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Base for loopback lora training setup, still working on proper sliders
This commit is contained in:
Jaret Burkett
609
jobs/process/TrainSliderProcess.py
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609
jobs/process/TrainSliderProcess.py
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@@ -0,0 +1,609 @@
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# ref:
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# - https://github.com/p1atdev/LECO/blob/main/train_lora.py
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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.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
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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 toolkit.lora import LoRANetwork, DEFAULT_TARGET_REPLACE, UNET_TARGET_REPLACE_MODULE_CONV, TRAINING_METHODS
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from leco import train_util, model_util
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from leco.prompt_util import PromptEmbedsCache, PromptEmbedsPair, ACTION_TYPES
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from leco import debug_util
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def flush():
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torch.cuda.empty_cache()
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gc.collect()
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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 SaveConfig:
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def __init__(self, **kwargs):
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self.save_every: int = kwargs.get('save_every', 1000)
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self.dtype: str = kwargs.get('save_dtype', 'float16')
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class LogingConfig:
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def __init__(self, **kwargs):
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self.log_every: int = kwargs.get('log_every', 100)
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self.verbose: bool = kwargs.get('verbose', False)
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self.use_wandb: bool = kwargs.get('use_wandb', False)
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class SampleConfig:
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def __init__(self, **kwargs):
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self.sample_every: int = kwargs.get('sample_every', 100)
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self.width: int = kwargs.get('width', 512)
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self.height: int = kwargs.get('height', 512)
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self.prompts: list[str] = kwargs.get('prompts', [])
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self.neg = kwargs.get('neg', False)
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self.seed = kwargs.get('seed', 0)
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self.walk_seed = kwargs.get('walk_seed', False)
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self.guidance_scale = kwargs.get('guidance_scale', 7)
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self.sample_steps = kwargs.get('sample_steps', 20)
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class NetworkConfig:
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def __init__(self, **kwargs):
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self.type: str = kwargs.get('type', 'lierla')
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self.rank: int = kwargs.get('rank', 4)
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self.alpha: float = kwargs.get('alpha', 1.0)
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class TrainConfig:
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def __init__(self, **kwargs):
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self.noise_scheduler: 'model_util.AVAILABLE_SCHEDULERS' = kwargs.get('noise_scheduler', 'ddpm')
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self.steps: int = kwargs.get('steps', 1000)
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self.lr = kwargs.get('lr', 1e-6)
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self.optimizer = kwargs.get('optimizer', 'adamw')
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self.lr_scheduler = kwargs.get('lr_scheduler', 'constant')
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self.max_denoising_steps: int = kwargs.get('max_denoising_steps', 50)
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self.batch_size: int = kwargs.get('batch_size', 1)
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self.dtype: str = kwargs.get('dtype', 'fp32')
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self.xformers = kwargs.get('xformers', False)
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self.train_unet = kwargs.get('train_unet', True)
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self.train_text_encoder = kwargs.get('train_text_encoder', True)
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class ModelConfig:
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def __init__(self, **kwargs):
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self.name_or_path: str = kwargs.get('name_or_path', None)
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self.is_v2: bool = kwargs.get('is_v2', False)
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self.is_v_pred: bool = kwargs.get('is_v_pred', False)
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if self.name_or_path is None:
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raise ValueError('name_or_path must be specified')
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class PromptSettingsOld:
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def __init__(self, **kwargs):
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self.target: str = kwargs.get('target', None)
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self.positive = kwargs.get('positive', None) # if None, target will be used
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self.unconditional = kwargs.get('unconditional', "") # default is ""
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self.neutral = kwargs.get('neutral', None) # if None, unconditional will be used
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self.action: ACTION_TYPES = kwargs.get('action', "erase") # default is "erase"
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self.guidance_scale: float = kwargs.get('guidance_scale', 1.0) # default is 1.0
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self.resolution: int = kwargs.get('resolution', 512) # default is 512
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self.dynamic_resolution: bool = kwargs.get('dynamic_resolution', False) # default is False
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self.batch_size: int = kwargs.get('batch_size', 1) # default is 1
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self.dynamic_crops: bool = kwargs.get('dynamic_crops', False) # default is False. only used when model is XL
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class TrainSliderProcess(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', {}))
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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.sd = None
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self.prompt_settings = self.get_prompt_settings()
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# added later
