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Merge branch 'main' into development

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- this syncs documentation and code
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Lincoln Stein
2022-10-09 14:47:27 -04:00
parent 3170c83d8d ce8c238ac4
commit 8faa06fb15
100 changed files with 1976 additions and 1107 deletions
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92
ldm/generate.py
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@ -19,7 +19,7 @@ import cv2
import skimage
from omegaconf import OmegaConf
from ldm.dream.generator.base import downsampling
from ldm.invoke.generator.base import downsampling
from PIL import Image, ImageOps
from torch import nn
from pytorch_lightning import seed_everything, logging
@ -28,33 +28,11 @@ from ldm.util import instantiate_from_config
from ldm.models.diffusion.ddim import DDIMSampler
from ldm.models.diffusion.plms import PLMSSampler
from ldm.models.diffusion.ksampler import KSampler
from ldm.dream.pngwriter import PngWriter
from ldm.dream.args import metadata_from_png
from ldm.dream.image_util import InitImageResizer
from ldm.dream.devices import choose_torch_device, choose_precision
from ldm.dream.conditioning import get_uc_and_c
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)
from ldm.invoke.pngwriter import PngWriter
from ldm.invoke.args import metadata_from_png
from ldm.invoke.image_util import InitImageResizer
from ldm.invoke.devices import choose_torch_device, choose_precision
from ldm.invoke.conditioning import get_uc_and_c
"""Simplified text to image API for stable diffusion/latent diffusion
@ -142,7 +120,8 @@ class Generate:
config = None,
gfpgan=None,
codeformer=None,
esrgan=None
esrgan=None,
free_gpu_mem=False,
):
models = OmegaConf.load(conf)
mconfig = models[model]
@ -169,6 +148,7 @@ class Generate:
self.gfpgan = gfpgan
self.codeformer = codeformer
self.esrgan = esrgan
self.free_gpu_mem = free_gpu_mem
# Note that in previous versions, there was an option to pass the
# device to Generate(). However the device was then ignored, so
@ -295,9 +275,9 @@ class Generate:
def process_image(image,seed):
image.save(f{'images/seed.png'})
The callback used by the prompt2png() can be found in ldm/dream_util.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.
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.
"""
# TODO: convert this into a getattr() loop
steps = steps or self.steps
@ -385,7 +365,8 @@ class Generate:
generator = self._make_txt2img()
generator.set_variation(
self.seed, variation_amount, with_variations)
self.seed, variation_amount, with_variations
)
results = generator.generate(
prompt,
iterations=iterations,
@ -521,7 +502,7 @@ class Generate:
)
elif tool == 'outcrop':
from ldm.dream.restoration.outcrop import Outcrop
from ldm.invoke.restoration.outcrop import Outcrop
extend_instructions = {}
for direction,pixels in _pairwise(opt.outcrop):
extend_instructions[direction]=int(pixels)
@ -558,7 +539,7 @@ class Generate:
image_callback = callback,
)
elif tool == 'outpaint':
from ldm.dream.restoration.outpaint import Outpaint
from ldm.invoke.restoration.outpaint import Outpaint
restorer = Outpaint(image,self)
return restorer.process(
opt,
@ -594,18 +575,14 @@ class Generate:
height,
)
if image.width < self.width and image.height < self.height:
print(f'>> WARNING: img2img and inpainting may produce unexpected results with initial images smaller than {self.width}x{self.height} in both dimensions')
# if image has a transparent area and no mask was provided, then try to generate mask
if self._has_transparency(image) and not mask:
print(
'>> Initial image has transparent areas. Will inpaint in these regions.')
if self._check_for_erasure(image):
print(
'>> WARNING: Colors underneath the transparent region seem to have been erased.\n',
'>> Inpainting will be suboptimal. Please preserve the colors when making\n',
'>> a transparency mask, or provide mask explicitly using --init_mask (-M).'
)
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)
init_mask = self._create_init_mask(image, width, height, fit=fit)
if (image.width * image.height) > (self.width * self.height):
print(">> This input is larger than your defaults. If you run out of memory, please use a smaller image.")
@ -621,39 +598,39 @@ class Generate:
def _make_base(self):
if not self.generators.get('base'):
from ldm.dream.generator import Generator
from ldm.invoke.generator import Generator
self.generators['base'] = Generator(self.model, self.precision)
return self.generators['base']
def _make_img2img(self):
if not self.generators.get('img2img'):
from ldm.dream.generator.img2img import Img2Img
from ldm.invoke.generator.img2img import Img2Img
self.generators['img2img'] = Img2Img(self.model, self.precision)
return self.generators['img2img']
def _make_embiggen(self):
if not self.generators.get('embiggen'):
from ldm.dream.generator.embiggen import Embiggen
from ldm.invoke.generator.embiggen import Embiggen
self.generators['embiggen'] = Embiggen(self.model, self.precision)
return self.generators['embiggen']
def _make_txt2img(self):
if not self.generators.get('txt2img'):
from ldm.dream.generator.txt2img import Txt2Img
from ldm.invoke.generator.txt2img import Txt2Img
self.generators['txt2img'] = Txt2Img(self.model, self.precision)
self.generators['txt2img'].free_gpu_mem = self.free_gpu_mem
return self.generators['txt2img']
def _make_txt2img2img(self):
if not self.generators.get('txt2img2'):
from ldm.dream.generator.txt2img2img import Txt2Img2Img
from ldm.invoke.generator.txt2img2img import Txt2Img2Img
self.generators['txt2img2'] = Txt2Img2Img(self.model, self.precision)
self.generators['txt2img2'].free_gpu_mem = self.free_gpu_mem
return self.generators['txt2img2']
def _make_inpaint(self):
if not self.generators.get('inpaint'):
from ldm.dream.generator.inpaint import Inpaint
from ldm.invoke.generator.inpaint import Inpaint
self.generators['inpaint'] = Inpaint(self.model, self.precision)
return self.generators['inpaint']
@ -784,7 +761,7 @@ class Generate:
print(msg)
# Be warned: config is the path to the model config file, not the dream conf file!
# Be warned: config is the path to the model config file, not the invoke conf file!
# Also note that we can get config and weights from self, so why do we need to
# pass them as args?
def _load_model_from_config(self, config, weights):
@ -920,6 +897,17 @@ class Generate:
colored += 1
return colored == 0
def _transparency_check_and_warning(self,image, mask):
if not mask:
print(
'>> Initial image has transparent areas. Will inpaint in these regions.')
if self._check_for_erasure(image):
print(
'>> WARNING: Colors underneath the transparent region seem to have been erased.\n',
'>> Inpainting will be suboptimal. Please preserve the colors when making\n',
'>> a transparency mask, or provide mask explicitly using --init_mask (-M).'
)
def _squeeze_image(self, image):
x, y, resize_needed = self._resolution_check(image.width, image.height)
if resize_needed: