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README.md

@@ -4,7 +4,7 @@ emoji: 🎨➡️🧨
 colorFrom: indigo
 colorTo: purple
 sdk: gradio
-sdk_version: 5.6.0
+sdk_version: 5.9.1
 app_file: app.py
 pinned: false
 license: mit

app.py

@@ -1,243 +1,251 @@
-import gradio as gr
-from convert_url_to_diffusers_multi_gr import convert_url_to_diffusers_repo, get_dtypes, FLUX_BASE_REPOS, SD35_BASE_REPOS
-from presets import (DEFAULT_DTYPE, schedulers, clips, t5s, sdxl_vaes, sdxl_loras, sdxl_preset_dict, sdxl_set_presets,
-                     sd15_vaes, sd15_loras, sd15_preset_dict, sd15_set_presets, flux_vaes, flux_loras, flux_preset_dict, flux_set_presets,
-                     sd35_vaes, sd35_loras, sd35_preset_dict, sd35_set_presets)
-
-css = """
-.title { font-size: 3em; align-items: center; text-align: center; }
-.info { align-items: center; text-align: center; }
-.block.result { margin: 1em 0; padding: 1em; box-shadow: 0 0 3px 3px #664422, 0 0 3px 2px #664422 inset; border-radius: 6px; background: #665544; }
-"""
-
-with gr.Blocks(theme="NoCrypt/miku@>=1.2.2", fill_width=True, css=css, delete_cache=(60, 3600)) as demo:
-    gr.Markdown("# Download SDXL / SD 1.5 / SD 3.5 / FLUX.1 safetensors and convert to HF🤗 Diffusers format and create your repo", elem_classes="title")
-    gr.Markdown(f"""
-### ⚠️IMPORTANT NOTICE⚠️<br>
-It's 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 HF account in advance.
-Keys and tokens could be set to **Secrets** (`HF_TOKEN`, `CIVITAI_API_KEY`) if it's placed in your own space.
-It saves you the trouble of typing them in.<br>
-It barely works in the CPU space, but larger files can be converted if duplicated on the more powerful **Zero GPU** space.
-In particular, conversion of FLUX.1 or SD 3.5 is almost impossible in CPU space.
-### The steps are the following:
-1. Paste a write-access token from [hf.co/settings/tokens](https://huggingface.co/settings/tokens).
-1. Input a model download url of the Hugging Face or Civitai or other sites.
-1. If you want to download a model from Civitai, paste a Civitai API Key.
-1. Input your HF user ID. e.g. 'yourid'.
-1. Input your new repo name. If empty, auto-complete. e.g. 'newrepo'.
-1. Set the parameters. If not sure, just use the defaults.
-1. Click "Submit".
-1. Patiently wait until the output changes. It takes approximately 2 to 3 minutes (on SDXL models downloading from HF).
-            """)
-    with gr.Column():
-        dl_url = gr.Textbox(label="URL to download", placeholder="https://huggingface.co/bluepen5805/blue_pencil-XL/blob/main/blue_pencil-XL-v7.0.0.safetensors", value="", max_lines=1)
-        with gr.Group():
-            with gr.Row():
-                hf_user = gr.Textbox(label="Your HF user ID", placeholder="username", value="", max_lines=1)
-                hf_repo = gr.Textbox(label="New repo name", placeholder="reponame", info="If empty, auto-complete", value="", max_lines=1)
-            with gr.Row(equal_height=True):
-                with gr.Column():
-                    hf_token = gr.Textbox(label="Your HF write token", placeholder="hf_...", value="", max_lines=1)
-                    gr.Markdown("Your token is available at [hf.co/settings/tokens](https://huggingface.co/settings/tokens).", elem_classes="info")
-                with gr.Column():
-                    civitai_key = gr.Textbox(label="Your Civitai API Key (Optional)", info="If you download model from Civitai...", placeholder="", value="", max_lines=1)
-                    gr.Markdown("Your Civitai API key is available at [https://civitai.com/user/account](https://civitai.com/user/account).", elem_classes="info")
-            with gr.Row():
-                is_upload_sf = gr.Checkbox(label="Upload single safetensors file into new repo", value=False)
-                is_private = gr.Checkbox(label="Create private repo", value=True)
-                is_overwrite = gr.Checkbox(label="Overwrite repo", value=False)
-        with gr.Tab("SDXL"):
-            with gr.Group():
-                sdxl_presets = gr.Radio(label="Presets", choices=list(sdxl_preset_dict.keys()), value=list(sdxl_preset_dict.keys())[0])
-                sdxl_mtype = gr.Textbox(value="SDXL", visible=False)
-                with gr.Row():
-                    sdxl_dtype = gr.Radio(label="Output data type", choices=get_dtypes(), value=DEFAULT_DTYPE)
-                    sdxl_ema = gr.Checkbox(label="Extract EMA", info="For SD 1.5", value=True, visible=False)
-                    sdxl_base_repo = gr.Dropdown(label="Base repo ID", choices=FLUX_BASE_REPOS, value=FLUX_BASE_REPOS[0], allow_custom_value=True, visible=False)
-                with gr.Accordion("Advanced settings", open=False):
-                    with gr.Row():
-                        sdxl_vae = gr.Dropdown(label="VAE", choices=sdxl_vaes, value="", allow_custom_value=True)
-                        sdxl_clip = gr.Dropdown(label="CLIP", choices=clips, value="", allow_custom_value=True)
-                        sdxl_t5 = gr.Dropdown(label="T5", choices=t5s, value="", allow_custom_value=True, visible=False)
-                        sdxl_scheduler = gr.Dropdown(label="Scheduler (Sampler)", choices=schedulers, value="Euler a")
-                    with gr.Row():
-                        with gr.Column():
-                            sdxl_lora1 = gr.Dropdown(label="LoRA1", choices=sdxl_loras, value="", allow_custom_value=True, min_width=320)
-                            sdxl_lora1s = gr.Slider(minimum=-2, maximum=2, step=0.01, value=1.00, label="LoRA1 weight scale")
-                        with gr.Column():
-                            sdxl_lora2 = gr.Dropdown(label="LoRA2", choices=sdxl_loras, value="", allow_custom_value=True, min_width=320)
-                            sdxl_lora2s = gr.Slider(minimum=-2, maximum=2, step=0.01, value=1.00, label="LoRA2 weight scale")
-                        with gr.Column():
-                            sdxl_lora3 = gr.Dropdown(label="LoRA3", choices=sdxl_loras, value="", allow_custom_value=True, min_width=320)
-                            sdxl_lora3s = gr.Slider(minimum=-2, maximum=2, step=0.01, value=1.00, label="LoRA3 weight scale")
-                        with gr.Column():
-                            sdxl_lora4 = gr.Dropdown(label="LoRA4", choices=sdxl_loras, value="", allow_custom_value=True, min_width=320)
-                            sdxl_lora4s = gr.Slider(minimum=-2, maximum=2, step=0.01, value=1.00, label="LoRA4 weight scale")
-                        with gr.Column():
-                            sdxl_lora5 = gr.Dropdown(label="LoRA5", choices=sdxl_loras, value="", allow_custom_value=True, min_width=320)
-                            sdxl_lora5s = gr.Slider(minimum=-2, maximum=2, step=0.01, value=1.00, label="LoRA5 weight scale")
-            sdxl_run_button = gr.Button(value="Submit", variant="primary")
-        with gr.Tab("SD 1.5"):
-            with gr.Group():
-                sd15_presets = gr.Radio(label="Presets", choices=list(sd15_preset_dict.keys()), value=list(sd15_preset_dict.keys())[0])
-                sd15_mtype = gr.Textbox(value="SD 1.5", visible=False)
-                with gr.Row():
-                    sd15_dtype = gr.Radio(label="Output data type", choices=get_dtypes(), value=DEFAULT_DTYPE)
-                    sd15_ema = gr.Checkbox(label="Extract EMA", info="For SD 1.5", value=True, visible=True)
-                    sd15_base_repo = gr.Dropdown(label="Base repo ID", choices=FLUX_BASE_REPOS, value=FLUX_BASE_REPOS[0], allow_custom_value=True, visible=False)
-                with gr.Accordion("Advanced settings", open=False):
-                    with gr.Row():
-                        sd15_vae = gr.Dropdown(label="VAE", choices=sd15_vaes, value="", allow_custom_value=True)
