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1565 lines
76 KiB
Python
1565 lines
76 KiB
Python
import os
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import random
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from collections import OrderedDict
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from typing import Union, Literal, List, Optional
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import numpy as np
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from diffusers import T2IAdapter, AutoencoderTiny, ControlNetModel
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import torch.functional as F
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from safetensors.torch import load_file
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from torch.utils.data import DataLoader, ConcatDataset
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from toolkit import train_tools
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from toolkit.basic import value_map, adain, get_mean_std
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from toolkit.clip_vision_adapter import ClipVisionAdapter
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from toolkit.config_modules import GuidanceConfig
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from toolkit.data_loader import get_dataloader_datasets
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from toolkit.data_transfer_object.data_loader import DataLoaderBatchDTO, FileItemDTO
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from toolkit.guidance import get_targeted_guidance_loss, get_guidance_loss, GuidanceType
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from toolkit.image_utils import show_tensors, show_latents
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from toolkit.ip_adapter import IPAdapter
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from toolkit.custom_adapter import CustomAdapter
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from toolkit.prompt_utils import PromptEmbeds, concat_prompt_embeds
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from toolkit.reference_adapter import ReferenceAdapter
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from toolkit.stable_diffusion_model import StableDiffusion, BlankNetwork
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from toolkit.train_tools import get_torch_dtype, apply_snr_weight, add_all_snr_to_noise_scheduler, \
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apply_learnable_snr_gos, LearnableSNRGamma
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import gc
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import torch
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from jobs.process import BaseSDTrainProcess
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from torchvision import transforms
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from diffusers import EMAModel
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import math
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from toolkit.train_tools import precondition_model_outputs_flow_match
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def flush():
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torch.cuda.empty_cache()
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gc.collect()
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adapter_transforms = transforms.Compose([
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transforms.ToTensor(),
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])
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class SDTrainer(BaseSDTrainProcess):
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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.assistant_adapter: Union['T2IAdapter', 'ControlNetModel', None]
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self.do_prior_prediction = False
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self.do_long_prompts = False
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self.do_guided_loss = False
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self.taesd: Optional[AutoencoderTiny] = None
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self._clip_image_embeds_unconditional: Union[List[str], None] = None
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self.negative_prompt_pool: Union[List[str], None] = None
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self.batch_negative_prompt: Union[List[str], None] = None
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self.scaler = torch.cuda.amp.GradScaler()
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# patch the scaler to allow fp16 training
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org_unscale_grads = self.scaler._unscale_grads_
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def _unscale_grads_replacer(optimizer, inv_scale, found_inf, allow_fp16):
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return org_unscale_grads(optimizer, inv_scale, found_inf, True)
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self.scaler._unscale_grads_ = _unscale_grads_replacer
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self.is_bfloat = self.train_config.dtype == "bfloat16" or self.train_config.dtype == "bf16"
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def before_model_load(self):
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pass
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def before_dataset_load(self):
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self.assistant_adapter = None
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# get adapter assistant if one is set
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if self.train_config.adapter_assist_name_or_path is not None:
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adapter_path = self.train_config.adapter_assist_name_or_path
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if self.train_config.adapter_assist_type == "t2i":
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# dont name this adapter since we are not training it
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self.assistant_adapter = T2IAdapter.from_pretrained(
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adapter_path, torch_dtype=get_torch_dtype(self.train_config.dtype)
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).to(self.device_torch)
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elif self.train_config.adapter_assist_type == "control_net":
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self.assistant_adapter = ControlNetModel.from_pretrained(
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adapter_path, torch_dtype=get_torch_dtype(self.train_config.dtype)
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).to(self.device_torch, dtype=get_torch_dtype(self.train_config.dtype))
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else:
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raise ValueError(f"Unknown adapter assist type {self.train_config.adapter_assist_type}")
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self.assistant_adapter.eval()
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self.assistant_adapter.requires_grad_(False)
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flush()
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if self.train_config.train_turbo and self.train_config.show_turbo_outputs:
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if self.model_config.is_xl:
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self.taesd = AutoencoderTiny.from_pretrained("madebyollin/taesdxl",
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torch_dtype=get_torch_dtype(self.train_config.dtype))
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else:
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self.taesd = AutoencoderTiny.from_pretrained("madebyollin/taesd",
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torch_dtype=get_torch_dtype(self.train_config.dtype))
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self.taesd.to(dtype=get_torch_dtype(self.train_config.dtype), device=self.device_torch)
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self.taesd.eval()
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self.taesd.requires_grad_(False)
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def hook_before_train_loop(self):
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if self.train_config.do_prior_divergence:
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self.do_prior_prediction = True
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# move vae to device if we did not cache latents
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if not self.is_latents_cached:
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self.sd.vae.eval()
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self.sd.vae.to(self.device_torch)
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else:
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# offload it. Already cached
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self.sd.vae.to('cpu')
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flush()
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add_all_snr_to_noise_scheduler(self.sd.noise_scheduler, self.device_torch)
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if self.adapter is not None:
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self.adapter.to(self.device_torch)
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# check if we have regs and using adapter and caching clip embeddings
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has_reg = self.datasets_reg is not None and len(self.datasets_reg) > 0
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is_caching_clip_embeddings = self.datasets is not None and any([self.datasets[i].cache_clip_vision_to_disk for i in range(len(self.datasets))])
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if has_reg and is_caching_clip_embeddings:
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# we need a list of unconditional clip image embeds from other datasets to handle regs
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unconditional_clip_image_embeds = []
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datasets = get_dataloader_datasets(self.data_loader)
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for i in range(len(datasets)):
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unconditional_clip_image_embeds += datasets[i].clip_vision_unconditional_cache
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if len(unconditional_clip_image_embeds) == 0:
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raise ValueError("No unconditional clip image embeds found. This should not happen")
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self._clip_image_embeds_unconditional = unconditional_clip_image_embeds
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if self.train_config.negative_prompt is not None:
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if os.path.exists(self.train_config.negative_prompt):
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with open(self.train_config.negative_prompt, 'r') as f:
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self.negative_prompt_pool = f.readlines()
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# remove empty
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self.negative_prompt_pool = [x.strip() for x in self.negative_prompt_pool if x.strip() != ""]
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else:
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# single prompt
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self.negative_prompt_pool = [self.train_config.negative_prompt]
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def process_output_for_turbo(self, pred, noisy_latents, timesteps, noise, batch):
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# to process turbo learning, we make one big step from our current timestep to the end
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# we then denoise the prediction on that remaining step and target our loss to our target latents
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# this currently only works on euler_a (that I know of). Would work on others, but needs to be coded to do so.
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# needs to be done on each item in batch as they may all have different timesteps
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batch_size = pred.shape[0]
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pred_chunks = torch.chunk(pred, batch_size, dim=0)
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noisy_latents_chunks = torch.chunk(noisy_latents, batch_size, dim=0)
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timesteps_chunks = torch.chunk(timesteps, batch_size, dim=0)
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latent_chunks = torch.chunk(batch.latents, batch_size, dim=0)
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noise_chunks = torch.chunk(noise, batch_size, dim=0)
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with torch.no_grad():
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# set the timesteps to 1000 so we can capture them to calculate the sigmas
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self.sd.noise_scheduler.set_timesteps(
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self.sd.noise_scheduler.config.num_train_timesteps,
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device=self.device_torch
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)
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train_timesteps = self.sd.noise_scheduler.timesteps.clone().detach()
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train_sigmas = self.sd.noise_scheduler.sigmas.clone().detach()
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# set the scheduler to one timestep, we build the step and sigmas for each item in batch for the partial step
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self.sd.noise_scheduler.set_timesteps(
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1,
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device=self.device_torch
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)
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denoised_pred_chunks = []
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target_pred_chunks = []
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for i in range(batch_size):
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pred_item = pred_chunks[i]
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noisy_latents_item = noisy_latents_chunks[i]
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timesteps_item = timesteps_chunks[i]
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latents_item = latent_chunks[i]
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noise_item = noise_chunks[i]
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with torch.no_grad():
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timestep_idx = [(train_timesteps == t).nonzero().item() for t in timesteps_item][0]
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single_step_timestep_schedule = [timesteps_item.squeeze().item()]
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# extract the sigma idx for our midpoint timestep
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sigmas = train_sigmas[timestep_idx:timestep_idx + 1].to(self.device_torch)
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end_sigma_idx = random.randint(timestep_idx, len(train_sigmas) - 1)
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end_sigma = train_sigmas[end_sigma_idx:end_sigma_idx + 1].to(self.device_torch)
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# add noise to our target
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# build the big sigma step. The to step will now be to 0 giving it a full remaining denoising half step
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# self.sd.noise_scheduler.sigmas = torch.cat([sigmas, torch.zeros_like(sigmas)]).detach()
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self.sd.noise_scheduler.sigmas = torch.cat([sigmas, end_sigma]).detach()
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# set our single timstep
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self.sd.noise_scheduler.timesteps = torch.from_numpy(
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np.array(single_step_timestep_schedule, dtype=np.float32)
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).to(device=self.device_torch)
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# set the step index to None so it will be recalculated on first step
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self.sd.noise_scheduler._step_index = None
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denoised_latent = self.sd.noise_scheduler.step(
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pred_item, timesteps_item, noisy_latents_item.detach(), return_dict=False
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)[0]
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residual_noise = (noise_item * end_sigma.flatten()).detach().to(self.device_torch, dtype=get_torch_dtype(
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self.train_config.dtype))
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# remove the residual noise from the denoised latents. Output should be a clean prediction (theoretically)
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denoised_latent = denoised_latent - residual_noise
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denoised_pred_chunks.append(denoised_latent)
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denoised_latents = torch.cat(denoised_pred_chunks, dim=0)
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# set the scheduler back to the original timesteps
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self.sd.noise_scheduler.set_timesteps(
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self.sd.noise_scheduler.config.num_train_timesteps,
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device=self.device_torch
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)
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output = denoised_latents / self.sd.vae.config['scaling_factor']
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output = self.sd.vae.decode(output).sample
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if self.train_config.show_turbo_outputs:
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# since we are completely denoising, we can show them here
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with torch.no_grad():
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show_tensors(output)
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# we return our big partial step denoised latents as our pred and our untouched latents as our target.
