Merge branch 'main' of github.com:huggingface/diffusers

examples/dreambooth/README_flux.md

@@ -8,7 +8,7 @@ The `train_dreambooth_flux.py` script shows how to implement the training proced
 > 
 > Flux can be quite expensive to run on consumer hardware devices and as a result finetuning it comes with high memory requirements - 
 > a LoRA with a rank of 16 (w/ all components trained) can exceed 40GB of VRAM for training.
-> For more tips & guidance on training on a resource-constrained device please visit [`@bghira`'s guide](documentation/quickstart/FLUX.md) 
+> For more tips & guidance on training on a resource-constrained device please visit [`@bghira`'s guide](https://github.com/bghira/SimpleTuner/blob/main/documentation/quickstart/FLUX.md) 
 
 
 > [!NOTE]
@@ -96,7 +96,7 @@ accelerate launch train_dreambooth_flux.py \
   --pretrained_model_name_or_path=$MODEL_NAME  \
   --instance_data_dir=$INSTANCE_DIR \
   --output_dir=$OUTPUT_DIR \
-  --mixed_precision="fp16" \
+  --mixed_precision="bf16" \
   --instance_prompt="a photo of sks dog" \
   --resolution=1024 \
   --train_batch_size=1 \
@@ -140,7 +140,7 @@ accelerate launch train_dreambooth_lora_flux.py \
   --pretrained_model_name_or_path=$MODEL_NAME  \
   --instance_data_dir=$INSTANCE_DIR \
   --output_dir=$OUTPUT_DIR \
-  --mixed_precision="fp16" \
+  --mixed_precision="bf16" \
   --instance_prompt="a photo of sks dog" \
   --resolution=512 \
   --train_batch_size=1 \
@@ -175,7 +175,7 @@ accelerate launch train_dreambooth_lora_flux.py \
   --pretrained_model_name_or_path=$MODEL_NAME  \
   --instance_data_dir=$INSTANCE_DIR \
   --output_dir=$OUTPUT_DIR \
-  --mixed_precision="fp16" \
+  --mixed_precision="bf16" \
   --train_text_encoder\
   --instance_prompt="a photo of sks dog" \
   --resolution=512 \

examples/dreambooth/requirements_flux.txt

@@ -0,0 +1,8 @@
+accelerate>=0.31.0
+torchvision
+transformers>=4.41.2
+ftfy
+tensorboard
+Jinja2
+peft>=0.11.1
+sentencepiece

