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Merge branch 'main' into release/invokeai-3-0-1

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This commit is contained in:
Lincoln Stein 2023-07-27 15:21:08 -04:00
commit 006075483d
9 changed files with 173 additions and 157 deletions
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2
README.md
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@ -123,7 +123,7 @@ and go to http://localhost:9090.
### Command-Line Installation (for developers and users familiar with Terminals) ### Command-Line Installation (for developers and users familiar with Terminals)
You must have Python 3.9 or 3.10 installed on your machine. Earlier or You must have Python 3.9 through 3.11 installed on your machine. Earlier or
later versions are not supported. later versions are not supported.
Node.js also needs to be installed along with yarn (can be installed with Node.js also needs to be installed along with yarn (can be installed with
the command `npm install -g yarn` if needed) the command `npm install -g yarn` if needed)

6
docs/installation/010_INSTALL_AUTOMATED.md
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@ -40,10 +40,8 @@ experimental versions later.
this, open up a command-line window ("Terminal" on Linux and this, open up a command-line window ("Terminal" on Linux and
Macintosh, "Command" or "Powershell" on Windows) and type `python Macintosh, "Command" or "Powershell" on Windows) and type `python
--version`. If Python is installed, it will print out the version --version`. If Python is installed, it will print out the version
number. If it is version `3.9.*` or `3.10.*`, you meet number. If it is version `3.9.*`, `3.10.*` or `3.11.*` you meet
requirements. We do not recommend using Python 3.11 or higher, requirements.
as not all the libraries that InvokeAI depends on work properly
with this version.
!!! warning "What to do if you have an unsupported version" !!! warning "What to do if you have an unsupported version"

4
docs/installation/020_INSTALL_MANUAL.md
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@ -32,7 +32,7 @@ gaming):
* **Python** * **Python**
version 3.9 or 3.10 (3.11 is not recommended). version 3.9 through 3.11
* **CUDA Tools** * **CUDA Tools**
@ -65,7 +65,7 @@ gaming):
To install InvokeAI with virtual environments and the PIP package To install InvokeAI with virtual environments and the PIP package
manager, please follow these steps: manager, please follow these steps:
1. Please make sure you are using Python 3.9 or 3.10. The rest of the install 1. Please make sure you are using Python 3.9 through 3.11. The rest of the install
procedure depends on this and will not work with other versions: procedure depends on this and will not work with other versions:
```bash ```bash

16
installer/install.sh.in
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@ -9,16 +9,20 @@ cd $scriptdir
function version { echo "$@" | awk -F. '{ printf("%d%03d%03d%03d\n", $1,$2,$3,$4); }'; } function version { echo "$@" | awk -F. '{ printf("%d%03d%03d%03d\n", $1,$2,$3,$4); }'; }
MINIMUM_PYTHON_VERSION=3.9.0 MINIMUM_PYTHON_VERSION=3.9.0
MAXIMUM_PYTHON_VERSION=3.11.0 MAXIMUM_PYTHON_VERSION=3.11.100
PYTHON="" PYTHON=""
for candidate in python3.10 python3.9 python3 python ; do for candidate in python3.11 python3.10 python3.9 python3 python ; do
if ppath=`which $candidate`; then if ppath=`which $candidate`; then
# when using `pyenv`, the executable for an inactive Python version will exist but will not be operational
# we check that this found executable can actually run
if [ $($candidate --version &>/dev/null; echo ${PIPESTATUS}) -gt 0 ]; then continue; fi
python_version=$($ppath -V | awk '{ print $2 }') python_version=$($ppath -V | awk '{ print $2 }')
if [ $(version $python_version) -ge $(version "$MINIMUM_PYTHON_VERSION") ]; then if [ $(version $python_version) -ge $(version "$MINIMUM_PYTHON_VERSION") ]; then
if [ $(version $python_version) -lt $(version "$MAXIMUM_PYTHON_VERSION") ]; then if [ $(version $python_version) -le $(version "$MAXIMUM_PYTHON_VERSION") ]; then
PYTHON=$ppath PYTHON=$ppath
break break
fi fi
fi fi
