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import os
from typing import TYPE_CHECKING
import torch
import yaml
from toolkit.config_modules import GenerateImageConfig, ModelConfig
from PIL import Image
from toolkit.models.base_model import BaseModel
from toolkit.basic import flush
from diffusers import AutoencoderKL
from toolkit.prompt_utils import PromptEmbeds
from toolkit.samplers.custom_flowmatch_sampler import CustomFlowMatchEulerDiscreteScheduler
from toolkit.dequantize import patch_dequantization_on_save
from toolkit.accelerator import unwrap_model
from optimum.quanto import freeze, QTensor
from toolkit.util.quantize import quantize, get_qtype
from transformers import T5TokenizerFast, T5EncoderModel
from .src import FLitePipeline, DiT
if TYPE_CHECKING:
from toolkit.data_transfer_object.data_loader import DataLoaderBatchDTO
scheduler_config = {
"base_image_seq_len": 256,
"base_shift": 0.5,
"max_image_seq_len": 4096,
"max_shift": 1.15,
"num_train_timesteps": 1000,
"shift": 3.0,
"use_dynamic_shifting": True
}
class FLiteModel(BaseModel):
arch = "f-lite"
def __init__(
self,
device,
model_config: ModelConfig,
dtype='bf16',
custom_pipeline=None,
noise_scheduler=None,
**kwargs
):
super().__init__(
device,
model_config,
dtype,
custom_pipeline,
noise_scheduler,
**kwargs
)
self.is_flow_matching = True
self.is_transformer = True
self.target_lora_modules = ['DiT']
# static method to get the noise scheduler
@staticmethod
def get_train_scheduler():
return CustomFlowMatchEulerDiscreteScheduler(**scheduler_config)
def get_bucket_divisibility(self):
# return the bucket divisibility for the model
return 16
def load_model(self):
dtype = self.torch_dtype
# will be updated if we detect a existing checkpoint in training folder
model_path = self.model_config.name_or_path
extras_path = self.model_config.extras_name_or_path
self.print_and_status_update("Loading transformer")
transformer = DiT.from_pretrained(
model_path,
subfolder="dit_model",
torch_dtype=dtype,
)
transformer.to(self.quantize_device, dtype=dtype)
if self.model_config.quantize:
# patch the state dict method
patch_dequantization_on_save(transformer)
quantization_type = get_qtype(self.model_config.qtype)
self.print_and_status_update("Quantizing transformer")
quantize(transformer, weights=quantization_type,
**self.model_config.quantize_kwargs)
freeze(transformer)
transformer.to(self.device_torch)
else:
transformer.to(self.device_torch, dtype=dtype)
flush()
self.print_and_status_update("Loading T5")
tokenizer = T5TokenizerFast.from_pretrained(
extras_path, subfolder="tokenizer", torch_dtype=dtype
)
text_encoder = T5EncoderModel.from_pretrained(
extras_path, subfolder="text_encoder", torch_dtype=dtype
)
text_encoder.to(self.device_torch, dtype=dtype)
flush()
if self.model_config.quantize_te:
self.print_and_status_update("Quantizing T5")
quantize(text_encoder, weights=get_qtype(
self.model_config.qtype))
freeze(text_encoder)
flush()
self.noise_scheduler = FLiteModel.get_train_scheduler()
self.print_and_status_update("Loading VAE")
vae = AutoencoderKL.from_pretrained(
extras_path,
subfolder="vae",
torch_dtype=dtype
)
vae = vae.to(self.device_torch, dtype=dtype)
self.print_and_status_update("Making pipe")
pipe: FLitePipeline = FLitePipeline(
text_encoder=None,
tokenizer=tokenizer,
vae=vae,
dit_model=None,
)
# for quantization, it works best to do these after making the pipe
pipe.text_encoder = text_encoder
pipe.dit_model = transformer
pipe.transformer = transformer
pipe.scheduler = self.noise_scheduler,
self.print_and_status_update("Preparing Model")
text_encoder = [pipe.text_encoder]
tokenizer = [pipe.tokenizer]
pipe.transformer = pipe.transformer.to(self.device_torch)
flush()
# just to make sure everything is on the right device and dtype
text_encoder[0].to(self.device_torch)
