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| from huggingface_hub import HfApi, ModelFilter | |
| from collections import defaultdict | |
| import pandas as pd | |
| import gradio as gr | |
| api = HfApi() | |
| filter = ModelFilter(library="diffusers") | |
| models = api.list_models(filter=filter) | |
| downloads = defaultdict(int) | |
| for model in models: | |
| is_counted = False | |
| for tag in model.tags: | |
| if tag.startswith("diffusers:"): | |
| is_counted = True | |
| downloads[tag[len("diffusers:"):]] += model.downloads | |
| if not is_counted: | |
| downloads["other"] += model.downloads | |
| # Remove 0 downloads | |
| downloads = {k: v for k,v in downloads.items() if v > 0} | |
| # Sort the dictionary by keys | |
| sorted_dict = dict(sorted(downloads.items(), key=lambda item: item[1], reverse=True)) | |
| # Convert the sorted dictionary to a DataFrame | |
| df = pd.DataFrame(list(sorted_dict.items()), columns=['Pipeline class', 'Downloads']) | |
| NOTE = """ | |
| This table shows the **total** number of downloads **per pipeline class** of `diffusers`. | |
| The pipeline classes are retrieved from the `_class_name` attribute of `model_index.json` or | |
| `config.json` depending on whether the diffusers repo is a pipeline repo or a model repo. | |
| **Note**: It's important to excatly understand how downloads are measured here. One should use this graph | |
| to figure out if a *"type"* of pipeline is used, not a specific pipeline is used. More specifically, we | |
| know from this graph that `stable-diffusion` checkpoints are highly used, **but** we don't know exactly which stable diffusion | |
| class is highly used. | |
| => So what conclusions can we draw from this graph? | |
| - 1.) `stable-diffusion` checkpoints are highly used and account for most downloads. | |
| - 2.) All `stable-diffusion` checkpoints are compatible with `StableDiffusionPipeline`, `StableDiffusionImg2ImgPipeline`, `StableDiffusionInpaintPipeline`, `StableDiffusionControlNetPipeline`, `StableDiffusionImg2ImgControlNetPipeline` or `StableDiffusionInpaintControlNetPipeline`, but all downloads contribute only to `StableDiffusionPipeline` here. This means we don't really know which pipeline class is used when the checkpoints are downloaded. | |
| - 3.) ControlNet is used a lot - it accounts for > 10% of all downloads | |
| - 4.) If a pipeline class **and** no compatible pipeline class shows up in the graph, we know that the pipeline class is not used a lot. For example: | |
| - `VersatileDiffusionPipeline` can only be used with "VersatileDiffusion" checkpoints and so here we no that all VersatileDiffusion classes combined have less than <2k monthly downloads. | |
| - `ConsistencyPipeline` can only be used with exactly this pipeline and here we're at < 100 monthly downloads | |
| - 5.) All LoRA and Textual Inversion downloads are grouped together in "other" for now. | |
| """ | |
| with gr.Blocks() as demo: | |
| gr.Markdown(NOTE) | |
| gr.DataFrame(df) | |
| demo.launch() | |