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3bdc93d | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 | """Sampling glue for Anima MLX flow models."""
from __future__ import annotations
from dataclasses import dataclass
from typing import Any, Callable, Sequence
from .scheduler import FlowSchedulerConfig, simple_sigmas, time_snr_shift
@dataclass(frozen=True)
class FlowStepResult:
model_output: Any
denoised: Any
next_latent: Any
sigma: Any
next_sigma: Any
timestep: Any
@dataclass(frozen=True)
class FlowLoopResult:
latent: Any
steps: tuple[FlowStepResult, ...]
def flow_timestep(sigma: Any, config: FlowSchedulerConfig = FlowSchedulerConfig()) -> Any:
return sigma * config.multiplier
def flow_denoised(latent: Any, model_output: Any, sigma: Any) -> Any:
return latent - model_output * _reshape_sigma(sigma, latent)
def flow_euler_step(latent: Any, model_output: Any, sigma: Any, next_sigma: Any) -> Any:
return latent + model_output * _reshape_sigma(next_sigma - sigma, latent)
def flow_one_step(
latent: Any,
model_output: Any,
sigma: Any,
next_sigma: Any,
config: FlowSchedulerConfig = FlowSchedulerConfig(),
) -> FlowStepResult:
timestep = flow_timestep(sigma, config)
denoised = flow_denoised(latent, model_output, sigma)
next_latent = flow_euler_step(latent, model_output, sigma, next_sigma)
return FlowStepResult(
model_output=model_output,
denoised=denoised,
next_latent=next_latent,
sigma=sigma,
next_sigma=next_sigma,
timestep=timestep,
)
def flow_euler_loop(
latent: Any,
sigmas: Sequence[Any],
predict_fn: Callable[[Any, Any], Any],
config: FlowSchedulerConfig = FlowSchedulerConfig(),
*,
store_steps: bool = True,
) -> FlowLoopResult:
import mlx.core as mx
if len(sigmas) < 2:
raise ValueError("sigmas must contain at least two values")
current = latent
results: list[FlowStepResult] = []
for index in range(len(sigmas) - 1):
sigma = _as_mx_scalar(sigmas[index])
next_sigma = _as_mx_scalar(sigmas[index + 1])
model_output = predict_fn(current, sigma)
if store_steps:
result = flow_one_step(current, model_output, sigma, next_sigma, config=config)
mx.eval(result.next_latent)
results.append(result)
current = result.next_latent
continue
current = flow_euler_step(current, model_output, sigma, next_sigma)
mx.eval(current)
return FlowLoopResult(latent=current, steps=tuple(results))
def er_sde_loop(
latent: Any,
sigmas: Sequence[Any],
predict_fn: Callable[[Any, Any], Any],
config: FlowSchedulerConfig = FlowSchedulerConfig(),
*,
seed: int | None = None,
s_noise: float = 1.0,
max_stage: int = 3,
num_integration_points: int = 200,
store_steps: bool = True,
) -> FlowLoopResult:
"""Run ComfyUI-style ER-SDE sampling for flow/CONST prediction models.
`predict_fn` returns the raw flow model output. The ER-SDE update itself uses
ComfyUI's denoised/x0 wrapper semantics, so this function converts raw model
output with `flow_denoised()` before applying the solver update.
