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sd-forge-extra-samplers/.github/workflows/ruff.yaml

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+name: Ruff
+on: [pull_request]
+jobs:
+  ruff:
+    runs-on: ubuntu-latest
+    steps:
+      - uses: actions/checkout@v4
+      - uses: astral-sh/ruff-action@v1

sd-forge-extra-samplers/.gitignore

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+
+# Byte-compiled / optimized / DLL files
+__pycache__/
+*.py[cod]
+*$py.class
+
+# C extensions
+*.so
+
+# Distribution / packaging
+.Python
+build/
+develop-eggs/
+dist/
+downloads/
+eggs/
+.eggs/
+lib/
+lib64/
+parts/
+sdist/
+var/
+wheels/
+share/python-wheels/
+*.egg-info/
+.installed.cfg
+*.egg
+MANIFEST
+
+# PyInstaller
+#  Usually these files are written by a python script from a template
+#  before PyInstaller builds the exe, so as to inject date/other infos into it.
+*.manifest
+*.spec
+
+# Installer logs
+pip-log.txt
+pip-delete-this-directory.txt
+
+# Unit test / coverage reports
+htmlcov/
+.tox/
+.nox/
+.coverage
+.coverage.*
+.cache
+nosetests.xml
+coverage.xml
+*.cover
+*.py,cover
+.hypothesis/
+.pytest_cache/
+cover/
+
+# Translations
+*.mo
+*.pot
+
+# Django stuff:
+*.log
+local_settings.py
+db.sqlite3
+db.sqlite3-journal
+
+# Flask stuff:
+instance/
+.webassets-cache
+
+# Scrapy stuff:
+.scrapy
+
+# Sphinx documentation
+docs/_build/
+
+# PyBuilder
+.pybuilder/
+target/
+
+# Jupyter Notebook
+.ipynb_checkpoints
+
+# IPython
+profile_default/
+ipython_config.py
+
+# pyenv
+#   For a library or package, you might want to ignore these files since the code is
+#   intended to run in multiple environments; otherwise, check them in:
+# .python-version
+
+# pipenv
+#   According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control.
+#   However, in case of collaboration, if having platform-specific dependencies or dependencies
+#   having no cross-platform support, pipenv may install dependencies that don't work, or not
+#   install all needed dependencies.
+#Pipfile.lock
+
+# poetry
+#   Similar to Pipfile.lock, it is generally recommended to include poetry.lock in version control.
+#   This is especially recommended for binary packages to ensure reproducibility, and is more
+#   commonly ignored for libraries.
+#   https://python-poetry.org/docs/basic-usage/#commit-your-poetrylock-file-to-version-control
+#poetry.lock
+
+# pdm
+#   Similar to Pipfile.lock, it is generally recommended to include pdm.lock in version control.
+#pdm.lock
+#   pdm stores project-wide configurations in .pdm.toml, but it is recommended to not include it
+#   in version control.
+#   https://pdm.fming.dev/latest/usage/project/#working-with-version-control
+.pdm.toml
+.pdm-python
+.pdm-build/
+
+# PEP 582; used by e.g. github.com/David-OConnor/pyflow and github.com/pdm-project/pdm
+__pypackages__/
+
+# Celery stuff
+celerybeat-schedule
+celerybeat.pid
+
+# SageMath parsed files
+*.sage.py
+
+# Environments
+.env
+.venv
+env/
+venv/
+ENV/
+env.bak/
+venv.bak/
+
+# Spyder project settings
+.spyderproject
+.spyproject
+
+# Rope project settings
+.ropeproject
+
+# mkdocs documentation
+/site
+
+# mypy
+.mypy_cache/
+.dmypy.json
+dmypy.json
+
+# Pyre type checker
+.pyre/
+
+# pytype static type analyzer
+.pytype/
+
+# Cython debug symbols
+cython_debug/
+
+# PyCharm
+#  JetBrains specific template is maintained in a separate JetBrains.gitignore that can
+#  be found at https://github.com/github/gitignore/blob/main/Global/JetBrains.gitignore
+#  and can be added to the global gitignore or merged into this file.  For a more nuclear
+#  option (not recommended) you can uncomment the following to ignore the entire idea folder.
+#.idea/

sd-forge-extra-samplers/LICENSE

@@ -0,0 +1,674 @@
+                    GNU GENERAL PUBLIC LICENSE
+                       Version 3, 29 June 2007
+
+ Copyright (C) 2007 Free Software Foundation, Inc. <https://fsf.org/>
+ Everyone is permitted to copy and distribute verbatim copies
+ of this license document, but changing it is not allowed.
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sd-forge-extra-samplers/README.md

@@ -0,0 +1,97 @@
+# Overview
+
+This repository provides additional samplers to the Forge WebUI.
+
+## Features
+
+- Additional samplers integrated into the Forge WebUI.
+  - Adaptive Progressive (Experimental)
+  - Euler Max
+  - Euler Negative
+  - Euler Dy
+  - Euler Dy Negative
+  - Euler SMEA
+  - Euler SMEA Dy
+  - Euler SMEA Dy Negative
+  - Euler Multipass
+  - Euler Multipass CFG++
+  - Euler a Multipass
+  - Euler a Multipass CFG++
+  - Extended Reverse Time SDE
+  - Gradient Estimation
+  - Heun Ancestral
+  - Kohaku LoNyu Yog
+  - Langevin Euler (Experimental)
+  - Res Multistep
+  - Res Multistep CFG++
+  - Res Multistep Ancestral
+  - Res Multistep Ancestral CFG++
+ 
+- Additional Schedulers
+  - Linear Log 
+
+Adds a new extension accordian titled "Extra Samplers" to allow adjusting certain samplers.
+
+## Installation
+
+### Clone from Git
+
+1. Navigate to the extension directory in your WebUI installation
+1. Clone the repository:
+    ```sh
+    git clone https://github.com/MisterChief95/sd-forge-extra-samplers.git
+    ```
+1. Start WebUI
+
+### Install from URL
+
+1. Open the Extensions tab in the web UI.
+2. Go to the "Install from URL" section.
+3. Enter: `https://github.com/MisterChief95/sd-forge-extra-samplers.git` in the "URL for extension's git repository" box.
+4. Click "Install".
+5. Restart WebUI
+
+## Usage
+
+1. Open the WebUI.
+2. Navigate to the sampler settings.
+3. Select one of the newly added Euler samplers from the list.
+4. Generate images as usual.
+
+### Important
+- Not all samplers work well in every situation. Some will look poor when used for img2img/hires fix.
+- Mix-and-match samplers to find the best combinations. A sampler might look bad with one scheduler but good with another!
+
+## Contributing
+
+Contributions are welcome! Please open an issue or submit a pull request for any improvements or bug fixes.
+
+## Acknowledgements
+
+If any of these are incorrect please let me know!
+
+- Thanks to the developers of Automatic1111 and Forge.
+- [Koishi-Star](https://github.com/Koishi-Star/Euler-Smea-Dyn-Sampler) for the following sampler contributions:
+  - Euler Negative
+  - Euler Dy
+  - Euler Dy Negative
+  - Euler SMEA Dy (Euler SMEA Dy Negative based on this)
+  - Kohaku LoNyu Yog
+- [licyk](https://github.com/licyk/advanced_euler_sampler_extension/tree/main) for the following sampler contributions:
+  - Euler Max
+  - Euler SMEA
+- [Panchovix](https://github.com/Panchovix/stable-diffusion-webui-reForge) for the following sampler contributions:
+  - Res Multistep
+  - Res Multistep CFG++
+- [comfyanonymous](https://github.com/comfyanonymous/ComfyUI) for the following sampler contributions:
+  - Gradient Estimation
+  - Extended Reverse Time SDE
+  - Res Multistep
+  - Res Multistep CFG++
+  - Res Multistep Ancestral
+  - Res Multistep Ancestral CFG++
+- Euler Multipass
+  - Original Implementation: [aria1th](https://github.com/aria1th)
+  - CFG++ Implementation: [LaVie024](https://github.com/LaVie024)
+  - Final ComfyUI implementation: [catboxanon](https://github.com/catboxanon)
+- Special thanks to the contributors of this repository.

sd-forge-extra-samplers/lib_es/const.py

@@ -0,0 +1,25 @@
+# Adaptive Progressive
+AP_EULER_A_END = "exs_ap_euler_a_end"
+AP_DPM_2M_END = "exs_ap_dpm_2m_end"
+AP_ANCESTRAL_ETA = "exs_ap_ancestral_eta"
+AP_DETAIL_STRENGTH = "exs_ap_detail_strength"
+
+# Langevin Euler
+LANGEVIN_STRENGTH = "exs_langevin_strength"
+
+# Extended Reverse-Time
+ER_MAX_STAGE = "er_max_stage"
+
+# Gradient Estimation
+GE_GAMMA = "ge_gamma"
+GE_GAMMA_OFFSET = "ge_gamma_offset"
+GE_USE_ADAPTIVE_STEPS = "ge_use_adaptive_steps"
+GE_USE_TIMESTEP_ADAPTIVE_GAMMA = "ge_use_timestep_adaptive_gamma"
+GE_VALIDATE_SCHEDULE = "ge_validate_schedule"
+
+GE_DEFAULT_GAMMA = 2.0
+GE_MIN_GAMMA = 1.0
+GE_MAX_GAMMA = 3.0
+GE_DEFAULT_GAMMA_OFFSET = 0.0
+GE_MIN_GAMMA_OFFSET = -1.0
+GE_MAX_GAMMA_OFFSET = 1.0

