[Flux Dreambooth LoRA] - te bug fixes & updates (#9139)

* add requirements + fix link to bghira's guide

* text ecnoder training fixes

* text encoder training fixes

* text encoder training fixes

* text encoder training fixes

* style

* add tests

* fix encode_prompt call

* style

* unpack_latents test

* fix lora saving

* remove default val for max_sequenece_length in encode_prompt

* remove default val for max_sequenece_length in encode_prompt

* style

* testing

* style

* testing

* testing

* style

* fix sizing issue

* style

* revert scaling

* style

* style

* scaling test

* style

* scaling test

* remove model pred operation left from pre-conditioning

* remove model pred operation left from pre-conditioning

* fix trainable params

* remove te2 from casting

* transformer to accelerator

* remove prints

* empty commit

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"})

examples/dreambooth/test_dreambooth_lora_flux.py

@@ -0,0 +1,165 @@
+# 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 DreamBoothLoRAFlux(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_lora_flux.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_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"})

examples/dreambooth/train_dreambooth_flux.py

@@ -1505,6 +1505,9 @@ def main(args):
                 model_input = vae.encode(pixel_values).latent_dist.sample()
                 model_input = (model_input - vae.config.shift_factor) * vae.config.scaling_factor
                 model_input = model_input.to(dtype=weight_dtype)
+
+                vae_scale_factor = 2 ** (len(vae.config.block_out_channels))
+
                 latent_image_ids = FluxPipeline._prepare_latent_image_ids(
                     model_input.shape[0],
                     model_input.shape[2],
@@ -1583,16 +1586,11 @@ def main(args):
 
                 model_pred = FluxPipeline._unpack_latents(
                     model_pred,
-                    height=int(model_input.shape[2]) * 8,
-                    width=int(model_input.shape[3]) * 8,
-                    vae_scale_factor=2
-                    ** (
-                        len(vae.config.block_out_channels)
-                    ),  # should this be 2 ** (len(vae.config.block_out_channels))?
+                    height=int(model_input.shape[2]),
+                    width=int(model_input.shape[3]),
+                    vae_scale_factor=vae_scale_factor,
                 )
 
-                model_pred = model_pred * (-sigmas) + noisy_model_input
-
                 # these weighting schemes use a uniform timestep sampling
                 # and instead post-weight the loss
                 weighting = compute_loss_weighting_for_sd3(weighting_scheme=args.weighting_scheme, sigmas=sigmas)

examples/dreambooth/train_dreambooth_lora_flux.py

@@ -319,7 +319,7 @@ def parse_args(input_args=None):
     parser.add_argument(
         "--max_sequence_length",
         type=int,
-        default=77,
+        default=512,
         help="Maximum sequence length to use with with the T5 text encoder",
     )
     parser.add_argument(
@@ -864,7 +864,7 @@ class PromptDataset(Dataset):
         return example
 
 
-def tokenize_prompt(tokenizer, prompt, max_sequence_length=512):
+def tokenize_prompt(tokenizer, prompt, max_sequence_length):
     text_inputs = tokenizer(
         prompt,
         padding="max_length",
@@ -885,20 +885,26 @@ def _encode_prompt_with_t5(
     prompt=None,
     num_images_per_prompt=1,
     device=None,
+    text_input_ids=None,
 ):
     prompt = [prompt] if isinstance(prompt, str) else prompt
     batch_size = len(prompt)
 
-    text_inputs = tokenizer(
-        prompt,
-        padding="max_length",
-        max_length=max_sequence_length,
-        truncation=True,
-        return_length=False,
-        return_overflowing_tokens=False,
-        return_tensors="pt",
-    )
-    text_input_ids = text_inputs.input_ids
+    if tokenizer is not None:
+        text_inputs = tokenizer(
+            prompt,
+            padding="max_length",
+            max_length=max_sequence_length,
+            truncation=True,
+            return_length=False,
+            return_overflowing_tokens=False,
+            return_tensors="pt",
+        )
+        text_input_ids = text_inputs.input_ids
+    else:
+        if text_input_ids is None:
+            raise ValueError("text_input_ids must be provided when the tokenizer is not specified")
+
     prompt_embeds = text_encoder(text_input_ids.to(device))[0]
 
     dtype = text_encoder.dtype
@@ -918,22 +924,28 @@ def _encode_prompt_with_clip(
     tokenizer,
     prompt: str,
     device=None,
+    text_input_ids=None,
     num_images_per_prompt: int = 1,
 ):
     prompt = [prompt] if isinstance(prompt, str) else prompt
     batch_size = len(prompt)
 
