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rework RNG to use generators instead of generating noises beforehand

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AUTOMATIC1111 2023-08-09 08:43:31 +03:00
parent d81d3fa8cd
commit 0d5dc9a6e7
5 changed files with 196 additions and 171 deletions
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81
modules/devices.py
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@ -3,7 +3,7 @@ import contextlib
from functools import lru_cache
import torch
from modules import errors, rng_philox
from modules import errors
if sys.platform == "darwin":
from modules import mac_specific
@ -96,84 +96,6 @@ def cond_cast_float(input):
nv_rng = None
def randn(seed, shape):
"""Generate a tensor with random numbers from a normal distribution using seed.
Uses the seed parameter to set the global torch seed; to generate more with that seed, use randn_like/randn_without_seed."""
from modules.shared import opts
manual_seed(seed)
if opts.randn_source == "NV":
return torch.asarray(nv_rng.randn(shape), device=device)
if opts.randn_source == "CPU" or device.type == 'mps':
return torch.randn(shape, device=cpu).to(device)
return torch.randn(shape, device=device)
def randn_local(seed, shape):
"""Generate a tensor with random numbers from a normal distribution using seed.
Does not change the global random number generator. You can only generate the seed's first tensor using this function."""
from modules.shared import opts
if opts.randn_source == "NV":
rng = rng_philox.Generator(seed)
return torch.asarray(rng.randn(shape), device=device)
local_device = cpu if opts.randn_source == "CPU" or device.type == 'mps' else device
local_generator = torch.Generator(local_device).manual_seed(int(seed))
return torch.randn(shape, device=local_device, generator=local_generator).to(device)
def randn_like(x):
"""Generate a tensor with random numbers from a normal distribution using the previously initialized genrator.
Use either randn() or manual_seed() to initialize the generator."""
from modules.shared import opts
if opts.randn_source == "NV":
return torch.asarray(nv_rng.randn(x.shape), device=x.device, dtype=x.dtype)
if opts.randn_source == "CPU" or x.device.type == 'mps':
return torch.randn_like(x, device=cpu).to(x.device)
return torch.randn_like(x)
def randn_without_seed(shape):
"""Generate a tensor with random numbers from a normal distribution using the previously initialized genrator.
Use either randn() or manual_seed() to initialize the generator."""
from modules.shared import opts
if opts.randn_source == "NV":
return torch.asarray(nv_rng.randn(shape), device=device)
if opts.randn_source == "CPU" or device.type == 'mps':
return torch.randn(shape, device=cpu).to(device)
return torch.randn(shape, device=device)
def manual_seed(seed):
"""Set up a global random number generator using the specified seed."""
from modules.shared import opts
if opts.randn_source == "NV":
global nv_rng
nv_rng = rng_philox.Generator(seed)
return
torch.manual_seed(seed)
def autocast(disable=False):
from modules import shared
@ -236,3 +158,4 @@ def first_time_calculation():
x = torch.zeros((1, 1, 3, 3)).to(device, dtype)
conv2d = torch.nn.Conv2d(1, 1, (3, 3)).to(device, dtype)
conv2d(x)

89
modules/processing.py
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@ -14,7 +14,7 @@ from skimage import exposure
from typing import Any, Dict, List
import modules.sd_hijack
from modules import devices, prompt_parser, masking, sd_samplers, lowvram, generation_parameters_copypaste, extra_networks, sd_vae_approx, scripts, sd_samplers_common, sd_unet, errors
from modules import devices, prompt_parser, masking, sd_samplers, lowvram, generation_parameters_copypaste, extra_networks, sd_vae_approx, scripts, sd_samplers_common, sd_unet, errors, rng
from modules.sd_hijack import model_hijack
from modules.sd_samplers_common import images_tensor_to_samples, decode_first_stage, approximation_indexes
from modules.shared import opts, cmd_opts, state
@ -186,6 +186,7 @@ class StableDiffusionProcessing:
self.cached_c = StableDiffusionProcessing.cached_c
self.uc = None
self.c = None
self.rng: rng.ImageRNG = None
self.user = None
@ -475,82 +476,9 @@ class Processed:
return self.token_merging_ratio_hr if for_hr else self.token_merging_ratio
# from https://discuss.pytorch.org/t/help-regarding-slerp-function-for-generative-model-sampling/32475/3
def slerp(val, low, high):
low_norm = low/torch.norm(low, dim=1, keepdim=True)
high_norm = high/torch.norm(high, dim=1, keepdim=True)
dot = (low_norm*high_norm).sum(1)
if dot.mean() > 0.9995:
return low * val + high * (1 - val)
omega = torch.acos(dot)
so = torch.sin(omega)
res = (torch.sin((1.0-val)*omega)/so).unsqueeze(1)*low + (torch.sin(val*omega)/so).unsqueeze(1) * 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, p=None):
eta_noise_seed_delta = opts.eta_noise_seed_delta or 0
xs = []
