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README.md

@@ -1,12 +1,13 @@
----
-title: Sd To Diffusers V2
-emoji: 👁
-colorFrom: blue
-colorTo: green
-sdk: gradio
-sdk_version: 4.38.1
-app_file: app.py
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
+---
+title: SD To Diffusers V2
+emoji: 🎨➡️🧨
+colorFrom: indigo
+colorTo: purple
+sdk: gradio
+sdk_version: 4.38.1
+app_file: app.py
+pinned: false
+license: mit
+---
+
+Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

app.py

@@ -0,0 +1,75 @@
+import gradio as gr
+from convert_url_to_diffusers_sd_gr import (
+    convert_url_to_diffusers_repo,
+    SCHEDULER_CONFIG_MAP,
+)
+
+vaes = [
+    "",
+    "https://huggingface.co/stabilityai/sd-vae-ft-mse-original/resolve/main/vae-ft-mse-840000-ema-pruned.ckpt",
+    "https://huggingface.co/stabilityai/sd-vae-ft-ema-original/resolve/main/vae-ft-ema-560000-ema-pruned.ckpt",
+]
+loras = [
+    "",
+    "https://huggingface.co/SPO-Diffusion-Models/SPO-SD-v1-5_4k-p_10ep_LoRA/blob/main/spo-sd-v1-5_4k-p_10ep_lora_diffusers.safetensors",
+]
+schedulers = list(SCHEDULER_CONFIG_MAP.keys())
+
+css = """"""
+
+with gr.Blocks(theme="NoCrypt/miku@>=1.2.2", css=css) as demo:
+    gr.Markdown("# Download and convert any Stable Diffusion 1.5 / 2.0 safetensors to Diffusers and create your repo")
+    gr.Markdown(
+        f"""
+**⚠️IMPORTANT NOTICE⚠️**<br>
+From an information security standpoint, it is dangerous to expose your access token or key to others.
+If you do use it, I recommend that you duplicate this space on your own account before doing so.
+Keys and tokens could be set to SECRET (HF_TOKEN, CIVITAI_API_KEY) if it's placed in your own space.
+It saves you the trouble of typing them in.<br>
+<br>
+**The steps are the following**:
+- Paste a write-access token from [hf.co/settings/tokens](https://huggingface.co/settings/tokens).
+- Input a model download url from the Hub or Civitai or other sites.
+- If you want to download a model from Civitai, paste a Civitai API Key.
+- Input your new repo name. e.g. 'yourid/newrepo'.
+- Set the parameters. If not sure, just use the defaults.
+- Click "Submit".
+- Patiently wait until the output changes.
+            """
+    )
+    with gr.Column():
+        dl_url = gr.Textbox(label="URL to download", placeholder="https://...", value="", max_lines=1)
+        repo_id = gr.Textbox(label="Your New Repo ID", placeholder="author/model", value="", max_lines=1)
+        hf_token = gr.Textbox(label="Your HF write token", placeholder="", value="", max_lines=1)
+        civitai_key = gr.Textbox(label="Your Civitai API Key (Optional)", info="If you download model from Civitai...", placeholder="", value="", max_lines=1)
+        is_half = gr.Checkbox(label="Half precision", value=True)
+        model_type = gr.Radio(label="Model type", choices=["v1", "v2"])
+        sample_size = gr.Radio(label="Sample size (px)", choices=[512, 768]),
+        ema = gr.Radio(label="Extract EMA or non-EMA?", choices=["ema", "non-ema"], value="ema"),
+        vae = gr.Dropdown(label="VAE", choices=vaes, value="", allow_custom_value=True)
+        scheduler = gr.Dropdown(label="Scheduler (Sampler)", choices=schedulers, value="Euler a")
+        lora1 = gr.Dropdown(label="LoRA1", choices=loras, value="", allow_custom_value=True)
+        lora1s = gr.Slider(minimum=-2, maximum=2, step=0.01, value=1.00, label="LoRA1 weight scale")
+        lora2 = gr.Dropdown(label="LoRA2", choices=loras, value="", allow_custom_value=True)
+        lora2s = gr.Slider(minimum=-2, maximum=2, step=0.01, value=1.00, label="LoRA2 weight scale")
+        lora3 = gr.Dropdown(label="LoRA3", choices=loras, value="", allow_custom_value=True)
+        lora3s = gr.Slider(minimum=-2, maximum=2, step=0.01, value=1.00, label="LoRA3 weight scale")
+        lora4 = gr.Dropdown(label="LoRA4", choices=loras, value="", allow_custom_value=True)
+        lora4s = gr.Slider(minimum=-2, maximum=2, step=0.01, value=1.00, label="LoRA4 weight scale")
+        lora5 = gr.Dropdown(label="LoRA5", choices=loras, value="", allow_custom_value=True)
+        lora5s = gr.Slider(minimum=-2, maximum=2, step=0.01, value=1.00, label="LoRA5 weight scale")
+        run_button = gr.Button(value="Submit")
+        repo_urls = gr.CheckboxGroup(visible=False, choices=[], value=None)
+        output_md = gr.Markdown(label="Output")
+
+    gr.on(
+        triggers=[run_button.click],
+        fn=convert_url_to_diffusers_repo,
+        inputs=[dl_url, repo_id, hf_token, civitai_key, repo_urls, is_half, vae, scheduler,
+                 lora1, lora1s, lora2, lora2s, lora3, lora3s, lora4, lora4s, lora5, lora5s,
+                 model_type, sample_size, ema],
+        outputs=[repo_urls, output_md],
+    )
+
+demo.queue()
+demo.launch()

