ComfyUI: Stable Video Diffusion (Workflow Tutorial)
TLDRIn this tutorial, Mali introduces ComfyUI's Stable Video Diffusion, a tool for creating animated videos from images. The video showcases various techniques, including frame control, subtle animations, and advanced workflows for fine-tuning image to video outputs. Mali demonstrates how to manipulate motion, camera movement, and facial expressions, providing a detailed guide for users to achieve dynamic video results using ComfyUI's features.
Takeaways
- π Mali introduces a tutorial on using Stability AI's Stable Video Diffusion model for creating animated images and videos.
- πΉ The video covers how to achieve frame control and subtle animations using AI-generated images or DSLR photos.
- π οΈ Mali demonstrates six 'comfy graphs' to fine-tune image to video output and shares workflow hacks for achieving desired results.
- π The tutorial includes links for downloading necessary models and software, such as FFMpeg, for video processing.
- π» The workflow involves using Comfy UI, which supports both stable video diffusion models and can be run locally.
- π¨ Mali explains the importance of image resizing and cropping for maintaining aspect ratios and precise alignment within the video frame.
- π The tutorial details the process of adjusting settings like CFG values, motion bucket IDs, and augmentation levels to control motion and animation.
- ποΈ Mali shows how to select video formats like MP4 for better quality and how to manage denoising levels for animation stability.
- ποΈ The video includes a trick for animating facial features like eyes blinking by using multiple images with different states (open and closed eyes).
- π The workflow also covers advanced techniques like noisy latent composition for combining elements from different images or videos.
- π The tutorial concludes with the availability of Json files and MP4 videos for YouTube channel members to replicate the workflows.
Q & A
What is the main topic of the video tutorial?
-The main topic of the video tutorial is the workflow for creating stable video diffusion using ComfyUI, a tool that supports the models released by Stability AI.
Who is the presenter of the tutorial?
-The presenter of the tutorial is Mali.
What are the two video models mentioned in the script, and how many frames can each generate?
-The two video models mentioned are the first stable video diffusion model, which generates 14 frames, and the second model, SVD XT, which is trained to generate 25 frames.
What is the recommended resolution for video generation in ComfyUI?
-The recommended resolution for video generation in ComfyUI is 1024x576, which works for both portrait and landscape orientations, with landscape being preferred.
What is the purpose of the 'video linear CFG guidance' node in the workflow?
-The 'video linear CFG guidance' node is used to control the level of detail and motion in the video output, starting with a minimum CFG value and ending with a value set in the case sampler.
Why is the 'VHS video combine' custom node used instead of the default 'save animated webp' node?
-The 'VHS video combine' custom node is used because it allows for exporting in various formats like GIF, webp, MP4, etc., within ComfyUI, whereas the default node only supports webp format, which requires additional custom software to convert to MP4.
What is the role of the 'image resize' node in the workflow?
-The 'image resize' node is used to maintain the aspect ratio and crop the image to ensure it aligns precisely as desired, regardless of whether the image is square, portrait, or landscape.
How does the 'augmentation level' setting affect the video generation?
-The 'augmentation level' setting adds noise to the generation, which affects the level of detail and motion. It is sensitive and can lead to poorer motion details if set too high or too low.
What is the recommended frame rate for the generated videos?
-The recommended frame rate for the generated videos is 10, as higher frame rates are not recommended due to the total video frame limit of 25.
Can the models be used to animate elements in a DSLR photo?
-Yes, the models can be used to animate elements in a DSLR photo by using techniques like noisy latent composition, which allows for adding specific animations like moving clouds in the sky.
What is the 'noisy latent composition' technique mentioned in the script?
-The 'noisy latent composition' technique is a method used to combine the effects of two videos by using a latent composite node, which allows for pasting one video's elements over another, such as adding moving clouds to a static image.
Outlines
π Introduction to Stability AI's Video Diffusion Model
Mali introduces the video tutorial on Stability AI's first model for stable video diffusion. The model allows for frame control and subtle animations on images, with six comfy graphs showcasing different image to video output fine-tuning techniques. Mali thanks new channel members and mentions the availability of resources like Json files and MP4 videos for YouTube channel members. The tutorial will cover the use of Comfy UI, custom nodes, and FFMpeg for video format conversion, focusing on the model's capabilities for video generation at 1024x576 resolution in both portrait and landscape orientations.
π Setting Up the Comfy UI Workflow for Video Generation
The paragraph details the initial steps in setting up the Comfy UI workflow for video generation using Stability AI's model. It includes installing necessary custom nodes like the W node suit, video helper suite, and image resize, as well as configuring the environment variables for FFMpeg. The workflow begins with the video model option and progresses through nodes for image to video conditioning, K-sampler, and VAE decode. Mali emphasizes the importance of maintaining image ratios and cropping for video output, using a candle image as an example to demonstrate motion control in the video.
