Instructions to use FrankCCCCC/ddpm-ema-10k_cfm-corr-400-ss0.0-ep100-ema-run1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use FrankCCCCC/ddpm-ema-10k_cfm-corr-400-ss0.0-ep100-ema-run1 with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("FrankCCCCC/ddpm-ema-10k_cfm-corr-400-ss0.0-ep100-ema-run1", dtype=torch.bfloat16, device_map="cuda") prompt = "Astronaut in a jungle, cold color palette, muted colors, detailed, 8k" image = pipe(prompt).images[0] - Notebooks
- Google Colab
- Kaggle
File size: 1,377 Bytes
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"save_model_per_ep": 5,
"checkpoint_per_ep": 5,
"ep_model_dir": "epochs",
"output_dir": "fm_cifar10",
"ckpt_dir": "ckpt",
"ckpt_data_file": "data.ckpt",
"is_save_all_model_epochs": false,
"args_key": "args",
"default_key": "default",
"final_key": "final",
"config_file": "config.json",
"optim_name": "adamw",
"shed_name": "poly_decay",
"sched_num_warmup_steps": 45000,
"optim_weight_decay": 0.0,
"optim_beta_1": 0.9,
"optim_beta_2": 0.999,
"optim_epsilon": 1e-08,
"sched_lr_end": 1e-07,
"sched_power": 1.0,
"sched_t_max": 200,
"sched_last_epoch": -1,
"training_result_file": "training_result.json",
"check_finished_file": "finished.json",
"project": "CFM_CORR_EMA_Final",
"name_postfix": "ema-run1",
"saving_strategy": "best_valid_loss",
"optim_lr": 0.0005,
"model_id": "google/ddpm-cifar10-32",
"train_dataset": "cifar10",
"traj_dataset": "data/traj_dataset/TRAJ-DS_PS-DM_DDPM_PSS-1000_SZ-10000",
"batch_size": 256,
"num_epochs": 100,
"num_train_timesteps": 1000,
"predictor_num_inference_steps": 1000,
"corrector_num_inference_steps": 100,
"sigma_min": 0.0,
"sigma_src": 0.0,
"corr_trained_timestep": 400,
"ds_size": 50000,
"seed": 43,
"device": "cuda:0",
"overwrite": false,
"weight_decay": 0.0
} |