mirror of
https://github.com/ostris/ai-toolkit.git
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219 lines
8.0 KiB
Python
219 lines
8.0 KiB
Python
import copy
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import random
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from collections import OrderedDict
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import os
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from contextlib import nullcontext
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from typing import Optional, Union, List
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from torch.utils.data import ConcatDataset, DataLoader
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from toolkit.data_loader import PairedImageDataset
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from toolkit.prompt_utils import concat_prompt_embeds
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from toolkit.stable_diffusion_model import StableDiffusion, PromptEmbeds
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from toolkit.train_tools import get_torch_dtype
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import gc
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from toolkit import train_tools
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import torch
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from jobs.process import BaseSDTrainProcess
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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 DatasetConfig:
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def __init__(self, **kwargs):
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self.pair_folder: str = kwargs.get('pair_folder', None)
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self.network_weight: float = kwargs.get('network_weight', 1.0)
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self.target_class: str = kwargs.get('target_class', '')
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self.size: int = kwargs.get('size', 512)
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class ReferenceSliderConfig:
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def __init__(self, **kwargs):
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self.additional_losses: List[str] = kwargs.get('additional_losses', [])
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self.datasets: List[DatasetConfig] = [DatasetConfig(**d) for d in kwargs.get('datasets', [])]
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class ImageReferenceSliderTrainerProcess(BaseSDTrainProcess):
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sd: StableDiffusion
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data_loader: DataLoader = None
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def __init__(self, process_id: int, job, config: OrderedDict, **kwargs):
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super().__init__(process_id, job, config, **kwargs)
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self.prompt_txt_list = None
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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.slider_config = ReferenceSliderConfig(**self.get_conf('slider', {}))
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def load_datasets(self):
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if self.data_loader is None:
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print(f"Loading datasets")
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datasets = []
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for dataset in self.slider_config.datasets:
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print(f" - Dataset: {dataset.pair_folder}")
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config = {
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'path': dataset.pair_folder,
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'size': dataset.size,
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'default_prompt': dataset.target_class,
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'network_weight': dataset.network_weight,
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}
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image_dataset = PairedImageDataset(config)
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datasets.append(image_dataset)
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concatenated_dataset = ConcatDataset(datasets)
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self.data_loader = DataLoader(
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concatenated_dataset,
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batch_size=self.train_config.batch_size,
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shuffle=True,
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num_workers=2
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)
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def before_model_load(self):
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pass
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def hook_before_train_loop(self):
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self.sd.vae.eval()
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self.sd.vae.to(self.device_torch)
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self.load_datasets()
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pass
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def hook_train_loop(self, batch):
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do_mirror_loss = 'mirror' in self.slider_config.additional_losses
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with torch.no_grad():
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imgs, prompts, base_network_weight = batch
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dtype = get_torch_dtype(self.train_config.dtype)
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imgs: torch.Tensor = imgs.to(self.device_torch, dtype=dtype)
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# split batched images in half so left is negative and right is positive
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negative_images, positive_images = torch.chunk(imgs, 2, dim=3)
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positive_latents = self.sd.encode_images(positive_images)
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negative_latents = self.sd.encode_images(negative_images)
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height = positive_images.shape[2]
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width = positive_images.shape[3]
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batch_size = positive_images.shape[0]
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if self.train_config.gradient_checkpointing:
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# may get disabled elsewhere
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self.sd.unet.enable_gradient_checkpointing()
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noise_scheduler = self.sd.noise_scheduler
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optimizer = self.optimizer
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lr_scheduler = self.lr_scheduler
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self.sd.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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timesteps = torch.randint(0, self.train_config.max_denoising_steps, (1,), device=self.device_torch)
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timesteps = timesteps.long()
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# get noise
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noise_positive = self.sd.get_latent_noise(
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pixel_height=height,
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pixel_width=width,
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batch_size=batch_size,
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noise_offset=self.train_config.noise_offset,
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).to(self.device_torch, dtype=dtype)
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if do_mirror_loss:
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# mirror the noise
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# torch shape is [batch, channels, height, width]
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noise_negative = torch.flip(noise_positive.clone(), dims=[3])
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else:
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noise_negative = noise_positive.clone()
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# Add noise to the latents according to the noise magnitude at each timestep
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# (this is the forward diffusion process)
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noisy_positive_latents = noise_scheduler.add_noise(positive_latents, noise_positive, timesteps)
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noisy_negative_latents = noise_scheduler.add_noise(negative_latents, noise_negative, timesteps)
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noisy_latents = torch.cat([noisy_positive_latents, noisy_negative_latents], dim=0)
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noise = torch.cat([noise_positive, noise_negative], dim=0)
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timesteps = torch.cat([timesteps, timesteps], dim=0)
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network_multiplier = [base_network_weight * 1.0, base_network_weight * -1.0]
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flush()
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loss_float = None
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loss_slide_float = None
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loss_mirror_float = None
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self.optimizer.zero_grad()
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noisy_latents.requires_grad = False
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# if training text encoder enable grads, else do context of no grad
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with torch.set_grad_enabled(self.train_config.train_text_encoder):
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# text encoding
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embedding_list = []
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# embed the prompts
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for prompt in prompts:
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embedding = self.sd.encode_prompt(prompt).to(self.device_torch, dtype=dtype)
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embedding_list.append(embedding)
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conditional_embeds = concat_prompt_embeds(embedding_list)
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conditional_embeds = concat_prompt_embeds([conditional_embeds, conditional_embeds])
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with self.network:
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assert self.network.is_active
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self.network.multiplier = network_multiplier
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noise_pred = self.sd.predict_noise(
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latents=noisy_latents,
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conditional_embeddings=conditional_embeds,
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timestep=timesteps,
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)
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if self.sd.prediction_type == 'v_prediction':
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# v-parameterization training
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target = noise_scheduler.get_velocity(noisy_latents, noise, timesteps)
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else:
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target = noise
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loss = torch.nn.functional.mse_loss(noise_pred.float(), target.float(), reduction="none")
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loss = loss.mean([1, 2, 3])
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# todo add snr gamma here
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loss = loss.mean()
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loss_slide_float = loss.item()
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if do_mirror_loss:
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noise_pred_pos, noise_pred_neg = torch.chunk(noise_pred, 2, dim=0)
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# mirror the negative
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noise_pred_neg = torch.flip(noise_pred_neg.clone(), dims=[3])
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loss_mirror = torch.nn.functional.mse_loss(noise_pred_pos.float(), noise_pred_neg.float(),
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reduction="none")
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loss_mirror = loss_mirror.mean([1, 2, 3])
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loss_mirror = loss_mirror.mean()
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loss_mirror_float = loss_mirror.item()
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loss += loss_mirror
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loss_float = loss.item()
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# back propagate loss to free ram
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loss.backward()
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flush()
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# apply gradients
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optimizer.step()
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lr_scheduler.step()
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# reset network
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self.network.multiplier = 1.0
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loss_dict = OrderedDict(
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{'loss': loss_float},
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)
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if do_mirror_loss:
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loss_dict['l/s'] = loss_slide_float
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loss_dict['l/m'] = loss_mirror_float
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return loss_dict
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# end hook_train_loop
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