fi fi
done done

2
invokeai/app/api/routers/models.py
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@ -90,7 +90,7 @@ async def update_model(
new_name=info.model_name, new_name=info.model_name,
new_base=info.base_model, new_base=info.base_model,
) )
logger.info(f"Successfully renamed {base_model}/{model_name}=>{info.base_model}/{info.model_name}") logger.info(f"Successfully renamed {base_model.value}/{model_name}=>{info.base_model}/{info.model_name}")
# update information to support an update of attributes # update information to support an update of attributes
model_name = info.model_name model_name = info.model_name
base_model = info.base_model base_model = info.base_model

242
invokeai/app/invocations/latent.py
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@ -12,7 +12,7 @@ from pydantic import BaseModel, Field, validator
from invokeai.app.invocations.metadata import CoreMetadata from invokeai.app.invocations.metadata import CoreMetadata
from invokeai.app.util.step_callback import stable_diffusion_step_callback from invokeai.app.util.step_callback import stable_diffusion_step_callback
from invokeai.backend.model_management.models.base import ModelType from invokeai.backend.model_management.models import ModelType, SilenceWarnings
from ...backend.model_management.lora import ModelPatcher from ...backend.model_management.lora import ModelPatcher
from ...backend.stable_diffusion import PipelineIntermediateState from ...backend.stable_diffusion import PipelineIntermediateState
@ -311,70 +311,71 @@ class TextToLatentsInvocation(BaseInvocation):
@torch.no_grad() @torch.no_grad()
def invoke(self, context: InvocationContext) -> LatentsOutput: def invoke(self, context: InvocationContext) -> LatentsOutput:
noise = context.services.latents.get(self.noise.latents_name) with SilenceWarnings():
noise = context.services.latents.get(self.noise.latents_name)
# Get the source node id (we are invoking the prepared node) # Get the source node id (we are invoking the prepared node)
graph_execution_state = context.services.graph_execution_manager.get(context.graph_execution_state_id) graph_execution_state = context.services.graph_execution_manager.get(context.graph_execution_state_id)
source_node_id = graph_execution_state.prepared_source_mapping[self.id] source_node_id = graph_execution_state.prepared_source_mapping[self.id]
def step_callback(state: PipelineIntermediateState): def step_callback(state: PipelineIntermediateState):
self.dispatch_progress(context, source_node_id, state) self.dispatch_progress(context, source_node_id, state)
def _lora_loader(): def _lora_loader():
for lora in self.unet.loras: for lora in self.unet.loras:
lora_info = context.services.model_manager.get_model( lora_info = context.services.model_manager.get_model(
**lora.dict(exclude={"weight"}), **lora.dict(exclude={"weight"}),
context=context,
)
yield (lora_info.context.model, lora.weight)
del lora_info
return
unet_info = context.services.model_manager.get_model(
**self.unet.unet.dict(),
context=context,
)
with ExitStack() as exit_stack, ModelPatcher.apply_lora_unet(
unet_info.context.model, _lora_loader()
), unet_info as unet:
noise = noise.to(device=unet.device, dtype=unet.dtype)
scheduler = get_scheduler(
context=context, context=context,
scheduler_info=self.unet.scheduler,
scheduler_name=self.scheduler,
) )
yield (lora_info.context.model, lora.weight)
del lora_info
return
unet_info = context.services.model_manager.get_model( pipeline = self.create_pipeline(unet, scheduler)
**self.unet.unet.dict(), conditioning_data = self.get_conditioning_data(context, scheduler, unet)
context=context,
)
with ExitStack() as exit_stack, ModelPatcher.apply_lora_unet(
unet_info.context.model, _lora_loader()
), unet_info as unet:
noise = noise.to(device=unet.device, dtype=unet.dtype)
scheduler = get_scheduler( control_data = self.prep_control_data(
context=context, model=pipeline,
scheduler_info=self.unet.scheduler, context=context,
scheduler_name=self.scheduler, control_input=self.control,
) latents_shape=noise.shape,
# do_classifier_free_guidance=(self.cfg_scale >= 1.0))
do_classifier_free_guidance=True,
exit_stack=exit_stack,
