''' Base class for invokeai.backend.generator.* including img2img, txt2img, and inpaint ''' from __future__ import annotations import os import os.path as osp import random import traceback from contextlib import nullcontext import cv2 import numpy as np import torch from PIL import Image, ImageFilter, ImageChops from diffusers import DiffusionPipeline from einops import rearrange from pathlib import Path from pytorch_lightning import seed_everything from tqdm import trange import invokeai.assets.web as web_assets from ..models.diffusion.ddpm import DiffusionWrapper from ldm.util import rand_perlin_2d downsampling = 8 CAUTION_IMG = 'caution.png' class Generator: downsampling_factor: int latent_channels: int precision: str model: DiffusionWrapper | DiffusionPipeline def __init__(self, model: DiffusionWrapper | DiffusionPipeline, precision: str): self.model = model self.precision = precision self.seed = None self.latent_channels = model.channels self.downsampling_factor = downsampling # BUG: should come from model or config self.safety_checker = None self.perlin = 0.0 self.threshold = 0 self.variation_amount = 0 self.with_variations = [] self.use_mps_noise = False self.free_gpu_mem = None self.caution_img = None # this is going to be overridden in img2img.py, txt2img.py and inpaint.py def get_make_image(self,prompt,**kwargs): """ Returns a function returning an image derived from the prompt and the initial image Return value depends on the seed at the time you call it """ raise NotImplementedError("image_iterator() must be implemented in a descendent class") def set_variation(self, seed, variation_amount, with_variations): self.seed = seed self.variation_amount = variation_amount self.with_variations = with_variations def generate(self,prompt,init_image,width,height,sampler, iterations=1,seed=None, image_callback=None, step_callback=None, threshold=0.0, perlin=0.0, h_symmetry_time_pct=None, v_symmetry_time_pct=None, safety_checker:dict=None, free_gpu_mem: bool=False, **kwargs): scope = nullcontext self.safety_checker = safety_checker self.free_gpu_mem = free_gpu_mem attention_maps_images = [] attention_maps_callback = lambda saver: attention_maps_images.append(saver.get_stacked_maps_image()) make_image = self.get_make_image( prompt, sampler = sampler, init_image = init_image, width = width, height = height, step_callback = step_callback, threshold = threshold, perlin = perlin, h_symmetry_time_pct = h_symmetry_time_pct, v_symmetry_time_pct = v_symmetry_time_pct, attention_maps_callback = attention_maps_callback, **kwargs ) results = [] seed = seed if seed is not None and seed >= 0 else self.new_seed() first_seed = seed seed, initial_noise = self.generate_initial_noise(seed, width, height) # There used to be an additional self.model.ema_scope() here, but it breaks # the inpaint-1.5 model. Not sure what it did.... ? with scope(self.model.device.type): for n in trange(iterations, desc='Generating'): x_T = None if self.variation_amount > 0: seed_everything(seed) target_noise = self.get_noise(width,height) x_T = self.slerp(self.variation_amount, initial_noise, target_noise) elif initial_noise is not None: # i.e. we specified particular variations x_T = initial_noise else: seed_everything(seed) try: x_T = self.get_noise(width,height) except: print('** An error occurred while getting initial noise **') print(traceback.format_exc()) image = make_image(x_T) if self.safety_checker is not None: image = self.safety_check(image) results.append([image, seed]) if image_callback is not None: attention_maps_image = None if len(attention_maps_images)==0 else attention_maps_images[-1] image_callback(image, seed, first_seed=first_seed, attention_maps_image=attention_maps_image) seed = self.new_seed() # Free up memory from the last generation. clear_cuda_cache = kwargs['clear_cuda_cache'] if 'clear_cuda_cache' in kwargs else None if clear_cuda_cache is not None: clear_cuda_cache() return results