前言：通道数与张量形状都在数值3和4之间变换，容易混淆。

1.通道数：

1.1 channel = 3

RGB 图像具有 3 个通道（红色、绿色和蓝色）。

1.2 channel = 4

Stable Diffusion has 4 latent channels。
如何理解卷积神经网络中的通道（channel）

2.张量形状

2.1 3D 张量

形状为 (C, H, W)，其中 C 是通道数，H 是高度，W 是宽度。这适用于单个图像。

2.2 4D 张量

2.2.1 通常

形状为 (B, C, H, W)，其中 B 是批次大小，C 是通道数，H 是高度，W 是宽度。这适用于多个图像（例如，批量处理）。

2.2.2 stable diffusion

在img2img中，将image用vae编码并按照timestep加噪：

		# This code copyed from diffusers.pipline_controlnet_img2img.py# 6. Prepare latent variableslatents = self.prepare_latents(image,latent_timestep,batch_size,num_images_per_prompt,prompt_embeds.dtype,device,generator,)

image的dim(维度)是3，而latents的dim为4。
让我们先看text2img的prepare_latents函数：

    # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.prepare_latentsdef prepare_latents(self, batch_size, num_channels_latents, height, width, dtype, device, generator, latents=None):shape = (batch_size, num_channels_latents, height // self.vae_scale_factor, width // self.vae_scale_factor)if isinstance(generator, list) and len(generator) != batch_size:raise ValueError(f"You have passed a list of generators of length {len(generator)}, but requested an effective batch"f" size of {batch_size}. Make sure the batch size matches the length of the generators.")if latents is None:latents = randn_tensor(shape, generator=generator, device=device, dtype=dtype)else:latents = latents.to(device)# scale the initial noise by the standard deviation required by the schedulerlatents = latents * self.scheduler.init_noise_sigmareturn latents

显然，shape已经规定了latents的dim(4)和排列顺序。
在img2img中：

    # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion_img2img.StableDiffusionImg2ImgPipeline.prepare_latentsdef prepare_latents(self, image, timestep, batch_size, num_images_per_prompt, dtype, device, generator=None):if not isinstance(image, (torch.Tensor, PIL.Image.Image, list)):raise ValueError(f"`image` has to be of type `torch.Tensor`, `PIL.Image.Image` or list but is {type(image)}")image = image.to(device=device, dtype=dtype)batch_size = batch_size * num_images_per_promptif image.shape[1] == 4:init_latents = imageelse:if isinstance(generator, list) and len(generator) != batch_size:raise ValueError(f"You have passed a list of generators of length {len(generator)}, but requested an effective batch"f" size of {batch_size}. Make sure the batch size matches the length of the generators.")elif isinstance(generator, list):init_latents = [self.vae.encode(image[i : i + 1]).latent_dist.sample(generator[i]) for i in range(batch_size)]init_latents = torch.cat(init_latents, dim=0)else:init_latents = self.vae.encode(image).latent_dist.sample(generator)init_latents = self.vae.config.scaling_factor * init_latentsif batch_size > init_latents.shape[0] and batch_size % init_latents.shape[0] == 0:# expand init_latents for batch_sizedeprecation_message = (f"You have passed {batch_size} text prompts (`prompt`), but only {init_latents.shape[0]} initial"" images (`image`). Initial images are now duplicating to match the number of text prompts. Note"" that this behavior is deprecated and will be removed in a version 1.0.0. Please make sure to update"" your script to pass as many initial images as text prompts to suppress this warning.")deprecate("len(prompt) != len(image)", "1.0.0", deprecation_message, standard_warn=False)additional_image_per_prompt = batch_size // init_latents.shape[0]init_latents = torch.cat([init_latents] * additional_image_per_prompt, dim=0)elif batch_size > init_latents.shape[0] and batch_size % init_latents.shape[0] != 0:raise ValueError(f"Cannot duplicate `image` of batch size {init_latents.shape[0]} to {batch_size} text prompts.")else:init_latents = torch.cat([init_latents], dim=0)shape = init_latents.shapenoise = randn_tensor(shape, generator=generator, device=device, dtype=dtype)# get latentsinit_latents = self.scheduler.add_noise(init_latents, noise, timestep)latents = init_latentsreturn latents

3.应用

3.1 问题

new_map = texture.permute(1, 2, 0)
RuntimeError: permute(sparse_coo): number of dimensions in the tensor input does not match the length of the desired ordering of dimensions i.e. input.dim() = 4 is not equal to len(dims) = 3

该问题是张量形状的问题，跟通道数毫无关系。

3.2 举例

问：4D 张量：形状为 (B, C, H, W)，其中C可以为3吗？
答：4D 张量的形状为 (B,C,H,W)，其中 C 表示通道数。通常情况下，C 可以为 3，这对应于 RGB 图像的三个颜色通道（红色、绿色和蓝色）。

3.3 张量可以理解为多维可变数组

print("sample:", sample.shape)
print("sample:", sample[0].shape)
print("sample:", sample[0][0].shape)

>>
sample: torch.Size([10, 4, 96, 96])
sample: torch.Size([4, 96, 96])
sample: torch.Size([96, 96])

由此可见，可以将张量形状为torch.size([10, 4, 96, 96])理解为一个4维可变数组。