223 lines
7.9 KiB
Python
223 lines
7.9 KiB
Python
import os
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import safetensors
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import torch
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import typing
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from transformers import CLIPTokenizer, T5TokenizerFast
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from modules import shared, devices, modelloader, sd_hijack_clip, prompt_parser
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from modules.models.sd3.other_impls import SDClipModel, SDXLClipG, T5XXLModel, SD3Tokenizer
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class SafetensorsMapping(typing.Mapping):
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def __init__(self, file):
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self.file = file
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def __len__(self):
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return len(self.file.keys())
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def __iter__(self):
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for key in self.file.keys():
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yield key
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def __getitem__(self, key):
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return self.file.get_tensor(key)
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CLIPL_URL = "https://huggingface.co/AUTOMATIC/stable-diffusion-3-medium-text-encoders/resolve/main/clip_l.safetensors"
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CLIPL_CONFIG = {
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"hidden_act": "quick_gelu",
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"hidden_size": 768,
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"intermediate_size": 3072,
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"num_attention_heads": 12,
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"num_hidden_layers": 12,
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}
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CLIPG_URL = "https://huggingface.co/AUTOMATIC/stable-diffusion-3-medium-text-encoders/resolve/main/clip_g.safetensors"
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CLIPG_CONFIG = {
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"hidden_act": "gelu",
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"hidden_size": 1280,
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"intermediate_size": 5120,
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"num_attention_heads": 20,
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"num_hidden_layers": 32,
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"textual_inversion_key": "clip_g",
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}
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T5_URL = "https://huggingface.co/AUTOMATIC/stable-diffusion-3-medium-text-encoders/resolve/main/t5xxl_fp16.safetensors"
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T5_CONFIG = {
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"d_ff": 10240,
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"d_model": 4096,
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"num_heads": 64,
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"num_layers": 24,
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"vocab_size": 32128,
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}
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class Sd3ClipLG(sd_hijack_clip.TextConditionalModel):
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def __init__(self, clip_l, clip_g):
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super().__init__()
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self.clip_l = clip_l
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self.clip_g = clip_g
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self.tokenizer = CLIPTokenizer.from_pretrained("openai/clip-vit-large-patch14")
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empty = self.tokenizer('')["input_ids"]
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self.id_start = empty[0]
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self.id_end = empty[1]
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self.id_pad = empty[1]
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self.return_pooled = True
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def tokenize(self, texts):
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return self.tokenizer(texts, truncation=False, add_special_tokens=False)["input_ids"]
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def encode_with_transformers(self, tokens):
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tokens_g = tokens.clone()
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for batch_pos in range(tokens_g.shape[0]):
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index = tokens_g[batch_pos].cpu().tolist().index(self.id_end)
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tokens_g[batch_pos, index+1:tokens_g.shape[1]] = 0
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l_out, l_pooled = self.clip_l(tokens)
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g_out, g_pooled = self.clip_g(tokens_g)
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lg_out = torch.cat([l_out, g_out], dim=-1)
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lg_out = torch.nn.functional.pad(lg_out, (0, 4096 - lg_out.shape[-1]))
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vector_out = torch.cat((l_pooled, g_pooled), dim=-1)
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lg_out.pooled = vector_out
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return lg_out
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def encode_embedding_init_text(self, init_text, nvpt):
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return torch.zeros((nvpt, 768+1280), device=devices.device) # XXX
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class Sd3T5(torch.nn.Module):
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def __init__(self, t5xxl):
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super().__init__()
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self.t5xxl = t5xxl
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self.tokenizer = T5TokenizerFast.from_pretrained("google/t5-v1_1-xxl")
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empty = self.tokenizer('', padding='max_length', max_length=2)["input_ids"]
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self.id_end = empty[0]
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self.id_pad = empty[1]
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def tokenize(self, texts):
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return self.tokenizer(texts, truncation=False, add_special_tokens=False)["input_ids"]
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def tokenize_line(self, line, *, target_token_count=None):
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if shared.opts.emphasis != "None":
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parsed = prompt_parser.parse_prompt_attention(line)
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else:
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parsed = [[line, 1.0]]
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tokenized = self.tokenize([text for text, _ in parsed])
