rework RNG to use generators instead of generating noises beforehand

modules/devices.py

@@ -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)
+

modules/processing.py

@@ -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
+    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)
-    xs = []
+    return g.next()
-
-    # 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
 
 
 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

modules/rng.py

@@ -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

modules/sd_samplers_common.py

@@ -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):
+    """This is here to replace torch.randn_like of k-diffusion.
-        # Using a deque to efficiently receive the sampler_noises in the same order as the previous index-based
+
-        # implementation.
+    k-diffusion has random_sampler argument for most samplers, but not for all, so
-        self.sampler_noises = deque(sampler_noises)
+    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:
+        return self.rng.next()
-            noise = self.sampler_noises.popleft()
-            if noise.shape == x.shape:
-                return noise
-
-        return devices.randn_like(x)
 
 
 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:

modules/shared.py

@@ -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