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initial SD3 support

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AUTOMATIC1111
2024-06-16 08:04:31 +03:00
parent a7116aa9a1
commit 5b2a60b8e2
14 changed files with 333 additions and 44 deletions
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27
modules/sd_vae_approx.py
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@@ -8,9 +8,9 @@ sd_vae_approx_models = {}
class VAEApprox(nn.Module):
def __init__(self):
def __init__(self, latent_channels=4):
super(VAEApprox, self).__init__()
self.conv1 = nn.Conv2d(4, 8, (7, 7))
self.conv1 = nn.Conv2d(latent_channels, 8, (7, 7))
self.conv2 = nn.Conv2d(8, 16, (5, 5))
self.conv3 = nn.Conv2d(16, 32, (3, 3))
self.conv4 = nn.Conv2d(32, 64, (3, 3))
@@ -40,7 +40,13 @@ def download_model(model_path, model_url):
def model():
model_name = "vaeapprox-sdxl.pt" if getattr(shared.sd_model, 'is_sdxl', False) else "model.pt"
if shared.sd_model.is_sd3:
model_name = "vaeapprox-sd3.pt"
elif shared.sd_model.is_sdxl:
model_name = "vaeapprox-sdxl.pt"
else:
model_name = "model.pt"
loaded_model = sd_vae_approx_models.get(model_name)
if loaded_model is None:
@@ -52,7 +58,7 @@ def model():
model_path = os.path.join(paths.models_path, "VAE-approx", model_name)
download_model(model_path, 'https://github.com/AUTOMATIC1111/stable-diffusion-webui/releases/download/v1.0.0-pre/' + model_name)
loaded_model = VAEApprox()
loaded_model = VAEApprox(latent_channels=shared.sd_model.latent_channels)
loaded_model.load_state_dict(torch.load(model_path, map_location='cpu' if devices.device.type != 'cuda' else None))
loaded_model.eval()
loaded_model.to(devices.device, devices.dtype)
@@ -64,7 +70,18 @@ def model():
def cheap_approximation(sample):
# https://discuss.huggingface.co/t/decoding-latents-to-rgb-without-upscaling/23204/2
if shared.sd_model.is_sdxl:
if shared.sd_model.is_sd3:
coeffs = [
[-0.0645, 0.0177, 0.1052], [ 0.0028, 0.0312, 0.0650],
[ 0.1848, 0.0762, 0.0360], [ 0.0944, 0.0360, 0.0889],
[ 0.0897, 0.0506, -0.0364], [-0.0020, 0.1203, 0.0284],
[ 0.0855, 0.0118, 0.0283], [-0.0539, 0.0658, 0.1047],
[-0.0057, 0.0116, 0.0700], [-0.0412, 0.0281, -0.0039],
[ 0.1106, 0.1171, 0.1220], [-0.0248, 0.0682, -0.0481],
[ 0.0815, 0.0846, 0.1207], [-0.0120, -0.0055, -0.0867],
[-0.0749, -0.0634, -0.0456], [-0.1418, -0.1457, -0.1259],
]
elif shared.sd_model.is_sdxl:
coeffs = [
[ 0.3448, 0.4168, 0.4395],
[-0.1953, -0.0290, 0.0250],