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🚧 post-rebase repair

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This commit is contained in:
Kevin Turner 2022-11-30 15:25:47 -08:00
parent adaa1c7c3e
commit 494936a8d2
8 changed files with 123 additions and 87 deletions
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4
.github/workflows/test-invoke-conda.yml vendored
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@ -86,14 +86,14 @@ jobs:
if: ${{ github.ref != 'refs/heads/main' && github.ref != 'refs/heads/development' }}
run: echo "TEST_PROMPTS=tests/validate_pr_prompt.txt" >> $GITHUB_ENV
- name: run preload_models.py
- name: run configure_invokeai.py
id: run-preload-models
run: |
if [ "${HAVE_SECRETS}" == true ] ; then
mkdir -p ~/.huggingface
echo -n '${{ secrets.HUGGINGFACE_TOKEN }}' > ~/.huggingface/token
fi
python scripts/preload_models.py \
python scripts/configure_invokeai.py \
--no-interactive --yes \
--full-precision # can't use fp16 weights without a GPU

44
backend/invoke_ai_web_server.py
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@ -1,33 +1,31 @@
import eventlet
import base64
import glob
import io
import json
import math
import mimetypes
import os
import shutil
import mimetypes
import traceback
import math
import io
import base64
import os
import json
from threading import Event
from uuid import uuid4
from werkzeug.utils import secure_filename
import eventlet
from PIL import Image
from PIL.Image import Image as ImageType
from flask import Flask, redirect, send_from_directory, request, make_response
from flask_socketio import SocketIO
from PIL import Image, ImageOps
from PIL.Image import Image as ImageType
from uuid import uuid4
from threading import Event
from werkzeug.utils import secure_filename
from ldm.invoke.args import Args, APP_ID, APP_VERSION, calculate_init_img_hash
from ldm.invoke.generator.diffusers_pipeline import PipelineIntermediateState
from ldm.invoke.pngwriter import PngWriter, retrieve_metadata
from ldm.invoke.prompt_parser import split_weighted_subprompts
from ldm.invoke.generator.inpaint import infill_methods
from backend.modules.parameters import parameters_to_command
from backend.modules.get_canvas_generation_mode import (
get_canvas_generation_mode,
)
from backend.modules.parameters import parameters_to_command
from ldm.invoke.args import Args, APP_ID, APP_VERSION, calculate_init_img_hash
from ldm.invoke.generator.diffusers_pipeline import PipelineIntermediateState
from ldm.invoke.generator.inpaint import infill_methods
from ldm.invoke.pngwriter import PngWriter, retrieve_metadata
from ldm.invoke.prompt_parser import split_weighted_subprompts
# Loading Arguments
opt = Args()
@ -251,7 +249,7 @@ class InvokeAIWebServer:
return candidate
assert "Frontend files cannot be found. Cannot continue"
def setup_app(self):
self.result_url = "outputs/"
self.init_image_url = "outputs/init-images/"
@ -776,10 +774,10 @@ class InvokeAIWebServer:
).convert("RGBA")
"""
The outpaint image and mask are pre-cropped by the UI, so the bounding box we pass
The outpaint image and mask are pre-cropped by the UI, so the bounding box we pass
to the generator should be:
{
"x": 0,
"x": 0,
"y": 0,
"width": original_bounding_box["width"],
"height": original_bounding_box["height"]
@ -799,7 +797,7 @@ class InvokeAIWebServer:
)
"""
Apply the mask to the init image, creating a "mask" image with
Apply the mask to the init image, creating a "mask" image with
transparency where inpainting should occur. This is the kind of
mask that prompt2image() needs.
"""

9
environments-and-requirements/environment-lin-cuda.yml
View File

@ -40,15 +40,6 @@ dependencies:
- torch-fidelity==0.3.0
- torchmetrics==0.7.0
- transformers==4.21.3
- diffusers~=0.7
- torchmetrics==0.7.0
- flask==2.1.3
- flask_socketio==5.3.0
- flask_cors==3.0.10
- dependency_injector==4.40.0
- eventlet
- getpass_asterisk
- kornia==0.6.0
- git+https://github.com/openai/CLIP.git@main#egg=clip
- git+https://github.com/Birch-san/k-diffusion.git@mps#egg=k-diffusion
- git+https://github.com/invoke-ai/clipseg.git@relaxed-python-requirement#egg=clipseg

