Delete convert_url_to_diffusers_sdxl.py

convert_url_to_diffusers_sdxl.py

@@ -1,300 +0,0 @@
-import argparse
-from pathlib import Path
-import os
-import torch
-from diffusers import StableDiffusionXLPipeline, AutoencoderKL
-# also requires aria, gdown, peft, huggingface_hub, safetensors, transformers, accelerate, pytorch_lightning
-
-
-def list_sub(a, b):
-    return [e for e in a if e not in b]
-
-
-def is_repo_name(s):
-    import re
-    return re.fullmatch(r'^[^/,\s\"\']+/[^/,\s\"\']+$', s)
-
-
-def download_thing(directory, url, civitai_api_key=""):
-    url = url.strip()
-    if "drive.google.com" in url:
-        original_dir = os.getcwd()
-        os.chdir(directory)
-        os.system(f"gdown --fuzzy {url}")
-        os.chdir(original_dir)
-    elif "huggingface.co" in url:
-        url = url.replace("?download=true", "")
-        if "/blob/" in url:
-            url = url.replace("/blob/", "/resolve/")
-            os.system(f"aria2c --console-log-level=error --summary-interval=10 -c -x 16 -k 1M -s 16 {url} -d {directory}  -o {url.split('/')[-1]}")
-        else:
-            os.system (f"aria2c --optimize-concurrent-downloads --console-log-level=error --summary-interval=10 -c -x 16 -k 1M -s 16 {url} -d {directory}  -o {url.split('/')[-1]}")
-    elif "civitai.com" in url:
-        if "?" in url:
-            url = url.split("?")[0]
-        if civitai_api_key:
-            url = url + f"?token={civitai_api_key}"
-            os.system(f"aria2c --console-log-level=error --summary-interval=10 -c -x 16 -k 1M -s 16 -d {directory} {url}")
-        else:
-            print("You need an API key to download Civitai models.")
-    else:
-        os.system(f"aria2c --console-log-level=error --summary-interval=10 -c -x 16 -k 1M -s 16 -d {directory} {url}")
-
-
-def get_local_model_list(dir_path):
-    model_list = []
-    valid_extensions = ('.safetensors')
-    for file in Path(dir_path).glob("*"):
-        if file.suffix in valid_extensions:
-            file_path = str(Path(f"{dir_path}/{file.name}"))
-            model_list.append(file_path)
-    return model_list
-
-
-def get_download_file(temp_dir, url, civitai_key):
-    if not "http" in url and is_repo_name(url) and not Path(url).exists():
-        print(f"Use HF Repo: {url}")
-        new_file = url
-    elif not "http" in url and Path(url).exists():
-        print(f"Use local file: {url}")
-        new_file = url
-    elif Path(f"{temp_dir}/{url.split('/')[-1]}").exists():
-        print(f"File to download alreday exists: {url}")
-        new_file = f"{temp_dir}/{url.split('/')[-1]}"
-    else:
-        print(f"Start downloading: {url}")
-        before = get_local_model_list(temp_dir)
-        try:
-            download_thing(temp_dir, url.strip(), civitai_key)
-        except Exception:
-            print(f"Download failed: {url}")
-            return ""
-        after = get_local_model_list(temp_dir)
-        new_file = list_sub(after, before)[0] if list_sub(after, before) else ""