)
pipeline = self.create_pipeline(unet, scheduler) # TODO: Verify the noise is the right size
conditioning_data = self.get_conditioning_data(context, scheduler, unet) result_latents, result_attention_map_saver = pipeline.latents_from_embeddings(
latents=torch.zeros_like(noise, dtype=torch_dtype(unet.device)),
noise=noise,
num_inference_steps=self.steps,
conditioning_data=conditioning_data,
control_data=control_data, # list[ControlNetData]
callback=step_callback,
)
control_data = self.prep_control_data( # https://discuss.huggingface.co/t/memory-usage-by-later-pipeline-stages/23699
model=pipeline, result_latents = result_latents.to("cpu")
context=context, torch.cuda.empty_cache()
control_input=self.control,
latents_shape=noise.shape,
# do_classifier_free_guidance=(self.cfg_scale >= 1.0))
do_classifier_free_guidance=True,
exit_stack=exit_stack,
)
# TODO: Verify the noise is the right size name = f"{context.graph_execution_state_id}__{self.id}"
result_latents, result_attention_map_saver = pipeline.latents_from_embeddings( context.services.latents.save(name, result_latents)
latents=torch.zeros_like(noise, dtype=torch_dtype(unet.device)), return build_latents_output(latents_name=name, latents=result_latents)
noise=noise,
num_inference_steps=self.steps,
conditioning_data=conditioning_data,
control_data=control_data, # list[ControlNetData]
callback=step_callback,
)
# https://discuss.huggingface.co/t/memory-usage-by-later-pipeline-stages/23699
result_latents = result_latents.to("cpu")
torch.cuda.empty_cache()
name = f"{context.graph_execution_state_id}__{self.id}"
context.services.latents.save(name, result_latents)
return build_latents_output(latents_name=name, latents=result_latents)
class LatentsToLatentsInvocation(TextToLatentsInvocation): class LatentsToLatentsInvocation(TextToLatentsInvocation):
@ -402,82 +403,83 @@ class LatentsToLatentsInvocation(TextToLatentsInvocation):
@torch.no_grad() @torch.no_grad()
def invoke(self, context: InvocationContext) -> LatentsOutput: def invoke(self, context: InvocationContext) -> LatentsOutput:
noise = context.services.latents.get(self.noise.latents_name) with SilenceWarnings(): # this quenches NSFW nag from diffusers
latent = context.services.latents.get(self.latents.latents_name) noise = context.services.latents.get(self.noise.latents_name)
latent = context.services.latents.get(self.latents.latents_name)
# Get the source node id (we are invoking the prepared node) # Get the source node id (we are invoking the prepared node)
graph_execution_state = context.services.graph_execution_manager.get(context.graph_execution_state_id) graph_execution_state = context.services.graph_execution_manager.get(context.graph_execution_state_id)
source_node_id = graph_execution_state.prepared_source_mapping[self.id] source_node_id = graph_execution_state.prepared_source_mapping[self.id]
def step_callback(state: PipelineIntermediateState): def step_callback(state: PipelineIntermediateState):
self.dispatch_progress(context, source_node_id, state) self.dispatch_progress(context, source_node_id, state)
def _lora_loader(): def _lora_loader():
for lora in self.unet.loras: for lora in self.unet.loras:
lora_info = context.services.model_manager.get_model( lora_info = context.services.model_manager.get_model(
**lora.dict(exclude={"weight"}), **lora.dict(exclude={"weight"}),
context=context,
)
yield (lora_info.context.model, lora.weight)
del lora_info
return
unet_info = context.services.model_manager.get_model(
**self.unet.unet.dict(),
context=context,
)
with ExitStack() as exit_stack, ModelPatcher.apply_lora_unet(
unet_info.context.model, _lora_loader()
), unet_info as unet:
noise = noise.to(device=unet.device, dtype=unet.dtype)
latent = latent.to(device=unet.device, dtype=unet.dtype)
scheduler = get_scheduler(
context=context, context=context,
scheduler_info=self.unet.scheduler,
scheduler_name=self.scheduler,
) )
yield (lora_info.context.model, lora.weight)
del lora_info
return
unet_info = context.services.model_manager.get_model( pipeline = self.create_pipeline(unet, scheduler)
**self.unet.unet.dict(), conditioning_data = self.get_conditioning_data(context, scheduler, unet)
context=context,
