examples/text_to_image/test_text_to_image_lora.py

#!/usr/bin/env python
# coding=utf-8
# Copyright 2024 The HuggingFace Inc. team. All rights reserved.
#
# 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

from diffusers import DiffusionPipeline  # noqa: E402


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 TextToImageLoRA(ExamplesTestsAccelerate):
    def test_text_to_image_lora_sdxl_checkpointing_checkpoints_total_limit(self):
        prompt = "a prompt"
        pipeline_path = "hf-internal-testing/tiny-stable-diffusion-xl-pipe"

        with tempfile.TemporaryDirectory() as tmpdir:
            # Run training script with checkpointing
            # max_train_steps == 6, checkpointing_steps == 2, checkpoints_total_limit == 2
            # Should create checkpoints at steps 2, 4, 6
            # with checkpoint at step 2 deleted

            initial_run_args = f"""
                examples/text_to_image/train_text_to_image_lora_sdxl.py
                --pretrained_model_name_or_path {pipeline_path}
                --dataset_name hf-internal-testing/dummy_image_text_data
                --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
                --checkpoints_total_limit=2
                """.split()

            run_command(self._launch_args + initial_run_args)

            pipe = DiffusionPipeline.from_pretrained(pipeline_path)
            pipe.load_lora_weights(tmpdir)
            pipe(prompt, num_inference_steps=1)

            # check checkpoint directories exist
            # checkpoint-2 should have been deleted
            self.assertEqual({x for x in os.listdir(tmpdir) if "checkpoint" in x}, {"checkpoint-4", "checkpoint-6"})

    def test_text_to_image_lora_checkpointing_checkpoints_total_limit(self):
        pretrained_model_name_or_path = "hf-internal-testing/tiny-stable-diffusion-pipe"
        prompt = "a prompt"

        with tempfile.TemporaryDirectory() as tmpdir:
            # Run training script with checkpointing
            # max_train_steps == 6, checkpointing_steps == 2, checkpoints_total_limit == 2
            # Should create checkpoints at steps 2, 4, 6
            # with checkpoint at step 2 deleted

            initial_run_args = f"""
                examples/text_to_image/train_text_to_image_lora.py
                --pretrained_model_name_or_path {pretrained_model_name_or_path}
                --dataset_name hf-internal-testing/dummy_image_text_data
                --resolution 64
                --center_crop
                --random_flip
                --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
                --checkpoints_total_limit=2
                --seed=0
                --num_validation_images=0
                """.split()

            run_command(self._launch_args + initial_run_args)

            pipe = DiffusionPipeline.from_pretrained(
                "hf-internal-testing/tiny-stable-diffusion-pipe", safety_checker=None
            )
            pipe.load_lora_weights(tmpdir)
            pipe(prompt, num_inference_steps=1)

            # check checkpoint directories exist
            # checkpoint-2 should have been deleted
            self.assertEqual({x for x in os.listdir(tmpdir) if "checkpoint" in x}, {"checkpoint-4", "checkpoint-6"})

    def test_text_to_image_lora_checkpointing_checkpoints_total_limit_removes_multiple_checkpoints(self):
        pretrained_model_name_or_path = "hf-internal-testing/tiny-stable-diffusion-pipe"
        prompt = "a prompt"

        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"""
                examples/text_to_image/train_text_to_image_lora.py
                --pretrained_model_name_or_path {pretrained_model_name_or_path}
                --dataset_name hf-internal-testing/dummy_image_text_data
                --resolution 64
                --center_crop
                --random_flip
                --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
                --num_validation_images=0
                """.split()

            run_command(self._launch_args + initial_run_args)

            pipe = DiffusionPipeline.from_pretrained(
                "hf-internal-testing/tiny-stable-diffusion-pipe", safety_checker=None
            )
            pipe.load_lora_weights(tmpdir)
            pipe(prompt, num_inference_steps=1)

            # check checkpoint directories exist
            self.assertEqual(
                {x for x in os.listdir(tmpdir) if "checkpoint" in x},
                {"checkpoint-2", "checkpoint-4"},
            )

