base on The code releasing for https://image-dream.github.io/ # ImageDream Reconstruction Peng Wang, Yichun Shi [Project Page](https://image-dream.github.io/) | [Paper](https://arxiv.org/abs/2312.02201) | [Demo]() [imagedream-threestudio-teaser](https://github.com/bytedance/ImageDream/assets/146033206/bcf67b1a-38f9-42cf-81df-b8b2f4fa007f) ## Installation This part is the same as original [MVDream-threestudio](https://github.com/bytedance/MVDream-threestudio). Skip it if you already have installed the environment. ## Quickstart Clone the modelcard on the [Huggingface ImageDream Model Page](https://huggingface.co/Peng-Wang/ImageDream/) under ```./extern/ImageDream/release_models/``` In the paper, we use the configuration with soft-shading. It would need an A100 GPU in most cases to compute normal: ```sh export PYTHONPATH=$PYTHONPATH:./extern/ImageDream image_file="./extern/ImageDream/assets/astronaut.png" ckpt_file="./extern/ImageDream/release_models/ImageDream/sd-v2.1-base-4view-ipmv.pt" cfg_file="./extern/ImageDream/imagedream/configs/sd_v2_base_ipmv.yaml" python3 launch.py \ --config configs/$method.yaml --train --gpu 0 \ name="imagedream-sd21-shading" tag="astronaut" \ system.prompt_processor.prompt="an astronaut riding a horse" \ system.prompt_processor.image_path="${image_file}" \ system.guidance.ckpt_path="${ckpt_file}" \ system.guidance.config_path="${cfg_file}" ``` ***For diffusion only model, refer to subdir*** ```./extern/ImageDream/``` ***Check*** ```./threestudio/scripts/run_imagedream.sh``` ***for a bash example.*** ## Credits - This code is forked from [threestudio](https://github.com/threestudio-project/threestudio) and [MVDream](https://github.com/bytedance/MVDream-threestudi) for SDS and 3D Generation. ## Tips 1. Place the object in the center and do not make it too large/small in the image. 2. If you have an object cutting image edge, in config, tuning the parameters range of elevation and fov to be a larger range, e.g. ```[0, 30]```, otherwise, you may do image outpainting and follow tips 1. 3. Check the results with ImageDream diffusion model before using it in 3D rendering to save time. ## PreComputed Results - Since there is some randomness in diffusion model and time costly to get baseline results. We put our pre-computed results for reproducing Tab.1 in the paper in a [hugging face dataset card](https://huggingface.co/datasets/Peng-Wang/ImageDream) ## Citing If you find ImageDream helpful, please consider citing: ``` bibtex @article{wang2023imagedream, title={ImageDream: Image-Prompt Multi-view Diffusion for 3D Generation}, author={Wang, Peng and Shi, Yichun}, journal={arXiv preprint arXiv:2312.02201}, year={2023} } ``` ", Assign "at most 3 tags" to the expected json: {"id":"6155","tags":[]} "only from the tags list I provide: []" returns me the "expected json"