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import copy
import gc
import inspect
import json
import random
import shutil
import typing
from typing import Optional, Union, List, Literal
import os
from collections import OrderedDict
import copy
import yaml
from PIL import Image
from diffusers.pipelines.stable_diffusion_xl.pipeline_stable_diffusion_xl import rescale_noise_cfg
from torch.nn import Parameter
from tqdm import tqdm
from torchvision.transforms import Resize, transforms
from toolkit.clip_vision_adapter import ClipVisionAdapter
from toolkit.custom_adapter import CustomAdapter
from toolkit.ip_adapter import IPAdapter
from toolkit.config_modules import ModelConfig, GenerateImageConfig, ModelArch
from toolkit.models.decorator import Decorator
from toolkit.paths import KEYMAPS_ROOT
from toolkit.prompt_utils import inject_trigger_into_prompt, PromptEmbeds, concat_prompt_embeds
from toolkit.reference_adapter import ReferenceAdapter
from toolkit.sd_device_states_presets import empty_preset
from toolkit.train_tools import get_torch_dtype, apply_noise_offset
import torch
from toolkit.pipelines import CustomStableDiffusionXLPipeline
from diffusers import StableDiffusionPipeline, StableDiffusionXLPipeline, T2IAdapter, DDPMScheduler, \
LCMScheduler, Transformer2DModel, AutoencoderTiny, ControlNetModel
import diffusers
from diffusers import \
AutoencoderKL, \
UNet2DConditionModel
from diffusers import PixArtAlphaPipeline
from transformers import CLIPTextModel, CLIPTokenizer, CLIPTextModelWithProjection
from toolkit.accelerator import get_accelerator, unwrap_model
from typing import TYPE_CHECKING
from toolkit.print import print_acc
if TYPE_CHECKING:
from toolkit.lora_special import LoRASpecialNetwork
from toolkit.data_transfer_object.data_loader import DataLoaderBatchDTO
# tell it to shut up
diffusers.logging.set_verbosity(diffusers.logging.ERROR)
SD_PREFIX_VAE = "vae"
SD_PREFIX_UNET = "unet"
SD_PREFIX_REFINER_UNET = "refiner_unet"
SD_PREFIX_TEXT_ENCODER = "te"
SD_PREFIX_TEXT_ENCODER1 = "te0"
SD_PREFIX_TEXT_ENCODER2 = "te1"
# prefixed diffusers keys
DO_NOT_TRAIN_WEIGHTS = [
"unet_time_embedding.linear_1.bias",
"unet_time_embedding.linear_1.weight",
"unet_time_embedding.linear_2.bias",
"unet_time_embedding.linear_2.weight",
"refiner_unet_time_embedding.linear_1.bias",
"refiner_unet_time_embedding.linear_1.weight",
"refiner_unet_time_embedding.linear_2.bias",
"refiner_unet_time_embedding.linear_2.weight",
]
DeviceStatePreset = Literal['cache_latents', 'generate']
class BlankNetwork:
def __init__(self):
self.multiplier = 1.0
self.is_active = True
self.is_merged_in = False
self.can_merge_in = False
def __enter__(self):
self.is_active = True
def __exit__(self, exc_type, exc_val, exc_tb):
self.is_active = False
def train(self):
pass
def flush():
torch.cuda.empty_cache()
gc.collect()
UNET_IN_CHANNELS = 4 # Stable Diffusion の in_channels は 4 で固定。XLも同じ。
# VAE_SCALE_FACTOR = 8 # 2 ** (len(vae.config.block_out_channels) - 1) = 8
class BaseModel:
# override these in child classes
arch = None
def __init__(
self,
device,
model_config: ModelConfig,
dtype='fp16',
custom_pipeline=None,
noise_scheduler=None,
**kwargs
):
self.accelerator = get_accelerator()
self.custom_pipeline = custom_pipeline
self.device = str(self.accelerator.device)
self.dtype = dtype
self.torch_dtype = get_torch_dtype(dtype)
self.device_torch = self.accelerator.device
self.vae_device_torch = self.accelerator.device
self.vae_torch_dtype = get_torch_dtype(model_config.vae_dtype)
self.te_device_torch = self.accelerator.device
self.te_torch_dtype = get_torch_dtype(model_config.te_dtype)
self.model_config = model_config
self.prediction_type = "v_prediction" if self.model_config.is_v_pred else "epsilon"
self.device_state = None
self.pipeline: Union[None, 'StableDiffusionPipeline',
'CustomStableDiffusionXLPipeline', 'PixArtAlphaPipeline']
self.vae: Union[None, 'AutoencoderKL']
self.model: Union[None, 'Transformer2DModel', 'UNet2DConditionModel']
self.text_encoder: Union[None, 'CLIPTextModel',
List[Union['CLIPTextModel', 'CLIPTextModelWithProjection']]]
self.tokenizer: Union[None, 'CLIPTokenizer', List['CLIPTokenizer']]
self.noise_scheduler: Union[None, 'DDPMScheduler'] = noise_scheduler
self.refiner_unet: Union[None, 'UNet2DConditionModel'] = None
self.assistant_lora: Union[None, 'LoRASpecialNetwork'] = None
# sdxl stuff
self.logit_scale = None
self.ckppt_info = None
self.is_loaded = False
# to hold network if there is one
self.network = None
self.adapter: Union['ControlNetModel', 'T2IAdapter',
'IPAdapter', 'ReferenceAdapter', None] = None
self.decorator: Union[Decorator, None] = None
self.arch: ModelArch = model_config.arch
self.use_text_encoder_1 = model_config.use_text_encoder_1
self.use_text_encoder_2 = model_config.use_text_encoder_2
self.config_file = None
self.is_flow_matching = False
self.quantize_device = self.device_torch
self.low_vram = self.model_config.low_vram
# merge in and preview active with -1 weight
self.invert_assistant_lora = False
self._after_sample_img_hooks = []
self._status_update_hooks = []
self.is_transformer = False
# properties for old arch for backwards compatibility
@property
def unet(self):
return self.model
# set unet to model
@unet.setter
def unet(self, value):
self.model = value
@property
def transformer(self):
return self.model
@transformer.setter
def transformer(self, value):
self.model = value
@property
def unet_unwrapped(self):
return unwrap_model(self.model)
@property
def model_unwrapped(self):
return unwrap_model(self.model)
@property
def is_xl(self):
return self.arch == 'sdxl'
@property
def is_v2(self):
