How to use generator to generate random image? about pixel2style2pixel

[yuval-alaluf]

Hi @zerollzeng ,
First, please note that the implementation for the generator was taken from rosinality.

With that said, in our repo we provide code that you can adopt for generating random samples using our pSp network and StyleGAN generator. Specifically, please refer to our inference.py script for an implementation for generating a random latent vector that we use for style mixing here:

pixel2style2pixel/scripts/inference.py

Lines 117 to 121 in ac7da53

	# get latent vector to inject into our input image
	vec_to_inject = np.random.randn(1, 512).astype('float32')
	_, latent_to_inject = net(torch.from_numpy(vec_to_inject).to("cuda"),
	input_code=True,
	return_latents=True)

Although here we only store latent_to_inject, you can make a few changes that will store the generated corresponding random face image for the random latent vector.

For example, the following code should be a good starting point for your needs:

n_images_to_generate = 10
generated_images = []
for _ in range(n_images_to_generate):
        random_vec = np.random.randn(1, 512).astype('float32')
        random_image, _ = net(torch.from_numpy(random_vec).to("cuda"), input_code=True, return_latents=True)
        generated_images.append(random_image)

Here, we generate random w vectors which are fed into pSp to randomly generate face images of size 256x256.

Please let me know if this is clear and if you have further questions.

[zerollzeng]

Oh thanks for your detailed explanation, I understand it now ::smile::
I'm reading your paper and code, doing some experiment, your jobs is fantastic! I think it would be great if we keep this issue open so that we can continue the discussion instead of open a new issue 👍

[zerollzeng]

I'm going to train your encoder network from scratch, here are some question I'm interested:

1. What's the difference between GradualStyleEncoder, BackboneEncoderUsingLastLayerIntoW, and BackboneEncoderUsingLastLayerIntoWPlus?
2. Does the num_layer's (50, 100, 152) affect the reconstruct result a lot?

[yuval-alaluf]

Thank you for the kind words!
Regarding your questions:

1. These architectures refer to official pSp encoder, the Naive W+ encoder, and W Encoder mentioned in the paper, respectively. The pSp architecture is explained in Section 3 in the paper and is illustrated in Figure 2. For an explanation of the two later architectures, please refer to the results shown in Section 4.1 (StyleGAN Inversion) in the paper where we discuss the ablation study performed.
2. In our experiments, we did not change the num_layers from the default value of 50. That is, in all experiments, we use a pretrained IR-SE50 model (which we linked in the README).

Please let me know if you have further questions.

[yuval-alaluf]

Closing due to inactivity.