95 lines
3.2 KiB
Python
95 lines
3.2 KiB
Python
import torch
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from PIL import Image, ImageOps
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from io import BytesIO
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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 folder_paths
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MAX_PREVIEW_RESOLUTION = 512
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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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if hasattr(Image, 'Resampling'):
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resampling = Image.Resampling.BILINEAR
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else:
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resampling = Image.ANTIALIAS
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preview_image = ImageOps.contain(preview_image, (MAX_PREVIEW_RESOLUTION, MAX_PREVIEW_RESOLUTION), resampling)
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preview_type = 1
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if preview_format == "JPEG":
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preview_type = 1
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elif preview_format == "PNG":
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preview_type = 2
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bytesIO = BytesIO()
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header = struct.pack(">I", preview_type)
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bytesIO.write(header)
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preview_image.save(bytesIO, format=preview_format, quality=95)
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preview_bytes = bytesIO.getvalue()
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return preview_bytes
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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.decoder(x0)[0].detach()
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# x_sample = self.taesd.unscale_latents(x_sample).div(4).add(0.5) # returns value in [-2, 2]
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x_sample = x_sample.sub(0.5).mul(2)
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x_sample = torch.clamp((x_sample + 1.0) / 2.0, min=0.0, max=1.0)
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x_sample = 255. * np.moveaxis(x_sample.cpu().numpy(), 0, 2)
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x_sample = x_sample.astype(np.uint8)
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preview_image = Image.fromarray(x_sample)
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return preview_image
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class Latent2RGBPreviewer(LatentPreviewer):
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def __init__(self, latent_rgb_factors):
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self.latent_rgb_factors = torch.tensor(latent_rgb_factors, device="cpu")
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def decode_latent_to_preview(self, x0):
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latent_image = x0[0].permute(1, 2, 0).cpu() @ self.latent_rgb_factors
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latents_ubyte = (((latent_image + 1) / 2)
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.clamp(0, 1) # change scale from -1..1 to 0..1
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.mul(0xFF) # to 0..255
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.byte()).cpu()
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return Image.fromarray(latents_ubyte.numpy())
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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 = folder_paths.get_full_path("vae_approx", latent_format.taesd_decoder_name)
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if method == LatentPreviewMethod.Auto:
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method = LatentPreviewMethod.Latent2RGB
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if taesd_decoder_path:
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method = LatentPreviewMethod.TAESD
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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).to(device)
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previewer = TAESDPreviewerImpl(taesd)
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else:
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print("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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previewer = Latent2RGBPreviewer(latent_format.latent_rgb_factors)
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return previewer
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