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README.md

@@ -2,28 +2,28 @@
 license: mit
 tags:
   - 3d-editing
-  - trellis
-  - controlnet
+  - 3d-generation
 ---
 
 # PartFlow — model weights
 
-Trained ControlNet denoisers for **PartFlow**, a two-stage image-conditioned
-3D editing model built on [TRELLIS](https://github.com/microsoft/TRELLIS).
+Trained stage models for **PartFlow**, a feedforward 3D editing network that
+learns from semantic-part transformations (project: *Pxform — Feedforward 3D
+Editing Learns from Semantic-Part Transformation*).
 
 Inference code: see the PartFlow GitHub repository.
 
 ## Contents
 
-| Path | Model | Description |
+| Path | Stage | Description |
 |---|---|---|
-| `stage1_ss/denoiser_ema0.9999_step0080000.pt` | `ControlledSparseStructureFlowModel` | Stage 1 — sparse-structure ControlNet (EMA, step 80k) |
+| `stage1_ss/denoiser_ema0.9999_step0080000.pt` | Stage 1 | Sparse-structure flow model (EMA, step 80k) |
 | `stage1_ss/config.json` | — | Stage 1 model config |
-| `stage2_slat/denoiser_ema0.9999_step0080000.pt` | `ControlledSLatFlowModel` | Stage 2 — structured-latent ControlNet (EMA, step 80k) |
+| `stage2_slat/denoiser_ema0.9999_step0080000.pt` | Stage 2 | Structured-latent (SLAT) flow model (EMA, step 80k) |
 | `stage2_slat/config.json` | — | Stage 2 model config |
 
 These are EMA weights (decay 0.9999). The frozen DINOv2 encoder and TRELLIS
-SS/SLat decoders are not included — PartFlow fetches them from their official
+SS/SLAT decoders are not included — PartFlow fetches them from their official
 repos at inference time.
 
 ## Usage