pipeline_configs

def t5gemma_postprocess_text(outputs: BaseEncoderOutput) -> torch.Tensor:
    """Return per-prompt last_hidden_state as a batched [B, L, D] tensor.
MagiHuman pads/trims the embedding to a fixed length in its own
`pad_or_trim` helper at pipeline time. Here we simply hand through
whatever the tokenizer produced — the latent-prep stage is responsible
for pad/trim so that the original context length can be preserved.
"""
hidden = outputs.last_hidden_state
assert torch.isnan(hidden).sum() == 0
# Keep the shape the tokenizer emitted; the pipeline stage handles
# pad-or-trim to t5_gemma_target_length=640.
return hidden</code></pre></details>
fastvideo.pipelines.basic.magi_human.pipeline_configs.MagiHumanBaseConfig
MagiHumanBaseConfig(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 = MagiHumanVideoConfig(), 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 = None, vae_tiling: bool = False, vae_sp: bool = False, image_encoder_config: EncoderConfig = EncoderConfig(), image_encoder_precision: str = 'fp32', image_encoder_configs: tuple[EncoderConfig, ...] | None = None, image_encoder_precisions: tuple[str, ...] | None = None, text_encoder_configs: tuple[EncoderConfig, ...] = (lambda: (T5GemmaEncoderConfig(),))(), text_encoder_precisions: tuple[str, ...] = (lambda: ('bf16',))(), preprocess_text_funcs: tuple[Callable[[str], str], ...] = (lambda: (preprocess_text,))(), postprocess_text_funcs: tuple[Callable[[BaseEncoderOutput], torch.Tensor], ...] = (lambda: (t5gemma_postprocess_text,))(), dmd_denoising_steps: list[int] | None = None, ti2v_task: bool = False, lucy_edit_task: bool = False, boundary_ratio: float | None = None, audio_vae_config: VAEConfig = OobleckVAEConfig(), precision: str = 'bf16', t5_gemma_target_length: int = 640, fps: int = 25, num_inference_steps: int = 32, video_txt_guidance_scale: float = 5.0, audio_txt_guidance_scale: float = 5.0, cfg_number: int = 2, vae_stride: tuple[int, int, int] = (4, 16, 16), z_dim: int = 48, frame_receptive_field: int = 11, coords_style: str = 'v2', ref_audio_offset: int = 1000, text_offset: int = 0, video_guidance_high_t_threshold: int = 500, video_guidance_low_t_value: float = 2.0)
Base MagiHuman text-to-AV pipeline config (prompt → video + audio).
MagiHuman's base model is a joint audio-visual generator. This config
wires up both the video VAE (Wan 2.2 TI2V-5B) and the audio VAE
(Stable Audio Open 1.0); the pipeline produces an mp4 with a muxed
audio track. The framework's WorkloadType enum has no T2AV
variant yet, so the registry entry uses WorkloadType.T2V as a
placeholder.
Source
@dataclass
class MagiHumanBaseConfig(PipelineConfig):
    """Base MagiHuman text-to-AV pipeline config (prompt → video + audio).
MagiHuman&#x27;s base model is a joint audio-visual generator. This config
wires up both the video VAE (Wan 2.2 TI2V-5B) and the audio VAE
(Stable Audio Open 1.0); the pipeline produces an mp4 with a muxed
audio track. The framework&#x27;s `WorkloadType` enum has no `T2AV`
variant yet, so the registry entry uses `WorkloadType.T2V` as a
placeholder.
&quot;&quot;&quot;

# DiT
dit_config: DiTConfig = field(default_factory=MagiHumanVideoConfig)
# VAE — Wan 2.2 TI2V-5B. Diffusers `vae/config.json` drives arch_config
# at load time, including z_dim=48 and scale_factor_temporal=4 /
# scale_factor_spatial=16.
vae_config: VAEConfig = field(default_factory=WanVAEConfig)
vae_tiling: bool = False
vae_sp: bool = False