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# you can do mse against the two here or run the denoised through the vae for pixel space loss against the
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# input tensor images.
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return output, batch.tensor.to(self.device_torch, dtype=get_torch_dtype(self.train_config.dtype))
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# you can expand these in a child class to make customization easier
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def calculate_loss(
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self,
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noise_pred: torch.Tensor,
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noise: torch.Tensor,
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noisy_latents: torch.Tensor,
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timesteps: torch.Tensor,
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batch: 'DataLoaderBatchDTO',
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mask_multiplier: Union[torch.Tensor, float] = 1.0,
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prior_pred: Union[torch.Tensor, None] = None,
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**kwargs
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):
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loss_target = self.train_config.loss_target
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is_reg = any(batch.get_is_reg_list())
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prior_mask_multiplier = None
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target_mask_multiplier = None
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dtype = get_torch_dtype(self.train_config.dtype)
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has_mask = batch.mask_tensor is not None
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with torch.no_grad():
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loss_multiplier = torch.tensor(batch.loss_multiplier_list).to(self.device_torch, dtype=torch.float32)
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if self.train_config.match_noise_norm:
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# match the norm of the noise
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noise_norm = torch.linalg.vector_norm(noise, ord=2, dim=(1, 2, 3), keepdim=True)
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noise_pred_norm = torch.linalg.vector_norm(noise_pred, ord=2, dim=(1, 2, 3), keepdim=True)
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noise_pred = noise_pred * (noise_norm / noise_pred_norm)
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if self.train_config.pred_scaler != 1.0:
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noise_pred = noise_pred * self.train_config.pred_scaler
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target = None
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if self.train_config.target_noise_multiplier != 1.0:
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noise = noise * self.train_config.target_noise_multiplier
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if self.train_config.correct_pred_norm or (self.train_config.inverted_mask_prior and prior_pred is not None and has_mask):
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if self.train_config.correct_pred_norm and not is_reg:
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with torch.no_grad():
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# this only works if doing a prior pred
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if prior_pred is not None:
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prior_mean = prior_pred.mean([2,3], keepdim=True)
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prior_std = prior_pred.std([2,3], keepdim=True)
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noise_mean = noise_pred.mean([2,3], keepdim=True)
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noise_std = noise_pred.std([2,3], keepdim=True)
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mean_adjust = prior_mean - noise_mean
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std_adjust = prior_std - noise_std
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mean_adjust = mean_adjust * self.train_config.correct_pred_norm_multiplier
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std_adjust = std_adjust * self.train_config.correct_pred_norm_multiplier
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target_mean = noise_mean + mean_adjust
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target_std = noise_std + std_adjust
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eps = 1e-5
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# match the noise to the prior
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noise = (noise - noise_mean) / (noise_std + eps)
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noise = noise * (target_std + eps) + target_mean
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noise = noise.detach()
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if self.train_config.inverted_mask_prior and prior_pred is not None and has_mask:
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assert not self.train_config.train_turbo
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with torch.no_grad():
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# we need to make the noise prediction be a masked blending of noise and prior_pred
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stretched_mask_multiplier = value_map(
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mask_multiplier,
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batch.file_items[0].dataset_config.mask_min_value,
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1.0,
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0.0,
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1.0
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)
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prior_mask_multiplier = 1.0 - stretched_mask_multiplier
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# target_mask_multiplier = mask_multiplier
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# mask_multiplier = 1.0
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target = noise
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# target = (noise * mask_multiplier) + (prior_pred * prior_mask_multiplier)
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# set masked multiplier to 1.0 so we dont double apply it
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# mask_multiplier = 1.0
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elif prior_pred is not None and not self.train_config.do_prior_divergence:
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assert not self.train_config.train_turbo
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# matching adapter prediction
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target = prior_pred
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elif self.sd.prediction_type == 'v_prediction':
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# v-parameterization training
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target = self.sd.noise_scheduler.get_velocity(batch.tensor, noise, timesteps)
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elif self.sd.is_flow_matching:
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target = (noise - batch.latents).detach()
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else:
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target = noise
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if target is None:
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target = noise
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pred = noise_pred
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if self.train_config.train_turbo:
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pred, target = self.process_output_for_turbo(pred, noisy_latents, timesteps, noise, batch)
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ignore_snr = False
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if loss_target == 'source' or loss_target == 'unaugmented':
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assert not self.train_config.train_turbo
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# ignore_snr = True
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if batch.sigmas is None:
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raise ValueError("Batch sigmas is None. This should not happen")
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# src https://github.com/huggingface/diffusers/blob/324d18fba23f6c9d7475b0ff7c777685f7128d40/examples/t2i_adapter/train_t2i_adapter_sdxl.py#L1190
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denoised_latents = noise_pred * (-batch.sigmas) + noisy_latents
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weighing = batch.sigmas ** -2.0
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if loss_target == 'source':
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# denoise the latent and compare to the latent in the batch
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target = batch.latents
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elif loss_target == 'unaugmented':
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# we have to encode images into latents for now
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# we also denoise as the unaugmented tensor is not a noisy diffirental
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with torch.no_grad():
