mistral3
HF-backed Mistral3 text encoder wrapper for full Flux2.
fastvideo.models.encoders.mistral3.Mistral3TextConfig
Mistral3TextConfig(arch_config: TextEncoderArchConfig = Mistral3TextArchConfig(), prefix: str = 'mistral3', quant_config: QuantizationConfig | None = None, lora_config: Any | None = None, is_chat_model: bool = True, treat_empty_as_dot: bool = False, *, _resolved_attention_backend: AttentionBackendEnum | None = None, chat_template_enable_thinking: bool = False)Top-level config for the Mistral3 full Flux2 text encoder.
fastvideo.models.encoders.mistral3.TextEncoder
TextEncoder(config: TextEncoderConfig)Base for native encoders with a model-specific forward output contract.
fastvideo.models.encoders.mistral3.TextEncoder.forward
forward(*args: Any, **kwargs: Any) -> TextEncoderOutputTfastvideo.models.encoders.mistral3.Mistral3ForConditionalGeneration
Mistral3ForConditionalGeneration(config: Mistral3TextConfig)Loads the Transformers Mistral3 implementation for Flux2 text encoding.
Source
class Mistral3ForConditionalGeneration(TextEncoder):
"""Loads the Transformers Mistral3 implementation for Flux2 text encoding."""
supports_hf_from_pretrained = True
def __init__(self, config: Mistral3TextConfig) -> None:
super().__init__(config)
self.config = config
@classmethod
def from_pretrained_local(
cls,
model_path: str,
model_config: Mistral3TextConfig,
dtype: torch.dtype,
device: torch.device,
) -> nn.Module:
from transformers import AutoModelForImageTextToText
model = AutoModelForImageTextToText.from_pretrained(
model_path,
local_files_only=True,
torch_dtype=dtype,
low_cpu_mem_usage=True,
).eval()
if device.type != "cpu":
model = model.to(device)
return model
def forward(self, *args: Any, **kwargs: Any) -> Any:
raise NotImplementedError("Mistral3ForConditionalGeneration is loaded through Transformers "
"via from_pretrained_local().")</code></pre></details>
fastvideo.models.encoders.mistral3.Mistral3ForConditionalGeneration.from_pretrained_local
from_pretrained_local(model_path: str, model_config: Mistral3TextConfig, dtype: torch.dtype, device: torch.device) -> nn.Module
Source
@classmethod
def from_pretrained_local(
cls,
model_path: str,
model_config: Mistral3TextConfig,
dtype: torch.dtype,
device: torch.device,
) -> nn.Module:
from transformers import AutoModelForImageTextToText
model = AutoModelForImageTextToText.from_pretrained(
model_path,
local_files_only=True,
torch_dtype=dtype,
low_cpu_mem_usage=True,
).eval()
if device.type != "cpu":
model = model.to(device)
return model</code></pre></details>
fastvideo.models.encoders.mistral3.Mistral3ForConditionalGeneration.forward
forward(*args: Any, **kwargs: Any) -> Any
Source
def forward(self, *args: Any, **kwargs: Any) -> Any:
raise NotImplementedError("Mistral3ForConditionalGeneration is loaded through Transformers "
"via from_pretrained_local().")