Perlin documentation and cleanups

Add OCSNoise PerlinSimple node

README.md

@@ -6,38 +6,31 @@ Experimental and mathematically unsound (but fun!) sampling for [ComfyUI](https:
 
 Feel free create a question in Discussions for usage help: [OCS Q&A Discussion](https://github.com/blepping/comfyui_overly_complicated_sampling/discussions/categories/q-a)
 
-_Note for Flux users_: Set `cfg1_uncond_optimization: true` in the `model` block for the main `OCS Sampler` as Flux does not use CFG. CFG++ and alt CFG++ features do not work with Flux.
-
 ## Features
 
 * Many different samplers.
 * Allows scheduling samplers (i.e. run `euler` for steps 1-4, then switch to `dpmpp_sde`).
 * CFG++ support (for some samplers).
-* Native support for Restart sigmas. (Restarts do not currently work with RF models like Flux.)
+* Native support for Restart sigmas.
 * Supports custom noise types.
 * Immiscible noise for sampling and Restart. See https://arxiv.org/abs/2406.12303 (note that it was designed for training not inference).
 * Allows splitting/combining steps in various ways for (potentially) more accurate sampling.
 * Supports Diffrax, torchdiffeq, torchode and torchsde solver backends. (SDE mode not recommended currently.)
 * Many tuneable parameters to play with.
-* Supports ancestral sampling (in a janky way) for rectified flow models like Flux, works for most basic samplers: does not work for SDE samplers currently.
-* Built in safe expression language that allows filtering and manipulating nearly all parameters during sampling.
+
 
 ## Credits
 
-I can move code around but sampling math and creating samplers generally beyond my ability. I didn't write any of the original samplers:
+I can move code around but sampling math and creating samplers is far beyond my ability. I didn't write any of the original samplers:
 
-* Euler, Heun++2, DPMPP SDE, DPMPP 2S, gradient estimation, RES multistep, DPM++ 2m, 2m SDE and 3m SDE samplers based on ComfyUI's implementation.
-* Reversible Heun, Reversible Heun 1s, RES, Trapezoidal, Bogacki, Reversible Bogacki, RK4, RKF45, dynamic RK(4), SENS and Euler Dancing samplers based on implementation from [https://github.com/Clybius/ComfyUI-Extra-Samplers](https://github.com/Clybius/ComfyUI-Extra-Samplers).
+* Euler, Heun++2, DPMPP SDE, DPMPP 2S, DPM++ 2m, 2m SDE and 3m SDE samplers based on ComfyUI's implementation.
+* Reversible Heun, Reversible Heun 1s, RES, Trapezoidal, Bogacki, Reversible Bogacki, RK4 and Euler Dancing samplers based on implementation from https://github.com/Clybius/ComfyUI-Extra-Samplers
 * TTM JVP sampler based on implementation written by Katherine Crowson (but yoinked from the Extra-Samplers repo mentioned above).
-* Distance sampler based on implementation from https://github.com/Extraltodeus/DistanceSampler
 * IPNDM, IPNDM_V and DEIS adapted from https://github.com/zju-pi/diff-sampler/blob/main/diff-solvers-main/solvers.py (I used the Comfy version as a reference).
-* PingPong sampler idea from https://github.com/ace-step/ACE-Step/ (implementation also referenced from that source).
 * Normal substep merge strategy based on implementation from https://github.com/Clybius/ComfyUI-Extra-Samplers
-* Immiscible noise processing based on implementation from https://github.com/kohya-ss/sd-scripts/pull/1395 and https://github.com/yhli123/Immiscible-Diffusion - idea for sampling with it and implementation help from https://github.com/Clybius
+* Immiscible noise processing based on implementation from https://github.com/kohya-ss/sd-scripts/pull/1395 and idea for sampling with it from https://github.com/Clybius
 * Precedence climbing (Pratt) expression parser based on implementation from https://github.com/andychu/pratt-parsing-demo
 
-Please notify me if I somehow missed appropriately crediting any code used here, any such ommissions are unintentional.
-
 This repo wouldn't be possible without building on the work of others. Thanks!
 
 ## Usage
@@ -98,9 +91,6 @@ reta: 1.0
 
 # Parameters related to restart sampling.
 restart:
-    # When enabled, out of order sigmas will be detected as restart.
-    # You can disable this if you want to use OCS for something like unsampling.
-    enabled: true
     # Scales the noise added by restart sampling.
     s_noise: 1.0
     # Immiscible block same as described below.
@@ -110,17 +100,13 @@ restart:
 
 # The noise block allows defining global noise sampling parameters.
 noise:
-    # You can disable this to allow GPU noise generation.
+    # You can disable this to allow GPU noise generation. I believe it only makes a difference for Brownian.
     cpu_noise: true
 
     # ComfyUI has a bug where if you disable add_noise in the sampler, no seed gets set. If you
     # are manually noising a sample and have add_noise turned off then you should enable this if
     # you want reproducible generations.
-    set_seed: true
-
-    # Only has an effect when set_seed is enabled. Will advance the RNG this many times to
-    # avoid the common mistake of using the same noise for sampling as the initial noise.
-    seed_offset: 1
+    set_seed: false
 
     # Global scale scale for generated noise
     scale: 1.0
@@ -137,10 +123,10 @@ noise:
 
     # When caching, the batch size for chunks of noise to generate in advance. Generating a batch of noise
     # can be more efficient than generating on demand when using a high number of substeps (>10) per step.
-    batch_size: 1
+    batch_size: 32
 
     # Whether to cache noise.
-    caching: true
+    caching: false
 
     # Interval (in full steps) to reset the cache. Brownian noise takes time into account so
     # if using Brownian with caching enabled you will generally want to reset each step.
@@ -187,31 +173,30 @@ noise:
         # See: https://docs.scipy.org/doc/scipy/reference/generated/scipy.optimize.linear_sum_assignment.html#scipy.optimize.linear_sum_assignment
         maximize: false
 
-        # Can be set to enable immiscible v2 mode. 0.0 is disabled, 0.1 is a reasonable value.
-        distance_scale: 0.0
-
-        # If null will use the same value as distance_scale.
-        distance_scale_ref: null
-
     filter: null
 
 
+# Model calls can be cached. This is very experimental: I don't recommend using it
+# unless you know what you're doing.
 model:
-    # When enabled, skips generating uncond when you have CFG set to 1. Disabled by
-    # default as stuff like CFG++ won't work without uncond. Useful to enable for
-    # models like Flux that don't actually use CFG.
-    cfg1_uncond_optimization: false
+    cache:
+        # The cache size.
+        size: 0
+
+        # Threshold for model call caching. For example if you have size=3 and threshold=1
+        # then model calls 1 through 3 will be cached, but model call 0 will not be (the first one).
+        # Additional explanation: Some samplers call the model multiple times per step. For example,
+        # Bogacki uses three model calls: 0, 1, 2
+        threshold: 1
+
+        # Maximum use count for cache items.
+        max_use: 1000000
 