32
ldm/generate.py
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@ -236,7 +236,7 @@ class Generate:
except Exception:
print('** An error was encountered while installing the safety checker:')
print(traceback.format_exc())
def prompt2png(self, prompt, outdir, **kwargs):
"""
Takes a prompt and an output directory, writes out the requested number
@ -330,7 +330,7 @@ class Generate:
infill_method = infill_methods[0], # The infill method to use
force_outpaint: bool = False,
enable_image_debugging = False,
**args,
): # eat up additional cruft
"""
@ -373,7 +373,7 @@ class Generate:
def process_image(image,seed):
image.save(f{'images/seed.png'})
The code used to save images to a directory can be found in ldm/invoke/pngwriter.py.
The code used to save images to a directory can be found in ldm/invoke/pngwriter.py.
It contains code to create the requested output directory, select a unique informative
name for each image, and write the prompt into the PNG metadata.
"""
@ -593,7 +593,7 @@ class Generate:
seed = opt.seed or args.seed
if seed is None or seed < 0:
seed = random.randrange(0, np.iinfo(np.uint32).max)
prompt = opt.prompt or args.prompt or ''
print(f'>> using seed {seed} and prompt "{prompt}" for {image_path}')
@ -645,7 +645,7 @@ class Generate:
opt.seed = seed
opt.prompt = prompt
if len(extend_instructions) > 0:
restorer = Outcrop(image,self,)
return restorer.process (
@ -687,7 +687,7 @@ class Generate:
image_callback = callback,
prefix = prefix
)
elif tool is None:
print(f'* please provide at least one postprocessing option, such as -G or -U')
return None
@ -710,13 +710,13 @@ class Generate:
if embiggen is not None:
return self._make_embiggen()
if inpainting_model_in_use:
return self._make_omnibus()
if ((init_image is not None) and (mask_image is not None)) or force_outpaint:
return self._make_inpaint()
if init_image is not None:
return self._make_img2img()
@ -747,7 +747,7 @@ class Generate:
if self._has_transparency(image):
self._transparency_check_and_warning(image, mask, force_outpaint)
init_mask = self._create_init_mask(image, width, height, fit=fit)
if (image.width * image.height) > (self.width * self.height) and self.size_matters:
print(">> This input is larger than your defaults. If you run out of memory, please use a smaller image.")
self.size_matters = False
@ -763,7 +763,7 @@ class Generate:
if invert_mask:
init_mask = ImageOps.invert(init_mask)
return init_image,init_mask
# lots o' repeated code here! Turn into a make_func()
@ -822,7 +822,7 @@ class Generate:
self.set_model(self.model_name)
def set_model(self,model_name):
"""
"""
Given the name of a model defined in models.yaml, will load and initialize it
and return the model object. Previously-used models will be cached.
"""
@ -834,7 +834,7 @@ class Generate:
if not cache.valid_model(model_name):
print(f'** "{model_name}" is not a known model name. Please check your models.yaml file')
return self.model
cache.print_vram_usage()
# have to get rid of all references to model in order
@ -843,7 +843,7 @@ class Generate:
self.sampler = None
self.generators = {}
gc.collect()
model_data = cache.get_model(model_name)
if model_data is None: # restore previous
model_data = cache.get_model(self.model_name)
@ -856,7 +856,7 @@ class Generate:
# uncache generators so they pick up new models
self.generators = {}
seed_everything(random.randrange(0, np.iinfo(np.uint32).max))
if self.embedding_path is not None:
self.model.embedding_manager.load(
@ -905,7 +905,7 @@ class Generate:
image_callback = None,
prefix = None,
):
for r in image_list:
image, seed = r
try:
@ -915,7 +915,7 @@ class Generate:
if self.gfpgan is None:
print('>> GFPGAN not found. Face restoration is disabled.')
else:
image = self.gfpgan.process(image, strength, seed)
image = self.gfpgan.process(image, strength, seed)
if facetool == 'codeformer':
if self.codeformer is None:
print('>> CodeFormer not found. Face restoration is disabled.')

