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run-batch.py
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run-batch.py
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# Copyright 2022 Dirk Moerenhout. All rights reserved.
#
# This program is free software: you can redistribute it and/or modify it under the terms
# of the GNU General Public License as published by the Free Software Foundation,
# either version 3 of the License, or (at your option) any later version.
#
# This program is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY;
# without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.
# See the GNU General Public License for more details.
#
# You should have received a copy of the GNU General Public License along with this program. If not,
# see <https://www.gnu.org/licenses/>.
# We need sys for argv
import sys
# We need os.path for isdir, isfile
import os.path
# Our settings are in json format
import json
# To be safe we force gc to lower RAM pressure
import gc
# We want to replace the text encoder in the pipeline
import functools
# We want to parse arguments
import argparse
# Numpy is used to provide a random generator
import numpy
# We need to load images for img2img
# We want to save data to PNG
from PIL import Image, PngImagePlugin
# The pipelines
from diffusers import OnnxStableDiffusionPipeline, OnnxStableDiffusionImg2ImgPipeline
# Model needed to load Text Encoder on CPU
from diffusers import OnnxRuntimeModel
# The schedulers
from diffusers import (
DDIMScheduler,
DEISMultistepScheduler,
DPMSolverMultistepScheduler,
DPMSolverSinglestepScheduler,
EulerAncestralDiscreteScheduler,
EulerDiscreteScheduler,
HeunDiscreteScheduler,
KDPM2DiscreteScheduler,
LMSDiscreteScheduler,
PNDMScheduler
)
# Support special text encoders
import lpw_pipe
# Default settings
defSettings = {
"width": 512,
"height": 512,
"reslist": [],
"steps": 30,
"stepslist": [],
"scale": 7.5,
"scalelist":[],
"seed":0,
"seedend":0,
"seedlist":[],
"task": "txt2img",
"model":"sd2_1-fp16",
"prompt": "",
"promptlist":[],
"negative_prompt": "",
"textenc": "standard",
"scheduler": "pndm",
"schedulerlist": [],
"strength": 0.9,
"strengthlist": []
}
parser = argparse.ArgumentParser()
parser.add_argument(
"--cpu-textenc",
action="store_true",
help="Load Text Encoder on CPU to save VRAM"
)
parser.add_argument(
"--subdirs",
action="store_true",
help="Add subdirs with settings.json to projects to run"
)
parser.add_argument(
'project',
nargs='+',
type=str,
help="Provide projects as directories that contain settings.json"
)
args = parser.parse_args()
projects=args.project
if args.subdirs:
for proj in args.project:
obj = os.scandir(proj)
for entry in obj:
if entry.is_dir():
if os.path.isfile(f"{proj}/{entry.name}/settings.json"):
projects.append(f"{proj}/{entry.name}")
for proj in projects:
print("Running project "+proj)
# Check for directory
if os.path.isdir(proj):
if os.path.isfile(proj+"/settings.json"):
with open(proj+"/settings.json", encoding="utf-8") as confFile:
projSettings=json.load(confFile)
# Merge dictionaries with project settings taking precedence
runSettings = defSettings | projSettings
# We need prompts
prereqmet=len(runSettings['prompt'])>0 or len(runSettings['promptlist'])>0
# We need a model
model="model/"+runSettings['model']
prereqmet=prereqmet and os.path.isfile(model+"/unet/model.onnx")
# We need a start image to do img2img
if runSettings['task']=="img2img":
infile=proj+"/input.png"
prereqmet = prereqmet and os.path.isfile(infile)
if prereqmet:
sched = {
"ddim": DDIMScheduler.from_pretrained(model, subfolder="scheduler"),
"deis": DEISMultistepScheduler.from_pretrained(model, subfolder="scheduler"),
"dpms_ms": DPMSolverMultistepScheduler.from_pretrained(model, subfolder="scheduler"),
"dpms_ss": DPMSolverSinglestepScheduler.from_pretrained(model, subfolder="scheduler"),
"euler_anc": EulerAncestralDiscreteScheduler.from_pretrained(model, subfolder="scheduler"),
"euler": EulerDiscreteScheduler.from_pretrained(model, subfolder="scheduler"),
"heun": HeunDiscreteScheduler.from_pretrained(model, subfolder="scheduler"),
"kdpm2": KDPM2DiscreteScheduler.from_pretrained(model, subfolder="scheduler"),
"lms": LMSDiscreteScheduler.from_pretrained(model, subfolder="scheduler"),
"pndm": PNDMScheduler.from_pretrained(model, subfolder="scheduler")
}
if runSettings['task']=="img2img":
