Added inbuild plugins and made one for image referenced. WIP

extensions_built_in/image_reference_slider_trainer/ImageReferenceSliderTrainerProcess.py

@@ -0,0 +1,202 @@
+import copy
+import random
+from collections import OrderedDict
+import os
+from typing import Optional, Union, List
+from torch.utils.data import ConcatDataset, DataLoader
+from toolkit.data_loader import PairedImageDataset
+from toolkit.prompt_utils import concat_prompt_embeds
+from toolkit.stable_diffusion_model import StableDiffusion
+from toolkit.train_tools import get_torch_dtype
+import gc
+from toolkit import train_tools
+import torch
+from jobs.process import BaseSDTrainProcess
+
+
+def flush():
+    torch.cuda.empty_cache()
+    gc.collect()
+
+
+class ReferenceSliderConfig:
+    def __init__(self, **kwargs):
+        self.slider_pair_folder: str = kwargs.get('slider_pair_folder', None)
+        self.resolutions: List[int] = kwargs.get('resolutions', [512])
+        self.batch_full_slide: bool = kwargs.get('batch_full_slide', True)
+        self.target_class: int = kwargs.get('target_class', '')
+
+
+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 res in self.slider_config.resolutions:
+                print(f" - Dataset: {self.slider_config.slider_pair_folder}")
+                config = {
+                    'path': self.slider_config.slider_pair_folder,
+                    'size': res,
+                    'default_prompt': self.slider_config.target_class
+                }
+                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 = batch
+            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)
+
+            height = positive_images.shape[2]
+            width = positive_images.shape[3]
+            batch_size = positive_images.shape[0]
+
+            # encode the images
+            positive_latents = self.sd.vae.encode(positive_images).latent_dist.sample()
+            positive_latents = positive_latents * 0.18215
+            negative_latents = self.sd.vae.encode(negative_images).latent_dist.sample()
+            negative_latents = negative_latents * 0.18215
+
+            embedding_list = []
+            negative_embedding_list = []
+            # embed the prompts
+            for prompt in prompts:
+                embedding = self.sd.encode_prompt(prompt).to(self.device_torch, dtype=dtype)
+                embedding_list.append(embedding)
+                # just empty for now
+                # todo cache this?
+                negative_embed = self.sd.encode_prompt('').to(self.device_torch, dtype=dtype)
+                negative_embedding_list.append(negative_embed)
+
+            conditional_embeds = concat_prompt_embeds(embedding_list)
+            unconditional_embeds = concat_prompt_embeds(negative_embedding_list)
+
+            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
+            loss_function = torch.nn.MSELoss()
+
+        def get_noise_pred(neg, pos, gs, cts, dn):
+            return self.sd.predict_noise(
+                latents=dn,
+                text_embeddings=train_tools.concat_prompt_embeddings(
+                    neg,  # negative prompt
+                    pos,  # positive prompt
+                    self.train_config.batch_size,
+                ),
+                timestep=cts,
+                guidance_scale=gs,
+            )
+
+        with torch.no_grad():
+            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, (batch_size,), device=self.device_torch)
+            timesteps = timesteps.long()
+
+            # get noise
+            noise = 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)
+
+            # 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, timesteps)
+            noisy_negative_latents = noise_scheduler.add_noise(negative_latents, noise, timesteps)
+
+        flush()
+
+        self.optimizer.zero_grad()
+        with self.network:
+            assert self.network.is_active
+            loss_list = []
+            for noisy_latents, network_multiplier in zip(
+                    [noisy_positive_latents, noisy_negative_latents],
+                    [1.0, -1.0],
+            ):
+                # do positive first
+                self.network.multiplier = network_multiplier
+
+                noise_pred = get_noise_pred(
+                    unconditional_embeds,
+                    conditional_embeds,
+                    1,
+                    timesteps,
+                    noisy_latents
+                )
+
+                if self.sd.is_v2:  # check is vpred, don't want to track it down right now
+                    # 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])
+
+                # todo add snr gamma here
+
+                loss = loss.mean()
+                # back propagate loss to free ram
+                loss.backward()
+                loss_list.append(loss.item())
+
+                flush()
+
+        # apply gradients
+        optimizer.step()
+        lr_scheduler.step()
+
+        loss_float = sum(loss_list) / len(loss_list)
+
+        # reset network
+        self.network.multiplier = 1.0
+
+        loss_dict = OrderedDict(
+            {'loss': loss_float},
+        )
+        return loss_dict
+        # end hook_train_loop