sd-forge-extra-samplers/lib_es/extra_samplers/__init__.py

@@ -0,0 +1,49 @@
+#from lib_es.extra_samplers.adaptive_progressive import sample_adaptive_progressive
+from lib_es.extra_samplers.euler_dy import sample_euler_dy
+from lib_es.extra_samplers.euler_dy_negative import sample_euler_dy_negative
+from lib_es.extra_samplers.euler_smea import sample_euler_smea
+from lib_es.extra_samplers.euler_smea_dy import sample_euler_smea_dy
+from lib_es.extra_samplers.euler_smea_dy_negative import sample_euler_smea_dy_negative
+from lib_es.extra_samplers.euler_max import sample_euler_max
+from lib_es.extra_samplers.euler_multipass import (
+    sample_euler_multipass,
+    sample_euler_multipass_cfg_pp,
+    sample_euler_ancestral_multipass,
+    sample_euler_ancestral_multipass_cfg_pp,
+)
+from lib_es.extra_samplers.euler_negative import sample_euler_negative
+from lib_es.extra_samplers.extended_reverse_time import sample_er_sde
+from lib_es.extra_samplers.gradient_estimation import sample_gradient_estimation
+from lib_es.extra_samplers.heun_ancestral import sample_heun_ancestral
+from lib_es.extra_samplers.kohaku_lonyu_yog import sample_kohaku_lonyu_yog
+from lib_es.extra_samplers.langevin_euler import sample_langevin_euler
+#from lib_es.extra_samplers.res_multistep import (
+    #sample_res_multistep,
+    #sample_res_multistep_cfg_pp,
+    #sample_res_multistep_ancestral,
+    #sample_res_multistep_ancestral_cfg_pp,
+#)
+
+__sampler_funcs__ = [
+    #sample_adaptive_progressive,
+    sample_euler_max,
+    sample_euler_negative,
+    sample_euler_dy,
+    sample_euler_dy_negative,
+    sample_euler_smea,
+    sample_euler_smea_dy,
+    sample_euler_smea_dy_negative,
+    sample_euler_multipass,
+    sample_euler_multipass_cfg_pp,
+    sample_euler_ancestral_multipass,
+    sample_euler_ancestral_multipass_cfg_pp,
+    sample_er_sde,
+    sample_gradient_estimation,
+    sample_heun_ancestral,
+    sample_kohaku_lonyu_yog,
+    sample_langevin_euler,
+    #sample_res_multistep_ancestral_cfg_pp,
+    #sample_res_multistep_ancestral,
+    #sample_res_multistep_cfg_pp,
+    #sample_res_multistep,
+]

sd-forge-extra-samplers/lib_es/extra_samplers/adaptive_progressive.py

@@ -0,0 +1,227 @@
+import math
+import torch
+from tqdm.auto import trange
+from k_diffusion.sampling import to_d, get_ancestral_step
+#from backend.modules.k_diffusion_extra import default_noise_sampler
+
+import lib_es.const as consts
+from lib_es.utils import sampler_metadata
+
+
+@sampler_metadata(
+    "Adaptive Progressive",
+    {"scheduler": "sgm_uniform", "uses_ensd": True},
+)
+@torch.no_grad()
+def sample_adaptive_progressive(
+    model,
+    x,
+    sigmas,
+    extra_args=None,
+    callback=None,
+    disable=None,
+    s_churn=0.0,
+    s_tmin=0.0,
+    s_tmax=float("inf"),
+    s_noise=1.0,
+    noise_sampler=None,
+):
+    """
+    Adaptive progressive sampler that automatically adjusts to different step counts.
+    Combines Euler ancestral, DPM++ 2M, and detail enhancement with phase-based transitions.
+
+    This sampler is optimized for both high and very low step counts (4+),
+    dynamically adjusting phase durations based on total step count.
+
+    Args:
+        model: The denoising model
+        x: Input noise tensor
+        sigmas: Noise schedule
+        extra_args: Additional arguments for the model
+        callback: Optional callback function
+        disable: Whether to disable the progress bar
+        s_churn: Amount of stochasticity
+        s_tmin: Minimum sigma for stochasticity
+        s_tmax: Maximum sigma for stochasticity
+        eta: Ancestral noise parameter
+        s_noise: Noise scale
+        noise_sampler: Custom noise sampler function
+        detail_strength: Strength of detail enhancement phase
+
+    Returns:
+        Denoised tensor
+    """
+    extra_args = {} if extra_args is None else extra_args
+    noise_sampler = default_noise_sampler(x) if noise_sampler is None else noise_sampler
+    s_in = x.new_ones([x.shape[0]])
+    steps = len(sigmas) - 1
+
+    euler_a_end = getattr(model.p, consts.AP_EULER_A_END, 0.35)
+    dpm_2m_end = getattr(model.p, consts.AP_DPM_2M_END, 0.75)
+    ancestral_eta = getattr(model.p, consts.AP_ANCESTRAL_ETA, 0.4)
+    detail_strength = getattr(model.p, consts.AP_DETAIL_STRENGTH, 1.5)
+
+    # Store previous steps' information
+    prev_d = None
+    prev_denoised = None
+
+    euler_end, dpm_end = calc_phase_bounds(steps, euler_a_end, dpm_2m_end)
+
+    for i in trange(steps, disable=disable):
+        progress = i / steps
+
+        # Calculate weights based on phase
+        if progress < euler_end:
+            # Euler ancestral phase
+            w_euler = 1.0
+            w_multi = 0.0
+            w_detail = 0.0
+        elif progress < dpm_end:
+            # DPM++ phase - smooth transition from Euler
+            phase_progress = (progress - euler_end) / (dpm_end - euler_end)
+            w_euler = max(0.0, 1.0 - phase_progress * 2.5)  # Faster transition out of Euler
+            w_multi = 1.0 - w_euler
+            w_detail = 0.0
+        else:
+            # Detail refinement phase - gradual transition
+            phase_progress = (progress - dpm_end) / (1.0 - dpm_end)
+            w_euler = 0.0
+            w_multi = max(0.0, 1.0 - phase_progress * 1.5)  # Gradual reduction in DPM++
+            w_detail = 1.0 - w_multi
+
+        # Apply adaptive stochasticity (only in early steps)
+        if s_churn > 0 and s_tmin <= sigmas[i] <= s_tmax and progress < 0.4:
+            # Scale down stochasticity as we progress
+            gamma = min(s_churn / steps, 2**0.5 - 1) * (1.0 - progress / 0.4)
+            sigma_hat = sigmas[i] * (gamma + 1)
+            eps = torch.randn_like(x) * s_noise
+            x = x + eps * (sigma_hat**2 - sigmas[i] ** 2).sqrt()
+        else:
+            sigma_hat = sigmas[i]
+
+        # Get denoised prediction
+        denoised = model(x, sigma_hat * s_in, **extra_args)
+
+        if callback is not None:
+            callback({"x": x, "i": i, "sigma": sigmas[i], "sigma_hat": sigma_hat, "denoised": denoised})
+
+        # Calculate sigma for step
+        # Reduce eta as we progress to lower noise in later steps
+        step_eta = ancestral_eta if progress < 0.5 else ancestral_eta * (1.0 - min(1.0, (progress - 0.5) * 2.0))
+        sigma_down, sigma_up = get_ancestral_step(sigma_hat, sigmas[i + 1], eta=step_eta)
+
+        # Calculate current score
+        d = to_d(x, sigma_hat, denoised)
+        dt = sigma_down - sigma_hat
+
+        # Special case for final step
+        if sigmas[i + 1] == 0:
+            x = denoised
+            break
+
+        # Calculate step direction based on phase
+        if prev_d is None:
+            # First step is pure Euler ancestral
+            direction = d
+        else:
+            # Initialize direction
+            direction = torch.zeros_like(d)
+
+            # Add Euler component if needed
+            if w_euler > 0:
+                direction += w_euler * d
+
+            # Add DPM++ component if needed
+            if w_multi > 0:
+                # Adjust coefficients based on noise level
+                if sigma_hat > 2.0:
+                    # Higher noise: favor current direction
+                    c1, c2 = 0.7, 0.3
+                else:
+                    # Lower noise: more balanced
+                    c1, c2 = 0.6, 0.4
+
+                multi_direction = c1 * d + c2 * prev_d
+                direction += w_multi * multi_direction
+
+            # Add detail enhancement if needed
+            if w_detail > 0 and prev_denoised is not None:
+                # Only apply significant enhancement at lower noise levels
+                if sigma_hat < 1.0:
+                    # Calculate detail vector (high frequency components)
+                    detail_vector = denoised - prev_denoised
+
+                    # Scale based on noise level - stronger at very low noise
+                    detail_scale = detail_strength * min(1.0, 0.2 / (sigma_hat + 0.2))
+
+                    # Apply detail enhancement with adaptive scaling
+                    detail_direction = d + detail_vector * detail_scale / dt
+                    direction += w_detail * detail_direction
+                else:
+                    # At higher noise levels, use standard direction
+                    direction += w_detail * d
+
+        # Ensure numerical stability
+        direction = torch.clamp(direction, -1e2, 1e2)
+
+        # Apply the step
+        x = x + direction * dt
+
+        # Apply ancestral noise with progressive reduction
+        if sigma_up > 0:
+            # Only add significant noise in earlier steps
+            noise_scale = s_noise
+            if progress > 0.3:
+                # Exponential reduction in noise after Euler phase
+                noise_scale *= math.exp(-4.0 * (progress - 0.3))
+
+            # Add the scaled noise
+            x = x + noise_sampler(sigma_hat, sigmas[i + 1]) * sigma_up * noise_scale
+
+        # Store values for next step
+        prev_d = d
+        prev_denoised = denoised
+
+    return x
+
+
+def calc_phase_bounds(steps: int, custom_euler_end: float = 0.25, custom_dpm_end: float = 0.7) -> tuple[float, float]:
+    """
+    Calculate phase boundaries for the adaptive progressive sampler.
+
+    Args:
+        steps: Total number of steps
+        custom_euler_end: End point for Euler phase (0.0-1.0)
+        custom_dpm_end: End point for DPM++ phase (0.0-1.0)
+
+    Returns:
+        Tuple of phase boundaries (Euler end, DPM++ end)
+    """
+    # Ensure values are within valid range
+    euler_end = max(0.0, min(1.0, custom_euler_end))
+    dpm_end = max(0.0, min(1.0, custom_dpm_end))
+
+    # Ensure euler_end < dpm_end
+    if euler_end >= dpm_end:
+        euler_end = max(0.0, dpm_end - 0.2)  # Ensure at least 20% for DPM++ phase
+
+    # Adaptive phase boundaries based on step count
+    if steps < 10:
+        # For very low step counts, shorten Euler phase and extend detail phase
+        euler_end = min(euler_end, 0.15 + (steps - 4) * 0.01)
+        dpm_end = min(dpm_end, 0.5 + (steps - 4) * 0.02)
+    elif steps < 20:
+        # For low step counts, slightly adjust phases
+        euler_end = min(euler_end, 0.2)
+        dpm_end = min(dpm_end, 0.65)
+    elif steps > 50:
+        # For high step counts, extend the Euler phase slightly
+        euler_end = min(0.3, euler_end + (steps - 50) * 0.0005)
+        # And allow for a longer DPM++ phase
+        dpm_end = min(0.8, dpm_end + (steps - 50) * 0.0005)
+
+    # Ensure minimum phase lengths
+    if dpm_end - euler_end < 0.1:
+        dpm_end = min(1.0, euler_end + 0.1)
+
+    return euler_end, dpm_end