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unaugmented_latents = self.sd.encode_images(batch.unaugmented_tensor).to(self.device_torch, dtype=dtype)
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unaugmented_latents = unaugmented_latents * self.train_config.latent_multiplier
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target = unaugmented_latents.detach()
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# Get the target for loss depending on the prediction type
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if self.sd.noise_scheduler.config.prediction_type == "epsilon":
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target = target # we are computing loss against denoise latents
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elif self.sd.noise_scheduler.config.prediction_type == "v_prediction":
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target = self.sd.noise_scheduler.get_velocity(target, noise, timesteps)
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else:
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raise ValueError(f"Unknown prediction type {self.sd.noise_scheduler.config.prediction_type}")
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# mse loss without reduction
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loss_per_element = (weighing.float() * (denoised_latents.float() - target.float()) ** 2)
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loss = loss_per_element
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else:
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if self.train_config.loss_type == "mae":
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loss = torch.nn.functional.l1_loss(pred.float(), target.float(), reduction="none")
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else:
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loss = torch.nn.functional.mse_loss(pred.float(), target.float(), reduction="none")
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# handle linear timesteps and only adjust the weight of the timesteps
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if self.sd.is_flow_matching and self.train_config.linear_timesteps:
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# calculate the weights for the timesteps
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timestep_weight = self.sd.noise_scheduler.get_weights_for_timesteps(timesteps).to(loss.device, dtype=loss.dtype)
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loss = loss * timestep_weight
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if self.train_config.do_prior_divergence and prior_pred is not None:
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loss = loss + (torch.nn.functional.mse_loss(pred.float(), prior_pred.float(), reduction="none") * -1.0)
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if self.train_config.train_turbo:
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mask_multiplier = mask_multiplier[:, 3:, :, :]
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# resize to the size of the loss
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mask_multiplier = torch.nn.functional.interpolate(mask_multiplier, size=(pred.shape[2], pred.shape[3]), mode='nearest')
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# multiply by our mask
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loss = loss * mask_multiplier
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prior_loss = None
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if self.train_config.inverted_mask_prior and prior_pred is not None and prior_mask_multiplier is not None:
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assert not self.train_config.train_turbo
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if self.train_config.loss_type == "mae":
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prior_loss = torch.nn.functional.l1_loss(pred.float(), prior_pred.float(), reduction="none")
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else:
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prior_loss = torch.nn.functional.mse_loss(pred.float(), prior_pred.float(), reduction="none")
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|
|
prior_loss = prior_loss * prior_mask_multiplier * self.train_config.inverted_mask_prior_multiplier
|
|
if torch.isnan(prior_loss).any():
|
|
print("Prior loss is nan")
|
|
prior_loss = None
|
|
else:
|
|
prior_loss = prior_loss.mean([1, 2, 3])
|
|
# loss = loss + prior_loss
|
|
# loss = loss + prior_loss
|
|
# loss = loss + prior_loss
|
|
loss = loss.mean([1, 2, 3])
|
|
# apply loss multiplier before prior loss
|
|
loss = loss * loss_multiplier
|
|
if prior_loss is not None:
|
|
loss = loss + prior_loss
|
|
|
|
if not self.train_config.train_turbo:
|
|
if self.train_config.learnable_snr_gos:
|
|
# add snr_gamma
|
|
loss = apply_learnable_snr_gos(loss, timesteps, self.snr_gos)
|
|
elif self.train_config.snr_gamma is not None and self.train_config.snr_gamma > 0.000001 and not ignore_snr:
|
|
# add snr_gamma
|
|
loss = apply_snr_weight(loss, timesteps, self.sd.noise_scheduler, self.train_config.snr_gamma,
|
|
fixed=True)
|
|
elif self.train_config.min_snr_gamma is not None and self.train_config.min_snr_gamma > 0.000001 and not ignore_snr:
|
|
# add min_snr_gamma
|
|
loss = apply_snr_weight(loss, timesteps, self.sd.noise_scheduler, self.train_config.min_snr_gamma)
|
|
|
|
loss = loss.mean()
|
|
|
|
# check for additional losses
|
|
if self.adapter is not None and hasattr(self.adapter, "additional_loss") and self.adapter.additional_loss is not None:
|
|
|
|
loss = loss + self.adapter.additional_loss.mean()
|
|
self.adapter.additional_loss = None
|
|
|
|
if self.train_config.target_norm_std:
|
|
# seperate out the batch and channels
|
|
pred_std = noise_pred.std([2, 3], keepdim=True)
|
|
norm_std_loss = torch.abs(self.train_config.target_norm_std_value - pred_std).mean()
|
|
loss = loss + norm_std_loss
|
|
|
|
|
|
return loss
|
|
|
|
def preprocess_batch(self, batch: 'DataLoaderBatchDTO'):
|
|
return batch
|
|
|
|
def get_guided_loss(
|
|
self,
|
|
noisy_latents: torch.Tensor,
|
|
conditional_embeds: PromptEmbeds,
|
|
match_adapter_assist: bool,
|
|
network_weight_list: list,
|
|
timesteps: torch.Tensor,
|
|
pred_kwargs: dict,
|
|
batch: 'DataLoaderBatchDTO',
|
|
noise: torch.Tensor,
|
|
unconditional_embeds: Optional[PromptEmbeds] = None,
|
|
**kwargs
|
|
):
|
|
loss = get_guidance_loss(
|
|
noisy_latents=noisy_latents,
|
|
conditional_embeds=conditional_embeds,
|
|
match_adapter_assist=match_adapter_assist,
|
|
network_weight_list=network_weight_list,
|
|
timesteps=timesteps,
|
|
pred_kwargs=pred_kwargs,
|
|
batch=batch,
|
|
noise=noise,
|
|
sd=self.sd,
|
|
unconditional_embeds=unconditional_embeds,
|
|
scaler=self.scaler,
|
|
**kwargs
|
|
)
|
|
|
|
return loss
|
|
|
|
def get_guided_loss_targeted_polarity(
|
|
self,
|
|
noisy_latents: torch.Tensor,
|
|
conditional_embeds: PromptEmbeds,
|
|
match_adapter_assist: bool,
|
|
network_weight_list: list,
|
|
timesteps: torch.Tensor,
|
|
pred_kwargs: dict,
|
|
batch: 'DataLoaderBatchDTO',
|
|
noise: torch.Tensor,
|
|
**kwargs
|
|
):
|
|
with torch.no_grad():
|
|
# Perform targeted guidance (working title)
|
|
dtype = get_torch_dtype(self.train_config.dtype)
|
|
|
|
conditional_latents = batch.latents.to(self.device_torch, dtype=dtype).detach()
|
|
unconditional_latents = batch.unconditional_latents.to(self.device_torch, dtype=dtype).detach()
|
|
|
|
mean_latents = (conditional_latents + unconditional_latents) / 2.0
|
|
|
|
unconditional_diff = (unconditional_latents - mean_latents)
|
|
conditional_diff = (conditional_latents - mean_latents)
|
|
|
|
# we need to determine the amount of signal and noise that would be present at the current timestep
|
|
# conditional_signal = self.sd.add_noise(conditional_diff, torch.zeros_like(noise), timesteps)
|
|
# unconditional_signal = self.sd.add_noise(torch.zeros_like(noise), unconditional_diff, timesteps)
|
|
# unconditional_signal = self.sd.add_noise(unconditional_diff, torch.zeros_like(noise), timesteps)
|
|
# conditional_blend = self.sd.add_noise(conditional_latents, unconditional_latents, timesteps)
|
|
# unconditional_blend = self.sd.add_noise(unconditional_latents, conditional_latents, timesteps)
|
|
|
|
# target_noise = noise + unconditional_signal
|
|
|
|
conditional_noisy_latents = self.sd.add_noise(
|
|
mean_latents,
|
|
noise,
|
|
timesteps
|
|
).detach()
|
|
|
|
unconditional_noisy_latents = self.sd.add_noise(
|
|
mean_latents,
|
|
noise,
|
|
timesteps
|
|
).detach()
|
|
|
|
# Disable the LoRA network so we can predict parent network knowledge without it
|
|
self.network.is_active = False
|
|
self.sd.unet.eval()
|
|
|
|
# Predict noise to get a baseline of what the parent network wants to do with the latents + noise.
|
|
# This acts as our control to preserve the unaltered parts of the image.
|
|
baseline_prediction = self.sd.predict_noise(
|
|
latents=unconditional_noisy_latents.to(self.device_torch, dtype=dtype).detach(),
|
|
conditional_embeddings=conditional_embeds.to(self.device_torch, dtype=dtype).detach(),
|
|
timestep=timesteps,
|
|
guidance_scale=1.0,
|
|
**pred_kwargs # adapter residuals in here
|
|
).detach()
|
|
|
|
# double up everything to run it through all at once
|
|
cat_embeds = concat_prompt_embeds([conditional_embeds, conditional_embeds])
|
|
cat_latents = torch.cat([conditional_noisy_latents, conditional_noisy_latents], dim=0)
|
|
cat_timesteps = torch.cat([timesteps, timesteps], dim=0)
|
|
|
|
# since we are dividing the polarity from the middle out, we need to double our network
|
|
# weights on training since the convergent point will be at half network strength
|
|
|
|
negative_network_weights = [weight * -2.0 for weight in network_weight_list]
|
|
positive_network_weights = [weight * 2.0 for weight in network_weight_list]