# Audio VAE — Stable Audio Open 1.0 (Oobleck), shared with the
# standalone Stable Audio pipeline. Lazy-loaded from
# `stabilityai/stable-audio-open-1.0` (HF gated, Apache 2.0).
audio_vae_config: VAEConfig = field(default_factory=OobleckVAEConfig)

# Denoising (flow-matching UniPC).
flow_shift: float | None = 5.0

# Text encoding
text_encoder_configs: tuple&#91;EncoderConfig, ...&#93; = field(default_factory=lambda: (T5GemmaEncoderConfig(), ))
postprocess_text_funcs: tuple&#91;Callable&#91;&#91;BaseEncoderOutput&#93;, torch.Tensor&#93;,
                              ...&#93; = field(default_factory=lambda: (t5gemma_postprocess_text, ))

# Precisions — the DiT runs bf16 internally, the text encoder is
# bf16-native, and the VAE decode path benefits from fp32 for long
# sequences.
precision: str = &quot;bf16&quot;
vae_precision: str = &quot;fp32&quot;
text_encoder_precisions: tuple&#91;str, ...&#93; = field(default_factory=lambda: (&quot;bf16&quot;, ))

# MagiHuman-specific defaults surfaced for the pipeline stages. These
# are pipeline-level knobs sourced from the upstream
# `EvaluationConfig` / `DataProxyConfig` (not `ModelConfig`), so they
# belong here, NOT on `MagiHumanArchConfig`.
t5_gemma_target_length: int = 640
fps: int = 25
num_inference_steps: int = 32
video_txt_guidance_scale: float = 5.0
audio_txt_guidance_scale: float = 5.0
cfg_number: int = 2

# VAE / data-proxy knobs (were on ArchConfig before; moved here).
vae_stride: tuple&#91;int, int, int&#93; = (4, 16, 16)
z_dim: int = 48
frame_receptive_field: int = 11
coords_style: str = &quot;v2&quot;
ref_audio_offset: int = 1000
text_offset: int = 0

# Video CFG step-dependent guidance: low-t steps use a relaxed scale.
# Upstream daVinci-MagiHuman/inference/pipeline/video_generate.py:426
# uses 5.0 for high-t and 2.0 for low-t with cutoff at t=500.
video_guidance_high_t_threshold: int = 500
video_guidance_low_t_value: float = 2.0

def __post_init__(self) -&gt; None:
    # Base text-to-AV does not need the VAE encoder (no reference-image
    # conditioning). Keep decoder only to save memory.
    self.vae_config.load_encoder = False
    self.vae_config.load_decoder = True</code></pre></details>
fastvideo.pipelines.basic.magi_human.pipeline_configs.MagiHumanBaseI2VConfig
MagiHumanBaseI2VConfig(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 = MagiHumanVideoConfig(), 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 = None, vae_tiling: bool = False, vae_sp: bool = False, image_encoder_config: EncoderConfig = EncoderConfig(), image_encoder_precision: str = 'fp32', image_encoder_configs: tuple[EncoderConfig, ...] | None = None, image_encoder_precisions: tuple[str, ...] | None = None, text_encoder_configs: tuple[EncoderConfig, ...] = (lambda: (T5GemmaEncoderConfig(),))(), text_encoder_precisions: tuple[str, ...] = (lambda: ('bf16',))(), preprocess_text_funcs: tuple[Callable[[str], str], ...] = (lambda: (preprocess_text,))(), postprocess_text_funcs: tuple[Callable[[BaseEncoderOutput], torch.Tensor], ...] = (lambda: (t5gemma_postprocess_text,))(), dmd_denoising_steps: list[int] | None = None, ti2v_task: bool = False, lucy_edit_task: bool = False, boundary_ratio: float | None = None, audio_vae_config: VAEConfig = OobleckVAEConfig(), precision: str = 'bf16', t5_gemma_target_length: int = 640, fps: int = 25, num_inference_steps: int = 32, video_txt_guidance_scale: float = 5.0, audio_txt_guidance_scale: float = 5.0, cfg_number: int = 2, vae_stride: tuple[int, int, int] = (4, 16, 16), z_dim: int = 48, frame_receptive_field: int = 11, coords_style: str = 'v2', ref_audio_offset: int = 1000, text_offset: int = 0, video_guidance_high_t_threshold: int = 500, video_guidance_low_t_value: float = 2.0, image_conditioning: bool = True)
Base MagiHuman text+image-to-AV pipeline config.
TI2V reuses the T2V DiT weights; the only pipeline-side difference is
that a reference image is encoded with the Wan VAE and reinserted into
the first video-latent frame before every denoise step.
Source
@dataclass
class MagiHumanBaseI2VConfig(MagiHumanBaseConfig):
    """Base MagiHuman text+image-to-AV pipeline config.
TI2V reuses the T2V DiT weights; the only pipeline-side difference is
that a reference image is encoded with the Wan VAE and reinserted into
the first video-latent frame before every denoise step.
&quot;&quot;&quot;

