# Copyright (c) 2022 Lincoln D. Stein (https://github.com/lstein) # Derived from source code carrying the following copyrights # Copyright (c) 2022 Machine Vision and Learning Group, LMU Munich # Copyright (c) 2022 Robin Rombach and Patrick Esser and contributors import torch import numpy as np import random import os import time import re import sys import traceback import transformers import io import hashlib import cv2 import skimage from omegaconf import OmegaConf from ldm.invoke.generator.base import downsampling from PIL import Image, ImageOps from torch import nn from pytorch_lightning import seed_everything, logging 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.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 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: from ldm.generate import Generate # Create an object with default values gr = Generate('stable-diffusion-1.4') # do the slow model initialization gr.load_model() # Do the fast inference & image generation. Any options passed here # override the default values assigned during class initialization # Will call load_model() if the model was not previously loaded and so # may be slow at first. # The method returns a list of images. Each row of the list is a sub-list of [filename,seed] results = gr.prompt2png(prompt = "an astronaut riding a horse", outdir = "./outputs/samples", iterations = 3) for row in results: print(f'filename={row[0]}') print(f'seed ={row[1]}') # Same thing, but using an initial image. results = gr.prompt2png(prompt = "an astronaut riding a horse", outdir = "./outputs/, iterations = 3, init_img = "./sketches/horse+rider.png") for row in results: print(f'filename={row[0]}') print(f'seed ={row[1]}') # Same thing, but we return a series of Image objects, which lets you manipulate them, # combine them, and save them under arbitrary names results = gr.prompt2image(prompt = "an astronaut riding a horse" outdir = "./outputs/") for row in results: im = row[0] seed = row[1] im.save(f'./outputs/samples/an_astronaut_riding_a_horse-{seed}.png') im.thumbnail(100,100).save('./outputs/samples/astronaut_thumb.jpg') Note that the old txt2img() and img2img() calls are deprecated but will still work. The full list of arguments to Generate() are: gr = Generate( # these values are set once and shouldn't be changed conf = path to configuration file ('configs/models.yaml') model = symbolic name of the model in the configuration file precision = float precision to be used # this value is sticky and maintained between generation calls sampler_name = ['ddim', 'k_dpm_2_a', 'k_dpm_2', 'k_euler_a', 'k_euler', 'k_heun', 'k_lms', 'plms'] // k_lms # these are deprecated - use conf and model instead weights = path to model weights ('models/ldm/stable-diffusion-v1/model.ckpt') config = path to model configuraiton ('configs/stable-diffusion/v1-inference.yaml') ) """ class Generate: """Generate class Stores default values for multiple configuration items """ def __init__( self, model = 'stable-diffusion-1.4', conf = 'configs/models.yaml', embedding_path = None, sampler_name = 'k_lms', ddim_eta = 0.0, # deterministic full_precision = False, precision = 'auto', # these are deprecated; if present they override values in the conf file weights = None, config = None, gfpgan=None, codeformer=None, esrgan=None, free_gpu_mem=False, ): models = OmegaConf.load(conf) mconfig = models[model] self.weights = mconfig.weights if weights is None else weights self.config = mconfig.config if config is None else config self.height = mconfig.height self.width = mconfig.width self.iterations = 1 self.steps = 50 self.cfg_scale = 7.5 self.sampler_name = sampler_name self.ddim_eta = 0.0 # same seed always produces same image self.precision = precision self.strength = 0.75 self.seamless = False self.embedding_path = embedding_path self.model = None # empty for now self.sampler = None self.device = None self.session_peakmem = None self.generators = {} self.base_generator = None self.seed = None 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 # it wasn't actually doing anything. This logic could be reinstated. device_type = choose_torch_device() self.device = torch.device(device_type) if full_precision: if self.precision != 'auto': raise ValueError('Remove --full_precision / -F if using --precision') print('Please remove deprecated --full_precision / -F') print('If auto config does not work you can use --precision=float32') self.precision = 'float32' if self.precision == 'auto': self.precision = choose_precision(self.device) # for VRAM usage statistics self.session_peakmem = torch.cuda.max_memory_allocated() if self._has_cuda else None transformers.logging.set_verbosity_error() # gets rid of annoying messages about random seed logging.getLogger('pytorch_lightning').setLevel(logging.ERROR) def prompt2png(self, prompt, outdir, **kwargs): """ Takes a prompt and an output directory, writes out the requested number of PNG files, and returns an array of [[filename,seed],[filename,seed]...] Optional named arguments are the same as those passed to Generate and prompt2image() """ results = self.prompt2image(prompt, **kwargs) pngwriter = PngWriter(outdir) prefix = pngwriter.unique_prefix() outputs = [] for image, seed in results: name = f'{prefix}.{seed}.png' path = pngwriter.save_image_and_prompt_to_png( image, dream_prompt=f'{prompt} -S{seed}', name=name) outputs.append([path, seed]) return outputs def txt2img(self, prompt, **kwargs): outdir = kwargs.pop('outdir', 'outputs/img-samples') return self.prompt2png(prompt, outdir, **kwargs) def img2img(self, prompt, **kwargs): outdir = kwargs.pop('outdir', 'outputs/img-samples') assert ( 'init_img' in kwargs ), 'call to img2img() must include the init_img argument' return self.prompt2png(prompt, outdir, **kwargs) def prompt2image( self, # these are common prompt, iterations = None, steps = None, seed = None, cfg_scale = None, ddim_eta = None, skip_normalize = False, image_callback = None, step_callback = None, width = None, height = None, sampler_name = None, seamless = False, log_tokenization = False, with_variations = None, variation_amount = 0.0, threshold = 0.0, perlin = 0.0, # these are specific to img2img and inpaint init_img = None, init_mask = None, fit = False, strength = None, init_color = None, # these are specific to embiggen (which also relies on img2img args) embiggen = None, embiggen_tiles = None, # these are specific to GFPGAN/ESRGAN facetool = None, gfpgan_strength = 0, codeformer_fidelity = None, save_original = False, upscale = None, # Set this True to handle KeyboardInterrupt internally catch_interrupts = False, hires_fix = False, **args, ): # eat up additional cruft """ ldm.generate.prompt2image() is the common entry point for txt2img() and img2img() It takes the following arguments: prompt // prompt string (no default) iterations // iterations (1); image count=iterations steps // refinement steps per iteration seed // seed for random number generator width // width of image, in multiples of 64 (512) height // height of image, in multiples of 64 (512) cfg_scale // how strongly the prompt influences the image (7.5) (must be >1) seamless // whether the generated image should tile init_img // path to an initial image strength // strength for noising/unnoising init_img. 0.0 preserves image exactly, 1.0 replaces it completely gfpgan_strength // strength for GFPGAN. 0.0 preserves image exactly, 1.0 replaces it completely ddim_eta // image randomness (eta=0.0 means the same seed always produces the same image) step_callback // a function or method that will be called each step image_callback // a function or method that will be called each time an image is generated with_variations // a weighted list [(seed_1, weight_1), (seed_2, weight_2), ...] of variations which should be applied before doing any generation variation_amount // optional 0-1 value to slerp from -S noise to random noise (allows variations on an image) threshold // optional value >=0 to add thresholding to latent values for k-diffusion samplers (0 disables) perlin // optional 0-1 value to add a percentage of perlin noise to the initial noise embiggen // scale factor relative to the size of the --init_img (-I), followed by ESRGAN upscaling strength (0-1.0), followed by minimum amount of overlap between tiles as a decimal ratio (0 - 1.0) or number of pixels embiggen_tiles // list of tiles by number in order to process and replace onto the image e.g. `0 2 4` To use the step callback, define a function that receives two arguments: - Image GPU data - The step number To use the image callback, define a function of method that receives two arguments, an Image object and the seed. You can then do whatever you like with the image, including converting it to different formats and manipulating it. For example: 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. 