|
|
cat_network_weight_list = positive_network_weights + negative_network_weights
|
|
|
|
# turn the LoRA network back on.
|
|
self.sd.unet.train()
|
|
self.network.is_active = True
|
|
|
|
self.network.multiplier = cat_network_weight_list
|
|
|
|
# do our prediction with LoRA active on the scaled guidance latents
|
|
prediction = self.sd.predict_noise(
|
|
latents=cat_latents.to(self.device_torch, dtype=dtype).detach(),
|
|
conditional_embeddings=cat_embeds.to(self.device_torch, dtype=dtype).detach(),
|
|
timestep=cat_timesteps,
|
|
guidance_scale=1.0,
|
|
**pred_kwargs # adapter residuals in here
|
|
)
|
|
|
|
pred_pos, pred_neg = torch.chunk(prediction, 2, dim=0)
|
|
|
|
pred_pos = pred_pos - baseline_prediction
|
|
pred_neg = pred_neg - baseline_prediction
|
|
|
|
pred_loss = torch.nn.functional.mse_loss(
|
|
pred_pos.float(),
|
|
unconditional_diff.float(),
|
|
reduction="none"
|
|
)
|
|
pred_loss = pred_loss.mean([1, 2, 3])
|
|
|
|
pred_neg_loss = torch.nn.functional.mse_loss(
|
|
pred_neg.float(),
|
|
conditional_diff.float(),
|
|
reduction="none"
|
|
)
|
|
pred_neg_loss = pred_neg_loss.mean([1, 2, 3])
|
|
|
|
loss = (pred_loss + pred_neg_loss) / 2.0
|
|
|
|
# loss = self.apply_snr(loss, timesteps)
|
|
loss = loss.mean()
|
|
loss.backward()
|
|
|
|
# detach it so parent class can run backward on no grads without throwing error
|
|
loss = loss.detach()
|
|
loss.requires_grad_(True)
|
|
|
|
return loss
|
|
|
|
def get_guided_loss_masked_polarity(
|
|
self,
|
|
noisy_latents: torch.Tensor,
|
|
conditional_embeds: PromptEmbeds,
|
|
match_adapter_assist: bool,
|
|
network_weight_list: list,
|
|
timesteps: torch.Tensor,
|
|
pred_kwargs: dict,
|
|
batch: 'DataLoaderBatchDTO',
|
|
noise: torch.Tensor,
|
|
**kwargs
|
|
):
|
|
with torch.no_grad():
|
|
# Perform targeted guidance (working title)
|
|
dtype = get_torch_dtype(self.train_config.dtype)
|
|
|
|
conditional_latents = batch.latents.to(self.device_torch, dtype=dtype).detach()
|
|
unconditional_latents = batch.unconditional_latents.to(self.device_torch, dtype=dtype).detach()
|
|
inverse_latents = unconditional_latents - (conditional_latents - unconditional_latents)
|
|
|
|
mean_latents = (conditional_latents + unconditional_latents) / 2.0
|
|
|
|
# unconditional_diff = (unconditional_latents - mean_latents)
|
|
# conditional_diff = (conditional_latents - mean_latents)
|
|
|
|
# we need to determine the amount of signal and noise that would be present at the current timestep
|
|
# conditional_signal = self.sd.add_noise(conditional_diff, torch.zeros_like(noise), timesteps)
|
|
# unconditional_signal = self.sd.add_noise(torch.zeros_like(noise), unconditional_diff, timesteps)
|
|
# unconditional_signal = self.sd.add_noise(unconditional_diff, torch.zeros_like(noise), timesteps)
|
|
# conditional_blend = self.sd.add_noise(conditional_latents, unconditional_latents, timesteps)
|
|
# unconditional_blend = self.sd.add_noise(unconditional_latents, conditional_latents, timesteps)
|
|
|
|
# make a differential mask
|
|
differential_mask = torch.abs(conditional_latents - unconditional_latents)
|
|
max_differential = \
|
|
differential_mask.max(dim=1, keepdim=True)[0].max(dim=2, keepdim=True)[0].max(dim=3, keepdim=True)[0]
|
|
differential_scaler = 1.0 / max_differential
|
|
differential_mask = differential_mask * differential_scaler
|
|
spread_point = 0.1
|
|
# adjust mask to amplify the differential at 0.1
|
|
differential_mask = ((differential_mask - spread_point) * 10.0) + spread_point
|
|
# clip it
|
|
differential_mask = torch.clamp(differential_mask, 0.0, 1.0)
|
|
|
|
# target_noise = noise + unconditional_signal
|
|
|
|
conditional_noisy_latents = self.sd.add_noise(
|
|
conditional_latents,
|
|
noise,
|
|
timesteps
|
|
).detach()
|
|
|
|
unconditional_noisy_latents = self.sd.add_noise(
|
|
unconditional_latents,
|
|
noise,
|
|
timesteps
|
|
).detach()
|
|
|
|
inverse_noisy_latents = self.sd.add_noise(
|
|
inverse_latents,
|
|
noise,
|
|
timesteps
|
|
).detach()
|
|
|
|
# Disable the LoRA network so we can predict parent network knowledge without it
|
|
self.network.is_active = False
|
|
self.sd.unet.eval()
|
|
|
|
# Predict noise to get a baseline of what the parent network wants to do with the latents + noise.
|
|
# This acts as our control to preserve the unaltered parts of the image.
|
|
# baseline_prediction = self.sd.predict_noise(
|
|
# latents=unconditional_noisy_latents.to(self.device_torch, dtype=dtype).detach(),
|
|
# conditional_embeddings=conditional_embeds.to(self.device_torch, dtype=dtype).detach(),
|
|
# timestep=timesteps,
|
|
# guidance_scale=1.0,
|
|
# **pred_kwargs # adapter residuals in here
|
|
# ).detach()
|
|
|
|
# double up everything to run it through all at once
|
|
cat_embeds = concat_prompt_embeds([conditional_embeds, conditional_embeds])
|
|
cat_latents = torch.cat([conditional_noisy_latents, unconditional_noisy_latents], dim=0)
|
|
cat_timesteps = torch.cat([timesteps, timesteps], dim=0)
|
|
|
|
# since we are dividing the polarity from the middle out, we need to double our network
|
|
# weights on training since the convergent point will be at half network strength
|
|
|
|
negative_network_weights = [weight * -1.0 for weight in network_weight_list]
|
|
positive_network_weights = [weight * 1.0 for weight in network_weight_list]
|
|
cat_network_weight_list = positive_network_weights + negative_network_weights
|
|
|
|
# turn the LoRA network back on.
|
|
self.sd.unet.train()
|
|
self.network.is_active = True
|
|
|
|
self.network.multiplier = cat_network_weight_list
|
|
|
|
# do our prediction with LoRA active on the scaled guidance latents
|
|
prediction = self.sd.predict_noise(
|
|
latents=cat_latents.to(self.device_torch, dtype=dtype).detach(),
|
|
conditional_embeddings=cat_embeds.to(self.device_torch, dtype=dtype).detach(),
|
|
timestep=cat_timesteps,
|
|
guidance_scale=1.0,
|
|
**pred_kwargs # adapter residuals in here
|
|
)
|
|
|
|
pred_pos, pred_neg = torch.chunk(prediction, 2, dim=0)
|
|
|
|
# create a loss to balance the mean to 0 between the two predictions
|
|
differential_mean_pred_loss = torch.abs(pred_pos - pred_neg).mean([1, 2, 3]) ** 2.0
|
|
|
|
# pred_pos = pred_pos - baseline_prediction
|
|
# pred_neg = pred_neg - baseline_prediction
|
|
|
|
pred_loss = torch.nn.functional.mse_loss(
|
|
pred_pos.float(),
|
|
noise.float(),
|
|
reduction="none"
|
|
)
|
|
# apply mask
|
|
pred_loss = pred_loss * (1.0 + differential_mask)
|
|
pred_loss = pred_loss.mean([1, 2, 3])
|
|
|
|
pred_neg_loss = torch.nn.functional.mse_loss(
|
|
pred_neg.float(),
|
|
noise.float(),
|
|
reduction="none"
|
|
)
|
|
# apply inverse mask
|
|
pred_neg_loss = pred_neg_loss * (1.0 - differential_mask)
|
|
pred_neg_loss = pred_neg_loss.mean([1, 2, 3])
|
|
|
|
# make a loss to balance to losses of the pos and neg so they are equal
|
|
# differential_mean_loss_loss = torch.abs(pred_loss - pred_neg_loss)
|
|
#
|
|
# differential_mean_loss = differential_mean_pred_loss + differential_mean_loss_loss
|
|
#
|
|
# # add a multiplier to balancing losses to make them the top priority
|
|
# differential_mean_loss = differential_mean_loss
|
|
|
|
# remove the grads from the negative as it is only a balancing loss
|
|
# pred_neg_loss = pred_neg_loss.detach()
|
|
|
|
# loss = pred_loss + pred_neg_loss + differential_mean_loss
|
|
loss = pred_loss + pred_neg_loss
|
|
|
|
# loss = self.apply_snr(loss, timesteps)
|
|
loss = loss.mean()
|
|
loss.backward()
|
|
|
|
# detach it so parent class can run backward on no grads without throwing error
|
|
loss = loss.detach()
|
|
loss.requires_grad_(True)
|
|
|
|
return loss
|
|
|
|
def get_prior_prediction(
|
|
self,
|
|
noisy_latents: torch.Tensor,
|
|
conditional_embeds: PromptEmbeds,
|
|
match_adapter_assist: bool,
|
|
network_weight_list: list,
|
|
timesteps: torch.Tensor,
|
|
pred_kwargs: dict,
|
|
batch: 'DataLoaderBatchDTO',
|
|
noise: torch.Tensor,
|
|
unconditional_embeds: Optional[PromptEmbeds] = None,
|
|
conditioned_prompts=None,
|
|
**kwargs
|
|
):
|
|
# todo for embeddings, we need to run without trigger words
|
|
was_unet_training = self.sd.unet.training
|
|
was_network_active = False
|
|
if self.network is not None:
|
|
was_network_active = self.network.is_active
|
|
self.network.is_active = False
|
|
can_disable_adapter = False
|
|
was_adapter_active = False
|
|
if self.adapter is not None and (isinstance(self.adapter, IPAdapter) or
|
|
isinstance(self.adapter, ReferenceAdapter) or
|
|
(isinstance(self.adapter, CustomAdapter))
|
|
):
|
|
can_disable_adapter = True
|
|
was_adapter_active = self.adapter.is_active
|
|
self.adapter.is_active = False
|
|
|
|
# do a prediction here so we can match its output with network multiplier set to 0.0
|
|
with torch.no_grad():
|
|
dtype = get_torch_dtype(self.train_config.dtype)
|
|
|
|
embeds_to_use = conditional_embeds.clone().detach()
|
|
# handle clip vision adapter by removing triggers from prompt and replacing with the class name
|
|
if (self.adapter is not None and isinstance(self.adapter, ClipVisionAdapter)) or self.embedding is not None:
|
|
prompt_list = batch.get_caption_list()
|
|
class_name = ''
|
|
|
|
triggers = ['[trigger]', '[name]']
