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03_generation.md

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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, --generate_name, --num_images, --num_classes(No need to set for models after version 1.1.4), --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/tools directory:

    Conditional Generation Command (version > 1.1.1)

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

    Unconditional Generation Command (version > 1.1.1)

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

    Conditional Generation Command (version <= 1.1.1)

    python generate.py --conditional --generate_name df --num_images 8 --class_name 0 --image_size 64 --weight_path /your/path/weight/model.pt --sample ddpm --network unet --act gelu

    Unconditional Generation Command (version <= 1.1.1)

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

  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, "weight_path", "/your/model/path/model.pt")
setattr(args, "result_path", "/your/save/path/")
# ...
# 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
--conditional Enable conditional generation bool If enabled, allows custom configuration, such as modifying classes or classifier-free guidance interpolation weights
--generate_name File name str File name to initialize the model for saving purposes
--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.
--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
--weight_path Path to model weights str Path to the model weights file, required for network generation
--result_path Save path str Path to save the generated images
--use_gpu Set the use GPU int Set the use GPU in generate, input is GPU's id
--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)
--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)
--class_name ✓ Class name int Index of the class to generate images. if class name is -1, the model would output one image per class.
--cfg_scale ✓ Classifier-free guidance weight int Weight for classifier-free guidance interpolation, for better generation model performance