            # resume and we should try to checkpoint at 6, where we'll have to remove
            # checkpoint-2 and checkpoint-4 instead of just a single previous checkpoint

            resume_run_args = f"""
                examples/text_to_image/train_text_to_image_lora.py
                --pretrained_model_name_or_path {pretrained_model_name_or_path}
                --dataset_name hf-internal-testing/dummy_image_text_data
                --resolution 64
                --center_crop
                --random_flip
                --train_batch_size 1
                --gradient_accumulation_steps 1
                --max_train_steps 8
                --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
                --checkpoints_total_limit=2
                --seed=0
                --num_validation_images=0
                """.split()

            run_command(self._launch_args + resume_run_args)

            pipe = DiffusionPipeline.from_pretrained(
                "hf-internal-testing/tiny-stable-diffusion-pipe", safety_checker=None
            )
            pipe.load_lora_weights(tmpdir)
            pipe(prompt, num_inference_steps=1)

            # check checkpoint directories exist
            self.assertEqual(
                {x for x in os.listdir(tmpdir) if "checkpoint" in x},
                {"checkpoint-6", "checkpoint-8"},
            )


class TextToImageLoRASDXL(ExamplesTestsAccelerate):
    def test_text_to_image_lora_sdxl(self):
        with tempfile.TemporaryDirectory() as tmpdir:
            test_args = f"""
                examples/text_to_image/train_text_to_image_lora_sdxl.py
                --pretrained_model_name_or_path hf-internal-testing/tiny-stable-diffusion-xl-pipe
                --dataset_name hf-internal-testing/dummy_image_text_data
                --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)

    def test_text_to_image_lora_sdxl_with_text_encoder(self):
        with tempfile.TemporaryDirectory() as tmpdir:
            test_args = f"""
                examples/text_to_image/train_text_to_image_lora_sdxl.py
                --pretrained_model_name_or_path hf-internal-testing/tiny-stable-diffusion-xl-pipe
                --dataset_name hf-internal-testing/dummy_image_text_data
                --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}
                --train_text_encoder
                """.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 `"unet"` or `"text_encoder"` or `"text_encoder_2"` in their names.
            keys = lora_state_dict.keys()
            starts_with_unet = all(
                k.startswith("unet") or k.startswith("text_encoder") or k.startswith("text_encoder_2") for k in keys
            )
            self.assertTrue(starts_with_unet)

    def test_text_to_image_lora_sdxl_text_encoder_checkpointing_checkpoints_total_limit(self):
        prompt = "a prompt"
        pipeline_path = "hf-internal-testing/tiny-stable-diffusion-xl-pipe"

        with tempfile.TemporaryDirectory() as tmpdir:
            # Run training script with checkpointing
            # max_train_steps == 6, checkpointing_steps == 2, checkpoints_total_limit == 2
            # Should create checkpoints at steps 2, 4, 6
            # with checkpoint at step 2 deleted

            initial_run_args = f"""
                examples/text_to_image/train_text_to_image_lora_sdxl.py
                --pretrained_model_name_or_path {pipeline_path}
                --dataset_name hf-internal-testing/dummy_image_text_data
                --resolution 64
                --train_batch_size 1
                --gradient_accumulation_steps 1
                --max_train_steps 6
                --learning_rate 5.0e-04
                --scale_lr
                --lr_scheduler constant
                --train_text_encoder
                --lr_warmup_steps 0
                --output_dir {tmpdir}
                --checkpointing_steps=2
                --checkpoints_total_limit=2
                """.split()

            run_command(self._launch_args + initial_run_args)

            pipe = DiffusionPipeline.from_pretrained(pipeline_path)
            pipe.load_lora_weights(tmpdir)
            pipe(prompt, num_inference_steps=1)

            # check checkpoint directories exist
            # checkpoint-2 should have been deleted
            self.assertEqual({x for x in os.listdir(tmpdir) if "checkpoint" in x}, {"checkpoint-4", "checkpoint-6"})