Upload extensions_built_in/image_reference_slider_trainer/ImageReferenceSliderTrainerProcess.py with huggingface_hub

extensions_built_in/image_reference_slider_trainer/ImageReferenceSliderTrainerProcess.py

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+import copy
+import random
+from collections import OrderedDict
+import os
+from contextlib import nullcontext
+from typing import Optional, Union, List
+from torch.utils.data import ConcatDataset, DataLoader
+
+from toolkit.config_modules import ReferenceDatasetConfig
+from toolkit.data_loader import PairedImageDataset
+from toolkit.prompt_utils import concat_prompt_embeds, split_prompt_embeds
+from toolkit.stable_diffusion_model import StableDiffusion, PromptEmbeds
+from toolkit.train_tools import get_torch_dtype, apply_snr_weight
+import gc
+from toolkit import train_tools
+import torch
+from jobs.process import BaseSDTrainProcess
+import random
+from toolkit.basic import value_map
+
+
+def flush():
+    torch.cuda.empty_cache()
+    gc.collect()
+
+
+class ReferenceSliderConfig:
+    def __init__(self, **kwargs):
+        self.additional_losses: List[str] = kwargs.get('additional_losses', [])
+        self.weight_jitter: float = kwargs.get('weight_jitter', 0.0)
+        self.datasets: List[ReferenceDatasetConfig] = [ReferenceDatasetConfig(**d) for d in kwargs.get('datasets', [])]
+
+
+class ImageReferenceSliderTrainerProcess(BaseSDTrainProcess):
+    sd: StableDiffusion
+    data_loader: DataLoader = None
+
+    def __init__(self, process_id: int, job, config: OrderedDict, **kwargs):
+        super().__init__(process_id, job, config, **kwargs)
+        self.prompt_txt_list = None
+        self.step_num = 0
+        self.start_step = 0
+        self.device = self.get_conf('device', self.job.device)
+        self.device_torch = torch.device(self.device)
+        self.slider_config = ReferenceSliderConfig(**self.get_conf('slider', {}))
+
+    def load_datasets(self):
+        if self.data_loader is None:
+            print(f"Loading datasets")
+            datasets = []
+            for dataset in self.slider_config.datasets:
+                print(f" - Dataset: {dataset.pair_folder}")
+                config = {
+                    'path': dataset.pair_folder,
+                    'size': dataset.size,
+                    'default_prompt': dataset.target_class,
+                    'network_weight': dataset.network_weight,
+                    'pos_weight': dataset.pos_weight,
+                    'neg_weight': dataset.neg_weight,
+                    'pos_folder': dataset.pos_folder,
+                    'neg_folder': dataset.neg_folder,
+                }
+                image_dataset = PairedImageDataset(config)
+                datasets.append(image_dataset)
+
+            concatenated_dataset = ConcatDataset(datasets)
+            self.data_loader = DataLoader(
+                concatenated_dataset,
+                batch_size=self.train_config.batch_size,
+                shuffle=True,
+                num_workers=2
+            )
+
+    def before_model_load(self):
+        pass
+
+    def hook_before_train_loop(self):
+        self.sd.vae.eval()
+        self.sd.vae.to(self.device_torch)
+        self.load_datasets()
+
+        pass
+
+    def hook_train_loop(self, batch):
+        with torch.no_grad():
+            imgs, prompts, network_weights = batch
+            network_pos_weight, network_neg_weight = network_weights
+
+            if isinstance(network_pos_weight, torch.Tensor):
+                network_pos_weight = network_pos_weight.item()
+            if isinstance(network_neg_weight, torch.Tensor):
+                network_neg_weight = network_neg_weight.item()
+
+            # get an array of random floats between -weight_jitter and weight_jitter
+            loss_jitter_multiplier = 1.0
+            weight_jitter = self.slider_config.weight_jitter
+            if weight_jitter > 0.0:
+                jitter_list = random.uniform(-weight_jitter, weight_jitter)
+                orig_network_pos_weight = network_pos_weight
+                network_pos_weight += jitter_list
+                network_neg_weight += (jitter_list * -1.0)
+                # penalize the loss for its distance from network_pos_weight
+                # a jitter_list of abs(3.0) on a weight of 5.0 is a 60% jitter
+                # so the loss_jitter_multiplier needs to be 0.4
+                loss_jitter_multiplier = value_map(abs(jitter_list), 0.0, weight_jitter, 1.0, 0.0)
+
+
+            # if items in network_weight list are tensors, convert them to floats
+
+            dtype = get_torch_dtype(self.train_config.dtype)
+            imgs: torch.Tensor = imgs.to(self.device_torch, dtype=dtype)
+            # split batched images in half so left is negative and right is positive
+            negative_images, positive_images = torch.chunk(imgs, 2, dim=3)
+
+            positive_latents = self.sd.encode_images(positive_images)
+            negative_latents = self.sd.encode_images(negative_images)
+
+            height = positive_images.shape[2]
+            width = positive_images.shape[3]
