Text-to-Image
Diffusers
Safetensors
Krea2Pipeline
krea
nunchaku
svdquant
int4
quantization
8-bit precision
Instructions to use lite-infer/krea-2-turbo-nunchaku-lite-int4_r32-bnb4-text-encoder with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use lite-infer/krea-2-turbo-nunchaku-lite-int4_r32-bnb4-text-encoder with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("lite-infer/krea-2-turbo-nunchaku-lite-int4_r32-bnb4-text-encoder", 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
- Local Apps Settings
- Draw Things
- DiffusionBee
Drop measurement-rig detail from the no-baseline note
Browse files
README.md
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@@ -38,7 +38,7 @@ NVIDIA RTX PRO 4000 Blackwell, settings as in the Run snippet below, one warmup
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measured runs, everything resident on the GPU with no offload. Latency covers
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the full pipeline call; VRAM is peak device usage.
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There is no dense bf16 row because
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## Output Comparison
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measured runs, everything resident on the GPU with no offload. Latency covers
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the full pipeline call; VRAM is peak device usage.
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There is no dense bf16 row because it does not fit on a 24 GiB card — the transformer alone is 26.3 GiB — while both quantized builds run resident. The NVFP4 build is 1.78x faster **on this GPU**, because NVIDIA RTX PRO 4000 Blackwell has native FP4 tensor cores that only NVFP4 can use. **INT4 is the build for Turing through Ada, where NVFP4 does not run at all**, and has not been benchmarked there.
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## Output Comparison
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