"""
import mlx.core as mx
if len(sigmas) < 2:
raise ValueError("sigmas must contain at least two values")
if max_stage <= 0:
raise ValueError("max_stage must be positive")
if num_integration_points <= 0:
raise ValueError("num_integration_points must be positive")
sigma_values = _offset_first_sigma_for_flow_snr([_as_float(value) for value in sigmas], config)
er_lambdas = [_er_lambda_from_sigma_value(value) for value in sigma_values]
point_indices = mx.arange(num_integration_points).astype(mx.float32)
noise_sampler = _normal_noise_sampler(latent, seed)
current = latent
old_denoised = None
old_denoised_d = None
results: list[FlowStepResult] = []
for index in range(len(sigma_values) - 1):
sigma = _as_mx_scalar(sigma_values[index])
next_sigma = _as_mx_scalar(sigma_values[index + 1])
model_output = predict_fn(current, sigma)
denoised = flow_denoised(current, model_output, sigma)
if sigma_values[index + 1] == 0.0:
next_latent = denoised
else:
er_lambda_s = _as_mx_scalar(er_lambdas[index])
er_lambda_t = _as_mx_scalar(er_lambdas[index + 1])
alpha_s = sigma / er_lambda_s
alpha_t = next_sigma / er_lambda_t
r_alpha = alpha_t / alpha_s
r = _er_sde_noise_scaler(er_lambda_t) / _er_sde_noise_scaler(er_lambda_s)
next_latent = r_alpha * r * current + alpha_t * (1.0 - r) * denoised
stage_used = min(max_stage, index + 1)
if stage_used >= 2:
dt = er_lambda_t - er_lambda_s
lambda_step_size = -dt / float(num_integration_points)
lambda_pos = er_lambda_t + point_indices * lambda_step_size
scaled_pos = _er_sde_noise_scaler(lambda_pos)
s = mx.sum(1.0 / scaled_pos) * lambda_step_size
previous_lambda = _as_mx_scalar(er_lambdas[index - 1])
denoised_d = (denoised - old_denoised) / (er_lambda_s - previous_lambda)
next_latent = next_latent + alpha_t * (dt + s * _er_sde_noise_scaler(er_lambda_t)) * denoised_d
if stage_used >= 3:
previous_previous_lambda = _as_mx_scalar(er_lambdas[index - 2])
s_u = mx.sum((lambda_pos - er_lambda_s) / scaled_pos) * lambda_step_size
denoised_u = (denoised_d - old_denoised_d) / ((er_lambda_s - previous_previous_lambda) / 2.0)
next_latent = next_latent + alpha_t * (
(dt * dt) / 2.0 + s_u * _er_sde_noise_scaler(er_lambda_t)
) * denoised_u
old_denoised_d = denoised_d
if s_noise > 0:
noise_scale = _sqrt_non_negative((er_lambda_t * er_lambda_t) - (er_lambda_s * er_lambda_s * r * r))
next_latent = next_latent + alpha_t * noise_sampler(sigma, next_sigma) * float(s_noise) * noise_scale
if store_steps:
result = FlowStepResult(
model_output=model_output,
denoised=denoised,
next_latent=next_latent,
sigma=sigma,
next_sigma=next_sigma,
timestep=flow_timestep(sigma, config),
)
mx.eval(result.next_latent)
results.append(result)
current = result.next_latent
else:
current = next_latent
mx.eval(current)
old_denoised = denoised
return FlowLoopResult(latent=current, steps=tuple(results))
def classifier_free_guidance(negative: Any, positive: Any, scale: float) -> Any:
return negative + (positive - negative) * float(scale)
def sigmas_for_steps(steps: int, config: FlowSchedulerConfig = FlowSchedulerConfig()) -> list[float]:
return simple_sigmas(steps, config)
def _reshape_sigma(sigma: Any, reference: Any) -> Any:
if hasattr(sigma, "shape") and len(sigma.shape) > 0:
return sigma.reshape(sigma.shape[:1] + (1,) * (len(reference.shape) - 1))
return sigma
def _as_mx_scalar(value: Any) -> Any:
import mlx.core as mx
if hasattr(value, "shape"):
return value
return mx.array(float(value), dtype=mx.float32)
def _as_float(value: Any) -> float:
if hasattr(value, "item"):
return float(value.item())
return float(value)
def _offset_first_sigma_for_flow_snr(sigmas: list[float], config: FlowSchedulerConfig, percent_offset: float = 1e-4) -> list[float]:
if len(sigmas) > 1 and sigmas[0] >= 1.0:
sigmas = sigmas.copy()
sigmas[0] = time_snr_shift(config.shift, 1.0 - percent_offset)
return sigmas
def _er_lambda_from_sigma_value(sigma: float) -> float:
if sigma >= 1.0:
raise ValueError("ER-SDE requires sigma < 1 after first-sigma SNR offset")
if sigma <= 0.0:
return 0.0
return sigma / (1.0 - sigma)
def _er_sde_noise_scaler(value: Any) -> Any:
import mlx.core as mx
return value * (mx.exp(value**0.3) + 10.0)
def _sqrt_non_negative(value: Any) -> Any:
import mlx.core as mx
return mx.sqrt(mx.maximum(value, mx.zeros_like(value)))
def _normal_noise_sampler(reference: Any, seed: int | None) -> Callable[[Any, Any], Any]:
import numpy as np
import mlx.core as mx
rng = np.random.default_rng(None if seed is None else int(seed))
def sample(_sigma: Any, _next_sigma: Any) -> Any:
noise = rng.standard_normal(reference.shape).astype(np.float32)
return mx.array(noise, dtype=reference.dtype)
return sample
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