Stable Diffusion怎么生成较长的视频

关键要点

研究表明，Stable Diffusion 本身生成较长视频有一定限制，但通过扩展工具如 AnimateDiff 和 Stable Video Diffusion（SVD）可以实现。
证据倾向于使用 AnimateDiff 在 ComfyUI 中，通过链式多段 16 帧生成，理论上可以创建无限长度的视频。
实际生成较长视频可能需要后处理，如帧插值，以确保流畅性，这可能带来意想不到的计算成本增加。

Stable Diffusion 是一个文本生成图像的模型，本身不直接支持长视频生成。但通过一些扩展工具和技巧，可以生成较长的视频序列。以下是主要方法，适合普通用户理解。

使用 AnimateDiff 在 ComfyUI 中生成

方法：使用 AnimateDiff 扩展，特别是在 ComfyUI 中，通过设置总帧数（如 64 帧）并保持上下文长度为 16，系统会自动分段生成并重叠，确保连续性。
优势：理论上可以生成任意长度的视频，适合需要动态内容的场景。
步骤：
安装 ComfyUI 和 AnimateDiff 节点。
加载工作流，设置总帧数和帧率（如 12 fps）。
使用提示调度调整内容变化。
生成后用工具合成视频文件。
示例：想生成 100 秒视频（12 fps），需设置 1200 帧，ComfyUI 会分段处理。

使用 Stable Video Diffusion（SVD）拼接

方法：SVD 可以生成 14 或 25 帧的短视频（约 2-4 秒），通过取最后一帧作为下一段的输入图像，拼接多个短片形成较长视频。
局限：拼接可能导致不连续，需后处理优化。
适合场景：需要高分辨率短片拼接的长视频，如广告片段。
意想不到的细节
生成长视频的计算成本可能远高于预期，尤其是在高帧率或高分辨率下，可能需要强大的 GPU 支持，这对普通用户来说可能是个挑战。

详细调研报告

Stable Diffusion 是一个基于扩散模型的文本生成图像工具，发布于 2022 年，主要用于生成静态图像。然而，用户需求扩展到视频生成，尤其是较长视频（超过几秒）的生成，这需要额外的工具和技巧。本报告详细探讨如何利用 Stable Diffusion 的扩展实现这一目标，涵盖原理、工具、步骤和局限。
背景与原理
Stable Diffusion 的核心是通过从噪声逐步生成图像，结合文本提示（prompt）和潜在空间（latent space）操作。生成视频需要引入时间维度（temporal dimension），让多帧图像保持一致性。现有方法主要依赖以下扩展：
AnimateDiff：一个插件，通过添加运动模块（Motion Module）到 U-Net，使模型生成多帧动画。
Stable Video Diffusion（SVD）：Stability AI 发布的图像到视频模型，基于 Stable Diffusion，生成短视频片段。
生成较长视频的方法

1. 使用 AnimateDiff 在 ComfyUI 中

ComfyUI 是一个基于节点的 Stable Diffusion 接口，高度可定制，适合复杂工作流。AnimateDiff 在 ComfyUI 中通过以下方式生成较长视频：
无限上下文长度技术：通过 Kosinkadink 的 ComfyUI-AnimateDiff-Evolved 节点，支持链式多段生成。用户设置总帧数（如 64 帧），上下文长度保持 16，系统自动分段重叠，确保连续性。例如，生成 64 帧视频，实际分 4 段，每段 16 帧，前后重叠部分保持一致。
提示调度（Prompt Scheduling）：通过调整提示随时间变化，创建叙事性内容。例如，从“一只猫走路”到“猫跳跃”，再到“猫落地”，形成故事线。
帧率与长度：帧率（如 12 fps）决定视频速度，总帧数决定长度。例如，1200 帧在 12 fps 下为 100 秒视频。
根据 Civitai 指南，用户可以设置图像加载上限为 0 以运行所有帧，或指定部分帧数，适合长视频生成。

2. 使用 Stable Video Diffusion（SVD）拼接

SVD 是 Stability AI 发布的图像到视频模型，支持生成 14 帧（SVD）或 25 帧（SVD-XT）的短视频，长度约 2-4 秒，帧率可定制（3-30 fps）。具体步骤：
生成第一段视频，取最后一帧作为下一段的输入图像，重复生成多段。
使用后处理工具（如帧插值）优化拼接处的连续性。
根据 Hugging Face 文档，SVD 适合高分辨率（576x1024）短片，但拼接可能导致运动不连续，需额外优化。
模型
帧数
最大长度（fps=10）
适用场景
SVD
14
~1.4 秒
高质量短片拼接
SVD-XT
25
~2.5 秒
稍长短片拼接
AnimateDiff
无限（分段）
理论无限，实际受计算限制
叙事性长视频生成

3. 后处理与优化

生成长视频后，可能需要：
帧插值：使用工具如 RIFE 或 FlowFrames 增加帧数，改善流畅性。
去闪烁（Deflickering）：通过 ControlNet 或其他方法减少帧间闪烁。
视频编辑：用软件如 Adobe Premiere 或 DaVinci Resolve 拼接和润色。
工具与平台支持
ComfyUI：推荐使用，节点化设计适合长视频工作流，支持 AnimateDiff 和提示调度。安装指南见 GitHub 仓库。
Automatic1111：也支持 AnimateDiff，但长视频生成较复杂，适合初学者短视频。
SVD 部署：可通过 Hugging Face Diffusers 库运行，需安装相关依赖，详见 官方文档。
局限与挑战
计算资源：长视频生成需要高性能 GPU，普通用户可能受限于显存（如 12GB 以上推荐）。
一致性：分段生成可能导致运动或内容不连续，需后处理优化。
生成时间：每段生成耗时长，总时间随帧数线性增加。
社区反馈：根据 Reddit 讨论（示例帖子），长视频生成仍需手动调整，效果因模型和提示而异。
实际案例
假设生成 30 秒视频（30 fps，900 帧）：
使用 AnimateDiff，设置 900 帧，上下文 16，ComfyUI 分段生成，每段重叠 8 帧，总耗时约 30 分钟（3070ti，6 步采样）。
使用 SVD，需生成 22 段（25 帧/段），拼接后需帧插值，耗时更长，效果可能不连贯。

结论

研究表明，生成较长视频的最佳方法是使用 AnimateDiff 在 ComfyUI 中，通过链式分段和提示调度实现理论无限长度。SVD 适合短片拼接，但需后处理优化。用户需权衡计算成本和效果，推荐从简单工作流开始，逐步优化。

How to Generate Longer Videos with Stable Diffusion

Key Takeaways

Research indicates that Stable Diffusion itself has limitations in generating longer videos, but extensions like AnimateDiff and Stable Video Diffusion (SVD) can achieve this.
The evidence favors using AnimateDiff in ComfyUI. By chaining multiple 16-frame segments, it is theoretically possible to create videos of infinite length.
Generating longer videos in practice may require post-processing, such as frame interpolation, to ensure smoothness, which may lead to unexpected increases in computational costs.

Stable Diffusion is a text-to-image model that does not directly support long video generation. However, longer video sequences can be generated using a few extensions and techniques. The following are the main methods, suitable for understanding by average users.

Using AnimateDiff in ComfyUI

Method: Use the AnimateDiff extension, especially in ComfyUI. By setting the total number of frames (e.g., 64 frames) and keeping the context length at 16, the system will automatically generate in segments and overlap them to ensure continuity.
Advantages: Theoretically capable of generating videos of any length, suitable for scenarios requiring dynamic content.
Steps:

  1. Install ComfyUI and AnimateDiff nodes.
  2. Load the workflow and set the total number of frames and frame rate (e.g., 12 fps).
  3. Use prompt scheduling to adjust content changes over time.
  4. Synthesize the video file with tools after generation.

Example: To generate a 100-second video (12 fps), you need to set 1200 frames, and ComfyUI will process it in segments.

Stitching with Stable Video Diffusion (SVD)

Method: SVD can generate short videos of 14 or 25 frames (about 2-4 seconds). By taking the last frame as the input image for the next segment, multiple short clips can be stitched together to form a longer video.
Limitations: Stitching may lead to discontinuities and requires post-processing optimization.
Suitable Scenarios: Long videos requiring the stitching of high-resolution short clips, such as advertising snippets.

Unexpected Details:
The computational cost of generating long videos may be much higher than expected, especially at high frame rates or high resolutions, which may require powerful GPU support. This can be a challenge for average users.

Detailed Research Report

Stable Diffusion is a diffusion-based text-to-image tool released in 2022, primarily used for generating static images. However, user demand has expanded to video generation, especially longer videos (over a few seconds), which requires additional tools and techniques. This report details how to leverage extensions of Stable Diffusion to achieve this goal, covering principles, tools, steps, and limitations.

Background and Principles

The core of Stable Diffusion works by progressively generating images from noise, combining text prompts and latent space operations. Generating video requires introducing a temporal dimension to keep multiple frames consistent. Existing methods mainly rely on the following extensions:

  • AnimateDiff: A plugin that enables the model to generate multi-frame animations by adding a Motion Module to the U-Net.
  • Stable Video Diffusion (SVD): An image-to-video model released by Stability AI, based on Stable Diffusion, which generates short video clips.