+            batch_size = positive_images.shape[0]
+
+            if self.train_config.gradient_checkpointing:
+                # may get disabled elsewhere
+                self.sd.unet.enable_gradient_checkpointing()
+
+            noise_scheduler = self.sd.noise_scheduler
+            optimizer = self.optimizer
+            lr_scheduler = self.lr_scheduler
+
+            self.sd.noise_scheduler.set_timesteps(
+                self.train_config.max_denoising_steps, device=self.device_torch
+            )
+
+            timesteps = torch.randint(0, self.train_config.max_denoising_steps, (1,), device=self.device_torch)
+            timesteps = timesteps.long()
+
+            # get noise
+            noise_positive = self.sd.get_latent_noise(
+                pixel_height=height,
+                pixel_width=width,
+                batch_size=batch_size,
+                noise_offset=self.train_config.noise_offset,
+            ).to(self.device_torch, dtype=dtype)
+
+            noise_negative = noise_positive.clone()
+
+            # Add noise to the latents according to the noise magnitude at each timestep
+            # (this is the forward diffusion process)
+            noisy_positive_latents = noise_scheduler.add_noise(positive_latents, noise_positive, timesteps)
+            noisy_negative_latents = noise_scheduler.add_noise(negative_latents, noise_negative, timesteps)
+
+            noisy_latents = torch.cat([noisy_positive_latents, noisy_negative_latents], dim=0)
+            noise = torch.cat([noise_positive, noise_negative], dim=0)
+            timesteps = torch.cat([timesteps, timesteps], dim=0)
+            network_multiplier = [network_pos_weight * 1.0, network_neg_weight * -1.0]
+
+        self.optimizer.zero_grad()
+        noisy_latents.requires_grad = False
+
+        # if training text encoder enable grads, else do context of no grad
+        with torch.set_grad_enabled(self.train_config.train_text_encoder):
+            # fix issue with them being tuples sometimes
+            prompt_list = []
+            for prompt in prompts:
+                if isinstance(prompt, tuple):
+                    prompt = prompt[0]
+                prompt_list.append(prompt)
+            conditional_embeds = self.sd.encode_prompt(prompt_list).to(self.device_torch, dtype=dtype)
+            conditional_embeds = concat_prompt_embeds([conditional_embeds, conditional_embeds])
+
+        # if self.model_config.is_xl:
+        #     # todo also allow for setting this for low ram in general, but sdxl spikes a ton on back prop
+        #     network_multiplier_list = network_multiplier
+        #     noisy_latent_list = torch.chunk(noisy_latents, 2, dim=0)
+        #     noise_list = torch.chunk(noise, 2, dim=0)
+        #     timesteps_list = torch.chunk(timesteps, 2, dim=0)
+        #     conditional_embeds_list = split_prompt_embeds(conditional_embeds)
+        # else:
+        network_multiplier_list = [network_multiplier]
+        noisy_latent_list = [noisy_latents]
+        noise_list = [noise]
+        timesteps_list = [timesteps]
+        conditional_embeds_list = [conditional_embeds]
+
+        losses = []
+        # allow to chunk it out to save vram
+        for network_multiplier, noisy_latents, noise, timesteps, conditional_embeds in zip(
+                network_multiplier_list, noisy_latent_list, noise_list, timesteps_list, conditional_embeds_list
+        ):
+            with self.network:
+                assert self.network.is_active
+
+                self.network.multiplier = network_multiplier
+
+                noise_pred = self.sd.predict_noise(
+                    latents=noisy_latents.to(self.device_torch, dtype=dtype),
+                    conditional_embeddings=conditional_embeds.to(self.device_torch, dtype=dtype),
+                    timestep=timesteps,
+                )
+                noise = noise.to(self.device_torch, dtype=dtype)
+
+                if self.sd.prediction_type == 'v_prediction':
+                    # v-parameterization training
+                    target = noise_scheduler.get_velocity(noisy_latents, noise, timesteps)
+                else:
+                    target = noise
+
+                loss = torch.nn.functional.mse_loss(noise_pred.float(), target.float(), reduction="none")
+                loss = loss.mean([1, 2, 3])
+
+                if self.train_config.min_snr_gamma is not None and self.train_config.min_snr_gamma > 0.000001:
+                    # add min_snr_gamma
+                    loss = apply_snr_weight(loss, timesteps, noise_scheduler, self.train_config.min_snr_gamma)
+
+                loss = loss.mean() * loss_jitter_multiplier
+
+                loss_float = loss.item()
+                losses.append(loss_float)
+
+                # back propagate loss to free ram
+                loss.backward()
+
+        # apply gradients
+        optimizer.step()
+        lr_scheduler.step()
+
+        # reset network
+        self.network.multiplier = 1.0
+
+        loss_dict = OrderedDict(
+            {'loss': sum(losses) / len(losses) if len(losses) > 0 else 0.0}
+        )
+
+        return loss_dict
+        # end hook_train_loop