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#!/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 json
import logging
import os
import shutil
import sys
import tempfile
import torch
from diffusers import VQModel
from diffusers.utils.testing_utils import require_timm
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)
@require_timm
class TextToImage(ExamplesTestsAccelerate):
@property
def test_vqmodel_config(self):
return {
"_class_name": "VQModel",
"_diffusers_version": "0.17.0.dev0",
"act_fn": "silu",
"block_out_channels": [
32,
],
"down_block_types": [
"DownEncoderBlock2D",
],
"in_channels": 3,
"latent_channels": 4,
"layers_per_block": 2,
"norm_num_groups": 32,
"norm_type": "spatial",
"num_vq_embeddings": 32,
"out_channels": 3,
"sample_size": 32,
"scaling_factor": 0.18215,
"up_block_types": [
"UpDecoderBlock2D",
],
"vq_embed_dim": 4,
}
@property
def test_discriminator_config(self):
return {
"_class_name": "Discriminator",
"_diffusers_version": "0.27.0.dev0",
"in_channels": 3,
"cond_channels": 0,
"hidden_channels": 8,
"depth": 4,
}
def get_vq_and_discriminator_configs(self, tmpdir):
vqmodel_config_path = os.path.join(tmpdir, "vqmodel.json")
discriminator_config_path = os.path.join(tmpdir, "discriminator.json")
with open(vqmodel_config_path, "w") as fp:
json.dump(self.test_vqmodel_config, fp)
with open(discriminator_config_path, "w") as fp:
json.dump(self.test_discriminator_config, fp)
return vqmodel_config_path, discriminator_config_path
def test_vqmodel(self):
with tempfile.TemporaryDirectory() as tmpdir:
vqmodel_config_path, discriminator_config_path = self.get_vq_and_discriminator_configs(tmpdir)
test_args = f"""
examples/vqgan/train_vqgan.py
--dataset_name hf-internal-testing/dummy_image_text_data
--resolution 32
--image_column image
--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
--model_config_name_or_path {vqmodel_config_path}
--discriminator_config_name_or_path {discriminator_config_path}
--output_dir {tmpdir}
""".split()
run_command(self._launch_args + test_args)
# save_pretrained smoke test
self.assertTrue(
os.path.isfile(os.path.join(tmpdir, "discriminator", "diffusion_pytorch_model.safetensors"))
)
self.assertTrue(os.path.isfile(os.path.join(tmpdir, "vqmodel", "diffusion_pytorch_model.safetensors")))
def test_vqmodel_checkpointing(self):
with tempfile.TemporaryDirectory() as tmpdir:
vqmodel_config_path, discriminator_config_path = self.get_vq_and_discriminator_configs(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/vqgan/train_vqgan.py
--dataset_name hf-internal-testing/dummy_image_text_data
--resolution 32
--image_column image
--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
--model_config_name_or_path {vqmodel_config_path}
--discriminator_config_name_or_path {discriminator_config_path}
--checkpointing_steps=2
--output_dir {tmpdir}
--seed=0
""".split()
run_command(self._launch_args + initial_run_args)
# check checkpoint directories exist
self.assertEqual(
{x for x in os.listdir(tmpdir) if "checkpoint" in x},
{"checkpoint-2", "checkpoint-4"},
)
# check can run an intermediate checkpoint
model = VQModel.from_pretrained(tmpdir, subfolder="checkpoint-2/vqmodel")
image = torch.randn(1, model.config.in_channels, model.config.sample_size, model.config.sample_size)
_ = model(image)
# Remove checkpoint 2 so that we can check only later checkpoints exist after resuming
shutil.rmtree(os.path.join(tmpdir, "checkpoint-2"))
self.assertEqual(
{x for x in os.listdir(tmpdir) if "checkpoint" in x},
{"checkpoint-4"},
)
# Run training script for 2 total steps resuming from checkpoint 4
resume_run_args = f"""
examples/vqgan/train_vqgan.py
--dataset_name hf-internal-testing/dummy_image_text_data
--resolution 32
--image_column image
--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
--model_config_name_or_path {vqmodel_config_path}
--discriminator_config_name_or_path {discriminator_config_path}
--checkpointing_steps=1
--resume_from_checkpoint={os.path.join(tmpdir, 'checkpoint-4')}
--output_dir {tmpdir}
--seed=0
""".split()
run_command(self._launch_args + resume_run_args)
# check can run new fully trained pipeline
model = VQModel.from_pretrained(tmpdir, subfolder="vqmodel")
image = torch.randn(1, model.config.in_channels, model.config.sample_size, model.config.sample_size)
_ = model(image)
# no checkpoint-2 -> check old checkpoints do not exist
# check new checkpoints exist
# In the current script, checkpointing_steps 1 is equivalent to checkpointing_steps 2 as after the generator gets trained for one step,
# the discriminator gets trained and loss and saving happens after that. Thus we do not expect to get a checkpoint-5
self.assertEqual(
{x for x in os.listdir(tmpdir) if "checkpoint" in x},
{"checkpoint-4", "checkpoint-6"},
)
def test_vqmodel_checkpointing_use_ema(self):
with tempfile.TemporaryDirectory() as tmpdir:
vqmodel_config_path, discriminator_config_path = self.get_vq_and_discriminator_configs(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/vqgan/train_vqgan.py
--dataset_name hf-internal-testing/dummy_image_text_data
--resolution 32
--image_column image
--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
--model_config_name_or_path {vqmodel_config_path}
--discriminator_config_name_or_path {discriminator_config_path}
--checkpointing_steps=2
--output_dir {tmpdir}
--use_ema
--seed=0
""".split()
run_command(self._launch_args + initial_run_args)
model = VQModel.from_pretrained(tmpdir, subfolder="vqmodel")
image = torch.randn(1, model.config.in_channels, model.config.sample_size, model.config.sample_size)
_ = model(image)
# check checkpoint directories exist
self.assertEqual(
{x for x in os.listdir(tmpdir) if "checkpoint" in x},
{"checkpoint-2", "checkpoint-4"},
)
# check can run an intermediate checkpoint
model = VQModel.from_pretrained(tmpdir, subfolder="checkpoint-2/vqmodel")
image = torch.randn(1, model.config.in_channels, model.config.sample_size, model.config.sample_size)
_ = model(image)
# 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 2 total steps resuming from checkpoint 4
resume_run_args = f"""
examples/vqgan/train_vqgan.py
--dataset_name hf-internal-testing/dummy_image_text_data
--resolution 32
--image_column image
--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
--model_config_name_or_path {vqmodel_config_path}
--discriminator_config_name_or_path {discriminator_config_path}
--checkpointing_steps=1
--resume_from_checkpoint={os.path.join(tmpdir, 'checkpoint-4')}
--output_dir {tmpdir}
--use_ema
--seed=0
""".split()
run_command(self._launch_args + resume_run_args)
# check can run new fully trained pipeline
model = VQModel.from_pretrained(tmpdir, subfolder="vqmodel")
image = torch.randn(1, model.config.in_channels, model.config.sample_size, model.config.sample_size)
_ = model(image)
# no checkpoint-2 -> check old checkpoints do not exist
# check new checkpoints exist
self.assertEqual(
{x for x in os.listdir(tmpdir) if "checkpoint" in x},
{"checkpoint-4", "checkpoint-6"},
)
def test_vqmodel_checkpointing_checkpoints_total_limit(self):
with tempfile.TemporaryDirectory() as tmpdir:
vqmodel_config_path, discriminator_config_path = self.get_vq_and_discriminator_configs(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/vqgan/train_vqgan.py
--dataset_name hf-internal-testing/dummy_image_text_data
--resolution 32
--image_column image
--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
--model_config_name_or_path {vqmodel_config_path}
--discriminator_config_name_or_path {discriminator_config_path}
--output_dir {tmpdir}
--checkpointing_steps=2
--checkpoints_total_limit=2
--seed=0
""".split()
run_command(self._launch_args + initial_run_args)
model = VQModel.from_pretrained(tmpdir, subfolder="vqmodel")
image = torch.randn(1, model.config.in_channels, model.config.sample_size, model.config.sample_size)
_ = model(image)
# 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_vqmodel_checkpointing_checkpoints_total_limit_removes_multiple_checkpoints(self):
with tempfile.TemporaryDirectory() as tmpdir:
vqmodel_config_path, discriminator_config_path = self.get_vq_and_discriminator_configs(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/vqgan/train_vqgan.py
--dataset_name hf-internal-testing/dummy_image_text_data
--resolution 32
--image_column image
--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
--model_config_name_or_path {vqmodel_config_path}
--discriminator_config_name_or_path {discriminator_config_path}
--checkpointing_steps=2
--output_dir {tmpdir}
--seed=0
""".split()
run_command(self._launch_args + initial_run_args)
model = VQModel.from_pretrained(tmpdir, subfolder="vqmodel")
image = torch.randn(1, model.config.in_channels, model.config.sample_size, model.config.sample_size)
_ = model(image)
# 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/vqgan/train_vqgan.py
--dataset_name hf-internal-testing/dummy_image_text_data
--resolution 32
--image_column image
--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
--model_config_name_or_path {vqmodel_config_path}
--discriminator_config_name_or_path {discriminator_config_path}
--output_dir {tmpdir}
--checkpointing_steps=2
--resume_from_checkpoint={os.path.join(tmpdir, 'checkpoint-4')}
--checkpoints_total_limit=2
--seed=0
""".split()
run_command(self._launch_args + resume_run_args)
model = VQModel.from_pretrained(tmpdir, subfolder="vqmodel")
image = torch.randn(1, model.config.in_channels, model.config.sample_size, model.config.sample_size)
_ = model(image)
# check checkpoint directories exist
self.assertEqual(
{x for x in os.listdir(tmpdir) if "checkpoint" in x},
{"checkpoint-6", "checkpoint-8"},
)
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