-    if not new_file:
-        print(f"Download failed: {url}")
-        return ""
-    print(f"Download completed: {url}")
-    return new_file
-
-
-from diffusers import (
-    DPMSolverMultistepScheduler,
-    DPMSolverSinglestepScheduler,
-    KDPM2DiscreteScheduler,
-    EulerDiscreteScheduler,
-    EulerAncestralDiscreteScheduler,
-    HeunDiscreteScheduler,
-    LMSDiscreteScheduler,
-    DDIMScheduler,
-    DEISMultistepScheduler,
-    UniPCMultistepScheduler,
-    LCMScheduler,
-    PNDMScheduler,
-    KDPM2AncestralDiscreteScheduler,
-    DPMSolverSDEScheduler,
-    EDMDPMSolverMultistepScheduler,
-    DDPMScheduler,
-    EDMEulerScheduler,
-    TCDScheduler,
-)
-
-
-SCHEDULER_CONFIG_MAP = {
-    "DPM++ 2M": (DPMSolverMultistepScheduler, {"use_karras_sigmas": False}),
-    "DPM++ 2M Karras": (DPMSolverMultistepScheduler, {"use_karras_sigmas": True}),
-    "DPM++ 2M SDE": (DPMSolverMultistepScheduler, {"use_karras_sigmas": False, "algorithm_type": "sde-dpmsolver++"}),
-    "DPM++ 2M SDE Karras": (DPMSolverMultistepScheduler, {"use_karras_sigmas": True, "algorithm_type": "sde-dpmsolver++"}),
-    "DPM++ 2S": (DPMSolverSinglestepScheduler, {"use_karras_sigmas": False}),
-    "DPM++ 2S Karras": (DPMSolverSinglestepScheduler, {"use_karras_sigmas": True}),
-    "DPM++ 1S": (DPMSolverMultistepScheduler, {"solver_order": 1}),
-    "DPM++ 1S Karras": (DPMSolverMultistepScheduler, {"solver_order": 1, "use_karras_sigmas": True}),
-    "DPM++ 3M": (DPMSolverMultistepScheduler, {"solver_order": 3}),
-    "DPM++ 3M Karras": (DPMSolverMultistepScheduler, {"solver_order": 3, "use_karras_sigmas": True}),
-    "DPM++ SDE": (DPMSolverSDEScheduler, {"use_karras_sigmas": False}),
-    "DPM++ SDE Karras": (DPMSolverSDEScheduler, {"use_karras_sigmas": True}),
-    "DPM2": (KDPM2DiscreteScheduler, {}),
-    "DPM2 Karras": (KDPM2DiscreteScheduler, {"use_karras_sigmas": True}),
-    "DPM2 a": (KDPM2AncestralDiscreteScheduler, {}),
-    "DPM2 a Karras": (KDPM2AncestralDiscreteScheduler, {"use_karras_sigmas": True}),
-    "Euler": (EulerDiscreteScheduler, {}),
-    "Euler a": (EulerAncestralDiscreteScheduler, {}),
-    "Euler trailing": (EulerDiscreteScheduler, {"timestep_spacing": "trailing", "prediction_type": "sample"}),
-    "Euler a trailing": (EulerAncestralDiscreteScheduler, {"timestep_spacing": "trailing"}),
-    "Heun": (HeunDiscreteScheduler, {}),
-    "Heun Karras": (HeunDiscreteScheduler, {"use_karras_sigmas": True}),
-    "LMS": (LMSDiscreteScheduler, {}),
-    "LMS Karras": (LMSDiscreteScheduler, {"use_karras_sigmas": True}),
-    "DDIM": (DDIMScheduler, {}),
-    "DDIM trailing": (DDIMScheduler, {"timestep_spacing": "trailing"}),
-    "DEIS": (DEISMultistepScheduler, {}),
-    "UniPC": (UniPCMultistepScheduler, {}),
-    "UniPC Karras": (UniPCMultistepScheduler, {"use_karras_sigmas": True}),
-    "PNDM": (PNDMScheduler, {}),
-    "Euler EDM": (EDMEulerScheduler, {}),
-    "Euler EDM Karras": (EDMEulerScheduler, {"use_karras_sigmas": True}),
