Merge branch 'main' into bugfix/convert-script

invokeai/app/invocations/params.py

@@ -6,8 +6,7 @@ from pydantic import Field
 
 from invokeai.app.invocations.prompt import PromptOutput
 
-from .baseinvocation import (BaseInvocation, BaseInvocationOutput,
+from .baseinvocation import BaseInvocation, BaseInvocationOutput, InvocationConfig, InvocationContext
-                             InvocationConfig, InvocationContext)
 from .math import FloatOutput, IntOutput
 
 # Pass-through parameter nodes - used by subgraphs
@@ -68,6 +67,7 @@ class ParamStringInvocation(BaseInvocation):
     def invoke(self, context: InvocationContext) -> StringOutput:
         return StringOutput(text=self.text)
 
+
 class ParamPromptInvocation(BaseInvocation):
     """A prompt input parameter"""
 

invokeai/frontend/web/src/features/gallery/components/CurrentImage/CurrentImageButtons.tsx

@@ -139,8 +139,19 @@ const CurrentImageButtons = (props: CurrentImageButtonsProps) => {
   useHotkeys('s', handleUseSeed, [imageDTO]);
 
   const handleUsePrompt = useCallback(() => {
-    recallBothPrompts(metadata?.positive_prompt, metadata?.negative_prompt);
+    recallBothPrompts(
-  }, [metadata?.negative_prompt, metadata?.positive_prompt, recallBothPrompts]);
+      metadata?.positive_prompt,
+      metadata?.negative_prompt,
+      metadata?.positive_style_prompt,
+      metadata?.negative_style_prompt
+    );
+  }, [
+    metadata?.negative_prompt,
+    metadata?.positive_prompt,
+    metadata?.positive_style_prompt,
+    metadata?.negative_style_prompt,
+    recallBothPrompts,
+  ]);
 
   useHotkeys('p', handleUsePrompt, [imageDTO]);
 

invokeai/frontend/web/src/features/gallery/components/ImageContextMenu/SingleSelectionMenuItems.tsx

@@ -102,8 +102,19 @@ const SingleSelectionMenuItems = (props: SingleSelectionMenuItemsProps) => {
 
   // Recall parameters handlers
   const handleRecallPrompt = useCallback(() => {
-    recallBothPrompts(metadata?.positive_prompt, metadata?.negative_prompt);
+    recallBothPrompts(
-  }, [metadata?.negative_prompt, metadata?.positive_prompt, recallBothPrompts]);
+      metadata?.positive_prompt,
+      metadata?.negative_prompt,
+      metadata?.positive_style_prompt,
+      metadata?.negative_style_prompt
+    );
+  }, [
+    metadata?.negative_prompt,
+    metadata?.positive_prompt,
+    metadata?.positive_style_prompt,
+    metadata?.negative_style_prompt,
+    recallBothPrompts,
+  ]);
 
   const handleRecallSeed = useCallback(() => {
     recallSeed(metadata?.seed);

invokeai/frontend/web/src/features/parameters/hooks/useRecallParameters.ts

@@ -1,5 +1,15 @@
 import { useAppToaster } from 'app/components/Toaster';
 import { useAppDispatch } from 'app/store/storeHooks';
+import {
+  refinerModelChanged,
+  setNegativeStylePromptSDXL,
+  setPositiveStylePromptSDXL,
+  setRefinerAestheticScore,
+  setRefinerCFGScale,
+  setRefinerScheduler,
+  setRefinerStart,
+  setRefinerSteps,
+} from 'features/sdxl/store/sdxlSlice';
 import { useCallback } from 'react';
 import { useTranslation } from 'react-i18next';
 import { UnsafeImageMetadata } from 'services/api/endpoints/images';
@@ -22,6 +32,10 @@ import {
   isValidMainModel,
   isValidNegativePrompt,
   isValidPositivePrompt,
+  isValidSDXLNegativeStylePrompt,
+  isValidSDXLPositiveStylePrompt,
+  isValidSDXLRefinerAestheticScore,
+  isValidSDXLRefinerStart,
   isValidScheduler,
   isValidSeed,
   isValidSteps,
@@ -74,17 +88,34 @@ export const useRecallParameters = () => {
    * Recall both prompts with toast
    */
   const recallBothPrompts = useCallback(
-    (positivePrompt: unknown, negativePrompt: unknown) => {
+    (
+      positivePrompt: unknown,
+      negativePrompt: unknown,
+      positiveStylePrompt: unknown,
+      negativeStylePrompt: unknown
+    ) => {
       if (
         isValidPositivePrompt(positivePrompt) ||
-        isValidNegativePrompt(negativePrompt)
+        isValidNegativePrompt(negativePrompt) ||
+        isValidSDXLPositiveStylePrompt(positiveStylePrompt) ||
+        isValidSDXLNegativeStylePrompt(negativeStylePrompt)
       ) {
         if (isValidPositivePrompt(positivePrompt)) {
           dispatch(setPositivePrompt(positivePrompt));
         }
+
         if (isValidNegativePrompt(negativePrompt)) {
           dispatch(setNegativePrompt(negativePrompt));
         }
+
+        if (isValidSDXLPositiveStylePrompt(positiveStylePrompt)) {
+          dispatch(setPositiveStylePromptSDXL(positiveStylePrompt));
+        }
+
+        if (isValidSDXLPositiveStylePrompt(negativeStylePrompt)) {
+          dispatch(setNegativeStylePromptSDXL(negativeStylePrompt));
+        }
+
         parameterSetToast();
         return;
       }
@@ -123,6 +154,36 @@ export const useRecallParameters = () => {
     [dispatch, parameterSetToast, parameterNotSetToast]
   );
 
