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| pipeline_tag: text-to-image | |
| library_name: pytorch | |
| license: apache-2.0 | |
| tags: | |
| - solintelligence | |
| - solpix | |
| - flow-matching | |
| - latent-image-generation | |
| - final-release | |
| <p align="center"> | |
| <img src="banner.png" alt="Sol Labs SolPix" width="100%"> | |
| </p> | |
| # SolPix | |
| SolPix turns a text prompt into a 512x512 image. Its roughly 49M-parameter flow transformer predicts image latents; Flan-T5 Base encodes the prompt, and the SANA 1.1 DC-AE decodes the result. Both external models stay frozen. | |
| ## Components | |
| | Setting | Value | | |
| |---|---| | |
| | Generator | Approximately 49M parameters | | |
| | Architecture | 15-block U-shaped joint text/image transformer | | |
| | Hidden width | 512 | | |
| | Attention | 8 heads, head dimension 64 | | |
| | FFN | SwiGLU, width 1,152 | | |
| | Long skip connections | 7 | | |
| | Conditioning | Shared adaptive layer normalization | | |
| | Local mixing | Depthwise 3x3 convolution | | |
| | Objective | Rectified flow matching with logit-normal time sampling | | |
| | Image latents | 32 channels, 32x spatial compression | | |
| | 512x512 latent grid | 16x16 | | |
| | Text encoder | Frozen `google/flan-t5-base`, 768 features, up to 96 tokens | | |
| | Decoder | Frozen SANA 1.1 DC-AE F32C32 | | |
| | Saved optimizer step | 210,000 | | |
| | Configured training schedule | 5,000,000 steps | | |
| The parameter count covers the generator only. Downloading the frozen text encoder and autoencoder adds separate dependencies. `SolPixTransformer2D` predicts latent velocity. `AutoencoderDCSol` loads the corresponding Diffusers `AutoencoderDC` for decoding. | |
| ## Generate an image | |
| Download the repository, install its requirements, and give `generate.py` a prompt and output path: | |
| ```bash | |
| python -m pip install -r requirements.txt | |
| python generate.py --prompt "A glass greenhouse in a quiet garden after rain" --output solpix.png | |
| ``` | |
| To choose a local checkpoint, seed, or sampling settings: | |
| ```bash | |
| python generate.py \ | |
| --checkpoint ./step_00210000.pt \ | |
| --prompt "A small red sailboat on a misty lake at sunrise" \ | |
| --seed 1234 --steps 40 --guidance-scale 3.5 \ | |
| --output ./solpix.png | |
| ``` | |
| On its first run, the helper downloads Flan-T5 Base and the pinned SANA DC-AE revision. It uses Euler integration with classifier-free guidance, running on CUDA when available. CPU inference is supported but slow. | |
| The flow path is `x_t = (1 - t) x_clean + t noise`. Sampling runs from `t=1` down to `t=0`; encoder and decoder identifiers and revision pins are in `config.json`. | |
| ## Data and checkpoint history | |
| We used [MONET v1.2.0](https://huggingface.co/datasets/jasperai/monet) with curation seed `20260924`. The split contains 174,603 training examples and a 9,300-example validation holdout. SANA F32C32 image latents and Flan-T5 Base caption states were encoded before training. | |
| MONET draws from CC12M, CommonCatalog-CC-BY, COYO, Diffusion-Aesthetic-4K, and LAION. Flux Klein, Flux Schnell, and Z-Image supply synthetic captions. Curation checks resolution, aesthetics, NSFW content, watermarks, and near duplicates. | |
| The source records include CC BY 4.0, Apache 2.0, Google permissive, and MIT license labels. A label on a record doesn't grant a new license to its contents. Images and dataset shards aren't redistributed in this repository. | |
| The Windows v1.0 continuation used BF16 on one RTX 3080 Ti, with batch size 4 and gradient accumulation 16. We released optimizer step 210,000. The documented 1.1 continuation retains that split and targets step 300,000. | |
