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from functools import partial
import sys
import torch
import torch.nn as nn
import torch.nn.functional as F
import weakref
from typing import Any, Dict, List, Optional, Tuple, Union, TYPE_CHECKING
from diffusers.models.transformers.transformer_flux import FluxTransformerBlock
from transformers import AutoModel, AutoTokenizer, Qwen2Model, LlamaModel, Qwen2Tokenizer, LlamaTokenizer
from toolkit import train_tools
from toolkit.prompt_utils import PromptEmbeds
from diffusers import Transformer2DModel
from toolkit.dequantize import patch_dequantization_on_save
if TYPE_CHECKING:
from toolkit.stable_diffusion_model import StableDiffusion, PixArtSigmaPipeline
from toolkit.custom_adapter import CustomAdapter
LLM = Union[Qwen2Model, LlamaModel]
LLMTokenizer = Union[Qwen2Tokenizer, LlamaTokenizer]
def new_context_embedder_forward(self, x):
if self._adapter_ref().is_active:
x = self._context_embedder_ref()(x)
else:
x = self._orig_forward(x)
return x
def new_block_forward(
self: FluxTransformerBlock,
hidden_states: torch.Tensor,
encoder_hidden_states: torch.Tensor,
temb: torch.Tensor,
image_rotary_emb: Optional[Tuple[torch.Tensor, torch.Tensor]] = None,
joint_attention_kwargs: Optional[Dict[str, Any]] = None,
) -> Tuple[torch.Tensor, torch.Tensor]:
if self._adapter_ref().is_active:
return self._new_block_ref()(hidden_states, encoder_hidden_states, temb, image_rotary_emb, joint_attention_kwargs)
else:
return self._orig_forward(hidden_states, encoder_hidden_states, temb, image_rotary_emb, joint_attention_kwargs)
class LLMAdapter(torch.nn.Module):
def __init__(
self,
adapter: 'CustomAdapter',
sd: 'StableDiffusion',
llm: LLM,
tokenizer: LLMTokenizer,
num_cloned_blocks: int = 0,
):
super(LLMAdapter, self).__init__()
self.adapter_ref: weakref.ref = weakref.ref(adapter)
self.sd_ref: weakref.ref = weakref.ref(sd)
self.llm_ref: weakref.ref = weakref.ref(llm)
self.tokenizer_ref: weakref.ref = weakref.ref(tokenizer)
self.num_cloned_blocks = num_cloned_blocks
self.apply_embedding_mask = False
# make sure we can pad
if tokenizer.pad_token is None:
tokenizer.pad_token = tokenizer.eos_token
# self.system_prompt = ""
self.system_prompt = "You are an assistant designed to generate superior images with the superior degree of image-text alignment based on textual prompts or user prompts. <Prompt Start> "
# determine length of system prompt
sys_prompt_tokenized = tokenizer(
[self.system_prompt],
padding="longest",
return_tensors="pt",
)
sys_prompt_tokenized_ids = sys_prompt_tokenized.input_ids[0]
self.system_prompt_length = sys_prompt_tokenized_ids.shape[0]
print(f"System prompt length: {self.system_prompt_length}")
self.hidden_size = llm.config.hidden_size
blocks = []
if sd.is_flux:
self.apply_embedding_mask = True
self.context_embedder = nn.Linear(
self.hidden_size, sd.unet.inner_dim)
self.sequence_length = 512
sd.unet.context_embedder._orig_forward = sd.unet.context_embedder.forward
sd.unet.context_embedder.forward = partial(
new_context_embedder_forward, sd.unet.context_embedder)
sd.unet.context_embedder._context_embedder_ref = weakref.ref(self.context_embedder)
# add a is active property to the context embedder
sd.unet.context_embedder._adapter_ref = self.adapter_ref
for idx in range(self.num_cloned_blocks):
block = FluxTransformerBlock(
dim=sd.unet.inner_dim,
num_attention_heads=24,
attention_head_dim=128,
)
# patch it in case it is quantized
patch_dequantization_on_save(sd.unet.transformer_blocks[idx])
state_dict = sd.unet.transformer_blocks[idx].state_dict()
for key, value in state_dict.items():
block.state_dict()[key].copy_(value)
blocks.append(block)
orig_block = sd.unet.transformer_blocks[idx]
orig_block._orig_forward = orig_block.forward
orig_block.forward = partial(
new_block_forward, orig_block)
orig_block._new_block_ref = weakref.ref(block)
orig_block._adapter_ref = self.adapter_ref
elif sd.is_lumina2:
self.context_embedder = nn.Linear(
self.hidden_size, sd.unet.hidden_size)
self.sequence_length = 256
else:
raise ValueError(
"llm adapter currently only supports flux or lumina2")
self.blocks = nn.ModuleList(blocks)
def _get_prompt_embeds(
self,
prompt: Union[str, List[str]],
max_sequence_length: int = 256,
) -> Tuple[torch.Tensor, torch.Tensor]:
tokenizer = self.tokenizer_ref()
text_encoder = self.llm_ref()
device = text_encoder.device
prompt = [prompt] if isinstance(prompt, str) else prompt
text_inputs = tokenizer(
prompt,
padding="max_length",
max_length=max_sequence_length + self.system_prompt_length,
truncation=True,
return_tensors="pt",
)
text_input_ids = text_inputs.input_ids.to(device)
prompt_attention_mask = text_inputs.attention_mask.to(device)
# remove the system prompt from the input and attention mask
prompt_embeds = text_encoder(
text_input_ids, attention_mask=prompt_attention_mask, output_hidden_states=True
)
prompt_embeds = prompt_embeds.hidden_states[-1]
prompt_embeds = prompt_embeds[:, self.system_prompt_length:]
prompt_attention_mask = prompt_attention_mask[:, self.system_prompt_length:]
dtype = text_encoder.dtype
prompt_embeds = prompt_embeds.to(dtype=dtype, device=device)
return prompt_embeds, prompt_attention_mask
# make a getter to see if is active
@property
def is_active(self):
return self.adapter_ref().is_active
def encode_text(self, prompt):
prompt = prompt if isinstance(prompt, list) else [prompt]
prompt = [self.system_prompt + p for p in prompt]
# prompt = [self.system_prompt + p for p in prompt]
prompt_embeds, prompt_attention_mask = self._get_prompt_embeds(
prompt=prompt,
max_sequence_length=self.sequence_length,
)
prompt_embeds = PromptEmbeds(
prompt_embeds,
attention_mask=prompt_attention_mask,
).detach()
return prompt_embeds
def forward(self, input):
return input
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