Upload app.py

app.py

@@ -15,6 +15,20 @@ loras = [
 ]
 schedulers = list(SCHEDULER_CONFIG_MAP.keys())
 
+preset_dict = {
+    "Default": [True, "", "Euler", "", 1.0, "", 1.0, "", 1.0, "", 1.0, "", 1.0, "v1", 768, "ema"],
+    "Bake in standard VAE": [True, "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, "v1", 768, "ema"],
+}
+
+
+def set_presets(preset: str="Default"):
+    p = []
+    if preset in preset_dict.keys(): p = preset_dict[preset]
+    else: p = 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], p[14], p[15]
+
+
 css = """"""
 
 with gr.Blocks(theme="NoCrypt/miku@>=1.2.2", css=css) as demo:
@@ -37,7 +51,7 @@ It saves you the trouble of typing them in.<br>
 - 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.
+- Patiently wait until the output changes. It takes approximately 1 minutes (downloading from HF).
             """
     )
     with gr.Column():
@@ -47,6 +61,7 @@ It saves you the trouble of typing them in.<br>
         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)
+        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")
@@ -77,6 +92,13 @@ It saves you the trouble of typing them in.<br>
                  model_type, sample_size, ema],
         outputs=[repo_urls, output_md],
     )
+    presets.change(
+        fn=set_presets,
+        inputs=[presets],
+        outputs=[is_half, vae, scheduler, lora1, lora1s, lora2, lora2s, lora3, lora3s, lora4, lora4s, lora5, lora5s,
+                 model_type, sample_size, ema],
+        queue=False,
+    )
 
 demo.queue()
 demo.launch()