mirror of
https://github.com/vladmandic/sdnext.git
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223 lines
9.2 KiB
Python
Executable File
223 lines
9.2 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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from pathlib import Path
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from util import Map, log
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from sdapi import get, post, close
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from grid import grid
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sys.path.append(os.path.join(os.path.dirname(__file__), '..'))
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from generate import sd, generate
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default = 'sd-v15-runwayml.ckpt [cc6cb27103]'
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embeddings = ['blonde', 'bruntette', 'sexy', 'naked', 'ti-mia', 'ti-lin', 'ti-kelly', 'ti-hanna', 'ti-rreid-random']
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exclude = ['sd-v20', 'sd-v21', 'inpainting', 'pix2pix']
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prompt = "photo of <keyword> <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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'format': '.jpg',
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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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'lora': {
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'strength': 1.0,
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},
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'hypernetwork': {
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'keyword': 'beautiful sexy woman',
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'strength': 1.0,
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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 + options.format)
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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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options.generate.prompt = options.generate.prompt.replace('<keyword>', 'beautiful woman')
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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 and not params.fixed:
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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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async def lora(params):
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cmdflags = await get('/sdapi/v1/cmd-flags')
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dir = cmdflags['lora_dir']
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if not os.path.exists(dir):
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log.error({ 'lora directory not found': dir })
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return
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models1 = [f for f in Path(dir).glob('*.safetensors')]
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models2 = [f for f in Path(dir).glob('*.ckpt')]
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models = [f.stem for f in models1 + models2]
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log.info({ 'loras': len(models) })
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for model in models:
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fn = os.path.join(dir, model + options.format)
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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({ 'lora preview exists': model })
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continue
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images = []
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labels = []
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t0 = time.time()
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import re
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keywords = re.sub('\d', '', model)
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keywords = keywords.replace('-v', ' ').replace('-', ' ').strip().split(' ')
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keyword = '\"' + '\" \"'.join(keywords) + '\"'
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options.generate.prompt = prompt.replace('<keyword>', keyword)
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options.generate.prompt = options.generate.prompt.replace('<embedding>', '')
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options.generate.prompt += f' <lora:{model}:{options.lora.strength}>'
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log.info({ 'lora generating': model, 'keyword': keyword, '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(keyword)
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else:
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log.error({ 'lora': model, 'keyword': keyword, '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({ 'lora 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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async def hypernetwork(params):
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cmdflags = await get('/sdapi/v1/cmd-flags')
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dir = cmdflags['hypernetwork_dir']
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if not os.path.exists(dir):
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log.error({ 'hypernetwork directory not found': dir })
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return
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models = [f.stem for f in Path(dir).glob('*.pt')]
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log.info({ 'loras': len(models) })
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for model in models:
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fn = os.path.join(dir, model + options.format)
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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({ 'hypernetwork preview exists': model })
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continue
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images = []
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labels = []
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t0 = time.time()
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keyword = options.hypernetwork.keyword
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options.generate.prompt = prompt.replace('<keyword>', options.hypernetwork.keyword)
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options.generate.prompt = options.generate.prompt.replace('<embedding>', '')
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options.generate.prompt = f' <hypernet:{model}:{options.hypernetwork.strength}> ' + options.generate.prompt
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log.info({ 'hypernetwork generating': model, 'keyword': keyword, '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(keyword)
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else:
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log.error({ 'hypernetwork': model, 'keyword': keyword, '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({ 'hypernetwork 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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async def create_previews(params):
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await models(params)
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await lora(params)
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await hypernetwork(params)
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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('--fixed', default = False, action='store_true', help = "do not change model")
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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(create_previews(params))
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