[Flux] Add advanced training script + support textual inversion infer…

…ence (#9434) * add ostris trainer to README & add cache latents of vae * add ostris trainer to README & add cache latents of vae * style * readme * add test for latent caching * add ostris noise scheduler https://github.com/ostris/ai-toolkit/blob/9ee1ef2a0a2a9a02b92d114a95f21312e5906e54/toolkit/samplers/custom_flowmatch_sampler.py#L95 * style * fix import * style * fix tests * style * --change upcasting of transformer? * update readme according to main * add pivotal tuning for CLIP * fix imports, encode_prompt call,add TextualInversionLoaderMixin to FluxPipeline for inference * TextualInversionLoaderMixin support for FluxPipeline for inference * move changes to advanced flux script, revert canonical * add latent caching to canonical script * revert changes to canonical script to keep it separate from #9160 * revert changes to canonical script to keep it separate from #9160 * style * remove redundant line and change code block placement to align with logic * add initializer_token arg * add transformer frac for range support from pure textual inversion to the orig pivotal tuning * support pure textual inversion - wip * adjustments to support pure textual inversion and transformer optimization in only part of the epochs * fix logic when using initializer token * fix pure_textual_inversion_condition * fix ti/pivotal loading of last validation run * remove embeddings loading for ti in final training run (to avoid adding huggingface hub dependency) * support pivotal for t5 * adapt pivotal for T5 encoder * adapt pivotal for T5 encoder and support in flux pipeline * t5 pivotal support + support fo pivotal for clip only or both * fix param chaining * fix param chaining * README first draft * readme * readme * readme * style * fix import * style * add fix from #9419 * add to readme, change function names * te lr changes * readme * change concept tokens logic * fix indices * change arg name * style * dummy test * revert dummy test * reorder pivoting * add warning in case the token abstraction is not the instance prompt * experimental - wip - specific block training * fix documentation and token abstraction processing * remove transformer block specification feature (for now) * style * fix copies * fix indexing issue when --initializer_concept has different amounts * add if TextualInversionLoaderMixin to all flux pipelines * style * fix import * fix imports * address review comments - remove necessary prints & comments, use pin_memory=True, use free_memory utils, unify warning and prints * style * logger info fix * make lora target modules configurable and change the default * make lora target modules configurable and change the default * style * make lora target modules configurable and change the default, add notes to readme * style * add tests * style * fix repo id * add updated requirements for advanced flux * fix indices of t5 pivotal tuning embeddings * fix path in test * remove `pin_memory` * fix filename of embedding * fix filename of embedding --------- Co-authored-by: Sayak Paul <spsayakpaul@gmail.com> Co-authored-by: YiYi Xu <yixu310@gmail.com>
huggingface · Oct 17, 2024 · 9a7f824 · 9a7f824
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diff --git a/examples/advanced_diffusion_training/README_flux.md b/examples/advanced_diffusion_training/README_flux.md
diff --git a/examples/advanced_diffusion_training/requirements_flux.txt b/examples/advanced_diffusion_training/requirements_flux.txt
@@ -0,0 +1,8 @@
+accelerate>=0.31.0
+torchvision
+transformers>=4.41.2
+ftfy
+tensorboard
+Jinja2
+peft>=0.11.1
+sentencepiece
diff --git a/examples/advanced_diffusion_training/test_dreambooth_lora_flux_advanced.py b/examples/advanced_diffusion_training/test_dreambooth_lora_flux_advanced.py
@@ -0,0 +1,283 @@
+# 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 sys
+import tempfile
+
+import safetensors
+
+
+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 DreamBoothLoRAFluxAdvanced(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/advanced_diffusion_training/train_dreambooth_lora_flux_advanced.py"
+
+    def test_dreambooth_lora_flux(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, "pytorch_lora_weights.safetensors")))
+
+            # make sure the state_dict has the correct naming in the parameters.
+            lora_state_dict = safetensors.torch.load_file(os.path.join(tmpdir, "pytorch_lora_weights.safetensors"))
+            is_lora = all("lora" in k for k in lora_state_dict.keys())
+            self.assertTrue(is_lora)
+
+            # when not training the text encoder, all the parameters in the state dict should start
+            # with `"transformer"` in their names.