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self.network = None
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self.scheduler = None
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self.is_flipped = False
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def flip_network(self):
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for param in self.network.parameters():
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# apply opposite weight to the network
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param.data = -param.data
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self.is_flipped = not self.is_flipped
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def get_prompt_settings(self):
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prompts = self.get_conf('prompts', required=True)
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prompt_settings = [PromptSettingsOld(**prompt) for prompt in prompts]
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# for i, prompt in enumerate(prompts):
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# prompt_settings[i].fill_prompts(prompt)
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return prompt_settings
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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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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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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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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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prompt = raw_prompt
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neg = self.sample_config.neg
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p_split = raw_prompt.split('--n')
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if len(p_split) > 1:
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prompt = p_split[0].strip()
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neg = p_split[1].strip()
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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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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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self.network.train()
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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
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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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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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self.print(f"Saved to {file_path}")
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def run(self):
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super().run()
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dtype = get_torch_dtype(self.train_config.dtype)
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modules = DEFAULT_TARGET_REPLACE
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loss = None
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if self.network_config.type == "c3lier":
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modules += UNET_TARGET_REPLACE_MODULE_CONV
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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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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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optimizer_type = self.train_config.optimizer.lower()
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# we call it something different than leco
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if optimizer_type == "dadaptation":
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optimizer_type = "dadaptadam"
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optimizer_module = train_util.get_optimizer(optimizer_type)
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optimizer = optimizer_module(
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self.network.prepare_optimizer_params(
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self.train_config.lr, self.train_config.lr, self.train_config.lr
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),
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lr=self.train_config.lr
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)
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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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criteria = torch.nn.MSELoss()
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if self.logging_config.verbose:
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print("Prompts")
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for settings in self.prompt_settings:
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print(settings)
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# debug
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# debug_util.check_requires_grad(network)
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# debug_util.check_training_mode(network)
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cache = PromptEmbedsCache()
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prompt_pairs: list[PromptEmbedsPair] = []
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with torch.no_grad():
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for settings in self.prompt_settings:
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self.print(settings)
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for prompt in [
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settings.target,
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settings.positive,
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settings.neutral,
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settings.unconditional,
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]:
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if cache[prompt] == None:
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cache[prompt] = train_util.encode_prompts(
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tokenizer, text_encoder, [prompt]
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)
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prompt_pairs.append(
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PromptEmbedsPair(
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criteria,
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cache[settings.target],
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cache[settings.positive],
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cache[settings.unconditional],
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cache[settings.neutral],
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settings,
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)
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)
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# move to cpu to save vram
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# tokenizer.to("cpu")
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text_encoder.to("cpu")
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flush()
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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(range(self.train_config.steps))
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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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with torch.no_grad():
