ComfyDock is a tool that allows you to easily manage your ComfyUI environments via Docker.
Common Challenges with ComfyUI
Custom Node Installation Issues: Installing new custom nodes can inadvertently change settings across the whole installation, potentially breaking the environment.
Workflow Compatibility: Workflows are often tested with specific custom nodes and ComfyUI versions. Running these workflows on different setups can lead to errors and frustration.
Security Risks: Installing custom nodes directly on your host machine increases the risk of malicious code execution.
How ComfyDock Helps
Environment Duplication: Easily duplicate your current environment before installing custom nodes. If something breaks, revert to the original environment effortlessly.
Deployment and Sharing: Workflow developers can commit their environments to a Docker image, which can be shared with others and run on cloud GPUs to ensure compatibility.
Enhanced Security: Containers help to isolate the environment, reducing the risk of malicious code impacting your host machine.
zopieux on Dec 5, 2024 : Ultralytics was attacked (or did it on purpose, waiting for a post mortem there), 8.3.41 contains nefarious code downloading and running a crypto miner hosted as a GitHub blob.
Enhanced Capabilities – Improved Prompt Understanding: Achieve more accurate prompt interpretation and stunning video dynamics. – Supports Various Video Ratios: Choose from 16:9, 9:16, 3:4, 4:3, and 1:1 ratios. – Upgraded Styles: Style functionality returns with options like Anime, Realistic, Clay, and 3D. It supports both text-to-video and image-to-video stylization.
New Features – Lipsync: The new Lipsync feature enables users to add text or upload audio, and PixVerse will automatically sync the characters’ lip movements in the generated video based on the text or audio. – Effect: Offers 8 creative effects, including Zombie Transformation, Wizard Hat, Monster Invasion, and other Halloween-themed effects, enabling one-click creativity. – Extend: Extend the generated video by an additional 5-8 seconds, with control over the content of the extended segment.
👍 SOTA Performance: Wan2.1 consistently outperforms existing open-source models and state-of-the-art commercial solutions across multiple benchmarks.
🚀 Supports Consumer-grade GPUs: The T2V-1.3B model requires only 8.19 GB VRAM, making it compatible with almost all consumer-grade GPUs. It can generate a 5-second 480P video on an RTX 4090 in about 4 minutes (without optimization techniques like quantization). Its performance is even comparable to some closed-source models.
🎉 Multiple tasks: Wan2.1 excels in Text-to-Video, Image-to-Video, Video Editing, Text-to-Image, and Video-to-Audio, advancing the field of video generation.
🔮 Visual Text Generation: Wan2.1 is the first video model capable of generating both Chinese and English text, featuring robust text generation that enhances its practical applications.
💪 Powerful Video VAE: Wan-VAE delivers exceptional efficiency and performance, encoding and decoding 1080P videos of any length while preserving temporal information, making it an ideal foundation for video and image generation.
Given an input video and a simple user-provided text instruction describing the desired content, our method synthesizes dynamic objects or complex scene effects that naturally interact with the existing scene over time. The position, appearance, and motion of the new content are seamlessly integrated into the original footage while accounting for camera motion, occlusions, and interactions with other dynamic objects in the scene, resulting in a cohesive and realistic output video.
They propose an end-to-end multimodality-conditioned human video generation framework named OmniHuman, which can generate human videos based on a single human image and motion signals (e.g., audio only, video only, or a combination of audio and video). In OmniHuman, we introduce a multimodality motion conditioning mixed training strategy, allowing the model to benefit from data scaling up of mixed conditioning. This overcomes the issue that previous end-to-end approaches faced due to the scarcity of high-quality data. OmniHuman significantly outperforms existing methods, generating extremely realistic human videos based on weak signal inputs, especially audio. It supports image inputs of any aspect ratio, whether they are portraits, half-body, or full-body images, delivering more lifelike and high-quality results across various scenarios.
Hunyuan3D 2.0, an advanced large-scale 3D synthesis system for generating high-resolution textured 3D assets. This system includes two foundation components: a large-scale shape generation model – Hunyuan3D-DiT, and a large-scale texture synthesis model – Hunyuan3D-Paint.