# if we have multiple seeds, this means we are working with batch size>1; this then
# enables the generation of additional tensors with noise that the sampler will use during its processing.
# Using those pre-generated tensors instead of simple torch.randn allows a batch with seeds [100, 101] to
# produce the same images as with two batches [100], [101].
if p is not None and p.sampler is not None and (len(seeds) > 1 and opts.enable_batch_seeds or eta_noise_seed_delta > 0):
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)
subnoise = None
if subseeds is not None and subseed_strength != 0:
subseed = 0 if i >= len(subseeds) else subseeds[i]
subnoise = devices.randn(subseed, noise_shape)
# randn results depend on device; gpu and cpu get different results for same seed;
# the way I see it, it's better to do this on CPU, so that everyone gets same result;
# but the original script had it like this, so I do not dare change it for now because
# it will break everyone's seeds.
noise = devices.randn(seed, noise_shape)
if subnoise is not None:
noise = slerp(subseed_strength, noise, subnoise)
if noise_shape != shape:
x = devices.randn(seed, shape)
dx = (shape[2] - noise_shape[2]) // 2
dy = (shape[1] - noise_shape[1]) // 2
w = noise_shape[2] if dx >= 0 else noise_shape[2] + 2 * dx
h = noise_shape[1] if dy >= 0 else noise_shape[1] + 2 * dy
tx = 0 if dx < 0 else dx
ty = 0 if dy < 0 else dy
dx = max(-dx, 0)
dy = max(-dy, 0)
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)
if eta_noise_seed_delta > 0:
devices.manual_seed(seed + eta_noise_seed_delta)
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
g = rng.ImageRNG(shape, seeds, subseeds=subseeds, subseed_strength=subseed_strength, seed_resize_from_h=seed_resize_from_h, seed_resize_from_w=seed_resize_from_w)
return g.next()
class DecodedSamples(list):
@ -769,6 +697,8 @@ def process_images_inner(p: StableDiffusionProcessing) -> Processed:
p.seeds = p.all_seeds[n * p.batch_size:(n + 1) * p.batch_size]
p.subseeds = p.all_subseeds[n * p.batch_size:(n + 1) * p.batch_size]
p.rng = rng.ImageRNG((opt_C, p.height // opt_f, p.width // opt_f), p.seeds, subseeds=p.subseeds, subseed_strength=p.subseed_strength, seed_resize_from_h=p.seed_resize_from_h, seed_resize_from_w=p.seed_resize_from_w)
if p.scripts is not None:
p.scripts.before_process_batch(p, batch_number=n, prompts=p.prompts, seeds=p.seeds, subseeds=p.subseeds)
@ -1072,7 +1002,7 @@ class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing):
def sample(self, conditioning, unconditional_conditioning, seeds, subseeds, subseed_strength, prompts):
self.sampler = sd_samplers.create_sampler(self.sampler_name, self.sd_model)
x = create_random_tensors([opt_C, self.height // opt_f, self.width // opt_f], seeds=seeds, subseeds=subseeds, subseed_strength=self.subseed_strength, seed_resize_from_h=self.seed_resize_from_h, seed_resize_from_w=self.seed_resize_from_w, p=self)
x = self.rng.next()
samples = self.sampler.sample(self, x, conditioning, unconditional_conditioning, image_conditioning=self.txt2img_image_conditioning(x))
del x
@ -1160,7 +1090,8 @@ class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing):
samples = samples[:, :, self.truncate_y//2:samples.shape[2]-(self.truncate_y+1)//2, self.truncate_x//2:samples.shape[3]-(self.truncate_x+1)//2]
noise = create_random_tensors(samples.shape[1:], seeds=seeds, subseeds=subseeds, subseed_strength=subseed_strength, p=self)
self.rng = rng.ImageRNG(samples.shape[1:], self.seeds, subseeds=self.subseeds, subseed_strength=self.subseed_strength, seed_resize_from_h=self.seed_resize_from_h, seed_resize_from_w=self.seed_resize_from_w)
noise = self.rng.next()
# GC now before running the next img2img to prevent running out of memory
devices.torch_gc()
@ -1418,7 +1349,7 @@ class StableDiffusionProcessingImg2Img(StableDiffusionProcessing):
self.image_conditioning = self.img2img_image_conditioning(image, self.init_latent, image_mask)
def sample(self, conditioning, unconditional_conditioning, seeds, subseeds, subseed_strength, prompts):
x = create_random_tensors([opt_C, self.height // opt_f, self.width // opt_f], seeds=seeds, subseeds=subseeds, subseed_strength=self.subseed_strength, seed_resize_from_h=self.seed_resize_from_h, seed_resize_from_w=self.seed_resize_from_w, p=self)
x = self.rng.next()
if self.initial_noise_multiplier != 1.0:
self.extra_generation_params["Noise multiplier"] = self.initial_noise_multiplier

171
modules/rng.py Normal file
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@ -0,0 +1,171 @@
import torch
from modules import devices, rng_philox, shared
def randn(seed, shape, generator=None):
"""Generate a tensor with random numbers from a normal distribution using seed.