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 width = width or self.width height = height or self.height seamless = seamless or self.seamless cfg_scale = cfg_scale or self.cfg_scale ddim_eta = ddim_eta or self.ddim_eta iterations = iterations or self.iterations strength = strength or self.strength self.seed = seed self.log_tokenization = log_tokenization self.step_callback = step_callback with_variations = [] if with_variations is None else with_variations # will instantiate the model or return it from cache model = self.load_model() for m in model.modules(): if isinstance(m, (nn.Conv2d, nn.ConvTranspose2d)): m.padding_mode = 'circular' if seamless else m._orig_padding_mode assert cfg_scale > 1.0, 'CFG_Scale (-C) must be >1.0' assert threshold >= 0.0, '--threshold must be >=0.0' assert ( 0.0 < strength < 1.0 ), 'img2img and inpaint strength can only work with 0.0 < strength < 1.0' assert ( 0.0 <= variation_amount <= 1.0 ), '-v --variation_amount must be in [0.0, 1.0]' assert ( 0.0 <= perlin <= 1.0 ), '--perlin must be in [0.0, 1.0]' assert ( (embiggen == None and embiggen_tiles == None) or ( (embiggen != None or embiggen_tiles != None) and init_img != None) ), 'Embiggen requires an init/input image to be specified' if len(with_variations) > 0 or variation_amount > 1.0: assert seed is not None,\ 'seed must be specified when using with_variations' if variation_amount == 0.0: assert iterations == 1,\ 'when using --with_variations, multiple iterations are only possible when using --variation_amount' assert all(0 <= weight <= 1 for _, weight in with_variations),\ f'variation weights must be in [0.0, 1.0]: got {[weight for _, weight in with_variations]}' width, height, _ = self._resolution_check(width, height, log=True) if sampler_name and (sampler_name != self.sampler_name): self.sampler_name = sampler_name self._set_sampler() tic = time.time() if self._has_cuda(): torch.cuda.reset_peak_memory_stats() results = list() init_image = None mask_image = None try: uc, c = get_uc_and_c( prompt, model =self.model, skip_normalize=skip_normalize, log_tokens =self.log_tokenization ) init_image,mask_image = self._make_images( init_img, init_mask, width, height, fit=fit, ) if (init_image is not None) and (mask_image is not None): generator = self._make_inpaint() elif (embiggen != None or embiggen_tiles != None): generator = self._make_embiggen() elif init_image is not None: generator = self._make_img2img() elif hires_fix: generator = self._make_txt2img2img() else: generator = self._make_txt2img() generator.set_variation( self.seed, variation_amount, with_variations ) results = generator.generate( prompt, iterations=iterations, seed=self.seed, sampler=self.sampler, steps=steps, cfg_scale=cfg_scale, conditioning=(uc, c), ddim_eta=ddim_eta, image_callback=image_callback, # called after the final image is generated step_callback=step_callback, # called after each intermediate image is generated width=width, 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 mask_image=mask_image, strength=strength, threshold=threshold, perlin=perlin, embiggen=embiggen, embiggen_tiles=embiggen_tiles, ) if init_color: self.correct_colors(image_list = results, reference_image_path = init_color, image_callback = image_callback) if upscale is not None or gfpgan_strength > 0: self.upscale_and_reconstruct(results, upscale = upscale, facetool = facetool, strength = gfpgan_strength, codeformer_fidelity = codeformer_fidelity, save_original = save_original, image_callback = image_callback) except RuntimeError as e: print(traceback.format_exc(), file=sys.stderr) print('>> Could not generate image.') except KeyboardInterrupt: if catch_interrupts: print('**Interrupted** Partial results will be returned.') else: raise KeyboardInterrupt toc = time.time() print('>> Usage stats:') print( f'>> {len(results)} image(s) generated in', '%4.2fs' % ( toc - tic) ) if self._has_cuda(): print( f'>> Max VRAM used for this generation:', '%4.2fG.' % (torch.cuda.max_memory_allocated() / 1e9), 'Current VRAM