Instructions to use syaffers/tiny-random-QwenImagePipeline with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use syaffers/tiny-random-QwenImagePipeline with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("syaffers/tiny-random-QwenImagePipeline", 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: 642 Bytes
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"_class_name": "AutoencoderKLQwenImage",
"_diffusers_version": "0.40.0",
"attn_scales": [],
"base_dim": 8,
"dim_mult": [
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"dropout": 0.0,
"input_channels": 3,
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"num_res_blocks": 1,
"temperal_downsample": [
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],
"z_dim": 16
}
|