add validation images for lora training

examples/lora_dreambooth/train_dreambooth.py

@@ -11,6 +11,7 @@ import warnings
 from pathlib import Path
 from typing import Optional
 
+import numpy as np
 import torch
 import torch.nn.functional as F
 import torch.utils.checkpoint
@@ -24,7 +25,13 @@ from transformers import AutoTokenizer, PretrainedConfig
 import datasets
 import diffusers
 import psutil
-from diffusers import AutoencoderKL, DDPMScheduler, DiffusionPipeline, UNet2DConditionModel
+from diffusers import (
+    AutoencoderKL,
+    DDPMScheduler,
+    DiffusionPipeline,
+    DPMSolverMultistepScheduler,
+    UNet2DConditionModel,
+)
 from diffusers.optimization import get_scheduler
 from diffusers.utils import check_min_version
 from diffusers.utils.import_utils import is_xformers_available
@@ -129,6 +136,27 @@ def parse_args(input_args=None):
             " class_data_dir, additional images will be sampled with class_prompt."
         ),
     )
+    parser.add_argument(
+        "--validation_prompt",
+        type=str,
+        default=None,
+        help="A prompt that is used during validation to verify that the model is learning.",
+    )
+    parser.add_argument(
+        "--num_validation_images",
+        type=int,
+        default=4,
+        help="Number of images that should be generated during validation with `validation_prompt`.",
+    )
+    parser.add_argument(
+        "--validation_steps",
+        type=int,
+        default=100,
+        help=(
+            "Run dreambooth validation every X steps. Dreambooth validation consists of running the prompt"
+            " `args.validation_prompt` multiple times: `args.num_validation_images`."
+        ),
+    )
     parser.add_argument(
         "--output_dir",
         type=str,
@@ -948,6 +976,54 @@ def main(args):
                 progress_bar.set_postfix(**logs)
                 accelerator.log(logs, step=global_step)
 
+                if (
+                    args.validation_prompt is not None
+                    and step + num_update_steps_per_epoch * epoch % args.validation_steps == 0
+                ):
+                    logger.info(
+                        f"Running validation... \n Generating {args.num_validation_images} images with prompt:"
+                        f" {args.validation_prompt}."
+                    )
+                    # create pipeline
+                    pipeline = DiffusionPipeline.from_pretrained(
+                        args.pretrained_model_name_or_path,
+                        safety_checker=None,
+                        revision=args.revision,
+                    )
+                    # set `keep_fp32_wrapper` to True because we do not want to remove
+                    # mixed precision hooks while we are still training
+                    pipeline.unet = accelerator.unwrap_model(unet, keep_fp32_wrapper=True)
+                    pipeline.text_encoder = accelerator.unwrap_model(text_encoder, keep_fp32_wrapper=True)
+                    pipeline.scheduler = DPMSolverMultistepScheduler.from_config(pipeline.scheduler.config)
+                    pipeline = pipeline.to(accelerator.device)
+                    pipeline.set_progress_bar_config(disable=True)
+
+                    # run inference
+                    generator = torch.Generator(device=accelerator.device).manual_seed(args.seed)
+                    images = []
+                    for _ in range(args.num_validation_images):
+                        image = pipeline(args.validation_prompt, num_inference_steps=25, generator=generator).images[0]
+                        images.append(image)
+
+                    for tracker in accelerator.trackers:
+                        if tracker.name == "tensorboard":
+                            np_images = np.stack([np.asarray(img) for img in images])
+                            tracker.writer.add_images("validation", np_images, epoch, dataformats="NHWC")
+                        if tracker.name == "wandb":
+                            import wandb
+
+                            tracker.log(
+                                {
+                                    "validation": [
+                                        wandb.Image(image, caption=f"{i}: {args.validation_prompt}")
+                                        for i, image in enumerate(images)
+                                    ]
+                                }
+                            )
+
+                    del pipeline
+                    torch.cuda.empty_cache()
+
                 if global_step >= args.max_train_steps:
                     break
         # Printing the GPU memory usage details such as allocated memory, peak memory, and total memory usage