mirror of
https://github.com/comfyanonymous/ComfyUI.git
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be95871adc
* initial gemma4 support * parity with reference implementation outputs can 100% match transformers with same sdpa flags, checkpoint this and then optimize * Cleanup, video fixes * cleanup, enable fused rms norm by default * update comment * Cleanup * Update sd.py * Various fixes * Add fp8 scaled embedding support * small fixes * Translate think tokens * Fix image encoder attention mask type So it works with basic attention * Handle thinking tokens different only for Gemma4 * Code cleanup * Update nodes_textgen.py * Use embed scale class instead of buffer Slight difference to HF, but technically more accurate and simpler code * Default to fused rms_norm * Update gemma4.py
78 lines
5.2 KiB
Python
78 lines
5.2 KiB
Python
from comfy import sd1_clip
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from .spiece_tokenizer import SPieceTokenizer
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import comfy.text_encoders.llama
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from comfy.text_encoders.lt import Gemma3_Tokenizer
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import comfy.utils
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class Gemma2BTokenizer(sd1_clip.SDTokenizer):
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def __init__(self, embedding_directory=None, tokenizer_data={}):
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tokenizer = tokenizer_data.get("spiece_model", None)
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special_tokens = {"<end_of_turn>": 107}
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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)
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def state_dict(self):
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return {"spiece_model": self.tokenizer.serialize_model()}
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class Gemma3_4BTokenizer(Gemma3_Tokenizer, sd1_clip.SDTokenizer):
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def __init__(self, embedding_directory=None, tokenizer_data={}):
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tokenizer = tokenizer_data.get("spiece_model", None)
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special_tokens = {"<image_soft_token>": 262144, "<end_of_turn>": 106}
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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)
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class LuminaTokenizer(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, name="gemma2_2b", tokenizer=Gemma2BTokenizer)
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class NTokenizer(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, name="gemma3_4b", tokenizer=Gemma3_4BTokenizer)
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class Gemma2_2BModel(sd1_clip.SDClipModel):
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def __init__(self, device="cpu", layer="hidden", layer_idx=-2, dtype=None, attention_mask=True, model_options={}):
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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)
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class Gemma3_4BModel(sd1_clip.SDClipModel):
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def __init__(self, device="cpu", layer="hidden", layer_idx=-2, dtype=None, attention_mask=True, model_options={}):
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llama_quantization_metadata = model_options.get("llama_quantization_metadata", None)
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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, 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)
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class Gemma3_4B_Vision_Model(sd1_clip.SDClipModel):
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def __init__(self, device="cpu", layer="hidden", layer_idx=-2, dtype=None, attention_mask=True, model_options={}):
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llama_quantization_metadata = model_options.get("llama_quantization_metadata", None)
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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, 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)
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def process_tokens(self, tokens, device):
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embeds, _, _, _ = super().process_tokens(tokens, device)
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return embeds
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class LuminaModel(sd1_clip.SD1ClipModel):
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def __init__(self, device="cpu", dtype=None, model_options={}, name="gemma2_2b", clip_model=Gemma2_2BModel):
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super().__init__(device=device, dtype=dtype, name=name, clip_model=clip_model, model_options=model_options)
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def te(dtype_llama=None, llama_quantization_metadata=None, model_type="gemma2_2b"):
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if model_type == "gemma2_2b":
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model = Gemma2_2BModel
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elif model_type == "gemma3_4b":
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model = Gemma3_4BModel
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elif model_type == "gemma3_4b_vision":
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model = Gemma3_4B_Vision_Model
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class LuminaTEModel_(LuminaModel):
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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, name=model_type, model_options=model_options, clip_model=model)
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return LuminaTEModel_
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