image_conditioning: bool = True

def __post_init__(self) -&gt; None:
    self.vae_config.load_encoder = True
    self.vae_config.load_decoder = True</code></pre></details>
fastvideo.pipelines.basic.magi_human.pipeline_configs.MagiHumanDistillConfig
MagiHumanDistillConfig(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 = MagiHumanVideoConfig(), 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 = None, vae_tiling: bool = False, vae_sp: bool = False, image_encoder_config: EncoderConfig = EncoderConfig(), image_encoder_precision: str = 'fp32', image_encoder_configs: tuple[EncoderConfig, ...] | None = None, image_encoder_precisions: tuple[str, ...] | None = None, text_encoder_configs: tuple[EncoderConfig, ...] = (lambda: (T5GemmaEncoderConfig(),))(), text_encoder_precisions: tuple[str, ...] = (lambda: ('bf16',))(), preprocess_text_funcs: tuple[Callable[[str], str], ...] = (lambda: (preprocess_text,))(), postprocess_text_funcs: tuple[Callable[[BaseEncoderOutput], torch.Tensor], ...] = (lambda: (t5gemma_postprocess_text,))(), dmd_denoising_steps: list[int] | None = None, ti2v_task: bool = False, lucy_edit_task: bool = False, boundary_ratio: float | None = None, audio_vae_config: VAEConfig = OobleckVAEConfig(), precision: str = 'bf16', t5_gemma_target_length: int = 640, fps: int = 25, num_inference_steps: int = 8, video_txt_guidance_scale: float = 5.0, audio_txt_guidance_scale: float = 5.0, cfg_number: int = 1, vae_stride: tuple[int, int, int] = (4, 16, 16), z_dim: int = 48, frame_receptive_field: int = 11, coords_style: str = 'v2', ref_audio_offset: int = 1000, text_offset: int = 0, video_guidance_high_t_threshold: int = 500, video_guidance_low_t_value: float = 2.0)
DMD-2 distilled MagiHuman text-to-AV pipeline config.
Same arch as base (identical 331 keys, same shapes, same module tree),
but trained via DMD-2 for 8-step inference without classifier-free
guidance. Weights are stored in fp32 upstream; the conversion script's
--cast-bf16 flag reduces the checkpoint to ~30 GB on disk.
Source
@dataclass
class MagiHumanDistillConfig(MagiHumanBaseConfig):
    """DMD-2 distilled MagiHuman text-to-AV pipeline config.
Same arch as base (identical 331 keys, same shapes, same module tree),
but trained via DMD-2 for 8-step inference without classifier-free
guidance. Weights are stored in fp32 upstream; the conversion script&#x27;s
`--cast-bf16` flag reduces the checkpoint to ~30 GB on disk.
&quot;&quot;&quot;