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tokens = []
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multipliers = []
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for text_tokens, (text, weight) in zip(tokenized, parsed):
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if text == 'BREAK' and weight == -1:
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continue
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tokens += text_tokens
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multipliers += [weight] * len(text_tokens)
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tokens += [self.id_end]
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multipliers += [1.0]
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if target_token_count is not None:
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if len(tokens) < target_token_count:
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tokens += [self.id_pad] * (target_token_count - len(tokens))
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multipliers += [1.0] * (target_token_count - len(tokens))
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else:
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tokens = tokens[0:target_token_count]
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multipliers = multipliers[0:target_token_count]
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return tokens, multipliers
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def forward(self, texts, *, token_count):
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if not self.t5xxl or not shared.opts.sd3_enable_t5:
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return torch.zeros((len(texts), token_count, 4096), device=devices.device, dtype=devices.dtype)
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tokens_batch = []
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for text in texts:
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tokens, multipliers = self.tokenize_line(text, target_token_count=token_count)
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tokens_batch.append(tokens)
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t5_out, t5_pooled = self.t5xxl(tokens_batch)
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return t5_out
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def encode_embedding_init_text(self, init_text, nvpt):
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return torch.zeros((nvpt, 4096), device=devices.device) # XXX
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class SD3Cond(torch.nn.Module):
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def __init__(self, *args, **kwargs):
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super().__init__(*args, **kwargs)
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self.tokenizer = SD3Tokenizer()
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with torch.no_grad():
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self.clip_g = SDXLClipG(CLIPG_CONFIG, device="cpu", dtype=devices.dtype)
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self.clip_l = SDClipModel(layer="hidden", layer_idx=-2, device="cpu", dtype=devices.dtype, layer_norm_hidden_state=False, return_projected_pooled=False, textmodel_json_config=CLIPL_CONFIG)
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if shared.opts.sd3_enable_t5:
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self.t5xxl = T5XXLModel(T5_CONFIG, device="cpu", dtype=devices.dtype)
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else:
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self.t5xxl = None
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self.model_lg = Sd3ClipLG(self.clip_l, self.clip_g)
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self.model_t5 = Sd3T5(self.t5xxl)
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def forward(self, prompts: list[str]):
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with devices.without_autocast():
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lg_out, vector_out = self.model_lg(prompts)
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t5_out = self.model_t5(prompts, token_count=lg_out.shape[1])
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lgt_out = torch.cat([lg_out, t5_out], dim=-2)
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return {
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'crossattn': lgt_out,
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'vector': vector_out,
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}
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def before_load_weights(self, state_dict):
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clip_path = os.path.join(shared.models_path, "CLIP")
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if 'text_encoders.clip_g.transformer.text_model.embeddings.position_embedding.weight' not in state_dict:
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clip_g_file = modelloader.load_file_from_url(CLIPG_URL, model_dir=clip_path, file_name="clip_g.safetensors")
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with safetensors.safe_open(clip_g_file, framework="pt") as file:
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self.clip_g.transformer.load_state_dict(SafetensorsMapping(file))
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if 'text_encoders.clip_l.transformer.text_model.embeddings.position_embedding.weight' not in state_dict:
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clip_l_file = modelloader.load_file_from_url(CLIPL_URL, model_dir=clip_path, file_name="clip_l.safetensors")
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with safetensors.safe_open(clip_l_file, framework="pt") as file:
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self.clip_l.transformer.load_state_dict(SafetensorsMapping(file), strict=False)
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if self.t5xxl and 'text_encoders.t5xxl.transformer.encoder.embed_tokens.weight' not in state_dict:
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t5_file = modelloader.load_file_from_url(T5_URL, model_dir=clip_path, file_name="t5xxl_fp16.safetensors")
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with safetensors.safe_open(t5_file, framework="pt") as file:
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self.t5xxl.transformer.load_state_dict(SafetensorsMapping(file), strict=False)
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def encode_embedding_init_text(self, init_text, nvpt):
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return self.model_lg.encode_embedding_init_text(init_text, nvpt)
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def tokenize(self, texts):
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return self.model_lg.tokenize(texts)
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def medvram_modules(self):
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return [self.clip_g, self.clip_l, self.t5xxl]
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def get_token_count(self, text):
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_, token_count = self.model_lg.process_texts([text])
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return token_count
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def get_target_prompt_token_count(self, token_count):
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return self.model_lg.get_target_prompt_token_count(token_count)
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