def sample_to_image(self,samples)->Image.Image: """ Given samples returned from a sampler, converts it into a PIL Image """ with torch.inference_mode(): image = self.model.decode_latents(samples) return self.model.numpy_to_pil(image)[0] def repaste_and_color_correct(self, result: Image.Image, init_image: Image.Image, init_mask: Image.Image, mask_blur_radius: int = 8) -> Image.Image: if init_image is None or init_mask is None: return result # 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 = init_mask.getchannel('A') if init_mask.mode == 'RGBA' else init_mask.convert('L') pil_init_image = init_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. init_rgb_pixels = np.asarray(init_image.convert('RGB'), dtype=np.uint8) init_a_pixels = np.asarray(pil_init_image.getchannel('A'), dtype=np.uint8) init_mask_pixels = np.asarray(pil_init_mask, dtype=np.uint8) # Get numpy version of result np_image = np.asarray(result, dtype=np.uint8) # Mask and calculate mean and standard deviation mask_pixels = init_a_pixels * init_mask_pixels > 0 np_init_rgb_pixels_masked = init_rgb_pixels[mask_pixels, :] np_image_masked = np_image[mask_pixels, :] if np_init_rgb_pixels_masked.size > 0: init_means = np_init_rgb_pixels_masked.mean(axis=0) init_std = np_init_rgb_pixels_masked.std(axis=0) gen_means = np_image_masked.mean(axis=0) gen_std = np_image_masked.std(axis=0) # Color correct np_matched_result = np_image.copy() np_matched_result[:,:,:] = (((np_matched_result[:,:,:].astype(np.float32) - gen_means[None,None,:]) / gen_std[None,None,:]) * init_std[None,None,:] + init_means[None,None,:]).clip(0, 255).astype(np.uint8) matched_result = Image.fromarray(np_matched_result, mode='RGB') else: matched_result = Image.fromarray(np_image, 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 = 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: blurred_init_mask = pil_init_mask multiplied_blurred_init_mask = ImageChops.multiply(blurred_init_mask, self.pil_image.split()[-1]) # Paste original on color-corrected generation (using blurred mask) 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 # these updated numbers for v1.5 are from @torridgristle v1_5_latent_rgb_factors = torch.tensor([ # R G B [ 0.3444, 0.1385, 0.0670], # L1 [ 0.1247, 0.4027, 0.1494], # L2 [-0.3192, 0.2513, 0.2103], # L3 [-0.1307, -0.1874, -0.7445] # L4 ], dtype=samples.dtype, device=samples.device) latent_image = samples[0].permute(1, 2, 0) @ v1_5_latent_rgb_factors latents_ubyte = (((latent_image + 1) / 2) .clamp(0, 1) # change scale from -1..1 to 0..1 .mul(0xFF) # to 0..255 .byte()).cpu() return Image.fromarray(latents_ubyte.numpy()) def generate_initial_noise(self, seed, width, height): initial_noise = None if self.variation_amount > 0 or len(self.with_variations) > 0: # use fixed initial noise plus random noise per iteration seed_everything(seed) initial_noise = self.get_noise(width,height) for v_seed, v_weight in self.with_variations: seed = v_seed seed_everything(seed) next_noise = self.get_noise(width,height) initial_noise = self.slerp(v_weight, initial_noise, next_noise) if self.variation_amount > 0: random.seed() # reset RNG to an actually random state, so we can get a random seed for variations seed = random.randrange(0,np.iinfo(np.uint32).max) return (seed, initial_noise) else: return (seed, None) # returns a tensor filled with random numbers from a normal distribution def get_noise(self,width,height): """ Returns a tensor filled with random numbers, either form a normal distribution (txt2img) or from the latent image (img2img, inpaint) """ raise NotImplementedError("get_noise() must be implemented in a descendent class") def get_perlin_noise(self,width,height): fixdevice = 'cpu' if (self.model.device.type == 'mps') else self.model.device # limit noise to only the diffusion image channels, not the mask channels input_channels = min(self.latent_channels, 4) # round up to the nearest block of 8 temp_width = int((width + 7) / 8) * 8 temp_height = int((height + 7) / 8) * 8 noise = torch.stack([ rand_perlin_2d((temp_height, temp_width), (8, 8), device = self.model.device).to(fixdevice) for _ in range(input_channels)], dim=0).to(self.model.device) return noise[0:4, 0:height, 0:width] def new_seed(self): self.seed = random.randrange(0, np.iinfo(np.uint32).max) return self.seed def slerp(self, t, v0, v1, DOT_THRESHOLD=0.9995): ''' Spherical linear interpolation Args: t (float/np.ndarray): Float value between 0.0 and 1.0 v0 (np.ndarray): Starting vector v1 (np.ndarray): Final vector DOT_THRESHOLD (float): Threshold for considering the two vectors as colineal. Not recommended to alter this. Returns: v2 (np.ndarray): Interpolation vector between v0 and v1 ''' inputs_are_torch = False if not isinstance(v0, np.ndarray): inputs_are_torch = True v0 = v0.detach().cpu().numpy() if not isinstance(v1, np.ndarray): inputs_are_torch = True v1 = v1.detach().cpu().numpy() dot = np.sum(v0 * v1 / (np.linalg.norm(v0) * np.linalg.norm(v1))) if np.abs(dot) > DOT_THRESHOLD: v2 = (1 - t) * v0 + t * v1 else: theta_0 = np.arccos(dot) sin_theta_0 = np.sin(theta_0) theta_t = theta_0 * t sin_theta_t = np.sin(theta_t) s0 = np.sin(theta_0 - theta_t) / sin_theta_0 s1 = sin_theta_t / sin_theta_0 v2 = s0 * v0 + s1 * v1 if inputs_are_torch: v2 = torch.from_numpy(v2).to(self.model.device) return v2 def safety_check(self,image:Image.Image): ''' If the CompViz safety checker flags an NSFW image, we blur it out. ''' import diffusers checker = self.safety_checker['checker'] extractor = self.safety_checker['extractor'] features = extractor([image], return_tensors="pt") features.to(self.model.device) # unfortunately checker requires the numpy version, so we have to convert back x_image = np.array(image).astype(np.float32) / 255.0 x_image = x_image[None].transpose(0, 3, 1, 2) diffusers.logging.set_verbosity_error() checked_image, has_nsfw_concept = checker(images=x_image, clip_input=features.pixel_values) if has_nsfw_concept[0]: print('** An image with potential non-safe content has been detected. A blurred image will be returned. **') return self.blur(image) else: return image def blur(self,input): blurry = input.filter(filter=ImageFilter.GaussianBlur(radius=32)) try: caution = self.get_caution_img() if caution: blurry.paste(caution,(0,0),caution) except FileNotFoundError: pass return blurry def get_caution_img(self): path = None if self.caution_img: return self.caution_img path = Path(web_assets.__path__[0]) / CAUTION_IMG caution = Image.open(path) self.caution_img = caution.resize((caution.width // 2, caution.height //2)) return self.caution_img # this is a handy routine for debugging use. Given a generated sample, # convert it into a PNG image and store it at the indicated path def save_sample(self, sample, filepath): image = self.sample_to_image(sample) dirname = os.path.dirname(filepath) or '.' if not os.path.exists(dirname): print(f'** creating directory {dirname}') os.makedirs(dirname, exist_ok=True) image.save(filepath,'PNG') def torch_dtype(self)->torch.dtype: return torch.float16 if self.precision == 'float16' else torch.float32 # returns a tensor filled with random numbers from a normal distribution def get_noise(self,width,height): device = self.model.device # limit noise to only the diffusion image channels, not the mask channels input_channels = min(self.latent_channels, 4) if self.use_mps_noise or device.type == 'mps': x = torch.randn([1, input_channels, height // self.downsampling_factor, width // self.downsampling_factor], dtype=self.torch_dtype(), device='cpu').to(device) else: x = torch.randn([1, input_channels, height // self.downsampling_factor, width // self.downsampling_factor], dtype=self.torch_dtype(), device=device) if self.perlin > 0.0: perlin_noise = self.get_perlin_noise(width // self.downsampling_factor, height // self.downsampling_factor) x = (1-self.perlin)*x + self.perlin*perlin_noise return x