+  /**
+   * Recall SDXL Positive Style Prompt with toast
+   */
+  const recallSDXLPositiveStylePrompt = useCallback(
+    (positiveStylePrompt: unknown) => {
+      if (!isValidSDXLPositiveStylePrompt(positiveStylePrompt)) {
+        parameterNotSetToast();
+        return;
+      }
+      dispatch(setPositiveStylePromptSDXL(positiveStylePrompt));
+      parameterSetToast();
+    },
+    [dispatch, parameterSetToast, parameterNotSetToast]
+  );
+
+  /**
+   * Recall SDXL Negative Style Prompt with toast
+   */
+  const recallSDXLNegativeStylePrompt = useCallback(
+    (negativeStylePrompt: unknown) => {
+      if (!isValidSDXLNegativeStylePrompt(negativeStylePrompt)) {
+        parameterNotSetToast();
+        return;
+      }
+      dispatch(setNegativeStylePromptSDXL(negativeStylePrompt));
+      parameterSetToast();
+    },
+    [dispatch, parameterSetToast, parameterNotSetToast]
+  );
+
   /**
    * Recall seed with toast
    */
@@ -271,6 +332,14 @@ export const useRecallParameters = () => {
         steps,
         width,
         strength,
+        positive_style_prompt,
+        negative_style_prompt,
+        refiner_model,
+        refiner_cfg_scale,
+        refiner_steps,
+        refiner_scheduler,
+        refiner_aesthetic_store,
+        refiner_start,
       } = metadata;
 
       if (isValidCfgScale(cfg_scale)) {
@@ -304,6 +373,38 @@ export const useRecallParameters = () => {
         dispatch(setImg2imgStrength(strength));
       }
 
+      if (isValidSDXLPositiveStylePrompt(positive_style_prompt)) {
+        dispatch(setPositiveStylePromptSDXL(positive_style_prompt));
+      }
+
+      if (isValidSDXLNegativeStylePrompt(negative_style_prompt)) {
+        dispatch(setNegativeStylePromptSDXL(negative_style_prompt));
+      }
+
+      if (isValidMainModel(refiner_model)) {
+        dispatch(refinerModelChanged(refiner_model));
+      }
+
+      if (isValidSteps(refiner_steps)) {
+        dispatch(setRefinerSteps(refiner_steps));
+      }
+
+      if (isValidCfgScale(refiner_cfg_scale)) {
+        dispatch(setRefinerCFGScale(refiner_cfg_scale));
+      }
+
+      if (isValidScheduler(refiner_scheduler)) {
+        dispatch(setRefinerScheduler(refiner_scheduler));
+      }
+
+      if (isValidSDXLRefinerAestheticScore(refiner_aesthetic_store)) {
+        dispatch(setRefinerAestheticScore(refiner_aesthetic_store));
+      }
+
+      if (isValidSDXLRefinerStart(refiner_start)) {
+        dispatch(setRefinerStart(refiner_start));
+      }
+
       allParameterSetToast();
     },
     [allParameterNotSetToast, allParameterSetToast, dispatch]
@@ -313,6 +414,8 @@ export const useRecallParameters = () => {
     recallBothPrompts,
     recallPositivePrompt,
     recallNegativePrompt,
+    recallSDXLPositiveStylePrompt,
+    recallSDXLNegativeStylePrompt,
     recallSeed,
     recallCfgScale,
     recallModel,

invokeai/frontend/web/src/features/parameters/types/parameterSchemas.ts

@@ -310,6 +310,39 @@ export type PrecisionParam = z.infer<typeof zPrecision>;
 export const isValidPrecision = (val: unknown): val is PrecisionParam =>
   zPrecision.safeParse(val).success;
 
