import math
import torch
import sys
from PIL import Image
from torch.nn import Parameter
from transformers import CLIPImageProcessor, CLIPVisionModelWithProjection, T5EncoderModel, CLIPTextModel, \
CLIPTokenizer, T5Tokenizer
from toolkit.data_transfer_object.data_loader import DataLoaderBatchDTO
from toolkit.models.clip_fusion import CLIPFusionModule
from toolkit.models.clip_pre_processor import CLIPImagePreProcessor
from toolkit.models.control_lora_adapter import ControlLoraAdapter
from toolkit.models.mean_flow_adapter import MeanFlowAdapter
from toolkit.models.i2v_adapter import I2VAdapter
from toolkit.models.subpixel_adapter import SubpixelAdapter
from toolkit.models.ilora import InstantLoRAModule
from toolkit.models.single_value_adapter import SingleValueAdapter
from toolkit.models.te_adapter import TEAdapter
from toolkit.models.te_aug_adapter import TEAugAdapter
from toolkit.models.vd_adapter import VisionDirectAdapter
from toolkit.models.redux import ReduxImageEncoder
from toolkit.photomaker import PhotoMakerIDEncoder, FuseModule, PhotoMakerCLIPEncoder
from toolkit.saving import load_ip_adapter_model, load_custom_adapter_model
from toolkit.train_tools import get_torch_dtype
from toolkit.models.pixtral_vision import PixtralVisionEncoderCompatible, PixtralVisionImagePreprocessorCompatible
import random
from toolkit.util.mask import generate_random_mask
from typing import TYPE_CHECKING, Union, Iterator, Mapping, Any, Tuple, List, Optional, Dict
from collections import OrderedDict
from toolkit.config_modules import AdapterConfig, AdapterTypes, TrainConfig
from toolkit.prompt_utils import PromptEmbeds
import weakref
if TYPE_CHECKING:
from toolkit.stable_diffusion_model import StableDiffusion
from transformers import (
ConvNextForImageClassification,
ConvNextImageProcessor,
UMT5EncoderModel, LlamaTokenizerFast, AutoModel, AutoTokenizer, BitsAndBytesConfig
)
from toolkit.models.size_agnostic_feature_encoder import SAFEImageProcessor, SAFEVisionModel
from toolkit.models.llm_adapter import LLMAdapter
import torch.nn.functional as F
class CustomAdapter(torch.nn.Module):
def __init__(self, sd: 'StableDiffusion', adapter_config: 'AdapterConfig', train_config: 'TrainConfig'):
super().__init__()
self.config = adapter_config
self.sd_ref: weakref.ref = weakref.ref(sd)
self.train_config = train_config
self.device = self.sd_ref().unet.device
self.image_processor: CLIPImageProcessor = None
self.input_size = 224
self.adapter_type: AdapterTypes = self.config.type
self.current_scale = 1.0
self.is_active = True
self.flag_word = "fla9wor0"
self.is_unconditional_run = False
self.is_sampling = False
self.vision_encoder: Union[PhotoMakerCLIPEncoder, CLIPVisionModelWithProjection] = None
self.fuse_module: FuseModule = None
self.lora: None = None
self.position_ids: Optional[List[int]] = None
self.num_control_images = self.config.num_control_images
self.token_mask: Optional[torch.Tensor] = None
# setup clip
self.setup_clip()
# add for dataloader
self.clip_image_processor = self.image_processor
self.clip_fusion_module: CLIPFusionModule = None
self.ilora_module: InstantLoRAModule = None
self.te: Union[T5EncoderModel, CLIPTextModel] = None
self.tokenizer: CLIPTokenizer = None
self.te_adapter: TEAdapter = None
self.te_augmenter: TEAugAdapter = None
self.vd_adapter: VisionDirectAdapter = None
self.single_value_adapter: SingleValueAdapter = None
self.redux_adapter: ReduxImageEncoder = None
self.control_lora: ControlLoraAdapter = None
self.mean_flow_adapter: MeanFlowAdapter = None
self.subpixel_adapter: SubpixelAdapter = None
self.i2v_adapter: I2VAdapter = None
self.conditional_embeds: Optional[torch.Tensor] = None
self.unconditional_embeds: Optional[torch.Tensor] = None
self.cached_control_image_0_1: Optional[torch.Tensor] = None
self.setup_adapter()
if self.adapter_type == 'photo_maker':
# try to load from our name_or_path
if self.config.name_or_path is not None and self.config.name_or_path.endswith('.bin'):
self.load_state_dict(torch.load(self.config.name_or_path, map_location=self.device), strict=False)
# add the trigger word to the tokenizer
if isinstance(self.sd_ref().tokenizer, list):
for tokenizer in self.sd_ref().tokenizer:
tokenizer.add_tokens([self.flag_word], special_tokens=True)
else:
self.sd_ref().tokenizer.add_tokens([self.flag_word], special_tokens=True)
elif self.config.name_or_path is not None:
loaded_state_dict = load_custom_adapter_model(
self.config.name_or_path,
self.sd_ref().device,
dtype=self.sd_ref().dtype,
)
self.load_state_dict(loaded_state_dict, strict=False)
@property
def do_direct_save(self):
# some adapters save their weights directly, others like ip adapters split the state dict
if self.config.train_only_image_encoder:
return True
if self.config.type in ['control_lora', 'subpixel', 'i2v', 'redux', 'mean_flow']:
return True
return False
def setup_adapter(self):
torch_dtype = get_torch_dtype(self.sd_ref().dtype)
if self.adapter_type == 'photo_maker':
sd = self.sd_ref()
embed_dim = sd.unet_unwrapped.config['cross_attention_dim']
self.fuse_module = FuseModule(embed_dim)
elif self.adapter_type == 'clip_fusion':
sd = self.sd_ref()
embed_dim = sd.unet_unwrapped.config['cross_attention_dim']
vision_tokens = ((self.vision_encoder.config.image_size // self.vision_encoder.config.patch_size) ** 2)
if self.config.image_encoder_arch == 'clip':
vision_tokens = vision_tokens + 1
self.clip_fusion_module = CLIPFusionModule(
text_hidden_size=embed_dim,
text_tokens=77,
vision_hidden_size=self.vision_encoder.config.hidden_size,
vision_tokens=vision_tokens
)
elif self.adapter_type == 'ilora':
vision_tokens = ((self.vision_encoder.config.image_size // self.vision_encoder.config.patch_size) ** 2)
if self.config.image_encoder_arch == 'clip':
vision_tokens = vision_tokens + 1
vision_hidden_size = self.vision_encoder.config.hidden_size
if self.config.clip_layer == 'image_embeds':
vision_tokens = 1
vision_hidden_size = self.vision_encoder.config.projection_dim
self.ilora_module = InstantLoRAModule(
vision_tokens=vision_tokens,
vision_hidden_size=vision_hidden_size,
head_dim=self.config.head_dim,
num_heads=self.config.num_heads,
sd=self.sd_ref(),
