Instructions to use olesheva/DavidLynch-Qwen-LoRA with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use olesheva/DavidLynch-Qwen-LoRA 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("olesheva/DavidLynch-Qwen-LoRA") prompt = "-" image = pipe(prompt).images[0] - Inference
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- Draw Things
- DiffusionBee
DavidLynch-LoRA

- Prompt
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- Prompt
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- Prompt
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Model description
This lora was trained for 90 epochs using onetrainer on a dataset of 63 high-res frames from Twin Peaks, Mulholland Drive, Blue Velvet etc. It focused on color grading, so no trigger words are required. For caption-based prompting I use qwen captioner to generate prompts for examples. Works normally on 1.0-1.2, you can mix it with another realism loras as well. Civit.ai:https://civitai.com/models/2044496?modelVersionId=2313953
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Model tree for olesheva/DavidLynch-Qwen-LoRA
Base model
Qwen/Qwen-Image