examples/dreambooth/test_dreambooth_flux.py

@@ -0,0 +1,203 @@
+# coding=utf-8
+# Copyright 2024 HuggingFace Inc.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import logging
+import os
+import shutil
+import sys
+import tempfile
+
+from diffusers import DiffusionPipeline, FluxTransformer2DModel
+
+
+sys.path.append("..")
+from test_examples_utils import ExamplesTestsAccelerate, run_command  # noqa: E402
+
+
+logging.basicConfig(level=logging.DEBUG)
+
+logger = logging.getLogger()
+stream_handler = logging.StreamHandler(sys.stdout)
+logger.addHandler(stream_handler)
+
+
+class DreamBoothFlux(ExamplesTestsAccelerate):
+    instance_data_dir = "docs/source/en/imgs"
+    instance_prompt = "photo"
+    pretrained_model_name_or_path = "hf-internal-testing/tiny-flux-pipe"
+    script_path = "examples/dreambooth/train_dreambooth_flux.py"
+
+    def test_dreambooth(self):
+        with tempfile.TemporaryDirectory() as tmpdir:
+            test_args = f"""
+                {self.script_path}
+                --pretrained_model_name_or_path {self.pretrained_model_name_or_path}
+                --instance_data_dir {self.instance_data_dir}
+                --instance_prompt {self.instance_prompt}
+                --resolution 64
+                --train_batch_size 1
+                --gradient_accumulation_steps 1
+                --max_train_steps 2
+                --learning_rate 5.0e-04
+                --scale_lr
+                --lr_scheduler constant
+                --lr_warmup_steps 0
+                --output_dir {tmpdir}
+                """.split()
+
+            run_command(self._launch_args + test_args)
+            # save_pretrained smoke test
+            self.assertTrue(os.path.isfile(os.path.join(tmpdir, "transformer", "diffusion_pytorch_model.safetensors")))
+            self.assertTrue(os.path.isfile(os.path.join(tmpdir, "scheduler", "scheduler_config.json")))
+
+    def test_dreambooth_checkpointing(self):
+        with tempfile.TemporaryDirectory() as tmpdir:
+            # Run training script with checkpointing
+            # max_train_steps == 4, checkpointing_steps == 2
+            # Should create checkpoints at steps 2, 4
+
+            initial_run_args = f"""
+                {self.script_path}
+                --pretrained_model_name_or_path {self.pretrained_model_name_or_path}
+                --instance_data_dir {self.instance_data_dir}
+                --instance_prompt {self.instance_prompt}
+                --resolution 64
+                --train_batch_size 1
+                --gradient_accumulation_steps 1
+                --max_train_steps 4
+                --learning_rate 5.0e-04
+                --scale_lr
+                --lr_scheduler constant
+                --lr_warmup_steps 0
+                --output_dir {tmpdir}
+                --checkpointing_steps=2
+                --seed=0
+                """.split()
+
+            run_command(self._launch_args + initial_run_args)
+
+            # check can run the original fully trained output pipeline
+            pipe = DiffusionPipeline.from_pretrained(tmpdir)
+            pipe(self.instance_prompt, num_inference_steps=1)
+
+            # check checkpoint directories exist
+            self.assertTrue(os.path.isdir(os.path.join(tmpdir, "checkpoint-2")))
+            self.assertTrue(os.path.isdir(os.path.join(tmpdir, "checkpoint-4")))
+
+            # check can run an intermediate checkpoint
+            transformer = FluxTransformer2DModel.from_pretrained(tmpdir, subfolder="checkpoint-2/transformer")
+            pipe = DiffusionPipeline.from_pretrained(self.pretrained_model_name_or_path, transformer=transformer)
+            pipe(self.instance_prompt, num_inference_steps=1)
+
+            # Remove checkpoint 2 so that we can check only later checkpoints exist after resuming
+            shutil.rmtree(os.path.join(tmpdir, "checkpoint-2"))
+
+            # Run training script for 7 total steps resuming from checkpoint 4
+
+            resume_run_args = f"""
+                {self.script_path}
+                --pretrained_model_name_or_path {self.pretrained_model_name_or_path}
+                --instance_data_dir {self.instance_data_dir}
+                --instance_prompt {self.instance_prompt}
+                --resolution 64
+                --train_batch_size 1
+                --gradient_accumulation_steps 1
+                --max_train_steps 6
+                --learning_rate 5.0e-04
+                --scale_lr
+                --lr_scheduler constant
+                --lr_warmup_steps 0
+                --output_dir {tmpdir}
+                --checkpointing_steps=2
+                --resume_from_checkpoint=checkpoint-4
+                --seed=0
+                """.split()
+
+            run_command(self._launch_args + resume_run_args)
+
+            # check can run new fully trained pipeline
+            pipe = DiffusionPipeline.from_pretrained(tmpdir)
+            pipe(self.instance_prompt, num_inference_steps=1)
+
+            # check old checkpoints do not exist
+            self.assertFalse(os.path.isdir(os.path.join(tmpdir, "checkpoint-2")))
+
+            # check new checkpoints exist
+            self.assertTrue(os.path.isdir(os.path.join(tmpdir, "checkpoint-4")))
+            self.assertTrue(os.path.isdir(os.path.join(tmpdir, "checkpoint-6")))
+
+    def test_dreambooth_checkpointing_checkpoints_total_limit(self):
+        with tempfile.TemporaryDirectory() as tmpdir:
+            test_args = f"""
+            {self.script_path}
+            --pretrained_model_name_or_path={self.pretrained_model_name_or_path}
+            --instance_data_dir={self.instance_data_dir}
+            --output_dir={tmpdir}
+            --instance_prompt={self.instance_prompt}
+            --resolution=64
+            --train_batch_size=1
+            --gradient_accumulation_steps=1
+            --max_train_steps=6
+            --checkpoints_total_limit=2
+            --checkpointing_steps=2
+            """.split()
+
+            run_command(self._launch_args + test_args)
+
+            self.assertEqual(
+                {x for x in os.listdir(tmpdir) if "checkpoint" in x},
+                {"checkpoint-4", "checkpoint-6"},
+            )
+
+    def test_dreambooth_checkpointing_checkpoints_total_limit_removes_multiple_checkpoints(self):
+        with tempfile.TemporaryDirectory() as tmpdir:
+            test_args = f"""
+            {self.script_path}
+            --pretrained_model_name_or_path={self.pretrained_model_name_or_path}
+            --instance_data_dir={self.instance_data_dir}
+            --output_dir={tmpdir}
+            --instance_prompt={self.instance_prompt}
+            --resolution=64
+            --train_batch_size=1
+            --gradient_accumulation_steps=1
+            --max_train_steps=4
+            --checkpointing_steps=2
+            """.split()
+
+            run_command(self._launch_args + test_args)
+
+            self.assertEqual(
+                {x for x in os.listdir(tmpdir) if "checkpoint" in x},
+                {"checkpoint-2", "checkpoint-4"},
+            )
+
+            resume_run_args = f"""
+            {self.script_path}
+            --pretrained_model_name_or_path={self.pretrained_model_name_or_path}
+            --instance_data_dir={self.instance_data_dir}
+            --output_dir={tmpdir}
+            --instance_prompt={self.instance_prompt}
+            --resolution=64
+            --train_batch_size=1
+            --gradient_accumulation_steps=1
+            --max_train_steps=8
+            --checkpointing_steps=2
+            --resume_from_checkpoint=checkpoint-4
+            --checkpoints_total_limit=2
+            """.split()
+
+            run_command(self._launch_args + resume_run_args)
+
+            self.assertEqual({x for x in os.listdir(tmpdir) if "checkpoint" in x}, {"checkpoint-6", "checkpoint-8"})