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| import os | |
| import gradio as gr | |
| import pandas as pd | |
| from apscheduler.schedulers.background import BackgroundScheduler | |
| from collections import Counter, defaultdict | |
| from datasets import load_dataset | |
| import datasets | |
| from huggingface_hub import HfApi, list_datasets | |
| api = HfApi(token=os.environ.get("HF_TOKEN", None)) | |
| def restart_space(): | |
| api.restart_space(repo_id="OpenGenAI/parti-prompts-leaderboard") | |
| parti_prompt_results = [] | |
| ORG = "diffusers-parti-prompts" | |
| SUBMISSIONS = { | |
| "kand2": None, | |
| "sdxl": None, | |
| "wuerst": None, | |
| "karlo": None, | |
| } | |
| LINKS = { | |
| "kand2": "https://huggingface.co/kandinsky-community/kandinsky-2-2-decoder", | |
| "sdxl": "https://huggingface.co/stabilityai/stable-diffusion-xl-base-1.0", | |
| "wuerst": "https://huggingface.co/warp-ai/wuerstchen", | |
| "karlo": "https://huggingface.co/kakaobrain/karlo-v1-alpha", | |
| } | |
| MODEL_KEYS = "-".join(SUBMISSIONS.keys()) | |
| SUBMISSION_ORG = f"result-{MODEL_KEYS}" | |
| submission_names = list(SUBMISSIONS.keys()) | |
| ds = load_dataset("nateraw/parti-prompts")["train"] | |
| parti_prompt_categories = ds["Category"] | |
| parti_prompt_challenge = ds["Challenge"] | |
| def load_submissions(): | |
| all_datasets = list_datasets(author=SUBMISSION_ORG) | |
| relevant_ids = [d.id for d in all_datasets] | |
| ids = defaultdict(list) | |
| challenges = defaultdict(list) | |
| categories = defaultdict(list) | |
| total_submissions = 0 | |
| for _id in relevant_ids: | |
| try: | |
| ds = load_dataset(_id)["train"] | |
| except: | |
| # skip dataset | |
| continue | |
| all_results = [] | |
| all_ids = [] | |
| for result, image_id in zip(ds["result"], ds["id"]): | |
| all_result = result.split(",") | |
| all_results += all_result | |
| all_ids += (len(all_result) * [image_id]) | |
| for result, image_id in zip(all_results, all_ids): | |
| if result == "": | |
| print(f"{result} was not solved by any model.") | |
| elif result not in submission_names: | |
| import ipdb; ipdb.set_trace() | |
| # Make sure that incorrect model names are not added | |
| continue | |
| ids[result].append(image_id) | |
| challenges[parti_prompt_challenge[image_id]].append(result) | |
| categories[parti_prompt_categories[image_id]].append(result) | |
| total_submissions += 1 | |
| all_values = sum(len(v) for v in ids.values()) | |
| main_dict = {k: float('{:.2}'.format(len(v)/all_values)) for k, v in ids.items()} | |
| challenges = {k: Counter(v) for k, v in challenges.items()} | |
| categories = {k: Counter(v) for k, v in categories.items()} | |
| return total_submissions, main_dict, challenges, categories | |
| def sort_by_highest_percentage(df): | |
| # Convert percentage values to numeric format | |
| df = df[df.loc[0].sort_values(ascending=False).index] | |
| return df | |
| def get_dataframe_all(): | |
| total_submissions, main, challenges, categories = load_submissions() | |
| main_frame = pd.DataFrame([main]) | |
| challenges_frame = pd.DataFrame.from_dict(challenges).fillna(0).T | |
| challenges_frame = challenges_frame.div(challenges_frame.sum(axis=1), axis=0) | |
| categories_frame = pd.DataFrame.from_dict(categories).fillna(0).T | |
| categories_frame = categories_frame.div(categories_frame.sum(axis=1), axis=0) | |
| main_frame = main_frame.rename(columns={"": "NOT SOLVED"}) | |
| categories_frame = categories_frame.rename(columns={"": "NOT SOLVED"}) | |
| challenges_frame = challenges_frame.rename(columns={"": "NOT SOLVED"}) | |
| main_frame = sort_by_highest_percentage(main_frame) | |
| main_frame = main_frame.applymap(lambda x: '{:.2%}'.format(x)) | |
| challenges_frame = challenges_frame.applymap(lambda x: '{:.2%}'.format(x)) | |
| categories_frame = categories_frame.applymap(lambda x: '{:.2%}'.format(x)) | |
| categories_frame = categories_frame.reindex(columns=main_frame.columns.to_list()) | |
| challenges_frame = challenges_frame.reindex(columns=main_frame.columns.to_list()) | |
| categories_frame = categories_frame.reset_index().rename(columns={'index': 'Category'}) | |
| challenges_frame = challenges_frame.reset_index().rename(columns={'index': 'Challenge'}) | |
| return total_submissions, main_frame, challenges_frame, categories_frame | |
| TITLE = "# Open Parti Prompts Leaderboard" | |
| DESCRIPTION = """ | |
