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import torch
from modules.sd_hijack_clip import FrozenCLIPEmbedderWithCustomWords
from ldm_patched.modules import model_management
from modules import sd_models
from modules.shared import opts
class SD3CLIP(torch.nn.Module):
def __init__(self, clip_components, embedding_directory=None):
super().__init__()
self.clip_l, self.clip_g, self.t5xxl = clip_components
self.tokenizer = SD3Tokenizer(embedding_directory)
self.patcher = model_management.ModelPatcher(self)
def encode_with_transformers(self, tokens):
move_clip_to_gpu()
# Process tokens for each component
z_l, pooled_l = self.clip_l(tokens['l'])
z_g, pooled_g = self.clip_g(tokens['g'])
z_t5, _ = self.t5xxl(tokens['t5xxl'])
# Combine outputs
z = torch.cat([z_l, z_g, z_t5], dim=-2)
pooled = torch.cat([pooled_l, pooled_g], dim=-1)
return z, pooled
def encode_from_tokens(self, tokens, return_pooled=False):
z, pooled = self.encode_with_transformers(tokens)
if return_pooled:
return z, pooled
return z
class SD3Tokenizer:
def __init__(self, embedding_directory=None):
self.clip_l = self.clip_g.clip_l.tokenizer
self.clip_g = self.clip_g.clip_g.tokenizer
self.t5xxl = self.t5xxl.tokenizer
def tokenize(self, text):
return {
"l": self.clip_l.tokenize(text),
"g": self.clip_g.tokenize(text),
"t5xxl": self.t5xxl.tokenize(text)
}
def move_clip_to_gpu():
if sd_models.model_data.sd_model is None:
print('Error: CLIP called before SD is loaded!')
return
model_management.load_model_gpu(sd_models.model_data.sd_model.forge_objects.clip.patcher) |