|
|
remove_tokens = []
|
|
|
|
if self.embed_config is not None:
|
|
triggers.append(self.embed_config.trigger)
|
|
for i in range(1, self.embed_config.tokens):
|
|
remove_tokens.append(f"{self.embed_config.trigger}_{i}")
|
|
if self.embed_config.trigger_class_name is not None:
|
|
class_name = self.embed_config.trigger_class_name
|
|
|
|
if self.adapter is not None:
|
|
triggers.append(self.adapter_config.trigger)
|
|
for i in range(1, self.adapter_config.num_tokens):
|
|
remove_tokens.append(f"{self.adapter_config.trigger}_{i}")
|
|
if self.adapter_config.trigger_class_name is not None:
|
|
class_name = self.adapter_config.trigger_class_name
|
|
|
|
for idx, prompt in enumerate(prompt_list):
|
|
for remove_token in remove_tokens:
|
|
prompt = prompt.replace(remove_token, '')
|
|
for trigger in triggers:
|
|
prompt = prompt.replace(trigger, class_name)
|
|
prompt_list[idx] = prompt
|
|
|
|
embeds_to_use = self.sd.encode_prompt(
|
|
prompt_list,
|
|
long_prompts=self.do_long_prompts).to(
|
|
self.device_torch,
|
|
dtype=dtype).detach()
|
|
|
|
# dont use network on this
|
|
# self.network.multiplier = 0.0
|
|
self.sd.unet.eval()
|
|
|
|
if self.adapter is not None and isinstance(self.adapter, IPAdapter):
|
|
# we need to remove the image embeds from the prompt
|
|
embeds_to_use: PromptEmbeds = embeds_to_use.clone().detach()
|
|
end_pos = embeds_to_use.text_embeds.shape[1] - self.adapter_config.num_tokens
|
|
embeds_to_use.text_embeds = embeds_to_use.text_embeds[:, :end_pos, :]
|
|
if unconditional_embeds is not None:
|
|
unconditional_embeds = unconditional_embeds.clone().detach()
|
|
unconditional_embeds.text_embeds = unconditional_embeds.text_embeds[:, :end_pos]
|
|
|
|
if unconditional_embeds is not None:
|
|
unconditional_embeds = unconditional_embeds.to(self.device_torch, dtype=dtype).detach()
|
|
|
|
prior_pred = self.sd.predict_noise(
|
|
latents=noisy_latents.to(self.device_torch, dtype=dtype).detach(),
|
|
conditional_embeddings=embeds_to_use.to(self.device_torch, dtype=dtype).detach(),
|
|
unconditional_embeddings=unconditional_embeds,
|
|
timestep=timesteps,
|
|
guidance_scale=self.train_config.cfg_scale,
|
|
rescale_cfg=self.train_config.cfg_rescale,
|
|
**pred_kwargs # adapter residuals in here
|
|
)
|
|
if was_unet_training:
|
|
self.sd.unet.train()
|
|
prior_pred = prior_pred.detach()
|
|
# remove the residuals as we wont use them on prediction when matching control
|
|
if match_adapter_assist and 'down_intrablock_additional_residuals' in pred_kwargs:
|
|
del pred_kwargs['down_intrablock_additional_residuals']
|
|
if match_adapter_assist and 'down_block_additional_residuals' in pred_kwargs:
|
|
del pred_kwargs['down_block_additional_residuals']
|
|
if match_adapter_assist and 'mid_block_additional_residual' in pred_kwargs:
|
|
del pred_kwargs['mid_block_additional_residual']
|
|
|
|
if can_disable_adapter:
|
|
self.adapter.is_active = was_adapter_active
|
|
# restore network
|
|
# self.network.multiplier = network_weight_list
|
|
if self.network is not None:
|
|
self.network.is_active = was_network_active
|
|
return prior_pred
|
|
|
|
def before_unet_predict(self):
|
|
pass
|
|
|
|
def after_unet_predict(self):
|
|
pass
|
|
|
|
def end_of_training_loop(self):
|
|
pass
|
|
|
|
def predict_noise(
|
|
self,
|
|
noisy_latents: torch.Tensor,
|
|
timesteps: Union[int, torch.Tensor] = 1,
|
|
conditional_embeds: Union[PromptEmbeds, None] = None,
|
|
unconditional_embeds: Union[PromptEmbeds, None] = None,
|
|
**kwargs,
|
|
):
|
|
dtype = get_torch_dtype(self.train_config.dtype)
|
|
return self.sd.predict_noise(
|
|
latents=noisy_latents.to(self.device_torch, dtype=dtype),
|
|
conditional_embeddings=conditional_embeds.to(self.device_torch, dtype=dtype),
|
|
unconditional_embeddings=unconditional_embeds,
|
|
timestep=timesteps,
|
|
guidance_scale=self.train_config.cfg_scale,
|
|
detach_unconditional=False,
|
|
rescale_cfg=self.train_config.cfg_rescale,
|
|
**kwargs
|
|
)
|
|
|
|
def hook_train_loop(self, batch: 'DataLoaderBatchDTO'):
|
|
self.timer.start('preprocess_batch')
|
|
batch = self.preprocess_batch(batch)
|
|
dtype = get_torch_dtype(self.train_config.dtype)
|
|
# sanity check
|
|
if self.sd.vae.dtype != self.sd.vae_torch_dtype:
|
|
self.sd.vae = self.sd.vae.to(self.sd.vae_torch_dtype)
|
|
if isinstance(self.sd.text_encoder, list):
|
|
for encoder in self.sd.text_encoder:
|
|
if encoder.dtype != self.sd.te_torch_dtype:
|
|
encoder.to(self.sd.te_torch_dtype)
|
|
else:
|
|
if self.sd.text_encoder.dtype != self.sd.te_torch_dtype:
|
|
self.sd.text_encoder.to(self.sd.te_torch_dtype)
|
|
|
|
noisy_latents, noise, timesteps, conditioned_prompts, imgs = self.process_general_training_batch(batch)
|
|
if self.train_config.do_cfg or self.train_config.do_random_cfg:
|
|
# pick random negative prompts
|
|
if self.negative_prompt_pool is not None:
|
|
negative_prompts = []
|
|
for i in range(noisy_latents.shape[0]):
|
|
num_neg = random.randint(1, self.train_config.max_negative_prompts)
|
|
this_neg_prompts = [random.choice(self.negative_prompt_pool) for _ in range(num_neg)]
|
|
this_neg_prompt = ', '.join(this_neg_prompts)
|
|
negative_prompts.append(this_neg_prompt)
|
|
self.batch_negative_prompt = negative_prompts
|
|
else:
|
|
self.batch_negative_prompt = ['' for _ in range(batch.latents.shape[0])]
|
|
|
|
if self.adapter and isinstance(self.adapter, CustomAdapter):
|
|
# condition the prompt
|
|
# todo handle more than one adapter image
|
|
self.adapter.num_control_images = 1
|
|
conditioned_prompts = self.adapter.condition_prompt(conditioned_prompts)
|
|
|
|
network_weight_list = batch.get_network_weight_list()
|
|
if self.train_config.single_item_batching:
|
|
network_weight_list = network_weight_list + network_weight_list
|
|
|
|
has_adapter_img = batch.control_tensor is not None
|
|
has_clip_image = batch.clip_image_tensor is not None
|
|
has_clip_image_embeds = batch.clip_image_embeds is not None
|
|
# force it to be true if doing regs as we handle those differently
|
|
if any([batch.file_items[idx].is_reg for idx in range(len(batch.file_items))]):
|
|
has_clip_image = True
|
|
if self._clip_image_embeds_unconditional is not None:
|
|
has_clip_image_embeds = True # we are caching embeds, handle that differently
|
|
has_clip_image = False
|
|
|
|
if self.adapter is not None and isinstance(self.adapter, IPAdapter) and not has_clip_image and has_adapter_img:
|
|
raise ValueError(
|
|
"IPAdapter control image is now 'clip_image_path' instead of 'control_path'. Please update your dataset config ")
|
|
|
|
match_adapter_assist = False
|
|
|
|
# check if we are matching the adapter assistant
|
|
if self.assistant_adapter:
|
|
if self.train_config.match_adapter_chance == 1.0:
|
|
match_adapter_assist = True
|
|
elif self.train_config.match_adapter_chance > 0.0:
|
|
match_adapter_assist = torch.rand(
|
|
(1,), device=self.device_torch, dtype=dtype
|
|
) < self.train_config.match_adapter_chance
|
|
|
|
self.timer.stop('preprocess_batch')
|
|
|
|
is_reg = False
|
|
with torch.no_grad():
|
|
loss_multiplier = torch.ones((noisy_latents.shape[0], 1, 1, 1), device=self.device_torch, dtype=dtype)
|
|
for idx, file_item in enumerate(batch.file_items):
|
|
if file_item.is_reg:
|
|
loss_multiplier[idx] = loss_multiplier[idx] * self.train_config.reg_weight
|
|
is_reg = True
|
|
|
|
adapter_images = None
|
|
sigmas = None
|
|
if has_adapter_img and (self.adapter or self.assistant_adapter):
|
|
with self.timer('get_adapter_images'):
|
|
# todo move this to data loader
|
|
if batch.control_tensor is not None:
|
|
adapter_images = batch.control_tensor.to(self.device_torch, dtype=dtype).detach()
|
|
# match in channels
|
|
if self.assistant_adapter is not None:
|
|
in_channels = self.assistant_adapter.config.in_channels
|
|
if adapter_images.shape[1] != in_channels:
|
|
# we need to match the channels
|
|
adapter_images = adapter_images[:, :in_channels, :, :]
|
|
else:
|
|
raise NotImplementedError("Adapter images now must be loaded with dataloader")
|
|
|
|
clip_images = None
|
|
if has_clip_image:
|
|
with self.timer('get_clip_images'):
|
|
# todo move this to data loader
|
|
if batch.clip_image_tensor is not None:
|
|
clip_images = batch.clip_image_tensor.to(self.device_torch, dtype=dtype).detach()
|
|
|
|
mask_multiplier = torch.ones((noisy_latents.shape[0], 1, 1, 1), device=self.device_torch, dtype=dtype)
|
|
if batch.mask_tensor is not None:
|
|
with self.timer('get_mask_multiplier'):
|
|
# upsampling no supported for bfloat16
|
|
mask_multiplier = batch.mask_tensor.to(self.device_torch, dtype=torch.float16).detach()
|
|
# scale down to the size of the latents, mask multiplier shape(bs, 1, width, height), noisy_latents shape(bs, channels, width, height)
|
|
mask_multiplier = torch.nn.functional.interpolate(
|
|
mask_multiplier, size=(noisy_latents.shape[2], noisy_latents.shape[3])
|
|
)
|
|
# expand to match latents
|
|
mask_multiplier = mask_multiplier.expand(-1, noisy_latents.shape[1], -1, -1)
|
|
mask_multiplier = mask_multiplier.to(self.device_torch, dtype=dtype).detach()
|
|
|
|
def get_adapter_multiplier():
|
|
if self.adapter and isinstance(self.adapter, T2IAdapter):
