SD3模型到底如何？StableDiffusion3全面评测！如何使用ComfyUI遍历题词 | 模型？（附赠测试工作流）！ #aigc #stablediffusion3

惫懒の欧阳川
15 Jun 202431:04

TLDR本期视频详细介绍了开源的StableDiffusion第三代模型（SD3），并探讨了其与前代模型的不同之处，如VAE解码增强和三种CLIP编码的引入。视频还演示了如何使用ComfyUI进行批处理操作，以及如何通过题词测试模型。此外，提供了不同模型的比较，包括SDXL、SD3和Cascade，展示了它们在不同风格和主题下的生成效果。最后，讨论了如何使用一键提词插件和在线生成平台Liblib进行图像生成，以及对SD3模型的初步评价和优化建议。

Takeaways

  • 😀 Stable Diffusion 3（SD3）模型是在SDXL基础上增强的新一代模型，具有更强大的VAE解码和对题词的理解能力。
  • 🔍 SD3模型引入了三种CLIP编码，增加了文本编码器，提高了对题词的精确控制能力。
  • 📈 SD3模型的训练数据量达到2B（20亿参数），相较于SDXL有显著增加。
  • 🖼️ Huggingface官网提供了不同版本的SD3模型下载，包括不同精度（FP16和8位）的模型，以适应不同硬件要求。
  • 🔗 对于国内用户，推荐使用哩布哩布AI网站下载模型，资源丰富且访问便捷。
  • 🔧 ComfyUI提供了批处理操作，可以配合题词进行模型测试，包括基础、强化和放大工作流。
  • 🎨 使用ComfyUI的Dynamic Prompts插件可以进行题词的遍历和批处理生成图像。
  • 🤖 视频中提到了通过调整CFG和采样算法等参数来优化生成图像的效果。
  • 📊 进行了SD3与其他模型（如SDXL和Cascade）的图像生成效果比较，发现SD3在风格表现上可能不如SDXL。
  • 🔄 视频展示了如何使用一键提词插件和字符串处理来生成多组不同风格的题词。
  • 🔍 最后，视频提到了使用哩布哩布在线生成平台测试SD3模型，并与本地生成效果进行对比。

Q & A

  • SD3模型的主要改进点有哪些？

    -SD3模型在VAE解码部分进行了增强，通道数提升到16。它对题词的理解以及元素融合更加完善，可以通过题词更精确地控制画面的某些部分。此外，它使用了三种CLIP编码，增加了文本编码器，并训练了更大的数据量，达到20亿参数。

  • 如何使用ComfyUI进行批处理操作？

    -ComfyUI可以通过安装Dynamic Prompts插件来进行题词的遍历和批处理操作。用户可以下载不同的题词卡，通过插件在ComfyUI中调用这些题词卡，实现自动化的图像生成。

  • 为什么SD3模型需要至少12G的显存？

    -SD3模型由于其庞大的参数量（20亿参数），对显存有较高的要求。官方建议至少需要12G显存，以确保模型能够顺利运行，尽管官方也提到8G显存在设置虚拟内存后也能运行，但速度可能无法保证。

  • 在huggingface官网上如何选择适合的SD3模型版本？

    -在huggingface官网上，用户可以根据模型的后缀来选择。没有后缀的模型不带CLIP编码，带有'clip'标识的模型包含基础的CLIP编码。第三代模型带'T5XXL'标识，提供了FP16和8精度的版本，用户可以根据显存大小和需求选择合适的版本。

  • 国内用户如何获取SD3模型及相关资源？

    -国内用户可以通过访问哩布哩布AI网站来获取SD3模型及相关资源。该网站提供了丰富的模型资源，包括独家模型和热门模型，同时也支持在线生成功能。

  • 为什么在使用SD3模型时，负面提词的强度被设置得很低？

    -由于SD3模型对题词的理解非常强，为了避免负面提词过于突出，影响生成效果，官方在工作流中将负面提词的强度设置得很低，仅在生成过程的前10%有提词作用，之后逐渐降低至无。

  • 如何使用ComfyUI的批处理功能生成多张不同风格的图像？

    -用户可以通过设置字符串连锁和帧节点，在ComfyUI中生成多张不同风格的图像。通过将不同的题词组件分配给不同的帧，然后使用FIZZ节点进行批次调度，可以实现一次性生成多张图像。