num_inference_steps: int = 8
cfg_number: int = 1  # DMD distilled models skip CFG.
# Lower flow_shift matches the distilled DMD schedule; if parity later
# shows drift, measure against `scheduler_config.json` generated by the
# conversion script for the distill subfolder.
flow_shift: float | None = 5.0</code></pre></details>
fastvideo.pipelines.basic.magi_human.pipeline_configs.MagiHumanDistillI2VConfig
MagiHumanDistillI2VConfig(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 = MagiHumanVideoConfig(), 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 = None, vae_tiling: bool = False, vae_sp: bool = False, image_encoder_config: EncoderConfig = EncoderConfig(), image_encoder_precision: str = 'fp32', image_encoder_configs: tuple[EncoderConfig, ...] | None = None, image_encoder_precisions: tuple[str, ...] | None = None, text_encoder_configs: tuple[EncoderConfig, ...] = (lambda: (T5GemmaEncoderConfig(),))(), text_encoder_precisions: tuple[str, ...] = (lambda: ('bf16',))(), preprocess_text_funcs: tuple[Callable[[str], str], ...] = (lambda: (preprocess_text,))(), postprocess_text_funcs: tuple[Callable[[BaseEncoderOutput], torch.Tensor], ...] = (lambda: (t5gemma_postprocess_text,))(), dmd_denoising_steps: list[int] | None = None, ti2v_task: bool = False, lucy_edit_task: bool = False, boundary_ratio: float | None = None, audio_vae_config: VAEConfig = OobleckVAEConfig(), precision: str = 'bf16', t5_gemma_target_length: int = 640, fps: int = 25, num_inference_steps: int = 8, video_txt_guidance_scale: float = 5.0, audio_txt_guidance_scale: float = 5.0, cfg_number: int = 1, vae_stride: tuple[int, int, int] = (4, 16, 16), z_dim: int = 48, frame_receptive_field: int = 11, coords_style: str = 'v2', ref_audio_offset: int = 1000, text_offset: int = 0, video_guidance_high_t_threshold: int = 500, video_guidance_low_t_value: float = 2.0, image_conditioning: bool = True)
DMD-2 distilled MagiHuman text+image-to-AV pipeline config.
Source
@dataclass
class MagiHumanDistillI2VConfig(MagiHumanDistillConfig):
    """DMD-2 distilled MagiHuman text+image-to-AV pipeline config."""
image_conditioning: bool = True

