mirror of
https://github.com/invoke-ai/InvokeAI
synced 2024-08-30 20:32:17 +00:00
9141132a5c
This commit does several things that improve the customizability of the CLI `outcrop` command: 1. When outcropping an image you can now add a `--new_prompt` option, to specify a new prompt to be applied to the outpainted region instead of the prompt used to generate the image. 2. Similarly you can provide a new seed using `--seed` (or `-S`). A seed less than zero will pick one randomly. 3. The metadata written into the outcropped file is now more informative about what was previously stored. 4. This PR also fixes the crash that happened when trying to outcrop an image that does not contain InvokeAI metadata. Other changes: - add error checking suggested by @Kyle0654 - add special case in invoke.py to allow -1 to be passed as seed. This now only occurs for postprocessing commands. Previously, -1 caused previous seed to be used, and this still applies to generate operations.
258 lines
10 KiB
Python
258 lines
10 KiB
Python
'''
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Base class for ldm.invoke.generator.*
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including img2img, txt2img, and inpaint
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'''
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import torch
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import numpy as np
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import random
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import os
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import traceback
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from tqdm import tqdm, trange
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from PIL import Image, ImageFilter
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from einops import rearrange, repeat
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from pytorch_lightning import seed_everything
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from ldm.invoke.devices import choose_autocast
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from ldm.util import rand_perlin_2d
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downsampling = 8
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CAUTION_IMG = 'assets/caution.png'
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class Generator():
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def __init__(self, model, precision):
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self.model = model
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self.precision = precision
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self.seed = None
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self.latent_channels = model.channels
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self.downsampling_factor = downsampling # BUG: should come from model or config
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self.safety_checker = None
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self.perlin = 0.0
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self.threshold = 0
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self.variation_amount = 0
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self.with_variations = []
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self.use_mps_noise = False
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self.free_gpu_mem = None
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# this is going to be overridden in img2img.py, txt2img.py and inpaint.py
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def get_make_image(self,prompt,**kwargs):
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"""
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Returns a function returning an image derived from the prompt and the initial image
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Return value depends on the seed at the time you call it
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"""
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raise NotImplementedError("image_iterator() must be implemented in a descendent class")
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def set_variation(self, seed, variation_amount, with_variations):
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self.seed = seed
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self.variation_amount = variation_amount
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self.with_variations = with_variations
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def generate(self,prompt,init_image,width,height,sampler, iterations=1,seed=None,
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image_callback=None, step_callback=None, threshold=0.0, perlin=0.0,
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safety_checker:dict=None,
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**kwargs):
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scope = choose_autocast(self.precision)
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self.safety_checker = safety_checker
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make_image = self.get_make_image(
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prompt,
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sampler = sampler,
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init_image = init_image,
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width = width,
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height = height,
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step_callback = step_callback,
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threshold = threshold,
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perlin = perlin,
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**kwargs
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)
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results = []
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seed = seed if seed is not None and seed >= 0 else self.new_seed()
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first_seed = seed
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seed, initial_noise = self.generate_initial_noise(seed, width, height)
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# There used to be an additional self.model.ema_scope() here, but it breaks
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# the inpaint-1.5 model. Not sure what it did.... ?
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with scope(self.model.device.type):
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for n in trange(iterations, desc='Generating'):
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x_T = None
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if self.variation_amount > 0:
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seed_everything(seed)
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target_noise = self.get_noise(width,height)
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x_T = self.slerp(self.variation_amount, initial_noise, target_noise)
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elif initial_noise is not None:
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# i.e. we specified particular variations
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x_T = initial_noise
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else:
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seed_everything(seed)
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try:
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x_T = self.get_noise(width,height)
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except:
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print('** An error occurred while getting initial noise **')
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print(traceback.format_exc())
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image = make_image(x_T)
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if self.safety_checker is not None:
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image = self.safety_check(image)
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results.append([image, seed])
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if image_callback is not None:
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image_callback(image, seed, first_seed=first_seed)
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seed = self.new_seed()
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return results
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def sample_to_image(self,samples)->Image.Image:
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"""
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Given samples returned from a sampler, converts
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it into a PIL Image
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"""
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x_samples = self.model.decode_first_stage(samples)
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x_samples = torch.clamp((x_samples + 1.0) / 2.0, min=0.0, max=1.0)
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if len(x_samples) != 1:
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raise Exception(
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f'>> expected to get a single image, but got {len(x_samples)}')
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x_sample = 255.0 * rearrange(
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x_samples[0].cpu().numpy(), 'c h w -> h w c'
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)
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return Image.fromarray(x_sample.astype(np.uint8))
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# write an approximate RGB image from latent samples for a single step to PNG
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def sample_to_lowres_estimated_image(self,samples):
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# origingally adapted from code by @erucipe and @keturn here:
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# https://discuss.huggingface.co/t/decoding-latents-to-rgb-without-upscaling/23204/7
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# these updated numbers for v1.5 are from @torridgristle
