Add high quality preview support for Flux2 latents (#13496)

comfy/latent_formats.py

@@ -224,6 +224,7 @@ class Flux2(LatentFormat):
 
         self.latent_rgb_factors_bias = [-0.0329, -0.0718, -0.0851]
         self.latent_rgb_factors_reshape = lambda t: t.reshape(t.shape[0], 32, 2, 2, t.shape[-2], t.shape[-1]).permute(0, 1, 4, 2, 5, 3).reshape(t.shape[0], 32, t.shape[-2] * 2, t.shape[-1] * 2)
+        self.taesd_decoder_name = "taef2_decoder"
 
     def process_in(self, latent):
         return latent

comfy/sd.py

@@ -479,7 +479,10 @@ class VAE:
                                                             encoder_config={'target': "comfy.ldm.modules.diffusionmodules.model.Encoder", 'params': encoder_config},
                                                             decoder_config={'target': "comfy.ldm.modules.temporal_ae.VideoDecoder", 'params': decoder_config})
             elif "taesd_decoder.1.weight" in sd:
-                self.latent_channels = sd["taesd_decoder.1.weight"].shape[1]
+                if isinstance(metadata, dict) and "tae_latent_channels" in metadata:
+                    self.latent_channels = metadata["tae_latent_channels"]
+                else:
+                    self.latent_channels = sd["taesd_decoder.1.weight"].shape[1]
                 self.first_stage_model = comfy.taesd.taesd.TAESD(latent_channels=self.latent_channels)
             elif "vquantizer.codebook.weight" in sd: #VQGan: stage a of stable cascade
                 self.first_stage_model = StageA()

comfy/taesd/taesd.py

@@ -17,32 +17,79 @@ class Clamp(nn.Module):
         return torch.tanh(x / 3) * 3
 
 class Block(nn.Module):
-    def __init__(self, n_in, n_out):
+    def __init__(self, n_in: int, n_out: int, use_midblock_gn: bool = False):
         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 = comfy.ops.disable_weight_init.Conv2d(n_in, n_out, 1, bias=False) if n_in != n_out else nn.Identity()
         self.fuse = nn.ReLU()
-    def forward(self, x):
+        if not use_midblock_gn:
+            self.pool = None
+            return
+        n_gn = n_in * 4
+        self.pool = nn.Sequential(
+            comfy.ops.disable_weight_init.Conv2d(n_in, n_gn, 1, bias=False),
+            comfy.ops.disable_weight_init.GroupNorm(4, n_gn),
+            nn.ReLU(inplace=True),
+            comfy.ops.disable_weight_init.Conv2d(n_gn, n_in, 1, bias=False),
+        )
+
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
+        if self.pool is not None:
+            x = x + self.pool(x)
         return self.fuse(self.conv(x) + self.skip(x))
 
-def Encoder(latent_channels=4):
-    return nn.Sequential(
-        conv(3, 64), Block(64, 64),
-        conv(64, 64, stride=2, bias=False), Block(64, 64), Block(64, 64), Block(64, 64),
-        conv(64, 64, stride=2, bias=False), Block(64, 64), Block(64, 64), Block(64, 64),
-        conv(64, 64, stride=2, bias=False), Block(64, 64), Block(64, 64), Block(64, 64),
-        conv(64, latent_channels),
-    )
+class Encoder(nn.Sequential):
+    def __init__(self, latent_channels: int = 4, use_gn: bool = False):
+        super().__init__(
+            conv(3, 64), Block(64, 64),
+            conv(64, 64, stride=2, bias=False), Block(64, 64), Block(64, 64), Block(64, 64),
+            conv(64, 64, stride=2, bias=False), Block(64, 64), Block(64, 64), Block(64, 64),
+            conv(64, 64, stride=2, bias=False), Block(64, 64, use_gn), Block(64, 64, use_gn), Block(64, 64, use_gn),
+            conv(64, latent_channels),
+        )
 
+class Decoder(nn.Sequential):
+    def __init__(self, latent_channels: int = 4, use_gn: bool = False):
+        super().__init__(
+            Clamp(), conv(latent_channels, 64), nn.ReLU(),
+            Block(64, 64, use_gn), Block(64, 64, use_gn), Block(64, 64, use_gn), 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 DecoderFlux2(Decoder):
+    def __init__(self, latent_channels: int = 128, use_gn: bool = True):
+        if latent_channels != 128 or not use_gn:
+            raise ValueError("Unexpected parameters for Flux2 TAE module")
+        super().__init__(latent_channels=32, use_gn=True)
+
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
+        B, C, H, W = x.shape
+        x = (
+            x
+            .reshape(B, 32, 2, 2, H, W)
+            .permute(0, 1, 4, 2, 5, 3)
+            .reshape(B, 32, H * 2, W * 2)
+        )
+        return super().forward(x)
+
+class EncoderFlux2(Encoder):
+    def __init__(self, latent_channels: int = 128, use_gn: bool = True):
+        if latent_channels != 128 or not use_gn:
+            raise ValueError("Unexpected parameters for Flux2 TAE module")
+        super().__init__(latent_channels=32, use_gn=True)
+
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
+        result = super().forward(x)
+        B, C, H, W = result.shape
+        return (
+            result
+            .reshape(B, C, H // 2, 2, W // 2, 2)
+            .permute(0, 1, 3, 5, 2, 4)
+            .reshape(B, 128, H // 2, W // 2)
+        )
 
