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https://github.com/ostris/ai-toolkit.git
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421 lines
15 KiB
Python
421 lines
15 KiB
Python
import time
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from collections import OrderedDict
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import os
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from toolkit.kohya_model_util import load_vae
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from toolkit.lora_special import LoRASpecialNetwork
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from toolkit.optimizer import get_optimizer
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from toolkit.paths import REPOS_ROOT
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import sys
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sys.path.append(REPOS_ROOT)
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sys.path.append(os.path.join(REPOS_ROOT, 'leco'))
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from diffusers import StableDiffusionPipeline
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from jobs.process import BaseTrainProcess
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from toolkit.metadata import get_meta_for_safetensors
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from toolkit.train_tools import get_torch_dtype, apply_noise_offset
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import gc
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import torch
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from tqdm import tqdm
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from leco import train_util, model_util
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from toolkit.config_modules import SaveConfig, LogingConfig, SampleConfig, NetworkConfig, TrainConfig, ModelConfig
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def flush():
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torch.cuda.empty_cache()
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gc.collect()
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UNET_IN_CHANNELS = 4 # Stable Diffusion の in_channels は 4 で固定。XLも同じ。
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VAE_SCALE_FACTOR = 8 # 2 ** (len(vae.config.block_out_channels) - 1) = 8
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class StableDiffusion:
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def __init__(self, vae, tokenizer, text_encoder, unet, noise_scheduler):
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self.vae = vae
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self.tokenizer = tokenizer
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self.text_encoder = text_encoder
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self.unet = unet
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self.noise_scheduler = noise_scheduler
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class BaseSDTrainProcess(BaseTrainProcess):
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def __init__(self, process_id: int, job, config: OrderedDict):
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super().__init__(process_id, job, config)
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self.step_num = 0
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self.start_step = 0
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self.device = self.get_conf('device', self.job.device)
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self.device_torch = torch.device(self.device)
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self.network_config = NetworkConfig(**self.get_conf('network', None))
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self.training_folder = self.get_conf('training_folder', self.job.training_folder)
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self.train_config = TrainConfig(**self.get_conf('train', {}))
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self.model_config = ModelConfig(**self.get_conf('model', {}))
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self.save_config = SaveConfig(**self.get_conf('save', {}))
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self.sample_config = SampleConfig(**self.get_conf('sample', {}))
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self.logging_config = LogingConfig(**self.get_conf('logging', {}))
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self.optimizer = None
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self.lr_scheduler = None
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self.sd = None
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# added later
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self.network = None
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self.scheduler = None
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def sample(self, step=None):
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sample_folder = os.path.join(self.save_root, 'samples')
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if not os.path.exists(sample_folder):
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os.makedirs(sample_folder, exist_ok=True)
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if self.network is not None:
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self.network.eval()
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# save current seed state for training
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rng_state = torch.get_rng_state()
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cuda_rng_state = torch.cuda.get_rng_state() if torch.cuda.is_available() else None
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original_device_dict = {
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'vae': self.sd.vae.device,
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'unet': self.sd.unet.device,
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'text_encoder': self.sd.text_encoder.device,
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# 'tokenizer': self.sd.tokenizer.device,
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}
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self.sd.vae.to(self.device_torch)
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self.sd.unet.to(self.device_torch)
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self.sd.text_encoder.to(self.device_torch)
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# self.sd.tokenizer.to(self.device_torch)
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# TODO add clip skip
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pipeline = StableDiffusionPipeline(
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vae=self.sd.vae,
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unet=self.sd.unet,
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text_encoder=self.sd.text_encoder,
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tokenizer=self.sd.tokenizer,
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scheduler=self.sd.noise_scheduler,
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safety_checker=None,
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feature_extractor=None,
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requires_safety_checker=False,
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)
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# disable progress bar
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pipeline.set_progress_bar_config(disable=True)
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start_seed = self.sample_config.seed
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start_multiplier = self.network.multiplier
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current_seed = start_seed
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pipeline.to(self.device_torch)
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with self.network:
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with torch.no_grad():
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if self.network is not None:
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assert self.network.is_active
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if self.logging_config.verbose:
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print("network_state", {
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'is_active': self.network.is_active,
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'multiplier': self.network.multiplier,
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})
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for i in tqdm(range(len(self.sample_config.prompts)), desc=f"Generating Samples - step: {step}"):
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raw_prompt = self.sample_config.prompts[i]
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neg = self.sample_config.neg
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multiplier = self.sample_config.network_multiplier
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p_split = raw_prompt.split('--')
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prompt = p_split[0].strip()
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if len(p_split) > 1:
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for split in p_split:
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flag = split[:1]
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content = split[1:].strip()
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if flag == 'n':
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neg = content
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elif flag == 'm':
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# multiplier
