You can not select more than 25 topics
Topics must start with a letter or number, can include dashes ('-') and can be up to 35 characters long.
66 lines
2.8 KiB
66 lines
2.8 KiB
from comfy import sd1_clip |
|
import torch |
|
import os |
|
|
|
class SDXLClipG(sd1_clip.SDClipModel): |
|
def __init__(self, device="cpu", max_length=77, freeze=True, layer="penultimate", layer_idx=None, dtype=None): |
|
if layer == "penultimate": |
|
layer="hidden" |
|
layer_idx=-2 |
|
|
|
textmodel_json_config = os.path.join(os.path.dirname(os.path.realpath(__file__)), "clip_config_bigg.json") |
|
super().__init__(device=device, freeze=freeze, layer=layer, layer_idx=layer_idx, textmodel_json_config=textmodel_json_config, dtype=dtype, |
|
special_tokens={"start": 49406, "end": 49407, "pad": 0}, layer_norm_hidden_state=False) |
|
|
|
def load_sd(self, sd): |
|
return super().load_sd(sd) |
|
|
|
class SDXLClipGTokenizer(sd1_clip.SDTokenizer): |
|
def __init__(self, tokenizer_path=None, embedding_directory=None): |
|
super().__init__(tokenizer_path, pad_with_end=False, embedding_directory=embedding_directory, embedding_size=1280, embedding_key='clip_g') |
|
|
|
|
|
class SDXLTokenizer: |
|
def __init__(self, embedding_directory=None): |
|
self.clip_l = sd1_clip.SDTokenizer(embedding_directory=embedding_directory) |
|
self.clip_g = SDXLClipGTokenizer(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) |
|
return out |
|
|
|
def untokenize(self, token_weight_pair): |
|
return self.clip_g.untokenize(token_weight_pair) |
|
|
|
class SDXLClipModel(torch.nn.Module): |
|
def __init__(self, device="cpu", dtype=None): |
|
super().__init__() |
|
self.clip_l = sd1_clip.SDClipModel(layer="hidden", layer_idx=-2, device=device, dtype=dtype, layer_norm_hidden_state=False) |
|
self.clip_g = SDXLClipG(device=device, dtype=dtype) |
|
|
|
def clip_layer(self, layer_idx): |
|
self.clip_l.clip_layer(layer_idx) |
|
self.clip_g.clip_layer(layer_idx) |
|
|
|
def reset_clip_layer(self): |
|
self.clip_g.reset_clip_layer() |
|
self.clip_l.reset_clip_layer() |
|
|
|
def encode_token_weights(self, token_weight_pairs): |
|
token_weight_pairs_g = token_weight_pairs["g"] |
|
token_weight_pairs_l = token_weight_pairs["l"] |
|
g_out, g_pooled = self.clip_g.encode_token_weights(token_weight_pairs_g) |
|
l_out, l_pooled = self.clip_l.encode_token_weights(token_weight_pairs_l) |
|
return torch.cat([l_out, g_out], dim=-1), g_pooled |
|
|
|
def load_sd(self, sd): |
|
if "text_model.encoder.layers.30.mlp.fc1.weight" in sd: |
|
return self.clip_g.load_sd(sd) |
|
else: |
|
return self.clip_l.load_sd(sd) |
|
|
|
class SDXLRefinerClipModel(sd1_clip.SD1ClipModel): |
|
def __init__(self, device="cpu", dtype=None): |
|
super().__init__(device=device, dtype=dtype, clip_name="g", clip_model=SDXLClipG)
|
|
|