convert_url_to_diffusers_sd.py

@@ -0,0 +1,307 @@
+import argparse
+from pathlib import Path
+import os
+import torch
+from diffusers import StableDiffusionPipeline, 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', '.ckpt', '.bin', '.pt', '.pth')
+    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
+tags:
+- text-to-image
+---
+Converted from [{orig_name}]({orig_url}).
+"""
+    else:
+        md = f"""---
+license: other
+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():
+        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) 
+    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_sd(url, civitai_key="", half=True, vae=None, scheduler="Euler", lora_dict={},
+                                 model_type="v1", sample_size=512, ema="ema"):
+    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(".", "_") #
+
+    extract_ema = True if ema == "ema" else False
+    
+    if model_type == "v1": #
+        config_url = "https://raw.githubusercontent.com/CompVis/stable-diffusion/main/configs/stable-diffusion/v1-inference.yaml"
+    elif model_type == "v2":
+        if sample_size == 512:
+            config_url = "https://raw.githubusercontent.com/Stability-AI/stablediffusion/main/configs/stable-diffusion/v2-inference.yaml"
+        else:
+            config_url = "https://raw.githubusercontent.com/Stability-AI/stablediffusion/main/configs/stable-diffusion/v2-inference-v.yaml"
+
+    pipe = None
+    if is_repo_name(new_file):
+        if half:
+            pipe = StableDiffusionPipeline.from_pretrained(new_file, extract_ema=extract_ema, requires_safety_checker=False, use_safetensors=True, torch_dtype=torch.float16)
+        else:
+            pipe = StableDiffusionPipeline.from_pretrained(new_file, extract_ema=extract_ema, requires_safety_checker=False,  use_safetensors=True)
+    else:
+        if half:
+            pipe = StableDiffusionPipeline.from_single_file(new_file, extract_ema=extract_ema, requires_safety_checker=False,  use_safetensors=True, torch_dtype=torch.float16)
+        else:
+            pipe = StableDiffusionPipeline.from_single_file(new_file, extract_ema=extract_ema, requires_safety_checker=False,  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)
+
+    return new_repo_name
+
+
+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("--model_type", default="v1", type=str, choices=["v1", "v2"], required=False, help="Extract EMA or non-EMA?")
+    parser.add_argument("--sample_size", default=512, type=int, choices=[512, 768], required=False, help="Sample size (px)")
+    parser.add_argument("--ema", default="ema", type=str, choices=["ema", "non-ema"], required=False, help="Extract EMA or non-EMA?")
+    parser.add_argument("--scheduler", default="Euler", 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 None in lora_dict.keys(): del lora_dict[None]
+
+    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_sd(args.url, args.civitai_key, args.half, args.vae, args.scheduler, lora_dict,
+                                 args.model_type, args.sample_size, args.ema)
+
+
+# Usage: python convert_url_to_diffusers_sd.py --url https://huggingface.co/Yntec/DreamPhotoGASM/blob/main/DreamPhotoGASM.safetensors
+# python convert_url_to_diffusers_sd.py --url https://huggingface.co/Yntec/DreamPhotoGASM/blob/main/DreamPhotoGASM.safetensors --scheduler "Euler a"
+# python convert_url_to_diffusers_sd.py --url https://huggingface.co/Yntec/DreamPhotoGASM/blob/main/DreamPhotoGASM.safetensors --loras ./loras