|
|
# training a t2i adapter, not using as assistant.
|
|
return 1.0
|
|
elif match_adapter_assist:
|
|
# training a texture. We want it high
|
|
adapter_strength_min = 0.9
|
|
adapter_strength_max = 1.0
|
|
else:
|
|
# training with assistance, we want it low
|
|
# adapter_strength_min = 0.4
|
|
# adapter_strength_max = 0.7
|
|
adapter_strength_min = 0.5
|
|
adapter_strength_max = 1.1
|
|
|
|
adapter_conditioning_scale = torch.rand(
|
|
(1,), device=self.device_torch, dtype=dtype
|
|
)
|
|
|
|
adapter_conditioning_scale = value_map(
|
|
adapter_conditioning_scale,
|
|
0.0,
|
|
1.0,
|
|
adapter_strength_min,
|
|
adapter_strength_max
|
|
)
|
|
return adapter_conditioning_scale
|
|
|
|
# flush()
|
|
with self.timer('grad_setup'):
|
|
|
|
# text encoding
|
|
grad_on_text_encoder = False
|
|
if self.train_config.train_text_encoder:
|
|
grad_on_text_encoder = True
|
|
|
|
if self.embedding is not None:
|
|
grad_on_text_encoder = True
|
|
|
|
if self.adapter and isinstance(self.adapter, ClipVisionAdapter):
|
|
grad_on_text_encoder = True
|
|
|
|
if self.adapter_config and self.adapter_config.type == 'te_augmenter':
|
|
grad_on_text_encoder = True
|
|
|
|
# have a blank network so we can wrap it in a context and set multipliers without checking every time
|
|
if self.network is not None:
|
|
network = self.network
|
|
else:
|
|
network = BlankNetwork()
|
|
|
|
# set the weights
|
|
network.multiplier = network_weight_list
|
|
self.optimizer.zero_grad(set_to_none=True)
|
|
|
|
# activate network if it exits
|
|
|
|
prompts_1 = conditioned_prompts
|
|
prompts_2 = None
|
|
if self.train_config.short_and_long_captions_encoder_split and self.sd.is_xl:
|
|
prompts_1 = batch.get_caption_short_list()
|
|
prompts_2 = conditioned_prompts
|
|
|
|
# make the batch splits
|
|
if self.train_config.single_item_batching:
|
|
if self.model_config.refiner_name_or_path is not None:
|
|
raise ValueError("Single item batching is not supported when training the refiner")
|
|
batch_size = noisy_latents.shape[0]
|
|
# chunk/split everything
|
|
noisy_latents_list = torch.chunk(noisy_latents, batch_size, dim=0)
|
|
noise_list = torch.chunk(noise, batch_size, dim=0)
|
|
timesteps_list = torch.chunk(timesteps, batch_size, dim=0)
|
|
conditioned_prompts_list = [[prompt] for prompt in prompts_1]
|
|
if imgs is not None:
|
|
imgs_list = torch.chunk(imgs, batch_size, dim=0)
|
|
else:
|
|
imgs_list = [None for _ in range(batch_size)]
|
|
if adapter_images is not None:
|
|
adapter_images_list = torch.chunk(adapter_images, batch_size, dim=0)
|
|
else:
|
|
adapter_images_list = [None for _ in range(batch_size)]
|
|
if clip_images is not None:
|
|
clip_images_list = torch.chunk(clip_images, batch_size, dim=0)
|
|
else:
|
|
clip_images_list = [None for _ in range(batch_size)]
|
|
mask_multiplier_list = torch.chunk(mask_multiplier, batch_size, dim=0)
|
|
if prompts_2 is None:
|
|
prompt_2_list = [None for _ in range(batch_size)]
|
|
else:
|
|
prompt_2_list = [[prompt] for prompt in prompts_2]
|
|
|
|
else:
|
|
noisy_latents_list = [noisy_latents]
|
|
noise_list = [noise]
|
|
timesteps_list = [timesteps]
|
|
conditioned_prompts_list = [prompts_1]
|
|
imgs_list = [imgs]
|
|
adapter_images_list = [adapter_images]
|
|
clip_images_list = [clip_images]
|
|
mask_multiplier_list = [mask_multiplier]
|
|
if prompts_2 is None:
|
|
prompt_2_list = [None]
|
|
else:
|
|
prompt_2_list = [prompts_2]
|
|
|
|
for noisy_latents, noise, timesteps, conditioned_prompts, imgs, adapter_images, clip_images, mask_multiplier, prompt_2 in zip(
|
|
noisy_latents_list,
|
|
noise_list,
|
|
timesteps_list,
|
|
conditioned_prompts_list,
|
|
imgs_list,
|
|
adapter_images_list,
|
|
clip_images_list,
|
|
mask_multiplier_list,
|
|
prompt_2_list
|
|
):
|
|
|
|
# if self.train_config.negative_prompt is not None:
|
|
# # add negative prompt
|
|
# conditioned_prompts = conditioned_prompts + [self.train_config.negative_prompt for x in
|
|
# range(len(conditioned_prompts))]
|
|
# if prompt_2 is not None:
|
|
# prompt_2 = prompt_2 + [self.train_config.negative_prompt for x in range(len(prompt_2))]
|
|
|
|
with (network):
|
|
# encode clip adapter here so embeds are active for tokenizer
|
|
if self.adapter and isinstance(self.adapter, ClipVisionAdapter):
|
|
with self.timer('encode_clip_vision_embeds'):
|
|
if has_clip_image:
|
|
conditional_clip_embeds = self.adapter.get_clip_image_embeds_from_tensors(
|
|
clip_images.detach().to(self.device_torch, dtype=dtype),
|
|
is_training=True,
|
|
has_been_preprocessed=True
|
|
)
|
|
else:
|
|
# just do a blank one
|
|
conditional_clip_embeds = self.adapter.get_clip_image_embeds_from_tensors(
|
|
torch.zeros(
|
|
(noisy_latents.shape[0], 3, 512, 512),
|
|
device=self.device_torch, dtype=dtype
|
|
),
|
|
is_training=True,
|
|
has_been_preprocessed=True,
|
|
drop=True
|
|
)
|
|
# it will be injected into the tokenizer when called
|
|
self.adapter(conditional_clip_embeds)
|
|
|
|
# do the custom adapter after the prior prediction
|
|
if self.adapter and isinstance(self.adapter, CustomAdapter) and has_clip_image:
|
|
quad_count = random.randint(1, 4)
|
|
self.adapter.train()
|
|
self.adapter.trigger_pre_te(
|
|
tensors_0_1=clip_images if not is_reg else None, # on regs we send none to get random noise
|
|
is_training=True,
|
|
has_been_preprocessed=True,
|
|
quad_count=quad_count,
|
|
batch_size=noisy_latents.shape[0]
|
|
)
|
|
|
|
with self.timer('encode_prompt'):
|
|
unconditional_embeds = None
|
|
if grad_on_text_encoder:
|
|
with torch.set_grad_enabled(True):
|
|
if isinstance(self.adapter, CustomAdapter):
|
|
self.adapter.is_unconditional_run = False
|
|
conditional_embeds = self.sd.encode_prompt(
|
|
conditioned_prompts, prompt_2,
|
|
dropout_prob=self.train_config.prompt_dropout_prob,
|
|
long_prompts=self.do_long_prompts).to(
|
|
self.device_torch,
|
|
dtype=dtype)
|
|
|
|
if self.train_config.do_cfg:
|
|
if isinstance(self.adapter, CustomAdapter):
|
|
self.adapter.is_unconditional_run = True
|
|
# todo only do one and repeat it
|
|
unconditional_embeds = self.sd.encode_prompt(
|
|
self.batch_negative_prompt,
|
|
self.batch_negative_prompt,
|
|
dropout_prob=self.train_config.prompt_dropout_prob,
|
|
long_prompts=self.do_long_prompts).to(
|
|
self.device_torch,
|
|
dtype=dtype)
|
|
if isinstance(self.adapter, CustomAdapter):
|
|
self.adapter.is_unconditional_run = False
|
|
else:
|
|
with torch.set_grad_enabled(False):
|
|
# make sure it is in eval mode
|
|
if isinstance(self.sd.text_encoder, list):
|
|
for te in self.sd.text_encoder:
|
|
te.eval()
|
|
else:
|
|
self.sd.text_encoder.eval()
|
|
if isinstance(self.adapter, CustomAdapter):
|
|
self.adapter.is_unconditional_run = False
|
|
conditional_embeds = self.sd.encode_prompt(
|
|
conditioned_prompts, prompt_2,
|
|