-                        sd15_clip = gr.Dropdown(label="CLIP", choices=clips, value="", allow_custom_value=True)
-                        sd15_t5 = gr.Dropdown(label="T5", choices=t5s, value="", allow_custom_value=True, visible=False)
-                        sd15_scheduler = gr.Dropdown(label="Scheduler (Sampler)", choices=schedulers, value="Euler")
-                    with gr.Row():
-                        with gr.Column():
-                            sd15_lora1 = gr.Dropdown(label="LoRA1", choices=sd15_loras, value="", allow_custom_value=True, min_width=320)
-                            sd15_lora1s = gr.Slider(minimum=-2, maximum=2, step=0.01, value=1.00, label="LoRA1 weight scale")
-                        with gr.Column():
-                            sd15_lora2 = gr.Dropdown(label="LoRA2", choices=sd15_loras, value="", allow_custom_value=True, min_width=320)
-                            sd15_lora2s = gr.Slider(minimum=-2, maximum=2, step=0.01, value=1.00, label="LoRA2 weight scale")
-                        with gr.Column():
-                            sd15_lora3 = gr.Dropdown(label="LoRA3", choices=sd15_loras, value="", allow_custom_value=True, min_width=320)
-                            sd15_lora3s = gr.Slider(minimum=-2, maximum=2, step=0.01, value=1.00, label="LoRA3 weight scale")
-                        with gr.Column():
-                            sd15_lora4 = gr.Dropdown(label="LoRA4", choices=sd15_loras, value="", allow_custom_value=True, min_width=320)
-                            sd15_lora4s = gr.Slider(minimum=-2, maximum=2, step=0.01, value=1.00, label="LoRA4 weight scale")
-                        with gr.Column():
-                            sd15_lora5 = gr.Dropdown(label="LoRA5", choices=sd15_loras, value="", allow_custom_value=True, min_width=320)
-                            sd15_lora5s = gr.Slider(minimum=-2, maximum=2, step=0.01, value=1.00, label="LoRA5 weight scale")
-            sd15_run_button = gr.Button(value="Submit", variant="primary")
-        with gr.Tab("FLUX.1"):
-            with gr.Group():
-                flux_presets = gr.Radio(label="Presets", choices=list(flux_preset_dict.keys()), value=list(flux_preset_dict.keys())[0])
-                flux_mtype = gr.Textbox(value="FLUX", visible=False)
-                with gr.Row():
-                    flux_dtype = gr.Radio(label="Output data type", choices=get_dtypes(), value="bf16")
-                    flux_ema = gr.Checkbox(label="Extract EMA", info="For SD 1.5", value=True, visible=False)
-                    flux_base_repo = gr.Dropdown(label="Base repo ID", choices=FLUX_BASE_REPOS, value=FLUX_BASE_REPOS[0], allow_custom_value=True, visible=True)
-                with gr.Accordion("Advanced settings", open=False):
-                    with gr.Row():
-                        flux_vae = gr.Dropdown(label="VAE", choices=flux_vaes, value="", allow_custom_value=True)
-                        flux_clip = gr.Dropdown(label="CLIP", choices=clips, value="", allow_custom_value=True)
-                        flux_t5 = gr.Dropdown(label="T5", choices=t5s, value="", allow_custom_value=True)
-                        flux_scheduler = gr.Dropdown(label="Scheduler (Sampler)", choices=[""], value="", visible=False)
-                    with gr.Row():
-                        with gr.Column():
-                            flux_lora1 = gr.Dropdown(label="LoRA1", choices=flux_loras, value="", allow_custom_value=True, min_width=320)
-                            flux_lora1s = gr.Slider(minimum=-2, maximum=2, step=0.01, value=1.00, label="LoRA1 weight scale")
-                        with gr.Column():
-                            flux_lora2 = gr.Dropdown(label="LoRA2", choices=flux_loras, value="", allow_custom_value=True, min_width=320)
-                            flux_lora2s = gr.Slider(minimum=-2, maximum=2, step=0.01, value=1.00, label="LoRA2 weight scale")
-                        with gr.Column():
-                            flux_lora3 = gr.Dropdown(label="LoRA3", choices=flux_loras, value="", allow_custom_value=True, min_width=320)
-                            flux_lora3s = gr.Slider(minimum=-2, maximum=2, step=0.01, value=1.00, label="LoRA3 weight scale")
-                        with gr.Column():
-                            flux_lora4 = gr.Dropdown(label="LoRA4", choices=flux_loras, value="", allow_custom_value=True, min_width=320)
-                            flux_lora4s = gr.Slider(minimum=-2, maximum=2, step=0.01, value=1.00, label="LoRA4 weight scale")
-                        with gr.Column():
-                            flux_lora5 = gr.Dropdown(label="LoRA5", choices=flux_loras, value="", allow_custom_value=True, min_width=320)
-                            flux_lora5s = gr.Slider(minimum=-2, maximum=2, step=0.01, value=1.00, label="LoRA5 weight scale")
-            flux_run_button = gr.Button(value="Submit", variant="primary")
-        with gr.Tab("SD 3.5"):
-            with gr.Group():
-                sd35_presets = gr.Radio(label="Presets", choices=list(sd35_preset_dict.keys()), value=list(sd35_preset_dict.keys())[0])
-                sd35_mtype = gr.Textbox(value="SD 3.5", visible=False)
-                with gr.Row():
-                    sd35_dtype = gr.Radio(label="Output data type", choices=get_dtypes(), value="bf16")
-                    sd35_ema = gr.Checkbox(label="Extract EMA", info="For SD 1.5", value=True, visible=False)
-                    sd35_base_repo = gr.Dropdown(label="Base repo ID", choices=SD35_BASE_REPOS, value=SD35_BASE_REPOS[0], allow_custom_value=True, visible=True)
-                with gr.Accordion("Advanced settings", open=False):
-                    with gr.Row():
-                        sd35_vae = gr.Dropdown(label="VAE", choices=sd35_vaes, value="", allow_custom_value=True)
-                        sd35_clip = gr.Dropdown(label="CLIP", choices=clips, value="", allow_custom_value=True)
-                        sd35_t5 = gr.Dropdown(label="T5", choices=t5s, value="", allow_custom_value=True)
-                        sd35_scheduler = gr.Dropdown(label="Scheduler (Sampler)", choices=[""], value="", visible=False)
-                    with gr.Row():
-                        with gr.Column():
-                            sd35_lora1 = gr.Dropdown(label="LoRA1", choices=sd35_loras, value="", allow_custom_value=True, min_width=320)
-                            sd35_lora1s = gr.Slider(minimum=-2, maximum=2, step=0.01, value=1.00, label="LoRA1 weight scale")
-                        with gr.Column():
-                            sd35_lora2 = gr.Dropdown(label="LoRA2", choices=sd35_loras, value="", allow_custom_value=True, min_width=320)
-                            sd35_lora2s = gr.Slider(minimum=-2, maximum=2, step=0.01, value=1.00, label="LoRA2 weight scale")
-                        with gr.Column():
-                            sd35_lora3 = gr.Dropdown(label="LoRA3", choices=sd35_loras, value="", allow_custom_value=True, min_width=320)
-                            sd35_lora3s = gr.Slider(minimum=-2, maximum=2, step=0.01, value=1.00, label="LoRA3 weight scale")
-                        with gr.Column():
-                            sd35_lora4 = gr.Dropdown(label="LoRA4", choices=sd35_loras, value="", allow_custom_value=True, min_width=320)
-                            sd35_lora4s = gr.Slider(minimum=-2, maximum=2, step=0.01, value=1.00, label="LoRA4 weight scale")
-                        with gr.Column():
-                            sd35_lora5 = gr.Dropdown(label="LoRA5", choices=sd35_loras, value="", allow_custom_value=True, min_width=320)
-                            sd35_lora5s = gr.Slider(minimum=-2, maximum=2, step=0.01, value=1.00, label="LoRA5 weight scale")
