def __init__(self, device="cpu", max_length=77, freeze=True, layer="penultimate", layer_idx=None, dtype=None, model_options={}):

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")

model_options = {**model_options, "model_name": "clip_g"}

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, return_projected_pooled=True, model_options=model_options)

def load_sd(self, sd):

def __init__(self, embedding_directory=None, tokenizer_data={}):

self.clip_l = sd1_clip.SDTokenizer(embedding_directory=embedding_directory, tokenizer_data=tokenizer_data)

self.clip_g = SDXLClipGTokenizer(embedding_directory=embedding_directory, tokenizer_data=tokenizer_data)

def tokenize_with_weights(self, text:str, return_word_ids=False, **kwargs):

out = {}

out["g"] = self.clip_g.tokenize_with_weights(text, return_word_ids, **kwargs)

out["l"] = self.clip_l.tokenize_with_weights(text, return_word_ids, **kwargs)

return out

def untokenize(self, token_weight_pair):

return self.clip_g.untokenize(token_weight_pair)

def state_dict(self):

def __init__(self, device="cpu", dtype=None, model_options={}):

super().__init__()

self.clip_l = sd1_clip.SDClipModel(layer="hidden", layer_idx=-2, device=device, dtype=dtype, layer_norm_hidden_state=False, model_options=model_options)

self.clip_g = SDXLClipG(device=device, dtype=dtype, model_options=model_options)

self.dtypes = set([dtype])

def set_clip_options(self, options):

self.clip_l.set_clip_options(options)

self.clip_g.set_clip_options(options)

def reset_clip_options(self):

self.clip_g.reset_clip_options()

self.clip_l.reset_clip_options()

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)

cut_to = min(l_out.shape[1], g_out.shape[1])

return torch.cat([l_out[:,:cut_to], g_out[:,:cut_to]], 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:

def __init__(self, device="cpu", max_length=77, freeze=True, layer="hidden", layer_idx=-1, dtype=None, model_options={}):

textmodel_json_config = os.path.join(os.path.dirname(os.path.realpath(__file__)), "clip_config_bigg.json")

model_options = {**model_options, "model_name": "clip_g"}

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": 49407}, layer_norm_hidden_state=False, enable_attention_masks=True, return_projected_pooled=True, model_options=model_options)

def load_sd(self, sd):