Apply the block to a Tensor, conditioned on a timestep embedding. :param x: an [N x C x ...] Tensor of features. :param emb:

Apply QKV attention. :param qkv: an [N x (H * 3 * C) x T] tensor of Qs, Ks, and Vs. :return: an [N x (H * C) x T] tensor afte

Apply QKV attention. :param qkv: an [N x (3 * H * C) x T] tensor of Qs, Ks, and Vs. :return: an [N x (H * C) x T] tensor afte

Apply the model to an input batch. :param x: an [N x C x ...] Tensor of inputs. :param timesteps: a 1-D batch of timesteps.

Apply the model to an input batch. :param x: an [N x C x ...] Tensor of inputs. :param timesteps: a 1-D batch of timesteps.

@param x: b,f0,h,w @param context: b,f1,d,h,w @return:

@param x: B,4,H,W @param t: B, @param clip_embed: B,M,768 @param volume_feats: B,C,D,H,W

@param x: B,4,H,W @param t: B, @param clip_embed: B,M,768 @param volume_feats: B,C,D,H,W

@param x: B,4,H,W @param t: B, @param clip_embed: B,M,768 @param volume_feats: B,C,D,H,W

:param input_im (PIL Image). :return image (H, W, 3) array in [0, 1].

Loads the schedulers state. Args: state_dict (dict): scheduler state. Should be an object returned from a call to

https://github.com/openai/guided-diffusion/blob/27c20a8fab9cb472df5d6bdd6c8d11c8f430b924/guided_diffusion/nn.py#L86 Take the mean over all no

Take the mean over all non-batch dimensions.