Uses the seed parameter to set the global torch seed; to generate more with that seed, use randn_like/randn_without_seed."""
manual_seed(seed)
if shared.opts.randn_source == "NV":
return torch.asarray((generator or nv_rng).randn(shape), device=devices.device)
if shared.opts.randn_source == "CPU" or devices.device.type == 'mps':
return torch.randn(shape, device=devices.cpu, generator=generator).to(devices.device)
return torch.randn(shape, device=devices.device, generator=generator)
def randn_local(seed, shape):
"""Generate a tensor with random numbers from a normal distribution using seed.
Does not change the global random number generator. You can only generate the seed's first tensor using this function."""
if shared.opts.randn_source == "NV":
rng = rng_philox.Generator(seed)
return torch.asarray(rng.randn(shape), device=devices.device)
local_device = devices.cpu if shared.opts.randn_source == "CPU" or devices.device.type == 'mps' else devices.device
local_generator = torch.Generator(local_device).manual_seed(int(seed))
return torch.randn(shape, device=local_device, generator=local_generator).to(devices.device)
def randn_like(x):
"""Generate a tensor with random numbers from a normal distribution using the previously initialized genrator.
Use either randn() or manual_seed() to initialize the generator."""
if shared.opts.randn_source == "NV":
return torch.asarray(nv_rng.randn(x.shape), device=x.device, dtype=x.dtype)
if shared.opts.randn_source == "CPU" or x.device.type == 'mps':
return torch.randn_like(x, device=devices.cpu).to(x.device)
return torch.randn_like(x)
def randn_without_seed(shape, generator=None):
"""Generate a tensor with random numbers from a normal distribution using the previously initialized genrator.
Use either randn() or manual_seed() to initialize the generator."""
if shared.opts.randn_source == "NV":
return torch.asarray((generator or nv_rng).randn(shape), device=devices.device)
if shared.opts.randn_source == "CPU" or devices.device.type == 'mps':
return torch.randn(shape, device=devices.cpu, generator=generator).to(devices.device)
return torch.randn(shape, device=devices.device, generator=generator)
def manual_seed(seed):
"""Set up a global random number generator using the specified seed."""
from modules.shared import opts
if opts.randn_source == "NV":
global nv_rng
nv_rng = rng_philox.Generator(seed)
return
torch.manual_seed(seed)
def create_generator(seed):
if shared.opts.randn_source == "NV":
return rng_philox.Generator(seed)
device = devices.cpu if shared.opts.randn_source == "CPU" or devices.device.type == 'mps' else devices.device
generator = torch.Generator(device).manual_seed(int(seed))
return generator
# from https://discuss.pytorch.org/t/help-regarding-slerp-function-for-generative-model-sampling/32475/3
def slerp(val, low, high):
low_norm = low/torch.norm(low, dim=1, keepdim=True)
high_norm = high/torch.norm(high, dim=1, keepdim=True)
dot = (low_norm*high_norm).sum(1)
if dot.mean() > 0.9995:
return low * val + high * (1 - val)
omega = torch.acos(dot)
so = torch.sin(omega)
res = (torch.sin((1.0-val)*omega)/so).unsqueeze(1)*low + (torch.sin(val*omega)/so).unsqueeze(1) * high
return res
class ImageRNG:
def __init__(self, shape, seeds, subseeds=None, subseed_strength=0.0, seed_resize_from_h=0, seed_resize_from_w=0):
self.shape = shape
self.seeds = seeds
self.subseeds = subseeds
self.subseed_strength = subseed_strength
self.seed_resize_from_h = seed_resize_from_h
self.seed_resize_from_w = seed_resize_from_w
self.generators = [create_generator(seed) for seed in seeds]
self.is_first = True
def first(self):
noise_shape = self.shape if self.seed_resize_from_h <= 0 or self.seed_resize_from_w <= 0 else (self.shape[0], self.seed_resize_from_h // 8, self.seed_resize_from_w // 8)
xs = []
