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Ernie-Image TextEncoder for DiffSynth-Studio.
Wraps transformers Ministral3Model to output text embeddings.
Pattern: lazy import + manual config dict + torch.nn.Module wrapper.
Only loads the text (language) model, ignoring vision components.
"""
import torch
class ErnieImageTextEncoder(torch.nn.Module):
"""
Text encoder using Ministral3Model (transformers).
Only the text_config portion of the full Mistral3Model checkpoint.
Uses the base model (no lm_head) since the checkpoint only has embeddings.
"""
def __init__(self):
super().__init__()
from transformers import Ministral3Config, Ministral3Model
text_config = {
"attention_dropout": 0.0,
"bos_token_id": 1,
"dtype": "bfloat16",
"eos_token_id": 2,
"head_dim": 128,
"hidden_act": "silu",
"hidden_size": 3072,
"initializer_range": 0.02,
"intermediate_size": 9216,
"max_position_embeddings": 262144,
"model_type": "ministral3",
"num_attention_heads": 32,
"num_hidden_layers": 26,
"num_key_value_heads": 8,
"pad_token_id": 11,
"rms_norm_eps": 1e-05,
"rope_parameters": {
"beta_fast": 32.0,
"beta_slow": 1.0,
"factor": 16.0,
"llama_4_scaling_beta": 0.1,
"mscale": 1.0,
"mscale_all_dim": 1.0,
"original_max_position_embeddings": 16384,
"rope_theta": 1000000.0,
"rope_type": "yarn",
"type": "yarn",
},
"sliding_window": None,
"tie_word_embeddings": True,
"use_cache": True,
"vocab_size": 131072,
}
config = Ministral3Config(**text_config)
self.model = Ministral3Model(config)
self.config = config
def forward(
self,
input_ids=None,
attention_mask=None,
position_ids=None,
**kwargs,
):
outputs = self.model(
input_ids=input_ids,
attention_mask=attention_mask,
position_ids=position_ids,
output_hidden_states=True,
return_dict=True,
**kwargs,
)
return (outputs.hidden_states,)
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