-            sd35_run_button = gr.Button(value="Submit", variant="primary")
-        with gr.Group():
-            repo_urls = gr.CheckboxGroup(visible=False, choices=[], value=[])
-            output_md = gr.Markdown(label="Output", value="<br><br>", elem_classes="result")
-            clear_button = gr.Button(value="Clear Output", variant="secondary")
-        gr.DuplicateButton(value="Duplicate Space")
-
-    gr.on(
-        triggers=[sdxl_run_button.click],
-        fn=convert_url_to_diffusers_repo,
-        inputs=[dl_url, hf_user, hf_repo, hf_token, civitai_key, is_private, is_overwrite, is_upload_sf, repo_urls,
-                sdxl_dtype, sdxl_vae, sdxl_clip, sdxl_t5, sdxl_scheduler, sdxl_ema, sdxl_base_repo, sdxl_mtype,
-                sdxl_lora1, sdxl_lora1s, sdxl_lora2, sdxl_lora2s, sdxl_lora3, sdxl_lora3s, sdxl_lora4, sdxl_lora4s, sdxl_lora5, sdxl_lora5s],
-        outputs=[repo_urls, output_md],
-    )
-    sdxl_presets.change(
-        fn=sdxl_set_presets,
-        inputs=[sdxl_presets],
-        outputs=[sdxl_dtype, sdxl_vae, sdxl_scheduler, sdxl_lora1, sdxl_lora1s, sdxl_lora2, sdxl_lora2s, sdxl_lora3, sdxl_lora3s,
-                 sdxl_lora4, sdxl_lora4s, sdxl_lora5, sdxl_lora5s],
-        queue=False,
-    )
-    gr.on(
-        triggers=[sd15_run_button.click],
-        fn=convert_url_to_diffusers_repo,
-        inputs=[dl_url, hf_user, hf_repo, hf_token, civitai_key, is_private, is_overwrite, is_upload_sf, repo_urls,
-                sd15_dtype, sd15_vae, sd15_clip, sd15_t5, sd15_scheduler, sd15_ema, sd15_base_repo, sd15_mtype,
-                sd15_lora1, sd15_lora1s, sd15_lora2, sd15_lora2s, sd15_lora3, sd15_lora3s, sd15_lora4, sd15_lora4s, sd15_lora5, sd15_lora5s],
-        outputs=[repo_urls, output_md],
-    )
-    sd15_presets.change(
-        fn=sd15_set_presets,
-        inputs=[sd15_presets],
-        outputs=[sd15_dtype, sd15_vae, sd15_scheduler, sd15_lora1, sd15_lora1s, sd15_lora2, sd15_lora2s, sd15_lora3, sd15_lora3s,
-                 sd15_lora4, sd15_lora4s, sd15_lora5, sd15_lora5s, sd15_ema],
-        queue=False,
-    )
-    gr.on(
-        triggers=[flux_run_button.click],
-        fn=convert_url_to_diffusers_repo,
-        inputs=[dl_url, hf_user, hf_repo, hf_token, civitai_key, is_private, is_overwrite, is_upload_sf, repo_urls,
-                flux_dtype, flux_vae, flux_clip, flux_t5, flux_scheduler, flux_ema, flux_base_repo, flux_mtype,
-                flux_lora1, flux_lora1s, flux_lora2, flux_lora2s, flux_lora3, flux_lora3s, flux_lora4, flux_lora4s, flux_lora5, flux_lora5s],
-        outputs=[repo_urls, output_md],
-    )
-    flux_presets.change(
-        fn=flux_set_presets,
-        inputs=[flux_presets],
-        outputs=[flux_dtype, flux_vae, flux_scheduler, flux_lora1, flux_lora1s, flux_lora2, flux_lora2s, flux_lora3, flux_lora3s,
-                 flux_lora4, flux_lora4s, flux_lora5, flux_lora5s, flux_base_repo],
-        queue=False,
-    )
-    gr.on(
-        triggers=[sd35_run_button.click],
-        fn=convert_url_to_diffusers_repo,
-        inputs=[dl_url, hf_user, hf_repo, hf_token, civitai_key, is_private, is_overwrite, is_upload_sf, repo_urls,
-                sd35_dtype, sd35_vae, sd35_clip, sd35_t5, sd35_scheduler, sd35_ema, sd35_base_repo, sd35_mtype,
-                sd35_lora1, sd35_lora1s, sd35_lora2, sd35_lora2s, sd35_lora3, sd35_lora3s, sd35_lora4, sd35_lora4s, sd35_lora5, sd35_lora5s],
-        outputs=[repo_urls, output_md],
-    )
-    sd35_presets.change(
-        fn=sd35_set_presets,
-        inputs=[sd35_presets],
-        outputs=[sd35_dtype, sd35_vae, sd35_scheduler, sd35_lora1, sd35_lora1s, sd35_lora2, sd35_lora2s, sd35_lora3, sd35_lora3s,
-                 sd35_lora4, sd35_lora4s, sd35_lora5, sd35_lora5s, sd35_base_repo],
-        queue=False,
-    )
-    clear_button.click(lambda: ([], "<br><br>"), None, [repo_urls, output_md], queue=False, show_api=False)
-
-demo.queue()
-demo.launch()
+import gradio as gr
+from convert_url_to_diffusers_multi_gr import convert_url_to_diffusers_repo, get_dtypes, FLUX_BASE_REPOS, SD35_BASE_REPOS
+from presets import (DEFAULT_DTYPE, schedulers, clips, t5s, sdxl_vaes, sdxl_loras, sdxl_preset_dict, sdxl_set_presets,
+                     sd15_vaes, sd15_loras, sd15_preset_dict, sd15_set_presets, flux_vaes, flux_loras, flux_preset_dict, flux_set_presets,
+                     sd35_vaes, sd35_loras, sd35_preset_dict, sd35_set_presets)
+import os
+
+
+HF_USER = os.getenv("HF_USER", "")
+HF_REPO = os.getenv("HF_REPO", "")
+HF_URL = os.getenv("HF_URL", "")
+HF_OW = os.getenv("HF_OW", False)
+HF_PR = os.getenv("HF_PR", False)
+
+css = """
+.title { font-size: 3em; align-items: center; text-align: center; }
+.info { align-items: center; text-align: center; }
+.block.result { margin: 1em 0; padding: 1em; box-shadow: 0 0 3px 3px #664422, 0 0 3px 2px #664422 inset; border-radius: 6px; background: #665544; }
+"""
+
+with gr.Blocks(theme="theNeofr/Syne", fill_width=True, css=css, delete_cache=(60, 3600)) as demo:
+    gr.Markdown("# Download SDXL / SD 1.5 / SD 3.5 / FLUX.1 safetensors and convert to HF🤗 Diffusers format and create your repo", elem_classes="title")
+    gr.Markdown(f"""
+### ⚠️IMPORTANT NOTICE⚠️<br>
+It's 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 HF account in advance.
+Keys and tokens could be set to **Secrets** (`HF_TOKEN`, `CIVITAI_API_KEY`) if it's placed in your own space.
+It saves you the trouble of typing them in.<br>
+It barely works in the CPU space, but larger files can be converted if duplicated on the more powerful **Zero GPU** space.
+In particular, conversion of FLUX.1 or SD 3.5 is almost impossible in CPU space.
+### The steps are the following:
+1. Paste a write-access token from [hf.co/settings/tokens](https://huggingface.co/settings/tokens).
+1. Input a model download url of the Hugging Face or Civitai or other sites.
+1. If you want to download a model from Civitai, paste a Civitai API Key.
+1. Input your HF user ID. e.g. 'yourid'.
+1. Input your new repo name. If empty, auto-complete. e.g. 'newrepo'.
+1. Set the parameters. If not sure, just use the defaults.
+1. Click "Submit".
+1. Patiently wait until the output changes. It takes approximately 2 to 3 minutes (on SDXL models downloading from HF).
+            """)
+    with gr.Column():
+        dl_url = gr.Textbox(label="URL to download", placeholder="https://huggingface.co/bluepen5805/blue_pencil-XL/blob/main/blue_pencil-XL-v7.0.0.safetensors",
+                            value=HF_URL, max_lines=1)
+        with gr.Group():
+            with gr.Row():
+                hf_user = gr.Textbox(label="Your HF user ID", placeholder="username", value=HF_USER, max_lines=1)
+                hf_repo = gr.Textbox(label="New repo name", placeholder="reponame", info="If empty, auto-complete", value=HF_REPO, max_lines=1)
+            with gr.Row(equal_height=True):
+                with gr.Column():
+                    hf_token = gr.Textbox(label="Your HF write token", placeholder="hf_...", value="", max_lines=1)
+                    gr.Markdown("Your token is available at [hf.co/settings/tokens](https://huggingface.co/settings/tokens).", elem_classes="info")
+                with gr.Column():
+                    civitai_key = gr.Textbox(label="Your Civitai API Key (Optional)", info="If you download model from Civitai...", placeholder="", value="", max_lines=1)
+                    gr.Markdown("Your Civitai API key is available at [https://civitai.com/user/account](https://civitai.com/user/account).", elem_classes="info")
+            with gr.Row():
+                is_upload_sf = gr.Checkbox(label="Upload single safetensors file into new repo", value=False)
+                is_private = gr.Checkbox(label="Create private repo", value=True)
