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feat: Add basic text generation support with native models, initially supporting Gemma3 (#12392)

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This commit is contained in:
Jukka Seppänen
2026-02-19 03:49:43 +02:00
committed by GitHub
parent f262444dd4
commit 6d11cc7354
9 changed files with 502 additions and 33 deletions
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comfy/text_encoders/lumina2.py
+29 -7
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@@ -1,23 +1,23 @@
from comfy import sd1_clip
from .spiece_tokenizer import SPieceTokenizer
import comfy.text_encoders.llama
from comfy.text_encoders.lt import Gemma3_Tokenizer
import comfy.utils
class Gemma2BTokenizer(sd1_clip.SDTokenizer):
def __init__(self, embedding_directory=None, tokenizer_data={}):
tokenizer = tokenizer_data.get("spiece_model", None)
super().__init__(tokenizer, pad_with_end=False, embedding_size=2304, embedding_key='gemma2_2b', tokenizer_class=SPieceTokenizer, has_end_token=False, pad_to_max_length=False, max_length=99999999, min_length=1, tokenizer_args={"add_bos": True, "add_eos": False}, tokenizer_data=tokenizer_data)
special_tokens = {"<end_of_turn>": 107}
super().__init__(tokenizer, pad_with_end=False, embedding_size=2304, embedding_key='gemma2_2b', tokenizer_class=SPieceTokenizer, has_end_token=False, pad_to_max_length=False, max_length=99999999, min_length=1, tokenizer_args={"add_bos": True, "add_eos": False, "special_tokens": special_tokens}, tokenizer_data=tokenizer_data)
def state_dict(self):
return {"spiece_model": self.tokenizer.serialize_model()}
class Gemma3_4BTokenizer(sd1_clip.SDTokenizer):
class Gemma3_4BTokenizer(Gemma3_Tokenizer, sd1_clip.SDTokenizer):
def __init__(self, embedding_directory=None, tokenizer_data={}):
tokenizer = tokenizer_data.get("spiece_model", None)
super().__init__(tokenizer, pad_with_end=False, embedding_size=2560, embedding_key='gemma3_4b', tokenizer_class=SPieceTokenizer, has_end_token=False, pad_to_max_length=False, max_length=99999999, min_length=1, tokenizer_args={"add_bos": True, "add_eos": False}, disable_weights=True, tokenizer_data=tokenizer_data)
def state_dict(self):
return {"spiece_model": self.tokenizer.serialize_model()}
special_tokens = {"<image_soft_token>": 262144, "<end_of_turn>": 106}
super().__init__(tokenizer, pad_with_end=False, embedding_size=2560, embedding_key='gemma3_4b', tokenizer_class=SPieceTokenizer, has_end_token=False, pad_to_max_length=False, max_length=99999999, min_length=1, tokenizer_args={"add_bos": True, "add_eos": False, "special_tokens": special_tokens}, disable_weights=True, tokenizer_data=tokenizer_data)
class LuminaTokenizer(sd1_clip.SD1Tokenizer):
def __init__(self, embedding_directory=None, tokenizer_data={}):
@@ -31,6 +31,9 @@ class Gemma2_2BModel(sd1_clip.SDClipModel):
def __init__(self, device="cpu", layer="hidden", layer_idx=-2, dtype=None, attention_mask=True, model_options={}):
super().__init__(device=device, layer=layer, layer_idx=layer_idx, textmodel_json_config={}, dtype=dtype, special_tokens={"start": 2, "pad": 0}, layer_norm_hidden_state=False, model_class=comfy.text_encoders.llama.Gemma2_2B, enable_attention_masks=attention_mask, return_attention_masks=attention_mask, model_options=model_options)
def generate(self, embeds, do_sample, max_length, temperature, top_k, top_p, min_p, repetition_penalty, seed):
return super().generate(embeds, do_sample, max_length, temperature, top_k, top_p, min_p, repetition_penalty, seed, stop_tokens=[107])
class Gemma3_4BModel(sd1_clip.SDClipModel):
def __init__(self, device="cpu", layer="hidden", layer_idx=-2, dtype=None, attention_mask=True, model_options={}):
llama_quantization_metadata = model_options.get("llama_quantization_metadata", None)
@@ -40,6 +43,23 @@ class Gemma3_4BModel(sd1_clip.SDClipModel):
super().__init__(device=device, layer=layer, layer_idx=layer_idx, textmodel_json_config={}, dtype=dtype, special_tokens={"start": 2, "pad": 0}, layer_norm_hidden_state=False, model_class=comfy.text_encoders.llama.Gemma3_4B, enable_attention_masks=attention_mask, return_attention_masks=attention_mask, model_options=model_options)
def generate(self, embeds, do_sample, max_length, temperature, top_k, top_p, min_p, repetition_penalty, seed):
return super().generate(embeds, do_sample, max_length, temperature, top_k, top_p, min_p, repetition_penalty, seed, stop_tokens=[106])
class Gemma3_4B_Vision_Model(sd1_clip.SDClipModel):
def __init__(self, device="cpu", layer="hidden", layer_idx=-2, dtype=None, attention_mask=True, model_options={}):
llama_quantization_metadata = model_options.get("llama_quantization_metadata", None)
if llama_quantization_metadata is not None:
model_options = model_options.copy()
model_options["quantization_metadata"] = llama_quantization_metadata
super().__init__(device=device, layer=layer, layer_idx=layer_idx, textmodel_json_config={}, dtype=dtype, special_tokens={"start": 2, "pad": 0}, layer_norm_hidden_state=False, model_class=comfy.text_encoders.llama.Gemma3_4B_Vision, enable_attention_masks=attention_mask, return_attention_masks=attention_mask, model_options=model_options)
def process_tokens(self, tokens, device):
embeds, _, _, embeds_info = super().process_tokens(tokens, device)
comfy.utils.normalize_image_embeddings(embeds, embeds_info, self.transformer.model.config.hidden_size ** 0.5)
return embeds
class LuminaModel(sd1_clip.SD1ClipModel):
def __init__(self, device="cpu", dtype=None, model_options={}, name="gemma2_2b", clip_model=Gemma2_2BModel):
super().__init__(device=device, dtype=dtype, name=name, clip_model=clip_model, model_options=model_options)
@@ -50,6 +70,8 @@ def te(dtype_llama=None, llama_quantization_metadata=None, model_type="gemma2_2b
model = Gemma2_2BModel
elif model_type == "gemma3_4b":
model = Gemma3_4BModel
elif model_type == "gemma3_4b_vision":
model = Gemma3_4B_Vision_Model
class LuminaTEModel_(LuminaModel):
def __init__(self, device="cpu", dtype=None, model_options={}):