config=self.config
)
elif self.adapter_type == 'text_encoder':
if self.config.text_encoder_arch == 't5':
te_kwargs = {}
# te_kwargs['load_in_4bit'] = True
# te_kwargs['load_in_8bit'] = True
te_kwargs['device_map'] = "auto"
te_is_quantized = True
self.te = T5EncoderModel.from_pretrained(
self.config.text_encoder_path,
torch_dtype=torch_dtype,
**te_kwargs
)
# self.te.to = lambda *args, **kwargs: None
self.tokenizer = T5Tokenizer.from_pretrained(self.config.text_encoder_path)
elif self.config.text_encoder_arch == 'pile-t5':
te_kwargs = {}
# te_kwargs['load_in_4bit'] = True
# te_kwargs['load_in_8bit'] = True
te_kwargs['device_map'] = "auto"
te_is_quantized = True
self.te = UMT5EncoderModel.from_pretrained(
self.config.text_encoder_path,
torch_dtype=torch_dtype,
**te_kwargs
)
# self.te.to = lambda *args, **kwargs: None
self.tokenizer = LlamaTokenizerFast.from_pretrained(self.config.text_encoder_path)
if self.tokenizer.pad_token is None:
self.tokenizer.add_special_tokens({'pad_token': '[PAD]'})
elif self.config.text_encoder_arch == 'clip':
self.te = CLIPTextModel.from_pretrained(self.config.text_encoder_path).to(self.sd_ref().unet.device,
dtype=torch_dtype)
self.tokenizer = CLIPTokenizer.from_pretrained(self.config.text_encoder_path)
else:
raise ValueError(f"unknown text encoder arch: {self.config.text_encoder_arch}")
self.te_adapter = TEAdapter(self, self.sd_ref(), self.te, self.tokenizer)
elif self.adapter_type == 'llm_adapter':
kwargs = {}
if self.config.quantize_llm:
bnb_kwargs = {
'load_in_4bit': True,
'bnb_4bit_quant_type': "nf4",
'bnb_4bit_compute_dtype': torch.bfloat16
}
quantization_config = BitsAndBytesConfig(**bnb_kwargs)
kwargs['quantization_config'] = quantization_config
kwargs['torch_dtype'] = torch_dtype
self.te = AutoModel.from_pretrained(
self.config.text_encoder_path,
**kwargs
)
else:
self.te = AutoModel.from_pretrained(self.config.text_encoder_path).to(
self.sd_ref().unet.device,
dtype=torch_dtype,
)
self.te.to = lambda *args, **kwargs: None
self.te.eval()
self.tokenizer = AutoTokenizer.from_pretrained(self.config.text_encoder_path)
self.llm_adapter = LLMAdapter(
adapter=self,
sd=self.sd_ref(),
llm=self.te,
tokenizer=self.tokenizer,
num_cloned_blocks=self.config.num_cloned_blocks,
)
self.llm_adapter.to(self.device, torch_dtype)
elif self.adapter_type == 'te_augmenter':
self.te_augmenter = TEAugAdapter(self, self.sd_ref())
elif self.adapter_type == 'vision_direct':
self.vd_adapter = VisionDirectAdapter(self, self.sd_ref(), self.vision_encoder)
elif self.adapter_type == 'single_value':
self.single_value_adapter = SingleValueAdapter(self, self.sd_ref(), num_values=self.config.num_tokens)
elif self.adapter_type == 'redux':
vision_hidden_size = self.vision_encoder.config.hidden_size
self.redux_adapter = ReduxImageEncoder(vision_hidden_size, 4096, self.device, torch_dtype)
elif self.adapter_type == 'mean_flow':
self.mean_flow_adapter = MeanFlowAdapter(
self,
sd=self.sd_ref(),
config=self.config,
train_config=self.train_config
)
elif self.adapter_type == 'control_lora':
self.control_lora = ControlLoraAdapter(
self,
sd=self.sd_ref(),
config=self.config,
train_config=self.train_config
)
elif self.adapter_type == 'i2v':
self.i2v_adapter = I2VAdapter(
self,
sd=self.sd_ref(),
config=self.config,
train_config=self.train_config,
image_processor=self.image_processor,
vision_encoder=self.vision_encoder,
)
elif self.adapter_type == 'subpixel':
self.subpixel_adapter = SubpixelAdapter(
self,
sd=self.sd_ref(),
config=self.config,
train_config=self.train_config
)
else:
raise ValueError(f"unknown adapter type: {self.adapter_type}")
def forward(self, *args, **kwargs):
# dont think this is used
# if self.adapter_type == 'photo_maker':
# id_pixel_values = args[0]
# prompt_embeds: PromptEmbeds = args[1]
# class_tokens_mask = args[2]
#
# grads_on_image_encoder = self.config.train_image_encoder and torch.is_grad_enabled()
#
# with torch.set_grad_enabled(grads_on_image_encoder):
# id_embeds = self.vision_encoder(self, id_pixel_values, do_projection2=False)
#
# if not grads_on_image_encoder:
# id_embeds = id_embeds.detach()
#
# prompt_embeds = prompt_embeds.detach()
#
# updated_prompt_embeds = self.fuse_module(
# prompt_embeds, id_embeds, class_tokens_mask
# )
#
# return updated_prompt_embeds
# else:
raise NotImplementedError
def edit_batch_raw(self, batch: DataLoaderBatchDTO):
# happens on a raw batch before latents are created
return batch
def edit_batch_processed(self, batch: DataLoaderBatchDTO):
# happens after the latents are processed
if self.adapter_type == "i2v":
return self.i2v_adapter.edit_batch_processed(batch)
return batch
def setup_clip(self):
adapter_config = self.config
sd = self.sd_ref()
if self.config.type in ["text_encoder", "llm_adapter", "single_value", "control_lora", "subpixel", "mean_flow"]:
return
if self.config.type == 'photo_maker':
try:
self.image_processor = CLIPImageProcessor.from_pretrained(self.config.image_encoder_path)
except EnvironmentError:
self.image_processor = CLIPImageProcessor()
if self.config.image_encoder_path is None:
self.vision_encoder = PhotoMakerCLIPEncoder()
else:
self.vision_encoder = PhotoMakerCLIPEncoder.from_pretrained(self.config.image_encoder_path)
elif self.config.image_encoder_arch == 'clip' or self.config.image_encoder_arch == 'clip+':
try:
self.image_processor = CLIPImageProcessor.from_pretrained(adapter_config.image_encoder_path)
except EnvironmentError:
self.image_processor = CLIPImageProcessor()
self.vision_encoder = CLIPVisionModelWithProjection.from_pretrained(
adapter_config.image_encoder_path,
ignore_mismatched_sizes=True).to(self.device, dtype=get_torch_dtype(self.sd_ref().dtype))
elif self.config.image_encoder_arch == 'siglip':
from transformers import SiglipImageProcessor, SiglipVisionModel
try:
self.image_processor = SiglipImageProcessor.from_pretrained(adapter_config.image_encoder_path)
except EnvironmentError:
self.image_processor = SiglipImageProcessor()
self.vision_encoder = SiglipVisionModel.from_pretrained(
adapter_config.image_encoder_path,
ignore_mismatched_sizes=True).to(self.device, dtype=get_torch_dtype(self.sd_ref().dtype))
elif self.config.image_encoder_arch == 'siglip2':