convert_url_to_diffusers_sd_gr.py

@@ -0,0 +1,366 @@
+import argparse
+from pathlib import Path
+import os
+import torch
+from diffusers import StableDiffusionPipeline, AutoencoderKL
+import gradio as gr
+# 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="", progress=gr.Progress(track_tqdm=True)):
+    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', '.ckpt', '.bin', '.pt', '.pth')
+    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, progress=gr.Progress(track_tqdm=True)):
+    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"]
+    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
+tags:
+- text-to-image
+---
+Converted from [{orig_name}]({orig_url}).
+"""
+    else:
+        md = f"""---
+license: other
+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, lora_dict={}, temp_dir=".", civitai_key=""):
+    if not lora_dict or not isinstance(lora_dict, dict): return
+    a_list = []
+    w_list = []
+    for k, v in lora_dict.items():
+        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) 
+    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_sd(url, civitai_key="", half=True, vae=None, scheduler="Euler", lora_dict={},
+                                  model_type="v1", sample_size=512, ema="ema", progress=gr.Progress(track_tqdm=True)):
+    progress(0, desc="Start converting...")
+    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(".", "_") #
+    if not is_repo_name(new_file): return ""
+
+    extract_ema = True if ema == "ema" else False
+
+    if model_type == "v1": #
+        config_url = "https://raw.githubusercontent.com/CompVis/stable-diffusion/main/configs/stable-diffusion/v1-inference.yaml"
+    elif model_type == "v2":
+        if sample_size == 512:
+            config_url = "https://raw.githubusercontent.com/Stability-AI/stablediffusion/main/configs/stable-diffusion/v2-inference.yaml"
+        else:
+            config_url = "https://raw.githubusercontent.com/Stability-AI/stablediffusion/main/configs/stable-diffusion/v2-inference-v.yaml"
+ 
+    pipe = None
+    if is_repo_name(new_file):
+        if half:
+            pipe = StableDiffusionPipeline.from_pretrained(new_file, extract_ema=extract_ema, requires_safety_checker=False, use_safetensors=True, torch_dtype=torch.float16)
+        else:
+            pipe = StableDiffusionPipeline.from_pretrained(new_file, extract_ema=extract_ema, requires_safety_checker=False,  use_safetensors=True)
+    else:
+        if half:
+            pipe = StableDiffusionPipeline.from_single_file(new_file, extract_ema=extract_ema, requires_safety_checker=False,  use_safetensors=True, torch_dtype=torch.float16)
+        else:
+            pipe = StableDiffusionPipeline.from_single_file(new_file, extract_ema=extract_ema, requires_safety_checker=False,  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)
+
+    progress(1, desc="Converted.")
+    return new_repo_name
+
+
+def is_repo_exists(repo_id):
+    from huggingface_hub import HfApi
+    api = HfApi()
+    try:
+        if api.repo_exists(repo_id=repo_id): return True
+        else: return False
+    except Exception as e:
+        print(f"Error: Failed to connect {repo_id}. ")
+        return True # for safe
+
+
+def create_diffusers_repo(new_repo_id, diffusers_folder, progress=gr.Progress(track_tqdm=True)):
+    from huggingface_hub import HfApi
+    import os
+    hf_token = os.environ.get("HF_TOKEN")
