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Running
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Zero
File size: 3,839 Bytes
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from .base_prompter import BasePrompter
from ..models.model_manager import ModelManager
from ..models import SD3TextEncoder1, SD3TextEncoder2, SD3TextEncoder3
from transformers import CLIPTokenizer, T5TokenizerFast
import os, torch
class SD3Prompter(BasePrompter):
def __init__(
self,
tokenizer_1_path=None,
tokenizer_2_path=None,
tokenizer_3_path=None
):
if tokenizer_1_path is None:
base_path = os.path.dirname(os.path.dirname(__file__))
tokenizer_1_path = os.path.join(base_path, "tokenizer_configs/stable_diffusion_3/tokenizer_1")
if tokenizer_2_path is None:
base_path = os.path.dirname(os.path.dirname(__file__))
tokenizer_2_path = os.path.join(base_path, "tokenizer_configs/stable_diffusion_3/tokenizer_2")
if tokenizer_3_path is None:
base_path = os.path.dirname(os.path.dirname(__file__))
tokenizer_3_path = os.path.join(base_path, "tokenizer_configs/stable_diffusion_3/tokenizer_3")
super().__init__()
self.tokenizer_1 = CLIPTokenizer.from_pretrained(tokenizer_1_path)
self.tokenizer_2 = CLIPTokenizer.from_pretrained(tokenizer_2_path)
self.tokenizer_3 = T5TokenizerFast.from_pretrained(tokenizer_3_path)
self.text_encoder_1: SD3TextEncoder1 = None
self.text_encoder_2: SD3TextEncoder2 = None
self.text_encoder_3: SD3TextEncoder3 = None
def fetch_models(self, text_encoder_1: SD3TextEncoder1 = None, text_encoder_2: SD3TextEncoder2 = None, text_encoder_3: SD3TextEncoder3 = None):
self.text_encoder_1 = text_encoder_1
self.text_encoder_2 = text_encoder_2
self.text_encoder_3 = text_encoder_3
def encode_prompt_using_clip(self, prompt, text_encoder, tokenizer, max_length, device):
input_ids = tokenizer(
prompt,
return_tensors="pt",
padding="max_length",
max_length=max_length,
truncation=True
).input_ids.to(device)
pooled_prompt_emb, prompt_emb = text_encoder(input_ids)
return pooled_prompt_emb, prompt_emb
def encode_prompt_using_t5(self, prompt, text_encoder, tokenizer, max_length, device):
input_ids = tokenizer(
prompt,
return_tensors="pt",
padding="max_length",
max_length=max_length,
truncation=True,
add_special_tokens=True,
).input_ids.to(device)
prompt_emb = text_encoder(input_ids)
prompt_emb = prompt_emb.reshape((1, prompt_emb.shape[0]*prompt_emb.shape[1], -1))
return prompt_emb
def encode_prompt(
self,
prompt,
positive=True,
device="cuda"
):
prompt = self.process_prompt(prompt, positive=positive)
# CLIP
pooled_prompt_emb_1, prompt_emb_1 = self.encode_prompt_using_clip(prompt, self.text_encoder_1, self.tokenizer_1, 77, device)
pooled_prompt_emb_2, prompt_emb_2 = self.encode_prompt_using_clip(prompt, self.text_encoder_2, self.tokenizer_2, 77, device)
# T5
if self.text_encoder_3 is None:
prompt_emb_3 = torch.zeros((prompt_emb_1.shape[0], 256, 4096), dtype=prompt_emb_1.dtype, device=device)
else:
prompt_emb_3 = self.encode_prompt_using_t5(prompt, self.text_encoder_3, self.tokenizer_3, 256, device)
prompt_emb_3 = prompt_emb_3.to(prompt_emb_1.dtype) # float32 -> float16
# Merge
prompt_emb = torch.cat([
torch.nn.functional.pad(torch.cat([prompt_emb_1, prompt_emb_2], dim=-1), (0, 4096 - 768 - 1280)),
prompt_emb_3
], dim=-2)
pooled_prompt_emb = torch.cat([pooled_prompt_emb_1, pooled_prompt_emb_2], dim=-1)
return prompt_emb, pooled_prompt_emb
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