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sample得到预测的噪声后怎么去除噪声呢？
我的任务是实现分割，输入的图片对是原图 分割mask图，在使用readme的教程中训练的我的代码，结果发现 模型参数ModelMeanType.EPSILON: noise,也就是unet预测的noise么，调用segmentation_sample.py函数得到的 sample, x_noisy, org, cal, cal_out = sample_fn( model, (batch_size, channels, args.image_size, args.image_size), img, step = args.diffusion_steps, clip_denoised=args.clip_denoised, model_kwargs=model_kwargs, ) sample是noise么，之后怎么处理得到我要的target分割图呢 我在可视化sample后发现和我想要的mask相差甚远，看了模型参数发现预测的是noise 值，是否有朋友知道有输入图 noise 变量 怎么得到mask图，请教各位互联网朋友们，感谢！
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Title: sampleHow to remove the noise after getting the predicted noise?
My task is to achieve segmentation. The input image pair is the original image segmentation mask map. I trained my code in the tutorial using the readme. It turns out that the model parameter ModelMeanType.EPSILON: noise, which is the noise predicted by unet, calls segmentation_sample. .py function obtained sample, x_noisy, org, cal, cal_out = sample_fn( model, (batch_size, channels, args.image_size, args.image_size), img, step = args.diffusion_steps, clip_denoised=args.clip_denoised, model_kwargs=model_kwargs, ) Is the sample noise? How can I get the target segmentation map I want? After visualizing the sample, I found that the mask was far from what I wanted. After looking at the model parameters, I found that the predicted noise value was. Does anyone know how to get the mask map with the noise variable in the input map? I would like to ask all Internet friends, thank you!