-    text_inputs = tokenizer(
-        prompt,
-        padding="max_length",
-        max_length=77,
-        truncation=True,
-        return_overflowing_tokens=False,
-        return_length=False,
-        return_tensors="pt",
-    )
+    if tokenizer is not None:
+        text_inputs = tokenizer(
+            prompt,
+            padding="max_length",
+            max_length=77,
+            truncation=True,
+            return_overflowing_tokens=False,
+            return_length=False,
+            return_tensors="pt",
+        )
+
+        text_input_ids = text_inputs.input_ids
+    else:
+        if text_input_ids is None:
+            raise ValueError("text_input_ids must be provided when the tokenizer is not specified")
 
-    text_input_ids = text_inputs.input_ids
     prompt_embeds = text_encoder(text_input_ids.to(device), output_hidden_states=False)
 
     # Use pooled output of CLIPTextModel
@@ -954,6 +966,7 @@ def encode_prompt(
     max_sequence_length,
     device=None,
     num_images_per_prompt: int = 1,
+    text_input_ids_list=None,
 ):
     prompt = [prompt] if isinstance(prompt, str) else prompt
     batch_size = len(prompt)
@@ -965,6 +978,7 @@ def encode_prompt(
         prompt=prompt,
         device=device if device is not None else text_encoders[0].device,
         num_images_per_prompt=num_images_per_prompt,
+        text_input_ids=text_input_ids_list[0] if text_input_ids_list else None,
     )
 
     prompt_embeds = _encode_prompt_with_t5(
@@ -974,6 +988,7 @@ def encode_prompt(
         prompt=prompt,
         num_images_per_prompt=num_images_per_prompt,
         device=device if device is not None else text_encoders[1].device,
+        text_input_ids=text_input_ids_list[1] if text_input_ids_list else None,
     )
 
     text_ids = torch.zeros(batch_size, prompt_embeds.shape[1], 3).to(device=device, dtype=dtype)
@@ -1127,14 +1142,11 @@ def main(args):
         args.pretrained_model_name_or_path, subfolder="transformer", revision=args.revision, variant=args.variant
     )
 
+    # We only train the additional adapter LoRA layers
     transformer.requires_grad_(False)
     vae.requires_grad_(False)
-    if args.train_text_encoder:
-        text_encoder_one.requires_grad_(True)
-        text_encoder_two.requires_grad_(False)
-    else:
-        text_encoder_one.requires_grad_(False)
-        text_encoder_two.requires_grad_(False)
+    text_encoder_one.requires_grad_(False)
+    text_encoder_two.requires_grad_(False)
 
     # For mixed precision training we cast all non-trainable weights (vae, text_encoder and transformer) to half-precision
     # as these weights are only used for inference, keeping weights in full precision is not required.
@@ -1151,9 +1163,9 @@ def main(args):
         )
 
     vae.to(accelerator.device, dtype=weight_dtype)
-    if not args.train_text_encoder:
-        text_encoder_one.to(accelerator.device, dtype=weight_dtype)
-        text_encoder_two.to(accelerator.device, dtype=weight_dtype)
+    transformer.to(accelerator.device, dtype=weight_dtype)
+    text_encoder_one.to(accelerator.device, dtype=weight_dtype)
+    text_encoder_two.to(accelerator.device, dtype=weight_dtype)
 
     if args.gradient_checkpointing:
         transformer.enable_gradient_checkpointing()
@@ -1168,6 +1180,14 @@ def main(args):
         target_modules=["to_k", "to_q", "to_v", "to_out.0"],
     )
     transformer.add_adapter(transformer_lora_config)
+    if args.train_text_encoder:
+        text_lora_config = LoraConfig(
+            r=args.rank,
+            lora_alpha=args.rank,
+            init_lora_weights="gaussian",
+            target_modules=["q_proj", "k_proj", "v_proj", "out_proj"],
+        )
+        text_encoder_one.add_adapter(text_lora_config)
 
     def unwrap_model(model):
         model = accelerator.unwrap_model(model)
@@ -1257,15 +1277,16 @@ def main(args):
     if args.mixed_precision == "fp16":
         models = [transformer]
         if args.train_text_encoder:
-            models.extend([text_encoder_one, text_encoder_two])
+            models.extend([text_encoder_one])
         # only upcast trainable parameters (LoRA) into fp32
         cast_training_params(models, dtype=torch.float32)
 
