Initial commit with folder contents

pyproject.toml

@@ -27,16 +27,16 @@ revision = "741f7c3ce8b383c54771c7003378a50191e9efe9"
 exclude = ["transformer"]
 
 [[tool.edge-maxxing.models]]
-repository = "CraftedHue/extra0usio0"
-revision = "5b04ff5b00d170334f508db412d9cc9e7803617b"
+repository = "CraftedHue/extra0Forg0"
+revision = "5bce637251277b7c6423d01c5cace7778bbf2e9f"
 
 [[tool.edge-maxxing.models]]
-repository = "CraftedHue/extra1usio1"
-revision = "73a7cf1e0a20350fca758bba7f7be81b1ac4f52b"
+repository = "CraftedHue/extra1Forg1"
+revision = "312290178758e8f4147b8c188faf8fe021a6b58e"
 
 [[tool.edge-maxxing.models]]
-repository = "CraftedHue/extra2usio2"
-revision = "d3aed408719722124428e041f74c3e09540a6890"
+repository = "CraftedHue/extra2Forg2"
+revision = "1394f2a464629a736898fd44ea31b9b98cdee3d2"
 
 
 [project.scripts]

src/pipeline.py

@@ -591,12 +591,12 @@ def load_pipeline() -> Pipeline:
     dtype, device = torch.bfloat16, "cuda"
     
     text_encoder_2 = T5EncoderModel.from_pretrained(
-        "CraftedHue/extra1usio1", revision = "73a7cf1e0a20350fca758bba7f7be81b1ac4f52b", torch_dtype=torch.bfloat16
+        "CraftedHue/extra1Forg1", revision = "312290178758e8f4147b8c188faf8fe021a6b58e", torch_dtype=torch.bfloat16
     ).to(memory_format=torch.channels_last)
     
-    vae = AutoencoderTiny.from_pretrained("CraftedHue/extra2usio2", revision="d3aed408719722124428e041f74c3e09540a6890", torch_dtype=dtype)
+    vae = AutoencoderTiny.from_pretrained("CraftedHue/extra2Forg2", revision="1394f2a464629a736898fd44ea31b9b98cdee3d2", torch_dtype=dtype)
     
-    path = os.path.join(HF_HUB_CACHE, "models--CraftedHue--extra0usio0/snapshots/5b04ff5b00d170334f508db412d9cc9e7803617b")
+    path = os.path.join(HF_HUB_CACHE, "models--CraftedHue--extra0Forg0/snapshots/5bce637251277b7c6423d01c5cace7778bbf2e9f")
     generator = torch.Generator(device=device)
     model = FluxTransformer2DModel.from_pretrained(path, torch_dtype=dtype, use_safetensors=False, generator= generator).to(memory_format=torch.channels_last)
     torch.backends.cudnn.benchmark = True