add NV option for Random number generator source setting, which allows to... (84b6fcd0) · Commits · github_fork / Stable Diffusion Webui

modules/devices.py

+37 −2

Original line number	Original line	Diff line number	Diff line
	@@ -3,7 +3,7 @@ import contextlib
	from functools import lru_cache		from functools import lru_cache

	import torch		import torch
	from modules import errors		from modules import errors, rng_philox

	if sys.platform == "darwin":		if sys.platform == "darwin":
	from modules import mac_specific		from modules import mac_specific
	@@ -90,23 +90,58 @@ def cond_cast_float(input):
	return input.float() if unet_needs_upcast else input		return input.float() if unet_needs_upcast else input


			nv_rng = None


	def randn(seed, shape):		def randn(seed, shape):
	from modules.shared import opts		from modules.shared import opts

	torch.manual_seed(seed)		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':		if opts.randn_source == "CPU" or device.type == 'mps':
	return torch.randn(shape, device=cpu).to(device)		return torch.randn(shape, device=cpu).to(device)

	return torch.randn(shape, device=device)		return torch.randn(shape, device=device)


			def randn_like(x):
			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):		def randn_without_seed(shape):
	from modules.shared import opts		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':		if opts.randn_source == "CPU" or device.type == 'mps':
	return torch.randn(shape, device=cpu).to(device)		return torch.randn(shape, device=cpu).to(device)

	return torch.randn(shape, device=device)		return torch.randn(shape, device=device)


			def manual_seed(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):		def autocast(disable=False):
	from modules import shared		from modules import shared

modules/processing.py

+3 −3

modules/rng_philox.py

0 → 100644

+100 −0

Original line number	Original line	Diff line number	Diff line
			"""RNG imitiating torch cuda randn on CPU. You are welcome.

			Usage:

			```
			g = Generator(seed=0)
			print(g.randn(shape=(3, 4)))
			```

			Expected output:
			```
			[[-0.92466259 -0.42534415 -2.6438457 0.14518388]
			[-0.12086647 -0.57972564 -0.62285122 -0.32838709]
			[-1.07454231 -0.36314407 -1.67105067 2.26550497]]
			```
			"""

			import numpy as np

			philox_m = [0xD2511F53, 0xCD9E8D57]
			philox_w = [0x9E3779B9, 0xBB67AE85]

			two_pow32_inv = np.array([2.3283064e-10], dtype=np.float32)
			two_pow32_inv_2pi = np.array([2.3283064e-10 * 6.2831855], dtype=np.float32)


			def uint32(x):
			"""Converts (N,) np.uint64 array into (2, N) np.unit32 array."""
			return np.moveaxis(x.view(np.uint32).reshape(-1, 2), 0, 1)


			def philox4_round(counter, key):
			"""A single round of the Philox 4x32 random number generator."""

			v1 = uint32(counter[0].astype(np.uint64) * philox_m[0])
			v2 = uint32(counter[2].astype(np.uint64) * philox_m[1])

			counter[0] = v2[1] ^ counter[1] ^ key[0]
			counter[1] = v2[0]
			counter[2] = v1[1] ^ counter[3] ^ key[1]
			counter[3] = v1[0]


			def philox4_32(counter, key, rounds=10):
			"""Generates 32-bit random numbers using the Philox 4x32 random number generator.

			Parameters:
			counter (numpy.ndarray): A 4xN array of 32-bit integers representing the counter values (offset into generation).
			key (numpy.ndarray): A 2xN array of 32-bit integers representing the key values (seed).
			rounds (int): The number of rounds to perform.

			Returns:
			numpy.ndarray: A 4xN array of 32-bit integers containing the generated random numbers.
			"""

			for _ in range(rounds - 1):
			philox4_round(counter, key)

			key[0] = key[0] + philox_w[0]
			key[1] = key[1] + philox_w[1]

			philox4_round(counter, key)
			return counter


			def box_muller(x, y):
			"""Returns just the first out of two numbers generated by Box–Muller transform algorithm."""
			u = x.astype(np.float32) * two_pow32_inv + two_pow32_inv / 2
			v = y.astype(np.float32) * two_pow32_inv_2pi + two_pow32_inv_2pi / 2

			s = np.sqrt(-2.0 * np.log(u))

			r1 = s * np.sin(v)
			return r1.astype(np.float32)


			class Generator:
			"""RNG that produces same outputs as torch.randn(..., device='cuda') on CPU"""

			def __init__(self, seed):
			self.seed = seed
			self.offset = 0

			def randn(self, shape):
			"""Generate a sequence of n standard normal random variables using the Philox 4x32 random number generator and the Box-Muller transform."""