| The *Open Parti Prompts Leaderboard* compares state-of-the-art, open-source text-to-image models to each other according to **human preferences**. \n\n | |
| Text-to-image models are notoriously difficult to evaluate. [FID](https://en.wikipedia.org/wiki/Fr%C3%A9chet_inception_distance) and | |
| [CLIP Score](https://en.wikipedia.org/wiki/Fr%C3%A9chet_inception_distance) are not enough to accurately state whether a text-to-image model can | |
| **generate "good" images**. "Good" is extremely difficult to put into numbers. \n\n | |
| Instead, the **Open Parti Prompts Leaderboard** uses human feedback from the community to compare images from different text-to-image models to each other. | |
| \n\n | |
| ❤️ ***Please take 3 minutes to contribute to the benchmark.*** \n | |
| 👉 ***Play one round of [Open Parti Prompts Game](https://huggingface.co/spaces/OpenGenAI/open-parti-prompts) to contribute 10 answers.*** 🤗 | |
| """ | |
| EXPLANATION = """\n\n | |
| ## How the is data collected 📊 \n\n | |
| In more detail, the [Open Parti Prompts Game](https://huggingface.co/spaces/OpenGenAI/open-parti-prompts) collects human preferences that state which generated image | |
| best fits a given prompt from the [Parti Prompts](https://huggingface.co/datasets/nateraw/parti-prompts) dataset. Parti Prompts has been designed to challenge | |
| text-to-image models on prompts of varying categories and difficulty. The images have been pre-generated from the models that are compared in this space. | |
| For more information of how the images were created, please refer to [Open Parti Prompts](https://huggingface.co/spaces/OpenGenAI/open-parti-prompts). | |
| The community's answers are then stored and used in this space to give a human-preference-based comparison of the different models. \n\n | |
| Currently the leaderboard includes the following models: | |
| - [Kandinsky 2.2](https://huggingface.co/kandinsky-community/kandinsky-2-2-decoder) | |
| - [Stable Diffusion XL](https://huggingface.co/stabilityai/stable-diffusion-xl-base-1.0) | |
| - [Wuerstchen](https://huggingface.co/warp-ai/wuerstchen) | |
| - [Karlo](https://huggingface.co/kakaobrain/karlo-v1-alpha) | |
| In the following you can see three result tables. The first shows the overall comparison of the 4 models. The score states, | |
| **the percentage at which images generated from the corresponding model are preferred over the image from all other models**. The second and third tables | |
| show you a breakdown analysis per category and per type of challenge as defined by [Parti Prompts](https://huggingface.co/datasets/nateraw/parti-prompts). | |
| """ | |
| GALLERY_COLUMN_NUM = len(SUBMISSIONS) | |
| def refresh(): | |
| return get_dataframe_all() | |
| with gr.Blocks() as demo: | |
| with gr.Column(visible=True) as intro_view: | |
| gr.Markdown(TITLE) | |
| gr.Markdown(DESCRIPTION) | |
| gr.Markdown(EXPLANATION) | |
| headers = list(SUBMISSIONS.keys()) | |
| datatype = "str" | |
| total_submissions, main_df, challenge_df, category_df = get_dataframe_all() | |
| with gr.Column(): | |
| gr.Markdown("# Open Parti Prompts") | |
| main_dataframe = gr.Dataframe( | |
| value=main_df, | |
| headers=main_df.columns.to_list(), | |
| datatype="str", | |
| row_count=main_df.shape[0], | |
| col_count=main_df.shape[1], | |
| interactive=False, | |
| ) | |
| with gr.Column(): | |
| gr.Markdown("## per category") | |
| cat_dataframe = gr.Dataframe( | |
| value=category_df, | |
| headers=category_df.columns.to_list(), | |
| datatype="str", | |
| row_count=category_df.shape[0], | |
| col_count=category_df.shape[1], | |
| interactive=False, | |
| ) | |
| with gr.Column(): | |
| gr.Markdown("## per challenge") | |
| chal_dataframe = gr.Dataframe( | |
| value=challenge_df, | |
| headers=challenge_df.columns.to_list(), | |
| datatype="str", | |
| row_count=challenge_df.shape[0], | |
| col_count=challenge_df.shape[1], | |
| interactive=False, | |
| ) | |
| with gr.Column(): | |
| gr.Markdown("## # Submissions") | |
| num_submissions = gr.Number(value=total_submissions, interactive=False) | |
| with gr.Row(): | |
| refresh_button = gr.Button("Refresh") | |
| refresh_button.click(refresh, inputs=[], outputs=[num_submissions, main_dataframe, cat_dataframe, chal_dataframe]) | |
| # Restart space every 20 minutes | |
| scheduler = BackgroundScheduler() | |
| scheduler.add_job(restart_space, 'interval', seconds=3600) | |
| scheduler.start() | |
| demo.launch() | |