Include VAE & LoRA data into PNG metadata

.gitignore

@@ -159,7 +159,7 @@ cython_debug/
 #  be found at https://github.com/github/gitignore/blob/main/Global/JetBrains.gitignore
 #  and can be added to the global gitignore or merged into this file.  For a more nuclear
 #  option (not recommended) you can uncomment the following to ignore the entire idea folder.
-#.idea/
+.idea/
 
 # vscode related
 .vscode

apps/stable_diffusion/src/utils/utils.py

@@ -757,6 +757,14 @@ def save_output_img(output_img, img_seed, extra_info={}):
     if args.ckpt_loc:
         img_model = Path(os.path.basename(args.ckpt_loc)).stem
 
+    img_vae = None
+    if args.custom_vae:
+        img_vae = Path(os.path.basename(args.custom_vae)).stem
+
+    img_lora = None
+    if args.use_lora:
+        img_lora = Path(os.path.basename(args.use_lora)).stem
+
     if args.output_img_format == "jpg":
         out_img_path = Path(generated_imgs_path, f"{out_img_name}.jpg")
         output_img.save(out_img_path, quality=95, subsampling=0)
@@ -767,7 +775,9 @@ def save_output_img(output_img, img_seed, extra_info={}):
         if args.write_metadata_to_png:
             pngInfo.add_text(
                 "parameters",
-                f"{args.prompts[0]}\nNegative prompt: {args.negative_prompts[0]}\nSteps:{args.steps}, Sampler: {args.scheduler}, CFG scale: {args.guidance_scale}, Seed: {img_seed}, Size: {args.width}x{args.height}, Model: {img_model}",
+                f"{args.prompts[0]}\nNegative prompt: {args.negative_prompts[0]}\nSteps: {args.steps},"
+                f"Sampler: {args.scheduler}, CFG scale: {args.guidance_scale}, Seed: {img_seed},"
+                f"Size: {args.width}x{args.height}, Model: {img_model}, VAE: {img_vae}, LoRA: {img_lora}",
             )
 
         output_img.save(out_img_path, "PNG", pnginfo=pngInfo)
@@ -778,6 +788,9 @@ def save_output_img(output_img, img_seed, extra_info={}):
                 "Image saved as png instead. Supported formats: png / jpg"
             )
 
+    # To be as low-impact as possible to the existing CSV format, we append
+    # "VAE" and "LORA" to the end. However, it does not fit the hierarchy of
+    # importance for each data point. Something to consider.
     new_entry = {
         "VARIANT": img_model,
         "SCHEDULER": args.scheduler,
@@ -791,6 +804,8 @@ def save_output_img(output_img, img_seed, extra_info={}):
         "WIDTH": args.width,
         "MAX_LENGTH": args.max_length,
         "OUTPUT": out_img_path,
+        "VAE": img_vae,
+        "LORA": img_lora,
     }
 
     new_entry.update(extra_info)

apps/stable_diffusion/web/ui/txt2img_ui.py

@@ -553,6 +553,9 @@ def txt2img_api(
                 height,
                 txt2img_custom_model,
                 txt2img_hf_model_id,
+                lora_weights,
+                lora_hf_id,
+                custom_vae,
             ],
             outputs=[
                 txt2img_png_info_img,
@@ -566,5 +569,8 @@ def txt2img_api(
                 height,
                 txt2img_custom_model,
                 txt2img_hf_model_id,
+                lora_weights,
+                lora_hf_id,
+                custom_vae,
             ],
         )

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

@@ -62,6 +62,82 @@ def parse_generation_parameters(x: str):
     return res
 
 
+def try_find_model_base_from_png_metadata(
+    file: str, folder: str = "models"
+) -> str:
+    custom = ""
+
+    # Remove extension from file info
+    if file.endswith(".safetensors") or file.endswith(".ckpt"):
+        file = Path(file).stem
+    # Check for the file name match with one of the local ckpt or safetensors files
+    if Path(get_custom_model_pathfile(file + ".ckpt", folder)).is_file():
+        custom = file + ".ckpt"
+    if Path(
+        get_custom_model_pathfile(file + ".safetensors", folder)
+    ).is_file():
+        custom = file + ".safetensors"
+
+    return custom
+
+
+def find_model_from_png_metadata(
+    key: str, metadata: dict[str, str | int]
+) -> tuple[str, str]:
+    png_hf_id = ""
+    png_custom = ""
+
+    if key in metadata:
+        model_file = metadata[key]
+        png_custom = try_find_model_base_from_png_metadata(model_file)
+        # Check for a model match with one of the default model list (ex: "Linaqruf/anything-v3.0")
+        if model_file in predefined_models:
+            png_custom = model_file
+        # If nothing had matched, check vendor/hf_model_id
+        if not png_custom and model_file.count("/"):
+            png_hf_id = model_file
+        # No matching model was found
+        if not png_custom and not png_hf_id:
+            print(
+                "Import PNG info: Unable to find a matching model for %s"
+                % model_file
+            )
+
+    return png_custom, png_hf_id
+
+
+def find_vae_from_png_metadata(
+    key: str, metadata: dict[str, str | int]
+) -> str:
+    vae_custom = ""
+
+    if key in metadata:
+        vae_file = metadata[key]
+        vae_custom = try_find_model_base_from_png_metadata(vae_file, "vae")
+
+    # VAE input is optional, should not print or throw an error if missing
+
+    return vae_custom
+
+
+def find_lora_from_png_metadata(
+    key: str, metadata: dict[str, str | int]
+) -> tuple[str, str]:
+    lora_hf_id = ""
+    lora_custom = ""
+
+    if key in metadata:
+        lora_file = metadata[key]
+        lora_custom = try_find_model_base_from_png_metadata(lora_file, "lora")
+        # If nothing had matched, check vendor/hf_model_id
+        if not lora_custom and lora_file.count("/"):
+            lora_hf_id = lora_file
+
+    # LoRA input is optional, should not print or throw an error if missing
+
+    return lora_custom, lora_hf_id
+
+
 def import_png_metadata(
     pil_data,
     prompt,
@@ -74,53 +150,46 @@ def import_png_metadata(
     height,
     custom_model,
     hf_model_id,
+    custom_lora,
+    hf_lora_id,
+    custom_vae,
 ):
     try:
         png_info = pil_data.info["parameters"]
         metadata = parse_generation_parameters(png_info)
-        png_hf_model_id = ""
-        png_custom_model = ""
-
-        if "Model" in metadata:
-            # Remove extension from model info
-            if metadata["Model"].endswith(".safetensors") or metadata[
-                "Model"
-            ].endswith(".ckpt"):
-                metadata["Model"] = Path(metadata["Model"]).stem
-            # Check for the model name match with one of the local ckpt or safetensors files
-            if Path(
-                get_custom_model_pathfile(metadata["Model"] + ".ckpt")
-            ).is_file():
-                png_custom_model = metadata["Model"] + ".ckpt"
-            if Path(
-                get_custom_model_pathfile(metadata["Model"] + ".safetensors")
-            ).is_file():
-                png_custom_model = metadata["Model"] + ".safetensors"
-            # Check for a model match with one of the default model list (ex: "Linaqruf/anything-v3.0")
-            if metadata["Model"] in predefined_models:
-                png_custom_model = metadata["Model"]
-            # If nothing had matched, check vendor/hf_model_id
-            if not png_custom_model and metadata["Model"].count("/"):
-                png_hf_model_id = metadata["Model"]
-            # No matching model was found
-            if not png_custom_model and not png_hf_model_id:
-                print(
-                    "Import PNG info: Unable to find a matching model for %s"
-                    % metadata["Model"]
-                )
+
+        (png_custom_model, png_hf_model_id) = find_model_from_png_metadata(
+            "Model", metadata
+        )
+        (lora_custom_model, lora_hf_model_id) = find_lora_from_png_metadata(
+            "LoRA", metadata
+        )
+        vae_custom_model = find_vae_from_png_metadata("VAE", metadata)
 
         negative_prompt = metadata["Negative prompt"]
         steps = int(metadata["Steps"])
         cfg_scale = float(metadata["CFG scale"])
         seed = int(metadata["Seed"])
         width = float(metadata["Size-1"])
         height = float(metadata["Size-2"])
+
         if "Model" in metadata and png_custom_model:
             custom_model = png_custom_model
             hf_model_id = ""
         if "Model" in metadata and png_hf_model_id:
             custom_model = "None"
             hf_model_id = png_hf_model_id
+
+        if "LoRA" in metadata and lora_custom_model:
+            custom_lora = lora_custom_model
+            hf_lora_id = ""
+        if "LoRA" in metadata and lora_hf_model_id:
+            custom_lora = "None"
+            hf_lora_id = lora_hf_model_id
+
+        if "VAE" in metadata and vae_custom_model:
+            custom_vae = vae_custom_model
+
         if "Prompt" in metadata:
             prompt = metadata["Prompt"]
         if "Sampler" in metadata:
@@ -149,4 +218,7 @@ def import_png_metadata(
         height,
         custom_model,
         hf_model_id,
+        custom_lora,
+        hf_lora_id,
+        custom_vae,
     )