Ai Animation in Stable Diffusion
TLDRIn this video, Sebastian Tus demonstrates the use of LCM (Latent Concept Modifiers) in Stable Diffusion to create animations with custom footage. He shares his experience using Blender's human generator and the DPM Plus+ s Caris, which he enhanced with the LCM. Sebastian explains the process of adjusting settings like sampling steps and resolution to improve the generation speed and quality. He also addresses the issue of occlusion in stable diffusion and how to avoid it. The video showcases the creation of a realistic image and an animated look, using different models and techniques. Sebastian also discusses the challenges of flickering in animations and how to mitigate them using DaVinci Resolve. He emphasizes the potential of using 3D applications like Blender for consistency in animations and the advantages of working with larger images for better results. The video concludes with a fusion clip example and an invitation for viewers to explore the topic further and share their thoughts.
Takeaways
- 🎬 Sebastian Tus introduces the use of LCM lures to create animations with personal footage in Stable Diffusion.
- 🚀 The presenter demonstrates how to add a prompt and apply LCM (Learned Conditional Model) to generate images.
- 🛠️ Sebastian uses Blender's human generator to create a base image and discusses the importance of occlusion in image generation.
- ⚙️ Settings adjustments are made, including sampling steps, resolution, and control net configurations, to optimize the generation process.
- 🔍 The presenter emphasizes the need for a specific image resolution (1920x1401) to avoid issues with occlusion.
- 🌟 A successful image generation is achieved without turning the yellow stripes into hands, showcasing the model's understanding of the image.
- 🔄 The process of recycling the seed number for a satisfactory image generation is explained.
- 🎨 The second example shows how to create an animated look using the eal dark Gold Max model and additional techniques to enhance color.
- 📊 The presenter discusses the trade-offs between image size and rendering time, and the benefits of using larger images for better quality.
- 🧩 Sebastian demonstrates the use of Fusion to combine different elements of an animation and reduce flickering.
- 💻 The presenter shares his preference for working with layers and using Stable Diffusion to generate the necessary layers for final images.
- ⏱️ The video concludes with a teaser for future explorations of the topic and an invitation for viewers to share their thoughts and applications.
Q & A
What is the main topic of the video?
-The main topic of the video is how to use LCM lures to create animations with your own footage using stable diffusion.
Who is the presenter of the video?
-The presenter of the video is Sebastian Tus.
What is the significance of the LCM lures in the context of the video?
-LCM lures are used to enhance the animation process in stable diffusion, allowing for more detailed and faster image generation.
What is the role of the oil array or DPM Plus+ s Caris in the animation process?
-The oil array or DPM Plus+ s Caris are used as part of the settings to generate images, but the presenter also mentions installing the LCM for better results.
How does reducing the sampling steps to eight benefit the image generation process?
-Reducing the sampling steps to eight allows for faster image generation, which is particularly useful when working with large images.
What is the resolution that the presenter uses for their image?
-The presenter uses a resolution of 1920 by 1401 for their image.
Why is occlusion a problem in stable diffusion?
-Occlusion is a problem in stable diffusion because if certain parts of the image are not included, the AI might misinterpret elements, such as thinking yellow stripes are hands.
What is the purpose of using the 'Venture resolve' in the video?
-The 'Venture resolve' is used to level up the stable diffusion game, likely referring to enhancing the quality and control of the animations.
What is the eal dark Gold Max model mentioned in the video?
-The eal dark Gold Max model is a resource used in the animation process that is no longer available on CID AI, but can be found through a link provided in the video description.
Why does the presenter suggest rendering out the animation without the helmet in Blender?
-Rendering out the animation without the helmet in Blender provides a more consistent face in the final animation, as the helmet can cause glitches and flickering.
How does the presenter address the issue of flickering in the white suit?
-The presenter acknowledges the flickering issue and suggests that it might be due to the shiny nature of the suit or the lack of training with the model. They also mention using DaVinci Resolve to de-flicker the animation.
What is the presenter's ultimate goal with the animations created using stable diffusion?
-The presenter's ultimate goal is to use the animations for live-action looking content, as they believe this is where the technology will really shine.
Outlines
😀 Introduction to LCM Lures and Animation Techniques
In this first paragraph, Sebastian introduces himself and expresses excitement about the topic of the video. He explains that he will demonstrate how to use LCM (Latent Concept Modifiers) lures to create animations with personal footage using stable diffusion, a technology that has been anticipated for a year. Sebastian showcases an image made with Blender's human generator and discusses the process of setting up the prompt for the animation, including the application of LCM Laura. He also details the technical settings required for the animation, such as sampling steps, resolution adjustments, and the use of control nets to ensure pixel-perfect results. The paragraph concludes with a real-time generation of an image to demonstrate the process's effectiveness and the importance of including specific parts of the image to avoid misinterpretation by the AI.
🎨 Creating Animated Effects and Post-Processing
The second paragraph focuses on creating an animated look for the character, as seen in the video teaser. Sebastian mentions the need for a specific model, no longer available on CID AI, but provides a link in the video description. He discusses the use of additional settings like moist mix vae to enhance colors and clip skip. The paragraph details the process of generating an image with less detail, which results in a faster rendering time. Sebastian then addresses the challenge of using a green screen background and the flexibility it provides for repositioning the character in the shot. He also talks about the potential for faster rendering with smaller images and upscaling, but shares his preference for higher resolution images due to better results. The paragraph concludes with a discussion on dealing with flickering in animations and the use of Blender to render animations without certain elements, like a helmet, to achieve a more consistent look. Sebastian also introduces the concept of fusing different elements in post-processing to create a final, cinematic image.
Mindmap
Keywords
💡LCM lures
💡Stable Diffusion
💡Venture Resolve
💡Prompt
💡Oil Array or DPM Plus+
💡Sampling Steps
💡Resolution
💡Denoising
💡Control Net
💡Batch Processing
💡Animated Look
💡Fusion Clip
Highlights
Sebastian Tus introduces a new method for creating animations using LCM lures in Stable Diffusion.
The process involves using Blender's human generator to create a base image.
LCM (Latent Concept Modifier) is not included by default in Automatic 1111 and must be installed.
Reducing sampling steps to eight increases generation speed.
Occlusion is a significant issue in Stable Diffusion, which can misinterpret image parts.
Adjusting the resolution to match the source image is crucial for maintaining detail.
CFG scale and D noising strength are parameters that can be fine-tuned for image quality.
Enabling Pixel Perfect and using control net units are key for image generation.
Large image generation can be time-consuming but yields higher quality results.
The method prevents unwanted transformations, such as turning stripes into hands.
Batch processing allows for the reuse of successful seeds for consistent results.
An alternative model, eal dark Gold Max, can be used for different animation styles.
Moist mix vae and clip skip are additional parameters that enhance color vibrancy.
Temporal net is used in the control net for smoother animations.
Flickering in animations can be reduced with post-processing in DaVinci Resolve.
Rendering without certain elements, like a helmet, can lead to more consistent results.
Blender can be used to create animations and then fuse them for a final, consistent output.
Stable Diffusion is used to generate layers for a final image, rather than a one-step solution.
The potential for live-action looking animations with Stable Diffusion is highlighted.
The method is still in early stages, with much to explore and improve.