Delete latent_preview.py

latent_preview.py

@@ -1,108 +0,0 @@
-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
-