mirror of
https://github.com/comfyanonymous/ComfyUI.git
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281 lines
13 KiB
Python
281 lines
13 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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from comfy.taesd.taesd import TAESD
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from comfy.sd import VAE
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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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default_preview_method = args.preview_method
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MAX_PREVIEW_RESOLUTION = args.preview_size
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VIDEO_TAES = ["taehv", "lighttaew2_2", "lighttaew2_1", "lighttaehy1_5", "taeltx_2"]
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def preview_to_image(latent_image, do_scale=True):
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if do_scale:
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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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)
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else:
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latents_ubyte = (latent_image.clamp(0, 1)
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.mul(0xFF) # to 0..255
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)
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if comfy.model_management.directml_enabled:
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latents_ubyte = latents_ubyte.to(dtype=torch.uint8)
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latents_ubyte = latents_ubyte.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 TAEHVPreviewerImpl(TAESDPreviewerImpl):
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def decode_latent_to_preview(self, x0):
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x_sample = self.taesd.decode(x0[:1, :, :1])[0][0]
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return preview_to_image(x_sample, do_scale=False)
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class Latent2RGBPreviewer(LatentPreviewer):
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def __init__(self, latent_rgb_factors, latent_rgb_factors_bias=None, latent_rgb_factors_reshape=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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self.latent_rgb_factors_reshape = latent_rgb_factors_reshape
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def decode_latent_to_preview(self, x0):
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if self.latent_rgb_factors_reshape is not None:
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x0 = self.latent_rgb_factors_reshape(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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if x0.ndim == 5:
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x0 = x0[0, :, 0]
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else:
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x0 = x0[0]
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latent_image = torch.nn.functional.linear(x0.movedim(0, -1), 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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class Trellis3DPreviewer(LatentPreviewer):
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"""Per-step preview for the Trellis2/Pixal3D cascade.
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Structure stage: x0 is a dense [B, 32, 16, 16, 16] grid — project the per-cell
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activation norm orthographically to a 2D occupancy heatmap (no decode, no coords).
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Texture stage: x0 is sparse [B, 32, N, 1] — splat the first 3 latent channels as
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pseudo-color onto the fixed voxel coords (read from the sampling side-channel).
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Shape stage adds no visible motion (coords are fixed, only sub-voxel detail
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evolves) and a full decode per step is too costly, so it's skipped.
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Both stages render through one orthographic point splatter (static view).
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"""
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_SIZE = 128
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_FILL = 0.9 # fraction of frame the texture splat spans (leaves a border)
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_STRUCTURE_ZOOM = 0.66 # <1 pulls the SS camera back, leaving margin around the blob
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def _splat(self, points, colors, rad):
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# points: [K, 3] voxel-index coords. colors: [K, 3] in [0, 1].
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# Center + isotropic-normalize, project orthographically front-on
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# (x->horizontal, y->up, z->depth), then splat a square footprint per point
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# with one global far->near sort (painter's).
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S = self._SIZE
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dev = points.device # keep every tensor here
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p = points.float()
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p = p - (p.amax(0) + p.amin(0)) * 0.5
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p = p / p.abs().amax().clamp(min=1e-8)
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x, y, z = p[:, 0], p[:, 1], p[:, 2]
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depth = z # into-screen
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m = self._FILL
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u = ((x * m * 0.5 + 0.5) * (S - 1)).long().clamp(0, S - 1)
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v = (((-y) * m * 0.5 + 0.5) * (S - 1)).long().clamp(0, S - 1) # image up = +y
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cols = colors.to(dev)
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us, vs, ds, cs = [], [], [], []
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for dv in range(-rad, rad + 1):
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for du in range(-rad, rad + 1):
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us.append((u + du).clamp(0, S - 1))
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vs.append((v + dv).clamp(0, S - 1))
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ds.append(depth)
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cs.append(cols)
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order = torch.cat(ds).argsort()
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img = torch.zeros(S, S, 3, device=dev)
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img[torch.cat(vs)[order], torch.cat(us)[order]] = torch.cat(cs)[order]
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return preview_to_image(img, do_scale=False)
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@staticmethod
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def _turbo(x):
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# Anton Mikhailov polynomial approximation of the turbo colormap. x: any shape
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# in [0, 1] -> (..., 3) RGB.
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x = x.clamp(0.0, 1.0)
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x2 = x * x; x3 = x2 * x; x4 = x2 * x2; x5 = x4 * x
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r = 0.13572138 + 4.61539260*x - 42.66032258*x2 + 132.13108234*x3 - 152.94239396*x4 + 59.28637943*x5
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g = 0.09140261 + 2.19418839*x + 4.84296658*x2 - 14.18503333*x3 + 4.27729857*x4 + 2.82956604*x5
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b = 0.10667330 + 12.64194608*x - 60.58204836*x2 + 110.36276771*x3 - 89.90310912*x4 + 27.34824973*x5
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return torch.stack([r, g, b], dim=-1).clamp(0.0, 1.0)
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def _structure(self, x0):
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# x0: [B, 32, D, H, W]; the model only consumes the first 8 channels.
