mirror of
https://github.com/invoke-ai/InvokeAI
synced 2024-08-30 20:32:17 +00:00
install the script as "invokeai-batch"
This commit is contained in:
Lincoln Stein
parent
fc164d5be2
commit
d912bab4c2
459
ldm/invoke/dynamic_prompts.py
Executable file
459
ldm/invoke/dynamic_prompts.py
Executable file
@ -0,0 +1,459 @@
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#!/usr/bin/env python
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"""
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Simple script to generate a file of InvokeAI prompts and settings
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that scan across steps and other parameters.
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"""
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import argparse
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import io
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import json
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import os
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import pydoc
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import re
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import shutil
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import sys
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import numpy as np
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from io import TextIOBase
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from itertools import product
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from pathlib import Path
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from multiprocessing import Process, Pipe
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from multiprocessing.connection import Connection
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from subprocess import PIPE, Popen
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from typing import Iterable, List
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import yaml
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from omegaconf import OmegaConf, dictconfig, listconfig
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def expand_prompts(
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template_file: Path,
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run_invoke: bool = False,
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invoke_model: str = None,
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invoke_outdir: Path = None,
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processes_per_gpu: int = 1
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):
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"""
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:param template_file: A YAML file containing templated prompts and args
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:param run_invoke: A boolean which if True will pass expanded prompts to invokeai CLI
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:param invoke_model: Name of the model to load when run_invoke is true; otherwise uses default
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:param invoke_outdir: Directory for outputs when run_invoke is true; otherwise uses default
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"""
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if template_file.name.endswith(".json"):
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with open(template_file, "r") as file:
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with io.StringIO(yaml.dump(json.load(file))) as fh:
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conf = OmegaConf.load(fh)
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else:
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conf = OmegaConf.load(template_file)
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# loading here to avoid long wait for help message
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import torch
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torch.multiprocessing.set_start_method('spawn')
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gpu_count = torch.cuda.device_count() if torch.cuda.is_available() else 1
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commands = expanded_invokeai_commands(conf, run_invoke)
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children = list()
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try:
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if run_invoke:
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# bring the big library into memory in order to share it in subprocesses
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import ldm.invoke.CLI
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invokeai_args = [shutil.which("invokeai"),"--from_file","-"]
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if invoke_model:
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invokeai_args.extend(("--model", invoke_model))
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if invoke_outdir:
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invokeai_args.extend(("--outdir", os.path.expanduser(invoke_outdir)))
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processes_to_launch = gpu_count * processes_per_gpu
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print(f'>> Spawning {processes_to_launch} invokeai processes across {gpu_count} CUDA gpus', file=sys.stderr)
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import ldm.invoke.CLI
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parent_conn, child_conn = Pipe()
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children = set()
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for i in range(processes_to_launch):
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p = Process(target=_run_invoke,
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args=(child_conn,
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parent_conn,
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invokeai_args,
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i%gpu_count,
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)
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)
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p.start()
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children.add(p)
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child_conn.close()
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sequence = 0
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for command in commands:
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sequence += 1
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parent_conn.send(command+f' --fnformat=dp.{sequence:04}.{{prompt}}.png')
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parent_conn.close()
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else:
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for command in commands:
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print(command)
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except KeyboardInterrupt:
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for p in children:
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p.terminate()
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class MessageToStdin(object):
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def __init__(self, connection: Connection):
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self.connection = connection
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self.linebuffer = list()
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def readline(self)->str:
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try:
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if len(self.linebuffer) == 0:
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message = self.connection.recv()
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self.linebuffer = message.split("\n")
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result = self.linebuffer.pop(0)
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return result
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except EOFError:
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return None
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class FilterStream(object):
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def __init__(self, stream: TextIOBase, include: re.Pattern=None, exclude: re.Pattern=None):
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self.stream = stream
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self.include = include
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self.exclude = exclude
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def write(self, data: str):
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if self.include and self.include.match(data):
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self.stream.write(data)
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self.stream.flush()
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elif self.exclude and not self.exclude.match(data):
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self.stream.write(data)
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self.stream.flush()
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def flush(self):
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self.stream.flush()
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def _run_invoke(conn_in: Connection, conn_out: Connection, args: List[str], gpu: int=0):