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multiplier = float(content)
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height = self.sample_config.height
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width = self.sample_config.width
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height = max(64, height - height % 8) # round to divisible by 8
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width = max(64, width - width % 8) # round to divisible by 8
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if self.sample_config.walk_seed:
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current_seed += i
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if self.network is not None:
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self.network.multiplier = multiplier
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torch.manual_seed(current_seed)
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torch.cuda.manual_seed(current_seed)
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img = pipeline(
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prompt,
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height=height,
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width=width,
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num_inference_steps=self.sample_config.sample_steps,
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guidance_scale=self.sample_config.guidance_scale,
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negative_prompt=neg,
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).images[0]
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step_num = ''
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if step is not None:
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# zero-pad 9 digits
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step_num = f"_{str(step).zfill(9)}"
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seconds_since_epoch = int(time.time())
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# zero-pad 2 digits
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i_str = str(i).zfill(2)
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filename = f"{seconds_since_epoch}{step_num}_{i_str}.png"
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output_path = os.path.join(sample_folder, filename)
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img.save(output_path)
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# clear pipeline and cache to reduce vram usage
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del pipeline
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torch.cuda.empty_cache()
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# restore training state
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torch.set_rng_state(rng_state)
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if cuda_rng_state is not None:
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torch.cuda.set_rng_state(cuda_rng_state)
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self.sd.vae.to(original_device_dict['vae'])
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self.sd.unet.to(original_device_dict['unet'])
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self.sd.text_encoder.to(original_device_dict['text_encoder'])
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if self.network is not None:
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self.network.train()
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self.network.multiplier = start_multiplier
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# self.sd.tokenizer.to(original_device_dict['tokenizer'])
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def update_training_metadata(self):
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self.add_meta(OrderedDict({"training_info": self.get_training_info()}))
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def get_training_info(self):
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info = OrderedDict({
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'step': self.step_num + 1
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})
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return info
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def save(self, step=None):
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if not os.path.exists(self.save_root):
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os.makedirs(self.save_root, exist_ok=True)
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step_num = ''
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if step is not None:
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# zeropad 9 digits
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step_num = f"_{str(step).zfill(9)}"
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self.update_training_metadata()
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filename = f'{self.job.name}{step_num}.safetensors'
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file_path = os.path.join(self.save_root, filename)
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# prepare meta
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save_meta = get_meta_for_safetensors(self.meta, self.job.name)
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if self.network is not None:
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# TODO handle dreambooth, fine tuning, etc
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self.network.save_weights(
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file_path,
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dtype=get_torch_dtype(self.save_config.dtype),
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metadata=save_meta
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)
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else:
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# TODO handle dreambooth, fine tuning, etc
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# will probably have to convert dict back to LDM
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ValueError("Non network training is not currently supported")
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self.print(f"Saved to {file_path}")
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# Called before the model is loaded
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def hook_before_model_load(self):
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# override in subclass
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pass
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def hook_add_extra_train_params(self, params):
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# override in subclass
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return params
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def hook_before_train_loop(self):
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pass
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def get_latent_noise(
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self,
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height=None,
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width=None,
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pixel_height=None,
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pixel_width=None,
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):
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if height is None and pixel_height is None:
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raise ValueError("height or pixel_height must be specified")
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if width is None and pixel_width is None:
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raise ValueError("width or pixel_width must be specified")
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if height is None:
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height = pixel_height // VAE_SCALE_FACTOR
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if width is None:
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width = pixel_width // VAE_SCALE_FACTOR
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noise = torch.randn(
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(
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self.train_config.batch_size,
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UNET_IN_CHANNELS,
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height,
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width,
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),
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device="cpu",
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)
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noise = apply_noise_offset(noise, self.train_config.noise_offset)
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return noise
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def hook_train_loop(self):
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# return loss
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return 0.0
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def run(self):
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super().run()
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### HOOK ###
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self.hook_before_model_load()
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dtype = get_torch_dtype(self.train_config.dtype)
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tokenizer, text_encoder, unet, noise_scheduler = model_util.load_models(
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self.model_config.name_or_path,
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scheduler_name=self.train_config.noise_scheduler,
