linting

Signed-off-by: vladmandic <mandic00@live.com>

.gitignore

@@ -1,7 +1,7 @@
 # defaults
+venv/
 __pycache__
 .ruff_cache
-/cache.json
 /*.json
 /*.yaml
 /params.txt
@@ -9,13 +9,14 @@ __pycache__
 /user.css
 /webui-user.bat
 /webui-user.sh
-/html/extensions.json
-/html/themes.json
+/data/metadata.json
+/data/extensions.json
+/data/cache.json
+/data/themes.json
 config_states
 node_modules
 pnpm-lock.yaml
 package-lock.json
-venv
 .history
 cache
 **/.DS_Store

.ruff.toml

@@ -1,3 +1,6 @@
+line-length = 250
+indent-width = 4
+target-version = "py310"
 exclude = [
     "venv",
     ".git",
@@ -13,7 +16,6 @@ exclude = [
     "modules/schedulers",
     "modules/teacache",
     "modules/seedvr",
-    "modules/res4lyf",
 
     "modules/control/proc",
     "modules/control/units",
@@ -42,9 +44,6 @@ exclude = [
     "extensions-builtin/sd-webui-agent-scheduler",
     "extensions-builtin/sdnext-modernui/node_modules",
 ]
-line-length = 250
-indent-width = 4
-target-version = "py310"
 
 [lint]
 select = [

CHANGELOG.md

@@ -4,7 +4,7 @@
 
 - **Models**
   - [Tongyi-MAI Z-Image Base](https://tongyi-mai.github.io/Z-Image-blog/)  
-    yup, its finally here, the full base model of Z-Image Turbo  
+    yup, its finally here, the full base model of **Z-Image**  
 - **Features**
   - **caption** tab support for Booru tagger models, thanks @CalamitousFelicitousness  
   - add SmilingWolf WD14/WaifuDiffusion tagger models, thanks @CalamitousFelicitousness  

data/cache.json

@@ -1,21 +0,0 @@
-{
-  "hashes": {
-    "checkpoint/tempestByVlad_baseV01": {
-      "mtime": 1763424610.0,
-      "sha256": "8bfad1722243955b3f94103c69079c280d348b14729251e86824972c1063b616"
-    },
-    "checkpoint/lyriel_v16": {
-      "mtime": 1763424496.0,
-      "sha256": "ec6f68ea6388951d53d6c8178c22aecfd1b4fedfe31f5a5814ddd7638c4eff37"
-    },
-    "checkpoint/v1-5-pruned-fp16-emaonly": {
-      "mtime": 1768913681.028832,
-      "sha256": "92954befdb6aacf52f86095eba54ac9262459bc21f987c0e51350f3679c4e45a"
-    },
-    "checkpoint/juggernautXL_juggXIByRundiffusion": {
-      "mtime": 1768913991.4270966,
-      "sha256": "33e58e86686f6b386c526682b5da9228ead4f91d994abd4b053442dc5b42719e"
-    }
-  },
-  "hashes-addnet": {}
-}

eslint.config.mjs

@@ -241,6 +241,9 @@ const jsonConfig = defineConfig([
     plugins: { json },
     language: 'json/json',
     extends: ['json/recommended'],
+    rules: {
+      'json/no-empty-keys': 'off',
+    },
   },
 ]);
 

modules/res4lyf/__init__.py

@@ -5,6 +5,7 @@ from .bong_tangent_scheduler import BongTangentScheduler
 from .common_sigma_scheduler import CommonSigmaScheduler
 from .deis_scheduler_alt import DEISMultistepScheduler
 from .etdrk_scheduler import ETDRKScheduler
+from .gauss_legendre_scheduler import GaussLegendreScheduler
 from .langevin_dynamics_scheduler import LangevinDynamicsScheduler
 from .lawson_scheduler import LawsonScheduler
 from .linear_rk_scheduler import LinearRKScheduler
@@ -17,11 +18,10 @@ from .res_singlestep_scheduler import RESSinglestepScheduler
 from .res_singlestep_sde_scheduler import RESSinglestepSDEScheduler
 from .res_unified_scheduler import RESUnifiedScheduler
 from .riemannian_flow_scheduler import RiemannianFlowScheduler
-from .simple_exponential_scheduler import SimpleExponentialScheduler
-from .gauss_legendre_scheduler import GaussLegendreScheduler
 from .rungekutta_44s_scheduler import RungeKutta44Scheduler
 from .rungekutta_57s_scheduler import RungeKutta57Scheduler
 from .rungekutta_67s_scheduler import RungeKutta67Scheduler
+from .simple_exponential_scheduler import SimpleExponentialScheduler
 from .specialized_rk_scheduler import SpecializedRKScheduler
 
 from .variants import (
@@ -88,7 +88,7 @@ from .variants import (
     GaussLegendre4SScheduler,
 )
 
