Skip to content
New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

Sign up for GitHub

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

[Fix] Fix README.md #41

Merged
merged 2 commits into from
Aug 28, 2023
Merged

[Fix] Fix README.md #41

Changes from all commits
Commits
Show all changes
2 commits
Select commit Hold shift + click to select a range
fdf4170
Update README.md
okotaku Aug 27, 2023
7bd1e85
Update README.md
okotaku Aug 27, 2023
File filter

Filter by extension

Filter by extension
Conversations
Failed to load comments.
Loading
Jump to
Jump to file
Failed to load files.
Loading
Diff view
Diff view
16 changes: 8 additions & 8 deletions README.md
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -44,15 +44,15 @@ pip install git+https://github.com/okotaku/diffengine.git

DiffEngine makes training easy through its pre-defined configs. These configs provide a streamlined way to start your training process. Here's how you can get started using one of the pre-defined configs:

1. **Choose a config**: You can find various pre-defined configs in the [`configs`](configs/) directory of the DiffEngine repository. For example, if you're interested in training a model for DreamBooth using the Stable Diffusion algorithm, you can use the [`configs/stable_diffusion_dreambooth/stable_diffusion_v15_dreambooth_lora_dog.py`](configs/stable_diffusion_dreambooth/stable_diffusion_v15_dreambooth_lora_dog.py).
1. **Choose a config**: You can find various pre-defined configs in the [`configs`](configs/) directory of the DiffEngine repository. For example, if you wish to train a DreamBooth model using the Stable Diffusion algorithm, you can use the [`configs/stable_diffusion_dreambooth/stable_diffusion_v15_dreambooth_lora_dog.py`](configs/stable_diffusion_dreambooth/stable_diffusion_v15_dreambooth_lora_dog.py).

2. **Start Training**: Open a terminal and use the following command to start training with the chosen config:
2. **Start Training**: Open a terminal and run the following command to start training with the selected config:

```bash
mim train diffengine stable_diffusion_v15_dreambooth_lora_dog.py
```

3. **Monitor Progress and get results**: The training process will begin, and you will monitor the progress of your training as it proceeds. The outputs of training will be located in the `work_dirs/stable_diffusion_v15_dreambooth_lora_dog` directory, specifically in the case of using the `stable_diffusion_v15_dreambooth_lora_dog` config.
3. **Monitor Progress and get results**: The training process will begin, and you can track its progress. The outputs of the training will be located in the `work_dirs/stable_diffusion_v15_dreambooth_lora_dog` directory, specifically when using the `stable_diffusion_v15_dreambooth_lora_dog` config.

```
work_dirs/stable_diffusion_v15_dreambooth_lora_dog
Expand All @@ -62,9 +62,9 @@ work_dirs/stable_diffusion_v15_dreambooth_lora_dog
| ├── 20230802_033741.json # log json file
| ├── config.py # config file for each experiment
| └── vis_image # visualized image from each step
├── step1199
├── step999
| └── pytorch_lora_weights.bin # weight for inferencing with diffusers.pipeline
├── iter_1200.pth # checkpoint from each step
├── iter_1000.pth # checkpoint from each step
├── last_checkpoint # last checkpoint, it can be used for resuming
└── stable_diffusion_v15_dreambooth_lora_dog.py # latest config file
```
Expand All @@ -73,14 +73,14 @@ An illustrative output example is provided below:

![img](https://github.com/okotaku/diffengine/assets/24734142/e4576779-e05f-42d0-a709-d6481eea87a9)

4. **Inference with diffusers.pipeline**: Once you have trained a model, specify the path to the saved model and utilize it for inference using the `diffusers.pipeline` module.
4. **Inference with diffusers.pipeline**: Once you have trained a model, simply specify the path to the saved model and inference by the `diffusers.pipeline` module.

```py
import torch
from diffusers import DiffusionPipeline

checkpoint = 'work_dirs/stable_diffusion_v15_lora_pokemon_blip/step10450'
prompt = 'yoda pokemon'
checkpoint = 'work_dirs/stable_diffusion_v15_dreambooth_lora_dog/step999'
prompt = 'A photo of sks dog in a bucket'

pipe = DiffusionPipeline.from_pretrained(
'runwayml/stable-diffusion-v1-5', torch_dtype=torch.float16)
Expand Down