Add files using upload-large-folder tool

.gitattributes

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pythonProject/diffusers-main/utils/update_metadata.py

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+# coding=utf-8
+# Copyright 2025 The HuggingFace Inc. team.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""
+Utility that updates the metadata of the Diffusers library in the repository `huggingface/diffusers-metadata`.
+
+Usage for an update (as used by the GitHub action `update_metadata`):
+
+```bash
+python utils/update_metadata.py
+```
+
+Script modified from:
+https://github.com/huggingface/transformers/blob/main/utils/update_metadata.py
+"""
+
+import argparse
+import os
+import tempfile
+
+import pandas as pd
+from datasets import Dataset
+from huggingface_hub import hf_hub_download, upload_folder
+
+from diffusers.pipelines.auto_pipeline import (
+    AUTO_IMAGE2IMAGE_PIPELINES_MAPPING,
+    AUTO_INPAINT_PIPELINES_MAPPING,
+    AUTO_TEXT2IMAGE_PIPELINES_MAPPING,
+)
+
+
+PIPELINE_TAG_JSON = "pipeline_tags.json"
+
+
+def get_supported_pipeline_table() -> dict:
+    """
+    Generates a dictionary containing the supported auto classes for each pipeline type,
+    using the content of the auto modules.
+    """
+    # All supported pipelines for automatic mapping.
+    all_supported_pipeline_classes = [
+        (class_name.__name__, "text-to-image", "AutoPipelineForText2Image")
+        for _, class_name in AUTO_TEXT2IMAGE_PIPELINES_MAPPING.items()
+    ]
+    all_supported_pipeline_classes += [
+        (class_name.__name__, "image-to-image", "AutoPipelineForImage2Image")
+        for _, class_name in AUTO_IMAGE2IMAGE_PIPELINES_MAPPING.items()
+    ]
+    all_supported_pipeline_classes += [
+        (class_name.__name__, "image-to-image", "AutoPipelineForInpainting")
+        for _, class_name in AUTO_INPAINT_PIPELINES_MAPPING.items()
+    ]
+    all_supported_pipeline_classes = list(set(all_supported_pipeline_classes))
+    all_supported_pipeline_classes.sort(key=lambda x: x[0])
+
+    data = {}
+    data["pipeline_class"] = [sample[0] for sample in all_supported_pipeline_classes]
+    data["pipeline_tag"] = [sample[1] for sample in all_supported_pipeline_classes]
+    data["auto_class"] = [sample[2] for sample in all_supported_pipeline_classes]
+
+    return data
+
+
+def update_metadata(commit_sha: str):
+    """
+    Update the metadata for the Diffusers repo in `huggingface/diffusers-metadata`.
+
+    Args:
+        commit_sha (`str`): The commit SHA on Diffusers corresponding to this update.
+    """
+    pipelines_table = get_supported_pipeline_table()
+    pipelines_table = pd.DataFrame(pipelines_table)
+    pipelines_dataset = Dataset.from_pandas(pipelines_table)
+
+    hub_pipeline_tags_json = hf_hub_download(
+        repo_id="huggingface/diffusers-metadata",
+        filename=PIPELINE_TAG_JSON,
+        repo_type="dataset",
+    )
+    with open(hub_pipeline_tags_json) as f:
+        hub_pipeline_tags_json = f.read()
+
+    with tempfile.TemporaryDirectory() as tmp_dir:
+        pipelines_dataset.to_json(os.path.join(tmp_dir, PIPELINE_TAG_JSON))
+
+        with open(os.path.join(tmp_dir, PIPELINE_TAG_JSON)) as f:
+            pipeline_tags_json = f.read()
+
+        hub_pipeline_tags_equal = hub_pipeline_tags_json == pipeline_tags_json
+        if hub_pipeline_tags_equal:
+            print("No updates, not pushing the metadata files.")
+            return
+
+        if commit_sha is not None:
+            commit_message = (
+                f"Update with commit {commit_sha}\n\nSee: https://github.com/huggingface/diffusers/commit/{commit_sha}"
+            )
+        else:
+            commit_message = "Update"
+
+        upload_folder(
+            repo_id="huggingface/diffusers-metadata",
+            folder_path=tmp_dir,
+            repo_type="dataset",
+            commit_message=commit_message,
+        )
+
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser()
+    parser.add_argument("--commit_sha", default=None, type=str, help="The sha of the commit going with this update.")
+    args = parser.parse_args()
+
+    update_metadata(args.commit_sha)

pythonProject/upload.py

@@ -0,0 +1,33 @@
+from huggingface_hub import HfApi
+
+# 参数配置
+folder_path = "E:\Qwen-image"                        # 本地文件夹路径
+# 必须包含：config.json, pytorch_model.bin, tokenizer_config.json 等
+path_in_repo = ""                                    # 上传到仓库根目录
+repo_id = "xiaoanyu123/QwenTEST"
+
+# 创建 HfApi 实例
+api = HfApi()
+
+# 执行大文件夹上传
+try:
+    print("🚀 开始上传大型文件夹...")
+    api.upload_large_folder(
+        repo_id=repo_id,
+        folder_path=folder_path,
+        repo_type="model",
+        # 类型：model, dataset, space
+        ignore_patterns=[
+            ".cache",  # 忽略所有 .cache 文件或文件夹
+            "__pycache__",  # 忽略 Python 编译缓存
+            ".git",  # 忽略 Git 版本控制文件
+            "pycharm/",  # 忽略整个 pycharm 文件夹
+            "testResources/",  # 忽略测试资源
+            "logs/",  # 忽略日志文件夹
+            "*.log",  # 忽略所有 .log 文件
+            "temp*",  # 忽略 temp 开头的文件/文件夹
+        ]
+    )
+    print("✅ 大型文件夹上传成功！")
+except Exception as e:
+    print(f"❌ 上传失败: {e}")

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