Comments (7)
this doesnt fix the issue i'm talking about:
to_rgb = AdaptiveConv2DMod(dim_out, channels, 1, num_conv_kernels = 1, demod = False)
num_conv_kernels is always 1 for rgb regardless of Generator setting
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but you still compute kernel weights for rgb branch
style_embed_split_dims.extend([
dim_in, # for first conv in resnet block
dim_kernel_mod, # first conv kernel selection
dim_out, # second conv in resnet block
dim_kernel_mod, # second conv kernel selection
dim_out, # to RGB conv
dim_kernel_mod, # RGB conv kernel selection
])
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but i agree that this path looks super convoluted :) i would rather move mod+kernel_mod projection inside AdaptiveConv2DMod
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gigagan-pytorch/gigagan_pytorch/gigagan_pytorch.py
Lines 896 to 897 in 9a364dd
0
here
i may move all that modulations into the adaptive conv2d mod at some point
its too confusing the way i have it currently
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@inspirit yup, that is intentional, based on point 2 in #33
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@inspirit 🤦♂️ yes you are right
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@inspirit yes i'll get around to it 😅
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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
- 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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