Fix output gallery for csv format inc. VAE & LoRA

* Update saving of img_details.csv to include headers for the first row
when creating new .csv files.
* Update parsing of parameter info by the output gallery from
img_details.csv to use headers when they are present
* Add VAE and LoRA fields to parameter info in output_gallery when
sourced from .csv or .json (was already picked up from .png sources)

apps/stable_diffusion/src/utils/utils.py

@@ -810,8 +810,11 @@ def save_output_img(output_img, img_seed, extra_info={}):
 
     new_entry.update(extra_info)
 
-    with open(csv_path, "a", encoding="utf-8") as csv_obj:
+    csv_mode = "a" if os.path.isfile(csv_path) else "w"
+    with open(csv_path, csv_mode, encoding="utf-8") as csv_obj:
         dictwriter_obj = DictWriter(csv_obj, fieldnames=list(new_entry.keys()))
+        if csv_mode == "w":
+            dictwriter_obj.writeheader()
         dictwriter_obj.writerow(new_entry)
         csv_obj.close()
 

apps/stable_diffusion/web/utils/metadata/csv_metadata.py

@@ -11,21 +11,35 @@ def has_csv(image_filename: str) -> bool:
     return os.path.exists(csv_path(image_filename))
 
 
-def parse_csv(image_filename: str):
-    # We use a reader instead of a DictReader here for images_details.csv files due to the lack of
-    # headers, and then match up the return list for each row with our guess at which column format
-    # the file is using.
-
+def matching_filename(image_filename: str, row):
     # we assume the final column of the csv has the original filename with full path and match that
-    # against the image_filename. We then exclude the filename from the output, hence the -1's.
+    # against the image_filename if we are given a list. Otherwise we assume a dict and and take
+    # the value of the OUTPUT key
+    return os.path.basename(image_filename) in (
+        row[-1] if isinstance(row, list) else row["OUTPUT"]
+    )
+
+
+def parse_csv(image_filename: str):
     csv_filename = csv_path(image_filename)
 
-    matches = [
-        humanize(row)
-        for row in csv.reader(open(csv_filename, "r", newline=""))
-        if row
-        and humanizable(row)
-        and os.path.basename(image_filename) in row[-1]
-    ]
+    with open(csv_filename, "r", newline="") as csv_file:
+        # We use a reader or DictReader here for images_details.csv depending on whether we think it
+        # has headers or not. Having headers means less guessing of the format.
+        has_header = csv.Sniffer().has_header(csv_file.read(2048))
+        csv_file.seek(0)
+
+        reader = (
+            csv.DictReader(csv_file) if has_header else csv.reader(csv_file)
+        )
+
+        matches = [
+            # we rely on humanize and humanizable to work out the parsing of the individual .csv rows
+            humanize(row)
+            for row in reader
+            if row
+            and (has_header or humanizable(row))
+            and matching_filename(image_filename, row)
+        ]
 
     return matches[0] if matches else {}

apps/stable_diffusion/web/utils/metadata/format.py

@@ -50,7 +50,22 @@
     },
 }
 
-PARAMS_FORMAT_LONGEST = PARAMS_FORMATS[max(PARAMS_FORMATS.keys())]
+PARAMS_FORMAT_CURRENT = {
+    "VARIANT": "Model",
+    "VAE": "VAE",
+    "LORA": "LoRA",
+    "SCHEDULER": "Sampler",
+    "PROMPT": "Prompt",
+    "NEG_PROMPT": "Negative prompt",
+    "SEED": "Seed",
+    "CFG_SCALE": "CFG scale",
+    "PRECISION": "Precision",
+    "STEPS": "Steps",
+    "HEIGHT": "Height",
+    "WIDTH": "Width",
+    "MAX_LENGTH": "Max Length",
+    "OUTPUT": "Filename",
+}
 
 
 def compact(metadata: dict) -> dict:
@@ -97,19 +112,20 @@ def humanize(metadata: dict | list[str], includes_filename=True) -> dict:
             )
 
     # For dictionaries we try to use the matching length parameter format if
-    # available, otherwise we use the longest. Then we swap keys in the
-    # metadata that match keys in the format for the friendlier name that we
-    # have set in the format value
+    # available, otherwise we just use the current format which is assumed to
+    # have everything currently known about. Then we swap keys in the metadata
+    # that match keys in the format for the friendlier name that we have set
+    # in the format value
     if isinstance(metadata, dict):
         if humanizable(metadata, includes_filename):
             format = PARAMS_FORMATS[lookup_key]
         else:
-            format = PARAMS_FORMAT_LONGEST
+            format = PARAMS_FORMAT_CURRENT
 
         return {
-            format[key]: value
-            for (key, value) in metadata.items()
-            if key in format.keys()
+            format[key]: metadata[key]
+            for key in format.keys()
+            if key in metadata.keys() and metadata[key]
         }
 
     raise TypeError("Can only humanize parameter lists or dictionaries")