+                gated = gr.Radio(label="Create gated repo", info="Gated repo must be public", choices=["auto", "manual", "False"], value="False")
+            with gr.Row():
+                is_overwrite = gr.Checkbox(label="Overwrite repo", value=HF_OW)
+                is_pr = gr.Checkbox(label="Create PR", value=HF_PR)
+        with gr.Tab("SDXL"):
+            with gr.Group():
+                sdxl_presets = gr.Radio(label="Presets", choices=list(sdxl_preset_dict.keys()), value=list(sdxl_preset_dict.keys())[0])
+                sdxl_mtype = gr.Textbox(value="SDXL", visible=False)
+                sdxl_dtype = gr.Radio(label="Output data type", choices=get_dtypes(), value=DEFAULT_DTYPE)
+                with gr.Accordion("Advanced settings", open=False):
+                    with gr.Row():
+                        sdxl_vae = gr.Dropdown(label="VAE", choices=sdxl_vaes, value="", allow_custom_value=True)
+                        sdxl_scheduler = gr.Dropdown(label="Scheduler (Sampler)", choices=schedulers, value="Euler a")
+                        sdxl_clip = gr.Dropdown(label="CLIP", choices=clips, value="", allow_custom_value=True)
+                    with gr.Column():
+                        with gr.Row():
+                            sdxl_lora1 = gr.Dropdown(label="LoRA1", choices=sdxl_loras, value="", allow_custom_value=True, min_width=320, scale=2)
+                            sdxl_lora1s = gr.Slider(minimum=-2, maximum=2, step=0.01, value=1.00, label="LoRA1 weight scale")
+                        with gr.Row():
+                            sdxl_lora2 = gr.Dropdown(label="LoRA2", choices=sdxl_loras, value="", allow_custom_value=True, min_width=320, scale=2)
+                            sdxl_lora2s = gr.Slider(minimum=-2, maximum=2, step=0.01, value=1.00, label="LoRA2 weight scale")
+                        with gr.Row():
+                            sdxl_lora3 = gr.Dropdown(label="LoRA3", choices=sdxl_loras, value="", allow_custom_value=True, min_width=320, scale=2)
+                            sdxl_lora3s = gr.Slider(minimum=-2, maximum=2, step=0.01, value=1.00, label="LoRA3 weight scale")
+                        with gr.Row():
+                            sdxl_lora4 = gr.Dropdown(label="LoRA4", choices=sdxl_loras, value="", allow_custom_value=True, min_width=320, scale=2)
+                            sdxl_lora4s = gr.Slider(minimum=-2, maximum=2, step=0.01, value=1.00, label="LoRA4 weight scale")
+                        with gr.Row():
+                            sdxl_lora5 = gr.Dropdown(label="LoRA5", choices=sdxl_loras, value="", allow_custom_value=True, min_width=320, scale=2)
+                            sdxl_lora5s = gr.Slider(minimum=-2, maximum=2, step=0.01, value=1.00, label="LoRA5 weight scale")
+            sdxl_run_button = gr.Button(value="Submit", variant="primary")
+        with gr.Tab("SD 1.5"):
+            with gr.Group():
+                sd15_presets = gr.Radio(label="Presets", choices=list(sd15_preset_dict.keys()), value=list(sd15_preset_dict.keys())[0])
+                sd15_mtype = gr.Textbox(value="SD 1.5", visible=False)
+                sd15_dtype = gr.Radio(label="Output data type", choices=get_dtypes(), value=DEFAULT_DTYPE)
+                with gr.Row():
+                    sd15_ema = gr.Checkbox(label="Extract EMA", value=True, visible=True)
+                    sd15_isize = gr.Radio(label="Image size", choices=["768", "512"], value="768")
+                    sd15_sc = gr.Checkbox(label="Safety checker", value=False)
+                with gr.Accordion("Advanced settings", open=False):
+                    with gr.Row():
+                        sd15_vae = gr.Dropdown(label="VAE", choices=sd15_vaes, value="", allow_custom_value=True)
+                        sd15_scheduler = gr.Dropdown(label="Scheduler (Sampler)", choices=schedulers, value="Euler")
+                        sd15_clip = gr.Dropdown(label="CLIP", choices=clips, value="", allow_custom_value=True)
+                    with gr.Column():
+                        with gr.Row():
+                            sd15_lora1 = gr.Dropdown(label="LoRA1", choices=sd15_loras, value="", allow_custom_value=True, min_width=320, scale=2)
+                            sd15_lora1s = gr.Slider(minimum=-2, maximum=2, step=0.01, value=1.00, label="LoRA1 weight scale")
+                        with gr.Row():
+                            sd15_lora2 = gr.Dropdown(label="LoRA2", choices=sd15_loras, value="", allow_custom_value=True, min_width=320, scale=2)
+                            sd15_lora2s = gr.Slider(minimum=-2, maximum=2, step=0.01, value=1.00, label="LoRA2 weight scale")
+                        with gr.Row():
+                            sd15_lora3 = gr.Dropdown(label="LoRA3", choices=sd15_loras, value="", allow_custom_value=True, min_width=320, scale=2)
+                            sd15_lora3s = gr.Slider(minimum=-2, maximum=2, step=0.01, value=1.00, label="LoRA3 weight scale")
+                        with gr.Row():
+                            sd15_lora4 = gr.Dropdown(label="LoRA4", choices=sd15_loras, value="", allow_custom_value=True, min_width=320, scale=2)
+                            sd15_lora4s = gr.Slider(minimum=-2, maximum=2, step=0.01, value=1.00, label="LoRA4 weight scale")
+                        with gr.Row():
+                            sd15_lora5 = gr.Dropdown(label="LoRA5", choices=sd15_loras, value="", allow_custom_value=True, min_width=320, scale=2)
+                            sd15_lora5s = gr.Slider(minimum=-2, maximum=2, step=0.01, value=1.00, label="LoRA5 weight scale")
+            sd15_run_button = gr.Button(value="Submit", variant="primary")
+        with gr.Tab("FLUX.1"):
+            with gr.Group():
+                flux_presets = gr.Radio(label="Presets", choices=list(flux_preset_dict.keys()), value=list(flux_preset_dict.keys())[0])
+                flux_mtype = gr.Textbox(value="FLUX", visible=False)
+                flux_dtype = gr.Radio(label="Output data type", choices=get_dtypes(), value="bf16")
+                flux_base_repo = gr.Dropdown(label="Base repo ID", choices=FLUX_BASE_REPOS, value=FLUX_BASE_REPOS[0], allow_custom_value=True, visible=True)
+                with gr.Accordion("Advanced settings", open=False):
+                    with gr.Row():
+                        flux_vae = gr.Dropdown(label="VAE", choices=flux_vaes, value="", allow_custom_value=True)
+                        flux_scheduler = gr.Dropdown(label="Scheduler (Sampler)", choices=[""], value="", visible=False)
+                    with gr.Row():
+                        flux_clip = gr.Dropdown(label="CLIP", choices=clips, value="", allow_custom_value=True)
+                        flux_t5 = gr.Dropdown(label="T5", choices=t5s, value="", allow_custom_value=True)
+                    with gr.Column():
+                        with gr.Row():
+                            flux_lora1 = gr.Dropdown(label="LoRA1", choices=flux_loras, value="", allow_custom_value=True, min_width=320, scale=2)
+                            flux_lora1s = gr.Slider(minimum=-2, maximum=2, step=0.01, value=1.00, label="LoRA1 weight scale")
+                        with gr.Row():
+                            flux_lora2 = gr.Dropdown(label="LoRA2", choices=flux_loras, value="", allow_custom_value=True, min_width=320, scale=2)
+                            flux_lora2s = gr.Slider(minimum=-2, maximum=2, step=0.01, value=1.00, label="LoRA2 weight scale")
+                        with gr.Row():
+                            flux_lora3 = gr.Dropdown(label="LoRA3", choices=flux_loras, value="", allow_custom_value=True, min_width=320, scale=2)
+                            flux_lora3s = gr.Slider(minimum=-2, maximum=2, step=0.01, value=1.00, label="LoRA3 weight scale")
