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Merge branch 'main' into feat/ui/consistent-param-layout

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blessedcoolant 2023-05-12 15:06:16 +12:00 committed by GitHub
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20 changed files with 160 additions and 132 deletions
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2
invokeai/app/invocations/generate.py
View File

@ -52,7 +52,7 @@ class TextToImageInvocation(BaseInvocation, SDImageInvocation):
width: int = Field(default=512, multiple_of=8, gt=0, description="The width of the resulting image", )
height: int = Field(default=512, multiple_of=8, gt=0, description="The height of the resulting image", )
cfg_scale: float = Field(default=7.5, ge=1, description="The Classifier-Free Guidance, higher values may result in a result closer to the prompt", )
scheduler: SAMPLER_NAME_VALUES = Field(default="k_lms", description="The scheduler to use" )
scheduler: SAMPLER_NAME_VALUES = Field(default="lms", description="The scheduler to use" )
model: str = Field(default="", description="The model to use (currently ignored)")
# fmt: on

38
invokeai/app/invocations/latent.py
View File

@ -17,6 +17,7 @@ from ...backend.stable_diffusion.diffusion.shared_invokeai_diffusion import Post
from ...backend.image_util.seamless import configure_model_padding
from ...backend.prompting.conditioning import get_uc_and_c_and_ec
from ...backend.stable_diffusion.diffusers_pipeline import ConditioningData, StableDiffusionGeneratorPipeline, image_resized_to_grid_as_tensor
from ...backend.stable_diffusion.schedulers import SCHEDULER_MAP
from .baseinvocation import BaseInvocation, BaseInvocationOutput, InvocationContext, InvocationConfig
import numpy as np
from ..services.image_storage import ImageType
@ -52,29 +53,20 @@ class NoiseOutput(BaseInvocationOutput):
#fmt: on
# TODO: this seems like a hack
scheduler_map = dict(
ddim=diffusers.DDIMScheduler,
dpmpp_2=diffusers.DPMSolverMultistepScheduler,
k_dpm_2=diffusers.KDPM2DiscreteScheduler,
k_dpm_2_a=diffusers.KDPM2AncestralDiscreteScheduler,
k_dpmpp_2=diffusers.DPMSolverMultistepScheduler,
k_euler=diffusers.EulerDiscreteScheduler,
k_euler_a=diffusers.EulerAncestralDiscreteScheduler,
k_heun=diffusers.HeunDiscreteScheduler,
k_lms=diffusers.LMSDiscreteScheduler,
plms=diffusers.PNDMScheduler,
)
SAMPLER_NAME_VALUES = Literal[
tuple(list(scheduler_map.keys()))
tuple(list(SCHEDULER_MAP.keys()))
]
def get_scheduler(scheduler_name:str, model: StableDiffusionGeneratorPipeline)->Scheduler:
scheduler_class = scheduler_map.get(scheduler_name,'ddim')
scheduler = scheduler_class.from_config(model.scheduler.config)
scheduler_class, scheduler_extra_config = SCHEDULER_MAP.get(scheduler_name, SCHEDULER_MAP['ddim'])
scheduler_config = model.scheduler.config
if "_backup" in scheduler_config:
scheduler_config = scheduler_config["_backup"]
scheduler_config = {**scheduler_config, **scheduler_extra_config, "_backup": scheduler_config}
scheduler = scheduler_class.from_config(scheduler_config)
# hack copied over from generate.py
if not hasattr(scheduler, 'uses_inpainting_model'):
scheduler.uses_inpainting_model = lambda: False
@ -148,7 +140,7 @@ class TextToLatentsInvocation(BaseInvocation):
noise: Optional[LatentsField] = Field(description="The noise to use")
steps: int = Field(default=10, gt=0, description="The number of steps to use to generate the image")
cfg_scale: float = Field(default=7.5, gt=0, description="The Classifier-Free Guidance, higher values may result in a result closer to the prompt", )
scheduler: SAMPLER_NAME_VALUES = Field(default="k_lms", description="The scheduler to use" )
scheduler: SAMPLER_NAME_VALUES = Field(default="lms", description="The scheduler to use" )
model: str = Field(default="", description="The model to use (currently ignored)")
seamless: bool = Field(default=False, description="Whether or not to generate an image that can tile without seams", )
seamless_axes: str = Field(default="", description="The axes to tile the image on, 'x' and/or 'y'")
@ -216,7 +208,7 @@ class TextToLatentsInvocation(BaseInvocation):
h_symmetry_time_pct=None,#h_symmetry_time_pct,
v_symmetry_time_pct=None#v_symmetry_time_pct,
),
).add_scheduler_args_if_applicable(model.scheduler, eta=None)#ddim_eta)
).add_scheduler_args_if_applicable(model.scheduler, eta=0.0)#ddim_eta)
return conditioning_data
@ -293,11 +285,7 @@ class LatentsToLatentsInvocation(TextToLatentsInvocation):
latent, device=model.device, dtype=latent.dtype
)
timesteps, _ = model.get_img2img_timesteps(
self.steps,
self.strength,
device=model.device,
)
timesteps, _ = model.get_img2img_timesteps(self.steps, self.strength)
result_latents, result_attention_map_saver = model.latents_from_embeddings(
latents=initial_latents,