     filter:
-        # Input to the model
         input: null
-        # Result after CFG calculation
         denoised: null
-        # Result after CFG calculation for JVP
         jdenoised: null
-        # Cond - positive prompt
         cond: null
-        # Uncond - negative prompt
         uncond: null
 ```
 
@@ -239,25 +224,15 @@ When running multiple substeps per step, the results will combined based on the
 
 * `simple`: Doesn't merge anything: only runs a single substep per step.
 * `divide`: Creates a linear schedule between the current sigma and the next and runs the substeps in sequence. The model is called at least once per substep.
-* `supreme_avg`: The model is called at least once per step (and possibly additional times for higher order samplers). Each substep shares the first model call result. The results are averaged together. *Note*: Since the first model call is shared and the initial input is the same for each substep, there is no point in running multiple identical substeps. Also note: This merge strategy doesn't work well with non-ancestral samplers (i.e. dpmpp_2m or any sampler with `eta: 0`).
+* `normal`: The model is called at least once per step (and possibly additional times for higher order samplers). Each substep shares the first model call result. The results are averaged together. *Note*: Since the first model call is shared and the initial input is the same for each substep, there is no point in running multiple identical substeps. Also note: This merge strategy doesn't work well with non-ancestral samplers (i.e. dpmpp_2m or any sampler with `eta: 0`).
 * `overshoot`: The model is called at least once per step. It will sample steps equal to the number of substeps, starting from the current step. Then it will restart back to the expected step.
-* `lookahead`: Similar to `overshoot`, it samples ahead based on the number of substeps. The last model prediction is used to do a Euler step to the expected step. *Note:* Very experimental, likely to change in the future.
-* `pingpong`: Works similar to `overshoot` and `lookahead` methods except it does a pingpong sampler style step to the expected sigma.
-* `dynamic`: Allows specifying the group parameters as an expression to be evaluated. See below.
+<!--
+* `average`: The model is called once at the beginning of the step and substeps share that result (but it may be called additional times for higher order samplers). This means substeps for samplers like reversible Euler, Heun 1s, DPM++ 2m SDE are essentially free. May be theoretically very unsound and inaccurate, requires manual tweaking of settings like `s_noise`. Supports the parameter `avgmerge_stretch`(`0.4`) which basically rolls back the current sigma and adds some noise (otherwise running a substep is deterministic and there would be no point to running a sampler like Euler more than once).
+* `sample`: Like `average` (and uses `avgmerge_stretch`) but instead of simply using the average, it does a sampler step toward that instead. You can plug in any substep sampler to the `merge_sampler_opt` input (if unconnected and the merge method is `sample` then Euler will be used). *Note*: Substeps in the attached sampler will be ignored.
+* `sample_uncached`: Similar to `sample`, however it calls the model per substep instead of caching the result and sharing it. Aside from sampling toward the result, it works more like the `normal` merge strategy. Theoretically it should be better because it's taking less shortcuts but results seem worse.
 
-**Dynamic Groups**: When `merge_method` is set to `dynamic` you must specify a `dynamic` block in the text parameters. The dynamic block may be either a string with the expression or a list of objects with an (optional) `when` key and a (required) `expression` key. The expression should return a dictionary of parameters you can set in the node (including both keys/values from the text parameters and widgets in the node). The first matching item will be used. Example:
-
-```yaml
-# Use the simple merge method when step < 3, otherwise use overshoot
-dynamic:
-    - when: step < 3
-      expression: dict(merge_method :> 'simple)
-    - expression: dict(merge_method :> 'overshoot)
-
-# You could also write it like this:
-dynamic: |
-    dict(merge_method :> (step < 3 ? 'simple : 'overshoot)
-```
+When using `average` and `sample` merge strategies and with model call caching enabled you can get away with setting substeps super high. Running something like 100 substeps is actually quite practical and seems to work well.
+-->
 
 #### Node Parameters
 
@@ -280,7 +255,6 @@ The left side group matches steps 0, 1, 2. The right side group matches all step
 -->
 
 * `restart_custom_noise`: Currently only used by the `overshoot` merge method.
-* `custom_noise`: Currently used by the `lookahead` merge method.
 
 #### Text Parameters
 
@@ -304,8 +278,6 @@ reta: 1.0
 #   cond: Shows the model cond prediction (basically the positive prompt).
 #   uncond: Shows the model uncond prediction (basically the negative prompt).
 #   raw: Shows the raw noisy latent input.
-#   noisy: 10% of the noise + denoised.
-#   diff: Multiplies the difference between cond and uncond.
 preview_mode: denoised
 
 # Expression.
@@ -323,26 +295,6 @@ restart:
     immiscible:
         size: 0
 
-# Only used by the lookahead merge method currently.
-lookahead:
-    # Works like normal samplers, essentially. Disabled by default.
-    eta: 0.0
-
-    # Scales the noise added by lookahead sampling.
-    s_noise: 1.0
-
-    # Controls how much noise to remove in the prediction phase. Higher values will remove more noise.
-    dt_factor: 1.0
-
-    # Immiscible block same as described above.
-    immiscible:
-        size: 0
-
-# Only used by the pingpong merge method.
-pingpong:
-    # Scales the noise added by lookahead sampling.
-    s_noise: 1.0
-
 pre_filter: null
 
 post_filter: null
@@ -358,44 +310,33 @@ post_filter: null
 
 In alphabetical order.
 
-* `adapter`: Wraps a normal ComfyUI `SAMPLER`. Attach a `SAMPLER` parameter to the node. Note: Samplers that do unusual stuff like try to manipulate the model won't work. ComfyUI's built-in CFG++ samplers in particular do not work here.
-* `blep_bas`: Batch Augmented Sampler. My own dumb experiment that expands the batch and averages the result. May be very slow/require a lot of VRAM. See parameters: `bas`
-* `blep_euler_cycle`: See parameters: `cycle_pct`.
-* `blep_weoon`: Wavelet-based second order sampler. Another dumb experiment. See parameters: `weoon`
-* `bogacki`: Bogacki-Shampine sampler. Also has a reversible variant.
-* `clybius_euler_dancing`: Pretty broken currently, will probably require increased `s_noise` values. See parameters: `deta`, `leap`, `deta_mode`.
-* `clybius_sens`: Reversible dpmpp_3m_sde variant. Supports a separate set of reversible parameters in `tsde_reversible`.
-* `deis`: See parameters: `history_limit`. Does not work well with ETA, I don't recommending leaving ETA at the default 1.
-* `dpm2`: Set `eta: 0` for non-ancestral variant.
-* `dpmpp_2m_sde`: Also supports reversible parameters. See parameters: `history_limit`.
-* `dpmpp_2m`: `eta` and `s_noise` parameters are ignored. See parameters: `history_limit`.
+* `adapter`: Wraps a normal ComfyUI `SAMPLER`. (Attach a `SAMPLER` parameter to the node.)
+* `bogacki`:
+* `deis`: See parameters: `history_limit`
+* `dpmpp_2m_sde`: See parameters: `history_limit`
+* `dpmpp_2m`: `eta` and `s_noise` parameters are ignored. See parameters: `history_limit`
 * `dpmpp_2s`
-* `dpmpp_3m_sde`: See parameters: `history_limit`.
+* `dpmpp_3m_sde`: See parameters: `history_limit`
 * `dpmpp_sde`
-* `dynamic`: Advanced step method that allows using an expression to determine the sampler parameters at each substep. See below for a more detailed explanation.
-* `euler`: If samplers came in vanilla.
-* `extraltodeus_distance`: Adaptive-ish/configurable step variant of Heun. Referenced from [https://github.com/Extraltodeus/DistanceSampler](https://github.com/Extraltodeus/DistanceSampler). See parameters: `distance`.
-* `gradient_estimation`
+* `euler_cycle`: See parameters: `cycle_pct`
+* `euler_dancing`: Pretty broken currently, will probably require increased `s_noise` values. See parameters: `deta`, `leap`, `deta_mode`
+* `euler`:
+* `heun`: Alternate Heun implementation. Supports reversible parameters. See parameters: `history_limit`
 * `heun_1s`: Alternate Heun one step implementation. Supports reversible parameters.
-* `heun`: Alternate Heun implementation. Supports reversible parameters. See parameters: `history_limit`.
-* `heunpp`: See parameters: `max_order`.
-* `ipndm_v`: See parameters: `history_limit`.
-* `ipndm`: See parameters: `history_limit`.
-* `pingpong`
-* `res`: Refined Exponential Solver. I believe this is a variant of Heun. Generally works very well.
-* `res_multistep`
-* `reversible_bogacki`: Reversible variant of Bockacki-Shampine.
-* `reversible_heun_1s`: Reversible variant of Heun 1 step. See parameters: `history_limit`.
-* `reversible_heun`: Reversible variant of Heun.
-* `rk_dynamic`: Variant of RK4 that lets you set `max_order` (you can also set it to `0` to choose an order dynamically, doesn't seem to work so well though).
-* `rk4`: Runge-Kutta 4th order sampler.
-* `rkf45`: 5 model call flavor of RK.
+* `heunpp`: See parameters: `max_order`
+* `ipndm_v`: See parameters: `history_limit`
+* `ipndm`: See parameters: `history_limit`
+* `res`
+* `reversible_bogacki`:
+* `reversible_heun`:
+* `reversible_heun_1s`: See parameters: `history_limit`
+* `rk4`:
 * `solver_diffrax`: Uses the [Diffrax](https://github.com/patrick-kidger/diffrax) solver backend. See `de_*` parameters below.
 * `solver_torchdiffeq`: Uses the [torchdiffeq](https://github.com/rtqichen/torchdiffeq) backend. See `de_*` parameters below.
 * `solver_torchode`: Uses the [torchode]((https://github.com/martenlienen/torchode)) backend. See `de_*` parameters below.
 * `solver_torchsde`: Uses the [torchsde](https://github.com/google-research/torchsde) backend. See `de_*` parameters below.
-* `trapezoidal_cycle`: See parameters: `cycle_pct`.
 * `trapezoidal`:
+* `trapezoidal_cycle`: See parameters: `cycle_pct`
 * `ttm_jvp`: TTM is a weird sampler. If you're using model caching you must make sure the entries TTM uses are populated first (by having it run before any other samplers that call the model multiple times). It may also not work with some other model patches and upscale methods. See parameters: `alternate_phi_2_calc`
 
 **Sampler Feature Support**
@@ -403,46 +344,36 @@ In alphabetical order.
 |Name|Cost|History|Order|Reversible|CFG++|
 |-|-|-|-|-|-|
 |`adapter`|?|?|?|?|?|
-|`blep_bas`|variable|||||
-|`blep_euler_cycle`|1||||X|
-|`blep_trapezoidal_cycle`|2|||||
-|`blep_weoon`|2|||||
 |`bogacki`|2|||||
-|`clybius_euler_dancing`|1|||||
-|`clybius_sens`|1|1||||
 |`deis`|1|1-3 (1)||||
 |`dpmpp_2m_sde`|1|1||||
 |`dpmpp_2m`|1|1||||
 |`dpmpp_2s`|2|||||
 |`dpmpp_3m_sde`|1|1-2 (2)||||
 |`dpmpp_sde`|2|||||
-|`dynamic`|?|?|?|?|?|
+|`euler_cycle`|1||||X|
+|`euler_dancing`|1|||||
 |`euler`|1||||X|
-|`extraltodeus_distance`|variable|||||
-|`gradient_estimation`|1|1||||
-|`heun_1s`|1|1||X||
 |`heun`|2|||X||
+|`heun_1s`|1|1||X||
 |`heunpp`|1-3||X|||
 |`ipndm_v`|1|1-3 (1)||||
 |`ipndm`|1|1-3 (1)||||
-|`pingpong`|1|||||
 |`res`|2|||||
-|`res_multistep`|1|1||||
 |`reversible_bogacki`|2|||X||
-|`reversible_heun_1s`|1|1||X||
 |`reversible_heun`|2|||X||
-|`rk4`|1-4|||||
+|`reversible_heun_1s`|1|1||X||
 |`rk4`|4|||||
-|`rkf45`|5|||||
 |`solver_diffrax`|variable|||||
 |`solver_torchdiffeq`|variable|||||
 |`solver_torchode`|variable|||||
 |`solver_torchsde`|variable|||||
 |`trapezoidal`|2|||||
+|`trapezoidal_cycle`|2|||||
 |`ttm_jvp`|2|||||
 
 
-`deis`, `ipndm*` and `gradient_estimation` do not seem to work well with ancestralness, I recommend `eta: 0.25` or disable it completely.
+`deis`, `ipndm*` do not seem to work well with ancestralness, I recommend `eta: 0.25` or disable it completely.
 
 **Solver Backend Samplers**:
 
@@ -465,23 +396,6 @@ When doing ancestral sampling, we actually _overshoot_ expected noise for the ne
 
 The difference with cycle is that instead of adding `noise * expected_noise_at_next_step`, we instead first add `noise * (expected_noise_at_next_step * (1.0 - cycle_pct))` and then we generate noise and scale it to `cycle_pct` and add it too. Just for example, suppose `cycle_pct` is `0.2`: we'll add 80% of the expected noise at the next step (`1.0 - 0.2 == 0.8`) and then generate the remaining 20% and add it in to meet the expected amount. I don't recommend setting `cycle_pct` to values over `0.5`, especially if using "weird" noise types.
 
-**Dynamic Step Method**: When `step_method` is set to `dynamic` you must specify a `dynamic` block in the text parameters. The dynamic block may be either a string with the expression or a list of objects with an (optional) `when` key and a (required) `expression` key. The first matching item will be used. The expression should return a dictionary of parameters you can set in the node (including both keys/values from the text parameters and widgets in the node). Example:
-
-```yaml
-# Use the rk4 merge method when step < 3, otherwise use euler
-dynamic:
-    - when: step < 3
-      expression: dict(step_method :> 'rk4)
-    - expression: dict(step_method :> 'euler)
-
-# You could also write it like this:
-dynamic: |
-    dict(step_method :> (step < 3 ? 'rk4 : 'euler)
-```
-
-*Note*: You can set all sampler parameters except `substeps` this way. Sampling will use the `substeps` value from the `OCS Substeps` node.
-
-
 #### Node Parameters
 
 * `substeps`(`1`): Number of substeps. Generally involves a model call per substep, so for example setting this to 4 would approximately quadruple sampling time.
@@ -504,13 +418,6 @@ s_noise: 1.0
 
 # ETA (basically ancestralness).
 eta: 1.0
-
-# If the ETA calculation fails, it will retry with eta - eta_retry_increment until it either
-# succeeds or eta becomes <= 0 (in which case ancestralness just gets disabled).
-# In other words, you can set ETA as high as you want, set eta_retry_increment to something like 0.1 and
-# it will just do whatever it takes to find an ETA that works.
-eta_retry_increment: 0
-
 # No effect unless both start and end are set. Will scale the eta value based on the
 # percentage of sampling. In other words, eta*dyn_eta_start at the beginning,
 # eta*dyn_eta_end at the end.
@@ -528,25 +435,15 @@ cfgpp: false
 
 ### Reversible Settings ###
 
-reversible:
-  # 0-indexed step where reversible sampling will start.
-  start_step: 0
-  # 0-indexed last step where reversible sampling will be used.
-  end_step: 9999
-  # Scale of the reversible correction. Can also be set to a negative value.
-  scale: 1.0
-  # Reversible ETA.
-  eta: 1.0
-
-  # No effect unless both start and end are set. Will scale the eta value based on the
-  # percentage of sampling. In other words, reta*dyn_reta_start at the beginning,
-  # reta*dyn_reta_end at the end.
-  dyn_eta_start: null
-  dyn_eta_end: null
-
-  eta_retry_increment: 0.0
-  # Might not do anything currently.
-  use_cfgpp: false
+# Reversible ETA (used for reversible samplers).
+reta: 1.0
+# Scale of the reversible correction. Can also be set to a negative value.
+reversible_scale: 1.0
+# No effect unless both start and end are set. Will scale the reta value based on the
+# percentage of sampling. In other words, reta*dyn_reta_start at the beginning,
+# reta*dyn_reta_end at the end.
+dyn_reta_start: null
+dyn_reta_end: null
 
 pre_filter: null
 
@@ -629,99 +526,6 @@ diffrax_g_time_scaling: false
 # i.e. if you'd get 1,2,3,4 as g values for the step, with this it would be 1,2,-3,-4.
 diffrax_g_split_time_mode: false
 
-# blep_bas sampler-specific parameters
-bas:
-  # Batch expansion factor. Whatever your original batch size was will be multiplied
-  # by this. If it's 0 then you just get normal Euler.
-  batch_multiplier: 2
-  # First 0-indexed step when BAS sampling will apply.
-  start_step: 0
-  # Last 0-index step when BAS sampling will apply.
-  end_step: 3
-
-  s_noise: 1.0
-  eta: 0.0
-  eta_retry_increment: 0.0
-
-  # List of weights for the denoised batches, with 0 being the original denoised.
-  # If the list is smaller than the batch size, the list will be padded with the
-  # last item.
-  # If set to null it will be calculated automatically.
-  # Example: [0.5, 1.0]
-  # Will use weight 0.5 for the original denoised and 1.0 for any other items.
-  denoised_factors: null
-
-  # If set to something other than 0 the supplied denoised_factors will be rebalanced
-  # to add up to this number.
-  denoised_factors_scale: 1.0
-
-  # Global multiplier on denoised for BAS steps.
-  denoised_multiplier: 1.0
-
-  # One of: restart, restart_noneta, simple
-  renoise_mode: restart
-
-  # Multiplier on the start sigma for BAS steps.
-  # Note that taking the multipliers into account sigma_next must be less than sigma.
-  fromstep_factor: 1.0
-
-  # Multiplier on the end sigma for BAS steps.
-  tostep_factor: 1.0
-
-  # Source for the downstep. Can be one of dt, sigma or sigma_next.
-  # dt means you get bsigma + (sigma_next - bsigma) * tostep_factor
-  # where bsigma = sigma * fromstep_factor
-  tostep_source: dt
-
-# blep_weoon sampler-specific options.
-# Parameters with "inv" in the name apply to the inverse wavelet operation.
-# When set to null, they will use the normal setting.
-weoon:
-  start_step: 0
-  end_step: 9999
-  eta: 0.0
-  eta_retry_increment: 0.0
-  s_noise: 1.0
-  # One of dwt, dwt1d, dtcwt
-  wavelet_mode: dwt
-  # Padding scheme used for wavelets
-  padding: periodization
-  # Padding scheme used for the inverse wavelet operation
-  inv_padding: null
-  # Wavelet type. Does not apply if wavelet_mode is dtcwt.
-  wave: db4
-  # Wavelet type used for the inverse wavelet operation. Does not apply if wavelet_mode is dtcwt.
-  inv_wave: null
-  # dtcwt qshift parameter. Only applies if the wavelet mode is dtcwt.
-  dtcwt_qshift: qshift_a
-  # dtcwt biort parameter. Only applies if the wavelet mode is dtcwt.
-  dtcwt_biort: near_sym_a
-  # dtcwt qshift parameter used for the inverse wavelet operation. Only applies if the wavelet mode is dtcwt.
-  dtcwt_inv_qshift: null
-  # dtcwt biort parameter used for the inverse wavelet operation. Only applies if the wavelet mode is dtcwt.
-  dtcwt_inv_biort: null
-  # Can be used to stretch the step down. I.E. 1.0 would be sigma -> sigma_next
-  # while 2.0 would be twice the distance between sigma and sigma_next.
-  downstep_scale: 1.0
-  # Blend scale for the downstep denoised lowpass wavelets
-  yl_strength: 1.0
-  # Blend scale for the downstep denoised highpass wavelets
-  yh_strength: 0.5
-  # Mode used for blending wavelets.
-  wavelet_blend_mode: lerp
-  # Blend mode for wavelet highpass, uses wavelet_blend_mode if null.
-  wavelet_blend_mode_yh: null
-  # Extra multipliers that can be applied to the low/highpass wavelets for the normal
-  # denoised or downstep denoised.
-  denoised_yl_multiplier: 1.0
-  denoised_yh_multiplier: 1.0
-  denoised_down_yl_multiplier: 1.0
-  denoised_down_yh_multiplier: 1.0
-  # Only applies when wavelet_mode is dwt1d. Can be:
-  #   2: Flatten starting at spatial dimensions
-  #   1: Flatten starting at channels dimension
-  #   0: Smash everything together!
-  flatten_start_dim: 2
 
 ### Other Sampler Specific Parameters ###
 
@@ -734,13 +538,10 @@ weoon:
 #   ipndm: 1 (max 3)
 #   ipndm_v: 1 (max 3)
 #   deis: 1 (max 3)
-#   clybius_sens: 2
 history_limit: 999 # Varies based on sampler.
 
 # Used for some samplers with variable order. List of samplers and default value below:
 #   heunpp2: 3
-#   rk_dynamic: 4 - Can also be set to 0 to try to dynamically adjust the order based on calculated
-#                   error from the last step (but that may not work well).
 max_order: 999 # Varies based on sampler.
 
 # Used for dpmpp_2m. One of midpoint, heun
@@ -769,50 +570,6 @@ dyn_deta_mode: "lerp"
 
 </details>
 
-### `OCS Param` and `OCS MultiParam`
-
-Allows specifying parameter inputs that can't be expressed with YAML, such as custom noise.
-
-#### Node Parameters
-
-* `key`: Determines the parameter input type.
-
-#### Input Parameters
-
-* `value`: Input for the actual parameter - must match the specified input type or you will get an error when you evaluate the workflow.
-* `params_opt`: Allows connecting another `OCS Param` or `OCS MultiParam` node to specify multiple parameters.
-
-#### Text Parameters
-
-Allows specifying extra parameters. You may use this block to rename a key, for example if your key type was `custom_noise` you could enter:
-
-```yaml
-rename: test
-```
-
-in the `OCS Param` node and:
-
-```yaml
-custom_noise: test
-```
-
-in the node that was connected to the params to have it use the custom noise specifically named `test`.
-
-### `OCS MultiParam`
-
-MultiParam is the same as Param except it has multiple optional inputs like `key_1`, `key_2`, etc.
-
-#### Text Parameters
-
-Same as `OCS Param` (see above), however if set you should use an object with a key corresponding to the index of the param. For example, if you wanted to rename `key_1` and `key_2` you would do something like:
-
-```yaml
-1:
-    rename: test1
-2:
-    rename: test2
-```
-
 ### `OCS SimpleRestartSchedule`
 
 Generates a restart schedule.
@@ -877,6 +634,7 @@ For more tuneable parameters, see the `OCSNoise PerlinAdvanced` node.
 * `res_height`: Number of periods of noise to generate along an axis. Comma-separated list, multiple items will apply to octaves in sequence.
 * `break_pattern`: Applies a function to break the Perlin pattern, making it more like normal noise. The value is the blend strength, where 1.0 indicates 100% pattern broken noise and 0.5 indicates 50% raw noise and 50% pattern broken noise. Generally should be at least 0.9 unless you want to generate colorful blobs.
 
+
 ***
 
 ### `OCSNoise PerlinAdvanced`

__init__.py

@@ -9,7 +9,5 @@ NODE_CLASS_MAPPINGS = {
     "OCS MultiParam": nodes.MultiParamNode,
     "OCS ModelSetMaxSigma": nodes.ModelSetMaxSigmaNode,
     "OCS SimpleRestartSchedule": nodes.SimpleRestartSchedule,
-    "OCS ApplyFilterLatent": nodes.ApplyFilterLatent,
-    "OCS ApplyFilterImage": nodes.ApplyFilterImage,
 } | custom_noise.NODE_CLASS_MAPPINGS
 __all__ = ["NODE_CLASS_MAPPINGS"]

docs/expression.md

@@ -150,9 +150,7 @@ Available in model filters, with the exception of the `input` filter.
 
 **Note on `unsafe_tensor_method` and `unsafe_torch`**: These functions are disabled by default. If the environment variable `COMFYUI_OCS_ALLOW_UNSAFE_EXPRESSIONS` is set to anything then you can use `unsafe_tensor_method` with a whitelisted set of methods (best effort to avoid anything actually unsafe). If the environment variable `COMFYUI_OCS_ALLOW_ALL_UNSAFE` is set to anything then `unsafe_torch` is enabled and `unsafe_tensor_method` will allow calling any method. ***WARNING***: Allowing _all_ unsafe with workflows you don't trust is _not_ recommended and a malicious workflow will likely have access to anything ComfyUI can access. It is effectively the same as letting the workflow run an arbitrary script on your system.
 
-Documentation TBD (check the source if you want to use them now): `t_copysign`, `t_gaussianblur2d`, `t_rgb_latent`, `t_snf_guidance`
-
-## Image Expression Functions
+## Tensor Expression Functions
 
 `IMG` used here to donate the type for functions that take an image. This may actually be an image batch rather than a single image.
 

py/custom_noise/__init__.py

@@ -1,11 +1,8 @@
+from . import noise_perlin
 from . import nodes
 
 NODE_CLASS_MAPPINGS = {
-    "OCSNoise PerlinSimple": nodes.PerlinSimpleNode,
-    "OCSNoise PerlinAdvanced": nodes.PerlinAdvancedNode,
-    "OCSNoise ImmiscibleReference": nodes.ImmiscibleReferenceNoiseNode,
+    "OCSNoise PerlinSimple": noise_perlin.PerlinSimpleNode,
+    "OCSNoise PerlinAdvanced": noise_perlin.PerlinAdvancedNode,
     "OCSNoise to SONAR_CUSTOM_NOISE": nodes.ToSonarNode,
-    "OCSNoise Conditioning": nodes.NoiseConditioningNode,
-    "OCSNoise OverrideSamplerNoise": nodes.SamplerNodeConfigOverride,
-    "OCSNoise ExpressionFilteredNoise": nodes.ExpressionFilteredNoiseNode,
 }

py/custom_noise/base.py

@@ -3,9 +3,7 @@ import torch
 
 from typing import Callable, Any
 
-from ..external import IntegratedNode
 from ..noise import scale_noise
-from ..nodes import WILDCARD_NOISE, NOISE_INPUT_TYPES_HINT
 
 
 class CustomNoiseItemBase(abc.ABC):
@@ -107,7 +105,7 @@ class CustomNoiseChain:
         return noise_sampler
 
 
-class CustomNoiseNodeBase(metaclass=IntegratedNode):
+class CustomNoiseNodeBase(abc.ABC):
     DESCRIPTION = "An Overly Complicated Sampling custom noise item."
     RETURN_TYPES = ("OCS_NOISE",)
     OUTPUT_TOOLTIPS = ("A custom noise chain.",)
@@ -153,9 +151,9 @@ class CustomNoiseNodeBase(metaclass=IntegratedNode):
         if include_chain:
             result["optional"] |= {
                 "ocs_noise_opt": (
-                    WILDCARD_NOISE,
+                    "OCS_NOISE",
                     {
-                        "tooltip": f"Optional input for more custom noise items.\n{NOISE_INPUT_TYPES_HINT}",
+                        "tooltip": "Optional input for more custom noise items.",
                     },
                 ),
             }

py/custom_noise/noise_immiscibleref.py

@@ -1,156 +0,0 @@
-import torch
-
-from ..noise import scale_noise, ImmiscibleNoise
-from .base import CustomNoiseItemBase
-
-
-class ImmiscibleReferenceItem(CustomNoiseItemBase):
-    def __init__(
-        self,
-        factor,
-        *,
-        size: int,
-        batching: str,
-        normalize_ref_scale: float,
-        normalize_noise_scale: float,
-        maximize: bool,
-        distance_scale: float,
-        distance_scale_ref: float,
-        blend: float,
-        noise,
-        blend_function,
-        normalize=None,
-        reference=None,
-        custom_noise_blend=None,
-        custom_noise_ref=None,
-    ):
-        if reference is None and custom_noise_ref is None:
-            raise ValueError(
-                "Either the reference latent or custom_noise_ref need to be supplied."
-            )
-        if reference is not None and custom_noise_ref is not None:
-            raise ValueError(
-                "One of reference latent or custom_noise_ref need to be supplied, but not both."
-            )
-        super().__init__(
-            factor,
-            size=size,
-            batching=batching,
-            normalize_ref_scale=normalize_ref_scale,
-            normalize_noise_scale=normalize_noise_scale,
-            maximize=maximize,
-            distance_scale=distance_scale,
-            distance_scale_ref=distance_scale_ref,
-            blend=blend,
-            blend_function=blend_function,
-            noise=noise,
-            reference=reference,
-            normalize=normalize,
-            custom_noise_ref=custom_noise_ref,
-            custom_noise_blend=custom_noise_blend,
-        )
-
-    def clone_key(self, k):
-        if k == "noise":
-            return self.noise.clone()
-        if k == "reference" and self.reference is not None:
-            return self.reference.clone()
-        if k == "custom_noise_ref" and self.custom_noise_ref is not None:
-            return self.custom_noise_ref.clone()
-        if k == "custom_noise_blend" and self.custom_noise_blend is not None:
-            return self.custom_noise_blend.clone()
-        return super().clone_key(k)
-
-    def make_noise_sampler(self, x, *args, normalized=True, **kwargs):
-        factor = self.factor
-        norm_noise_scale = self.normalize_noise_scale
-        normalize = self.get_normalize("normalize", normalized)
-        norm_ref = self.normalize_ref_scale
-
-        ns = self.noise.make_noise_sampler(x, *args, normalized=False, **kwargs)
-        batching = self.batching
-        if "_" in batching:
-            batchings = batching.split("_")
-            batching = batchings[0]
-            dual_mode = True
-        else:
-            dual_mode = False
-        immiscible = ImmiscibleNoise(
-            size=self.size,
-            batching=batching,
-            distance_scale=self.distance_scale,
-            distance_scale_ref=self.distance_scale_ref,
-            maximize=self.maximize,
-        )
-        if dual_mode:
-            immiscible2 = immiscible = ImmiscibleNoise(
-                size=self.size,
-                batching=batchings[-1],
-                distance_scale=self.distance_scale,
-                distance_scale_ref=self.distance_scale_ref,
-                maximize=self.maximize,
-            )
-        if self.reference is not None:
-            ns_ref = None
-            ref_latent = self.reference.detach().clone().to(x)
-            if norm_ref:
-                ref_latent = scale_noise(ref_latent, norm_ref, normalized=True)
-        else:
-            ref_latent = None
-            ns_ref = self.custom_noise_ref.make_noise_sampler(
-                x, *args, normalized=False, **kwargs
-            )
-        blend = self.blend
-        blend_function = self.blend_function
-        blending = self.blend != 1.0
-        if self.custom_noise_blend is not None and blending:
-            ns_blend = self.custom_noise_blend.make_noise_sampler(
-                x, *args, normalized=False, **kwargs
-            )
-        else:
-            ns_blend = None
-        repeat_count = max(1, self.size) + int(blending)
-        batch_size = x.shape[0]
-        blend_in_batch = blending and ns_blend is None
-
-        def noise_sampler(s, sn, *args, **kwargs):
-            if ns_ref is not None:
-                ref_latent = ns_ref(s, sn)
-                if norm_ref:
-                    ref_latent = scale_noise(ref_latent, norm_ref, normalized=True)
-
-            noise_batch = torch.cat(tuple(ns(s, sn) for _ in range(repeat_count)))
-            nb_input = scale_noise(
-                noise_batch[batch_size * int(blend_in_batch) :],
-                1.0 if norm_noise_scale == 0 else norm_noise_scale,
-                normalized=norm_noise_scale != 0,
-            )
-            immiscible_noise = immiscible.unbatch(
-                immiscible.immiscible(
-                    immiscible.batch(nb_input),
-                    immiscible.batch(ref_latent),
-                ),
-                ref_latent.shape,
-            )
-            if dual_mode:
-                immiscible_noise = (
-                    immiscible2.unbatch(
-                        immiscible2.immiscible(
-                            immiscible2.batch(nb_input),
-                            immiscible2.batch(ref_latent),
-                        ),
-                        ref_latent.shape,
-                    )
-                    .add_(immiscible_noise)
-                    .mul_(0.5)
-                )
-            immiscible_noise = scale_noise(immiscible_noise, normalized=normalize)
-            if blend != 1:
-                immiscible_noise = blend_function(
-                    noise_batch[:batch_size] if ns_blend is None else ns_blend(s, sn),
-                    immiscible_noise,
-                    blend,
-                )
-            return scale_noise(immiscible_noise, factor, normalized=normalize)
-
-        return noise_sampler

py/custom_noise/noise_perlin.py

@@ -1,14 +1,11 @@
-import itertools
-import math
-
 import torch
+import math
+import itertools
 
-from comfy import model_management
-
-from .. import filtering
+from .base import CustomNoiseItemBase, CustomNoiseNodeBase, NormalizeNoiseNodeMixin
 from ..latent import normalize_to_scale
 from ..noise import scale_noise
-from .base import CustomNoiseItemBase, NormalizeNoiseNodeMixin
+from ..filtering import BLENDING_MODES
 
 # Perlin generation routines based on https://github.com/Extraltodeus/noise_latent_perlinpinpin which was based on https://gist.github.com/vadimkantorov/ac1b097753f217c5c11bc2ff396e0a57 which was based on https://github.com/pvigier/perlin-numpy
 
@@ -53,13 +50,13 @@ def rand_perlin(
     *,
     tileable=DEFAULTS.tileable,
     fade=DEFAULTS.fade,
-    blend=torch.lerp,
+    blend=BLENDING_MODES[DEFAULTS.blend],
     generator=DEFAULTS.generator,
     device=DEFAULTS.device,
 ):
     dims = len(res)
     didxs = tuple(range(dims))
-    delta, d = zip(*((res[i] / shape[i], int(round(shape[i] / res[i]))) for i in didxs))
+    delta, d = zip(*((res[i] / shape[i], int(shape[i] // res[i])) for i in didxs))
 
     grid = (
         torch.stack(
@@ -117,16 +114,14 @@ def rand_perlin(
     shape_slices = tuple(slice(0, shape[i]) for i in didxs)
 
     def tile_grads(slices):
-        result = gradients[tuple(slice(*slices[i]) for i in didxs)]
+        result = gradients[*(slice(*slices[i]) for i in didxs)]
         for i in didxs:
             result = result.repeat_interleave(d[i], i)
         return result
 
     def dot(grad, shift):
         return (
-            torch.stack(
-                tuple(grid[(*shape_slices, i)] + shift[i] for i in didxs), dim=-1
-            )
+            torch.stack(tuple(grid[*shape_slices, i] + shift[i] for i in didxs), dim=-1)
             * grad[shape_slices]
         ).sum(dim=-1)
 
@@ -166,7 +161,7 @@ def generate_fractal_noise(
     initial_frequency=DEFAULTS.initial_frequency,
     tileable=DEFAULTS.tileable,
     fade=DEFAULTS.fade,
-    blend=torch.lerp,
+    blend=BLENDING_MODES[DEFAULTS.blend],
     generator=DEFAULTS.generator,
     device=DEFAULTS.device,
 ):
@@ -234,8 +229,8 @@ def create_noisy_latents_perlin(
     res=DEFAULTS.res,
     break_pattern=DEFAULTS.break_pattern,
     channels=4,
-    blend=torch.lerp,
-    pattern_break_blend=torch.lerp,
+    blend=BLENDING_MODES[DEFAULTS.blend],
+    pattern_break_blend=BLENDING_MODES[DEFAULTS.pattern_break_blend],
     depth_over_channels=DEFAULTS.depth_over_channels,
     pad=DEFAULTS.pad,
     initial_frequency=DEFAULTS.initial_frequency,
@@ -429,20 +424,15 @@ class PerlinItem(CustomNoiseItemBase):
     ):
         normalized = self.get_normalize("normalized", normalized)
         cpu = cpu if self.device == "default" else self.device == "cpu"
-        device = "cpu" if cpu else model_management.get_torch_device()
+        device = torch.device(0 if not cpu else "cpu")
         noise_chunk = None
         noise_index = self.initial_depth
         max_idx = None
-        if x.ndim < 4:
-            raise ValueError("Can only handle latents with 4+ dimensions")
-        orig_shape = x.shape
-        b = x.shape[0]
-        c = math.prod(x.shape[1:-2])  # Hack to deal with video models.
-        h, w = x.shape[-2:]
+        b, c, h, w = x.shape
         x_device, x_dtype = x.device, x.dtype
         del x
-        blend = filtering.BLENDING_MODES[self.blend]
-        pattern_break_blend = filtering.BLENDING_MODES[self.pattern_break_blend]
+        blend = BLENDING_MODES[self.blend]
+        pattern_break_blend = BLENDING_MODES[self.pattern_break_blend]
 
         def noise_sampler(_s, _sn):
             nonlocal noise_chunk, noise_index, max_idx
@@ -489,7 +479,266 @@ class PerlinItem(CustomNoiseItemBase):
                 noise_index = 0
                 if not self.wrap_depth:
                     noise_chunk = None
-            result = scale_noise(noise, self.factor, normalized=normalized)
-            return result.reshape(orig_shape) if result.shape != orig_shape else result
+            return scale_noise(noise, self.factor, normalized=normalized)
 
         return noise_sampler
+
+
+class PerlinAdvancedNode(CustomNoiseNodeBase, NormalizeNoiseNodeMixin):
+    DESCRIPTION = "Advanced Perlin noise generator, allows generating 2D or 3D Perlin noise. See the OCSNoise PerlinSimple node for less tuneable parameters."
+
+    @classmethod
+    def INPUT_TYPES(cls):
+        result = super().INPUT_TYPES()
+        result["required"] |= {
+            "depth": (
+                "INT",
+                {
+                    "default": DEFAULTS.depth,
+                    "tooltip": "When non-zero, 3D perlin noise will be generated.",
+                },
+            ),
+            "detail_level": (
+                "FLOAT",
+                {
+                    "default": DEFAULTS.detail_level,
+                    "tooltip": "Controls the detail level of the noise when break_pattern is non-zero. No effect when using 100% raw Perlin noise.",
+                },
+            ),
+            "octaves": (
+                "INT",
+                {
+                    "default": DEFAULTS.octaves,
+                    "tooltip": "Generally controls the detail level of the noise. Each octave involves generating a layer of noise so there is a performance cost to increasing octaves.",
+                },
+            ),
+            "persistence": (
+                "STRING",
+                {
+                    "default": DEFAULTS.get_commasep("persistence"),
