Upload latent_preview.py

latent_preview.py

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+import torch
+from PIL import Image
+from comfy.cli_args import args, LatentPreviewMethod
+from comfy.taesd.taesd import TAESD
+import comfy.model_management
+import folder_paths
+import comfy.utils
+import logging
+
+MAX_PREVIEW_RESOLUTION = args.preview_size
+
+def preview_to_image(latent_image):
+        latents_ubyte = (((latent_image + 1.0) / 2.0).clamp(0, 1)  # change scale from -1..1 to 0..1
+                            .mul(0xFF)  # to 0..255
+                            )
+        if comfy.model_management.directml_enabled:
+                latents_ubyte = latents_ubyte.to(dtype=torch.uint8)
+        latents_ubyte = latents_ubyte.to(device="cpu", dtype=torch.uint8, non_blocking=comfy.model_management.device_supports_non_blocking(latent_image.device))
+
+        return Image.fromarray(latents_ubyte.numpy())
+
+class LatentPreviewer:
+    def decode_latent_to_preview(self, x0):
+        pass
+
+    def decode_latent_to_preview_image(self, preview_format, x0):
+        preview_image = self.decode_latent_to_preview(x0)
+        return ("JPEG", preview_image, MAX_PREVIEW_RESOLUTION)
+
+class TAESDPreviewerImpl(LatentPreviewer):
+    def __init__(self, taesd):
+        self.taesd = taesd
+
+    def decode_latent_to_preview(self, x0):
+        x_sample = self.taesd.decode(x0[:1])[0].movedim(0, 2)
+        return preview_to_image(x_sample)
+
+
+class Latent2RGBPreviewer(LatentPreviewer):
+    def __init__(self, latent_rgb_factors, latent_rgb_factors_bias=None):
+        self.latent_rgb_factors = torch.tensor(latent_rgb_factors, device="cpu").transpose(0, 1)
+        self.latent_rgb_factors_bias = None
+        if latent_rgb_factors_bias is not None:
+            self.latent_rgb_factors_bias = torch.tensor(latent_rgb_factors_bias, device="cpu")
+
+    def decode_latent_to_preview(self, x0):
+        self.latent_rgb_factors = self.latent_rgb_factors.to(dtype=x0.dtype, device=x0.device)
+        if self.latent_rgb_factors_bias is not None:
+            self.latent_rgb_factors_bias = self.latent_rgb_factors_bias.to(dtype=x0.dtype, device=x0.device)
+
+        if x0.ndim == 5:
+            x0 = x0[0, :, 0]
+        else:
+            x0 = x0[0]
+
+        latent_image = torch.nn.functional.linear(x0.movedim(0, -1), self.latent_rgb_factors, bias=self.latent_rgb_factors_bias)
+        # latent_image = x0[0].permute(1, 2, 0) @ self.latent_rgb_factors
+
+        return preview_to_image(latent_image)
+
+
+def get_previewer(device, latent_format):
+    previewer = None
+    method = args.preview_method
+    if method != LatentPreviewMethod.NoPreviews:
+        # TODO previewer methods
+        taesd_decoder_path = None
+        if latent_format.taesd_decoder_name is not None:
+            taesd_decoder_path = next(
+                (fn for fn in folder_paths.get_filename_list("vae_approx")
+                    if fn.startswith(latent_format.taesd_decoder_name)),
+                ""
+            )
+            taesd_decoder_path = folder_paths.get_full_path("vae_approx", taesd_decoder_path)
+
+        if method == LatentPreviewMethod.Auto:
+            method = LatentPreviewMethod.Latent2RGB
+
+        if method == LatentPreviewMethod.TAESD:
+            if taesd_decoder_path:
+                taesd = TAESD(None, taesd_decoder_path, latent_channels=latent_format.latent_channels).to(device)
+                previewer = TAESDPreviewerImpl(taesd)
+            else:
+                logging.warning("Warning: TAESD previews enabled, but could not find models/vae_approx/{}".format(latent_format.taesd_decoder_name))
+
+        if previewer is None:
+            if latent_format.latent_rgb_factors is not None:
+                previewer = Latent2RGBPreviewer(latent_format.latent_rgb_factors, latent_format.latent_rgb_factors_bias)
+    return previewer
+
+def prepare_callback(model, steps, x0_output_dict=None):
+    preview_format = "JPEG"
+    if preview_format not in ["JPEG", "PNG"]:
+        preview_format = "JPEG"
+
+    previewer = get_previewer(model.load_device, model.model.latent_format)
+
+    pbar = comfy.utils.ProgressBar(steps)
+    def callback(step, x0, x, total_steps):
+        if x0_output_dict is not None:
+            x0_output_dict["x0"] = x0
+
+        preview_bytes = None
+        if previewer:
+            preview_bytes = previewer.decode_latent_to_preview_image(preview_format, x0)
+        pbar.update_absolute(step + 1, total_steps, preview_bytes)
+    return callback
+