Update README.md

README.md

@@ -123,6 +123,9 @@ def main():
 
 ```
 
+### You'll need to snip out the __orig layer extensions that got snapped into it when I saved.
+Still not quite sure how to fix that without just editing before saving, but I think it's causing some sort of additional effects that I'm unaware of.
+I don't want to save as pt because they are considered unsafe and I don't want this to be considered unsafe for use.
 
 You can inference the test version using stable-diffusion-15 as an example test.
 The CLIP_L responses fall apart when too many nodes hit those guidance bells, but it's definitely a powerful first test using divergent systems.
@@ -171,6 +174,10 @@ t5_mod = T5EncoderModel.from_pretrained(
     torch_dtype=DTYPE
 ).to(DEVICE).eval()
 
+# 1c) Velocity Adapter local directory
+local_adapter_directory = "roba_adapter_step_19500.safetensors" # opens the state below.
+
+
 # 1c) Adapter
 import torch
 import torch.nn as nn
@@ -364,19 +371,22 @@ scheduler = EulerAncestralDiscreteScheduler.from_pretrained(
     "runwayml/stable-diffusion-v1-5", subfolder="scheduler"
 )
 
-# 1c) T5 + Adapter
+# 1c) T5
 t5_tok = T5TokenizerFast.from_pretrained("t5-small")
 t5_mod = T5EncoderModel.from_pretrained(
     "AbstractPhil/T5-Small-Human-Attentive-Try2-Pass3",
     torch_dtype=DTYPE
 ).to(DEVICE).eval()
 
+# 1d) velocity prediction adapter
 adapter = RobustVelocityAdapter(out_tokens=64).to(DEVICE).eval()
-state   = load_safetensors("roba_adapter_step_19500.safetensors", device="cpu")
+state   = load_safetensors(local_adapter_directory, device="cpu")
 clean   = {k.replace("_orig_mod.", ""): v for k, v in state.items()}
 adapter.load_state_dict(clean, strict=False)
 adapter.to(DEVICE).eval()
 
+
+
 # 2) GENERATION FUNCTION
 @torch.no_grad()
 def generate_image_with_adapter(