Methods for Generating Longer Videos

1. Using AnimateDiff in ComfyUI

ComfyUI is a node-based Stable Diffusion interface that is highly customizable and suitable for complex workflows. AnimateDiff generates longer videos in ComfyUI in the following ways:

  • Infinite Context Length Technology: Through Kosinkadink’s ComfyUI-AnimateDiff-Evolved node, chained multi-segment generation is supported. Users set the total number of frames (e.g., 64 frames), and the context length is kept at 16. The system automatically segments and overlaps to ensure continuity. For example, generating a 64-frame video is actually divided into 4 segments of 16 frames each, with overlapping parts at the beginning and end maintained consistent.
  • Prompt Scheduling: Create narrative content by adjusting prompts to change over time. For example, from “a cat walking” to “a cat jumping” to “a cat landing,” forming a storyline.
  • Frame Rate and Length: Frame rate (e.g., 12 fps) determines video speed, and total frames determine length. For example, 1200 frames at 12 fps is a 100-second video.
    According to the Civitai guide, users can set the image load cap to 0 to run all frames or specify a partial frame count, which is suitable for long video generation.

2. Stitching with Stable Video Diffusion (SVD)

SVD is an image-to-video model released by Stability AI that supports generating short videos of 14 frames (SVD) or 25 frames (SVD-XT), with a length of about 2-4 seconds and a customizable frame rate (3-30 fps). Specific steps:

  1. Generate the first video segment.
  2. Take the last frame as the input image for the next segment.
  3. Repeat to generate multiple segments.
  4. Use post-processing tools (such as frame interpolation) to optimize continuity at the seams.
    According to Hugging Face documentation, SVD is suitable for high-resolution (576x1024) short clips, but stitching can cause motion discontinuity and requires additional optimization.
Model Frames Max Length (fps=10) Applicable Scenario
SVD 14 ~1.4 seconds High-quality short clip stitching
SVD-XT 25 ~2.5 seconds Slightly longer short clip stitching
AnimateDiff Infinite (Segmented) Theoretically infinite, practically limited by computation Narrative long video generation

3. Post-Processing and Optimization

After generating a long video, you may need:

  • Frame Interpolation: Use tools like RIFE or FlowFrames to increase the number of frames and improve smoothness.
  • Deflickering: Reduce inter-frame flicker through ControlNet or other methods.
  • Video Editing: Use software like Adobe Premiere or DaVinci Resolve for stitching and polishing.

Tools and Platform Support

  • ComfyUI: Highly recommended. Its node-based design is suitable for long video workflows and supports AnimateDiff and prompt scheduling. Installation guides can be found in the GitHub repository.
  • Automatic1111: Also supports AnimateDiff, but long video generation is more complex and more suitable for beginner short videos.
  • SVD Deployment: It can be run via the Hugging Face Diffusers library, requiring the installation of relevant dependencies. See the official documentation for details.

Limitations and Challenges

  • Computational Resources: Long video generation requires high-performance GPUs, and average users may be limited by VRAM (12GB+ recommended).
  • Consistency: Segmented generation may lead to motion or content discontinuity, requiring post-processing optimization.
  • Generation Time: Each segment takes a long time to generate, and total time increases linearly with the number of frames.
  • Community Feedback: According to Reddit discussions (example post), long video generation still requires manual tuning, and results vary by model and prompt.

Practical Meaning

Suppose generating a 30-second video (30 fps, 900 frames):

  • Using AnimateDiff, setting 900 frames, context 16, ComfyUI segmented generation, with each segment overlapping 8 frames, takes about 30 minutes (3070ti, 6-step sampling).
  • Using SVD requires generating 22 segments (25 frames/segment). Stitching requires frame interpolation, which takes longer and may result in incoherent effects.

Conclusion

Research suggests that the best way to generate longer videos is to use AnimateDiff in ComfyUI, achieving theoretically infinite length through chained segmentation and prompt scheduling. SVD is suitable for short clip stitching but requires post-processing optimization. Users need to weigh computational costs against results, and it is recommended to start with a simple workflow and optimize gradually.

What is AnimateDiff

AnimateDiff 是一个基于 Stable Diffusion 的扩展工具，专门用来生成短视频或动画。它通过在图像生成的基础上引入时间维度（temporal dimension），让原本静态的 Stable Diffusion 模型能够输出动态的内容。简单来说，它是一个“让图片动起来的魔法”，特别适合生成简单的循环动画或短视频片段。
下面我用通俗的语言详细讲解 AnimateDiff 的原理、实现方式、在 ComfyUI 中的使用，以及它的优缺点和应用场景。

1. AnimateDiff 的基本原理

Stable Diffusion 本质上是为生成单张图像设计的，每次从噪声生成一张图。AnimateDiff 的核心创新是让这个过程扩展到多帧（frames），生成一系列连贯的图像，形成动画。具体怎么做到的呢？
时间一致性：AnimateDiff 在模型中加入了“时间层”（temporal layer），让每一帧的生成不仅考虑当前内容，还参考前后帧，确保动画看起来流畅，而不是一堆乱跳的图片。
预训练模块：它通常以一个独立的“运动模块”（Motion Module）的形式存在，这个模块是专门训练过的，能够理解物体移动、变形等动态规律，然后应用到 Stable Diffusion 的 U-Net 上。
帧间插值：生成的每一帧不是完全独立计算，而是通过时间维度共享信息，类似于视频编码中的帧间预测，保证动画的连贯性。
通俗点说，AnimateDiff 就像给 Stable Diffusion 加了个“动画导演”，告诉它：“别只画一张图，要画一组连起来的图，还要动得自然！”

2. AnimateDiff 的实现方式

AnimateDiff 的实现主要依赖以下几个步骤：
（1）Motion Module（运动模块）
这是一个预训练的神经网络模块，通常基于 Transformer 或卷积网络，专门负责处理时间维度的信息。
它会被插入到 Stable Diffusion 的 U-Net 中，增强模型对动态的理解能力。
训练时，Motion Module 看了大量视频数据，学会了如何让物体移动、变形或循环。
（2）与 Stable Diffusion 结合
AnimateDiff 不直接改动 Stable Diffusion 的原始权重，而是通过加载 Motion Module，在生成过程中动态调整 U-Net 的行为。
用户依然可以用文字提示（prompt）控制内容，但生成的不再是单张图，而是一组帧（比如 16 帧或 32 帧）。
（3）帧数和循环
AnimateDiff 通常生成固定帧数的短动画（常见是 16 或 32 帧），可以设置为循环播放（looping），形成 GIF 那样的效果。
帧数由 Motion Module 的设计决定，用户可以在一定程度上调整，但受限于预训练模型。
（4）后处理（可选）
生成的帧可以用插帧工具（比如 RIFE 或 SVP）进一步平滑，或用视频编辑软件合成更长的动画。

3. 在 ComfyUI 中的使用

AnimateDiff 在 ComfyUI 中作为一个扩展插件集成，使用时需要安装相关组件并加载预训练的 Motion Module。以下是一个典型的工作流：
常用节点和流程
Load Checkpoint：加载基础 Stable Diffusion 模型（比如 SD 1.5 或 SDXL）。
Load AnimateDiff Model：加载 AnimateDiff 的 Motion Module（比如 mm_sd_v15.ckpt），通常从社区下载。
CLIP Text Encode：输入动画的文字描述（比如“一只猫跳跃”）。
Empty Latent Image：设置动画的画布尺寸（比如 512x512）。
AnimateDiff Sampler：替代普通的 KSampler，专门用于生成多帧内容。需要指定帧数（比如 16 帧）和采样步数。
VAE Decode：将生成的潜在帧解码成图像序列。
Save Animated GIF/Video：将帧序列保存为 GIF 或 MP4 文件。
配置要点
Motion Module：需要下载 AnimateDiff 的预训练模型（常见的有 v1、v2、v3 等版本），放入 ComfyUI 的模型目录。
帧数：在 AnimateDiff Sampler 中设置，通常 16-32 帧适合短动画。
时间强度：部分版本支持调整运动幅度（motion scale），控制动画的剧烈程度。
配合其他工具：可以用 ControlNet 控制每帧的姿势，或用 LoRA 调整风格。
安装步骤
在 ComfyUI 的 Manager 中搜索并安装 AnimateDiff 扩展。
下载 Motion Module 文件（比如从 Hugging Face 或 GitHub）。
将文件放入 ComfyUI/models/animatediff 文件夹。
重启 ComfyUI，节点就会出现在界面上。

4. AnimateDiff 的优缺点

优点
简单高效：不需要从头训练模型，直接用预训练模块就能生成动画。
灵活性：支持 Stable Diffusion 的所有特性（文字提示、LoRA、ControlNet），动画风格多变。
社区支持：有很多预训练模块和教程，入门门槛低。
缺点
动画长度有限：通常只能生成短动画（16-32 帧），不适合长视频。
一致性挑战：复杂场景或大幅运动可能导致帧间不连贯（比如物体突然变形）。
资源需求：生成动画比单张图更耗显存和时间，尤其是配合 SDXL 时。
细节控制有限：只能大致控制运动方向，具体帧的细节难以精确调整。

5. 应用场景举例

循环 GIF：生成“跳舞的小猫”或“旋转的星空”这样的简单动画。
概念展示：快速制作动态的设计原型，比如“飞翔的龙”。
艺术创作：结合 LoRA 生成特定风格的动画，比如“赛博朋克城市闪烁的霓虹灯”。
游戏素材：制作简单的角色动作序列。

6. 一个生活化的比喻

把 Stable Diffusion 想象成一个画师，平时只会画静止的画。AnimateDiff 就像给他加了个“动画魔法棒”，让他学会画连环画。每次挥动魔法棒，他都能画出一小段故事，比如“猫咪跳起来又落地”，但他画不了完整的电影，只能画个短片段。

7. 进阶用法

结合 ControlNet：用姿势图或边缘图控制每帧的动作，比如让角色按指定路径移动。
多模块融合：加载多个 Motion Module，混合不同运动风格。
后处理优化：用外部工具（如 DAIN 或 FlowFrames）插帧，让动画更丝滑。

总结

AnimateDiff 是 Stable Diffusion 的“动画化身”，通过引入 Motion Module，它把静态图像生成扩展到了动态领域。在 ComfyUI 中，它以节点的形式无缝集成，适合生成短小精悍的动画内容。虽然它有帧数和一致性的局限，但对于快速制作创意动画来说，已经是非常强大的工具了。

What is AnimateDiff

AnimateDiff is an extension tool based on Stable Diffusion, designed specifically to generate short videos or animations. By introducing a temporal dimension on top of image generation, it enables the originally static Stable Diffusion model to output dynamic content. Simply put, it is “magic that makes pictures move,” particularly suitable for generating simple loop animations or short video clips.

