About the training code

[davidz123]

Great job!
I would like to ask if there are any plans to release the training code.

[ZhenbangDu]

Hi. Thank you for your interest in our work! Unfortunately, due to JD.com's policies and the use of internal APIs during the training process, we are unable to upload the original training code. However, you may refer to the ReFL training code available at https://github.com/THUDM/ImageReward/blob/main/ImageReward/ReFL.py. Our training approach is broadly similar, with the additional requirement of implementing classifier-free guidance, as detailed in the following link: https://github.com/huggingface/diffusers/blob/c977966502b70f4758c83ee5a855b48398042b03/src/diffusers/pipelines/controlnet/pipeline_controlnet_inpaint.py#L1433:

noise_pred_uncond, noise_pred_text = noise_pred.chunk(2)
noise_pred = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)

Additionally, you can formulate our $L_{CC}$ just as follows:

l_cc = ((noise_pred_text - noise_pred_uncond) - (teacher_noise_pred_text - teacher_noise_pred_uncond)) ** 2

where it is necessary to maintain a frozen teacher model. If you have any further questions, please feel free to contact me at dzb99@hust.edu.cn.

[EricWillims]

Why is there no training code? As a paper included in the top conference ECCV2024, all the codes involved in the paper should be made public, including the training code and even some training details. I hope the author can provide the source code so that we can repeat the effect of the model mentioned in the paper. Thank you very much