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94 lines
3.2 KiB
94 lines
3.2 KiB
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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