Various Fixes for Flax Dreambooth (#1782)

* Various Fixes for Flax Dreambooth

- Correctly update the progress bar every epoch
- Allow specifying a pretrained VAE
- Allow specifying a revision to pretrained models
- Cache compiled models between invocations (speeds up TPU execution a lot!)
- Save intermediate checkpoints by specifying `save_steps`

* Don't die when save_steps is not set.

* Address CR

* Address comments

* Apply suggestions from code review

Co-authored-by: Pedro Cuenca <pedro@huggingface.co>

Co-authored-by: Suraj Patil <surajp815@gmail.com>
Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com>
Co-authored-by: Pedro Cuenca <pedro@huggingface.co>

examples/dreambooth/train_dreambooth_flax.py

@@ -28,6 +28,7 @@ from flax import jax_utils
 from flax.training import train_state
 from flax.training.common_utils import shard
 from huggingface_hub import HfFolder, Repository, whoami
+from jax.experimental.compilation_cache import compilation_cache as cc
 from PIL import Image
 from torchvision import transforms
 from tqdm.auto import tqdm
@@ -37,6 +38,9 @@ from transformers import CLIPFeatureExtractor, CLIPTokenizer, FlaxCLIPTextModel,
 # Will error if the minimal version of diffusers is not installed. Remove at your own risks.
 check_min_version("0.10.0.dev0")
 
+# Cache compiled models across invocations of this script.
+cc.initialize_cache(os.path.expanduser("~/.cache/jax/compilation_cache"))
+
 logger = logging.getLogger(__name__)
 
 
@@ -49,6 +53,19 @@ def parse_args():
         required=True,
         help="Path to pretrained model or model identifier from huggingface.co/models.",
     )
+    parser.add_argument(
+        "--pretrained_vae_name_or_path",
+        type=str,
+        default=None,
+        help="Path to pretrained vae or vae identifier from huggingface.co/models.",
+    )
+    parser.add_argument(
+        "--revision",
+        type=str,
+        default=None,
+        required=False,
+        help="Revision of pretrained model identifier from huggingface.co/models.",
+    )
     parser.add_argument(
         "--tokenizer_name",
         type=str,
@@ -103,6 +120,7 @@ def parse_args():
         default="text-inversion-model",
         help="The output directory where the model predictions and checkpoints will be written.",
     )
+    parser.add_argument("--save_steps", type=int, default=None, help="Save a checkpoint every X steps.")
     parser.add_argument("--seed", type=int, default=0, help="A seed for reproducible training.")
     parser.add_argument(
         "--resolution",
@@ -332,7 +350,7 @@ def main():
 
         if cur_class_images < args.num_class_images:
             pipeline, params = FlaxStableDiffusionPipeline.from_pretrained(
-                args.pretrained_model_name_or_path, safety_checker=None
+                args.pretrained_model_name_or_path, safety_checker=None, revision=args.revision
             )
             pipeline.set_progress_bar_config(disable=True)
 
@@ -383,7 +401,11 @@ def main():
     if args.tokenizer_name:
         tokenizer = CLIPTokenizer.from_pretrained(args.tokenizer_name)
     elif args.pretrained_model_name_or_path:
-        tokenizer = CLIPTokenizer.from_pretrained(args.pretrained_model_name_or_path, subfolder="tokenizer")
+        tokenizer = CLIPTokenizer.from_pretrained(
+            args.pretrained_model_name_or_path, subfolder="tokenizer", revision=args.revision
+        )
+    else:
+        raise NotImplementedError("No tokenizer specified!")
 
     train_dataset = DreamBoothDataset(
         instance_data_root=args.instance_data_dir,
@@ -437,15 +459,23 @@ def main():
     elif args.mixed_precision == "bf16":
         weight_dtype = jnp.bfloat16
 