-def Decoder(latent_channels=4):
-    return nn.Sequential(
-        Clamp(), conv(latent_channels, 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 = 3
@@ -51,8 +98,15 @@ class TAESD(nn.Module):
     def __init__(self, encoder_path=None, decoder_path=None, latent_channels=4):
         """Initialize pretrained TAESD on the given device from the given checkpoints."""
         super().__init__()
-        self.taesd_encoder = Encoder(latent_channels=latent_channels)
-        self.taesd_decoder = Decoder(latent_channels=latent_channels)
+        if latent_channels == 128:
+            encoder_class = EncoderFlux2
+            decoder_class = DecoderFlux2
+        else:
+            encoder_class = Encoder
+            decoder_class = Decoder
+        self.taesd_encoder = encoder_class(latent_channels=latent_channels)
+        self.taesd_decoder = decoder_class(latent_channels=latent_channels)
+
         self.vae_scale = torch.nn.Parameter(torch.tensor(1.0))
         self.vae_shift = torch.nn.Parameter(torch.tensor(0.0))
         if encoder_path is not None:
@@ -61,19 +115,19 @@ class TAESD(nn.Module):
             self.taesd_decoder.load_state_dict(comfy.utils.load_torch_file(decoder_path, safe_load=True))
 
     @staticmethod
-    def scale_latents(x):
+    def scale_latents(x: torch.Tensor) -> torch.Tensor:
         """raw latents -> [0, 1]"""
         return x.div(2 * TAESD.latent_magnitude).add(TAESD.latent_shift).clamp(0, 1)
 
     @staticmethod
-    def unscale_latents(x):
+    def unscale_latents(x: torch.Tensor) -> torch.Tensor:
         """[0, 1] -> raw latents"""
         return x.sub(TAESD.latent_shift).mul(2 * TAESD.latent_magnitude)
 
-    def decode(self, x):
+    def decode(self, x: torch.Tensor) -> torch.Tensor:
         x_sample = self.taesd_decoder((x - self.vae_shift) * self.vae_scale)
         x_sample = x_sample.sub(0.5).mul(2)
         return x_sample
 
-    def encode(self, x):
+    def encode(self, x: torch.Tensor) -> torch.Tensor:
         return (self.taesd_encoder(x * 0.5 + 0.5) / self.vae_scale) + self.vae_shift

nodes.py

@@ -728,50 +728,26 @@ class LoraLoaderModelOnly(LoraLoader):
 
 class VAELoader:
     video_taes = ["taehv", "lighttaew2_2", "lighttaew2_1", "lighttaehy1_5", "taeltx_2"]
-    image_taes = ["taesd", "taesdxl", "taesd3", "taef1"]
+    image_taes = ["taesd", "taesdxl", "taesd3", "taef1", "taef2"]
+
     @staticmethod
     def vae_list(s):
         vaes = folder_paths.get_filename_list("vae")
         approx_vaes = folder_paths.get_filename_list("vae_approx")
-        sdxl_taesd_enc = False
-        sdxl_taesd_dec = False
-        sd1_taesd_enc = False
-        sd1_taesd_dec = False
-        sd3_taesd_enc = False
-        sd3_taesd_dec = False
-        f1_taesd_enc = False
-        f1_taesd_dec = False
-
+        have_img_encoder, have_img_decoder = set(), set()
         for v in approx_vaes:
-            if v.startswith("taesd_decoder."):
-                sd1_taesd_dec = True
-            elif v.startswith("taesd_encoder."):
-                sd1_taesd_enc = True
-            elif v.startswith("taesdxl_decoder."):
-                sdxl_taesd_dec = True
-            elif v.startswith("taesdxl_encoder."):
-                sdxl_taesd_enc = True
-            elif v.startswith("taesd3_decoder."):
-                sd3_taesd_dec = True
-            elif v.startswith("taesd3_encoder."):
-                sd3_taesd_enc = True
-            elif v.startswith("taef1_encoder."):
-                f1_taesd_dec = True
-            elif v.startswith("taef1_decoder."):
-                f1_taesd_enc = True
-            else:
+            parts = v.split("_", 1)
+            if len(parts) != 2 or parts[0] not in s.image_taes:
                 for tae in s.video_taes:
                     if v.startswith(tae):
                         vaes.append(v)
-
-        if sd1_taesd_dec and sd1_taesd_enc:
-            vaes.append("taesd")
-        if sdxl_taesd_dec and sdxl_taesd_enc:
-            vaes.append("taesdxl")
-        if sd3_taesd_dec and sd3_taesd_enc:
-            vaes.append("taesd3")
-        if f1_taesd_dec and f1_taesd_enc:
-            vaes.append("taef1")
+                        break
+                continue
+            if parts[1].startswith("encoder."):
+                have_img_encoder.add(parts[0])
+            elif parts[1].startswith("decoder."):
+                have_img_decoder.add(parts[0])
+        vaes += [k for k in have_img_decoder if k in have_img_encoder]
         vaes.append("pixel_space")
         return vaes
 
@@ -827,6 +803,11 @@ class VAELoader:
             else:
                 vae_path = folder_paths.get_full_path_or_raise("vae", vae_name)
             sd, metadata = comfy.utils.load_torch_file(vae_path, return_metadata=True)
+        if vae_name == "taef2":
+            if metadata is None:
+                metadata = {"tae_latent_channels": 128}
+            else:
+                metadata["tae_latent_channels"] = 128
         vae = comfy.sd.VAE(sd=sd, metadata=metadata)
         vae.throw_exception_if_invalid()
         return (vae,)