Generating images by text request
About project
Development of an application that allows users to generate images online according to a given text request. Our customer has successfully launched a commercial service using this product.
Value of the idea
Save time and resources
Personalization and Uniqueness
Creating high-quality images requires considerable effort, time and resources.
The project saves time, which is especially valuable for entrepreneurs, marketers and creative professionals who need to quickly get graphic materials for their tasks.
The project saves time, which is especially valuable for entrepreneurs, marketers and creative professionals who need to quickly get graphic materials for their tasks.
Monetization potential
An application that can generate images on demand provides customizable settings and styles. This allows users to create unique and original images that match their needs and preferences.
The project can be monetized in many ways, for example, by offering paid plans with advanced capabilities, selling licenses for the use of images, etc.
Case Use Areas
Quick and convenient creation of unique and attractive images has many applications:
- Marketing and Advertising
- Web Design and Development
- Social Media and Content Marketing
- Publishing houses and creative agencies
- Online shopping and e-commerce
- Education and training
- Personal projects and hobbies
About development
Stages of work:
1. Refine the task.
2. The study of modern approaches, the choice of Stable Diffusion and the first experiments.
3. Select libraries, create a model that solves the problem. Testing and refinement.
4. Preparing the model for implementation in backend.
5. Create backend, shared backend components.
6. Testing, debugging.
7. Send project to customer to connect to frontend and deploy.
2. The study of modern approaches, the choice of Stable Diffusion and the first experiments.
3. Select libraries, create a model that solves the problem. Testing and refinement.
4. Preparing the model for implementation in backend.
5. Create backend, shared backend components.
6. Testing, debugging.
7. Send project to customer to connect to frontend and deploy.
1. Refine the task.
2. The study of modern approaches, the choice of Stable Diffusion and the first experiments.
3. Select libraries, create a model that solves the problem. Testing and refinement.
4. Preparing the model for implementation in backend.
5. Create backend, shared backend components.
6. Testing, debugging.
7. Send project to customer to connect to frontend and deploy.
Project team
Nikolay D.
Developer
Victor Sh.
Developer
