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Hypernetwork-MonkeyPatch-Extension
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fix weight usage

pull/51/head
aria1th 2023-03-02 19:49:25 +09:00
parent c3ada0eeb4
commit 95b9506d33
1 changed files with 2 additions and 10 deletions
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12
patches/external_pr/dataset.py
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@ -116,21 +116,19 @@ class PersonalizedBase(Dataset):
npimage = (npimage / 127.5 - 1.0).astype(np.float32)
torchdata = torch.from_numpy(npimage).permute(2, 0, 1).to(device=device, dtype=torch.float32)
latent_sample = None
latent_sample = model.get_first_stage_encoding(latent_dist).squeeze().to(devices.cpu)
weight = torch.ones_like(latent_sample)
with torch.autocast("cuda"):
latent_dist = model.encode_first_stage(torchdata.unsqueeze(dim=0))
if latent_sampling_method == "once" or (
latent_sampling_method == "deterministic" and not isinstance(latent_dist,
DiagonalGaussianDistribution)):
latent_sample = model.get_first_stage_encoding(latent_dist).squeeze().to(devices.cpu)
latent_sampling_method = "once"
entry = DatasetEntry(filename=path, filename_text=filename_text, latent_sample=latent_sample)
elif latent_sampling_method == "deterministic":
# Works only for DiagonalGaussianDistribution
latent_dist.std = 0
latent_sample = model.get_first_stage_encoding(latent_dist).squeeze().to(devices.cpu)
entry = DatasetEntry(filename=path, filename_text=filename_text, latent_sample=latent_sample)
elif latent_sampling_method == "random":
if latent_sampling_std != -1:
@ -144,8 +142,6 @@ class PersonalizedBase(Dataset):
if use_weight and 'A' in image.getbands():
alpha_channel = image.getchannel('A')
if use_weight and alpha_channel is not None:
if latent_sample is None:
latent_sample = model.get_first_stage_encoding(latent_dist).squeeze().to(devices.cpu)
channels, *latent_size = latent_sample.shape
weight_img = alpha_channel.resize(latent_size)
npweight = np.array(weight_img).astype(np.float32)
@ -155,12 +151,8 @@ class PersonalizedBase(Dataset):
weight -= weight.min()
weight /= weight.mean()
elif use_weight:
if latent_sample is None:
latent_sample = model.get_first_stage_encoding(latent_dist).squeeze().to(devices.cpu)
#If an image does not have a alpha channel, add a ones weight map anyway so we can stack it later
weight = torch.ones_like(latent_sample)
else:
weight = None
entry.weight = weight
if not (self.tag_drop_out != 0 or self.shuffle_tags):
entry.cond_text = self.create_text(filename_text)