joliGEN v3.0.0

This release adds Consistency Models and REST API for GAN and diffusion models inference.

Features

• api inference for gan and diffusion (6fd43d8)
• inference server option returns base64 image (5bc8f44)
• ml: consistency models as diffusion models for inpainting, with conditioning (de9d725)
• scripts: canny thresholds grid plots (6d94ffa)
• specialized helpers for each model (f361414)

Bug Fixes

• correct help for image generation scripts (76ce6b4)
• enable model export during training (79525b8)
• not force reloading dinov2 models (800967d)
• ref_in option (2927574)
• resize image to test base64 api diffusion (2708a6f)

Docker images:

• GPU (CUDA only): docker pull docker.jolibrain.com/joligen_server:v3.0.0
• All images available from https://docker.jolibrain.com/#!/taglist/joligen_server

joliGEN 2.0.0

Features

• ml: dinov2 discriminator with registers (7fcf790)
• ml: DinoV2 feature-based projected discriminator (c67ffa8)
• ml: SigLIP based projected discriminators (5e10a86)
• optimization eps value control (0556987)
• pix2pix task for palette (7e47139)
• scripts: adding a video generation script for gans (85d1922)

Bug Fixes

• amp for discriminators (811ba3d)
• APA augmentation on multiple discriminators (becb3eb)
• docker release script (f1c56de)
• end of training metrics computation (e1f213c)
• init_metrics directory and metrics on CPU (0b77943)
• load size for rectangular images, resize ref image for inference (965e1bf)
• ml: inference for diffusion with reference image (df8c504)

Docker images:

• GPU (CUDA only): `docker pull docker.jolibrain.com/joligen_server:v$tag`
• All images available from https://docker.jolibrain.com/#!/taglist/joligen_server

v1.0.0

joliGEN: Generative AI Toolset (Changelog)

1.0.0 (2023-10-06)

Docker

• GPU (CUDA only): `docker pull docker.jolibrain.com/joligen_server:v1.0.0`
• All images available from https://docker.jolibrain.com/#!/taglist/joligen_server

Features

• add a server endpoint to delete files (30b2143)
• add choices for all options (ed43b82)
• add ddim inference (0196134)
• add DDPM tutorial on the VITON-HD dataset (c932d73)
• add FastAPI server to run training (f517462)
• add lambda for semantic losses (aab53fe)
• add LPIPS metric (f1e0526)
• add miou compute to tests (c0033ef)
• add new metrics (f3c84cd)
• add palette model (b7db294)
• add psnr metric (7135458)
• add sampling options to test (a2958dc)
• add SRC and hDCE losses (ddfcc97)
• add test for doc generation (41526f8)
• add test on cycle_gan_semantic_mask (3eeff76)
• add tests for reference image dataloaders (ae6405e)
• added D noise to CUT with semantics (31aa4a3)
• added optimizers and options (505cac2)
• allow control of projected discriminator interpolation (dbffec5)
• allow ViT custom resolution at D projector init (82e6e83)
• api: display current commit at startup (6f90be8)
• aug: affine transforms for semantics (170b0f8)
• aug: configurable online mask delta augmentation by x and y axis (dfa6459)
• aug: select bbox category through the path sanitization functionality (a8d3f48)
• auto download segformer weights (083cc5e)
• backward while computing MSE criterion loss (1b87906)
• bbox as sam prompt (a39c5bd)
• bbox prompt for sam (1fa9cae)
• bilinear interpolation of attention heads when dimension does not match, useful for segformer G (eed9494)
• bw model export (8e43efa)
• check code format when PR (eeb56cb)
• choices for canny random thresholds (9573fc1)
• class weights for semantic segmentation network with cross entropy (4274f1e)
• classifier training on domain B (fa343c0)
• commandline saving (6eb503e)
• commandline script for joligan server calls (48ae23b)
• compute_feats for unet G (9f1109e)
• conditioning for palette (b9854ee)
• config json for client script (174dce9)
• context for D (b0d3c7b)
• contrastive classifier noise (7193e0e)
• contrastive loss for D (deb2ec4)
• cut_semantic model (b20a943)
• D accuracy (26ead91)
• data: random noise in images for object insertion (42cf13d)
• DDP (68f24da)
• deceiving D for GAN training (2e2113f)
• depth model as projector (10ffc28)
• depth prediction and depth discriminator (01bc62b)
• diff augment (054509c)
• diffusion inference with old and new models (9c4c5a9)
• display augmented images (2126253)
• display test images (a1de083)
• doc options auto update (1b08f92)
• doc: add JSON config examples (5332213)
• doc: basic server REST API (a757d17)
• doc: datasets (dfe2343)
• doc: DDPM conditioning training and inference examples (e694a29)
• doc: models (be1fe34)
• doc: refactored README with links to documentation (b5bf121)
• doc: reference image conditioning (70aeb32)
• doc: remove overview (2360527)
• doc: server, client, docker (68a5b96)
• doc: tips (3fea9ca)
• doc: training (a4b720d)
• doc: update inference models and examples (3c43a7b)
• doc: updated FAQ (88b417c)
• doc: updated model export (e692f78)
• edge detection techniques (78202ea)
• export for unet_mha (b4c3cfd)
• extract bbox from img (fb64ef0)
• first recut model (aaa4069)
• first test (4ac8cd9)
• fixed bbox size for online creation and bbox size randomization ([5cd6227]...