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