mirror of
https://github.com/comfyanonymous/ComfyUI.git
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107 lines
5.2 KiB
Python
107 lines
5.2 KiB
Python
"""Krea 2 (K2) text encoder: Qwen3-VL-4B language model, 12-layer tap.
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K2 conditions on a stack of hidden states from 12 layers of Qwen3-VL-4B
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(reference taps ``hidden_states[2,5,8,...,35]``), kept as a ``(B, 12, seq, 2560)`` tensor and
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consumed by the DiT's internal ``txtfusion`` adapter. Comfy carries conditioning as a 3D tensor,
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so the 12-layer stack is flattened to ``(B, seq, 12*2560)`` here and unpacked inside the model.
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"""
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import os
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import numbers
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import torch
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from transformers import Qwen2Tokenizer
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import comfy.text_encoders.llama
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from comfy import sd1_clip
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# tap k == hidden_states[k] (no offset).
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KREA2_TAP_LAYERS = [2, 5, 8, 11, 14, 17, 20, 23, 26, 29, 32, 35]
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QWEN3VL_4B_CONFIG = {"rope_theta": 5000000.0, "final_norm": False, "lm_head": False}
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# Identical system template to Qwen-Image; Krea2 strips the system+user-opening prefix.
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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"
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class Qwen3VL4BTokenizer(sd1_clip.SDTokenizer):
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def __init__(self, embedding_directory=None, tokenizer_data={}):
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tokenizer_path = os.path.join(os.path.dirname(os.path.realpath(__file__)), "qwen25_tokenizer")
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super().__init__(tokenizer_path, pad_with_end=False, embedding_directory=embedding_directory,
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embedding_size=2560, embedding_key='qwen3vl_4b', tokenizer_class=Qwen2Tokenizer,
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has_start_token=False, has_end_token=False, pad_to_max_length=False,
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max_length=99999999, min_length=1, pad_token=151643, tokenizer_data=tokenizer_data)
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class Krea2Tokenizer(sd1_clip.SD1Tokenizer):
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def __init__(self, embedding_directory=None, tokenizer_data={}):
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super().__init__(embedding_directory=embedding_directory, tokenizer_data=tokenizer_data,
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name="qwen3vl_4b", tokenizer=Qwen3VL4BTokenizer)
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self.llama_template = KREA2_TEMPLATE
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def tokenize_with_weights(self, text, return_word_ids=False, llama_template=None, **kwargs):
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if text.startswith('<|im_start|>'):
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llama_text = text
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elif llama_template is None:
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llama_text = self.llama_template.format(text)
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else:
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llama_text = llama_template.format(text)
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return super().tokenize_with_weights(llama_text, return_word_ids=return_word_ids, disable_weights=True, **kwargs)
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class Qwen3VL4BModel(sd1_clip.SDClipModel):
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def __init__(self, device="cpu", layer="hidden", layer_idx=None, dtype=None, attention_mask=True, model_options={}):
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super().__init__(device=device, layer=KREA2_TAP_LAYERS, layer_idx=None,
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textmodel_json_config=dict(QWEN3VL_4B_CONFIG),
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dtype=dtype, special_tokens={"pad": 151643}, layer_norm_hidden_state=False,
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model_class=comfy.text_encoders.llama.Qwen3_4B,
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enable_attention_masks=attention_mask, return_attention_masks=attention_mask,
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model_options=model_options)
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class Krea2TEModel(sd1_clip.SD1ClipModel):
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def __init__(self, device="cpu", dtype=None, model_options={}):
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super().__init__(device=device, dtype=dtype, name="qwen3vl_4b", clip_model=Qwen3VL4BModel, model_options=model_options)
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def encode_token_weights(self, token_weight_pairs, template_end=-1):
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out, pooled, extra = super().encode_token_weights(token_weight_pairs) # out: (B, 12, seq, 2560)
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tok_pairs = token_weight_pairs["qwen3vl_4b"][0]
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# Strip the system + user-opening prefix
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count_im_start = 0
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if template_end == -1:
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for i, v in enumerate(tok_pairs):
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elem = v[0]
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if not torch.is_tensor(elem) and isinstance(elem, numbers.Integral):
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if elem == 151644 and count_im_start < 2:
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template_end = i
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count_im_start += 1
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if out.shape[2] > (template_end + 3):
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if tok_pairs[template_end + 1][0] == 872: # "user"
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if tok_pairs[template_end + 2][0] == 198: # "\n"
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template_end += 3
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out = out[:, :, template_end:]
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b, n, seq, h = out.shape
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# Flatten the 12-layer axis into the feature dim: (B, seq, 12*2560). Unpacked in the model.
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out = out.permute(0, 2, 1, 3).reshape(b, seq, n * h)
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if "attention_mask" in extra:
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extra["attention_mask"] = extra["attention_mask"][:, template_end:]
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if extra["attention_mask"].sum() == torch.numel(extra["attention_mask"]):
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extra.pop("attention_mask")
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return out, pooled, extra
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def te(dtype_llama=None, llama_quantization_metadata=None):
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class Krea2TEModel_(Krea2TEModel):
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def __init__(self, device="cpu", dtype=None, model_options={}):
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if llama_quantization_metadata is not None:
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model_options = model_options.copy()
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model_options["quantization_metadata"] = llama_quantization_metadata
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if dtype_llama is not None:
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dtype = dtype_llama
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super().__init__(device=device, dtype=dtype, model_options=model_options)
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return Krea2TEModel_
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