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v1_5_latent_rgb_factors = torch.tensor([
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# R G B
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[ 0.3444, 0.1385, 0.0670], # L1
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[ 0.1247, 0.4027, 0.1494], # L2
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[-0.3192, 0.2513, 0.2103], # L3
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[-0.1307, -0.1874, -0.7445] # L4
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], dtype=samples.dtype, device=samples.device)
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latent_image = samples[0].permute(1, 2, 0) @ v1_5_latent_rgb_factors
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latents_ubyte = (((latent_image + 1) / 2)
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.clamp(0, 1) # change scale from -1..1 to 0..1
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.mul(0xFF) # to 0..255
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.byte()).cpu()
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return Image.fromarray(latents_ubyte.numpy())
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def generate_initial_noise(self, seed, width, height):
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initial_noise = None
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if self.variation_amount > 0 or len(self.with_variations) > 0:
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# use fixed initial noise plus random noise per iteration
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seed_everything(seed)
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initial_noise = self.get_noise(width,height)
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for v_seed, v_weight in self.with_variations:
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seed = v_seed
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seed_everything(seed)
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next_noise = self.get_noise(width,height)
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initial_noise = self.slerp(v_weight, initial_noise, next_noise)
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if self.variation_amount > 0:
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random.seed() # reset RNG to an actually random state, so we can get a random seed for variations
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seed = random.randrange(0,np.iinfo(np.uint32).max)
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return (seed, initial_noise)
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else:
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return (seed, None)
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# returns a tensor filled with random numbers from a normal distribution
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def get_noise(self,width,height):
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"""
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Returns a tensor filled with random numbers, either form a normal distribution
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(txt2img) or from the latent image (img2img, inpaint)
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"""
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raise NotImplementedError("get_noise() must be implemented in a descendent class")
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def get_perlin_noise(self,width,height):
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fixdevice = 'cpu' if (self.model.device.type == 'mps') else self.model.device
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return torch.stack([rand_perlin_2d((height, width), (8, 8), device = self.model.device).to(fixdevice) for _ in range(self.latent_channels)], dim=0).to(self.model.device)
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def new_seed(self):
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self.seed = random.randrange(0, np.iinfo(np.uint32).max)
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return self.seed
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def slerp(self, t, v0, v1, DOT_THRESHOLD=0.9995):
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'''
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Spherical linear interpolation
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Args:
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t (float/np.ndarray): Float value between 0.0 and 1.0
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v0 (np.ndarray): Starting vector
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v1 (np.ndarray): Final vector
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DOT_THRESHOLD (float): Threshold for considering the two vectors as
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colineal. Not recommended to alter this.
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Returns:
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v2 (np.ndarray): Interpolation vector between v0 and v1
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'''
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inputs_are_torch = False
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if not isinstance(v0, np.ndarray):
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inputs_are_torch = True
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v0 = v0.detach().cpu().numpy()
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if not isinstance(v1, np.ndarray):
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inputs_are_torch = True
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v1 = v1.detach().cpu().numpy()
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dot = np.sum(v0 * v1 / (np.linalg.norm(v0) * np.linalg.norm(v1)))
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if np.abs(dot) > DOT_THRESHOLD:
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v2 = (1 - t) * v0 + t * v1
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else:
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theta_0 = np.arccos(dot)
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sin_theta_0 = np.sin(theta_0)
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theta_t = theta_0 * t
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sin_theta_t = np.sin(theta_t)
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s0 = np.sin(theta_0 - theta_t) / sin_theta_0
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s1 = sin_theta_t / sin_theta_0
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v2 = s0 * v0 + s1 * v1
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if inputs_are_torch:
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v2 = torch.from_numpy(v2).to(self.model.device)
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return v2
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def safety_check(self,image:Image.Image):
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'''
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If the CompViz safety checker flags an NSFW image, we
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blur it out.
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'''
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import diffusers
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checker = self.safety_checker['checker']
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extractor = self.safety_checker['extractor']
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features = extractor([image], return_tensors="pt")
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features.to(self.model.device)
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# unfortunately checker requires the numpy version, so we have to convert back
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x_image = np.array(image).astype(np.float32) / 255.0
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x_image = x_image[None].transpose(0, 3, 1, 2)
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diffusers.logging.set_verbosity_error()
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checked_image, has_nsfw_concept = checker(images=x_image, clip_input=features.pixel_values)
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if has_nsfw_concept[0]:
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print('** An image with potential non-safe content has been detected. A blurred image will be returned. **')
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return self.blur(image)
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else:
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return image
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def blur(self,input):
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blurry = input.filter(filter=ImageFilter.GaussianBlur(radius=32))
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try:
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caution = Image.open(CAUTION_IMG)
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caution = caution.resize((caution.width // 2, caution.height //2))
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blurry.paste(caution,(0,0),caution)
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except FileNotFoundError:
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pass
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return blurry
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# this is a handy routine for debugging use. Given a generated sample,
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# convert it into a PNG image and store it at the indicated path
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def save_sample(self, sample, filepath):
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image = self.sample_to_image(sample)
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dirname = os.path.dirname(filepath) or '.'
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if not os.path.exists(dirname):
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print(f'** creating directory {dirname}')
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os.makedirs(dirname, exist_ok=True)
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image.save(filepath,'PNG')
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