25
invokeai/backend/args.py
View File

@ -108,17 +108,20 @@ APP_VERSION = invokeai.version.__version__
SAMPLER_CHOICES = [
"ddim",
"k_dpm_2_a",
"k_dpm_2",
"k_dpmpp_2_a",
"k_dpmpp_2",
"k_euler_a",
"k_euler",
"k_heun",
"k_lms",
"plms",
# diffusers:
"ddpm",
"deis",
"lms",
"pndm",
"heun",
"euler",
"euler_k",
"euler_a",
"kdpm_2",
"kdpm_2_a",
"dpmpp_2s",
"dpmpp_2m",
"dpmpp_2m_k",
"unipc",
]
PRECISION_CHOICES = [
@ -631,7 +634,7 @@ class Args(object):
choices=SAMPLER_CHOICES,
metavar="SAMPLER_NAME",
help=f'Set the default sampler. Supported samplers: {", ".join(SAMPLER_CHOICES)}',
default="k_lms",
default="lms",
)
render_group.add_argument(
"--log_tokenization",

26
invokeai/backend/generate.py
View File

@ -37,6 +37,7 @@ from .safety_checker import SafetyChecker
from .prompting import get_uc_and_c_and_ec
from .prompting.conditioning import log_tokenization
from .stable_diffusion import HuggingFaceConceptsLibrary
from .stable_diffusion.schedulers import SCHEDULER_MAP
from .util import choose_precision, choose_torch_device
def fix_func(orig):
@ -141,7 +142,7 @@ class Generate:
model=None,
conf="configs/models.yaml",
embedding_path=None,
sampler_name="k_lms",
sampler_name="lms",
ddim_eta=0.0, # deterministic
full_precision=False,
precision="auto",
@ -1047,29 +1048,12 @@ class Generate:
def _set_scheduler(self):
default = self.model.scheduler
# See https://github.com/huggingface/diffusers/issues/277#issuecomment-1371428672
scheduler_map = dict(
ddim=diffusers.DDIMScheduler,
dpmpp_2=diffusers.DPMSolverMultistepScheduler,
k_dpm_2=diffusers.KDPM2DiscreteScheduler,
k_dpm_2_a=diffusers.KDPM2AncestralDiscreteScheduler,
# DPMSolverMultistepScheduler is technically not `k_` anything, as it is neither
# the k-diffusers implementation nor included in EDM (Karras 2022), but we can
# provide an alias for compatibility.
k_dpmpp_2=diffusers.DPMSolverMultistepScheduler,
k_euler=diffusers.EulerDiscreteScheduler,
k_euler_a=diffusers.EulerAncestralDiscreteScheduler,
k_heun=diffusers.HeunDiscreteScheduler,
k_lms=diffusers.LMSDiscreteScheduler,
plms=diffusers.PNDMScheduler,
)
if self.sampler_name in scheduler_map:
sampler_class = scheduler_map[self.sampler_name]
if self.sampler_name in SCHEDULER_MAP:
sampler_class, sampler_extra_config = SCHEDULER_MAP[self.sampler_name]
msg = (
f"Setting Sampler to {self.sampler_name} ({sampler_class.__name__})"
)
self.sampler = sampler_class.from_config(self.model.scheduler.config)
self.sampler = sampler_class.from_config({**self.model.scheduler.config, **sampler_extra_config})
else:
msg = (
f" Unsupported Sampler: {self.sampler_name} "+