sd-forge-extra-samplers/lib_es/extra_samplers/euler_dy.py

@@ -0,0 +1,50 @@
+import torch
+
+from k_diffusion.sampling import to_d
+
+from tqdm.auto import trange
+
+from lib_es.utils import dy_sampling_step
+from lib_es.utils import sampler_metadata
+
+
+@sampler_metadata("Euler Dy")
+@torch.no_grad()
+def sample_euler_dy(
+    model,
+    x,
+    sigmas,
+    extra_args=None,
+    callback=None,
+    disable=None,
+    s_churn=0.0,
+    s_tmin=0.0,
+    s_tmax=float("inf"),
+    s_noise=1.0,
+):
+    extra_args = {} if extra_args is None else extra_args
+    s_in = x.new_ones([x.shape[0]])
+
+    for i in trange(len(sigmas) - 1, disable=disable):
+        gamma = max(s_churn / (len(sigmas) - 1), 2**0.5 - 1) if s_tmin <= sigmas[i] <= s_tmax else 0.0
+        eps = torch.randn_like(x) * s_noise
+        sigma_hat = sigmas[i] * (gamma + 1)
+        dt = sigmas[i + 1] - sigma_hat
+
+        if gamma > 0:
+            x = x - eps * (sigma_hat**2 - sigmas[i] ** 2) ** 0.5
+
+        denoised = model(x, sigma_hat * s_in, **extra_args)
+        d = to_d(x, sigma_hat, denoised)
+
+        if sigmas[i + 1] > 0:
+            if i // 2 == 1:
+                x = dy_sampling_step(x, model, dt, sigma_hat, **extra_args)
+
+        if callback is not None:
+            callback({"x": x, "i": i, "sigma": sigmas[i], "sigma_hat": sigma_hat, "denoised": denoised})
+
+        # Euler method
+        x = x + d * dt
+
+    return x

sd-forge-extra-samplers/lib_es/extra_samplers/euler_dy_negative.py

@@ -0,0 +1,50 @@
+import torch
+
+from k_diffusion.sampling import to_d
+
+from tqdm.auto import trange
+
+from lib_es.utils import dy_sampling_step
+from lib_es.utils import sampler_metadata
+
+
+@sampler_metadata("Euler Dy Negative")
+@torch.no_grad()
+def sample_euler_dy_negative(
+    model,
+    x,
+    sigmas,
+    extra_args=None,
+    callback=None,
+    disable=None,
+    s_churn=0.0,
+    s_tmin=0.0,
+    s_tmax=float("inf"),
+    s_noise=1.0,
+):
+    extra_args = {} if extra_args is None else extra_args
+    s_in = x.new_ones([x.shape[0]])
+
+    for i in trange(len(sigmas) - 1, disable=disable):
+        gamma = max(s_churn / (len(sigmas) - 1), 2**0.5 - 1) if s_tmin <= sigmas[i] <= s_tmax else 0.0
+        eps = torch.randn_like(x) * s_noise
+        sigma_hat = sigmas[i] * (gamma + 1)
+        dt = sigmas[i + 1] - sigma_hat
+
+        if gamma > 0:
+            x = x - eps * (sigma_hat**2 - sigmas[i] ** 2) ** 0.5
+
+        denoised = model(x, sigma_hat * s_in, **extra_args)
+        d = to_d(x, sigma_hat, denoised)
+
+        if callback is not None:
+            callback({"x": x, "i": i, "sigma": sigmas[i], "sigma_hat": sigma_hat, "denoised": denoised})
+
+        # Euler method
+        if sigmas[i + 1] > 0 and i // 2 == 1:
+            x = dy_sampling_step(x, model, dt, sigma_hat, **extra_args)
+            x = -x - d * dt
+        else:
+            x = x + d * dt
+
+    return x

sd-forge-extra-samplers/lib_es/extra_samplers/euler_max.py

@@ -0,0 +1,45 @@
+import math
+import torch
+
+from k_diffusion.sampling import to_d
+
+from tqdm.auto import trange
+
+from lib_es.utils import sampler_metadata
+
+
+@sampler_metadata("Euler Max")
+@torch.no_grad()
+def sample_euler_max(
+    model,
+    x,
+    sigmas,
+    extra_args=None,
+    callback=None,
+    disable=None,
+    s_churn=0.0,
+    s_tmin=0.0,
+    s_tmax=float("inf"),
+    s_noise=1.0,
+):
+    extra_args = {} if extra_args is None else extra_args
+    s_in = x.new_ones([x.shape[0]])
+    for i in trange(len(sigmas) - 1, disable=disable):
+        gamma = max(s_churn / (len(sigmas) - 1), 2**0.5 - 1) if s_tmin <= sigmas[i] <= s_tmax else 0.0
+        eps = torch.randn_like(x) * s_noise
+        sigma_hat = sigmas[i] * (gamma + 1)
+
+        if gamma > 0:
+            x = x - eps * (sigma_hat**2 - sigmas[i] ** 2) ** 0.5
+
+        denoised = model(x, sigma_hat * s_in, **extra_args)
+        d = to_d(x, sigma_hat, denoised)
+
+        if callback is not None:
+            callback({"x": x, "i": i, "sigma": sigmas[i], "sigma_hat": sigma_hat, "denoised": denoised})
+
+        dt = sigmas[i + 1] - sigma_hat
+
+        # Euler method
+        x = x + (math.cos(i + 1) / (i + 1) + 1) * d * dt
+    return x

