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add XL support for live previews: approx and TAESD

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AUTOMATIC1111
2023-07-13 17:24:54 +03:00
parent 6f23da603d
commit b8159d0919
3 changed files with 40 additions and 25 deletions
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37
modules/sd_vae_approx.py
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@@ -2,9 +2,9 @@ import os
import torch
from torch import nn
from modules import devices, paths
from modules import devices, paths, shared
sd_vae_approx_model = None
sd_vae_approx_models = {}
class VAEApprox(nn.Module):
@@ -31,19 +31,34 @@ class VAEApprox(nn.Module):
return x
def download_model(model_path, model_url):
if not os.path.exists(model_path):
os.makedirs(os.path.dirname(model_path), exist_ok=True)
print(f'Downloading VAEApprox model to: {model_path}')
torch.hub.download_url_to_file(model_url, model_path)
def model():
global sd_vae_approx_model
model_name = "vaeapprox-sdxl.pt" if getattr(shared.sd_model, 'is_sdxl', False) else "model.pt"
loaded_model = sd_vae_approx_models.get(model_name)
if sd_vae_approx_model is None:
model_path = os.path.join(paths.models_path, "VAE-approx", "model.pt")
sd_vae_approx_model = VAEApprox()
if loaded_model is None:
model_path = os.path.join(paths.models_path, "VAE-approx", model_name)
if not os.path.exists(model_path):
model_path = os.path.join(paths.script_path, "models", "VAE-approx", "model.pt")
sd_vae_approx_model.load_state_dict(torch.load(model_path, map_location='cpu' if devices.device.type != 'cuda' else None))
sd_vae_approx_model.eval()
sd_vae_approx_model.to(devices.device, devices.dtype)
model_path = os.path.join(paths.script_path, "models", "VAE-approx", model_name)
return sd_vae_approx_model
if not os.path.exists(model_path):
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.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)
sd_vae_approx_models[model_name] = loaded_model
return loaded_model
def cheap_approximation(sample):