Instructions to use zeyuren2002/EvalMDE with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use zeyuren2002/EvalMDE with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("zeyuren2002/EvalMDE", dtype=torch.bfloat16, device_map="cuda") prompt = "Astronaut in a jungle, cold color palette, muted colors, detailed, 8k" image = pipe(prompt).images[0] - Notebooks
- Google Colab
- Kaggle
File size: 2,797 Bytes
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#
# Copyright 2025 Ziyang Song, USTC. All rights reserved.
#
# This file has been modified from the original version.
# Original copyright (c) 2023 Bingxin Ke, ETH Zurich. 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.
# --------------------------------------------------------------------------
# If you find this code useful, we kindly ask you to cite our paper in your work.
# Please find bibtex at: https://github.com/indu1ge/DepthMaster#-citation
# More information about the method can be found at https://indu1ge.github.io/DepthMaster_page
# --------------------------------------------------------------------------
import omegaconf
from omegaconf import OmegaConf
def recursive_load_config(config_path: str) -> OmegaConf:
conf = OmegaConf.load(config_path)
output_conf = OmegaConf.create({})
# Load base config. Later configs on the list will overwrite previous
base_configs = conf.get("base_config", default_value=None)
if base_configs is not None:
assert isinstance(base_configs, omegaconf.listconfig.ListConfig)
for _path in base_configs:
assert (
_path != config_path
), "Circulate merging, base_config should not include itself."
_base_conf = recursive_load_config(_path)
output_conf = OmegaConf.merge(output_conf, _base_conf)
# Merge configs and overwrite values
output_conf = OmegaConf.merge(output_conf, conf)
return output_conf
def find_value_in_omegaconf(search_key, config):
result_list = []
if isinstance(config, omegaconf.DictConfig):
for key, value in config.items():
if key == search_key:
result_list.append(value)
elif isinstance(value, (omegaconf.DictConfig, omegaconf.ListConfig)):
result_list.extend(find_value_in_omegaconf(search_key, value))
elif isinstance(config, omegaconf.ListConfig):
for item in config:
if isinstance(item, (omegaconf.DictConfig, omegaconf.ListConfig)):
result_list.extend(find_value_in_omegaconf(search_key, item))
return result_list
if "__main__" == __name__:
conf = recursive_load_config("config/train_base.yaml")
print(OmegaConf.to_yaml(conf))
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