update the wrong registration information for T2VTurbo and adding back VC2

arena_elo/video_generation_model_info.json

@@ -31,7 +31,7 @@
     },
     "StableVideoDiffusion": {
         "Link": "https://fal.ai/models/fal-ai/fast-svd/text-to-video/api",
-        "License": "stable-video-diffusion-nc-community",
+        "License": "SVD-nc-community",
         "Organization": "Stability AI"
     },
     "T2VTurbo": {

model/model_registry.py

@@ -258,15 +258,6 @@ register_model_info(
     "AnimateDiff Turbo is a lightning version of AnimateDiff.",
 )
 
-"""
-register_model_info(
-    ["videogenhub_LaVie_generation"],
-    "LaVie",
-    "https://github.com/Vchitect/LaVie",
-    "LaVie is a video generation model with cascaded latent diffusion models.",
-)
-
-
 register_model_info(
     ["videogenhub_VideoCrafter2_generation"],
     "VideoCrafter2",
@@ -274,6 +265,13 @@ register_model_info(
     "VideoCrafter2 is a T2V model that disentangling motion from appearance.",
 )
 
+"""
+register_model_info(
+    ["videogenhub_LaVie_generation"],
+    "LaVie",
+    "https://github.com/Vchitect/LaVie",
+    "LaVie is a video generation model with cascaded latent diffusion models.",
+)
 register_model_info(
     ["videogenhub_ModelScope_generation"],
     "ModelScope",
@@ -303,7 +301,7 @@ register_model_info(
 )
     
 register_model_info(
-    ["fal_T2VTurbo_generation"],
+    ["fal_T2VTurbo_text2video"],
     "T2V-Turbo",
     "https://github.com/Ji4chenLi/t2v-turbo",
     "Video Consistency Model with Mixed Reward Feedback.",

model/models/__init__.py

@@ -18,7 +18,7 @@ IMAGE_EDITION_MODELS = ['imagenhub_CycleDiffusion_edition', 'imagenhub_Pix2PixZe
 VIDEO_GENERATION_MODELS = ['fal_AnimateDiff_text2video',
                            'fal_AnimateDiffTurbo_text2video',
                            #'videogenhub_LaVie_generation', 
-                           #'videogenhub_VideoCrafter2_generation',
+                           'videogenhub_VideoCrafter2_generation',
                            #'videogenhub_ModelScope_generation', 
                            'videogenhub_CogVideoX_generation', 'videogenhub_OpenSora12_generation',
                            #'videogenhub_OpenSora_generation', 

serve/leaderboard.py

@@ -22,20 +22,6 @@ basic_component_values = [None] * 6
 leader_component_values = [None] * 5
 
 
-# def make_leaderboard_md(elo_results):
-#     leaderboard_md = f"""
-# # 🏆 Chatbot Arena Leaderboard
-# | [Blog](https://lmsys.org/blog/2023-05-03-arena/) | [GitHub](https://github.com/lm-sys/FastChat) | [Paper](https://arxiv.org/abs/2306.05685) | [Dataset](https://github.com/lm-sys/FastChat/blob/main/docs/dataset_release.md) | [Twitter](https://twitter.com/lmsysorg) | [Discord](https://discord.gg/HSWAKCrnFx) |
-
-# This leaderboard is based on the following three benchmarks.
-# - [Chatbot Arena](https://lmsys.org/blog/2023-05-03-arena/) - a crowdsourced, randomized battle platform. We use 100K+ user votes to compute Elo ratings.
-# - [MT-Bench](https://arxiv.org/abs/2306.05685) - a set of challenging multi-turn questions. We use GPT-4 to grade the model responses.
-# - [MMLU](https://arxiv.org/abs/2009.03300) (5-shot) - a test to measure a model's multitask accuracy on 57 tasks.
-
-# 💻 Code: The Arena Elo ratings are computed by this [notebook]({notebook_url}). The MT-bench scores (single-answer grading on a scale of 10) are computed by [fastchat.llm_judge](https://github.com/lm-sys/FastChat/tree/main/fastchat/llm_judge). The MMLU scores are mostly computed by [InstructEval](https://github.com/declare-lab/instruct-eval). Higher values are better for all benchmarks. Empty cells mean not available. Last updated: November, 2023.
-# """
-#     return leaderboard_md
-
 def make_leaderboard_md(elo_results):
     leaderboard_md = f"""
 # 🏆 GenAI-Arena Leaderboard
@@ -324,31 +310,6 @@ def build_leaderboard_tab(elo_results_file, leaderboard_table_file, show_plot=Tr
 
     leader_component_values[:] = [md, p1, p2, p3, p4]
 
-    """
-    with gr.Row():
-        with gr.Column():
-            gr.Markdown(
-                "#### Figure 1: Fraction of Model A Wins for All Non-tied A vs. B Battles"
-            )
-            plot_1 = gr.Plot(p1, show_label=False)
-        with gr.Column():
-            gr.Markdown(
-                "#### Figure 2: Battle Count for Each Combination of Models (without Ties)"
-            )
-            plot_2 = gr.Plot(p2, show_label=False)
-    with gr.Row():
-        with gr.Column():
-            gr.Markdown(
-                "#### Figure 3: Bootstrap of Elo Estimates (1000 Rounds of Random Sampling)"
-            )
-            plot_3 = gr.Plot(p3, show_label=False)
-        with gr.Column():
-            gr.Markdown(
-                "#### Figure 4: Average Win Rate Against All Other Models (Assuming Uniform Sampling and No Ties)"
-            )
-            plot_4 = gr.Plot(p4, show_label=False)
-    """
-
     from .utils import acknowledgment_md
 
     gr.Markdown(acknowledgment_md)