec, list | tuple) and len(spec) &gt;= 1 and spec&#91;0&#93; is not None)

    deferred = &#91;&#93;
    for key in (&quot;text_encoder&quot;, &quot;tokenizer&quot;, &quot;audio_vae&quot;, &quot;vae&quot;):
        if key in self.required_config_modules and not _is_bundled(key):
            self.required_config_modules.remove(key)
            deferred.append(key)

    try:
        modules = super().load_modules(fastvideo_args, loaded_modules)
    finally:
        for key in deferred:
            if key not in self.required_config_modules:
                self.required_config_modules.append(key)

    # For each lazy-load key, prefer whatever super() already loaded
    # (a bundled subfolder, or a caller-provided override merged in
    # via `loaded_modules`). Fall back to the caller-provided
    # `loaded_modules` entry for keys absent from model_index.json
    # (super() never iterates those). Otherwise lazy-load from the
    # canonical upstream HF repo.
    def _resolve(key: str) -&gt; bool:
        &quot;&quot;&quot;Return True if `modules&#91;key&#93;` is already populated.&quot;&quot;&quot;
        if modules.get(key) is not None:
            return True
        if loaded_modules and key in loaded_modules:
            modules&#91;key&#93; = loaded_modules&#91;key&#93;
            return True
        return False

    if not _resolve(&quot;text_encoder&quot;):
        logger.info(&quot;Building T5-Gemma text encoder (lazy-load from %s)&quot;, _T5GEMMA_HF_ID)
        enc_config = T5GemmaEncoderConfig()
        enc_config.arch_config.t5gemma_model_path = _T5GEMMA_HF_ID
        modules&#91;&quot;text_encoder&quot;&#93; = T5GemmaEncoderModel(enc_config)

    if not _resolve(&quot;tokenizer&quot;):
        logger.info(&quot;Loading T5-Gemma tokenizer from %s&quot;, _T5GEMMA_HF_ID)
        modules&#91;&quot;tokenizer&quot;&#93; = AutoTokenizer.from_pretrained(_T5GEMMA_HF_ID)

    if not _resolve(&quot;audio_vae&quot;):
        logger.info(
            &quot;Building Stable Audio Open 1.0 VAE (lazy-load from %s) — &quot;
            &quot;requires HF terms accepted for gated repo&quot;,
            _SA_AUDIO_HF_ID,
        )
        audio_config = OobleckVAEConfig()
        audio_config.pretrained_path = _SA_AUDIO_HF_ID
        modules&#91;&quot;audio_vae&quot;&#93; = SAAudioVAEModel(audio_config)

    if not _resolve(&quot;vae&quot;):
        modules&#91;&quot;vae&quot;&#93; = self._load_video_vae(fastvideo_args)

    return modules

def _load_video_vae(self, fastvideo_args: FastVideoArgs) -&gt; Any:
    &quot;&quot;&quot;Resolve the video VAE: prefer a bundled ``vae/`` subfolder in
    the converted repo (legacy), fall back to lazy-downloading the
    Wan 2.2 TI2V-5B VAE shards from upstream.

    Either way the load goes through FastVideo&#x27;s standard
    ``VAELoader`` so the result is the same FV ``AutoencoderKLWan``
    nn.Module that the bundled path produces.
    &quot;&quot;&quot;
    from fastvideo.models.loader.component_loader import VAELoader

    bundled = Path(self.model_path) / &quot;vae&quot;
    if bundled.is_dir() and (bundled / &quot;config.json&quot;).is_file():
        logger.info(&quot;Loading bundled video VAE from %s&quot;, bundled)
        return VAELoader().load(str(bundled), fastvideo_args)

    from huggingface_hub import snapshot_download

    logger.info(
        &quot;Bundled vae/ not found at %s; lazy-loading Wan 2.2 TI2V-5B VAE from %s&quot;,
        self.model_path,
        _WAN_VAE_HF_ID,
    )
    snapshot = snapshot_download(
        repo_id=_WAN_VAE_HF_ID,
        allow_patterns=&#91;&quot;vae/*&quot;&#93;,
    )
    vae_dir = os.path.join(snapshot, &quot;vae&quot;)
    if not os.path.isdir(vae_dir):
        raise RuntimeError(
            f&quot;snapshot_download returned &#123;snapshot&#125; but no vae/ &quot;
            f&quot;subfolder was found inside it. Check that &#123;_WAN_VAE_HF_ID&#125; &quot;
            &quot;still exposes a Diffusers-format vae/ folder.&quot;, )
    return VAELoader().load(vae_dir, fastvideo_args)