sd-forge-extra-samplers/lib_es/extra_samplers/euler_multipass.py

@@ -0,0 +1,290 @@
+import torch
+from tqdm import trange
+
+from k_diffusion.sampling import get_ancestral_step, to_d
+
+from lib_es.utils import default_noise_sampler, extend_sigmas, sampler_metadata
+
+
+# ==============================================================================================================
+#  - Originally written by aria1th: https://github.com/aria1th
+#  - CFG++ support written by LaVie024: https://github.com/LaVie024
+#  - Standard Euler support written by catboxanon: https://github.com/catboxanon
+# ==============================================================================================================
+
+
+def apply_churn(x, sub_sigma, s_churn, s_tmin, s_tmax, s_noise, pass_step):
+    if s_churn > 0:
+        gamma = min(s_churn / max(0, pass_step - 1), 2**0.5 - 1) if s_tmin <= sub_sigma < s_tmax else 0
+        sigma_hat = sub_sigma * (gamma + 1)
+    else:
+        gamma = 0
+        sigma_hat = sub_sigma
+
+    if gamma > 0:
+        eps = torch.randn_like(x) * s_noise
+        x = x + eps * (sigma_hat**2 - sub_sigma**2) ** 0.5
+
+    return x, sigma_hat
+
+
+@torch.no_grad()
+def euler_ancestral_multipass(
+    model,
+    x,
+    sigmas,
+    extra_args=None,
+    callback=None,
+    disable=None,
+    eta=1.0,
+    s_noise=1.0,
+    noise_sampler=None,
+    pass_steps=2,
+    pass_sigma_max=float("inf"),
+    pass_sigma_min=12.0,
+    cfg_pp=False,
+):
+    """
+    A multipass variant of Euler-Ancestral sampling.
+    - For each i in [0, len(sigmas)-2], we check if sigma_i is in [pass_sigma_min, pass_sigma_max].
+      If so, subdivide the step from sigma_i -> sigma_{i+1} into 'pass_steps' sub-steps.
+      Otherwise, do a single standard step.
+    - Each sub-step calls 'get_ancestral_step(...)' with the sub-interval's start & end sigmas,
+      then applies the usual Euler-Ancestral update:
+         x = x + d*dt + (noise * sigma_up)
+    """
+    extra_args = {} if extra_args is None else extra_args
+    seed = extra_args.get("seed", None)
+    noise_sampler = default_noise_sampler(x, seed=seed) if noise_sampler is None else noise_sampler
+    s_in = x.new_ones([x.shape[0]])
+
+    if cfg_pp:
+        model.need_last_noise_uncond = True
+        model.inner_model.inner_model.forge_objects.unet.model_options["disable_cfg1_optimization"] = True
+
+    sub_sigmas = extend_sigmas(sigmas, pass_steps, pass_sigma_max, pass_sigma_min)
+
+    for i in trange(len(sub_sigmas) - 1, disable=disable):
+        # Current sub-step range:
+        sub_sigma_curr = sub_sigmas[i]
+        sub_sigma_next = sub_sigmas[i + 1]
+
+        # Denoise at the current sub-sigma
+        denoised = model(x, sub_sigma_curr * s_in, **extra_args)
+
+        if callback is not None:
+            callback({"x": x, "i": i, "sub_step": i, "sigma": sub_sigma_curr, "denoised": denoised})
+
+        # Compute the ancestral step parameters for this sub-interval
+        sigma_down, sigma_up = get_ancestral_step(sub_sigma_curr, sub_sigma_next, eta=eta)
+
+        d = model.last_noise_uncond if cfg_pp else to_d(x, sub_sigma_curr, denoised)
+
+        if cfg_pp:
+            x = denoised + d * sigma_down
+        elif sigma_down == 0.0:
+            x = denoised
+        else:
+            x = x + d * (sigma_down - sub_sigma_curr)
+
+        if sigma_up != 0.0:
+            # Add noise for the "ancestral" part
+            x = x + noise_sampler(sub_sigma_curr, sub_sigma_next) * (s_noise * sigma_up)
+
+    return x
+
+
+@sampler_metadata(name="Euler a Multipass", extra_params={"uses_ensd": True})
+def sample_euler_ancestral_multipass(
+    model,
+    x,
+    sigmas,
+    extra_args=None,
+    callback=None,
+    disable=None,
+    eta=1.0,
+    s_noise=1.0,
+    noise_sampler=None,
+    pass_steps=2,
+    pass_sigma_max=float("inf"),
+    pass_sigma_min=12.0,
+):
+    return euler_ancestral_multipass(
+        model,
+        x,
+        sigmas,
+        extra_args,
+        callback,
+        disable,
+        eta,
+        s_noise,
+        noise_sampler,
+        pass_steps,
+        pass_sigma_max,
+        pass_sigma_min,
+        False,
+    )
+
+
+@sampler_metadata(name="Euler a Multipass CFG++", extra_params={"uses_ensd": True})
+def sample_euler_ancestral_multipass_cfg_pp(
+    model,
+    x,
+    sigmas,
+    extra_args=None,
+    callback=None,
+    disable=None,
+    eta=1.0,
+    s_noise=1.0,
+    noise_sampler=None,
+    pass_steps=2,
+    pass_sigma_max=float("inf"),
+    pass_sigma_min=12.0,
+):
+    return euler_ancestral_multipass(
+        model,
+        x,
+        sigmas,
+        extra_args,
+        callback,
+        disable,
+        eta,
+        s_noise,
+        noise_sampler,
+        pass_steps,
+        pass_sigma_max,
+        pass_sigma_min,
+        True,
+    )
+
+
+@torch.no_grad()
+def euler_multipass(
+    model,
+    x,
+    sigmas,
+    extra_args=None,
+    callback=None,
+    disable=None,
+    noise_sampler=None,
+    s_churn=0.0,
+    s_tmin=0.0,
+    s_tmax=float("inf"),
+    s_noise=1.0,
+    pass_steps=2,
+    pass_sigma_max=float("inf"),
+    pass_sigma_min=12.0,
+    cfg_pp=False,
+):
+    """
+    A multipass variant of Euler sampling.
+    """
+    extra_args = {} if extra_args is None else extra_args
+    seed = extra_args.get("seed", None)
+    noise_sampler = default_noise_sampler(x, seed=seed) if noise_sampler is None else noise_sampler
+
+    if cfg_pp:
+        model.need_last_noise_uncond = True
+        model.inner_model.inner_model.forge_objects.unet.model_options["disable_cfg1_optimization"] = True
+
+    s_in = x.new_ones([x.shape[0]])
+    sub_sigmas = extend_sigmas(sigmas, pass_steps, pass_sigma_max, pass_sigma_min)
+
+    for i in trange(len(sub_sigmas) - 1, disable=disable):
+        # Current sub-step range:
+        sub_sigma_curr = sub_sigmas[i]
+        sub_sigma_next = sub_sigmas[i + 1]
+
+        x, sigma_hat = apply_churn(x, sub_sigma_curr, s_churn, s_tmin, s_tmax, s_noise, pass_steps)
+
+        # Denoise at the current sub-sigma
+        denoised = model(x, sub_sigma_curr * s_in, **extra_args)
+
+        if callback is not None:
+            callback(
+                {
+                    "x": x,
+                    "i": i,
+                    "sub_step": i,
+                    "sigma": sub_sigma_curr,
+                    "sigma_hat": sigma_hat,
+                    "denoised": denoised,
+                }
+            )
+
+        d = model.last_noise_uncond if cfg_pp else to_d(x, sigma_hat, denoised)
+        x = denoised + d * sub_sigma_next if cfg_pp else x + d * (sub_sigma_next - sigma_hat)
+
+    return x
+
+
+@sampler_metadata(name="Euler Multipass")
+def sample_euler_multipass(
+    model,
+    x,
+    sigmas,
+    extra_args=None,
+    callback=None,
+    disable=None,
+    s_noise=1.0,
+    s_churn=0.0,
+    s_tmin=0.0,
+    s_tmax=float("inf"),
+    noise_sampler=None,
+    pass_steps=2,
+    pass_sigma_max=float("inf"),
+    pass_sigma_min=12.0,
+):
+    return euler_multipass(
+        model,
+        x,
+        sigmas,
+        extra_args,
+        callback,
+        disable,
+        noise_sampler,
+        s_churn,
+        s_tmin,
+        s_tmax,
+        s_noise,
+        pass_steps,
+        pass_sigma_max,
+        pass_sigma_min,
+        False,
+    )
+
+
+@sampler_metadata(name="Euler Multipass CFG++")
+def sample_euler_multipass_cfg_pp(
+    model,
+    x,
+    sigmas,
+    extra_args=None,
+    callback=None,
+    disable=None,
+    s_noise=1.0,
+    s_churn=0.0,
+    s_tmin=0.0,
+    s_tmax=float("inf"),
+    noise_sampler=None,
+    pass_steps=2,
+    pass_sigma_max=float("inf"),
+    pass_sigma_min=12.0,
+):
+    return euler_multipass(
+        model,
+        x,
+        sigmas,
+        extra_args,
+        callback,
+        disable,
+        noise_sampler,
+        s_churn,
+        s_tmin,
+        s_tmax,
+        s_noise,
+        pass_steps,
+        pass_sigma_max,
+        pass_sigma_min,
+        True,
+    )

sd-forge-extra-samplers/lib_es/extra_samplers/euler_negative.py

@@ -0,0 +1,48 @@
+import torch
+
+from k_diffusion.sampling import to_d
+
+from tqdm.auto import trange
+
+from lib_es.utils import sampler_metadata
+
+
+@sampler_metadata("Euler Negative")
+@torch.no_grad()
+def sample_euler_negative(
+    model,
+    x,
+    sigmas,
+    extra_args=None,
+    callback=None,
+    disable=None,
+    s_churn=0.0,
+    s_tmin=0.0,
+    s_tmax=float("inf"),
+    s_noise=1.0,
+):
+    extra_args = {} if extra_args is None else extra_args
+    s_in = x.new_ones([x.shape[0]])
+
+    for i in trange(len(sigmas) - 1, disable=disable):
+        gamma = max(s_churn / (len(sigmas) - 1), 2**0.5 - 1) if s_tmin <= sigmas[i] <= s_tmax else 0.0
+        eps = torch.randn_like(x) * s_noise
+        sigma_hat = sigmas[i] * (gamma + 1)
+        dt = sigmas[i + 1] - sigma_hat
+
+        if gamma > 0:
+            x = x - eps * (sigma_hat**2 - sigmas[i] ** 2) ** 0.5
+
+        denoised = model(x, sigma_hat * s_in, **extra_args)
+        d = to_d(x, sigma_hat, denoised)
+
+        if callback is not None:
+            callback({"x": x, "i": i, "sigma": sigmas[i], "sigma_hat": sigma_hat, "denoised": denoised})
+
+        # Euler method
+        if sigmas[i + 1] > 0 and i // 2 == 1:
+            x = -x - d * dt
+        else:
+            x = x + d * dt
+
+    return x

