Instructions to use Efficient-Large-Model/Sana_600M_512px_diffusers with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Sana
How to use Efficient-Large-Model/Sana_600M_512px_diffusers with Sana:
# Load the model and infer image from text import torch from app.sana_pipeline import SanaPipeline from torchvision.utils import save_image sana = SanaPipeline("configs/sana_config/1024ms/Sana_1600M_img1024.yaml") sana.from_pretrained("hf://Efficient-Large-Model/Sana_600M_512px_diffusers") image = sana( prompt='a cyberpunk cat with a neon sign that says "Sana"', height=1024, width=1024, guidance_scale=5.0, pag_guidance_scale=2.0, num_inference_steps=18, ) - Notebooks
- Google Colab
- Kaggle
File size: 840 Bytes
4cf38fe | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 | {
"_class_name": "DPMSolverMultistepScheduler",
"_diffusers_version": "0.32.0.dev0",
"algorithm_type": "dpmsolver++",
"beta_end": 0.02,
"beta_schedule": "linear",
"beta_start": 0.0001,
"dynamic_thresholding_ratio": 0.995,
"euler_at_final": false,
"final_sigmas_type": "zero",
"flow_shift": 3.0,
"lambda_min_clipped": -Infinity,
"lower_order_final": true,
"num_train_timesteps": 1000,
"prediction_type": "flow_prediction",
"rescale_betas_zero_snr": false,
"sample_max_value": 1.0,
"solver_order": 2,
"solver_type": "midpoint",
"steps_offset": 0,
"thresholding": false,
"timestep_spacing": "linspace",
"trained_betas": null,
"use_beta_sigmas": false,
"use_exponential_sigmas": false,
"use_flow_sigmas": true,
"use_karras_sigmas": false,
"use_lu_lambdas": false,
"variance_type": null
}
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