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TimestepEmbedder
TimestepEmbedder: 9 implementations from 9 papers ran on one shared input (census 2026-09-22, battery b986f7e04d79); they produced 5 distinct outputs across 2 buckets, one shared input per bucket.
Identical values to six decimals (the recorded digest) on the shared input are agreement on those inputs, not equivalence. Implementations are compared only within one bucket, the positional (rank, kind, dtype) of each array argument; the argument name is not part of the key because the harness draws the shared array from (rank, kind) and casts it to the dtype, whatever the name; each bucket's shared input is fixed by that key, so members of one bucket saw bitwise-identical inputs under their own scalar arguments. A cluster is the set of members whose recorded output digest is identical. Nothing here says which computation a paper's method intended, and nothing reproduces a paper's results.
Not compared, and not in the tables or the counts above:
- Every examined implementation of this name that took an array argument ran on the shared input.
- No implementation of this name ran on its own fixture arguments only: every one that ran took an array argument.
- Every compared output was digested.
- 9 compared members are class-bearing (tagged class): the class was constructed with seeded weights in evaluation mode and then called, so its recorded output depends on that initialisation as well as on the forward computation.
Bucket 1 of 2: arg 1: rank 1, kind int, dtype int64
8 implementations from 8 papers share this bucket (rank, kind, dtype of each array argument, positional; each member's recorded signature, argument name included, is shown under it); 4 distinct outputs, largest cluster first. Values are the first 8 of the recorded output, flattened.
| Cluster (same digest to six decimals) | Members | Shared output on this bucket's input |
|---|---|---|
| 3 implementations 3 papers 8698b24e75aa recorded values identical |
class models.py 36e69cc6 recorded t:1/int/int64 class models.py 7a14af05 recorded t:1/int/int64
models/rfmsr.py a64a2a0d recorded t:1/int/int64 |
[-0.00585138, -0.252235, -0.101584, 0.237988, -0.0847021, -0.0422496, -0.0150573, 0.0116102, …] shape [8, 64] · float32 · Tensor |
| 3 implementations 3 papers ae4f4bbc49dd recorded values identical |
class Model.py 13972686 recorded t:1/int/int64 class src/models/denoisers/dit.py 4f5374da recorded t:1/int/int64
src/models/transformer/JiT.py b6702ea9 recorded t:1/int/int64 |
[0.0931006, 0.174504, -0.398712, -0.274677, -0.152235, 0.114349, -0.164447, 0.283258, …] shape [8, 8] · float32 · Tensor |
| 1 implementation 1 paper a780766b4b71 |
src/models/controlnet.py ec75a1f9 recorded t:1/int/int64 |
[0.122364, 0.26327, 0.0848194, -0.25548, 0.21163, -0.383041, -0.187202, 0.0890273, …] shape [8, 12] · float32 · Tensor |
| 1 implementation 1 paper e17c34d76a9e |
class DiffusionRet/models/modeling.py 29b441f9 recorded timesteps:1/int/int64 |
[-0.246242, 0.453031, 0.183401, 0.374649, -0.206935, 0.49242, 0.103967, 0.328675, …] shape [8, 4] · float32 · Tensor |
Bucket 2 of 2: arg 1: rank 1, kind float, dtype float32
1 implementation from 1 paper share this bucket (rank, kind, dtype of each array argument, positional; each member's recorded signature, argument name included, is shown under it); 1 distinct output, largest cluster first. Values are the first 8 of the recorded output, flattened.
| Cluster (same digest to six decimals) | Members | Shared output on this bucket's input |
|---|---|---|
| 1 implementation 1 paper b10f18935581 |
models/dit_gumbel.py 1a545c77 recorded t:1/float/float32 |
[0.131186, 0.148842, -0.40112, -0.346325, -0.379667, 0.160614, -0.0759736, 0.229224, …] shape [8, 8] · float32 · Tensor |
Identical values to six decimals (the recorded digest) on the shared input are agreement on those inputs, not equivalence; where a cluster's members carry recorded values, the largest difference among them is shown under the cluster. Paper titles are the archive's archive 2025-07-28 where the paper is in the archive and the graph's where it was added by Syntology; papers with no page here are shown by their recorded paper id only. A paper count above the implementation count means one implementation (one code sha) is held from several papers' repositories and counts once. Per-sample status, licence and fingerprint records for each paper are on its paper page. JSON twin: /census/timestepembedder.json.
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