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655 lines
24 KiB
Python
655 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 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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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.pngwriter 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__(
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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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# just to keep track of this parameter when regenerating prompt
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latent_diffusion_weights=False,
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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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self.session_peakmem = torch.cuda.max_memory_allocated()
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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(
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outdir, prompt, kwargs.get('batch_size', self.batch_size)
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)
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for r in results:
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# gets written into the PNG
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metadata_str = f'prompt2png("{prompt}" {kwargs} seed={r[1]}'
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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 (
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'init_img' in kwargs
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), '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(
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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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gfpgan_strength=None,
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save_original=False,
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upscale=None,
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variants=None,
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**args,
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): # 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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gfpgan_strength // strength for GFPGAN. 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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image_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 = (
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self.load_model()
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) # 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 (
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0.0 <= strength <= 1.0
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), '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(
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f'Height and width must be multiples of 64. Resizing to {h}x{w}.'
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)
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height = h
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width = w
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scope = autocast if self.precision == 'autocast' else nullcontext
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tic = time.time()
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torch.cuda.torch.cuda.reset_peak_memory_stats()
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results = list()
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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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images_iterator = self._img2img(
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prompt,
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precision_scope=scope,
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batch_size=batch_size,
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steps=steps,
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cfg_scale=cfg_scale,
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ddim_eta=ddim_eta,
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skip_normalize=skip_normalize,
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init_img=init_img,
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strength=strength,
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)
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else:
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images_iterator = self._txt2img(
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prompt,
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precision_scope=scope,
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batch_size=batch_size,
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steps=steps,
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cfg_scale=cfg_scale,
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ddim_eta=ddim_eta,
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skip_normalize=skip_normalize,
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width=width,
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height=height,
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)
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with scope(self.device.type), self.model.ema_scope():
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for n in trange(iterations, desc='Generating'):
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seed_everything(seed)
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iter_images = next(images_iterator)
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for image in iter_images:
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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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seed = self._new_seed()
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if upscale is not None or gfpgan_strength > 0:
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for result in results:
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image, seed = result
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try:
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if upscale is not None:
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from ldm.gfpgan.gfpgan_tools import (
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real_esrgan_upscale,
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)
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if len(upscale) < 2:
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upscale.append(0.75)
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image = real_esrgan_upscale(
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image,
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upscale[1],
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int(upscale[0]),
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prompt,
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seed,
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)
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if gfpgan_strength > 0:
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from ldm.gfpgan.gfpgan_tools import _run_gfpgan
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image = _run_gfpgan(
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image, gfpgan_strength, prompt, seed, 1
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)
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except Exception as e:
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print(
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f'Error running RealESRGAN - Your image was not upscaled.\n{e}'
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)
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if image_callback is not None:
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if save_original:
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image_callback(image, seed)
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else:
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image_callback(image, seed, upscaled=True)
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except KeyboardInterrupt:
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print('*interrupted*')
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print(
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'Partial results will be returned; if --grid was requested, nothing will be returned.'
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)
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except RuntimeError as e:
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print(str(e))
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print('Are you sure your system has an adequate NVIDIA GPU?')
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toc = time.time()
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self.session_peakmem = max(
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self.session_peakmem, torch.cuda.max_memory_allocated()
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)
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print('Usage stats:')
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print(
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f' {len(results)} image(s) generated in', '%4.2fs' % (toc - tic)
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)
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print(
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f' Max VRAM used for this generation:',
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'%4.2fG' % (torch.cuda.max_memory_allocated() / 1e9),
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)
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print(
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f' Max VRAM used since script start: ',
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'%4.2fG' % (self.session_peakmem / 1e9),
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)
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return results
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@torch.no_grad()
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def _txt2img(
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self,
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prompt,
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precision_scope,
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batch_size,
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steps,
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cfg_scale,
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ddim_eta,
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skip_normalize,
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width,
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height,
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):
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"""
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An infinite iterator of images from the prompt.
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"""
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sampler = self.sampler
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while True:
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uc, c = self._get_uc_and_c(prompt, batch_size, skip_normalize)
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shape = [
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self.latent_channels,
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height // self.downsampling_factor,
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width // self.downsampling_factor,
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]
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samples, _ = sampler.sample(
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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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)
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yield self._samples_to_images(samples)
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@torch.no_grad()
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def _img2img(
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self,
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prompt,
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precision_scope,
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batch_size,
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steps,
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cfg_scale,
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ddim_eta,
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skip_normalize,
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init_img,
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strength,
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):
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"""
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An infinite iterator of images from the prompt and the initial image
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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(
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f"sampler '{self.sampler_name}' is not yet supported. Using DDM sampler"
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)
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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(
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self.model.encode_first_stage(init_image)
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) # move to latent space
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sampler.make_schedule(
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ddim_num_steps=steps, ddim_eta=ddim_eta, verbose=False
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)
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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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while True:
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uc, c = self._get_uc_and_c(prompt, batch_size, skip_normalize)
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# encode (scaled latent)
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z_enc = sampler.stochastic_encode(
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init_latent, torch.tensor([t_enc] * batch_size).to(self.device)
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)
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# decode it
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samples = sampler.decode(
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z_enc,
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c,
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t_enc,
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unconditional_guidance_scale=cfg_scale,
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unconditional_conditioning=uc,
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)
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yield self._samples_to_images(samples)
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# TODO: does this actually need to run every loop? does anything in it vary by random seed?
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def _get_uc_and_c(self, prompt, batch_size, skip_normalize):
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uc = self.model.get_learned_conditioning(batch_size * [''])
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# weighted sub-prompts
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subprompts, weights = T2I._split_weighted_subprompts(prompt)
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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(
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c,
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self.model.get_learned_conditioning(
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batch_size * [subprompts[i]]
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),
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alpha=weight,
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)
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else: # just standard 1 prompt
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c = self.model.get_learned_conditioning(batch_size * [prompt])
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return (uc, c)
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def _samples_to_images(self, samples):
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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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images = list()
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for x_sample in x_samples:
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x_sample = 255.0 * rearrange(
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x_sample.cpu().numpy(), 'c h w -> h w c'
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)
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image = Image.fromarray(x_sample.astype(np.uint8))
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images.append(image)
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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 _get_device(self):
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if torch.cuda.is_available():
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return torch.device('cuda')
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elif torch.backends.mps.is_available():
|
|
return torch.device('mps')
|
|
else:
|
|
return torch.device('cpu')
|
|
|
|
def load_model(self):
|
|
"""Load and initialize the model from configuration variables passed at object creation time"""
|
|
if self.model is None:
|
|
seed_everything(self.seed)
|
|
try:
|
|
config = OmegaConf.load(self.config)
|
|
self.device = self._get_device()
|
|
model = self._load_model_from_config(config, self.weights)
|
|
if self.embedding_path is not None:
|
|
model.embedding_manager.load(
|
|
self.embedding_path, self.full_precision
|
|
)
|
|
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:
|
|
raise SystemExit
|
|
|
|
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)
|
|
|
|
return self.model
|
|
|
|
def _load_model_from_config(self, config, ckpt):
|
|
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 more accurate but VRAM-intensive full precision.'
|
|
)
|
|
model.half()
|
|
return model
|
|
|
|
def _load_img(self, path):
|
|
with Image.open(path) as img:
|
|
image = img.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 2.0 * image - 1.0
|
|
|
|
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
|