26
invokeai/backend/generator/base.py
View File

@ -31,6 +31,7 @@ from ..util.util import rand_perlin_2d
from ..safety_checker import SafetyChecker
from ..prompting.conditioning import get_uc_and_c_and_ec
from ..stable_diffusion.diffusers_pipeline import StableDiffusionGeneratorPipeline
from ..stable_diffusion.schedulers import SCHEDULER_MAP
downsampling = 8
@ -71,19 +72,6 @@ class InvokeAIGeneratorOutput:
# we are interposing a wrapper around the original Generator classes so that
# old code that calls Generate will continue to work.
class InvokeAIGenerator(metaclass=ABCMeta):
scheduler_map = dict(
ddim=diffusers.DDIMScheduler,
dpmpp_2=diffusers.DPMSolverMultistepScheduler,
k_dpm_2=diffusers.KDPM2DiscreteScheduler,
k_dpm_2_a=diffusers.KDPM2AncestralDiscreteScheduler,
k_dpmpp_2=diffusers.DPMSolverMultistepScheduler,
k_euler=diffusers.EulerDiscreteScheduler,
k_euler_a=diffusers.EulerAncestralDiscreteScheduler,
k_heun=diffusers.HeunDiscreteScheduler,
k_lms=diffusers.LMSDiscreteScheduler,
plms=diffusers.PNDMScheduler,
)
def __init__(self,
model_info: dict,
params: InvokeAIGeneratorBasicParams=InvokeAIGeneratorBasicParams(),
@ -175,14 +163,20 @@ class InvokeAIGenerator(metaclass=ABCMeta):
'''
Return list of all the schedulers that we currently handle.
'''
return list(self.scheduler_map.keys())
return list(SCHEDULER_MAP.keys())
def load_generator(self, model: StableDiffusionGeneratorPipeline, generator_class: Type[Generator]):
return generator_class(model, self.params.precision)
def get_scheduler(self, scheduler_name:str, model: StableDiffusionGeneratorPipeline)->Scheduler:
scheduler_class = self.scheduler_map.get(scheduler_name,'ddim')
scheduler = scheduler_class.from_config(model.scheduler.config)
scheduler_class, scheduler_extra_config = SCHEDULER_MAP.get(scheduler_name, SCHEDULER_MAP['ddim'])
scheduler_config = model.scheduler.config
if "_backup" in scheduler_config:
scheduler_config = scheduler_config["_backup"]
scheduler_config = {**scheduler_config, **scheduler_extra_config, "_backup": scheduler_config}
scheduler = scheduler_class.from_config(scheduler_config)
# hack copied over from generate.py
if not hasattr(scheduler, 'uses_inpainting_model'):
scheduler.uses_inpainting_model = lambda: False

3
invokeai/backend/model_management/convert_ckpt_to_diffusers.py
View File

@ -47,6 +47,7 @@ from diffusers import (
LDMTextToImagePipeline,
LMSDiscreteScheduler,
PNDMScheduler,
UniPCMultistepScheduler,
StableDiffusionPipeline,
UNet2DConditionModel,
)
@ -1209,6 +1210,8 @@ def load_pipeline_from_original_stable_diffusion_ckpt(
scheduler = EulerAncestralDiscreteScheduler.from_config(scheduler.config)
elif scheduler_type == "dpm":
scheduler = DPMSolverMultistepScheduler.from_config(scheduler.config)
elif scheduler_type == 'unipc':
scheduler = UniPCMultistepScheduler.from_config(scheduler.config)
elif scheduler_type == "ddim":
scheduler = scheduler
else:

2
invokeai/backend/model_management/model_manager.py
View File

@ -1228,7 +1228,7 @@ class ModelManager(object):
sha.update(chunk)
hash = sha.hexdigest()
toc = time.time()
self.logger.debug(f"sha256 = {hash} ({count} files hashed in", "%4.2fs)" % (toc - tic))
self.logger.debug(f"sha256 = {hash} ({count} files hashed in {toc - tic:4.2f}s)")
with open(hashpath, "w") as f:
f.write(hash)
return hash

31
invokeai/backend/stable_diffusion/diffusers_pipeline.py
View File

@ -509,10 +509,13 @@ class StableDiffusionGeneratorPipeline(StableDiffusionPipeline):
run_id=None,
callback: Callable[[PipelineIntermediateState], None] = None,
) -> tuple[torch.Tensor, Optional[AttentionMapSaver]]:
if self.scheduler.config.get("cpu_only", False):
scheduler_device = torch.device('cpu')
else:
scheduler_device = self._model_group.device_for(self.unet)
if timesteps is None:
self.scheduler.set_timesteps(
num_inference_steps, device=self._model_group.device_for(self.unet)
)
self.scheduler.set_timesteps(num_inference_steps, device=scheduler_device)
timesteps = self.scheduler.timesteps
infer_latents_from_embeddings = GeneratorToCallbackinator(
self.generate_latents_from_embeddings, PipelineIntermediateState
@ -726,11 +729,7 @@ class StableDiffusionGeneratorPipeline(StableDiffusionPipeline):
run_id=None,
callback=None,
) -> InvokeAIStableDiffusionPipelineOutput:
timesteps, _ = self.get_img2img_timesteps(
num_inference_steps,
strength,
device=self._model_group.device_for(self.unet),
)
timesteps, _ = self.get_img2img_timesteps(num_inference_steps, strength)
result_latents, result_attention_maps = self.latents_from_embeddings(
latents=initial_latents if strength < 1.0 else torch.zeros_like(
initial_latents, device=initial_latents.device, dtype=initial_latents.dtype
@ -756,13 +755,19 @@ class StableDiffusionGeneratorPipeline(StableDiffusionPipeline):
return self.check_for_safety(output, dtype=conditioning_data.dtype)
def get_img2img_timesteps(
self, num_inference_steps: int, strength: float, device
self, num_inference_steps: int, strength: float, device=None
) -> (torch.Tensor, int):
img2img_pipeline = StableDiffusionImg2ImgPipeline(**self.components)
assert img2img_pipeline.scheduler is self.scheduler
img2img_pipeline.scheduler.set_timesteps(num_inference_steps, device=device)
if self.scheduler.config.get("cpu_only", False):
scheduler_device = torch.device('cpu')
else:
scheduler_device = self._model_group.device_for(self.unet)
img2img_pipeline.scheduler.set_timesteps(num_inference_steps, device=scheduler_device)
timesteps, adjusted_steps = img2img_pipeline.get_timesteps(
num_inference_steps, strength, device=device
num_inference_steps, strength, device=scheduler_device
)
# Workaround for low strength resulting in zero timesteps.
# TODO: submit upstream fix for zero-step img2img
@ -796,9 +801,7 @@ class StableDiffusionGeneratorPipeline(StableDiffusionPipeline):
if init_image.dim() == 3:
init_image = init_image.unsqueeze(0)
timesteps, _ = self.get_img2img_timesteps(
num_inference_steps, strength, device=device
)
timesteps, _ = self.get_img2img_timesteps(num_inference_steps, strength)
# 6. Prepare latent variables
# can't quite use upstream StableDiffusionImg2ImgPipeline.prepare_latents

1
invokeai/backend/stable_diffusion/schedulers/__init__.py Normal file
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@ -0,0 +1 @@
from .schedulers import SCHEDULER_MAP

