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https://github.com/invoke-ai/InvokeAI
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552 lines
24 KiB
Python
552 lines
24 KiB
Python
# Copyright (c) 2022 Lincoln D. Stein (https://github.com/lstein)
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# Derived from source code carrying the following copyrights
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# Copyright (c) 2022 Machine Vision and Learning Group, LMU Munich
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# Copyright (c) 2022 Robin Rombach and Patrick Esser and contributors
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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 sys
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import os
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from omegaconf import OmegaConf
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from PIL import Image
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from tqdm import tqdm, trange
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from itertools import islice
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from einops import rearrange, repeat
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from torchvision.utils import make_grid
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from pytorch_lightning import seed_everything
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from torch import autocast
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from contextlib import contextmanager, nullcontext
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import transformers
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import time
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import re
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import traceback
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from ldm.util import instantiate_from_config
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from ldm.models.diffusion.ddim import DDIMSampler
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from ldm.models.diffusion.plms import PLMSSampler
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from ldm.models.diffusion.ksampler import KSampler
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from ldm.dream_util import PngWriter
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"""Simplified text to image API for stable diffusion/latent diffusion
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Example Usage:
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from ldm.simplet2i import T2I
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# Create an object with default values
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t2i = T2I(model = <path> // models/ldm/stable-diffusion-v1/model.ckpt
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config = <path> // configs/stable-diffusion/v1-inference.yaml
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iterations = <integer> // how many times to run the sampling (1)
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batch_size = <integer> // how many images to generate per sampling (1)
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steps = <integer> // 50
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seed = <integer> // current system time
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sampler_name= ['ddim', 'k_dpm_2_a', 'k_dpm_2', 'k_euler_a', 'k_euler', 'k_heun', 'k_lms', 'plms'] // k_lms
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grid = <boolean> // false
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width = <integer> // image width, multiple of 64 (512)
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height = <integer> // image height, multiple of 64 (512)
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cfg_scale = <float> // unconditional guidance scale (7.5)
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)
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# do the slow model initialization
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t2i.load_model()
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# Do the fast inference & image generation. Any options passed here
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# override the default values assigned during class initialization
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# Will call load_model() if the model was not previously loaded and so
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# may be slow at first.
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# The method returns a list of images. Each row of the list is a sub-list of [filename,seed]
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results = t2i.prompt2png(prompt = "an astronaut riding a horse",
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outdir = "./outputs/samples",
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iterations = 3)
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for row in results:
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print(f'filename={row[0]}')
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print(f'seed ={row[1]}')
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# Same thing, but using an initial image.
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results = t2i.prompt2png(prompt = "an astronaut riding a horse",
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outdir = "./outputs/,
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iterations = 3,
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init_img = "./sketches/horse+rider.png")
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for row in results:
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print(f'filename={row[0]}')
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print(f'seed ={row[1]}')
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# Same thing, but we return a series of Image objects, which lets you manipulate them,
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# combine them, and save them under arbitrary names
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results = t2i.prompt2image(prompt = "an astronaut riding a horse"
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outdir = "./outputs/")
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for row in results:
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im = row[0]
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seed = row[1]
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im.save(f'./outputs/samples/an_astronaut_riding_a_horse-{seed}.png')
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im.thumbnail(100,100).save('./outputs/samples/astronaut_thumb.jpg')
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Note that the old txt2img() and img2img() calls are deprecated but will
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still work.
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"""
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class T2I:
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"""T2I class
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Attributes
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----------
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model
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config
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iterations
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batch_size
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steps
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seed
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sampler_name
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width
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height
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cfg_scale
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latent_channels
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downsampling_factor
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precision
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strength
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embedding_path
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The vast majority of these arguments default to reasonable values.