+                    "tooltip": "Controls how rough the generated noise is. Lower values will result in smoother noise, higher values will look more like Gaussian noise. Comma-separated list, multiple items will apply to octaves in sequence.",
+                },
+            ),
+            "lacunarity_height": (
+                "STRING",
+                {
+                    "default": DEFAULTS.get_commasep("lacunarity", 0),
+                    "tooltip": "Lacunarity controls the frequency multiplier between successive octaves. Only has an effect when octaves is greater than one. Comma-separated list, multiple items will apply to octaves in sequence.",
+                },
+            ),
+            "lacunarity_width": (
+                "STRING",
+                {
+                    "default": DEFAULTS.get_commasep("lacunarity", 1),
+                    "tooltip": "Lacunarity controls the frequency multiplier between successive octaves. Only has an effect when octaves is greater than one. Comma-separated list, multiple items will apply to octaves in sequence.",
+                },
+            ),
+            "lacunarity_depth": (
+                "STRING",
+                {
+                    "default": DEFAULTS.get_commasep("lacunarity", 2),
+                    "tooltip": "Lacunarity controls the frequency multiplier between successive octaves. Only has an effect when depth is non-zero and octaves is greater than one. Comma-separated list, multiple items will apply to octaves in sequence.",
+                },
+            ),
+            "res_height": (
+                "STRING",
+                {
+                    "default": DEFAULTS.get_commasep("res", 0),
+                    "tooltip": "Number of periods of noise to generate along an axis. Comma-separated list, multiple items will apply to octaves in sequence.",
+                },
+            ),
+            "res_width": (
+                "STRING",
+                {
+                    "default": DEFAULTS.get_commasep("res", 1),
+                    "tooltip": "Number of periods of noise to generate along an axis. Comma-separated list, multiple items will apply to octaves in sequence.",
+                },
+            ),
+            "res_depth": (
+                "STRING",
+                {
+                    "default": DEFAULTS.get_commasep("res", 2),
+                    "tooltip": "Number of periods of noise to generate along an axis. Only has an effect when depth is non-zero. Comma-separated list, multiple items will apply to octaves in sequence.",
+                },
+            ),
+            "break_pattern": (
+                "FLOAT",
+                {
+                    "default": DEFAULTS.break_pattern,
+                    "tooltip": "Applies a function to break the Perlin pattern, making it more like normal noise. The value is the blend strength, where 1.0 indicates 100% pattern broken noise and 0.5 indicates 50% raw noise and 50% pattern broken noise. Generally should be at least 0.9 unless you want to generate colorful blobs.",
+                },
+            ),
+            "initial_depth": (
+                "INT",
+                {
+                    "default": DEFAULTS.initial_depth,
+                    "tooltip": "First zero-based depth index the noise generator will return. Only has an effect when depth is non-zero.",
+                },
+            ),
+            "wrap_depth": (
+                "INT",
+                {
+                    "default": DEFAULTS.wrap_depth,
+                    "tooltip": "If non-zero, instead of generating a new chunk of noise when the last slice is used will instead jump back to the specified zero-based depth index. Only has an effect when depth is non-zero.",
+                },
+            ),
+            "max_depth": (
+                "INT",
+                {
+                    "default": DEFAULTS.max_depth,
+                    "tooltip": "Basically crops the depth dimension to the specified value (inclusive). Negative values start from the end, the default of -1 does no cropping. Only has an effect when depth is non-zero.",
+                },
+            ),
+            "tileable_height": (
+                "BOOLEAN",
+                {
+                    "default": DEFAULTS.tileable[0],
+                    "tooltip": "Makes the specified dimension tileable.",
+                },
+            ),
+            "tileable_width": (
+                "BOOLEAN",
+                {
+                    "default": DEFAULTS.tileable[1],
+                    "tooltip": "Makes the specified dimension tileable.",
+                },
+            ),
+            "tileable_depth": (
+                "BOOLEAN",
+                {
+                    "default": DEFAULTS.tileable[2],
+                    "tooltip": "Makes the specified dimension tileable. Only has an effect when depth is non-zero.",
+                },
+            ),
+            "blend": (
+                tuple(BLENDING_MODES.keys()),
+                {
+                    "default": "lerp",
+                    "tooltip": "Blending function used when generating Perlin noise. When set to values other than LERP may not work at all or may not actually generate Perlin noise.",
+                },
+            ),
+            "pattern_break_blend": (
+                tuple(BLENDING_MODES.keys()),
+                {
+                    "default": "lerp",
+                    "tooltip": "Blending function used to blend pattern broken noise with raw noise.",
+                },
+            ),
+            "depth_over_channels": (
+                "BOOLEAN",
+                {
+                    "default": DEFAULTS.depth_over_channels,
+                    "tooltip": "When disabled, each channel will have its own separate 3D noise pattern. When enabled, depth is multiplied by the number of channels and each channel is a slice of depth. Only has an effect when depth is non-zero.",
+                },
+            ),
+            "pad_height": (
+                "INT",
+                {
+                    "default": DEFAULTS.pad[0],
+                    "min": 0,
+                    "tooltip": "Pads the specified dimension by the size. Equal padding will be added on both sides and cropped out after generation.",
+                },
+            ),
+            "pad_width": (
+                "INT",
+                {
+                    "default": DEFAULTS.pad[1],
+                    "min": 0,
+                    "tooltip": "Pads the specified dimension by the size. Equal padding will be added on both sides and cropped out after generation.",
+                },
+            ),
+            "pad_depth": (
+                "INT",
+                {
+                    "default": DEFAULTS.pad[2],
+                    "min": 0,
+                    "tooltip": "Pads the specified dimension by the size. Equal padding will be added on both sides and cropped out after generation. Only has an effect when depth is non-zero.",
+                },
+            ),
+            "initial_amplitude": (
+                "FLOAT",
+                {
+                    "default": DEFAULTS.initial_amplitude,
+                    "tooltip": "Controls the amplitude for the first octave.",
+                },
+            ),
+            "initial_frequency_height": (
+                "FLOAT",
+                {
+                    "default": DEFAULTS.initial_frequency[0],
+                    "tooltip": "Controls the frequency for the first octave for the this axis.",
+                },
+            ),
+            "initial_frequency_width": (
+                "FLOAT",
+                {
+                    "default": DEFAULTS.initial_frequency[1],
+                    "tooltip": "Controls the frequency for the first octave for the this axis.",
+                },
+            ),
+            "initial_frequency_depth": (
+                "FLOAT",
+                {
+                    "default": DEFAULTS.initial_frequency[2],
+                    "tooltip": "Controls the frequency for the first octave for the this axis.",
+                },
+            ),
+            "normalize": (
+                ("default", "forced", "off"),
+                {
+                    "tooltip": "Controls whether the output noise is normalized after generation.",
+                },
+            ),
+            "device": (
+                ("default", "cpu", "gpu"),
+                {
+                    "default": "default",
+                    "tooltip": "Controls what device is used to generate the noise. GPU noise may be slightly faster but you will get different results on different GPUs.",
+                },
+            ),
+        }
+        return result
+
+    @classmethod
+    def get_item_class(cls):
+        return PerlinItem
+
+
+class PerlinSimpleNode(PerlinAdvancedNode):
+    DESCRIPTION = "Simplified Perlin noise generator, allows generating 2D or 3D Perlin noise. See the OCSNoise PerlinAdvanced node for more tuneable parameters."
+
+    _COPY_KEYS = {
+        "factor",
+        "rescale",
+        "depth",
+        "detail_level",
+        "octaves",
+        "persistence",
+        "break_pattern",
+    }
+
+    @classmethod
+    def INPUT_TYPES(cls):
+        result = super().INPUT_TYPES()
+        orig_reqs = result["required"]
+        reqs = {k: v for k, v in orig_reqs.items() if k in cls._COPY_KEYS}
+        reqs["lacunarity"] = orig_reqs["lacunarity_height"]
+        reqs["res"] = orig_reqs["res_height"]
+        result["required"] = reqs
+        return result
+
+    @classmethod
+    def get_item_class(cls):
+        def wrapper(factor, *, lacunarity, res, **kwargs):
+            return PerlinItem(
+                factor,
+                lacunarity_height=lacunarity,
+                lacunarity_width=lacunarity,
+                lacunarity_depth=lacunarity,
+                res_height=res,
+                res_width=res,
+                res_depth=res,
+                **kwargs,
+            )
+
+        return wrapper

py/expression/expression.py

@@ -1,8 +1,6 @@
 import re
 import operator
 
-from tqdm import tqdm
-
 from .parser import Parser, ParserSpec, ParseError
 from .types import (
     Empty,
@@ -60,7 +58,7 @@ class Expression:
 
     def eval(self, handlers, *args, **kwargs):
         if self.expr != ExpOp("default"):
-            tqdm.write(f"* OCS: EVAL: {self.expr}")
+            print("\nEVAL", self.expr)
         if not isinstance(self.expr, ExpBase):
             return self.expr
         return self.expr.eval(handlers, *args, **kwargs)
@@ -83,25 +81,20 @@ class Expression:
     def fixup_token(cls, t):
         if t == "":
             return t
-        if t[0] == "'":
-            return ExpSym(t[1:])
         t = t.lower()
         val = cls.FIXUP.get(t, Empty)
         if val is not Empty:
             return val
         if t[0] == "`":
             return ExpBinOp(t.strip("`"))
+        if t[0] == "'":
+            return ExpSym(t[1:])
         if (len(t) > 1 and t[0] == "-" and t[1].isdigit()) or t[0].isdigit():
             return float(t) if "." in t else int(t)
         return ExpOp(t)
 
     @classmethod
     def tokenize(cls, s):
-        s = "\n".join(
-            line.rstrip("\r")
-            for line in s.split("\n")
-            if not line.lstrip().startswith("#")
-        )
         yield from (cls.fixup_token(m.group(1)) for m in cls.EXPR_RE.finditer(s))
 
 

py/expression/handler.py

@@ -178,7 +178,7 @@ class EqHandler(BinopLogicHandler):
         return a1 == a2
 
 
-class NeqHandler(EqHandler):
+class NeqHandler(BinopLogicHandler):
     def handle(self, *args, **kwargs):
         return not super().handle(*args, **kwargs)
 
@@ -212,8 +212,6 @@ class BetweenHandler(BaseHandler):  # Inclusive
 
     def handle(self, obj, getter):
         value, low, high = self.safe_get_all(obj, getter)
-        if low > high:
-            low, high = high, low
         return low <= value <= high
 
 
@@ -283,17 +281,9 @@ class GetHandler(BaseHandler):
 
 class S_Handler(BaseHandler):
     input_validators = (
-        Arg.one_of(
-            "start",
-            (ValidateArg.validate_none, ValidateArg.validate_integer),
-            default=None,
-        ),
-        Arg.one_of(
-            "end",
-            (ValidateArg.validate_none, ValidateArg.validate_integer),
-            default=None,
-        ),
-        Arg.integer("step", 1),
+        Arg.integer("start", None),
+        Arg.integer("end", None),
+        Arg.integer("step", None),
     )
 
     def handle(self, obj, getter):

py/expression/validation.py

@@ -1,4 +1,3 @@
-import contextlib
 import functools
 
 from ..latent import ImageBatch
@@ -53,10 +52,6 @@ class Arg:
             name, default=default, validator=ValidateArg.validate_numscalar_sequence
         )
 
-    @classmethod
-    def tensor_slice(cls, name, default=Empty):
-        return cls(name, default=default, validator=ValidateArg.validate_tensor_slice)
-
     @classmethod
     def sequence(cls, name, default=Empty, *, item_validator=None):
         return cls(
@@ -67,16 +62,6 @@ class Arg:
             ),
         )
 
-    @classmethod
-    def nested_sequence(cls, name, default=Empty, *, item_validator=None):
-        return cls(
-            name,
-            default=default,
-            validator=functools.partial(
-                ValidateArg.validate_nested_sequence, item_validator=item_validator
-            ),
-        )
-
     @classmethod
     def string(cls, name, default=Empty):
         return cls(name, default=default, validator=ValidateArg.validate_string)
@@ -113,10 +98,7 @@ class ValidateArg:
 
     def __init__(self, name, *args, kwargslist=(), group=all, **kwargs):
         if not isinstance(name, (list, tuple)):
-            name = (name,)
-            args = ((args,),)
-            kwargslist = kwargs
-            kwargs = {}
+            return self.__init__((name,), (args,), group=group, kwargslist=kwargs)
         self.valfuns = (getattr(self, f"validate_{n}", None) for n in name)
         if not all(self.valfuns):
             raise ValueError("Unknown validator")
@@ -149,22 +131,6 @@ class ValidateArg:
             raise ValidateError(f"Expected numeric argument at {idx}, got {type(val)}")
         return val
 
-    @classmethod
-    def validate_tensor_slice_item(cls, idx, val):
-        with contextlib.suppress(ValidateError):
-            ok = (
-                val in {Ellipsis, None}
-                or isinstance(val, (int, slice))
-                or cls.validate_sequence(
-                    idx, val, item_validator=ValidateArg.validate_integer
-                )
-            )
-            if ok:
-                return val
-        raise ValidateError(
-            f"Expected none, int, slice, tuple of int or ellipsis argument at {idx}, got {type(val)}"
-        )
-
     @classmethod
     def validate_integer(cls, idx, val):
         if not isinstance(val, int):
@@ -194,29 +160,7 @@ class ValidateArg:
         try:
             return tuple(item_validator(iidx, v) for iidx, v in enumerate(val))
         except ValidateError as exc:
-            raise ValidateError(
-                f"Item validation failed for sequence argument at {idx}: {exc}"
-            )
-
-    @classmethod
-    def validate_nested_sequence(cls, idx, val, *, item_validator=None, depth=0):
-        if not isinstance(val, (list, tuple)):
-            raise ValidateError(
-                f"Expected nested sequence argument at {idx}, depth {depth} but got {type(val)}"
-            )
-        try:
-            return tuple(
-                cls.validate_nested_sequence(
-                    idx, v, item_validator=item_validator, depth=depth + 1
-                )
-                if isinstance(v, (list, tuple))
-                else (item_validator(iidx, v) if item_validator is not None else v)
-                for iidx, v in enumerate(val)
-            )
-        except ValidateError as exc:
-            raise ValidateError(
-                f"Item validation failed for nested sequence argument at {idx}, depth {depth}: {exc}"
-            )
+            raise ValidateError(f"Item validation failed for in sequence: {exc}")
 
     @classmethod
     def validate_numscalar_sequence(cls, idx, val):
@@ -224,11 +168,20 @@ class ValidateArg:
             idx, val, item_validator=cls.validate_numeric_scalar
         )
 
-    @classmethod
-    def validate_tensor_slice(cls, idx, val):
-        return cls.validate_sequence(
-            idx, val, item_validator=cls.validate_tensor_slice_item
-        )
+    # @classmethod
+    # def validate_numscalar_sequence(cls, idx, val):
+    #     if not isinstance(val, (list, tuple)):
+    #         raise ValidateError(f"Expected sequence argument at {idx}, got {type(val)}")
+    #     try:
+    #         _ = all(
+    #             cls.validate_numeric_scalar(f"{idx}[{i}]", v) is not None
+    #             for i, v in enumerate(val)
+    #         )
+    #     except ValidateError as exc:
+    #         raise ValidateError(
+    #             f"Expected numeric sequence argument at {idx}, got {type(val)}: {exc}"
+    #         )
+    #     return val
 
     @classmethod
     def validate_string(cls, idx, val):
@@ -242,12 +195,6 @@ class ValidateArg:
             raise ValidateError(f"Expected boolean argument at {idx}, got {type(val)}")
         return val
 
-    @classmethod
-    def validate_none(cls, idx, val):
-        if val is not None:
-            raise ValidateError(f"Expected none argument at {idx}, got {type(val)}")
-        return val
-
     @classmethod
     def validate_passthrough(cls, idx, val):
         return val

py/expression_handlers.py

@@ -3,45 +3,31 @@ import os
 import torch
 import numpy as np
 import PIL.Image as PILImage
-from functools import partial
 
 from . import expression as expr
 from . import latent
-from . import unsafe_expression_whitelists
 
 from .external import MODULES as EXT
-from .utils import scale_noise, resolve_value, quantile_normalize
-from .latent import OCSTAESD, ImageBatch, normalize_to_scale
-
+from .utils import scale_noise, resolve_value
+from .latent import OCSTAESD, ImageBatch
 
 ALLOW_UNSAFE = os.environ.get("COMFYUI_OCS_ALLOW_UNSAFE_EXPRESSIONS") is not None
 ALLOW_ALL_UNSAFE = os.environ.get("COMFYUI_OCS_ALLOW_ALL_UNSAFE") is not None
 
-EXT_BLEH = EXT_SONAR = None
+EXT_BLEH = EXT.get("bleh")
+EXT_SONAR = EXT.get("sonar")
+EXT_NNLATENTUPSCALE = EXT.get("nnlatentupscale")
 
-BLENDING_MODES = {
-    "lerp": torch.lerp,
-}
+if "bleh" in EXT:
+    BLENDING_MODES = EXT_BLEH.latent_utils.BLENDING_MODES
+else:
+    BLENDING_MODES = {
+        "lerp": lambda a, b, t: (1 - t) * a + t * b,
+    }
 
 HANDLERS = {}
 
 
-def init_integrations(integrations):
-    global EXT_BLEH, EXT_SONAR, BLENDING_MODES, HANDLERS
-    EXT_BLEH = integrations.bleh
-    EXT_SONAR = integrations.sonar
-    if EXT_BLEH is not None:
-        BLENDING_MODES = EXT_BLEH.latent_utils.BLENDING_MODES
-        HANDLERS["t_bleh_enhance"] = BlehEnhanceHandler()
-    if EXT_SONAR is not None:
-        HANDLERS["t_sonar_power_filter"] = SonarPowerFilterHandler()
-    if integrations.nnlatentupscale is not None:
-        HANDLERS["t_scale_nnlatentupscale"] = ScaleNNLatentUpscaleHandler()
-
-
-EXT.register_init_handler(init_integrations)
-
-
 class NormHandler(expr.BaseHandler):
     input_validators = (
         expr.Arg.tensor("tensor"),
@@ -56,114 +42,6 @@ class NormHandler(expr.BaseHandler):
     validate_output = expr.Arg.tensor("output")
 
 
-class QuantileNormHandler(NormHandler):
-    input_validators = (
-        expr.Arg.tensor("tensor"),
-        expr.Arg.numeric("quantile", 0.75),
-        expr.Arg.integer("dim", 1),
-        expr.Arg.boolean("flatten", True),
-        expr.Arg.numeric("norm_factor", 1.0),
-        expr.Arg.numeric("norm_power", 0.5),
-        expr.Arg.string("mode", "clamp"),
-    )
-
-    def handle(self, obj, getter):
-        tensor, quantile, dim, flatten, norm_factor, norm_power, mode = (
-            self.safe_get_all(obj, getter)
-        )
-        return quantile_normalize(
-            tensor,
-            quantile=quantile,
-            dim=dim,
-            flatten=flatten,
-            nq_fac=norm_factor,
-            pow_fac=norm_power,
-            strategy=mode,
-        )
-
-
-class NormToScaleHandler(NormHandler):
-    input_validators = (
-        expr.Arg.tensor("tensor"),
-        expr.Arg.numeric("target_min", 0.0),
-        expr.Arg.numeric("target_max", 1.0),
-        expr.Arg.numscalar_sequence("dim", (-3, -2, -1)),
-    )
-
-    def handle(self, obj, getter):
-        tensor, tmin, tmax, dim = self.safe_get_all(obj, getter)
-        return normalize_to_scale(tensor, tmin, tmax, dim=dim)
-
-
-class ClampHandler(NormHandler):
-    input_validators = (
-        expr.Arg.tensor("tensor"),
-        expr.Arg.numeric("min", 0.0),
-        expr.Arg.numeric("max", 1.0),
-    )
-
-    def handle(self, obj, getter):
-        tensor, tmin, tmax = self.safe_get_all(obj, getter)
-        return torch.clamp(tensor, min=tmin, max=tmax)
-
-
-class StackHandler(NormHandler):
-    input_validators = (
-        expr.Arg.sequence("tensors", item_validator=expr.ValidateArg.validate_tensor),
-        expr.Arg.integer("dim", 1),
-    )
-
-    def handle(self, obj, getter):
-        tensors, dim = self.safe_get_all(obj, getter)
-        return torch.stack(tensors, dim)
-
-
-class CatHandler(StackHandler):
-    def handle(self, obj, getter):
-        tensors, dim = self.safe_get_all(obj, getter)
-        return torch.cat(tensors, dim)
-
-
-class ReshapeHandler(NormHandler):
-    input_validators = (
-        expr.Arg.tensor("tensor"),
-        expr.Arg.numscalar_sequence("shape"),
-    )
-
-    def handle(self, obj, getter):
-        tensor, shape = self.safe_get_all(obj, getter)
-        return torch.reshape(tensor.clone(), shape)
-
-
-class IndexedCopyHandler(NormHandler):
-    input_validators = (
-        expr.Arg.tensor("tensor_dest"),
-        expr.Arg.tensor("tensor_src"),
-        expr.Arg.tensor_slice("slice"),
-    )
-
-    def handle(self, obj, getter):
-        tensor1, tensor2, tensor_slice = self.safe_get_all(obj, getter)
-        result = tensor1.clone()
-        result[tensor_slice] = tensor2[tensor_slice]
-        return result
-
-
-class NewLikeHandler(NormHandler):
-    input_validators = (
-        expr.Arg.tensor("tensor"),
-        expr.Arg.nested_sequence(
-            "values",
-            default=(),
-            item_validator=expr.ValidateArg.validate_numeric_scalar,
-        ),
-    )
-
-    def handle(self, obj, getter):
-        tensor, values = self.safe_get_all(obj, getter)
-        return torch.tensor(values, dtype=tensor.dtype, device=tensor.device)
-
-
 class MeanHandler(NormHandler):
     input_validators = (
         expr.Arg.tensor("tensor"),
@@ -190,23 +68,11 @@ class RollHandler(NormHandler):
     input_validators = (
         expr.Arg.tensor("tensor"),
         expr.Arg.numeric_scalar("amount", 0.5),
-        expr.Arg.one_of(
-            "dim",
-            (
-                expr.ValidateArg.validate_integer,
-                partial(
-                    expr.ValidateArg.validate_sequence,
-                    item_validator=expr.ValidateArg.validate_integer,
-                ),
-            ),
-            default=-2,
-        ),
+        expr.Arg.numscalar_sequence("dim", (-2,)),
     )
 
     def handle(self, obj, getter):
         tensor, amount, dim = self.safe_get_all(obj, getter)
-        if not isinstance(dim, tuple):
-            dim = (dim,)
         if isinstance(amount, float) and amount < 1.0 and amount > -1.0:
             if len(dim) > 1:
                 raise ValueError(
@@ -236,45 +102,14 @@ class FlipHandler(NormHandler):
             return torch.flip(tensor, (dim,))
         result = tensor.detach().clone()
         pivot = tensor.shape[dim] // 2
-        out_slice = tuple(
+        out_slice = (
             np.s_[:] if d != dim else np.s_[pivot:] for d in range(tensor.ndim)
         )
-        in_slice = tuple(
-            np.s_[:] if d != dim else np.s_[:pivot] for d in range(tensor.ndim)
-        )
-        result[out_slice] = torch.flip(tensor[in_slice], dims=(dim,))
+        in_slice = (np.s_[:] if d != dim else np.s_[:pivot] for d in range(tensor.ndim))
+        result[*out_slice] = torch.flip(tensor[*in_slice], dims=(dim,))
         return result
 
 
-class CopySignHandler(NormHandler):
-    input_validators = (
-        expr.Arg.tensor("tensor"),
-        expr.Arg.tensor("other"),
-    )
-
-    def handle(self, obj, getter):
-        return torch.copysign(*self.safe_get_all(obj, getter))
-
-
-class CloneHandler(NormHandler):
-    input_validators = (expr.Arg.tensor("tensor"),)
-
-    def handle(self, obj, getter):
-        return self.safe_get("tensor", obj, getter).clone()
-
-
-class NewFullHandler(NormHandler):
-    input_validators = (
-        expr.Arg.tensor("tensor"),
-        expr.Arg.numscalar_sequence("shape"),
-        expr.Arg.numeric("value", 0.0),
-    )
-
-    def handle(self, obj, getter):
-        tensor, shape, value = self.safe_get_all(obj, getter)
-        return tensor.new_full(shape, value)
-
-
 class BlendHandler(NormHandler):
     input_validators = (
         expr.Arg.tensor("tensor1"),
@@ -295,12 +130,11 @@ class ContrastAdaptiveSharpeningHandler(NormHandler):
     input_validators = (
         expr.Arg.tensor("tensor"),
         expr.Arg.numeric("scale", 0.5),
-        expr.Arg.boolean("normalize", True),
     )
 
     def handle(self, obj, getter):
-        t, scale, normalize = self.safe_get_all(obj, getter)
-        return latent.contrast_adaptive_sharpening(t, scale, normalize=normalize)
+        t, scale = self.safe_get_all(obj, getter)
+        return latent.contrast_adaptive_sharpening(t, scale)
 
 
 class ScaleHandler(NormHandler):
@@ -330,6 +164,7 @@ class ScaleHandler(NormHandler):
             scale = tuple(int(v) for v in scale)
         else:
             scale = (int(t.shape[-2] * scale[0]), int(t.shape[-1] * scale[1]))
+        print("SCALE", t.shape[-2:], "->", scale)
         if not all(v > 0 for v in scale):
             raise ValueError(f"Invalid scale: scale values must be > 0, got: {scale!r}")
         return latent.scale_samples(t, scale[1], scale[0], mode=mode)
@@ -345,8 +180,7 @@ class NoiseHandler(NormHandler):
         t, typ = self.safe_get_all(obj, getter)
         ctx = getter.ctx
         smin, smax, s, sn = (
-            ctx.get_var(k, default=0.0)
-            for k in ("sigma_min", "sigma_max", "sigma", "sigma_next")
+            ctx.get_var(k) for k in ("sigma_min", "sigma_max", "sigma", "sigma_next")
         )
         ns = latent.get_noise_sampler(typ, t, smin, smax, normalized=False)
         return ns(s, sn)
@@ -360,55 +194,6 @@ class ShapeHandler(expr.BaseHandler):
         return expr.types.ExpTuple((*t.shape,))
 
 
-class GaussianBlur2DHandler(NormHandler):
-    input_validators = (
-        expr.Arg.tensor("tensor"),
-        expr.Arg.integer("kernel_size"),
-        expr.Arg.numeric_scalar("sigma"),
-    )
-
-    def handle(self, obj, getter):
-        return latent.gaussian_blur_2d(*self.safe_get_all(obj, getter))
-
-
-class SNFGuidanceHandler(NormHandler):
-    input_validators = (
-        expr.Arg.tensor("t_tensor"),
-        expr.Arg.tensor("s_tensor"),
-        expr.Arg.integer("t_kernel_size", 3),
-        expr.Arg.numeric_scalar("t_sigma", 1),
-        expr.Arg.integer("s_kernel_size", 3),
-        expr.Arg.numeric_scalar("s_sigma", 1),
-    )
-
-    def handle(self, obj, getter):
-        return latent.snf_guidance(*self.safe_get_all(obj, getter))
-
-
-class RGBLatentHandler(expr.BaseHandler):
-    input_validators = (
-        expr.Arg.tensor("reference"),
-        expr.Arg.numscalar_sequence("rgb"),
-    )
-
-    def handle(self, obj, getter):
-        ctx = getter.ctx.constants.ctx
-        model = ctx.get("model")
-        if model is None:
-            raise ValueError("Ohno")
-        reference, rgb = self.safe_get_all(obj, getter)
-        if len(rgb) != 3 or not all(0 <= v <= 1.0 for v in rgb):
-            raise ValueError("Bad RGB parameter")
-        img = torch.tensor(
-            tuple(v * 2 - 1.0 for v in rgb),
-            device=reference.device,
-            dtype=reference.dtype,
-        ).view(1, 3)
-        latent = torch.zeros_like(reference).movedim(1, -1)
-        latent += model.latent_format.rgb_to_latent(img)
-        return latent.movedim(-1, 1)
-
-
 class TAESDDecodeHandler(expr.BaseHandler):
     input_validators = (
         expr.Arg.tensor("tensor"),
@@ -497,9 +282,295 @@ class UnsafeTorchTensorMethodHandler(NormHandler):
 
         whitelist = AlwaysContains()
     elif ALLOW_UNSAFE:
-        whitelist = unsafe_expression_whitelists.TORCH_FUNCTION_WHITELIST
+        whitelist = {
+            "abs",
+            "absolute",
+            "acos",
+            "acosh",
+            "add",
+            "addbmm",
+            "addcdiv",
+            "addcmul",
+            "addmm",
+            "addmv",
+            "addr",
+            "adjoint",
+            "all",
+            "allclose",
+            "amax",
+            "amin",
+            "aminmax",
+            "angle",
+            "any",
+            "arccos",
+            "arccosh",
+            "arcsin",
+            "arcsinh",
+            "arctan",
+            "arctan2",
+            "arctanh",
+            "argmax",
+            "argmin",
+            "argsort",
+            "argwhere",
+            "as_strided",
+            "asin",
+            "asinh",
+            "atan",
+            "atan2",
+            "atanh",
+            "baddbmm",
+            "bernoulli",
+            "bincount",
+            "bitwise_and",
+            "bitwise_left_shift",
+            "bitwise_not",
+            "bitwise_or",
+            "bitwise_right_shift",
+            "bitwise_xor",
+            "bmm",
+            "broadcast_to",
+            "ceil",
+            "cholesky",
+            "cholesky_inverse",
+            "cholesky_solve",
+            "chunk",
+            "clamp",
+            "clip",
+            "clone",
+            "conj",
+            "conj_physical",
+            "contiguous",
+            "copysign",
+            "corrcoef",
+            "cos",
+            "cosh",
+            "count_nonzero",
+            "cov",
+            "cross",
+            "cummax",
+            "cummin",
+            "cumprod",
+            "cumsum",
+            "deg2rad",
+            "det",
+            "detach",
+            "diag",
+            "diag_embed",
+            "diagflat",
+            "diagonal",
+            "diagonal_scatter",
+            "diff",
+            "digamma",
+            "dim",
+            "dist",
+            "div",
+            "divide",
+            "dot",
+            "dsplit",
+            "eq",
+            "equal",
+            "erf",
+            "erfc",
+            "erfinv",
+            "exp",
+            "expand",
+            "expand_as",
+            "expm1",
+            "fix",
+            "flatten",
+            "flip",
+            "fliplr",
+            "flipud",
+            "float_power",
+            "floor",
+            "floor_divide",
+            "fmax",
+            "fmin",
+            "fmod",
+            "frac",
+            "frexp",
+            "gather",
+            "gcd",
+            "ge",
+            "geqrf",
+            "ger",
+            "greater",
+            "greater_equal",
+            "gt",
+            "hardshrink",
+            "heaviside",
+            "histc",
+            "hsplit",
+            "hypot",
+            "i0",
+            "igamma",
+            "igammac",
+            "index_add",
+            "index_copy",
+            "index_fill",
+            "index_put",
+            "index_reduce",
+            "index_select",
+            "inner",
+            "inverse",
+            "isclose",
+            "isfinite",
+            "isinf",
+            "isnan",
+            "isneginf",
+            "isposinf",
+            "kthvalue",
+            "lcm()",
+            "ldexp",
+            "le",
+            "lerp",
+            "less",
+            "less_equal",
+            "lgamma",
+            "log",
+            "log10",
+            "log1p",
+            "log2",
+            "logaddexp",
+            "logaddexp2",
+            "logcumsumexp",
+            "logdet",
+            "logical_and",
+            "logical_not",
+            "logical_or",
+            "logical_xor",
+            "logit",
+            "logsumexp",
+            "lt",
+            "lu",
+            "lu_solve",
+            "masked_fill",
+            "masked_scatter",
+            "masked_select",
+            "matmul",
+            "matrix_exp",
+            "max",
+            "maximum",
+            "mean",
+            "median",
+            "min",
+            "minimum",
+            "mm",
+            "mode",
+            "moveaxis",
+            "movedim",
+            "msort",
+            "mul",
+            "multinomial",
+            "multiply",
+            "mv",
+            "mvlgamma",
+            "nan_to_num",
+            "nanmean",
+            "nanmedian",
+            "nanquantile",
+            "nansum",
+            "narrow",
+            "narrow_copy",
+            "ne",
+            "neg",
+            "negative",
+            "new_empty",
+            "new_full",
+            "new_ones",
+            "new_zeros",
+            "nextafter",
+            "nonzero",
+            "norm",
+            "not_equal",
+            "numel",
+            "orgqr",
+            "ormqr",
+            "outer",
+            "permute",
+            "polygamma",
+            "positive",
+            "pow",
+            "prod",
+            "qr",
+            "quantile",
+            "rad2deg",
+            "ravel",
+            "reciprocal",
+            "remainder",
+            "renorm",
+            "repeat",
+            "repeat_interleave",
+            "reshape",
+            "reshape_as",
+            "resolve_conj",
+            "resolve_neg",
+            "roll",
+            "rot90",
+            "round",
+            "rsqrt",
+            "scatter",
+            "scatter_add",
+            "scatter_reduce",
+            "select",
+            "select_scatter",
+            "sgn",
+            "sigmoid",
+            "sign",
+            "signbit",
+            "sin",
+            "sinc",
+            "sinh",
+            "slice_scatter",
+            "slogdet",
+            "smm",
+            "softmax",
+            "sort",
+            "sparse_mask",
+            "split",
+            "sqrt",
+            "square",
+            "squeeze",
+            "sspaddmm",
+            "std",
+            "stft",
+            "sub",
+            "subtract",
+            "sum",
+            "sum_to_size",
+            "svd",
+            "swapaxes",
+            "swapdims",
+            "t",
+            "take",
+            "take_along_dim",
+            "tan",
+            "tanh",
+            "tensor_split",
+            "tile",
+            "topk",
+            "transpose",
+            "triangular_solve",
+            "tril",
+            "triu",
+            "true_divide",
+            "trunc",
+            "unflatten",
+            "unfold",
+            "unique",
+            "unique_consecutive",
+            "unsqueeze",
+            "var",
+            "vdot",
+            "view",
+            "view_as",
+            "vsplit",
+            "where",
+            "xlogy",
+        }
     else:
-        whitelist = frozenset()
+        whitelist = set()
 
     def handle(self, obj, getter):
         if "__method" in obj.kwargs or "__tensor" in obj.kwargs:
@@ -538,120 +609,119 @@ class UnsafeTorchHandler(expr.BaseHandler):
             return resolve_value(keys, torch)
 
 
-class BlehEnhanceHandler(expr.BaseHandler):
-    input_validators = (
-        expr.Arg.tensor("tensor"),
-        expr.Arg.string("mode"),
-        expr.Arg.numeric_scalar("scale", 1.0),
-    )
-    output_validator = expr.Arg.tensor("output")
+if EXT_BLEH:
 
-    def handle(self, obj, getter):
-        tensor, mode, scale = self.safe_get_all(obj, getter)
-        return EXT_BLEH.latent_utils.enhance_tensor(
-            tensor, mode, scale=scale, adjust_scale=False
+    class BlehEnhanceHandler(expr.BaseHandler):
+        input_validators = (
+            expr.Arg.tensor("tensor"),
+            expr.Arg.string("mode"),
+            expr.Arg.numeric_scalar("scale", 1.0),
         )
+        output_validator = expr.Arg.tensor("output")
 
-
-class SonarPowerFilterHandler(expr.BaseHandler):
-    input_validators = (
-        expr.Arg.tensor("tensor"),
-        expr.Arg.present("filter"),
-    )
-    output_validator = expr.Arg.tensor("output")
-
-    default_power_filter = {
-        "mix": 1.0,
-        "normalization_factor": 1.0,
-        "common_mode": 0.0,
-        "channel_correlation": "1,1,1,1,1,1",
-    }
-
-    @classmethod
-    def make_power_filter(cls, fdict, *, toplevel=True):
-        fdict = fdict.copy()
-        compose_with = fdict.pop("compose_with", None)
-        if compose_with:
-            if not isinstance(compose_with, dict):
-                raise TypeError("compose_with must be a dictionary")
-            fdict["compose_with"] = cls.make_power_filter(compose_with, toplevel=False)
-        topargs = {k: fdict.pop(k, dv) for k, dv in cls.default_power_filter.items()}
-        power_filter = EXT_SONAR.powernoise.PowerFilter(**fdict)
-        if not toplevel:
-            return power_filter
-        cc = topargs.get("channel_correlation")
-        if cc is not None:
-            if not isinstance(cc, (list, tuple)) or not all(
-                isinstance(v, (int, float)) for v in cc
-            ):
-                raise TypeError(
-                    "Bad channel correlation type: must be comma separated string or numeric sequence"
-                )
-            topargs["channel_correlation"] = ",".join(repr(v) for v in cc)
-        return EXT_SONAR.powernoise.PowerNoiseItem(
-            1, power_filter=power_filter, time_brownian=True, **topargs
-        )
-
-    def handle(self, obj, getter):
-        tensor, filter_def = self.safe_get_all(obj, getter)
-        if not isinstance(filter_def, dict):
-            raise TypeError("filter argument must be a dictionary")
-        power_filter = self.make_power_filter(filter_def)
-        filter_rfft = power_filter.make_filter(tensor.shape).to(
-            tensor.device, non_blocking=True
-        )
-        ns = power_filter.make_noise_sampler_internal(
-            tensor,
-            lambda *_unused, latent=tensor: latent,
-            filter_rfft,
-            normalized=False,
-        )
-        return ns(None, None)
-
-
-class ScaleNNLatentUpscaleHandler(expr.BaseHandler):
-    input_validators = (
-        expr.Arg.tensor("tensor"),
-        expr.Arg.string("mode", "sd1"),
-        expr.Arg.numeric_scalar("scale", 2.0),
-    )
-    output_validator = expr.Arg.tensor("output")
-
-    def handle(self, obj, getter):
-        tensor, mode, scale = self.safe_get_all(obj, getter)
-        if mode not in {"sd1", "sdxl"}:
-            raise ValueError(
-                "Bad mode for t_scale_nnlatentupscale: must be either sd15 or sdxl"
+        def handle(self, obj, getter):
+            tensor, mode, scale = self.safe_get_all(obj, getter)
+            return EXT_BLEH.latent_utils.enhance_tensor(
+                tensor, mode, scale=scale, adjust_scale=False
             )
-        return latent.scale_nnlatentupscale(mode, tensor, scale)
 
+    HANDLERS["t_bleh_enhance"] = BlehEnhanceHandler()
+
+if EXT_SONAR:
+
+    class SonarPowerFilterHandler(expr.BaseHandler):
+        input_validators = (
+            expr.Arg.tensor("tensor"),
+            expr.Arg.present("filter"),
+        )
+        output_validator = expr.Arg.tensor("output")
+
+        default_power_filter = {
+            "mix": 1.0,
+            "normalization_factor": 1.0,
+            "common_mode": 0.0,
+            "channel_correlation": "1,1,1,1,1,1",
+        }
+
+        @classmethod
+        def make_power_filter(cls, fdict, *, toplevel=True):
+            fdict = fdict.copy()
+            compose_with = fdict.pop("compose_with", None)
+            if compose_with:
+                if not isinstance(compose_with, dict):
+                    raise TypeError("compose_with must be a dictionary")
+                fdict["compose_with"] = cls.make_power_filter(
+                    compose_with, toplevel=False
+                )
+            topargs = {
+                k: fdict.pop(k, dv) for k, dv in cls.default_power_filter.items()
+            }
+            power_filter = EXT_SONAR.powernoise.PowerFilter(**fdict)
+            if not toplevel:
+                return power_filter
+            cc = topargs.get("channel_correlation")
+            if cc is not None:
+                if not isinstance(cc, (list, tuple)) or not all(
+                    isinstance(v, (int, float)) for v in cc
+                ):
+                    raise TypeError(
+                        "Bad channel correlation type: must be comma separated string or numeric sequence"
+                    )
+                topargs["channel_correlation"] = ",".join(repr(v) for v in cc)
+            return EXT_SONAR.powernoise.PowerNoiseItem(
+                1, power_filter=power_filter, time_brownian=True, **topargs
+            )
+
+        def handle(self, obj, getter):
+            tensor, filter_def = self.safe_get_all(obj, getter)
+            if not isinstance(filter_def, dict):
+                raise TypeError("filter argument must be a dictionary")
+            power_filter = self.make_power_filter(filter_def)
+            filter_rfft = power_filter.make_filter(tensor.shape).to(
+                tensor.device, non_blocking=True
+            )
+            ns = power_filter.make_noise_sampler_internal(
+                tensor,
+                lambda *_unused, latent=tensor: latent,
+                filter_rfft,
+                normalized=False,
+            )
+            return ns(None, None)
+
+    HANDLERS["t_sonar_power_filter"] = SonarPowerFilterHandler()
+
+if EXT_NNLATENTUPSCALE:
+    from .latent import scale_nnlatentupscale
+
+    class ScaleNNLatentUpscaleHandler(expr.BaseHandler):
+        input_validators = (
+            expr.Arg.tensor("tensor"),
+            expr.Arg.string("mode", "sd1"),
+            expr.Arg.numeric_scalar("scale", 2.0),
+        )
+        output_validator = expr.Arg.tensor("output")
+
+        def handle(self, obj, getter):
+            tensor, mode, scale = self.safe_get_all(obj, getter)
+            if mode not in {"sd1", "sdxl"}:
+                raise ValueError(
+                    "Bad mode for t_scale_nnlatentupscale: must be either sd15 or sdxl"
+                )
+            return scale_nnlatentupscale(mode, tensor, scale)
+
+    HANDLERS["t_scale_nnlatentupscale"] = ScaleNNLatentUpscaleHandler()
 
 TENSOR_OP_HANDLERS = {
     "t_norm": NormHandler(),
-    "t_quantilenorm": QuantileNormHandler(),
-    "t_normtoscale": NormToScaleHandler(),
-    "t_normalize_to_scale": NormToScaleHandler(),
-    "t_reshape": ReshapeHandler(),
-    "t_clamp": ClampHandler(),
-    "t_cat": CatHandler(),
-    "t_stack": StackHandler(),
-    "t_indexed_copy": IndexedCopyHandler(),
-    "t_new_like": NewLikeHandler(),
     "t_mean": MeanHandler(),
     "t_std": StdHandler(),
     "t_blend": BlendHandler(),
     "t_roll": RollHandler(),
     "t_flip": FlipHandler(),
-    "t_clone": CloneHandler(),
-    "t_newfull": NewFullHandler(),
-    "t_copysign": CopySignHandler(),
     "t_contrast_adaptive_sharpening": ContrastAdaptiveSharpeningHandler(),
     "t_scale": ScaleHandler(),
     "t_noise": NoiseHandler(),
     "t_shape": ShapeHandler(),
-    "t_gaussianblur2d": GaussianBlur2DHandler(),
-    "t_rgb_latent": RGBLatentHandler(),
-    "t_snf_guidance": SNFGuidanceHandler(),
     "t_taesd_decode": TAESDDecodeHandler(),
     "unsafe_tensor_method": UnsafeTorchTensorMethodHandler(),
     "unsafe_torch": UnsafeTorchHandler(),

py/external.py

@@ -1,120 +1,21 @@
 import contextlib
 import importlib
-import sys
-from functools import partial
-from types import ModuleType
-from typing import Callable, NamedTuple
 
+MODULES = {}
 
-class Integrations:
-    class Integration(NamedTuple):
-        key: str
-        module_name: str
-        handler: Callable | None = None
+with contextlib.suppress(ImportError, NotImplementedError):
+    bleh = importlib.import_module("custom_nodes.ComfyUI-bleh")
+    bleh_version = getattr(bleh, "BLEH_VERSION", -1)
+    if bleh_version < 1:
+        raise NotImplementedError
+    MODULES["bleh"] = bleh.py
 
-    def __init__(self):
-        self.initialized = False
-        self.modules = {}
-        self.init_handlers = []
-        self.handlers = []
-
-    def __getitem__(self, key):
-        return self.modules[key]
-
-    def __contains__(self, key):
-        return key in self.modules
-
-    def __getattr__(self, key):
-        return self.modules.get(key)
-
-    def get(self, key, default=None):
-        return self.modules.get(key, default)
-
-    @staticmethod
-    def get_custom_node(module_name: str, key: str) -> ModuleType | None:
-        bi_module = sys.modules.get("_blepping_integrations", {}).get(key)
-        if bi_module is not None:
-            return bi_module
-        module_key = f"custom_nodes.{module_name}"
-        with contextlib.suppress(StopIteration):
-            spec = importlib.util.find_spec(module_key)
-            if spec is None:
-                return None
-            return next(
-                v
-                for v in sys.modules.copy().values()
-                if hasattr(v, "__spec__")
-                and v.__spec__ is not None
-                and v.__spec__.origin == spec.origin
-            )
-        return None
-
-    def register_init_handler(self, handler):
-        self.init_handlers.append(handler)
-
-    def register_integration(self, key: str, module_name: str, handler=None) -> None:
-        if self.initialized:
-            raise ValueError(
-                "Internal error: Cannot register integration after initialization",
-            )
-        if any(item[0] == key or item[1] == module_name for item in self.handlers):
-            errstr = (
-                f"Module {module_name} ({key}) already in integration handlers list!"
-            )
-            raise ValueError(errstr)
-        self.handlers.append(self.Integration(key, module_name, handler))
-
-    def initialize(self) -> None:
-        if self.initialized:
-            return
-        self.initialized = True
-        for ih in self.handlers:
-            module = self.get_custom_node(ih.module_name, ih.key)
-            if module is None:
-                continue
-            if ih.handler is not None:
-                module = ih.handler(module)
-            if module is not None:
-                self.modules[ih.key] = module
-
-        for init_handler in self.init_handlers:
-            init_handler(self)
-
-
-class OCSIntegrations(Integrations):
-    def __init__(self, *args: list, **kwargs: dict):
-        super().__init__(*args, **kwargs)
-        self.register_integration("bleh", "ComfyUI-bleh", self.bleh_integration)
-        self.register_integration("sonar", "ComfyUI-sonar", self.sonar_integration)
-        self.register_integration("nnlatentupscale", "ComfyUi_NNLatentUpscale")
-        self.register_integration("tiled_diffusion", "ComfyUI-TiledDiffusion")
-
-    @classmethod
-    def bleh_integration(cls, module: ModuleType) -> ModuleType | None:
-        bleh_version = getattr(module, "BLEH_VERSION", -1)
-        if bleh_version < 1:
-            return None
-        return module.py
-
-    @classmethod
-    def sonar_integration(cls, module: ModuleType) -> ModuleType | None:
-        return module.py
-
-
-MODULES = OCSIntegrations()
-
-
-class IntegratedNode(type):
-    @staticmethod
-    def wrap_INPUT_TYPES(orig_method: Callable, *args: list, **kwargs: dict) -> dict:
-        MODULES.initialize()
-        return orig_method(*args, **kwargs)
-
-    def __new__(cls: type, name: str, bases: tuple, attrs: dict) -> object:
-        obj = type.__new__(cls, name, bases, attrs)
-        if hasattr(obj, "INPUT_TYPES"):
-            obj.INPUT_TYPES = partial(cls.wrap_INPUT_TYPES, obj.INPUT_TYPES)
-        return obj
+with contextlib.suppress(ImportError, NotImplementedError):
+    MODULES["sonar"] = importlib.import_module("custom_nodes.ComfyUI-sonar").py
 
+with contextlib.suppress(ImportError, NotImplementedError):
+    MODULES["nnlatentupscale"] = importlib.import_module(
+        "custom_nodes.ComfyUi_NNLatentUpscale"
+    )
 
 __all__ = ("MODULES",)

py/filtering.py

@@ -10,33 +10,23 @@ from .utils import fallback
 
 OD = collections.OrderedDict
 
-BLENDING_MODES = {
-    "lerp": torch.lerp,
+EXT_BLEH = EXT.get("bleh")
+EXT_SONAR = EXT.get("sonar")
+
+if "bleh" in EXT:
+    BLENDING_MODES = EXT_BLEH.latent_utils.BLENDING_MODES
+else:
+    BLENDING_MODES = {
+        "lerp": lambda a, b, t: (1 - t) * a + t * b,
+    }
+
+BLENDING_MODES = BLENDING_MODES | {
     "a_only": lambda a, b, t: a * t,
     "b_only": lambda a, b, t: b * t,
-    "inject": lambda a, b, t: (b * t).add_(a),
 }
 
 FILTER = {}
 
-EXT_BLEH = None
-
-
-def init_integrations(integrations):
-    global BLENDING_MODES, FILTER, EXT_BLEH
-    EXT_BLEH = integrations.bleh
-    if EXT_BLEH is not None:
-        BLENDING_MODES = EXT_BLEH.latent_utils.BLENDING_MODES | BLENDING_MODES
-        FILTER |= {
-            "bleh_enhance": BlehEnhanceFilter,
-            "bleh_ops": BlehOpsFilter,
-        }
-    ext_sonar = integrations.sonar
-    if ext_sonar is not None:
-        FILTER["sonar_power_filter"] = SonarPowerFilter
-
-
-EXT.register_init_handler(init_integrations)
 
 FILTER_HANDLERS = expr.HandlerContext(
     expr.BASIC_HANDLERS | expression_handlers.HANDLERS
@@ -44,9 +34,8 @@ FILTER_HANDLERS = expr.HandlerContext(
 
 
 class FilterRefs:
-    def __init__(self, kvs=None, *, ctx=None):
+    def __init__(self, kvs=None):
         self.kvs = fallback(kvs, {})
-        self.ctx = fallback(ctx, {})
 
     def get(self, k, default=None):
         return self.kvs.get(k, default)
@@ -58,14 +47,13 @@ class FilterRefs:
         self.kvs[k] = v
 
     def clone(self):
-        return self.__class__(self.kvs.copy(), ctx=self.ctx)
+        return self.__class__(self.kvs.copy())
 
     def __or__(self, other):
-        return self.__class__(self.kvs | other.kvs, ctx=self.ctx | other.ctx)
+        return self.__class__(self.kvs | other.kvs)
 
     def __ior__(self, other):
         self.kvs |= other.kvs
-        self.ctx |= other.ctx
         return self
 
     def __delitem__(self, k):
@@ -83,34 +71,26 @@ class FilterRefs:
     def __iter__(self):
         return self.kvs.__iter__()
 
-    def items(self):
-        return self.kvs.items()
-
     @classmethod
     def from_ss(cls, ss, *, have_current=False):
         ms = ss.model.model_sampling
-        fr = cls(
-            {
-                "step": ss.step,
-                "substep": ss.substep,
-                "dt": ss.dt,
-                "sigma_idx": ss.idx,
-                "sigma": ss.sigma,
-                "sigma_next": ss.sigma_next,
-                "sigma_down": ss.sigma_down,
-                "sigma_up": ss.sigma_up,
-                "sigma_prev": ss.sigma_prev,
-                "hist_len": len(ss.hist),
-                "sigma_min": ms.sigma_min.item(),
-                "sigma_max": ms.sigma_max.item(),
-                "step_pct": float(ss.step / ss.total_steps),
-                "total_steps": ss.total_steps,
-                "sampling_pct": (999 - ms.timestep(ss.sigma).item()) / 999,
-                "is_rectified_flow": ss.model.is_rectified_flow,
-                "original_cfg_scale": ss.model.inner_cfg_scale,
-            },
-            ctx={"ss": ss, "model": ss.model},
-        )
+        fr = cls({
+            "step": ss.step,
+            "substep": ss.substep,
+            "dt": ss.dt,
+            "sigma_idx": ss.idx,
+            "sigma": ss.sigma,
+            "sigma_next": ss.sigma_next,
+            "sigma_down": ss.sigma_down,
+            "sigma_up": ss.sigma_up,
+            "sigma_prev": ss.sigma_prev,
+            "hist_len": len(ss.hist),
+            "sigma_min": ms.sigma_min.item(),
+            "sigma_max": ms.sigma_max.item(),
+            "step_pct": float(ss.step / ss.total_steps),
+            "total_steps": ss.total_steps,
+            "sampling_pct": (999 - ms.timestep(ss.sigma).item()) / 999,
+        })
         if have_current and len(ss.hist) > 0:
             fr |= cls.from_mr(ss.hcur)
             fr["d"] = ss.d
@@ -246,12 +226,6 @@ class Filter:
         refs = drefs if refs is None else refs | drefs
         return ops.eval(FILTER_HANDLERS.clone(constants=refs, variables={}))
 
-    def __str__(self):
-        prettyvals = ", ".join(
-            f"{k}={getattr(self, k, None)!s}" for k in self.default_options.keys()
-        )
-        return f"<Filter({self.name}): {prettyvals}>"
-
 
 class SimpleFilter(Filter):
     name = "simple"
@@ -419,88 +393,100 @@ class NormalizeFilter_:
 
 Normalize = NormalizeFilter
 
+if EXT_BLEH:
 
-class BlehEnhanceFilter(Filter):
-    name = "bleh_enhance"
-    default_options = Filter.default_options | {
-        "enhance_mode": None,
-        "enhance_scale": 1.0,
+    class BlehEnhanceFilter(Filter):
+        name = "bleh_enhance"
+        default_options = Filter.default_options | {
+            "enhance_mode": None,
+            "enhance_scale": 1.0,
+        }
+
+        def filter(self, latent, *args, **kwargs):
+            if self.enhance_mode is None or self.enhance_scale == 1:
+                return latent
+            return EXT_BLEH.latent_utils.enhance_tensor(
+                latent, self.enhance_mode, scale=self.enhance_scale, adjust_scale=False
+            )
+
+    class BlehOpsFilter(Filter):
+        name = "bleh_ops"
+        default_options = Filter.default_options | {"ops": ()}
+
+        def __init__(self, **kwargs):
+            super().__init__(**kwargs)
+            if isinstance(self.ops, (tuple, list)):
+                self.ops = EXT_BLEH.nodes.ops.RuleGroup(
+                    tuple(
+                        r
+                        for rs in self.ops
+                        for r in EXT_BLEH.nodes.ops.Rule.from_dict(rs)
+                    )
+                )
+                return
+            if not isinstance(self.ops, str):
+                raise ValueError("ops key must be a YAML string or list of object")
+            self.ops = EXT_BLEH.nodes.ops.RuleGroup.from_yaml(self.ops)
+
+        def filter(self, latent, ref_latent, *args, refs=None, **kwargs):
+            if not self.ops:
+                return latent
+            refs = fallback(refs, {})
+            bops = EXT_BLEH.nodes.ops
+            state = {
+                bops.CondType.TYPE: bops.PatchType.LATENT,
+                bops.CondType.PERCENT: 0.0,
+                bops.CondType.BLOCK: -1,
+                bops.CondType.STAGE: -1,
+                bops.CondType.STEP: refs.get("step", 0),
+                bops.CondType.STEP_EXACT: refs.get("step", -1),
+                "h": latent,
+                "hsp": ref_latent,