+/**
+ * Zod schema for SDXL refiner aesthetic score parameter
+ */
+export const zSDXLRefinerAestheticScore = z.number().min(1).max(10);
+/**
+ * Type alias for SDXL refiner aesthetic score parameter, inferred from its zod schema
+ */
+export type SDXLRefinerAestheticScoreParam = z.infer<
+  typeof zSDXLRefinerAestheticScore
+>;
+/**
+ * Validates/type-guards a value as a SDXL refiner aesthetic score parameter
+ */
+export const isValidSDXLRefinerAestheticScore = (
+  val: unknown
+): val is SDXLRefinerAestheticScoreParam =>
+  zSDXLRefinerAestheticScore.safeParse(val).success;
+
+/**
+ * Zod schema for SDXL start parameter
+ */
+export const zSDXLRefinerstart = z.number().min(0).max(1);
+/**
+ * Type alias for SDXL start, inferred from its zod schema
+ */
+export type SDXLRefinerStartParam = z.infer<typeof zSDXLRefinerstart>;
+/**
+ * Validates/type-guards a value as a SDXL refiner aesthetic score parameter
+ */
+export const isValidSDXLRefinerStart = (
+  val: unknown
+): val is SDXLRefinerStartParam => zSDXLRefinerstart.safeParse(val).success;
+
 // /**
 //  * Zod schema for BaseModelType
 //  */

invokeai/frontend/web/src/features/sdxl/components/ParamSDXLConcatButton.tsx

@@ -21,8 +21,8 @@ export default function ParamSDXLConcatButton() {
 
   return (
     <IAIIconButton
-      aria-label="Concat"
+      aria-label="Concatenate Prompt & Style"
-      tooltip="Concatenates Basic Prompt with Style (Recommended)"
+      tooltip="Concatenate Prompt & Style"
       variant="outline"
       isChecked={shouldConcatSDXLStylePrompt}
       onClick={handleShouldConcatPromptChange}

notebooks/Stable_Diffusion_AI_Notebook.ipynb

@@ -107,6 +107,7 @@
    "source": [
     "# @title 5. Load small ML models required\n",
     "import gc\n",
+    "\n",
     "%cd /content/InvokeAI/\n",
     "!python scripts/preload_models.py\n",
     "gc.collect()"
@@ -132,7 +133,8 @@
    "source": [
     "# @title 6. Mount google Drive\n",
     "from google.colab import drive\n",
-        "drive.mount('/content/drive')"
+    "\n",
+    "drive.mount(\"/content/drive\")"
    ]
   },
   {
@@ -183,7 +185,7 @@
     "    print(\"❗ Symlink already created\")\n",
     "else:\n",
     "    src = model_path\n",
-        "    dst = '/content/InvokeAI/models/ldm/stable-diffusion-v1/model.ckpt'\n",
+    "    dst = \"/content/InvokeAI/models/ldm/stable-diffusion-v1/model.ckpt\"\n",
     "    os.symlink(src, dst)\n",
     "    print(\"✅ Symbolic link created successfully\")"
    ]