π§ Fine-Tuning Video Parameters for Motion and Detail
This section delves into fine-tuning the video generation parameters, such as the CFG value and motion bucket ID, which determine camera motion and detail levels. Mali explains the relationship between these settings and their impact on the video output, using different values to illustrate the effects on motion and detail. The paragraph also covers the importance of the augmentation level, which adds noise and detail to the generation, and how it should be carefully adjusted to avoid poor motion details. The tutorial progresses with practical examples, including changing the frame rate and experimenting with different formats like GIF, webp, and MP4 for better quality.
π¨ Advanced Techniques for Image Animation and Facial Expressions
Mali explores advanced techniques for animating specific parts of an image, such as making a woman's hand wave or creating a blinking effect in a portrait image. The tutorial covers the use of different samplers and schedulers to achieve these animations, as well as the importance of the motion bucket level in determining which parts of the image will move. The paragraph also introduces tricks for animating facial features, such as eyes, by using ancestral samplers and adjusting the motion bucket level, demonstrating how to achieve more natural and subtle animations.
π€ Creating Subtle Animations with Multi-Image Inputs
The focus shifts to creating subtle animations like blinking eyes and lip movements using multi-image inputs. Mali demonstrates how providing the AI with a set of images in a specific order can influence the animation outcome, without the need for frame interpolation. The paragraph explains the process of setting up image loaders and batch nodes to feed the AI with a sequence of images for the desired animation. It also discusses the challenges of maintaining color consistency and offers solutions, such as adjusting the ratio of open to closed eye images to improve the animation quality.
π΄ββοΈ Animating Complex Motions with the Right Parameters
This section discusses animating complex motions, such as forward movement, using the right parameters to avoid unwanted camera panning or background movement. Mali uses a motorbike example to illustrate the effect of the augmentation level on motion details and how sensitive this setting is. The tutorial shows how to adjust the motion bucket and CFG values to achieve a smooth forward motion, and how changing the sampler and scheduler can affect the animation outcome, with a special note on avoiding pedal movement animation due to model limitations.
π Combining Videos with Noisy Latent Composition
Mali introduces a complex workflow for combining videos using a technique called noisy latent composition. The aim is to create a video with slow-moving waves and time-lapsed clouds in the sky from a DSLR photo. The paragraph explains the process of creating groups for each video, adjusting image sizes, and using nodes like image to video, image size to number, and number to text for precise resolution control. It also covers the importance of the order in which prompts and outputs are combined for the final video composition, using conditioning combine nodes and a latent composite node to layer the cloud animation over the main image.
π Finalizing the Video Output with Text to Image Integration
The final paragraph outlines the process of finalizing the video output by integrating a text to image workflow. It describes setting up the text to image group with the appropriate aspect ratio and connecting it to the video processing group. The tutorial emphasizes adjusting the denoise value for the final output to ensure a close match to the previous video layers and using the feather value to blend the layers seamlessly. Mali concludes the tutorial by making the video output look beautiful and mentioning that all Json files will be available for YouTube members, hoping the tutorial was helpful.
Mindmap
Keywords
π‘Stable Video Diffusion
π‘ComfyUI
π‘Frame Control
π‘Latent Noise Composition
π‘CFG Guidance
π‘K Sampler
π‘VH Video Combine
π‘Image Resize
π‘Augmentation Level
π‘CRF Value
π‘Ping Pong Effect
Highlights
Introduction to ComfyUI and Stable Video Diffusion by Stability AI.
Frame control in video diffusion by animating specific elements.
Creating subtle animations for hair and eyes using AI-generated images.
Utilizing latent noise composition for creating short videos from DSLR photos.
Six comfy graphs showcasing different image to video output fine-tuning examples.
Comfy UI supports both Stable Video Diffusion models released by Stability AI.
Local running of Comfy UI on a 4090 GPU with 100% usage during processing.
Model training for video generation at 14 and 25 frames, respectively.
Resolution capabilities of the models at 1024x576 for both portrait and landscape.
Installation and update requirements for Comfy Manager and custom nodes.
Importance of the image resize node for maintaining aspect ratio and precise alignment.
Explanation of the video model option and nodes used in the workflow.
Technique for animating only certain elements in a video using AI images.
Adjusting settings to control motion and camera movement in video diffusion.
Use of the 'VHS video combine' custom node for exporting various video formats.
Optimization of augmentation level for achieving the desired motion details.
Frame rate adjustment for better video quality and compatibility.
Manipulating motion bucket ID and sampler CFG for different animation effects.
Advanced workflow for creating blinking animations using multi-image method.
Tips for animating facial features like eyes and lips in portrait images.
Combining effects with noisy latent composition using DSLR photos.
Final workflow explanation for creating a video with specific motion effects.
Availability of JSON files and MP4 videos for YouTube channel members.