text_encoder[0].requires_grad_(False)
text_encoder[0].eval()
pipe.transformer = pipe.transformer.to(self.device_torch)
flush()
# save it to the model class
self.vae = vae
self.text_encoder = text_encoder # list of text encoders
self.tokenizer = tokenizer # list of tokenizers
self.model = pipe.transformer
self.pipeline = pipe
self.print_and_status_update("Model Loaded")
def get_generation_pipeline(self):
scheduler = FLiteModel.get_train_scheduler()
# it has built in scheduler. Basically euler flowmatching
pipeline = FLitePipeline(
text_encoder=unwrap_model(self.text_encoder[0]),
tokenizer=self.tokenizer[0],
vae=unwrap_model(self.vae),
dit_model=unwrap_model(self.transformer)
)
pipeline.transformer = pipeline.dit_model
pipeline.scheduler = scheduler
return pipeline
def generate_single_image(
self,
pipeline: FLitePipeline,
gen_config: GenerateImageConfig,
conditional_embeds: PromptEmbeds,
unconditional_embeds: PromptEmbeds,
generator: torch.Generator,
extra: dict,
):
extra['negative_prompt_embeds'] = unconditional_embeds.text_embeds
img = pipeline(
prompt_embeds=conditional_embeds.text_embeds,
negative_prompt_embeds=unconditional_embeds.text_embeds,
height=gen_config.height,
width=gen_config.width,
num_inference_steps=gen_config.num_inference_steps,
guidance_scale=gen_config.guidance_scale,
latents=gen_config.latents,
generator=generator,
).images[0]
return img
def get_noise_prediction(
self,
latent_model_input: torch.Tensor,
timestep: torch.Tensor, # 0 to 1000 scale
text_embeddings: PromptEmbeds,
**kwargs
):
cast_dtype = self.unet.dtype
noise_pred = self.unet(
latent_model_input.to(
self.device_torch, cast_dtype
),
text_embeddings.text_embeds.to(
self.device_torch, cast_dtype
),
timestep / 1000,
)
if isinstance(noise_pred, QTensor):
noise_pred = noise_pred.dequantize()
return noise_pred
def get_prompt_embeds(self, prompt: str) -> PromptEmbeds:
if isinstance(prompt, str):
prompts = [prompt]
else:
prompts = prompt
if self.pipeline.text_encoder.device != self.device_torch:
self.pipeline.text_encoder.to(self.device_torch)
prompt_embeds, negative_embeds = self.pipeline.encode_prompt(
prompt=prompts,
negative_prompt=None,
device=self.text_encoder[0].device,
dtype=self.torch_dtype,
)
pe = PromptEmbeds(prompt_embeds)
return pe
def get_model_has_grad(self):
# return from a weight if it has grad
return False
def get_te_has_grad(self):
# return from a weight if it has grad
return False
def save_model(self, output_path, meta, save_dtype):
# only save the unet
transformer: DiT = unwrap_model(self.model)
# diffusers
# only save the unet
transformer: DiT = unwrap_model(self.transformer)
transformer.save_pretrained(
save_directory=os.path.join(output_path, 'dit_model'),
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)
def get_loss_target(self, *args, **kwargs):
noise = kwargs.get('noise')
batch = kwargs.get('batch')
# return (noise - batch.latents).detach()
return (batch.latents - noise).detach()
def convert_lora_weights_before_save(self, state_dict):
# currently starte with transformer. but needs to start with diffusion_model. for comfyui
new_sd = {}
for key, value in state_dict.items():
new_key = key.replace("transformer.", "diffusion_model.")
new_sd[new_key] = value
return new_sd
def convert_lora_weights_before_load(self, state_dict):
# saved as diffusion_model. but needs to be transformer. for ai-toolkit
new_sd = {}
for key, value in state_dict.items():
new_key = key.replace("diffusion_model.", "transformer.")
new_sd[new_key] = value
return new_sd
def get_base_model_version(self):
return "f-lite"
def get_stepped_pred(self, pred, noise):
# just used for DFE support
latents = pred + noise
return latents
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