def initialize_pipeline(self, fastvideo_args: FastVideoArgs) -&gt; None:
    # MagiHuman applies `flow_shift` during timestep setup; keep the
    # scheduler constructor at its default no-op shift.
    self.modules&#91;&quot;scheduler&quot;&#93; = FlowUniPCMultistepScheduler()

def create_pipeline_stages(self, fastvideo_args: FastVideoArgs) -&gt; None:
    self._add_input_and_conditioning_stages(fastvideo_args)
    self._add_base_latent_and_denoising_stages(fastvideo_args)
    self._add_decode_stages()

def _add_input_and_conditioning_stages(self, fastvideo_args: FastVideoArgs) -&gt; None:
    self.add_stage(
        stage_name=&quot;input_validation_stage&quot;,
        stage=InputValidationStage(),
    )

    self.add_stage(
        stage_name=&quot;prompt_encoding_stage&quot;,
        stage=TextEncodingStage(
            text_encoders=&#91;self.get_module(&quot;text_encoder&quot;)&#93;,
            tokenizers=&#91;self.get_module(&quot;tokenizer&quot;)&#93;,
        ),
    )

    self._add_reference_image_stage(fastvideo_args)

def _add_base_latent_and_denoising_stages(self, fastvideo_args: FastVideoArgs) -&gt; None:
    pc = fastvideo_args.pipeline_config
    dit_arch = pc.dit_config.arch_config

    # Data-proxy + eval knobs come from the PipelineConfig (`pc`).
    # Only DiT-architecture fields live on `dit_arch` now.
    self.add_stage(
        stage_name=&quot;latent_preparation_stage&quot;,
        stage=MagiHumanLatentPreparationStage(
            vae_stride=tuple(pc.vae_stride),
            z_dim=pc.z_dim,
            patch_size=tuple(dit_arch.patch_size),
            fps=pc.fps,
            t5_gemma_target_length=pc.t5_gemma_target_length,
            coords_style=pc.coords_style,
            text_offset=pc.text_offset,
            audio_in_channels=dit_arch.audio_in_channels,
        ),
    )

    self.add_stage(
        stage_name=&quot;denoising_stage&quot;,
        stage=MagiHumanDenoisingStage(
            transformer=self.get_module(&quot;transformer&quot;),
            scheduler=self.get_module(&quot;scheduler&quot;),
            patch_size=tuple(dit_arch.patch_size),
            video_in_channels=dit_arch.video_in_channels,
            audio_in_channels=dit_arch.audio_in_channels,
            video_txt_guidance_scale=pc.video_txt_guidance_scale,
            audio_txt_guidance_scale=pc.audio_txt_guidance_scale,
            cfg_number=pc.cfg_number,
            coords_style=pc.coords_style,
            video_guidance_high_t_threshold=pc.video_guidance_high_t_threshold,
            video_guidance_low_t_value=pc.video_guidance_low_t_value,
        ),
    )

def _add_decode_stages(self) -&gt; None:
    self.add_stage(
        stage_name=&quot;decoding_stage&quot;,
        stage=DecodingStage(vae=self.get_module(&quot;vae&quot;), pipeline=self),
    )

    self.add_stage(
        stage_name=&quot;audio_decoding_stage&quot;,
        stage=MagiHumanAudioDecodingStage(audio_vae=self.get_module(&quot;audio_vae&quot;), ),
    )

def _add_reference_image_stage(self, fastvideo_args: FastVideoArgs) -&gt; None:
    return</code></pre></details>
fastvideo.pipelines.basic.magi_human.magi_human_pipeline.MagiHumanPipeline.load_modules
load_modules(fastvideo_args: FastVideoArgs, loaded_modules: dict[str, Any] | None = None) -> dict[str, Any]
Load the variant-specific transformer + scheduler from the
converted MagiHuman repo and lazy-load the four cross-variant
shared components from their canonical upstream HF repos:

text_encoder, tokenizer -> google/t5gemma-9b-9b-ul2
    (gated, requires HF token with accepted terms of use)
audio_vae -> stabilityai/stable-audio-open-1.0 (gated)
vae -> Wan-AI/Wan2.2-TI2V-5B-Diffusers

