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84 lines
3.4 KiB
84 lines
3.4 KiB
1 year ago
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from comfy import sd1_clip
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import torch
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import os
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class SDXLClipG(sd1_clip.SD1ClipModel):
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def __init__(self, device="cpu", max_length=77, freeze=True, layer="penultimate", layer_idx=None):
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textmodel_json_config = os.path.join(os.path.dirname(os.path.realpath(__file__)), "clip_config_bigg.json")
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super().__init__(device=device, freeze=freeze, textmodel_json_config=textmodel_json_config)
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self.empty_tokens = [[49406] + [49407] + [0] * 75]
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self.text_projection = torch.nn.Parameter(torch.empty(1280, 1280))
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self.layer_norm_hidden_state = False
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if layer == "last":
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pass
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elif layer == "penultimate":
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layer_idx = -1
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self.clip_layer(layer_idx)
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elif self.layer == "hidden":
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assert layer_idx is not None
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assert abs(layer_idx) < 32
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self.clip_layer(layer_idx)
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else:
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raise NotImplementedError()
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def clip_layer(self, layer_idx):
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if layer_idx < 0:
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layer_idx -= 1 #The real last layer of SD2.x clip is the penultimate one. The last one might contain garbage.
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if abs(layer_idx) >= 32:
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self.layer = "hidden"
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self.layer_idx = -2
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else:
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self.layer = "hidden"
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self.layer_idx = layer_idx
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class SDXLClipGTokenizer(sd1_clip.SD1Tokenizer):
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def __init__(self, tokenizer_path=None, embedding_directory=None):
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super().__init__(tokenizer_path, pad_with_end=False, embedding_directory=embedding_directory, embedding_size=1280)
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class SDXLTokenizer(sd1_clip.SD1Tokenizer):
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def __init__(self, embedding_directory=None):
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self.clip_l = sd1_clip.SD1Tokenizer(embedding_directory=embedding_directory)
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self.clip_g = SDXLClipGTokenizer(embedding_directory=embedding_directory)
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def tokenize_with_weights(self, text:str, return_word_ids=False):
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out = {}
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out["g"] = self.clip_g.tokenize_with_weights(text, return_word_ids)
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out["l"] = self.clip_l.tokenize_with_weights(text, return_word_ids)
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return out
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def untokenize(self, token_weight_pair):
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return self.clip_g.untokenize(token_weight_pair)
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class SDXLClipModel(torch.nn.Module):
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def __init__(self, device="cpu"):
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super().__init__()
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self.clip_l = sd1_clip.SD1ClipModel(layer="hidden", layer_idx=11, device=device)
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self.clip_l.layer_norm_hidden_state = False
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self.clip_g = SDXLClipG(device=device)
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def clip_layer(self, layer_idx):
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self.clip_l.clip_layer(layer_idx)
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self.clip_g.clip_layer(layer_idx)
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def encode_token_weights(self, token_weight_pairs):
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token_weight_pairs_g = token_weight_pairs["g"]
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token_weight_pairs_l = token_weight_pairs["l"]
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g_out, g_pooled = self.clip_g.encode_token_weights(token_weight_pairs_g)
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l_out, l_pooled = self.clip_l.encode_token_weights(token_weight_pairs_l)
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return torch.cat([l_out, g_out], dim=-1), g_pooled
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class SDXLRefinerClipModel(torch.nn.Module):
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def __init__(self, device="cpu"):
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super().__init__()
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self.clip_g = SDXLClipG(device=device)
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def clip_layer(self, layer_idx):
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self.clip_g.clip_layer(layer_idx)
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def encode_token_weights(self, token_weight_pairs):
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token_weight_pairs_g = token_weight_pairs["g"]
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g_out, g_pooled = self.clip_g.encode_token_weights(token_weight_pairs_g)
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return g_out, g_pooled
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