The shape generative model, built on a scalable flow-based diffusion transformer, aims to create geometry that properly aligns with a given condition image, laying a solid foundation for downstream applications. The texture synthesis model, benefiting from strong geometric and diffusion priors, produces high-resolution and vibrant texture maps for either generated or hand-crafted meshes. Furthermore, we build Hunyuan3D-Studio – a versatile, user-friendly production platform that simplifies the re-creation process of 3D assets.
It allows both professional and amateur users to manipulate or even animate their meshes efficiently. We systematically evaluate our models, showing that Hunyuan3D 2.0 outperforms previous state-of-the-art models, including the open-source models and closed-source models in geometry details, condition alignment, texture quality, and e.t.c.
Invoke is a powerful, secure, and easy-to-deploy generative AI platform for professional studios to create visual media. Train models on your intellectual property, control every aspect of the production process, and maintain complete ownership of your data, in perpetuity.
Stable Diffusion is a latent diffusion model that generates AI images from text. Instead of operating in the high-dimensional image space, it first compresses the image into the latent space.
Stable Diffusion belongs to a class of deep learning models called diffusion models. They are generative models, meaning they are designed to generate new data similar to what they have seen in training. In the case of Stable Diffusion, the data are images.
Why is it called the diffusion model? Because its math looks very much like diffusion in physics. Let’s go through the idea.
To reverse the diffusion, we need to know how much noise is added to an image. The answer is teaching a neural network model to predict the noise added. It is called the noise predictor in Stable Diffusion. It is a U-Net model.
After training, we have a noise predictor capable of estimating the noise added to an image.
Diffusion models like Google’s Imagen and Open AI’s DALL-E are in pixel space. They have used some tricks to make the model faster but still not enough.
Stable Diffusion is designed to solve the speed problem. Here’s how.
Stable Diffusion is a latent diffusion model. Instead of operating in the high-dimensional image space, it first compresses the image into the latent space. The latent space is 48 times smaller so it reaps the benefit of crunching a lot fewer numbers.
It is done using a technique called the variational autoencoder. Yes, that’s precisely what the VAE files are, but I will make it crystal clear later.
The Variational Autoencoder (VAE) neural network has two parts: (1) an encoder and (2) a decoder. The encoder compresses an image to a lower dimensional representation in the latent space. The decoder restores the image from the latent space.
You may wonder why the VAE can compress an image into a much smaller latent space without losing information. The reason is, unsurprisingly, natural images are not random. They have high regularity: A face follows a specific spatial relationship between the eyes, nose, cheek, and mouth. A dog has 4 legs and is a particular shape.
In other words, the high dimensionality of images is artifactual. Natural images can be readily compressed into the much smaller latent space without losing any information. This is called the manifold hypothesis in machine learning.
Where does the text prompt enter the picture?
This is where conditioning comes in. The purpose of conditioning is to steer the noise predictor so that the predicted noise will give us what we want after subtracting from the image.
The text prompt is not the only way a Stable Diffusion model can be conditioned. ControlNet conditions the noise predictor with detected outlines, human poses, etc, and achieves excellent controls over image generations.
This write-up won’t be complete without explaining Classifier-Free Guidance (CFG), a value AI artists tinker with every day. To understand what it is, we will need to first touch on its predecessor, classifier guidance…
The classifier guidance scale is a parameter for controlling how closely should the diffusion process follow the label.
Classifier-free guidance, in its authors’ terms, is a way to achieve “classifier guidance without a classifier”. They put the classifier part as conditioning of the noise predictor U-Net, achieving the so-called “classifier-free” (i.e., without a separate image classifier) guidance in image generation.
The SDXL model is the official upgrade to the v1 and v2 models. The model is released as open-source software. The total number of parameters of the SDXL model is 6.6 billion, compared with 0.98 billion for the v1.5 model.
The SDXL model is, in practice, two models. You run the base model, followed by the refiner model. The base model sets the global composition. The refiner model adds finer details.
The IPAdapter are very powerful models for image-to-image conditioning. The subject or even just the style of the reference image(s) can be easily transferred to a generation. Think of it as a 1-image lora. They are an effective and lightweight adapter to achieve image prompt capability for the pre-trained text-to-image diffusion models. An IP-Adapter with only 22M parameters can achieve comparable or even better performance to a fine-tuned image prompt model.
Once the IP-Adapter is trained, it can be directly reusable on custom models fine-tuned from the same base model.
The IP-Adapter is fully compatible with existing controllable tools, e.g., ControlNet and T2I-Adapter.
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