Toast AI

What is Toast AI?

Toast AIIt is an AI creation platform that combines a model-sharing community, cloud-based execution, and a creative workspace. Both the official company profile and the current user agreement list Shanghai Biyou Huiyang Intelligent Technology Co., Ltd. as the operator. It is suitable for users who wish to try out different image models online, reuse creative parameters from the community, or further train their own style models. The commonly known “TusiArt” and “tusiart.com” are previously used brand names and entry points; the current official designation is “tusi.cn.”

The key to Toast lies in “choosing the right model, how to run it, and how to reuse it.” Users can not only enter a prompt but also view the model type, version, infrastructure, and author’s notes before deciding how to generate content. Not all models uploaded to the community were developed by Toast itself; users should also review the model authors’ licenses and the platform’s service terms separately.

What features are available from the Model Library to the Workspace?

Model Browsing and Online Generation:existModel LibrarySelect a base model or LoRA, read the preview and usage instructions, and then run it in the cloud. The base model determines the overall generation capabilities and art style, while LoRA is typically used to supplement specific styles, characters, or concepts. LoRA cannot be freely mixed and matched across different base models; before selecting one, check the compatibility list and trigger words listed on the model card.

Reuse of Community Content:According to the official description, the feature supports “one-click replication” of community-created works. Its practical value lies in enabling creators to start with existing generation parameters, understand the relationship between the model, the prompt, and the resulting image, and then modify their own subject matter and composition. Reusing parameters does not imply obtaining commercial licensing rights to the original author’s work or characters, nor does it guarantee the generation of an identical image.

Workflows and AI Tools:The platform provides access to workflows and AI tools. According to the official template tutorials, the tools allow users to specify prompts, images, and options such as Seed, CFG, Steps, Checkpoint, and LoRA, organizing a complex process into an easy-to-use form. Beginners can start by using established tools to complete specific tasks, while advanced users can then explore the underlying combinations. For details on which tools support image or video inputs and outputs, please refer to the specific tool’s page.

Online Training:Toast provides a workflow for uploading datasets, cropping, labeling, selecting base models, and configuring training parameters. It is ideal for converting a licensed set of materials into reusable model assets. The training feature does not eliminate the need for data preparation: duplicate samples, incorrect annotations, or inconsistencies in the appearance of subjects can all cause the model to learn unwanted content.

Here's how to create a cover image the first time you use it

Taking the “cover illustration for the plant knowledge column” as an example, the goal is to create a background image that can be further edited in layout software.

1. Finding the Right Starting Point:Search for relevant results—such as plants or illustrations—in the model library or community. Open the model details and first review the base type, example prompts, suggested parameters, and license. Once you find a result that closely matches your goal, you can use the "Create the Same" feature to load its generation configuration.
2. Paint a clear picture rather than piling on adjectives:Change the subject to “A potted monstera plant on a wooden table by the window, with its leaves spread out, against an off-white wall, bathed in soft morning light; a flat illustration with space reserved on the right side of the image for the title.” If the model creator requests an English label or specific keywords, adjust accordingly as instructed.
3. Control variables:Start by using the base model and main parameters from the example, changing only the theme. When adding LoRA, verify compatibility with the base model and try the weights as recommended by the author. Do not change the base model, sampling parameters, and multiple LoRA models at the same time, as it will be difficult to determine which modification caused the changes in the image.
4. Generate and filter:Select the scale based on the final layout, confirm the resource usage, and then submit. Prioritize checking the leaf structure, the edges of the flowerpot, and the white space around the title; once the orientation is correct, adjust the background or composition. You can record the selected model version, prompt, seed, and other available parameters to avoid having to start from scratch next time.
5. Download and complete the submission:Save the original image that passed the review, add accurate column titles and labels in the design tool, and export it in the dimensions required for social media. If the model isn’t good at handling text, there’s no need to have it repeatedly generate entire paragraphs of text.

With this closed-loop approach, you first determine whether “a single image can be used for layout,” and then consider creating a batch of materials from the same series. When a consistent art style is required, you can continue using the same base model and style settings; only when existing models cannot meet specific, recurring needs should you evaluate the time and cost of training a new model.

How do I train my own LoRA?

Based onOfficial Online Training GuideThe process begins with organizing the source material: upload licensed images on the same theme but with variations in angle, background, or pose; crop them according to the base model’s requirements; and review the automatic tags to correct any errors. Next, select a base model and a preset suitable for the task, set the trigger words and preview prompts, confirm the cost, and begin training.

After training, you should test the candidate versions by feeding them back into the corresponding base model, rather than just looking at the training preview images. You can set a fixed set of test prompts that specify different scenes, compositions, or poses, and observe whether the model retains the desired features while also responding to the new requirements. If the model consistently replicates the training background or only produces images from a single angle, you should first review the dataset and the extent of training. When using the model in production, keep a record of the trained version, base model, and testing conditions to avoid mistakenly attributing differences in results to ineffective prompts when switching base models.

Who Should Use It? How to Choose Between SeaArt and Midjourney

Toast is suitable for three types of projects: style experiments in illustration or concept design; series of assets that require repeated reuse of parameters; and creators who want to gradually learn LoRA training using ready-made models. E-commerce designers can also use it to explore backgrounds and atmospheres, but the shapes, materials, and logos of products should be based on real products; generated images should not be used as proof of a product’s authenticity.

SeaArtIt also offers cloud-based model selection, LoRA, and training, making it a comparison platform more similar to Toast. When making a choice, it’s worth comparing whether the required models are actually runnable, whether parameters can be reused, whether post-training testing and management are convenient, and the resource consumption for your typical tasks. The size of a model library alone does not indicate a project’s production efficiency.

MidjourneyIt also supports style references and style intensity control, but its usage is primarily centered around its proprietary generation system. If you simply want to quickly explore visual directions, you can compare how the two platforms render the same creative concept; if your focus is on selecting specific community models, combining LoRA models, and saving parameters, Toast’s Model Workbench is worth a closer look. This is a difference in workflow, not a unified ranking of image quality between the two.

Permissions and fees for the materials must be reviewed on a case-by-case basis.

Generation, training, asset storage, and quotas for different models may be billed separately; the “Free” description on the homepage does not supersede the usage details on the task submission page. Before commercial use, you must verify the platform’s rules, the licenses for the selected model and LoRA, the rights to the input materials, and whether the output contains images of others, brands, or protected elements. For materials not publicly available to clients, first confirm the upload scope, visibility, and project requirements. Download and back up important results promptly; do not rely on the workspace history as your long-term archive.