)
with ExitStack() as exit_stack, ModelPatcher.apply_lora_unet(
unet_info.context.model, _lora_loader()
), unet_info as unet:
noise = noise.to(device=unet.device, dtype=unet.dtype)
latent = latent.to(device=unet.device, dtype=unet.dtype)
scheduler = get_scheduler( control_data = self.prep_control_data(
context=context, model=pipeline,
scheduler_info=self.unet.scheduler, context=context,
scheduler_name=self.scheduler, control_input=self.control,
) latents_shape=noise.shape,
# do_classifier_free_guidance=(self.cfg_scale >= 1.0))
do_classifier_free_guidance=True,
exit_stack=exit_stack,
)
pipeline = self.create_pipeline(unet, scheduler) # TODO: Verify the noise is the right size
conditioning_data = self.get_conditioning_data(context, scheduler, unet) initial_latents = (
latent if self.strength < 1.0 else torch.zeros_like(latent, device=unet.device, dtype=latent.dtype)
)
control_data = self.prep_control_data( timesteps, _ = pipeline.get_img2img_timesteps(
model=pipeline, self.steps,
context=context, self.strength,
control_input=self.control, device=unet.device,
latents_shape=noise.shape, )
# do_classifier_free_guidance=(self.cfg_scale >= 1.0))
do_classifier_free_guidance=True,
exit_stack=exit_stack,
)
# TODO: Verify the noise is the right size result_latents, result_attention_map_saver = pipeline.latents_from_embeddings(
initial_latents = ( latents=initial_latents,
latent if self.strength < 1.0 else torch.zeros_like(latent, device=unet.device, dtype=latent.dtype) timesteps=timesteps,
) noise=noise,
num_inference_steps=self.steps,
conditioning_data=conditioning_data,
control_data=control_data, # list[ControlNetData]
callback=step_callback,
)
timesteps, _ = pipeline.get_img2img_timesteps( # https://discuss.huggingface.co/t/memory-usage-by-later-pipeline-stages/23699
self.steps, result_latents = result_latents.to("cpu")
self.strength, torch.cuda.empty_cache()
device=unet.device,
)
result_latents, result_attention_map_saver = pipeline.latents_from_embeddings( name = f"{context.graph_execution_state_id}__{self.id}"
latents=initial_latents, context.services.latents.save(name, result_latents)
timesteps=timesteps,
noise=noise,
num_inference_steps=self.steps,
conditioning_data=conditioning_data,
control_data=control_data, # list[ControlNetData]
callback=step_callback,
)
# https://discuss.huggingface.co/t/memory-usage-by-later-pipeline-stages/23699
result_latents = result_latents.to("cpu")
torch.cuda.empty_cache()
name = f"{context.graph_execution_state_id}__{self.id}"
context.services.latents.save(name, result_latents)
return build_latents_output(latents_name=name, latents=result_latents) return build_latents_output(latents_name=name, latents=result_latents)
@ -490,7 +492,7 @@ class LatentsToImageInvocation(BaseInvocation):
# Inputs # Inputs
latents: Optional[LatentsField] = Field(description="The latents to generate an image from") latents: Optional[LatentsField] = Field(description="The latents to generate an image from")
vae: VaeField = Field(default=None, description="Vae submodel") vae: VaeField = Field(default=None, description="Vae submodel")
tiled: bool = Field(default=False, description="Decode latents by overlapping tiles(less memory consumption)") tiled: bool = Field(default=False, description="Decode latents by overlaping tiles (less memory consumption)")
fp32: bool = Field(DEFAULT_PRECISION == "float32", description="Decode in full precision") fp32: bool = Field(DEFAULT_PRECISION == "float32", description="Decode in full precision")
metadata: Optional[CoreMetadata] = Field( metadata: Optional[CoreMetadata] = Field(
default=None, description="Optional core metadata to be written to the image" default=None, description="Optional core metadata to be written to the image"

6
invokeai/backend/model_management/model_manager.py
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@ -401,7 +401,11 @@ class ModelManager(object):
base_model: BaseModelType, base_model: BaseModelType,
model_type: ModelType, model_type: ModelType,
) -> str: ) -> str:
return f"{base_model}/{model_type}/{model_name}" # In 3.11, the behavior of (str,enum) when interpolated into a