+                        with gr.Row():
+                            flux_lora4 = gr.Dropdown(label="LoRA4", choices=flux_loras, value="", allow_custom_value=True, min_width=320, scale=2)
+                            flux_lora4s = gr.Slider(minimum=-2, maximum=2, step=0.01, value=1.00, label="LoRA4 weight scale")
+                        with gr.Row():
+                            flux_lora5 = gr.Dropdown(label="LoRA5", choices=flux_loras, value="", allow_custom_value=True, min_width=320, scale=2)
+                            flux_lora5s = gr.Slider(minimum=-2, maximum=2, step=0.01, value=1.00, label="LoRA5 weight scale")
+            flux_run_button = gr.Button(value="Submit", variant="primary")
+        with gr.Tab("SD 3.5"):
+            with gr.Group():
+                sd35_presets = gr.Radio(label="Presets", choices=list(sd35_preset_dict.keys()), value=list(sd35_preset_dict.keys())[0])
+                sd35_mtype = gr.Textbox(value="SD 3.5", visible=False)
+                sd35_dtype = gr.Radio(label="Output data type", choices=get_dtypes(), value="bf16")
+                sd35_base_repo = gr.Dropdown(label="Base repo ID", choices=SD35_BASE_REPOS, value=SD35_BASE_REPOS[0], allow_custom_value=True, visible=True)
+                with gr.Accordion("Advanced settings", open=False):
+                    with gr.Row():
+                        sd35_vae = gr.Dropdown(label="VAE", choices=sd35_vaes, value="", allow_custom_value=True)
+                        sd35_scheduler = gr.Dropdown(label="Scheduler (Sampler)", choices=[""], value="", visible=False)
+                    with gr.Row():
+                        sd35_clip = gr.Dropdown(label="CLIP", choices=clips, value="", allow_custom_value=True)
+                        sd35_t5 = gr.Dropdown(label="T5", choices=t5s, value="", allow_custom_value=True)
+                    with gr.Column():
+                        with gr.Row():
+                            sd35_lora1 = gr.Dropdown(label="LoRA1", choices=sd35_loras, value="", allow_custom_value=True, min_width=320, scale=2)
+                            sd35_lora1s = gr.Slider(minimum=-2, maximum=2, step=0.01, value=1.00, label="LoRA1 weight scale")
+                        with gr.Row():
+                            sd35_lora2 = gr.Dropdown(label="LoRA2", choices=sd35_loras, value="", allow_custom_value=True, min_width=320, scale=2)
+                            sd35_lora2s = gr.Slider(minimum=-2, maximum=2, step=0.01, value=1.00, label="LoRA2 weight scale")
+                        with gr.Row():
+                            sd35_lora3 = gr.Dropdown(label="LoRA3", choices=sd35_loras, value="", allow_custom_value=True, min_width=320, scale=2)
+                            sd35_lora3s = gr.Slider(minimum=-2, maximum=2, step=0.01, value=1.00, label="LoRA3 weight scale")
+                        with gr.Row():
+                            sd35_lora4 = gr.Dropdown(label="LoRA4", choices=sd35_loras, value="", allow_custom_value=True, min_width=320, scale=2)
+                            sd35_lora4s = gr.Slider(minimum=-2, maximum=2, step=0.01, value=1.00, label="LoRA4 weight scale")
+                        with gr.Row():
+                            sd35_lora5 = gr.Dropdown(label="LoRA5", choices=sd35_loras, value="", allow_custom_value=True, min_width=320, scale=2)
+                            sd35_lora5s = gr.Slider(minimum=-2, maximum=2, step=0.01, value=1.00, label="LoRA5 weight scale")
+            sd35_run_button = gr.Button(value="Submit", variant="primary")
+        adv_args = gr.Textbox(label="Advanced arguments", value="", visible=False)
+        with gr.Group():
+            repo_urls = gr.CheckboxGroup(visible=False, choices=[], value=[])
+            output_md = gr.Markdown(label="Output", value="<br><br>", elem_classes="result")
+            clear_button = gr.Button(value="Clear Output", variant="secondary")
+        gr.DuplicateButton(value="Duplicate Space")
+
+    gr.Markdown("This webui was redesigned with ❤ by [theNeofr](https://huggingface.co/theNeofr)")
+    gr.on(
+        triggers=[sdxl_run_button.click],
+        fn=convert_url_to_diffusers_repo,
+        inputs=[dl_url, hf_user, hf_repo, hf_token, civitai_key, is_private, gated, is_overwrite, is_pr, is_upload_sf, repo_urls,
+                sdxl_dtype, sdxl_vae, sdxl_clip, flux_t5, sdxl_scheduler, sd15_ema, sd15_isize, sd15_sc, flux_base_repo, sdxl_mtype,
+                sdxl_lora1, sdxl_lora1s, sdxl_lora2, sdxl_lora2s, sdxl_lora3, sdxl_lora3s, sdxl_lora4, sdxl_lora4s, sdxl_lora5, sdxl_lora5s, adv_args],
+        outputs=[repo_urls, output_md],
+    )
+    sdxl_presets.change(
+        fn=sdxl_set_presets,
+        inputs=[sdxl_presets],
+        outputs=[sdxl_dtype, sdxl_vae, sdxl_scheduler, sdxl_lora1, sdxl_lora1s, sdxl_lora2, sdxl_lora2s, sdxl_lora3, sdxl_lora3s,
+                 sdxl_lora4, sdxl_lora4s, sdxl_lora5, sdxl_lora5s],
+        queue=False,
+    )
+    gr.on(
+        triggers=[sd15_run_button.click],
+        fn=convert_url_to_diffusers_repo,
+        inputs=[dl_url, hf_user, hf_repo, hf_token, civitai_key, is_private, gated, is_overwrite, is_pr, is_upload_sf, repo_urls,
+                sd15_dtype, sd15_vae, sd15_clip, flux_t5, sd15_scheduler, sd15_ema, sd15_isize, sd15_sc, flux_base_repo, sd15_mtype,
+                sd15_lora1, sd15_lora1s, sd15_lora2, sd15_lora2s, sd15_lora3, sd15_lora3s, sd15_lora4, sd15_lora4s, sd15_lora5, sd15_lora5s, adv_args],
+        outputs=[repo_urls, output_md],
+    )
+    sd15_presets.change(
+        fn=sd15_set_presets,
+        inputs=[sd15_presets],
+        outputs=[sd15_dtype, sd15_vae, sd15_scheduler, sd15_lora1, sd15_lora1s, sd15_lora2, sd15_lora2s, sd15_lora3, sd15_lora3s,
+                 sd15_lora4, sd15_lora4s, sd15_lora5, sd15_lora5s, sd15_ema],
+        queue=False,
+    )
+    gr.on(
+        triggers=[flux_run_button.click],
+        fn=convert_url_to_diffusers_repo,
+        inputs=[dl_url, hf_user, hf_repo, hf_token, civitai_key, is_private, gated, is_overwrite, is_pr, is_upload_sf, repo_urls,
+                flux_dtype, flux_vae, flux_clip, flux_t5, flux_scheduler, sd15_ema, sd15_isize, sd15_sc, flux_base_repo, flux_mtype,
+                flux_lora1, flux_lora1s, flux_lora2, flux_lora2s, flux_lora3, flux_lora3s, flux_lora4, flux_lora4s, flux_lora5, flux_lora5s, adv_args],
+        outputs=[repo_urls, output_md],
+    )
+    flux_presets.change(
+        fn=flux_set_presets,
+        inputs=[flux_presets],
+        outputs=[flux_dtype, flux_vae, flux_scheduler, flux_lora1, flux_lora1s, flux_lora2, flux_lora2s, flux_lora3, flux_lora3s,
+                 flux_lora4, flux_lora4s, flux_lora5, flux_lora5s, flux_base_repo],
+        queue=False,
+    )
+    gr.on(
+        triggers=[sd35_run_button.click],
+        fn=convert_url_to_diffusers_repo,
+        inputs=[dl_url, hf_user, hf_repo, hf_token, civitai_key, is_private, gated, is_overwrite, is_pr, is_upload_sf, repo_urls,
+                sd35_dtype, sd35_vae, sd35_clip, sd35_t5, sd35_scheduler, sd15_ema, sd15_isize, sd15_sc, sd35_base_repo, sd35_mtype,
+                sd35_lora1, sd35_lora1s, sd35_lora2, sd35_lora2s, sd35_lora3, sd35_lora3s, sd35_lora4, sd35_lora4s, sd35_lora5, sd35_lora5s, adv_args],
+        outputs=[repo_urls, output_md],
+    )
+    sd35_presets.change(
+        fn=sd35_set_presets,
+        inputs=[sd35_presets],
+        outputs=[sd35_dtype, sd35_vae, sd35_scheduler, sd35_lora1, sd35_lora1s, sd35_lora2, sd35_lora2s, sd35_lora3, sd35_lora3s,
+                 sd35_lora4, sd35_lora4s, sd35_lora5, sd35_lora5s, sd35_base_repo],
+        queue=False,
+    )
+    clear_button.click(lambda: ([], "<br><br>"), None, [repo_urls, output_md], queue=False, show_api=False)
+
+demo.queue()
+demo.launch(ssr_mode=False)