return self.arch == 'sd2'
@property
def is_ssd(self):
return self.arch == 'ssd'
@property
def is_v3(self):
return self.arch == 'sd3'
@property
def is_vega(self):
return self.arch == 'vega'
@property
def is_pixart(self):
return self.arch == 'pixart'
@property
def is_auraflow(self):
return self.arch == 'auraflow'
@property
def is_flux(self):
return self.arch == 'flux'
@property
def is_lumina2(self):
return self.arch == 'lumina2'
def get_bucket_divisibility(self):
if self.vae is None:
return 8
try:
divisibility = 2 ** (len(self.vae.config['block_out_channels']) - 1)
except:
# if we have a custom vae, it might not have this
divisibility = 8
# flux packs this again,
if self.is_flux:
divisibility = divisibility * 2
return divisibility
# these must be implemented in child classes
def load_model(self):
# override this in child classes
raise NotImplementedError(
"load_model must be implemented in child classes")
def get_generation_pipeline(self):
# override this in child classes
raise NotImplementedError(
"get_generation_pipeline must be implemented in child classes")
def generate_single_image(
self,
pipeline,
gen_config: GenerateImageConfig,
conditional_embeds: PromptEmbeds,
unconditional_embeds: PromptEmbeds,
generator: torch.Generator,
extra: dict,
):
# override this in child classes
raise NotImplementedError(
"generate_single_image must be implemented in child classes")
def get_noise_prediction(
latent_model_input: torch.Tensor,
timestep: torch.Tensor, # 0 to 1000 scale
text_embeddings: PromptEmbeds,
**kwargs
):
raise NotImplementedError(
"get_noise_prediction must be implemented in child classes")
def get_prompt_embeds(self, prompt: str) -> PromptEmbeds:
raise NotImplementedError(
"get_prompt_embeds must be implemented in child classes")
def get_model_has_grad(self):
raise NotImplementedError(
"get_model_has_grad must be implemented in child classes")
def get_te_has_grad(self):
raise NotImplementedError(
"get_te_has_grad must be implemented in child classes")
def save_model(self, output_path, meta, save_dtype):
# todo handle dtype without overloading anything (vram, cpu, etc)
unwrap_model(self.pipeline).save_pretrained(
save_directory=output_path,
safe_serialization=True,
)
# save out meta config
meta_path = os.path.join(output_path, 'aitk_meta.yaml')
with open(meta_path, 'w') as f:
yaml.dump(meta, f)
# end must be implemented in child classes
def te_train(self):
if isinstance(self.text_encoder, list):
for te in self.text_encoder:
te.train()
elif self.text_encoder is not None:
self.text_encoder.train()
def te_eval(self):
if isinstance(self.text_encoder, list):
for te in self.text_encoder:
te.eval()
elif self.text_encoder is not None:
self.text_encoder.eval()
def _after_sample_image(self, img_num, total_imgs):
# process all hooks
for hook in self._after_sample_img_hooks:
hook(img_num, total_imgs)
def add_after_sample_image_hook(self, func):
self._after_sample_img_hooks.append(func)
def _status_update(self, status: str):
for hook in self._status_update_hooks:
hook(status)
def print_and_status_update(self, status: str):
print_acc(status)
self._status_update(status)
def add_status_update_hook(self, func):
self._status_update_hooks.append(func)
@torch.no_grad()
def generate_images(
self,
image_configs: List[GenerateImageConfig],
sampler=None,
pipeline: Union[None, StableDiffusionPipeline,
StableDiffusionXLPipeline] = None,
):
network = unwrap_model(self.network)
merge_multiplier = 1.0
flush()
# if using assistant, unfuse it
if self.model_config.assistant_lora_path is not None:
print_acc("Unloading assistant lora")
if self.invert_assistant_lora:
self.assistant_lora.is_active = True
# move weights on to the device
self.assistant_lora.force_to(
self.device_torch, self.torch_dtype)
else:
self.assistant_lora.is_active = False
if self.model_config.inference_lora_path is not None:
print_acc("Loading inference lora")
self.assistant_lora.is_active = True
# move weights on to the device
self.assistant_lora.force_to(self.device_torch, self.torch_dtype)
if network is not None:
network.eval()
# check if we have the same network weight for all samples. If we do, we can merge in th
# the network to drastically speed up inference
unique_network_weights = set(
[x.network_multiplier for x in image_configs])
if len(unique_network_weights) == 1 and network.can_merge_in:
can_merge_in = True
merge_multiplier = unique_network_weights.pop()
network.merge_in(merge_weight=merge_multiplier)
else:
network = BlankNetwork()
self.save_device_state()
self.set_device_state_preset('generate')
# save current seed state for training
rng_state = torch.get_rng_state()
cuda_rng_state = torch.cuda.get_rng_state() if torch.cuda.is_available() else None
if pipeline is None:
pipeline = self.get_generation_pipeline()
try:
pipeline.set_progress_bar_config(disable=True)
except:
pass
start_multiplier = 1.0
if network is not None:
start_multiplier = network.multiplier
# pipeline.to(self.device_torch)
with network:
with torch.no_grad():
if network is not None:
assert network.is_active
for i in tqdm(range(len(image_configs)), desc=f"Generating Images", leave=False):
gen_config = image_configs[i]
extra = {}
validation_image = None
if self.adapter is not None and gen_config.adapter_image_path is not None:
validation_image = Image.open(gen_config.adapter_image_path)
if ".inpaint." not in gen_config.adapter_image_path:
validation_image = validation_image.convert("RGB")
else:
# make sure it has an alpha
if validation_image.mode != "RGBA":
raise ValueError("Inpainting images must have an alpha channel")
if isinstance(self.adapter, T2IAdapter):