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from ldm.invoke.CLI import main
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print(f'>> Process {os.getpid()} running on GPU {gpu}', file=sys.stderr)
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conn_out.close()
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os.environ['CUDA_VISIBLE_DEVICES'] = f"{gpu}"
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sys.argv = args
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sys.stdin = MessageToStdin(conn_in)
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sys.stdout = FilterStream(sys.stdout,include=re.compile('^\[\d+\]'))
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sys.stderr = FilterStream(sys.stdout,exclude=re.compile('^(>>|\s*\d+%|Fetching)'))
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main()
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def _filter_output(stream: TextIOBase):
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while line := stream.readline():
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if re.match('^\[\d+\]',line):
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print(line)
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def main():
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parser = argparse.ArgumentParser(
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description=HELP,
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)
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parser.add_argument(
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"template_file",
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type=Path,
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nargs="?",
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help="path to a template file, use --example to generate an example file",
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)
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parser.add_argument(
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"--example",
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action="store_true",
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default=False,
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help=f'Print an example template file in YAML format. Use "{sys.argv[0]} --example > example.yaml" to save output to a file',
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)
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parser.add_argument(
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"--json-example",
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action="store_true",
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default=False,
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help=f'Print an example template file in json format. Use "{sys.argv[0]} --json-example > example.json" to save output to a file',
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)
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parser.add_argument(
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"--instructions",
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"-i",
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dest="instructions",
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action="store_true",
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default=False,
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help="Print verbose instructions.",
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)
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parser.add_argument(
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"--invoke",
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action="store_true",
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help="Execute invokeai using specified optional --model, --processes_per_gpu and --outdir",
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)
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parser.add_argument(
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"--model",
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help="Feed the generated prompts to the invokeai CLI using the indicated model. Will be overriden by a model: section in template file.",
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)
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parser.add_argument(
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"--outdir", type=Path, help="Write images and log into indicated directory"
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)
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parser.add_argument(
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"--processes_per_gpu",
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type=int,
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default=1,
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help="When executing invokeai, how many parallel processes to execute per CUDA GPU.",
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)
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opt = parser.parse_args()
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if opt.example:
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print(EXAMPLE_TEMPLATE_FILE)
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sys.exit(0)
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if opt.json_example:
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print(_yaml_to_json(EXAMPLE_TEMPLATE_FILE))
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sys.exit(0)
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if opt.instructions:
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pydoc.pager(INSTRUCTIONS)
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sys.exit(0)
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if not opt.template_file:
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parser.print_help()
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sys.exit(-1)
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expand_prompts(
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template_file=opt.template_file,
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run_invoke=opt.invoke,
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invoke_model=opt.model,
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invoke_outdir=opt.outdir,
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processes_per_gpu=opt.processes_per_gpu,
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)
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def expanded_invokeai_commands(conf: OmegaConf, always_switch_models: bool=False)->List[List[str]]:
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models = expand_values(conf.get("model"))
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steps = expand_values(conf.get("steps")) or [30]
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cfgs = expand_values(conf.get("cfg")) or [7.5]
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samplers = expand_values(conf.get("sampler")) or ["ddim"]
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seeds = expand_values(conf.get("seed")) or [0]
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dimensions = expand_values(conf.get("dimensions")) or ["512x512"]
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init_img = expand_values(conf.get('init_img')) or ['']
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perlin = expand_values(conf.get('perlin')) or [0]
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threshold = expand_values(conf.get('threshold')) or [0]
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strength = expand_values(conf.get('strength')) or [0.75]
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prompts = expand_prompt(conf.get("prompt")) or ["banana sushi"]
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cross_product = product(
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*[models, seeds, prompts, samplers, cfgs, steps, perlin, threshold, init_img, strength, dimensions]
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)
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previous_model = None
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result = list()
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for p in cross_product:
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(model, seed, prompt, sampler, cfg, step, perlin, threshold, init_img, strength, dimensions) = tuple(p)
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(width, height) = dimensions.split("x")
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switch_args = f"!switch {model}\n" if always_switch_models or previous_model != model else ''
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image_args = f'-I{init_img} -f{strength}' if init_img else ''
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command = f'{switch_args}{prompt} -S{seed} -A{sampler} -C{cfg} -s{step} {image_args} --perlin={perlin} --threshold={threshold} -W{width} -H{height}'
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result.append(command)
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previous_model = model
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return result
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def expand_prompt(
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stanza: str | dict | listconfig.ListConfig | dictconfig.DictConfig,
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) -> list | range:
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if not stanza:
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return None