convert_url_to_diffusers_multi_gr.py

@@ -4,19 +4,20 @@ 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, AutoTokenizer, T5EncoderModel, BitsAndBytesConfig as TFBitsAndBytesConfig
+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)
+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)
 
 
@@ -204,7 +205,7 @@ def convert_sd35_fp8_cpu(new_file: str, new_dir: str, dtype: str, base_repo: str
 
 #@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, base_repo: str, civitai_key: str, lora_dict: dict,
+                           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:
@@ -223,6 +224,9 @@ def load_and_save_pipeline(pipe, model_type: str, url: str, new_file: str, new_d
         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
         
@@ -234,12 +238,19 @@ def load_and_save_pipeline(pipe, model_type: str, url: str, new_file: str, new_d
             pipe.scheduler = sconf[0].from_config(pipe.scheduler.config, **sconf[1])
             pipe.save_pretrained(new_dir)
         elif model_type == "SD 1.5":
-            if is_repo_name(url): pipe = StableDiffusionPipeline.from_pretrained(url, extract_ema=ema, requires_safety_checker=False,
-                                                                                 use_safetensors=True, **kwargs, **dkwargs, token=hf_token)
-            else: pipe = StableDiffusionPipeline.from_single_file(new_file, extract_ema=ema, requires_safety_checker=False, use_safetensors=True, **kwargs, **dkwargs)
+            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":
@@ -282,31 +293,34 @@ def load_and_save_pipeline(pipe, model_type: str, url: str, new_file: str, new_d
             pipe.save_pretrained(new_dir)
     except Exception as e:
         print(f"Failed to load pipeline. {e}")
-        raise Exception("Failed to load pipeline.") from e
+        raise Exception(f"Failed to load pipeline. {e}") 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, base_repo: str="", mtype: str="", lora_dict: dict={}, is_local: bool=True, progress=gr.Progress(track_tqdm=True)):
+                             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
+        pipe = None
 
         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}")
+            if not new_file or Path(new_file).suffix.lower() not in set([".safetensors", ".ckpt", ".bin", ".sft"]):
+                safe_clean(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}")
 
@@ -362,7 +376,7 @@ def convert_url_to_diffusers(url: str, civitai_key: str="", is_upload_sf: bool=F
                 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, base_repo, civitai_key, lora_dict,
+        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)
@@ -375,35 +389,35 @@ def convert_url_to_diffusers(url: str, civitai_key: str="", is_upload_sf: bool=F
         return new_dir
     except Exception as e:
         print(f"Failed to convert. {e}")
-        raise Exception("Failed to convert.") from e
+        raise Exception(f"Failed to convert. {e}") 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, is_overwrite: 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,
+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, progress=gr.Progress(track_tqdm=True)):
+                                  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 and os.environ.get("HF_USER"): hf_user = os.environ.get("HF_USER") # default username
         if not hf_user: raise gr.Error(f"Invalid user name: {hf_user}")
-        if not hf_repo and os.environ.get("HF_REPO"): hf_repo = os.environ.get("HF_REPO") # default reponame
-        if not is_overwrite and os.environ.get("HF_OW"): is_overwrite = os.environ.get("HF_OW") # for debugging
-        if not dl_url and os.environ.get("HF_URL"): dl_url = os.environ.get("HF_URL") # for debugging
+        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, base_repo, mtype, lora_dict, is_local)
+        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): raise gr.Error(f"Repo already exists: {new_repo_id}")
-        repo_url = upload_repo(new_repo_id, new_path, is_private)
+        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)

