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78 lines
3.9 KiB
Python
78 lines
3.9 KiB
Python
"""Ideogram 4 text encoder: Qwen3-VL-8B language model, 13-layer tap.
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Ideogram 4 conditions on the concatenation of hidden states from 13 layers of
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Qwen3-VL (layers 0,3,...,33,35), giving a 4096*13 = 53248-dim feature per token.
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"""
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import os
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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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# Reference taps outputs of layers (0,3,...,35); comfy captures layer inputs, offset by +1.
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IDEOGRAM4_TAP_LAYERS = [1, 4, 7, 10, 13, 16, 19, 22, 25, 28, 31, 34, 36]
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class Qwen3VLTokenizer(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=4096, embedding_key='qwen3vl_8b', 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 Ideogram4Tokenizer(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_8b", tokenizer=Qwen3VLTokenizer)
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self.llama_template = "<|im_start|>user\n{}<|im_end|>\n<|im_start|>assistant\n"
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def tokenize_with_weights(self, text, return_word_ids=False, llama_template=None, **kwargs):
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if 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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# Qwen3-VL-8B = 5e6 (vs plain Qwen3-8B's 1e6)
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# final_norm/lm_head off -> Ideogram only reads raw tapped hidden states
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QWEN3VL_8B_CONFIG = {"rope_theta": 5000000.0, "final_norm": False, "lm_head": False}
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class Qwen3VL8BModel(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=IDEOGRAM4_TAP_LAYERS, layer_idx=None,
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textmodel_json_config=dict(QWEN3VL_8B_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_8B,
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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 Ideogram4TEModel(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_8b", clip_model=Qwen3VL8BModel, model_options=model_options)
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def encode_token_weights(self, token_weight_pairs):
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out, pooled, extra = super().encode_token_weights(token_weight_pairs)
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b, n, seq, h = out.shape # (B, n_taps=13, seq, 4096) stacked in ascending layer order.
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out = out.permute(0, 2, 3, 1).reshape(b, seq, h * n) # (B, seq, 4096*13). permute -> (B, seq, H, taps).
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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 Ideogram4TEModel_(Ideogram4TEModel):
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def __init__(self, device="cpu", dtype=None, model_options={}):
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if dtype_llama is not None:
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dtype = dtype_llama
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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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super().__init__(device=device, dtype=dtype, model_options=model_options)
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return Ideogram4TEModel_
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