Instructions to use wavespeed/Qwen-Image-bf16 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use wavespeed/Qwen-Image-bf16 with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("wavespeed/Qwen-Image-bf16", 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
Add model card metadata: base_model, license, pipeline_tag, tags
Browse files
README.md
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---
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base_model: Qwen/Qwen-Image
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library_name: diffusers
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license: apache-2.0
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pipeline_tag: text-to-image
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tags:
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- qwen-image
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- text-to-image
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- bf16
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- diffusers
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---
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# Qwen-Image-bf16
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An unmodified bf16 copy of [Qwen/Qwen-Image](https://huggingface.co/Qwen/Qwen-Image),
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resharded for WaveSpeed's loader.
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## What was changed
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Nothing numerically. Tensor names, shapes, dtypes (bf16) and total parameter
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bytes are identical to upstream; the transformer is simply written as 5 shards
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instead of 9, which suits how our serving nodes stream weights from cache.
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**If you are not us, use [`Qwen/Qwen-Image`](https://huggingface.co/Qwen/Qwen-Image)
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instead.** This repository exists as a pinned serving artifact and offers no
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advantage over the original.
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The quantized variants derived from this baseline are
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[`wavespeed/Qwen-Image-e4m3`](https://huggingface.co/wavespeed/Qwen-Image-e4m3)
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(fp8) — those do differ from upstream.
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## License
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Apache-2.0, inherited from Qwen-Image.
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