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: formatted_filenames = ", ".join(current_component_filenames) raise ValueError( f"Requested extension ({extension}) and variant ({variant}) not present for {current_component}." f" Available files for this component: {formatted_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", cache_examples=False, ) as demo: demo.launch(show_error=True)