+                "target": "h",
+            }
+            self.ops.eval(state, toplevel=True)
+            return state["h"]
+
+    FILTER |= {
+        "bleh_enhance": BlehEnhanceFilter,
+        "bleh_ops": BlehOpsFilter,
     }
 
-    def filter(self, latent, *args, **kwargs):
-        if self.enhance_mode is None or self.enhance_scale == 1:
-            return latent
-        return EXT_BLEH.latent_utils.enhance_tensor(
-            latent, self.enhance_mode, scale=self.enhance_scale, adjust_scale=False
-        )
+if EXT_SONAR:
 
+    class SonarPowerFilter(Filter):
+        name = "sonar_power_filter"
+        default_options = Filter.default_options
 
-class BlehOpsFilter(Filter):
-    name = "bleh_ops"
-    default_options = Filter.default_options | {"ops": ()}
-
-    def __init__(self, **kwargs):
-        super().__init__(**kwargs)
-        if isinstance(self.ops, (tuple, list)):
-            self.ops = EXT_BLEH.nodes.ops.RuleGroup(
-                tuple(
-                    r for rs in self.ops for r in EXT_BLEH.nodes.ops.Rule.from_dict(rs)
+        def __init__(self, **kwargs):
+            super().__init__(**kwargs)
+            power_filter = self.options.pop("power_filter", None)
+            if power_filter is None:
+                self.power_filter = None
+                return
+            if not isinstance(power_filter, dict):
+                raise ValueError("power_filter key must be dict or null")
+            self.power_filter = (
+                expression_handlers.SonarPowerFilterHandler.make_power_filter(
+                    power_filter
                 )
             )
-            return
-        if not isinstance(self.ops, str):
-            raise ValueError("ops key must be a YAML string or list of object")
-        self.ops = EXT_BLEH.nodes.ops.RuleGroup.from_yaml(self.ops)
 
-    def filter(self, latent, ref_latent, *args, refs=None, **kwargs):
-        if not self.ops:
-            return latent
-        refs = fallback(refs, {})
-        bops = EXT_BLEH.nodes.ops
-        state = {
-            bops.CondType.TYPE: bops.PatchType.LATENT,
-            bops.CondType.PERCENT: 0.0,
-            bops.CondType.BLOCK: -1,
-            bops.CondType.STAGE: -1,
-            bops.CondType.STEP: refs.get("step", 0),
-            bops.CondType.STEP_EXACT: refs.get("step", -1),
-            "h": latent,
-            "hsp": ref_latent,
-            "target": "h",
-        }
-        self.ops.eval(state, toplevel=True)
-        return state["h"]
+        def filter(self, latent, ref_latent, *args, refs=None, **kwargs):
+            if not self.power_filter:
+                return latent
+            filter_rfft = self.power_filter.make_filter(latent.shape).to(
+                latent.device, non_blocking=True
+            )
+            ns = self.power_filter.make_noise_sampler_internal(
+                latent,
+                lambda *_unused, latent=latent: latent,
+                filter_rfft,
+                normalized=False,
+            )
+            return ns(None, None)
 
-
-class SonarPowerFilter(Filter):
-    name = "sonar_power_filter"
-    default_options = Filter.default_options
-
-    def __init__(self, **kwargs):
-        super().__init__(**kwargs)
-        power_filter = self.options.pop("power_filter", None)
-        if power_filter is None:
-            self.power_filter = None
-            return
-        if not isinstance(power_filter, dict):
-            raise ValueError("power_filter key must be dict or null")
-        self.power_filter = (
-            expression_handlers.SonarPowerFilterHandler.make_power_filter(power_filter)
-        )
-
-    def filter(self, latent, ref_latent, *args, refs=None, **kwargs):
-        if not self.power_filter:
-            return latent
-        filter_rfft = self.power_filter.make_filter(latent.shape).to(
-            latent.device, non_blocking=True
-        )
-        ns = self.power_filter.make_noise_sampler_internal(
-            latent,
-            lambda *_unused, latent=latent: latent,
-            filter_rfft,
-            normalized=False,
-        )
-        return ns(None, None)
+    FILTER |= {"sonar_power_filter": SonarPowerFilter}
 
 
 def make_filter(args):

py/latent.py

@@ -11,20 +11,6 @@ from comfy import latent_formats
 
 from .external import MODULES as EXT
 
-EXT_NNLATENTUPSCALE = None
-
-
-def init_integrations(integrations):
-    global get_noise_sampler, EXT_NNLATENTUPSCALE
-
-    ext_sonar = integrations.sonar
-    if ext_sonar is not None:
-        get_noise_sampler = ext_sonar.noise.get_noise_sampler
-    EXT_NNLATENTUPSCALE = EXT.nnlatentupscale
-
-
-EXT.register_init_handler(init_integrations)
-
 
 def normalize_to_scale(latent, target_min, target_max, *, dim=(-3, -2, -1)):
     min_val, max_val = (
@@ -39,33 +25,32 @@ def normalize_to_scale(latent, target_min, target_max, *, dim=(-3, -2, -1)):
     )
 
 
-# Improvements by https://github.com/Clybius
 # The following is modified to work with latent images of ~0 mean from https://github.com/Jamy-L/Pytorch-Contrast-Adaptive-Sharpening/tree/main.
-# The algorithm is directly implemented from FidelityFX's source code that can be found here: https://github.com/GPUOpen-Effects/FidelityFX-CAS/blob/master/ffx-cas/ffx_cas.h.
-def contrast_adaptive_sharpening(  # noqa: PLR0914
-    x,
-    amount=0.8,
-    *,
-    normalize=True,
-    epsilon=1e-06,
-):
-    orig_shape = x.shape
-    if x.ndim == 5:
-        x = x.reshape(orig_shape[0], orig_shape[1] * orig_shape[2], *orig_shape[-2:])
-    elif x.ndim != 4:
-        raise ValueError(
-            "Contrast-adaptive sharpening requires a tensor with 4 or 5 dimensions",
-        )
+def contrast_adaptive_sharpening(x, amount=0.8, *, epsilon=1e-06):
+    """
+    Performs a contrast adaptive sharpening on the batch of images x.
+    The algorithm is directly implemented from FidelityFX's source code,
+    that can be found here
+    https://github.com/GPUOpen-Effects/FidelityFX-CAS/blob/master/ffx-cas/ffx_cas.h
 
-    def on_abs_stacked(tensor_list, f, *args: list, **kwargs: dict):
+    Parameters
+    ----------
+    x : Tensor
+        Image or stack of images, of shape [batch, channels, ny, nx].
+        Batch and channel dimensions can be ommited.
+    amount : int [0, 1]
+        Amount of sharpening to do, 0 being minimum and 1 maximum
+
+    Returns
+    -------
+    Tensor
+        Processed stack of images.
+
+    """
+
+    def on_abs_stacked(tensor_list, f, *args, **kwargs):
         return f(torch.abs(torch.stack(tensor_list)), *args, **kwargs)[0]
 
-    if normalize:
-        luminance = torch.linalg.vector_norm(x, dim=1, keepdim=True).add_(1e-08)
-        x = x / luminance
-        orig_mean = x.mean(dim=(-3, -2, -1), keepdim=True)
-        x -= orig_mean
-
     x_padded = F.pad(x, pad=(1, 1, 1, 1))
     x_padded = torch.complex(x_padded, torch.zeros_like(x_padded))
     # each side gets padded with 1 pixel
@@ -117,12 +102,7 @@ def contrast_adaptive_sharpening(  # noqa: PLR0914
     div = torch.reciprocal(1 + 4 * w)
     output = ((b + d + f + h) * w + e) * div
 
-    output = output.real
-    for ob, xb in zip(x, output):
-        ob.clamp_(*xb.aminmax())
-    if normalize:
-        output = output.add_(orig_mean).mul_(luminance)
-    return output.reshape(*orig_shape)
+    return output.real.clamp(x.min(), x.max())
 
 
 class ImageBatch(tuple):
@@ -165,11 +145,24 @@ class OCSTAESD:
     @classmethod
     def decode(cls, fmt, latent):
         latent_format = cls.latent_formats[fmt]
+        # rv = latent_format.process_out(1.0)
         filename = cls.get_taesd_path(cls.get_decoder_name(fmt))
         model = TAESD(
             decoder_path=filename, latent_channels=latent_format.latent_channels
         ).to(latent.device)
+        # print("DEC INPUT ORIG", latent.min(), latent.max())
+        # if torch.any(latent.max() > rv) or torch.any(latent.min() < -rv):
+        #     sv = latent.new((-rv, rv))
+        #     latent = normalize_to_scale(
+        #         latent,
+        #         latent.amin(dim=(-3, -2, -1), keepdim=True).maximum(sv[0]),
+        #         latent.amax(dim=(-3, -2, -1), keepdim=True).minimum(sv[1]),
+        #         dim=(-3, -2, -1),
+        #     )
+        # print("DEC INPUT", latent.min(), latent.max())
+        # result = model.decode(latent.clamp(-rv, rv)).movedim(1, 3)
         result = model.decode(latent).movedim(1, 3)
+        # print("DEC RESULT", result.shape, result.isnan().any().item())
         return ImageBatch(
             latent_preview.preview_to_image(result[batch_idx])
             for batch_idx in range(result.shape[0])
@@ -197,143 +190,80 @@ class OCSTAESD:
             encoder_path=filename, latent_channels=latent_format.latent_channels
         ).to(device=latent.device)
         result = model.encode(cls.img_to_encoder_input(imgbatch).to(latent.device))
+        # print(
+        #     "ENC RESULT ORIG",
+        #     result.min(),
+        #     result.max(),
+        # )
+        # if torch.any(result.max() > rv) or torch.any(result.min() < -rv):
+        #     sv = result.new((-rv, rv))
+        #     result = normalize_to_scale(
+        #         result,
+        #         result.amin(dim=(-3, -2, -1), keepdim=True).maximum(sv[0]),
+        #         result.amax(dim=(-3, -2, -1), keepdim=True).minimum(sv[1]),
+        #         dim=(-3, -2, -1),
+        #     )
+        # print(
+        #     "ENC RESULT",
+        #     result.shape,
+        #     result.isnan().any().item(),
+        #     result.min(),
+        #     result.max(),
+        # )
         return result.to(latent.dtype).clamp(-rv, rv)
 
 
-bleh_scale_samples = None
-UPSCALE_METHODS = ("bicubic", "bislerp", "bilinear", "nearest-exact", "area")
+if "bleh" in EXT:
+    scale_samples = EXT["bleh"].latent_utils.scale_samples
+    UPSCALE_METHODS = EXT["bleh"].latent_utils.UPSCALE_METHODS
+else:
+    UPSCALE_METHODS = ("bicubic", "bislerp", "bilinear", "nearest-exact", "area")
+
+    def scale_samples(
+        samples,
+        width,
+        height,
+        mode="bicubic",
+        sigma=None,  # noqa: ARG001
+    ):
+        if mode == "bislerp":
+            return bislerp(samples, width, height)
+        return F.interpolate(samples, size=(height, width), mode=mode)
 
 
-def scale_samples(
-    samples,
-    width,
-    height,
-    mode="bicubic",
-    sigma=None,  # noqa: ARG001
-):
-    global bleh_scale_samples, UPSCALE_METHODS
-    if bleh_scale_samples is None:
-        bleh = EXT.get("bleh")
-        if bleh is not None:
-            bleh_scale_samples = bleh.latent_utils.scale_samples
-            UPSCALE_METHODS = bleh.latent_utils.UPSCALE_METHODS
-        else:
-            bleh_scale_samples = False
-    if bleh_scale_samples:
-        return bleh_scale_samples(samples, width, height, mode=mode, sigma=sigma)
-    if mode == "bislerp":
-        return bislerp(samples, width, height)
-    return F.interpolate(samples, size=(height, width), mode=mode)
+if "sonar" in EXT:
+    get_noise_sampler = EXT["sonar"].noise.get_noise_sampler
+else:
+
+    def get_noise_sampler(noise_type, x, *_args: list, **_kwargs: dict):
+        if noise_type != "gaussian":
+            raise ValueError("Only gaussian noise supported")
+        return lambda _s, _sn: torch.randn_like(x)
 
 
-def get_noise_sampler(noise_type, x, *_args: list, **_kwargs: dict):  # noqa: F811
-    if noise_type != "gaussian":
-        raise ValueError("Only gaussian noise supported")
-    return lambda _s, _sn: torch.randn_like(x)
+if "nnlatentupscale" in EXT:
 
-
-def scale_nnlatentupscale(mode, latent, scale=2.0, *, scale_factor=0.13025):
-    if EXT_NNLATENTUPSCALE is None:
-        raise RuntimeError("nnlatentupscale integration not available")
-    mode = {"sdxl": "SDXL", "sd1": "SD 1.x"}.get(mode)
-    if mode is None:
-        raise ValueError("Bad mode")
-    node = EXT_NNLATENTUPSCALE.NNLatentUpscale()
-    model = EXT_NNLATENTUPSCALE.latent_resizer.LatentResizer.load_model(
-        node.weight_path[mode], latent.device, latent.dtype
-    ).to(device=latent.device)
-    result = (
-        model(scale_factor * latent, scale=scale).to(
-            dtype=latent.dtype, device=latent.device
+    def scale_nnlatentupscale(
+        mode,
+        latent,
+        scale=2.0,
+        *,
+        scale_factor=0.13025,
+        __nlu_module=EXT["nnlatentupscale"],
+    ):
+        module = __nlu_module
+        mode = {"sdxl": "SDXL", "sd1": "SD 1.x"}.get(mode)
+        if mode is None:
+            raise ValueError("Bad mode")
+        node = module.NNLatentUpscale()
+        model = module.latent_resizer.LatentResizer.load_model(
+            node.weight_path[mode], latent.device, latent.dtype
+        ).to(device=latent.device)
+        result = (
+            model(scale_factor * latent, scale=scale).to(
+                dtype=latent.dtype, device=latent.device
+            )
+            / scale_factor
         )
-        / scale_factor
-    )
-    del model
-    return result
-
-
-# Gaussian blur
-def gaussian_blur_2d(img, kernel_size, sigma):
-    height = img.shape[-1]
-    kernel_size = min(kernel_size, height - (height % 2 - 1))
-    ksize_half = (kernel_size - 1) * 0.5
-
-    x = torch.linspace(-ksize_half, ksize_half, steps=kernel_size)
-
-    pdf = torch.exp(-0.5 * (x / sigma).pow(2))
-
-    x_kernel = pdf / pdf.sum()
-    x_kernel = x_kernel.to(device=img.device, dtype=img.dtype)
-
-    kernel2d = torch.mm(x_kernel[:, None], x_kernel[None, :])
-    kernel2d = kernel2d.expand(img.shape[-3], 1, kernel2d.shape[0], kernel2d.shape[1])
-
-    padding = [kernel_size // 2, kernel_size // 2, kernel_size // 2, kernel_size // 2]
-
-    img = torch.nn.functional.pad(img, padding, mode="reflect")
-    img = torch.nn.functional.conv2d(img, kernel2d, groups=img.shape[-3])
-
-    return img
-
-
-# Saliency-adaptive Noise Fusion based on High-fidelity Person-centric Subject-to-Image Synthesis (Wang et al.)
-# https://github.com/CodeGoat24/Face-diffuser/blob/edff1a5178ac9984879d9f5e542c1d0f0059ca5f/facediffuser/pipeline.py#L535-L562
-def snf_guidance(
-    t_guidance: torch.Tensor,
-    s_guidance: torch.Tensor,
-    t_kernel_size=3,
-    t_sigma=1,
-    s_kernel_size=3,
-    s_sigma=1,
-):
-    b, c, h, w = shape = t_guidance.shape
-
-    t_softmax, s_softmax = (
-        torch.softmax(
-            gaussian_blur_2d(torch.abs(t), ks, sig).reshape(b * c, h * w),
-            dim=1,
-        ).reshape(*shape)
-        for t, ks, sig in (
-            (t_guidance, t_kernel_size, t_sigma),
-            (s_guidance, s_kernel_size, s_sigma),
-        )
-    )
-    guidance_stacked = torch.stack((t_guidance, s_guidance), dim=0)
-    argeps = torch.argmax(
-        torch.stack((t_softmax, s_softmax), dim=0), dim=0, keepdim=True
-    )
-    return torch.gather(guidance_stacked, dim=0, index=argeps).squeeze(0)
-
-
-class OCSLatentFormat:
-    def __init__(self, device, latent_format):
-        if latent_format.latent_rgb_factors is None:
-            self.rgb_factors = None
-            return
-        self.rgb_factors = torch.tensor(
-            latent_format.latent_rgb_factors, device=device, dtype=torch.float
-        ).t()
-        # Thanks for Joviax for the help implementing this!
-        self.rgb_factors_inv = torch.linalg.pinv(self.rgb_factors)
-        bias = getattr(latent_format, "latent_rgb_factors_bias", None)
-        self.rgb_factors_bias = (
-            None
-            if bias is None
-            else torch.tensor(bias, device=device, dtype=torch.float)
-        )
-
-    def latent_to_rgb(self, latent: torch.Tensor) -> torch.Tensor:
-        # NCHW -> NHWC
-        if self.latent_factors is None:
-            raise ValueError("No RGB factors for latent type!")
-        return torch.nn.functional.linear(
-            latent.movedim(1, -1), self.rgb_factors, bias=self.rgb_factors_bias
-        )
-
-    def rgb_to_latent(self, img: torch.Tensor) -> torch.Tensor:
-        # NHWC
-        if self.latent_factors is None:
-            raise ValueError("No RGB factors for latent type!")
-        if self.rgb_factors_bias is not None:
-            img = img - self.rgb_factors_bias
-        return torch.nn.functional.linear(img, self.rgb_factors_inv)
+        del model
+        return result

py/model.py

@@ -1,3 +1,5 @@
+from collections import namedtuple
+
 import torch
 
 import comfy
@@ -6,7 +8,6 @@ from comfy.k_diffusion.sampling import to_d
 from . import filtering
 
 from .utils import fallback
-from .latent import OCSLatentFormat
 
 
 class History:
@@ -42,14 +43,12 @@ class ModelResult:
         sigma,
         x,
         denoised,
-        have_uncond=True,
         **kwargs,
     ):
         self.call_idx = call_idx
         self.sigma = sigma
         self.x = x
         self.denoised = denoised
-        self.have_uncond = have_uncond
         for k in ("denoised_uncond", "denoised_cond", "tangents", "jdenoised"):
             setattr(self, k, kwargs.pop(k, None))
         if len(kwargs) != 0:
@@ -73,33 +72,6 @@ class ModelResult:
             x = x - denoised * alt_cfgpp_scale + denoised_uncond * alt_cfgpp_scale
         return to_d(x, sigma, denoised if not cfgpp else denoised_uncond)
 
-    def get_split_prediction(
-        self,
-        *,
-        x=None,
-        d=None,
-        sigma=None,
-        denoised=None,
-        denoised_uncond=None,
-        alt_cfgpp_scale=0,
-        cfgpp=False,
-    ):
-        denoised = fallback(denoised, self.denoised)
-        denoised_uncond = fallback(denoised_uncond, self.denoised_uncond)
-        x = fallback(x, self.x)
-        sigma = fallback(sigma, self.sigma)
-        if d is None:
-            d = self.to_d(
-                x=x,
-                sigma=sigma,
-                denoised=denoised,
-                denoised_uncond=denoised_uncond,
-                alt_cfgpp_scale=alt_cfgpp_scale,
-                cfgpp=cfgpp,
-            )
-        denoised_pred = denoised if alt_cfgpp_scale == 0 else x - d * sigma
-        return (denoised_pred, d)
-
     @property
     def d(self):
         return self.to_d()
@@ -122,44 +94,27 @@ class ModelResult:
             setattr(obj, k, val)
         return obj
 
-    def get_error(self, other, *, override=None, alt_cfgpp_scale=0, cfgpp=False):
-        slf = fallback(override, self)
-        first, second = (other, slf) if other.sigma > slf.sigma else (slf, other)
-        if first.sigma == second.sigma:
-            return 0.0
