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Merge branch 'main' into bugfix/convert-script

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Lincoln Stein 2023-07-29 17:30:40 -04:00 committed by GitHub
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8 changed files with 493 additions and 329 deletions
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4
invokeai/app/invocations/params.py
View File

@ -6,8 +6,7 @@ from pydantic import Field
from invokeai.app.invocations.prompt import PromptOutput
from .baseinvocation import (BaseInvocation, BaseInvocationOutput,
InvocationConfig, InvocationContext)
from .baseinvocation import BaseInvocation, BaseInvocationOutput, 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"""

15
invokeai/frontend/web/src/features/gallery/components/CurrentImage/CurrentImageButtons.tsx
View File

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

15
invokeai/frontend/web/src/features/gallery/components/ImageContextMenu/SingleSelectionMenuItems.tsx
View File

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

107
invokeai/frontend/web/src/features/parameters/hooks/useRecallParameters.ts
View File

@ -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,

33
invokeai/frontend/web/src/features/parameters/types/parameterSchemas.ts
View File

@ -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
// */

4
invokeai/frontend/web/src/features/sdxl/components/ParamSDXLConcatButton.tsx
View File

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

6
notebooks/Stable_Diffusion_AI_Notebook.ipynb
View File

@ -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\")"
]

40
scripts/orig_scripts/latent_imagenet_diffusion.ipynb
View File

@ -52,17 +52,17 @@
"name": "stdout",
"text": [
"Cloning into 'latent-diffusion'...\n",
"remote: Enumerating objects: 992, 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: Total 992 (delta 375), reused 564 (delta 253), pack-reused 297\u001B[K\n",
"remote: Enumerating objects: 992, 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: 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: Counting objects: 100% (525/525), 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: Enumerating objects: 1335, 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: 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",
" min=0.0, max=1.0)\n",
" x_samples_ddim = torch.clamp((x_samples_ddim + 1.0) / 2.0, 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": {