7
ldm/invoke/generator/base.py
View File

@ -9,9 +9,10 @@ import os.path as osp
import random
import traceback
import cv2
import numpy as np
import torch
from PIL import Image, ImageFilter
from PIL import Image, ImageFilter, ImageChops
from diffusers import DiffusionPipeline
from einops import rearrange
from pytorch_lightning import seed_everything
@ -169,7 +170,7 @@ class Generator:
# Blur the mask out (into init image) by specified amount
if mask_blur_radius > 0:
nm = np.asarray(pil_init_mask, dtype=np.uint8)
nmd = cv.erode(nm, kernel=np.ones((3,3), dtype=np.uint8), iterations=int(mask_blur_radius / 2))
nmd = cv2.erode(nm, kernel=np.ones((3,3), dtype=np.uint8), iterations=int(mask_blur_radius / 2))
pmd = Image.fromarray(nmd, mode='L')
blurred_init_mask = pmd.filter(ImageFilter.BoxBlur(mask_blur_radius))
else:
@ -181,8 +182,6 @@ class Generator:
matched_result.paste(init_image, (0,0), mask = multiplied_blurred_init_mask)
return matched_result
def sample_to_lowres_estimated_image(self,samples):
# origingally adapted from code by @erucipe and @keturn here:
# https://discuss.huggingface.co/t/decoding-latents-to-rgb-without-upscaling/23204/7

45
ldm/invoke/model_cache.py
View File

@ -21,9 +21,6 @@ from typing import Union
import torch
import transformers
import textwrap
import contextlib
from typing import Union
from omegaconf import OmegaConf
from omegaconf.errors import ConfigAttributeError
from picklescan.scanner import scan_file_path
@ -99,7 +96,7 @@ class ModelCache(object):
assert self.current_model,'** FATAL: no current model to restore to'
print(f'** restoring {self.current_model}')
self.get_model(self.current_model)
return None
return
self.current_model = model_name
self._push_newest_model(model_name)
@ -219,7 +216,7 @@ class ModelCache(object):
if model_format == 'ckpt':
weights = mconfig.weights
print(f'>> Loading {model_name} from {weights}')
model, width, height, model_hash = self._load_ckpt_model(mconfig)
model, width, height, model_hash = self._load_ckpt_model(model_name, mconfig)
elif model_format == 'diffusers':
model, width, height, model_hash = self._load_diffusers_model(mconfig)
else:
@ -237,10 +234,10 @@ class ModelCache(object):
)
return model, width, height, model_hash
def _load_ckpt_model(self, mconfig):
def _load_ckpt_model(self, model_name, mconfig):
config = mconfig.config
weights = mconfig.weights
vae = mconfig.get('vae', None)
vae = mconfig.get('vae')
width = mconfig.width
height = mconfig.height
@ -249,10 +246,22 @@ class ModelCache(object):
if not os.path.isabs(weights):
weights = os.path.normpath(os.path.join(Globals.root,weights))
# scan model
self._scan_model(model_name, weights)
self.scan_model(model_name, weights)
c = OmegaConf.load(config)
with open(weights, 'rb') as f:
print(f'>> Loading {model_name} from {weights}')
# for usage statistics
if self._has_cuda():
torch.cuda.reset_peak_memory_stats()
torch.cuda.empty_cache()
tic = time.time()
# this does the work
if not os.path.isabs(config):
config = os.path.join(Globals.root,config)
omega_config = OmegaConf.load(config)
with open(weights,'rb') as f:
weight_bytes = f.read()
model_hash = self._cached_sha256(weights, weight_bytes)
sd = torch.load(io.BytesIO(weight_bytes), map_location='cpu')
@ -289,6 +298,18 @@ class ModelCache(object):
if isinstance(module, (torch.nn.Conv2d, torch.nn.ConvTranspose2d)):
module._orig_padding_mode = module.padding_mode
# usage statistics
toc = time.time()
print(f'>> Model loaded in', '%4.2fs' % (toc - tic))
if self._has_cuda():
print(
'>> Max VRAM used to load the model:',
'%4.2fG' % (torch.cuda.max_memory_allocated() / 1e9),
'\n>> Current VRAM usage:'
'%4.2fG' % (torch.cuda.memory_allocated() / 1e9),
)
return model, width, height, model_hash
def _load_diffusers_model(self, mconfig):
@ -308,6 +329,8 @@ class ModelCache(object):
print(f'>> Loading diffusers model from {name_or_path}')
# TODO: scan weights maybe?
if self.precision == 'float16':
print(' | Using faster float16 precision')
pipeline_args.update(revision="fp16", torch_dtype=torch.float16)
@ -342,7 +365,7 @@ class ModelCache(object):
else:
raise ValueError("Model config must specify either repo_name or path.")
def offload_model(self, model_name:str):
def offload_model(self, model_name:str) -> None:
'''
Offload the indicated model to CPU. Will call
_make_cache_room() to free space if needed.

