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| import torch | |
| from PIL import Image | |
| import struct | |
| import numpy as np | |
| from fcbh.cli_args import args, LatentPreviewMethod | |
| from fcbh.taesd.taesd import TAESD | |
| import folder_paths | |
| import fcbh.utils | |
| MAX_PREVIEW_RESOLUTION = 512 | |
| 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].detach() | |
| x_sample = torch.clamp((x_sample + 1.0) / 2.0, min=0.0, max=1.0) | |
| x_sample = 255. * np.moveaxis(x_sample.cpu().numpy(), 0, 2) | |
| x_sample = x_sample.astype(np.uint8) | |
| preview_image = Image.fromarray(x_sample) | |
| return preview_image | |
| class Latent2RGBPreviewer(LatentPreviewer): | |
| def __init__(self, latent_rgb_factors): | |
| self.latent_rgb_factors = torch.tensor(latent_rgb_factors, device="cpu") | |
| def decode_latent_to_preview(self, x0): | |
| latent_image = x0[0].permute(1, 2, 0).cpu() @ self.latent_rgb_factors | |
| latents_ubyte = (((latent_image + 1) / 2) | |
| .clamp(0, 1) # change scale from -1..1 to 0..1 | |
| .mul(0xFF) # to 0..255 | |
| .byte()).cpu() | |
| return Image.fromarray(latents_ubyte.numpy()) | |
| 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 taesd_decoder_path: | |
| method = LatentPreviewMethod.TAESD | |
| if method == LatentPreviewMethod.TAESD: | |
| if taesd_decoder_path: | |
| taesd = TAESD(None, taesd_decoder_path).to(device) | |
| previewer = TAESDPreviewerImpl(taesd) | |
| else: | |
| print("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) | |
| 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 = fcbh.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 | |