147 lines
6.7 KiB
Python
147 lines
6.7 KiB
Python
from comfy import sd1_clip
|
|
from comfy import sdxl_clip
|
|
from transformers import T5TokenizerFast
|
|
import comfy.text_encoders.t5
|
|
import torch
|
|
import os
|
|
import comfy.model_management
|
|
import logging
|
|
|
|
class T5XXLModel(sd1_clip.SDClipModel):
|
|
def __init__(self, device="cpu", layer="last", layer_idx=None, dtype=None, attention_mask=False, 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, enable_attention_masks=attention_mask, return_attention_masks=attention_mask, 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, 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=77)
|
|
|
|
|
|
class SD3Tokenizer:
|
|
def __init__(self, embedding_directory=None, tokenizer_data={}):
|
|
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.clip_g = sdxl_clip.SDXLClipGTokenizer(embedding_directory=embedding_directory)
|
|
self.t5xxl = T5XXLTokenizer(embedding_directory=embedding_directory)
|
|
|
|
def tokenize_with_weights(self, text:str, return_word_ids=False):
|
|
out = {}
|
|
out["g"] = self.clip_g.tokenize_with_weights(text, return_word_ids)
|
|
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_g.untokenize(token_weight_pair)
|
|
|
|
def state_dict(self):
|
|
return {}
|
|
|
|
class SD3ClipModel(torch.nn.Module):
|
|
def __init__(self, clip_l=True, clip_g=True, t5=True, dtype_t5=None, t5_attention_mask=False, device="cpu", dtype=None, model_options={}):
|
|
super().__init__()
|
|
self.dtypes = set()
|
|
if clip_l:
|
|
clip_l_class = model_options.get("clip_l_class", sd1_clip.SDClipModel)
|
|
self.clip_l = clip_l_class(layer="hidden", layer_idx=-2, device=device, dtype=dtype, layer_norm_hidden_state=False, return_projected_pooled=False, model_options=model_options)
|
|
self.dtypes.add(dtype)
|
|
else:
|
|
self.clip_l = None
|
|
|
|
if clip_g:
|
|
self.clip_g = sdxl_clip.SDXLClipG(device=device, dtype=dtype, model_options=model_options)
|
|
self.dtypes.add(dtype)
|
|
else:
|
|
self.clip_g = None
|
|
|
|
if t5:
|
|
dtype_t5 = comfy.model_management.pick_weight_dtype(dtype_t5, dtype, device)
|
|
self.t5_attention_mask = t5_attention_mask
|
|
self.t5xxl = T5XXLModel(device=device, dtype=dtype_t5, model_options=model_options, attention_mask=self.t5_attention_mask)
|
|
self.dtypes.add(dtype_t5)
|
|
else:
|
|
self.t5xxl = None
|
|
|
|
logging.debug("Created SD3 text encoder with: clip_l {}, clip_g {}, t5xxl {}:{}".format(clip_l, clip_g, t5, dtype_t5))
|
|
|
|
def set_clip_options(self, options):
|
|
if self.clip_l is not None:
|
|
self.clip_l.set_clip_options(options)
|
|
if self.clip_g is not None:
|
|
self.clip_g.set_clip_options(options)
|
|
if self.t5xxl is not None:
|
|
self.t5xxl.set_clip_options(options)
|
|
|
|
def reset_clip_options(self):
|
|
if self.clip_l is not None:
|
|
self.clip_l.reset_clip_options()
|
|
if self.clip_g is not None:
|
|
self.clip_g.reset_clip_options()
|
|
if self.t5xxl is not None:
|
|
self.t5xxl.reset_clip_options()
|
|
|
|
def encode_token_weights(self, token_weight_pairs):
|
|
token_weight_pairs_l = token_weight_pairs["l"]
|
|
token_weight_pairs_g = token_weight_pairs["g"]
|
|
token_weight_pairs_t5 = token_weight_pairs["t5xxl"]
|
|
lg_out = None
|
|
pooled = None
|
|
out = None
|
|
extra = {}
|
|
|
|
if len(token_weight_pairs_g) > 0 or len(token_weight_pairs_l) > 0:
|
|
if self.clip_l is not None:
|
|
lg_out, l_pooled = self.clip_l.encode_token_weights(token_weight_pairs_l)
|
|
else:
|
|
l_pooled = torch.zeros((1, 768), device=comfy.model_management.intermediate_device())
|
|
|
|
if self.clip_g is not None:
|
|
g_out, g_pooled = self.clip_g.encode_token_weights(token_weight_pairs_g)
|
|
if lg_out is not None:
|
|
cut_to = min(lg_out.shape[1], g_out.shape[1])
|
|
lg_out = torch.cat([lg_out[:,:cut_to], g_out[:,:cut_to]], dim=-1)
|
|
else:
|
|
lg_out = torch.nn.functional.pad(g_out, (768, 0))
|
|
else:
|
|
g_out = None
|
|
g_pooled = torch.zeros((1, 1280), device=comfy.model_management.intermediate_device())
|
|
|
|
if lg_out is not None:
|
|
lg_out = torch.nn.functional.pad(lg_out, (0, 4096 - lg_out.shape[-1]))
|
|
out = lg_out
|
|
pooled = torch.cat((l_pooled, g_pooled), dim=-1)
|
|
|
|
if self.t5xxl is not None:
|
|
t5_output = self.t5xxl.encode_token_weights(token_weight_pairs_t5)
|
|
t5_out, t5_pooled = t5_output[:2]
|
|
if self.t5_attention_mask:
|
|
extra["attention_mask"] = t5_output[2]["attention_mask"]
|
|
|
|
if lg_out is not None:
|
|
out = torch.cat([lg_out, t5_out], dim=-2)
|
|
else:
|
|
out = t5_out
|
|
|
|
if out is None:
|
|
out = torch.zeros((1, 77, 4096), device=comfy.model_management.intermediate_device())
|
|
|
|
if pooled is None:
|
|
pooled = torch.zeros((1, 768 + 1280), device=comfy.model_management.intermediate_device())
|
|
|
|
return out, pooled, extra
|
|
|
|
def load_sd(self, sd):
|
|
if "text_model.encoder.layers.30.mlp.fc1.weight" in sd:
|
|
return self.clip_g.load_sd(sd)
|
|
elif "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 sd3_clip(clip_l=True, clip_g=True, t5=True, dtype_t5=None, t5_attention_mask=False):
|
|
class SD3ClipModel_(SD3ClipModel):
|
|
def __init__(self, device="cpu", dtype=None, model_options={}):
|
|
super().__init__(clip_l=clip_l, clip_g=clip_g, t5=t5, dtype_t5=dtype_t5, t5_attention_mask=t5_attention_mask, device=device, dtype=dtype, model_options=model_options)
|
|
return SD3ClipModel_
|