init_image = Image.open(infile).convert("RGB")
if args.cpu_textenc:
cputextenc=OnnxRuntimeModel.from_pretrained(model+"/text_encoder")
pipe = OnnxStableDiffusionImg2ImgPipeline.from_pretrained(
model,
provider="DmlExecutionProvider",
revision="onnx",
scheduler=sched['pndm'],
text_encoder=cputextenc,
safety_checker=None,
feature_extractor=None
)
else:
pipe = OnnxStableDiffusionImg2ImgPipeline.from_pretrained(
model,
provider="DmlExecutionProvider",
revision="onnx",
scheduler=sched['pndm'],
safety_checker=None,
feature_extractor=None
)
else:
if args.cpu_textenc:
cputextenc=OnnxRuntimeModel.from_pretrained(model+"/text_encoder")
pipe = OnnxStableDiffusionPipeline.from_pretrained(
model,
provider="DmlExecutionProvider",
revision="onnx",
scheduler=sched['pndm'],
text_encoder=cputextenc,
safety_checker=None,
feature_extractor=None
)
else:
pipe = OnnxStableDiffusionPipeline.from_pretrained(
model,
provider="DmlExecutionProvider",
revision="onnx",
scheduler=sched['pndm'],
safety_checker=None,
feature_extractor=None
)
if runSettings['textenc'] == "lpw":
pipe._encode_prompt = functools.partial(lpw_pipe._encode_prompt, pipe)
generator = numpy.random
# Set schedulers for projects
if len(runSettings['schedulerlist'])==0:
schedulerlist=[runSettings['scheduler']]
else:
schedulerlist=runSettings['schedulerlist']
# Set seeds for project
if len(runSettings['seedlist'])==0:
if runSettings['seed']>runSettings['seedend']:
runSettings['seedend']=runSettings['seed']
seedlist=range(runSettings['seed'],runSettings['seedend']+1)
else:
seedlist=runSettings['seedlist']
# Set resolustions for project
if len(runSettings['reslist'])==0:
restuples=[(runSettings['width'],runSettings['height'])]
else:
restuples=[]
for resstr in runSettings['reslist']:
restuples.append(tuple(map(int, resstr.split("x"))))
# Set steps for project
if len(runSettings['stepslist'])==0:
stepslist=[runSettings['steps']]
else:
stepslist=runSettings['stepslist']
# Set guidance scales for project
if len(runSettings['scalelist'])==0:
scalelist=[runSettings['scale']]
else:
scalelist=runSettings['scalelist']
# Set prompts for project
if len(runSettings['promptlist'])==0:
promptlist=[runSettings['prompt']]
else:
promptlist=runSettings['promptlist']
# Set strengths for project
if len(runSettings['strengthlist'])==0:
strengthlist=[runSettings['strength']]
else:
strengthlist=runSettings['strengthlist']
imgnr=len(schedulerlist)*len(promptlist)*len(seedlist)*len(restuples)*len(stepslist)*len(scalelist)*len(strengthlist)
imgdone=0
for scheduler in schedulerlist:
if not sched[scheduler]:
scheduler="pndm"
pipe.scheduler=sched[scheduler]
promptnum=0
for prompt in promptlist:
for seed in seedlist:
for res in restuples:
for steps in stepslist:
for scale in scalelist:
for strength in strengthlist:
if runSettings['task']=="img2img":
filename=(
f"{proj}/result-p{promptnum}-seed{seed}-{res[0]}x{res[1]}-"+
f"-steps-{steps}-{scheduler}-scale-"+str(scale).replace(".","_")+
"-strength-"+str(strength).replace(".","_")+".png"
)
else:
filename=(
f"{proj}/result-p{promptnum}-seed{seed}-{res[0]}x{res[1]}-"+
f"-steps-{steps}-{scheduler}-scale-"+str(scale).replace(".","_")+".png"
)
if not os.path.isfile(filename):
generator.seed(seed)
if runSettings['task']=="img2img":
image = pipe(
image=init_image,
strength=strength,
prompt=prompt,
negative_prompt=runSettings['negative_prompt'],
num_inference_steps=steps,
guidance_scale=scale,
generator=generator).images[0]
else:
image = pipe(
prompt=prompt,
negative_prompt=runSettings['negative_prompt'],
width=res[0],
height=res[1],
num_inference_steps=steps,
guidance_scale=scale,
generator = generator).images[0]
metadata = PngImagePlugin.PngInfo()
metadata.add_text("Generator","Stable Diffusion ONNX https://github.com/Amblyopius/Stable-Diffusion-ONNX-FP16")
metadata.add_text("SD Model (local name)",model)
metadata.add_text("SD Prompt",prompt)
metadata.add_text("SD Negative Prompt",runSettings['negative_prompt'])
metadata.add_text("SD Scheduler",scheduler)
metadata.add_text("SD Steps",str(steps))
metadata.add_text("SD Guidance Scale",str(scale))
image.save(filename, pnginfo = metadata)
else:
print("Skipping existing image!")
imgdone+=1
print(f"Finished {imgdone}/{imgnr}")
promptnum+=1
del pipe
gc.collect()
else:
print("Minimum requirements not met! Skipping")
else:
print("Settings not found! Skipping")
else:
print("Path not found! Skipping")