-    "DPM++ 2M EDM": (EDMDPMSolverMultistepScheduler, {"solver_order": 2, "solver_type": "midpoint", "final_sigmas_type": "zero", "algorithm_type": "dpmsolver++"}),
-    "DPM++ 2M EDM Karras": (EDMDPMSolverMultistepScheduler, {"use_karras_sigmas": True, "solver_order": 2, "solver_type": "midpoint", "final_sigmas_type": "zero", "algorithm_type": "dpmsolver++"}),
-    "DDPM": (DDPMScheduler, {}),
-
-    "DPM++ 2M Lu": (DPMSolverMultistepScheduler, {"use_lu_lambdas": True}),
-    "DPM++ 2M Ef": (DPMSolverMultistepScheduler, {"euler_at_final": True}),
-    "DPM++ 2M SDE Lu": (DPMSolverMultistepScheduler, {"use_lu_lambdas": True, "algorithm_type": "sde-dpmsolver++"}),
-    "DPM++ 2M SDE Ef": (DPMSolverMultistepScheduler, {"algorithm_type": "sde-dpmsolver++", "euler_at_final": True}),
-
-    "LCM": (LCMScheduler, {}),
-    "TCD": (TCDScheduler, {}),
-    "LCM trailing": (LCMScheduler, {"timestep_spacing": "trailing"}),
-    "TCD trailing": (TCDScheduler, {"timestep_spacing": "trailing"}),
-    "LCM Auto-Loader": (LCMScheduler, {}),
-    "TCD Auto-Loader": (TCDScheduler, {}),
-}
-
-
-def get_scheduler_config(name):
-    if not name in SCHEDULER_CONFIG_MAP.keys(): return SCHEDULER_CONFIG_MAP["Euler a"]
-    return SCHEDULER_CONFIG_MAP[name]
-
-
-def save_readme_md(dir, url):
-    orig_url = ""
-    orig_name = ""
-    if is_repo_name(url): 
-        orig_name = url
-        orig_url = f"https://huggingface.co/{url}/"
-    elif "http" in url:
-        orig_name = url
-        orig_url = url
-    if orig_name and orig_url:
-       md = f"""---
-license: other
-language:
-- en
-library_name: diffusers
-pipeline_tag: text-to-image
-tags:
-- text-to-image
----
-Converted from [{orig_name}]({orig_url}).
-"""
-    else:
-        md = f"""---
-license: other
-language:
-- en
-library_name: diffusers
-pipeline_tag: text-to-image
-tags:
-- text-to-image
----
-"""
-    path = str(Path(dir, "README.md"))
-    with open(path, mode='w', encoding="utf-8") as f:
-        f.write(md)
-
-
-def fuse_loras(pipe, civitai_key="", lora_dict={}, temp_dir="."):
-    if not lora_dict or not isinstance(lora_dict, dict): return pipe
-    a_list = []
-    w_list = []
-    for k, v in lora_dict.items():
-        if not k: continue
-        new_lora_file = get_download_file(temp_dir, k, civitai_key)
-        if not new_lora_file or not Path(new_lora_file).exists():
-            print(f"LoRA not found: {k}")
-            continue
-        w_name = Path(new_lora_file).name
-        a_name = Path(new_lora_file).stem
-        pipe.load_lora_weights(new_lora_file, weight_name = w_name, adapter_name = a_name)
-        a_list.append(a_name)
-        w_list.append(v)
-    if not a_list: return pipe
-    pipe.set_adapters(a_list, adapter_weights=w_list)
-    pipe.fuse_lora(adapter_names=a_list, lora_scale=1.0)
-    pipe.unload_lora_weights()
-    return pipe
-
-
-def convert_url_to_diffusers_sdxl(url, civitai_key="", half=True, vae=None, scheduler="Euler a", lora_dict={}):
-    temp_dir = "."
-    new_file = get_download_file(temp_dir, url, civitai_key)
-    if not new_file:
-        print(f"Not found: {url}")
-        return
-    new_repo_name = Path(new_file).stem.replace(" ", "_").replace(",", "_").replace(".", "_") #
-
-    pipe = None
-    if is_repo_name(url):
-        if half:
-            pipe = StableDiffusionXLPipeline.from_pretrained(new_file, use_safetensors=True, torch_dtype=torch.float16)
-        else:
-            pipe = StableDiffusionXLPipeline.from_pretrained(new_file, use_safetensors=True)
-    else:
-        if half:
-            pipe = StableDiffusionXLPipeline.from_single_file(new_file, use_safetensors=True, torch_dtype=torch.float16)
-        else:
-            pipe = StableDiffusionXLPipeline.from_single_file(new_file, use_safetensors=True)
-
-    new_vae_file = ""
-    if vae:
-        if is_repo_name(vae):
-            if half:
-                pipe.vae = AutoencoderKL.from_pretrained(vae, torch_dtype=torch.float16)
-            else:
-                pipe.vae = AutoencoderKL.from_pretrained(vae)
-        else:
-            new_vae_file = get_download_file(temp_dir, vae, civitai_key)
-            if new_vae_file and half:
-                pipe.vae = AutoencoderKL.from_single_file(new_vae_file, torch_dtype=torch.float16)
-            elif new_vae_file:
-                pipe.vae = AutoencoderKL.from_single_file(new_vae_file)
-
-    pipe = fuse_loras(pipe, lora_dict, temp_dir, civitai_key)
-
-    sconf = get_scheduler_config(scheduler)
-    pipe.scheduler = sconf[0].from_config(pipe.scheduler.config, **sconf[1])
-
-    if half:
-        pipe.save_pretrained(new_repo_name, safe_serialization=True, use_safetensors=True)
-    else:
-        pipe.save_pretrained(new_repo_name, safe_serialization=True, use_safetensors=True)
-
-    if Path(new_repo_name).exists():
-        save_readme_md(new_repo_name, url)
-
-
-if __name__ == "__main__":
-    parser = argparse.ArgumentParser()
-
-    parser.add_argument("--url", default=None, type=str, required=True, help="URL of the model to convert.")
-    parser.add_argument("--half", default=True, help="Save weights in half precision.")
-    parser.add_argument("--scheduler", default="Euler a", type=str, choices=list(SCHEDULER_CONFIG_MAP.keys()), required=False, help="Scheduler name to use.")
-    parser.add_argument("--vae", default=None, type=str, required=False, help="URL of the VAE to use.")
-    parser.add_argument("--civitai_key", default=None, type=str, required=False, help="Civitai API Key (If you want to download file from Civitai).")
-    parser.add_argument("--lora1", default=None, type=str, required=False, help="URL of the LoRA to use.")
-    parser.add_argument("--lora1s", default=1.0, type=float, required=False, help="LoRA weight scale of --lora1.")
-    parser.add_argument("--lora2", default=None, type=str, required=False, help="URL of the LoRA to use.")
-    parser.add_argument("--lora2s", default=1.0, type=float, required=False, help="LoRA weight scale of --lora2.")
-    parser.add_argument("--lora3", default=None, type=str, required=False, help="URL of the LoRA to use.")
-    parser.add_argument("--lora3s", default=1.0, type=float, required=False, help="LoRA weight scale of --lora3.")
-    parser.add_argument("--lora4", default=None, type=str, required=False, help="URL of the LoRA to use.")
-    parser.add_argument("--lora4s", default=1.0, type=float, required=False, help="LoRA weight scale of --lora4.")
-    parser.add_argument("--lora5", default=None, type=str, required=False, help="URL of the LoRA to use.")
-    parser.add_argument("--lora5s", default=1.0, type=float, required=False, help="LoRA weight scale of --lora5.")
-    parser.add_argument("--loras", default=None, type=str, required=False, help="Folder of the LoRA to use.")
-
-    args = parser.parse_args()
-    assert args.url is not None, "Must provide a URL!"
-
-    lora_dict = {args.lora1: args.lora1s, args.lora2: args.lora2s, args.lora3: args.lora3s, args.lora4: args.lora4s, args.lora5: args.lora5s}
-
-    if args.loras and Path(args.loras).exists():
-        for p in Path(args.loras).glob('**/*.safetensors'):
-            lora_dict[str(p)] = 1.0
-
-    convert_url_to_diffusers_sdxl(args.url, args.civitai_key, args.half, args.vae, args.scheduler, lora_dict)
-
-
-# Usage: python convert_url_to_diffusers_sdxl.py --url https://huggingface.co/bluepen5805/anima_pencil-XL/blob/main/anima_pencil-XL-v5.0.0.safetensors
-# python convert_url_to_diffusers_sdxl.py --url https://huggingface.co/bluepen5805/anima_pencil-XL/blob/main/anima_pencil-XL-v5.0.0.safetensors --scheduler "Euler a"
-# python convert_url_to_diffusers_sdxl.py --url https://huggingface.co/bluepen5805/anima_pencil-XL/blob/main/anima_pencil-XL-v5.0.0.safetensors --loras ./loras