utilization:', '%4.2fG' % (torch.cuda.memory_allocated() / 1e9), ) self.session_peakmem = max( self.session_peakmem, torch.cuda.max_memory_allocated() ) print( f'>> Max VRAM used since script start: ', '%4.2fG' % (self.session_peakmem / 1e9), ) return results # this needs to be generalized to all sorts of postprocessors, which should be wrapped # in a nice harmonized call signature. For now we have a bunch of if/elses! def apply_postprocessor( self, image_path, tool = 'gfpgan', # one of 'upscale', 'gfpgan', 'codeformer', 'outpaint', or 'embiggen' gfpgan_strength = 0.0, codeformer_fidelity = 0.75, upscale = None, out_direction = None, outcrop = [], save_original = True, # to get new name callback = None, opt = None, ): # retrieve the seed from the image; seed = None image_metadata = None prompt = None args = metadata_from_png(image_path) seed = args.seed prompt = args.prompt print(f'>> retrieved seed {seed} and prompt "{prompt}" from {image_path}') if not seed: print('* Could not recover seed for image. Replacing with 42. This will not affect image quality') seed = 42 # try to reuse the same filename prefix as the original file. # we take everything up to the first period prefix = None m = re.match('^([^.]+)\.',os.path.basename(image_path)) if m: prefix = m.groups()[0] # face fixers and esrgan take an Image, but embiggen takes a path image = Image.open(image_path) # used by multiple postfixers uc, c = get_uc_and_c( prompt, model =self.model, skip_normalize=opt.skip_normalize, log_tokens =opt.log_tokenization ) if tool in ('gfpgan','codeformer','upscale'): if tool == 'gfpgan': facetool = 'gfpgan' elif tool == 'codeformer': facetool = 'codeformer' elif tool == 'upscale': facetool = 'gfpgan' # but won't be run gfpgan_strength = 0 return self.upscale_and_reconstruct( [[image,seed]], facetool = facetool, strength = gfpgan_strength, codeformer_fidelity = codeformer_fidelity, save_original = save_original, upscale = upscale, image_callback = callback, prefix = prefix, ) elif tool == 'outcrop': from ldm.invoke.restoration.outcrop import Outcrop extend_instructions = {} for direction,pixels in _pairwise(opt.outcrop): extend_instructions[direction]=int(pixels) restorer = Outcrop(image,self,) return restorer.process ( extend_instructions, opt = opt, orig_opt = args, image_callback = callback, prefix = prefix, ) elif tool == 'embiggen': # fetch the metadata from the image generator = self._make_embiggen() opt.strength = 0.40 print(f'>> Setting img2img strength to {opt.strength} for happy embiggening') # embiggen takes a image path (sigh) generator.generate( prompt, sampler = self.sampler, steps = opt.steps, cfg_scale = opt.cfg_scale, ddim_eta = self.ddim_eta, conditioning= (uc, c), init_img = image_path, # not the Image! (sigh) init_image = image, # embiggen wants both! (sigh) strength = opt.strength, width = opt.width, height = opt.height, embiggen = opt.embiggen, embiggen_tiles = opt.embiggen_tiles, image_callback = callback, ) elif tool == 'outpaint': from ldm.invoke.restoration.outpaint import Outpaint restorer = Outpaint(image,self) return restorer.process( opt, args, 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 else: print(f'* postprocessing tool {tool} is not yet supported') return None def _make_images( self, img, mask, width, height, fit=False, ): init_image = None init_mask = None if not img: return None, None image = self._load_img( img, width, 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): 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): print(">> This input is larger than your defaults. If you run out of memory, please use a smaller image.") init_image = self._create_init_image(image,width,height,fit=fit) # this returns a torch tensor if mask: mask_image = self._load_img( mask, width, height) # this returns an Image init_mask = self._create_init_mask(mask_image,width,height,fit=fit) return init_image, init_mask def _make_base(self): if not self.generators.get('base'): 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.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.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.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.