# not sure why this is double??
validation_image = validation_image.resize(
(gen_config.width * 2, gen_config.height * 2))
extra['image'] = validation_image
extra['adapter_conditioning_scale'] = gen_config.adapter_conditioning_scale
if isinstance(self.adapter, ControlNetModel):
validation_image = validation_image.resize(
(gen_config.width, gen_config.height))
extra['image'] = validation_image
extra['controlnet_conditioning_scale'] = gen_config.adapter_conditioning_scale
if isinstance(self.adapter, CustomAdapter) and self.adapter.control_lora is not None:
validation_image = validation_image.resize((gen_config.width, gen_config.height))
extra['control_image'] = validation_image
extra['control_image_idx'] = gen_config.ctrl_idx
if isinstance(self.adapter, IPAdapter) or isinstance(self.adapter, ClipVisionAdapter):
transform = transforms.Compose([
transforms.ToTensor(),
])
validation_image = transform(validation_image)
if isinstance(self.adapter, CustomAdapter):
# todo allow loading multiple
transform = transforms.Compose([
transforms.ToTensor(),
])
validation_image = transform(validation_image)
self.adapter.num_images = 1
if isinstance(self.adapter, ReferenceAdapter):
# need -1 to 1
validation_image = transforms.ToTensor()(validation_image)
validation_image = validation_image * 2.0 - 1.0
validation_image = validation_image.unsqueeze(0)
self.adapter.set_reference_images(validation_image)
if network is not None:
network.multiplier = gen_config.network_multiplier
torch.manual_seed(gen_config.seed)
torch.cuda.manual_seed(gen_config.seed)
generator = torch.manual_seed(gen_config.seed)
if self.adapter is not None and isinstance(self.adapter, ClipVisionAdapter) \
and gen_config.adapter_image_path is not None:
# run through the adapter to saturate the embeds
conditional_clip_embeds = self.adapter.get_clip_image_embeds_from_tensors(
validation_image)
self.adapter(conditional_clip_embeds)
if self.adapter is not None and isinstance(self.adapter, CustomAdapter):
# handle condition the prompts
gen_config.prompt = self.adapter.condition_prompt(
gen_config.prompt,
is_unconditional=False,
)
gen_config.prompt_2 = gen_config.prompt
gen_config.negative_prompt = self.adapter.condition_prompt(
gen_config.negative_prompt,
is_unconditional=True,
)
gen_config.negative_prompt_2 = gen_config.negative_prompt
if self.adapter is not None and isinstance(self.adapter, CustomAdapter) and validation_image is not None:
self.adapter.trigger_pre_te(
tensors_0_1=validation_image,
is_training=False,
has_been_preprocessed=False,
quad_count=4
)
# encode the prompt ourselves so we can do fun stuff with embeddings
if isinstance(self.adapter, CustomAdapter):
self.adapter.is_unconditional_run = False
conditional_embeds = self.encode_prompt(
gen_config.prompt, gen_config.prompt_2, force_all=True)
if isinstance(self.adapter, CustomAdapter):
self.adapter.is_unconditional_run = True
unconditional_embeds = self.encode_prompt(
gen_config.negative_prompt, gen_config.negative_prompt_2, force_all=True
)
if isinstance(self.adapter, CustomAdapter):
self.adapter.is_unconditional_run = False
# allow any manipulations to take place to embeddings
gen_config.post_process_embeddings(
conditional_embeds,
unconditional_embeds,
)
if self.decorator is not None:
# apply the decorator to the embeddings
conditional_embeds.text_embeds = self.decorator(
conditional_embeds.text_embeds)
unconditional_embeds.text_embeds = self.decorator(
unconditional_embeds.text_embeds, is_unconditional=True)
if self.adapter is not None and isinstance(self.adapter, IPAdapter) \
and gen_config.adapter_image_path is not None:
# apply the image projection
conditional_clip_embeds = self.adapter.get_clip_image_embeds_from_tensors(
validation_image)
unconditional_clip_embeds = self.adapter.get_clip_image_embeds_from_tensors(validation_image,
True)
conditional_embeds = self.adapter(
conditional_embeds, conditional_clip_embeds, is_unconditional=False)
unconditional_embeds = self.adapter(
unconditional_embeds, unconditional_clip_embeds, is_unconditional=True)
if self.adapter is not None and isinstance(self.adapter, CustomAdapter):
conditional_embeds = self.adapter.condition_encoded_embeds(
tensors_0_1=validation_image,
prompt_embeds=conditional_embeds,
is_training=False,
has_been_preprocessed=False,
is_generating_samples=True,
)
unconditional_embeds = self.adapter.condition_encoded_embeds(
tensors_0_1=validation_image,
prompt_embeds=unconditional_embeds,
is_training=False,
has_been_preprocessed=False,
is_unconditional=True,
is_generating_samples=True,
)
if self.adapter is not None and isinstance(self.adapter, CustomAdapter) and len(
gen_config.extra_values) > 0:
extra_values = torch.tensor([gen_config.extra_values], device=self.device_torch,
dtype=self.torch_dtype)
# apply extra values to the embeddings
self.adapter.add_extra_values(
extra_values, is_unconditional=False)
self.adapter.add_extra_values(torch.zeros_like(
extra_values), is_unconditional=True)
pass # todo remove, for debugging
if self.refiner_unet is not None and gen_config.refiner_start_at < 1.0:
# if we have a refiner loaded, set the denoising end at the refiner start
extra['denoising_end'] = gen_config.refiner_start_at
extra['output_type'] = 'latent'
if not self.is_xl:
raise ValueError(
"Refiner is only supported for XL models")
conditional_embeds = conditional_embeds.to(
self.device_torch, dtype=self.unet.dtype)
unconditional_embeds = unconditional_embeds.to(
self.device_torch, dtype=self.unet.dtype)
img = self.generate_single_image(
pipeline,
gen_config,
conditional_embeds,
unconditional_embeds,
generator,
extra,
)
gen_config.save_image(img, i)
gen_config.log_image(img, i)
self._after_sample_image(i, len(image_configs))
flush()
if self.adapter is not None and isinstance(self.adapter, ReferenceAdapter):
self.adapter.clear_memory()
