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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, tokenizer_data={}):
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, tokenizer_data={}):
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)
def state_dict(self):
return {}
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)
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)
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)
class StableCascadeClipGTokenizer(sd1_clip.SDTokenizer):
def __init__(self, tokenizer_path=None, embedding_directory=None, tokenizer_data={}):
super().__init__(tokenizer_path, pad_with_end=True, embedding_directory=embedding_directory, embedding_size=1280, embedding_key='clip_g')
class StableCascadeTokenizer(sd1_clip.SD1Tokenizer):
def __init__(self, embedding_directory=None, tokenizer_data={}):
super().__init__(embedding_directory=embedding_directory, tokenizer_data=tokenizer_data, clip_name="g", tokenizer=StableCascadeClipGTokenizer)
class StableCascadeClipG(sd1_clip.SDClipModel):
def __init__(self, device="cpu", max_length=77, freeze=True, layer="hidden", layer_idx=-1, dtype=None):
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": 49407}, layer_norm_hidden_state=False, enable_attention_masks=True)
def load_sd(self, sd):
return super().load_sd(sd)
class StableCascadeClipModel(sd1_clip.SD1ClipModel):
def __init__(self, device="cpu", dtype=None):
super().__init__(device=device, dtype=dtype, clip_name="g", clip_model=StableCascadeClipG)
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