mirror of
https://github.com/vladmandic/automatic
synced 2026-08-29 08:31:00 +02:00
Vladimir Mandic
113 lines
4.5 KiB
Python
Executable File
113 lines
4.5 KiB
Python
Executable File
#!/bin/env python
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import os
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import sys
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import json
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import time
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import asyncio
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import argparse
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sys.path.append(os.path.join(os.path.dirname(__file__), 'modules'))
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from generate import sd, generate
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from modules.util import Map, log
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from modules.sdapi import get, post, close
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from modules.grid import grid
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embeddings = ['blonde', 'bruntette', 'sexy', 'naked', 'mia', 'lin', 'kelly', 'hanna', 'rreid-random-v0']
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exclude = ['sd-v20', 'sd-v21', 'inpainting', 'pix2pix']
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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"
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options = Map({
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'generate': {
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'restore_faces': True,
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'prompt': '',
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'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',
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'steps': 30,
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'batch_size': 4,
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'n_iter': 1,
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'seed': -1,
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'sampler_name': 'DPM2 Karras',
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'cfg_scale': 7,
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'width': 512,
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'height': 512
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},
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'paths': {
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"root": "/mnt/c/Users/mandi/OneDrive/Generative/Generate",
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"generate": "image",
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"upscale": "upscale",
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"grid": "grid"
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},
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'options': {
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"sd_model_checkpoint": "sd-v15-runwayml",
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"sd_vae": "vae-ft-mse-840000-ema-pruned.ckpt"
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}
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})
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async def models(params):
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global sd
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data = await get('/sdapi/v1/sd-models')
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all = [m['title'] for m in data]
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models = []
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excluded = []
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for m in all: # loop through all registered models
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ok = True
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for e in exclude: # check if model is excluded
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if e in m:
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excluded.append(m)
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ok = False
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break
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if len(params.input) > 0: # check if model is included in cmd line
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found = m if m in params.input else None
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if found is None:
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found = [i for i in params.input if m.startswith(i)]
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if len(found) == 0:
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ok = False
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break
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if ok:
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short = m.split(' [')[0]
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short = short.replace('.ckpt', '').replace('.safetensors', '')
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models.append(short)
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log.info({ 'models preview' })
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log.info({ 'models': len(models), 'excluded': len(excluded) })
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log.info({ 'embeddings': len(embeddings) })
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log.info({ 'batch size': options.generate.batch_size })
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log.info({ 'total jobs': len(models) * len(embeddings) * options.generate.batch_size, 'per-model': len(embeddings) * options.generate.batch_size })
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log.info(json.dumps(options, indent=2))
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# models = ['sd-v15-runwayml.ckpt [cc6cb27103]']
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for model in models:
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fn = os.path.join(params.output, model + '.jpg')
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if os.path.exists(fn):
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log.info({ 'model': model, 'model preview exists': fn })
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continue
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opt = await get('/sdapi/v1/options')
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opt['sd_model_checkpoint'] = model
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await post('/sdapi/v1/options', opt)
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images = []
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labels = []
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t0 = time.time()
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for embedding in embeddings:
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options.generate.prompt = prompt.replace('<embedding>', f'\"{embedding}\"')
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log.info({ 'model': model, 'embedding': embedding, 'prompt': options.generate.prompt })
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data = await generate(options = options, quiet=True)
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if 'image' in data:
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for img in data['image']:
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images.append(img)
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labels.append(embedding)
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else:
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log.error({ 'model': model, 'embedding': embedding, 'error': data })
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t1 = time.time()
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image = grid(images = images, labels = labels, border = 8)
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image.save(fn)
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t = t1 - t0
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its = 1.0 * options.generate.batch_size * len(images) / t
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log.info({ 'model': model, 'created preview': fn, 'images': len(images), 'grid': [image.width, image.height], 'time': round(t, 2), 'its': round(its, 2) })
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await close()
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if __name__ == '__main__':
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parser = argparse.ArgumentParser(description = 'generate model previews')
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parser.add_argument('--output', type = str, default = '', required = False, help = 'output directory')
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parser.add_argument('input', type = str, nargs = '*')
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params = parser.parse_args()
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asyncio.run(models(params))
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