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* adding rich arg, adding coldkeys and hotokeys * moving rich to payload from headers * bump version --------- Co-authored-by: benliang99 <caliangben@gmail.com>
Adding two finetuned image models to expand validator challenges
Updated transformers version to fix tokenizer initialization error
* Made gpu id specification consistent across synthetic image generation models * Changed gpu_id to device * Docstring grammar * add neuron.device to SyntheticImageGenerator init * Fixed variable names * adding device to start_validator.sh * deprecating old/biased random prompt generation * properly clear gpu of moderation pipeline * simplifying usage of self.device * fixing moderation pipeline device * explicitly defining model/tokenizer for moderation pipeline to avoid accelerate auto device management * deprecating random prompt generation --------- Co-authored-by: benliang99 <caliangben@gmail.com>
bump version
* simple video challenge implementation wip * dummy multimodal miner * constants reorg * updating verify_models script with t2v * fixing MODEL_PIPELINE init * cleanup * __init__.py * hasattr fix * num_frames must be divisible by 8 * fixing dict iteration * dummy response for videos * fixing small bugs * fixing video logging and compression * apply image transforms uniformly to frames of video * transform list of tensor to pil for synapse prep * cleaning up vali forward * miner function signatures to use Synapse base class instead of ImageSynapse * vali requirements imageio and moviepy * attaching separate video and image forward functions * separating blacklist and priority fns for image/video synapses * pred -> prediction * initial synth video challenge flow * initial video cache implementation * video cache cleanup * video zip downloads * wip fairly large refactor of data generation, functionality and form * generalized hf zip download fn * had claude improve video_cache formatting * vali forward cleanup * cleanup + turning back on randomness for real/fake * fix relative import * wip moving video datasets to vali config * Adding optimization flags to vali config * check if captioning model already loaded * async SyntheticDataGenerator wip * async zip download * ImageCache wip * proper gpu clearing for moderation pipeline * sdg cleanup * new cache system WIP * image/video cache updates * cleaning up unused metadata arg, improving logging * fixed frame sampling, parquet image extraction, image sampling * synth data cache wip * Moving sgd to its own pm2 process * synthetic data gen memory management update * mochi-1-preview * util cleanup, new requirements * ensure SyntheticDataGenerator process waits for ImageCache to populate * adding new t2i models from main * Fixing t2v model output saving * miner cleanup * Moving tall model weights to bitmind hf org * removing test video pkl * fixing circular import * updating usage of hf_hub_download according to some breaking huggingface_hub changes * adding ffmpeg to vali reqs * adding back in video models in async generation after testing * renaming UCF directory to DFB, since it now contains TALL * remaining renames for UCF -> DFB * pyffmpegg * video compatible data augmentations * Default values for level, data_aug_params for failure case * switching image challenges back on * using sample variable to store data for all challenge types * disabling sequential_cpu_offload for CogVideoX5b * logging metadata fields to w&b * log challenge metadata * bump version * adding context manager for generation w different dtypes * variable name fix in ComposeWithTransforms * fixing broken DFB stuff in tall_detector.py * removing unnecessary logging * fixing outdated variable names * cache refactor; moving shared functionality to BaseCache * finally automating w&b project setting * improving logs * improving validator forward structure * detector ABC cleanup + function headers * adding try except for miner performance history loading * fixing import * cleaning up vali logging * pep8 formatting video_utils * cleaning up start_validator.sh, starting validator process before data gen * shortening vali challenge timer * moving data generation management to its own script & added w&B logging * run_data_generator.py * fixing full_path variable name * changing w&b name for data generator * yaml > json gang * simplifying ImageCache.sample to always return one sample * adding option to skip a challenge if no data are available in cache * adding config vars for image/video detector * cleaning up miner class, moving blacklist/priority to base * updating call to image_cache.sample() * fixing mochi gen to 84 frames * fixing video data padding for miners * updating setup script to create new .env file * fixing weight loading after detector refactor * model/detector separation for TALL & modifying base DFB code to allow device configuration * standardizing video detector input to a frames tensor * separation of concerns; moving all video preprocessing to detector class * pep8 cleanup * reformatting if statements * temporarily removing initial dataset class * standardizing config loading across video and image models * finished VideoDataloader and supporting components * moved save config file out of trian script * backwards compatibility for ucf training * moving data augmentation from RealFakeDataset to Dataset subclasses for video aug support * cleaning up data augmentation and target_image_size * import cleanup * gitignore update * fixing typos picked up by flake8 * fixing function name ty flake8 * fixing test fixtures * disabling pytests for now, some are broken after refactor and its 4am
Combined requirements installation
Video UAT fixes
* new cache structure and related config vars * refactored vai forward to be more modular * cleanup * restructure wip * added dataset to cache dir hierachy, cleaned up data classes, better error reporting for missing frames * fixing cache access order * bugfixes for semisynthetic image cache and safer pruning * config and logging cleanup * cache clear for this release --------- Co-authored-by: Dylan Uys <dylan.uys@gmai.com>
* inpainting pipeline import * removing cache clear from autoupdate * eidon data * version bump * removing filetype key from bm-eidon-image
* improved dendrite class with proper connection pool management to deal w these pesky broken pipes * logging and indendation * version bump * updating connection pool config * removing cache clear * reward transform + logging updates
Release 2.2.6
dylanuys
approved these changes
Mar 28, 2025
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Release 2.2.6 – MidJourney Incentivization Update
Release Date: March 25, 2025
This release addresses a key gap in incentivizing MidJourney-like image generation on the BitMind Subnet. Because MidJourney is a closed model without a public API, the subnet has previously lacked mechanisms to evaluate or reward contributions aligned with its output style. Version 2.2.5 introduces new models and datasets to close this gap.
Updates
Integration of MidJourney V6 LoRA Model
Kvikontent’s MidJourney V6 LoRA model, based on Stable Diffusion, has been integrated into the subnet. This allows the synthetic image generator to produce outputs in a MidJourney-like style.
config.pyto register and load this model.Relevant code:
Addition of MidJourney-Like Datasets
Two static datasets have been added to the subnet’s dataset registry to support evaluation and training on MidJourney-style images:
JourneyDB
Curated synthetic dataset designed to reflect MidJourney aesthetics.
View on Hugging Face
GenImage MidJourney
Additional dataset with synthetic images emulating MidJourney’s style.
View on Hugging Face
Impact
These additions enable the subnet to better evaluate, incentivize, and align synthetic image generation with the visual qualities seen in MidJourney outputs. This is a foundational step toward incentivizing contributions in high-value, closed-model style domains.
For background, see our related white paper: Survival of the Fittest Detectors (PDF)