first attempt to produce crrect seeds in batch

This commit is contained in:
AUTOMATIC 2022-09-13 21:49:58 +03:00
parent 85b97cc49c
commit 9d40212485
3 changed files with 51 additions and 2 deletions

View File

@ -48,3 +48,13 @@ def randn(seed, shape):
torch.manual_seed(seed)
return torch.randn(shape, device=device)
def randn_without_seed(shape):
# Pytorch currently doesn't handle setting randomness correctly when the metal backend is used.
if device.type == 'mps':
generator = torch.Generator(device=cpu)
noise = torch.randn(shape, generator=generator, device=cpu).to(device)
return noise
return torch.randn(shape, device=device)

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@ -119,8 +119,14 @@ def slerp(val, low, high):
return res
def create_random_tensors(shape, seeds, subseeds=None, subseed_strength=0.0, seed_resize_from_h=0, seed_resize_from_w=0):
def create_random_tensors(shape, seeds, subseeds=None, subseed_strength=0.0, seed_resize_from_h=0, seed_resize_from_w=0, p=None):
xs = []
if p is not None and p.sampler is not None and len(seeds) > 1:
sampler_noises = [[] for _ in range(p.sampler.number_of_needed_noises(p))]
else:
sampler_noises = None
for i, seed in enumerate(seeds):
noise_shape = shape if seed_resize_from_h <= 0 or seed_resize_from_w <= 0 else (shape[0], seed_resize_from_h//8, seed_resize_from_w//8)
@ -155,9 +161,17 @@ def create_random_tensors(shape, seeds, subseeds=None, subseed_strength=0.0, see
x[:, ty:ty+h, tx:tx+w] = noise[:, dy:dy+h, dx:dx+w]
noise = x
if sampler_noises is not None:
cnt = p.sampler.number_of_needed_noises(p)
for j in range(cnt):
sampler_noises[j].append(devices.randn_without_seed(tuple(noise_shape)))
xs.append(noise)
if sampler_noises is not None:
p.sampler.sampler_noises = [torch.stack(n).to(shared.device) for n in sampler_noises]
x = torch.stack(xs).to(shared.device)
return x
@ -254,7 +268,7 @@ def process_images(p: StableDiffusionProcessing) -> Processed:
comments += model_hijack.comments
# we manually generate all input noises because each one should have a specific seed
x = create_random_tensors([opt_C, p.height // opt_f, p.width // opt_f], seeds=seeds, subseeds=subseeds, subseed_strength=p.subseed_strength, seed_resize_from_h=p.seed_resize_from_h, seed_resize_from_w=p.seed_resize_from_w)
x = create_random_tensors([opt_C, p.height // opt_f, p.width // opt_f], seeds=seeds, subseeds=subseeds, subseed_strength=p.subseed_strength, seed_resize_from_h=p.seed_resize_from_h, seed_resize_from_w=p.seed_resize_from_w, p=p)
if p.n_iter > 1:
shared.state.job = f"Batch {n+1} out of {p.n_iter}"

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@ -93,6 +93,10 @@ class VanillaStableDiffusionSampler:
self.mask = None
self.nmask = None
self.init_latent = None
self.sampler_noises = None
def number_of_needed_noises(self, p):
return 0
def sample_img2img(self, p, x, noise, conditioning, unconditional_conditioning):
t_enc = int(min(p.denoising_strength, 0.999) * p.steps)
@ -171,16 +175,37 @@ def extended_trange(count, *args, **kwargs):
shared.total_tqdm.update()
original_randn_like = torch.randn_like
class KDiffusionSampler:
def __init__(self, funcname, sd_model):
self.model_wrap = k_diffusion.external.CompVisDenoiser(sd_model)
self.funcname = funcname
self.func = getattr(k_diffusion.sampling, self.funcname)
self.model_wrap_cfg = CFGDenoiser(self.model_wrap)
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"])
def number_of_needed_noises(self, p):
return p.steps
def randn_like(self, x):
noise = self.sampler_noises[self.sampler_noise_index] if self.sampler_noises is not None and self.sampler_noise_index < len(self.sampler_noises) else None
if noise is not None and x.shape == noise.shape:
res = noise
else:
print('generating')
res = original_randn_like(x)
self.sampler_noise_index += 1
return res
def sample_img2img(self, p, x, noise, conditioning, unconditional_conditioning):
t_enc = int(min(p.denoising_strength, 0.999) * p.steps)
sigmas = self.model_wrap.get_sigmas(p.steps)