debugging: print iso time of app launch, model & dataset links

app.py

@@ -25,6 +25,9 @@ _model = InferenceUNetPseudo3D(
         hf_auth_token = os.environ.get('HUGGING_FACE_HUB_TOKEN', None)
 )
 
+import datetime
+print(datetime.datetime.now(datetime.timezone.utc).isoformat())
+
 if _model.failed != False:
     trace = f'```{_model.failed}```'
     with gr.Blocks(title = 'Make-A-Video Stable Diffusion JAX', analytics_enabled = False) as demo:
@@ -151,9 +154,10 @@ with gr.Blocks(title = 'Make-A-Video Stable Diffusion JAX', analytics_enabled =
                         Only the new temporal layers have been fine tuned on a dataset of videos themed around dance.
                         The model has been trained for 80 epochs on a dataset of 18,000 Videos with 120 frames each, randomly selecting a 24 frame range from each sample.
 
-                        See model and dataset links in the metadata.
+                        Model: [TempoFunk/makeavid-sd-jax](https://huggingface.co/TempoFunk/makeavid-sd-jax)
+                        Datasets: [TempoFunk/tempofunk-sdance](https://huggingface.co/datasets/TempoFunk/tempofunk-sdance), [TempoFunk/small](https://huggingface.co/datasets/TempoFunk/small)
 
-                        Model implementation and training code can be found at <https://github.com/lopho/makeavid-sd-tpu>
+                        Model implementation and training code can be found at <https://github.com/lopho/makeavid-sd-tpu> (WIP)
             """)
         with gr.Column():
             intro3 = gr.Markdown("""