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 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.set_adapters(a_list, adapter_weights=w_list) pipe.fuse_lora(adapter_names=a_list, lora_scale=1.0) pipe.unload_lora_weights() 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) 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