def __post_init__(self) -&gt; None:
    self.vae_config.load_encoder = True
    self.vae_config.load_decoder = True</code></pre></details>
fastvideo.pipelines.basic.magi_human.pipeline_configs.MagiHumanSR540pConfig
MagiHumanSR540pConfig(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 = MagiHumanVideoConfig(), 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 = None, vae_tiling: bool = False, vae_sp: bool = False, image_encoder_config: EncoderConfig = EncoderConfig(), image_encoder_precision: str = 'fp32', image_encoder_configs: tuple[EncoderConfig, ...] | None = None, image_encoder_precisions: tuple[str, ...] | None = None, text_encoder_configs: tuple[EncoderConfig, ...] = (lambda: (T5GemmaEncoderConfig(),))(), text_encoder_precisions: tuple[str, ...] = (lambda: ('bf16',))(), preprocess_text_funcs: tuple[Callable[[str], str], ...] = (lambda: (preprocess_text,))(), postprocess_text_funcs: tuple[Callable[[BaseEncoderOutput], torch.Tensor], ...] = (lambda: (t5gemma_postprocess_text,))(), dmd_denoising_steps: list[int] | None = None, ti2v_task: bool = False, lucy_edit_task: bool = False, boundary_ratio: float | None = None, audio_vae_config: VAEConfig = OobleckVAEConfig(), precision: str = 'bf16', t5_gemma_target_length: int = 640, fps: int = 25, num_inference_steps: int = 32, video_txt_guidance_scale: float = 5.0, audio_txt_guidance_scale: float = 5.0, cfg_number: int = 2, vae_stride: tuple[int, int, int] = (4, 16, 16), z_dim: int = 48, frame_receptive_field: int = 11, coords_style: str = 'v2', ref_audio_offset: int = 1000, text_offset: int = 0, video_guidance_high_t_threshold: int = 500, video_guidance_low_t_value: float = 2.0, noise_value: int = 220, sr_audio_noise_scale: float = 0.7, 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, sr_height: int = 512, sr_width: int = 896, sr_local_attn_layers: tuple[int, ...] = ())
Two-stage MagiHuman base + SR-540p text-to-AV pipeline config.
Source
@dataclass
class MagiHumanSR540pConfig(MagiHumanBaseConfig):
    """Two-stage MagiHuman base + SR-540p text-to-AV pipeline config."""
noise_value: int = 220
sr_audio_noise_scale: float = 0.7
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
# Upstream example/sr_540p uses sr_height=512, sr_width=896. Despite the
# marketing name, these are the VAE/patch-aligned dimensions actually run.
sr_height: int = 512
sr_width: int = 896
sr_local_attn_layers: tuple&#91;int, ...&#93; = ()</code></pre></details>
fastvideo.pipelines.basic.magi_human.pipeline_configs.MagiHumanSR540pI2VConfig
MagiHumanSR540pI2VConfig(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 = MagiHumanVideoConfig(), 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 = None, vae_tiling: bool = False, vae_sp: bool = False, image_encoder_config: EncoderConfig = EncoderConfig(), image_encoder_precision: str = 'fp32', image_encoder_configs: tuple[EncoderConfig, ...] | None = None, image_encoder_precisions: tuple[str, ...] | None = None, text_encoder_configs: tuple[EncoderConfig, ...] = (lambda: (T5GemmaEncoderConfig(),))(), text_encoder_precisions: tuple[str, ...] = (lambda: ('bf16',))(), preprocess_text_funcs: tuple[Callable[[str], str], ...] = (lambda: (preprocess_text,))(), postprocess_text_funcs: tuple[Callable[[BaseEncoderOutput], torch.Tensor], ...] = (lambda: (t5gemma_postprocess_text,))(), dmd_denoising_steps: list[int] | None = None, ti2v_task: bool = False, lucy_edit_task: bool = False, boundary_ratio: float | None = None, audio_vae_config: VAEConfig = OobleckVAEConfig(), precision: str = 'bf16', t5_gemma_target_length: int = 640, fps: int = 25, num_inference_steps: int = 32, video_txt_guidance_scale: float = 5.0, audio_txt_guidance_scale: float = 5.0, cfg_number: int = 2, vae_stride: tuple[int, int, int] = (4, 16, 16), z_dim: int = 48, frame_receptive_field: int = 11, coords_style: str = 'v2', ref_audio_offset: int = 1000, text_offset: int = 0, video_guidance_high_t_threshold: int = 500, video_guidance_low_t_value: float = 2.0, noise_value: int = 220, sr_audio_noise_scale: float = 0.7, 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, sr_height: int = 512, sr_width: int = 896, sr_local_attn_layers: tuple[int, ...] = (), image_conditioning: bool = True)
Two-stage MagiHuman base + SR-540p text+image-to-AV config.
Source
@dataclass
class MagiHumanSR540pI2VConfig(MagiHumanSR540pConfig):
    """Two-stage MagiHuman base + SR-540p text+image-to-AV config."""
image_conditioning: bool = True

