import os import spaces import argparse from pathlib import Path import os import torch from diffusers import (DiffusionPipeline, AutoencoderKL, FlowMatchEulerDiscreteScheduler, StableDiffusionXLPipeline, StableDiffusionPipeline, FluxPipeline, FluxTransformer2DModel, SD3Transformer2DModel, StableDiffusion3Pipeline) from transformers import CLIPTokenizer, CLIPTextModel, CLIPTextModelWithProjection, CLIPFeatureExtractor, AutoTokenizer, T5EncoderModel, BitsAndBytesConfig as TFBitsAndBytesConfig from huggingface_hub import save_torch_state_dict, snapshot_download from diffusers.loaders.single_file_utils import (convert_flux_transformer_checkpoint_to_diffusers, convert_sd3_transformer_checkpoint_to_diffusers, convert_sd3_t5_checkpoint_to_diffusers) from diffusers.pipelines.stable_diffusion import StableDiffusionSafetyChecker import safetensors.torch import gradio as gr import shutil import gc import tempfile # also requires aria, gdown, peft, huggingface_hub, safetensors, transformers, accelerate, pytorch_lightning from utils import (get_token, set_token, is_repo_exists, is_repo_name, get_download_file, upload_repo, gate_repo) from sdutils import (SCHEDULER_CONFIG_MAP, get_scheduler_config, fuse_loras, DTYPE_DEFAULT, get_dtype, get_dtypes, get_model_type_from_key, get_process_dtype) @spaces.GPU def fake_gpu(): pass try: from diffusers import BitsAndBytesConfig is_nf4 = True except Exception: is_nf4 = False FLUX_BASE_REPOS = ["camenduru/FLUX.1-dev-diffusers", "black-forest-labs/FLUX.1-schnell", "John6666/flux1-dev-fp8-flux", "John6666/flux1-schnell-fp8-flux"] FLUX_T5_URL = "https://huggingface.co/camenduru/FLUX.1-dev/blob/main/t5xxl_fp8_e4m3fn.safetensors" SD35_BASE_REPOS = ["adamo1139/stable-diffusion-3.5-large-ungated", "adamo1139/stable-diffusion-3.5-large-turbo-ungated"] SD35_T5_URL = "https://huggingface.co/adamo1139/stable-diffusion-3.5-large-turbo-ungated/blob/main/text_encoders/t5xxl_fp8_e4m3fn.safetensors" TEMP_DIR = tempfile.mkdtemp() IS_ZERO = os.environ.get("SPACES_ZERO_GPU") is not None IS_CUDA = torch.cuda.is_available() def safe_clean(path: str): try: if Path(path).exists(): if Path(path).is_dir(): shutil.rmtree(str(Path(path))) else: Path(path).unlink() print(f"Deleted: {path}") else: print(f"File not found: {path}") except Exception as e: print(f"Failed to delete: {path} {e}") 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 save_module(model, name: str, dir: str, dtype: str="fp8", progress=gr.Progress(track_tqdm=True)): # doesn't work if name in ["vae", "transformer", "unet"]: pattern = "diffusion_pytorch_model{suffix}.safetensors" else: pattern = "model{suffix}.safetensors" if name in ["transformer", "unet"]: size = "10GB" else: size = "5GB" path = str(Path(f"{dir.removesuffix('/')}/{name}")) os.makedirs(path, exist_ok=True) progress(0, desc=f"Saving {name} to {dir}...") print(f"Saving {name} to {dir}...") model.to("cpu") sd = dict(model.state_dict()) new_sd = {} for key in list(sd.keys()): q = sd.pop(key) if dtype == "fp8": new_sd[key] = q if q.dtype == torch.float8_e4m3fn else q.to(torch.float8_e4m3fn) else: new_sd[key] = q del sd gc.collect() save_torch_state_dict(state_dict=new_sd, save_directory=path, filename_pattern=pattern, max_shard_size=size) del new_sd gc.collect() def save_module_sd(sd: dict, name: str, dir: str, dtype: str="fp8", progress=gr.Progress(track_tqdm=True)): if name in ["vae", "transformer", "unet"]: pattern = "diffusion_pytorch_model{suffix}.safetensors" else: pattern = "model{suffix}.safetensors" if name in ["transformer", "unet"]: size = "10GB" else: size = "5GB" path = str(Path(f"{dir.removesuffix('/')}/{name}")) os.makedirs(path, exist_ok=True) progress(0, desc=f"Saving state_dict of {name} to {dir}...") print(f"Saving state_dict of {name} to {dir}...") new_sd = {} for key in list(sd.keys()): q = sd.pop(key).to("cpu") if dtype == "fp8": new_sd[key] = q if q.dtype == torch.float8_e4m3fn else q.to(torch.float8_e4m3fn) else: new_sd[key] = q save_torch_state_dict(state_dict=new_sd, save_directory=path, filename_pattern=pattern, max_shard_size=size) del new_sd gc.collect() def convert_flux_fp8_cpu(new_file: str, new_dir: str, dtype: str, base_repo: str, civitai_key: str, kwargs: dict, progress=gr.Progress(track_tqdm=True)): temp_dir = TEMP_DIR down_dir = str(Path(f"{TEMP_DIR}/down")) os.makedirs(down_dir, exist_ok=True) hf_token = get_token() progress(0.25, desc=f"Loading {new_file}...") orig_sd = safetensors.torch.load_file(new_file) progress(0.3, desc=f"Converting {new_file}...") conv_sd = convert_flux_transformer_checkpoint_to_diffusers(orig_sd) del orig_sd gc.collect() progress(0.35, desc=f"Saving {new_file}...") save_module_sd(conv_sd, "transformer", new_dir, dtype) del conv_sd gc.collect() progress(0.5, desc=f"Loading text_encoder_2 from {FLUX_T5_URL}...") t5_file = get_download_file(temp_dir, FLUX_T5_URL, civitai_key) if not t5_file: raise Exception(f"Safetensors file not found: {FLUX_T5_URL}") t5_sd = safetensors.torch.load_file(t5_file) safe_clean(t5_file) save_module_sd(t5_sd, "text_encoder_2", new_dir, dtype) del t5_sd gc.collect() progress(0.6, desc=f"Loading other components from {base_repo}...") pipe = FluxPipeline.from_pretrained(base_repo, transformer=None, text_encoder_2=None, use_safetensors=True, **kwargs, torch_dtype=torch.bfloat16, token=hf_token) pipe.save_pretrained(new_dir) progress(0.75, desc=f"Loading nontensor files from {base_repo}...") snapshot_download(repo_id=base_repo, local_dir=down_dir, token=hf_token, force_download=True, ignore_patterns=["*.safetensors", "*.sft", ".*", "README*", "*.md", "*.index", "*.jpg", "*.jpeg", "*.png", "*.webp"]) shutil.copytree(down_dir, new_dir, ignore=shutil.ignore_patterns(".*", "README*", "*.md", "*.jpg", "*.jpeg", "*.png", "*.webp"), dirs_exist_ok=True) safe_clean(down_dir) def convert_sd35_fp8_cpu(new_file: str, new_dir: str, dtype: str, base_repo: str, civitai_key: str, kwargs: dict, progress=gr.Progress(track_tqdm=True)): temp_dir = TEMP_DIR down_dir = str(Path(f"{TEMP_DIR}/down")) os.makedirs(down_dir, exist_ok=True) hf_token = get_token() progress(0.25, desc=f"Loading {new_file}...") orig_sd = safetensors.torch.load_file(new_file) progress(0.3, desc=f"Converting {new_file}...") conv_sd = convert_sd3_transformer_checkpoint_to_diffusers(orig_sd) del orig_sd gc.collect() progress(0.35, desc=f"Saving {new_file}...") save_module_sd(conv_sd, "transformer", new_dir, dtype) del conv_sd gc.collect() progress(0.5, desc=f"Loading text_encoder_3 from {SD35_T5_URL}...") t5_file = get_download_file(temp_dir, SD35_T5_URL, civitai_key) if not t5_file: raise Exception(f"Safetensors file not found: {SD35_T5_URL}") t5_sd = safetensors.torch.load_file(t5_file) safe_clean(t5_file) conv_t5_sd = convert_sd3_t5_checkpoint_to_diffusers(t5_sd) del t5_sd gc.collect() save_module_sd(conv_t5_sd, "text_encoder_3", new_dir, dtype) del conv_t5_sd gc.collect() progress(0.6, desc=f"Loading other components from {base_repo}...") pipe = StableDiffusion3Pipeline.from_pretrained(base_repo, transformer=None, text_encoder_3=None, use_safetensors=True, **kwargs, torch_dtype=torch.bfloat16, token=hf_token) pipe.save_pretrained(new_dir) progress(0.75, desc=f"Loading nontensor files from {base_repo}...") snapshot_download(repo_id=base_repo, local_dir=down_dir, token=hf_token, force_download=True, ignore_patterns=["*.safetensors", "*.sft", ".