-    # Optimization parameters
-    transformer_parameters_with_lr = {"params": transformer.parameters(), "lr": args.learning_rate}
+    transformer_lora_parameters = list(filter(lambda p: p.requires_grad, transformer.parameters()))
     if args.train_text_encoder:
         text_lora_parameters_one = list(filter(lambda p: p.requires_grad, text_encoder_one.parameters()))
 
+    # Optimization parameters
+    transformer_parameters_with_lr = {"params": transformer_lora_parameters, "lr": args.learning_rate}
     if args.train_text_encoder:
         # different learning rate for text encoder and unet
         text_parameters_one_with_lr = {
@@ -1420,14 +1441,18 @@ def main(args):
                 prompt_embeds = torch.cat([prompt_embeds, class_prompt_hidden_states], dim=0)
                 pooled_prompt_embeds = torch.cat([pooled_prompt_embeds, class_pooled_prompt_embeds], dim=0)
                 text_ids = torch.cat([text_ids, class_text_ids], dim=0)
-        # if we're optimizing the text encoder (both if instance prompt is used for all images or custom prompts) we need to tokenize and encode the
-        # batch prompts on all training steps
+        # if we're optimizing the text encoder (both if instance prompt is used for all images or custom prompts)
+        # we need to tokenize and encode the batch prompts on all training steps
         else:
             tokens_one = tokenize_prompt(tokenizer_one, args.instance_prompt, max_sequence_length=77)
-            tokens_two = tokenize_prompt(tokenizer_two, args.instance_prompt, max_sequence_length=512)
+            tokens_two = tokenize_prompt(
+                tokenizer_two, args.instance_prompt, max_sequence_length=args.max_sequence_length
+            )
             if args.with_prior_preservation:
                 class_tokens_one = tokenize_prompt(tokenizer_one, args.class_prompt, max_sequence_length=77)
-                class_tokens_two = tokenize_prompt(tokenizer_two, args.class_prompt, max_sequence_length=512)
+                class_tokens_two = tokenize_prompt(
+                    tokenizer_two, args.class_prompt, max_sequence_length=args.max_sequence_length
+                )
                 tokens_one = torch.cat([tokens_one, class_tokens_one], dim=0)
                 tokens_two = torch.cat([tokens_two, class_tokens_two], dim=0)
 
@@ -1545,6 +1570,8 @@ def main(args):
         transformer.train()
         if args.train_text_encoder:
             text_encoder_one.train()
+            # set top parameter requires_grad = True for gradient checkpointing works
+            accelerator.unwrap_model(text_encoder_one).text_model.embeddings.requires_grad_(True)
 
         for step, batch in enumerate(train_dataloader):
             models_to_accumulate = [transformer]
@@ -1562,12 +1589,33 @@ def main(args):
                         )
                     else:
                         tokens_one = tokenize_prompt(tokenizer_one, prompts, max_sequence_length=77)
-                        tokens_two = tokenize_prompt(tokenizer_two, prompts, max_sequence_length=512)
+                        tokens_two = tokenize_prompt(
+                            tokenizer_two, prompts, max_sequence_length=args.max_sequence_length
+                        )
+                        prompt_embeds, pooled_prompt_embeds, text_ids = encode_prompt(
+                            text_encoders=[text_encoder_one, text_encoder_two],
+                            tokenizers=[None, None],
+                            text_input_ids_list=[tokens_one, tokens_two],
+                            max_sequence_length=args.max_sequence_length,
+                            prompt=prompts,
+                        )
+                else:
+                    if args.train_text_encoder:
+                        prompt_embeds, pooled_prompt_embeds, text_ids = encode_prompt(
+                            text_encoders=[text_encoder_one, text_encoder_two],
+                            tokenizers=[None, None],
+                            text_input_ids_list=[tokens_one, tokens_two],
+                            max_sequence_length=args.max_sequence_length,
+                            prompt=args.instance_prompt,
+                        )
 
