--- license: other license_name: bespoke-lora-trained-license license_link: https://multimodal.art/civitai-licenses?allowNoCredit=True&allowCommercialUse=Sell&allowDerivatives=True&allowDifferentLicense=True tags: - text-to-image - stable-diffusion - lora - diffusers - template:sd-lora - migrated - character base_model: black-forest-labs/FLUX.1-dev instance_prompt: widget: - text: 'silver silk dress, solo, simple background, perfectly round sunglasses, upper body, cannyquest, 1girl, standing straight' output: url: >- 26676267.jpeg - text: 'upper body, 1girl, cannyquest, standing straight, perfectly round sunglasses, silver silk dress, simple background, solo' output: url: >- 26676266.jpeg - text: 'perfectly round sunglasses, upper body, standing straight, cannyquest, simple background, 1girl, solo, silver silk dress' output: url: >- 26676268.jpeg --- # Canny Quest ([CivitAI](https://civitai.com/models/)) ## Model description

This model was created to be a shortcut to the fictional character Canny Quest from Pixel Perfect Beauties.

Main features:

blonde, silver silk dress, perfectly round sunglasses, pearl necklace

Trigger World:

cannyquest

## Download model Weights for this model are available in Safetensors format. [Download](/marceloxp/canny-quest/tree/main) them in the Files & versions tab. ## Use it with the [🧨 diffusers library](https://github.com/huggingface/diffusers) ```py from diffusers import AutoPipelineForText2Image import torch device = "cuda" if torch.cuda.is_available() else "cpu" pipeline = AutoPipelineForText2Image.from_pretrained('black-forest-labs/FLUX.1-dev', torch_dtype=torch.bfloat16).to(device) pipeline.load_lora_weights('marceloxp/canny-quest', weight_name='Canny_Quest-000004.safetensors') image = pipeline('perfectly round sunglasses, upper body, standing straight, cannyquest, simple background, 1girl, solo, silver silk dress').images[0] ``` For more details, including weighting, merging and fusing LoRAs, check the [documentation on loading LoRAs in diffusers](https://huggingface.co/docs/diffusers/main/en/using-diffusers/loading_adapters)