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Merge branch 'development' into development

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Peter Baylies 2022-10-04 18:40:42 -04:00 committed by GitHub
commit 5d911b43c0
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2 changed files with 6 additions and 13 deletions
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2
ldm/dream/generator/txt2img.py
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@ -32,7 +32,7 @@ class Txt2Img(Generator):
if self.free_gpu_mem and self.model.model.device != self.model.device: if self.free_gpu_mem and self.model.model.device != self.model.device:
self.model.model.to(self.model.device) self.model.model.to(self.model.device)
sampler.make_schedule(ddim_num_steps=steps, ddim_eta=ddim_eta, verbose=True) sampler.make_schedule(ddim_num_steps=steps, ddim_eta=ddim_eta, verbose=False)
samples, _ = sampler.sample( samples, _ = sampler.sample(
batch_size = 1, batch_size = 1,

17
ldm/models/diffusion/ksampler.py
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@ -79,17 +79,9 @@ class KSampler(Sampler):
ddim_eta=0.0, ddim_eta=0.0,
verbose=False, verbose=False,
) )
self.model = outer_model self.model = outer_model
self.ddim_num_steps = ddim_num_steps self.ddim_num_steps = ddim_num_steps
sigmas = K.sampling.get_sigmas_karras( sigmas = self.model.get_sigmas(ddim_num_steps)
n=ddim_num_steps,
sigma_min=self.model.sigmas[0].item(),
sigma_max=self.model.sigmas[-1].item(),
rho=7.,
device=self.device,
# Birch-san recommends this, but it doesn't match the call signature in his branch of k-diffusion
# concat_zero=False
)
self.sigmas = sigmas self.sigmas = sigmas
# ALERT: We are completely overriding the sample() method in the base class, which # ALERT: We are completely overriding the sample() method in the base class, which
@ -133,7 +125,8 @@ class KSampler(Sampler):
# sigmas = self.model.get_sigmas(S) # sigmas = self.model.get_sigmas(S)
# sigmas are now set up in make_schedule - we take the last steps items # sigmas are now set up in make_schedule - we take the last steps items
sigmas = self.sigmas[-S:] sigmas = self.sigmas[-S-1:]
if x_T is not None: if x_T is not None:
x = x_T * sigmas[0] x = x_T * sigmas[0]
else: else:
@ -147,7 +140,7 @@ class KSampler(Sampler):
'uncond': unconditional_conditioning, 'uncond': unconditional_conditioning,
'cond_scale': unconditional_guidance_scale, 'cond_scale': unconditional_guidance_scale,
} }
print(f'>> Sampling with k__{self.schedule}') print(f'>> Sampling with k_{self.schedule}')
return ( return (
K.sampling.__dict__[f'sample_{self.schedule}']( K.sampling.__dict__[f'sample_{self.schedule}'](
model_wrap_cfg, x, sigmas, extra_args=extra_args, model_wrap_cfg, x, sigmas, extra_args=extra_args,