【新生代AI绘画模型】Cascade 到底有多强？| 独立版一键安装包，精准控制，风格还原，远超SDXL！#cascade
TLDRThe video discusses the new AI painting model, Cascade, launched by spiletia, which has gained significant attention for its precision and style replication capabilities, surpassing SDXL. The model's open-source availability and local deployment options are highlighted, along with its efficiency, hardware compatibility, and potential applications across various creative fields. The video creator's positive experience with Cascade's high image yield, accuracy, and aesthetic reproduction is shared, emphasizing its potential as a creative tool for both professionals and enthusiasts.
Takeaways
- 🚀 The AI field is rapidly developing with new models like Open AI's GPT5 and Google's Gimni Pro 1.5.
- 🎨 The new painting model Cascade by spiletia has gained significant attention for its breakthroughs in the painting field.
- 🌐 Cascade is open-source and can be deployed locally, offering high-quality image generation with increased efficiency.
- 🔍 Cascade introduces architectural improvements over previous models, including a high-compression latent space for better efficiency.
- 🔜 The model operates at a faster rate compared to previous versions, with potential for 5-6 times the operation speed.
- 📚 The training framework of previous models can be migrated to Cascade, including Alora training and other components.
- 🛠️ The generation process involves three steps with different models responsible for encoding, compressing, and generating images.
- 🏗️ Model C, with 3.6 billion parameters, significantly improves detail generation and accuracy over SDXL.
- 🎭 Cascade's generated images exhibit a high degree of realism and style reproduction, closely resembling real pictures.
- 🔄 The model's ability to understand and follow style inscriptions (CFG) allows for a variety of artistic outputs without the need for complex prompts.
- 📈 The deployment process for Cascade can be complex, but community developers have created user-friendly versions for easier installation and use.
Q & A
What is the main topic of discussion in Ouyang's video?
-The main topic of discussion is the new AI painting model called Cascade, launched by spiletia, and its capabilities and improvements over the previous model SDXL.
How does the Cascade model differ from the previous model in terms of architecture?
-The Cascade model differs in that it has undergone changes from the previous architecture, including a higher compression rate in the latent space, which is now compressed in multiples of 8, resulting in a 24x24 potential space compared to the previous 128x128.
What are the improvements that Cascade brings in terms of efficiency?
-Cascade improves efficiency by requiring less computing power to operate in the potential space, and its operation rate is 5-6 times faster than the previous SDXL model.
How has the training framework of the previous models been integrated into Cascade?
-The training framework of all previous models, such as fine-tuning of large models, Alora training, contranet, ipadapter, and LCM, has been opened and can be migrated to the Cascade model.
Can you explain the three-step generation process of the Cascade model?
-The three-step generation process involves Model A, which is a VAE responsible for image encoding; Model B, which further compresses the images and generates initial noise; and Model C, the latent Generator, which is responsible for the complete image generation process.
What are the parameter counts for the different stages of the Cascade model?
-Model A contains 20 million parameters, Model B comes in two versions with 700 million and 1.5 billion parameters, and Model C is available in 1 billion and 3.6 billion parameter versions.
What is Ouyang's evaluation of the image yield and availability generated by the Cascade model?
-Ouyang found that the image availability is over 90%, meaning that in most cases, the generated images can be used directly without needing to try again or adjust the prompt words.
How is the deployment process of the Cascade model for users in China?
-The deployment process is quite troublesome, but community developers have organized a user version that simplifies the process to a one-click installation package, which only requires decompression and running a batch file.
What are the challenges faced during the deployment of the Cascade model?
-The challenges include the complexity of deploying the official project, the need for network configuration, and the loading of model processes which can take 12-15 hours due to the large parameter sizes.
How does Ouyang describe the future development direction of AI in the field of painting and creation?
-Ouyang believes that AI will increasingly provide realistic materials and blur the boundaries with traditional human creations, but it will remain a tool for providing materials and inspiration rather than reaching the level of true human creativity and emotional expression.
What is Ouyang's advice for those interested in testing the Cascade model without local deployment?
-Ouyang suggests using a test page like Hagen face to directly generate and test images without the need for local deployment, although there is no way to download or save the generated images from the online test.
Outlines
🚀 Introduction to AI Developments and New Models
The paragraph discusses the rapid advancements in the field of AI, highlighting recent releases like Open AI's GPT5, Google's Gimni Pro 1.5, and the Open I video generation model Sora. It emphasizes the attention garnered by Sora and the AI field's explosive growth. The speaker, Ouyang, shares his experiences with the influx of information on the internet and introduces a new painting model called Cascade, launched by spiletia. The model is noted for its efficiency and open-source availability, enabling local deployment. Ouyang's focus on practical projects is stressed, and an overview of the Cascade model's features and improvements over previous models is provided, including its high compression rate and efficiency, which allows for faster operation and training framework compatibility with previous models.