def __post_init__(self) -&gt; None:
    self.vae_config.load_encoder = True
    self.vae_config.load_decoder = True</code></pre></details>
fastvideo.pipelines.basic.magi_human.pipeline_configs.MagiHumanSR1080pConfig
MagiHumanSR1080pConfig(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 = MagiHumanVideoConfig(), 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 = None, vae_tiling: bool = False, vae_sp: bool = False, image_encoder_config: EncoderConfig = EncoderConfig(), image_encoder_precision: str = 'fp32', image_encoder_configs: tuple[EncoderConfig, ...] | None = None, image_encoder_precisions: tuple[str, ...] | None = None, text_encoder_configs: tuple[EncoderConfig, ...] = (lambda: (T5GemmaEncoderConfig(),))(), text_encoder_precisions: tuple[str, ...] = (lambda: ('bf16',))(), preprocess_text_funcs: tuple[Callable[[str], str], ...] = (lambda: (preprocess_text,))(), postprocess_text_funcs: tuple[Callable[[BaseEncoderOutput], torch.Tensor], ...] = (lambda: (t5gemma_postprocess_text,))(), dmd_denoising_steps: list[int] | None = None, ti2v_task: bool = False, lucy_edit_task: bool = False, boundary_ratio: float | None = None, audio_vae_config: VAEConfig = OobleckVAEConfig(), precision: str = 'bf16', t5_gemma_target_length: int = 640, fps: int = 25, num_inference_steps: int = 32, video_txt_guidance_scale: float = 5.0, audio_txt_guidance_scale: float = 5.0, cfg_number: int = 2, vae_stride: tuple[int, int, int] = (4, 16, 16), z_dim: int = 48, frame_receptive_field: int = 11, coords_style: str = 'v2', ref_audio_offset: int = 1000, text_offset: int = 0, video_guidance_high_t_threshold: int = 500, video_guidance_low_t_value: float = 2.0, noise_value: int = 220, sr_audio_noise_scale: float = 0.7, 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, sr_height: int = 1080, sr_width: int = 1920, sr_local_attn_layers: tuple[int, ...] = _SR_1080P_LOCAL_ATTN_LAYERS)
Two-stage MagiHuman base + SR-1080p text-to-AV pipeline config.
Source
@dataclass
class MagiHumanSR1080pConfig(MagiHumanSR540pConfig):
    """Two-stage MagiHuman base + SR-1080p text-to-AV pipeline config."""
sr_height: int = 1080
sr_width: int = 1920
sr_local_attn_layers: tuple&#91;int, ...&#93; = _SR_1080P_LOCAL_ATTN_LAYERS</code></pre></details>
fastvideo.pipelines.basic.magi_human.pipeline_configs.MagiHumanSR1080pI2VConfig
MagiHumanSR1080pI2VConfig(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 = MagiHumanVideoConfig(), 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 = None, vae_tiling: bool = False, vae_sp: bool = False, image_encoder_config: EncoderConfig = EncoderConfig(), image_encoder_precision: str = 'fp32', image_encoder_configs: tuple[EncoderConfig, ...] | None = None, image_encoder_precisions: tuple[str, ...] | None = None, text_encoder_configs: tuple[EncoderConfig, ...] = (lambda: (T5GemmaEncoderConfig(),))(), text_encoder_precisions: tuple[str, ...] = (lambda: ('bf16',))(), preprocess_text_funcs: tuple[Callable[[str], str], ...] = (lambda: (preprocess_text,))(), postprocess_text_funcs: tuple[Callable[[BaseEncoderOutput], torch.Tensor], ...] = (lambda: (t5gemma_postprocess_text,))(), dmd_denoising_steps: list[int] | None = None, ti2v_task: bool = False, lucy_edit_task: bool = False, boundary_ratio: float | None = None, audio_vae_config: VAEConfig = OobleckVAEConfig(), precision: str = 'bf16', t5_gemma_target_length: int = 640, fps: int = 25, num_inference_steps: int = 32, video_txt_guidance_scale: float = 5.0, audio_txt_guidance_scale: float = 5.0, cfg_number: int = 2, vae_stride: tuple[int, int, int] = (4, 16, 16), z_dim: int = 48, frame_receptive_field: int = 11, coords_style: str = 'v2', ref_audio_offset: int = 1000, text_offset: int = 0, video_guidance_high_t_threshold: int = 500, video_guidance_low_t_value: float = 2.0, noise_value: int = 220, sr_audio_noise_scale: float = 0.7, 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, sr_height: int = 1080, sr_width: int = 1920, sr_local_attn_layers: tuple[int, ...] = _SR_1080P_LOCAL_ATTN_LAYERS, image_conditioning: bool = True)
Two-stage MagiHuman base + SR-1080p text+image-to-AV config.
Source
@dataclass
class MagiHumanSR1080pI2VConfig(MagiHumanSR1080pConfig):
    """Two-stage MagiHuman base + SR-1080p text+image-to-AV config."""
image_conditioning: bool = True

def __post_init__(self) -&gt; None:
    self.vae_config.load_encoder = True
    self.vae_config.load_decoder = True</code></pre></details>