                 # Convert images to latent space
                 model_input = vae.encode(pixel_values).latent_dist.sample()
                 model_input = (model_input - vae.config.shift_factor) * vae.config.scaling_factor
                 model_input = model_input.to(dtype=weight_dtype)
+
+                vae_scale_factor = 2 ** (len(vae.config.block_out_channels))
+
                 latent_image_ids = FluxPipeline._prepare_latent_image_ids(
                     model_input.shape[0],
                     model_input.shape[2],
@@ -1575,7 +1623,6 @@ def main(args):
                     accelerator.device,
                     weight_dtype,
                 )
-
                 # Sample noise that we'll add to the latents
                 noise = torch.randn_like(model_input)
                 bsz = model_input.shape[0]
@@ -1613,49 +1660,24 @@ def main(args):
                     guidance = None
 
                 # Predict the noise residual
-                if not args.train_text_encoder:
-                    model_pred = transformer(
-                        hidden_states=packed_noisy_model_input,
-                        # YiYi notes: divide it by 1000 for now because we scale it by 1000 in the transforme rmodel (we should not keep it but I want to keep the inputs same for the model for testing)
-                        timestep=timesteps / 1000,
-                        guidance=guidance,
-                        pooled_projections=pooled_prompt_embeds,
-                        encoder_hidden_states=prompt_embeds,
-                        txt_ids=text_ids,
-                        img_ids=latent_image_ids,
-                        return_dict=False,
-                    )[0]
-                else:
-                    prompt_embeds, pooled_prompt_embeds, text_ids = encode_prompt(
-                        text_encoders=[text_encoder_one, text_encoder_two],
-                        tokenizers=None,
-                        prompt=None,
-                        text_input_ids_list=[tokens_one, tokens_two],
-                    )
-                    model_pred = transformer(
-                        hidden_states=packed_noisy_model_input,
-                        # YiYi notes: divide it by 1000 for now because we scale it by 1000 in the transforme rmodel (we should not keep it but I want to keep the inputs same for the model for testing)
-                        timestep=timesteps / 1000,
-                        guidance=guidance,
-                        pooled_projections=pooled_prompt_embeds,
-                        encoder_hidden_states=prompt_embeds,
-                        txt_ids=text_ids,
-                        img_ids=latent_image_ids,
-                        return_dict=False,
-                    )[0]
-
+                model_pred = transformer(
+                    hidden_states=packed_noisy_model_input,
+                    # YiYi notes: divide it by 1000 for now because we scale it by 1000 in the transforme rmodel (we should not keep it but I want to keep the inputs same for the model for testing)
+                    timestep=timesteps / 1000,
+                    guidance=guidance,
+                    pooled_projections=pooled_prompt_embeds,
+                    encoder_hidden_states=prompt_embeds,
+                    txt_ids=text_ids,
+                    img_ids=latent_image_ids,
+                    return_dict=False,
+                )[0]
                 model_pred = FluxPipeline._unpack_latents(
                     model_pred,
-                    height=int(model_input.shape[2]) * 8,
-                    width=int(model_input.shape[3]) * 8,
-                    vae_scale_factor=2
-                    ** (
-                        len(vae.config.block_out_channels)
-                    ),  # should this be 2 ** (len(vae.config.block_out_channels))?
+                    height=int(model_input.shape[2] * vae_scale_factor / 2),
+                    width=int(model_input.shape[3] * vae_scale_factor / 2),
+                    vae_scale_factor=vae_scale_factor,
                 )
 
-                model_pred = model_pred * (-sigmas) + noisy_model_input
-
                 # these weighting schemes use a uniform timestep sampling
                 # and instead post-weight the loss
                 weighting = compute_loss_weighting_for_sd3(weighting_scheme=args.weighting_scheme, sigmas=sigmas)
@@ -1783,7 +1805,7 @@ def main(args):
         FluxPipeline.save_lora_weights(
             save_directory=args.output_dir,
             transformer_lora_layers=transformer_lora_layers,
-            text_encoder_one_lora_layers=text_encoder_lora_layers,
+            text_encoder_lora_layers=text_encoder_lora_layers,
         )
 
         # Final inference