EACHLABS
Adapt any picture of a face into another image
Avg Run Time: 17.000s
Model Slug: become-image
Playground
Input
Enter a URL or choose a file from your computer.
Invalid URL.
image/jpeg, image/png, image/jpg, image/webp (Max 50MB)
Enter a URL or choose a file from your computer.
Invalid URL.
image/jpeg, image/png, image/jpg, image/webp (Max 50MB)
Output
Example Result
Preview and download your result.

API & SDK
Create a Prediction
Send a POST request to create a new prediction. This will return a prediction ID that you'll use to check the result. The request should include your model inputs and API key.
Get Prediction Result
Poll the prediction endpoint with the prediction ID until the result is ready. The API uses long-polling, so you'll need to repeatedly check until you receive a success status.
Readme
Overview
become-image — Image-to-Image AI Model
become-image from eachlabs transforms any picture of a face into another image with precise adaptation, solving the challenge of seamless facial integration across diverse scenes and styles for creators and developers. Developed by eachlabs as part of the eachlabs family, this image-to-image AI model excels at maintaining facial identity while altering backgrounds, lighting, or expressions via natural language prompts. Ideal for AI photo editing for e-commerce or personalized visuals, become-image delivers high-fidelity results up to 1024x1024 resolution, drawing on advanced flow transformer architecture for consistent, photorealistic outputs.
Technical Specifications
What Sets become-image Apart
become-image stands out in the competitive landscape of image-to-image AI models with its specialized focus on facial adaptation, enabling users to swap faces into target images while preserving intricate details like skin texture and expressions that generic editors often distort. This capability supports single-reference editing with strong spatial logic, allowing precise placement and lighting matching in complex compositions.
- Face-specific adaptation: Seamlessly integrates a source face into any target image, maintaining identity consistency superior to broad-spectrum models; this empowers quick personalization for avatars or product mockups without retraining.
- High-resolution facial editing up to 1024x1024: Handles detailed edits at scales rivaling larger models, with outputs in standard formats like PNG/JPEG; users benefit from professional-grade results on consumer hardware, typically in seconds.
- Natural language-driven controls: Uses text prompts for edits like "adapt this face to a cyberpunk portrait with neon lighting," combined with hex color adjustments; this simplifies workflows for edit images with AI tasks, reducing manual masking.
Processing leverages efficient inference, requiring modest VRAM, and supports aspect ratios for versatile applications in AI image editor API integrations.
Key Considerations
Image Quality: High-resolution images yield better transformation results.
Prompt Clarity: Clear and specific prompts guide the Image to Become more effectively.
Parameter Tuning: Experiment with different parameter settings to achieve desired outcomes.
Safety Checker: Disabling the safety checker may result in inappropriate content; proceed with caution.
Tips & Tricks
How to Use become-image on Eachlabs
Access become-image through Eachlabs Playground by uploading a source face image and target image, adding a descriptive prompt like "adapt the face to match the target's pose and lighting," then selecting resolution up to 1024x1024. Integrate via API or SDK with POST requests including image URLs, prompt, and optional CFG scale (around 5.0 for best results); expect high-quality PNG/JPEG outputs in seconds, optimized for scalable image-to-image apps.
---Capabilities
Image to Become transforms facial images into various artistic styles based on user prompts.
Image to Become generates multiple variations of transformed images.
Incorporate user-defined parameters to fine-tune the transformation process.
What Can I Use It For?
Use Cases for become-image
For designers crafting personalized marketing visuals, become-image adapts a model's face onto diverse body types or outfits, ensuring brand-consistent e-commerce photos without costly reshoots—perfect for automated image editing API pipelines.
Developers building avatar generators feed a user-uploaded selfie plus a prompt like "adapt this face into a medieval knight portrait with chainmail helmet and torchlight shadows," yielding consistent identities across game assets or social profiles via the become-image API.
Content creators targeting social media use it for rapid face swaps in memes or videos, maintaining expressions during style transfers to cinematic or cartoon looks, streamlining image to image AI model workflows for viral content.
Marketers in fashion leverage facial adaptation for virtual try-ons, integrating customer faces into catalog images with realistic lighting, boosting engagement through hyper-personalized eachlabs image-to-image campaigns.
Things to Be Aware Of
Experiment with Prompts for Image to Become: Use diverse and imaginative prompts to explore various transformation styles.
Adjust Parameters: Fine-tune parameters like denoising strength and prompt strength to achieve desired effects.
Combine with Other Models: Integrate outputs with other models or editing tools to enhance creativity.
Safety Checker: Use the safety checker to ensure appropriate content generation.
Limitations
Input Dependency: The quality of the output is highly dependent on the input image and prompt clarity.
Overfitting: Extreme parameter values may lead to overfitting, resulting in less natural images.
Safety: Disabling the safety checker can lead to the generation of inappropriate content.
Output Format: PNG
Pricing
Pricing Detail
This model runs at a cost of $0.001080 per second.
The average execution time is 17 seconds, but this may vary depending on your input data.
The average cost per run is $0.018360
Pricing Type: Execution Time
Cost Per Second means the total cost is calculated based on how long the model runs. Instead of paying a fixed fee per run, you are charged for every second the model is actively processing. This pricing method provides flexibility, especially for models with variable execution times, because you only pay for the actual time used.
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