Below, I will explain in detail the principles, implementation methods, usage in ComfyUI, as well as its pros, cons, and application scenarios of AnimateDiff in simple language.

1. Basic Principle of AnimateDiff

Stable Diffusion is essentially designed for generating single images, creating one picture from noise at a time. The core innovation of AnimateDiff is to extend this process to multiple frames, generating a series of coherent images to form an animation. How is this achieved specifically?

  • Temporal Consistency: AnimateDiff adds a “temporal layer” to the model, ensuring that the generation of each frame considers not only the current content but also references the preceding and succeeding frames, making the animation look smooth rather than a pile of jittery pictures.
  • Pre-trained Module: It typically exists in the form of an independent “Motion Module”. This module is specially trained to understand dynamic laws such as object movement and deformation, and then applies them to the U-Net of Stable Diffusion.
  • Inter-frame Interpolation: Each generated frame is not calculated completely independently but shares information through the temporal dimension, similar to inter-frame prediction in video coding, ensuring the coherence of the animation.

To put it simply, AnimateDiff is like adding an “animation director” to Stable Diffusion, telling it: “Don’t just draw one picture, draw a set of connected pictures, and make them move naturally!”

2. Implementation of AnimateDiff

The implementation of AnimateDiff mainly relies on the following steps:

(1) Motion Module

  • This is a pre-trained neural network module, usually based on Transformers or convolutional networks, specifically responsible for processing information in the temporal dimension.
  • It is inserted into the U-Net of Stable Diffusion to enhance the model’s ability to understand dynamics.
  • During training, the Motion Module watches a large amount of video data and learns how objects move, deform, or loop.

(2) Combining with Stable Diffusion

  • AnimateDiff does not directly modify the original weights of Stable Diffusion but dynamically adjusts the behavior of the U-Net during the generation process by loading the Motion Module.
  • Users can still use text prompts to control content, but the output is no longer a single image, but a set of frames (e.g., 16 frames or 32 frames).

(3) Frame Count and Looping

  • AnimateDiff usually generates short animations with a fixed number of frames (commonly 16 or 32 frames), which can be set to loop, forming a GIF-like effect.
  • The number of frames is determined by the design of the Motion Module. Users can adjust it to a certain extent, but it is limited by the pre-trained model.

(4) Post-processing (Optional)

  • The generated frames can be further smoothed using frame interpolation tools (such as RIFE or SVP) or synthesized into longer animations using video editing software.

3. Usage in ComfyUI

AnimateDiff is integrated as an extension plugin in ComfyUI. To use it, you need to install the relevant components and load a pre-trained Motion Module. Here is a typical workflow:

Common Nodes and Process

  • Load Checkpoint: Load the base Stable Diffusion model (such as SD 1.5 or SDXL).
  • Load AnimateDiff Model: Load the Motion Module of AnimateDiff (such as mm_sd_v15.ckpt), usually downloaded from the community.
  • CLIP Text Encode: Input the text description of the animation (such as “a cat jumping”).
  • Empty Latent Image: Set the canvas size of the animation (such as 512x512).
  • AnimateDiff Sampler: Replaces the ordinary KSampler, specifically used for generating multi-frame content. You need to specify the number of frames (such as 16 frames) and sampling steps.
  • VAE Decode: Decodes the generated latent frames into an image sequence.
  • Save Animated GIF/Video: Saves the frame sequence as a GIF or MP4 file.

Configuration Points

  • Motion Module: You need to download the pre-trained model of AnimateDiff (common versions include v1, v2, v3, etc.) and put it into the ComfyUI model directory.
  • Frame Count: Set in the AnimateDiff Sampler, usually 16-32 frames are suitable for short animations.
  • Motion Scale: Some versions support adjusting the motion amplitude effectively controlling the intensity of the animation.
  • Coordination with Other Tools: You can use ControlNet to control the pose of each frame, or use LoRA to adjust the style.

Installation Steps

  1. Search for and install the AnimateDiff extension in the ComfyUI Manager.
  2. Download the Motion Module file (e.g., from Hugging Face or GitHub).
  3. Place the file in the ComfyUI/models/animatediff folder.
  4. Restart ComfyUI, and the nodes will appear on the interface.

4. Pros and Cons of AnimateDiff

Pros

  • Simple and Efficient: No need to train a model from scratch; you can generate animations directly using pre-trained modules.
  • Flexibility: Supports all features of Stable Diffusion (text prompts, LoRA, ControlNet), allowing for varied animation styles.
  • Community Support: There are many pre-trained modules and tutorials, making the entry barrier low.

Cons

  • Limited Animation Length: Usually only capable of generating short animations (16-32 frames), not suitable for long videos.
  • Consistency Challenges: Complex scenes or large movements may lead to incoherence between frames (e.g., objects suddenly deforming).
  • Resource Demands: Generating animations consumes more VRAM and time than generating single images, especially when used with SDXL.
  • Limited Detail Control: Can only roughly control the direction of movement; details of specific frames are difficult to adjust precisely.

5. Application Scenarios Examples

  • Looping GIF: Generating simple animations like “a dancing kitten” or “a rotating starry sky”.
  • Concept Showcase: Quickly creating dynamic design prototypes, such as “a flying dragon”.
  • Art Creation: Combining with LoRA to generate animations with specific styles, such as “neon lights flashing in a cyberpunk city”.
  • Game Assets: Making simple character action sequences.

6. A Real-Life Metaphor

Imagine Stable Diffusion as a painter who usually only draws still pictures. AnimateDiff is like giving him an “animation magic wand”, enabling him to draw comic strips. Every time he waves the magic wand, he can draw a short story, like “a cat jumps up and lands”, but he cannot draw a complete movie, only a short clip.

7. Advanced Usage

  • Combine with ControlNet: Use pose maps or edge maps to control the action of each frame, for example, making a character move along a specified path.
  • Multi-Module Fusion: Load multiple Motion Modules to mix different motion styles.
  • Post-processing Optimization: Use external tools (such as DAIN or FlowFrames) for frame interpolation to make the animation smoother.

Summary

AnimateDiff is the “animated avatar” of Stable Diffusion. By introducing the Motion Module, it extends static image generation to the dynamic realm. In ComfyUI, it is seamlessly integrated in the form of nodes, suitable for generating short and concise animation content. Although it has limitations in frame count and consistency, it is already a very powerful tool for quickly creating creative animations.

ComfyUI 有哪些基本节点

ComfyUI 是一个基于节点（node-based）的 Stable Diffusion 用户界面，因其高度可定制化和灵活性而受到广泛欢迎。它的核心在于通过连接各种功能节点来构建图像生成的工作流（workflow）。以下是一些 ComfyUI 中常用的组件（节点），我会用通俗的语言介绍它们的用途，帮助你快速上手！

1. Load Checkpoint（加载检查点）

作用：这是工作流的基础节点，用来加载 Stable Diffusion 模型（比如 SD 1.5、SDXL 等）。它会同时加载模型的三个部分：U-Net（生成核心）、CLIP（文字理解）和 VAE（图像编码解码）。
通俗解释：就像给画家准备画笔和颜料，这个节点把“画图的大脑”加载进来。
常见用法：选择一个 .ckpt 或 .safetensors 文件，启动整个生成过程。

2. CLIP Text Encode（CLIP 文本编码）

作用：把你输入的文字提示（prompt）转化成模型能理解的“数字语言”（嵌入向量），分为正向提示（positive prompt）和负向提示（negative prompt）。
通俗解释：相当于把你的描述翻译给画家听，比如“画一只猫”或者“别画得太模糊”。
常见用法：接上 Load Checkpoint 的 CLIP 输出，一个用于想要的内容，一个用于避免的内容。

3. KSampler（采样器）

作用：控制从噪声生成图像的过程，可以选择不同的采样方法（比如 Euler、DPM++）和步数（steps）。
通俗解释：就像画家画画时决定用几笔完成作品，步数多画得细致但慢，步数少则快但粗糙。
常见用法：连接 U-Net 模型和 CLIP 编码后的提示，调整步数（20-50 常见）和 CFG（引导强度，7-12 常见）。

4. VAE Decode（VAE 解码）

作用：把 KSampler 生成的“潜在图像”（latent image，一种压缩数据）解码成最终的像素图像。
通俗解释：相当于把画家的草稿变成成品画。
常见用法：接上 KSampler 的输出和 Load Checkpoint 的 VAE 输出，生成可见图像。

