03_generation.md

Generation

Start Your First Generation

Command Generation

1. Open the generate.py file and locate the --weight_path parameter. Modify the path in the parameter to the path of your model weights, for example /your/path/weight/model.pt.

2. Set the necessary parameters such as --conditional,--latent, --generate_name, --num_images, --class_name, --image_size, --result_path, etc. If no parameters are set, the default settings will be used. There are two ways to set the parameters: one is to directly modify the parser in the if __name__ == "__main__": section of the generate.py file, and the other is to use the following commands in the console while in the /your/path/Defect-Diffusion-Model/iddm/tools directory:

   Conditional Generation Command

   python generate.py --generate_name df --num_images 8 --mode class --class_name 0 --image_size 64 --weight_path /your/path/weight/model.pt --sample ddpm

   Unconditional Generation Command

   python generate.py --generate_name df --num_images 8 --image_size 64 --weight_path /your/path/weight/model.pt --sample ddpm

   Conditional Latent Generation Command

   python generate.py --conditional --latent --generate_name df --num_images 8 --mode class --class_name 0 --image_size 64 --weight_path /your/path/weight/model.pt --sample ddim --autoencoder_ckpt /your/path/weight/autoencoder_model.pt

   Unconditional Latent Generation Command

   python generate.py --latent --generate_name df --num_images 8 --image_size 64 --weight_path /your/path/weight/model.pt --sample ddim --autoencoder_ckpt /your/path/weight/autoencoder_model.pt

3. Wait for the generation process to complete.

Python generation

from iddm.tools.generate import Generator, init_generate_args

# Initialize generation arguments, or you can configure them by entering the init_generate_args method
args = init_generate_args()
# Customize your parameters
setattr(args, "latent", True)
setattr(args, "autoencoder_ckpt", "/your/VAE/model/path/model.pt")
setattr(args, "weight_path", "/your/model/path/model.pt")
setattr(args, "result_path", "/your/save/path/")
setattr(args, "mode", "class")
setattr(args, "class_name", 0)
# ...
# Or use args["parameter_name"] = "your setting"
gen_model = Generator(gen_args=args, deploy=False)
# Number of generations
num_images = 2
for i in range(num_images):
   gen_model.generate(index=i)

Generation Parameters

Parameter Explanation

Warning

--sample, --network and --act do not need to set for models after version 1.1.1

--num_classes do not need to set for models after version 1.1.4

Parameter Name	Conditional	Usage	Type	Description
--generate_name		File name	str	File name to initialize the model for saving purposes.
--latent		Enable latent diffusion model	bool	If enabled, the model will use latent diffusion.
--image_format		Generated image format	str	Generated image format, jpg/png/jpeg. Recommend to use png for better generation quality.
--num_images		Number of generated images	int	Number of images to generate.
--use_ema		Set the EMA model	bool	Set the EMA model for generation
--weight_path		Path to model weights	str	Path to the model weights file, required for network generation.
--autoencoder_ckpt		VAE model weight path	str	VAE model weight path.
--result_path		Save path	str	Path to save the generated images.
--sample		Sampling method	str	Set the sampling method type, currently supporting DDPM and DDIM. (No need to set for models after version 1.1.1)
--image_size		Input image size	int	Size of input images, adaptive input/output size. if class name is -1 and conditional is True, the model would output one image per class.
--use_gpu		Set the use GPU	int	Set the use GPU in generate, input is GPU's id.
--mode	✓	Guided generation	str	Select the generation mode, Category-guided or Text-guided generation.
--class_name	✓	Class name	str	Index of the class to generate images. if class name is -1, the model would output one image per class.
--text	✓	Text description	str	TODO: Text description for generation.
--cfg_scale	✓	Classifier-free guidance weight	int	Weight for classifier-free guidance interpolation, for better generation model performance.
--conditional		Enable conditional generation	bool	If enabled, allows custom configuration, such as modifying classes or classifier-free guidance interpolation weights.
--network		Training network	str	Set the training network, currently supporting UNet, CSPDarkUNet. (No need to set for models after version 1.1.1)
--act		Activation function	str	Activation function selection. Currently supports gelu, silu, relu, relu6 and lrelu. If you do not set the same activation function as the model, mosaic phenomenon will occur. (No need to set for models after version 1.1.1)
--num_classes	✓	Number of classes	int	Number of classes for classification. (No need to set for models after version 1.1.1)