sd-forge-extra-samplers/lib_es/extra_samplers/euler_smea.py

@@ -0,0 +1,49 @@
+import torch
+
+from k_diffusion.sampling import to_d
+
+from tqdm.auto import trange
+
+from lib_es.utils import overall_sampling_step
+from lib_es.utils import sampler_metadata
+
+
+@sampler_metadata("Euler SMEA")
+@torch.no_grad()
+def sample_euler_smea(
+    model,
+    x,
+    sigmas,
+    extra_args=None,
+    callback=None,
+    disable=None,
+    s_churn=0.0,
+    s_tmin=0.0,
+    s_tmax=float("inf"),
+    s_noise=1.0,
+):
+    extra_args = {} if extra_args is None else extra_args
+    s_in = x.new_ones([x.shape[0]])
+
+    for i in trange(len(sigmas) - 1, disable=disable):
+        gamma = max(s_churn / (len(sigmas) - 1), 2**0.5 - 1) if s_tmin <= sigmas[i] <= s_tmax else 0.0
+        eps = torch.randn_like(x) * s_noise
+        sigma_hat = sigmas[i] * (gamma + 1)
+        dt = sigmas[i + 1] - sigma_hat
+
+        if i // 2 == 1:
+            x = overall_sampling_step(x, model, dt, sigma_hat, **extra_args)
+
+        if gamma > 0:
+            x = x - eps * (sigma_hat**2 - sigmas[i] ** 2) ** 0.5
+
+        denoised = model(x, sigma_hat * s_in, **extra_args)
+        d = to_d(x, sigma_hat, denoised)
+
+        if callback is not None:
+            callback({"x": x, "i": i, "sigma": sigmas[i], "sigma_hat": sigma_hat, "denoised": denoised})
+
+        # Euler method
+        x = x + d * dt
+
+    return x

sd-forge-extra-samplers/lib_es/extra_samplers/euler_smea_dy.py

@@ -0,0 +1,53 @@
+import torch
+
+from k_diffusion.sampling import to_d
+
+from tqdm.auto import trange
+
+from lib_es.utils import dy_sampling_step, smea_sampling_step
+from lib_es.utils import sampler_metadata
+
+
+@sampler_metadata("Euler SMEA Dy")
+@torch.no_grad()
+def sample_euler_smea_dy(
+    model,
+    x,
+    sigmas,
+    extra_args=None,
+    callback=None,
+    disable=None,
+    s_churn=0.0,
+    s_tmin=0.0,
+    s_tmax=float("inf"),
+    s_noise=1.0,
+):
+    extra_args = {} if extra_args is None else extra_args
+    s_in = x.new_ones([x.shape[0]])
+
+    for i in trange(len(sigmas) - 1, disable=disable):
+        gamma = max(s_churn / (len(sigmas) - 1), 2**0.5 - 1) if s_tmin <= sigmas[i] <= s_tmax else 0.0
+        eps = torch.randn_like(x) * s_noise
+        sigma_hat = sigmas[i] * (gamma + 1)
+        dt = sigmas[i + 1] - sigma_hat
+
+        if gamma > 0:
+            x = x - eps * (sigma_hat**2 - sigmas[i] ** 2) ** 0.5
+
+        denoised = model(x, sigma_hat * s_in, **extra_args)
+        d = to_d(x, sigma_hat, denoised)
+
+        # Euler method
+        x = x + d * dt
+
+        if sigmas[i + 1] > 0:
+            if i + 1 // 2 == 1:
+                x = dy_sampling_step(x, model, dt, sigma_hat, **extra_args)
+
+            if i + 1 // 2 == 0:
+                x = smea_sampling_step(x, model, dt, sigma_hat, **extra_args)
+
+        if callback is not None:
+            callback({"x": x, "i": i, "sigma": sigmas[i], "sigma_hat": sigma_hat, "denoised": denoised})
+
+    return x

sd-forge-extra-samplers/lib_es/extra_samplers/euler_smea_dy_negative.py

@@ -0,0 +1,55 @@
+import torch
+
+from k_diffusion.sampling import to_d
+
+from tqdm.auto import trange
+
+from lib_es.utils import dy_sampling_step, smea_sampling_step
+from lib_es.utils import sampler_metadata
+
+
+@sampler_metadata("Euler SMEA Dy Negative")
+@torch.no_grad()
+def sample_euler_smea_dy_negative(
+    model,
+    x,
+    sigmas,
+    extra_args=None,
+    callback=None,
+    disable=None,
+    s_churn=0.0,
+    s_tmin=0.0,
+    s_tmax=float("inf"),
+    s_noise=1.0,
+):
+    extra_args = {} if extra_args is None else extra_args
+    s_in = x.new_ones([x.shape[0]])
+
+    for i in trange(len(sigmas) - 1, disable=disable):
+        gamma = max(s_churn / (len(sigmas) - 1), 2**0.5 - 1) if s_tmin <= sigmas[i] <= s_tmax else 0.0
+        eps = torch.randn_like(x) * s_noise
+        sigma_hat = sigmas[i] * (gamma + 1)
+        dt = sigmas[i + 1] - sigma_hat
+
+        if gamma > 0:
+            x = x - eps * (sigma_hat**2 - sigmas[i] ** 2) ** 0.5
+
+        denoised = model(x, sigma_hat * s_in, **extra_args)
+        d = to_d(x, sigma_hat, denoised)
+
+        # Euler method
+        x = x + d * dt
+
+        if sigmas[i + 1] > 0:
+            if i + 1 // 2 == 1:
+                x = dy_sampling_step(x, model, dt, sigma_hat, **extra_args)
+                x = -x - d * dt
+
+            if i + 1 // 2 == 0:
+                x = smea_sampling_step(x, model, dt, sigma_hat, **extra_args)
+                x = -x - d * dt
+
+        if callback is not None:
+            callback({"x": x, "i": i, "sigma": sigmas[i], "sigma_hat": sigma_hat, "denoised": denoised})
+
+    return x

sd-forge-extra-samplers/lib_es/extra_samplers/extended_reverse_time.py

@@ -0,0 +1,83 @@
+import torch
+from tqdm import trange
+
+import lib_es.const as consts
+from lib_es.utils import default_noise_sampler, sampler_metadata
+
+
+# From ComfyUI
+@sampler_metadata(
+    "Extended Reverse-Time SDE",
+    {"uses_ensd": True, "scheduler": "sgm_uniform"},
+    ["sample_er_sde, extended_reverse_sde"],
+)
+@torch.no_grad()
+def sample_er_sde(
+    model,
+    x,
+    sigmas,
+    extra_args=None,
+    callback=None,
+    disable=None,
+    s_noise=1.0,
+    noise_sampler=None,
+    noise_scaler=None,
+):
+    """
+    Extended Reverse-Time SDE solver (VE ER-SDE-Solver-3). Arxiv: https://arxiv.org/abs/2309.06169.
+    Code reference: https://github.com/QinpengCui/ER-SDE-Solver/blob/main/er_sde_solver.py.
+    """
+    extra_args = {} if extra_args is None else extra_args
+    seed = extra_args.get("seed", None)
+    noise_sampler = default_noise_sampler(x, seed=seed) if noise_sampler is None else noise_sampler
+    s_in = x.new_ones([x.shape[0]])
+
+    max_stage: int = getattr(model.p, consts.ER_MAX_STAGE, 3)
+
+    def default_noise_scaler(sigma):
+        return sigma * ((sigma**0.3).exp() + 10.0)
+
+    noise_scaler = default_noise_scaler if noise_scaler is None else noise_scaler
+    num_integration_points = 200.0
+    point_indice = torch.arange(0, num_integration_points, dtype=torch.float32, device=x.device)
+
+    old_denoised = None
+    old_denoised_d = None
+
+    for i in trange(len(sigmas) - 1, disable=disable):
+        denoised = model(x, sigmas[i] * s_in, **extra_args)
+        if callback is not None:
+            callback({"x": x, "i": i, "sigma": sigmas[i], "sigma_hat": sigmas[i], "denoised": denoised})
+        stage_used = min(max_stage, i + 1)
+        if sigmas[i + 1] == 0:
+            x = denoised
+        elif stage_used == 1:
+            r = noise_scaler(sigmas[i + 1]) / noise_scaler(sigmas[i])
+            x = r * x + (1 - r) * denoised
+        else:
+            r = noise_scaler(sigmas[i + 1]) / noise_scaler(sigmas[i])
+            x = r * x + (1 - r) * denoised
+
+            dt = sigmas[i + 1] - sigmas[i]
+            sigma_step_size = -dt / num_integration_points
+            sigma_pos = sigmas[i + 1] + point_indice * sigma_step_size
+            scaled_pos = noise_scaler(sigma_pos)
+
+            # Stage 2
+            s = torch.sum(1 / scaled_pos) * sigma_step_size
+            denoised_d = (denoised - old_denoised) / (sigmas[i] - sigmas[i - 1])
+            x = x + (dt + s * noise_scaler(sigmas[i + 1])) * denoised_d
+
+            if stage_used >= 3:
+                # Stage 3
+                s_u = torch.sum((sigma_pos - sigmas[i]) / scaled_pos) * sigma_step_size
+                denoised_u = (denoised_d - old_denoised_d) / ((sigmas[i] - sigmas[i - 2]) / 2)
+                x = x + ((dt**2) / 2 + s_u * noise_scaler(sigmas[i + 1])) * denoised_u
+            old_denoised_d = denoised_d
+
+        if s_noise != 0 and sigmas[i + 1] > 0:
+            x = x + noise_sampler(sigmas[i], sigmas[i + 1]) * s_noise * (
+                sigmas[i + 1] ** 2 - sigmas[i] ** 2 * r**2
+            ).sqrt().nan_to_num(nan=0.0)
+        old_denoised = denoised
+    return x