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if isinstance(stanza, listconfig.ListConfig):
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return stanza
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if isinstance(stanza, str):
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return [stanza]
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if not isinstance(stanza, dictconfig.DictConfig):
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raise ValueError(f"Unrecognized template: {stanza}")
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if not (template := stanza.get("template")):
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raise KeyError('"prompt" section must contain a "template" definition')
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fragment_labels = re.findall("{([^{}]+?)}", template)
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if len(fragment_labels) == 0:
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return [template]
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fragments = [[{x: y} for y in stanza.get(x)] for x in fragment_labels]
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dicts = merge(product(*fragments))
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return [template.format(**x) for x in dicts]
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def merge(dicts: Iterable) -> List[dict]:
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result = list()
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for x in dicts:
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to_merge = dict()
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for item in x:
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to_merge = to_merge | item
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result.append(to_merge)
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return result
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def expand_values(stanza: str | dict | listconfig.ListConfig) -> list | range:
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if not stanza:
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return None
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if isinstance(stanza, listconfig.ListConfig):
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return stanza
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elif match := re.match("^(-?\d+);(-?\d+)(;(\d+))?", str(stanza)):
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(start, stop, step) = (int(match.group(1)), int(match.group(2)), int(match.group(4)) or 1)
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return range(start, stop+step, step)
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elif match := re.match("^(-?[\d.]+);(-?[\d.]+)(;([\d.]+))?", str(stanza)):
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(start, stop, step) = (float(match.group(1)), float(match.group(2)), float(match.group(4)) or 1.0)
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return np.arange(start, stop+step, step).tolist()
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else:
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return [stanza]
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def _yaml_to_json(yaml_input: str) -> str:
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"""
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Converts a yaml string into a json string. Used internally
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to generate the example template file.
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"""
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with io.StringIO(yaml_input) as yaml_in:
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data = yaml.safe_load(yaml_in)
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return json.dumps(data, indent=2)
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HELP = """
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This script takes a prompt template file that contains multiple
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alternative values for the prompt and its generation arguments (such
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as steps). It then expands out the prompts using all combinations of
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arguments and either prints them to the terminal's standard output, or
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feeds the prompts directly to the invokeai command-line interface.
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Call this script again with --instructions (-i) for verbose instructions.
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"""
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INSTRUCTIONS = f"""
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== INTRODUCTION ==
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This script takes a prompt template file that contains multiple
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alternative values for the prompt and its generation arguments (such
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as steps). It then expands out the prompts using all combinations of
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arguments and either prints them to the terminal's standard output, or
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feeds the prompts directly to the invokeai command-line interface.
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If the optional --invoke argument is provided, then the generated
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prompts will be fed directly to invokeai for image generation. You
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will likely want to add the --outdir option in order to save the image
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files to their own folder.
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{sys.argv[0]} --invoke --outdir=/tmp/outputs my_template.yaml
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If --invoke isn't specified, the expanded prompts will be printed to
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output. You can capture them to a file for inspection and editing this
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way:
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{sys.argv[0]} my_template.yaml > prompts.txt
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And then feed them to invokeai this way:
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invokeai --outdir=/tmp/outputs < prompts.txt
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Note that after invokeai finishes processing the list of prompts, the
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output directory will contain a markdown file named `log.md`
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containing annotated images. You can open this file using an e-book
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reader such as the cross-platform Calibre eBook reader
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(https://calibre-ebook.com/).
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== FORMAT OF THE TEMPLATES FILE ==
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This will generate an example template file that you can get
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started with:
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{sys.argv[0]} --example > example.yaml
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An excerpt from the top of this file looks like this:
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model:
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- stable-diffusion-1.5
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- stable-diffusion-2.1-base
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steps: 30:50:1 # start steps at 30 and go up to 50, incrementing by 1 each time
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seed: 50 # fixed constant, seed=50
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cfg: # list of CFG values to try
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- 7
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- 8
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- 12
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prompt: a walk in the park # constant value
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In more detail, the template file can one or more of the
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following sections:
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- model:
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- steps:
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- seed:
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- cfg:
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- sampler:
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- prompt:
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- init_img:
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- perlin:
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- threshold:
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- strength
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- Each section can have a constant value such as this:
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steps: 50
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- Or a range of numeric values in the format:
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steps: <start>;<stop>;<step> (note semicolon, not colon!)
