convert_url_to_diffusers_multi_gr.py

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)})<br>"
        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)