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noise_scheduler.set_timesteps(
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self.train_config.max_denoising_steps, device=self.device_torch
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)
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optimizer.zero_grad()
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prompt_pair: PromptEmbedsPair = prompt_pairs[
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torch.randint(0, len(prompt_pairs), (1,)).item()
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]
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# 1 ~ 49 random from 1 to 49
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timesteps_to = torch.randint(
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1, self.train_config.max_denoising_steps, (1,)
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).item()
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height, width = (
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prompt_pair.resolution,
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prompt_pair.resolution,
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)
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if prompt_pair.dynamic_resolution:
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height, width = train_util.get_random_resolution_in_bucket(
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prompt_pair.resolution
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)
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if self.logging_config.verbose:
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self.print("guidance_scale:", prompt_pair.guidance_scale)
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self.print("resolution:", prompt_pair.resolution)
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self.print("dynamic_resolution:", prompt_pair.dynamic_resolution)
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if prompt_pair.dynamic_resolution:
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self.print("bucketed resolution:", (height, width))
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self.print("batch_size:", prompt_pair.batch_size)
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latents = train_util.get_initial_latents(
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noise_scheduler, prompt_pair.batch_size, height, width, 1
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).to(self.device_torch, dtype=dtype)
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|
||||
with self.network:
|
||||
assert self.network.is_active
|
||||
# A little denoised one is returned
|
||||
denoised_latents = train_util.diffusion(
|
||||
unet,
|
||||
noise_scheduler,
|
||||
latents, # pass simple noise latents
|
||||
train_util.concat_embeddings(
|
||||
prompt_pair.unconditional,
|
||||
prompt_pair.target,
|
||||
prompt_pair.batch_size,
|
||||
),
|
||||
start_timesteps=0,
|
||||
total_timesteps=timesteps_to,
|
||||
guidance_scale=3,
|
||||
)
|
||||
|
||||
noise_scheduler.set_timesteps(1000)
|
||||
|
||||
current_timestep = noise_scheduler.timesteps[
|
||||
int(timesteps_to * 1000 / self.train_config.max_denoising_steps)
|
||||
]
|
||||
|
||||
# with network: Only empty LoRA is enabled outside with network :
|
||||
positive_latents = train_util.predict_noise(
|
||||
unet,
|
||||
noise_scheduler,
|
||||
current_timestep,
|
||||
denoised_latents,
|
||||
train_util.concat_embeddings(
|
||||
prompt_pair.unconditional,
|
||||
prompt_pair.positive,
|
||||
prompt_pair.batch_size,
|
||||
),
|
||||
guidance_scale=1,
|
||||
).to("cpu", dtype=torch.float32)
|
||||
neutral_latents = train_util.predict_noise(
|
||||
unet,
|
||||
noise_scheduler,
|
||||
current_timestep,
|
||||
denoised_latents,
|
||||
train_util.concat_embeddings(
|
||||
prompt_pair.unconditional,
|
||||
prompt_pair.neutral,
|
||||
prompt_pair.batch_size,
|
||||
),
|
||||
guidance_scale=1,
|
||||
).to("cpu", dtype=torch.float32)
|
||||
unconditional_latents = train_util.predict_noise(
|
||||
unet,
|
||||
noise_scheduler,
|
||||
current_timestep,
|
||||
denoised_latents,
|
||||
train_util.concat_embeddings(
|
||||
prompt_pair.unconditional,
|
||||
prompt_pair.unconditional,
|
||||
prompt_pair.batch_size,
|
||||
),
|
||||
guidance_scale=1,
|
||||
).to("cpu", dtype=torch.float32)
|
||||
# if self.logging_config.verbose:
|
||||
# self.print("positive_latents:", positive_latents[0, 0, :5, :5])
|
||||
# self.print("neutral_latents:", neutral_latents[0, 0, :5, :5])
|
||||
# self.print("unconditional_latents:", unconditional_latents[0, 0, :5, :5])
|
||||
|
||||
with self.network:
|
||||
target_latents = train_util.predict_noise(
|
||||
unet,
|
||||
noise_scheduler,
|
||||
current_timestep,
|
||||
denoised_latents,
|
||||
train_util.concat_embeddings(
|
||||
prompt_pair.unconditional,
|
||||
prompt_pair.target,
|
||||
prompt_pair.batch_size,
|
||||
),
|
||||
guidance_scale=1,
|
||||
).to("cpu", dtype=torch.float32)
|
||||
|
||||
# if self.logging_config.verbose:
|
||||
# self.print("target_latents:", target_latents[0, 0, :5, :5])
|
||||
|
||||
positive_latents.requires_grad = False
|
||||
neutral_latents.requires_grad = False
|
||||
unconditional_latents.requires_grad = False
|
||||
|
||||
loss = prompt_pair.loss(
|
||||
target_latents=target_latents,
|
||||
positive_latents=positive_latents,
|
||||
neutral_latents=neutral_latents,
|
||||
unconditional_latents=unconditional_latents,
|
||||
)
|
||||
loss_float = loss.item()
|
||||
if self.train_config.optimizer.startswith('dadaptation'):
|
||||
learning_rate = (
|
||||
optimizer.param_groups[0]["d"] *
|
||||
optimizer.param_groups[0]["lr"]
|
||||
)
|
||||
else:
|
||||
learning_rate = optimizer.param_groups[0]['lr']
|
||||
|
||||
self.progress_bar.set_postfix_str(f"lr: {learning_rate:.1e} loss: {loss.item():.3e}")
|
||||
|
||||
loss.backward()
|
||||
optimizer.step()
|
||||
lr_scheduler.step()
|
||||
|
||||
del (
|
||||
positive_latents,
|
||||
neutral_latents,
|
||||
unconditional_latents,
|
||||
target_latents,
|
||||
latents,
|
||||
)
|
||||
flush()
|
||||
|
||||
# don't do on first step
|
||||
if self.step_num != self.start_step:
|
||||
# pause progress bar
|
||||
self.progress_bar.unpause() # makes it so doesn't track time
|
||||
if self.sample_config.sample_every and self.step_num % self.sample_config.sample_every == 0:
|
||||
# print above the progress bar
|
||||
self.sample(self.step_num)
|
||||
|
||||
if self.save_config.save_every and self.step_num % self.save_config.save_every == 0:
|
||||
# print above the progress bar
|
||||
self.print(f"Saving at step {self.step_num}")
|
||||
self.save(self.step_num)
|
||||
|
||||
if self.logging_config.log_every and self.step_num % self.logging_config.log_every == 0:
|
||||
# log to tensorboard
|
||||
if self.writer is not None:
|
||||
# get avg loss
|
||||
self.writer.add_scalar(f"loss", loss_float, self.step_num)
|
||||
if self.train_config.optimizer.startswith('dadaptation'):
|
||||
learning_rate = (
|
||||
optimizer.param_groups[0]["d"] *
|
||||
optimizer.param_groups[0]["lr"]
|
||||
)
|
||||
else:
|
||||
learning_rate = optimizer.param_groups[0]['lr']
|
||||
self.writer.add_scalar(f"lr", learning_rate, self.step_num)
|
||||
self.progress_bar.refresh()
|
||||
|
||||
# sets progress bar to match out step
|
||||
self.progress_bar.update(step - self.progress_bar.n)
|
||||
# end of step
|
||||
self.step_num = step
|
||||
|
||||
self.save()
|
||||
|
||||
del (
|
||||
unet,
|
||||
noise_scheduler,
|
||||
loss,
|
||||
optimizer,
|
||||
self.network,
|
||||
tokenizer,
|
||||
text_encoder,
|
||||
)
|
||||
|
||||
flush()
|
||||
Reference in New Issue
Block a user