Text-to-Image
Diffusers
Safetensors
image-to-image
quantization
w4a4
svdquant
gptq
nunchaku
8-bit precision
Instructions to use ModelsLab/Qwen-Image-2.1-W4A4-int4 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use ModelsLab/Qwen-Image-2.1-W4A4-int4 with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("ModelsLab/Qwen-Image-2.1-W4A4-int4", 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
Record the reference-free text-accuracy sweep
Browse files
README.md
CHANGED
|
@@ -50,6 +50,10 @@ Only the transformer is quantized. The Qwen3-VL text encoder stays bf16 and is a
|
|
| 50 |
16.3 GB of a 21.5 GB resident total, so it, not this file, decides how many reference
|
| 51 |
images fit on a card.
|
| 52 |
|
|
|
|
|
|
|
|
|
|
|
|
|
| 53 |
## Requirements
|
| 54 |
|
| 55 |
Packed for a specific kernel. The layout is an MMA fragment swizzle, so a different
|
|
|
|
| 50 |
16.3 GB of a 21.5 GB resident total, so it, not this file, decides how many reference
|
| 51 |
images fit on a card.
|
| 52 |
|
| 53 |
+
## Text accuracy against bf16
|
| 54 |
+
|
| 55 |
+
The nvfp4 build of this same recipe scored 15/16 against bf16's 14/16 on a reference-free seed sweep (8 seeds, two text prompts, text read against the prompt) — indistinguishable. This int4 build was not put through that sweep; its LPIPS is 0.1836 against the nvfp4 build's 0.1425, so treat it as the same recipe at slightly lower fidelity, on the far wider set of cards it runs on.
|
| 56 |
+
|
| 57 |
## Requirements
|
| 58 |
|
| 59 |
Packed for a specific kernel. The layout is an MMA fragment swizzle, so a different
|