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- Or a list of values in the format:
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- value1
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- value2
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- value3
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The "prompt:" section is special. It can accept a constant value:
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prompt: a walk in the woods in the style of donatello
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Or it can accept a list of prompts:
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prompt:
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- a walk in the woods
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- a walk on the beach
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Or it can accept a templated list of prompts. These allow you to
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define a series of phrases, each of which is a list. You then combine
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them together into a prompt template in this way:
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prompt:
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style:
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- oil painting
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- watercolor
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- comic book
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- studio photography
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subject:
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- sunny meadow in the mountains
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- gathering storm in the mountains
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template: a {{subject}} in the style of {{style}}
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In the example above, the phrase names "style" and "subject" are
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examples only. You can use whatever you like. However, the "template:"
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field is required. The output will be:
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"a sunny meadow in the mountains in the style of an oil painting"
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"a sunny meadow in the mountains in the style of watercolor masterpiece"
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...
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"a gathering storm in the mountains in the style of an ink sketch"
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== SUPPORT FOR JSON FORMAT ==
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For those who prefer the JSON format, this script will accept JSON
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template files as well. Please run "{sys.argv[0]} --json-example"
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to print out a version of the example template file in json format.
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You may save it to disk and use it as a starting point for your own
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template this way:
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{sys.argv[0]} --json-example > template.json
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"""
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EXAMPLE_TEMPLATE_FILE = """
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model: stable-diffusion-1.5
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steps: 30;50;10
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seed: 50
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dimensions: 512x512
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perlin: 0.0
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threshold: 0
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cfg:
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- 7
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- 12
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sampler:
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- k_euler_a
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- k_lms
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prompt:
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style:
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- oil painting
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- watercolor
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location:
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- the mountains