Backwards-compatible with bundled converted repos: if any of
these subfolders is present locally and listed in
model_index.json, the standard component loader picks it up
via super(). Otherwise the loader is told to skip the entry and
we lazy-load it here.
Source
def load_modules(
    self,
    fastvideo_args: FastVideoArgs,
    loaded_modules: dict[str, Any] | None = None,
) -> dict[str, Any]:
    """Load the variant-specific transformer + scheduler from the
    converted MagiHuman repo and lazy-load the four cross-variant
    shared components from their canonical upstream HF repos:
  * text_encoder, tokenizer -&gt; ``google/t5gemma-9b-9b-ul2``
    (gated, requires HF token with accepted terms of use)
  * audio_vae -&gt; ``stabilityai/stable-audio-open-1.0`` (gated)
  * vae -&gt; ``Wan-AI/Wan2.2-TI2V-5B-Diffusers``

Backwards-compatible with bundled converted repos: if any of
these subfolders is present locally and listed in
``model_index.json``, the standard component loader picks it up
via super(). Otherwise the loader is told to skip the entry and
we lazy-load it here.
&quot;&quot;&quot;
# T5-Gemma is gated: expose `HF_API_KEY` as `HF_TOKEN` if needed.
_ensure_hf_token_env()

# Resolve to a local cache path so we can inspect
# model_index.json before invoking super(). `maybe_download_model`
# is idempotent for local paths; super() repeats the call cheaply
# via `_load_config`.
local_path = maybe_download_model(self.model_path)

# Identify which cross-variant shared keys are bundled in the
# converted repo (declared in model_index.json with a non-null
# spec) versus absent (the umbrella scheme). Bundled keys stay
# in `required_config_modules` and are loaded normally by super()
# from `&lt;model_path&gt;/&lt;key&gt;/`. Absent keys are temporarily
# dropped so super() does not fail the &quot;every required entry
# must appear in model_index.json&quot; check, then lazy-loaded
# below.
model_index: dict&#91;str, Any&#93; = &#123;&#125;
try:
    with open(Path(local_path) / &quot;model_index.json&quot;) as f:
        model_index = json.load(f)
except (FileNotFoundError, json.JSONDecodeError):
    pass

def _is_bundled(key: str) -&gt; bool:
    spec = model_index.get(key)
    return (isinstance(spec, list | tuple) and len(spec) &gt;= 1 and spec&#91;0&#93; is not None)

deferred = &#91;&#93;
for key in (&quot;text_encoder&quot;, &quot;tokenizer&quot;, &quot;audio_vae&quot;, &quot;vae&quot;):
    if key in self.required_config_modules and not _is_bundled(key):
        self.required_config_modules.remove(key)
        deferred.append(key)

try:
    modules = super().load_modules(fastvideo_args, loaded_modules)
finally:
    for key in deferred:
        if key not in self.required_config_modules:
            self.required_config_modules.append(key)

# For each lazy-load key, prefer whatever super() already loaded
# (a bundled subfolder, or a caller-provided override merged in
# via `loaded_modules`). Fall back to the caller-provided
# `loaded_modules` entry for keys absent from model_index.json
# (super() never iterates those). Otherwise lazy-load from the
# canonical upstream HF repo.
def _resolve(key: str) -&gt; bool:
    &quot;&quot;&quot;Return True if `modules&#91;key&#93;` is already populated.&quot;&quot;&quot;
    if modules.get(key) is not None:
        return True
    if loaded_modules and key in loaded_modules:
        modules&#91;key&#93; = loaded_modules&#91;key&#93;
        return True
    return False

if not _resolve(&quot;text_encoder&quot;):
    logger.info(&quot;Building T5-Gemma text encoder (lazy-load from %s)&quot;, _T5GEMMA_HF_ID)
    enc_config = T5GemmaEncoderConfig()
    enc_config.arch_config.t5gemma_model_path = _T5GEMMA_HF_ID
    modules&#91;&quot;text_encoder&quot;&#93; = T5GemmaEncoderModel(enc_config)

if not _resolve(&quot;tokenizer&quot;):
    logger.info(&quot;Loading T5-Gemma tokenizer from %s&quot;, _T5GEMMA_HF_ID)
    modules&#91;&quot;tokenizer&quot;&#93; = AutoTokenizer.from_pretrained(_T5GEMMA_HF_ID)