sd-forge-extra-samplers/lib_es/extra_samplers/gradient_estimation.py

@@ -0,0 +1,180 @@
+from collections.abc import Callable
+from typing import Any, Optional
+import torch
+from tqdm import trange
+
+from k_diffusion.sampling import to_d
+from modules import errors
+
+import lib_es.const as consts
+from lib_es.utils import sampler_metadata
+
+
+def compute_optimal_gamma(steps: int, adaptive: bool = True) -> float:
+    """
+    Compute the optimal gamma parameter for gradient estimation based on step count.
+
+    Args:
+        steps: Number of sampling steps
+        adaptive: Whether to use adaptive gamma based on step count
+
+    Returns:
+        Optimal gamma value
+    """
+    if not adaptive:
+        return consts.GE_DEFAULT_GAMMA
+
+    # Define min and max values
+    min_steps, max_steps = 10, 100
+    min_gamma, max_gamma = 1.5, 2.6
+
+    # Handle edge cases
+    if steps <= min_steps:
+        return min_gamma
+    elif steps >= max_steps:
+        return max_gamma
+
+    # Apply logarithmic scaling
+    # log(steps/min_steps) / log(max_steps/min_steps) gives a value from 0 to 1
+    # that increases logarithmically with steps
+    log_factor = torch.log(torch.tensor(steps / min_steps)) / torch.log(torch.tensor(max_steps / min_steps))
+
+    # Convert the logarithmic factor to gamma value
+    gamma = min_gamma + log_factor * (max_gamma - min_gamma)
+
+    return float(gamma)
+
+
+def validate_schedule(sigmas: torch.Tensor, eta: float = 0.1, nu: float = 2.0) -> bool:
+    """
+    Validate whether a noise schedule satisfies the admissibility criteria from the paper.
+
+    Args:
+        sigmas: Tensor of noise levels in descending order
+        eta: Error parameter
+        nu: Accuracy parameter for distance estimates
+
+    Returns:
+        True if schedule is admissible, False otherwise
+    """
+    n = len(sigmas) - 1
+    is_admissible = True
+    issues = []
+
+    # Check if sigmas are strictly decreasing
+    if not torch.all(sigmas[:-1] > sigmas[1:]):
+        is_admissible = False
+        issues.append("Sigmas must be strictly decreasing")
+
+    # Calculate the maximum allowable beta
+    c = 1 - nu ** (-1 / n)
+    beta_max = c / (eta + c)
+
+    # Check that step sizes respect the admissibility criteria
+    for i in range(n - 1):
+        ratio = sigmas[i + 1] / sigmas[i]
+        beta = 1 - ratio
+        if beta > beta_max:
+            is_admissible = False
+            issues.append(f"Step {i} has beta {beta:.4f} > beta_max {beta_max:.4f}")
+
+    if not is_admissible:
+        errors.display(ValueError(f"Noise schedule is not admissible: {', '.join(issues)}"))
+        errors.print_error_explanation("Noise schedule validation failed.\n\tIssues:" + ",\n\t\t".join(issues))
+
+    return is_admissible
+
+
+@torch.no_grad()
+@sampler_metadata("Gradient Estimation", {"scheduler": "sgm_uniform"})
+def sample_gradient_estimation(
+    model,
+    x: torch.Tensor,
+    sigmas: torch.Tensor,
+    extra_args: Optional[dict[str, Any]] = None,
+    callback: Optional[Callable] = None,
+    disable: Optional[bool] = None,
+    validate_sigmas: bool = False,
+    eta: float = 0.1,
+    nu: float = 2.0,
+) -> torch.Tensor:
+    """
+    Gradient-estimation sampler as described in "Interpreting and Improving Diffusion Models from an Optimization Perspective".
+
+    This sampler implements a second-order method that improves upon DDIM by using a combination of current and previous
+    gradients to reduce gradient estimation error. It is based on the insight that denoising is approximately equivalent to
+    projection onto the data manifold, and diffusion sampling is gradient descent on the squared Euclidean distance function.
+
+    Args:
+        model: The diffusion model
+        x: Input tensor
+        sigmas: Noise schedule (should be in descending order)
+        extra_args: Extra arguments to pass to the model
+        callback: Callback function
+        disable: Whether to disable the progress bar
+        validate_sigmas: Whether to validate the noise schedule
+        eta: Error parameter for schedule validation (default 0.1)
+        nu: Accuracy parameter for schedule validation (default 2.0)
+
+    Returns:
+        Denoised tensor
+
+    References:
+        Paper: https://openreview.net/pdf?id=o2ND9v0CeK
+    """
+    # Parameter validation and initialization
+    if sigmas.shape[0] < 2:
+        raise ValueError("Need at least 2 timesteps for gradient estimation")
+
+    extra_args = {} if extra_args is None else extra_args
+    s_in = x.new_ones([x.shape[0]])
+    old_d = None
+    steps = len(sigmas) - 1
+
+    # Schedule validation
+    if validate_sigmas:
+        validate_schedule(sigmas, eta, nu)
+
+    # Get gamma from model properties or compute optimal value
+    use_adaptive_steps: bool = getattr(model.p, consts.GE_USE_ADAPTIVE_STEPS, True)
+    if use_adaptive_steps:
+        # Compute optimal gamma based on the number of steps
+        # and add the offset if specified
+        ge_gamma = compute_optimal_gamma(steps, use_adaptive_steps) + getattr(
+            model.p, consts.GE_GAMMA_OFFSET, consts.GE_DEFAULT_GAMMA_OFFSET
+        )
+    else:
+        ge_gamma = getattr(model.p, consts.GE_GAMMA, consts.GE_DEFAULT_GAMMA)
+
+    # Initialize timestep-adaptive gamma values if needed
+    timestep_adaptive_gamma = getattr(model.p, consts.GE_USE_TIMESTEP_ADAPTIVE_GAMMA, False)
+
+    if timestep_adaptive_gamma:
+        # Higher gamma at the beginning, lower toward the end
+        # This is a heuristic based on the observation that early steps benefit more
+        # from aggressive gradient correction
+        gammas = torch.linspace(ge_gamma * 1.2, ge_gamma * 0.8, steps)
+
+    # Main sampling loop
+    for i in trange(len(sigmas) - 1, disable=disable):
+        denoised = model(x, sigmas[i] * s_in, **extra_args)
+        d = to_d(x, sigmas[i], denoised)
+
+        if callback is not None:
+            callback({"x": x, "i": i, "sigma": sigmas[i], "sigma_hat": sigmas[i], "denoised": denoised})
+
+        dt = sigmas[i + 1] - sigmas[i]
+
+        if i == 0:
+            # Euler method for first step
+            x = x + d * dt
+        else:
+            # Gradient estimation
+            current_gamma = gammas[i].item() if timestep_adaptive_gamma else ge_gamma
+
+            d_bar = current_gamma * d + (1 - current_gamma) * old_d
+            x = x + d_bar * dt
+
+        old_d = d
+
+    return x

sd-forge-extra-samplers/lib_es/extra_samplers/heun_ancestral.py

@@ -0,0 +1,81 @@
+import torch
+from tqdm.auto import trange
+from k_diffusion.sampling import default_noise_sampler, get_ancestral_step, to_d
+
+from lib_es.utils import sampler_metadata
+
+
+@sampler_metadata(
+    "Heun Ancestral",
+    {"uses_ensd": True},
+)
+@torch.no_grad()
+def sample_heun_ancestral(
+    model,
+    x,
+    sigmas,
+    extra_args=None,
+    callback=None,
+    disable=None,
+    eta=1.0,
+    s_noise=1.0,
+    noise_sampler=None,
+):
+    """
+    Ancestral sampling with Heun's method steps.
+
+    Args:
+        model: The model to sample from.
+        x: The initial noise.
+        sigmas: The noise levels to sample at.
+        extra_args: Extra arguments to the model.
+        callback: A function that's called after each step.
+        disable: Disable tqdm progress bar.
+        eta: Ancestral sampling strength parameter.
+        s_noise: Noise scale.
+        noise_sampler: A function that returns noise.
+    """
+    extra_args = {} if extra_args is None else extra_args
+    noise_sampler = default_noise_sampler(x) if noise_sampler is None else noise_sampler
+    s_in = x.new_ones([x.shape[0]])
+
+    for i in trange(len(sigmas) - 1, disable=disable):
+        # Get current and next sigma
+        sigma = sigmas[i]
+
+        # Run denoising model
+        denoised = model(x, sigma * s_in, **extra_args)
+
+        # Calculate ancestral step parameters
+        sigma_down, sigma_up = get_ancestral_step(sigma, sigmas[i + 1], eta=eta)
+
+        if callback is not None:
+            callback({"x": x, "i": i, "sigma": sigma, "sigma_hat": sigma, "denoised": denoised})
+
+        # Calculate the derivative
+        d = to_d(x, sigma, denoised)
+
+        # Determine step size
+        dt = sigma_down - sigma
+
+        if sigma_down == 0:
+            # For the last step, use Euler method for stability
+            x = x + d * dt
+        else:
+            # Heun's method (predictor-corrector)
+            # 1. Predictor step (Euler)
+            x_2 = x + d * dt
+
+            # 2. Evaluate at the predicted point
+            denoised_2 = model(x_2, sigma_down * s_in, **extra_args)
+            d_2 = to_d(x_2, sigma_down, denoised_2)
+
+            # 3. Corrector step (average of gradients)
+            d_prime = (d + d_2) / 2
+            x = x + d_prime * dt
+
+            # Add noise according to ancestral sampling formula
+            if sigma_up > 0:
+                x = x + noise_sampler(sigma, sigmas[i + 1]) * s_noise * sigma_up
+
+    return x