scripts/orig_scripts/latent_imagenet_diffusion.ipynb

@@ -52,17 +52,17 @@
      "name": "stdout",
      "text": [
       "Cloning into 'latent-diffusion'...\n",
-      "remote: Enumerating objects: 992, done.\u001B[K\n",
+      "remote: Enumerating objects: 992, done.\u001b[K\n",
-      "remote: Counting objects: 100% (695/695), done.\u001B[K\n",
+      "remote: Counting objects: 100% (695/695), done.\u001b[K\n",
-      "remote: Compressing objects: 100% (397/397), done.\u001B[K\n",
+      "remote: Compressing objects: 100% (397/397), done.\u001b[K\n",
-      "remote: Total 992 (delta 375), reused 564 (delta 253), pack-reused 297\u001B[K\n",
+      "remote: Total 992 (delta 375), reused 564 (delta 253), pack-reused 297\u001b[K\n",
       "Receiving objects: 100% (992/992), 30.78 MiB | 29.43 MiB/s, done.\n",
       "Resolving deltas: 100% (510/510), done.\n",
       "Cloning into 'taming-transformers'...\n",
-      "remote: Enumerating objects: 1335, done.\u001B[K\n",
+      "remote: Enumerating objects: 1335, done.\u001b[K\n",
-      "remote: Counting objects: 100% (525/525), done.\u001B[K\n",
+      "remote: Counting objects: 100% (525/525), done.\u001b[K\n",
-      "remote: Compressing objects: 100% (493/493), done.\u001B[K\n",
+      "remote: Compressing objects: 100% (493/493), done.\u001b[K\n",
-      "remote: Total 1335 (delta 58), reused 481 (delta 30), pack-reused 810\u001B[K\n",
+      "remote: Total 1335 (delta 58), reused 481 (delta 30), pack-reused 810\u001b[K\n",
       "Receiving objects: 100% (1335/1335), 412.35 MiB | 30.53 MiB/s, done.\n",
       "Resolving deltas: 100% (267/267), done.\n",
       "Obtaining file:///content/taming-transformers\n",
@@ -73,9 +73,9 @@
       "Installing collected packages: taming-transformers\n",
       "  Running setup.py develop for taming-transformers\n",
       "Successfully installed taming-transformers-0.0.1\n",
-      "\u001B[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\n",
+      "\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\n",
       "tensorflow 2.8.0 requires tf-estimator-nightly==2.8.0.dev2021122109, which is not installed.\n",
-      "arviz 0.11.4 requires typing-extensions<4,>=3.7.4.3, but you have typing-extensions 4.1.1 which is incompatible.\u001B[0m\n"
+      "arviz 0.11.4 requires typing-extensions<4,>=3.7.4.3, but you have typing-extensions 4.1.1 which is incompatible.\u001b[0m\n"
      ]
     }
    ],
@@ -87,8 +87,9 @@
     "!pip install omegaconf>=2.0.0 pytorch-lightning>=1.0.8 torch-fidelity einops\n",
     "\n",
     "import sys\n",
+    "\n",
     "sys.path.append(\".\")\n",
-    "sys.path.append('./taming-transformers')\n",
+    "sys.path.append(\"./taming-transformers\")\n",
     "from taming.models import vqgan"
    ]
   },
@@ -299,32 +300,35 @@
     "        )\n",
     "\n",
     "        for class_label in classes:\n",
-    "            print(f\"rendering {n_samples_per_class} examples of class '{class_label}' in {ddim_steps} steps and using s={scale:.2f}.\")\n",
+    "            print(\n",
+    "                f\"rendering {n_samples_per_class} examples of class '{class_label}' in {ddim_steps} steps and using s={scale:.2f}.\"\n",
+    "            )\n",
     "            xc = torch.tensor(n_samples_per_class * [class_label])\n",
     "            c = model.get_learned_conditioning({model.cond_stage_key: xc.to(model.device)})\n",
     "\n",
-    "            samples_ddim, _ = sampler.sample(S=ddim_steps,\n",
+    "            samples_ddim, _ = sampler.sample(\n",
+    "                S=ddim_steps,\n",
     "                conditioning=c,\n",
     "                batch_size=n_samples_per_class,\n",
     "                shape=[3, 64, 64],\n",
     "                verbose=False,\n",
     "                unconditional_guidance_scale=scale,\n",
     "                unconditional_conditioning=uc,\n",
-    "                                             eta=ddim_eta)\n",
+    "                eta=ddim_eta,\n",
+    "            )\n",
     "\n",
     "            x_samples_ddim = model.decode_first_stage(samples_ddim)\n",
-    "            x_samples_ddim = torch.clamp((x_samples_ddim+1.0)/2.0, \n",
+    "            x_samples_ddim = torch.clamp((x_samples_ddim + 1.0) / 2.0, min=0.0, max=1.0)\n",
-    "                                         min=0.0, max=1.0)\n",
     "            all_samples.append(x_samples_ddim)\n",
     "\n",
     "\n",
     "# display as grid\n",
     "grid = torch.stack(all_samples, 0)\n",
-    "grid = rearrange(grid, 'n b c h w -> (n b) c h w')\n",
+    "grid = rearrange(grid, \"n b c h w -> (n b) c h w\")\n",
     "grid = make_grid(grid, nrow=n_samples_per_class)\n",
     "\n",
     "# to image\n",
-    "grid = 255. * rearrange(grid, 'c h w -> h w c').cpu().numpy()\n",
+    "grid = 255.0 * rearrange(grid, \"c h w -> h w c\").cpu().numpy()\n",
     "Image.fromarray(grid.astype(np.uint8))"
    ],
    "metadata": {