# clear pipeline and cache to reduce vram usage
del pipeline
torch.cuda.empty_cache()
# restore training state
torch.set_rng_state(rng_state)
if cuda_rng_state is not None:
torch.cuda.set_rng_state(cuda_rng_state)
self.restore_device_state()
if network is not None:
network.train()
network.multiplier = start_multiplier
self.unet.to(self.device_torch, dtype=self.torch_dtype)
if network.is_merged_in:
network.merge_out(merge_multiplier)
# self.tokenizer.to(original_device_dict['tokenizer'])
# refuse loras
if self.model_config.assistant_lora_path is not None:
print_acc("Loading assistant lora")
if self.invert_assistant_lora:
self.assistant_lora.is_active = False
# move weights off the device
self.assistant_lora.force_to('cpu', self.torch_dtype)
else:
self.assistant_lora.is_active = True
if self.model_config.inference_lora_path is not None:
print_acc("Unloading inference lora")
self.assistant_lora.is_active = False
# move weights off the device
self.assistant_lora.force_to('cpu', self.torch_dtype)
flush()
def get_latent_noise(
self,
height=None,
width=None,
pixel_height=None,
pixel_width=None,
batch_size=1,
noise_offset=0.0,
):
VAE_SCALE_FACTOR = 2 ** (
len(self.vae.config['block_out_channels']) - 1)
if height is None and pixel_height is None:
raise ValueError("height or pixel_height must be specified")
if width is None and pixel_width is None:
raise ValueError("width or pixel_width must be specified")
if height is None:
height = pixel_height // VAE_SCALE_FACTOR
if width is None:
width = pixel_width // VAE_SCALE_FACTOR
num_channels = self.unet_unwrapped.config['in_channels']
if self.is_flux:
# has 64 channels in for some reason
num_channels = 16
noise = torch.randn(
(
batch_size,
num_channels,
height,
width,
),
device=self.unet.device,
)
noise = apply_noise_offset(noise, noise_offset)
return noise
def get_latent_noise_from_latents(
self,
latents: torch.Tensor,
noise_offset=0.0
):
noise = torch.randn_like(latents)
noise = apply_noise_offset(noise, noise_offset)
return noise
def add_noise(
self,
original_samples: torch.FloatTensor,
noise: torch.FloatTensor,
timesteps: torch.IntTensor,
**kwargs,
) -> torch.FloatTensor:
original_samples_chunks = torch.chunk(
original_samples, original_samples.shape[0], dim=0)
noise_chunks = torch.chunk(noise, noise.shape[0], dim=0)
timesteps_chunks = torch.chunk(timesteps, timesteps.shape[0], dim=0)
if len(timesteps_chunks) == 1 and len(timesteps_chunks) != len(original_samples_chunks):
timesteps_chunks = [timesteps_chunks[0]] * \
len(original_samples_chunks)
noisy_latents_chunks = []
for idx in range(original_samples.shape[0]):
noisy_latents = self.noise_scheduler.add_noise(original_samples_chunks[idx], noise_chunks[idx],
timesteps_chunks[idx])
noisy_latents_chunks.append(noisy_latents)
noisy_latents = torch.cat(noisy_latents_chunks, dim=0)
return noisy_latents
def predict_noise(
self,
latents: torch.Tensor,
text_embeddings: Union[PromptEmbeds, None] = None,
timestep: Union[int, torch.Tensor] = 1,
guidance_scale=7.5,
guidance_rescale=0,
add_time_ids=None,
conditional_embeddings: Union[PromptEmbeds, None] = None,
unconditional_embeddings: Union[PromptEmbeds, None] = None,
is_input_scaled=False,
detach_unconditional=False,
rescale_cfg=None,
return_conditional_pred=False,
guidance_embedding_scale=1.0,
bypass_guidance_embedding=False,
batch: Union[None, 'DataLoaderBatchDTO'] = None,
**kwargs,
):
conditional_pred = None
# get the embeddings
if text_embeddings is None and conditional_embeddings is None:
raise ValueError(
"Either text_embeddings or conditional_embeddings must be specified")
if text_embeddings is None and unconditional_embeddings is not None:
text_embeddings = concat_prompt_embeds([
unconditional_embeddings, # negative embedding
conditional_embeddings, # positive embedding
])
elif text_embeddings is None and conditional_embeddings is not None:
# not doing cfg
text_embeddings = conditional_embeddings
# CFG is comparing neg and positive, if we have concatenated embeddings
# then we are doing it, otherwise we are not and takes half the time.
do_classifier_free_guidance = True
# check if batch size of embeddings matches batch size of latents
if isinstance(text_embeddings.text_embeds, list):
te_batch_size = text_embeddings.text_embeds[0].shape[0]
else:
te_batch_size = text_embeddings.text_embeds.shape[0]
if latents.shape[0] == te_batch_size:
do_classifier_free_guidance = False
elif latents.shape[0] * 2 != te_batch_size:
raise ValueError(
"Batch size of latents must be the same or half the batch size of text embeddings")
latents = latents.to(self.device_torch)
text_embeddings = text_embeddings.to(self.device_torch)
timestep = timestep.to(self.device_torch)
# if timestep is zero dim, unsqueeze it
if len(timestep.shape) == 0:
timestep = timestep.unsqueeze(0)
# if we only have 1 timestep, we can just use the same timestep for all
if timestep.shape[0] == 1 and latents.shape[0] > 1:
# check if it is rank 1 or 2
if len(timestep.shape) == 1:
timestep = timestep.repeat(latents.shape[0])
else:
timestep = timestep.repeat(latents.shape[0], 0)
# handle t2i adapters
if 'down_intrablock_additional_residuals' in kwargs:
# go through each item and concat if doing cfg and it doesnt have the same shape
for idx, item in enumerate(kwargs['down_intrablock_additional_residuals']):
if do_classifier_free_guidance and item.shape[0] != text_embeddings.text_embeds.shape[0]:
kwargs['down_intrablock_additional_residuals'][idx] = torch.cat([
item] * 2, dim=0)
# handle controlnet
if 'down_block_additional_residuals' in kwargs and 'mid_block_additional_residual' in kwargs:
# go through each item and concat if doing cfg and it doesnt have the same shape
for idx, item in enumerate(kwargs['down_block_additional_residuals']):
if do_classifier_free_guidance and item.shape[0] != text_embeddings.text_embeds.shape[0]:
kwargs['down_block_additional_residuals'][idx] = torch.cat([
item] * 2, dim=0)
for idx, item in enumerate(kwargs['mid_block_additional_residual']):
if do_classifier_free_guidance and item.shape[0] != text_embeddings.text_embeds.shape[0]:
kwargs['mid_block_additional_residual'][idx] = torch.cat(
[item] * 2, dim=0)
def scale_model_input(model_input, timestep_tensor):
if is_input_scaled:
return model_input
mi_chunks = torch.chunk(model_input, model_input.shape[0], dim=0)
timestep_chunks = torch.chunk(
timestep_tensor, timestep_tensor.shape[0], dim=0)
out_chunks = []
# unsqueeze if timestep is zero dim
for idx in range(model_input.shape[0]):
# if scheduler has step_index
if hasattr(self.noise_scheduler, '_step_index'):
self.noise_scheduler._step_index = None
out_chunks.append(
self.noise_scheduler.scale_model_input(
mi_chunks[idx], timestep_chunks[idx])
)
return torch.cat(out_chunks, dim=0)
with torch.no_grad():
if do_classifier_free_guidance:
# if we are doing classifier free guidance, need to double up
latent_model_input = torch.cat([latents] * 2, dim=0)
timestep = torch.cat([timestep] * 2)
else:
latent_model_input = latents
latent_model_input = scale_model_input(
latent_model_input, timestep)
# check if we need to concat timesteps
if isinstance(timestep, torch.Tensor) and len(timestep.shape) > 1:
ts_bs = timestep.shape[0]
if ts_bs != latent_model_input.shape[0]:
if ts_bs == 1:
timestep = torch.cat(
[timestep] * latent_model_input.shape[0])
elif ts_bs * 2 == latent_model_input.shape[0]:
timestep = torch.cat([timestep] * 2, dim=0)
else:
raise ValueError(
f"Batch size of latents {latent_model_input.shape[0]} must be the same or half the batch size of timesteps {timestep.shape[0]}")
# predict the noise residual
if self.unet.device != self.device_torch:
self.unet.to(self.device_torch)
if self.unet.dtype != self.torch_dtype:
self.unet = self.unet.to(dtype=self.torch_dtype)
# check if get_noise prediction has guidance_embedding_scale
# if it does not, we dont pass it
signatures = inspect.signature(self.get_noise_prediction).parameters
if 'guidance_embedding_scale' in signatures:
kwargs['guidance_embedding_scale'] = guidance_embedding_scale
if 'bypass_guidance_embedding' in signatures:
kwargs['bypass_guidance_embedding'] = bypass_guidance_embedding
if 'batch' in signatures:
kwargs['batch'] = batch
noise_pred = self.get_noise_prediction(
latent_model_input=latent_model_input,
timestep=timestep,
text_embeddings=text_embeddings,
**kwargs
)
conditional_pred = noise_pred
if do_classifier_free_guidance:
# perform guidance
noise_pred_uncond, noise_pred_text = noise_pred.chunk(2, dim=0)
conditional_pred = noise_pred_text
if detach_unconditional:
noise_pred_uncond = noise_pred_uncond.detach()
noise_pred = noise_pred_uncond + guidance_scale * (
noise_pred_text - noise_pred_uncond
)
if rescale_cfg is not None and rescale_cfg != guidance_scale:
with torch.no_grad():
# do cfg at the target rescale so we can match it
target_pred_mean_std = noise_pred_uncond + rescale_cfg * (
noise_pred_text - noise_pred_uncond
)
target_mean = target_pred_mean_std.mean(
[1, 2, 3], keepdim=True).detach()
target_std = target_pred_mean_std.std(
[1, 2, 3], keepdim=True).detach()
pred_mean = noise_pred.mean(
[1, 2, 3], keepdim=True).detach()
pred_std = noise_pred.std([1, 2, 3], keepdim=True).detach()
# match the mean and std
noise_pred = (noise_pred - pred_mean) / pred_std
noise_pred = (noise_pred * target_std) + target_mean
# https://github.com/huggingface/diffusers/blob/7a91ea6c2b53f94da930a61ed571364022b21044/src/diffusers/pipelines/stable_diffusion_xl/pipeline_stable_diffusion_xl.py#L775
if guidance_rescale > 0.0:
# Based on 3.4. in https://arxiv.org/pdf/2305.08891.pdf
noise_pred = rescale_noise_cfg(
noise_pred, noise_pred_text, guidance_rescale=guidance_rescale)
if return_conditional_pred:
return noise_pred, conditional_pred
return noise_pred
def step_scheduler(self, model_input, latent_input, timestep_tensor, noise_scheduler=None):
if noise_scheduler is None:
noise_scheduler = self.noise_scheduler
# // sometimes they are on the wrong device, no idea why
if isinstance(noise_scheduler, DDPMScheduler) or isinstance(noise_scheduler, LCMScheduler):
try:
noise_scheduler.betas = noise_scheduler.betas.to(
self.device_torch)
noise_scheduler.alphas = noise_scheduler.alphas.to(
self.device_torch)
noise_scheduler.alphas_cumprod = noise_scheduler.alphas_cumprod.to(
self.device_torch)
except Exception as e:
pass
mi_chunks = torch.chunk(model_input, model_input.shape[0], dim=0)
latent_chunks = torch.chunk(latent_input, latent_input.shape[0], dim=0)
timestep_chunks = torch.chunk(
timestep_tensor, timestep_tensor.shape[0], dim=0)
out_chunks = []
if len(timestep_chunks) == 1 and len(mi_chunks) > 1:
# expand timestep to match
timestep_chunks = timestep_chunks * len(mi_chunks)
for idx in range(model_input.shape[0]):
# Reset it so it is unique for the
if hasattr(noise_scheduler, '_step_index'):
noise_scheduler._step_index = None
if hasattr(noise_scheduler, 'is_scale_input_called'):
noise_scheduler.is_scale_input_called = True
out_chunks.append(
noise_scheduler.step(mi_chunks[idx], timestep_chunks[idx], latent_chunks[idx], return_dict=False)[
0]
)
return torch.cat(out_chunks, dim=0)
# ref: https://github.com/huggingface/diffusers/blob/0bab447670f47c28df60fbd2f6a0f833f75a16f5/src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion.py#L746
def diffuse_some_steps(
self,
latents: torch.FloatTensor,
text_embeddings: PromptEmbeds,
total_timesteps: int = 1000,
start_timesteps=0,
guidance_scale=1,
add_time_ids=None,
bleed_ratio: float = 0.5,
bleed_latents: torch.FloatTensor = None,
is_input_scaled=False,
return_first_prediction=False,
**kwargs,
):
timesteps_to_run = self.noise_scheduler.timesteps[start_timesteps:total_timesteps]
first_prediction = None
for timestep in tqdm(timesteps_to_run, leave=False):
