mirror of
https://git.datalinker.icu/comfyanonymous/ComfyUI
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165 lines
8.4 KiB
Python
165 lines
8.4 KiB
Python
from comfy import sd1_clip
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import comfy.text_encoders.llama
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from .qwen_image import QwenImageTokenizer, QwenImageTEModel
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from transformers import ByT5Tokenizer
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import os
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import re
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import torch
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import numbers
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class ByT5SmallTokenizer(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__)), "byt5_tokenizer")
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super().__init__(tokenizer_path, pad_with_end=False, embedding_size=1472, embedding_key='byt5_small', tokenizer_class=ByT5Tokenizer, has_start_token=False, pad_to_max_length=False, max_length=99999999, min_length=1, tokenizer_data=tokenizer_data)
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class HunyuanImageTokenizer(QwenImageTokenizer):
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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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self.llama_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|>"
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# self.llama_template_images = "{}"
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self.byt5 = ByT5SmallTokenizer(embedding_directory=embedding_directory, tokenizer_data=tokenizer_data)
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def tokenize_with_weights(self, text:str, return_word_ids=False, **kwargs):
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out = super().tokenize_with_weights(text, return_word_ids, **kwargs)
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# ByT5 processing for HunyuanImage
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text_prompt_texts = []
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pattern_quote_double = r'\"(.*?)\"'
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pattern_quote_chinese_single = r'‘(.*?)’'
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pattern_quote_chinese_double = r'“(.*?)”'
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matches_quote_double = re.findall(pattern_quote_double, text)
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matches_quote_chinese_single = re.findall(pattern_quote_chinese_single, text)
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matches_quote_chinese_double = re.findall(pattern_quote_chinese_double, text)
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text_prompt_texts.extend(matches_quote_double)
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text_prompt_texts.extend(matches_quote_chinese_single)
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text_prompt_texts.extend(matches_quote_chinese_double)
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if len(text_prompt_texts) > 0:
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out['byt5'] = self.byt5.tokenize_with_weights(''.join(map(lambda a: 'Text "{}". '.format(a), text_prompt_texts)), return_word_ids, **kwargs)
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return out
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class HunyuanImageRefinerTokenizer(HunyuanImageTokenizer):
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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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self.llama_template = "<|start_header_id|>system<|end_header_id|>\n\nDescribe the image by detailing the color, shape, size, texture, quantity, text, spatial relationships of the objects and background:<|eot_id|>\n<|start_header_id|>user<|end_header_id|>\n\n{}<|eot_id|>"
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class Qwen25_7BVLIModel(sd1_clip.SDClipModel):
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def __init__(self, device="cpu", layer="hidden", layer_idx=-3, dtype=None, attention_mask=True, model_options={}):
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llama_scaled_fp8 = model_options.get("qwen_scaled_fp8", None)
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if llama_scaled_fp8 is not None:
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model_options = model_options.copy()
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model_options["scaled_fp8"] = llama_scaled_fp8
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super().__init__(device=device, layer=layer, layer_idx=layer_idx, textmodel_json_config={}, dtype=dtype, special_tokens={"pad": 151643}, layer_norm_hidden_state=False, model_class=comfy.text_encoders.llama.Qwen25_7BVLI, enable_attention_masks=attention_mask, return_attention_masks=attention_mask, model_options=model_options)
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class ByT5SmallModel(sd1_clip.SDClipModel):
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def __init__(self, device="cpu", layer="last", layer_idx=None, dtype=None, model_options={}):
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textmodel_json_config = os.path.join(os.path.dirname(os.path.realpath(__file__)), "byt5_config_small_glyph.json")
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super().__init__(device=device, layer=layer, layer_idx=layer_idx, textmodel_json_config=textmodel_json_config, dtype=dtype, model_options=model_options, special_tokens={"end": 1, "pad": 0}, model_class=comfy.text_encoders.t5.T5, enable_attention_masks=True, zero_out_masked=True)
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class HunyuanImageTEModel(sd1_clip.SD1ClipModel):
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def __init__(self, byt5=True, device="cpu", dtype=None, model_options={}):
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super().__init__(device=device, dtype=dtype, name="qwen25_7b", clip_model=Qwen25_7BVLIModel, model_options=model_options)
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if byt5:
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self.byt5_small = ByT5SmallModel(device=device, dtype=dtype, model_options=model_options)
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else:
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self.byt5_small = None
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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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tok_pairs = token_weight_pairs["qwen25_7b"][0]
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count_im_start = 0
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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):
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if 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[1] > (template_end + 3):
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if tok_pairs[template_end + 1][0] == 872:
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if tok_pairs[template_end + 2][0] == 198:
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template_end += 3
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out = out[:, template_end:]
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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") # attention mask is useless if no masked elements
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# noqa: W293
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if self.byt5_small is not None and "byt5" in token_weight_pairs:
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byt5_out = self.byt5_small.encode_token_weights(token_weight_pairs["byt5"])
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extra["conditioning_byt5small"] = byt5_out[0]
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return out, pooled, extra
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def set_clip_options(self, options):
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super().set_clip_options(options)
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if self.byt5_small is not None:
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self.byt5_small.set_clip_options(options)
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def reset_clip_options(self):
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super().reset_clip_options()
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if self.byt5_small is not None:
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self.byt5_small.reset_clip_options()
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def load_sd(self, sd):
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if "encoder.block.0.layer.0.SelfAttention.o.weight" in sd:
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return self.byt5_small.load_sd(sd)
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else:
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return super().load_sd(sd)
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class HunyuanImageRefinerTEModel(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="qwen25_7b", clip_model=Qwen25_7BVLIModel, 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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tok_pairs = token_weight_pairs["qwen25_7b"][0]
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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):
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if isinstance(elem, numbers.Integral):
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if elem == 6171:
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template_end = i
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break
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out = out[:, template_end-1:]
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extra["attention_mask"] = extra["attention_mask"][:, template_end-1:]
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if extra["attention_mask"].sum() == torch.numel(extra["attention_mask"]):
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extra.pop("attention_mask") # attention mask is useless if no masked elements
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return out, pooled, extra
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def te(byt5=True, dtype_llama=None, llama_scaled_fp8=None, refiner=False):
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class HunyuanImageTEModel_(HunyuanImageTEModel):
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def __init__(self, device="cpu", dtype=None, model_options={}):
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if llama_scaled_fp8 is not None and "scaled_fp8" not in model_options:
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model_options = model_options.copy()
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model_options["qwen_scaled_fp8"] = llama_scaled_fp8
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if dtype_llama is not None:
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dtype = dtype_llama
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super().__init__(byt5=byt5, device=device, dtype=dtype, model_options=model_options)
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class HunyuanImageTEModel_refiner(HunyuanImageRefinerTEModel):
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def __init__(self, device="cpu", dtype=None, model_options={}):
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if llama_scaled_fp8 is not None and "scaled_fp8" not in model_options:
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model_options = model_options.copy()
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model_options["qwen_scaled_fp8"] = llama_scaled_fp8
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if dtype_llama is not None:
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dtype = dtype_llama
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assert refiner, "refiner must be True"
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assert not byt5, "byt5 must be False"
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super().__init__(device=device, dtype=dtype, model_options=model_options)
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return HunyuanImageTEModel_refiner if refiner else HunyuanImageTEModel_
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