from transformers import SiglipImageProcessor, SiglipVisionModel
try:
self.image_processor = SiglipImageProcessor.from_pretrained(adapter_config.image_encoder_path)
except EnvironmentError:
self.image_processor = SiglipImageProcessor()
self.vision_encoder = SiglipVisionModel.from_pretrained(
adapter_config.image_encoder_path,
ignore_mismatched_sizes=True).to(self.device, dtype=get_torch_dtype(self.sd_ref().dtype))
elif self.config.image_encoder_arch == 'pixtral':
self.image_processor = PixtralVisionImagePreprocessorCompatible(
max_image_size=self.config.pixtral_max_image_size,
)
self.vision_encoder = PixtralVisionEncoderCompatible.from_pretrained(
adapter_config.image_encoder_path,
).to(self.device, dtype=get_torch_dtype(self.sd_ref().dtype))
elif self.config.image_encoder_arch == 'safe':
try:
self.image_processor = SAFEImageProcessor.from_pretrained(adapter_config.image_encoder_path)
except EnvironmentError:
self.image_processor = SAFEImageProcessor()
self.vision_encoder = SAFEVisionModel(
in_channels=3,
num_tokens=self.config.safe_tokens,
num_vectors=sd.unet_unwrapped.config['cross_attention_dim'],
reducer_channels=self.config.safe_reducer_channels,
channels=self.config.safe_channels,
downscale_factor=8
).to(self.device, dtype=get_torch_dtype(self.sd_ref().dtype))
elif self.config.image_encoder_arch == 'convnext':
try:
self.image_processor = ConvNextImageProcessor.from_pretrained(adapter_config.image_encoder_path)
except EnvironmentError:
print(f"could not load image processor from {adapter_config.image_encoder_path}")
self.image_processor = ConvNextImageProcessor(
size=320,
image_mean=[0.48145466, 0.4578275, 0.40821073],
image_std=[0.26862954, 0.26130258, 0.27577711],
)
self.vision_encoder = ConvNextForImageClassification.from_pretrained(
adapter_config.image_encoder_path,
use_safetensors=True,
).to(self.device, dtype=get_torch_dtype(self.sd_ref().dtype))
else:
raise ValueError(f"unknown image encoder arch: {adapter_config.image_encoder_arch}")
self.input_size = self.vision_encoder.config.image_size
if self.config.quad_image: # 4x4 image
# self.clip_image_processor.config
# We do a 3x downscale of the image, so we need to adjust the input size
preprocessor_input_size = self.vision_encoder.config.image_size * 2
# update the preprocessor so images come in at the right size
if 'height' in self.image_processor.size:
self.image_processor.size['height'] = preprocessor_input_size
self.image_processor.size['width'] = preprocessor_input_size
elif hasattr(self.image_processor, 'crop_size'):
self.image_processor.size['shortest_edge'] = preprocessor_input_size
self.image_processor.crop_size['height'] = preprocessor_input_size
self.image_processor.crop_size['width'] = preprocessor_input_size
if self.config.image_encoder_arch == 'clip+':
# self.image_processor.config
# We do a 3x downscale of the image, so we need to adjust the input size
preprocessor_input_size = self.vision_encoder.config.image_size * 4
# update the preprocessor so images come in at the right size
self.image_processor.size['shortest_edge'] = preprocessor_input_size
self.image_processor.crop_size['height'] = preprocessor_input_size
self.image_processor.crop_size['width'] = preprocessor_input_size
self.preprocessor = CLIPImagePreProcessor(
input_size=preprocessor_input_size,
clip_input_size=self.vision_encoder.config.image_size,
)
if 'height' in self.image_processor.size:
self.input_size = self.image_processor.size['height']
else:
self.input_size = self.image_processor.crop_size['height']
def load_state_dict(self, state_dict: Mapping[str, Any], strict: bool = True):
strict = False
if self.config.train_only_image_encoder and 'vd_adapter' not in state_dict and 'dvadapter' not in state_dict:
# we are loading pure clip weights.
self.vision_encoder.load_state_dict(state_dict, strict=strict)
if 'lora_weights' in state_dict:
# todo add LoRA
# self.sd_ref().pipeline.load_lora_weights(state_dict["lora_weights"], adapter_name="photomaker")
# self.sd_ref().pipeline.fuse_lora()
pass
if 'clip_fusion' in state_dict:
self.clip_fusion_module.load_state_dict(state_dict['clip_fusion'], strict=strict)
if 'id_encoder' in state_dict and (self.adapter_type == 'photo_maker' or self.adapter_type == 'clip_fusion'):
self.vision_encoder.load_state_dict(state_dict['id_encoder'], strict=strict)
# check to see if the fuse weights are there
fuse_weights = {}
for k, v in state_dict['id_encoder'].items():
if k.startswith('fuse_module'):
k = k.replace('fuse_module.', '')
fuse_weights[k] = v
if len(fuse_weights) > 0:
try:
self.fuse_module.load_state_dict(fuse_weights, strict=strict)
except Exception as e:
print(e)
# force load it
print(f"force loading fuse module as it did not match")
current_state_dict = self.fuse_module.state_dict()
for k, v in fuse_weights.items():
if len(v.shape) == 1:
current_state_dict[k] = v[:current_state_dict[k].shape[0]]
elif len(v.shape) == 2:
current_state_dict[k] = v[:current_state_dict[k].shape[0], :current_state_dict[k].shape[1]]
elif len(v.shape) == 3:
current_state_dict[k] = v[:current_state_dict[k].shape[0], :current_state_dict[k].shape[1],
:current_state_dict[k].shape[2]]
elif len(v.shape) == 4:
current_state_dict[k] = v[:current_state_dict[k].shape[0], :current_state_dict[k].shape[1],
:current_state_dict[k].shape[2], :current_state_dict[k].shape[3]]
else:
raise ValueError(f"unknown shape: {v.shape}")
self.fuse_module.load_state_dict(current_state_dict, strict=strict)
if 'te_adapter' in state_dict:
self.te_adapter.load_state_dict(state_dict['te_adapter'], strict=strict)
if 'llm_adapter' in state_dict:
self.llm_adapter.load_state_dict(state_dict['llm_adapter'], strict=strict)
if 'te_augmenter' in state_dict:
self.te_augmenter.load_state_dict(state_dict['te_augmenter'], strict=strict)
if 'vd_adapter' in state_dict:
self.vd_adapter.load_state_dict(state_dict['vd_adapter'], strict=strict)
if 'dvadapter' in state_dict:
self.vd_adapter.load_state_dict(state_dict['dvadapter'], strict=False)
if 'sv_adapter' in state_dict:
self.single_value_adapter.load_state_dict(state_dict['sv_adapter'], strict=strict)
if 'vision_encoder' in state_dict:
self.vision_encoder.load_state_dict(state_dict['vision_encoder'], strict=strict)
if 'fuse_module' in state_dict:
self.fuse_module.load_state_dict(state_dict['fuse_module'], strict=strict)
if 'ilora' in state_dict:
try:
self.ilora_module.load_state_dict(state_dict['ilora'], strict=strict)
except Exception as e:
print(e)
if 'redux_up' in state_dict:
# state dict is seperated. so recombine it
new_dict = {}
for k, v in state_dict.items():
for k2, v2 in v.items():
new_dict[k + '.' + k2] = v2
self.redux_adapter.load_state_dict(new_dict, strict=True)
if self.adapter_type == 'control_lora':
# state dict is seperated. so recombine it
new_dict = {}
for k, v in state_dict.items():
for k2, v2 in v.items():
new_dict[k + '.' + k2] = v2
self.control_lora.load_weights(new_dict, strict=strict)
if self.adapter_type == 'mean_flow':
# state dict is seperated. so recombine it
new_dict = {}
for k, v in state_dict.items():
for k2, v2 in v.items():
new_dict[k + '.' + k2] = v2
self.mean_flow_adapter.load_weights(new_dict, strict=strict)
if self.adapter_type == 'i2v':
# state dict is seperated. so recombine it
new_dict = {}
for k, v in state_dict.items():
for k2, v2 in v.items():
new_dict[k + '.' + k2] = v2
self.i2v_adapter.load_weights(new_dict, strict=strict)
if self.adapter_type == 'subpixel':
# state dict is seperated. so recombine it
new_dict = {}
for k, v in state_dict.items():
for k2, v2 in v.items():
new_dict[k + '.' + k2] = v2
self.subpixel_adapter.load_weights(new_dict, strict=strict)
pass
def state_dict(self) -> OrderedDict:
state_dict = OrderedDict()
if self.config.train_only_image_encoder:
return self.vision_encoder.state_dict()
if self.adapter_type == 'photo_maker':
if self.config.train_image_encoder:
state_dict["id_encoder"] = self.vision_encoder.state_dict()
state_dict["fuse_module"] = self.fuse_module.state_dict()
# todo save LoRA
return state_dict
elif self.adapter_type == 'clip_fusion':
if self.config.train_image_encoder:
state_dict["vision_encoder"] = self.vision_encoder.state_dict()
state_dict["clip_fusion"] = self.clip_fusion_module.state_dict()
return state_dict
elif self.adapter_type == 'text_encoder':
state_dict["te_adapter"] = self.te_adapter.state_dict()
return state_dict
elif self.adapter_type == 'llm_adapter':
state_dict["llm_adapter"] = self.llm_adapter.state_dict()
return state_dict
elif self.adapter_type == 'te_augmenter':
if self.config.train_image_encoder:
state_dict["vision_encoder"] = self.vision_encoder.state_dict()
state_dict["te_augmenter"] = self.te_augmenter.state_dict()
return state_dict
elif self.adapter_type == 'vision_direct':
state_dict["dvadapter"] = self.vd_adapter.state_dict()
# if self.config.train_image_encoder: # always return vision encoder
state_dict["vision_encoder"] = self.vision_encoder.state_dict()
return state_dict
elif self.adapter_type == 'single_value':
state_dict["sv_adapter"] = self.single_value_adapter.state_dict()
return state_dict
elif self.adapter_type == 'ilora':
if self.config.train_image_encoder:
state_dict["vision_encoder"] = self.vision_encoder.state_dict()
state_dict["ilora"] = self.ilora_module.state_dict()
return state_dict
elif self.adapter_type == 'redux':
d = self.redux_adapter.state_dict()
for k, v in d.items():
state_dict[k] = v
return state_dict
elif self.adapter_type == 'control_lora':
d = self.control_lora.get_state_dict()
for k, v in d.items():
state_dict[k] = v
return state_dict
elif self.adapter_type == 'mean_flow':
d = self.mean_flow_adapter.get_state_dict()
for k, v in d.items():
state_dict[k] = v
return state_dict
elif self.adapter_type == 'i2v':
d = self.i2v_adapter.get_state_dict()
for k, v in d.items():
state_dict[k] = v
return state_dict
elif self.adapter_type == 'subpixel':
d = self.subpixel_adapter.get_state_dict()
for k, v in d.items():
state_dict[k] = v
return state_dict
else:
raise NotImplementedError
def add_extra_values(self, extra_values: torch.Tensor, is_unconditional=False):
if self.adapter_type == 'single_value':
if is_unconditional:
self.unconditional_embeds = extra_values.to(self.device, get_torch_dtype(self.sd_ref().dtype))
else:
self.conditional_embeds = extra_values.to(self.device, get_torch_dtype(self.sd_ref().dtype))
def condition_noisy_latents(self, latents: torch.Tensor, batch:DataLoaderBatchDTO):
with torch.no_grad():
# todo add i2v start frame conditioning here
if self.adapter_type in ['i2v']:
return self.i2v_adapter.condition_noisy_latents(latents, batch)
elif self.adapter_type in ['control_lora']:
# inpainting input is 0-1 (bs, 4, h, w) on batch.inpaint_tensor
# 4th channel is the mask with 1 being keep area and 0 being area to inpaint.
sd: StableDiffusion = self.sd_ref()
inpainting_latent = None
if self.config.has_inpainting_input:
do_dropout = random.random() < self.config.control_image_dropout
# do random mask if we dont have one
inpaint_tensor = batch.inpaint_tensor
if inpaint_tensor is None and not do_dropout:
# generate a random one since we dont have one
# this will make random blobs, invert the blobs for now as we normanlly inpaint the alpha
inpaint_tensor = 1 - generate_random_mask(
batch_size=latents.shape[0],
height=latents.shape[2],
width=latents.shape[3],
device=latents.device,
).to(latents.device, latents.dtype)
if inpaint_tensor is not None and not do_dropout:
if inpaint_tensor.shape[1] == 4:
# get just the mask
inpainting_tensor_mask = inpaint_tensor[:, 3:4, :, :].to(latents.device, dtype=latents.dtype)
elif inpaint_tensor.shape[1] == 3:
# rgb mask. Just get one channel
inpainting_tensor_mask = inpaint_tensor[:, 0:1, :, :].to(latents.device, dtype=latents.dtype)
else:
inpainting_tensor_mask = inpaint_tensor
# # use our batch latents so we cna avoid ancoding again
inpainting_latent = batch.latents
# resize the mask to match the new encoded size
inpainting_tensor_mask = F.interpolate(inpainting_tensor_mask, size=(inpainting_latent.shape[2], inpainting_latent.shape[3]), mode='bilinear')
inpainting_tensor_mask = inpainting_tensor_mask.to(latents.device, latents.dtype)
do_mask_invert = False
if self.config.invert_inpaint_mask_chance > 0.0:
do_mask_invert = random.random() < self.config.invert_inpaint_mask_chance
if do_mask_invert:
# invert the mask
inpainting_tensor_mask = 1 - inpainting_tensor_mask
# mask out the inpainting area, it is currently 0 for inpaint area, and 1 for keep area
# we are zeroing our the latents in the inpaint area not on the pixel space.
inpainting_latent = inpainting_latent * inpainting_tensor_mask
# mask needs to be 1 for inpaint area and 0 for area to leave alone. So flip it.
inpainting_tensor_mask = 1 - inpainting_tensor_mask
# leave the mask as 0-1 and concat on channel of latents
inpainting_latent = torch.cat((inpainting_latent, inpainting_tensor_mask), dim=1)
else:
# we have iinpainting but didnt get a control. or we are doing a dropout
# the input needs to be all zeros for the latents and all 1s for the mask
inpainting_latent = torch.zeros_like(latents)
# add ones for the mask since we are technically inpainting everything
inpainting_latent = torch.cat((inpainting_latent, torch.ones_like(inpainting_latent[:, :1, :, :])), dim=1)
if self.config.num_control_images == 1:
# this is our only control
control_latent = inpainting_latent.to(latents.device, latents.dtype)
latents = torch.cat((latents, control_latent), dim=1)
return latents.detach()
if control_tensor is None:
# concat zeros onto the latents
ctrl = torch.zeros(
latents.shape[0], # bs
latents.shape[1] * self.num_control_images, # ch
latents.shape[2],
latents.shape[3],
device=latents.device,
dtype=latents.dtype
)
if inpainting_latent is not None:
# inpainting always comes first
ctrl = torch.cat((inpainting_latent, ctrl), dim=1)
latents = torch.cat((latents, ctrl), dim=1)
return latents.detach()
# if we have multiple control tensors, they come in like [bs, num_control_images, ch, h, w]
# if we have 1, it comes in like [bs, ch, h, w]
# stack out control tensors to be [bs, ch * num_control_images, h, w]
control_tensor = batch.control_tensor.to(latents.device, dtype=latents.dtype)
control_tensor_list = []
if len(control_tensor.shape) == 4:
control_tensor_list.append(control_tensor)
else:
# reshape
control_tensor = control_tensor.view(
control_tensor.shape[0],
control_tensor.shape[1] * control_tensor.shape[2],
control_tensor.shape[3],
control_tensor.shape[4]
)
control_tensor_list = control_tensor.chunk(self.num_control_images, dim=1)
control_latent_list = []
for control_tensor in control_tensor_list:
do_dropout = random.random() < self.config.control_image_dropout
if do_dropout:
# dropout with noise
control_latent_list.append(torch.zeros_like(batch.latents))
else:
# it is 0-1 need to convert to -1 to 1
control_tensor = control_tensor * 2 - 1
control_tensor = control_tensor.to(sd.vae_device_torch, dtype=sd.torch_dtype)
# if it is not the size of batch.tensor, (bs,ch,h,w) then we need to resize it
if control_tensor.shape[2] != batch.tensor.shape[2] or control_tensor.shape[3] != batch.tensor.shape[3]:
control_tensor = F.interpolate(control_tensor, size=(batch.tensor.shape[2], batch.tensor.shape[3]), mode='bicubic')
# encode it
control_latent = sd.encode_images(control_tensor).to(latents.device, latents.dtype)
control_latent_list.append(control_latent)
# stack them on the channel dimension
control_latent = torch.cat(control_latent_list, dim=1)
if inpainting_latent is not None:
# inpainting always comes first
control_latent = torch.cat((inpainting_latent, control_latent), dim=1)
# concat it onto the latents
latents = torch.cat((latents, control_latent), dim=1)
return latents.detach()
return latents
def condition_prompt(
self,
prompt: Union[List[str], str],
is_unconditional: bool = False,
):
if self.adapter_type in ['clip_fusion', 'ilora', 'vision_direct', 'redux', 'control_lora', 'subpixel', 'i2v', 'mean_flow']:
return prompt
elif self.adapter_type == 'text_encoder':
# todo allow for training
with torch.no_grad():
# encode and save the embeds
if is_unconditional:
self.unconditional_embeds = self.te_adapter.encode_text(prompt).detach()
else:
self.conditional_embeds = self.te_adapter.encode_text(prompt).detach()
elif self.adapter_type == 'llm_adapter':
# todo allow for training
with torch.no_grad():
# encode and save the embeds
if is_unconditional:
self.unconditional_embeds = self.llm_adapter.encode_text(prompt).detach()
else:
self.conditional_embeds = self.llm_adapter.encode_text(prompt).detach()
return prompt
elif self.adapter_type == 'photo_maker':
if is_unconditional:
return prompt
else:
with torch.no_grad():
was_list = isinstance(prompt, list)
if not was_list:
prompt_list = [prompt]
else:
prompt_list = prompt
new_prompt_list = []
token_mask_list = []
for prompt in prompt_list:
our_class = None
# find a class in the prompt
prompt_parts = prompt.split(' ')
prompt_parts = [p.strip().lower() for p in prompt_parts if len(p) > 0]
new_prompt_parts = []
tokened_prompt_parts = []
for idx, prompt_part in enumerate(prompt_parts):
new_prompt_parts.append(prompt_part)
tokened_prompt_parts.append(prompt_part)
if prompt_part in self.config.class_names:
our_class = prompt_part
# add the flag word
tokened_prompt_parts.append(self.flag_word)
if self.num_control_images > 1:
# add the rest
for _ in range(self.num_control_images - 1):
new_prompt_parts.extend(prompt_parts[idx + 1:])
# add the rest
tokened_prompt_parts.extend(prompt_parts[idx + 1:])
new_prompt_parts.extend(prompt_parts[idx + 1:])
break
prompt = " ".join(new_prompt_parts)
tokened_prompt = " ".join(tokened_prompt_parts)
if our_class is None:
# add the first one to the front of the prompt
tokened_prompt = self.config.class_names[0] + ' ' + self.flag_word + ' ' + prompt
our_class = self.config.class_names[0]
prompt = " ".join(
[self.config.class_names[0] for _ in range(self.num_control_images)]) + ' ' + prompt
# add the prompt to the list
new_prompt_list.append(prompt)
# tokenize them with just the first tokenizer
tokenizer = self.sd_ref().tokenizer
if isinstance(tokenizer, list):
tokenizer = tokenizer[0]
flag_token = tokenizer.convert_tokens_to_ids(self.flag_word)
tokenized_prompt = tokenizer.encode(prompt)
tokenized_tokened_prompt = tokenizer.encode(tokened_prompt)
flag_idx = tokenized_tokened_prompt.index(flag_token)
class_token = tokenized_prompt[flag_idx - 1]
boolean_mask = torch.zeros(flag_idx - 1, dtype=torch.bool)
boolean_mask = torch.cat((boolean_mask, torch.ones(self.num_control_images, dtype=torch.bool)))
boolean_mask = boolean_mask.to(self.device)
# zero pad it to 77
boolean_mask = F.pad(boolean_mask, (0, 77 - boolean_mask.shape[0]), value=False)
token_mask_list.append(boolean_mask)
self.token_mask = torch.cat(token_mask_list, dim=0).to(self.device)
prompt_list = new_prompt_list
if not was_list:
prompt = prompt_list[0]
else:
prompt = prompt_list
return prompt
else:
return prompt
def condition_encoded_embeds(
self,
tensors_0_1: torch.Tensor,
prompt_embeds: PromptEmbeds,
is_training=False,
has_been_preprocessed=False,
is_unconditional=False,
quad_count=4,
is_generating_samples=False,
) -> PromptEmbeds:
if self.adapter_type == 'text_encoder':
# replace the prompt embed with ours
if is_unconditional:
return self.unconditional_embeds.clone()
return self.conditional_embeds.clone()
if self.adapter_type == 'llm_adapter':
# replace the prompt embed with ours
if is_unconditional:
prompt_embeds.text_embeds = self.unconditional_embeds.text_embeds.clone()
prompt_embeds.attention_mask = self.unconditional_embeds.attention_mask.clone()
return prompt_embeds
prompt_embeds.text_embeds = self.conditional_embeds.text_embeds.clone()