timestep = timestep.unsqueeze_(0)
noise_pred, conditional_pred = self.predict_noise(
latents,
text_embeddings,
timestep,
guidance_scale=guidance_scale,
add_time_ids=add_time_ids,
is_input_scaled=is_input_scaled,
return_conditional_pred=True,
**kwargs,
)
# some schedulers need to run separately, so do that. (euler for example)
if return_first_prediction and first_prediction is None:
first_prediction = conditional_pred
latents = self.step_scheduler(noise_pred, latents, timestep)
# if not last step, and bleeding, bleed in some latents
if bleed_latents is not None and timestep != self.noise_scheduler.timesteps[-1]:
latents = (latents * (1 - bleed_ratio)) + \
(bleed_latents * bleed_ratio)
# only skip first scaling
is_input_scaled = False
# return latents_steps
if return_first_prediction:
return latents, first_prediction
return latents
def encode_prompt(
self,
prompt,
prompt2=None,
num_images_per_prompt=1,
force_all=False,
long_prompts=False,
max_length=None,
dropout_prob=0.0,
) -> PromptEmbeds:
# sd1.5 embeddings are (bs, 77, 768)
prompt = prompt
# if it is not a list, make it one
if not isinstance(prompt, list):
prompt = [prompt]
if prompt2 is not None and not isinstance(prompt2, list):
prompt2 = [prompt2]
return self.get_prompt_embeds(prompt)
@torch.no_grad()
def encode_images(
self,
image_list: List[torch.Tensor],
device=None,
dtype=None
):
if device is None:
device = self.vae_device_torch
if dtype is None:
dtype = self.vae_torch_dtype
latent_list = []
# Move to vae to device if on cpu
if self.vae.device == 'cpu':
self.vae.to(device)
self.vae.eval()
self.vae.requires_grad_(False)
# move to device and dtype
image_list = [image.to(device, dtype=dtype) for image in image_list]
VAE_SCALE_FACTOR = 2 ** (
len(self.vae.config['block_out_channels']) - 1)
# resize images if not divisible by 8
for i in range(len(image_list)):
image = image_list[i]
if image.shape[1] % VAE_SCALE_FACTOR != 0 or image.shape[2] % VAE_SCALE_FACTOR != 0:
image_list[i] = Resize((image.shape[1] // VAE_SCALE_FACTOR * VAE_SCALE_FACTOR,
image.shape[2] // VAE_SCALE_FACTOR * VAE_SCALE_FACTOR))(image)
images = torch.stack(image_list)
if isinstance(self.vae, AutoencoderTiny):
latents = self.vae.encode(images, return_dict=False)[0]
else:
latents = self.vae.encode(images).latent_dist.sample()
shift = self.vae.config['shift_factor'] if self.vae.config['shift_factor'] is not None else 0
# flux ref https://github.com/black-forest-labs/flux/blob/c23ae247225daba30fbd56058d247cc1b1fc20a3/src/flux/modules/autoencoder.py#L303
# z = self.scale_factor * (z - self.shift_factor)
latents = self.vae.config['scaling_factor'] * (latents - shift)
latents = latents.to(device, dtype=dtype)
return latents
def decode_latents(
self,
latents: torch.Tensor,
device=None,
dtype=None
):
if device is None:
device = self.device
if dtype is None:
dtype = self.torch_dtype
# Move to vae to device if on cpu
if self.vae.device == 'cpu':
self.vae.to(self.device)
latents = latents.to(device, dtype=dtype)
latents = (
latents / self.vae.config['scaling_factor']) + self.vae.config['shift_factor']
images = self.vae.decode(latents).sample
images = images.to(device, dtype=dtype)
return images
def encode_image_prompt_pairs(
self,
prompt_list: List[str],
image_list: List[torch.Tensor],
device=None,
dtype=None
):
# todo check image types and expand and rescale as needed
# device and dtype are for outputs
if device is None:
device = self.device
if dtype is None:
dtype = self.torch_dtype
embedding_list = []
latent_list = []
# embed the prompts
for prompt in prompt_list:
embedding = self.encode_prompt(prompt).to(
self.device_torch, dtype=dtype)
embedding_list.append(embedding)
return embedding_list, latent_list
def get_weight_by_name(self, name):
# weights begin with te{te_num}_ for text encoder
# weights begin with unet_ for unet_
if name.startswith('te'):
key = name[4:]
# text encoder
te_num = int(name[2])
if isinstance(self.text_encoder, list):
return self.text_encoder[te_num].state_dict()[key]
else:
return self.text_encoder.state_dict()[key]
elif name.startswith('unet'):
key = name[5:]
# unet
return self.unet.state_dict()[key]
raise ValueError(f"Unknown weight name: {name}")
def inject_trigger_into_prompt(self, prompt, trigger=None, to_replace_list=None, add_if_not_present=False):
return inject_trigger_into_prompt(
prompt,
trigger=trigger,
to_replace_list=to_replace_list,
add_if_not_present=add_if_not_present,
)
def state_dict(self, vae=True, text_encoder=True, unet=True):
state_dict = OrderedDict()
if vae:
for k, v in self.vae.state_dict().items():
new_key = k if k.startswith(
f"{SD_PREFIX_VAE}") else f"{SD_PREFIX_VAE}_{k}"
state_dict[new_key] = v
if text_encoder:
if isinstance(self.text_encoder, list):
for i, encoder in enumerate(self.text_encoder):
for k, v in encoder.state_dict().items():
new_key = k if k.startswith(
f"{SD_PREFIX_TEXT_ENCODER}{i}_") else f"{SD_PREFIX_TEXT_ENCODER}{i}_{k}"
state_dict[new_key] = v
else:
for k, v in self.text_encoder.state_dict().items():
new_key = k if k.startswith(
f"{SD_PREFIX_TEXT_ENCODER}_") else f"{SD_PREFIX_TEXT_ENCODER}_{k}"
state_dict[new_key] = v
if unet:
for k, v in self.unet.state_dict().items():
new_key = k if k.startswith(
f"{SD_PREFIX_UNET}_") else f"{SD_PREFIX_UNET}_{k}"
state_dict[new_key] = v
return state_dict
def named_parameters(self, vae=True, text_encoder=True, unet=True, refiner=False, state_dict_keys=False) -> \
OrderedDict[
str, Parameter]:
named_params: OrderedDict[str, Parameter] = OrderedDict()
if vae:
for name, param in self.vae.named_parameters(recurse=True, prefix=f"{SD_PREFIX_VAE}"):
named_params[name] = param
if text_encoder:
if isinstance(self.text_encoder, list):
for i, encoder in enumerate(self.text_encoder):
if self.is_xl and not self.model_config.use_text_encoder_1 and i == 0:
# dont add these params
continue