+            starts_with_transformer = all(key.startswith("transformer") for key in lora_state_dict.keys())
+            self.assertTrue(starts_with_transformer)
+
+    def test_dreambooth_lora_text_encoder_flux(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
+                --train_text_encoder
+                --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, "pytorch_lora_weights.safetensors")))
+
+            # make sure the state_dict has the correct naming in the parameters.
+            lora_state_dict = safetensors.torch.load_file(os.path.join(tmpdir, "pytorch_lora_weights.safetensors"))
+            is_lora = all("lora" in k for k in lora_state_dict.keys())
+            self.assertTrue(is_lora)
+
+            starts_with_expected_prefix = all(
+                (key.startswith("transformer") or key.startswith("text_encoder")) for key in lora_state_dict.keys()
+            )
+            self.assertTrue(starts_with_expected_prefix)
+
+    def test_dreambooth_lora_pivotal_tuning_flux_clip(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
+                --train_text_encoder_ti
+                --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, "pytorch_lora_weights.safetensors")))
+            # make sure embeddings were also saved
+            self.assertTrue(os.path.isfile(os.path.join(tmpdir, f"{os.path.basename(tmpdir)}_emb.safetensors")))
+
+            # make sure the state_dict has the correct naming in the parameters.
+            lora_state_dict = safetensors.torch.load_file(os.path.join(tmpdir, "pytorch_lora_weights.safetensors"))
+            is_lora = all("lora" in k for k in lora_state_dict.keys())
+            self.assertTrue(is_lora)
+
+            # make sure the state_dict has the correct naming in the parameters.
+            textual_inversion_state_dict = safetensors.torch.load_file(
+                os.path.join(tmpdir, f"{os.path.basename(tmpdir)}_emb.safetensors")
+            )
+            is_clip = all("clip_l" in k for k in textual_inversion_state_dict.keys())
+            self.assertTrue(is_clip)
+
+            # when performing pivotal tuning, all the parameters in the state dict should start
+            # with `"transformer"` in their names.
+            starts_with_transformer = all(key.startswith("transformer") for key in lora_state_dict.keys())
+            self.assertTrue(starts_with_transformer)
+
+    def test_dreambooth_lora_pivotal_tuning_flux_clip_t5(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
+                --train_text_encoder_ti
+                --enable_t5_ti
+                --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, "pytorch_lora_weights.safetensors")))
+            # make sure embeddings were also saved
+            self.assertTrue(os.path.isfile(os.path.join(tmpdir, f"{os.path.basename(tmpdir)}_emb.safetensors")))
+
+            # make sure the state_dict has the correct naming in the parameters.
+            lora_state_dict = safetensors.torch.load_file(os.path.join(tmpdir, "pytorch_lora_weights.safetensors"))
+            is_lora = all("lora" in k for k in lora_state_dict.keys())
+            self.assertTrue(is_lora)
+
+            # make sure the state_dict has the correct naming in the parameters.
+            textual_inversion_state_dict = safetensors.torch.load_file(
+                os.path.join(tmpdir, f"{os.path.basename(tmpdir)}_emb.safetensors")
+            )
+            is_te = all(("clip_l" in k or "t5" in k) for k in textual_inversion_state_dict.keys())
+            self.assertTrue(is_te)
+
+            # when performing pivotal tuning, all the parameters in the state dict should start
+            # with `"transformer"` in their names.
+            starts_with_transformer = all(key.startswith("transformer") for key in lora_state_dict.keys())
+            self.assertTrue(starts_with_transformer)
+
+    def test_dreambooth_lora_latent_caching(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
+                --cache_latents
+                --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, "pytorch_lora_weights.safetensors")))
+
+            # make sure the state_dict has the correct naming in the parameters.
+            lora_state_dict = safetensors.torch.load_file(os.path.join(tmpdir, "pytorch_lora_weights.safetensors"))
+            is_lora = all("lora" in k for k in lora_state_dict.keys())
+            self.assertTrue(is_lora)
+
+            # when not training the text encoder, all the parameters in the state dict should start
+            # with `"transformer"` in their names.
+            starts_with_transformer = all(key.startswith("transformer") for key in lora_state_dict.keys())
+            self.assertTrue(starts_with_transformer)
+
+    def test_dreambooth_lora_flux_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_lora_flux_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"})