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# Dense orthographic max-projection -> filled occupancy heatmap (turbo-colored,
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# intensity-weighted so empty space stays black).
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act = x0[0, :min(8, x0.shape[1])].float().norm(dim=0) # [D, H, W]
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proj = act.amax(dim=2) # project along one axis
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proj = (proj - proj.amin()) / (proj.amax() - proj.amin() + 1e-8)
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inner = max(1, int(round(self._SIZE * self._STRUCTURE_ZOOM)))
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img = torch.nn.functional.interpolate(proj[None, None], size=(inner, inner), mode="nearest")
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pad = self._SIZE - inner
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pl, pt = pad // 2, pad // 2
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gray = torch.nn.functional.pad(img, (pl, pad - pl, pt, pad - pt))[0, 0] # [S, S], zero margin
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rgb = self._turbo(gray) * gray.unsqueeze(-1) # [S, S, 3], black where empty
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return preview_to_image(rgb, do_scale=False)
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@staticmethod
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def _latent_color(latent):
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# Prefer the calibrated latent->base_color map (fit from real decoded
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# albedo by VaeDecodeTextureTrellis); fall back to PCA pseudo-color until
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# a texture decode has trained it.
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try:
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from comfy.ldm.trellis2 import sampling_preview
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factors = sampling_preview.get_tex_rgb()
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except Exception:
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factors = None
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if factors is not None:
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W, b = factors
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rgb = latent @ W.to(latent) + b.to(latent)
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return rgb.clamp(0, 1)
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return Trellis3DPreviewer._pca_color(latent)
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@staticmethod
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def _pca_color(latent):
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# latent: [n, C]. Map the 3 directions of maximum variance to RGB.
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# Higher contrast and more coherent than picking 3 fixed channels.
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X = latent - latent.mean(dim=0, keepdim=True)
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cov = (X.transpose(0, 1) @ X) / max(X.shape[0] - 1, 1) # [C, C]
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_, evecs = torch.linalg.eigh(cov) # ascending eigenvalues
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pcs = evecs[:, -3:] # [C, 3] top-3 components
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# Deterministic sign per component (largest-magnitude entry positive) to
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# stop the preview's hues from flickering as the latent rotates each step.
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sign = torch.sign(pcs[pcs.abs().argmax(dim=0), torch.arange(3, device=pcs.device)])
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pcs = pcs * sign.clamp(min=-1.0)
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proj = X @ pcs # [n, 3]
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pmin = proj.amin(dim=0, keepdim=True)
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pmax = proj.amax(dim=0, keepdim=True)
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return ((proj - pmin) / (pmax - pmin + 1e-8)).clamp(0, 1)
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def _texture(self, x0, coords, model_frame=None):
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if coords.shape[-1] == 4:
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b0 = coords[:, 0] == 0
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spatial = coords[b0][:, 1:4].float()
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else:
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spatial = coords[:, :3].float()
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n0 = spatial.shape[0]
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if n0 == 0:
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return None
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if model_frame == "z_up":
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spatial = torch.stack([spatial[:, 0], spatial[:, 2], -spatial[:, 1]], dim=-1)
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latent = x0[0, :, :n0, 0].float().transpose(0, 1) # [n0, C]
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colors = self._latent_color(latent) # [n0, 3]
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res = float(spatial.abs().max().item()) + 1.0
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rad = max(1, int(round(self._SIZE * self._FILL / max(res, 1) / 2)))
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return self._splat(spatial, colors, rad)
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def decode_latent_to_preview(self, x0):
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try:
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from comfy.ldm.trellis2 import sampling_preview
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ctx = sampling_preview.get_context()
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if x0.ndim == 5:
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return self._structure(x0)
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mode = ctx.get("mode")
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coords = ctx.get("coords")
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if mode == "texture_generation" and coords is not None:
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return self._texture(x0, coords, model_frame=ctx.get("model_frame"))
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except Exception as e:
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logging.debug(f"Trellis3DPreviewer: skipping preview ({e})")
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return None
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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 preview_image is None:
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return None
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return ("JPEG", preview_image, MAX_PREVIEW_RESOLUTION)
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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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if getattr(latent_format, "trellis3d_preview", False):
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return Trellis3DPreviewer()
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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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if latent_format.taesd_decoder_name in VIDEO_TAES:
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taesd = VAE(comfy.utils.load_torch_file(taesd_decoder_path))
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taesd.first_stage_model.show_progress_bar = False
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previewer = TAEHVPreviewerImpl(taesd)
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else:
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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, latent_format.latent_rgb_factors_reshape)
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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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def set_preview_method(override: str = None):
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if override and override != "default":
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method = LatentPreviewMethod.from_string(override)
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if method is not None:
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args.preview_method = method
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return
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args.preview_method = default_preview_method
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