22
invokeai/backend/stable_diffusion/schedulers/schedulers.py Normal file
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@ -0,0 +1,22 @@
from diffusers import DDIMScheduler, DPMSolverMultistepScheduler, KDPM2DiscreteScheduler, \
KDPM2AncestralDiscreteScheduler, EulerDiscreteScheduler, EulerAncestralDiscreteScheduler, \
HeunDiscreteScheduler, LMSDiscreteScheduler, PNDMScheduler, UniPCMultistepScheduler, \
DPMSolverSinglestepScheduler, DEISMultistepScheduler, DDPMScheduler
SCHEDULER_MAP = dict(
ddim=(DDIMScheduler, dict()),
ddpm=(DDPMScheduler, dict()),
deis=(DEISMultistepScheduler, dict()),
lms=(LMSDiscreteScheduler, dict()),
pndm=(PNDMScheduler, dict()),
heun=(HeunDiscreteScheduler, dict()),
euler=(EulerDiscreteScheduler, dict(use_karras_sigmas=False)),
euler_k=(EulerDiscreteScheduler, dict(use_karras_sigmas=True)),
euler_a=(EulerAncestralDiscreteScheduler, dict()),
kdpm_2=(KDPM2DiscreteScheduler, dict()),
kdpm_2_a=(KDPM2AncestralDiscreteScheduler, dict()),
dpmpp_2s=(DPMSolverSinglestepScheduler, dict()),
dpmpp_2m=(DPMSolverMultistepScheduler, dict(use_karras_sigmas=False)),
dpmpp_2m_k=(DPMSolverMultistepScheduler, dict(use_karras_sigmas=True)),
unipc=(UniPCMultistepScheduler, dict(cpu_only=True))
)

23
invokeai/backend/web/modules/parameters.py
View File

@ -4,17 +4,20 @@ from .parse_seed_weights import parse_seed_weights
SAMPLER_CHOICES = [
"ddim",
"k_dpm_2_a",
"k_dpm_2",
"k_dpmpp_2_a",
"k_dpmpp_2",
"k_euler_a",
"k_euler",
"k_heun",
"k_lms",
"plms",
# diffusers:
"ddpm",
"deis",
"lms",
"pndm",
"heun",
"euler",
"euler_k",
"euler_a",
"kdpm_2",
"kdpm_2_a",
"dpmpp_2s",
"dpmpp_2m",
"dpmpp_2m_k",
"unipc",
]

2
invokeai/frontend/web/docs/README.md
View File

@ -37,7 +37,7 @@ From `invokeai/frontend/web/` run `yarn install` to get everything set up.
Start everything in dev mode:
1. Start the dev server: `yarn dev`
2. Start the InvokeAI UI per usual: `invokeai --web`
2. Start the InvokeAI Nodes backend: `python scripts/invokeai-new.py --web # run from the repo root`
3. Point your browser to the dev server address e.g. <http://localhost:5173/>
### Production builds

29
invokeai/frontend/web/src/app/constants.ts
View File

@ -2,17 +2,28 @@
export const DIFFUSERS_SCHEDULERS: Array<string> = [
'ddim',
'plms',
'k_lms',
'dpmpp_2',
'k_dpm_2',
'k_dpm_2_a',
'k_dpmpp_2',
'k_euler',
'k_euler_a',
'k_heun',
'ddpm',
'deis',
'lms',
'pndm',
'heun',
'euler',
'euler_k',
'euler_a',
'kdpm_2',
'kdpm_2_a',
'dpmpp_2s',
'dpmpp_2m',
'dpmpp_2m_k',
'unipc',
];
export const IMG2IMG_DIFFUSERS_SCHEDULERS = DIFFUSERS_SCHEDULERS.filter(
(scheduler) => {
return scheduler !== 'dpmpp_2s';
}
);
// Valid image widths
export const WIDTHS: Array<number> = Array.from(Array(64)).map(
(_x, i) => (i + 1) * 64