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"""
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def __init__(self,
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batch_size=1,
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iterations = 1,
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steps=50,
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seed=None,
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cfg_scale=7.5,
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weights="models/ldm/stable-diffusion-v1/model.ckpt",
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config = "configs/stable-diffusion/v1-inference.yaml",
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width=512,
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height=512,
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sampler_name="klms",
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latent_channels=4,
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downsampling_factor=8,
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ddim_eta=0.0, # deterministic
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precision='autocast',
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full_precision=False,
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strength=0.75, # default in scripts/img2img.py
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embedding_path=None,
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latent_diffusion_weights=False, # just to keep track of this parameter when regenerating prompt
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device='cuda'
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):
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self.batch_size = batch_size
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self.iterations = iterations
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self.width = width
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self.height = height
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self.steps = steps
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self.cfg_scale = cfg_scale
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self.weights = weights
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self.config = config
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self.sampler_name = sampler_name
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self.latent_channels = latent_channels
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self.downsampling_factor = downsampling_factor
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self.ddim_eta = ddim_eta
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self.precision = precision
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self.full_precision = full_precision
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self.strength = strength
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self.embedding_path = embedding_path
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self.model = None # empty for now
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self.sampler = None
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self.latent_diffusion_weights=latent_diffusion_weights
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self.device = device
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if seed is None:
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self.seed = self._new_seed()
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else:
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self.seed = seed
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transformers.logging.set_verbosity_error()
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def prompt2png(self,prompt,outdir,**kwargs):
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'''
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Takes a prompt and an output directory, writes out the requested number
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of PNG files, and returns an array of [[filename,seed],[filename,seed]...]
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Optional named arguments are the same as those passed to T2I and prompt2image()
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'''
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results = self.prompt2image(prompt,**kwargs)
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pngwriter = PngWriter(outdir,prompt,kwargs.get('batch_size',self.batch_size))
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for r in results:
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metadata_str = f'prompt2png("{prompt}" {kwargs} seed={r[1]}' # gets written into the PNG
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pngwriter.write_image(r[0],r[1])
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return pngwriter.files_written
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def txt2img(self,prompt,**kwargs):
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outdir = kwargs.get('outdir','outputs/img-samples')
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return self.prompt2png(prompt,outdir,**kwargs)
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def img2img(self,prompt,**kwargs):
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outdir = kwargs.get('outdir','outputs/img-samples')
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assert 'init_img' in kwargs,'call to img2img() must include the init_img argument'
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return self.prompt2png(prompt,outdir,**kwargs)
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def prompt2image(self,
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# these are common
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prompt,
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batch_size=None,
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iterations=None,
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steps=None,
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seed=None,
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cfg_scale=None,
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ddim_eta=None,
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skip_normalize=False,
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image_callback=None,
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# these are specific to txt2img
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width=None,
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height=None,
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# these are specific to img2img
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init_img=None,
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strength=None,
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variants=None,
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**args): # eat up additional cruft
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'''
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ldm.prompt2image() is the common entry point for txt2img() and img2img()
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It takes the following arguments:
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prompt // prompt string (no default)
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iterations // iterations (1); image count=iterations x batch_size
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batch_size // images per iteration (1)
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steps // refinement steps per iteration
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seed // seed for random number generator
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width // width of image, in multiples of 64 (512)
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height // height of image, in multiples of 64 (512)
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cfg_scale // how strongly the prompt influences the image (7.5) (must be >1)
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init_img // path to an initial image - its dimensions override width and height
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strength // strength for noising/unnoising init_img. 0.0 preserves image exactly, 1.0 replaces it completely
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ddim_eta // image randomness (eta=0.0 means the same seed always produces the same image)
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variants // if >0, the 1st generated image will be passed back to img2img to generate the requested number of variants
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callback // a function or method that will be called each time an image is generated
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To use the callback, define a function of method that receives two arguments, an Image object
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and the seed. You can then do whatever you like with the image, including converting it to
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different formats and manipulating it. For example:
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def process_image(image,seed):
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image.save(f{'images/seed.png'})
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The callback used by the prompt2png() can be found in ldm/dream_util.py. It contains code
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to create the requested output directory, select a unique informative name for each image, and
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write the prompt into the PNG metadata.
