103 lines
4.0 KiB
Python
103 lines
4.0 KiB
Python
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) # change scale from -1..1 to 0..1
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.mul(0xFF) # to 0..255
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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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latent_image = torch.nn.functional.linear(x0[0].permute(1, 2, 0), self.latent_rgb_factors, bias=self.latent_rgb_factors_bias)
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# latent_image = x0[0].permute(1, 2, 0) @ self.latent_rgb_factors
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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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# TODO previewer methods
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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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