Text-to-Image
Diffusers
NeuronStableDiffusionXLPipeline
stable-diffusion
lora
template:sd-lora
Neuron
Inferentia
Instructions to use Shekswess/Canopus-Interior-Architecture-0.1-Neuron with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use Shekswess/Canopus-Interior-Architecture-0.1-Neuron with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("stabilityai/stable-diffusion-xl-base-1.0", dtype=torch.bfloat16, device_map="cuda") pipe.load_lora_weights("Shekswess/Canopus-Interior-Architecture-0.1-Neuron") prompt = "Interior room of the house with plants, a chair and candles, space to relax, soft lighting, pastel colors, style of ultrafine detail, high quality photo --ar 2:3 --v 5" image = pipe(prompt).images[0] - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- Draw Things
- DiffusionBee
Download scheduler/scheduler_config.json from Shekswess/Canopus-Interior-Architecture-0.1-Neuron: direct link, hf CLI and curl.
- Browser
- Download file 613 Bytes
-
https://huggingface.co/Shekswess/Canopus-Interior-Architecture-0.1-Neuron/resolve/main/scheduler/scheduler_config.json
- Command line
-
hf download hf://Shekswess/Canopus-Interior-Architecture-0.1-Neuron/scheduler/scheduler_config.json
-
curl -L -o scheduler_config.json https://huggingface.co/Shekswess/Canopus-Interior-Architecture-0.1-Neuron/resolve/main/scheduler/scheduler_config.json
613 Bytes
| { | |
| "_class_name": "EulerDiscreteScheduler", | |
| "_diffusers_version": "0.30.3", | |
| "beta_end": 0.012, | |
| "beta_schedule": "scaled_linear", | |
| "beta_start": 0.00085, | |
| "clip_sample": false, | |
| "final_sigmas_type": "zero", | |
| "interpolation_type": "linear", | |
| "num_train_timesteps": 1000, | |
| "prediction_type": "epsilon", | |
| "rescale_betas_zero_snr": false, | |
| "sample_max_value": 1.0, | |
| "set_alpha_to_one": false, | |
| "sigma_max": null, | |
| "sigma_min": null, | |
| "skip_prk_steps": true, | |
| "steps_offset": 1, | |
| "timestep_spacing": "leading", | |
| "timestep_type": "discrete", | |
| "trained_betas": null, | |
| "use_karras_sigmas": false | |
| } | |