+    if args.pretrained_vae_name_or_path:
+        # TODO(patil-suraj): Upload flax weights for the VAE
+        vae_arg, vae_kwargs = (args.pretrained_vae_name_or_path, {"from_pt": True})
+    else:
+        vae_arg, vae_kwargs = (args.pretrained_model_name_or_path, {"subfolder": "vae", "revision": args.revision})
+
     # Load models and create wrapper for stable diffusion
     text_encoder = FlaxCLIPTextModel.from_pretrained(
-        args.pretrained_model_name_or_path, subfolder="text_encoder", dtype=weight_dtype
+        args.pretrained_model_name_or_path, subfolder="text_encoder", dtype=weight_dtype, revision=args.revision
     )
     vae, vae_params = FlaxAutoencoderKL.from_pretrained(
-        args.pretrained_model_name_or_path, subfolder="vae", dtype=weight_dtype
+        vae_arg,
+        dtype=weight_dtype,
+        **vae_kwargs,
     )
     unet, unet_params = FlaxUNet2DConditionModel.from_pretrained(
-        args.pretrained_model_name_or_path, subfolder="unet", dtype=weight_dtype
+        args.pretrained_model_name_or_path, subfolder="unet", dtype=weight_dtype, revision=args.revision
     )
 
     # Optimization
@@ -597,6 +627,39 @@ def main():
     logger.info(f"  Total train batch size (w. parallel & distributed) = {total_train_batch_size}")
     logger.info(f"  Total optimization steps = {args.max_train_steps}")
 
+    def checkpoint(step=None):
+        # Create the pipeline using the trained modules and save it.
+        scheduler, _ = FlaxPNDMScheduler.from_pretrained(
+                "CompVis/stable-diffusion-v1-4", subfolder="scheduler"
+        )
+        safety_checker = FlaxStableDiffusionSafetyChecker.from_pretrained(
+            "CompVis/stable-diffusion-safety-checker", from_pt=True
+        )
+        pipeline = FlaxStableDiffusionPipeline(
+            text_encoder=text_encoder,
+            vae=vae,
+            unet=unet,
+            tokenizer=tokenizer,
+            scheduler=scheduler,
+            safety_checker=safety_checker,
+            feature_extractor=CLIPFeatureExtractor.from_pretrained("openai/clip-vit-base-patch32"),
+        )
+
+        outdir = os.path.join(args.output_dir, str(step)) if step else args.output_dir
+        pipeline.save_pretrained(
+            outdir,
+            params={
+                "text_encoder": get_params_to_save(text_encoder_state.params),
+                "vae": get_params_to_save(vae_params),
+                "unet": get_params_to_save(unet_state.params),
+                "safety_checker": safety_checker.params,
+            },
+        )
+
+        if args.push_to_hub:
+            message = f"checkpoint-{step}" if step is not None else "End of training"
+            repo.push_to_hub(commit_message=message, blocking=False, auto_lfs_prune=True)
+
     global_step = 0
 
     epochs = tqdm(range(args.num_train_epochs), desc="Epoch ... ", position=0)
@@ -615,9 +678,11 @@ def main():
             )
             train_metrics.append(train_metric)
 
-            train_step_progress_bar.update(1)
+            train_step_progress_bar.update(jax.local_device_count())
 
             global_step += 1
+            if jax.process_index() == 0 and args.save_steps and global_step % args.save_steps == 0:
+                checkpoint(global_step)
             if global_step >= args.max_train_steps:
                 break
 
@@ -626,36 +691,8 @@ def main():
         train_step_progress_bar.close()
         epochs.write(f"Epoch... ({epoch + 1}/{args.num_train_epochs} | Loss: {train_metric['loss']})")
 
-    # Create the pipeline using using the trained modules and save it.
     if jax.process_index() == 0:
-        scheduler = FlaxPNDMScheduler(
+        checkpoint()
-            beta_start=0.00085, beta_end=0.012, beta_schedule="scaled_linear", skip_prk_steps=True
-        )
-        safety_checker = FlaxStableDiffusionSafetyChecker.from_pretrained(
-            "CompVis/stable-diffusion-safety-checker", from_pt=True
-        )
-        pipeline = FlaxStableDiffusionPipeline(
-            text_encoder=text_encoder,
-            vae=vae,
-            unet=unet,
-            tokenizer=tokenizer,
-            scheduler=scheduler,
-            safety_checker=safety_checker,
-            feature_extractor=CLIPFeatureExtractor.from_pretrained("openai/clip-vit-base-patch32"),
-        )
-
-        pipeline.save_pretrained(
-            args.output_dir,
-            params={
-                "text_encoder": get_params_to_save(text_encoder_state.params),
-                "vae": get_params_to_save(vae_params),
-                "unet": get_params_to_save(unet_state.params),
-                "safety_checker": safety_checker.params,
-            },
-        )
-
-        if args.push_to_hub:
-            repo.push_to_hub(commit_message="End of training", blocking=False, auto_lfs_prune=True)
 
 
 if __name__ == "__main__":