gatilin/tokenflow_clipb_32k_visual_codec

TokenFlow CLIP-B 32K with enhanced RGB decoder

The complete official tokenflow_clipb_32k_enhanced.pt model mapping: trained CLIP-B/16 semantic encoder, pixel encoder, both codebooks, multi-scale residual quantizer, semantic decoder and enhanced RGB decoder. Source weights: ByteVisionLab/TokenFlow. All 649 tensor entries retain original names, dtypes and values.

from huggingface_hub import snapshot_download
import sys, torch
folder = snapshot_download('gatilin/tokenflow_clipb_32k_visual_codec')
sys.path.insert(0, folder)
from component import load_model
codec = load_model(folder)
x = torch.zeros(1, 3, 256, 256) # RGB NCHW in [-1, 1]
with torch.inference_mode():
    quantized, losses, ids = codec.encode(x)
    semantic_features, reconstructed_rgb = codec.decode_code(ids)

This release follows the official enhanced-decoder reconstruction script with teacher=clipb_224, infer_interpolate=True, and 32768 codes. Scales are [1,2,4,6,8,10,12,14,16]; IDs concatenate the square grids in that order. Both semantic and pixel codebook vectors are required to reconstruct the residual representation. The trained CLIP visual weights are already included; the adapter constructs the exact original visual architecture and strictly loads them without an extra CLIP weight download.

Native classes are pinned to source revision 9812da01c0695f4989060cc4eea053715e9e4441. The two adaptations are documented: parameter device/dtype helper compatibility, and construction of the already-checkpointed CLIP branch without its redundant network download. Original tensor computation classes are retained. See source_reconstruction.sh, code_provenance.json and validation.json.

Validation covers all source tensor payload hashes, strict loading, nine-scale ID geometry and bounds, residual lookup reconstruction, finite semantic/RGB outputs, and exact decode-code parity with canonical lookup decoding. Synthetic reconstruction does not establish image quality or full MLLM/VQA/text-to-image performance. Weight-card MIT terms and source-code Apache-2.0 terms are recorded separately in LICENSE and CODE_LICENSE; original copyright notices remain in the native files.

CLIP_LICENSE separately preserves the original OpenAI MIT license and attribution for the bundled CLIP-derived visual transformer.