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
| # Copyright (c) 2025 ByteDance Ltd. and/or its affiliates | |
| # | |
| # 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. | |
| # | |
| # Adapted from [VGGT-Long](https://github.com/DengKaiCQ/VGGT-Long) | |
| import yaml | |
| def load_config(path, default_path=None): | |
| """ | |
| Loads config file. | |
| Args: | |
| path (str): path to config file. | |
| default_path (str, optional): whether to use default path. Defaults to None. | |
| Returns: | |
| cfg (dict): config dict. | |
| """ | |
| # load configuration from per scene/dataset cfg. | |
| with open(path) as f: | |
| cfg_special = yaml.full_load(f) | |
| inherit_from = cfg_special.get("inherit_from") | |
| if inherit_from is not None: | |
| cfg = load_config(inherit_from, default_path) | |
| elif default_path is not None: | |
| with open(default_path) as f: | |
| cfg = yaml.full_load(f) | |
| else: | |
| cfg = dict() | |
| # merge per dataset cfg. and main cfg. | |
| update_recursive(cfg, cfg_special) | |
| return cfg | |
| def update_recursive(dict1, dict2): | |
| """ | |
| Update two config dictionaries recursively. dict1 get masked by dict2, and we retuen dict1. | |
| Args: | |
| dict1 (dict): first dictionary to be updated. | |
| dict2 (dict): second dictionary which entries should be used. | |
| """ | |
| for k, v in dict2.items(): | |
| if k not in dict1: | |
| dict1[k] = dict() | |
| if isinstance(v, dict): | |
| update_recursive(dict1[k], v) | |
| else: | |
| dict1[k] = v | |