if self.is_xl and not self.model_config.use_text_encoder_2 and i == 1:
# dont add these params
continue
for name, param in encoder.named_parameters(recurse=True, prefix=f"{SD_PREFIX_TEXT_ENCODER}{i}"):
named_params[name] = param
else:
for name, param in self.text_encoder.named_parameters(recurse=True, prefix=f"{SD_PREFIX_TEXT_ENCODER}"):
named_params[name] = param
if unet:
if self.is_flux or self.is_lumina2 or self.is_transformer:
for name, param in self.unet.named_parameters(recurse=True, prefix="transformer"):
named_params[name] = param
else:
for name, param in self.unet.named_parameters(recurse=True, prefix=f"{SD_PREFIX_UNET}"):
named_params[name] = param
if self.model_config.ignore_if_contains is not None:
# remove params that contain the ignore_if_contains from named params
for key in list(named_params.keys()):
if any([s in key for s in self.model_config.ignore_if_contains]):
del named_params[key]
if self.model_config.only_if_contains is not None:
# remove params that do not contain the only_if_contains from named params
for key in list(named_params.keys()):
if not any([s in key for s in self.model_config.only_if_contains]):
del named_params[key]
if refiner:
for name, param in self.refiner_unet.named_parameters(recurse=True, prefix=f"{SD_PREFIX_REFINER_UNET}"):
named_params[name] = param
# convert to state dict keys, jsut replace . with _ on keys
if state_dict_keys:
new_named_params = OrderedDict()
for k, v in named_params.items():
# replace only the first . with an _
new_key = k.replace('.', '_', 1)
new_named_params[new_key] = v
named_params = new_named_params
return named_params
def save(self, output_file: str, meta: OrderedDict, save_dtype=get_torch_dtype('fp16'), logit_scale=None):
self.save_model(
output_path=output_file,
meta=meta,
save_dtype=save_dtype
)
def prepare_optimizer_params(
self,
unet=False,
text_encoder=False,
text_encoder_lr=None,
unet_lr=None,
refiner_lr=None,
refiner=False,
default_lr=1e-6,
):
# todo maybe only get locon ones?
# not all items are saved, to make it match, we need to match out save mappings
# and not train anything not mapped. Also add learning rate
version = 'sd1'
if self.is_xl:
version = 'sdxl'
if self.is_v2:
version = 'sd2'
mapping_filename = f"stable_diffusion_{version}.json"
mapping_path = os.path.join(KEYMAPS_ROOT, mapping_filename)
with open(mapping_path, 'r') as f:
mapping = json.load(f)
ldm_diffusers_keymap = mapping['ldm_diffusers_keymap']
trainable_parameters = []
# we use state dict to find params
if unet:
named_params = self.named_parameters(
vae=False, unet=unet, text_encoder=False, state_dict_keys=True)
unet_lr = unet_lr if unet_lr is not None else default_lr
params = []
for param in named_params.values():
if param.requires_grad:
params.append(param)
param_data = {"params": params, "lr": unet_lr}
trainable_parameters.append(param_data)
print_acc(f"Found {len(params)} trainable parameter in unet")
if text_encoder:
named_params = self.named_parameters(
vae=False, unet=False, text_encoder=text_encoder, state_dict_keys=True)
text_encoder_lr = text_encoder_lr if text_encoder_lr is not None else default_lr
params = []
for key, diffusers_key in ldm_diffusers_keymap.items():
if diffusers_key in named_params and diffusers_key not in DO_NOT_TRAIN_WEIGHTS:
if named_params[diffusers_key].requires_grad:
params.append(named_params[diffusers_key])
param_data = {"params": params, "lr": text_encoder_lr}
trainable_parameters.append(param_data)
print_acc(
f"Found {len(params)} trainable parameter in text encoder")
if refiner:
named_params = self.named_parameters(vae=False, unet=False, text_encoder=False, refiner=True,
state_dict_keys=True)
refiner_lr = refiner_lr if refiner_lr is not None else default_lr
params = []
for key, diffusers_key in ldm_diffusers_keymap.items():
diffusers_key = f"refiner_{diffusers_key}"
if diffusers_key in named_params and diffusers_key not in DO_NOT_TRAIN_WEIGHTS:
if named_params[diffusers_key].requires_grad:
params.append(named_params[diffusers_key])
param_data = {"params": params, "lr": refiner_lr}
trainable_parameters.append(param_data)
print_acc(f"Found {len(params)} trainable parameter in refiner")
return trainable_parameters
def save_device_state(self):
# saves the current device state for all modules
# this is useful for when we want to alter the state and restore it
unet_has_grad = self.get_model_has_grad()
self.device_state = {
**empty_preset,
'vae': {
'training': self.vae.training,
'device': self.vae.device,
},
'unet': {
'training': self.unet.training,
'device': self.unet.device,
'requires_grad': unet_has_grad,
},
}
if isinstance(self.text_encoder, list):
self.device_state['text_encoder']: List[dict] = []
for encoder in self.text_encoder:
te_has_grad = self.get_te_has_grad()
self.device_state['text_encoder'].append({
'training': encoder.training,
'device': encoder.device,
# todo there has to be a better way to do this
'requires_grad': te_has_grad
})
else:
te_has_grad = self.get_te_has_grad()
self.device_state['text_encoder'] = {
'training': self.text_encoder.training,
'device': self.text_encoder.device,
'requires_grad': te_has_grad
}
if self.adapter is not None:
if isinstance(self.adapter, IPAdapter):
requires_grad = self.adapter.image_proj_model.training
adapter_device = self.unet.device
elif isinstance(self.adapter, T2IAdapter):
requires_grad = self.adapter.adapter.conv_in.weight.requires_grad
adapter_device = self.adapter.device
elif isinstance(self.adapter, ControlNetModel):
requires_grad = self.adapter.conv_in.training
adapter_device = self.adapter.device
elif isinstance(self.adapter, ClipVisionAdapter):
requires_grad = self.adapter.embedder.training
adapter_device = self.adapter.device
elif isinstance(self.adapter, CustomAdapter):
requires_grad = self.adapter.training
adapter_device = self.adapter.device
elif isinstance(self.adapter, ReferenceAdapter):
