repo_description: The most powerful and modular diffusion model GUI, api and backend with a graph/nodes interface.
repo_topics: ai comfy comfyui python pytorch stable-diffusion
code: import torch
  from PIL import Image
  from comfy.cli_args import args, LatentPreviewMethod
  from comfy.taesd.taesd import TAESD
  from comfy.sd import VAE
  import comfy.model_management
  import folder_paths
  import comfy.utils
  import logging
  
  default_preview_method = args.preview_method
  
  MAX_PREVIEW_RESOLUTION = args.preview_size
  VIDEO_TAES = ["taehv", "lighttaew2_2", "lighttaew2_1", "lighttaehy1_5", "taeltx_2"]
  
  def preview_to_image(latent_image, do_scale=True):
          if do_scale:
              latents_ubyte = (((latent_image + 1.0) / 2.0).clamp(0, 1)  # change scale from -1..1 to 0..1
                                  .mul(0xFF)  # to 0..255
                                  )
          else:
              latents_ubyte = (latent_image.clamp(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 TAEHVPreviewerImpl(TAESDPreviewerImpl):
      def decode_latent_to_preview(self, x0):
          x_sample = self.taesd.decode(x0[:1, :, :1])[0][0]
          return preview_to_image(x_sample, do_scale=False)
  
  class Latent2RGBPreviewer(LatentPreviewer):
      def __init__(self, latent_rgb_factors, latent_rgb_factors_bias=None, latent_rgb_factors_reshape=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")
          self.latent_rgb_factors_reshape = latent_rgb_factors_reshape
  
      def decode_latent_to_preview(self, x0):
          if self.latent_rgb_factors_reshape is not None:
              x0 = self.latent_rgb_factors_reshape(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:
                  if latent_format.taesd_decoder_name in VIDEO_TAES:
                      taesd = VAE(comfy.utils.load_torch_file(taesd_decoder_path))
                      taesd.first_stage_model.show_progress_bar = False
                      previewer = TAEHVPreviewerImpl(taesd)
                  else:
                      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, latent_format.latent_rgb_factors_reshape)
      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
  
  def set_preview_method(override: str = None):
      if override and override != "default":
          method = LatentPreviewMethod.from_string(override)
          if method is not None:
              args.preview_method = method
              return
      args.preview_method = default_preview_method
  
  