mirror of
https://github.com/comfyanonymous/ComfyUI.git
synced 2026-01-11 23:00:51 +08:00
89 lines
3.6 KiB
Python
89 lines
3.6 KiB
Python
import copy
|
|
|
|
import torch
|
|
from transformers import T5TokenizerFast
|
|
|
|
from .sd3_clip import T5XXLModel
|
|
from .. import sd1_clip, model_management
|
|
from ..component_model import files
|
|
|
|
|
|
class T5XXLTokenizer(sd1_clip.SDTokenizer):
|
|
def __init__(self, embedding_directory=None, tokenizer_data=None):
|
|
if tokenizer_data is None:
|
|
tokenizer_data = dict()
|
|
tokenizer_path = files.get_package_as_path("comfy.text_encoders.t5_tokenizer")
|
|
super().__init__(tokenizer_path, embedding_directory=embedding_directory, pad_with_end=False, embedding_size=4096, embedding_key='t5xxl', tokenizer_class=T5TokenizerFast, has_start_token=False, pad_to_max_length=False, max_length=99999999, min_length=256)
|
|
|
|
|
|
class FluxTokenizer:
|
|
def __init__(self, embedding_directory=None, tokenizer_data=None):
|
|
if tokenizer_data is None:
|
|
tokenizer_data = dict()
|
|
clip_l_tokenizer_class = tokenizer_data.get("clip_l_tokenizer_class", sd1_clip.SDTokenizer)
|
|
self.clip_l = clip_l_tokenizer_class(embedding_directory=embedding_directory)
|
|
self.t5xxl = T5XXLTokenizer(embedding_directory=embedding_directory)
|
|
|
|
def tokenize_with_weights(self, text: str, return_word_ids=False):
|
|
out = {
|
|
"l": self.clip_l.tokenize_with_weights(text, return_word_ids),
|
|
"t5xxl": self.t5xxl.tokenize_with_weights(text, return_word_ids)
|
|
}
|
|
return out
|
|
|
|
def untokenize(self, token_weight_pair):
|
|
return self.clip_l.untokenize(token_weight_pair)
|
|
|
|
def state_dict(self):
|
|
return {}
|
|
|
|
def clone(self):
|
|
return copy.copy(self)
|
|
|
|
|
|
class FluxClipModel(torch.nn.Module):
|
|
def __init__(self, dtype_t5=None, device="cpu", dtype=None, model_options=None):
|
|
super().__init__()
|
|
if model_options is None:
|
|
model_options = {}
|
|
dtype_t5 = model_management.pick_weight_dtype(dtype_t5, dtype, device)
|
|
clip_l_class = model_options.get("clip_l_class", sd1_clip.SDClipModel)
|
|
self.clip_l = clip_l_class(device=device, dtype=dtype, return_projected_pooled=False, model_options=model_options)
|
|
self.t5xxl = T5XXLModel(device=device, dtype=dtype_t5, model_options=model_options)
|
|
self.dtypes = {dtype, dtype_t5}
|
|
|
|
def set_clip_options(self, options):
|
|
self.clip_l.set_clip_options(options)
|
|
self.t5xxl.set_clip_options(options)
|
|
|
|
def reset_clip_options(self):
|
|
self.clip_l.reset_clip_options()
|
|
self.t5xxl.reset_clip_options()
|
|
|
|
def encode_token_weights(self, token_weight_pairs):
|
|
token_weight_pairs_l = token_weight_pairs["l"]
|
|
token_weight_pairs_t5 = token_weight_pairs["t5xxl"]
|
|
|
|
t5_out, t5_pooled = self.t5xxl.encode_token_weights(token_weight_pairs_t5)
|
|
l_out, l_pooled = self.clip_l.encode_token_weights(token_weight_pairs_l)
|
|
return t5_out, l_pooled
|
|
|
|
def load_sd(self, sd):
|
|
if "text_model.encoder.layers.1.mlp.fc1.weight" in sd:
|
|
return self.clip_l.load_sd(sd)
|
|
else:
|
|
return self.t5xxl.load_sd(sd)
|
|
|
|
|
|
def flux_clip(dtype_t5=None, t5xxl_scaled_fp8=None):
|
|
class FluxClipModel_(FluxClipModel):
|
|
def __init__(self, device="cpu", dtype=None, model_options=None):
|
|
if model_options is None:
|
|
model_options = {}
|
|
if t5xxl_scaled_fp8 is not None and "t5xxl_scaled_fp8" not in model_options:
|
|
model_options = model_options.copy()
|
|
model_options["t5xxl_scaled_fp8"] = t5xxl_scaled_fp8
|
|
super().__init__(dtype_t5=dtype_t5, device=device, dtype=dtype, model_options=model_options)
|
|
|
|
return FluxClipModel_
|