Instructions to use AzhureRaven/rico-diffusion with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use AzhureRaven/rico-diffusion with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("AzhureRaven/rico-diffusion", dtype=torch.bfloat16, device_map="cuda") prompt = "red Toolbar Upper Top containing Text Left Login inside and white Input Upper Top and white Input Lower Top and red Button login Upper Middle, Android UI, Medical, white background" image = pipe(prompt).images[0] - Notebooks
- Google Colab
- Kaggle
- Local Apps
- Draw Things
- DiffusionBee
Rename rico_diffusion_v2_comp_icon.json to ed2-config/rico_diffusion_v2_comp_icon.json
7823b14 verified | { | |
| "attn_type": "sdp", | |
| "batch_size": 11, | |
| "ckpt_every_n_minutes": null, | |
| "clip_grad_norm": null, | |
| "clip_skip": 2, | |
| "cond_dropout": 0.00, | |
| "data_root": "/home/azhure/Tesis/Icon", | |
| "disable_amp": false, | |
| "disable_textenc_training": false, | |
| "flip_p": 0.0, | |
| "gpuid": 0, | |
| "gradient_checkpointing": true, | |
| "grad_accum": 1, | |
| "logdir": "logs", | |
| "log_step": 25, | |
| "loss_type": "huber", | |
| "max_epochs": 2, | |
| "optimizer_config": "optimizer_rico_comp.json", | |
| "project_name": "rico_diffusion_v2_comp_icon", | |
| "resolution": 384, | |
| "resume_ckpt": "findlast", | |
| "sample_prompts": "sample_prompts.txt", | |
| "sample_steps": 999999999, | |
| "save_ckpt_dir": "model_output", | |
| "save_ckpts_from_n_epochs": 0, | |
| "save_every_n_epochs": 10, | |
| "save_optimizer": false, | |
| "scale_lr": false, | |
| "seed": 555, | |
| "keep_tags": 3, | |
| "shuffle_tags": false, | |
| "validation_config": "validation_rico_off.json", | |
| "zero_frequency_noise_ratio": 0.02, | |
| "min_snr_gamma": 5.0, | |
| "enable_zero_terminal_snr": true, | |
| "no_save_ckpt": true | |
| } |