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- a desert
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object:
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- luxurious dwelling
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- crude tent
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template: a {object} in {location}, in the style of {style}
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"""
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if __name__ == "__main__":
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main()
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@ -118,6 +118,7 @@ requires-python = ">=3.9, <3.11"
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"invokeai-configure" = "ldm.invoke.config.invokeai_configure:main"
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"invokeai-merge" = "ldm.invoke.merge_diffusers:main"
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"invokeai-ti" = "ldm.invoke.training.textual_inversion:main"
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"invokeai-batch" = "ldm.invoke.dynamic_prompts:main"
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[project.urls]
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"Bug Reports" = "https://github.com/invoke-ai/InvokeAI/issues"
|
||||
|
||||
@ -5,453 +5,5 @@ Simple script to generate a file of InvokeAI prompts and settings
|
||||
that scan across steps and other parameters.
|
||||
"""
|
||||
|
||||
import argparse
|
||||
import io
|
||||
import json
|
||||
import os
|
||||
import pydoc
|
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import re
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import shutil
|
||||
import sys
|
||||
import numpy as np
|
||||
from io import TextIOBase
|
||||
from itertools import product
|
||||
from pathlib import Path
|
||||
from multiprocessing import Process, Pipe
|
||||
from multiprocessing.connection import Connection
|
||||
from subprocess import PIPE, Popen
|
||||
from typing import Iterable, List
|
||||
|
||||
import yaml
|
||||
from omegaconf import OmegaConf, dictconfig, listconfig
|
||||
|
||||
|
||||
def expand_prompts(
|
||||
template_file: Path,
|
||||
run_invoke: bool = False,
|
||||
invoke_model: str = None,
|
||||
invoke_outdir: Path = None,
|
||||
processes_per_gpu: int = 1
|
||||
):
|
||||
"""
|
||||
:param template_file: A YAML file containing templated prompts and args
|
||||
:param run_invoke: A boolean which if True will pass expanded prompts to invokeai CLI
|
||||
:param invoke_model: Name of the model to load when run_invoke is true; otherwise uses default
|
||||
:param invoke_outdir: Directory for outputs when run_invoke is true; otherwise uses default
|
||||
"""
|
||||
if template_file.name.endswith(".json"):
|
||||
with open(template_file, "r") as file:
|
||||
with io.StringIO(yaml.dump(json.load(file))) as fh:
|
||||
conf = OmegaConf.load(fh)
|
||||
else:
|
||||
conf = OmegaConf.load(template_file)
|
||||
|
||||
# loading here to avoid long wait for help message
|
||||
import torch
|
||||
torch.multiprocessing.set_start_method('spawn')
|
||||
gpu_count = torch.cuda.device_count() if torch.cuda.is_available() else 1
|
||||
commands = expanded_invokeai_commands(conf, run_invoke)
|
||||
children = list()
|
||||
|
||||
try:
|
||||
if run_invoke:
|
||||
invokeai_args = [shutil.which("invokeai"),"--from_file","-"]
|
||||
if invoke_model:
|
||||
invokeai_args.extend(("--model", invoke_model))
|
||||
if invoke_outdir:
|
||||
invokeai_args.extend(("--outdir", os.path.expanduser(invoke_outdir)))
|
||||
|
||||
processes_to_launch = gpu_count * processes_per_gpu
|
||||
print(f'>> Spawning {processes_to_launch} invokeai processes across {gpu_count} CUDA gpus', file=sys.stderr)
|
||||
import ldm.invoke.CLI
|
||||
parent_conn, child_conn = Pipe()
|
||||
children = set()
|
||||
for i in range(processes_to_launch):
|
||||
p = Process(target=_run_invoke,
|
||||
args=(child_conn,
|
||||
parent_conn,
|
||||
invokeai_args,
|
||||
i%gpu_count,
|
||||
)
|
||||
)
|
||||
p.start()
|
||||
children.add(p)
|
||||
child_conn.close()
|
||||
sequence = 0
|
||||
for command in commands:
|
||||
sequence += 1
|
||||
parent_conn.send(command+f' --fnformat=dp.{sequence:04}.{{prompt}}.png')
|
||||
parent_conn.close()
|
||||
else:
|
||||
for command in commands:
|
||||
print(command)
|
||||
except KeyboardInterrupt:
|
||||
for p in children:
|
||||
p.terminate()
|
||||
|
||||
class MessageToStdin(object):
|
||||
def __init__(self, connection: Connection):
|
||||
self.connection = connection
|
||||
self.linebuffer = list()
|
||||
|
||||
def readline(self)->str:
|
||||
try:
|
||||
if len(self.linebuffer) == 0:
|
||||
message = self.connection.recv()
