86 lines
3.4 KiB
Python
86 lines
3.4 KiB
Python
import torch
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from PIL import Image
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from comfy.cli_args import args, LatentPreviewMethod
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import comfy.model_management
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import comfy.utils
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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 ("GIF", preview_image, MAX_PREVIEW_RESOLUTION)
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class Latent2RGBPreviewer(LatentPreviewer):
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def __init__(self):
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latent_rgb_factors = [[-0.0395, -0.0331, 0.0445],
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[ 0.0696, 0.0795, 0.0518],
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[ 0.0135, -0.0945, -0.0282],
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[ 0.0108, -0.0250, -0.0765],
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[-0.0209, 0.0032, 0.0224],
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[-0.0804, -0.0254, -0.0639],
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[-0.0991, 0.0271, -0.0669],
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[-0.0646, -0.0422, -0.0400],
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[-0.0696, -0.0595, -0.0894],
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[-0.0799, -0.0208, -0.0375],
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[ 0.1166, 0.1627, 0.0962],
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[ 0.1165, 0.0432, 0.0407],
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[-0.2315, -0.1920, -0.1355],
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[-0.0270, 0.0401, -0.0821],
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[-0.0616, -0.0997, -0.0727],
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[ 0.0249, -0.0469, -0.1703]]
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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 = torch.tensor([0.0259, -0.0192, -0.0761], 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,
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bias=self.latent_rgb_factors_bias)
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return preview_to_image(latent_image)
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def get_previewer():
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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 method
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if method == LatentPreviewMethod.Auto:
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method = LatentPreviewMethod.Latent2RGB
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if previewer is None:
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previewer = Latent2RGBPreviewer()
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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()
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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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