| ## Reading the samples | |
| We haven't run a formal image-quality or prompt-following benchmark on this checkpoint. The gallery shows generated examples, without supplying a held-out quality estimate. Composition errors, artifacts, and weak text or fine-detail rendering remain limitations. | |
| There is no built-in safety classifier. Dataset filtering doesn't remove every bias or unwanted association. Flan-T5 and SANA DC-AE have separate licenses and usage terms. | |
| All 15 samples below use the released checkpoint at 512x512, with 32 Euler steps, guidance scale 3.5, and the pinned SANA DC-AE decoder. Their files, prompts, seeds, and SHA-256 values are recorded in `samples/`. | |
| ### Sample 01 | |
|  | |
| Prompt: Three Black men sharing french fries at a neighborhood diner, candid documentary photography. | |
| Seed: 260926 | |
| ### Sample 02 | |
|  | |
| Prompt: A red fox standing in fresh snow beneath pine trees at winter dawn, wildlife photography. | |
| Seed: 260927 | |
| ### Sample 03 | |
|  | |
| Prompt: A glass greenhouse filled with ferns after rain, soft natural light, botanical photograph. | |
| Seed: 260928 | |
| ### Sample 04 | |
|  | |
| Prompt: A handmade cobalt blue teapot on a pale stone table, clean studio product photograph. | |
| Seed: 260929 | |
| ### Sample 05 | |
|  | |
| Prompt: A white sailboat crossing a calm blue bay at golden hour, fine art landscape photograph. | |
| Seed: 260930 | |
| ### Sample 06 | |
|  | |
| Prompt: An orange cat curled on a wooden chair in a sunlit bookshop, cozy editorial photograph. | |
| Seed: 260931 | |
| ### Sample 07 | |
|  | |
| Prompt: A small street cafe reflected in wet pavement at night, warm window light, city photograph. | |
| Seed: 260932 | |
| ### Sample 08 | |
|  | |
| Prompt: A wooden lighthouse on a rocky coast under a cloudy sky, atmospheric landscape photograph. | |
| Seed: 260933 | |
| ### Sample 09 | |
|  | |
| Prompt: A bowl of ripe peaches on a kitchen counter, morning light, natural still life photograph. | |
| Seed: 260934 | |
| ### Sample 10 | |
|  | |
| Prompt: A snow-covered cabin among tall pine trees at blue hour, quiet winter landscape photograph. | |
| Seed: 260935 | |
| ### Sample 11 | |
|  | |
| Prompt: A baker placing fresh bread on a cooling rack in a bright kitchen, documentary photograph. | |
| Seed: 260936 | |
| ### Sample 12 | |
|  | |
| Prompt: A goldfinch perched on a thin branch among spring blossoms, close-up wildlife photograph. | |
| Seed: 260937 | |
| ### Sample 13 | |
|  | |
| Prompt: A red bicycle leaning against a brick wall on a leafy neighborhood street, lifestyle photograph. | |
| Seed: 260938 | |
| ### Sample 14 | |
|  | |
| Prompt: A lemon cake with a slice cut out on a ceramic plate, bright tabletop food photograph. | |
| Seed: 260939 | |
| ### Sample 15 | |
|  | |
| Prompt: A small observatory beneath a clear star-filled sky, distant mountains, night landscape photograph. | |
| Seed: 260940 | |
| ## Files | |
| - `step_00210000.pt`: EMA and raw weights, optimizer state, configuration, and training arguments. | |
| - `solpix/`: the transformer, decoder adapter, configuration, data, and training components. | |
| - `generate.py`: prompt-to-image generation. `train.py` starts training; `sample_latents.py` samples latents. | |
| - `samples/`: the 15 PNGs and their metadata. `config.json` records the architecture and external-model manifest. | |
| ## License | |
| The code, checkpoint weights, configuration, model card, and supplied banner use [Apache 2.0](LICENSE). Attribution is in [NOTICE](NOTICE). Dataset, Flan-T5, and SANA DC-AE licenses apply separately. | |