automatic/cli/models.py

121 lines
4.7 KiB
Python
Executable File

#!/bin/env python
import os
import sys
import json
import time
import asyncio
import argparse
sys.path.append(os.path.join(os.path.dirname(__file__), 'modules'))
from generate import sd, generate
from modules.util import Map, log
from modules.sdapi import get, post, close
from modules.grid import grid
default = 'sd-v15-runwayml.ckpt [cc6cb27103]'
embeddings = ['blonde', 'bruntette', 'sexy', 'naked', 'mia', 'lin', 'kelly', 'hanna', 'rreid-random-v0']
exclude = ['sd-v20', 'sd-v21', 'inpainting', 'pix2pix']
prompt = "photo of beautiful woman <embedding>, photograph, posing, pose, high detailed, intricate, elegant, sharp focus, skin texture, looking forward, facing camera, 135mm, shot on dslr, canon 5d, 4k, modelshoot style, cinematic lighting"
options = Map({
'generate': {
'restore_faces': True,
'prompt': '',
'negative_prompt': 'digital art, cgi, render, foggy, blurry, blurred, duplicate, ugly, mutilated, mutation, mutated, out of frame, bad anatomy, disfigured, deformed, censored, low res, low resolution, watermark, text, poorly drawn face, poorly drawn hands, signature',
'steps': 30,
'batch_size': 4,
'n_iter': 1,
'seed': -1,
'sampler_name': 'DPM2 Karras',
'cfg_scale': 7,
'width': 512,
'height': 512
},
'paths': {
"root": "/mnt/c/Users/mandi/OneDrive/Generative/Generate",
"generate": "image",
"upscale": "upscale",
"grid": "grid"
},
'options': {
"sd_model_checkpoint": "sd-v15-runwayml",
"sd_vae": "vae-ft-mse-840000-ema-pruned.ckpt"
}
})
async def models(params):
global sd
data = await get('/sdapi/v1/sd-models')
all = [m['title'] for m in data]
models = []
excluded = []
for m in all: # loop through all registered models
ok = True
for e in exclude: # check if model is excluded
if e in m:
excluded.append(m)
ok = False
break
if len(params.input) > 0: # check if model is included in cmd line
found = m if m in params.input else None
if found is None:
found = [i for i in params.input if m.startswith(i)]
if len(found) == 0:
ok = False
break
if ok:
short = m.split(' [')[0]
short = short.replace('.ckpt', '').replace('.safetensors', '')
models.append(short)
log.info({ 'models preview' })
log.info({ 'models': len(models), 'excluded': len(excluded) })
log.info({ 'embeddings': embeddings })
log.info({ 'total jobs': len(models) * len(embeddings) * options.generate.batch_size, 'per-model': len(embeddings) * options.generate.batch_size })
log.info(json.dumps(options, indent=2))
for model in models:
fn = os.path.join(params.output, model + '.jpg')
if os.path.exists(fn) and len(params.input) == 0: # if model preview exists and not manually included
log.info({ 'model preview exists': model })
continue
log.info({ 'model load': model })
opt = await get('/sdapi/v1/options')
opt['sd_model_checkpoint'] = model
await post('/sdapi/v1/options', opt)
images = []
labels = []
t0 = time.time()
for embedding in embeddings:
options.generate.prompt = prompt.replace('<embedding>', f'\"{embedding}\"')
log.info({ 'model generating': model, 'embedding': embedding, 'prompt': options.generate.prompt })
data = await generate(options = options, quiet=True)
if 'image' in data:
for img in data['image']:
images.append(img)
labels.append(embedding)
else:
log.error({ 'model': model, 'embedding': embedding, 'error': data })
t1 = time.time()
image = grid(images = images, labels = labels, border = 8)
image.save(fn)
t = t1 - t0
its = 1.0 * options.generate.steps * len(images) / t
log.info({ 'model preview created': model, 'image': fn, 'images': len(images), 'grid': [image.width, image.height], 'time': round(t, 2), 'its': round(its, 2) })
opt = await get('/sdapi/v1/options')
if opt['sd_model_checkpoint'] != default:
log.info({ 'model set default': default })
opt['sd_model_checkpoint'] = default
await post('/sdapi/v1/options', opt)
await close()
if __name__ == '__main__':
parser = argparse.ArgumentParser(description = 'generate model previews')
parser.add_argument('--output', type = str, default = '', required = False, help = 'output directory')
parser.add_argument('input', type = str, nargs = '*')
params = parser.parse_args()
asyncio.run(models(params))