prompt_embeds.attention_mask = self.conditional_embeds.attention_mask.clone()
return prompt_embeds
if self.adapter_type == 'ilora':
return prompt_embeds
if self.adapter_type == 'photo_maker' or self.adapter_type == 'clip_fusion' or self.adapter_type == 'redux':
if is_unconditional:
# we dont condition the negative embeds for photo maker
return prompt_embeds.clone()
with torch.no_grad():
# on training the clip image is created in the dataloader
if not has_been_preprocessed:
# tensors should be 0-1
if tensors_0_1.ndim == 3:
tensors_0_1 = tensors_0_1.unsqueeze(0)
# training tensors are 0 - 1
tensors_0_1 = tensors_0_1.to(self.device, dtype=torch.float16)
# if images are out of this range throw error
if tensors_0_1.min() < -0.3 or tensors_0_1.max() > 1.3:
raise ValueError("image tensor values must be between 0 and 1. Got min: {}, max: {}".format(
tensors_0_1.min(), tensors_0_1.max()
))
clip_image = self.image_processor(
images=tensors_0_1,
return_tensors="pt",
do_resize=True,
do_rescale=False,
do_convert_rgb=True
).pixel_values
else:
clip_image = tensors_0_1
clip_image = clip_image.to(self.device, dtype=get_torch_dtype(self.sd_ref().dtype)).detach()
if self.config.quad_image:
# split the 4x4 grid and stack on batch
ci1, ci2 = clip_image.chunk(2, dim=2)
ci1, ci3 = ci1.chunk(2, dim=3)
ci2, ci4 = ci2.chunk(2, dim=3)
to_cat = []
for i, ci in enumerate([ci1, ci2, ci3, ci4]):
if i < quad_count:
to_cat.append(ci)
else:
break
clip_image = torch.cat(to_cat, dim=0).detach()
if self.adapter_type == 'photo_maker':
# Embeddings need to be (b, num_inputs, c, h, w) for now, just put 1 input image
clip_image = clip_image.unsqueeze(1)
with torch.set_grad_enabled(is_training):
if is_training and self.config.train_image_encoder:
self.vision_encoder.train()
clip_image = clip_image.requires_grad_(True)
id_embeds = self.vision_encoder(
clip_image,
do_projection2=isinstance(self.sd_ref().text_encoder, list),
)
else:
with torch.no_grad():
self.vision_encoder.eval()
id_embeds = self.vision_encoder(
clip_image, do_projection2=isinstance(self.sd_ref().text_encoder, list)
).detach()
prompt_embeds.text_embeds = self.fuse_module(
prompt_embeds.text_embeds,
id_embeds,
self.token_mask
)
return prompt_embeds
elif self.adapter_type == 'clip_fusion':
with torch.set_grad_enabled(is_training):
if is_training and self.config.train_image_encoder:
self.vision_encoder.train()
clip_image = clip_image.requires_grad_(True)
id_embeds = self.vision_encoder(
clip_image,
output_hidden_states=True,
)
else:
with torch.no_grad():
self.vision_encoder.eval()
id_embeds = self.vision_encoder(
clip_image, output_hidden_states=True
)
img_embeds = id_embeds['last_hidden_state']
if self.config.quad_image:
# get the outputs of the quat
chunks = img_embeds.chunk(quad_count, dim=0)
chunk_sum = torch.zeros_like(chunks[0])
for chunk in chunks:
chunk_sum = chunk_sum + chunk
# get the mean of them
img_embeds = chunk_sum / quad_count
if not is_training or not self.config.train_image_encoder:
img_embeds = img_embeds.detach()
prompt_embeds.text_embeds = self.clip_fusion_module(
prompt_embeds.text_embeds,
img_embeds
)
return prompt_embeds
elif self.adapter_type == 'redux':
with torch.set_grad_enabled(is_training):
if is_training and self.config.train_image_encoder:
self.vision_encoder.train()
clip_image = clip_image.requires_grad_(True)
id_embeds = self.vision_encoder(
clip_image,
output_hidden_states=True,
)
else:
with torch.no_grad():
self.vision_encoder.eval()
id_embeds = self.vision_encoder(
clip_image, output_hidden_states=True
)
img_embeds = id_embeds['last_hidden_state']
if self.config.quad_image:
# get the outputs of the quat
chunks = img_embeds.chunk(quad_count, dim=0)
chunk_sum = torch.zeros_like(chunks[0])
for chunk in chunks:
chunk_sum = chunk_sum + chunk
# get the mean of them
img_embeds = chunk_sum / quad_count
if not is_training or not self.config.train_image_encoder:
img_embeds = img_embeds.detach()
img_embeds = self.redux_adapter(img_embeds.to(self.device, get_torch_dtype(self.sd_ref().dtype)))
prompt_embeds.text_embeds = torch.cat((prompt_embeds.text_embeds, img_embeds), dim=-2)
return prompt_embeds
else:
return prompt_embeds
def get_empty_clip_image(self, batch_size: int, shape=None) -> torch.Tensor:
with torch.no_grad():
if shape is None:
shape = [batch_size, 3, self.input_size, self.input_size]
tensors_0_1 = torch.rand(shape, device=self.device)
noise_scale = torch.rand([tensors_0_1.shape[0], 1, 1, 1], device=self.device,
dtype=get_torch_dtype(self.sd_ref().dtype))
tensors_0_1 = tensors_0_1 * noise_scale
# tensors_0_1 = tensors_0_1 * 0
mean = torch.tensor(self.clip_image_processor.image_mean).to(
self.device, dtype=get_torch_dtype(self.sd_ref().dtype)
).detach()
std = torch.tensor(self.clip_image_processor.image_std).to(
self.device, dtype=get_torch_dtype(self.sd_ref().dtype)
).detach()
tensors_0_1 = torch.clip((255. * tensors_0_1), 0, 255).round() / 255.0
clip_image = (tensors_0_1 - mean.view([1, 3, 1, 1])) / std.view([1, 3, 1, 1])
return clip_image.detach()
def train(self, mode: bool = True):
if self.config.train_image_encoder:
self.vision_encoder.train(mode)
super().train(mode)
def trigger_pre_te(
self,
tensors_0_1: Optional[torch.Tensor]=None,
tensors_preprocessed: Optional[torch.Tensor]=None, # preprocessed by the dataloader
is_training=False,
has_been_preprocessed=False,
batch_tensor: Optional[torch.Tensor]=None,
quad_count=4,
batch_size=1,
) -> PromptEmbeds:
if tensors_0_1 is not None:
# actual 0 - 1 image
self.cached_control_image_0_1 = tensors_0_1
else:
# image has been processed through the dataloader and is prepped for vision encoder
self.cached_control_image_0_1 = None
if batch_tensor is not None and self.cached_control_image_0_1 is None:
# convert it to 0 - 1
to_cache = batch_tensor / 2 + 0.5
# videos come in (bs, num_frames, channels, height, width)
# images come in (bs, channels, height, width)
# if it is a video, just grad first frame
if len(to_cache.shape) == 5:
to_cache = to_cache[:, 0:1, :, :, :]
to_cache = to_cache.squeeze(1)
self.cached_control_image_0_1 = to_cache
if tensors_preprocessed is not None and has_been_preprocessed:
tensors_0_1 = tensors_preprocessed
# if self.adapter_type == 'ilora' or self.adapter_type == 'vision_direct' or self.adapter_type == 'te_augmenter':
if self.adapter_type in ['ilora', 'vision_direct', 'te_augmenter', 'i2v']:
skip_unconditional = self.sd_ref().is_flux
if tensors_0_1 is None:
tensors_0_1 = self.get_empty_clip_image(batch_size)
has_been_preprocessed = True
with torch.no_grad():
# on training the clip image is created in the dataloader
if not has_been_preprocessed:
# tensors should be 0-1
if tensors_0_1.ndim == 3:
tensors_0_1 = tensors_0_1.unsqueeze(0)
# training tensors are 0 - 1
tensors_0_1 = tensors_0_1.to(self.device, dtype=torch.float16)
# if images are out of this range throw error
if tensors_0_1.min() < -0.3 or tensors_0_1.max() > 1.3:
raise ValueError("image tensor values must be between 0 and 1. Got min: {}, max: {}".format(
tensors_0_1.min(), tensors_0_1.max()
))
clip_image = self.image_processor(
images=tensors_0_1,
return_tensors="pt",
do_resize=True,
do_rescale=False,
).pixel_values
else:
clip_image = tensors_0_1
# if is pixtral
if self.config.image_encoder_arch == 'pixtral' and self.config.pixtral_random_image_size:
# get the random size
random_size = random.randint(256, self.config.pixtral_max_image_size)