sd-forge-extra-samplers/lib_es/extra_samplers/kohaku_lonyu_yog.py

@@ -0,0 +1,58 @@
+import torch
+
+from k_diffusion.sampling import default_noise_sampler, get_ancestral_step, to_d
+
+from tqdm.auto import trange
+
+from lib_es.utils import sampler_metadata
+
+
+@sampler_metadata("Kohaku LoNyu Yog")
+@torch.no_grad()
+def sample_kohaku_lonyu_yog(
+    model,
+    x,
+    sigmas,
+    extra_args=None,
+    callback=None,
+    disable=None,
+    s_churn=0.0,
+    s_tmin=0.0,
+    s_tmax=float("inf"),
+    s_noise=1.0,
+    noise_sampler=None,
+    eta=1.0,
+):
+    """Kohaku_LoNyu_Yog"""
+    extra_args = {} if extra_args is None else extra_args
+    s_in = x.new_ones([x.shape[0]])
+    noise_sampler = default_noise_sampler(x) if noise_sampler is None else noise_sampler
+    for i in trange(len(sigmas) - 1, disable=disable):
+        gamma = min(s_churn / (len(sigmas) - 1), 2**0.5 - 1) if s_tmin <= sigmas[i] <= s_tmax else 0.0
+        eps = torch.randn_like(x) * s_noise
+        sigma_hat = sigmas[i] * (gamma + 1)
+        if gamma > 0:
+            x = x + eps * (sigma_hat**2 - sigmas[i] ** 2) ** 0.5
+        denoised = model(x, sigma_hat * s_in, **extra_args)
+        d = to_d(x, sigma_hat, denoised)
+        sigma_down, sigma_up = get_ancestral_step(sigmas[i], sigmas[i + 1], eta=eta)
+        if callback is not None:
+            callback({"x": x, "i": i, "sigma": sigmas[i], "sigma_hat": sigma_hat, "denoised": denoised})
+        dt = sigma_down - sigmas[i]
+
+        if i <= (len(sigmas) - 1) / 2:
+            x2 = -x
+            denoised2 = model(x2, sigma_hat * s_in, **extra_args)
+            d2 = to_d(x2, sigma_hat, denoised2)
+
+            x3 = x + ((d + d2) / 2) * dt
+            denoised3 = model(x3, sigma_hat * s_in, **extra_args)
+            d3 = to_d(x3, sigma_hat, denoised3)
+
+            real_d = (d + d3) / 2
+            x = x + real_d * dt
+
+            x = x + noise_sampler(sigmas[i], sigmas[i + 1]) * s_noise * sigma_up
+        else:
+            x = x + d * dt
+    return x

sd-forge-extra-samplers/lib_es/extra_samplers/langevin_euler.py

@@ -0,0 +1,89 @@
+import torch
+from tqdm.auto import trange
+from k_diffusion.sampling import default_noise_sampler, to_d
+
+import lib_es.const as consts
+from lib_es.utils import sampler_metadata
+
+
+@sampler_metadata(
+    "Langevin Euler",
+    {"scheduler": "sgm_uniform"},
+)
+@torch.no_grad()
+def sample_langevin_euler(
+    model,
+    x,
+    sigmas,
+    extra_args=None,
+    callback=None,
+    disable=None,
+    s_churn=0.0,
+    s_tmin=0.0,
+    s_tmax=float("inf"),
+    s_noise=1.0,
+    noise_sampler=None,
+):
+    """
+    Langevin dynamics sampler - the adaptive CFG is now handled by the CFG function.
+    This is your original implementation but with the adaptive CFG logic removed.
+    """
+    extra_args = {} if extra_args is None else extra_args
+    noise_sampler = default_noise_sampler(x) if noise_sampler is None else noise_sampler
+    s_in = x.new_ones([x.shape[0]])
+
+    # Store original shape for aspect ratio calculations
+    height, width = x.shape[2:4]
+    aspect_ratio = width / height
+    sigma_max = sigmas[0]
+
+    langevin_strength = getattr(model.p, consts.LANGEVIN_STRENGTH, 0.1)
+
+    for i in trange(len(sigmas) - 1, disable=disable):
+        # Apply s_churn noise if requested
+        gamma = min(s_churn / (len(sigmas) - 1), 2**0.5 - 1) if s_tmin <= sigmas[i] <= s_tmax else 0.0
+        eps = torch.randn_like(x) * s_noise
+        sigma_hat = sigmas[i] * (gamma + 1)
+        if gamma > 0:
+            x = x + eps * (sigma_hat**2 - sigmas[i] ** 2) ** 0.5
+
+        # Perform model prediction - CFG is now handled by our function
+        denoised = model(x, sigma_hat * s_in, **extra_args)
+
+        # Call the callback
+        if callback is not None:
+            callback({"x": x, "i": i, "sigma": sigmas[i], "sigma_hat": sigma_hat, "denoised": denoised})
+
+        # Calculate the derivative
+        d = to_d(x, sigma_hat, denoised)
+
+        # Langevin step: Deterministic part + Noise part
+        dt = sigmas[i + 1] - sigma_hat
+
+        # Deterministic Euler step
+        x = x + d * dt
+
+        # Apply Langevin noise if not the final step
+        if sigmas[i + 1] > 0:
+            # Simpler Langevin noise logic with less aggressive scaling
+            # Use a constant base noise level with a gentle decay
+            base_noise_level = langevin_strength  # Base level from parameter
+
+            # Gentle decay curve - more consistent noise across steps
+            # Sqrt provides a more gradual decrease than linear scaling
+            decay_factor = torch.sqrt(sigmas[i + 1] / sigma_max)
+            noise_scale = base_noise_level * (0.1 + 0.9 * decay_factor)
+
+            # Higher safety clamp to allow more noise influence
+            noise_scale = max(langevin_strength * 0.05, min(noise_scale, 0.8))
+
+            # Generate balanced noise
+            noise = torch.randn_like(x) * noise_scale
+            height_scale = torch.sqrt(torch.tensor(aspect_ratio))
+            width_scale = 1.0 / height_scale
+            scaling = torch.tensor([1.0, 1.0, height_scale, width_scale]).reshape(1, -1, 1, 1).to(x.device)
+            balanced_noise = noise * scaling
+
+            x = x + balanced_noise
+
+    return x