*", "README*", "*.md", "*.index", "*.jpg", "*.jpeg", "*.png", "*.webp"]) shutil.copytree(down_dir, new_dir, ignore=shutil.ignore_patterns(".*", "README*", "*.md", "*.jpg", "*.jpeg", "*.png", "*.webp"), dirs_exist_ok=True) safe_clean(down_dir) #@spaces.GPU(duration=60) def load_and_save_pipeline(pipe, model_type: str, url: str, new_file: str, new_dir: str, dtype: str, scheduler: str, ema: bool, image_size: str, is_safety_checker: bool, base_repo: str, civitai_key: str, lora_dict: dict, my_vae, my_clip_tokenizer, my_clip_encoder, my_t5_tokenizer, my_t5_encoder, kwargs: dict, dkwargs: dict, progress=gr.Progress(track_tqdm=True)): try: hf_token = get_token() temp_dir = TEMP_DIR qkwargs = {} tfqkwargs = {} if is_nf4: nf4_config = BitsAndBytesConfig(load_in_4bit=True, bnb_4bit_quant_type="nf4", bnb_4bit_use_double_quant=True, bnb_4bit_compute_dtype=torch.bfloat16) nf4_config_tf = TFBitsAndBytesConfig(load_in_4bit=True, bnb_4bit_quant_type="nf4", bnb_4bit_use_double_quant=True, bnb_4bit_compute_dtype=torch.bfloat16) else: nf4_config = None nf4_config_tf = None if dtype == "NF4" and nf4_config is not None and nf4_config_tf is not None: qkwargs["quantization_config"] = nf4_config tfqkwargs["quantization_config"] = nf4_config_tf #print(f"model_type:{model_type}, dtype:{dtype}, scheduler:{scheduler}, ema:{ema}, base_repo:{base_repo}") #print("lora_dict:", lora_dict, "kwargs:", kwargs, "dkwargs:", dkwargs) #t5 = None if model_type == "SDXL": if is_repo_name(url): pipe = StableDiffusionXLPipeline.from_pretrained(url, use_safetensors=True, **kwargs, **dkwargs, token=hf_token) else: pipe = StableDiffusionXLPipeline.from_single_file(new_file, use_safetensors=True, **kwargs, **dkwargs) pipe = fuse_loras(pipe, lora_dict, temp_dir, civitai_key, dkwargs) sconf = get_scheduler_config(scheduler) pipe.scheduler = sconf[0].from_config(pipe.scheduler.config, **sconf[1]) pipe.save_pretrained(new_dir) elif model_type == "SD 1.5": if is_safety_checker: safety_checker = StableDiffusionSafetyChecker.from_pretrained("CompVis/stable-diffusion-safety-checker") feature_extractor = CLIPFeatureExtractor.from_pretrained("openai/clip-vit-base-patch32") kwargs["requires_safety_checker"] = True kwargs["safety_checker"] = safety_checker kwargs["feature_extractor"] = feature_extractor else: kwargs["requires_safety_checker"] = False if is_repo_name(url): pipe = StableDiffusionPipeline.from_pretrained(url, extract_ema=ema, use_safetensors=True, **kwargs, **dkwargs, token=hf_token) else: pipe = StableDiffusionPipeline.from_single_file(new_file, extract_ema=ema, use_safetensors=True, **kwargs, **dkwargs) pipe = fuse_loras(pipe, lora_dict, temp_dir, civitai_key, dkwargs) sconf = get_scheduler_config(scheduler) pipe.scheduler = sconf[0].from_config(pipe.scheduler.config, **sconf[1]) if image_size != "512": pipe.vae = AutoencoderKL.from_config(pipe.vae.config, sample_size=int(image_size)) pipe.save_pretrained(new_dir) elif model_type == "FLUX": if dtype != "fp8": if is_repo_name(url): transformer = FluxTransformer2DModel.from_pretrained(url, subfolder="transformer", config=base_repo, **dkwargs, **qkwargs) #if my_t5_encoder is None: # t5 = T5EncoderModel.from_pretrained(url, subfolder="text_encoder_2", config=base_repo, **dkwargs, **tfqkwargs) # kwargs["text_encoder_2"] = t5 pipe = FluxPipeline.from_pretrained(url, transformer=transformer, use_safetensors=True, **kwargs, **dkwargs, token=hf_token) else: transformer = FluxTransformer2DModel.from_single_file(new_file, subfolder="transformer", config=base_repo, **dkwargs, **qkwargs) #if my_t5_encoder is None: # t5 = T5EncoderModel.from_pretrained(base_repo, subfolder="text_encoder_2", config=base_repo, **dkwargs, **tfqkwargs) # kwargs["text_encoder_2"] = t5 pipe = FluxPipeline.from_pretrained(base_repo, transformer=transformer, use_safetensors=True, **kwargs, **dkwargs, token=hf_token) pipe = fuse_loras(pipe, lora_dict, temp_dir, civitai_key, dkwargs) pipe.save_pretrained(new_dir) elif not is_repo_name(url): convert_flux_fp8_cpu(new_file, new_dir, dtype, base_repo, civitai_key, kwargs) elif model_type == "SD 3.5": if dtype != "fp8": if is_repo_name(url): transformer = SD3Transformer2DModel.from_pretrained(url, subfolder="transformer", config=base_repo, **dkwargs, **qkwargs) #if my_t5_encoder is None: # t5 = T5EncoderModel.from_pretrained(url, subfolder="text_encoder_3", config=base_repo, **dkwargs, **tfqkwargs) # kwargs["text_encoder_3"] = t5 pipe = StableDiffusion3Pipeline.from_pretrained(url, transformer=transformer, use_safetensors=True, **kwargs, **dkwargs, token=hf_token) else: transformer = SD3Transformer2DModel.from_single_file(new_file, subfolder="transformer", config=base_repo, **dkwargs, **qkwargs) #if my_t5_encoder is None: # t5 = T5EncoderModel.from_pretrained(base_repo, subfolder="text_encoder_3", config=base_repo, **dkwargs, **tfqkwargs) # kwargs["text_encoder_3"] = t5 pipe = StableDiffusion3Pipeline.from_pretrained(base_repo, transformer=transformer, use_safetensors=True, **kwargs, **dkwargs, token=hf_token) pipe = fuse_loras(pipe, lora_dict, temp_dir, civitai_key, dkwargs) pipe.save_pretrained(new_dir) elif not is_repo_name(url): convert_sd35_fp8_cpu(new_file, new_dir, dtype, base_repo, civitai_key, kwargs) else: # unknown model type if is_repo_name(url): pipe = DiffusionPipeline.from_pretrained(url, use_safetensors=True, **kwargs, **dkwargs, token=hf_token) else: pipe = DiffusionPipeline.from_single_file(new_file, use_safetensors=True, **kwargs, **dkwargs) pipe = fuse_loras(pipe, lora_dict, temp_dir, civitai_key, dkwargs) pipe.save_pretrained(new_dir) except Exception as e: print(f"Failed to load pipeline. {e}") raise Exception("Failed to load pipeline.") from e finally: return pipe def convert_url_to_diffusers(url: str, civitai_key: str="", is_upload_sf: bool=False, dtype: str="fp16", vae: str="", clip: str="", t5: str="", scheduler: str="Euler a", ema: bool=True, image_size: str="768", safety_checker: bool=False, base_repo: str="", mtype: str="", lora_dict: dict={}, is_local: bool=True, progress=gr.Progress(track_tqdm=True)): try: hf_token = get_token() progress(0, desc="Start converting...") temp_dir = TEMP_DIR if is_repo_name(url) and is_repo_exists(url): new_file = url model_type = mtype else: new_file = get_download_file(temp_dir, url, civitai_key) if not new_file: raise Exception(f"Safetensors file not found: {url}") model_type = get_model_type_from_key(new_file) new_dir = Path(new_file).stem.replace(" ", "_").replace(",", "_").replace(".", "_") # kwargs = {} dkwargs = {} if dtype != DTYPE_DEFAULT: dkwargs["torch_dtype"] = get_process_dtype(dtype, model_type) pipe = None print(f"Model type: {model_type} / VAE: {vae} / CLIP: {clip} / T5: {t5} / Scheduler: {scheduler} / dtype: {dtype} / EMA: {ema} / Base repo: {base_repo} / LoRAs: {lora_dict}") my_vae = None if vae: progress(0, desc=f"Loading VAE: {vae}...") if is_repo_name(vae): my_vae = AutoencoderKL.from_pretrained(vae, **dkwargs, token=hf_token) else: new_vae_file = get_download_file(temp_dir, vae, civitai_key) my_vae = AutoencoderKL.from_single_file(new_vae_file, **dkwargs) if new_vae_file else None safe_clean(new_vae_file) if my_vae: kwargs["vae"] = my_vae my_clip_tokenizer = None my_clip_encoder = None if clip: progress(0, desc=f"Loading CLIP: {clip}...") if is_repo_name(clip): my_clip_tokenizer = CLIPTokenizer.from_pretrained(clip, token=hf_token) if model_type == "SD 3.5": my_clip_encoder = CLIPTextModelWithProjection.from_pretrained(clip, **dkwargs, token=hf_token) else: my_clip_encoder = CLIPTextModel.from_pretrained(clip, **dkwargs, token=hf_token) else: new_clip_file = get_download_file(temp_dir, clip, civitai_key) if model_type == "SD 3.5": my_clip_encoder = CLIPTextModelWithProjection.from_single_file(new_clip_file, **dkwargs) if new_clip_file else None else: my_clip_encoder = CLIPTextModel.from_single_file(new_clip_file, **dkwargs) if new_clip_file else None safe_clean(new_clip_file) if model_type == "SD 3.5": if my_clip_tokenizer: kwargs["tokenizer"] = my_clip_tokenizer kwargs["tokenizer_2"] = my_clip_tokenizer if my_clip_encoder: kwargs["text_encoder"] = my_clip_encoder kwargs["text_encoder_2"] = my_clip_encoder else: if my_clip_tokenizer: kwargs["tokenizer"] = my_clip_tokenizer if my_clip_encoder: kwargs["text_encoder"] = my_clip_encoder my_t5_tokenizer = None my_t5_encoder = None if t5: progress(0, desc=f"Loading T5: {t5}...") if is_repo_name(t5): my_t5_tokenizer = AutoTokenizer.from_pretrained(t5, token=hf_token) my_t5_encoder = T5EncoderModel.from_pretrained(t5, **dkwargs, token=hf_token) else: new_t5_file = get_download_file(temp_dir, t5, civitai_key) my_t5_encoder = T5EncoderModel.from_single_file(new_t5_file, **dkwargs) if new_t5_file else None safe_clean(new_t5_file) if model_type == "SD 3.5": if my_t5_tokenizer: kwargs["tokenizer_3"] = my_t5_tokenizer if my_t5_encoder: kwargs["text_encoder_3"] = my_t5_encoder else: if my_t5_tokenizer: kwargs["tokenizer_2"] = my_t5_tokenizer if my_t5_encoder: kwargs["text_encoder_2"] = my_t5_encoder pipe = load_and_save_pipeline(pipe, model_type, url, new_file, new_dir, dtype, scheduler, ema, image_size, safety_checker, base_repo, civitai_key, lora_dict, my_vae, my_clip_tokenizer, my_clip_encoder, my_t5_tokenizer, my_t5_encoder, kwargs, dkwargs) if Path(new_dir).exists(): save_readme_md(new_dir, url) if not is_local: if not is_repo_name(new_file) and is_upload_sf: shutil.move(str(Path(new_file).resolve()), str(Path(new_dir, Path(new_file).name).resolve())) else: safe_clean(new_file) progress(1, desc="Converted.") return new_dir except Exception as e: print(f"Failed to convert. {e}") raise Exception("Failed to convert.") from e finally: del pipe torch.cuda.empty_cache() gc.collect() def convert_url_to_diffusers_repo(dl_url: str, hf_user: str, hf_repo: str, hf_token: str, civitai_key="", is_private: bool=True, gated: str="False", is_overwrite: bool=False, is_pr: bool=False, is_upload_sf: bool=False, urls: list=[], dtype: str="fp16", vae: str="", clip: str="", t5: str="", scheduler: str="Euler a", ema: bool=True, image_size: str="768", safety_checker: bool=False, base_repo: str="", mtype: str="", lora1: str="", lora1s=1.0, lora2: str="", lora2s=1.0, lora3: str="", lora3s=1.0, lora4: str="", lora4s=1.0, lora5: str="", lora5s=1.0, args: str="", progress=gr.Progress(track_tqdm=True)): try: is_local = False if not civitai_key and os.environ.get("CIVITAI_API_KEY"): civitai_key = os.environ.get("CIVITAI_API_KEY") # default Civitai API key if not hf_token and os.environ.get("HF_TOKEN"): hf_token = os.environ.get("HF_TOKEN") # default HF write token if not hf_user: raise gr.Error(f"Invalid user name: {hf_user}") if gated != "False" and is_private: raise gr.Error(f"Gated repo must be public") set_token(hf_token) lora_dict = {lora1: lora1s, lora2: lora2s, lora3: lora3s, lora4: lora4s, lora5: lora5s} new_path = convert_url_to_diffusers(dl_url, civitai_key, is_upload_sf, dtype, vae, clip, t5, scheduler, ema, image_size, safety_checker, base_repo, mtype, lora_dict, is_local) if not new_path: return "" new_repo_id = f"{hf_user}/{Path(new_path).stem}" if hf_repo != "": new_repo_id = f"{hf_user}/{hf_repo}" if not is_repo_name(new_repo_id): raise gr.Error(f"Invalid repo name: {new_repo_id}") if not is_overwrite and is_repo_exists(new_repo_id) and not is_pr: raise gr.Error(f"Repo already exists: {new_repo_id}") repo_url = upload_repo(new_repo_id, new_path, is_private, is_pr) gate_repo(new_repo_id, gated) safe_clean(new_path) if not urls: urls = [] urls.append(repo_url) md = "### Your new repo:\n" for u in urls: md += f"[{str(u).split('/')[-2]}/{str(u).split('/')[-1]}]({str(u)})
" return gr.update(value=urls, choices=urls), gr.update(value=md) except Exception as e: print(f"Error occured. {e}") raise gr.Error(f"Error occured. {e}") if __name__ == "__main__": parser = argparse.ArgumentParser() parser.add_argument("--url", type=str, required=True, help="URL of the model to convert.") parser.add_argument("--dtype", default="fp16", type=str, choices=get_dtypes(), help='Output data type. (Default: "fp16")') 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="", type=str, required=False, help="URL or Repo ID of the VAE to use.") parser.add_argument("--clip", default="", type=str, required=False, help="URL or Repo ID of the CLIP to use.") parser.add_argument("--t5", default="", type=str, required=False, help="URL or Repo ID of the T5 to use.") parser.add_argument("--base", default="", type=str, required=False, help="Repo ID of the base repo.") parser.add_argument("--nonema", action="store_true", default=False, help="Don't extract EMA (for SD 1.5).") parser.add_argument("--civitai_key", default="", type=str, required=False, help="Civitai API Key (If you want to download file from Civitai).") parser.add_argument("--lora1", default="", 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="", 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="", 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="", 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="", 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="", 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!" is_local = True 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 ema = not args.nonema mtype = "SDXL" convert_url_to_diffusers(args.url, args.civitai_key, args.dtype, args.vae, args.clip, args.t5, args.scheduler, ema, args.base, mtype, lora_dict, is_local)