# images are already sized for max size, we have to fit them to the pixtral patch size to reduce / enlarge it farther.
h, w = clip_image.shape[2], clip_image.shape[3]
current_base_size = int(math.sqrt(w * h))
ratio = current_base_size / random_size
if ratio > 1:
w = round(w / ratio)
h = round(h / ratio)
width_tokens = (w - 1) // self.image_processor.image_patch_size + 1
height_tokens = (h - 1) // self.image_processor.image_patch_size + 1
assert width_tokens > 0
assert height_tokens > 0
new_image_size = (
width_tokens * self.image_processor.image_patch_size,
height_tokens * self.image_processor.image_patch_size,
)
# resize the image
clip_image = F.interpolate(clip_image, size=new_image_size, mode='bicubic', align_corners=False)
batch_size = clip_image.shape[0]
if self.config.control_image_dropout > 0 and is_training:
clip_batch = torch.chunk(clip_image, batch_size, dim=0)
unconditional_batch = torch.chunk(self.get_empty_clip_image(batch_size, shape=clip_image.shape).to(
clip_image.device, dtype=clip_image.dtype
), batch_size, dim=0)
combine_list = []
for i in range(batch_size):
do_dropout = random.random() < self.config.control_image_dropout
if do_dropout:
# dropout with noise
combine_list.append(unconditional_batch[i])
else:
combine_list.append(clip_batch[i])
clip_image = torch.cat(combine_list, dim=0)
if self.adapter_type in ['vision_direct', 'te_augmenter', 'i2v'] and not skip_unconditional:
# add an unconditional so we can save it
unconditional = self.get_empty_clip_image(batch_size, shape=clip_image.shape).to(
clip_image.device, dtype=clip_image.dtype
)
clip_image = torch.cat([unconditional, clip_image], dim=0)
clip_image = clip_image.to(self.device, dtype=get_torch_dtype(self.sd_ref().dtype)).detach()
if self.config.quad_image:
# split the 4x4 grid and stack on batch
ci1, ci2 = clip_image.chunk(2, dim=2)
ci1, ci3 = ci1.chunk(2, dim=3)
ci2, ci4 = ci2.chunk(2, dim=3)
to_cat = []
for i, ci in enumerate([ci1, ci2, ci3, ci4]):
if i < quad_count:
to_cat.append(ci)
else:
break
clip_image = torch.cat(to_cat, dim=0).detach()
if self.adapter_type == 'ilora':
with torch.set_grad_enabled(is_training):
if is_training and self.config.train_image_encoder:
self.vision_encoder.train()
clip_image = clip_image.requires_grad_(True)
id_embeds = self.vision_encoder(
clip_image,
output_hidden_states=True,
)
else:
with torch.no_grad():
self.vision_encoder.eval()
id_embeds = self.vision_encoder(
clip_image, output_hidden_states=True
)
if self.config.clip_layer == 'penultimate_hidden_states':
img_embeds = id_embeds.hidden_states[-2]
elif self.config.clip_layer == 'last_hidden_state':
img_embeds = id_embeds.hidden_states[-1]
elif self.config.clip_layer == 'image_embeds':
img_embeds = id_embeds.image_embeds
else:
raise ValueError(f"unknown clip layer: {self.config.clip_layer}")
if self.config.quad_image:
# get the outputs of the quat
chunks = img_embeds.chunk(quad_count, dim=0)
chunk_sum = torch.zeros_like(chunks[0])
for chunk in chunks:
chunk_sum = chunk_sum + chunk
# get the mean of them
img_embeds = chunk_sum / quad_count
if not is_training or not self.config.train_image_encoder:
img_embeds = img_embeds.detach()
self.ilora_module(img_embeds)
# if self.adapter_type == 'vision_direct' or self.adapter_type == 'te_augmenter':
if self.adapter_type in ['vision_direct', 'te_augmenter', 'i2v']:
with torch.set_grad_enabled(is_training):
if is_training and self.config.train_image_encoder:
self.vision_encoder.train()
clip_image = clip_image.requires_grad_(True)
else:
with torch.no_grad():
self.vision_encoder.eval()
self.vision_encoder.to(self.device)
clip_output = self.vision_encoder(
clip_image.to(self.device, dtype=get_torch_dtype(self.sd_ref().dtype)),
output_hidden_states=True,
)
if self.config.clip_layer == 'penultimate_hidden_states':
# they skip last layer for ip+
# https://github.com/tencent-ailab/IP-Adapter/blob/f4b6742db35ea6d81c7b829a55b0a312c7f5a677/tutorial_train_plus.py#L403C26-L403C26
clip_image_embeds = clip_output.hidden_states[-2]
elif self.config.clip_layer == 'last_hidden_state':
clip_image_embeds = clip_output.hidden_states[-1]
else:
if hasattr(clip_output, 'image_embeds'):
clip_image_embeds = clip_output.image_embeds
elif hasattr(clip_output, 'pooler_output'):