23
invokeai/frontend/web/src/app/types/invokeai.ts
View File

@ -47,15 +47,20 @@ export type CommonGeneratedImageMetadata = {
postprocessing: null | Array<ESRGANMetadata | FacetoolMetadata>;
sampler:
| 'ddim'
| 'k_dpm_2_a'
| 'k_dpm_2'
| 'k_dpmpp_2_a'
| 'k_dpmpp_2'
| 'k_euler_a'
| 'k_euler'
| 'k_heun'
| 'k_lms'
| 'plms';
| 'ddpm'
| 'deis'
| 'lms'
| 'pndm'
| 'heun'
| 'euler'
| 'euler_k'
| 'euler_a'
| 'kdpm_2'
| 'kdpm_2_a'
| 'dpmpp_2s'
| 'dpmpp_2m'
| 'dpmpp_2m_k'
| 'unipc';
prompt: Prompt;
seed: number;
variations: SeedWeights;

2
invokeai/frontend/web/src/features/nodes/examples/iterationGraph.ts
View File

@ -20,7 +20,7 @@ export const iterationGraph = {
model: '',
progress_images: false,
prompt: 'dog',
sampler_name: 'k_lms',
sampler_name: 'lms',
seamless: false,
steps: 11,
type: 'txt2img',

15
invokeai/frontend/web/src/features/parameters/components/Parameters/Core/ParamSampler.tsx
View File

@ -1,8 +1,12 @@
import { DIFFUSERS_SCHEDULERS } from 'app/constants';
import {
DIFFUSERS_SCHEDULERS,
IMG2IMG_DIFFUSERS_SCHEDULERS,
} from 'app/constants';
import { RootState } from 'app/store/store';
import { useAppDispatch, useAppSelector } from 'app/store/storeHooks';
import IAISelect from 'common/components/IAISelect';
import { setSampler } from 'features/parameters/store/generationSlice';
import { activeTabNameSelector } from 'features/ui/store/uiSelectors';
import { ChangeEvent, memo, useCallback } from 'react';
import { useTranslation } from 'react-i18next';
@ -10,6 +14,9 @@ const ParamSampler = () => {
const sampler = useAppSelector(
(state: RootState) => state.generation.sampler
);
const activeTabName = useAppSelector(activeTabNameSelector);
const dispatch = useAppDispatch();
const { t } = useTranslation();
@ -23,7 +30,11 @@ const ParamSampler = () => {
label={t('parameters.sampler')}
value={sampler}
onChange={handleChange}
validValues={DIFFUSERS_SCHEDULERS}
validValues={
activeTabName === 'img2img' || activeTabName == 'unifiedCanvas'
? IMG2IMG_DIFFUSERS_SCHEDULERS
: DIFFUSERS_SCHEDULERS
}
minWidth={36}
/>
);

2
invokeai/frontend/web/src/features/parameters/store/generationSlice.ts
View File

@ -51,7 +51,7 @@ export const initialGenerationState: GenerationState = {
perlin: 0,
prompt: '',
negativePrompt: '',
sampler: 'k_lms',
sampler: 'lms',
seamBlur: 16,
seamSize: 96,
seamSteps: 30,

2
invokeai/frontend/web/src/i18n.ts
View File

@ -3,7 +3,7 @@ import LanguageDetector from 'i18next-browser-languagedetector';
import Backend from 'i18next-http-backend';
import { initReactI18next } from 'react-i18next';
import translationEN from '../dist/locales/en.json';
import translationEN from '../public/locales/en.json';
import { LOCALSTORAGE_PREFIX } from 'app/store/constants';
if (import.meta.env.MODE === 'package') {

2
tests/inpainting/original.json
View File

@ -23,7 +23,7 @@
],
"threshold": 0,
"postprocessing": null,
"sampler": "k_lms",
"sampler": "lms",
"variations": [],
"type": "txt2img"
}

2
tests/nodes/test_png_metadata_service.py
View File

@ -17,7 +17,7 @@ valid_metadata = {
"width": 512,
"height": 512,
"cfg_scale": 7.5,
"scheduler": "k_lms",
"scheduler": "lms",
"model": "stable-diffusion-1.5",
},
}