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
TLDR本期视频深入探讨了开源的Stable Diffusion 3（SD3）模型，介绍了其基于SDXL架构的改进，如VAE解码增强、更精准的题词控制能力以及新增的文本编码器。视频还展示了如何使用ComfyUI进行批处理操作，并通过不同模型的比较测试，展示了SD3在图像生成中的表现。同时，提供了测试工作流和使用心得，帮助新手快速入门并优化模型使用体验。
Takeaways
- 😀 Stable Diffusion 3（SD3）模型基于SDXL架构进行了增强，特别是在VAE解码部分，通道数提升到16，增强了对题词的理解及元素融合能力。
- 🔍 SD3模型引入了三种CLIP编码，比前代SDXL增加了文本编码器，使得模型能够更精确地控制画面的某些部分。
- 📈 SD3训练使用了更大的数据集，参数量达到20亿，相比SDXL有显著增加。
- 💾 huggingface官网提供了不同版本的SD3模型下载，包括不同精度的模型，如FP16和8位精度，以适应不同硬件要求。
- 🔗 对于国内用户，推荐使用哩布哩布（liblib）网站下载模型，该网站资源丰富，访问速度快，且有独家模型。
- 🛠️ ComfyUI提供了批处理操作和三个工作流样本，包括基础、prompt强化和放大工作流，方便用户进行图像生成。
- 🎨 使用SD3模型进行图像生成时，可以通过调整CFG、采样器步数和调度器等参数来优化图像效果。
- 🔧 视频提到了如何使用ComfyUI和Dynamic Prompts插件进行题词遍历和批处理，以生成多样化的图像。
- 📝 视频还介绍了如何通过修改负面提词和采样算法来改善图像生成效果，避免过拟合。
- 🆚 进行了SD3与其他模型（如SDXL和Cascade）的比较测试，发现SD3在风格表现上可能不如其他模型强烈。
- 🔄 视频最后提到了使用Lib网站的在线生成功能，发现其生成效果与本地模型生成存在差异，暗示可能需要进一步优化本地模型配置。
Q & A
SD3模型有哪些主要的改进点？
-SD3模型在VAE解码部分进行了增强，通道数增加到16，对题词的理解及元素融合更加完善，可以通过题词更精确地控制画面部分。它使用了三种CLIP编码，增加了文本编码器，训练数据量达到2B，即20亿参数，比之前的SDXL模型多很多。
在huggingface官网上，如何根据后缀区分不同的模型版本？
-在huggingface官网上，没有后缀的模型表示不带CLIP编码。带有'clip'后缀的版本带有基础的CLIP编码，即i和l的编码。而带有'T5XXL'后缀的是第三代模型，带有第三种CLIP编码。FP16和8表示模型的精度，其中8精度是更小的模型。
为什么说SD3模型对显存的要求相对较高？
-SD3模型由于参数量达到20亿，即使是FP16精度的模型大小也达到了15G，因此对显存的要求相对较高。官方建议至少需要12G显存，虽然8G显存理论上也可以运行，但可能需要使用虚拟内存，速度无法保证。
国内用户如何获取模型资源，有哪些推荐的网站？
-对于国内用户，推荐使用哩布哩布（liblib）网站，这是一个国内比较完善的绘画模型资源网站，除了常用的模型外，还有许多原创作者发布的独家模型，适合东方人的使用习惯。
如何使用ComfyUI进行批处理操作？
-在ComfyUI中，可以使用Dynamic Prompts插件进行题词的遍历。通过设置不同的文档和通配符，可以生成多张不同的图像。此外，还可以使用FIZZ节点进行更精细的批处理设置，实现自动填写题词和批量生成图像。
在视频中提到的三种工作流分别是什么？
-视频中提到的三种工作流是：basic基础工作流，prompt强化工作流，以及upscale放大工作流。basic工作流是最基本的设置，prompt强化工作流针对题词进行了特别优化，而放大工作流主要用于图像的放大处理。
SD3模型的负面处理方式有什么特点？
-SD3模型的负面处理方式通过降低负面提词的强度和使用时间来减少其影响。负面提词在生成过程中的前10%有作用，之后逐渐减弱直至没有作用，实现了一个缓出的效果。
如何使用ComfyUI生成带有特定风格的图像？
-在ComfyUI中，可以通过设置正向和负向文本编码器，并选择合适的CLIP模型来生成带有特定风格的图像。例如，使用不同的CLIP模型（如Clip L、Clip G和T5的CLIP）可以分别控制背景色彩、氛围、形状和主体特征等。
视频中提到的一键提词插件有什么作用？
-一键提词插件可以快速生成题词，它可以根据选择的风格、类型、主体等元素自动组合成合适的题词。这个插件简化了题词的创建过程，但生成的题词精度可能需要根据具体模型进行调整。
如何使用ComfyUI进行多模型的比较测试？
-可以创建一个包含多个模型的工作流，例如SDXL、SD3和Cascade模型，并使用相同的题词或略有差异的题词进行图像生成。通过比较不同模型生成的图像，可以评估它们在风格、细节和整体效果上的表现差异。
视频中提到了哪些模型在人物图像生成上的表现？
-视频中提到，在人物图像生成上，SDXL模型表现较好，能够体现出整体的风格。Cascade模型在动漫风格上表现一般，而SD3模型则在风格性和细节上有所不足，可能需要进一步的优化和调整。
Outlines
😀 Introduction to SD3 Model and ComfyUI Batch Processing
The video begins with an introduction to the newly open-sourced SD third-generation model, highlighting its improved architecture based on SDXL with enhanced VAE decoding and better integration of prompts and elements. The host also discusses ComfyUI batch processing operations and guides viewers to the SD official website. The explanation covers the new features of SD3, including three types of CLIP encoding and a larger training dataset of 2 billion parameters. It also touches on model variants available on Huggingface, the requirements for running the models, and the importance of setting up virtual memory for lower VRAM options.