presets.py

@@ -1,134 +1,147 @@
-from sdutils import get_dtypes, SCHEDULER_CONFIG_MAP
-
-
-DEFAULT_DTYPE = get_dtypes()[0]
-schedulers = list(SCHEDULER_CONFIG_MAP.keys())
-
-
-clips = [
-    "",
-    "openai/clip-vit-large-patch14",
-]
-
-
-t5s = [
-    "",
-    "https://huggingface.co/camenduru/FLUX.1-dev/blob/main/t5xxl_fp8_e4m3fn.safetensors",
-]
-
-
-sdxl_vaes = [
-    "",
-    "https://huggingface.co/madebyollin/sdxl-vae-fp16-fix/blob/main/sdxl.vae.safetensors",
-    "https://huggingface.co/nubby/blessed-sdxl-vae-fp16-fix/blob/main/sdxl_vae-fp16fix-blessed.safetensors",
-    "https://huggingface.co/John6666/safetensors_converting_test/blob/main/xlVAEC_e7.safetensors",
-    "https://huggingface.co/John6666/safetensors_converting_test/blob/main/xlVAEC_f1.safetensors",
-]
-
-
-sdxl_loras = [
-    "",
-    "https://huggingface.co/SPO-Diffusion-Models/SPO-SDXL_4k-p_10ep_LoRA/blob/main/spo_sdxl_10ep_4k-data_lora_diffusers.safetensors",
-    "https://huggingface.co/wangfuyun/PCM_Weights/blob/main/sdxl/pcm_sdxl_smallcfg_2step_converted.safetensors",
-    "https://huggingface.co/wangfuyun/PCM_Weights/blob/main/sdxl/pcm_sdxl_smallcfg_4step_converted.safetensors",
-    "https://huggingface.co/wangfuyun/PCM_Weights/blob/main/sdxl/pcm_sdxl_smallcfg_8step_converted.safetensors",
-    "https://huggingface.co/wangfuyun/PCM_Weights/blob/main/sdxl/pcm_sdxl_normalcfg_8step_converted.safetensors",
-    "https://huggingface.co/wangfuyun/PCM_Weights/blob/main/sdxl/pcm_sdxl_normalcfg_16step_converted.safetensors",
-    "https://huggingface.co/ByteDance/Hyper-SD/blob/main/Hyper-SDXL-1step-lora.safetensors",
-    "https://huggingface.co/ByteDance/Hyper-SD/blob/main/Hyper-SDXL-2steps-lora.safetensors",
-    "https://huggingface.co/ByteDance/Hyper-SD/blob/main/Hyper-SDXL-4steps-lora.safetensors",
-    "https://huggingface.co/ByteDance/Hyper-SD/blob/main/Hyper-SDXL-8steps-CFG-lora.safetensors",
-    "https://huggingface.co/ByteDance/Hyper-SD/blob/main/Hyper-SDXL-12steps-CFG-lora.safetensors",
-    "https://huggingface.co/latent-consistency/lcm-lora-sdxl/blob/main/pytorch_lora_weights.safetensors",
-]
-
-
-sdxl_preset_dict = {
-    "Default": [DEFAULT_DTYPE, "", "Euler a", "", 1.0, "", 1.0, "", 1.0, "", 1.0, "", 1.0],
-    "Bake in standard VAE": [DEFAULT_DTYPE, "https://huggingface.co/madebyollin/sdxl-vae-fp16-fix/blob/main/sdxl.vae.safetensors",
-                             "Euler a", "", 1.0, "", 1.0, "", 1.0, "", 1.0, "", 1.0],
-    "Hyper-SDXL / SPO": [DEFAULT_DTYPE, "https://huggingface.co/madebyollin/sdxl-vae-fp16-fix/blob/main/sdxl.vae.safetensors",
-     "TCD", "https://huggingface.co/ByteDance/Hyper-SD/blob/main/Hyper-SDXL-8steps-CFG-lora.safetensors", 1.0,
-     "https://huggingface.co/SPO-Diffusion-Models/SPO-SDXL_4k-p_10ep_LoRA/blob/main/spo_sdxl_10ep_4k-data_lora_diffusers.safetensors",
-       1.0, "", 1.0, "", 1.0, "", 1.0],
-}
-
-
-def sdxl_set_presets(preset: str="Default"):
-    p = []
-    if preset in sdxl_preset_dict.keys(): p = sdxl_preset_dict[preset]
-    else: p = sdxl_preset_dict["Default"]
-    return p[0], p[1], p[2], p[3], p[4], p[5], p[6], p[7], p[8], p[9], p[10], p[11], p[12]
-
-
-sd15_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",
-]
-
-
-sd15_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",
-]
-
-
-sd15_preset_dict = {
-    "Default": [DEFAULT_DTYPE, "", "Euler", "", 1.0, "", 1.0, "", 1.0, "", 1.0, "", 1.0, True],
-    "Bake in standard VAE": [DEFAULT_DTYPE, "https://huggingface.co/stabilityai/sd-vae-ft-mse-original/resolve/main/vae-ft-mse-840000-ema-pruned.ckpt",
-                             "Euler", "", 1.0, "", 1.0, "", 1.0, "", 1.0, "", 1.0, True],
-}
-
-
-def sd15_set_presets(preset: str="Default"):
-    p = []
-    if preset in sd15_preset_dict.keys(): p = sd15_preset_dict[preset]
-    else: p = sd15_preset_dict["Default"]
-    return p[0], p[1], p[2], p[3], p[4], p[5], p[6], p[7], p[8], p[9], p[10], p[11], p[12], p[13]
-
-
-flux_vaes = [
-    "",
-]
-
-
-flux_loras = [
-    "",
-]
-
-
-flux_preset_dict = {
-    "dev": ["bf16", "", "", "", 1.0, "", 1.0, "", 1.0, "", 1.0, "", 1.0, "camenduru/FLUX.1-dev-diffusers"],
-    "schnell": ["bf16", "", "", "", 1.0, "", 1.0, "", 1.0, "", 1.0, "", 1.0, "black-forest-labs/FLUX.1-schnell"],
-}
-
-
-def flux_set_presets(preset: str="dev"):
-    p = []
-    if preset in flux_preset_dict.keys(): p = flux_preset_dict[preset]
-    else: p = flux_preset_dict["dev"]
-    return p[0], p[1], p[2], p[3], p[4], p[5], p[6], p[7], p[8], p[9], p[10], p[11], p[12], p[13]
-
-
-
-sd35_vaes = [
-    "",
-]
-
-
-sd35_loras = [
-    "",
-]
-
-
-sd35_preset_dict = {
-    "Default": ["bf16", "", "", "", 1.0, "", 1.0, "", 1.0, "", 1.0, "", 1.0, "adamo1139/stable-diffusion-3.5-large-ungated"],
-}
-
-
-def sd35_set_presets(preset: str="dev"):
-    p = []
-    if preset in sd35_preset_dict.keys(): p = sd35_preset_dict[preset]
-    else: p = sd35_preset_dict["Default"]
-    return p[0], p[1], p[2], p[3], p[4], p[5], p[6], p[7], p[8], p[9], p[10], p[11], p[12], p[13]
+from sdutils import get_dtypes, SCHEDULER_CONFIG_MAP
+import gradio as gr
+
+
+DEFAULT_DTYPE = get_dtypes()[0]
+schedulers = list(SCHEDULER_CONFIG_MAP.keys())
+
+
+clips = [
+    "",
+    "openai/clip-vit-large-patch14",
+]
+
+
+t5s = [
+    "",
+    "https://huggingface.co/camenduru/FLUX.1-dev/blob/main/t5xxl_fp8_e4m3fn.safetensors",
+]
+
+
+sdxl_vaes = [
+    "",
+    "https://huggingface.co/madebyollin/sdxl-vae-fp16-fix/blob/main/sdxl.vae.safetensors",
+    "https://huggingface.co/nubby/blessed-sdxl-vae-fp16-fix/blob/main/sdxl_vae-fp16fix-blessed.safetensors",
+    "https://huggingface.co/John6666/safetensors_converting_test/blob/main/xlVAEC_e7.safetensors",
+    "https://huggingface.co/John6666/safetensors_converting_test/blob/main/xlVAEC_f1.safetensors",
+]
+
+
+sdxl_loras = [
+    "",
+    "https://huggingface.co/SPO-Diffusion-Models/SPO-SDXL_4k-p_10ep_LoRA/blob/main/spo_sdxl_10ep_4k-data_lora_diffusers.safetensors",
+    "https://huggingface.co/wangfuyun/PCM_Weights/blob/main/sdxl/pcm_sdxl_smallcfg_2step_converted.safetensors",
+    "https://huggingface.co/wangfuyun/PCM_Weights/blob/main/sdxl/pcm_sdxl_smallcfg_4step_converted.safetensors",
+    "https://huggingface.co/wangfuyun/PCM_Weights/blob/main/sdxl/pcm_sdxl_smallcfg_8step_converted.safetensors",
+    "https://huggingface.co/wangfuyun/PCM_Weights/blob/main/sdxl/pcm_sdxl_normalcfg_8step_converted.safetensors",
+    "https://huggingface.co/wangfuyun/PCM_Weights/blob/main/sdxl/pcm_sdxl_normalcfg_16step_converted.safetensors",
+    "https://huggingface.co/ByteDance/Hyper-SD/blob/main/Hyper-SDXL-1step-lora.safetensors",
+    "https://huggingface.co/ByteDance/Hyper-SD/blob/main/Hyper-SDXL-2steps-lora.safetensors",
+    "https://huggingface.co/ByteDance/Hyper-SD/blob/main/Hyper-SDXL-4steps-lora.safetensors",
+    "https://huggingface.co/ByteDance/Hyper-SD/blob/main/Hyper-SDXL-8steps-CFG-lora.safetensors",
+    "https://huggingface.co/ByteDance/Hyper-SD/blob/main/Hyper-SDXL-12steps-CFG-lora.safetensors",
+    "https://huggingface.co/latent-consistency/lcm-lora-sdxl/blob/main/pytorch_lora_weights.safetensors",
+]
+
+
+sdxl_preset_items = ["dtype", "vae", "scheduler", "lora1", "lora1s", "lora2", "lora2s", "lora3", "lora3s", "lora4", "lora4s", "lora5", "lora5s"]
+sdxl_preset_dict = {
+    "Default": [DEFAULT_DTYPE, "", "Euler a", "", 1.0, "", 1.0, "", 1.0, "", 1.0, "", 1.0],
+    "Bake in standard VAE": [DEFAULT_DTYPE, "https://huggingface.co/madebyollin/sdxl-vae-fp16-fix/blob/main/sdxl.vae.safetensors",
+                             "Euler a", "", 1.0, "", 1.0, "", 1.0, "", 1.0, "", 1.0],
+    "Hyper-SDXL / SPO": [DEFAULT_DTYPE, "https://huggingface.co/madebyollin/sdxl-vae-fp16-fix/blob/main/sdxl.vae.safetensors",
+     "TCD", "https://huggingface.co/ByteDance/Hyper-SD/blob/main/Hyper-SDXL-8steps-CFG-lora.safetensors", 1.0,
+     "https://huggingface.co/SPO-Diffusion-Models/SPO-SDXL_4k-p_10ep_LoRA/blob/main/spo_sdxl_10ep_4k-data_lora_diffusers.safetensors",
+       1.0, "", 1.0, "", 1.0, "", 1.0],
+}
+
+
+def sdxl_set_presets(preset: str="Default"):
+    p = []
+    if preset in sdxl_preset_dict.keys(): p = sdxl_preset_dict[preset]
+    else: p = sdxl_preset_dict["Default"]
+    if len(p) != len(sdxl_preset_items): raise gr.Error("Invalid preset.")
+    print("Setting SDXL preset:", ", ".join([f"{x}:{y}" for x, y in zip(sdxl_preset_items, p)]))
+    return p[0], p[1], p[2], p[3], p[4], p[5], p[6], p[7], p[8], p[9], p[10], p[11], p[12]
+
+
+sd15_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",
+]
+
+
+sd15_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",
+]
+
+
+sd15_preset_items = ["dtype", "vae", "scheduler", "lora1", "lora1s", "lora2", "lora2s", "lora3", "lora3s", "lora4", "lora4s", "lora5", "lora5s", "ema"]
+sd15_preset_dict = {
+    "Default": [DEFAULT_DTYPE, "", "Euler", "", 1.0, "", 1.0, "", 1.0, "", 1.0, "", 1.0, True],
+    "Bake in standard VAE": [DEFAULT_DTYPE, "https://huggingface.co/stabilityai/sd-vae-ft-mse-original/resolve/main/vae-ft-mse-840000-ema-pruned.ckpt",
+                             "Euler", "", 1.0, "", 1.0, "", 1.0, "", 1.0, "", 1.0, True],
+}
+
+
+def sd15_set_presets(preset: str="Default"):
+    p = []
+    if preset in sd15_preset_dict.keys(): p = sd15_preset_dict[preset]
+    else: p = sd15_preset_dict["Default"]
+    if len(p) != len(sd15_preset_items): raise gr.Error("Invalid preset.")
+    print("Setting SD1.5 preset:", ", ".join([f"{x}:{y}" for x, y in zip(sd15_preset_items, p)]))
+    return p[0], p[1], p[2], p[3], p[4], p[5], p[6], p[7], p[8], p[9], p[10], p[11], p[12], p[13]
+
+
+flux_vaes = [
+    "",
+]
+
+
+flux_loras = [
+    "",
+]
+
+
+flux_preset_items = ["dtype", "vae", "scheduler", "lora1", "lora1s", "lora2", "lora2s", "lora3", "lora3s", "lora4", "lora4s", "lora5", "lora5s", "base_repo"]
+flux_preset_dict = {
+    "dev": ["bf16", "", "", "", 1.0, "", 1.0, "", 1.0, "", 1.0, "", 1.0, "camenduru/FLUX.1-dev-diffusers"],
+    "schnell": ["bf16", "", "", "", 1.0, "", 1.0, "", 1.0, "", 1.0, "", 1.0, "black-forest-labs/FLUX.1-schnell"],
+}
+
+
+def flux_set_presets(preset: str="dev"):
+    p = []
+    if preset in flux_preset_dict.keys(): p = flux_preset_dict[preset]
+    else: p = flux_preset_dict["dev"]
+    if len(p) != len(flux_preset_items): raise gr.Error("Invalid preset.")
+    print("Setting FLUX.1 preset:", ", ".join([f"{x}:{y}" for x, y in zip(flux_preset_items, p)]))
+    return p[0], p[1], p[2], p[3], p[4], p[5], p[6], p[7], p[8], p[9], p[10], p[11], p[12], p[13]
+
+
+
+sd35_vaes = [
+    "",
+]
+
+
+sd35_loras = [
+    "",
+]
+
+
+sd35_preset_items = ["dtype", "vae", "scheduler", "lora1", "lora1s", "lora2", "lora2s", "lora3", "lora3s", "lora4", "lora4s", "lora5", "lora5s", "base_repo"]
+sd35_preset_dict = {
+    "Default": ["bf16", "", "", "", 1.0, "", 1.0, "", 1.0, "", 1.0, "", 1.0, "adamo1139/stable-diffusion-3.5-large-ungated"],
+}
+
+
+def sd35_set_presets(preset: str="dev"):
+    p = []
+    if preset in sd35_preset_dict.keys(): p = sd35_preset_dict[preset]
+    else: p = sd35_preset_dict["Default"]
+    if len(p) != len(sd35_preset_items): raise gr.Error("Invalid preset.")
+    print("Setting SD3.5 preset:", ", ".join([f"{x}:{y}" for x, y in zip(sd35_preset_items, p)]))
+    return p[0], p[1], p[2], p[3], p[4], p[5], p[6], p[7], p[8], p[9], p[10], p[11], p[12], p[13]

