The Entry Deselsion 1.5 / 2.1 / XL 1.0, Datl-e, years old … years ago, Deffion models showed a wonderful quality for the remarkable generation. However, while producing a large quality in common sense, this is difficult to produce high quality for special questions, for example to produce pictures on a specific style, that was always visible to training data.
We can also find the entire model with a large number of photos, explaining the concepts needed to fix the problem from scratch. However, this does not sound effective. First, we need a large set of ideas, and second, it's just a lot of expensive and time-consuming.
There are solutions, however, that, have been given a few pictures and an hour to laugh, can give the models of interruption to produce the right quality in new ideas.
Below, I cover the Dreamambooth, Lora, networks, network networks, text conversion, IP adrolnets are widely used to perform customization models and situations. The concept after all these ways is to memorize a new idea that tries to read it, however, each process is approaching differently.
DEFFION Composition
Before falling down in different ways that help to find out conditions of interruption models, let's restart what import models.
The original idea of the disturbance models to train model to rebuild a harmonious image from noise. In the training phase, gradually adds small gaussian sounds and rebuild the image via Iteratively by displaying the model to predict sounds (delete target).
The original image vision of the image appeared to be a practical and unwanted structure that focus on the findings of it, and all additional sounds are being made at the lower high point.
To add text information to the Deffysion model, the first time we forward the Encerer-Encoder (generally) to produce corridor in the formation of pay attention.
The idea is to take unusual word; Usually, the name of {SKS} is used and teaches a model to map the name {SKS]in a feature we would like to read. This, for example, can style that model has never seen you, such as Van GOG. We will show a lot of his drawings and get well with the phrase “boot painting at {SKS}”. We can customize the same generation, for example, learn how to create photos of a specific, for example “{SKS} in the mountains” in a person's selfies set.
To save the information learned from the pre-training station, Dreamambooth promotes the model that does not radiate significantly from the original, trained text by adding text pairs produced by the original model in Orely planning
When and when and when
Dramambooth produces the best quality of all the means; However, the process may affect the concepts already learned since the review of all model. Training schedule also reduces the number of concepts that can understand. Training takes time, takes 1-2 hours. If we decide to introduce several new ideas at once, we will have to keep testing centers in two models, making a lot of space.
Deviation of text, paper, code
Thinking after the text of the text is that the information stored in the Latent Space for Prouncesion Models is great. Therefore, the style or state that wants to produce a deffion model is already known, but we have no access token. Therefore, instead of planning a resolution model you need when eating in unusual words “at {SKS}”
When and when and when
It takes too little space, as the token will be saved. It is fast and quick to train, with a central training of 20-30 minutes. However, it comes with its mistakes – as we plan for a particular Vector directing the model to produce a specific style, will not use normal for this style.
Lora's division is proposed in large language models and began to be changed in the Effession model by Mu Ryu. Loras concept of Loras that instead of humiliating the rest of the model, the most expensive, can compare part of the new instruments that will be well organized for the work in the original model.
In the Effession Models, rotting of positions is used to cross the layers of attention and you are responsible for integrating immediate information and photo. The mass matrics wo, WQ, wk, and WV in these layers installed by Lora.
When and when and when
Loras take too little training (5-15 minutes) – We review a few parameters in comparison with the rest of the model, and unlike Dreathambooth, they take a small space. However, small models have good size with loras prove the worst quality compared to Dramambooth.
Hyper-network networks, paper, code
Hyper-network networks, somehow, extensions in Loras. Instead of learning a little incorporation that can change the model effect, we train a different network that can predict these newly installed Nxibals.
Having model to predict a particular idea can teach you hypernetwork several concepts – the same spending on many activities.
When can you use and no
Hyperenetworks, not taking care of one style, but instead of generally able to generate plethora usually lead like good quality as other ways and can take the recommended training period. On the side of the benefits, they can keep many concepts there are other forms of one ideal for one idea.
Instead of controlling the implication of the image from time to time, IP adapter raises how to control the production of image without changes in the lower model.
The basic idea behind the IP adapter is a path of attention that allows the combination of the source to the symbols of pictures produced. This is available by adding a different attention layer, allowing the model to read the pictures specific features.
When can you use and no
The IP Addavers can be limited, flexible and fast. However, their performance depends largely on the quality and variation of training information. The IP Adventurs usually performs better by providing stylistic qualities (eg Mark Chagall painting) we would like to see in a produced photograph and fight direct information, such as the pose.
The control paper is proposed to add a Text-to-image model in any sync, allows the good form of image.
By the original, Controlnet is a model of the Devinder's previously trained Devinder model, such as the installation, Prompt, Noise and Control data (eg a deep map, world symbols, etc.). To direct generation, middle of the center of the control, which is not heard from a Frozen Effession Actival.
This injection is available with zero-colfolutions, where the weights and 1 × 1 1 1 1 1 1 1 1 1 1 1 ratio are starting like zeros and gradually read a logical transformation during training. This is similar to how Loras are trained – the good at 0's and started studying in the ID-based work.
When can you use and no
Controlnets are parallels when we want to control the structure of a building, for example, through the world's symbols, depth maps, or edge. As a result of the need to renew all the metals of all model, the training can cost time; However, these methods also allow the best fried infrastructure with strong control symbols.
Summary
- Dramambooth: Full-up models of styles of styles, high control level; However, it takes a long training and is ready for only one purpose.
- Textual deviation: Reading based on new ideas, low quality control, however, is fast training.
- Lora: Simple style models of new styles / letters, middle control level, while fast training
- Hypernetworks: A separate model for predicting lora instrument for a given control application. Low control level of additional styles. It takes time to train.
- IP-Adapter: The soft style / content of the content with indicators, central level of stylistic, lane and works well.
- Controlnet: Control by the pose, depth, and the edges are very accurate; However, it takes a long time to train.
Good practice: For the best results, a combination of IP-Adapter, by its soft-tylistic guides and a pose control and item control, will produce excellent results.
If you want to go for more details about Fifesion, check this article, that I have been best written in any lesson learning quality and math. If you want to have an intuitive meaning of mathematics by cool analysis Check this video or this video.
By looking at the details of Controlnets, I found this very useful description, this article and this article can be a good intro.
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