from comfy import sd1_clip import comfy.text_encoders.llama from .qwen_image import QwenImageTokenizer, QwenImageTEModel from transformers import ByT5Tokenizer import os import re import torch import numbers class ByT5SmallTokenizer(sd1_clip.SDTokenizer): def __init__(self, embedding_directory=None, tokenizer_data={}): tokenizer_path = os.path.join(os.path.dirname(os.path.realpath(__file__)), "byt5_tokenizer") 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) class HunyuanImageTokenizer(QwenImageTokenizer): def __init__(self, embedding_directory=None, tokenizer_data={}): super().__init__(embedding_directory=embedding_directory, tokenizer_data=tokenizer_data) 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|>" # self.llama_template_images = "{}" self.byt5 = ByT5SmallTokenizer(embedding_directory=embedding_directory, tokenizer_data=tokenizer_data) def tokenize_with_weights(self, text:str, return_word_ids=False, **kwargs): out = super().tokenize_with_weights(text, return_word_ids, **kwargs) # ByT5 processing for HunyuanImage text_prompt_texts = [] pattern_quote_double = r'\"(.*?)\"' pattern_quote_chinese_single = r'‘(.*?)’' pattern_quote_chinese_double = r'“(.*?)”' matches_quote_double = re.findall(pattern_quote_double, text) matches_quote_chinese_single = re.findall(pattern_quote_chinese_single, text) matches_quote_chinese_double = re.findall(pattern_quote_chinese_double, text) text_prompt_texts.extend(matches_quote_double) text_prompt_texts.extend(matches_quote_chinese_single) text_prompt_texts.extend(matches_quote_chinese_double) if len(text_prompt_texts) > 0: out['byt5'] = self.byt5.tokenize_with_weights(''.join(map(lambda a: 'Text "{}". '.format(a), text_prompt_texts)), return_word_ids, **kwargs) return out class HunyuanImageRefinerTokenizer(HunyuanImageTokenizer): def __init__(self, embedding_directory=None, tokenizer_data={}): super().__init__(embedding_directory=embedding_directory, tokenizer_data=tokenizer_data) 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|>" class Qwen25_7BVLIModel(sd1_clip.SDClipModel): def __init__(self, device="cpu", layer="hidden", layer_idx=-3, dtype=None, attention_mask=True, model_options={}): llama_scaled_fp8 = model_options.get("qwen_scaled_fp8", None) if llama_scaled_fp8 is not None: model_options = model_options.copy() model_options["scaled_fp8"] = llama_scaled_fp8 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) class ByT5SmallModel(sd1_clip.SDClipModel): def __init__(self, device="cpu", layer="last", layer_idx=None, dtype=None, model_options={}): textmodel_json_config = os.path.join(os.path.dirname(os.path.realpath(__file__)), "byt5_config_small_glyph.json") 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) class HunyuanImageTEModel(sd1_clip.SD1ClipModel): def __init__(self, byt5=True, device="cpu", dtype=None, model_options={}): super().__init__(device=device, dtype=dtype, name="qwen25_7b", clip_model=Qwen25_7BVLIModel, model_options=model_options) if byt5: self.byt5_small = ByT5SmallModel(device=device, dtype=dtype, model_options=model_options) else: self.byt5_small = None def encode_token_weights(self, token_weight_pairs): out, pooled, extra = super().encode_token_weights(token_weight_pairs) tok_pairs = token_weight_pairs["qwen25_7b"][0] count_im_start = 0 for i, v in enumerate(tok_pairs): elem = v[0] if not torch.is_tensor(elem): if isinstance(elem, numbers.Integral): if elem == 151644 and count_im_start < 2: template_end = i count_im_start += 1 if out.shape[1] > (template_end + 3): if tok_pairs[template_end + 1][0] == 872: if tok_pairs[template_end + 2][0] == 198: template_end += 3 out = out[:, template_end:] extra["attention_mask"] = extra["attention_mask"][:, template_end:] if extra["attention_mask"].sum() == torch.numel(extra["attention_mask"]): extra.pop("attention_mask") # attention mask is useless if no masked elements # noqa: W293 if self.byt5_small is not None and "byt5" in token_weight_pairs: byt5_out = self.byt5_small.encode_token_weights(token_weight_pairs["byt5"]) extra["conditioning_byt5small"] = byt5_out[0] return out, pooled, extra def set_clip_options(self, options): super().set_clip_options(options) if self.byt5_small is not None: self.byt5_small.set_clip_options(options) def reset_clip_options(self): super().reset_clip_options() if self.byt5_small is not None: self.byt5_small.reset_clip_options() def load_sd(self, sd): if "encoder.block.0.layer.0.SelfAttention.o.weight" in sd: return self.byt5_small.load_sd(sd) else: return super().load_sd(sd) class HunyuanImageRefinerTEModel(sd1_clip.SD1ClipModel): def __init__(self, device="cpu", dtype=None, model_options={}): super().__init__(device=device, dtype=dtype, name="qwen25_7b", clip_model=Qwen25_7BVLIModel, model_options=model_options) def encode_token_weights(self, token_weight_pairs): out, pooled, extra = super().encode_token_weights(token_weight_pairs) tok_pairs = token_weight_pairs["qwen25_7b"][0] for i, v in enumerate(tok_pairs): elem = v[0] if not torch.is_tensor(elem): if isinstance(elem, numbers.Integral): if elem == 6171: template_end = i break out = out[:, template_end-1:] extra["attention_mask"] = extra["attention_mask"][:, template_end-1:] if extra["attention_mask"].sum() == torch.numel(extra["attention_mask"]): extra.pop("attention_mask") # attention mask is useless if no masked elements return out, pooled, extra def te(byt5=True, dtype_llama=None, llama_scaled_fp8=None, refiner=False): class HunyuanImageTEModel_(HunyuanImageTEModel): def __init__(self, device="cpu", dtype=None, model_options={}): if llama_scaled_fp8 is not None and "scaled_fp8" not in model_options: model_options = model_options.copy() model_options["qwen_scaled_fp8"] = llama_scaled_fp8 if dtype_llama is not None: dtype = dtype_llama super().__init__(byt5=byt5, device=device, dtype=dtype, model_options=model_options) class HunyuanImageTEModel_refiner(HunyuanImageRefinerTEModel): def __init__(self, device="cpu", dtype=None, model_options={}): if llama_scaled_fp8 is not None and "scaled_fp8" not in model_options: model_options = model_options.copy() model_options["qwen_scaled_fp8"] = llama_scaled_fp8 if dtype_llama is not None: dtype = dtype_llama assert refiner, "refiner must be True" assert not byt5, "byt5 must be False" super().__init__(device=device, dtype=dtype, model_options=model_options) return HunyuanImageTEModel_refiner if refiner else HunyuanImageTEModel_