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Inherit TE from Qwen3VL

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kijai 2026-06-23 00:25:56 +03:00
parent fd5252096a
commit 2f0e8842f7
2 changed files with 16 additions and 37 deletions
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3
comfy/sd.py
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@ -1630,7 +1630,8 @@ def load_text_encoder_state_dicts(state_dicts=[], embedding_directory=None, clip
clip_data[0] = comfy.utils.state_dict_prefix_replace(clip_data[0], {"model.language_model.": "model.", "model.visual.": "visual.", "lm_head.": "model.lm_head."}) clip_data[0] = comfy.utils.state_dict_prefix_replace(clip_data[0], {"model.language_model.": "model.", "model.visual.": "visual.", "lm_head.": "model.lm_head."})
clip_target.clip = comfy.text_encoders.boogu.te(**llama_detect(clip_data)) clip_target.clip = comfy.text_encoders.boogu.te(**llama_detect(clip_data))
clip_target.tokenizer = comfy.text_encoders.boogu.BooguTokenizer clip_target.tokenizer = comfy.text_encoders.boogu.BooguTokenizer
elif clip_type == CLIPType.KREA2: # Krea2: Qwen3-VL-4B LM (12-layer tap) elif clip_type == CLIPType.KREA2 and te_model == TEModel.QWEN3VL_4B: # Krea2: full Qwen3-VL-4B (12-layer tap for conditioning + multimodal generate).
clip_data[0] = comfy.utils.state_dict_prefix_replace(clip_data[0], {"model.language_model.": "model.", "model.visual.": "visual.", "lm_head.": "model.lm_head."})
clip_target.clip = comfy.text_encoders.krea2.te(**llama_detect(clip_data)) clip_target.clip = comfy.text_encoders.krea2.te(**llama_detect(clip_data))
clip_target.tokenizer = comfy.text_encoders.krea2.Krea2Tokenizer clip_target.tokenizer = comfy.text_encoders.krea2.Krea2Tokenizer
elif clip_type in (CLIPType.FLUX, CLIPType.FLUX2): # Flux2 Klein reuses the Qwen3-VL LM (3-layer tap -> 12288); visual unused. elif clip_type in (CLIPType.FLUX, CLIPType.FLUX2): # Flux2 Klein reuses the Qwen3-VL LM (3-layer tap -> 12288); visual unused.

50
comfy/text_encoders/krea2.py
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@ -1,4 +1,4 @@
"""Krea 2 (K2) text encoder: Qwen3-VL-4B language model, 12-layer tap. """Krea 2 (K2) text encoder: Qwen3-VL-4B, 12-layer tap.
K2 conditions on a stack of hidden states from 12 layers of Qwen3-VL-4B K2 conditions on a stack of hidden states from 12 layers of Qwen3-VL-4B
(reference taps ``hidden_states[2,5,8,...,35]``), kept as a ``(B, 12, seq, 2560)`` tensor and (reference taps ``hidden_states[2,5,8,...,35]``), kept as a ``(B, 12, seq, 2560)`` tensor and
@ -6,61 +6,39 @@ consumed by the DiT's internal ``txtfusion`` adapter. Comfy carries conditioning
so the 12-layer stack is flattened to ``(B, seq, 12*2560)`` here and unpacked inside the model. so the 12-layer stack is flattened to ``(B, seq, 12*2560)`` here and unpacked inside the model.
""" """
import os
import numbers import numbers
import torch import torch
from transformers import Qwen2Tokenizer
import comfy.text_encoders.llama import comfy.text_encoders.qwen3vl
from comfy import sd1_clip from comfy import sd1_clip
# tap k == hidden_states[k] (no offset). # tap k == hidden_states[k] (no offset).
KREA2_TAP_LAYERS = [2, 5, 8, 11, 14, 17, 20, 23, 26, 29, 32, 35] KREA2_TAP_LAYERS = [2, 5, 8, 11, 14, 17, 20, 23, 26, 29, 32, 35]
QWEN3VL_4B_CONFIG = {"rope_theta": 5000000.0, "final_norm": False, "lm_head": False}
# Identical system template to Qwen-Image; Krea2 strips the system+user-opening prefix. # Identical system template to Qwen-Image; Krea2 strips the system+user-opening prefix.
