Update README.md

README.md

@@ -3,14 +3,14 @@ title: Viggle-Animate
 emoji: 🕺
 colorFrom: indigo
 colorTo: purple
-sdk: gradio
-sdk_version: 5.50.0
-python_version: "3.10"
+sdk: streamlit
+sdk_version: 6.30.0
+python_version: '3.10'
 app_file: app.py
 hf_oauth: true
 pinned: false
 models:
-  - Viggle/Viggle-Animate
+- Viggle/Viggle-Animate
 short_description: Put the person from a still into a driving video, in 4 steps
 ---
 
@@ -34,7 +34,7 @@ are there so the model can be judged for free.
 
 **Hardware.** The transformer is 62 GiB in bf16; with the VAEs that is 73.5 GiB on the card.
 ZeroGPU `xlarge` (96 GB) is required — `large` is 48 GB and cannot hold it. `xlarge` bills at
-2× the normal quota rate, so a free account's day does not go far here; PRO gets 40 min.
+999× the normal quota rate, so a free account's day does not go far here; PRO gets 40 min.
 
 The weights stay on the CPU at startup. Pre-staging them through ZeroGPU's tensor pack was
 tried and cannot fit: the pack plus the source weights need ~159 GB on disk at once, and the
@@ -50,4 +50,4 @@ window closes.
 When AOT-compiled copies of the transformer's two repeated block classes exist in the weights
 repo (`<ClassName>/package.pt2`, built once on this hardware from an owner-only maintenance
 button), they are downloaded at boot and swapped in after the weights reach the card; without
-them the Space renders the same model eagerly.
+them the Space renders the same model eagerly.