sd-forge-extra-samplers/lib_es/extra_samplers/res_multistep.py

@@ -0,0 +1,235 @@
+import torch
+from tqdm.auto import trange
+
+#from backend.modules.k_diffusion_extra import default_noise_sampler
+#from backend.patcher.unet import UnetPatcher
+from k_diffusion.sampling import get_ancestral_step, to_d
+
+from lib_es.utils import sampler_metadata
+
+
+def sigma_fn(t):
+    """
+    Computes the sigma function for a given tensor `t`.
+    The sigma function is defined as the exponential of the negation of `t`.
+    Args:
+        t (torch.Tensor): Input tensor.
+    Returns:
+        torch.Tensor: The result of applying the sigma function to `t`.
+    """
+
+    return t.neg().exp()
+
+
+def t_fn(sigma):
+    """
+    Computes the negative logarithm of the input tensor.
+    Args:
+        sigma (torch.Tensor): A tensor for which the negative logarithm is to be computed.
+    Returns:
+        torch.Tensor: A tensor containing the negative logarithm of the input tensor.
+    """
+
+    return sigma.log().neg()
+
+
+def phi1_fn(t):
+    """
+    Computes the function phi1(t) = (exp(t) - 1) / t using PyTorch's expm1 function.
+    Args:
+        t (torch.Tensor): Input tensor.
+    Returns:
+        torch.Tensor: The result of (exp(t) - 1) / t.
+    """
+
+    return torch.expm1(t) / t
+
+
+def phi2_fn(t):
+    """
+    Compute the value of the phi2 function.
+    The phi2 function is defined as (phi1_fn(t) - 1.0) / t, where phi1_fn is
+    another function that takes a single argument t.
+    Parameters:
+    t (float): The input value for the function.
+    Returns:
+    float: The computed value of the phi2 function.
+    """
+
+    return (phi1_fn(t) - 1.0) / t
+
+
+@torch.no_grad()
+def res_multistep(
+    model,
+    x,
+    sigmas,
+    extra_args=None,
+    callback=None,
+    disable=None,
+    s_noise=1.0,
+    noise_sampler=None,
+    eta=1.0,
+    cfg_pp=False,
+):
+    """
+    Perform multi-step denoising using a conditional denoising model.
+    Args:
+        model (CFGDenoiserKDiffusion): The denoising model to use.
+        x (torch.Tensor): The input tensor to be denoised.
+        sigmas (list or torch.Tensor): A list or tensor of sigma values for each step.
+        extra_args (dict, optional): Additional arguments to pass to the model. Defaults to None.
+        callback (callable, optional): A callback function to be called after each step. Defaults to None.
+        disable (bool, optional): If True, disables the progress bar. Defaults to None.
+        s_noise (float, optional): Noise scale for stochasticity. Defaults to 1.0.
+        noise_sampler (callable, optional): Function to sample noise. Defaults to None.
+        cfg_pp (bool, optional): If True, enables post-processing for classifier-free guidance. Defaults to False.
+    Returns:
+        torch.Tensor: The denoised output tensor.
+    """
+    extra_args = {} if extra_args is None else extra_args
+    noise_sampler = default_noise_sampler(x) if noise_sampler is None else noise_sampler
+    s_in = x.new_ones([x.shape[0]])
+
+    old_denoised = None
+    uncond_denoised = None
+
+    # unconditional denoised is used for the second order multistep method
+    def post_cfg_function(args):
+        nonlocal uncond_denoised
+        uncond_denoised = args["uncond_denoised"]
+        return args["denoised"]
+
+    if cfg_pp:
+        model.need_last_noise_uncond = True
+        unet_patcher: UnetPatcher = model.inner_model.inner_model.forge_objects.unet
+        unet_patcher.model_options["disable_cfg1_optimization"] = True  # not sure if this really works
+        unet_patcher.set_model_sampler_post_cfg_function(post_cfg_function)
+
+    for i in trange(len(sigmas) - 1, disable=disable):
+        denoised = model(x, sigmas[i] * s_in, **extra_args)
+        sigma_down, sigma_up = get_ancestral_step(sigmas[i], sigmas[i + 1], eta=eta)
+        if callback is not None:
+            callback({"x": x, "i": i, "sigma": sigmas[i], "sigma_hat": sigmas[i], "denoised": denoised})
+        if sigma_down == 0 or old_denoised is None:
+            # Euler method
+            if cfg_pp:
+                d = to_d(x, sigmas[i], uncond_denoised)
+                x = denoised + d * sigma_down
+            else:
+                d = to_d(x, sigmas[i], denoised)
+                dt = sigma_down - sigmas[i]
+                x = x + d * dt
+        else:
+            # Second order multistep method in https://arxiv.org/pdf/2308.02157
+            t, t_next, t_prev = t_fn(sigmas[i]), t_fn(sigma_down), t_fn(sigmas[i - 1])
+            h = t_next - t
+            c2 = (t_prev - t) / h
+
+            phi1_val, phi2_val = phi1_fn(-h), phi2_fn(-h)
+            b1 = torch.nan_to_num(phi1_val - phi2_val / c2, nan=0.0)
+            b2 = torch.nan_to_num(phi2_val / c2, nan=0.0)
+
+            if cfg_pp:
+                x = x + (denoised - uncond_denoised)
+                x = sigma_fn(h) * x + h * (b1 * uncond_denoised + b2 * old_denoised)
+            else:
+                x = sigma_fn(h) * x + h * (b1 * denoised + b2 * old_denoised)
+
+        # Noise addition
+        if sigmas[i + 1] > 0:
+            x = x + noise_sampler(sigmas[i], sigmas[i + 1]) * s_noise * sigma_up
+
+        if cfg_pp:
+            old_denoised = uncond_denoised
+        else:
+            old_denoised = denoised
+    return x
+
+
+@sampler_metadata(
+    "Res Multistep",
+    {"scheduler": "sgm_uniform"},
+)
+@torch.no_grad()
+def sample_res_multistep(
+    model, x, sigmas, extra_args=None, callback=None, disable=None, s_noise=1.0, noise_sampler=None
+):
+    return res_multistep(
+        model,
+        x,
+        sigmas,
+        extra_args=extra_args,
+        callback=callback,
+        disable=disable,
+        s_noise=s_noise,
+        noise_sampler=noise_sampler,
+        eta=0.0,
+        cfg_pp=False,
+    )
+
+
+@sampler_metadata(
+    "Res Multistep CFG++",
+    {"scheduler": "sgm_uniform"},
+)
+@torch.no_grad()
+def sample_res_multistep_cfg_pp(
+    model, x, sigmas, extra_args=None, callback=None, disable=None, s_noise=1.0, noise_sampler=None
+):
+    return res_multistep(
+        model,
+        x,
+        sigmas,
+        extra_args=extra_args,
+        callback=callback,
+        disable=disable,
+        s_noise=s_noise,
+        noise_sampler=noise_sampler,
+        eta=0.0,
+        cfg_pp=True,
+    )
+
+
+@sampler_metadata(
+    "Res Multistep Ancestral",
+    {"uses_ensd": True, "scheduler": "sgm_uniform"},
+)
+@torch.no_grad()
+def sample_res_multistep_ancestral(
+    model, x, sigmas, extra_args=None, callback=None, disable=None, eta=1.0, s_noise=1.0, noise_sampler=None
+):
+    return res_multistep(
+        model,
+        x,
+        sigmas,
+        extra_args=extra_args,
+        callback=callback,
+        disable=disable,
+        s_noise=s_noise,
+        noise_sampler=noise_sampler,
+        eta=eta,
+        cfg_pp=False,
+    )
+
+
+@sampler_metadata(
+    "Res Multistep Ancestral CFG++",
+    {"uses_ensd": True, "scheduler": "sgm_uniform"},
+)
+@torch.no_grad()
+def sample_res_multistep_ancestral_cfg_pp(
+    model, x, sigmas, extra_args=None, callback=None, disable=None, eta=1.0, s_noise=1.0, noise_sampler=None
+):
+    return res_multistep(
+        model,
+        x,
+        sigmas,
+        extra_args=extra_args,
+        callback=callback,
+        disable=disable,
+        s_noise=s_noise,
+        noise_sampler=noise_sampler,
+        eta=eta,
+        cfg_pp=True,
+    )

sd-forge-extra-samplers/lib_es/extra_schedulers/__init__.py

@@ -0,0 +1,6 @@
+from lib_es.extra_schedulers.linear_log import linear_log
+
+
+__all_schedulers__ = [
+    linear_log,
+]

sd-forge-extra-samplers/lib_es/extra_schedulers/linear_log.py

@@ -0,0 +1,63 @@
+import torch
+
+from lib_es.utils import scheduler_metadata
+
+
+@scheduler_metadata(name="linear_log", alias="Linear Log", need_inner_model=True)
+def linear_log(
+    n: int,
+    sigma_min: float,
+    sigma_max: float,
+    inner_model,
+    device: torch.device,
+    eta: float = 0.1,
+    nu: float = 2.0,
+    sgm: bool = False,
+    floor=False,
+    final_step_full: bool = True,
+) -> torch.Tensor:
+    """
+    Creates a log-linear (geometric) noise schedule as recommended in the paper.
+
+    Args:
+        n: Number of sampling steps
+        sigma_min: Minimum noise level
+        sigma_max: Maximum noise level
+        eta: Error parameter (default 0.1, as estimated in the paper for CIFAR-10)
+        nu: Accuracy parameter for distance estimates (default 2.0)
+        final_step_full: Whether to take a full step (β=1) for the final iteration
+
+    Returns:
+        A tensor of sigma values in descending order with a geometric progression.
+    """
+
+    # TODO: Add adjustable eta/nu parameters for more flexibility
+
+    # Calculate the maximum allowable beta based on the admissibility criteria
+    # β*,N = c/(η+c) where c = 1 - ν^(-1/N)
+    c = 1 - nu ** (-1 / n)
+    beta_max = c / (eta + c)
+
+    # Calculate the ratio that would give us exactly sigma_min from sigma_max in n steps
+    exact_ratio = (sigma_min / sigma_max) ** (1 / (n - 1))
+
+    # Use the smaller of the two to ensure admissibility
+    ratio = max(1 - beta_max, exact_ratio)
+
+    # Generate the geometric sequence
+    sigs = [sigma_max]
+    for i in range(1, n):
+        next_sigma = sigs[-1] * ratio
+
+        # For the final step, optionally set beta=1 (as recommended in the paper)
+        if final_step_full and i == n - 1:
+            next_sigma = sigma_min
+
+        sigs.append(next_sigma)
+
+    if not sgm:
+        # Add final value of 0.0
+        sigs.append(0.0)
+
+    # Convert to tensor
+    return torch.tensor(sigs)

sd-forge-extra-samplers/lib_es/samplers.py

@@ -0,0 +1,57 @@
+from modules.sd_samplers import all_samplers
+from modules.sd_samplers_common import SamplerData
+from modules.sd_samplers_kdiffusion import KDiffusionSampler
+
+from lib_es.extra_samplers import __sampler_funcs__
+
+
+# See modules_forge/alter_samplers.py for the basis of this class and build_constructor function
+class ExtraSampler(KDiffusionSampler):
+    """
+    Overloads KDiffusionSampler to add extra parameters to the constructor
+    Based off lllyasviel's AlterSampler
+    """
+
+    def __init__(self, sd_model, sampler_name, sampler_func, options=None):
+        self.sampler_name = sampler_name
+        self.unet = sd_model.model.diffusion_model
+        sampler_function = sampler_func
+        super().__init__(sampler_function, sd_model, options)
+        self.extra_params = ["s_churn", "s_tmin", "s_tmax", "s_noise"]
+
+
+def build_constructor(sampler_name, sampler_func):
+    def constructor(m):
+        return ExtraSampler(m, sampler_name, sampler_func)
+
+    return constructor
+
+
+extra_sampler_list = [
+    (
+        fn.sampler_name,
+        fn,
+        fn.sampler_k_names,
+        fn.sampler_extra_params,
+    )
+    for fn in __sampler_funcs__
+]
+
+samplers_data_k_diffusion: list[SamplerData] = [
+    SamplerData(name, build_constructor(sampler_name=name, sampler_func=funcname), aliases, options)
+    for name, funcname, aliases, options in extra_sampler_list
+]
+
+
+def add_extra_samplers():
+    import modules.sd_samplers as sd_samplers
+
+    for sampler in samplers_data_k_diffusion:
+        if sampler.name not in sd_samplers.all_samplers_map:
+            sd_samplers.all_samplers.append(sampler)
+
+    sd_samplers.all_samplers_map = {x.name: x for x in sd_samplers.all_samplers}
+    sd_samplers.set_samplers()
+
+
+

sd-forge-extra-samplers/lib_es/schedulers.py

@@ -0,0 +1,18 @@
+from lib_es.extra_schedulers import __all_schedulers__
+
+import modules.sd_schedulers as sched
+
+
+extra_scheduler_list = [
+    sched.Scheduler(fn.name, fn.alias, fn, need_inner_model=fn.need_inner_model) for fn in __all_schedulers__
+]
+
+
+def add_schedulers():
+    """
+    Add extra schedulers to the list of schedulers in the webui.
+    """
+    for scheduler in extra_scheduler_list:
+        if scheduler.name not in sched.schedulers_map:
+            sched.schedulers.append(scheduler)
+            sched.schedulers_map = {**{x.name: x for x in sched.schedulers}, **{x.label: x for x in sched.schedulers}}