KREA2_TEMPLATE = "<|im_start|>system\nDescribe the image by detailing the color, shape, size, texture, quantity, text, spatial relationships of the objects and background:<|im_end|>\n<|im_start|>user\n{}<|im_end|>\n<|im_start|>assistant\n" KREA2_TEMPLATE = "<|im_start|>system\nDescribe the image by detailing the color, shape, size, texture, quantity, text, spatial relationships of the objects and background:<|im_end|>\n<|im_start|>user\n{}<|im_end|>\n<|im_start|>assistant\n"
class Qwen3VL4BTokenizer(sd1_clip.SDTokenizer): class Krea2Tokenizer(comfy.text_encoders.qwen3vl.Qwen3VLTokenizer):
def __init__(self, embedding_directory=None, tokenizer_data={}): def __init__(self, embedding_directory=None, tokenizer_data={}):
tokenizer_path = os.path.join(os.path.dirname(os.path.realpath(__file__)), "qwen25_tokenizer") super().__init__(embedding_directory=embedding_directory, tokenizer_data=tokenizer_data, model_type="qwen3vl_4b")
super().__init__(tokenizer_path, pad_with_end=False, embedding_directory=embedding_directory, self.llama_template = KREA2_TEMPLATE # conditioning template; image text-gen uses qwen3vl's default image template.
embedding_size=2560, embedding_key='qwen3vl_4b', tokenizer_class=Qwen2Tokenizer,
has_start_token=False, has_end_token=False, pad_to_max_length=False, def tokenize_with_weights(self, text, return_word_ids=False, llama_template=None, images=[], prevent_empty_text=False, thinking=True, **kwargs):
max_length=99999999, min_length=1, pad_token=151643, tokenizer_data=tokenizer_data) # Krea2 conditions on the no-think template; thinking=True drops the empty <think> block qwen3vl adds.
return super().tokenize_with_weights(text, return_word_ids=return_word_ids, llama_template=llama_template, images=images, prevent_empty_text=prevent_empty_text, thinking=thinking, **kwargs)
class Krea2Tokenizer(sd1_clip.SD1Tokenizer): class Krea2Qwen3VLClipModel(comfy.text_encoders.qwen3vl.Qwen3VLClipModel):
def __init__(self, embedding_directory=None, tokenizer_data={}): def __init__(self, device="cpu", dtype=None, attention_mask=True, model_options={}):
super().__init__(embedding_directory=embedding_directory, tokenizer_data=tokenizer_data, super().__init__(device=device, layer=KREA2_TAP_LAYERS, layer_idx=None, dtype=dtype,
name="qwen3vl_4b", tokenizer=Qwen3VL4BTokenizer) attention_mask=attention_mask, model_options=model_options, model_type="qwen3vl_4b")
self.llama_template = KREA2_TEMPLATE
def tokenize_with_weights(self, text, return_word_ids=False, llama_template=None, **kwargs):
if text.startswith('<|im_start|>'):
llama_text = text
elif llama_template is None:
llama_text = self.llama_template.format(text)
else:
llama_text = llama_template.format(text)
return super().tokenize_with_weights(llama_text, return_word_ids=return_word_ids, disable_weights=True, **kwargs)
class Qwen3VL4BModel(sd1_clip.SDClipModel):
def __init__(self, device="cpu", layer="hidden", layer_idx=None, dtype=None, attention_mask=True, model_options={}):
super().__init__(device=device, layer=KREA2_TAP_LAYERS, layer_idx=None,
textmodel_json_config=dict(QWEN3VL_4B_CONFIG),
dtype=dtype, special_tokens={"pad": 151643}, layer_norm_hidden_state=False,
model_class=comfy.text_encoders.llama.Qwen3_4B,
enable_attention_masks=attention_mask, return_attention_masks=attention_mask,
model_options=model_options)
class Krea2TEModel(sd1_clip.SD1ClipModel): class Krea2TEModel(sd1_clip.SD1ClipModel):
def __init__(self, device="cpu", dtype=None, model_options={}): def __init__(self, device="cpu", dtype=None, model_options={}):
super().__init__(device=device, dtype=dtype, name="qwen3vl_4b", clip_model=Qwen3VL4BModel, model_options=model_options) super().__init__(device=device, dtype=dtype, name="qwen3vl_4b", clip_model=Krea2Qwen3VLClipModel, model_options=model_options)
def encode_token_weights(self, token_weight_pairs, template_end=-1): def encode_token_weights(self, token_weight_pairs, template_end=-1):
out, pooled, extra = super().encode_token_weights(token_weight_pairs) # out: (B, 12, seq, 2560) out, pooled, extra = super().encode_token_weights(token_weight_pairs) # out: (B, 12, seq, 2560)