5. Save Image（保存图像）

作用：把生成的图像保存到硬盘上。
通俗解释：就像把画好的画裱起来存档。
常见用法：接上 VAE Decode 的输出，指定保存路径和文件名。

6. Empty Latent Image（空潜在图像）

作用：生成一个空白的“画布”（潜在空间的噪声），指定图像尺寸（比如 512x512 或 1024x1024）。
通俗解释：给画家一张白纸，让他从零开始画。
常见用法：作为 KSampler 的输入起点，尺寸要符合模型要求（SD 1.5 用 512x512，SDXL 用 1024x1024）。

7. Preview Image（预览图像）

作用：在界面上直接显示生成的图像，不用保存就能看结果。
通俗解释：让画家先给你看一眼成品，觉得行再保存。
常见用法：接上 VAE Decode 的输出，方便调试工作流。

8. Conditioning (Set Area)（条件区域设置）

作用：给提示加上区域限制，比如“左边画猫，右边画狗”。
通俗解释：告诉画家在画布的哪块地方画什么。
常见用法：结合 CLIP Text Encode，用于局部控制生成内容。

9. LoRA Loader（LoRA 加载器）

作用：加载 LoRA 模型，微调主模型的风格或特征（比如动漫风、特定角色）。
通俗解释：给画家加个“风格滤镜”，让他画得更有个性。
常见用法：接上 Load Checkpoint 的 MODEL 和 CLIP 输出，调整 LoRA 强度（通常 0.5-1.0）。

10. ControlNet（控制网络）

作用：通过额外的参考图（比如线稿、边缘图、姿势图）控制生成图像的结构。
通俗解释：给画家一张草图，让他照着画细节。
常见用法：需要配合 ControlNet 模型文件，接上 KSampler，输入参考图像。

11. VAE Encode（VAE 编码）

作用：把一张普通图片编码成潜在空间的表示，用于图像到图像（img2img）生成。
通俗解释：把一张旧画交给画家，让他改一改。
常见用法：输入现有图片，接上 KSampler 开始改造。

12. Upscale Model（放大模型）

作用：加载超分辨率模型（比如 ESRGAN、SwinIR）来放大图像。
通俗解释：给画加个放大镜，让它变得更清晰。
常见用法：接上生成的图像，进一步提升分辨率。
一个简单工作流的例子
一个基础的文字到图像工作流可能是这样的：
Load Checkpoint：加载 SDXL 模型。
CLIP Text Encode：输入“一只猫在阳光下”。
Empty Latent Image：设置 1024x1024 的画布。
KSampler：用 30 步和 Euler 方法生成。
VAE Decode：把结果解码成图像。
Preview Image：预览一下。

总结

这些组件是 ComfyUI 的“基本工具箱”，掌握它们就能搭建简单的生成流程。随着需求增加，你可能会用到更多高级节点，比如：
Latent Upscale（潜在空间放大）
Inpaint（修图节点）
AnimateDiff（动画生成）
ComfyUI 的魅力在于它的模块化设计，你可以根据需要自由组合这些节点。如果你是新手，建议从默认工作流开始，逐步尝试添加 LoRA 或 ControlNet 等功能。有什么具体想深入了解的组件，随时问我！

What are the Basic Nodes in ComfyUI

ComfyUI is a node-based user interface for Stable Diffusion, widely popular for its high customizability and flexibility. Its core lies in building image generation workflows by connecting various functional nodes. Here are some commonly used components (nodes) in ComfyUI. I will explain their purposes in plain language to help you get started quickly!

1. Load Checkpoint

Function: This is the foundational node of a workflow, used to load Stable Diffusion models (such as SD 1.5, SDXL, etc.). It loads three parts of the model simultaneously: U-Net (generation core), CLIP (text understanding), and VAE (image encoding/decoding).
Plain Explanation: It’s like preparing brushes and paints for a painter; this node loads the “drawing brain”.
Common Usage: Select a .ckpt or .safetensors file to start the entire generation process.

2. CLIP Text Encode

Function: Converts your text prompts into “digital language” (embedding vectors) that the model can understand, divided into positive prompts and negative prompts.
Plain Explanation: Equivalent to translating your description for the painter, such as “draw a cat” or “don’t make it too blurry”.
Common Usage: Connect to the CLIP output of Load Checkpoint, one for desired content and one for content to avoid.

3. KSampler

Function: Controls the process of generating images from noise. You can choose different sampling methods (such as Euler, DPM++) and steps.
Plain Explanation: Like a painter deciding how many strokes to use to finish a work. More steps mean more detail but slower, fewer steps mean faster but rougher.
Common Usage: Connect the U-Net model and CLIP encoded prompts, adjust steps (20-50 is common) and CFG (guidance scale, 7-12 is common).

4. VAE Decode

Function: Decodes the “latent image” (compressed data) generated by KSampler into the final pixel image.
Plain Explanation: Equivalent to turning the painter’s draft into a finished painting.
Common Usage: Connect the output of KSampler and the VAE output of Load Checkpoint to generate a visible image.

5. Save Image

Function: Saves the generated image to the hard drive.
Plain Explanation: Like framing the finished painting and archiving it.
Common Usage: Connect the output of VAE Decode, specify the save path and filename.

6. Empty Latent Image

Function: Generates a blank “canvas” (noise in latent space), specifying image dimensions (such as 512x512 or 1024x1024).
Plain Explanation: Giving the painter a blank sheet of paper to start drawing from scratch.
Common Usage: As the input starting point for KSampler, dimensions should match model requirements (512x512 for SD 1.5, 1024x1024 for SDXL).

7. Preview Image

Function: Displays the generated image directly on the interface without saving it.
Plain Explanation: Letting the painter show you the finished product first; save it if you like it.
Common Usage: Connect the output of VAE Decode for easy workflow debugging.

8. Conditioning (Set Area)

Function: Adds area restrictions to prompts, such as “draw a cat on the left, draw a dog on the right”.
Plain Explanation: Telling the painter what to draw in which part of the canvas.
Common Usage: Combined with CLIP Text Encode, used for local control of generated content.

9. LoRA Loader

Function: Loads LoRA models to fine-tune the style or features of the main model (such as anime style, specific characters).
Plain Explanation: Adding a “style filter” to the painter to make the drawing more personalized.
Common Usage: Connect the MODEL and CLIP outputs of Load Checkpoint, adjust LoRA strength (usually 0.5-1.0).

10. ControlNet

Function: Controls the structure of the generated image through additional reference images (such as line art, edge maps, pose maps).
Plain Explanation: Giving the painter a sketch to follow for details.
Common Usage: Requires a ControlNet model file, connects to KSampler, inputs reference image.

11. VAE Encode

Function: Encodes a normal image into a representation in latent space, used for image-to-image (img2img) generation.
Plain Explanation: Handing an old painting to the painter and asking them to modify it.
Common Usage: Input existing image, connect to KSampler to start modification.

12. Upscale Model

Function: Loads super-resolution models (such as ESRGAN, SwinIR) to upscale images.
Plain Explanation: Giving the painting a magnifying glass to make it clearer.
Common Usage: Connect the generated image to further improve resolution.

Example of a Simple Workflow

A basic text-to-image workflow might look like this:

  1. Load Checkpoint: Load SDXL model.
  2. CLIP Text Encode: Input “A cat in the sunlight”.
  3. Empty Latent Image: Set a 1024x1024 canvas.
  4. KSampler: Generate using 30 steps and Euler method.
  5. VAE Decode: Decode the result into an image.
  6. Preview Image: Take a look.

Summary

These components are the “basic toolbox” of ComfyUI. Mastering them allows you to build simple generation flows. As your needs increase, you might use more advanced nodes, such as:

  • Latent Upscale
  • Inpaint
  • AnimateDiff (Animation generation)

The charm of ComfyUI lies in its modular design, allowing you to freely combine these nodes as needed. If you are a beginner, it is recommended to start with the default workflow and gradually try adding features like LoRA or ControlNet. If you have any specific components you want to know more about, feel free to ask!

What is SDXL

SDXL，全称是“Stable Diffusion XL”，是Stable Diffusion的一个升级版本，由Stability AI团队开发。它在原来的Stable Diffusion基础上做了大幅改进，目标是生成更高分辨率、更高质量、更细腻的图像，同时保持生成效率和灵活性。简单来说，SDXL是一个更强大、更精致的图像生成模型。
下面我用通俗的语言介绍一下SDXL的特点、原理和它跟普通Stable Diffusion的区别：

1. SDXL的基本特点

更高分辨率：普通Stable Diffusion默认生成512x512的图像，SDXL可以轻松生成1024x1024甚至更高分辨率的图像，细节更丰富，适合打印或大屏幕展示。
图像质量更好：生成的图像更清晰，纹理更自然，色彩和光影也更协调，整体看起来更“专业”。
理解能力更强：它能更好地理解复杂的文字提示（prompt），生成的内容更符合描述，尤其是细节部分。
架构升级：模型更大、更复杂，但通过优化设计，依然能在普通设备上运行。

2. SDXL的实现原理

SDXL仍然基于扩散模型（Diffusion Model），核心思想和普通Stable Diffusion差不多：从噪声开始，一步步“雕刻”出图像。不过，它在几个关键地方做了改进：
更大的模型规模：SDXL的神经网络（主要是U-Net）参数更多，层数更深，能捕捉更复杂的图像特征。
双重文本编码器：它用了两个CLIP模型（一个小的ViT-L，一个大的OpenCLIP ViT-BigG），分别处理文字提示的不同层次。小模型抓细节，大模型抓整体概念，结合起来让生成的图更贴合描述。
改进的VAE：变分自编码器（VAE）升级了，压缩和解压图像的能力更强，保证高分辨率下细节不丢失。
训练数据优化：SDXL用的是更高质量、更多样化的数据集，训练时还加入了一些去噪技巧，让模型学得更“聪明”。

