Comments (8)
I think the multi scale input may not be produced by resize, it's produced from every to_rbg layer like https://github.com/JiauZhang/GigaGAN/blob/main/model.py#L254
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@gooobot ohh got it! so the generator will need to output all the
rgb
at all stages, and be able to pass it into the discriminator. i can get that done tomorrow morning, thank you! 🙏
if a real image taken from the dataset is fed into the discriminator, then it would be resized as the code does now?Yes, the generator will output images with [4x, 8x, ..., 32x, 64x] resolution when use_multi_scale is enabled.
That's what I mean! @lucidrains Thank you for you great effort!
from gigagan-pytorch.
@potato123-hash hello! so i'm actually doing that automatically here. let me know if i misunderstood that part of the paper
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@gooobot ohh got it! so the generator will need to output all the rgb
at all stages, and be able to pass it into the discriminator. i can get that done tomorrow morning, thank you! 🙏
if a real image taken from the dataset is fed into the discriminator, then it would be resized as the code does now?
from gigagan-pytorch.
@gooobot ohh got it! so the generator will need to output all the
rgb
at all stages, and be able to pass it into the discriminator. i can get that done tomorrow morning, thank you! 🙏if a real image taken from the dataset is fed into the discriminator, then it would be resized as the code does now?
Yes, the generator will output images with [4x, 8x, ..., 32x, 64x] resolution when use_multi_scale is enabled.
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@potato123-hash @gooobot ok, makes sense! will get this all fixed tomorrow! thank you for raising this issue
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@potato123-hash do you want to try 0.0.16 and see if that fits your intuition?
import torch
from gigagan_pytorch.gigagan_pytorch import (
TextEncoder,
Generator,
Discriminator,
StyleNetwork
)
text_encoder = TextEncoder(
dim = 512,
depth = 2
).cuda()
discr = Discriminator(
dim = 64,
dim_max = 512,
image_size = 256,
text_encoder = text_encoder,
use_glu = True,
num_skip_layers_excite = 4,
unconditional = False
).cuda()
style_network = StyleNetwork(
dim = 64,
depth = 4,
dim_text_latent = text_encoder.dim
).cuda()
generator = Generator(
dim = 64,
style_network = style_network,
text_encoder = text_encoder,
image_size = 256,
dim_max = 512,
use_glu = True,
num_skip_layers_excite = 4
).cuda()
# mock data
real_images = torch.randn(1, 3, 256, 256).cuda()
texts = ['a happy dog wagging her tail']
# generator
image, rgbs = generator(
texts = texts,
batch_size = 1,
return_all_rgbs = True
)
# discriminator
logits, *_ = discr(
image,
rgbs,
real_images = real_images,
texts = texts
)
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feel free to reopen if it isn't resolved!
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Related Issues (20)
- Confused about this project?
- NaN losses after hours of training (UPSAMPLER) HOT 16
- How to implement this model to enhance my input images? Do I have to train the model to use? HOT 2
- Weights of Gigagan Upscaler HOT 1
- Turn on/off gradients computation between generator/discriminator HOT 2
- Wrong order of resolutions list HOT 1
- to_rgb branch has only 1 learnable kernel HOT 7
- Gradient Penalty is very high in the start HOT 10
- How to use this model for SR ?
- Has Anyone Trained This Model Yet? HOT 2
- The text-to-image tasks
- Config to reproduce paper
- question about code in unet_upsampler.py HOT 1
- the loss became nan after a few train steps HOT 2
- [News] Videogigagan is published. HOT 4
- Standard Attention vs ViTGAN Attention HOT 1
- support 256x256 images HOT 1
- Loss turned `NaN` when custom training HOT 1
- How to adapt this network to image to image translation task
- Video? HOT 2
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