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Merge pull request #16164 from AUTOMATIC1111/sd3_textual_inversion

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sd3 TI support
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AUTOMATIC1111 2024-07-13 09:27:46 +03:00 committed by GitHub
commit b4d62a05af
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3 changed files with 26 additions and 5 deletions
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8
modules/models/sd3/other_impls.py
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@ -5,6 +5,8 @@ import math
from torch import nn from torch import nn
from transformers import CLIPTokenizer, T5TokenizerFast from transformers import CLIPTokenizer, T5TokenizerFast
from modules import sd_hijack
################################################################################################# #################################################################################################
### Core/Utility ### Core/Utility
@ -110,9 +112,9 @@ class CLIPEncoder(torch.nn.Module):
class CLIPEmbeddings(torch.nn.Module): class CLIPEmbeddings(torch.nn.Module):
def __init__(self, embed_dim, vocab_size=49408, num_positions=77, dtype=None, device=None): def __init__(self, embed_dim, vocab_size=49408, num_positions=77, dtype=None, device=None, textual_inversion_key="clip_l"):
super().__init__() super().__init__()
self.token_embedding = torch.nn.Embedding(vocab_size, embed_dim, dtype=dtype, device=device) self.token_embedding = sd_hijack.TextualInversionEmbeddings(vocab_size, embed_dim, dtype=dtype, device=device, textual_inversion_key=textual_inversion_key)
self.position_embedding = torch.nn.Embedding(num_positions, embed_dim, dtype=dtype, device=device) self.position_embedding = torch.nn.Embedding(num_positions, embed_dim, dtype=dtype, device=device)
def forward(self, input_tokens): def forward(self, input_tokens):
@ -127,7 +129,7 @@ class CLIPTextModel_(torch.nn.Module):
intermediate_size = config_dict["intermediate_size"] intermediate_size = config_dict["intermediate_size"]
intermediate_activation = config_dict["hidden_act"] intermediate_activation = config_dict["hidden_act"]
super().__init__() super().__init__()
self.embeddings = CLIPEmbeddings(embed_dim, dtype=torch.float32, device=device) self.embeddings = CLIPEmbeddings(embed_dim, dtype=torch.float32, device=device, textual_inversion_key=config_dict.get('textual_inversion_key', 'clip_l'))
self.encoder = CLIPEncoder(num_layers, embed_dim, heads, intermediate_size, intermediate_activation, dtype, device) self.encoder = CLIPEncoder(num_layers, embed_dim, heads, intermediate_size, intermediate_activation, dtype, device)
self.final_layer_norm = nn.LayerNorm(embed_dim, dtype=dtype, device=device) self.final_layer_norm = nn.LayerNorm(embed_dim, dtype=dtype, device=device)

6
modules/models/sd3/sd3_cond.py
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@ -40,6 +40,7 @@ CLIPG_CONFIG = {
"intermediate_size": 5120, "intermediate_size": 5120,
"num_attention_heads": 20, "num_attention_heads": 20,
"num_hidden_layers": 32, "num_hidden_layers": 32,
"textual_inversion_key": "clip_g",
} }
T5_URL = "https://huggingface.co/AUTOMATIC/stable-diffusion-3-medium-text-encoders/resolve/main/t5xxl_fp16.safetensors" T5_URL = "https://huggingface.co/AUTOMATIC/stable-diffusion-3-medium-text-encoders/resolve/main/t5xxl_fp16.safetensors"
@ -204,7 +205,10 @@ class SD3Cond(torch.nn.Module):
self.t5xxl.transformer.load_state_dict(SafetensorsMapping(file), strict=False) self.t5xxl.transformer.load_state_dict(SafetensorsMapping(file), strict=False)
def encode_embedding_init_text(self, init_text, nvpt): def encode_embedding_init_text(self, init_text, nvpt):
return torch.tensor([[0]], device=devices.device) # XXX return self.model_lg.encode_embedding_init_text(init_text, nvpt)
def tokenize(self, texts):
return self.model_lg.tokenize(texts)
def medvram_modules(self): def medvram_modules(self):
return [self.clip_g, self.clip_l, self.t5xxl] return [self.clip_g, self.clip_l, self.t5xxl]

17
modules/sd_hijack.py
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@ -359,13 +359,28 @@ class EmbeddingsWithFixes(torch.nn.Module):
vec = embedding.vec[self.textual_inversion_key] if isinstance(embedding.vec, dict) else embedding.vec vec = embedding.vec[self.textual_inversion_key] if isinstance(embedding.vec, dict) else embedding.vec
emb = devices.cond_cast_unet(vec) emb = devices.cond_cast_unet(vec)
emb_len = min(tensor.shape[0] - offset - 1, emb.shape[0]) emb_len = min(tensor.shape[0] - offset - 1, emb.shape[0])
tensor = torch.cat([tensor[0:offset + 1], emb[0:emb_len], tensor[offset + 1 + emb_len:]]) tensor = torch.cat([tensor[0:offset + 1], emb[0:emb_len], tensor[offset + 1 + emb_len:]]).to(dtype=inputs_embeds.dtype)
vecs.append(tensor) vecs.append(tensor)
return torch.stack(vecs) return torch.stack(vecs)
class TextualInversionEmbeddings(torch.nn.Embedding):
def __init__(self, num_embeddings: int, embedding_dim: int, textual_inversion_key='clip_l', **kwargs):
super().__init__(num_embeddings, embedding_dim, **kwargs)
self.embeddings = model_hijack
self.textual_inversion_key = textual_inversion_key
@property
def wrapped(self):
return super().forward
def forward(self, input_ids):
return EmbeddingsWithFixes.forward(self, input_ids)
def add_circular_option_to_conv_2d(): def add_circular_option_to_conv_2d():
conv2d_constructor = torch.nn.Conv2d.__init__ conv2d_constructor = torch.nn.Conv2d.__init__