dropout_prob=self.train_config.prompt_dropout_prob,
|
|
long_prompts=self.do_long_prompts).to(
|
|
self.device_torch,
|
|
dtype=dtype)
|
|
if self.train_config.do_cfg:
|
|
if isinstance(self.adapter, CustomAdapter):
|
|
self.adapter.is_unconditional_run = True
|
|
unconditional_embeds = self.sd.encode_prompt(
|
|
self.batch_negative_prompt,
|
|
dropout_prob=self.train_config.prompt_dropout_prob,
|
|
long_prompts=self.do_long_prompts).to(
|
|
self.device_torch,
|
|
dtype=dtype)
|
|
if isinstance(self.adapter, CustomAdapter):
|
|
self.adapter.is_unconditional_run = False
|
|
|
|
# detach the embeddings
|
|
conditional_embeds = conditional_embeds.detach()
|
|
if self.train_config.do_cfg:
|
|
unconditional_embeds = unconditional_embeds.detach()
|
|
|
|
# flush()
|
|
pred_kwargs = {}
|
|
|
|
if has_adapter_img:
|
|
if (self.adapter and isinstance(self.adapter, T2IAdapter)) or (self.assistant_adapter and isinstance(self.assistant_adapter, T2IAdapter)):
|
|
with torch.set_grad_enabled(self.adapter is not None):
|
|
adapter = self.assistant_adapter if self.assistant_adapter is not None else self.adapter
|
|
adapter_multiplier = get_adapter_multiplier()
|
|
with self.timer('encode_adapter'):
|
|
down_block_additional_residuals = adapter(adapter_images)
|
|
if self.assistant_adapter:
|
|
# not training. detach
|
|
down_block_additional_residuals = [
|
|
sample.to(dtype=dtype).detach() * adapter_multiplier for sample in
|
|
down_block_additional_residuals
|
|
]
|
|
else:
|
|
down_block_additional_residuals = [
|
|
sample.to(dtype=dtype) * adapter_multiplier for sample in
|
|
down_block_additional_residuals
|
|
]
|
|
|
|
pred_kwargs['down_intrablock_additional_residuals'] = down_block_additional_residuals
|
|
|
|
if self.adapter and isinstance(self.adapter, IPAdapter):
|
|
with self.timer('encode_adapter_embeds'):
|
|
# number of images to do if doing a quad image
|
|
quad_count = random.randint(1, 4)
|
|
image_size = self.adapter.input_size
|
|
if has_clip_image_embeds:
|
|
# todo handle reg images better than this
|
|
if is_reg:
|
|
# get unconditional image imbeds from cache
|
|
embeds = [
|
|
load_file(random.choice(batch.clip_image_embeds_unconditional)) for i in
|
|
range(noisy_latents.shape[0])
|
|
]
|
|
conditional_clip_embeds = self.adapter.parse_clip_image_embeds_from_cache(
|
|
embeds,
|
|
quad_count=quad_count
|
|
)
|
|
|
|
if self.train_config.do_cfg:
|
|
embeds = [
|
|
load_file(random.choice(batch.clip_image_embeds_unconditional)) for i in range(noisy_latents.shape[0])
|
|
]
|
|
unconditional_clip_embeds = self.adapter.parse_clip_image_embeds_from_cache(
|
|
embeds,
|
|
quad_count=quad_count
|
|
)
|
|
|
|
else:
|
|
conditional_clip_embeds = self.adapter.parse_clip_image_embeds_from_cache(
|
|
batch.clip_image_embeds,
|
|
quad_count=quad_count
|
|
)
|
|
if self.train_config.do_cfg:
|
|
unconditional_clip_embeds = self.adapter.parse_clip_image_embeds_from_cache(
|
|
batch.clip_image_embeds_unconditional,
|
|
quad_count=quad_count
|
|
)
|
|
elif is_reg:
|
|
# we will zero it out in the img embedder
|
|
clip_images = torch.zeros(
|
|
(noisy_latents.shape[0], 3, image_size, image_size),
|
|
device=self.device_torch, dtype=dtype
|
|
).detach()
|
|
# drop will zero it out
|
|
conditional_clip_embeds = self.adapter.get_clip_image_embeds_from_tensors(
|
|
clip_images,
|
|
drop=True,
|
|
is_training=True,
|
|
has_been_preprocessed=False,
|
|
quad_count=quad_count
|
|
)
|
|
if self.train_config.do_cfg:
|
|
unconditional_clip_embeds = self.adapter.get_clip_image_embeds_from_tensors(
|
|
torch.zeros(
|
|
(noisy_latents.shape[0], 3, image_size, image_size),
|
|
device=self.device_torch, dtype=dtype
|
|
).detach(),
|
|
is_training=True,
|
|
drop=True,
|
|
has_been_preprocessed=False,
|
|
quad_count=quad_count
|
|
)
|
|
elif has_clip_image:
|
|
conditional_clip_embeds = self.adapter.get_clip_image_embeds_from_tensors(
|
|
clip_images.detach().to(self.device_torch, dtype=dtype),
|
|
is_training=True,
|
|
has_been_preprocessed=True,
|
|
quad_count=quad_count,
|
|
# do cfg on clip embeds to normalize the embeddings for when doing cfg
|
|
# cfg_embed_strength=3.0 if not self.train_config.do_cfg else None
|
|
# cfg_embed_strength=3.0 if not self.train_config.do_cfg else None
|
|
)
|
|
if self.train_config.do_cfg:
|
|
unconditional_clip_embeds = self.adapter.get_clip_image_embeds_from_tensors(
|
|
clip_images.detach().to(self.device_torch, dtype=dtype),
|
|
is_training=True,
|
|
drop=True,
|
|
has_been_preprocessed=True,
|
|
quad_count=quad_count
|
|
)
|
|
else:
|
|
print("No Clip Image")
|
|
print([file_item.path for file_item in batch.file_items])
|
|
raise ValueError("Could not find clip image")
|
|
|
|
if not self.adapter_config.train_image_encoder:
|
|
# we are not training the image encoder, so we need to detach the embeds
|
|
conditional_clip_embeds = conditional_clip_embeds.detach()
|
|
if self.train_config.do_cfg:
|
|
unconditional_clip_embeds = unconditional_clip_embeds.detach()
|
|
|
|
with self.timer('encode_adapter'):
|
|
self.adapter.train()
|
|
conditional_embeds = self.adapter(conditional_embeds.detach(), conditional_clip_embeds)
|
|
if self.train_config.do_cfg:
|
|
unconditional_embeds = self.adapter(unconditional_embeds.detach(),
|
|
unconditional_clip_embeds)
|
|
|
|
if self.adapter and isinstance(self.adapter, ReferenceAdapter):
|
|
# pass in our scheduler
|
|
self.adapter.noise_scheduler = self.lr_scheduler
|
|
if has_clip_image or has_adapter_img:
|
|
img_to_use = clip_images if has_clip_image else adapter_images
|
|
# currently 0-1 needs to be -1 to 1
|
|
reference_images = ((img_to_use - 0.5) * 2).detach().to(self.device_torch, dtype=dtype)
|
|
self.adapter.set_reference_images(reference_images)
|
|
self.adapter.noise_scheduler = self.sd.noise_scheduler
|
|
elif is_reg:
|
|
self.adapter.set_blank_reference_images(noisy_latents.shape[0])
|
|
else:
|
|
self.adapter.set_reference_images(None)
|
|
|
|
prior_pred = None
|
|
|
|
do_reg_prior = False
|
|
# if is_reg and (self.network is not None or self.adapter is not None):
|
|
# # we are doing a reg image and we have a network or adapter
|
|
# do_reg_prior = True
|
|
|
|
do_inverted_masked_prior = False
|
|
if self.train_config.inverted_mask_prior and batch.mask_tensor is not None:
|
|
do_inverted_masked_prior = True
|
|
|
|
do_correct_pred_norm_prior = self.train_config.correct_pred_norm
|
|
|
|
do_guidance_prior = False
|
|
|
|
if batch.unconditional_latents is not None:
|
|
# for this not that, we need a prior pred to normalize
|
|
guidance_type: GuidanceType = batch.file_items[0].dataset_config.guidance_type
|
|
if guidance_type == 'tnt':
|
|
do_guidance_prior = True
|
|
|
|
if ((
|
|
has_adapter_img and self.assistant_adapter and match_adapter_assist) or self.do_prior_prediction or do_guidance_prior or do_reg_prior or do_inverted_masked_prior or self.train_config.correct_pred_norm):
