Update src/pipeline.py

src/pipeline.py

@@ -31,8 +31,9 @@ def load_pipeline() -> Pipeline:
     # basepath = os.path.join(HF_HUB_CACHE, "models--manbeast3b--Flux.1.schnell_eagle5_1_0.1_unst_8/snapshots/3666a458a53e7dc83adfecb0bf955a0b4d575843")
     # basepath = os.path.join(HF_HUB_CACHE, "models--manbeast3b--Flux.1.schnell_eagle5_1_0.1_unst_13/snapshots/b3bdda899cd1961ec9b97bffde3ded31afa73ce3")
     # basepath = os.path.join(HF_HUB_CACHE, "models--manbeast3b--Flux.1.schnell_eagle5_1_0.1_unst_10/snapshots/20e4cf6ce3cc658237dfd6aae1d5f14bc6b3d1a4")
-    # pipeline.vae.encoder.load_state_dict(torch.load(os.path.join(basepath, "encoder.pth")), strict=False)
-    # pipeline.vae.decoder.load_state_dict(torch.load(os.path.join(basepath, "decoder.pth")), strict=False)
+    basepath = os.path.join(HF_HUB_CACHE, "models--manbeast3b--Flux.1.schnell_eagle5_1_0.1_unst_8_try/snapshots/0d3ce1d07195ccfe8eafe821ee80b34d74a3c2d7")
+    pipeline.vae.encoder.load_state_dict(torch.load(os.path.join(basepath, "encoder.pth")), strict=False)
+    pipeline.vae.decoder.load_state_dict(torch.load(os.path.join(basepath, "decoder.pth")), strict=False)
     quantize_(pipeline.vae, int8_weight_only())
     pipeline.to("cuda")
     for _ in range(3):