🔍 Exploring Model Variants and Settings on Huggingface
This paragraph delves into the different model variants available on Huggingface, explaining the significance of the 'clip' suffix in model names and the inclusion of text encoders in the latest models. It discusses the file sizes and precision levels of the models, with a focus on the FP16 and 8-bit precision models, and the hardware requirements for running them. The host also provides insights into the Chinese AI painting model resource website 'Liblib', which is recommended for its convenience and exclusive model offerings.
🛠️ ComfyUI Workflows and Model Testing
The script moves on to describe the ComfyUI workflows provided by Huggingface, including basic, prompt enhancement, and upscale workflows. The host provides a walkthrough of the basic workflow, discussing the process of loading models without CLIP encoding and the unique negative prompt handling that seems to reduce the prominence of negative prompts over time. The video then demonstrates the generation of images using the official prompts, with adjustments to sampling algorithms and schedulers to achieve better results.
🎨 Advanced Prompt Techniques and Batch Processing
The host introduces advanced prompt techniques, including the use of three separate CLIP models for different aspects of image generation. The video explains how these models contribute to the overall image, with one responsible for background and atmosphere, and another for the main subject. The script also covers the process of batch processing in ComfyUI, using a plugin for dynamic prompts and discussing the challenges and settings involved in generating multiple images with varied prompts.
📈 Comparative Analysis of SD Models
This section presents a comparative analysis of different SD models, including SDXL, SD3, and Cascade. The host describes a custom test workflow that generates images using these models with the same prompts to evaluate their performance. The results show varying levels of style and detail, with SDXL generally performing better in terms of style representation, while SD3 and Cascade have their own unique strengths and weaknesses.
🤖 Fine-Tuning and Online Generation Testing
The final part of the script discusses the fine-tuning of the models and the testing of the online generation feature on the Liblib website. The host speculates on the potential reasons for the discrepancies in image quality between local and online generation and suggests that further optimization might be needed for the SD3 model. The video concludes with an invitation for viewers to share their findings and experiences with the models and workflows discussed.
📝 Conclusion and Future Exploration
In conclusion, the host summarizes the findings from the video, emphasizing the need for further exploration and optimization of the SD3 model. They express a personal preference for the SDXL model based on the tests conducted and invite viewers to engage in discussions and share their insights. The video ends with a reminder to support the channel and a tease for future content related to AI painting models and techniques.
Mindmap
Keywords
💡SD3 model
💡ComfyUI
💡VAE (Variational Autoencoder)
💡CLIP encoding
💡Huggingface
💡FP16 precision
💡Batch processing
💡Prompts
💡Liblib AI
💡Texture synthesis
💡Batch调度
Highlights
Introduction to the newly open-sourced SD3 (Stable Diffusion 3) model and its architectural enhancements based on SDXL.
VAE decoding in SD3 has been significantly strengthened with 16 channels for more precise control over image elements through prompts.
SD3 incorporates three CLIP encoders, adding a text encoder to the existing 'l' and 'g' models for better prompt understanding and element fusion.
Training data for SD3 is vast, with 2 billion parameters, marking a significant increase from the SDXL model.
Explanation of model variants available on Huggingface, including those with and without CLIP encoding, and different precision levels like FP16 and FP8.
Requirement of at least 12GB of VRAM for optimal performance with SD3, though 8GB can be used with potential speed trade-offs.
Guide on where to download models for users, particularly highlighting the Liblib AI platform as a resource for model downloads.
Liblib AI's support for the SD3 model and its online generation capabilities, comparing speeds to high-end hardware like the Nvidia 4090.
Review of Huggingface's ComfyUI sample workflows for basic, prompt-strengthened, and upscale tasks in SD3.
Analysis of the negative prompt handling in SD3's basic workflow, explaining the reduction of negative prompt strength over time.
Discussion on SD3's sampling algorithms and the model pipeline's role in image generation optimization.
Demonstration of generating images using official prompts and adjusting parameters like CFG and sampling steps for better results.
Comparison of different sampling methods like Euler a and DDIM, and their impact on image generation quality.
Introduction to the Dynamic Prompts plugin for ComfyUI and its use for batch processing and iterating over prompts.
Tutorial on setting up batch processing for image generation using frame nodes and string concatenation in ComfyUI.
Testing of different models including SDXL, SD3, and Cascade, comparing their performance in generating images with various prompts.
Observations on the limitations of SD3 in generating detailed and stylistically consistent images compared to SDXL and Cascade.
Final thoughts on the SD3 model as a base model requiring further optimization and community feedback for improvement.
