by Lodestones
Community-distilled 8.9B model based on FLUX.1-schnell architecture. Apache 2.0 licensed alternative to Flux with competitive quality. Available in HD and Flash variants for different quality/speed tradeoffs.
VRAM requirements, GPU fit, and setup notes for Chroma, including 8GB/12GB fit guidance where relevant. Recommended runtimes: ComfyUI and Diffusers support. Best download size: ~8.9 GB at FP8.
Your hardware
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Measured quality metrics for Chroma outputs.
How often humans prefer this model's output (0-100%)
Visual quality and composition rating (5-9 scale)
Compare which GPUs can run Chroma at different precisions. FP8 uses less memory than FP16 when available, and the grade shows how comfortably each GPU handles the workload.
| Resolution | VRAM Required | RTX 4090 24GB | RTX 3060 12GB | RTX 4060 8GB | MacBook Pro M4 Pro 24GB |
|---|---|---|---|---|---|
| 512×512 | 21.7 GB | A● | F● | F● | F● |
| 768×768 | 21.8 GB | A● | F● | F● | F● |
| 1024×1024 | 22.0 GB | A● | F● | F● | F● |
| Resolution | VRAM Required | RTX 4090 24GB | RTX 3060 12GB | RTX 4060 8GB | MacBook Pro M4 Pro 24GB |
|---|---|---|---|---|---|
| 512×512 | 15.3 GB | S | D | F | A |
| 768×768 | 15.5 GB | S | D | F | A |
| 1024×1024 | 15.7 GB | S | D | F | A |
from diffusers import FluxPipeline
import torch
pipe = FluxPipeline.from_pretrained(
"lodestones/Chroma",
torch_dtype=torch.float16
)
pipe.to("cuda")
image = pipe(
prompt="your prompt here",
num_inference_steps=4,
height=1024,
width=1024,
).images[0]
image.save("output.png")Get started
Setup instructions for running Chroma locally
1. Download the model
Get the checkpoint from HuggingFace
2. Place in:
ComfyUI/models/checkpoints/3. Launch ComfyUI
python main.pyVRAM allocation at 1024×1024 on RTX 4090 24GB (24 GB)
Time per image at 1024×1024, 28 steps, FP16.
Download Chroma in the precision that matches your GPU. Lower precision usually means less VRAM pressure, while higher precision keeps more quality.
Early LoRA ecosystem. Some Flux LoRAs may be partially compatible.
Frequently asked questions
Chroma (8.9B parameters) requires approximately 22.0 GB of VRAM at FP16 precision for standard 1024×1024 image generation. If you want a lighter setup, lower precisions like FP8 can reduce memory use when available.
Chroma usually needs more than 8GB for comfortable local use. Check the VRAM table above for the exact resolution and precision trade-off.
Chroma is marked for ComfyUI and Diffusers support in our catalog, so those are the runtimes we recommend first for local setup. If your workflow uses another front end, check the model's available formats and workflow notes above before downloading.
Yes, the RTX 4090 (24 GB VRAM) can run Chroma comfortably at FP16. Expected generation time is around ~7.3s per image at 1024×1024.
There are currently no known ControlNet adapters for Chroma. Check Hugging Face and Civitai for community-contributed adapters.
Early LoRA ecosystem. Some Flux LoRAs may be partially compatible. The LoRA ecosystem for Chroma is rated as "minimal". Each LoRA adds roughly 0.2 GB of extra VRAM.
On a reference GPU (RTX 4090 24GB), Chroma generates a 1024×1024 image in approximately ~7.3s at FP16 with 28 inference steps. Faster GPUs with higher memory bandwidth will produce images more quickly.
See also