|
|
with self.timer('prior predict'):
|
|
prior_pred = self.get_prior_prediction(
|
|
noisy_latents=noisy_latents,
|
|
conditional_embeds=conditional_embeds,
|
|
match_adapter_assist=match_adapter_assist,
|
|
network_weight_list=network_weight_list,
|
|
timesteps=timesteps,
|
|
pred_kwargs=pred_kwargs,
|
|
noise=noise,
|
|
batch=batch,
|
|
unconditional_embeds=unconditional_embeds,
|
|
conditioned_prompts=conditioned_prompts
|
|
)
|
|
if prior_pred is not None:
|
|
prior_pred = prior_pred.detach()
|
|
|
|
|
|
# do the custom adapter after the prior prediction
|
|
if self.adapter and isinstance(self.adapter, CustomAdapter) and has_clip_image:
|
|
quad_count = random.randint(1, 4)
|
|
self.adapter.train()
|
|
conditional_embeds = self.adapter.condition_encoded_embeds(
|
|
tensors_0_1=clip_images,
|
|
prompt_embeds=conditional_embeds,
|
|
is_training=True,
|
|
has_been_preprocessed=True,
|
|
quad_count=quad_count
|
|
)
|
|
if self.train_config.do_cfg and unconditional_embeds is not None:
|
|
unconditional_embeds = self.adapter.condition_encoded_embeds(
|
|
tensors_0_1=clip_images,
|
|
prompt_embeds=unconditional_embeds,
|
|
is_training=True,
|
|
has_been_preprocessed=True,
|
|
is_unconditional=True,
|
|
quad_count=quad_count
|
|
)
|
|
|
|
if self.adapter and isinstance(self.adapter, CustomAdapter) and batch.extra_values is not None:
|
|
self.adapter.add_extra_values(batch.extra_values.detach())
|
|
|
|
if self.train_config.do_cfg:
|
|
self.adapter.add_extra_values(torch.zeros_like(batch.extra_values.detach()), is_unconditional=True)
|
|
|
|
if has_adapter_img:
|
|
if (self.adapter and isinstance(self.adapter, ControlNetModel)) or (self.assistant_adapter and isinstance(self.assistant_adapter, ControlNetModel)):
|
|
if self.train_config.do_cfg:
|
|
raise ValueError("ControlNetModel is not supported with CFG")
|
|
with torch.set_grad_enabled(self.adapter is not None):
|
|
adapter: ControlNetModel = self.assistant_adapter if self.assistant_adapter is not None else self.adapter
|
|
adapter_multiplier = get_adapter_multiplier()
|
|
with self.timer('encode_adapter'):
|
|
# add_text_embeds is pooled_prompt_embeds for sdxl
|
|
added_cond_kwargs = {}
|
|
if self.sd.is_xl:
|
|
added_cond_kwargs["text_embeds"] = conditional_embeds.pooled_embeds
|
|
added_cond_kwargs['time_ids'] = self.sd.get_time_ids_from_latents(noisy_latents)
|
|
down_block_res_samples, mid_block_res_sample = adapter(
|
|
noisy_latents,
|
|
timesteps,
|
|
encoder_hidden_states=conditional_embeds.text_embeds,
|
|
controlnet_cond=adapter_images,
|
|
conditioning_scale=1.0,
|
|
guess_mode=False,
|
|
added_cond_kwargs=added_cond_kwargs,
|
|
return_dict=False,
|
|
)
|
|
pred_kwargs['down_block_additional_residuals'] = down_block_res_samples
|
|
pred_kwargs['mid_block_additional_residual'] = mid_block_res_sample
|
|
|
|
|
|
self.before_unet_predict()
|
|
# do a prior pred if we have an unconditional image, we will swap out the giadance later
|
|
if batch.unconditional_latents is not None or self.do_guided_loss:
|
|
# do guided loss
|
|
loss = self.get_guided_loss(
|
|
noisy_latents=noisy_latents,
|
|
conditional_embeds=conditional_embeds,
|
|
match_adapter_assist=match_adapter_assist,
|
|
network_weight_list=network_weight_list,
|
|
timesteps=timesteps,
|
|
pred_kwargs=pred_kwargs,
|
|
batch=batch,
|
|
noise=noise,
|
|
unconditional_embeds=unconditional_embeds,
|
|
mask_multiplier=mask_multiplier,
|
|
prior_pred=prior_pred,
|
|
)
|
|
|
|
else:
|
|
with self.timer('predict_unet'):
|
|
if unconditional_embeds is not None:
|
|
unconditional_embeds = unconditional_embeds.to(self.device_torch, dtype=dtype).detach()
|
|
noise_pred = self.predict_noise(
|
|
noisy_latents=noisy_latents.to(self.device_torch, dtype=dtype),
|
|
timesteps=timesteps,
|
|
conditional_embeds=conditional_embeds.to(self.device_torch, dtype=dtype),
|
|
unconditional_embeds=unconditional_embeds,
|
|
**pred_kwargs
|
|
)
|
|
self.after_unet_predict()
|
|
|
|
with self.timer('calculate_loss'):
|
|
noise = noise.to(self.device_torch, dtype=dtype).detach()
|
|
loss = self.calculate_loss(
|
|
noise_pred=noise_pred,
|
|
noise=noise,
|
|
noisy_latents=noisy_latents,
|
|
timesteps=timesteps,
|
|
batch=batch,
|
|
mask_multiplier=mask_multiplier,
|
|
prior_pred=prior_pred,
|
|
)
|
|
# check if nan
|
|
if torch.isnan(loss):
|
|
print("loss is nan")
|
|
loss = torch.zeros_like(loss).requires_grad_(True)
|
|
|
|
|
|
with self.timer('backward'):
|
|
# todo we have multiplier seperated. works for now as res are not in same batch, but need to change
|
|
loss = loss * loss_multiplier.mean()
|
|
# IMPORTANT if gradient checkpointing do not leave with network when doing backward
|
|
# it will destroy the gradients. This is because the network is a context manager
|
|
# and will change the multipliers back to 0.0 when exiting. They will be
|
|
# 0.0 for the backward pass and the gradients will be 0.0
|
|
# I spent weeks on fighting this. DON'T DO IT
|
|
# with fsdp_overlap_step_with_backward():
|
|
# if self.is_bfloat:
|
|
# loss.backward()
|
|
# else:
|
|
self.scaler.scale(loss).backward()
|
|
# flush()
|
|
|
|
if not self.is_grad_accumulation_step:
|
|
# fix this for multi params
|
|
if self.train_config.optimizer != 'adafactor':
|
|
self.scaler.unscale_(self.optimizer)
|
|
if isinstance(self.params[0], dict):
|
|
for i in range(len(self.params)):
|
|
torch.nn.utils.clip_grad_norm_(self.params[i]['params'], self.train_config.max_grad_norm)
|
|
else:
|
|
torch.nn.utils.clip_grad_norm_(self.params, self.train_config.max_grad_norm)
|
|
# only step if we are not accumulating
|
|
with self.timer('optimizer_step'):
|
|
# self.optimizer.step()
|
|
self.scaler.step(self.optimizer)
|
|
self.scaler.update()
|
|
self.optimizer.zero_grad(set_to_none=True)
|
|
if self.ema is not None:
|
|
with self.timer('ema_update'):
|
|
self.ema.update()
|
|
else:
|
|
# gradient accumulation. Just a place for breakpoint
|
|
pass
|
|
|
|
# TODO Should we only step scheduler on grad step? If so, need to recalculate last step
|
|
with self.timer('scheduler_step'):
|
|
self.lr_scheduler.step()
|
|
|
|
if self.embedding is not None:
|
|
with self.timer('restore_embeddings'):
|
|
# Let's make sure we don't update any embedding weights besides the newly added token
|
|
self.embedding.restore_embeddings()
|
|
if self.adapter is not None and isinstance(self.adapter, ClipVisionAdapter):
|
|
with self.timer('restore_adapter'):
|
|
# Let's make sure we don't update any embedding weights besides the newly added token
|
|
self.adapter.restore_embeddings()
|
|
|
|
loss_dict = OrderedDict(
|
|
{'loss': loss.item()}
|
|
)
|
|
|
|
self.end_of_training_loop()
|
|
|
|
return loss_dict
|