-__all__ = [
+__all__ = [ # noqa: RUF022
     # Base
     "RESUnifiedScheduler",
     "RESMultistepScheduler",

modules/res4lyf/deis_scheduler_alt.py

@@ -0,0 +1,327 @@
+from typing import List, Optional, Tuple, Union
+
+import numpy as np
+import torch
+from diffusers.configuration_utils import ConfigMixin, register_to_config
+from diffusers.schedulers.scheduling_utils import SchedulerMixin, SchedulerOutput
+
+
+def get_def_integral_2(a, b, start, end, c):
+    coeff = (end**3 - start**3) / 3 - (end**2 - start**2) * (a + b) / 2 + (end - start) * a * b
+    return coeff / ((c - a) * (c - b))
+
+
+def get_def_integral_3(a, b, c, start, end, d):
+    coeff = (end**4 - start**4) / 4 - (end**3 - start**3) * (a + b + c) / 3 + (end**2 - start**2) * (a * b + a * c + b * c) / 2 - (end - start) * a * b * c
+    return coeff / ((d - a) * (d - b) * (d - c))
+
+
+class DEISMultistepScheduler(SchedulerMixin, ConfigMixin):
+    """
+    DEISMultistepScheduler: Diffusion Explicit Iterative Sampler with high-order multistep.
+    Adapted from the RES4LYF repository.
+    """
+
+    order = 1
+
+    @register_to_config
+    def __init__(
+        self,
+        num_train_timesteps: int = 1000,
+        beta_start: float = 0.00085,
+        beta_end: float = 0.012,
+        beta_schedule: str = "linear",
+        trained_betas: Optional[Union[np.ndarray, List[float]]] = None,
+        prediction_type: str = "epsilon",
+        use_karras_sigmas: bool = False,
+        use_exponential_sigmas: bool = False,
+        use_beta_sigmas: bool = False,
+        use_flow_sigmas: bool = False,
+        sigma_min: Optional[float] = None,
+        sigma_max: Optional[float] = None,
+        rho: float = 7.0,
+        shift: Optional[float] = None,
+        base_shift: float = 0.5,
+        max_shift: float = 1.15,
+        use_dynamic_shifting: bool = False,
+        timestep_spacing: str = "linspace",
+        solver_order: int = 2,
+        clip_sample: bool = False,
+        sample_max_value: float = 1.0,
+        set_alpha_to_one: bool = False,
+        skip_prk_steps: bool = False,
+        interpolation_type: str = "linear",
+        steps_offset: int = 0,
+        timestep_type: str = "discrete",
+        rescale_betas_zero_snr: bool = False,
+        final_sigmas_type: str = "zero",
+    ):
+        if trained_betas is not None:
+            self.betas = torch.tensor(trained_betas, dtype=torch.float32)
+        elif beta_schedule == "linear":
+            self.betas = torch.linspace(beta_start, beta_end, num_train_timesteps, dtype=torch.float32)
+        elif beta_schedule == "scaled_linear":
+            self.betas = torch.linspace(beta_start**0.5, beta_end**0.5, num_train_timesteps, dtype=torch.float32) ** 2
+        else:
+            raise NotImplementedError(f"{beta_schedule} is not implemented")
+
+        self.alphas = 1.0 - self.betas
+        self.alphas_cumprod = torch.cumprod(self.alphas, dim=0)
+
+        self.num_inference_steps = None
+        self.timesteps = torch.from_numpy(np.arange(0, num_train_timesteps)[::-1].copy())
+        self.sigmas = None
+
+        # Internal state
+        self.model_outputs = []
+        self.hist_samples = []
+        self._step_index = None
+        self._sigmas_cpu = None
+        self.all_coeffs = []
+
+    def set_timesteps(
+        self,
+        num_inference_steps: int,
+        device: Union[str, torch.device] = None,
+        mu: Optional[float] = None,
+    ):
+        self.num_inference_steps = num_inference_steps
+
+        # 1. Spacing
+        if self.config.timestep_spacing == "linspace":
+            timesteps = np.linspace(self.config.num_train_timesteps - 1, 0, num_inference_steps, dtype=float).copy()
+        elif self.config.timestep_spacing == "leading":
+            step_ratio = self.config.num_train_timesteps // num_inference_steps
