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feat: Support Qwen3.5 text generation models (#12771)

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This commit is contained in:
Jukka Seppänen
2026-03-26 04:48:28 +02:00
committed by GitHub
parent 6580a6bc01
commit 404d7b9978
8 changed files with 496842 additions and 29 deletions
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comfy/sd.py
+28 -5
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@@ -61,6 +61,7 @@ import comfy.text_encoders.newbie
import comfy.text_encoders.anima
import comfy.text_encoders.ace15
import comfy.text_encoders.longcat_image
import comfy.text_encoders.qwen35
import comfy.model_patcher
import comfy.lora
@@ -425,13 +426,13 @@ class CLIP:
def get_key_patches(self):
return self.patcher.get_key_patches()
def generate(self, tokens, do_sample=True, max_length=256, temperature=1.0, top_k=50, top_p=0.95, min_p=0.0, repetition_penalty=1.0, seed=None):
def generate(self, tokens, do_sample=True, max_length=256, temperature=1.0, top_k=50, top_p=0.95, min_p=0.0, repetition_penalty=1.0, seed=None, presence_penalty=0.0):
self.cond_stage_model.reset_clip_options()
self.load_model(tokens)
self.cond_stage_model.set_clip_options({"layer": None})
self.cond_stage_model.set_clip_options({"execution_device": self.patcher.load_device})
return self.cond_stage_model.generate(tokens, do_sample=do_sample, max_length=max_length, temperature=temperature, top_k=top_k, top_p=top_p, min_p=min_p, repetition_penalty=repetition_penalty, seed=seed)
return self.cond_stage_model.generate(tokens, do_sample=do_sample, max_length=max_length, temperature=temperature, top_k=top_k, top_p=top_p, min_p=min_p, repetition_penalty=repetition_penalty, seed=seed, presence_penalty=presence_penalty)
def decode(self, token_ids, skip_special_tokens=True):
return self.tokenizer.decode(token_ids, skip_special_tokens=skip_special_tokens)
@@ -1228,6 +1229,11 @@ class TEModel(Enum):
QWEN3_8B = 20
QWEN3_06B = 21
GEMMA_3_4B_VISION = 22
QWEN35_08B = 23
QWEN35_2B = 24
QWEN35_4B = 25
QWEN35_9B = 26
QWEN35_27B = 27
def detect_te_model(sd):
@@ -1267,6 +1273,17 @@ def detect_te_model(sd):
return TEModel.QWEN25_3B
if weight.shape[0] == 512:
return TEModel.QWEN25_7B
if "model.language_model.layers.0.linear_attn.A_log" in sd and "model.language_model.layers.0.input_layernorm.weight" in sd:
weight = sd['model.language_model.layers.0.input_layernorm.weight']
if weight.shape[0] == 1024:
return TEModel.QWEN35_08B
if weight.shape[0] == 2560:
return TEModel.QWEN35_4B
if weight.shape[0] == 4096:
return TEModel.QWEN35_9B
if weight.shape[0] == 5120:
return TEModel.QWEN35_27B
return TEModel.QWEN35_2B
if "model.layers.0.post_attention_layernorm.weight" in sd:
weight = sd['model.layers.0.post_attention_layernorm.weight']
if 'model.layers.0.self_attn.q_norm.weight' in sd:
@@ -1299,11 +1316,12 @@ def t5xxl_detect(clip_data):
return {}
def llama_detect(clip_data):
weight_name = "model.layers.0.self_attn.k_proj.weight"
weight_names = ["model.layers.0.self_attn.k_proj.weight", "model.layers.0.linear_attn.in_proj_a.weight"]
for sd in clip_data:
if weight_name in sd:
return comfy.text_encoders.hunyuan_video.llama_detect(sd)
for weight_name in weight_names:
if weight_name in sd:
return comfy.text_encoders.hunyuan_video.llama_detect(sd)
return {}
@@ -1431,6 +1449,11 @@ def load_text_encoder_state_dicts(state_dicts=[], embedding_directory=None, clip
elif te_model == TEModel.JINA_CLIP_2:
clip_target.clip = comfy.text_encoders.jina_clip_2.JinaClip2TextModelWrapper
clip_target.tokenizer = comfy.text_encoders.jina_clip_2.JinaClip2TokenizerWrapper
elif te_model in (TEModel.QWEN35_08B, TEModel.QWEN35_2B, TEModel.QWEN35_4B, TEModel.QWEN35_9B, TEModel.QWEN35_27B):
clip_data[0] = comfy.utils.state_dict_prefix_replace(clip_data[0], {"model.language_model.": "model.", "model.visual.": "visual.", "lm_head.": "model.lm_head."})
qwen35_type = {TEModel.QWEN35_08B: "qwen35_08b", TEModel.QWEN35_2B: "qwen35_2b", TEModel.QWEN35_4B: "qwen35_4b", TEModel.QWEN35_9B: "qwen35_9b", TEModel.QWEN35_27B: "qwen35_27b"}[te_model]
clip_target.clip = comfy.text_encoders.qwen35.te(**llama_detect(clip_data), model_type=qwen35_type)
clip_target.tokenizer = comfy.text_encoders.qwen35.tokenizer(model_type=qwen35_type)
elif te_model == TEModel.QWEN3_06B:
clip_target.clip = comfy.text_encoders.anima.te(**llama_detect(clip_data))
clip_target.tokenizer = comfy.text_encoders.anima.AnimaTokenizer