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from comfy import sd1_clip
import torch
import os
class SDXLClipG(sd1_clip.SD1ClipModel):
def __init__(self, device="cpu", max_length=77, freeze=True, layer="penultimate", layer_idx=None, textmodel_path=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, textmodel_path=textmodel_path, dtype=dtype)
self.empty_tokens = [[49406] + [49407] + [0] * 75]
self.layer_norm_hidden_state = False
def load_sd(self, sd):
return super().load_sd(sd)
class SDXLClipGTokenizer(sd1_clip.SD1Tokenizer):
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(sd1_clip.SD1Tokenizer):
def __init__(self, embedding_directory=None):
self.clip_l = sd1_clip.SD1Tokenizer(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.SD1ClipModel(layer="hidden", layer_idx=11, device=device, dtype=dtype)
self.clip_l.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(torch.nn.Module):
def __init__(self, device="cpu", dtype=None):
super().__init__()
self.clip_g = SDXLClipG(device=device, dtype=dtype)
def clip_layer(self, layer_idx):
self.clip_g.clip_layer(layer_idx)
def reset_clip_layer(self):
self.clip_g.reset_clip_layer()
def encode_token_weights(self, token_weight_pairs):
token_weight_pairs_g = token_weight_pairs["g"]
g_out, g_pooled = self.clip_g.encode_token_weights(token_weight_pairs_g)
return g_out, g_pooled
def load_sd(self, sd):
return self.clip_g.load_sd(sd)
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