区分stable diffusion中的通道数与张量形状
- 1.通道数：
-
- [1.1 channel = 3](#1.1 channel = 3)
- [1.2 channel = 4](#1.2 channel = 4)
- 2.张量形状
-
- [2.1 3D 张量](#2.1 3D 张量)
- [2.2 4D 张量](#2.2 4D 张量)
-
- [2.2.1 通常](#2.2.1 通常)
- [2.2.2 stable diffusion](#2.2.2 stable diffusion)
- 3.应用
-
- [3.1 问题](#3.1 问题)
- [3.2 举例](#3.2 举例)
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：
python
# This code copyed from diffusers.pipline_controlnet_img2img.py
# 6. Prepare latent variables
latents = 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：
python
# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.prepare_latents
def 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 scheduler
latents = latents * self.scheduler.init_noise_sigma
return latents显然，shape已经规定了latents的dim(4)和排列顺序。
在img2img中：
python
# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion_img2img.StableDiffusionImg2ImgPipeline.prepare_latents
def 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_prompt
if image.shape[1] == 4:
init_latents = image
else:
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_latents
if batch_size > init_latents.shape[0] and batch_size % init_latents.shape[0] == 0:
# expand init_latents for batch_size
deprecation_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.shape
noise = randn_tensor(shape, generator=generator, device=device, dtype=dtype)
# get latents
init_latents = self.scheduler.add_noise(init_latents, noise, timestep)
latents = init_latents
return latents3.应用
3.1 问题
python
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）。