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'''
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steps = steps or self.steps
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seed = seed or self.seed
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width = width or self.width
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height = height or self.height
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cfg_scale = cfg_scale or self.cfg_scale
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ddim_eta = ddim_eta or self.ddim_eta
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batch_size = batch_size or self.batch_size
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iterations = iterations or self.iterations
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strength = strength or self.strength
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model = self.load_model() # will instantiate the model or return it from cache
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assert cfg_scale>1.0, "CFG_Scale (-C) must be >1.0"
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assert 0. <= strength <= 1., 'can only work with strength in [0.0, 1.0]'
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w = int(width/64) * 64
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h = int(height/64) * 64
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if h != height or w != width:
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print(f'Height and width must be multiples of 64. Resizing to {h}x{w}')
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height = h
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width = w
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data = [batch_size * [prompt]]
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scope = autocast if self.precision=="autocast" else nullcontext
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tic = time.time()
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results = list()
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def prompt_callback(image, seed):
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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)
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try:
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if init_img:
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assert os.path.exists(init_img),f'{init_img}: File not found'
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self._img2img(prompt,
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data=data,precision_scope=scope,
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batch_size=batch_size,iterations=iterations,
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steps=steps,seed=seed,cfg_scale=cfg_scale,ddim_eta=ddim_eta,
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skip_normalize=skip_normalize,
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init_img=init_img,strength=strength,variants=variants,
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callback=prompt_callback)
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else:
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self._txt2img(prompt,
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data=data,precision_scope=scope,
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batch_size=batch_size,iterations=iterations,
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steps=steps,seed=seed,cfg_scale=cfg_scale,ddim_eta=ddim_eta,
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skip_normalize=skip_normalize,
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width=width,height=height,
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callback=prompt_callback)
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except KeyboardInterrupt:
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print('*interrupted*')
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print('Partial results will be returned; if --grid was requested, nothing will be returned.')
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except RuntimeError as e:
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print(str(e))
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toc = time.time()
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print(f'{len(results)} images generated in',"%4.2fs"% (toc-tic))
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return results
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@torch.no_grad()
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def _txt2img(self,prompt,
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data,precision_scope,
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batch_size,iterations,
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steps,seed,cfg_scale,ddim_eta,
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skip_normalize,
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width,height,
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callback): # the callback is called each time a new Image is generated
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"""
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Generate an image from the prompt, writing iteration images into the outdir
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The output is a list of lists in the format: [[image1,seed1], [image2,seed2],...]
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"""
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sampler = self.sampler
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images = list()
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image_count = 0
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# Gawd. Too many levels of indent here. Need to refactor into smaller routines!
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with precision_scope(self.device.type), self.model.ema_scope():
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all_samples = list()
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for n in trange(iterations, desc="Sampling"):
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seed_everything(seed)
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for prompts in tqdm(data, desc="data", dynamic_ncols=True):
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uc = None
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if cfg_scale != 1.0:
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uc = self.model.get_learned_conditioning(batch_size * [""])
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if isinstance(prompts, tuple):
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prompts = list(prompts)
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# weighted sub-prompts
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subprompts,weights = T2I._split_weighted_subprompts(prompts[0])
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if len(subprompts) > 1:
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# i dont know if this is correct.. but it works
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c = torch.zeros_like(uc)
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# get total weight for normalizing
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totalWeight = sum(weights)
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# normalize each "sub prompt" and add it
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for i in range(0,len(subprompts)):
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weight = weights[i]
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if not skip_normalize:
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weight = weight / totalWeight
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c = torch.add(c,self.model.get_learned_conditioning(subprompts[i]), alpha=weight)
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else: # just standard 1 prompt
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c = self.model.get_learned_conditioning(prompts)
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shape = [self.latent_channels, height // self.downsampling_factor, width // self.downsampling_factor]
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samples_ddim, _ = sampler.sample(S=steps,
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conditioning=c,
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batch_size=batch_size,
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shape=shape,
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verbose=False,
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unconditional_guidance_scale=cfg_scale,
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unconditional_conditioning=uc,
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eta=ddim_eta)
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x_samples_ddim = self.model.decode_first_stage(samples_ddim)
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x_samples_ddim = torch.clamp((x_samples_ddim + 1.0) / 2.0, min=0.0, max=1.0)
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for x_sample in x_samples_ddim:
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x_sample = 255. * rearrange(x_sample.cpu().numpy(), 'c h w -> h w c')
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image = Image.fromarray(x_sample.astype(np.uint8))
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images.append([image,seed])
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if callback is not None:
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callback(image,seed)
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seed = self._new_seed()
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return images
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@torch.no_grad()
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def _img2img(self,prompt,
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data,precision_scope,
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batch_size,iterations,
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steps,seed,cfg_scale,ddim_eta,
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skip_normalize,
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init_img,strength,variants,
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callback):
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"""
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Generate an image from the prompt and the initial image, writing iteration images into the outdir
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The output is a list of lists in the format: [[image,seed1], [image,seed2],...]
