Instructions to use Miayan/freelit-checkpoint with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use Miayan/freelit-checkpoint with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("Miayan/freelit-checkpoint", 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: 2,298 Bytes
c8fa304 | 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 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 | experiment:
project_name: relighting_indoors
output_dir: train_r3_diff_ctrl_phys
logging_dir: logs
report_to: tensorboard
model:
pretrained_model_name_or_path: stable-diffusion-2-1
revision: null
variant: null
tokenizer_name: null
hf_repo_id: Miayan/project
hf_version: null
pretrain_unet_path: scribblelight_controlnet/checkpoint-10000
resume_from_checkpoint: latest
enable_ambient_cond: true
light_encoder:
cross_dim: 768
K: 4
Bi: 4
Bc: 4
Ba: 4
albedo_estimator:
enabled: false
version: v2
load_stage: 3
frozen_components:
- ord_model
- iid_model
- col_model
trainable_components: []
data:
dataset_cache_dir: ./.hf_cache
data_hf_repo_id: Miayan/physical-relighting-dataset
data_split: test
resolution: 512
dataloader_num_workers: 0
condition_mode: v1
relighting_impl: ori
use_ambient_in_controlnet: false
use_color_on_lightmap: true
colors:
- - 255
- 255
- 255
- - 255
- 0
- 0
- - 0
- 255
- 0
- - 0
- 0
- 255
- - 255
- 255
- 0
- - 255
- 165
- 0
- - 128
- 0
- 128
- - 255
- 192
- 203
- - 0
- 255
- 255
- - 255
- 0
- 255
intensities:
- 0.0
- 0.2
- 0.4
- 0.6
- 0.8
- 1.0
training:
batch_size: 1
num_epochs: 20
max_train_steps: 100000
max_train_samples: null
seed: 42
mixed_precision: 'no'
allow_tf32: true
gradient_accumulation_steps: 1
checkpointing_steps: 5000
checkpoints_total_limit: 4
learning_rate: 5.0e-06
scale_lr: false
lr_scheduler: constant
lr_warmup_steps: 500
adam_beta1: 0.9
adam_beta2: 0.999
adam_weight_decay: 0.01
adam_epsilon: 1.0e-08
max_grad_norm: 1.0
use_8bit_adam: false
set_grads_to_none: false
losses:
phys_loss:
enabled: true
weight: 1.0
latent_loss:
enabled: true
weight: 1.0
image_loss:
enabled: true
weight: 1.0
sup_structure_loss:
enabled: false
weight: 1.0
enable_decay: true
start_weight: 1.0
end_weight: 0.1
decay_steps: 5000
self_recon_loss:
enabled: false
mode: teacher
weight: 2.0
affine_alignment: true
epsilon: 0.05
staging:
switch_epoch: 5
stage1_weight: 2.0
stage2_weight: 0.5
consistency_loss:
enabled: false
weight: 1.0
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