clip_image_embeds = clip_output.pooler_output
# TODO should we always norm image embeds?
# get norm embeddings
# l2_norm = torch.norm(clip_image_embeds, p=2)
# clip_image_embeds = clip_image_embeds / l2_norm
if not is_training or not self.config.train_image_encoder:
clip_image_embeds = clip_image_embeds.detach()
if self.adapter_type == 'te_augmenter':
clip_image_embeds = self.te_augmenter(clip_image_embeds)
if self.adapter_type == 'vision_direct':
clip_image_embeds = self.vd_adapter(clip_image_embeds)
# save them to the conditional and unconditional
try:
if skip_unconditional:
self.unconditional_embeds, self.conditional_embeds = None, clip_image_embeds
else:
self.unconditional_embeds, self.conditional_embeds = clip_image_embeds.chunk(2, dim=0)
except ValueError:
raise ValueError(f"could not split the clip image embeds into 2. Got shape: {clip_image_embeds.shape}")
def parameters(self, recurse: bool = True) -> Iterator[Parameter]:
if self.config.train_only_image_encoder:
yield from self.vision_encoder.parameters(recurse)
return
if self.config.type == 'photo_maker':
yield from self.fuse_module.parameters(recurse)
if self.config.train_image_encoder:
yield from self.vision_encoder.parameters(recurse)
elif self.config.type == 'clip_fusion':
yield from self.clip_fusion_module.parameters(recurse)
if self.config.train_image_encoder:
yield from self.vision_encoder.parameters(recurse)
elif self.config.type == 'ilora':
yield from self.ilora_module.parameters(recurse)
if self.config.train_image_encoder:
yield from self.vision_encoder.parameters(recurse)
elif self.config.type == 'text_encoder':
for attn_processor in self.te_adapter.adapter_modules:
yield from attn_processor.parameters(recurse)
elif self.config.type == 'llm_adapter':
yield from self.llm_adapter.parameters(recurse)
elif self.config.type == 'vision_direct':
if self.config.train_scaler:
# only yield the self.block_scaler = torch.nn.Parameter(torch.tensor([1.0] * num_modules)
yield self.vd_adapter.block_scaler
else:
for attn_processor in self.vd_adapter.adapter_modules:
yield from attn_processor.parameters(recurse)
if self.config.train_image_encoder:
yield from self.vision_encoder.parameters(recurse)
if self.vd_adapter.resampler is not None:
yield from self.vd_adapter.resampler.parameters(recurse)
if self.vd_adapter.pool is not None:
yield from self.vd_adapter.pool.parameters(recurse)
if self.vd_adapter.sparse_autoencoder is not None:
yield from self.vd_adapter.sparse_autoencoder.parameters(recurse)
elif self.config.type == 'te_augmenter':
yield from self.te_augmenter.parameters(recurse)
if self.config.train_image_encoder:
yield from self.vision_encoder.parameters(recurse)
elif self.config.type == 'single_value':
yield from self.single_value_adapter.parameters(recurse)
elif self.config.type == 'redux':
yield from self.redux_adapter.parameters(recurse)
elif self.config.type == 'control_lora':
param_list = self.control_lora.get_params()
for param in param_list:
yield param
elif self.config.type == 'mean_flow':
param_list = self.mean_flow_adapter.get_params()
for param in param_list:
yield param
elif self.config.type == 'i2v':
param_list = self.i2v_adapter.get_params()
for param in param_list:
yield param
elif self.config.type == 'subpixel':
param_list = self.subpixel_adapter.get_params()
for param in param_list:
yield param
else:
raise NotImplementedError
def enable_gradient_checkpointing(self):
if hasattr(self.vision_encoder, "enable_gradient_checkpointing"):
self.vision_encoder.enable_gradient_checkpointing()
elif hasattr(self.vision_encoder, 'gradient_checkpointing'):
self.vision_encoder.gradient_checkpointing = True
def get_additional_save_metadata(self) -> Dict[str, Any]:
additional = {}
if self.config.type == 'ilora':
extra = self.ilora_module.get_additional_save_metadata()
for k, v in extra.items():
additional[k] = v
additional['clip_layer'] = self.config.clip_layer
additional['image_encoder_arch'] = self.config.head_dim
return additional
def post_weight_update(self):
# do any kind of updates after the weight update
if self.config.type == 'vision_direct':
self.vd_adapter.post_weight_update()
pass
import math
import torch
import sys
from PIL import Image
from torch.nn import Parameter
from transformers import CLIPImageProcessor, CLIPVisionModelWithProjection, T5EncoderModel, CLIPTextModel, \
CLIPTokenizer, T5Tokenizer
from toolkit.data_transfer_object.data_loader import DataLoaderBatchDTO
from toolkit.models.clip_fusion import CLIPFusionModule
from toolkit.models.clip_pre_processor import CLIPImagePreProcessor
from toolkit.models.control_lora_adapter import ControlLoraAdapter
from toolkit.models.mean_flow_adapter import MeanFlowAdapter
from toolkit.models.i2v_adapter import I2VAdapter
from toolkit.models.subpixel_adapter import SubpixelAdapter
from toolkit.models.ilora import InstantLoRAModule
from toolkit.models.single_value_adapter import SingleValueAdapter
from toolkit.models.te_adapter import TEAdapter
from toolkit.models.te_aug_adapter import TEAugAdapter
from toolkit.models.vd_adapter import VisionDirectAdapter
from toolkit.models.redux import ReduxImageEncoder
from toolkit.photomaker import PhotoMakerIDEncoder, FuseModule, PhotoMakerCLIPEncoder
from toolkit.saving import load_ip_adapter_model, load_custom_adapter_model
from toolkit.train_tools import get_torch_dtype
from toolkit.models.pixtral_vision import PixtralVisionEncoderCompatible, PixtralVisionImagePreprocessorCompatible
import random
from toolkit.util.mask import generate_random_mask
from typing import TYPE_CHECKING, Union, Iterator, Mapping, Any, Tuple, List, Optional, Dict
from collections import OrderedDict
from toolkit.config_modules import AdapterConfig, AdapterTypes, TrainConfig
from toolkit.prompt_utils import PromptEmbeds
import weakref
if TYPE_CHECKING:
from toolkit.stable_diffusion_model import StableDiffusion
from transformers import (
ConvNextForImageClassification,
ConvNextImageProcessor,
UMT5EncoderModel, LlamaTokenizerFast, AutoModel, AutoTokenizer, BitsAndBytesConfig
)
from toolkit.models.size_agnostic_feature_encoder import SAFEImageProcessor, SAFEVisionModel
from toolkit.models.llm_adapter import LLMAdapter
import torch.nn.functional as F
class CustomAdapter(torch.nn.Module):
def __init__(self, sd: 'StableDiffusion', adapter_config: 'AdapterConfig', train_config: 'TrainConfig'):
super().__init__()
self.config = adapter_config
self.sd_ref: weakref.ref = weakref.ref(sd)
self.train_config = train_config
self.device = self.sd_ref().unet.device
self.image_processor: CLIPImageProcessor = None
self.input_size = 224
self.adapter_type: AdapterTypes = self.config.type
self.current_scale = 1.0
self.is_active = True
self.flag_word = "fla9wor0"
self.is_unconditional_run = False
self.is_sampling = False
self.vision_encoder: Union[PhotoMakerCLIPEncoder, CLIPVisionModelWithProjection] = None
self.fuse_module: FuseModule = None
self.lora: None = None
self.position_ids: Optional[List[int]] = None
self.num_control_images = self.config.num_control_images
self.token_mask: Optional[torch.Tensor] = None
# setup clip
self.setup_clip()
# add for dataloader
self.clip_image_processor = self.image_processor
self.clip_fusion_module: CLIPFusionModule = None
self.ilora_module: InstantLoRAModule = None
self.te: Union[T5EncoderModel, CLIPTextModel] = None
self.tokenizer: CLIPTokenizer = None
self.te_adapter: TEAdapter = None
self.te_augmenter: TEAugAdapter = None
self.vd_adapter: VisionDirectAdapter = None
self.single_value_adapter: SingleValueAdapter = None
self.redux_adapter: ReduxImageEncoder = None
self.control_lora: ControlLoraAdapter = None
self.mean_flow_adapter: MeanFlowAdapter = None
self.subpixel_adapter: SubpixelAdapter = None
self.i2v_adapter: I2VAdapter = None
self.conditional_embeds: Optional[torch.Tensor] = None
self.unconditional_embeds: Optional[torch.Tensor] = None
self.cached_control_image_0_1: Optional[torch.Tensor] = None