Text-to-Image
Diffusers
Safetensors
QwenImagePipeline
nvidia
ModelOpt
AutoModel
qwen-image
dmd2
few-step
Instructions to use nvidia/Qwen-Image-Flash with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use nvidia/Qwen-Image-Flash with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("nvidia/Qwen-Image-Flash", torch_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
Format evaluation dataset fields
Browse files
README.md
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## Evaluation Dataset:
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**Link:** [Qwen-Image-Bench](https://arxiv.org/abs/2605.28091), [OneIG-Bench-EN](https://oneig-bench.github.io/)
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**Data Collection Method by dataset:** Manually-collected
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**Properties:** Qwen-Image-Bench: 1,000 prompts; OneIG-Bench-EN: 1,120 English prompts across five categories.
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## Inference:
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## Evaluation Dataset:
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**Link:** [Qwen-Image-Bench](https://arxiv.org/abs/2605.28091), [OneIG-Bench-EN](https://oneig-bench.github.io/)
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**Data Collection Method by dataset:** Manually-collected
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**Labeling Method by dataset:** Automated
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**Properties:** Qwen-Image-Bench: 1,000 prompts; OneIG-Bench-EN: 1,120 English prompts across five categories.
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## Inference:
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