|
||||
self.linebuffer = message.split("\n")
|
||||
result = self.linebuffer.pop(0)
|
||||
return result
|
||||
except EOFError:
|
||||
return None
|
||||
|
||||
class FilterStream(object):
|
||||
def __init__(self, stream: TextIOBase, include: re.Pattern=None, exclude: re.Pattern=None):
|
||||
self.stream = stream
|
||||
self.include = include
|
||||
self.exclude = exclude
|
||||
|
||||
def write(self, data: str):
|
||||
if self.include and self.include.match(data):
|
||||
self.stream.write(data)
|
||||
self.stream.flush()
|
||||
elif self.exclude and not self.exclude.match(data):
|
||||
self.stream.write(data)
|
||||
self.stream.flush()
|
||||
|
||||
def flush(self):
|
||||
self.stream.flush()
|
||||
|
||||
def _run_invoke(conn_in: Connection, conn_out: Connection, args: List[str], gpu: int=0):
|
||||
print(f'>> Process {os.getpid()} running on GPU {gpu}', file=sys.stderr)
|
||||
conn_out.close()
|
||||
os.environ['CUDA_VISIBLE_DEVICES'] = f"{gpu}"
|
||||
from ldm.invoke.CLI import main
|
||||
sys.argv = args
|
||||
sys.stdin = MessageToStdin(conn_in)
|
||||
sys.stdout = FilterStream(sys.stdout,include=re.compile('^\[\d+\]'))
|
||||
sys.stderr = FilterStream(sys.stdout,exclude=re.compile('^(>>|\s*\d+%|Fetching)'))
|
||||
main()
|
||||
|
||||
def _filter_output(stream: TextIOBase):
|
||||
while line := stream.readline():
|
||||
if re.match('^\[\d+\]',line):
|
||||
print(line)
|
||||
|
||||
def main():
|
||||
parser = argparse.ArgumentParser(
|
||||
description=HELP,
|
||||
)
|
||||
parser.add_argument(
|
||||
"template_file",
|
||||
type=Path,
|
||||
nargs="?",
|
||||
help="path to a template file, use --example to generate an example file",
|
||||
)
|
||||
parser.add_argument(
|
||||
"--example",
|
||||
action="store_true",
|
||||
default=False,
|
||||
help=f'Print an example template file in YAML format. Use "{sys.argv[0]} --example > example.yaml" to save output to a file',
|
||||
)
|
||||
parser.add_argument(
|
||||
"--json-example",
|
||||
action="store_true",
|
||||
default=False,
|
||||
help=f'Print an example template file in json format. Use "{sys.argv[0]} --json-example > example.json" to save output to a file',
|
||||
)
|
||||
parser.add_argument(
|
||||
"--instructions",
|
||||
"-i",
|
||||
dest="instructions",
|
||||
action="store_true",
|
||||
default=False,
|
||||
help="Print verbose instructions.",
|
||||
)
|
||||
parser.add_argument(
|
||||
"--invoke",
|
||||
action="store_true",
|
||||
help="Execute invokeai using specified optional --model, --processes_per_gpu and --outdir",
|
||||
)
|
||||
parser.add_argument(
|
||||
"--model",
|
||||
help="Feed the generated prompts to the invokeai CLI using the indicated model. Will be overriden by a model: section in template file.",
|
||||
)
|
||||
parser.add_argument(
|
||||
"--outdir", type=Path, help="Write images and log into indicated directory"
|
||||
)
|
||||
parser.add_argument(
|
||||
"--processes_per_gpu",
|
||||
type=int,
|
||||
default=1,
|
||||
help="When executing invokeai, how many parallel processes to execute per CUDA GPU.",
|
||||
)
|
||||
opt = parser.parse_args()
|
||||
|
||||
if opt.example:
|
||||
print(EXAMPLE_TEMPLATE_FILE)
|
||||
sys.exit(0)
|
||||
|
||||
if opt.json_example:
|
||||
print(_yaml_to_json(EXAMPLE_TEMPLATE_FILE))
|
||||
sys.exit(0)
|
||||
|
||||
if opt.instructions:
|
||||
pydoc.pager(INSTRUCTIONS)
|
||||
sys.exit(0)
|
||||
|
||||
if not opt.template_file:
|
||||
parser.print_help()
|
||||
sys.exit(-1)
|
||||
|
||||
expand_prompts(
|
||||
template_file=opt.template_file,
|
||||
run_invoke=opt.invoke,
|
||||
invoke_model=opt.model,
|
||||
invoke_outdir=opt.outdir,
|
||||
processes_per_gpu=opt.processes_per_gpu,
|
||||
)
|
||||
|
||||
def expanded_invokeai_commands(conf: OmegaConf, always_switch_models: bool=False)->List[List[str]]:
|
||||
models = expand_values(conf.get("model"))
|
||||
steps = expand_values(conf.get("steps")) or [30]
|
||||
cfgs = expand_values(conf.get("cfg")) or [7.5]
|
||||
samplers = expand_values(conf.get("sampler")) or ["ddim"]
|
||||
seeds = expand_values(conf.get("seed")) or [0]
|
||||
dimensions = expand_values(conf.get("dimensions")) or ["512x512"]
|
||||
init_img = expand_values(conf.get('init_img')) or ['']
|
||||
perlin = expand_values(conf.get('perlin')) or [0]
|
||||
threshold = expand_values(conf.get('threshold')) or [0]
|
||||
strength = expand_values(conf.get('strength')) or [0.75]
|
||||
prompts = expand_prompt(conf.get("prompt")) or ["banana sushi"]
|
||||
|
||||
cross_product = product(
|
||||
*[models, seeds, prompts, samplers, cfgs, steps, perlin, threshold, init_img, strength, dimensions]
|
||||
)
|
||||
previous_model = None
|
||||
|
||||
result = list()
|
||||
