mirror of
https://git.datalinker.icu/comfyanonymous/ComfyUI
synced 2026-11-13 22:17:01 +08:00
99 lines
4.3 KiB
Python
99 lines
4.3 KiB
Python
from comfy import sd1_clip
|
|
import comfy.text_encoders.t5
|
|
import comfy.model_management
|
|
from transformers import T5TokenizerFast
|
|
import torch
|
|
import os
|
|
|
|
class T5XXLModel(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__)), "t5_config_xxl.json")
|
|
super().__init__(device=device, layer=layer, layer_idx=layer_idx, textmodel_json_config=textmodel_json_config, dtype=dtype, special_tokens={"end": 1, "pad": 0}, model_class=comfy.text_encoders.t5.T5, model_options=model_options)
|
|
|
|
class T5XXLTokenizer(sd1_clip.SDTokenizer):
|
|
def __init__(self, embedding_directory=None, tokenizer_data={}):
|
|
tokenizer_path = os.path.join(os.path.dirname(os.path.realpath(__file__)), "t5_tokenizer")
|
|
super().__init__(tokenizer_path, 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=768)
|
|
|
|
|
|
class FluxTokenizer:
|
|
def __init__(self, embedding_directory=None, tokenizer_data={}):
|
|
self.clip_l = sd1_clip.SDTokenizer(embedding_directory=embedding_directory)
|
|
self.t5xxl = T5XXLTokenizer(embedding_directory=embedding_directory)
|
|
|
|
def tokenize_with_weights(self, text:str, return_word_ids=False):
|
|
out = {}
|
|
out["l"] = self.clip_l.tokenize_with_weights(text, return_word_ids)
|
|
out["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 {}
|
|
|
|
|
|
class FluxClipModel(torch.nn.Module):
|
|
def __init__(self, dtype_t5=None, device="cpu", dtype=None, model_options={}):
|
|
super().__init__()
|
|
dtype_t5 = comfy.model_management.pick_weight_dtype(dtype_t5, dtype, device)
|
|
self.clip_l = sd1_clip.SDClipModel(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 = set([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:
|
|
quantize_model(self.t5xxl.transformer, sd)
|
|
return self.t5xxl.load_sd(sd)
|
|
|
|
def flux_clip(dtype_t5=None):
|
|
class FluxClipModel_(FluxClipModel):
|
|
def __init__(self, device="cpu", dtype=None, model_options={}):
|
|
super().__init__(dtype_t5=dtype_t5, device=device, dtype=dtype, model_options=model_options)
|
|
return FluxClipModel_
|
|
|
|
def quantize_model(model: torch.nn.Module, new_state_dict):
|
|
for name, module in model.named_modules():
|
|
device = next(module.parameters()).device
|
|
if not isinstance(module, torch.nn.Linear):
|
|
continue
|
|
weight = new_state_dict[f"{name}.weight"].to(device)
|
|
if module.bias is not None:
|
|
module.bias.data = module.bias.data.to(torch.float16).to(device)
|
|
qweight, scale = fp8_quantize(weight)
|
|
module.weight.data = module.weight.data.to(torch.float8_e4m3fn)
|
|
module.weight.data.copy_(qweight.data)
|
|
module.register_buffer("scale", scale.to(device))
|
|
new_state_dict.pop(f"{name}.weight")
|
|
|
|
|
|
def fp8_quantize(weight, qdtype=torch.float8_e4m3fn):
|
|
device = weight.device
|
|
finfo = torch.finfo(qdtype)
|
|
|
|
scale = finfo.max / weight.abs().max().clamp(min=1e-12)
|
|
|
|
qweight = (weight * scale).clamp(min=finfo.min, max=finfo.max)
|
|
|
|
qweight = qweight.to(qdtype)
|
|
scale = scale.float().reciprocal()
|
|
return qweight, scale |