3. 和普通Stable Diffusion的区别

打个比喻，普通Stable Diffusion像一个手艺不错的画师，能画出好看的图，但细节和尺寸有限；SDXL像是同一个画师升级成了大师级，工具更精良，画布更大，作品更震撼。具体区别有：
分辨率：普通版默认512x512，SDXL默认1024x1024。
细节表现：SDXL生成的图像细节更丰富，比如皮肤纹理、头发光泽、背景层次感都更强。
提示响应：SDXL对复杂提示（像“穿着蓝色斗篷的骑士站在夕阳下的城堡前”）理解更到位，不容易跑偏。
资源需求：SDXL模型更大，需要更多显存（推荐12GB以上），但优化后普通电脑也能跑。

4. SDXL的优势和局限

优势：
高质量输出：适合专业用途，比如艺术创作、商业设计。
更强的可控性：配合ControlNet、LoRA等工具，效果更惊艳。
社区支持：发布后被广泛使用，有很多预训练模型和插件可用。
局限：
硬件要求更高：显存不够的话跑起来会慢。
生成速度稍慢：因为模型更复杂，每张图生成时间比普通版长一点。

5. 一个生活化的比喻

普通Stable Diffusion像一台家用打印机，能打出不错的照片，但放大后有点模糊。SDXL像是专业摄影店的高端打印机，能输出大幅高清海报，连细微的纹路都清晰可见。它还是那个“从噪声雕刻图像”的原理，但工具更高级，成品更精美。

6. SDXL的应用场景

艺术创作：生成大幅画作或高质量插图。
设计原型：快速生成产品概念图或场景草稿。
个性化定制：配合微调工具生成特定风格或角色的图像。

总结

SDXL是Stable Diffusion的“豪华升级版”，通过更大的模型、更强的文本理解和优化的VAE，实现了更高分辨率和更高质量的图像生成。它保留了Stable Diffusion的核心优势（灵活、开源），同时把图像品质推到了新高度，非常适合需要精美输出的用户。

What is SDXL

SDXL, short for “Stable Diffusion XL”, is an upgraded version of Stable Diffusion developed by the Stability AI team. It has made significant improvements based on the original Stable Diffusion, aiming to generate higher resolution, higher quality, and more detailed images, while maintaining generation efficiency and flexibility. Simply put, SDXL is a more powerful and refined image generation model.

Below I will introduce the features and principles of SDXL and how it differs from ordinary Stable Diffusion in simple language:

1. Basic Features of SDXL

  • Higher Resolution: Ordinary Stable Diffusion defaults to generating 512x512 images, while SDXL can easily generate 1024x1024 or even higher resolution images with richer details, suitable for printing or large screen display.
  • Better Image Quality: The generated images are clearer, textures are more natural, colors and lighting are more coordinated, and the overall look is more “professional”.
  • Stronger Understanding: It can better understand complex text prompts, and the generated content is more consistent with the description, especially in terms of details.
  • Architecture Upgrade: The model is larger and more complex, but through optimized design, it can still run on consumer devices.

2. Implementation Principle of SDXL

SDXL is still based on the Diffusion Model, and its core idea is similar to ordinary Stable Diffusion: starting from noise and “sculpting” the image step by step. However, it has made improvements in several key areas:

  • Larger Model Scale: SDXL’s neural network (mainly U-Net) has more parameters and deeper layers, capable of capturing more complex image features.
  • Dual Text Encoders: It uses two CLIP models (a small ViT-L and a large OpenCLIP ViT-BigG) to process different levels of text prompts respectively. The small model captures details, and the large model captures overall concepts. Combining them makes the generated picture fit the description better.
  • Improved VAE: The Variational Autoencoder (VAE) has been upgraded, with stronger capabilities to compress and decompress images, ensuring that details are not lost at high resolutions.
  • Training Data Optimization: SDXL uses a higher quality and more diverse dataset, and some denoising techniques were added during training to make the model learn “smarter”.

3. Difference from Ordinary Stable Diffusion

To use a metaphor, ordinary Stable Diffusion is like a skilled painter who can draw good pictures, but the details and size are limited; SDXL is like the same painter upgraded to a master level, with better tools, a larger canvas, and more stunning works. Specific differences include:

  • Resolution: Ordinary version defaults to 512x512, SDXL defaults to 1024x1024.
  • Detail Performance: SDXL generates images with richer details, such as skin texture, hair luster, and background layering are stronger.
  • Prompt Response: SDXL understands complex prompts (like “a knight in a blue cloak standing in front of a castle under the sunset”) better and is less likely to deviate.
  • Resource Requirements: SDXL model is larger and requires more VRAM (recommended 12GB or more), but after optimization, ordinary computers can also run it.

4. Pros and Cons of SDXL

Pros:

  • High Quality Output: Suitable for professional use, such as artistic creation and commercial design.
  • Stronger Controllability: When used with tools like ControlNet and LoRA, the effect is even more amazing.
  • Community Support: Widely used after release, with many pre-trained models and plugins available.

Cons:

  • Higher Hardware Requirements: It will run slowly if VRAM is insufficient.
  • Slightly Slower Generation Speed: Because the model is more complex, the generation time for each image is longer than the ordinary version.

5. A Real-Life Metaphor

Ordinary Stable Diffusion is like a home printer that can print good photos, but they are a bit blurry when enlarged. SDXL is like a high-end printer in a professional photo shop, capable of outputting large high-definition posters where even fine lines are clearly visible. It is still the principle of “sculpting images from noise”, but the tools are more advanced and the finished product is more exquisite.

6. Application Scenarios of SDXL

  • Art Creation: Generating large paintings or high-quality illustrations.
  • Design Prototyping: Quickly generating product concept maps or scene drafts.
  • Personalized Customization: Generating images of specific styles or characters with fine-tuning tools.

Summary

SDXL is the “luxury upgraded version” of Stable Diffusion. Through a larger model, stronger text understanding, and optimized VAE, it achieves higher resolution and higher quality image generation. It retains the core advantages of Stable Diffusion (flexible, open source) while pushing image quality to a new height, making it very suitable for users who need exquisite output.

What is ControlNet

ControlNet是一种增强Stable Diffusion功能的强大工具，它可以让用户更精确地控制生成图像的内容和结构。简单来说，它就像给Stable Diffusion加了一个“遥控器”，让你不仅能通过文字描述生成图片，还能通过额外的条件（比如线稿、姿势或边缘图）精确指定图像的样子。
下面我用通俗的语言解释一下ControlNet的原理和作用：

1. ControlNet的基本概念

通常，Stable Diffusion只靠文字提示（如“一只猫坐在树上”）来生成图像，但结果可能不够精准，比如猫的姿势、位置不好控制。ControlNet的思路是：除了文字，我再给你一个“蓝图”或“参考图”，你按这个蓝图来画。这样生成的图像就能更好地符合你的期望。
这个“蓝图”可以是很多东西，比如：
一张手绘线稿（控制形状和轮廓）。
一张边缘图（从照片提取的边缘信息）。
一个姿势图（比如人体骨骼关键点）。
甚至是一张深度图（控制物体的远近关系）。
ControlNet会把这些“蓝图”信息融入生成过程，让图像既有创意，又能严格遵循你的控制。

2. ControlNet怎么工作？

ControlNet本质上是一个额外的神经网络模块，它和Stable Diffusion的U-Net结构紧密合作。工作流程大致是这样的：
输入条件：你给ControlNet一张参考图（比如线稿）和文字描述。
分析蓝图：ControlNet分析这张参考图，提取出形状、结构等关键信息。
指导生成：它把这些信息传递给Stable Diffusion的U-Net，告诉它“在生成图像时，别跑偏，按这个结构来”。
融合文字：同时，文字提示还是照常起作用，确保图像内容和描述一致。
结果就是，生成的图像既符合文字描述，又严格尊重参考图的结构。

3. 在Stable Diffusion里的作用

ControlNet让Stable Diffusion从“自由发挥”变成了“精准定制”。比如：
你画一个简单的猫的线稿，输入“一只橘猫”，ControlNet就能生成一张橘猫的图像，而且姿势和线稿一模一样。
你给一张照片的边缘图，输入“赛博朋克城市”，它会生成一个赛博朋克风格的城市，但布局和原图一致。
它特别适合需要精确控制的场景，比如艺术创作、设计草图变真实图像，或者调整已有图片的风格。

4. 一个生活化的比喻

把Stable Diffusion想象成一个画家，平时他听你描述（“画一只猫”）后自由发挥，画风可能五花八门。ControlNet就像你递给他一张草图，说：“照这个画，别乱改布局。”画家就老老实实按草图画，但颜色、细节还是按你的描述来填。这样画出来的作品既有你的创意，又符合你的具体要求。

5. ControlNet的优缺点

优点：
精确控制：生成的图像结构完全可控，不再全靠运气。
灵活性高：支持各种条件输入（线稿、边缘、姿势等）。
扩展性强：可以用在不同任务上，比如图像修复、风格转换。
缺点：
需要额外输入：得准备参考图，比纯文字提示多一步。
计算量稍大：比单独用Stable Diffusion多用点资源。

6. 常见应用举例

线稿上色：你画个黑白线稿，ControlNet帮你生成彩色成品。
姿势控制：用OpenPose生成的骨骼图，让人物按指定姿势生成。
风格化改造：拿一张照片的边缘图，生成不同风格的版本。

总结

ControlNet是Stable Diffusion的“精确导航系统”，通过参考图给模型加了一层结构约束，让你能更细致地控制生成结果。它特别适合那些需要“既要有创意，又要听话”的场景，把生成图像的自由度和可控性结合得更好。

What is ControlNet

ControlNet is a powerful tool that enhances the functionality of Stable Diffusion, allowing users to more precisely control the content and structure of generated images. Simply put, it is like adding a “remote control” to Stable Diffusion, allowing you to not only generate images through text descriptions but also precisely specify the look of the image through additional conditions (such as sketches, poses, or edge maps).

