Image-Text-to-Video
Diffusers
Safetensors
MiniMaxH3ModularPipeline
text-to-video
image-to-video
video-to-video
text-to-audio-video
image-to-audio-video
image-text-to-audio-video
video-to-audio-video
audio-to-audio-video
audio-video-generation
multimodal
synchronized-audio-video
reference-to-audio-video
File size: 3,532 Bytes
5d9b308 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 | # SPDX-License-Identifier: Apache-2.0
# Remote entry: self-contained MiniMax H3 audio VAE (DAC-lineage encoder + BigVGAN decoder).
# Loaded via config.json:auto_map with trust_remote_code; weights are safetensors-only.
from __future__ import annotations
import json
from pathlib import Path
import torch.nn as nn
# --- dependency manifest ---
# diffusers' dynamic-module loader only copies ONE level of relative
# imports into its cache; list every bundle module here so all files
# are copied, letting their own second-level imports resolve.
from .dac_activations import SnakeBeta as _dep_dac_activations # noqa: F401
from .dac_alias_free_act import Activation1d as _dep_dac_alias_free_act # noqa: F401
from .dac_alias_free_filter import kaiser_sinc_filter1d as _dep_dac_alias_free_filter # noqa: F401
from .dac_alias_free_resample import UpSample1d as _dep_dac_alias_free_resample # noqa: F401
from .dac_attn_proj import GeGluMlp as _dep_dac_attn_proj # noqa: F401
from .dac_bigvgan import AttrDict as _dep_dac_bigvgan # noqa: F401
from .dac_audio_vae import AttrDict as _dep_dac_audio_vae # noqa: F401
from .dac_utils import init_weights as _dep_dac_utils # noqa: F401
# --- end dependency manifest ---
from safetensors.torch import load_file
from .dac_audio_vae import DacAudioVAE
def _load_yaml(path: Path) -> dict:
try:
import yaml
except ImportError as exc:
raise ImportError("MiniMax H3 audio VAE requires PyYAML.") from exc
with path.open("r", encoding="utf-8") as f:
return yaml.safe_load(f)
class MiniMaxH3AudioVAE(nn.Module):
def __init__(self, model: nn.Module) -> None:
super().__init__()
self.model = model
@classmethod
def from_pretrained(cls, pretrained_model_name_or_path: str, **kwargs):
component_dir = Path(pretrained_model_name_or_path)
with (component_dir / "config.json").open("r", encoding="utf-8") as f:
config = json.load(f)
audio_config = _load_yaml(component_dir / config["source_config_path"])
if "source_safetensors_path" not in config:
raise KeyError(
"source_safetensors_path is required; pickle checkpoints are not supported"
)
if "source_metadata_path" not in config:
raise KeyError(
"source_metadata_path is required when source_safetensors_path is set"
)
state_dict = load_file(
component_dir / config["source_safetensors_path"], device="cpu"
)
with (component_dir / config["source_metadata_path"]).open(
"r", encoding="utf-8"
) as f:
metadata_doc = json.load(f)
metadata = metadata_doc["metadata"]["kwargs"]
model = DacAudioVAE(
encoder_rates=metadata["encoder_rates"],
decoder_rates=metadata["decoder_rates"],
attn_proj=metadata["attn_proj"],
decoder_type=metadata["decoder_type"],
decoder_dim=audio_config["model_config"]["decoder_dim"],
vae_latent_channels=audio_config["model_config"]["vae_latent_channels"],
sample_rate=metadata["sample_rate"],
)
model.load_state_dict(state_dict, strict=True)
return cls(model.eval())
def decode(self, *args, **kwargs):
return self.model.decode(*args, **kwargs)
def __getattr__(self, name: str):
try:
return super().__getattr__(name)
except AttributeError:
return getattr(self.model, name)
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