Instructions to use threecrowco/VolkClipartQwen with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use threecrowco/VolkClipartQwen with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("Qwen/Qwen-Image", dtype=torch.bfloat16, device_map="cuda") pipe.load_lora_weights("threecrowco/VolkClipartQwen") prompt = "-" image = pipe(prompt).images[0] - Inference
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- Draw Things
- DiffusionBee
Download adapter_config.json from threecrowco/VolkClipartQwen: direct link, hf CLI and curl.
- Browser
- Download file 209 Bytes
-
https://huggingface.co/threecrowco/VolkClipartQwen/resolve/main/adapter_config.json
- Command line
-
hf download hf://threecrowco/VolkClipartQwen/adapter_config.json
-
curl -L -o adapter_config.json https://huggingface.co/threecrowco/VolkClipartQwen/resolve/main/adapter_config.json
209 Bytes
| { | |
| "peft_type": "LORA", | |
| "r": 16, | |
| "lora_alpha": 16, | |
| "bias": "none", | |
| "target_modules": [ | |
| "attn.q_proj", | |
| "attn.k_proj", | |
| "attn.v_proj", | |
| "attn.out_proj", | |
| "mlp.fc1", | |
| "mlp.fc2" | |
| ] | |
| } | |