Add tiling and latent upscaler steps to metadata

Also fix txt metadata labels when also embedding metadata

ui/easydiffusion/utils/save_utils.py

@@ -30,9 +30,11 @@ TASK_TEXT_MAPPING = {
     "lora_alpha": "LoRA Strength",
     "use_hypernetwork_model": "Hypernetwork model",
     "hypernetwork_strength": "Hypernetwork Strength",
+    "tiling": "Seamless Tiling",
     "use_face_correction": "Use Face Correction",
     "use_upscale": "Use Upscaling",
     "upscale_amount": "Upscale By",
+    "latent_upscaler_steps": "Latent Upscaler Steps"
 }
 
 time_placeholders = {
@@ -169,21 +171,23 @@ def save_images_to_disk(images: list, filtered_images: list, req: GenerateImageR
             output_quality=task_data.output_quality,
             output_lossless=task_data.output_lossless,
         )
-        if task_data.metadata_output_format.lower() in ["json", "txt", "embed"]:
-            save_dicts(
-                metadata_entries,
-                save_dir_path,
-                file_name=make_filter_filename,
-                output_format=task_data.metadata_output_format,
-                file_format=task_data.output_format,
-            )
+        if task_data.metadata_output_format:
+            for metadata_output_format in task_data.metadata_output_format.split(","):
+                if metadata_output_format.lower() in ["json", "txt", "embed"]:
+                    save_dicts(
+                        metadata_entries,
+                        save_dir_path,
+                        file_name=make_filter_filename,
+                        output_format=task_data.metadata_output_format,
+                        file_format=task_data.output_format,
+                    )
 
 
 def get_metadata_entries_for_request(req: GenerateImageRequest, task_data: TaskData):
     metadata = get_printable_request(req, task_data)
 
     # if text, format it in the text format expected by the UI
-    is_txt_format = task_data.metadata_output_format.lower() == "txt"
+    is_txt_format = task_data.metadata_output_format and "txt" in task_data.metadata_output_format.lower().split(",")
     if is_txt_format:
         metadata = {TASK_TEXT_MAPPING[key]: val for key, val in metadata.items() if key in TASK_TEXT_MAPPING}
 
@@ -215,10 +219,12 @@ def get_printable_request(req: GenerateImageRequest, task_data: TaskData):
         del metadata["hypernetwork_strength"]
     if task_data.use_lora_model is None and "lora_alpha" in metadata:
         del metadata["lora_alpha"]
+    if task_data.use_upscale is not "latent_upscaler" and "latent_upscaler_steps" in metadata:
+        del metadata["latent_upscaler_steps"]
 
     app_config = app.getConfig()
     if not app_config.get("test_diffusers", False):
-        for key in (x for x in ["use_lora_model", "lora_alpha", "clip_skip"] if x in metadata):
+        for key in (x for x in ["use_lora_model", "lora_alpha", "clip_skip", "tiling", "latent_upscaler_steps"] if x in metadata):
             del metadata[key]
 
     return metadata

ui/media/js/auto-save.js

@@ -35,6 +35,7 @@ const SETTINGS_IDS_LIST = [
     "gfpgan_model",
     "use_upscale",
     "upscale_amount",
+    "latent_upscaler_steps",
     "block_nsfw",
     "show_only_filtered_image",
     "upscale_model",

ui/media/js/dnd.js

@@ -221,6 +221,14 @@ const TASK_MAPPING = {
         readUI: () => upscaleAmountField.value,
         parse: (val) => val,
     },
+    latent_upscaler_steps: {
+        name: "Latent Upscaler Steps",
+        setUI: (latent_upscaler_steps) => {
+            latentUpscalerStepsField.value = latent_upscaler_steps
+        },
+        readUI: () => latentUpscalerStepsField.value,
+        parse: (val) => val,
+    },
     sampler_name: {
         name: "Sampler",
         setUI: (sampler_name) => {