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Merge pull request #12 from pollockjj/issue_87
Issue 87: fix Trellis2 batch semantics collapse shape and texture samples
This commit is contained in:
John Pollock
GitHub
commit
141f13a3ae
@ -786,6 +786,7 @@ class Trellis2(nn.Module):
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# 32 -> 512px path, 64 -> 1024px path.
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uses_1024_conditioning = self.img2shape.resolution == 64
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coords = transformer_options.get("coords", None)
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coord_counts = transformer_options.get("coord_counts")
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mode = transformer_options.get("generation_mode", "structure_generation")
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is_512_run = False
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timestep = timestep.to(self.dtype)
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@ -811,15 +812,33 @@ class Trellis2(nn.Module):
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cond = context
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shape_rule = sigmas < self.guidance_interval[0] or sigmas > self.guidance_interval[1]
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txt_rule = sigmas < self.guidance_interval_txt[0] or sigmas > self.guidance_interval_txt[1]
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dense_out = None
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cond_or_uncond = transformer_options.get("cond_or_uncond") or []
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def cond_group_indices(batch_groups):
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if len(cond_or_uncond) == batch_groups:
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cond_groups = [i for i, marker in enumerate(cond_or_uncond) if marker == 0]
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if len(cond_groups) > 0:
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return cond_groups
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return [batch_groups - 1]
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if not_struct_mode:
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orig_bsz = x.shape[0]
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rule = txt_rule if mode == "texture_generation" else shape_rule
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if rule and orig_bsz > 1:
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x_eval = x[1].unsqueeze(0)
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t_eval = timestep[1].unsqueeze(0) if timestep.shape[0] > 1 else timestep
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c_eval = cond
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logical_batch = coord_counts.shape[0] if coord_counts is not None else 1
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if rule and orig_bsz > logical_batch:
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batch_groups = orig_bsz // logical_batch
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selected_groups = cond_group_indices(batch_groups)
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x_groups = x.reshape(batch_groups, logical_batch, *x.shape[1:])
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x_eval = x_groups[selected_groups].reshape(-1, *x.shape[1:])
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if timestep.shape[0] > 1:
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t_groups = timestep.reshape(batch_groups, logical_batch, *timestep.shape[1:])
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t_eval = t_groups[selected_groups].reshape(-1, *timestep.shape[1:])
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else:
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t_eval = timestep
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c_groups = context.reshape(batch_groups, logical_batch, *context.shape[1:])
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c_eval = c_groups[selected_groups].reshape(-1, *context.shape[1:])
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else:
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x_eval = x
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t_eval = timestep
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@ -828,23 +847,107 @@ class Trellis2(nn.Module):
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B, N, C = x_eval.shape
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if mode in ["shape_generation", "texture_generation"]:
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feats_flat = x_eval.reshape(-1, C)
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if coord_counts is not None:
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logical_batch = coord_counts.shape[0]
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if B % logical_batch != 0:
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raise ValueError(
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f"Trellis2 coord_counts batch {logical_batch} doesn't divide latent batch {B}"
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)
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if int(coord_counts.sum().item()) != coords.shape[0]:
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raise ValueError(
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f"Trellis2 coord_counts total {int(coord_counts.sum().item())} does not match coords rows {coords.shape[0]}"
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)
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batch_ids = coords[:, 0].to(torch.int64)
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order = torch.argsort(batch_ids, stable=True)
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sorted_coords = coords.index_select(0, order)
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sorted_batch_ids = batch_ids.index_select(0, order)
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offsets = coord_counts.cumsum(0) - coord_counts
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coords_by_batch = []
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for i in range(logical_batch):
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count = int(coord_counts[i].item())
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start = int(offsets[i].item())
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coords_i = sorted_coords[start:start + count]
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ids_i = sorted_batch_ids[start:start + count]
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if coords_i.shape[0] != count or not torch.all(ids_i == i):
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raise ValueError(
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f"Trellis2 coords rows for batch {i} expected {count}, got {coords_i.shape[0]}"
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)
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coords_by_batch.append(coords_i)
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repeat_factor = B // logical_batch
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sparse_outs = []
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active_coord_counts = []
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for rep in range(repeat_factor):
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for i in range(logical_batch):
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out_index = rep * logical_batch + i
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count = int(coord_counts[i].item())
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if count > N:
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raise ValueError(
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f"Trellis2 coord count {count} exceeds latent token dimension {N} for batch {i}"
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)
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coords_i = coords_by_batch[i].clone()
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coords_i[:, 0] = 0
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feats_i = x_eval[out_index, :count].clone()
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x_st_i = SparseTensor(feats=feats_i, coords=coords_i.to(torch.int32))
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t_i = t_eval[out_index].unsqueeze(0).clone() if t_eval.shape[0] > 1 else t_eval
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c_i = c_eval[out_index].unsqueeze(0).clone() if c_eval.shape[0] > 1 else c_eval
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# inflate coords [N, 4] -> [B*N, 4]
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coords_list = []
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for i in range(B):
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c = coords.clone()
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c[:, 0] = i
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coords_list.append(c)
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if mode == "shape_generation":
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if is_512_run:
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sparse_out = self.img2shape_512(x_st_i, t_i, c_i)
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else:
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sparse_out = self.img2shape(x_st_i, t_i, c_i)
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else:
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slat = transformer_options.get("shape_slat")
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if slat is None:
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raise ValueError("shape_slat can't be None")
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if slat.ndim == 3:
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if slat.shape[0] != logical_batch:
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raise ValueError(
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f"shape_slat batch {slat.shape[0]} doesn't match coord_counts batch {logical_batch}"
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)
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if slat.shape[1] < count:
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raise ValueError(
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f"shape_slat tokens {slat.shape[1]} can't cover coord count {count} for batch {i}"
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)
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slat_feats = slat[i, :count].to(x_st_i.device)
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else:
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slat_feats = slat[:count].to(x_st_i.device)
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x_st_i = x_st_i.replace(feats=torch.cat([x_st_i.feats, slat_feats], dim=-1))
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sparse_out = self.shape2txt(x_st_i, t_i, c_i)
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batched_coords = torch.cat(coords_list, dim=0)
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sparse_outs.append(sparse_out.feats)
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active_coord_counts.append(count)
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out_channels = sparse_outs[0].shape[-1]
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padded = sparse_outs[0].new_zeros((B, N, out_channels))
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for out_index, (count, feats_i) in enumerate(zip(active_coord_counts, sparse_outs)):
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padded[out_index, :count] = feats_i
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dense_out = padded.transpose(1, 2).unsqueeze(-1)
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elif coords.shape[0] == N:
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feats_flat = x_eval.reshape(-1, C)
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coords_list = []
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for i in range(B):
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c = coords.clone()
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c[:, 0] = i
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coords_list.append(c)
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batched_coords = torch.cat(coords_list, dim=0)
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elif coords.shape[0] == B * N:
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feats_flat = x_eval.reshape(-1, C)
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batched_coords = coords
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else:
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raise ValueError(
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f"Trellis2 expected coords rows {N} or {B * N}, got {coords.shape[0]}"
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)
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else:
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batched_coords = coords
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feats_flat = x_eval
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x_st = SparseTensor(feats=feats_flat, coords=batched_coords.to(torch.int32))
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if dense_out is None:
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x_st = SparseTensor(feats=feats_flat, coords=batched_coords.to(torch.int32))
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if mode == "shape_generation":
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if dense_out is not None:
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out = dense_out
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elif mode == "shape_generation":
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if is_512_run:
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out = self.img2shape_512(x_st, t_eval, c_eval)
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else:
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@ -856,23 +959,96 @@ class Trellis2(nn.Module):
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if slat is None:
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raise ValueError("shape_slat can't be None")
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base_slat_feats = slat[:N]
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slat_feats_batched = base_slat_feats.repeat(B, 1).to(x_st.device)
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if slat.ndim == 3:
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if coord_counts is not None:
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logical_batch = coord_counts.shape[0]
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if slat.shape[0] != logical_batch:
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raise ValueError(
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f"shape_slat batch {slat.shape[0]} doesn't match coord_counts batch {logical_batch}"
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)
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if B % logical_batch != 0:
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raise ValueError(
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f"Trellis2 coord_counts batch {logical_batch} doesn't divide latent batch {B}"
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)
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repeat_factor = B // logical_batch
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slat_list = []
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for _ in range(repeat_factor):
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for i in range(logical_batch):
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count = int(coord_counts[i].item())
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if slat.shape[1] < count:
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raise ValueError(
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f"shape_slat tokens {slat.shape[1]} can't cover coord count {count} for batch {i}"
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)
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slat_list.append(slat[i, :count])
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slat_feats_batched = torch.cat(slat_list, dim=0).to(x_st.device)
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else:
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if slat.shape[0] != B:
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raise ValueError(f"shape_slat batch {slat.shape[0]} doesn't match latent batch {B}")
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if slat.shape[1] != N:
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raise ValueError(f"shape_slat tokens {slat.shape[1]} doesn't match latent tokens {N}")
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slat_feats_batched = slat.reshape(B * N, -1).to(x_st.device)
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else:
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base_slat_feats = slat[:N]
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slat_feats_batched = base_slat_feats.repeat(B, 1).to(x_st.device)
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x_st = x_st.replace(feats=torch.cat([x_st.feats, slat_feats_batched], dim=-1))
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out = self.shape2txt(x_st, t_eval, c_eval)
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else: # structure
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orig_bsz = x.shape[0]
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if shape_rule and orig_bsz > 1:
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half = orig_bsz // 2
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x = x[half:]
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timestep = timestep[half:] if timestep.shape[0] > 1 else timestep
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out = self.structure_model(x, timestep, cond if shape_rule and orig_bsz > 1 else context)
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if shape_rule and orig_bsz > 1:
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out = out.repeat(2, 1, 1, 1, 1)
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batch_groups = len(cond_or_uncond) if len(cond_or_uncond) > 0 and orig_bsz % len(cond_or_uncond) == 0 else 1
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logical_batch = orig_bsz // batch_groups
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if logical_batch > 1:
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x_groups = x.reshape(batch_groups, logical_batch, *x.shape[1:])
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if timestep.shape[0] > 1:
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t_groups = timestep.reshape(batch_groups, logical_batch, *timestep.shape[1:])
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else:
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t_groups = timestep
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c_groups = context.reshape(batch_groups, logical_batch, *context.shape[1:])
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if shape_rule and batch_groups > 1:
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selected_group_indices = cond_group_indices(batch_groups)
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else:
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selected_group_indices = list(range(batch_groups))
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out_groups = []
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for sample_index in range(logical_batch):
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if shape_rule and batch_groups > 1:
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x_i = x_groups[selected_group_indices, sample_index]
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if timestep.shape[0] > 1:
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t_i = t_groups[selected_group_indices, sample_index]
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else:
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t_i = timestep
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c_i = c_groups[selected_group_indices, sample_index]
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else:
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x_i = x_groups[selected_group_indices, sample_index]
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if timestep.shape[0] > 1:
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t_i = t_groups[selected_group_indices, sample_index]
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else:
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t_i = timestep
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c_i = c_groups[selected_group_indices, sample_index]
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out_groups.append(self.structure_model(x_i, t_i, c_i))
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out = out_groups[0].new_zeros((orig_bsz, *out_groups[0].shape[1:]))
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for sample_index, out_sample in enumerate(out_groups):
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if shape_rule and batch_groups > 1:
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repeated = out_sample[0]
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for group_index in range(batch_groups):
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out[group_index * logical_batch + sample_index] = repeated
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else:
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for local_group_index, group_index in enumerate(selected_group_indices):
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out[group_index * logical_batch + sample_index] = out_sample[local_group_index]
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else:
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if shape_rule and orig_bsz > 1:
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half = orig_bsz // 2
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x = x[half:]
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timestep = timestep[half:] if timestep.shape[0] > 1 else timestep
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out = self.structure_model(x, timestep, cond if shape_rule and orig_bsz > 1 else context)
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if shape_rule and orig_bsz > 1:
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out = out.repeat(2, 1, 1, 1, 1)
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if not_struct_mode:
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out = out.feats
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out = out.view(B, N, -1).transpose(1, 2).unsqueeze(-1)
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if rule and orig_bsz > 1:
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out = out.repeat(orig_bsz, 1, 1, 1)
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if dense_out is None:
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out = out.feats
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out = out.view(B, N, -1).transpose(1, 2).unsqueeze(-1)
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if rule and orig_bsz > B:
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out = out.repeat(orig_bsz // B, 1, 1, 1)
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return out
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@ -7,6 +7,50 @@ import logging
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import comfy.nested_tensor
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def prepare_noise_inner(latent_image, generator, noise_inds=None):
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coord_counts = getattr(latent_image, "trellis_coord_counts", None)
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if coord_counts is not None:
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if coord_counts.ndim != 1:
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raise ValueError(f"Trellis2 coord_counts must be 1D, got shape {tuple(coord_counts.shape)}")
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if coord_counts.shape[0] != latent_image.size(0):
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raise ValueError(
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f"Trellis2 coord_counts length {coord_counts.shape[0]} does not match latent batch {latent_image.size(0)}"
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)
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if (coord_counts < 0).any() or (coord_counts > latent_image.size(2)).any():
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raise ValueError(
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f"Trellis2 coord_counts must be within [0, {latent_image.size(2)}], got {coord_counts.tolist()}"
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)
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noise = torch.zeros(latent_image.size(), dtype=torch.float32, layout=latent_image.layout, device="cpu")
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if noise_inds is None:
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noise_inds = np.arange(latent_image.size(0), dtype=np.int64)
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else:
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noise_inds = np.asarray(noise_inds, dtype=np.int64)
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if noise_inds.shape[0] != latent_image.size(0):
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raise ValueError(
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f"Trellis2 noise_inds length {noise_inds.shape[0]} does not match latent batch {latent_image.size(0)}"
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)
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base_seed = int(generator.initial_seed())
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unique_inds = np.unique(noise_inds)
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sample_noises = {}
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for noise_index in unique_inds.tolist():
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rows = np.flatnonzero(noise_inds == noise_index)
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max_count = max(int(coord_counts[row].item()) for row in rows.tolist())
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local_generator = torch.Generator(device="cpu")
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local_generator.manual_seed(base_seed + int(noise_index))
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sample_noises[int(noise_index)] = torch.randn(
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[1, latent_image.size(1), max_count, latent_image.size(3)],
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dtype=torch.float32,
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layout=latent_image.layout,
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generator=local_generator,
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device="cpu",
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)
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for batch_index, noise_index in enumerate(noise_inds.tolist()):
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count = int(coord_counts[batch_index].item())
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sample_noise = sample_noises[int(noise_index)]
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noise[batch_index:batch_index + 1, :, :count, :] = sample_noise[:, :, :count, :]
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return noise.to(dtype=latent_image.dtype)
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if noise_inds is None:
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return torch.randn(latent_image.size(), dtype=torch.float32, layout=latent_image.layout, generator=generator, device="cpu").to(dtype=latent_image.dtype)
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@ -96,6 +96,114 @@ def shape_norm(shape_latent, coords):
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samples = samples * std + mean
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return samples
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def infer_batched_coord_layout(coords):
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if coords.ndim != 2 or coords.shape[1] != 4:
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raise ValueError(f"Expected Trellis2 coords with shape [N, 4], got {tuple(coords.shape)}")
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if coords.shape[0] == 0:
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raise ValueError("Trellis2 coords can't be empty")
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batch_ids = coords[:, 0].to(torch.int64)
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if (batch_ids < 0).any():
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raise ValueError(f"Trellis2 batch ids must be non-negative, got {batch_ids.unique(sorted=True).tolist()}")
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batch_size = int(batch_ids.max().item()) + 1
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counts = torch.bincount(batch_ids, minlength=batch_size)
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if (counts == 0).any():
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raise ValueError(f"Non-contiguous Trellis2 batch ids in coords: {batch_ids.unique(sorted=True).tolist()}")
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max_tokens = int(counts.max().item())
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return batch_size, counts, max_tokens
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def split_batched_coords(coords, coord_counts):
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if coord_counts.ndim != 1:
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raise ValueError(f"Trellis2 coord_counts must be 1D, got shape {tuple(coord_counts.shape)}")
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if (coord_counts < 0).any():
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raise ValueError(f"Trellis2 coord_counts must be non-negative, got {coord_counts.tolist()}")
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if int(coord_counts.sum().item()) != coords.shape[0]:
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raise ValueError(
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f"Trellis2 coord_counts total {int(coord_counts.sum().item())} does not match coords rows {coords.shape[0]}"
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)
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batch_ids = coords[:, 0].to(torch.int64)
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order = torch.argsort(batch_ids, stable=True)
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sorted_coords = coords.index_select(0, order)
|
||||
sorted_batch_ids = batch_ids.index_select(0, order)
|
||||
|
||||
offsets = coord_counts.cumsum(0) - coord_counts
|
||||
items = []
|
||||
for i in range(coord_counts.shape[0]):
|
||||
count = int(coord_counts[i].item())
|
||||
start = int(offsets[i].item())
|
||||
coords_i = sorted_coords[start:start + count]
|
||||
ids_i = sorted_batch_ids[start:start + count]
|
||||
if coords_i.shape[0] != count or not torch.all(ids_i == i):
|
||||
raise ValueError(f"Trellis2 coords rows for batch {i} expected {count}, got {coords_i.shape[0]}")
|
||||
items.append(coords_i)
|
||||
return items
|
||||
|
||||
|
||||
def normalize_batch_index(batch_index):
|
||||
if batch_index is None:
|
||||
return None
|
||||
if isinstance(batch_index, int):
|
||||
return [int(batch_index)]
|
||||
return list(batch_index)
|
||||
|
||||
|
||||
def resolve_sample_indices(batch_index, batch_size):
|
||||
sample_indices = normalize_batch_index(batch_index)
|
||||
if sample_indices is None:
|
||||
return list(range(batch_size))
|
||||
if len(sample_indices) != batch_size:
|
||||
raise ValueError(
|
||||
f"Trellis2 batch_index length {len(sample_indices)} does not match batch size {batch_size}"
|
||||
)
|
||||
return sample_indices
|
||||
|
||||
|
||||
def resolve_singleton_sample_index(batch_index):
|
||||
sample_indices = normalize_batch_index(batch_index)
|
||||
if sample_indices is None:
|
||||
return 0
|
||||
if len(sample_indices) != 1:
|
||||
raise ValueError(
|
||||
f"Trellis2 batch_index must be an int or single-element iterable for singleton coords, got {sample_indices}"
|
||||
)
|
||||
return int(sample_indices[0])
|
||||
|
||||
|
||||
def flatten_batched_sparse_latent(samples, coords, coord_counts):
|
||||
samples = samples.squeeze(-1).transpose(1, 2)
|
||||
if coord_counts is None:
|
||||
return samples.reshape(-1, samples.shape[-1]), coords
|
||||
|
||||
coords_items = split_batched_coords(coords, coord_counts)
|
||||
feat_list = []
|
||||
coord_list = []
|
||||
for i, coords_i in enumerate(coords_items):
|
||||
count = int(coord_counts[i].item())
|
||||
feat_list.append(samples[i, :count])
|
||||
coord_list.append(coords_i)
|
||||
|
||||
return torch.cat(feat_list, dim=0), torch.cat(coord_list, dim=0)
|
||||
|
||||
|
||||
def split_batched_sparse_latent(samples, coords, coord_counts):
|
||||
samples = samples.squeeze(-1).transpose(1, 2)
|
||||
if coord_counts is None:
|
||||
return [(samples.reshape(-1, samples.shape[-1]), coords)]
|
||||
|
||||
coords_items = split_batched_coords(coords, coord_counts)
|
||||
items = []
|
||||
for i, coords_i in enumerate(coords_items):
|
||||
count = int(coord_counts[i].item())
|
||||
items.append((samples[i, :count], coords_i))
|
||||
return items
|
||||
|
||||
|
||||
def paint_mesh_with_voxels(mesh, voxel_coords, voxel_colors, resolution):
|
||||
"""
|
||||
Generic function to paint a mesh using nearest-neighbor colors from a sparse voxel field.
|
||||
@ -169,12 +277,32 @@ class VaeDecodeShapeTrellis(IO.ComfyNode):
|
||||
|
||||
vae = vae.first_stage_model
|
||||
coords = samples["coords"]
|
||||
coord_counts = samples.get("coord_counts")
|
||||
|
||||
samples = samples["samples"]
|
||||
samples = samples.squeeze(-1).transpose(1, 2).reshape(-1, 32).to(device)
|
||||
samples = shape_norm(samples, coords)
|
||||
if coord_counts is None:
|
||||
samples, coords = flatten_batched_sparse_latent(samples, coords, coord_counts)
|
||||
samples = shape_norm(samples.to(device), coords.to(device))
|
||||
mesh, subs = vae.decode_shape_slat(samples, resolution)
|
||||
else:
|
||||
split_items = split_batched_sparse_latent(samples, coords, coord_counts)
|
||||
mesh = []
|
||||
subs_per_sample = []
|
||||
for feats_i, coords_i in split_items:
|
||||
coords_i = coords_i.to(device).clone()
|
||||
coords_i[:, 0] = 0
|
||||
sample_i = shape_norm(feats_i.to(device), coords_i)
|
||||
mesh_i, subs_i = vae.decode_shape_slat(sample_i, resolution)
|
||||
mesh.append(mesh_i[0])
|
||||
subs_per_sample.append(subs_i)
|
||||
|
||||
subs = []
|
||||
for stage_index in range(len(subs_per_sample[0])):
|
||||
stage_tensors = [sample_subs[stage_index] for sample_subs in subs_per_sample]
|
||||
feats_list = [stage_tensor.feats for stage_tensor in stage_tensors]
|
||||
coords_list = [stage_tensor.coords for stage_tensor in stage_tensors]
|
||||
subs.append(SparseTensor.from_tensor_list(feats_list, coords_list))
|
||||
|
||||
mesh, subs = vae.decode_shape_slat(samples, resolution)
|
||||
face_list = [m.faces for m in mesh]
|
||||
vert_list = [m.vertices for m in mesh]
|
||||
if all(v.shape == vert_list[0].shape for v in vert_list) and all(f.shape == face_list[0].shape for f in face_list):
|
||||
@ -210,12 +338,14 @@ class VaeDecodeTextureTrellis(IO.ComfyNode):
|
||||
|
||||
vae = vae.first_stage_model
|
||||
coords = samples["coords"]
|
||||
coord_counts = samples.get("coord_counts")
|
||||
|
||||
samples = samples["samples"]
|
||||
samples = samples.squeeze(-1).transpose(1, 2).reshape(-1, 32).to(device)
|
||||
samples, coords = flatten_batched_sparse_latent(samples, coords, coord_counts)
|
||||
samples = samples.to(device)
|
||||
std = tex_slat_normalization["std"].to(samples)
|
||||
mean = tex_slat_normalization["mean"].to(samples)
|
||||
samples = SparseTensor(feats = samples, coords=coords)
|
||||
samples = SparseTensor(feats = samples, coords=coords.to(device))
|
||||
samples = samples * std + mean
|
||||
|
||||
voxel = vae.decode_tex_slat(samples, shape_subs)
|
||||
@ -271,9 +401,16 @@ class VaeDecodeStructureTrellis2(IO.ComfyNode):
|
||||
load_device = comfy.model_management.get_torch_device()
|
||||
offload_device = comfy.model_management.vae_offload_device()
|
||||
decoder = decoder.to(load_device)
|
||||
batch_index = normalize_batch_index(samples.get("batch_index"))
|
||||
samples = samples["samples"]
|
||||
samples = samples.to(load_device)
|
||||
decoded = decoder(samples)>0
|
||||
if samples.shape[0] > 1:
|
||||
decoded_items = []
|
||||
for i in range(samples.shape[0]):
|
||||
decoded_items.append(decoder(samples[i:i + 1]) > 0)
|
||||
decoded = torch.cat(decoded_items, dim=0)
|
||||
else:
|
||||
decoded = decoder(samples) > 0
|
||||
decoder.to(offload_device)
|
||||
current_res = decoded.shape[2]
|
||||
|
||||
@ -281,6 +418,8 @@ class VaeDecodeStructureTrellis2(IO.ComfyNode):
|
||||
ratio = current_res // resolution
|
||||
decoded = torch.nn.functional.max_pool3d(decoded.float(), ratio, ratio, 0) > 0.5
|
||||
out = Types.VOXEL(decoded.squeeze(1).float())
|
||||
if batch_index is not None:
|
||||
out.batch_index = normalize_batch_index(batch_index)
|
||||
return IO.NodeOutput(out)
|
||||
|
||||
class Trellis2UpsampleCascade(IO.ComfyNode):
|
||||
@ -305,32 +444,93 @@ class Trellis2UpsampleCascade(IO.ComfyNode):
|
||||
device = comfy.model_management.get_torch_device()
|
||||
comfy.model_management.load_model_gpu(vae.patcher)
|
||||
|
||||
feats = shape_latent_512["samples"].squeeze(-1).transpose(1, 2).reshape(-1, 32).to(device)
|
||||
coords_512 = shape_latent_512["coords"].to(device)
|
||||
|
||||
slat = shape_norm(feats, coords_512)
|
||||
|
||||
coord_counts = shape_latent_512.get("coord_counts")
|
||||
batch_index = normalize_batch_index(shape_latent_512.get("batch_index"))
|
||||
decoder = vae.first_stage_model.shape_dec
|
||||
|
||||
slat.feats = slat.feats.to(next(decoder.parameters()).dtype)
|
||||
hr_coords = decoder.upsample(slat, upsample_times=4)
|
||||
|
||||
lr_resolution = 512
|
||||
hr_resolution = int(target_resolution)
|
||||
target_resolution = int(target_resolution)
|
||||
|
||||
if coord_counts is None:
|
||||
feats, coords_512 = flatten_batched_sparse_latent(
|
||||
shape_latent_512["samples"],
|
||||
shape_latent_512["coords"],
|
||||
coord_counts,
|
||||
)
|
||||
feats = feats.to(device)
|
||||
coords_512 = coords_512.to(device)
|
||||
slat = shape_norm(feats, coords_512)
|
||||
slat.feats = slat.feats.to(next(decoder.parameters()).dtype)
|
||||
hr_coords = decoder.upsample(slat, upsample_times=4)
|
||||
|
||||
hr_resolution = target_resolution
|
||||
while True:
|
||||
quant_coords = torch.cat([
|
||||
hr_coords[:, :1],
|
||||
((hr_coords[:, 1:] + 0.5) / lr_resolution * (hr_resolution // 16)).int(),
|
||||
], dim=1)
|
||||
final_coords = quant_coords.unique(dim=0)
|
||||
num_tokens = final_coords.shape[0]
|
||||
|
||||
if num_tokens < max_tokens or hr_resolution <= 1024:
|
||||
break
|
||||
hr_resolution -= 128
|
||||
|
||||
return IO.NodeOutput(final_coords,)
|
||||
|
||||
items = split_batched_sparse_latent(
|
||||
shape_latent_512["samples"],
|
||||
shape_latent_512["coords"],
|
||||
coord_counts,
|
||||
)
|
||||
decoder_dtype = next(decoder.parameters()).dtype
|
||||
|
||||
sample_hr_coords = []
|
||||
for feats_i, coords_i in items:
|
||||
feats_i = feats_i.to(device)
|
||||
coords_i = coords_i.to(device).clone()
|
||||
coords_i[:, 0] = 0
|
||||
slat_i = shape_norm(feats_i, coords_i)
|
||||
slat_i.feats = slat_i.feats.to(decoder_dtype)
|
||||
sample_hr_coords.append(decoder.upsample(slat_i, upsample_times=4))
|
||||
|
||||
hr_resolution = target_resolution
|
||||
while True:
|
||||
quant_coords = torch.cat([
|
||||
hr_coords[:, :1],
|
||||
((hr_coords[:, 1:] + 0.5) / lr_resolution * (hr_resolution // 16)).int(),
|
||||
], dim=1)
|
||||
final_coords = quant_coords.unique(dim=0)
|
||||
num_tokens = final_coords.shape[0]
|
||||
|
||||
if num_tokens < max_tokens or hr_resolution <= 1024:
|
||||
exceeds_limit = False
|
||||
for hr_coords_i in sample_hr_coords:
|
||||
quant_coords_i = torch.cat([
|
||||
hr_coords_i[:, :1],
|
||||
((hr_coords_i[:, 1:] + 0.5) / lr_resolution * (hr_resolution // 16)).int(),
|
||||
], dim=1)
|
||||
if quant_coords_i.unique(dim=0).shape[0] >= max_tokens:
|
||||
exceeds_limit = True
|
||||
break
|
||||
if not exceeds_limit or hr_resolution <= 1024:
|
||||
break
|
||||
hr_resolution -= 128
|
||||
|
||||
return IO.NodeOutput(final_coords,)
|
||||
final_coords_list = []
|
||||
output_coord_counts = []
|
||||
for sample_offset, hr_coords_i in enumerate(sample_hr_coords):
|
||||
quant_coords_i = torch.cat([
|
||||
hr_coords_i[:, :1],
|
||||
((hr_coords_i[:, 1:] + 0.5) / lr_resolution * (hr_resolution // 16)).int(),
|
||||
], dim=1)
|
||||
final_coords_i = quant_coords_i.unique(dim=0)
|
||||
final_coords_i = final_coords_i.clone()
|
||||
final_coords_i[:, 0] = sample_offset
|
||||
final_coords_list.append(final_coords_i)
|
||||
output_coord_counts.append(int(final_coords_i.shape[0]))
|
||||
|
||||
normalized_batch_index = normalize_batch_index(batch_index)
|
||||
output = {
|
||||
"coords": torch.cat(final_coords_list, dim=0),
|
||||
"coord_counts": torch.tensor(output_coord_counts, dtype=torch.int64),
|
||||
"resolutions": torch.full((len(final_coords_list),), int(hr_resolution), dtype=torch.int64),
|
||||
}
|
||||
if normalized_batch_index is not None:
|
||||
output["batch_index"] = normalized_batch_index
|
||||
|
||||
return IO.NodeOutput(output,)
|
||||
|
||||
dino_mean = torch.tensor([0.485, 0.456, 0.406]).view(1, 3, 1, 1)
|
||||
dino_std = torch.tensor([0.229, 0.224, 0.225]).view(1, 3, 1, 1)
|
||||
@ -484,7 +684,8 @@ class EmptyShapeLatentTrellis2(IO.ComfyNode):
|
||||
category="latent/3d",
|
||||
inputs=[
|
||||
IO.AnyType.Input("structure_or_coords"),
|
||||
IO.Model.Input("model")
|
||||
IO.Model.Input("model"),
|
||||
IO.Int.Input("seed", default=0, min=0, max=0xffffffffffffffff),
|
||||
],
|
||||
outputs=[
|
||||
IO.Latent.Output(),
|
||||
@ -493,14 +694,25 @@ class EmptyShapeLatentTrellis2(IO.ComfyNode):
|
||||
)
|
||||
|
||||
@classmethod
|
||||
def execute(cls, structure_or_coords, model):
|
||||
def execute(cls, structure_or_coords, model, seed):
|
||||
# to accept the upscaled coords
|
||||
is_512_pass = False
|
||||
coord_counts = None
|
||||
coord_resolutions = None
|
||||
batch_index = None
|
||||
|
||||
if hasattr(structure_or_coords, "data") and structure_or_coords.data.ndim == 4:
|
||||
decoded = structure_or_coords.data.unsqueeze(1)
|
||||
coords = torch.argwhere(decoded.bool())[:, [0, 2, 3, 4]].int()
|
||||
is_512_pass = True
|
||||
batch_index = normalize_batch_index(getattr(structure_or_coords, "batch_index", None))
|
||||
|
||||
elif isinstance(structure_or_coords, dict):
|
||||
coords = structure_or_coords["coords"].int()
|
||||
coord_counts = structure_or_coords.get("coord_counts")
|
||||
coord_resolutions = structure_or_coords.get("resolutions")
|
||||
batch_index = normalize_batch_index(structure_or_coords.get("batch_index"))
|
||||
is_512_pass = False
|
||||
|
||||
elif isinstance(structure_or_coords, torch.Tensor) and structure_or_coords.ndim == 2:
|
||||
coords = structure_or_coords.int()
|
||||
@ -509,8 +721,31 @@ class EmptyShapeLatentTrellis2(IO.ComfyNode):
|
||||
else:
|
||||
raise ValueError(f"Invalid input to EmptyShapeLatent: {type(structure_or_coords)}")
|
||||
in_channels = 32
|
||||
# image like format
|
||||
latent = torch.randn(1, in_channels, coords.shape[0], 1)
|
||||
batch_size, inferred_coord_counts, max_tokens = infer_batched_coord_layout(coords)
|
||||
if coord_counts is not None:
|
||||
coord_counts = coord_counts.to(dtype=torch.int64, device=coords.device)
|
||||
if coord_counts.shape != inferred_coord_counts.shape or not torch.equal(coord_counts, inferred_coord_counts):
|
||||
raise ValueError(
|
||||
f"Trellis2 coord_counts metadata {coord_counts.tolist()} does not match coords layout {inferred_coord_counts.tolist()}"
|
||||
)
|
||||
else:
|
||||
coord_counts = inferred_coord_counts
|
||||
if batch_size == 1:
|
||||
sample_index = resolve_singleton_sample_index(batch_index)
|
||||
generator = torch.Generator(device="cpu")
|
||||
generator.manual_seed(int(seed) + sample_index)
|
||||
latent = torch.randn(1, in_channels, coords.shape[0], 1, generator=generator)
|
||||
else:
|
||||
sample_indices = resolve_sample_indices(batch_index, batch_size)
|
||||
latent = torch.zeros(batch_size, in_channels, max_tokens, 1)
|
||||
for i, sample_index in enumerate(sample_indices):
|
||||
count = int(coord_counts[i].item())
|
||||
generator = torch.Generator(device="cpu")
|
||||
generator.manual_seed(int(seed) + int(sample_index))
|
||||
latent_i = torch.randn(1, in_channels, count, 1, generator=generator)
|
||||
latent[i, :, :count] = latent_i[0]
|
||||
if coord_counts is not None:
|
||||
latent.trellis_coord_counts = coord_counts.clone()
|
||||
model = model.clone()
|
||||
model.model_options = model.model_options.copy()
|
||||
if "transformer_options" in model.model_options:
|
||||
@ -519,11 +754,20 @@ class EmptyShapeLatentTrellis2(IO.ComfyNode):
|
||||
model.model_options["transformer_options"] = {}
|
||||
|
||||
model.model_options["transformer_options"]["coords"] = coords
|
||||
if coord_counts is not None:
|
||||
model.model_options["transformer_options"]["coord_counts"] = coord_counts
|
||||
if is_512_pass:
|
||||
model.model_options["transformer_options"]["generation_mode"] = "shape_generation_512"
|
||||
else:
|
||||
model.model_options["transformer_options"]["generation_mode"] = "shape_generation"
|
||||
return IO.NodeOutput({"samples": latent, "coords": coords, "type": "trellis2"}, model)
|
||||
output = {"samples": latent, "coords": coords, "type": "trellis2"}
|
||||
if batch_index is not None:
|
||||
output["batch_index"] = normalize_batch_index(batch_index)
|
||||
if coord_counts is not None:
|
||||
output["coord_counts"] = coord_counts
|
||||
if coord_resolutions is not None:
|
||||
output["resolutions"] = coord_resolutions
|
||||
return IO.NodeOutput(output, model)
|
||||
|
||||
class EmptyTextureLatentTrellis2(IO.ComfyNode):
|
||||
@classmethod
|
||||
@ -534,7 +778,8 @@ class EmptyTextureLatentTrellis2(IO.ComfyNode):
|
||||
inputs=[
|
||||
IO.Voxel.Input("structure_or_coords"),
|
||||
IO.Latent.Input("shape_latent"),
|
||||
IO.Model.Input("model")
|
||||
IO.Model.Input("model"),
|
||||
IO.Int.Input("seed", default=0, min=0, max=0xffffffffffffffff),
|
||||
],
|
||||
outputs=[
|
||||
IO.Latent.Output(),
|
||||
@ -543,20 +788,68 @@ class EmptyTextureLatentTrellis2(IO.ComfyNode):
|
||||
)
|
||||
|
||||
@classmethod
|
||||
def execute(cls, structure_or_coords, shape_latent, model):
|
||||
def execute(cls, structure_or_coords, shape_latent, model, seed):
|
||||
channels = 32
|
||||
coord_counts = None
|
||||
batch_index = None
|
||||
if hasattr(structure_or_coords, "data") and structure_or_coords.data.ndim == 4:
|
||||
decoded = structure_or_coords.data.unsqueeze(1)
|
||||
coords = torch.argwhere(decoded.bool())[:, [0, 2, 3, 4]].int()
|
||||
batch_index = normalize_batch_index(getattr(structure_or_coords, "batch_index", None))
|
||||
|
||||
elif isinstance(structure_or_coords, dict):
|
||||
coords = structure_or_coords["coords"].int()
|
||||
coord_counts = structure_or_coords.get("coord_counts")
|
||||
batch_index = normalize_batch_index(structure_or_coords.get("batch_index"))
|
||||
|
||||
elif isinstance(structure_or_coords, torch.Tensor) and structure_or_coords.ndim == 2:
|
||||
coords = structure_or_coords.int()
|
||||
else:
|
||||
raise ValueError(
|
||||
"structure_or_coords must be a voxel input with data.ndim == 4, "
|
||||
f'a dict containing "coords", or a 2D torch.Tensor; got {type(structure_or_coords).__name__}'
|
||||
)
|
||||
|
||||
shape_batch_index = normalize_batch_index(shape_latent.get("batch_index"))
|
||||
if batch_index is None:
|
||||
batch_index = shape_batch_index
|
||||
shape_latent = shape_latent["samples"]
|
||||
batch_size, inferred_coord_counts, max_tokens = infer_batched_coord_layout(coords)
|
||||
if coord_counts is not None:
|
||||
coord_counts = coord_counts.to(dtype=torch.int64, device=coords.device)
|
||||
if coord_counts.shape != inferred_coord_counts.shape or not torch.equal(coord_counts, inferred_coord_counts):
|
||||
raise ValueError(
|
||||
f"Trellis2 coord_counts metadata {coord_counts.tolist()} does not match coords layout {inferred_coord_counts.tolist()}"
|
||||
)
|
||||
else:
|
||||
coord_counts = inferred_coord_counts
|
||||
if shape_latent.ndim == 4:
|
||||
shape_latent = shape_latent.squeeze(-1).transpose(1, 2).reshape(-1, channels)
|
||||
if shape_latent.shape[0] != batch_size:
|
||||
raise ValueError(
|
||||
f"shape_latent batch {shape_latent.shape[0]} doesn't match coords batch {batch_size}"
|
||||
)
|
||||
shape_latent = shape_latent.squeeze(-1).transpose(1, 2)
|
||||
if shape_latent.shape[1] < max_tokens:
|
||||
raise ValueError(
|
||||
f"shape_latent tokens {shape_latent.shape[1]} can't cover coords max tokens {max_tokens}"
|
||||
)
|
||||
|
||||
latent = torch.randn(1, channels, coords.shape[0], 1)
|
||||
if batch_size == 1:
|
||||
sample_index = resolve_singleton_sample_index(batch_index)
|
||||
generator = torch.Generator(device="cpu")
|
||||
generator.manual_seed(int(seed) + sample_index)
|
||||
latent = torch.randn(1, channels, coords.shape[0], 1, generator=generator)
|
||||
else:
|
||||
sample_indices = resolve_sample_indices(batch_index, batch_size)
|
||||
latent = torch.zeros(batch_size, channels, max_tokens, 1)
|
||||
for i, sample_index in enumerate(sample_indices):
|
||||
count = int(coord_counts[i].item())
|
||||
generator = torch.Generator(device="cpu")
|
||||
generator.manual_seed(int(seed) + int(sample_index))
|
||||
latent_i = torch.randn(1, channels, count, 1, generator=generator)
|
||||
latent[i, :, :count] = latent_i[0]
|
||||
if coord_counts is not None:
|
||||
latent.trellis_coord_counts = coord_counts.clone()
|
||||
model = model.clone()
|
||||
model.model_options = model.model_options.copy()
|
||||
if "transformer_options" in model.model_options:
|
||||
@ -565,9 +858,16 @@ class EmptyTextureLatentTrellis2(IO.ComfyNode):
|
||||
model.model_options["transformer_options"] = {}
|
||||
|
||||
model.model_options["transformer_options"]["coords"] = coords
|
||||
if coord_counts is not None:
|
||||
model.model_options["transformer_options"]["coord_counts"] = coord_counts
|
||||
model.model_options["transformer_options"]["generation_mode"] = "texture_generation"
|
||||
model.model_options["transformer_options"]["shape_slat"] = shape_latent
|
||||
return IO.NodeOutput({"samples": latent, "coords": coords, "type": "trellis2"}, model)
|
||||
output = {"samples": latent, "coords": coords, "type": "trellis2"}
|
||||
if batch_index is not None:
|
||||
output["batch_index"] = normalize_batch_index(batch_index)
|
||||
if coord_counts is not None:
|
||||
output["coord_counts"] = coord_counts
|
||||
return IO.NodeOutput(output, model)
|
||||
|
||||
|
||||
class EmptyStructureLatentTrellis2(IO.ComfyNode):
|
||||
@ -578,17 +878,30 @@ class EmptyStructureLatentTrellis2(IO.ComfyNode):
|
||||
category="latent/3d",
|
||||
inputs=[
|
||||
IO.Int.Input("batch_size", default=1, min=1, max=4096, tooltip="The number of latent images in the batch."),
|
||||
IO.Int.Input("batch_index_start", default=0, min=0, max=4096, tooltip="Starting sample index for per-sample sampler noise."),
|
||||
IO.Int.Input("seed", default=0, min=0, max=0xffffffffffffffff),
|
||||
],
|
||||
outputs=[
|
||||
IO.Latent.Output(),
|
||||
]
|
||||
)
|
||||
@classmethod
|
||||
def execute(cls, batch_size):
|
||||
def execute(cls, batch_size, batch_index_start, seed):
|
||||
in_channels = 8
|
||||
resolution = 16
|
||||
latent = torch.randn(batch_size, in_channels, resolution, resolution, resolution)
|
||||
return IO.NodeOutput({"samples": latent, "type": "trellis2"})
|
||||
sample_indices = [int(batch_index_start) + i for i in range(batch_size)]
|
||||
latent = torch.zeros(batch_size, in_channels, resolution, resolution, resolution)
|
||||
for i, sample_index in enumerate(sample_indices):
|
||||
generator = torch.Generator(device="cpu")
|
||||
generator.manual_seed(int(seed) + sample_index)
|
||||
latent[i] = torch.randn(1, in_channels, resolution, resolution, resolution, generator=generator)[0]
|
||||
output = {
|
||||
"samples": latent,
|
||||
"type": "trellis2",
|
||||
}
|
||||
if batch_size > 1 or batch_index_start != 0:
|
||||
output["batch_index"] = sample_indices
|
||||
return IO.NodeOutput(output)
|
||||
|
||||
def simplify_fn(vertices, faces, colors=None, target=100000):
|
||||
if vertices.ndim == 3:
|
||||
|
||||
@ -123,5 +123,203 @@ class TestRunConditioningRestore(unittest.TestCase):
|
||||
self.assertFalse(hasattr(inner_model, "image_size"))
|
||||
|
||||
|
||||
class DummyCloneModel:
|
||||
def __init__(self):
|
||||
self.model_options = {}
|
||||
|
||||
def clone(self):
|
||||
cloned = DummyCloneModel()
|
||||
cloned.model_options = self.model_options.copy()
|
||||
return cloned
|
||||
|
||||
|
||||
class TestTrellisBatchSemantics(unittest.TestCase):
|
||||
def test_empty_structure_latent_is_deterministic_and_propagates_sample_indices(self):
|
||||
batch_output = nodes_trellis2.EmptyStructureLatentTrellis2.execute(2, 0, 17)[0]
|
||||
single_output = nodes_trellis2.EmptyStructureLatentTrellis2.execute(1, 5, 17)[0]
|
||||
|
||||
expected_batch = torch.zeros(2, 8, 16, 16, 16)
|
||||
expected_batch[0] = torch.randn(1, 8, 16, 16, 16, generator=torch.Generator(device="cpu").manual_seed(17))[0]
|
||||
expected_batch[1] = torch.randn(1, 8, 16, 16, 16, generator=torch.Generator(device="cpu").manual_seed(18))[0]
|
||||
expected_single = torch.randn(1, 8, 16, 16, 16, generator=torch.Generator(device="cpu").manual_seed(22))
|
||||
|
||||
self.assertTrue(torch.equal(batch_output["samples"], expected_batch))
|
||||
self.assertEqual(batch_output["batch_index"], [0, 1])
|
||||
self.assertTrue(torch.equal(single_output["samples"], expected_single))
|
||||
self.assertEqual(single_output["batch_index"], [5])
|
||||
|
||||
def test_empty_shape_latent_is_deterministic_and_propagates_batch_index(self):
|
||||
coords = torch.tensor(
|
||||
[
|
||||
[1, 5, 5, 5],
|
||||
[0, 1, 1, 1],
|
||||
[1, 6, 6, 6],
|
||||
[0, 2, 2, 2],
|
||||
[1, 7, 7, 7],
|
||||
],
|
||||
dtype=torch.int32,
|
||||
)
|
||||
structure = {
|
||||
"coords": coords,
|
||||
"coord_counts": torch.tensor([2, 3], dtype=torch.int64),
|
||||
"batch_index": [4, 9],
|
||||
}
|
||||
|
||||
output, _ = nodes_trellis2.EmptyShapeLatentTrellis2.execute(structure, DummyCloneModel(), 23)
|
||||
|
||||
expected = torch.zeros(2, 32, 3, 1)
|
||||
expected[0, :, :2, :] = torch.randn(1, 32, 2, 1, generator=torch.Generator(device="cpu").manual_seed(27))[0]
|
||||
expected[1, :, :3, :] = torch.randn(1, 32, 3, 1, generator=torch.Generator(device="cpu").manual_seed(32))[0]
|
||||
|
||||
self.assertTrue(torch.equal(output["samples"], expected))
|
||||
self.assertTrue(torch.equal(output["coord_counts"], torch.tensor([2, 3], dtype=torch.int64)))
|
||||
self.assertEqual(output["batch_index"], [4, 9])
|
||||
|
||||
def test_empty_shape_latent_keeps_singleton_coord_counts(self):
|
||||
structure = {
|
||||
"coords": torch.tensor(
|
||||
[
|
||||
[0, 1, 1, 1],
|
||||
[0, 2, 2, 2],
|
||||
],
|
||||
dtype=torch.int32,
|
||||
),
|
||||
}
|
||||
|
||||
output, _ = nodes_trellis2.EmptyShapeLatentTrellis2.execute(structure, DummyCloneModel(), 11)
|
||||
|
||||
self.assertTrue(torch.equal(output["coord_counts"], torch.tensor([2], dtype=torch.int64)))
|
||||
|
||||
def test_empty_shape_latent_rejects_multi_index_singleton(self):
|
||||
structure = {
|
||||
"coords": torch.tensor(
|
||||
[
|
||||
[0, 1, 1, 1],
|
||||
[0, 2, 2, 2],
|
||||
],
|
||||
dtype=torch.int32,
|
||||
),
|
||||
"batch_index": [5, 6],
|
||||
}
|
||||
|
||||
with self.assertRaises(ValueError):
|
||||
nodes_trellis2.EmptyShapeLatentTrellis2.execute(structure, DummyCloneModel(), 11)
|
||||
|
||||
def test_empty_texture_latent_rejects_multi_index_singleton(self):
|
||||
coords = torch.tensor(
|
||||
[
|
||||
[0, 1, 1, 1],
|
||||
[0, 2, 2, 2],
|
||||
],
|
||||
dtype=torch.int32,
|
||||
)
|
||||
structure = {"coords": coords, "batch_index": [7, 8]}
|
||||
shape_latent = {"samples": torch.zeros(1, 32, 2, 1)}
|
||||
|
||||
with self.assertRaises(ValueError):
|
||||
nodes_trellis2.EmptyTextureLatentTrellis2.execute(
|
||||
structure,
|
||||
shape_latent,
|
||||
DummyCloneModel(),
|
||||
13,
|
||||
)
|
||||
|
||||
def test_empty_texture_latent_rejects_invalid_structure_input(self):
|
||||
with self.assertRaises(ValueError):
|
||||
nodes_trellis2.EmptyTextureLatentTrellis2.execute(
|
||||
"bad-input",
|
||||
{"samples": torch.zeros(1, 32, 2, 1)},
|
||||
DummyCloneModel(),
|
||||
13,
|
||||
)
|
||||
|
||||
def test_empty_texture_latent_uses_shape_batch_index_for_seed_fallback(self):
|
||||
coords = torch.tensor(
|
||||
[
|
||||
[0, 1, 1, 1],
|
||||
[1, 2, 2, 2],
|
||||
[1, 3, 3, 3],
|
||||
],
|
||||
dtype=torch.int32,
|
||||
)
|
||||