if not _resolve(&quot;audio_vae&quot;):
    logger.info(
        &quot;Building Stable Audio Open 1.0 VAE (lazy-load from %s) — &quot;
        &quot;requires HF terms accepted for gated repo&quot;,
        _SA_AUDIO_HF_ID,
    )
    audio_config = OobleckVAEConfig()
    audio_config.pretrained_path = _SA_AUDIO_HF_ID
    modules&#91;&quot;audio_vae&quot;&#93; = SAAudioVAEModel(audio_config)

if not _resolve(&quot;vae&quot;):
    modules&#91;&quot;vae&quot;&#93; = self._load_video_vae(fastvideo_args)

return modules</code></pre></details>
fastvideo.pipelines.basic.magi_human.magi_human_pipeline.MagiHumanPipeline.initialize_pipeline
initialize_pipeline(fastvideo_args: FastVideoArgs) -> None
Source
def initialize_pipeline(self, fastvideo_args: FastVideoArgs) -> None:
    # MagiHuman applies `flow_shift` during timestep setup; keep the
    # scheduler constructor at its default no-op shift.
    self.modules["scheduler"] = FlowUniPCMultistepScheduler()
fastvideo.pipelines.basic.magi_human.magi_human_pipeline.MagiHumanPipeline.create_pipeline_stages
create_pipeline_stages(fastvideo_args: FastVideoArgs) -> None
Source
def create_pipeline_stages(self, fastvideo_args: FastVideoArgs) -> None:
    self._add_input_and_conditioning_stages(fastvideo_args)
    self._add_base_latent_and_denoising_stages(fastvideo_args)
    self._add_decode_stages()
fastvideo.pipelines.basic.magi_human.magi_human_pipeline.MagiHumanI2VPipeline
MagiHumanI2VPipeline(model_path: str, fastvideo_args: FastVideoArgs | TrainingArgs, required_config_modules: list[str] | None = None, loaded_modules: dict[str, torch.nn.Module] | None = None)
MagiHuman text+image-to-AV pipeline using the T2V DiT weights.
Source
class MagiHumanI2VPipeline(MagiHumanPipeline):
    """MagiHuman text+image-to-AV pipeline using the T2V DiT weights."""
def _add_reference_image_stage(self, fastvideo_args: FastVideoArgs) -&gt; None:
    pc = fastvideo_args.pipeline_config
    self.add_stage(
        stage_name=&quot;reference_image_stage&quot;,
        stage=MagiHumanReferenceImageStage(
            vae=self.get_module(&quot;vae&quot;),
            vae_scale_factor=pc.vae_stride&#91;1&#93;,
        ),
    )</code></pre></details>
fastvideo.pipelines.basic.magi_human.magi_human_pipeline.MagiHumanSRPipeline
MagiHumanSRPipeline(model_path: str, fastvideo_args: FastVideoArgs | TrainingArgs, required_config_modules: list[str] | None = None, loaded_modules: dict[str, torch.nn.Module] | None = None)
Two-stage MagiHuman base + SR-540p text-to-AV pipeline.
Source
class MagiHumanSRPipeline(MagiHumanPipeline):
    """Two-stage MagiHuman base + SR-540p text-to-AV pipeline."""
_required_config_modules = &#91;
    &quot;text_encoder&quot;,
    &quot;tokenizer&quot;,
    &quot;vae&quot;,
    &quot;transformer&quot;,
    &quot;sr_transformer&quot;,
    &quot;scheduler&quot;,
    &quot;audio_vae&quot;,
&#93;

def create_pipeline_stages(self, fastvideo_args: FastVideoArgs) -&gt; None:
    self._add_input_and_conditioning_stages(fastvideo_args)
    self._add_base_latent_and_denoising_stages(fastvideo_args)
    self._add_sr_latent_and_denoising_stages(fastvideo_args)
    self._add_decode_stages()

def _add_sr_latent_and_denoising_stages(self, fastvideo_args: FastVideoArgs) -&gt; None:
    pc = fastvideo_args.pipeline_config
    dit_arch = pc.dit_config.arch_config
    sr_transformer = self.get_module(&quot;sr_transformer&quot;)
    sr_local_attn_layers = tuple(getattr(pc, &quot;sr_local_attn_layers&quot;, ()))
    if sr_local_attn_layers and hasattr(sr_transformer, &quot;configure_local_attention&quot;):
        sr_transformer.configure_local_attention(
            sr_local_attn_layers,
            frame_receptive_field=pc.frame_receptive_field,
        )