Below, I will explain the principle and function of ControlNet in simple language:

1. Basic Concepts of ControlNet

Usually, Stable Diffusion relies only on text prompts (such as “a cat sitting on a tree”) to generate images, but the results may not be precise enough, for example, the cat’s pose or position is hard to control. The idea of ControlNet is: besides text, I will give you a “blueprint” or “reference image”, and you draw according to this blueprint. In this way, the generated image can better meet your expectations.

This “blueprint” can be many things, such as:

  • A hand-drawn sketch (to control shape and contour).
  • An edge map (edge information extracted from photos).
  • A pose map (such as human skeletal keypoints).
  • Or even a depth map (typically controlling the distance relationship of objects).

ControlNet integrates this “blueprint” information into the generation process, making the image both creative and strictly following your control.

2. How Does ControlNet Work?

ControlNet is essentially an additional neural network module that works closely with the U-Net structure of Stable Diffusion. The workflow is roughly like this:

  1. Input Condition: You give ControlNet a reference image (such as a sketch) and a text description.
  2. Analyze Blueprint: ControlNet analyzes this reference image and extracts key information such as shape and structure.
  3. Guide Generation: It passes this information to the U-Net of Stable Diffusion, telling it “when generating the image, don’t deviate, follow this structure”.
  4. Fuse Text: At the same time, the text prompt works as usual ensuring the image content matches the description.

The result is that the generated image not only conforms to the text description but also strictly respects the structure of the reference image.

3. Role in Stable Diffusion

ControlNet transforms Stable Diffusion from “free play” to “precise customization”. For example:

  • You draw a simple sketch of a cat, input “an orange cat”, and ControlNet can generate an image of an orange cat with the exact same pose as the sketch.
  • You give an edge map of a photo, input “Cyberpunk city”, and it will generate a cyberpunk-style city, but the layout is consistent with the original image.

It is particularly suitable for scenarios requiring precise control, such as artistic creation, turning design sketches into real images, or adjusting the style of existing pictures.

4. A Real-Life Metaphor

Imagine Stable Diffusion as a painter who usually listens to your description (“draw a cat”) and then plays freely, with styles that may vary widely. ControlNet is like you handing him a sketch and saying: “Draw according to this, don’t change the layout randomly.” The painter then draws honestly according to the sketch, but fills in colors and details according to your description. The resulting work has both your creativity and meets your specific requirements.

5. Pros and Cons of ControlNet

Pros:

  • Precise Control: The structure of the generated image is completely controllable, no longer relying entirely on luck.
  • High Flexibility: Supports various condition inputs (sketches, edges, poses, etc.).
  • Strong Extensibility: Can be used in different tasks such as image inpainting and style transfer.

Cons:

  • Need Extra Input: Requires preparing a reference image, which is one more step than pure text prompts.
  • Slightly Higher Computation: Consumes slightly more resources than using Stable Diffusion alone.

6. Common Application Examples

  • Coloring Sketches: You draw a black and white sketch, and ControlNet helps you generate a colored finished product.
  • Pose Control: Using skeletal maps generated by OpenPose to make characters generate according to specified poses.
  • Stylization: Take an edge map of a photo and generate versions in different styles.

Summary

ControlNet is the “precise navigation system” of Stable Diffusion. By adding a layer of structural constraint to the model through reference images, it allows you to control the generation results in more detail. It is particularly suitable for those scenarios that need to be “both creative and obedient”, combining the freedom and controllability of image generation better.

What is Hypernetwork

Hypernetwork（超网络）是另一种用来改进和增强Stable Diffusion这类生成模型的技术，类似于LoRA，但它的思路和工作方式有点不同。简单来说，Hypernetwork是一个“动态调参大师”，它不直接改模型的参数，而是通过一个独立的“小网络”来预测和调整大模型的某些部分，让它生成更符合特定需求的内容，比如某种艺术风格或特定主题的图像。
下面我用通俗的语言解释一下Hypernetwork的原理和作用：

1. Hypernetwork的基本概念

想象Stable Diffusion是一个超级复杂的机器，里面有很多层“齿轮”（神经网络层），这些齿轮的参数决定了它生成什么风格的图像。Hypernetwork就像一个聪明的小助手，它不直接调整这些齿轮，而是跑去造一个“调节器”，这个调节器会根据你的需求，动态地告诉齿轮们：“嘿，你们应该这样转，才能画出用户想要的东西！”
换句话说，Hypernetwork是一个独立的小网络，专门用来生成或调整大模型（比如Stable Diffusion）的参数。

2. Hypernetwork怎么工作？

独立的小网络：Hypernetwork是一个额外的神经网络，规模比Stable Diffusion小很多。它接收一些输入（比如文字提示或条件），然后输出一组“调整方案”。
动态调参：这些调整方案会被应用到Stable Diffusion的某些层（通常是U-Net里的层），临时改变它们的表现方式。
生成图像：调整后的Stable Diffusion按照新参数运作，生成符合特定风格或特征的图像。
它的特别之处在于，每次生成图像时，Hypernetwork都会根据输入重新计算调整方案，所以它很灵活，能适应不同的需求。

3. 和LoRA的区别

Hypernetwork和LoRA有点像，但做事的方式不同：
LoRA：直接给模型加一个固定的“小配件”，训练好后就固定了，用的时候直接加载。
Hypernetwork：不固定加配件，而是用一个小网络动态生成调整方案，相当于每次都“现场定制”。
打个比喻，LoRA像给自行车换了个固定的新轮胎，Hypernetwork像是每次骑车前根据路况现场调整轮胎气压和齿轮。

4. 在Stable Diffusion里的作用

在Stable Diffusion里，Hypernetwork通常用来微调模型的表现，比如让它更好地生成某种风格（像油画风、像素风）或某个特定对象（比如某个动漫角色）。它主要影响U-Net的部分，帮助模型在生成图像时更精准地捕捉你想要的特征。

5. 一个生活化的比喻

把Stable Diffusion想象成一个大厨，Hypernetwork就像他的私人调料师。大厨会做菜，但每次做之前，调料师会根据顾客的口味（比如“要辣”或“要甜”），现场配一瓶特别的调料交给大厨。大厨用这瓶调料炒菜，菜就变成了顾客想要的味道。Hypernetwork就是那个灵活配调料的角色。

6. Hypernetwork的优缺点

优点：
灵活性强，能动态适应不同需求。
可以影响模型的多个部分，效果可能更全面。
缺点：
训练和使用时计算量比LoRA大一些。
文件体积通常也比LoRA大，不太方便分享。

总结

Hypernetwork是一个“动态调整专家”，通过一个小网络来临时改变Stable Diffusion的行为，让它生成更符合特定需求的图像。相比LoRA的固定微调，Hypernetwork更像是一个实时定制工具，适合需要高度灵活性的场景。

What is Hypernetwork

Hypernetwork is another technology used to improve and enhance generative models like Stable Diffusion, similar to LoRA, but its approach and way of working are slightly different. Simply put, a Hypernetwork is a “dynamic tuning master”. It does not directly change the parameters of the model, but predicts and adjusts certain parts of the large model through an independent “small network”, enabling it to generate content that better meets specific needs, such as a certain art style or images of specific themes.

Below I will explain the principle and function of Hypernetwork in simple language:

1. Basic Concepts of Hypernetwork

Imagine Stable Diffusion is a super complex machine with many layers of “gears” (neural network layers) inside. The parameters of these gears determine what style of images it generates. Hypernetwork is like a smart little assistant. It does not directly adjust these gears, but goes to build a “regulator”. This regulator will dynamically tell the gears according to your needs: “Hey, you should turn like this to draw what the user wants!”

In other words, Hypernetwork is an independent, small network specifically designed to generate or adjust the parameters of a large model (such as Stable Diffusion).

2. How Does Hypernetwork Work?

  • Independent Small Network: Hypernetwork is an additional neural network, much smaller in scale than Stable Diffusion. It receives some inputs (such as text prompts or conditions) and then outputs a set of “adjustment plans”.
  • Dynamic Tuning: These adjustment plans will be applied to certain layers of Stable Diffusion (usually layers in U-Net), temporarily changing their behavior.
  • Generate Image: The adjusted Stable Diffusion operates according to the new parameters, generating images that conform to specific styles or characteristics.

Its special feature is that every time an image is generated, the Hypernetwork recalculates the adjustment plan based on the input, so it is very flexible and can adapt to different needs.

3. Difference from LoRA

Hypernetwork and LoRA are somewhat similar, but they do things differently:

  • LoRA: Directly adds a fixed “small accessory” to the model. Once trained, it is fixed and loaded directly when used.
  • Hypernetwork: Does not add fixed accessories, but uses a small network to dynamically generate adjustment plans, which is equivalent to “customizing on the spot” every time.

To use a metaphor, LoRA is like changing a fixed new tire for a bicycle, while Hypernetwork is like adjusting the tire pressure and gears on the spot according to road conditions before every ride.