# todo update this!!
requires_grad = True
adapter_device = self.adapter.device
else:
raise ValueError(f"Unknown adapter type: {type(self.adapter)}")
self.device_state['adapter'] = {
'training': self.adapter.training,
'device': adapter_device,
'requires_grad': requires_grad,
}
if self.refiner_unet is not None:
self.device_state['refiner_unet'] = {
'training': self.refiner_unet.training,
'device': self.refiner_unet.device,
'requires_grad': self.refiner_unet.conv_in.weight.requires_grad,
}
def restore_device_state(self):
# restores the device state for all modules
# this is useful for when we want to alter the state and restore it
if self.device_state is None:
return
self.set_device_state(self.device_state)
self.device_state = None
def set_device_state(self, state):
if state['vae']['training']:
self.vae.train()
else:
self.vae.eval()
self.vae.to(state['vae']['device'])
if state['unet']['training']:
self.unet.train()
else:
self.unet.eval()
self.unet.to(state['unet']['device'])
if state['unet']['requires_grad']:
self.unet.requires_grad_(True)
else:
self.unet.requires_grad_(False)
if isinstance(self.text_encoder, list):
for i, encoder in enumerate(self.text_encoder):
if isinstance(state['text_encoder'], list):
if state['text_encoder'][i]['training']:
encoder.train()
else:
encoder.eval()
encoder.to(state['text_encoder'][i]['device'])
encoder.requires_grad_(
state['text_encoder'][i]['requires_grad'])
else:
if state['text_encoder']['training']:
encoder.train()
else:
encoder.eval()
encoder.to(state['text_encoder']['device'])
encoder.requires_grad_(
state['text_encoder']['requires_grad'])
else:
if state['text_encoder']['training']:
self.text_encoder.train()
else:
self.text_encoder.eval()
self.text_encoder.to(state['text_encoder']['device'])
self.text_encoder.requires_grad_(
state['text_encoder']['requires_grad'])
if self.adapter is not None:
self.adapter.to(state['adapter']['device'])
self.adapter.requires_grad_(state['adapter']['requires_grad'])
if state['adapter']['training']:
self.adapter.train()
else:
self.adapter.eval()
if self.refiner_unet is not None:
self.refiner_unet.to(state['refiner_unet']['device'])
self.refiner_unet.requires_grad_(
state['refiner_unet']['requires_grad'])
if state['refiner_unet']['training']:
self.refiner_unet.train()
else:
self.refiner_unet.eval()
flush()
def set_device_state_preset(self, device_state_preset: DeviceStatePreset):
# sets a preset for device state
# save current state first
self.save_device_state()
active_modules = []
training_modules = []
if device_state_preset in ['cache_latents']:
active_modules = ['vae']
if device_state_preset in ['cache_clip']:
active_modules = ['clip']
if device_state_preset in ['generate']:
active_modules = ['vae', 'unet',
'text_encoder', 'adapter', 'refiner_unet']
state = copy.deepcopy(empty_preset)
# vae
state['vae'] = {
'training': 'vae' in training_modules,
'device': self.vae_device_torch if 'vae' in active_modules else 'cpu',
'requires_grad': 'vae' in training_modules,
}
# unet
state['unet'] = {
'training': 'unet' in training_modules,
'device': self.device_torch if 'unet' in active_modules else 'cpu',
'requires_grad': 'unet' in training_modules,
}
if self.refiner_unet is not None:
state['refiner_unet'] = {
'training': 'refiner_unet' in training_modules,
'device': self.device_torch if 'refiner_unet' in active_modules else 'cpu',
'requires_grad': 'refiner_unet' in training_modules,
}
# text encoder
if isinstance(self.text_encoder, list):
state['text_encoder'] = []
for i, encoder in enumerate(self.text_encoder):
state['text_encoder'].append({
'training': 'text_encoder' in training_modules,
'device': self.te_device_torch if 'text_encoder' in active_modules else 'cpu',
'requires_grad': 'text_encoder' in training_modules,
})
else:
state['text_encoder'] = {
'training': 'text_encoder' in training_modules,
'device': self.te_device_torch if 'text_encoder' in active_modules else 'cpu',
'requires_grad': 'text_encoder' in training_modules,
}
if self.adapter is not None:
state['adapter'] = {
'training': 'adapter' in training_modules,
'device': self.device_torch if 'adapter' in active_modules else 'cpu',
'requires_grad': 'adapter' in training_modules,
}
self.set_device_state(state)
def text_encoder_to(self, *args, **kwargs):
if isinstance(self.text_encoder, list):
for encoder in self.text_encoder:
encoder.to(*args, **kwargs)
else:
self.text_encoder.to(*args, **kwargs)
def convert_lora_weights_before_save(self, state_dict):
# can be overridden in child classes to convert weights before saving
return state_dict
def convert_lora_weights_before_load(self, state_dict):
# can be overridden in child classes to convert weights before loading
return state_dict
def condition_noisy_latents(self, latents: torch.Tensor, batch:'DataLoaderBatchDTO'):
# can be overridden in child classes to condition latents before noise prediction
return latents
def get_transformer_block_names(self) -> Optional[List[str]]:
# override in child classes to get transformer block names for lora targeting
return None
def get_base_model_version(self) -> str:
# override in child classes to get the base model version
return "unknown"
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