app.py

from huggingface_hub import model_info, hf_hub_download
import gradio as gr
import json

COMPONENT_FILTER = [
    "scheduler",
    "feature_extractor",
    "tokenizer",
    "tokenizer_2",
    "_class_name",
    "_diffusers_version",
]


def format_size(num: int) -> str:
    """Format size in bytes into a human-readable string.
    Taken from https://stackoverflow.com/a/1094933
    """
    num_f = float(num)
    for unit in ["", "K", "M", "G", "T", "P", "E", "Z"]:
        if abs(num_f) < 1000.0:
            return f"{num_f:3.1f}{unit}"
        num_f /= 1000.0
    return f"{num_f:.1f}Y"


def format_output(pipeline_id, memory_mapping):
    markdown_str = f"## {pipeline_id}\n"
    if memory_mapping:
        for component, memory in memory_mapping.items():
            markdown_str += f"* {component}: {format_size(memory)}\n"
    return markdown_str


def load_model_index(pipeline_id, token=None, revision=None):
    index_path = hf_hub_download(repo_id=pipeline_id, filename="model_index.json", revision=revision, token=token)
    with open(index_path, "r") as f:
        index_dict = json.load(f)
    return index_dict


def get_component_wise_memory(pipeline_id, token=None, variant=None, revision=None, extension=".safetensors"):
    if token == "":
        token = None

    if revision == "":
        revision = None

    if variant == "fp32":
        variant = None

    print(f"pipeline_id: {pipeline_id}, variant: {variant}, revision: {revision}, extension: {extension}")

    files_in_repo = model_info(pipeline_id, revision=revision, token=token, files_metadata=True).siblings
    index_dict = load_model_index(pipeline_id, token=token, revision=revision)

    # Check if all the concerned components have the checkpoints in the requested "variant" and "extension".
    print(f"Index dict: {index_dict}")
    for current_component in index_dict:
        if (
            current_component not in COMPONENT_FILTER
            and isinstance(index_dict[current_component], list)
            and len(index_dict[current_component]) == 2
        ):
            current_component_fileobjs = list(filter(lambda x: current_component in x.rfilename, files_in_repo))
            if current_component_fileobjs:
                current_component_filenames = [fileobj.rfilename for fileobj in current_component_fileobjs]
                condition = (  # noqa: E731
                    lambda filename: extension in filename and variant in filename
                    if variant is not None
                    else lambda filename: extension in filename
                )
                variant_present_with_extension = any(condition(filename) for filename in current_component_filenames)
                if not variant_present_with_extension:
                    raise ValueError(
                        f"Requested extension ({extension}) and variant ({variant}) not present for {current_component}. Available files for this component:\n{current_component_filenames}."
                    )
            else:
                raise ValueError(f"Problem with {current_component}.")

    # Handle text encoder separately when it's sharded.
    is_text_encoder_shared = any(".index.json" in file_obj.rfilename for file_obj in files_in_repo)
    component_wise_memory = {}
    if is_text_encoder_shared:
        for current_file in files_in_repo:
            if "text_encoder" in current_file.rfilename:
                if not current_file.rfilename.endswith(".json") and current_file.rfilename.endswith(extension):
                    if variant is not None and variant in current_file.rfilename:
                        selected_file = current_file
                    else:
                        selected_file = current_file
                    if "text_encoder" not in component_wise_memory:
                        component_wise_memory["text_encoder"] = selected_file.size
                    else:
                        component_wise_memory["text_encoder"] += selected_file.size

    # Handle pipeline components.
    if is_text_encoder_shared:
        COMPONENT_FILTER.append("text_encoder")

    for current_file in files_in_repo:
        if all(substring not in current_file.rfilename for substring in COMPONENT_FILTER):
            is_folder = len(current_file.rfilename.split("/")) == 2
            if is_folder and current_file.rfilename.split("/")[0] in index_dict:
                selected_file = None
                if not current_file.rfilename.endswith(".json") and current_file.rfilename.endswith(extension):
                    component = current_file.rfilename.split("/")[0]
                    if (
                        variant is not None
                        and variant in current_file.rfilename
                        and "ema" not in current_file.rfilename
                    ):
                        selected_file = current_file
                    elif variant is None and "ema" not in current_file.rfilename:
                        selected_file = current_file

                    if selected_file is not None:
                        component_wise_memory[component] = selected_file.size

    return format_output(pipeline_id, component_wise_memory)


with gr.Interface(
    title="Compute component-wise memory of a 🧨 Diffusers pipeline.",
    description="Pipelines containing text encoders with sharded checkpoints are also supported"
    " (PixArt-Alpha, for example) 🤗",
    fn=get_component_wise_memory,
    inputs=[
        gr.components.Textbox(lines=1, label="pipeline_id", info="Example: runwayml/stable-diffusion-v1-5"),
        gr.components.Textbox(lines=1, label="hf_token", info="Pass this in case of private repositories."),
        gr.components.Radio(
            ["fp32", "fp16", "bf16"],
            label="variant",
            info="Precision to use for calculation.",
        ),
        gr.components.Textbox(lines=1, label="revision", info="Repository revision to use."),
        gr.components.Radio(
            [".bin", ".safetensors"],
            label="extension",
            info="Extension to use.",
        ),
    ],
    outputs=[gr.Markdown(label="Output")],
    examples=[
        ["runwayml/stable-diffusion-v1-5", None, "fp32", None, ".safetensors"],
        ["stabilityai/stable-diffusion-xl-base-1.0", None, "fp16", None, ".safetensors"],
        ["PixArt-alpha/PixArt-XL-2-1024-MS", None, "fp32", None, ".safetensors"],
        ["stabilityai/stable-cascade", None, "bf16", None, ".safetensors"],
        ["Deci/DeciDiffusion-v2-0", None, "fp32", None, ".safetensors"],
    ],
    theme=gr.themes.Soft(),
    allow_flagging="never",
) as demo:
    demo.launch(show_error=True)