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svdquant and others stuff

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Signed-off-by: Vladimir Mandic <mandic00@live.com>
This commit is contained in:
Vladimir Mandic
2025-04-17 22:14:11 -04:00
parent 1fd746c75e
commit cbef571f90
7 changed files with 16 additions and 6 deletions
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modules/processing_vae.py
+2 -2
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@@ -2,7 +2,6 @@ import os
import time
import numpy as np
import torch
import torchvision.transforms.functional as TF
from modules import shared, devices, sd_models, sd_vae, sd_vae_taesd, errors
@@ -316,6 +315,7 @@ def vae_decode(latents, model, output_type='np', vae_type='Full', width=None, he
def vae_encode(image, model, vae_type='Full'): # pylint: disable=unused-variable
import torchvision.transforms.functional as f
if shared.state.interrupted or shared.state.skipped:
return []
if not hasattr(model, 'vae') and hasattr(model, 'pipe'):
@@ -323,7 +323,7 @@ def vae_encode(image, model, vae_type='Full'): # pylint: disable=unused-variable
if not hasattr(model, 'vae'):
shared.log.error('VAE not found in model')
return []
tensor = TF.to_tensor(image.convert("RGB")).unsqueeze(0).to(devices.device, devices.dtype_vae)
tensor = f.to_tensor(image.convert("RGB")).unsqueeze(0).to(devices.device, devices.dtype_vae)
if vae_type == 'Full':
tensor = tensor * 2 - 1
latents = full_vae_encode(image=tensor, model=shared.sd_model)