+    api = HfApi()
+    try:
+        progress(0, desc="Start uploading...")
+        api.create_repo(repo_id=new_repo_id, token=hf_token)
+        for path in Path(diffusers_folder).glob("*"):
+            if path.is_dir():
+                api.upload_folder(repo_id=new_repo_id, folder_path=str(path), path_in_repo=path.name, token=hf_token)
+            elif path.is_file():
+                api.upload_file(repo_id=new_repo_id, path_or_fileobj=str(path), path_in_repo=path.name, token=hf_token)
+        progress(1, desc="Uploaded.")
+        url = f"https://huggingface.co/{new_repo_id}"
+    except Exception as e:
+        print(f"Error: Failed to upload to {new_repo_id}. ")
+        return ""
+    return url
+
+
+def convert_url_to_diffusers_repo(dl_url, new_repo_id, hf_token, civitai_key="", repo_urls=[], half=True, vae=None,
+                                  scheduler="Euler a", lora1=None, lora1s=1.0, lora2=None, lora2s=1.0, lora3=None, lora3s=1.0,
+                                  lora4=None, lora4s=1.0, lora5=None, lora5s=1.0,
+                                  model_type="v1", sample_size=512, ema="ema", progress=gr.Progress(track_tqdm=True)):
+    if hf_token and not os.environ.get("HF_TOKEN"): os.environ['HF_TOKEN'] = hf_token
+    if not civitai_key and os.environ.get("CIVITAI_API_KEY"): civitai_key = os.environ.get("CIVITAI_API_KEY")
+    if not is_repo_name(new_repo_id):
+        print(f"Invalid repo name: {new_repo_id}")
+        progress(1, desc=f"Invalid repo name: {new_repo_id}")
+        return ""
+    if is_repo_exists(new_repo_id):
+        print(f"Repo already exists: {new_repo_id}")
+        progress(1, desc=f"Repo already exists: {new_repo_id}")
+        return ""
+    lora_dict = {lora1: lora1s, lora2: lora2s, lora3: lora3s, lora4: lora4s, lora5: lora5s}
+    if None in lora_dict.keys(): del lora_dict[None]
+    new_path = convert_url_to_diffusers_sd(dl_url, civitai_key, half, vae, scheduler, lora_dict,
+                                            model_type, sample_size, ema)
+    if not new_path: return ""
+    repo_url = create_diffusers_repo(new_repo_id, new_path)
+    if not repo_urls: repo_urls = []
+    repo_urls.append(repo_url)
+    md = "Your new repo:<br>"
+    for u in repo_urls:
+        md += f"[{str(u).split('/')[-2]}/{str(u).split('/')[-1]}]({str(u)})<br>"
+    return gr.update(value=repo_urls, choices=repo_urls), gr.update(value=md)
+
+
+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("--model_type", default="v1", type=str, choices=["v1", "v2"], required=False, help="Extract EMA or non-EMA?")
+    parser.add_argument("--sample_size", default=512, type=int, choices=[512, 768], required=False, help="Sample size (px)")
+    parser.add_argument("--ema", default="ema", type=str, choices=["ema", "non-ema"], required=False, help="Extract EMA or non-EMA?")
+    parser.add_argument("--scheduler", default="Euler", 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 None in lora_dict.keys(): del lora_dict[None]
+
+    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_sd(args.url, args.civitai_key, args.half, args.vae, args.scheduler, lora_dict,
+                                 args.model_type, args.sample_size, args.ema)

requirements.txt

@@ -0,0 +1,9 @@
+huggingface_hub
+safetensors
+transformers
+accelerate
+git+https://github.com/huggingface/diffusers
+pytorch_lightning
+peft
+aria2
+gdown