mirror of https://github.com/vladmandic/automatic
130 lines
5.0 KiB
Python
Executable File
130 lines
5.0 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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default = 'sd-v15-runwayml.ckpt [cc6cb27103]'
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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 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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if len(params.input) > 0: # check if model is included in cmd line
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filtered = []
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for m in params.input:
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if m in models:
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filtered.append(m)
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else:
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log.error({ 'model not found': m })
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return
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models = filtered
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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': embeddings })
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cmdflags = await get('/sdapi/v1/cmd-flags')
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opt = await get('/sdapi/v1/options')
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if params.output != '':
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dir = params.output
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else:
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dir = os.path.abspath(os.path.join(cmdflags['hypernetwork_dir'], '..', 'Stable-diffusion'))
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log.info({ 'output directory': dir })
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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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for model in models:
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fn = os.path.join(dir, model + '.png')
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if os.path.exists(fn) and len(params.input) == 0: # if model preview exists and not manually included
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log.info({ 'model preview exists': model })
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continue
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log.info({ 'model load': model })
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opt['sd_model_checkpoint'] = model
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await post('/sdapi/v1/options', opt)
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opt = await get('/sdapi/v1/options')
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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 generating': 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.steps * len(images) / t
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log.info({ 'model preview created': model, 'image': fn, 'images': len(images), 'grid': [image.width, image.height], 'time': round(t, 2), 'its': round(its, 2) })
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opt = await get('/sdapi/v1/options')
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if opt['sd_model_checkpoint'] != default:
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log.info({ 'model set default': default })
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opt['sd_model_checkpoint'] = default
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await post('/sdapi/v1/options', opt)
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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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