4. Role in Stable Diffusion

In Stable Diffusion, Hypernetwork is usually used to fine-tune the model’s performance, such as making it better at generating a certain style (like oil painting style, pixel style) or a specific object (like a certain anime character). It mainly affects parts of the U-Net, helping the model capture the features you want more precisely when generating images.

5. A Real-Life Metaphor

Imagine Stable Diffusion as a chef, and Hypernetwork as his private spice master. The chef knows how to cook, but before each cooking session, the spice master will mix a special bottle of seasoning on the spot according to the customer’s taste (such as “spicy” or “sweet”) and give it to the chef. The chef uses this bottle of seasoning to cook, and the dish becomes the flavor the customer wants. Hypernetwork is that flexible role of mixing spices.

6. Pros and Cons of Hypernetwork

Pros:

  • High flexibility, can dynamically adapt to different needs.
  • Can affect multiple parts of the model, potentially leading to more comprehensive effects.

Cons:

  • The computational load during training and use is slightly larger than LoRA.
  • The file size is usually larger than LoRA, making it less convenient to share.

Summary

Hypernetwork is a “dynamic adjustment expert” that temporarily changes the behavior of Stable Diffusion through a small network, allowing it to generate images that better meet specific needs. Compared to LoRA’s fixed fine-tuning, Hypernetwork is more like a real-time customization tool, suitable for scenarios requiring high flexibility.

What is VAE

VAE，全称是“Variational Autoencoder”（变分自编码器），是Stable Diffusion这类生成模型里一个很重要的组件。简单来说，它就像一个“图像压缩大师”，能把复杂的图片压缩成一个简化的“代码”，然后还能根据这个代码把图片还原出来。在Stable Diffusion里，VAE的角色是帮模型更高效地处理图像。
下面用通俗的语言解释一下VAE的原理和作用：

1. VAE的基本想法

想象你有一张高清照片，里面细节特别多，直接处理它需要很大算力。VAE就像一个聪明助手，先把这张照片“浓缩”成一个很小的“信息包”（专业点叫“潜在表示”），这个信息包保留了照片的核心特征，但体积小得多。然后，需要的时候，它还能根据这个信息包把照片大致还原回去。
这个过程有点像把一张大图压成一个zip文件，然后再解压出来，虽然可能有点细节损失，但整体样子还在。

2. VAE怎么工作？

VAE分成两部分：编码器（Encoder）和解码器（Decoder）。
编码器：负责把图片压缩成一个小的“代码”。它会分析图片，找出最重要的特征（比如形状、颜色、结构），然后用一串数字表示这些特征。
解码器：负责把这个“代码”还原成图片。它根据这串数字，重新画出一张尽量接近原图的图像。
但VAE不是简单地压缩和解压，它有个特别的地方：它生成的“代码”不是固定的，而是带有一些随机性（变分的意思就在这），这样可以让模型更有创造力，能生成各种不同的图像。

3. 在Stable Diffusion里的作用

Stable Diffusion用VAE来提升效率。前面我提到，Stable Diffusion在“潜在空间”里操作，而不是直接处理高清图像。这个潜在空间就是VAE帮着构建的：
生成图像时，模型先在潜在空间里从噪声雕刻出一个“代码”。
然后VAE的解码器把这个代码“解压”成最终的高清图像。
这样做的好处是，潜在空间比原始图像小得多，计算起来更快、更省资源。VAE就像一个桥梁，把复杂的图像世界和简化的代码世界连了起来。

4. 一个生活化的比喻

把VAE想象成一个速写画家。你给他看一张风景照，他快速画个简笔画（编码），只勾勒出山、树、太阳的大概轮廓。之后，你让他根据这个简笔画再画一幅完整的画（解码），他就能还原出风景，虽然细节可能有点不同。这种“简笔画”能力让Stable Diffusion能高效工作，还能生成各种新变化。

5. VAE的优点和局限

优点：压缩图像节省算力，还能让生成过程更灵活，适合创造新内容。
局限：解码时可能会丢一些细节，所以生成的图有时不够完美，需要其他技术配合优化。

总结

在Stable Diffusion里，VAE是一个“幕后功臣”，它把大图像压缩成小代码，让模型能在潜在空间里高效雕刻图像，最后再把结果还原成高清图。可以说，没有VAE，Stable Diffusion的生成过程会慢很多，也没那么灵活。

VAE 演示

What is VAE

VAE, short for “Variational Autoencoder”, is a very important component in generative models like Stable Diffusion. Simply put, it acts like an “image compression master” that can compress complex images into a simplified “code” and then restore the image from this code. In Stable Diffusion, the role of VAE is to help the model process images more efficiently.

Below is an explanation of the principle and function of VAE in simple language:

1. Basic Idea of VAE

Imagine you have a high-definition photo with lots of details. Processing it directly requires a lot of computing power. VAE is like a smart assistant that first “condenses” this photo into a very small “information package” (technically called “latent representation”). This information package retains the core features of the photo but is much smaller in volume. Then, when needed, it can roughly restore the photo based on this information package.

This process is a bit like compressing a large image into a zip file and then unzipping it. Although there might be some loss of detail, the overall appearance remains.

2. How Does VAE Work?

VAE consists of two parts: Encoder and Decoder.

  • Encoder: Responsible for compressing the image into a small “code”. It analyzes the image, identifying the most important features (such as shape, color, structure), and then represents these features with a string of numbers.
  • Decoder: Responsible for restoring this “code” into an image. It redraws an image as close to the original as possible based on this string of numbers.

But VAE is not just simple compression and decompression. It has a special feature: the “code” it generates is not fixed, but carries some randomness (this is where “Variational” comes in). This makes the model more creative and capable of generating various different images.

3. Role in Stable Diffusion

Stable Diffusion uses VAE to improve efficiency. As I mentioned before, Stable Diffusion operates in “latent space” rather than directly processing high-definition images. This latent space is built with the help of VAE:

  1. When generating an image, the model first sculpts a “code” from noise in the latent space.
  2. Then, VAE’s decoder “decompresses” this code into the final high-definition image.

The benefit of this is that the latent space is much smaller than the original image, making calculations faster and saving resources. VAE is like a bridge connecting the complex image world and the simplified code world.

4. A Real-Life Metaphor

Imagine VAE as a sketch artist. You show him a landscape photo, and he quickly draws a simple sketch (encoding), outlining only the general contours of mountains, trees, and the sun. Afterward, you ask him to draw a complete painting based on this sketch (decoding), and he can restore the landscape, although the details might be slightly different. This “sketching” ability allows Stable Diffusion to work efficiently and generate variety of new changes.

5. Pros and Cons of VAE

  • Pros: Compresses images to save computing power, makes the generation process more flexible, and is suitable for creating new content.
  • Cons: Some details might be lost during decoding, so the generated image is sometimes not perfect and needs other technologies to optimize.

Summary

In Stable Diffusion, VAE is an “unsung hero”. It compresses large images into small codes, allowing the model to efficiently sculpt images in the latent space, and finally restores the result into a high-definition image. It can be said that without VAE, the generation process of Stable Diffusion would be much slower and less flexible.

VAE Demo

What is LoRA

LoRA，全称是“Low-Rank Adaptation”（低秩适应），是一种用来改进和个性化Stable Diffusion这类大模型的技术。简单来说，它是一个轻量级的“插件”，可以让模型快速学会一些新东西，比如特定的艺术风格、某个角色形象，或者其他你想要的特征，而不用重新训练整个大模型。
下面用通俗的语言解释一下LoRA的原理和工作方式：

1. 为什么需要LoRA？

Stable Diffusion这种模型训练一次很费时间和资源，而且它学到的知识是“广而泛”的，比如它能生成猫、狗、风景，但如果你想要它专门画“梵高风格的猫”或者“某个动漫角色”，直接让它改头换面太麻烦了。要么重新训练整个模型（费时费力），要么就得想个聪明办法——LoRA就是这个聪明办法。

2. LoRA怎么工作？

想象Stable Diffusion是一个超级复杂的机器，里面有很多“旋钮”控制图像生成。这些旋钮的设置是训练好的，决定了模型的基本能力。LoRA不直接动这些旋钮，而是给机器加装了一些“小配件”。
这些小配件很特别：
轻量：它们只调整模型的一小部分参数，而不是全部。

低秩：用数学的说法，它只关心最重要的变化方向（“低秩”是指用少量数据就能表达关键信息），所以效率很高。

可插拔：你可以用不同的LoRA配件，让模型快速切换风格或主题。

比如，你训练一个LoRA来学“赛博朋克风格”，装上这个LoRA后，模型生成的图像就带上了赛博朋克的味道；换成“卡通风格”的LoRA，生成的图又变成卡通风。

3. 训练LoRA的过程

训练LoRA就像教模型一个新技能。你给它看一些目标图像（比如一堆赛博朋克画作），然后让LoRA记住这些图像的特征。训练时，Stable Diffusion的大部分参数保持不动，只有LoRA这部分小配件被调整。这样既节省时间，又不会破坏模型原来的能力。

4. 用LoRA的好处

省资源：训练和使用LoRA比重新训练整个模型便宜多了，普通电脑也能跑。

灵活性：你可以收集一堆LoRA，随时切换，比如今天用“写实风”，明天用“水彩风”。

共享方便：LoRA文件很小，几兆字节就能搞定，方便社区用户分享和下载。

5. 一个生活化的比喻

把Stable Diffusion想象成一个超级厉害的厨师，会做各种菜。LoRA就像是给厨师一本新菜谱，告诉他“加点辣椒，做川菜”或者“用奶油，做法式甜点”。厨师的基本功不变，只是按菜谱小调一下，菜就变出新花样了。

总结

LoRA是一个高效的“微调工具”，让Stable Diffusion这种大模型变得更灵活、更个性化。它通过加装轻量配件，快速教模型新技能，用最小的代价实现大变化。你在网上看到的很多Stable Diffusion生成作品，可能都用了LoRA来定制风格或主题。

What is LoRA

LoRA, which stands for “Low-Rank Adaptation,” is a technique used to improve and personalize large models like Stable Diffusion. Simply put, it is a lightweight “plugin” that allows the model to quickly learn some new things, such as a specific art style, a specific character design, or other features you want, without retraining the entire large model.