    self.add_stage(
        stage_name=&quot;sr_latent_preparation_stage&quot;,
        stage=MagiHumanSRLatentPreparationStage(
            vae=self.get_module(&quot;vae&quot;),
            vae_stride=tuple(pc.vae_stride),
            patch_size=tuple(dit_arch.patch_size),
            noise_value=pc.noise_value,
            sr_audio_noise_scale=pc.sr_audio_noise_scale,
            sr_height=pc.sr_height,
            sr_width=pc.sr_width,
            vae_scale_factor=pc.vae_stride&#91;1&#93;,
        ),
    )
    self.add_stage(
        stage_name=&quot;sr_denoising_stage&quot;,
        stage=MagiHumanSRDenoisingStage(
            transformer=sr_transformer,
            scheduler=self.get_module(&quot;scheduler&quot;),
            patch_size=tuple(dit_arch.patch_size),
            video_in_channels=dit_arch.video_in_channels,
            audio_in_channels=dit_arch.audio_in_channels,
            sr_num_inference_steps=pc.sr_num_inference_steps,
            sr_video_txt_guidance_scale=pc.sr_video_txt_guidance_scale,
            use_cfg_trick=pc.use_cfg_trick,
            cfg_trick_start_frame=pc.cfg_trick_start_frame,
            cfg_trick_value=pc.cfg_trick_value,
            cfg_number=pc.cfg_number,
            coords_style=&quot;v1&quot;,
        ),
    )</code></pre></details>
fastvideo.pipelines.basic.magi_human.magi_human_pipeline.MagiHumanSRPipeline.create_pipeline_stages
create_pipeline_stages(fastvideo_args: FastVideoArgs) -> None
Source
def create_pipeline_stages(self, fastvideo_args: FastVideoArgs) -> None:
    self._add_input_and_conditioning_stages(fastvideo_args)
    self._add_base_latent_and_denoising_stages(fastvideo_args)
    self._add_sr_latent_and_denoising_stages(fastvideo_args)
    self._add_decode_stages()
fastvideo.pipelines.basic.magi_human.magi_human_pipeline.MagiHumanSRI2VPipeline
MagiHumanSRI2VPipeline(model_path: str, fastvideo_args: FastVideoArgs | TrainingArgs, required_config_modules: list[str] | None = None, loaded_modules: dict[str, torch.nn.Module] | None = None)
Two-stage MagiHuman base + SR-540p text+image-to-AV pipeline.
Source
class MagiHumanSRI2VPipeline(MagiHumanSRPipeline):
    """Two-stage MagiHuman base + SR-540p text+image-to-AV pipeline."""
def _add_reference_image_stage(self, fastvideo_args: FastVideoArgs) -&gt; None:
    pc = fastvideo_args.pipeline_config
    self.add_stage(
        stage_name=&quot;reference_image_stage&quot;,
        stage=MagiHumanReferenceImageStage(
            vae=self.get_module(&quot;vae&quot;),
            vae_scale_factor=pc.vae_stride&#91;1&#93;,
        ),
    )</code></pre></details>
fastvideo.pipelines.basic.magi_human.magi_human_pipeline.MagiHumanSR1080pPipeline
MagiHumanSR1080pPipeline(model_path: str, fastvideo_args: FastVideoArgs | TrainingArgs, required_config_modules: list[str] | None = None, loaded_modules: dict[str, torch.nn.Module] | None = None)
Two-stage MagiHuman base + SR-1080p text-to-AV pipeline.
The stage chain is identical to SR-540p. The paired pipeline config enables
block-sparse local-window attention on 32 SR-DiT layers and requests the
1080p latent target.
Source
class MagiHumanSR1080pPipeline(MagiHumanSRPipeline):
    """Two-stage MagiHuman base + SR-1080p text-to-AV pipeline.
The stage chain is identical to SR-540p. The paired pipeline config enables
block-sparse local-window attention on 32 SR-DiT layers and requests the
1080p latent target.
&quot;&quot;&quot;</code></pre></details>
fastvideo.pipelines.basic.magi_human.magi_human_pipeline.MagiHumanSR1080pI2VPipeline
MagiHumanSR1080pI2VPipeline(model_path: str, fastvideo_args: FastVideoArgs | TrainingArgs, required_config_modules: list[str] | None = None, loaded_modules: dict[str, torch.nn.Module] | None = None)
Two-stage MagiHuman base + SR-1080p text+image-to-AV pipeline.
Source
class MagiHumanSR1080pI2VPipeline(MagiHumanSRI2VPipeline):
    """Two-stage MagiHuman base + SR-1080p text+image-to-AV pipeline."""