Text-to-Image
Diffusers
Safetensors
AXQReferenceDiffusionPipeline
axquant
axq
nvfp4
reference-storage
research-only
development
Instructions to use AutomatosX/AX-Image-2.1-Turbo-AXQ-NVFP4 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use AutomatosX/AX-Image-2.1-Turbo-AXQ-NVFP4 with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("AutomatosX/AX-Image-2.1-Turbo-AXQ-NVFP4", 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
File size: 983 Bytes
aad6de7 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 | {
"device": "mps",
"diffusers_version": "0.41.0",
"execution": "BF16 reconstruction; ordinary PyTorch operators; MPS CPU fallback enabled",
"hardware": "Mac Studio M2 Ultra 192 GB",
"height": 512,
"load_and_image_generation": "passed",
"manifest_sha256": "571e02255ddd7d3c4eedd7b805d4545289b93d042c9ee0db761b5d680783eb6e",
"native_low_precision_execution": false,
"output_file": "reference-output.png",
"output_pixel_stddev": 81.6553239994759,
"output_sha256": "ba44f8031147893b0dcf9e497bcbecde75055377973dab426fe8cc3be602f24a",
"prompt": "A red cube on a plain white background",
"quality_certified": false,
"sample_sigmas": [
1.0,
0.978453,
0.95418,
0.926626,
0.89508,
0.845148,
0.704534,
0.414568
],
"schema_version": "axquant.diffusion-reference-runtime.v1",
"seed": 0,
"source_model": "Qwen/Qwen-Image-2.1-Turbo",
"steps": 8,
"torch_version": "2.14.1",
"transformers_version": "5.19.0",
"width": 512
}
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