errors msg

frontend/src/lib/App.svelte

@@ -119,7 +119,7 @@
 							const imgBase64 = data.output.data[0] as string;
 							const isNSWF = data.output.data[1] as boolean;
 							if (isNSWF) {
-								throw new Error('Potential NFSW content, please try again');
+								throw new Error('NFSW');
 							}
 							const imgBlob = await base64ToBlob(imgBase64);
 							const imgURL = await uploadImage(imgBlob, prompt);
@@ -197,7 +197,7 @@
 </div>
 
 <div class="fixed bottom-0 left-0 right-0 z-10 my-2">
-	<Menu on:prompt={onPrompt} />
+	<Menu on:prompt={onPrompt} {isLoading} />
 </div>
 
 <style lang="postcss" scoped>

frontend/src/lib/Menu.svelte

@@ -2,7 +2,7 @@
 	import { onMount, createEventDispatcher } from 'svelte';
 	import PPButton from '$lib/Buttons/PPButton.svelte';
 	const dispatch = createEventDispatcher();
-
+	export let isLoading = false;
 	const onKeyup = (e: KeyboardEvent) => {
 		if (e.key === 'Enter') {
 			dispatch('prompt');
@@ -17,5 +17,5 @@
 </script>
 
 <div class="grid grid-cols-1 gap-3 w-max mx-auto">
-	<PPButton on:click={() => dispatch('prompt')} />
+	<PPButton {isLoading} on:click={() => dispatch('prompt')} />
 </div>

frontend/src/lib/PaintFrame.svelte

@@ -13,7 +13,7 @@
 	import { onMount, createEventDispatcher } from 'svelte';
 
 	import { useMyPresence } from '$lib/liveblocks';
-	import { canvasEl, maskEl } from '$lib/store';
+	import { canvasEl, maskEl, loadingState } from '$lib/store';
 
 	import { Status } from './types';
 	const myPresence = useMyPresence();
@@ -218,6 +218,17 @@
 					<div class="py-3">
 						<PPButton {isLoading} on:click={() => dispatch('prompt')} />
 					</div>
+					{#if $loadingState !== ''}
+						<div class="p-3 bg-white rounded-lg font-mono">
+							{#if $loadingState === 'NFSW'}
+								<h2 class="text-red-500 text-2xl font-bold">NSFW Alert</h2>
+								<h3 class="text-red-500 text-lg">
+									Possible NSFW result detected, please try again
+								</h3>
+							{/if}
+							<p>{$loadingState}</p>
+						</div>
+					{/if}
 				</div>
 				<div
 					class="absolute left-full"

stablediffusion-infinity/app.py

@@ -104,29 +104,29 @@ def run_outpaint(
     process_size = 512
 
     mask_sum = mask.sum()
-    if mask_sum >= WHITES:
-        print("inpaiting with fixed Mask")
-        mask = np.array(MASK)[:, :, 0]
-        img, mask = functbl[fill_mode](img, mask)
-        init_image = Image.fromarray(img)
-        mask = 255 - mask
-        mask = skimage.measure.block_reduce(mask, (8, 8), np.max)
-        mask = mask.repeat(8, axis=0).repeat(8, axis=1)
-        mask_image = Image.fromarray(mask)
-
-        # mask_image=mask_image.filter(ImageFilter.GaussianBlur(radius = 8))
-        with autocast("cuda"):
-            images = inpaint(
-                prompt=prompt_text,
-                init_image=init_image.resize(
-                    (process_size, process_size), resample=SAMPLING_MODE
-                ),
-                mask_image=mask_image.resize((process_size, process_size)),
-                strength=strength,
-                num_inference_steps=step,
-                guidance_scale=guidance,
-            )
-    elif mask_sum > 0 and mask_sum < WHITES:
+    # if mask_sum >= WHITES:
+    #     print("inpaiting with fixed Mask")
+    #     mask = np.array(MASK)[:, :, 0]
+    #     img, mask = functbl[fill_mode](img, mask)
+    #     init_image = Image.fromarray(img)
+    #     mask = 255 - mask
+    #     mask = skimage.measure.block_reduce(mask, (8, 8), np.max)
+    #     mask = mask.repeat(8, axis=0).repeat(8, axis=1)
+    #     mask_image = Image.fromarray(mask)
+
+    #     # mask_image=mask_image.filter(ImageFilter.GaussianBlur(radius = 8))
+    #     with autocast("cuda"):
+    #         images = inpaint(
+    #             prompt=prompt_text,
+    #             init_image=init_image.resize(
+    #                 (process_size, process_size), resample=SAMPLING_MODE
+    #             ),
+    #             mask_image=mask_image.resize((process_size, process_size)),
+    #             strength=strength,
+    #             num_inference_steps=step,
+    #             guidance_scale=guidance,
+    #         )
+    if mask_sum > 0:
         print("inpainting")
         img, mask = functbl[fill_mode](img, mask)
         init_image = Image.fromarray(img)