Open-Source Benchmarking and Evaluation for Image and Video Diffusion Models
DreamLayer is benchmarking infrastructure for AI researchers, ML engineers, labs, and developers. It automates prompts, seeds, configs, metric scoring, and reproducible run logging so teams can compare image and video generation models faster and more consistently.
Benchmark Image and Video Models Faster
Automate prompts, seeds, configs, and scoring so image and video model benchmarks run faster and more reproducibly.
Reproducible by Design
Every run is logged with prompts, seeds, configs, outputs, and metrics so benchmark results stay traceable, consistent, and repeatable.
Built for AI Research and Model Evaluation
Compare image and video generation models using reproducible metrics such as CLIP Score, FID, precision, recall, F1, LPIPS, SSIM, and PSNR.
Run DreamLayer Locally for Reproducible Model Benchmarking
Use the lightweight open-source version to benchmark models locally, evaluate outputs with built-in metrics, and compare results before scaling to larger research workflows.
Try For Free
Benchmarking Workflow Automation
Replace manual pipeline wiring with a single benchmarking workflow. DreamLayer centralizes prompts, seeds, configs, and metric evaluation so every benchmark run starts reproducibly and stays easy to compare.
One config for model comparisons and parameter sweeps
No manual scripts for prompt setup or metric logging
Reproducible benchmark runs from the start
Prompt and Seed Management for Controlled Evaluation
Apply batch prompts and consistent seeds across models so experiments stay controlled, reproducible, and easier to compare.
Load hundreds of prompts at once
Apply multiple seeds per benchmark run
Keep prompts, seeds, and configs consistent across models
Built-In Evaluation Metrics for Image and Video Models
DreamLayer supports common image and video evaluation metrics for benchmarking model outputs, including CLIP Score, FID, precision, recall, F1, LPIPS, SSIM, PSNR, and temporal consistency metrics.
CLIP Score, FID, precision, recall, and F1 logged automatically
Metrics tied to prompts, seeds, outputs, and configs for full traceability
Export-ready evaluation results for papers, reports, and leaderboards
Reproducibility, Reporting, and Benchmark Exports
Every experiment is frozen into a reproducible benchmark bundle. Export CSVs, JSON, configs, images, and evaluation results in one place for model comparison, internal review, or paper appendices.
One-click export to CSV, JSON, and benchmark bundles
Every run logged with prompts, seeds, configs, and metrics
Shareable and replayable results for teams and research workflows
Save Weeks of Work
Replace days of pipeline wiring with one clean config. DreamLayer centralizes prompts, seeds, and settings so every run starts reproducible.
One config for all models and sweeps
No custom scripts or manual logging
Reproducible runs from the start
Prompt & Seed Management
Batch prompts are auto-applied across models with no manual setup. DreamLayer ensures consistent prompts and seeds so experiments are always reproducible.
Load 100s of prompts instantly
Auto-apply multiple seeds per run
Guaranteed reproducibility across models
Evaluation Metrics
No more coding CLIPScore, FID, or F1 by hand. DreamLayer runs every benchmark automatically and logs results with prompts, seeds, and configs
CLIP, FID, Precision, Recall, F1 auto-scored
Metrics tied to every run for full traceability
Export-ready for papers and reports
Reproducibility & Reporting
Every experiment is frozen and packaged into a reproducible bundle. Export CSVs, configs, images, and documentation in one click.
One-click export to CSV, JSON, and README
Every run logged with seeds, prompts, and configs
Shareable and replayable for papers or collaboration