+            timesteps = (np.arange(0, num_inference_steps) * step_ratio).round()[::-1].copy().astype(float)
+        elif self.config.timestep_spacing == "trailing":
+            step_ratio = self.config.num_train_timesteps / num_inference_steps
+            timesteps = (np.arange(num_inference_steps, 0, -step_ratio)).round().copy().astype(float)
+            timesteps -= step_ratio
+        else:
+            raise ValueError(f"timestep_spacing must be one of 'linspace', 'leading', or 'trailing', got {self.config.timestep_spacing}")
+
+        if self.config.timestep_spacing == "trailing":
+            timesteps = np.maximum(timesteps, 0)
+
+        sigmas = np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5)
+        log_sigmas_all = np.log(sigmas)
+        if self.config.interpolation_type == "linear":
+            sigmas = np.interp(timesteps, np.arange(len(sigmas)), sigmas)
+        elif self.config.interpolation_type == "log_linear":
+            sigmas = np.exp(np.interp(timesteps, np.arange(len(sigmas)), np.log(sigmas)))
+        else:
+            raise ValueError(f"interpolation_type must be one of 'linear' or 'log_linear', got {self.config.interpolation_type}")
+
+        # 2. Sigma Schedule
+        if self.config.use_karras_sigmas:
+            sigma_min = self.config.sigma_min if self.config.sigma_min is not None else sigmas[-1]
+            sigma_max = self.config.sigma_max if self.config.sigma_max is not None else sigmas[0]
+            rho = self.config.rho
+            ramp = np.linspace(0, 1, num_inference_steps)
+            sigmas = (sigma_max ** (1 / rho) + ramp * (sigma_min ** (1 / rho) - sigma_max ** (1 / rho))) ** rho
+        elif self.config.use_exponential_sigmas:
+            sigma_min = self.config.sigma_min if self.config.sigma_min is not None else sigmas[-1]
+            sigma_max = self.config.sigma_max if self.config.sigma_max is not None else sigmas[0]
+            sigmas = np.exp(np.linspace(np.log(sigma_max), np.log(sigma_min), num_inference_steps))
+        elif self.config.use_beta_sigmas:
+            sigma_min = self.config.sigma_min if self.config.sigma_min is not None else sigmas[-1]
+            sigma_max = self.config.sigma_max if self.config.sigma_max is not None else sigmas[0]
+            alpha, beta = 0.6, 0.6
+            ramp = np.linspace(0, 1, num_inference_steps)
+            try:
+                import torch.distributions as dist
+
+                b = dist.Beta(alpha, beta)
+                ramp = b.sample((num_inference_steps,)).sort().values.numpy()
+            except Exception:
+                pass
+            sigmas = sigma_max * (1 - ramp) + sigma_min * ramp
+        elif self.config.use_flow_sigmas:
+            sigmas = np.linspace(1.0, 1 / 1000, num_inference_steps)
+
+        # 3. Shifting
+        if self.config.use_dynamic_shifting and mu is not None:
+            sigmas = mu * sigmas / (1 + (mu - 1) * sigmas)
+        elif self.config.shift is not None:
+            sigmas = self.config.shift * sigmas / (1 + (self.config.shift - 1) * sigmas)
+
+        # Map back to timesteps
+        timesteps = np.interp(np.log(np.maximum(sigmas, 1e-10)), log_sigmas_all, np.arange(len(log_sigmas_all)))
+
+        self.sigmas = torch.from_numpy(np.append(sigmas, 0.0)).to(device=device, dtype=torch.float32)
+        self.timesteps = torch.from_numpy(timesteps + self.config.steps_offset).to(device=device, dtype=torch.float32)
+
+        self._sigmas_cpu = self.sigmas.detach().cpu().numpy()
+
+        # Precompute coefficients
+        self.all_coeffs = []
+        num_steps = len(timesteps)
+        for i in range(num_steps):
+            sigma_t = self._sigmas_cpu[i]
+            sigma_next = self._sigmas_cpu[i + 1]
+
+            if sigma_next <= 0:
+                coeffs = None
+            else:
+                current_order = min(i + 1, self.config.solver_order)
+                if current_order == 1:
+                    coeffs = [sigma_next - sigma_t]
+                else:
+                    ts = [self._sigmas_cpu[i - j] for j in range(current_order)]
+                    t_next = sigma_next
+                    if current_order == 2:
+                        t_cur, t_prev1 = ts[0], ts[1]
+                        coeff_cur = ((t_next - t_prev1) ** 2 - (t_cur - t_prev1) ** 2) / (2 * (t_cur - t_prev1))
+                        coeff_prev1 = (t_next - t_cur) ** 2 / (2 * (t_prev1 - t_cur))
+                        coeffs = [coeff_cur, coeff_prev1]
+                    elif current_order == 3:
+                        t_cur, t_prev1, t_prev2 = ts[0], ts[1], ts[2]
+                        coeffs = [
+                            get_def_integral_2(t_prev1, t_prev2, t_cur, t_next, t_cur),
+                            get_def_integral_2(t_cur, t_prev2, t_cur, t_next, t_prev1),
+                            get_def_integral_2(t_cur, t_prev1, t_cur, t_next, t_prev2),
+                        ]
+                    elif current_order == 4:
+                        t_cur, t_prev1, t_prev2, t_prev3 = ts[0], ts[1], ts[2], ts[3]
+                        coeffs = [
+                            get_def_integral_3(t_prev1, t_prev2, t_prev3, t_cur, t_next, t_cur),
+                            get_def_integral_3(t_cur, t_prev2, t_prev3, t_cur, t_next, t_prev1),
+                            get_def_integral_3(t_cur, t_prev1, t_prev3, t_cur, t_next, t_prev2),
+                            get_def_integral_3(t_cur, t_prev1, t_prev2, t_cur, t_next, t_prev3),
+                        ]
+                    else:
+                        coeffs = [(sigma_next - sigma_t) / sigma_t]  # Fallback to Euler
+            self.all_coeffs.append(coeffs)
+
+        # Reset history
+        self.model_outputs = []
+        self.hist_samples = []
+        self._step_index = None
+
+    @property
+    def step_index(self):
+        """
+        The index counter for the current timestep. It will increase 1 after each scheduler step.
+        """
+        return self._step_index
+
+    def index_for_timestep(self, timestep, schedule_timesteps=None):
+        if self._step_index is not None:
+            return self._step_index
+
+        if schedule_timesteps is None:
+            schedule_timesteps = self.timesteps
+
+        if isinstance(schedule_timesteps, torch.Tensor):
+            schedule_timesteps = schedule_timesteps.detach().cpu().numpy()
+
+        if isinstance(timestep, torch.Tensor):
+            timestep = timestep.detach().cpu().numpy()
+
+        return np.abs(schedule_timesteps - timestep).argmin().item()
+
+    def _init_step_index(self, timestep):
+        if self._step_index is None:
+            self._step_index = self.index_for_timestep(timestep)
+
+    def scale_model_input(self, sample: torch.Tensor, timestep: Union[float, torch.Tensor]) -> torch.Tensor:
+        return sample
+
+    def step(
+        self,
+        model_output: torch.Tensor,
+        timestep: Union[float, torch.Tensor],
+        sample: torch.Tensor,
+        return_dict: bool = True,
+    ) -> Union[SchedulerOutput, Tuple]:
+        if self._step_index is None:
+            self._init_step_index(timestep)
+
+        step_index = self._step_index
+        sigma_t = self.sigmas[step_index]
+        # Calculate alpha_t and sigma_t (NSR)
+        alpha_t = 1 / (sigma_t**2 + 1) ** 0.5
+        sigma_actual = sigma_t * alpha_t
+
+        if self.config.prediction_type == "epsilon":
+            denoised = (sample / alpha_t) - sigma_t * model_output
+        elif self.config.prediction_type == "v_prediction":
+            denoised = alpha_t * sample - sigma_actual * model_output
+        elif self.config.prediction_type == "flow_prediction":
