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README.md

@@ -1,13 +1,13 @@
----
-title: Download and Convert SD1.5 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 SD1.5 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

@@ -29,10 +29,14 @@ 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], p[13], p[14], p[15]
 
 
-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 1.5 / 2.0 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 1.5 / 2.0 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/sd-to-diffusers-v2/tree/main/local).
@@ -55,34 +59,46 @@ 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/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_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)
-            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.Group():
+            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)
+            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):
+                with gr.Row():
+                    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")
+                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")
+                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_sd.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():
@@ -208,7 +208,7 @@ def fuse_loras(pipe, civitai_key="", lora_dict={}, temp_dir="."):
         pipe.load_lora_weights(new_lora_file, weight_name = w_name, adapter_name = a_name)
         a_list.append(a_name)
         w_list.append(v)
-    if not a_list: return
+    if not a_list: return pipe
     pipe.set_adapters(a_list, adapter_weights=w_list)
     pipe.fuse_lora(adapter_names=a_list, lora_scale=1.0)
     pipe.unload_lora_weights()

convert_url_to_diffusers_sd_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():
@@ -237,7 +237,7 @@ def fuse_loras(pipe, lora_dict={}, temp_dir=".", civitai_key="", hf_token=""):
         pipe.load_lora_weights(new_lora_file, weight_name = w_name, adapter_name = a_name)
         a_list.append(a_name)
         w_list.append(v)
-    if not a_list: return
+    if not a_list: return pipe
     pipe.set_adapters(a_list, adapter_weights=w_list)
     pipe.fuse_lora(adapter_names=a_list, lora_scale=1.0)
     pipe.unload_lora_weights()

local/convert_url_to_diffusers_sd.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():
@@ -208,7 +208,7 @@ def fuse_loras(pipe, civitai_key="", lora_dict={}, temp_dir="."):
         pipe.load_lora_weights(new_lora_file, weight_name = w_name, adapter_name = a_name)
         a_list.append(a_name)
         w_list.append(v)
-    if not a_list: return
+    if not a_list: return pipe
     pipe.set_adapters(a_list, adapter_weights=w_list)
     pipe.fuse_lora(adapter_names=a_list, lora_scale=1.0)
     pipe.unload_lora_weights()