# string has changed. The next two lines are defensive.
base_model = BaseModelType(base_model)
model_type = ModelType(model_type)
return f"{base_model.value}/{model_type.value}/{model_name}"
@classmethod @classmethod
def parse_key(cls, model_key: str) -> Tuple[str, BaseModelType, ModelType]: def parse_key(cls, model_key: str) -> Tuple[str, BaseModelType, ModelType]:

42
pyproject.toml
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@ -5,7 +5,7 @@ build-backend = "setuptools.build_meta"
[project] [project]
name = "InvokeAI" name = "InvokeAI"
description = "An implementation of Stable Diffusion which provides various new features and options to aid the image generation process" description = "An implementation of Stable Diffusion which provides various new features and options to aid the image generation process"
requires-python = ">=3.9, <3.11" requires-python = ">=3.9, <3.12"
readme = { content-type = "text/markdown", file = "README.md" } readme = { content-type = "text/markdown", file = "README.md" }
keywords = ["stable-diffusion", "AI"] keywords = ["stable-diffusion", "AI"]
dynamic = ["version"] dynamic = ["version"]
@ -32,16 +32,16 @@ classifiers = [
'Topic :: Scientific/Engineering :: Image Processing', 'Topic :: Scientific/Engineering :: Image Processing',
] ]
dependencies = [ dependencies = [
"accelerate~=0.16", "accelerate~=0.21.0",
"albumentations", "albumentations",
"click", "click",
"clip_anytorch", # replacing "clip @ https://github.com/openai/CLIP/archive/eaa22acb90a5876642d0507623e859909230a52d.zip", "clip_anytorch", # replacing "clip @ https://github.com/openai/CLIP/archive/eaa22acb90a5876642d0507623e859909230a52d.zip",
"compel==2.0.0", "compel~=2.0.0",
"controlnet-aux>=0.0.6", "controlnet-aux>=0.0.6",
"timm==0.6.13", # needed to override timm latest in controlnet_aux, see https://github.com/isl-org/ZoeDepth/issues/26 "timm==0.6.13", # needed to override timm latest in controlnet_aux, see https://github.com/isl-org/ZoeDepth/issues/26
"datasets", "datasets",
"diffusers[torch]~=0.18.1", "diffusers[torch]~=0.18.2",
"dnspython==2.2.1", "dnspython~=2.4.0",
"dynamicprompts", "dynamicprompts",
"easing-functions", "easing-functions",
"einops", "einops",
@ -54,37 +54,37 @@ dependencies = [
"flask_cors==3.0.10", "flask_cors==3.0.10",
"flask_socketio==5.3.0", "flask_socketio==5.3.0",
"flaskwebgui==1.0.3", "flaskwebgui==1.0.3",
"gfpgan==1.3.8",
"huggingface-hub>=0.11.1", "huggingface-hub>=0.11.1",
"invisible-watermark>=0.2.0", # needed to install SDXL base and refiner using their repo_ids "invisible-watermark~=0.2.0", # needed to install SDXL base and refiner using their repo_ids
"matplotlib", # needed for plotting of Penner easing functions "matplotlib", # needed for plotting of Penner easing functions
"mediapipe", # needed for "mediapipeface" controlnet model "mediapipe", # needed for "mediapipeface" controlnet model
"npyscreen", "npyscreen",
"numpy<1.24", "numpy==1.24.4",
"omegaconf", "omegaconf",
"opencv-python", "opencv-python",
"picklescan", "picklescan",
"pillow", "pillow",
"prompt-toolkit", "prompt-toolkit",
"pympler==1.0.1", "pydantic==1.10.10",
"pympler~=1.0.1",
"pypatchmatch", "pypatchmatch",
'pyperclip', 'pyperclip',
"pyreadline3", "pyreadline3",
"python-multipart==0.0.6", "python-multipart",
"pytorch-lightning==1.7.7", "pytorch-lightning",
"realesrgan", "realesrgan",
"requests==2.28.2", "requests~=2.28.2",
"rich~=13.3", "rich~=13.3",
"safetensors~=0.3.0", "safetensors~=0.3.0",
"scikit-image>=0.19", "scikit-image~=0.21.0",
"send2trash", "send2trash",
"test-tube>=0.7.5", "test-tube~=0.7.5",
"torch~=2.0.0", "torch~=2.0.1",
"torchvision>=0.14.1", "torchvision~=0.15.2",
"torchmetrics==0.11.4", "torchmetrics~=1.0.1",
"torchsde==0.2.5", "torchsde~=0.2.5",
"transformers~=4.31.0", "transformers~=4.31.0",
"uvicorn[standard]==0.21.1", "uvicorn[standard]~=0.21.1",
"windows-curses; sys_platform=='win32'", "windows-curses; sys_platform=='win32'",
] ]

10
scripts/probe-model.py
View File

@ -1,8 +1,16 @@
#!/bin/env python #!/bin/env python
import argparse
import sys import sys
from pathlib import Path from pathlib import Path
from invokeai.backend.model_management.model_probe import ModelProbe from invokeai.backend.model_management.model_probe import ModelProbe
info = ModelProbe().probe(Path(sys.argv[1])) parser = argparse.ArgumentParser(description="Probe model type")
parser.add_argument(
"model_path",
type=Path,
)
args = parser.parse_args()
info = ModelProbe().probe(args.model_path)
print(info) print(info)