# CQFIO/PhotographicImageSynthesis

Photographic Image Synthesis with Cascaded Refinement Networks

Repository: https://github.com/CQFIO/PhotographicImageSynthesis
Canonical: https://ross.abutalabs.com/products/photographicimagesynthesis
Homepage: https://cqf.io/ImageSynthesis/
Language: Python
License Family: other
Topics: image-synthesis, cascaded-refinement-networks, tensorflow
Last push: 2022-02-07T16:54:09+00:00

## Health v2 (maintenance only)
Score: 32/100 (v2, computed 2026-09-02T17:46:02.011165+00:00)
- activity 0, release rhythm 35, longevity 100
- inputs: {"age_days": 3323, "days_push": 1668, "days_rel": null, "gap_med": null, "n_releases_24m": 0}
- flags: no_releases, no_license
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 1248, forks 222 (observed 2026-08-28T04:04:07.869474+00:00)

## What it is
A TensorFlow implementation of the ICCV 2017 paper 'Photographic Image Synthesis with Cascaded Refinement Networks', which synthesizes photographic images from semantic label maps using a feedforward network trained with direct regression. It includes pretrained models for 512p and 1024p synthesis and training scripts for progressive fine-tuning.

## Use cases
- generate realistic images from semantic segmentation maps
- synthesize photographic street scenes from layout sketches
- render indoor scenes from semantic layouts
- train an image synthesis model at 256p and fine-tune to higher resolutions
- reproduce ICCV 2017 image synthesis research results

## When to choose
- you need to generate photographic images conditioned on semantic layouts
- you want a non-GAN, regression-based image synthesis baseline
- you are reproducing or building on the cascaded refinement networks paper
- you need high-resolution (up to 2-megapixel) layout-to-image synthesis

## When to avoid
- you need actively maintained code or modern framework support (TensorFlow >=1.0 era)
- you want GAN-based or diffusion-based image generation
- you need a production-ready image generation service
- you cannot access a CUDA GPU with sufficient memory for high-resolution synthesis

## Facets
- artifact type: library
- maturity: maintenance
- function: machine-learning, deep-learning, image-processing
- domain: machine-learning, computer-vision, image-processing, artificial-intelligence
- platform: python
- tags: image-synthesis, semantic-layout, cascaded-refinement-networks, tensorflow, generative-models, research-code, iccv-2017, linux, gpu

## Member repositories
- CQFIO/PhotographicImageSynthesis (main) score 32

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:04:07.869474+00:00.
- Health v2: computed from the inputs above; adoption is never an input.
- Inferred fields (summary, facets, guidance): AI-extracted, prompt v1, taxonomy v1, on 2026-08-30T05:07:49.587502+00:00, confidence not recorded.
  - readme: https://github.com/CQFIO/PhotographicImageSynthesis (fetched 2026-08-28T04:04:07.869474+00:00, sha ed89e76f6bd5)
  - homepage: https://cqf.io/ImageSynthesis/ (fetched 2026-08-29T12:19:00.035678+00:00, sha fd8ce45ca36c)
- Data as of 2026-08-30T08:39:29.467469+00:00.