for p in cross_product:
|
||||
(model, seed, prompt, sampler, cfg, step, perlin, threshold, init_img, strength, dimensions) = tuple(p)
|
||||
(width, height) = dimensions.split("x")
|
||||
switch_args = f"!switch {model}\n" if always_switch_models or previous_model != model else ''
|
||||
image_args = f'-I{init_img} -f{strength}' if init_img else ''
|
||||
command = f'{switch_args}{prompt} -S{seed} -A{sampler} -C{cfg} -s{step} {image_args} --perlin={perlin} --threshold={threshold} -W{width} -H{height}'
|
||||
result.append(command)
|
||||
previous_model = model
|
||||
return result
|
||||
|
||||
|
||||
def expand_prompt(
|
||||
stanza: str | dict | listconfig.ListConfig | dictconfig.DictConfig,
|
||||
) -> list | range:
|
||||
if not stanza:
|
||||
return None
|
||||
if isinstance(stanza, listconfig.ListConfig):
|
||||
return stanza
|
||||
if isinstance(stanza, str):
|
||||
return [stanza]
|
||||
if not isinstance(stanza, dictconfig.DictConfig):
|
||||
raise ValueError(f"Unrecognized template: {stanza}")
|
||||
|
||||
if not (template := stanza.get("template")):
|
||||
raise KeyError('"prompt" section must contain a "template" definition')
|
||||
|
||||
fragment_labels = re.findall("{([^{}]+?)}", template)
|
||||
if len(fragment_labels) == 0:
|
||||
return [template]
|
||||
fragments = [[{x: y} for y in stanza.get(x)] for x in fragment_labels]
|
||||
dicts = merge(product(*fragments))
|
||||
return [template.format(**x) for x in dicts]
|
||||
|
||||
|
||||
def merge(dicts: Iterable) -> List[dict]:
|
||||
result = list()
|
||||
for x in dicts:
|
||||
to_merge = dict()
|
||||
for item in x:
|
||||
to_merge = to_merge | item
|
||||
result.append(to_merge)
|
||||
return result
|
||||
|
||||
|
||||
def expand_values(stanza: str | dict | listconfig.ListConfig) -> list | range:
|
||||
if not stanza:
|
||||
return None
|
||||
if isinstance(stanza, listconfig.ListConfig):
|
||||
return stanza
|
||||
elif match := re.match("^(-?\d+);(-?\d+)(;(\d+))?", str(stanza)):
|
||||
(start, stop, step) = (int(match.group(1)), int(match.group(2)), int(match.group(4)) or 1)
|
||||
return range(start, stop+step, step)
|
||||
elif match := re.match("^(-?[\d.]+);(-?[\d.]+)(;([\d.]+))?", str(stanza)):
|
||||
(start, stop, step) = (float(match.group(1)), float(match.group(2)), float(match.group(4)) or 1.0)
|
||||
return np.arange(start, stop+step, step).tolist()
|
||||
else:
|
||||
return [stanza]
|
||||
|
||||
|
||||
def _yaml_to_json(yaml_input: str) -> str:
|
||||
"""
|
||||
Converts a yaml string into a json string. Used internally
|
||||
to generate the example template file.
|
||||
"""
|
||||
with io.StringIO(yaml_input) as yaml_in:
|
||||
data = yaml.safe_load(yaml_in)
|
||||
return json.dumps(data, indent=2)
|
||||
|
||||
|
||||
HELP = """
|
||||
This script takes a prompt template file that contains multiple
|
||||
alternative values for the prompt and its generation arguments (such
|
||||
as steps). It then expands out the prompts using all combinations of
|
||||
arguments and either prints them to the terminal's standard output, or
|
||||
feeds the prompts directly to the invokeai command-line interface.
|
||||
|
||||
Call this script again with --instructions (-i) for verbose instructions.
|
||||
"""
|
||||
|
||||
INSTRUCTIONS = f"""
|
||||
== INTRODUCTION ==
|
||||
This script takes a prompt template file that contains multiple
|
||||
alternative values for the prompt and its generation arguments (such
|
||||
as steps). It then expands out the prompts using all combinations of
|
||||
arguments and either prints them to the terminal's standard output, or
|
||||
feeds the prompts directly to the invokeai command-line interface.
|
||||
|
||||
If the optional --invoke argument is provided, then the generated
|
||||
prompts will be fed directly to invokeai for image generation. You
|
||||
will likely want to add the --outdir option in order to save the image
|
||||
files to their own folder.
|
||||
|
||||
{sys.argv[0]} --invoke --outdir=/tmp/outputs my_template.yaml
|
||||
|
||||
If --invoke isn't specified, the expanded prompts will be printed to
|
||||
output. You can capture them to a file for inspection and editing this
|
||||
way:
|
||||
|
||||
{sys.argv[0]} my_template.yaml > prompts.txt
|
||||
|
||||
And then feed them to invokeai this way:
|
||||
|
||||
invokeai --outdir=/tmp/outputs < prompts.txt
|
||||
|
||||
Note that after invokeai finishes processing the list of prompts, the
|
||||
output directory will contain a markdown file named `log.md`
|
||||
containing annotated images. You can open this file using an e-book
|
||||
reader such as the cross-platform Calibre eBook reader
|
||||
(https://calibre-ebook.com/).
