Textual inv make save log both steps (huggingface#2178)

* Initial commit

* removed images

* Made logging the same as save

* Removed logging function

* Quality fixes

* Quality fixes

* Tested

* Added support back for validation_epochs

* Fixing styles

* Did changes

* Change to log_validation

* Add extra space after wandb import

* Add extra space after wandb

Co-authored-by: Will Berman <[email protected]>

* Fixed spacing

---------

Co-authored-by: Will Berman <[email protected]>

Author: 2 people

examples/textual_inversion/textual_inversion.py

@@ -18,6 +18,7 @@
 import math
 import os
 import random
+import warnings
 from pathlib import Path
 from typing import Optional
 
@@ -54,6 +55,9 @@
 from diffusers.utils.import_utils import is_xformers_available
 
 
+if is_wandb_available():
+    import wandb
+
 if version.parse(version.parse(PIL.__version__).base_version) >= version.parse("9.1.0"):
     PIL_INTERPOLATION = {
         "linear": PIL.Image.Resampling.BILINEAR,
@@ -79,6 +83,50 @@
 logger = get_logger(__name__)
 
 
+def log_validation(text_encoder, tokenizer, unet, vae, args, accelerator, weight_dtype, epoch):
+    logger.info(
+        f"Running validation... \n Generating {args.num_validation_images} images with prompt:"
+        f" {args.validation_prompt}."
+    )
+    # create pipeline (note: unet and vae are loaded again in float32)
+    pipeline = DiffusionPipeline.from_pretrained(
+        args.pretrained_model_name_or_path,
+        text_encoder=accelerator.unwrap_model(text_encoder),
+        tokenizer=tokenizer,
+        unet=unet,
+        vae=vae,
+        revision=args.revision,
+        torch_dtype=weight_dtype,
+    )
+    pipeline.scheduler = DPMSolverMultistepScheduler.from_config(pipeline.scheduler.config)
+    pipeline = pipeline.to(accelerator.device)
+    pipeline.set_progress_bar_config(disable=True)
+
+    # run inference
+    generator = None if args.seed is None else torch.Generator(device=accelerator.device).manual_seed(args.seed)
+    images = []
+    for _ in range(args.num_validation_images):
+        with torch.autocast("cuda"):
+            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":
+            tracker.log(
+                {
+                    "validation": [
+                        wandb.Image(image, caption=f"{i}: {args.validation_prompt}") for i, image in enumerate(images)
+                    ]
+                }
+            )
+
+    del pipeline
+    torch.cuda.empty_cache()
+
+
 def save_progress(text_encoder, placeholder_token_id, accelerator, args, save_path):
     logger.info("Saving embeddings")
     learned_embeds = accelerator.unwrap_model(text_encoder).get_input_embeddings().weight[placeholder_token_id]
@@ -268,12 +316,22 @@ def parse_args():
         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 validation every X steps. Validation consists of running the prompt"
+            " `args.validation_prompt` multiple times: `args.num_validation_images`"
+            " and logging the images."
+        ),
+    )
     parser.add_argument(
         "--validation_epochs",
         type=int,
-        default=50,
+        default=None,
         help=(
-            "Run validation every X epochs. Validation consists of running the prompt"
+            "Deprecated in favor of validation_steps. Run validation every X epochs. Validation consists of running the prompt"
             " `args.validation_prompt` multiple times: `args.num_validation_images`"
             " and logging the images."
         ),
@@ -488,7 +546,6 @@ def main():
     if args.report_to == "wandb":
         if not is_wandb_available():
             raise ImportError("Make sure to install wandb if you want to use it for logging during training.")
-        import wandb
 
     # Make one log on every process with the configuration for debugging.
     logging.basicConfig(
@@ -627,6 +684,15 @@ def main():
     train_dataloader = torch.utils.data.DataLoader(
         train_dataset, batch_size=args.train_batch_size, shuffle=True, num_workers=args.dataloader_num_workers
     )
+    if args.validation_epochs is not None:
+        warnings.warn(
+            f"FutureWarning: You are doing logging with validation_epochs={args.validation_epochs}."
+            " Deprecated validation_epochs in favor of `validation_steps`"
+            f"Setting `args.validation_steps` to {args.validation_epochs * len(train_dataset)}",
+            FutureWarning,
+            stacklevel=2,
+        )
+        args.validation_steps = args.validation_epochs * len(train_dataset)
 
     # Scheduler and math around the number of training steps.
     overrode_max_train_steps = False
@@ -683,7 +749,6 @@ def main():
     logger.info(f"  Total optimization steps = {args.max_train_steps}")
     global_step = 0
     first_epoch = 0
-
     # Potentially load in the weights and states from a previous save
     if args.resume_from_checkpoint:
         if args.resume_from_checkpoint != "latest":
@@ -783,60 +848,15 @@ def main():
                         save_path = os.path.join(args.output_dir, f"checkpoint-{global_step}")
                         accelerator.save_state(save_path)
                         logger.info(f"Saved state to {save_path}")
+                if args.validation_prompt is not None and global_step % args.validation_steps == 0:
+                    log_validation(text_encoder, tokenizer, unet, vae, args, accelerator, weight_dtype, epoch)
 
             logs = {"loss": loss.detach().item(), "lr": lr_scheduler.get_last_lr()[0]}
             progress_bar.set_postfix(**logs)
             accelerator.log(logs, step=global_step)
 
             if global_step >= args.max_train_steps:
                 break
-
-        if args.validation_prompt is not None and epoch % args.validation_epochs == 0:
-            logger.info(
-                f"Running validation... \n Generating {args.num_validation_images} images with prompt:"
-                f" {args.validation_prompt}."
-            )
-            # create pipeline (note: unet and vae are loaded again in float32)
-            pipeline = DiffusionPipeline.from_pretrained(
-                args.pretrained_model_name_or_path,
-                text_encoder=accelerator.unwrap_model(text_encoder),
-                tokenizer=tokenizer,
-                unet=unet,
-                vae=vae,
-                revision=args.revision,
-                torch_dtype=weight_dtype,
-            )
-            pipeline.scheduler = DPMSolverMultistepScheduler.from_config(pipeline.scheduler.config)
-            pipeline = pipeline.to(accelerator.device)
-            pipeline.set_progress_bar_config(disable=True)
-
-            # run inference
-            generator = (
-                None if args.seed is None else torch.Generator(device=accelerator.device).manual_seed(args.seed)
-            )
-            images = []
-            for _ in range(args.num_validation_images):
-                with torch.autocast("cuda"):
-                    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":
-                    tracker.log(
-                        {
-                            "validation": [
-                                wandb.Image(image, caption=f"{i}: {args.validation_prompt}")
-                                for i, image in enumerate(images)
-                            ]
-                        }
-                    )
-
-            del pipeline
-            torch.cuda.empty_cache()
-
     # Create the pipeline using using the trained modules and save it.
     accelerator.wait_for_everyone()
     if accelerator.is_main_process: