make response types match

api/onnx_web/chain/result.py

@@ -1,13 +1,14 @@
 from json import dumps
 from logging import getLogger
 from os import path
-from typing import Any, List, Optional
+from typing import Any, List, Optional, Tuple
 
 import numpy as np
 from PIL import Image
 
 from ..convert.utils import resolve_tensor
 from ..params import Border, HighresParams, ImageParams, Size, UpscaleParams
+from ..server.context import ServerContext
 from ..server.load import get_extra_hashes
 from ..utils import hash_file
 
@@ -55,8 +56,8 @@ class ImageMetadata:
         self.loras = loras
         self.models = models
 
-    def to_auto1111(self, server, outputs) -> str:
-        model_name = path.basename(path.normpath(self.params.model))
+    def get_model_hash(self, model: Optional[str] = None) -> Tuple[str, str]:
+        model_name = path.basename(path.normpath(model or self.params.model))
         logger.debug("getting model hash for %s", model_name)
 
         model_hash = get_extra_hashes().get(model_name, None)
@@ -66,7 +67,10 @@ class ImageMetadata:
                 with open(model_hash_path, "r") as f:
                     model_hash = f.readline().rstrip(",. \n\t\r")
 
-        model_hash = model_hash or "unknown"
+        return (model_name, model_hash or "unknown")
+
+    def to_exif(self, server) -> str:
+        model_name, model_hash = self.get_model_hash()
         hash_map = {
             model_name: model_hash,
         }
@@ -112,15 +116,17 @@ class ImageMetadata:
             f"Hashes: {dumps(hash_map)}"
         )
 
-    def tojson(self, server, outputs):
+    def tojson(self, server: ServerContext, output: List[str]):
         json = {
             "input_size": self.size.tojson(),
-            "outputs": outputs,
+            "outputs": output,
             "params": self.params.tojson(),
-            "inversions": {},
-            "loras": {},
+            "inversions": [],
+            "loras": [],
+            "models": [],
         }
 
+        # fix up some fields
         json["params"]["model"] = path.basename(self.params.model)
         json["params"]["scheduler"] = self.params.scheduler  # TODO: why tho?
 
@@ -145,14 +151,21 @@ class ImageMetadata:
                 hash = hash_file(
                     resolve_tensor(path.join(server.model_path, "inversion", name))
                 ).upper()
-                json["inversions"][name] = {"weight": weight, "hash": hash}
+                json["inversions"].append(
+                    {"name": name, "weight": weight, "hash": hash}
+                )
 
         if self.loras is not None:
             for name, weight in self.loras:
                 hash = hash_file(
                     resolve_tensor(path.join(server.model_path, "lora", name))
                 ).upper()
-                json["loras"][name] = {"weight": weight, "hash": hash}
+                json["loras"].append({"name": name, "weight": weight, "hash": hash})
+
+        if self.models is not None:
+            for name, weight in self.models:
+                name, hash = self.get_model_hash()
+                json["models"].append({"name": name, "weight": weight, "hash": hash})
 
         return json
 

api/onnx_web/output.py

@@ -98,7 +98,7 @@ def save_image(
             exif.add_text("model", server.server_version)
             exif.add_text(
                 "parameters",
-                metadata.to_auto1111(server, [output]),
+                metadata.to_exif(server, [output]),
             )
 
         image.save(path, format=server.image_format, pnginfo=exif)
@@ -111,7 +111,7 @@ def save_image(
                         encoding="unicode",
                     ),
                     ExifIFD.UserComment: UserComment.dump(
-                        metadata.to_auto1111(server, [output]),
+                        metadata.to_exif(server, [output]),
                         encoding="unicode",
                     ),
                     ImageIFD.Make: "onnx-web",

gui/src/components/card/ImageCard.tsx

@@ -12,7 +12,6 @@ import { ClientContext, ConfigContext, OnnxState, StateContext } from '../../sta
 import { range, visibleIndex } from '../../utils.js';
 import { BLEND_SOURCES } from '../../constants.js';
 import { JobResponse, SuccessJobResponse } from '../../types/api-v2.js';
-import { getApiRoot } from '../../config.js';
 
 export interface ImageCardProps {
   image: SuccessJobResponse;
@@ -110,8 +109,8 @@ export function ImageCard(props: ImageCardProps) {
 
   const url = useMemo(() => client.outputURL(image, index), [image, index]);
 
-  const model = getLabel('model', metadata[index].model);
-  const scheduler = getLabel('scheduler', metadata[index].scheduler);
+  const model = getLabel('model', metadata[index].models[0].name);
+  const scheduler = getLabel('scheduler', metadata[index].params.scheduler);
 
   return <Card sx={{ maxWidth: config.params.width.default }} elevation={2}>
     <CardMedia sx={{ height: config.params.height.default }}

gui/src/components/card/LoadingCard.tsx

@@ -36,10 +36,6 @@ export function LoadingCard(props: LoadingCardProps) {
     refetchInterval: POLL_TIME,
   });
 
-  function getReady() {
-    return doesExist(ready.data) && ready.data[0].status === JobStatus.SUCCESS;
-  }
-
   function renderProgress() {
     const progress = getProgress(ready.data);
     const total = getTotal(ready.data);
@@ -57,10 +53,10 @@ export function LoadingCard(props: LoadingCardProps) {
   }, [cancel.status]);
 
   useEffect(() => {
-    if (ready.status === 'success' && getReady()) {
+    if (ready.status === 'success') {
       setReady(ready.data[0]);
     }
-  }, [ready.status, getReady(), getProgress(ready.data)]);
+  }, [ready.status, getStatus(ready.data), getProgress(ready.data)]);
 
   return <Card sx={{ maxWidth: params.width.default }}>
     <CardContent sx={{ height: params.height.default }}>
@@ -134,3 +130,11 @@ export function getTotal(data: Maybe<Array<JobResponse>>) {
 
   return 0;
 }
+
+function getStatus(data: Maybe<Array<JobResponse>>) {
+  if (doesExist(data)) {
+    return data[0].status;
+  }
+
+  return JobStatus.PENDING;
+}

gui/src/types/api-v2.ts

@@ -19,16 +19,18 @@ export interface NetworkMetadata {
 
 export interface ImageMetadata<TParams extends BaseImgParams, TType extends JobType> {
   input_size: Size;
-  size: Size;
   outputs: Array<string>;
   params: TParams;
+
   inversions: Array<NetworkMetadata>;
   loras: Array<NetworkMetadata>;
-  model: string;
-  scheduler: string;
-  border: unknown;
+  models: Array<NetworkMetadata>;
+
+  border: unknown; // TODO: type
   highres: HighresParams;
   upscale: UpscaleParams;
+  size: Size;
+
   type: TType;
 }