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v2=self.model_config.is_v2,
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v_pred=self.model_config.is_v_pred,
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)
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# just for now or of we want to load a custom one
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# put on cpu for now, we only need it when sampling
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vae = load_vae(self.model_config.name_or_path, dtype=dtype).to('cpu', dtype=dtype)
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vae.eval()
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self.sd = StableDiffusion(vae, tokenizer, text_encoder, unet, noise_scheduler)
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text_encoder.to(self.device_torch, dtype=dtype)
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text_encoder.eval()
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unet.to(self.device_torch, dtype=dtype)
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if self.train_config.xformers:
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unet.enable_xformers_memory_efficient_attention()
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unet.requires_grad_(False)
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unet.eval()
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if self.network_config is not None:
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self.network = LoRASpecialNetwork(
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text_encoder=text_encoder,
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unet=unet,
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lora_dim=self.network_config.rank,
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multiplier=1.0,
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alpha=self.network_config.alpha
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)
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self.network.force_to(self.device_torch, dtype=dtype)
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self.network.apply_to(
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text_encoder,
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unet,
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self.train_config.train_text_encoder,
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self.train_config.train_unet
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)
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self.network.prepare_grad_etc(text_encoder, unet)
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params = self.network.prepare_optimizer_params(
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text_encoder_lr=self.train_config.lr,
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unet_lr=self.train_config.lr,
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default_lr=self.train_config.lr
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)
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else:
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params = []
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# assume dreambooth/finetune
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if self.train_config.train_text_encoder:
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text_encoder.requires_grad_(True)
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text_encoder.train()
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params += text_encoder.parameters()
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if self.train_config.train_unet:
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unet.requires_grad_(True)
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unet.train()
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params += unet.parameters()
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### HOOK ###
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params = self.hook_add_extra_train_params(params)
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optimizer_type = self.train_config.optimizer.lower()
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optimizer = get_optimizer(params, optimizer_type, learning_rate=self.train_config.lr,
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optimizer_params=self.train_config.optimizer_params)
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self.optimizer = optimizer
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lr_scheduler = train_util.get_lr_scheduler(
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self.train_config.lr_scheduler,
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optimizer,
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max_iterations=self.train_config.steps,
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lr_min=self.train_config.lr / 100, # not sure why leco did this, but ill do it to
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)
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self.lr_scheduler = lr_scheduler
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### HOOK ###
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self.hook_before_train_loop()
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# sample first
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self.print("Generating baseline samples before training")
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self.sample(0)
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self.progress_bar = tqdm(
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total=self.train_config.steps,
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desc=self.job.name,
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leave=True
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)
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self.step_num = 0
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for step in range(self.train_config.steps):
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# todo handle dataloader here maybe, not sure
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### HOOK ###
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loss = self.hook_train_loop()
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# don't do on first step
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if self.step_num != self.start_step:
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# pause progress bar
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self.progress_bar.unpause() # makes it so doesn't track time
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if self.sample_config.sample_every and self.step_num % self.sample_config.sample_every == 0:
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# print above the progress bar
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self.sample(self.step_num)
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if self.save_config.save_every and self.step_num % self.save_config.save_every == 0:
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# print above the progress bar
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self.print(f"Saving at step {self.step_num}")
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self.save(self.step_num)
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if self.logging_config.log_every and self.step_num % self.logging_config.log_every == 0:
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# log to tensorboard
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if self.writer is not None:
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# get avg loss
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self.writer.add_scalar(f"loss", loss, self.step_num)
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if self.train_config.optimizer.startswith('dadaptation'):
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learning_rate = (
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optimizer.param_groups[0]["d"] *
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optimizer.param_groups[0]["lr"]
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)
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else:
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learning_rate = optimizer.param_groups[0]['lr']
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self.writer.add_scalar(f"lr", learning_rate, self.step_num)
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self.progress_bar.refresh()
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# sets progress bar to match out step
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self.progress_bar.update(step - self.progress_bar.n)
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# end of step
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self.step_num = step
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self.sample(self.step_num + 1)
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print("")
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self.save()
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del (
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self.sd,
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unet,
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noise_scheduler,
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optimizer,
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self.network,
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tokenizer,
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text_encoder,
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)
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flush()
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