Below is an explanation of the principle and working method of LoRA in simple language:

1. Why Do We Need LoRA?

Training a model like Stable Diffusion takes a lot of time and resources, and the knowledge it learns is “broad and general”. For example, it can generate cats, dogs, and landscapes, but if you want it to specifically draw “Van Gogh style cats” or “a certain anime character”, it is too troublesome to make it change completely directly. You either have to retrain the entire model (time-consuming and laborious), or you have to think of a smart way—LoRA is this smart way.

2. How Does LoRA Work?

Imagine Stable Diffusion as a super complex machine with many “knobs” controlling image generation. The settings of these knobs are pre-trained and determine the basic capabilities of the model. LoRA does not directly touch these knobs, but adds some “small accessories” to the machine.

These small accessories are very special:

  • Lightweight: They only adjust a small part of the model’s parameters, not all of them.
  • Low-Rank: In mathematical terms, it only cares about the most important directions of change (“low-rank” means using a small amount of data to express key information), so it is very efficient.
  • Pluggable: You can use different LoRA accessories to let the model quickly switch styles or themes.

For example, if you train a LoRA to learn “Cyberpunk style”, after installing this LoRA, the images generated by the model will take on a Cyberpunk flavor; switch to a “Cartoon style” LoRA, and the generated images will turn into cartoon style.

3. The Process of Training LoRA

Training LoRA is like teaching the model a new skill. You show it some target images (such as a pile of Cyberpunk paintings), and then let LoRA remember the features of these images. During training, most parameters of Stable Diffusion remain unchanged, and only the small accessories of LoRA are adjusted. This saves time and does not destroy the original capabilities of the model.

4. Benefits of Using LoRA

  • Resource Saving: Training and using LoRA is much cheaper than retraining the entire model, and it can run on ordinary computers.
  • Flexibility: You can collect a bunch of LoRAs and switch them at any time, for example, use “Realistic Style” today and “Watercolor Style” tomorrow.
  • Easy Sharing: LoRA files are very small, usually just a few megabytes, making them convenient for community users to share and download.

5. A Real-Life Metaphor

Imagine Stable Diffusion as a super skilled chef who can cook various dishes. LoRA is like giving the chef a new recipe, telling him “add some chili to make Sichuan cuisine” or “use cream to make French dessert”. The chef’s basic skills remain unchanged, just a slight adjustment according to the recipe, and the dishes will change into new patterns.

Summary

LoRA is an efficient “fine-tuning tool” that makes large models like Stable Diffusion more flexible and personalized. By installing lightweight accessories, it quickly teaches the model new skills, achieving big changes at minimum cost. Many Stable Diffusion generation works you see online likely used LoRA to customize style or theme.

Stable Diffusion的内部机制和实现原理

Stable Diffusion是一种生成图像的AI模型，特别擅长根据文字描述生成逼真的图片。它的核心思想是利用“扩散过程”（diffusion process），从随机噪声开始，一步步“修复”出一张清晰的图像。下面我分几个部分来解释。

1. 从噪声到图像：扩散的逆过程

想象一下，你有一张照片，然后故意给它加上一堆杂乱的噪点，直到它完全看不出原来的样子，变成一团随机斑点。Stable Diffusion的工作原理有点像反过来操作：它从一团纯噪声开始，通过一系列计算，逐步去除噪声，最终生成一张有意义的图片。
这个“去除噪声”的过程并不是随便猜的，而是靠模型学过的规律来一步步调整。模型知道如何根据给定的条件（比如你输入的文字描述），把噪声“雕琢”成符合描述的图像。

2. 训练过程：教模型认清噪声和图像

Stable Diffusion是怎么学会这个本领的呢？它在训练时会看大量的图片，然后研究这些图片被加噪点后会变成什么样。具体来说：
拿一张清晰的图，加一点点噪点，记下来；
再多加一点噪点，又记下来；
反复加噪点，直到图片完全变成一团乱七八糟的噪声。
通过这个过程，模型学会了从“清晰图像”到“纯噪声”的变化规律。然后在生成图像时，它就反着来：从纯噪声开始，预测每一步该怎么减少噪点，最终还原出一张清晰图。

3. 文字引导：如何听懂你的描述

你输入“一只猫坐在阳光下”，Stable Diffusion为什么能生成对应的图像呢？这里用到了一个叫CLIP的助手。CLIP是一个专门理解文字和图像关系的模型，它能把你的文字描述转化为一种“数学语言”，然后告诉Stable Diffusion：“嘿，你生成的图像得朝这个方向走！”
所以，Stable Diffusion一边从噪声中“雕刻”图像，一边参考CLIP给的指引，确保生成的图和你的描述匹配。

4. U-Net：幕后的雕刻大师

具体干活儿的“工具”是模型里一个叫U-Net的结构。U-Net长得像一个U形网络，特别擅长处理图像。它会看当前这团噪声，然后预测下一步该怎么调整，才能让图像更清晰、更符合目标。每次调整都是一小步，经过几十步甚至上百步，噪声就变成了你想要的图。

5. 节省资源的秘密：潜在空间

直接处理高清大图会很费计算资源，所以Stable Diffusion有个聪明办法：它先把图像压缩到一个叫“潜在空间”的小空间里。这个空间就像是图像的“精简版”，信息量少但关键特征都在。然后它在这个小空间里操作噪声，等雕刻得差不多，再把结果“解压”成高清大图。这样既快又省力。

总结一下
Stable Diffusion的实现原理可以用一个比喻来概括：它就像一个雕刻家，从一块杂乱的石头（噪声）开始，根据你的描述（文字提示），一点点凿掉多余的部分，最终雕出精美的雕塑（图像）。它靠训练学会了雕刻的规律，用U-Net动手干活，用CLIP听懂你的要求，还用潜在空间来提高效率。

Internal Mechanisms and Implementation Principles of Stable Diffusion

Stable Diffusion is an AI model that generates images, particularly excelling at creating realistic pictures based on text descriptions. Its core idea utilizes a “diffusion process,” starting from random noise and “restoring” it step by step into a clear image. I will explain this in several parts below.

1. From Noise to Image: The Reverse Process of Diffusion

Imagine you have a photograph, and you deliberately add a pile of messy noise to it until it is completely unrecognizable, turning into a blob of random spots. Stable Diffusion works somewhat like the reverse of this operation: it starts from a blob of pure noise and, through a series of calculations, gradually removes the noise to finally generate a meaningful image.
This process of “removing noise” is not a random guess but is adjusted step by step based on patterns the model has learned. The model knows how to “sculpt” the noise into an image that fits the description based on given conditions (such as the text description you input).

2. Training Process: Teaching the Model to Recognize Noise and Images

How did Stable Diffusion learn this skill? During training, it looks at a massive number of images and studies what happens to these images after noise is added. Specifically:

  1. Take a clear image, add a little bit of noise, and record it;
  2. Add a bit more noise, and record it again;
  3. Repeatedly add noise until the image turns completely into a messy blob of noise.
    Through this process, the model learns the laws of change from “clear image” to “pure noise.” Then, when generating an image, it does the reverse: starting from pure noise, it predicts how to reduce noise at each step, finally restoring a clear picture.

3. Text Guidance: How It Understands Your Description

When you input “a cat sitting in the sunlight,” why can Stable Diffusion generate the corresponding image? Here, an assistant called CLIP is used. CLIP is a model specifically designed to understand the relationship between text and images. It can translate your text description into a “mathematical language” and then tell Stable Diffusion: “Hey, the image you are generating needs to go in this direction!”
So, while Stable Diffusion “sculpts” the image from noise, it also refers to the guidance given by CLIP to ensure that the generated picture matches your description.

4. U-Net: The Sculpting Master Behind the Scenes

The specific “tool” that does the work is a structure in the model called U-Net. U-Net looks like a U-shaped network and is particularly good at processing images. It looks at the current blob of noise and then predicts how to adjust it in the next step to make the image clearer and more consistent with the goal. Each adjustment is a small step. After dozens or even hundreds of steps, the noise turns into the picture you want.

5. The Secret to Saving Resources: Latent Space

Directly processing high-definition large images consumes a lot of computing resources, so Stable Diffusion has a clever method: it first compresses individual images into a small space called “Latent Space.” This space is like a “condensed version” of the image; the amount of information is small, but the key features are all there. Then it operates on the noise in this small space, and when the sculpting is about done, it “decompresses” the result into a high-definition large image. This is both fast and efficient.

Summary

The implementation principle of Stable Diffusion can be summarized with a metaphor: it is like a sculptor who starts from a messy stone (noise) and, based on your description (text prompt), chips away the excess parts bit by bit, finally sculpting an exquisite sculpture (image). It relies on training to learn the laws of sculpting, uses U-Net to do the hands-on work, uses CLIP to understand your requests, and also uses latent space to improve efficiency.

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