Picture Generation Model – Webkul Blog

Making AI-based images has developed into a vital tool for technical applications, such as creating automatic content, data visualization, and the tasks of making prototype design.

This system uses diffusion -based architecture that produces high loyalty images from textual descriptions.

Also facilitates effective iterations in the workflow of engineering, research, and product development.

This blog explains five leading models: Google Nano Banana, Openai’s Dall-E 3, Midjourney, Qwen3-VL, and Google Imagen 4.

We will discuss in detail the architecture, abilities, and cases of their use such as making images, editing images, removal of background, and addition or removal of objects etc.

This analysis is mainly focused on their documented specifications and the latest updates.

The core principle of making AI images

Making AI images mainly depends on the diffusion model, which iteratively reduces noise in a random input that helps align with the pattern learned from the dataset of large-scale texts.

These models support tasks such as the synthesis of text-drawing, image editing, and transfer of styles such as tasks.

Variations in training data, optimization techniques, and inference efficiency distinguish each implementation.

With multimodal integration, they can handle combined inputs such as text and images for enhanced output.

Google Nano Banana: Efficient Conversation Image Editing

Nano Banana, developed by Google Deepmind and available in the Gemini ecosystem, is a lightweight model based on Flash Gemini 2.5, which was released in August 2025.

This is specialized in making real-time images and editing tasks in the conversation interface, which makes it suitable for interactive prototyping.

The main features are:

• Inference speed: The time of making sub-seconds, is optimized for cellular and fire spread.
• Multimodal Interaction: Supports repeated improvements through natural language, maintaining semantic consistency in all editing.
• Editing ability: Activating adjustments to the ratio of inpainting, strengths, and aspects, with strong performance in combining images uploaded by users.

Nano Banana is ideal for developers who want to build dynamic tools, such as augmented reality or automatic UI mockup, because of low latency.

OPENAI DALL-E 3: SYNTHESIS OF HIGH TEXT-TEXT TEXT

Dall-E 3 OpenAI, introduced in 2023 and perfected until 2025, efficient in interpreting complex instructions with fine-grained controls.

This is integrated with chatgpt and external application strength such as image creators, emphasizing accuracy and also providing security in company arrangements.

The main features are:

• Fast understanding: Sophisticated natural language processing ensures focus on detailed specifications, reducing hallucinations in output.
• Safety mechanism: Combining classification for content moderation, with continuous updates to overcome bias in representation.
• Scalability: Supports variable resolution and is integrated with broader opening fire for chain workflows.

This model is suitable for users who need reliable output for documentation, simulation images, or data augmentation in machine learning pipes.

MidJourney: Community -oriented Artistic Rendering

The MidJourney V7 model, default since June 2025 after April release, shows the diversity of 3D forces and extension.

The main features are:

• Parameterization: Offering remix functions, style weight, and style explorers for fine-tuning aesthetics.
• Extended modality: Produce 3D models such as Neural Radiance (NERF) and short video clips from static prompt.
• Collaborative Framework: Utilizing user feedback loops for model iterations, supporting a special parameter set.

Midjourney is very suitable for creative engineering tasks, such as producing reference assets that are useful for game development or architectural visualization references.

QWEN3-VL: Excellence of Open Sources in Editing Multimodal

Qwen3-VL, released in September 2025 by the Qwen Alibaba team, is an open-source vision model series (Variant Dense and MOE).

Also very good in multimodal understanding rather than direct generation.

They are mainly used for image and video analysis work; They complete the pipeline paths through tasks such as spatial reasoning, background removal, adding objects, or removal.

It also supports OCR in 32 languages ​​and control of visual agents.

The main features are:

• Visual reasoning: 2D/3D foundation, object localization, and mixing event time in the video.
• Multimodal Fusion: Match the performance of LLM in the text while handling documents, GUI, and long videos.
• Agent feature: Produce code (for example, HTML/CSS from the image) and control the interface for task automation.

The QWEN3-VL model focuses on post-generating verification, delay, or editing guide through understanding, and can be used by hugging the face.

Google Imagen 4: Optimized for photorealistic output

Imagen 4, Google diffusion models are generally available in August 2025 through Gemini API, prioritizing fotorealism and the task of production scale efficiency.

It supports image resolution of up to 2K and is designed to integrate AI Vertex.

The main features are:

• Rendering Quality: Using a multilevel diffusion stage for sharp texture and lighting loyalty.
• Responsible AI features: Including synthetic watermarks, quick re -writing for compliance, and safety filters that can be configured.
• Application option: Allows batch processing and real-time inference for high throughput applications.

Imagen 4 is recommended for cases of industrial use, including rendering products and scientific illustrations, which require higher visual accuracy.

Image Model Application

1) Try virtual

Virtual Try-On (VTON) allows customers to visualize how clothes will be seen in them.

A sophisticated AI -based system that brings virtual trial experiences to life with amazing realism and accuracy.

His sophisticated ability allows retailers to give customers to shopping, interactive, and personal shopping experiences that connect imagination with reality.

2) Background Elimination

BG Remover allows users to precisely identify and separate the front background objects from their background.

This allows a smooth change of background or removal for images of e-commerce products, professional portraits, or creative compositions.

3) Elimination and addition of objects

Users can easily delete unwanted objects from images or add new elements, make it ideal for photo editing, prepare marketing material, or make imaginative scenes.

4) Improvement and recovery of images

We can improve low resolution images, delete noise, and restore old or damaged photos, benefit photographers, historians, and professional film restoration.

5) Editing Image and Inpainting/Outpainting

We can fill in the parts that are lost (inpainting) or expand images outside the original limit (excess), creating a larger and more complete visual.

We can also edit complex styles, such as changing the landscape season.

Conclusion

These models advance the process of making images from the independent generation (Dall-E 3, MidJourney, Imagen, Nano Banana) to an integrated multimodal system (QWEN3-VL).

Google offers provide an entry point that can be discredited, OpenAI ensures precision, Midjourney fosters creativity, and QWEN3-VL adds open-source depth to understand heavy tasks.

All of these models provide sophisticated results in the case of their specific use, so choose according to your needs, quality, workflow latency, and integration requirements.

“For more information, visit the webkul-in which e-commerce dream of flying!”