  • SD3模型的采样算法有哪些优化？

    -SD3模型的采样算法包括离散算法、连续算法以及Cascate算法等。用户可以根据自己的需求在模型管道中选择不同的算法进行优化。

  • 为什么SD3模型的官方推荐采样步数只有28步？

    -SD3模型的官方推荐采样步数为28步，这可能是因为模型在设计时已经考虑到了效率和效果的平衡，使得在较少的步数下也能获得满意的生成效果。

  • 如何通过ComfyUI使用一键提词插件生成图像？

    -用户可以在ComfyUI中安装并使用一键提词插件，通过选择不同的风格、主体、类型等选项，插件会自动生成相应的题词。然后，用户可以将生成的题词应用到模型中，快速生成图像。

Outlines

00:00

🚀 Introduction to SD3 Model and ComfyUI Batch Operations

The video begins with an introduction to the newly open-sourced SD third-generation model, highlighting its enhanced architecture based on SDXL with improved VAE decoding and better integration of prompts and elements. The host also discusses ComfyUI's batch processing operations and guides viewers to the SD official website, explaining the differences in the new model, including the addition of a text encoder and the increase in training data to 2 billion parameters. The video then navigates to Huggingface's website to explain the various model versions available, focusing on the new T5XXL model with three CLIP encodings and the different precision options provided, such as FP16 and 8-bit precision. The host also addresses the system requirements for running these models, mentioning the need for at least 12GB of VRAM and the potential use of virtual memory for lower-end systems.

05:01

🔍 Exploring Model Variants and Negative Prompting Techniques

This paragraph delves into the specifics of different model variants available on Huggingface, the absence of CLIP encoding in certain models, and the necessity to download additional CLIP models for those without. It also explores the concept of negative prompting, explaining how the absence of prompt intensity can lead to a 'zeroing out' effect in the generated images. The host discusses the timing settings for negative prompts and how they are merged to create a linear transition effect, ultimately reducing the negative prompt's impact. Additionally, the video touches on the sampling algorithms and model pipelines in SD3, suggesting that while they are not the most critical components, they do offer some room for optimization.

10:02

🎨 Testing SD3 with Official Prompts and Adjusting Settings

The host proceeds to test the SD3 model using official prompts, noting the model's 10GB size which includes three CLIP models, eliminating the need for separate downloads. The video showcases the sampling parameters provided by the model and the host's attempt to generate an image, commenting on the slightly 'greasy' appearance of the generated face. Adjustments are made to the CFG value and the sampling method, with the host trying different schedulers and model algorithms to achieve a more desirable result. The video emphasizes the importance of following the negative prompt approach for better image quality and provides a comparison between different work flows, such as basic, prompt强化, and upscale.

15:02

📚 Utilizing Dynamic Prompts for Image Iteration

The paragraph introduces the concept of dynamic prompts for image iteration, mentioning a plugin called 'Dynamic' that facilitates the use of wildcards in prompts. The host guides viewers on how to install the plugin and use it to generate images with varying styles and elements by referencing different text files containing prompt cards. The video also highlights the resources available on the Lib website for downloading prompt cards and models, emphasizing the convenience and speed of accessing these resources for users in China.

20:03

🌐 Batch Processing and Model Comparison

The host discusses the process of batch processing in ComfyUI, explaining how to use the FIZZ node for batch generation and the challenges of integrating dynamic prompts into the batch process. The video demonstrates how to set up a batch process using string concatenation and frame nodes to generate multiple images with different prompts. Additionally, the host compares three different models: SDXL, SD3, and Cascade, using a macro prompt plugin to generate a variety of images and assess the strengths and weaknesses of each model in terms of style and detail.

25:04

🖌️ Fine-Tuning and Testing SD3 with Different Prompts

In this section, the host fine-tunes the SD3 model by adjusting weights and testing it with different prompts, including landscapes and portraits. The video compares the results with SDXL and Cascade models, noting that while SDXL shows better style representation, SD3's outputs are more generic. The host also experiments with various themes and styles, such as watercolor and anime, to see how SD3 handles different artistic interpretations and discusses the potential reasons for the discrepancies in image quality.

30:04

🌐 Online Generation Test and Community Engagement

The final paragraph wraps up the video with a test of the online generation feature on the Lib website using the SD3 model. The host compares the results with local generation and discusses the potential reasons for any differences observed. The video concludes with an invitation for viewers to share their experiences and optimizations in the comments section and to join a community group for further discussion and support. The host also hints at future content that will explore more about the models and their applications.

Mindmap

Keywords

💡SD3 model

The SD3 model refers to the third generation of the Stable Diffusion model, an AI-based image synthesis tool that has been recently open-sourced. It represents a significant advancement in the field of AI-generated art, with improved capabilities in understanding and integrating prompts into image generation. In the video, the presenter discusses the architectural enhancements of the SD3 model, such as the increased number of channels in the VAE decoding part and the addition of a third CLIP encoder, which contributes to more precise control over image elements.

