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README.md

@@ -1,13 +1,13 @@
----
-title: Download and Convert SDXL To Diffusers V2
-emoji: 🎨➡️🧨
-colorFrom: indigo
-colorTo: purple
-sdk: gradio
-sdk_version: 4.44.0
-app_file: app.py
-pinned: false
-license: mit
----
-
+---
+title: Download and Convert SDXL To Diffusers V2
+emoji: 🎨➡️🧨
+colorFrom: indigo
+colorTo: purple
+sdk: gradio
+sdk_version: 5.0.2
+app_file: app.py
+pinned: false
+license: mit
+---
+
 Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

app.py

@@ -46,10 +46,14 @@ def set_presets(preset: str="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]
 
 
-css = """"""
+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", css=css) as demo:
-    gr.Markdown("# Download and convert any Stable Diffusion XL safetensors to Diffusers and create your repo")
+with gr.Blocks(theme="NoCrypt/miku@>=1.2.2", fill_width=True, css=css, delete_cache=(60, 3600)) as demo:
+    gr.Markdown("# Download and convert any Stable Diffusion XL safetensors to Diffusers and create your repo", elem_classes="title")
     gr.Markdown(
         f"""
 - [A CLI version of this tool (without uploading-related function) is available here](https://huggingface.co/spaces/John6666/sdxl-to-diffusers-v2/tree/main/local).
@@ -73,30 +77,40 @@ It saves you the trouble of typing them in.<br>
     )
     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)
-        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_private = gr.Checkbox(label="Create private repo", value=True)
+        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():
+            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)
+        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)
         presets = gr.Radio(label="Presets", choices=list(preset_dict.keys()), value="Default")
         with gr.Accordion("Advanced settings", open=False):
             is_half = gr.Checkbox(label="Half precision", value=True)
-            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")
+            with gr.Row():
+                vae = gr.Dropdown(label="VAE", choices=vaes, value="", allow_custom_value=True)
+                scheduler = gr.Dropdown(label="Scheduler (Sampler)", choices=schedulers, value="Euler a")
+            with gr.Row():
+                with gr.Column():
+                    lora1 = gr.Dropdown(label="LoRA1", choices=loras, value="", allow_custom_value=True, min_width=320)
+                    lora1s = gr.Slider(minimum=-2, maximum=2, step=0.01, value=1.00, label="LoRA1 weight scale")
+                with gr.Column():
+                    lora2 = gr.Dropdown(label="LoRA2", choices=loras, value="", allow_custom_value=True, min_width=320)
+                    lora2s = gr.Slider(minimum=-2, maximum=2, step=0.01, value=1.00, label="LoRA2 weight scale")
+                with gr.Column():
+                    lora3 = gr.Dropdown(label="LoRA3", choices=loras, value="", allow_custom_value=True, min_width=320)
+                    lora3s = gr.Slider(minimum=-2, maximum=2, step=0.01, value=1.00, label="LoRA3 weight scale")
+                with gr.Column():
+                    lora4 = gr.Dropdown(label="LoRA4", choices=loras, value="", allow_custom_value=True, min_width=320)
+                    lora4s = gr.Slider(minimum=-2, maximum=2, step=0.01, value=1.00, label="LoRA4 weight scale")
+                with gr.Column():
+                    lora5 = gr.Dropdown(label="LoRA5", choices=loras, value="", allow_custom_value=True, min_width=320)
+                    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")
+        output_md = gr.Markdown(label="Output", value="<br><br>", elem_classes="result")
     gr.DuplicateButton(value="Duplicate Space")
 
     gr.on(

convert_url_to_diffusers_sdxl.py

@@ -194,7 +194,7 @@ tags:
 
 
 def fuse_loras(pipe, civitai_key="", lora_dict={}, temp_dir="."):
-    if not lora_dict or not isinstance(lora_dict, dict): return
+    if not lora_dict or not isinstance(lora_dict, dict): return pipe
     a_list = []
     w_list = []
     for k, v in lora_dict.items():

convert_url_to_diffusers_sdxl_gr.py

@@ -223,7 +223,7 @@ tags:
 
 
 def fuse_loras(pipe, lora_dict={}, temp_dir=".", civitai_key="", hf_token=""):
-    if not lora_dict or not isinstance(lora_dict, dict): return
+    if not lora_dict or not isinstance(lora_dict, dict): return pipe
     a_list = []
     w_list = []
     for k, v in lora_dict.items():

local/convert_url_to_diffusers_sdxl.py

@@ -194,7 +194,7 @@ tags:
 
 
 def fuse_loras(pipe, civitai_key="", lora_dict={}, temp_dir="."):
-    if not lora_dict or not isinstance(lora_dict, dict): return
+    if not lora_dict or not isinstance(lora_dict, dict): return pipe
     a_list = []
     w_list = []
     for k, v in lora_dict.items():