🛠️ Deployment and Usage of the Cascade Model
This paragraph delves into the challenges of deploying the Cascade model in China and the availability of a user-friendly version encapsulated by community developers. The process involves downloading a compressed package, extracting it, and running a batch file to set up the environment. The speaker outlines the requirements and steps for deployment, including the creation of a file and running a command. The model's initial run loads default configurations and model processes. The paragraph also discusses the model's inference speed, training framework, and the three-step generation process involving VAE, noise generation, and a latent generator. Ouyang shares his positive experience with the model's accuracy, restoration ability, and image yield.
🎨 AI in Art and Style Reproduction
The speaker reflects on the increasing realism of AI-generated images and their diminishing AI 'flavor'. He uses examples from movies and anime to illustrate how the AI model can capture and reproduce various artistic styles accurately. The discussion includes the generation of a superhero resembling Iron Man and an anime character from Dragon Ball. The model's ability to adjust to different styles and produce high-quality images is highlighted, as well as its speed and accuracy in rendering images based on given prompts. The paragraph also touches on the potential of AI in providing creative inspiration and materials for various applications.
🤖 Comparison of AI Models and Future Prospects
This section compares the new AI model with existing ones like SDXL, emphasizing the improved accuracy and style imitation capabilities of the new model. The speaker provides examples of generated images and discusses the differences in detail and overall harmony. The paragraph then explores the broader implications of AI development, particularly in terms of ethics and the potential blurring of lines between AI-generated and real content. The speaker asserts that AI, regardless of its advancements, remains a tool for providing materials and inspiration rather than a creator of humanistic content. The limitations of AI in understanding and expressing human emotions and artistry are also discussed.
🌐 Commercialization and Ethical Considerations of AI
The final paragraph discusses the commercial potential of AI in fields like advertising, film and television, and self-media. The speaker expresses interest in AI's capability to process existing video materials and its implications for semi-automatic commercialization. The challenges of customization in advertising and film are mentioned, along with the importance of maintaining character consistency and expressiveness. The speaker also shares thoughts on the future of AI and its ethical considerations, emphasizing that the ultimate purpose of AI creation is to serve human expression and emotion. The paragraph concludes with the speaker's intention to provide a configured image file and installation method for the Cascade model, as well as an invitation to join an AI exchange group for further support and discussion.
Mindmap
Keywords
💡AI
💡Cascade
💡SDXL
💡Sora
💡OpenAI's GPT5
💡Google's Gimni Pro 1.5
💡latent space compression
💡fine-tuning
💡inference speed
💡one-click installation
💡CFG
Highlights
The new AI painting model Cascade has been launched, marking a breakthrough in the field of AI painting.
Cascade is开源项目，可以本地部署和运行，提高用户的便利性。
Cascade模型基于原始的SD模型进行了两项主要改进，提升了整体性能。
模型的潜在空间经过42倍压缩，相较于之前的1024压缩至24倍24，效率显著提高。
Cascade的推理速度是之前SDXL的5-6倍，大幅提升了操作效率。
Cascade模型允许之前模型的训练框架迁移，例如Alora训练、contranet、ipadapter和LCM等。
Cascade的生成过程分为三步，由三个不同的模型负责，分别是VAE、压缩和生成噪声以及潜在生成器。
模型a包含2000万参数，模型b提供7亿和15亿参数的两个版本，模型c则有10亿和36亿参数的版本。
36亿参数版本的模型c在生成细节方面表现更优，准确性和文本理解能力远超SDXL。
Cascade的图像产出率超过90%，在大多数情况下可以直接使用生成的图像。
部署Cascade项目在中国有一定的难度，但社区开发者提供了用户版，简化了部署过程。
部署过程中需要配置网络，否则无法加载模型，影响使用体验。
Cascade模型的前端问题导致无法实时预览图像，否则渲染速度会极慢。
Cascade模型在美学和风格再现方面表现出色，生成的图像越来越少AI味道，更接近真实图片。
通过改变种子值和使用高级设置，可以轻松调整生成图像的风格和细节。
Cascade模型能够准确理解复杂的文本提示，并准确恢复场景，展现了高文本准确性。
未来AI的发展将使得人们更难区分AI生成内容和真实内容，需要我们更加注意。
尽管AI在视觉方面表现出色，但仍然只是提供材料和灵感的工具，远未达到创造性和创作水平。
AI生成的视频和图片目前只能作为材料使用，无法产生真正的人文内容或故事内容。
未来AI的商业化方向可能集中在广告、影视和短视频自媒体等领域。
