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Parent(s):
Duplicate from diffusers/sdxl-to-diffusers
Browse filesCo-authored-by: Patrick von Platen <patrickvonplaten@users.noreply.huggingface.co>
- .gitattributes +34 -0
- README.md +14 -0
- __pycache__/app.cpython-310.pyc +0 -0
- __pycache__/convert.cpython-310.pyc +0 -0
- app.py +31 -0
- convert.py +69 -0
- hf_utils.py +50 -0
- requirements.txt +7 -0
- utils.py +6 -0
.gitattributes
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*.7z filter=lfs diff=lfs merge=lfs -text
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*.bin filter=lfs diff=lfs merge=lfs -text
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*.bz2 filter=lfs diff=lfs merge=lfs -text
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*.pt filter=lfs diff=lfs merge=lfs -text
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*.rar filter=lfs diff=lfs merge=lfs -text
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*.safetensors filter=lfs diff=lfs merge=lfs -text
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saved_model/**/* filter=lfs diff=lfs merge=lfs -text
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README.md
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---
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title: SD-XL To Diffusers
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emoji: 🎨➡️🧨
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colorFrom: indigo
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colorTo: purple
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sdk: gradio
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sdk_version: 3.31.0
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app_file: app.py
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pinned: true
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license: mit
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duplicated_from: diffusers/sdxl-to-diffusers
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---
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Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
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__pycache__/app.cpython-310.pyc
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Binary file (1.31 kB). View file
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__pycache__/convert.cpython-310.pyc
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Binary file (2.75 kB). View file
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app.py
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import gradio as gr
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from convert import convert
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DESCRIPTION = """
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The steps are the following:
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- Paste a read-access token from hf.co/settings/tokens. Read access is enough given that we will open a PR against the source repo.
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- Input a model id from the Hub
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- Input the filename from the root dir of the repo that you would like to convert, e.g. 'xl-pruned.safetensors'
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- Click "Submit"
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- That's it! You'll get feedback if it works or not, and if it worked, you'll get the URL of the opened PR 🔥
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⚠️ If you encounter weird error messages, please have a look into the Logs and feel free to open a PR to correct the error messages.
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"""
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demo = gr.Interface(
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title="Convert any Stable Diffusion XL checkpoint to Diffusers and open a PR",
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description=DESCRIPTION,
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allow_flagging="never",
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article="Check out the [Diffusers repo on GitHub](https://github.com/huggingface/diffusers)",
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inputs=[
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gr.Text(max_lines=1, label="your_hf_token"),
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gr.Text(max_lines=1, label="model_id"),
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gr.Text(max_lines=1, label="filename"),
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],
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outputs=[gr.Markdown(label="output")],
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fn=convert,
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).queue(max_size=10, concurrency_count=1)
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demo.launch(show_api=True)
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convert.py
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import gradio as gr
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import requests
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import os
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import shutil
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from pathlib import Path
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from typing import Any
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from tempfile import TemporaryDirectory
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from typing import Optional
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import torch
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from io import BytesIO
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from huggingface_hub import CommitInfo, Discussion, HfApi, hf_hub_download
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from huggingface_hub.file_download import repo_folder_name
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from diffusers import StableDiffusionXLPipeline
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from transformers import CONFIG_MAPPING
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COMMIT_MESSAGE = " This PR adds fp32 and fp16 weights in safetensors format to {}"
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def convert_single(model_id: str, filename: str, folder: str, progress: Any):
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progress(0, desc="Downloading model")
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local_file = os.path.join(model_id, filename)
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ckpt_file = local_file if os.path.isfile(local_file) else hf_hub_download(repo_id=model_id, filename=filename)
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pipeline = StableDiffusionXLPipeline.from_single_file(ckpt_file)
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pipeline.save_pretrained(folder, safe_serialization=True)
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pipeline = pipeline.to(torch_dtype=torch.float16)
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pipeline.save_pretrained(folder, safe_serialization=True, variant="fp16")
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return folder
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def previous_pr(api: "HfApi", model_id: str, pr_title: str) -> Optional["Discussion"]:
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try:
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discussions = api.get_repo_discussions(repo_id=model_id)
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except Exception:
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return None
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for discussion in discussions:
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if discussion.status == "open" and discussion.is_pull_request and discussion.title == pr_title:
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details = api.get_discussion_details(repo_id=model_id, discussion_num=discussion.num)
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if details.target_branch == "refs/heads/main":
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return discussion
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def convert(token: str, model_id: str, filename: str, progress=gr.Progress()):
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api = HfApi()
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pr_title = "Adding `diffusers` weights of this model"
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with TemporaryDirectory() as d:
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folder = os.path.join(d, repo_folder_name(repo_id=model_id, repo_type="models"))
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os.makedirs(folder)
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new_pr = None
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try:
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folder = convert_single(model_id, filename, folder, progress)
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progress(0.7, desc="Uploading to Hub")
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new_pr = api.upload_folder(folder_path=folder, path_in_repo="./", repo_id=model_id, repo_type="model", token=token, commit_message=pr_title, commit_description=COMMIT_MESSAGE.format(model_id), create_pr=True)
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pr_number = new_pr.split("%2F")[-1].split("/")[0]
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link = f"Pr created at: {'https://huggingface.co/' + os.path.join(model_id, 'discussions', pr_number)}"
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progress(1, desc="Done")
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except Exception as e:
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raise gr.exceptions.Error(str(e))
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finally:
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shutil.rmtree(folder)
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return link
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hf_utils.py
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from huggingface_hub import get_hf_file_metadata, hf_hub_url, hf_hub_download, scan_cache_dir, whoami, list_models
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def get_my_model_names(token):
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try:
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author = whoami(token=token)
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model_infos = list_models(author=author["name"], use_auth_token=token)
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return [model.modelId for model in model_infos], None
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except Exception as e:
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return [], e
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def download_file(repo_id: str, filename: str, token: str):
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"""Download a file from a repo on the Hugging Face Hub.
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Returns:
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file_path (:obj:`str`): The path to the downloaded file.
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revision (:obj:`str`): The commit hash of the file.
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"""
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md = get_hf_file_metadata(hf_hub_url(repo_id=repo_id, filename=filename), token=token)
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revision = md.commit_hash
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file_path = hf_hub_download(repo_id=repo_id, filename=filename, revision=revision, token=token)
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return file_path, revision
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def delete_file(revision: str):
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"""Delete a file from local cache.
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Args:
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revision (:obj:`str`): The commit hash of the file.
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Returns:
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None
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"""
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scan_cache_dir().delete_revisions(revision).execute()
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def get_pr_url(api, repo_id, title):
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try:
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discussions = api.get_repo_discussions(repo_id=repo_id)
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except Exception:
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return None
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for discussion in discussions:
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if (
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discussion.status == "open"
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and discussion.is_pull_request
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and discussion.title == title
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):
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return f"https://huggingface.co/{repo_id}/discussions/{discussion.num}"
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requirements.txt
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huggingface_hub
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safetensors
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transformers
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accelerate
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git+https://github.com/huggingface/diffusers
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omegaconf
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pytorch_lightning
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utils.py
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def is_google_colab():
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try:
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import google.colab
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return True
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except:
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return False
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