44
ldm/models/diffusion/cross_attention_control.py
View File

@ -248,33 +248,33 @@ def inject_attention_function(unet, context: Context):
cross_attention_modules = [(name, module) for (name, module) in unet.named_modules()
if type(module).__name__ == "CrossAttention"]
for identifier, module in cross_attention_modules:
module.identifier = identifier
try:
module.set_attention_slice_wrangler(attention_slice_wrangler)
module.set_slicing_strategy_getter(
lambda module: context.get_slicing_strategy(identifier)
)
except AttributeError as e:
if is_attribute_error_about(e, 'set_attention_slice_wrangler'):
warnings.warn(f"TODO: implement for {type(module)}") # TODO
else:
raise
for identifier, module in cross_attention_modules:
module.identifier = identifier
try:
module.set_attention_slice_wrangler(attention_slice_wrangler)
module.set_slicing_strategy_getter(
lambda module: context.get_slicing_strategy(identifier)
)
except AttributeError as e:
if is_attribute_error_about(e, 'set_attention_slice_wrangler'):
warnings.warn(f"TODO: implement for {type(module)}") # TODO
else:
raise
def remove_attention_function(unet):
cross_attention_modules = [module for (_, module) in unet.named_modules()
if type(module).__name__ == "CrossAttention"]
for module in cross_attention_modules:
try:
# clear wrangler callback
module.set_attention_slice_wrangler(None)
module.set_slicing_strategy_getter(None)
except AttributeError as e:
if is_attribute_error_about(e, 'set_attention_slice_wrangler'):
warnings.warn(f"TODO: implement for {type(module)}") # TODO
else:
raise
for module in cross_attention_modules:
try:
# clear wrangler callback
module.set_attention_slice_wrangler(None)
module.set_slicing_strategy_getter(None)
except AttributeError as e:
if is_attribute_error_about(e, 'set_attention_slice_wrangler'):
warnings.warn(f"TODO: implement for {type(module)}") # TODO
else:
raise
def is_attribute_error_about(error: AttributeError, attribute: str):

25
scripts/configure_invokeai.py
View File

@ -34,6 +34,12 @@ warnings.filterwarnings('ignore')
import torch
transformers.logging.set_verbosity_error()
try:
from ldm.invoke.model_cache import ModelCache
except ImportError:
sys.path.append('.')
from ldm.invoke.model_cache import ModelCache
#--------------------------globals-----------------------
Model_dir = 'models'
Weights_dir = 'ldm/stable-diffusion-v1/'
@ -267,6 +273,19 @@ def download_weight_datasets(models:dict, access_token:str):
print(f'Successfully installed {keys}')
return successful
#---------------------------------------------
def is_huggingface_authenticated():
# huggingface_hub 0.10 API isn't great for this, it could be OSError, ValueError,
# maybe other things, not all end-user-friendly.
# noinspection PyBroadException
try:
response = hf_whoami()
if response.get('id') is not None:
return True
except Exception:
pass
return False
#---------------------------------------------
def hf_download_with_resume(repo_id:str, model_dir:str, model_name:str, access_token:str=None)->bool:
model_dest = os.path.join(model_dir, model_name)
@ -749,6 +768,12 @@ def main():
action=argparse.BooleanOptionalAction,
default=True,
help='run in interactive mode (default)')
parser.add_argument('--full-precision',
dest='full_precision',
action=argparse.BooleanOptionalAction,
type=bool,
default=False,
help='use 32-bit weights instead of faster 16-bit weights')
parser.add_argument('--yes','-y',
dest='yes_to_all',
action='store_true',