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Kyle0654 inpaint improvement - with refactoring from PR #1221 (#1)

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* Removed duplicate fix_func for MPS

* add support for loading VAE autoencoders

To add a VAE autoencoder to an existing model:

1. Download the appropriate autoencoder and put it into
   models/ldm/stable-diffusion

   Note that you MUST use a VAE that was written for the
   original CompViz Stable Diffusion codebase. For v1.4,
   that would be the file named vae-ft-mse-840000-ema-pruned.ckpt
   that you can download from https://huggingface.co/stabilityai/sd-vae-ft-mse-original

2. Edit config/models.yaml to contain the following stanza, modifying `weights`
   and `vae` as required to match the weights and vae model file names. There is
   no requirement to rename the VAE file.

~~~
stable-diffusion-1.4:
  weights: models/ldm/stable-diffusion-v1/sd-v1-4.ckpt
  description: Stable Diffusion v1.4
  config: configs/stable-diffusion/v1-inference.yaml
  vae: models/ldm/stable-diffusion-v1/vae-ft-mse-840000-ema-pruned.ckpt
  width: 512
  height: 512
~~~

3. Alternatively from within the `invoke.py` CLI, you may use the command
   `!editmodel stable-diffusion-1.4` to bring up a simple editor that will
   allow you to add the path to the VAE.

4. If you are just installing InvokeAI for the first time, you can also
   use `!import_model models/ldm/stable-diffusion/sd-v1.4.ckpt` instead
   to create the configuration from scratch.

5. That's it!

* ported code refactor changes from PR #1221

- pass a PIL.Image to img2img and inpaint rather than tensor
- To support clipseg, inpaint needs to accept an "L" or "1" format
  mask. Made the appropriate change.

* minor fixes to inpaint code

1. If tensors are passed to inpaint as init_image and/or init_mask, then
   the post-generation image fixup code will be skipped.

2. Post-generation image fixup will work with either a black and white "L"
   or "RGB"  mask, or an "RGBA" mask.

Co-authored-by: wfng92 <43742196+wfng92@users.noreply.github.com>
This commit is contained in:
Lincoln Stein 2022-10-22 23:09:38 -04:00 committed by GitHub
parent 3b01e6e423
commit 93cba3fba5
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6 changed files with 129 additions and 114 deletions
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26
configs/models.yaml
View File

@ -1,20 +1,22 @@
# This file describes the alternative machine learning models
# available to the dream script.
# available to the dream script.
#
# To add a new model, follow the examples below. Each
# model requires a model config file, a weights file,
# and the width and height of the images it
# was trained on.
stable-diffusion-1.4:
config: configs/stable-diffusion/v1-inference.yaml
weights: models/ldm/stable-diffusion-v1/model.ckpt
description: Stable Diffusion inference model version 1.4
width: 512
height: 512
config: configs/stable-diffusion/v1-inference.yaml
weights: models/ldm/stable-diffusion-v1/model.ckpt
vae: models/ldm/stable-diffusion-v1/vae-ft-mse-840000-ema-pruned.ckpt
description: Stable Diffusion inference model version 1.4
width: 512
height: 512
stable-diffusion-1.5:
config: configs/stable-diffusion/v1-inference.yaml
weights: models/ldm/stable-diffusion-v1/v1-5-pruned-emaonly.ckpt
description: Stable Diffusion inference model version 1.5
width: 512
height: 512
config: configs/stable-diffusion/v1-inference.yaml
weights: models/ldm/stable-diffusion-v1/v1-5-pruned-emaonly.ckpt
description: Stable Diffusion inference model version 1.5
width: 512
height: 512
vae: models/ldm/stable-diffusion-v1/vae-ft-mse-840000-ema-pruned.ckpt
default: true

72
ldm/generate.py
View File

@ -58,24 +58,6 @@ torch.multinomial = fix_func(torch.multinomial)
# this is fallback model in case no default is defined
FALLBACK_MODEL_NAME='stable-diffusion-1.4'
def fix_func(orig):
if hasattr(torch.backends, 'mps') and torch.backends.mps.is_available():
def new_func(*args, **kw):
device = kw.get("device", "mps")
kw["device"]="cpu"
return orig(*args, **kw).to(device)
return new_func
return orig
torch.rand = fix_func(torch.rand)
torch.rand_like = fix_func(torch.rand_like)
torch.randn = fix_func(torch.randn)
torch.randn_like = fix_func(torch.randn_like)
torch.randint = fix_func(torch.randint)
torch.randint_like = fix_func(torch.randint_like)
torch.bernoulli = fix_func(torch.bernoulli)
torch.multinomial = fix_func(torch.multinomial)
"""Simplified text to image API for stable diffusion/latent diffusion
Example Usage:
@ -411,7 +393,7 @@ class Generate:
log_tokens =self.log_tokenization
)
init_image,mask_image,pil_image,pil_mask = self._make_images(
init_image, mask_image = self._make_images(
init_img,
init_mask,
width,
@ -451,8 +433,6 @@ class Generate:
height=height,
init_img=init_img, # embiggen needs to manipulate from the unmodified init_img
init_image=init_image, # notice that init_image is different from init_img
pil_image=pil_image,
pil_mask=pil_mask,
mask_image=mask_image,
strength=strength,
threshold=threshold,
@ -644,7 +624,7 @@ class Generate:
init_image = None
init_mask = None
if not img:
return None, None, None, None
return None, None
image = self._load_img(img)
@ -654,23 +634,22 @@ class Generate:
# if image has a transparent area and no mask was provided, then try to generate mask
if self._has_transparency(image):
self._transparency_check_and_warning(image, mask)
# this returns a torch tensor
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
init_image = self._create_init_image(image,width,height,fit=fit) # this returns a torch tensor
init_image = self._create_init_image(image,width,height,fit=fit)
if mask:
mask_image = self._load_img(mask) # this returns an Image
mask_image = self._load_img(mask)
init_mask = self._create_init_mask(mask_image,width,height,fit=fit)
elif text_mask:
init_mask = self._txt2mask(image, text_mask, width, height, fit=fit)
return init_image, init_mask, image, mask_image
return init_image,init_mask
def _make_base(self):
if not self.generators.get('base'):
@ -887,33 +866,15 @@ class Generate:
def _create_init_image(self, image, width, height, fit=True):
image = image.convert('RGB')
if fit:
image = self._fit_image(image, (width, height))
else:
image = self._squeeze_image(image)
image = np.array(image).astype(np.float32) / 255.0
image = image[None].transpose(0, 3, 1, 2)
image = torch.from_numpy(image)
image = 2.0 * image - 1.0
return image.to(self.device)
image = self._fit_image(image, (width, height)) if fit else self._squeeze_image(image)
return image
def _create_init_mask(self, image, width, height, fit=True):
# convert into a black/white mask
image = self._image_to_mask(image)
image = image.convert('RGB')
# now we adjust the size
if fit:
image = self._fit_image(image, (width, height))
else:
image = self._squeeze_image(image)
image = image.resize((image.width//downsampling, image.height //
downsampling), resample=Image.Resampling.NEAREST)
image = np.array(image)
image = image.astype(np.float32) / 255.0
image = image[None].transpose(0, 3, 1, 2)
image = torch.from_numpy(image)
return image.to(self.device)
image = self._fit_image(image, (width, height)) if fit else self._squeeze_image(image)
return image
# The mask is expected to have the region to be inpainted
# with alpha transparency. It converts it into a black/white
@ -930,7 +891,6 @@ class Generate:
mask = ImageOps.invert(mask)
return mask
# TODO: The latter part of this method repeats code from _create_init_mask()
def _txt2mask(self, image:Image, text_mask:list, width, height, fit=True) -> Image:
prompt = text_mask[0]
confidence_level = text_mask[1] if len(text_mask)>1 else 0.5
@ -940,18 +900,8 @@ class Generate:
segmented = self.txt2mask.segment(image, prompt)
mask = segmented.to_mask(float(confidence_level))
mask = mask.convert('RGB')
# now we adjust the size
if fit:
mask = self._fit_image(mask, (width, height))
else:
mask = self._squeeze_image(mask)
mask = mask.resize((mask.width//downsampling, mask.height //
downsampling), resample=Image.Resampling.NEAREST)
mask = np.array(mask)
mask = mask.astype(np.float32) / 255.0
mask = mask[None].transpose(0, 3, 1, 2)
mask = torch.from_numpy(mask)
return mask.to(self.device)
mask = self._fit_image(mask, (width, height)) if fit else self._squeeze_image(mask)
return mask
def _has_transparency(self, image):
if image.info.get("transparency", None) is not None:

20
ldm/invoke/generator/img2img.py
View File

@ -4,9 +4,12 @@ ldm.invoke.generator.img2img descends from ldm.invoke.generator
import torch
import numpy as np
from ldm.invoke.devices import choose_autocast
from ldm.invoke.generator.base import Generator
from ldm.models.diffusion.ddim import DDIMSampler
import PIL
from torch import Tensor
from PIL import Image
from ldm.invoke.devices import choose_autocast
from ldm.invoke.generator.base import Generator
from ldm.models.diffusion.ddim import DDIMSampler
class Img2Img(Generator):
def __init__(self, model, precision):
@ -25,6 +28,9 @@ class Img2Img(Generator):
ddim_num_steps=steps, ddim_eta=ddim_eta, verbose=False
)
if isinstance(init_image, PIL.Image.Image):
init_image = self._image_to_tensor(init_image)
scope = choose_autocast(self.precision)
with scope(self.model.device.type):
self.init_latent = self.model.get_first_stage_encoding(
@ -68,3 +74,11 @@ class Img2Img(Generator):
shape = init_latent.shape
x = (1-self.perlin)*x + self.perlin*self.get_perlin_noise(shape[3], shape[2])
return x
def _image_to_tensor(self, image:Image, normalize:bool=True)->Tensor:
image = np.array(image).astype(np.float32) / 255.0
image = image[None].transpose(0, 3, 1, 2)
image = torch.from_numpy(image)
if normalize:
image = 2.0 * image - 1.0
return image.to(self.model.device)

101
ldm/invoke/generator/inpaint.py
View File

@ -6,6 +6,7 @@ import torch
import torchvision.transforms as T
import numpy as np
import cv2 as cv
import PIL
from PIL import Image, ImageFilter
from skimage.exposure.histogram_matching import match_histograms
from einops import rearrange, repeat
@ -13,16 +14,19 @@ from ldm.invoke.devices import choose_autocast
from ldm.invoke.generator.img2img import Img2Img
from ldm.models.diffusion.ddim import DDIMSampler
from ldm.models.diffusion.ksampler import KSampler
from ldm.invoke.generator.base import downsampling
class Inpaint(Img2Img):
def __init__(self, model, precision):
self.init_latent = None
self.pil_image = None
self.pil_mask = None
self.mask_blur_radius = 0
super().__init__(model, precision)
@torch.no_grad()
def get_make_image(self,prompt,sampler,steps,cfg_scale,ddim_eta,
conditioning,init_image,mask_image,strength,
pil_image: Image.Image, pil_mask: Image.Image,
mask_blur_radius: int = 8,
step_callback=None,inpaint_replace=False, **kwargs):
"""
@ -31,17 +35,22 @@ class Inpaint(Img2Img):
the time you call it. kwargs are 'init_latent' and 'strength'
"""
# Get the alpha channel of the mask
pil_init_mask = pil_mask.getchannel('A')
pil_init_image = pil_image.convert('RGBA') # Add an alpha channel if one doesn't exist
if isinstance(init_image, PIL.Image.Image):
self.pil_image = init_image
init_image = self._image_to_tensor(init_image)
# Build an image with only visible pixels from source to use as reference for color-matching.
# Note that this doesn't use the mask, which would exclude some source image pixels from the
# histogram and cause slight color changes.
init_rgb_pixels = np.asarray(pil_image.convert('RGB'), dtype=np.uint8).reshape(pil_image.width * pil_image.height, 3)
init_a_pixels = np.asarray(pil_init_image.getchannel('A'), dtype=np.uint8).reshape(pil_init_mask.width * pil_init_mask.height)
init_rgb_pixels = init_rgb_pixels[init_a_pixels > 0]
init_rgb_pixels = init_rgb_pixels.reshape(1, init_rgb_pixels.shape[0], init_rgb_pixels.shape[1]) # Filter to just pixels that have any alpha, this is now our histogram
if isinstance(mask_image, PIL.Image.Image):
self.pil_mask = mask_image
mask_image = mask_image.resize(
(
mask_image.width // downsampling,
mask_image.height // downsampling
),
resample=Image.Resampling.NEAREST
)
mask_image = self._image_to_tensor(mask_image,normalize=False)
self.mask_blur_radius = mask_blur_radius
# klms samplers not supported yet, so ignore previous sampler
if isinstance(sampler,KSampler):
@ -96,30 +105,50 @@ class Inpaint(Img2Img):
mask = mask_image,
init_latent = self.init_latent
)
# Get PIL result
gen_result = self.sample_to_image(samples).convert('RGB')
# Get numpy version
np_gen_result = np.asarray(gen_result, dtype=np.uint8)
# Color correct
np_matched_result = match_histograms(np_gen_result, init_rgb_pixels, channel_axis=-1)
matched_result = Image.fromarray(np_matched_result, mode='RGB')
# 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.dilate(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:
blurred_init_mask = pil_init_mask
# Paste original on color-corrected generation (using blurred mask)
matched_result.paste(pil_image, (0,0), mask = blurred_init_mask)
return matched_result
return self.sample_to_image(samples)
return make_image
def sample_to_image(self, samples)->Image:
gen_result = super().sample_to_image(samples).convert('RGB')
if self.pil_image is None or self.pil_mask is None:
return gen_result
pil_mask = self.pil_mask
pil_image = self.pil_image
mask_blur_radius = self.mask_blur_radius
# Get the original alpha channel of the mask if there is one.
# Otherwise it is some other black/white image format ('1', 'L' or 'RGB')
pil_init_mask = pil_mask.getchannel('A') if pil_mask.mode == 'RGBA' else pil_mask.convert('L')
pil_init_image = pil_image.convert('RGBA') # Add an alpha channel if one doesn't exist
# Build an image with only visible pixels from source to use as reference for color-matching.
# Note that this doesn't use the mask, which would exclude some source image pixels from the
# histogram and cause slight color changes.
init_rgb_pixels = np.asarray(pil_image.convert('RGB'), dtype=np.uint8).reshape(pil_image.width * pil_image.height, 3)
init_a_pixels = np.asarray(pil_init_image.getchannel('A'), dtype=np.uint8).reshape(pil_init_mask.width * pil_init_mask.height)
init_rgb_pixels = init_rgb_pixels[init_a_pixels > 0]
init_rgb_pixels = init_rgb_pixels.reshape(1, init_rgb_pixels.shape[0], init_rgb_pixels.shape[1]) # Filter to just pixels that have any alpha, this is now our histogram
# Get numpy version
np_gen_result = np.asarray(gen_result, dtype=np.uint8)
# Color correct
np_matched_result = match_histograms(np_gen_result, init_rgb_pixels, channel_axis=-1)
matched_result = Image.fromarray(np_matched_result, mode='RGB')
# 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.dilate(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:
blurred_init_mask = pil_init_mask
# Paste original on color-corrected generation (using blurred mask)
matched_result.paste(pil_image, (0,0), mask = blurred_init_mask)
return matched_result

10
ldm/invoke/model_cache.py
View File

@ -13,6 +13,7 @@ import gc
import hashlib
import psutil
import transformers
import os
from sys import getrefcount
from omegaconf import OmegaConf
from omegaconf.errors import ConfigAttributeError
@ -193,6 +194,7 @@ class ModelCache(object):
mconfig = self.config[model_name]
config = mconfig.config
weights = mconfig.weights
vae = mconfig.get('vae',None)
width = mconfig.width
height = mconfig.height
@ -222,9 +224,17 @@ class ModelCache(object):
else:
print(' | Using more accurate float32 precision')
# look and load a matching vae file. Code borrowed from AUTOMATIC1111 modules/sd_models.py
if vae and os.path.exists(vae):
print(f' | Loading VAE weights from: {vae}')
vae_ckpt = torch.load(vae, map_location="cpu")
vae_dict = {k: v for k, v in vae_ckpt["state_dict"].items() if k[0:4] != "loss"}
model.first_stage_model.load_state_dict(vae_dict, strict=False)
model.to(self.device)
# model.to doesn't change the cond_stage_model.device used to move the tokenizer output, so set it here
model.cond_stage_model.device = self.device
model.eval()
for m in model.modules():

14
scripts/invoke.py
View File

@ -493,6 +493,16 @@ def add_weights_to_config(model_path:str, gen, opt, completer):
new_config['config'] = input('Configuration file for this model: ')
done = os.path.exists(new_config['config'])
done = False
completer.complete_extensions(('.vae.pt','.vae','.ckpt'))
while not done:
vae = input('VAE autoencoder file for this model [None]: ')
if os.path.exists(vae):
new_config['vae'] = vae
done = True
else:
done = len(vae)==0
completer.complete_extensions(None)
for field in ('width','height'):
@ -537,8 +547,8 @@ def edit_config(model_name:str, gen, opt, completer):
conf = config[model_name]
new_config = {}
completer.complete_extensions(('.yaml','.yml','.ckpt','.vae'))
for field in ('description', 'weights', 'config', 'width','height'):
completer.complete_extensions(('.yaml','.yml','.ckpt','.vae.pt'))
for field in ('description', 'weights', 'vae', 'config', 'width','height'):
completer.linebuffer = str(conf[field]) if field in conf else ''
new_value = input(f'{field}: ')
new_config[field] = int(new_value) if field in ('width','height') else new_value