utils.py

@@ -130,18 +130,17 @@ def download_thing(directory, url, civitai_api_key="", progress=gr.Progress(trac
         if "drive.google.com" in url:
             original_dir = os.getcwd()
             os.chdir(directory)
-            os.system(f"gdown --fuzzy {url}")
+            subprocess.run(f"gdown --fuzzy {url}", shell=True)
             os.chdir(original_dir)
         elif "huggingface.co" in url:
             url = url.replace("?download=true", "")
             if "/blob/" in url: url = url.replace("/blob/", "/resolve/")
             download_hf_file(directory, url)
         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}")
+                url = f"'{url}&token={civitai_api_key}'" if "?" in url else f"{url}?token={civitai_api_key}"
+                print(f"Downloading {url}")
+                subprocess.run(f"aria2c --console-log-level=error --summary-interval=10 -c -x 16 -k 1M -s 16 -d {directory} {url}", shell=True)
             else:
                 print("You need an API key to download Civitai models.")
         else:
@@ -152,7 +151,7 @@ def download_thing(directory, url, civitai_api_key="", progress=gr.Progress(trac
 
 def get_local_file_list(dir_path):
     file_list = []
-    for file in Path(dir_path).glob("**/*.*"):
+    for file in Path(dir_path).glob("*/*.*"):
         if file.is_file():
             file_path = str(file)
             file_list.append(file_path)
@@ -181,8 +180,8 @@ def get_download_file(temp_dir, url, civitai_key, progress=gr.Progress(track_tqd
             return ""
         print(f"Download completed: {url}")
         return new_file
-    except Exception:
-        print(f"Download failed: {url}")
+    except Exception as e:
+        print(f"Download failed: {url} {e}")
         return ""
 
 
@@ -198,17 +197,13 @@ def download_repo(repo_id: str, dir_path: str, progress=gr.Progress(track_tqdm=T
         return False
 
 
-def upload_repo(repo_id: str, dir_path: str, is_private: bool, progress=gr.Progress(track_tqdm=True)): # for diffusers repo
+def upload_repo(repo_id: str, dir_path: str, is_private: bool, is_pr: bool=False, progress=gr.Progress(track_tqdm=True)): # for diffusers repo
     hf_token = get_token()
     api = HfApi(token=hf_token)
     try:
         progress(0, desc="Start uploading...")
         api.create_repo(repo_id=repo_id, token=hf_token, private=is_private, exist_ok=True)
-        for path in Path(dir_path).glob("*"):
-            if path.is_dir():
-                api.upload_folder(repo_id=repo_id, folder_path=str(path), path_in_repo=path.name, token=hf_token)
-            elif path.is_file():
-                api.upload_file(repo_id=repo_id, path_or_fileobj=str(path), path_in_repo=path.name, token=hf_token)
+        api.upload_folder(repo_id=repo_id, folder_path=dir_path, path_in_repo="", create_pr=is_pr, token=hf_token)
         progress(1, desc="Uploaded.")
         return get_hf_url(repo_id, "model")
     except Exception as e:
@@ -216,6 +211,18 @@ def upload_repo(repo_id: str, dir_path: str, is_private: bool, progress=gr.Progr
         return ""
 
 
+def gate_repo(repo_id: str, gated_str: str, repo_type: str="model"):
+    hf_token = get_token()
+    api = HfApi(token=hf_token)
+    try:
+        if gated_str == "auto": gated = "auto"
+        elif gated_str == "manual": gated = "manual"
+        else: gated = False
+        api.update_repo_settings(repo_id=repo_id, gated=gated, repo_type=repo_type, token=hf_token)
+    except Exception as e:
+        print(f"Error: Failed to update settings {repo_id}. {e}")
+
+
 HF_SUBFOLDER_NAME = ["None", "user_repo"]