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.invoke.generator.inpaint import Inpaint self.generators['inpaint'] = Inpaint(self.model, self.precision) return self.generators['inpaint'] def load_model(self): """Load and initialize the model from configuration variables passed at object creation time""" if self.model is None: seed_everything(random.randrange(0, np.iinfo(np.uint32).max)) try: model = self._load_model_from_config(self.config, self.weights) if self.embedding_path is not None: model.embedding_manager.load( self.embedding_path, self.precision == 'float32' or self.precision == 'autocast' ) self.model = model.to(self.device) # model.to doesn't change the cond_stage_model.device used to move the tokenizer output, so set it here self.model.cond_stage_model.device = self.device except AttributeError as e: print(f'>> Error loading model. {str(e)}', file=sys.stderr) print(traceback.format_exc(), file=sys.stderr) raise SystemExit from e self._set_sampler() for m in self.model.modules(): if isinstance(m, (nn.Conv2d, nn.ConvTranspose2d)): m._orig_padding_mode = m.padding_mode return self.model def correct_colors(self, image_list, reference_image_path, image_callback = None): reference_image = Image.open(reference_image_path) correction_target = cv2.cvtColor(np.asarray(reference_image), cv2.COLOR_RGB2LAB) for r in image_list: image, seed = r image = cv2.cvtColor(np.asarray(image), cv2.COLOR_RGB2LAB) image = skimage.exposure.match_histograms(image, correction_target, channel_axis=2) image = Image.fromarray( cv2.cvtColor(image, cv2.COLOR_LAB2RGB).astype("uint8") ) if image_callback is not None: image_callback(image, seed) else: r[0] = image def upscale_and_reconstruct(self, image_list, facetool = 'gfpgan', upscale = None, strength = 0.0, codeformer_fidelity = 0.75, save_original = False, image_callback = None, prefix = None, ): for r in image_list: image, seed = r try: if strength > 0: if self.gfpgan is not None or self.codeformer is not None: if facetool == 'gfpgan': if self.gfpgan is None: print('>> GFPGAN not found. Face restoration is disabled.') else: image = self.gfpgan.process(image, strength, seed) if facetool == 'codeformer': if self.codeformer is None: print('>> CodeFormer not found. Face restoration is disabled.') else: cf_device = 'cpu' if str(self.device) == 'mps' else self.device image = self.codeformer.process(image=image, strength=strength, device=cf_device, seed=seed, fidelity=codeformer_fidelity) else: print(">> Face Restoration is disabled.") if upscale is not None: if self.esrgan is not None: if len(upscale) < 2: upscale.append(0.75) image = self.esrgan.process( image, upscale[1], seed, int(upscale[0])) else: print(">> ESRGAN is disabled. Image not upscaled.") except Exception as e: print( f'>> Error running RealESRGAN or GFPGAN. Your image was not upscaled.\n{e}' ) if image_callback is not None: image_callback(image, seed, upscaled=True, use_prefix=prefix) else: r[0] = image # to help WebGUI - front end to generator util function def sample_to_image(self, samples): return self._make_base().sample_to_image(samples) def _set_sampler(self): msg = f'>> Setting Sampler to {self.sampler_name}' if self.sampler_name == 'plms': self.sampler = PLMSSampler(self.model, device=self.device) elif self.sampler_name == 'ddim': self.sampler = DDIMSampler(self.model, device=self.device) elif self.sampler_name == 'k_dpm_2_a': self.sampler = KSampler( self.model, 'dpm_2_ancestral', device=self.device ) elif self.sampler_name == 'k_dpm_2': self.sampler = KSampler(self.model, 'dpm_2', device=self.device) elif self.sampler_name == 'k_euler_a': self.sampler = KSampler( self.model, 'euler_ancestral', device=self.device ) elif self.sampler_name == 'k_euler': self.sampler = KSampler(self.model, 'euler', device=self.device) elif self.sampler_name == 'k_heun': self.sampler = KSampler(self.model, 'heun', device=self.device) elif self.sampler_name == 'k_lms': self.sampler = KSampler(self.model, 'lms', device=self.device) else: msg = f'>> Unsupported Sampler: {self.sampler_name}, Defaulting to plms' self.sampler = PLMSSampler(self.model, device=self.device) print(msg) # 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): print(f'>> Loading model from {weights}') # for usage statistics device_type = choose_torch_device() if device_type == 'cuda': torch.cuda.reset_peak_memory_stats() tic = time.time() # this does the work c = OmegaConf.load(config) with open(weights,'rb') as f: weight_bytes = f.read() self.model_hash = self._cached_sha256(weights,weight_bytes) pl_sd = torch.load(io.BytesIO(weight_bytes), map_location='cpu') del weight_bytes sd = pl_sd['state_dict'] model = instantiate_from_config(c.model) m, u = model.load_state_dict(sd, strict=False) if self.precision == 'float16': print('>> Using faster float16 precision') model.to(torch.float16) else: print('>> Using more accurate float32 precision') model.to(self.device) model.eval() # 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 def _load_img(self, img, width, height)->Image: if isinstance(img, Image.Image): image = img print( f'>> using provided input image of size {image.width}x{image.height}' ) elif isinstance(img, str): assert os.path.exists(img), f'>> {img}: File not found' image = Image.open(img) print( f'>> loaded input image of size {image.width}x{image.height} from {img}' ) else: image = Image.open(img) print( f'>> loaded input image of size {image.width}x{image.height}' ) image = ImageOps.exif_transpose(image) return image 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) 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) # The mask is expected to have the region to be inpainted # with alpha transparency. It converts it into a black/white # image with the transparent part black. def _image_to_mask(self, mask_image, invert=False) -> Image: # Obtain the mask from the transparency channel mask = Image.new(mode="L", size=mask_image.size, color=255) mask.putdata(mask_image.getdata(band=3)) if invert: mask = ImageOps.invert(mask) return mask def _has_transparency(self, image): if image.info.get("transparency", None) is not None: return True if image.mode == "P": transparent = image.info.get("transparency", -1) for _, index in image.getcolors(): if index == transparent: return True elif image.mode == "RGBA": extrema = image.getextrema() if extrema[3][0] < 255: return True return False def _check_for_erasure(self, image): width, height = image.size pixdata = image.load() colored = 0 for y in range(height): for x in range(width): if pixdata[x, y][3] == 0: r, g, b, _ = pixdata[x, y] if (r, g, b) != (0, 0, 0) and \ (r, g, b) != (255, 255, 255): 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: return InitImageResizer(image).resize(x, y) return image def _fit_image(self, image, max_dimensions): w, h = max_dimensions print( f'>> image will be resized to fit inside a box {w}x{h} in size.' ) if image.width > image.height: h = None # by setting h to none, we tell InitImageResizer to fit into the width and calculate height elif image.height > image.width: w = None # ditto for w else: pass # note that InitImageResizer does the multiple of 64 truncation internally image = InitImageResizer(image).resize(w, h) print( f'>> after adjusting image dimensions to be multiples of 64, init image is {image.width}x{image.height}' ) return image def _resolution_check(self, width, height, log=False): resize_needed = False w, h = map( lambda x: x - x % 64, (width, height) ) # resize to integer multiple of 64 if h != height or w != width: if log: print( f'>> Provided width and height must be multiples of 64. Auto-resizing to {w}x{h}' ) height = h width = w resize_needed = True return width, height, resize_needed def _has_cuda(self): return self.device.type == 'cuda' def _cached_sha256(self,path,data): dirname = os.path.dirname(path) basename = os.path.basename(path) base, _ = os.path.splitext(basename) hashpath = os.path.join(dirname,base+'.sha256') if os.path.exists(hashpath) and os.path.getmtime(path) <= os.path.getmtime(hashpath): with open(hashpath) as f: hash = f.read() return hash print(f'>> Calculating sha256 hash of weights file') tic = time.time() sha = hashlib.sha256() sha.update(data) hash = sha.hexdigest() toc = time.time() print(f'>> sha256 = {hash}','(%4.2fs)' % (toc - tic)) with open(hashpath,'w') as f: f.write(hash) return hash def write_intermediate_images(self,modulus,path): counter = -1 if not os.path.exists(path): os.makedirs(path) def callback(img): nonlocal counter counter += 1 if counter % modulus != 0: return; image = self.sample_to_image(img) image.save(os.path.join(path,f'{counter:03}.png'),'PNG') return callback def _pairwise(iterable): "s -> (s0, s1), (s2, s3), (s4, s5), ..." a = iter(iterable) return zip(a, a)