Add Tiny AE live preview

modules/sd_samplers_common.py

@@ -2,7 +2,7 @@ from collections import namedtuple
 import numpy as np
 import torch
 from PIL import Image
-from modules import devices, processing, images, sd_vae_approx
+from modules import devices, processing, images, sd_vae_approx, sd_vae_taesd
 
 from modules.shared import opts, state
 import modules.shared as shared
@@ -22,21 +22,26 @@ def setup_img2img_steps(p, steps=None):
     return steps, t_enc
 
 
-approximation_indexes = {"Full": 0, "Approx NN": 1, "Approx cheap": 2}
+approximation_indexes = {"Full": 0, "Tiny AE": 1, "Approx NN": 2, "Approx cheap": 3}
 
 
 def single_sample_to_image(sample, approximation=None):
     if approximation is None:
         approximation = approximation_indexes.get(opts.show_progress_type, 0)
 
-    if approximation == 2:
+    if approximation == 1:
+        x_sample = sd_vae_taesd.decode()(sample.to(devices.device, devices.dtype).unsqueeze(0))[0].detach()
+        x_sample = sd_vae_taesd.TAESD.unscale_latents(x_sample)
+        x_sample = torch.clamp((x_sample * 0.25) + 0.5, 0, 1)
+    else:
+        if approximation == 3:
             x_sample = sd_vae_approx.cheap_approximation(sample)
-    elif approximation == 1:
+        elif approximation == 2:
             x_sample = sd_vae_approx.model()(sample.to(devices.device, devices.dtype).unsqueeze(0))[0].detach()
         else:
             x_sample = processing.decode_first_stage(shared.sd_model, sample.unsqueeze(0))[0]
-
         x_sample = torch.clamp((x_sample + 1.0) / 2.0, min=0.0, max=1.0)
+
     x_sample = 255. * np.moveaxis(x_sample.cpu().numpy(), 0, 2)
     x_sample = x_sample.astype(np.uint8)
     return Image.fromarray(x_sample)

modules/sd_vae_taesd.py

@@ -0,0 +1,76 @@
+"""
+Tiny AutoEncoder for Stable Diffusion
+(DNN for encoding / decoding SD's latent space)
+
+https://github.com/madebyollin/taesd
+"""
+import os
+import torch
+import torch.nn as nn
+
+from modules import devices, paths_internal
+
+sd_vae_taesd = None
+
+
+def conv(n_in, n_out, **kwargs):
+    return nn.Conv2d(n_in, n_out, 3, padding=1, **kwargs)
+
+
+class Clamp(nn.Module):
+    @staticmethod
+    def forward(x):
+        return torch.tanh(x / 3) * 3
+
+
+class Block(nn.Module):
+    def __init__(self, n_in, n_out):
+        super().__init__()
+        self.conv = nn.Sequential(conv(n_in, n_out), nn.ReLU(), conv(n_out, n_out), nn.ReLU(), conv(n_out, n_out))
+        self.skip = nn.Conv2d(n_in, n_out, 1, bias=False) if n_in != n_out else nn.Identity()
+        self.fuse = nn.ReLU()
+
+    def forward(self, x):
+        return self.fuse(self.conv(x) + self.skip(x))
+
+
+def decoder():
+    return nn.Sequential(
+        Clamp(), conv(4, 64), nn.ReLU(),
+        Block(64, 64), Block(64, 64), Block(64, 64), nn.Upsample(scale_factor=2), conv(64, 64, bias=False),
+        Block(64, 64), Block(64, 64), Block(64, 64), nn.Upsample(scale_factor=2), conv(64, 64, bias=False),
+        Block(64, 64), Block(64, 64), Block(64, 64), nn.Upsample(scale_factor=2), conv(64, 64, bias=False),
+        Block(64, 64), conv(64, 3),
+    )
+
+
+class TAESD(nn.Module):
+    latent_magnitude = 2
+    latent_shift = 0.5
+
+    def __init__(self, decoder_path="taesd_decoder.pth"):
+        """Initialize pretrained TAESD on the given device from the given checkpoints."""
+        super().__init__()
+        self.decoder = decoder()
+        self.decoder.load_state_dict(
+            torch.load(decoder_path, map_location='cpu' if devices.device.type != 'cuda' else None))
+
+    @staticmethod
+    def unscale_latents(x):
+        """[0, 1] -> raw latents"""
+        return x.sub(TAESD.latent_shift).mul(2 * TAESD.latent_magnitude)
+
+
+def decode():
+    global sd_vae_taesd
+
+    if sd_vae_taesd is None:
+        model_path = os.path.join(paths_internal.models_path, "VAE-approx", "taesd_decoder.pth")
+        if os.path.exists(model_path):
+            sd_vae_taesd = TAESD(model_path)
+            sd_vae_taesd.eval()
+            sd_vae_taesd.to(devices.device, devices.dtype)
+        else:
+            raise FileNotFoundError('Tiny AE mdoel not found')
+
+    return sd_vae_taesd.decoder

modules/shared.py

@@ -425,7 +425,7 @@ options_templates.update(options_section(('ui', "Live previews"), {
     "live_previews_enable": OptionInfo(True, "Show live previews of the created image"),
     "show_progress_grid": OptionInfo(True, "Show previews of all images generated in a batch as a grid"),
     "show_progress_every_n_steps": OptionInfo(10, "Show new live preview image every N sampling steps. Set to -1 to show after completion of batch.", gr.Slider, {"minimum": -1, "maximum": 32, "step": 1}),
-    "show_progress_type": OptionInfo("Approx NN", "Image creation progress preview mode", gr.Radio, {"choices": ["Full", "Approx NN", "Approx cheap"]}),
+    "show_progress_type": OptionInfo("Tiny AE", "Image creation progress preview mode", gr.Radio, {"choices": ["Full", "Tiny AE", "Approx NN", "Approx cheap"]}),
     "live_preview_content": OptionInfo("Prompt", "Live preview subject", gr.Radio, {"choices": ["Combined", "Prompt", "Negative prompt"]}),
     "live_preview_refresh_period": OptionInfo(1000, "Progressbar/preview update period, in milliseconds")
 }))

webui.py

@@ -144,10 +144,21 @@ Use --skip-version-check commandline argument to disable this check.
             """.strip())
 
 
+def check_taesd():
+    from modules.paths_internal import models_path
+
+    model_url = 'https://github.com/madebyollin/taesd/raw/main/taesd_decoder.pth'
+    model_path = os.path.join(models_path, "VAE-approx", "taesd_decoder.pth")
+    if not os.path.exists(model_path):
+        print('download taesd model')
+        torch.hub.download_url_to_file(model_url, os.path.dirname(model_path))
+
+
 def initialize():
     fix_asyncio_event_loop_policy()
 
     check_versions()
+    check_taesd()
 
     extensions.list_extensions()
     localization.list_localizations(cmd_opts.localizations_dir)