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"""
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# PLMS sampler not supported yet, so ignore previous sampler
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if self.sampler_name!='ddim':
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print(f"sampler '{self.sampler_name}' is not yet supported. Using DDM sampler")
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sampler = DDIMSampler(self.model, device=self.device)
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else:
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sampler = self.sampler
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init_image = self._load_img(init_img).to(self.device)
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init_image = repeat(init_image, '1 ... -> b ...', b=batch_size)
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with precision_scope(self.device.type):
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init_latent = self.model.get_first_stage_encoding(self.model.encode_first_stage(init_image)) # move to latent space
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sampler.make_schedule(ddim_num_steps=steps, ddim_eta=ddim_eta, verbose=False)
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t_enc = int(strength * steps)
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# print(f"target t_enc is {t_enc} steps")
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images = list()
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with precision_scope(self.device.type), self.model.ema_scope():
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all_samples = list()
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for n in trange(iterations, desc="Sampling"):
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seed_everything(seed)
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for prompts in tqdm(data, desc="data", dynamic_ncols=True):
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uc = None
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if cfg_scale != 1.0:
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uc = self.model.get_learned_conditioning(batch_size * [""])
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if isinstance(prompts, tuple):
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prompts = list(prompts)
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# weighted sub-prompts
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subprompts,weights = T2I._split_weighted_subprompts(prompts[0])
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if len(subprompts) > 1:
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# i dont know if this is correct.. but it works
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c = torch.zeros_like(uc)
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# get total weight for normalizing
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totalWeight = sum(weights)
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# normalize each "sub prompt" and add it
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for i in range(0,len(subprompts)):
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weight = weights[i]
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if not skip_normalize:
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weight = weight / totalWeight
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c = torch.add(c,self.model.get_learned_conditioning(subprompts[i]), alpha=weight)
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else: # just standard 1 prompt
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c = self.model.get_learned_conditioning(prompts)
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# encode (scaled latent)
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z_enc = sampler.stochastic_encode(init_latent, torch.tensor([t_enc]*batch_size).to(self.device))
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# decode it
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samples = sampler.decode(z_enc, c, t_enc, unconditional_guidance_scale=cfg_scale,
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unconditional_conditioning=uc,)
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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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for x_sample in x_samples:
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x_sample = 255. * rearrange(x_sample.cpu().numpy(), 'c h w -> h w c')
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image = Image.fromarray(x_sample.astype(np.uint8))
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images.append([image,seed])
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if callback is not None:
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callback(image,seed)
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seed = self._new_seed()
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return images
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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 load_model(self):
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""" Load and initialize the model from configuration variables passed at object creation time """