for i, (seed, generator) in enumerate(zip(self.seeds, self.generators)):
subnoise = None
if self.subseeds is not None and self.subseed_strength != 0:
subseed = 0 if i >= len(self.subseeds) else self.subseeds[i]
subnoise = randn(subseed, noise_shape)
if noise_shape != self.shape:
noise = randn(seed, noise_shape)
else:
noise = randn(seed, self.shape, generator=generator)
if subnoise is not None:
noise = slerp(self.subseed_strength, noise, subnoise)
if noise_shape != self.shape:
x = randn(seed, self.shape, generator=generator)
dx = (self.shape[2] - noise_shape[2]) // 2
dy = (self.shape[1] - noise_shape[1]) // 2
w = noise_shape[2] if dx >= 0 else noise_shape[2] + 2 * dx
h = noise_shape[1] if dy >= 0 else noise_shape[1] + 2 * dy
tx = 0 if dx < 0 else dx
ty = 0 if dy < 0 else dy
dx = max(-dx, 0)
dy = max(-dy, 0)
x[:, ty:ty + h, tx:tx + w] = noise[:, dy:dy + h, dx:dx + w]
noise = x
xs.append(noise)
eta_noise_seed_delta = shared.opts.eta_noise_seed_delta or 0
if eta_noise_seed_delta:
self.generators = [create_generator(seed + eta_noise_seed_delta) for seed in self.seeds]
return torch.stack(xs).to(shared.device)
def next(self):
if self.is_first:
self.is_first = False
return self.first()
xs = []
for generator in self.generators:
x = randn_without_seed(self.shape, generator=generator)
xs.append(x)
return torch.stack(xs).to(shared.device)
devices.randn = randn
devices.randn_local = randn_local
devices.randn_like = randn_like
devices.randn_without_seed = randn_without_seed
devices.manual_seed = manual_seed

24
modules/sd_samplers_common.py
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@ -1,5 +1,5 @@
import inspect
from collections import namedtuple, deque
from collections import namedtuple
import numpy as np
import torch
from PIL import Image
@ -132,10 +132,15 @@ replace_torchsde_browinan()
class TorchHijack:
def __init__(self, sampler_noises):
# Using a deque to efficiently receive the sampler_noises in the same order as the previous index-based
# implementation.
self.sampler_noises = deque(sampler_noises)
"""This is here to replace torch.randn_like of k-diffusion.
k-diffusion has random_sampler argument for most samplers, but not for all, so
this is needed to properly replace every use of torch.randn_like.
We need to replace to make images generated in batches to be same as images generated individually."""
def __init__(self, p):
self.rng = p.rng
def __getattr__(self, item):
if item == 'randn_like':
@ -147,12 +152,7 @@ class TorchHijack:
raise AttributeError(f"'{type(self).__name__}' object has no attribute '{item}'")
def randn_like(self, x):
if self.sampler_noises:
noise = self.sampler_noises.popleft()
if noise.shape == x.shape:
return noise
return devices.randn_like(x)
return self.rng.next()
class Sampler:
@ -215,7 +215,7 @@ class Sampler:
self.eta = p.eta if p.eta is not None else getattr(opts, self.eta_option_field, 0.0)
self.s_min_uncond = getattr(p, 's_min_uncond', 0.0)
k_diffusion.sampling.torch = TorchHijack(self.sampler_noises if self.sampler_noises is not None else [])
k_diffusion.sampling.torch = TorchHijack(p)
extra_params_kwargs = {}
for param_name in self.extra_params:

2
modules/shared.py
View File

@ -16,7 +16,7 @@ import modules.interrogate
import modules.memmon
import modules.styles
import modules.devices as devices
from modules import localization, script_loading, errors, ui_components, shared_items, cmd_args
from modules import localization, script_loading, errors, ui_components, shared_items, cmd_args, rng # noqa: F401
from modules.paths_internal import models_path, script_path, data_path, sd_configs_path, sd_default_config, sd_model_file, default_sd_model_file, extensions_dir, extensions_builtin_dir # noqa: F401
from ldm.models.diffusion.ddpm import LatentDiffusion
from typing import Optional