			n = 1
			for x in shape:
			n *= x

			counter = np.zeros((4, n), dtype=np.uint32)
			counter[0] = self.offset
			counter[2] = np.arange(n, dtype=np.uint32) # up to 2^32 numbers can be generated - if you want more you'd need to spill into counter[3]
			self.offset += 1

			key = uint32(np.array([[self.seed] * n], dtype=np.uint64))

			g = philox4_32(counter, key)

			return box_muller(g[0], g[1]).reshape(shape) # discard g[2] and g[3]

modules/sd_samplers_kdiffusion.py

+1 −4

Original line number	Original line	Diff line number	Diff line
	@@ -260,10 +260,7 @@ class TorchHijack:
	if noise.shape == x.shape:		if noise.shape == x.shape:
	return noise		return noise

	if opts.randn_source == "CPU" or x.device.type == 'mps':		return devices.randn_like(x)
	return torch.randn_like(x, device=devices.cpu).to(x.device)
	else:
	return torch.randn_like(x)


	class KDiffusionSampler:		class KDiffusionSampler:

modules/shared.py

+1 −1

Original line number	Original line	Diff line number	Diff line
	@@ -428,7 +428,7 @@ options_templates.update(options_section(('sd', "Stable Diffusion"), {
	"CLIP_stop_at_last_layers": OptionInfo(1, "Clip skip", gr.Slider, {"minimum": 1, "maximum": 12, "step": 1}).link("wiki", "https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Features#clip-skip").info("ignore last layers of CLIP network; 1 ignores none, 2 ignores one layer"),		"CLIP_stop_at_last_layers": OptionInfo(1, "Clip skip", gr.Slider, {"minimum": 1, "maximum": 12, "step": 1}).link("wiki", "https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Features#clip-skip").info("ignore last layers of CLIP network; 1 ignores none, 2 ignores one layer"),
	"upcast_attn": OptionInfo(False, "Upcast cross attention layer to float32"),		"upcast_attn": OptionInfo(False, "Upcast cross attention layer to float32"),
	"auto_vae_precision": OptionInfo(True, "Automaticlly revert VAE to 32-bit floats").info("triggers when a tensor with NaNs is produced in VAE; disabling the option in this case will result in a black square image"),		"auto_vae_precision": OptionInfo(True, "Automaticlly revert VAE to 32-bit floats").info("triggers when a tensor with NaNs is produced in VAE; disabling the option in this case will result in a black square image"),
	"randn_source": OptionInfo("GPU", "Random number generator source.", gr.Radio, {"choices": ["GPU", "CPU"]}).info("changes seeds drastically; use CPU to produce the same picture across different videocard vendors"),		"randn_source": OptionInfo("GPU", "Random number generator source.", gr.Radio, {"choices": ["GPU", "CPU", "NV"]}).info("changes seeds drastically; use CPU to produce the same picture across different videocard vendors; use NV to produce same picture as on NVidia videocards"),
	}))		}))

	options_templates.update(options_section(('sdxl', "Stable Diffusion XL"), {		options_templates.update(options_section(('sdxl', "Stable Diffusion XL"), {

Original line number	Original line	Diff line number	Diff line
	@@ -492,7 +492,7 @@ def create_random_tensors(shape, seeds, subseeds=None, subseed_strength=0.0, see
	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)		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		subnoise = None
	if subseeds is not None:		if subseeds is not None and subseed_strength != 0:
	subseed = 0 if i >= len(subseeds) else subseeds[i]		subseed = 0 if i >= len(subseeds) else subseeds[i]

	subnoise = devices.randn(subseed, noise_shape)		subnoise = devices.randn(subseed, noise_shape)
	@@ -524,7 +524,7 @@ def create_random_tensors(shape, seeds, subseeds=None, subseed_strength=0.0, see
	cnt = p.sampler.number_of_needed_noises(p)		cnt = p.sampler.number_of_needed_noises(p)

	if eta_noise_seed_delta > 0:		if eta_noise_seed_delta > 0:
	torch.manual_seed(seed + eta_noise_seed_delta)		devices.manual_seed(seed + eta_noise_seed_delta)

	for j in range(cnt):		for j in range(cnt):
	sampler_noises[j].append(devices.randn_without_seed(tuple(noise_shape)))		sampler_noises[j].append(devices.randn_without_seed(tuple(noise_shape)))
	@@ -636,7 +636,7 @@ def create_infotext(p, all_prompts, all_seeds, all_subseeds, comments=None, iter
	"Token merging ratio": None if token_merging_ratio == 0 else token_merging_ratio,		"Token merging ratio": None if token_merging_ratio == 0 else token_merging_ratio,
	"Token merging ratio hr": None if not enable_hr or token_merging_ratio_hr == 0 else token_merging_ratio_hr,		"Token merging ratio hr": None if not enable_hr or token_merging_ratio_hr == 0 else token_merging_ratio_hr,
	"Init image hash": getattr(p, 'init_img_hash', None),		"Init image hash": getattr(p, 'init_img_hash', None),
	"RNG": opts.randn_source if opts.randn_source != "GPU" else None,		"RNG": opts.randn_source if opts.randn_source != "GPU" and opts.randn_source != "NV" else None,
	"NGMS": None if p.s_min_uncond == 0 else p.s_min_uncond,		"NGMS": None if p.s_min_uncond == 0 else p.s_min_uncond,
	**p.extra_generation_params,		**p.extra_generation_params,
	"Version": program_version() if opts.add_version_to_infotext else None,		"Version": program_version() if opts.add_version_to_infotext else None,

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