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feat(txt2img): allow from_file to work with len(lines) < batch_size (#349)

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Olivier Louvignes 2022-09-06 12:41:08 +02:00 committed by GitHub
parent 720e5cd651
commit eef788981c
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1 changed files with 8 additions and 3 deletions
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11
scripts/orig_scripts/txt2img.py
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@ -232,7 +232,12 @@ def main():
print(f"reading prompts from {opt.from_file}") print(f"reading prompts from {opt.from_file}")
with open(opt.from_file, "r") as f: with open(opt.from_file, "r") as f:
data = f.read().splitlines() data = f.read().splitlines()
data = list(chunk(data, batch_size)) if (len(data) >= batch_size):
data = list(chunk(data, batch_size))
else:
while (len(data) < batch_size):
data.append(data[-1])
data = [data]
sample_path = os.path.join(outpath, "samples") sample_path = os.path.join(outpath, "samples")
os.makedirs(sample_path, exist_ok=True) os.makedirs(sample_path, exist_ok=True)
@ -264,7 +269,7 @@ def main():
prompts = list(prompts) prompts = list(prompts)
c = model.get_learned_conditioning(prompts) c = model.get_learned_conditioning(prompts)
shape = [opt.C, opt.H // opt.f, opt.W // opt.f] shape = [opt.C, opt.H // opt.f, opt.W // opt.f]
if not opt.klms: if not opt.klms:
samples_ddim, _ = sampler.sample(S=opt.ddim_steps, samples_ddim, _ = sampler.sample(S=opt.ddim_steps,
conditioning=c, conditioning=c,
@ -284,7 +289,7 @@ def main():
model_wrap_cfg = CFGDenoiser(model_wrap) model_wrap_cfg = CFGDenoiser(model_wrap)
extra_args = {'cond': c, 'uncond': uc, 'cond_scale': opt.scale} extra_args = {'cond': c, 'uncond': uc, 'cond_scale': opt.scale}
samples_ddim = K.sampling.sample_lms(model_wrap_cfg, x, sigmas, extra_args=extra_args) samples_ddim = K.sampling.sample_lms(model_wrap_cfg, x, sigmas, extra_args=extra_args)
x_samples_ddim = model.decode_first_stage(samples_ddim) x_samples_ddim = model.decode_first_stage(samples_ddim)
x_samples_ddim = torch.clamp((x_samples_ddim + 1.0) / 2.0, min=0.0, max=1.0) x_samples_ddim = torch.clamp((x_samples_ddim + 1.0) / 2.0, min=0.0, max=1.0)