File size: 1,664 Bytes
140017c | 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 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 | ---
base_model: Qwen/Qwen-Image
library_name: diffusers
license: apache-2.0
pipeline_tag: text-to-image
tags:
- qwen-image
- text-to-image
- quantized
- fp8
- e4m3
- diffusers
base_model_relation: quantized
---
# Qwen-Image-e4m3
FP8 (e4m3) dynamically-quantized [Qwen-Image](https://huggingface.co/Qwen/Qwen-Image),
saved as a complete `QwenImagePipeline`.
## What was changed
All 60 blocks of the `QwenImageTransformer2DModel` are quantized to
`e4m3_e4m3_dynamic` — `float8_e4m3fn` weights with dynamically scaled
`float8_e4m3fn` activations. The Qwen2.5-VL text encoder, the VAE and the
transformer's non-block tensors are untouched and stay in bf16. The transformer
drops from ~40.9 GB to ~20.5 GB.
Quantization was done with WaveSpeed's `xelerate.ao.quantize`. Weights are
stored as pickled `.bin` shards, so loading requires `use_safetensors=False`.
FP8 matmul needs Hopper (H100/H200) or newer to be faster than bf16; on older
GPUs the weights are dequantized on the fly and you only get the memory saving.
One caveat worth repeating from our own testing: on Qwen-Image, running fp8
*weight-and-activation* matmul under a fully fused fast path produces visible
quality loss. The configuration published here — dynamic per-tensor activation
scaling with bf16 accumulation — is the one that holds up.
## Usage
```python
import torch
from diffusers import QwenImagePipeline
pipe = QwenImagePipeline.from_pretrained(
"wavespeed/Qwen-Image-e4m3",
torch_dtype=torch.bfloat16,
use_safetensors=False,
).to("cuda")
image = pipe("a chalkboard menu written in neat cursive").images[0]
```
## License
Apache-2.0, inherited from Qwen-Image.
|