diff options
Diffstat (limited to 'modules/sd_samplers.py')
| -rw-r--r-- | modules/sd_samplers.py | 25 |
1 files changed, 20 insertions, 5 deletions
diff --git a/modules/sd_samplers.py b/modules/sd_samplers.py index f77fe43f..d478c5bc 100644 --- a/modules/sd_samplers.py +++ b/modules/sd_samplers.py @@ -175,7 +175,19 @@ def extended_trange(count, *args, **kwargs): shared.total_tqdm.update()
-original_randn_like = torch.randn_like
+class TorchHijack:
+ def __init__(self, kdiff_sampler):
+ self.kdiff_sampler = kdiff_sampler
+
+ def __getattr__(self, item):
+ if item == 'randn_like':
+ return self.kdiff_sampler.randn_like
+
+ if hasattr(torch, item):
+ return getattr(torch, item)
+
+ raise AttributeError("'{}' object has no attribute '{}'".format(type(self).__name__, item))
+
class KDiffusionSampler:
def __init__(self, funcname, sd_model):
@@ -186,8 +198,6 @@ class KDiffusionSampler: self.sampler_noises = None
self.sampler_noise_index = 0
- k_diffusion.sampling.torch.randn_like = self.randn_like
-
def callback_state(self, d):
store_latent(d["denoised"])
@@ -200,8 +210,7 @@ class KDiffusionSampler: if noise is not None and x.shape == noise.shape:
res = noise
else:
- print('generating')
- res = original_randn_like(x)
+ res = torch.randn_like(x)
self.sampler_noise_index += 1
return res
@@ -223,6 +232,9 @@ class KDiffusionSampler: if hasattr(k_diffusion.sampling, 'trange'):
k_diffusion.sampling.trange = lambda *args, **kwargs: extended_trange(*args, **kwargs)
+ if self.sampler_noises is not None:
+ k_diffusion.sampling.torch = TorchHijack(self)
+
return self.func(self.model_wrap_cfg, xi, sigma_sched, extra_args={'cond': conditioning, 'uncond': unconditional_conditioning, 'cond_scale': p.cfg_scale}, disable=False, callback=self.callback_state)
def sample(self, p, x, conditioning, unconditional_conditioning):
@@ -232,6 +244,9 @@ class KDiffusionSampler: if hasattr(k_diffusion.sampling, 'trange'):
k_diffusion.sampling.trange = lambda *args, **kwargs: extended_trange(*args, **kwargs)
+ if self.sampler_noises is not None:
+ k_diffusion.sampling.torch = TorchHijack(self)
+
samples_ddim = self.func(self.model_wrap_cfg, x, sigmas, extra_args={'cond': conditioning, 'uncond': unconditional_conditioning, 'cond_scale': p.cfg_scale}, disable=False, callback=self.callback_state)
return samples_ddim
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