+            alpha_t = 1.0
+            denoised = sample - sigma_t * model_output
+        elif self.config.prediction_type == "sample":
+            denoised = model_output
+        else:
+            raise ValueError(f"prediction_type error: {self.config.prediction_type}")
+
+        if self.config.clip_sample:
+            denoised = denoised.clamp(-self.config.sample_max_value, self.config.sample_max_value)
+
+        # DEIS coefficients are precomputed in set_timesteps
+        coeffs = self.all_coeffs[step_index]
+
+        sigma_next = self.sigmas[step_index + 1]
+        alpha_next = 1 / (sigma_next**2 + 1) ** 0.5 if sigma_next > 0 else 1.0
+
+        if coeffs is None:
+            prev_sample = denoised
+        else:
+            current_order = len(coeffs)
+            if current_order == 1:
+                # 1st order step (Euler) in normalized space
+                prev_sample_norm = (sigma_next / sigma_t) * (sample / alpha_t) + (1 - sigma_next / sigma_t) * denoised
+                prev_sample = prev_sample_norm * alpha_next
+            else:
+                # Xs: [x0_curr, x0_prev1, x0_prev2, ...]
+                x0s = [denoised, *self.model_outputs[::-1][: current_order - 1]]
+
+                # Normalize DEIS coefficients to get weights for x0 interpolation
+                # sum(coeffs) = sigma_next - sigma_t
+                delta_sigma = sigma_next - sigma_t
+                if abs(delta_sigma) > 1e-8:
+                    weights = [c / delta_sigma for c in coeffs]
+                else:
+                    weights = [1.0] + [0.0] * (current_order - 1)
+
+                mixed_x0 = 0
+                for i in range(current_order):
+                    mixed_x0 = mixed_x0 + weights[i] * x0s[i]
+
+                # Stable update in normalized space
+                prev_sample_norm = (sigma_next / sigma_t) * (sample / alpha_t) + (1 - sigma_next / sigma_t) * mixed_x0
+                prev_sample = prev_sample_norm * alpha_next
+
+        # Store state (always store x0)
+        self.model_outputs.append(denoised)
+        self.hist_samples.append(sample)
+
+        if len(self.model_outputs) > 4:
+            self.model_outputs.pop(0)
+            self.hist_samples.pop(0)
+
+        if self._step_index is not None:
+            self._step_index += 1
+
+        if not return_dict:
+            return (prev_sample,)
+        return SchedulerOutput(prev_sample=prev_sample)
+
+    def add_noise(
+        self,
+        original_samples: torch.Tensor,
+        noise: torch.Tensor,
+        timesteps: torch.Tensor,
+    ) -> torch.Tensor:
+        step_indices = [self.index_for_timestep(t) for t in timesteps]
+        sigma = self.sigmas[step_indices].flatten()
+        while len(sigma.shape) < len(original_samples.shape):
+            sigma = sigma.unsqueeze(-1)
+        return original_samples + noise * sigma
+
+    @property
+    def init_noise_sigma(self):
+        return 1.0
+
+    def __len__(self):
+        return self.config.num_train_timesteps

modules/res4lyf/variants.py

@@ -2,6 +2,7 @@ from .abnorsett_scheduler import ABNorsettScheduler
 from .common_sigma_scheduler import CommonSigmaScheduler
 from .deis_scheduler_alt import DEISMultistepScheduler
 from .etdrk_scheduler import ETDRKScheduler
+from .gauss_legendre_scheduler import GaussLegendreScheduler
 from .lawson_scheduler import LawsonScheduler
 from .linear_rk_scheduler import LinearRKScheduler
 from .lobatto_scheduler import LobattoScheduler
@@ -13,7 +14,6 @@ from .res_singlestep_scheduler import RESSinglestepScheduler
 from .res_singlestep_sde_scheduler import RESSinglestepSDEScheduler
 from .res_unified_scheduler import RESUnifiedScheduler
 from .riemannian_flow_scheduler import RiemannianFlowScheduler
-from .gauss_legendre_scheduler import GaussLegendreScheduler
 
 # RES Unified Variants