💡ComfyUI

ComfyUI is mentioned as a user interface for working with AI models like Stable Diffusion. It allows for batch processing operations, which are essential for testing and generating multiple images based on different prompts. The script describes how ComfyUI can be used to load models and manage prompts efficiently, showcasing its role in facilitating the process of AI-driven image creation.

💡VAE decoding

VAE, or Variational Autoencoder, is a type of neural network architecture that is used in the SD3 model for decoding, which is a part of the image generation process. The script highlights that the VAE decoding part of the SD3 model has been significantly enhanced, with an increase in the number of channels to 16, allowing for more detailed and accurate image synthesis.

💡CLIP encoder

CLIP, or Contrastive Language-Image Pre-training, is a multimodal model that connects an image and the text describing it. The SD3 model incorporates three types of CLIP encoders, which is an increase from the two found in the SDXL model. These encoders help the model to better understand and integrate textual prompts into the generated images, as discussed in the video.

💡Huggingface

Huggingface is a platform mentioned in the script as a resource for AI models, including the Stable Diffusion models. It provides different versions of the models, including those with and without CLIP encoding, and in various precision formats like FP16 and FP8. The video explains how to identify and choose the appropriate model based on the presence of CLIP encoding and the required system resources.

💡Batch processing

Batch processing in the context of the video refers to the ability to generate multiple images in one go, using different prompts or settings. The script explains how ComfyUI facilitates batch processing, which is crucial for testing the SD3 model with various prompts and parameters to evaluate its performance and capabilities.

💡Prompts

Prompts are textual descriptions or commands that guide the AI model in generating images. The script discusses how the SD3 model can interpret prompts more accurately due to its enhanced architecture, allowing users to control specific aspects of the generated images through precise prompts.

💡Liblib AI

Liblib AI is a resource website for AI models and related resources, particularly popular among Chinese users. The video mentions it as a platform where users can find a variety of models, including exclusive ones that may not be available on other platforms like Huggingface. It also provides an online generation feature for models like SD3.

💡Sampling algorithm

The sampling algorithm is a part of the AI model's process for generating images based on prompts. The SD3 model's sampling algorithm is discussed in the script, with mentions of different steps and parameters like CFG (which affects style intensity) and the choice of sampling method, such as DDIM, that can influence the outcome of the generated images.

💡Texture inversion

Texture inversion is a term used in the context of image generation to describe the process of enhancing or inverting the textures in an image. Although not explicitly defined in the script, it is implied in the discussion of image quality and the adjustments made to the sampling process to avoid issues like overly shiny or greasy-looking textures in the generated images.

💡Batch scheduling

Batch scheduling is a method for organizing and executing multiple tasks or processes in a batch, which in the video's context, refers to generating multiple images with different prompts in an automated sequence. The script explains how to set up batch scheduling in ComfyUI to leverage the SD3 model's capabilities for bulk image generation.

Highlights

Introduction to the newly open-sourced SD3 model and its architecture enhancements based on SDXL.

VAE decoding in SD3 has been significantly strengthened with 16 channels.

Improved understanding and integration of prompts, allowing for more precise control over image elements.

SD3 utilizes three CLIP encoders, adding a text encoder to the existing two.

Training data size has increased to 2 billion parameters, a significant leap from SDXL.

Differentiating between model versions on Huggingface by their suffixes to identify CLIP encoding inclusion.

SD3 model versions available in FP16 and INT8 precisions, with file sizes reaching up to 15GB.

The requirement of at least 12GB of VRAM for optimal performance of the SD3 model.

Introduction to the Liblib AI platform as a resource for model downloads and community engagement.

Liblib AI's online generation capabilities compared with local generation performance.

ComfyUI's batch processing operations to test the model with various prompts.

Explanation of the basic, prompt-enhanced, and upscale workflows provided by Huggingface for ComfyUI.

Analysis of the negative prompt handling in SD3 to prevent overemphasis on certain image aspects.

SD3's sampling algorithms and their impact on image generation quality.

Testing the official prompt words for image generation and adjusting parameters for better results.

Comparative analysis of image generation between SD3, SDXL, and Cascade models.

Use of the Dynamic Prompts plugin for batch processing and iterating over prompts.

Methodology for setting up batch generation using FIZZ nodes and string concatenation in ComfyUI.

Online generation tests using Liblib AI's platform to compare with local model results.

Discussion on the potential need for further optimization of the SD3 model based on test results.

Invitation for viewers to share their findings and optimizations in the comment section for community discussion.

Final thoughts on the current state of the SD3 model and its comparison with other models like SDXL and Cascade.