Instructions to use lodestones/chroma-h3 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use lodestones/chroma-h3 with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("MiniMaxAI/MiniMax-H3", dtype=torch.bfloat16, device_map="cuda") pipe.load_lora_weights("lodestones/chroma-h3") prompt = "Astronaut in a jungle, cold color palette, muted colors, detailed, 8k" image = pipe(prompt).images[0] - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- Draw Things
- DiffusionBee
chroma-h3
An image ("stills") fine-tune of MiniMaxAI/MiniMax-H3: the video DiT run with a single latent frame, trained on text-image pairs.
Root: the full H3 diffusers pipeline with the fine-tune merged into
transformer/(bf16). Every other component (Qwen3-VL text encoder, tokenizer, processor, video/audio VAEs, scheduler) is the unchanged base.lora/lora.safetensors: the same fine-tune as a LoRA on the base H3 transformer (fp32, rank 256, alpha 256, apply at strength 1). Keys are the H3 transformer's own module names (<module>.lora_A/<module>.lora_B); target regex(transformer_blocks|token_refiner\.refiner_blocks)\.\d+\.ff\.net\.(0\.proj|2).lora/lora_vspace_dmd8.safetensors: few-step version (experimental, DMD update 8700 of 12000). Apply it to the stock MiniMaxAI/MiniMax-H3 transformer, not to this repo's merged one: it already contains the fine-tune above plus a data-space DMD student distilled from it at CFG 3. Sample with plain Euler at CFG 1, 4 or 8 steps (shift 3 for stills, the usual shift 12 for video). fp32, scales folded intolora_B(metadataalpha: rank), so apply at strength 1 with scale 1; rank 384 on the block FFNs (fine-tune + student), 128 on block attention (student), 256 on the token-refiner FFNs (fine-tune). Trained on stills only, it also carries over to video (4/8 steps, coherent motion).
Training
- LoRA rank 256 / alpha 256 on the feed-forward Linears of all transformer blocks and the token-refiner blocks; attention, AdaLN and embeddings untouched.
- 13605 updates at a global batch of 48 images (one pass over the data), lr 0.0001, flow matching with timestep shift 3.0, aspect-ratio buckets around 1024^2, 10% empty-prompt dropout; the audio AdaLN slice is dropped during training (image mode), the merged model keeps it unchanged.
- Captions: an even mix of booru tags, short / brief / Midjourney-style summaries, long captions and scene graphs.
Use
Generate a single frame (1 latent frame) at ~1MP. The fine-tune no longer behaves as guidance-distilled: use classifier-free guidance (CFG ~3 with an empty negative prompt) and ~30 Euler steps at shift 3.
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