rollingdepth_src/diffusers/examples/t2i_adapter/test_t2i_adapter.py

# 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


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 T2IAdapter(ExamplesTestsAccelerate):
    def test_t2i_adapter_sdxl(self):
        with tempfile.TemporaryDirectory() as tmpdir:
            test_args = f"""
            examples/t2i_adapter/train_t2i_adapter_sdxl.py
            --pretrained_model_name_or_path=hf-internal-testing/tiny-stable-diffusion-xl-pipe
            --adapter_model_name_or_path=hf-internal-testing/tiny-adapter
            --dataset_name=hf-internal-testing/fill10
            --output_dir={tmpdir}
            --resolution=64
            --train_batch_size=1
            --gradient_accumulation_steps=1
            --max_train_steps=9
            --checkpointing_steps=2
            """.split()

            run_command(self._launch_args + test_args)

            self.assertTrue(os.path.isfile(os.path.join(tmpdir, "diffusion_pytorch_model.safetensors")))