structure = {"coords": coords}
|
||||
shape_latent = {
|
||||
"samples": torch.zeros(2, 32, 2, 1),
|
||||
"batch_index": [4, 9],
|
||||
}
|
||||
|
||||
output, _ = nodes_trellis2.EmptyTextureLatentTrellis2.execute(
|
||||
structure,
|
||||
shape_latent,
|
||||
DummyCloneModel(),
|
||||
13,
|
||||
)
|
||||
|
||||
expected = torch.zeros(2, 32, 2, 1)
|
||||
expected[0, :, :1, :] = torch.randn(1, 32, 1, 1, generator=torch.Generator(device="cpu").manual_seed(17))[0]
|
||||
expected[1, :, :2, :] = torch.randn(1, 32, 2, 1, generator=torch.Generator(device="cpu").manual_seed(22))[0]
|
||||
|
||||
self.assertTrue(torch.equal(output["samples"], expected))
|
||||
self.assertEqual(output["batch_index"], [4, 9])
|
||||
|
||||
def test_flatten_batched_sparse_latent_validates_coord_counts(self):
|
||||
samples = torch.zeros(2, 32, 3, 1)
|
||||
coords = torch.tensor(
|
||||
[
|
||||
[0, 1, 1, 1],
|
||||
[1, 2, 2, 2],
|
||||
[1, 3, 3, 3],
|
||||
],
|
||||
dtype=torch.int32,
|
||||
)
|
||||
coord_counts = torch.tensor([2, 1], dtype=torch.int64)
|
||||
|
||||
with self.assertRaises(ValueError):
|
||||
nodes_trellis2.flatten_batched_sparse_latent(samples, coords, coord_counts)
|
||||
|
||||
def test_infer_batched_coord_layout_rejects_negative_batch_ids(self):
|
||||
coords = torch.tensor(
|
||||
[
|
||||
[-1, 1, 1, 1],
|
||||
[0, 2, 2, 2],
|
||||
],
|
||||
dtype=torch.int32,
|
||||
)
|
||||
|
||||
with self.assertRaises(ValueError):
|
||||
nodes_trellis2.infer_batched_coord_layout(coords)
|
||||
|
||||
def test_split_batched_coords_validates_total_count(self):
|
||||
coords = torch.tensor(
|
||||
[
|
||||
[0, 1, 1, 1],
|
||||
[1, 2, 2, 2],
|
||||
[1, 3, 3, 3],
|
||||
],
|
||||
dtype=torch.int32,
|
||||
)
|
||||
coord_counts = torch.tensor([1, 1], dtype=torch.int64)
|
||||
|
||||
with self.assertRaises(ValueError):
|
||||
nodes_trellis2.split_batched_coords(coords, coord_counts)
|
||||
|
||||
def test_empty_shape_latent_preserves_resolutions_key(self):
|
||||
structure = {
|
||||
"coords": torch.tensor(
|
||||
[
|
||||
[0, 1, 1, 1],
|
||||
[0, 2, 2, 2],
|
||||
],
|
||||
dtype=torch.int32,
|
||||
),
|
||||
"resolutions": torch.tensor([1024], dtype=torch.int64),
|
||||
}
|
||||
|
||||
output, model = nodes_trellis2.EmptyShapeLatentTrellis2.execute(structure, DummyCloneModel(), 11)
|
||||
|
||||
self.assertTrue(torch.equal(output["resolutions"], torch.tensor([1024], dtype=torch.int64)))
|
||||
self.assertNotIn("coord_resolutions", model.model_options["transformer_options"])
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
unittest.main()
|
||||
|
||||
76
tests-unit/comfy_test/sample_test.py
Normal file
76
tests-unit/comfy_test/sample_test.py
Normal file
@ -0,0 +1,76 @@
|
||||
import unittest
|
||||
|
||||
import torch
|
||||
|
||||
import comfy.sample
|
||||
|
||||
|
||||
class TestPrepareNoiseInnerTrellis(unittest.TestCase):
|
||||
def test_coord_counts_noise_matches_per_index_prefix_draws(self):
|
||||
latent = torch.zeros(2, 4, 5, 1)
|
||||
latent.trellis_coord_counts = torch.tensor([3, 5], dtype=torch.int64)
|
||||
|
||||
generator = torch.Generator(device="cpu")
|
||||
generator.manual_seed(123)
|
||||
noise = comfy.sample.prepare_noise_inner(latent, generator)
|
||||
|
||||
expected = torch.zeros_like(noise, dtype=torch.float32)
|
||||
row0 = torch.Generator(device="cpu")
|
||||
row0.manual_seed(123)
|
||||
expected[0, :, :3, :] = torch.randn(1, 4, 3, 1, generator=row0)[0]
|
||||
row1 = torch.Generator(device="cpu")
|
||||
row1.manual_seed(124)
|
||||
expected[1] = torch.randn(1, 4, 5, 1, generator=row1)[0]
|
||||
|
||||
self.assertTrue(torch.equal(noise.float(), expected))
|
||||
self.assertTrue(torch.equal(noise[0, :, 3:, :], torch.zeros_like(noise[0, :, 3:, :])))
|
||||
|
||||
def test_coord_counts_noise_inds_share_prefixes_for_duplicates(self):
|
||||
latent = torch.zeros(2, 4, 5, 1)
|
||||
latent.trellis_coord_counts = torch.tensor([3, 5], dtype=torch.int64)
|
||||
|
||||
generator = torch.Generator(device="cpu")
|
||||
generator.manual_seed(456)
|
||||
noise = comfy.sample.prepare_noise_inner(latent, generator, noise_inds=[7, 7])
|
||||
|
||||
replay = torch.Generator(device="cpu")
|
||||
replay.manual_seed(463)
|
||||
expected1 = torch.randn(1, 4, 5, 1, generator=replay)
|
||||
expected0 = expected1[:, :, :3, :]
|
||||
|
||||
self.assertTrue(torch.equal(noise[0:1, :, :3, :], expected0))
|
||||
self.assertTrue(torch.equal(noise[1:2, :, :5, :], expected1))
|
||||
self.assertTrue(torch.equal(noise[0, :, 3:, :], torch.zeros_like(noise[0, :, 3:, :])))
|
||||
|
||||
def test_coord_counts_noise_inds_length_must_match_batch(self):
|
||||
latent = torch.zeros(2, 4, 5, 1)
|
||||
latent.trellis_coord_counts = torch.tensor([3, 5], dtype=torch.int64)
|
||||
|
||||
generator = torch.Generator(device="cpu")
|
||||
generator.manual_seed(456)
|
||||
|
||||
with self.assertRaises(ValueError):
|
||||
comfy.sample.prepare_noise_inner(latent, generator, noise_inds=[7])
|
||||
|
||||
def test_coord_counts_metadata_must_match_batch_and_bounds(self):
|
||||
generator = torch.Generator(device="cpu")
|
||||
generator.manual_seed(456)
|
||||
|
||||
latent = torch.zeros(2, 4, 5, 1)
|
||||
latent.trellis_coord_counts = torch.tensor([[3, 5]], dtype=torch.int64)
|
||||
with self.assertRaises(ValueError):
|
||||
comfy.sample.prepare_noise_inner(latent, generator)
|
||||
|
||||
latent = torch.zeros(2, 4, 5, 1)
|
||||
latent.trellis_coord_counts = torch.tensor([3], dtype=torch.int64)
|
||||
with self.assertRaises(ValueError):
|
||||
comfy.sample.prepare_noise_inner(latent, generator)
|
||||
|
||||
latent = torch.zeros(2, 4, 5, 1)
|
||||
latent.trellis_coord_counts = torch.tensor([3, 6], dtype=torch.int64)
|
||||
with self.assertRaises(ValueError):
|
||||
comfy.sample.prepare_noise_inner(latent, generator)
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
unittest.main()
|
||||
Loading…
Reference in New Issue
Block a user