Upload 5 files

app.py

@@ -33,34 +33,37 @@ It saves you the trouble of typing them in.<br>
 - 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'.
+- Input your HF user ID. e.g. 'yourid'.
+- Input your new repo name. If empty, auto-complete. e.g. '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)
+        dl_url = gr.Textbox(label="URL to download", placeholder="https://huggingface.co/SG161222/RealVisXL_V4.0/blob/main/RealVisXL_V4.0.safetensors", value="", max_lines=1)
+        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)
+        hf_token = gr.Textbox(label="Your HF write token", placeholder="hf_...", 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_upload_sf = gr.Checkbox(label="Upload single safetensors file into new repo", value=False)
-        is_half = gr.Checkbox(label="Half precision", value=True)
-        model_type = gr.Radio(label="Model type", choices=["v1", "v2"], value="v1")
-        sample_size = gr.Radio(label="Sample size (px)", choices=[512, 768], value=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")
-        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")
+        with gr.Accordion("Advanced settings", open=False):
+            is_half = gr.Checkbox(label="Half precision", value=True)
+            model_type = gr.Radio(label="Model type", choices=["v1", "v2"], value="v1")
+            sample_size = gr.Radio(label="Sample size (px)", choices=[512, 768], value=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")
+            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")
@@ -69,7 +72,7 @@ It saves you the trouble of typing them in.<br>
     gr.on(
         triggers=[run_button.click],
         fn=convert_url_to_diffusers_repo_sd,
-        inputs=[dl_url, repo_id, hf_token, civitai_key, is_upload_sf, repo_urls, is_half, vae, scheduler,
+        inputs=[dl_url, hf_user, hf_repo, hf_token, civitai_key, is_upload_sf, 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],

convert_url_to_diffusers_sd_gr.py

@@ -312,12 +312,23 @@ def create_diffusers_repo(new_repo_id, diffusers_folder, progress=gr.Progress(tr
     return url
 
 
-def convert_url_to_diffusers_repo_sd(dl_url, new_repo_id, hf_token, civitai_key="", is_upload_sf=False, repo_urls=[], half=True, vae=None,
+def convert_url_to_diffusers_repo_sd(dl_url, hf_user, hf_repo, hf_token, civitai_key="", is_upload_sf=False, repo_urls=[], half=True, vae=None,
                                   scheduler="Euler", 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=768, ema="ema", progress=gr.Progress(track_tqdm=True)):
+    if not hf_user:
+        print(f"Invalid user name: {hf_user}")
+        progress(1, desc=f"Invalid user name: {hf_user}")
+        return gr.update(value=repo_urls, choices=repo_urls), gr.update(value="")
     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")
+    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, is_upload_sf, half, vae, scheduler, lora_dict,
+                                            model_type, sample_size, ema)
+    if not new_path: return gr.update(value=repo_urls, choices=repo_urls), gr.update(value="")
+    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):
         print(f"Invalid repo name: {new_repo_id}")
         progress(1, desc=f"Invalid repo name: {new_repo_id}")
@@ -326,11 +337,6 @@ def convert_url_to_diffusers_repo_sd(dl_url, new_repo_id, hf_token, civitai_key=
         print(f"Repo already exists: {new_repo_id}")
         progress(1, desc=f"Repo already exists: {new_repo_id}")
         return gr.update(value=repo_urls, choices=repo_urls), gr.update(value="")
-    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, is_upload_sf, half, vae, scheduler, lora_dict,
-                                            model_type, sample_size, ema)
-    if not new_path: return gr.update(value=repo_urls, choices=repo_urls), gr.update(value="")
     repo_url = create_diffusers_repo(new_repo_id, new_path)
     if not repo_urls: repo_urls = []
     repo_urls.append(repo_url)

local/convert_url_to_diffusers_sd.py

@@ -168,6 +168,10 @@ def save_readme_md(dir, url):
     if orig_name and orig_url:
        md = f"""---
 license: other
+language:
+- en
+library_name: diffusers
+pipeline_tag: text-to-image
 tags:
 - text-to-image
 ---
@@ -176,6 +180,10 @@ Converted from [{orig_name}]({orig_url}).
     else:
         md = f"""---
 license: other
+language:
+- en
+library_name: diffusers
+pipeline_tag: text-to-image
 tags:
 - text-to-image
 ---