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if self.model is None:
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seed_everything(self.seed)
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try:
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config = OmegaConf.load(self.config)
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self.device = torch.device(self.device) if torch.cuda.is_available() else torch.device("cpu")
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model = self._load_model_from_config(config,self.weights)
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if self.embedding_path is not None:
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model.embedding_manager.load(self.embedding_path)
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self.model = model.to(self.device)
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# model.to doesn't change the cond_stage_model.device used to move the tokenizer output, so set it here
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self.model.cond_stage_model.device = self.device
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except AttributeError:
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raise SystemExit
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msg = f'setting sampler to {self.sampler_name}'
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if self.sampler_name=='plms':
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self.sampler = PLMSSampler(self.model, device=self.device)
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elif self.sampler_name == 'ddim':
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self.sampler = DDIMSampler(self.model, device=self.device)
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elif self.sampler_name == 'k_dpm_2_a':
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self.sampler = KSampler(self.model, 'dpm_2_ancestral', device=self.device)
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elif self.sampler_name == 'k_dpm_2':
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self.sampler = KSampler(self.model, 'dpm_2', device=self.device)
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elif self.sampler_name == 'k_euler_a':
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self.sampler = KSampler(self.model, 'euler_ancestral', device=self.device)
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|
elif self.sampler_name == 'k_euler':
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|
self.sampler = KSampler(self.model, 'euler', device=self.device)
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|
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)
|
|
|
|
return self.model
|
|
|
|
def _load_model_from_config(self, config, ckpt):
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|
print(f"Loading model from {ckpt}")
|
|
pl_sd = torch.load(ckpt, map_location="cpu")
|
|
# if "global_step" in pl_sd:
|
|
# print(f"Global Step: {pl_sd['global_step']}")
|
|
sd = pl_sd["state_dict"]
|
|
model = instantiate_from_config(config.model)
|
|
m, u = model.load_state_dict(sd, strict=False)
|
|
model.to(self.device)
|
|
model.eval()
|
|
if self.full_precision:
|
|
print('Using slower but more accurate full-precision math (--full_precision)')
|
|
else:
|
|
print('Using half precision math. Call with --full_precision to use slower but more accurate full precision.')
|
|
model.half()
|
|
return model
|
|
|
|
def _load_img(self,path):
|
|
image = Image.open(path).convert("RGB")
|
|
w, h = image.size
|
|
print(f"loaded input image of size ({w}, {h}) from {path}")
|
|
w, h = map(lambda x: x - x % 32, (w, h)) # resize to integer multiple of 32
|
|
image = image.resize((w, h), resample=Image.Resampling.LANCZOS)
|
|
image = np.array(image).astype(np.float32) / 255.0
|
|
image = image[None].transpose(0, 3, 1, 2)
|
|
image = torch.from_numpy(image)
|
|
return 2.*image - 1.
|
|
|
|
def _split_weighted_subprompts(text):
|
|
"""
|
|
grabs all text up to the first occurrence of ':'
|
|
uses the grabbed text as a sub-prompt, and takes the value following ':' as weight
|
|
if ':' has no value defined, defaults to 1.0
|
|
repeats until no text remaining
|
|
"""
|
|
remaining = len(text)
|
|
prompts = []
|
|
weights = []
|
|
while remaining > 0:
|
|
if ":" in text:
|
|
idx = text.index(":") # first occurrence from start
|
|
# grab up to index as sub-prompt
|
|
prompt = text[:idx]
|
|
remaining -= idx
|
|
# remove from main text
|
|
text = text[idx+1:]
|
|
# find value for weight
|
|
if " " in text:
|
|
idx = text.index(" ") # first occurence
|
|
else: # no space, read to end
|
|
idx = len(text)
|
|
if idx != 0:
|
|
try:
|
|
weight = float(text[:idx])
|
|
except: # couldn't treat as float
|
|
print(f"Warning: '{text[:idx]}' is not a value, are you missing a space?")
|
|
weight = 1.0
|
|
else: # no value found
|
|
weight = 1.0
|
|
# remove from main text
|
|
remaining -= idx
|
|
text = text[idx+1:]
|
|
# append the sub-prompt and its weight
|
|
prompts.append(prompt)
|
|
weights.append(weight)
|
|
else: # no : found
|
|
if len(text) > 0: # there is still text though
|
|
# take remainder as weight 1
|
|
prompts.append(text)
|
|
weights.append(1.0)
|
|
remaining = 0
|
|
return prompts, weights
|