|
||||
|
||||
== FORMAT OF THE TEMPLATES FILE ==
|
||||
|
||||
This will generate an example template file that you can get
|
||||
started with:
|
||||
|
||||
{sys.argv[0]} --example > example.yaml
|
||||
|
||||
An excerpt from the top of this file looks like this:
|
||||
|
||||
model:
|
||||
- stable-diffusion-1.5
|
||||
- stable-diffusion-2.1-base
|
||||
steps: 30:50:1 # start steps at 30 and go up to 50, incrementing by 1 each time
|
||||
seed: 50 # fixed constant, seed=50
|
||||
cfg: # list of CFG values to try
|
||||
- 7
|
||||
- 8
|
||||
- 12
|
||||
prompt: a walk in the park # constant value
|
||||
|
||||
In more detail, the template file can one or more of the
|
||||
following sections:
|
||||
- model:
|
||||
- steps:
|
||||
- seed:
|
||||
- cfg:
|
||||
- sampler:
|
||||
- prompt:
|
||||
- init_img:
|
||||
- perlin:
|
||||
- threshold:
|
||||
- strength
|
||||
|
||||
- Each section can have a constant value such as this:
|
||||
steps: 50
|
||||
- Or a range of numeric values in the format:
|
||||
steps: <start>;<stop>;<step> (note semicolon, not colon!)
|
||||
- Or a list of values in the format:
|
||||
- value1
|
||||
- value2
|
||||
- value3
|
||||
|
||||
The "prompt:" section is special. It can accept a constant value:
|
||||
|
||||
prompt: a walk in the woods in the style of donatello
|
||||
|
||||
Or it can accept a list of prompts:
|
||||
|
||||
prompt:
|
||||
- a walk in the woods
|
||||
- a walk on the beach
|
||||
|
||||
Or it can accept a templated list of prompts. These allow you to
|
||||
define a series of phrases, each of which is a list. You then combine
|
||||
them together into a prompt template in this way:
|
||||
|
||||
prompt:
|
||||
style:
|
||||
- oil painting
|
||||
- watercolor
|
||||
- comic book
|
||||
- studio photography
|
||||
subject:
|
||||
- sunny meadow in the mountains
|
||||
- gathering storm in the mountains
|
||||
template: a {{subject}} in the style of {{style}}
|
||||
|
||||
In the example above, the phrase names "style" and "subject" are
|
||||
examples only. You can use whatever you like. However, the "template:"
|
||||
field is required. The output will be:
|
||||
|
||||
"a sunny meadow in the mountains in the style of an oil painting"
|
||||
"a sunny meadow in the mountains in the style of watercolor masterpiece"
|
||||
...
|
||||
"a gathering storm in the mountains in the style of an ink sketch"
|
||||
|
||||
== SUPPORT FOR JSON FORMAT ==
|
||||
|
||||
For those who prefer the JSON format, this script will accept JSON
|
||||
template files as well. Please run "{sys.argv[1]} --json-example"
|
||||
to print out a version of the example template file in json format.
|
||||
You may save it to disk and use it as a starting point for your own
|
||||
template this way:
|
||||
|
||||
{sys.argv[1]} --json-example > template.json
|
||||
"""
|
||||
|
||||
EXAMPLE_TEMPLATE_FILE = """
|
||||
model: stable-diffusion-1.5
|
||||
steps: 30;50;10
|
||||
seed: 50
|
||||
dimensions: 512x512
|
||||
perlin: 0.0
|
||||
threshold: 0
|
||||
cfg:
|
||||
- 7
|
||||
- 12
|
||||
sampler:
|
||||
- k_euler_a
|
||||
- k_lms
|
||||
prompt:
|
||||
style:
|
||||
- oil painting
|
||||
- watercolor
|
||||
location:
|
||||
- the mountains
|
||||
- a desert
|
||||
object:
|
||||
- luxurious dwelling
|
||||
- crude tent
|
||||
template: a {object} in {location}, in the style of {style}
|
||||
"""
|
||||
|
||||
if __name__ == "__main__":
|
||||
main()
|
||||
import ldm.invoke.dynamic_prompts
|
||||
ldm.invoke.dynamic_prompts.main()
|
||||
|
||||
Loading…
Reference in New Issue
Block a user