Instructions to use SceneWorks/chroma1-base-mlx with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use SceneWorks/chroma1-base-mlx with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("SceneWorks/chroma1-base-mlx", dtype=torch.bfloat16, device_map="cuda") prompt = "Astronaut in a jungle, cold color palette, muted colors, detailed, 8k" image = pipe(prompt).images[0] - Notebooks
- Google Colab
- Kaggle
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Download bf16/README.md from SceneWorks/chroma1-base-mlx: direct link, hf CLI and curl.
- Browser
- Download file 1.82 kB
-
https://huggingface.co/SceneWorks/chroma1-base-mlx/resolve/main/bf16/README.md
- Command line
-
hf download hf://SceneWorks/chroma1-base-mlx/bf16/README.md
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curl -L -o README.md https://huggingface.co/SceneWorks/chroma1-base-mlx/resolve/main/bf16/README.md
1.82 kB
| license: apache-2.0 | |
| pipeline_tag: text-to-image | |
| base_model: lodestones/Chroma1-Base | |
| tags: | |
| - mlx | |
| - apple-silicon | |
| - chroma | |
| - flux | |
| - text-to-image | |
| # Chroma1-Base — MLX packed tiers (SceneWorks) | |
| Pre-quantized, packed-load MLX tiers of [`lodestones/Chroma1-Base`](https://huggingface.co/lodestones/Chroma1-Base) | |
| for on-device inference in [SceneWorks](https://github.com/SceneWorks) via `mlx-gen-chroma`. | |
| Chroma1-Base is an 8.9B-parameter FLUX.1-schnell-derived text-to-image DiT (Apache-2.0). This repo | |
| re-hosts it as three self-contained tiers so SceneWorks can load a tier directly with **no dense | |
| transient and no in-app quantization** (the loader packed-detects group-wise affine weights via | |
| `{base}.scales`): | |
| | Tier subdir | Transformer weights | Notes | | |
| |-------------|--------------------|-------| | |
| | `bf16/` | dense bf16 | verbatim mirror of the source diffusers snapshot | | |
| | `q8/` | packed Q8 (group 64) | transformer block Linears only | | |
| | `q4/` | packed Q4 (group 64) | transformer block Linears only (default tier) | | |
| **Quant scope.** Only the DiT `transformer/` matmul-heavy block Linears are quantized (the double | |
| blocks' attention + FFN and the single blocks' attention + `proj_mlp`/`proj_out`). The transformer's | |
| `x_embedder`/`context_embedder`/`proj_out` and the distilled-guidance Approximator, the shared T5-XXL | |
| text encoder, and the FLUX.1 VAE stay dense in every tier. The Q4/Q8 packing is byte-identical to the | |
| load-time quantization seam (weights cast to bf16 first, MLX group-wise affine at group size 64). | |
| Each tier subdir is a complete diffusers-layout turnkey (`transformer/ text_encoder/ vae/ tokenizer/ | |
| scheduler/ model_index.json`). | |
| ## License | |
| Apache-2.0, inherited from the upstream model. See `LICENSE`. Upstream: | |
| `lodestones/Chroma1-Base`. | |