-        d = first.to_d(alt_cfgpp_scale=alt_cfgpp_scale, cfgpp=cfgpp)
-        d_pred = second.to_d(
-            x=first.x + d * (second.sigma - first.sigma),
-            alt_cfgpp_scale=alt_cfgpp_scale,
-            cfgpp=cfgpp,
-        )
-        return torch.linalg.norm(d_pred.sub_(d)).div_(torch.linalg.norm(d)).item()
+
+ModelCallCacheConfig = namedtuple(
+    "ModelCallCacheConfig", ("size", "max_use", "threshold"), defaults=(0, 1000000, 1)
+)
 
 
-class OCSModel:
+class ModelCallCache:
     def __init__(
         self,
         model,
-        x: torch.Tensor,
-        s_in: torch.Tensor,
-        extra_args: dict,
+        x,
+        s_in,
+        extra_args,
         *,
-        cache: dict | None = None,
-        filter: dict | None = None,
-        cfg1_uncond_optimization: bool = False,
-        cfg_scale_override: int | float | None = None,
-    ) -> None:
+        cache=None,
+        filter=None,
+    ):
+        self.cache = ModelCallCacheConfig(**fallback(cache, {}))
         filtargs = fallback(filter, {}).copy()
         self.filters = {}
-        for key in (
-            "input",
-            "denoised",
-            "jdenoised",
-            "cond",
-            "uncond",
-            "postcfg",
-            "precfg",
-        ):
+        for key in ("input", "denoised", "jdenoised", "cond", "uncond", "x"):
             filt = filtargs.pop(key, None)
             if filt is None:
                 continue
@@ -167,34 +122,25 @@ class OCSModel:
         self.model = model
         self.s_in = s_in
         self.extra_args = extra_args
-        self.cfg1_uncond_optimization = cfg1_uncond_optimization
-        self.cfg_scale_override = cfg_scale_override
-        self.is_rectified_flow = isinstance(
-            model.inner_model.inner_model.model_sampling, comfy.model_sampling.CONST
-        )
-        self.latent_format = OCSLatentFormat(
-            x.device, model.inner_model.inner_model.latent_format
-        )
+        if self.cache.size < 1:
+            return
+        self.reset_cache()
 
-    def maybe_filter(
-        self, name: str, latent: torch.Tensor, *args: list, **kwargs: dict
-    ) -> torch.Tensor:
+    def maybe_filter(self, name, latent, *args, **kwargs):
         filt = self.filters.get(name)
         if filt is None:
             return latent
         return filt.apply(latent, *args, **kwargs)
 
-    def filter_result(
-        self, result: ModelResult, *args: list, **kwargs: dict
-    ) -> ModelResult:
+    def filter_result(self, result, *args, **kwargs):
         if not self.filters:
             return result
         result = result.clone()
-        for key in ("denoised", "cond", "uncond", "jdenoised"):
+        for key in ("denoised", "cond", "uncond", "jdenoised", "x"):
             filt = self.filters.get(key)
             if filt is None:
                 continue
-            attk = f"denoised_{key}" if key in {"cond", "uncond"} else key
+            attk = f"denoised_{key}" if key in ("cond", "uncond") else key
             inpval = getattr(result, attk, None)
             if inpval is None:
                 continue
@@ -202,123 +148,87 @@ class OCSModel:
         return result
 
     @staticmethod
-    def _fr_add_mr(fr: filtering.FilterRefs, mr: ModelResult) -> filtering.FilterRefs:
+    def _fr_add_mr(fr, mr):
         frmr = filtering.FilterRefs.from_mr(mr)
         fr.kvs |= {f"{k}_curr": v for k, v in frmr.kvs.items()}
         return fr
 
-    def call_model(
-        self, x: torch.Tensor, sigma: torch.Tensor, **kwargs: dict
-    ) -> torch.Tensor:
+    def reset_cache(self):
+        size = self.cache.size
+        self.slot = [None] * size
+        self.slot_use = [self.cache.max_use] * size
+
+    def get(self, idx, *, jvp=False):
+        idx -= self.cache.threshold
+        if (
+            idx >= self.cache.size
+            or idx < 0
+            or self.slot[idx] is None
+            or self.slot_use[idx] < 1
+        ):
+            return None
+        result = self.slot[idx]
+        if jvp and result.jdenoised is None:
+            return None
+        self.slot_use[idx] -= 1
+        return result
+
+    def set(self, idx, mr):
+        idx -= self.cache.threshold
+        if idx < 0 or idx >= self.cache.size:
+            return
+        self.slot_use[idx] = self.cache.max_use
+        self.slot[idx] = mr
+
+    def call_model(self, x, sigma, **kwargs):
         return self.model(x, sigma * self.s_in, **self.extra_args | kwargs)
 
     @property
     def model_sampling(self):
         return self.model.inner_model.inner_model.model_sampling
 
-    @property
-    def inner_cfg_scale(self) -> None | int | float:
-        maybe_cfg_scale = getattr(self.model.inner_model, "cfg", None)
-        return maybe_cfg_scale if isinstance(maybe_cfg_scale, (int, float)) else None
-
-    def set_inner_cfg_scale(self, scale: None | int | float) -> None | int | float:
-        eff_scale = self.cfg_scale_override
-        if scale is not None:
-            eff_scale = None if scale < 0 else scale
-        if eff_scale is None or eff_scale < 0:
-            return None
-        curr_cfg_scale = self.inner_cfg_scale
-        if curr_cfg_scale is None:
-            return None
-        self.model.inner_model.cfg = eff_scale
-        return curr_cfg_scale
-
     def __call__(
         self,
-        x: torch.Tensor,
-        sigma: torch.Tensor,
+        x,
+        sigma,
         *,
-        call_index: int = 0,
+        call_index=0,
         ss,
         s_in=None,
         tangents=None,
-        require_uncond: bool = False,
-        cfg_scale_override: None | int = None,
+        return_cached=False,
         **kwargs,
-    ) -> ModelResult:
+    ):
         filter_refs = ss.refs | filtering.FilterRefs({
             "model_call": call_index,
             "orig_x": x,
         })
+        result = self.get(call_index, jvp=tangents is not None)
+        # print(
+        #     f"MODEL: idx={call_index}, size={self.size}, threshold={self.threshold}, cached={result is not None}"
+        # )
+        if result is not None:
+            self._fr_add_mr(filter_refs, result)
+            result = self.filter_result(result, default_ref=x, refs=filter_refs)
+            return (result, True) if return_cached else result
 
         comfy.model_management.throw_exception_if_processing_interrupted()
 
         model_options = self.extra_args.get("model_options", {}).copy()
         denoised_cond = denoised_uncond = None
-        have_uncond = True
 
         def postcfg(args):
-            nonlocal denoised_cond, denoised_uncond, have_uncond
+            nonlocal denoised_cond, denoised_uncond
             denoised_uncond = args["uncond_denoised"]
             denoised_cond = args["cond_denoised"]
-            result = args["denoised"]
-            if "postcfg" in self.filters:
-                result = self.maybe_filter(
-                    "postcfg",
-                    result,
-                    refs=filter_refs
-                    | filtering.FilterRefs({
-                        "postcfg_input": args["input"],
-                        "postcfg_sigma": args["sigma"],
-                        "postcfg_denoised_cond": denoised_cond,
-                        "postcfg_denoised_uncond": denoised_uncond,
-                    }),
-                )
-            if denoised_uncond is None:
-                have_uncond = False
-                denoised_uncond = denoised_cond
-            return result
+            return args["denoised"]
 
-        def precfg(args):
-            conds_out = args["conds_out"]
-            precfg_refs = (
-                filter_refs
-                | filtering.FilterRefs({
-                    "precfg_input": args["input"],
-                    "precfg_sigma": args["sigma"],
-                    "precfg_cond_scale": args["cond_scale"],
-                })
-                | filtering.FilterRefs({
-                    f"precfg_cond_{idx}": cond for idx, cond in enumerate(conds_out)
-                })
+        extra_args = self.extra_args | {
+            "model_options": comfy.model_patcher.set_model_options_post_cfg_function(
+                model_options, postcfg, disable_cfg1_optimization=True
             )
-            return [
-                self.maybe_filter(
-                    "precfg",
-                    curr_cond,
-                    refs=precfg_refs | filtering.FilterRefs({"cond_idx": cond_idx}),
-                )
-                for cond_idx, curr_cond in enumerate(conds_out)
-            ]
-
-        orig_cfg_scale = self.set_inner_cfg_scale(cfg_scale_override)
-
-        model_options = comfy.model_patcher.set_model_options_post_cfg_function(
-            model_options,
-            postcfg,
-            disable_cfg1_optimization=require_uncond
-            or not self.cfg1_uncond_optimization,
-        )
-        if "precfg" in self.filters:
-            model_options = comfy.model_patcher.set_model_options_pre_cfg_function(
-                model_options,
-                precfg,
-            )
-
-        extra_args = self.extra_args | {"model_options": model_options}
+        }
         s_in = fallback(s_in, self.s_in)
-        if s_in.shape[0] != x.shape[0]:
-            s_in = self.s_in = x.new_ones((x.shape[0],))
         x = self.maybe_filter("input", x, refs=filter_refs)
 
         def call_model(x, sigma, **kwargs):
@@ -326,31 +236,29 @@ class OCSModel:
 
         if tangents is None:
             denoised = call_model(x, sigma, **kwargs)
-            self.set_inner_cfg_scale(orig_cfg_scale)
             mr = ModelResult(
                 call_index,
                 sigma,
                 x,
                 denoised,
-                have_uncond=have_uncond,
                 denoised_uncond=denoised_uncond,
                 denoised_cond=denoised_cond,
             )
+            self.set(call_index, mr)
             self._fr_add_mr(filter_refs, mr)
             mr = self.filter_result(mr, default_ref=x, refs=filter_refs)
-            return mr
+            return (mr, False) if return_cached else mr
         denoised, denoised_prime = torch.func.jvp(call_model, (x, sigma), tangents)
-        self.set_inner_cfg_scale(orig_cfg_scale)
         mr = ModelResult(
             call_index,
             sigma,
             x,
             denoised,
-            have_uncond=have_uncond,
             jdenoised=denoised_prime,
             denoised_uncond=denoised_uncond,
             denoised_cond=denoised_cond,
         )
+        self.set(call_index, mr)
         self._fr_add_mr(filter_refs, mr)
         mr = self.filter_result(mr, default_ref=x, refs=filter_refs)
-        return mr
+        return (mr, False) if return_cached else mr

py/nodes.py

@@ -1,75 +1,21 @@
-import comfy
 import yaml
 
-import torch
+import comfy
 
-from tqdm import tqdm
-
-from .external import MODULES, IntegratedNode
-from .restart import Restart
 from .sampling import composable_sampler
+from .substep_sampling import StepSamplerChain, StepSamplerGroups, ParamGroup
 from .step_samplers import STEP_SAMPLERS
 from .substep_merging import MERGE_SUBSTEPS_CLASSES
-from .substep_sampling import ParamGroup, StepSamplerChain, StepSamplerGroups
-from .filtering import make_filter
+from .restart import Restart
 
-try:
-    from comfy_execution import validation as comfy_validation
-
-    if not hasattr(comfy_validation, "validate_node_input"):
-        raise NotImplementedError
-    HAVE_COMFY_UNION_TYPE = comfy_validation.validate_node_input("B", "A,B")
-except (ImportError, NotImplementedError):
-    HAVE_COMFY_UNION_TYPE = False
-except Exception as exc:
-    HAVE_COMFY_UNION_TYPE = False
-    tqdm.write(
-        f"** OCS: Warning, caught unexpected exception trying to detect ComfyUI union type support. Disabling. Exception: {exc}"
-    )
-
-PARAM_INPUT_TYPES = frozenset((
-    "IMAGE",
-    "OCS_NOISE",
-    "SAMPLER",
-    "SIGMAS",
-    "SONAR_CUSTOM_NOISE",
-    "UPSCALE_MODEL",
-    "VAE",
-))
-
-NOISE_INPUT_TYPES = frozenset(("SONAR_CUSTOM_NOISE", "OCS_NOISE"))
-
-if not HAVE_COMFY_UNION_TYPE:
-
-    class Wildcard(str):
-        __slots__ = ("whitelist",)
-
-        @classmethod
-        def __new__(cls, s, *args: list, whitelist=None, **kwargs: dict):
-            result = super().__new__(s, *args, **kwargs)
-            result.whitelist = whitelist
-            return result
-
-        def __ne__(self, other):
-            return False if self.whitelist is None else other not in self.whitelist
-
-    WILDCARD_NOISE = Wildcard("*", whitelist=NOISE_INPUT_TYPES)
-    WILDCARD_PARAM = Wildcard("*", whitelist=PARAM_INPUT_TYPES)
-else:
-    WILDCARD_NOISE = ",".join(NOISE_INPUT_TYPES)
-    WILDCARD_PARAM = ",".join(PARAM_INPUT_TYPES)
-
-PARAM_INPUT_TYPES_HINT = (
-    f"The following input types are supported: {', '.join(PARAM_INPUT_TYPES)}"
-)
-NOISE_INPUT_TYPES_HINT = (
-    f"The following input types are supported: {', '.join(NOISE_INPUT_TYPES)}"
-)
-
-DEFAULT_YAML_PARAMS = "# YAML/JSON parameters\n"
+DEFAULT_YAML_PARAMS = """\
+# JSON or YAML parameters
+s_noise: 1.0
+eta: 1.0
+"""
 
 
-class SamplerNode(metaclass=IntegratedNode):
+class SamplerNode:
     RETURN_TYPES = ("SAMPLER",)
     CATEGORY = "sampling/custom_sampling/OCS"
     DESCRIPTION = "Overly Complicated Sampling main sampler node. Can be connected to a SamplerCustom or other sampler node that supports a SAMPLER input."
@@ -86,8 +32,7 @@ class SamplerNode(metaclass=IntegratedNode):
                 "groups": (
                     "OCS_GROUPS",
                     {
-                        "tooltip": "Connect OCS substep groups here which are output from the OCS Group node.",
-                        "forceInput": True,
+                        "tooltip": "Connect OCS substep groups here which are output from the OCS Group node."
                     },
                 ),
             },
@@ -96,14 +41,12 @@ class SamplerNode(metaclass=IntegratedNode):
                     "OCS_PARAMS",
                     {
                         "tooltip": "Optionally connect parameters like custom noise here. Output from the OCS Param or OCS MultiParam nodes.",
-                        "forceInput": True,
                     },
                 ),
                 "parameters": (
                     "STRING",
                     {
-                        "default": "",
-                        "placeholder": DEFAULT_YAML_PARAMS,
+                        "default": DEFAULT_YAML_PARAMS,
                         "multiline": True,
                         "dynamicPrompts": False,
                         "tooltip": "The text parameter block allows setting custom parameters using YAML (recommended) or JSON. Optional, may be left blank.",
@@ -119,7 +62,6 @@ class SamplerNode(metaclass=IntegratedNode):
         params_opt=None,
         parameters="",
     ):
-        MODULES.initialize()
         options = {}
         parameters = parameters.strip()
         if parameters:
@@ -138,7 +80,7 @@ class SamplerNode(metaclass=IntegratedNode):
         )
 
 
-class GroupNode(metaclass=IntegratedNode):
+class GroupNode:
     RETURN_TYPES = ("OCS_GROUPS",)
     CATEGORY = "sampling/custom_sampling/OCS"
     DESCRIPTION = "Over Complicated Sampling group definition node."
@@ -188,7 +130,6 @@ class GroupNode(metaclass=IntegratedNode):
                     "OCS_SUBSTEPS",
                     {
                         "tooltip": "Connect output from an OCS Substeps node here.",
-                        "forceInput": True,
                     },
                 ),
             },
@@ -197,21 +138,18 @@ class GroupNode(metaclass=IntegratedNode):
                     "OCS_GROUPS",
                     {
                         "tooltip": "You may optionally connect the output from another OCS Group node here. Only one group per step is used, matching (based on time or other constraints) starts with the OCS Group node furthest from the OCS Sampler.",
-                        "forceInput": True,
                     },
                 ),
                 "params_opt": (
                     "OCS_PARAMS",
                     {
                         "tooltip": "Optionally connect parameters like custom noise here. Output from the OCS Param or OCS MultiParam nodes.",
-                        "forceInput": True,
                     },
                 ),
                 "parameters": (
                     "STRING",
                     {
-                        "default": "",
-                        "placeholder": DEFAULT_YAML_PARAMS,
+                        "default": DEFAULT_YAML_PARAMS,
                         "multiline": True,
                         "dynamicPrompts": False,
                         "tooltip": "The text parameter block allows setting custom parameters using YAML (recommended) or JSON. Optional, may be left blank.",
@@ -232,7 +170,6 @@ class GroupNode(metaclass=IntegratedNode):
         params_opt=None,
         parameters="",
     ):
-        MODULES.initialize()
         group = StepSamplerGroups() if groups_opt is None else groups_opt.clone()
         chain = substeps.clone()
         chain.merge_method = merge_method
@@ -253,7 +190,7 @@ class GroupNode(metaclass=IntegratedNode):
         return (group,)
 
 
-class SubstepsNode(metaclass=IntegratedNode):
+class SubstepsNode:
     RETURN_TYPES = ("OCS_SUBSTEPS",)
     CATEGORY = "sampling/custom_sampling/OCS"
     DESCRIPTION = "Overly Complicated Sampling substeps definition node. Used to define a sampler type and other sampler-specific parameters."
@@ -288,21 +225,18 @@ class SubstepsNode(metaclass=IntegratedNode):
                     "OCS_SUBSTEPS",
                     {
                         "tooltip": "Optionally connect another OCS Substeps node here. Substeps will run in order, starting from the OCS Substeps node FURTHEST from the OCS Group node.",
-                        "forceInput": True,
                     },
                 ),
                 "params_opt": (
                     "OCS_PARAMS",
                     {
                         "tooltip": "Optionally connect parameters like custom noise here. Output from the OCS Param or OCS MultiParam nodes.",
-                        "forceInput": True,
                     },
                 ),
                 "parameters": (
                     "STRING",
                     {
-                        "default": "",
-                        "placeholder": f"{DEFAULT_YAML_PARAMS}s_noise: 1.0\neta: 1.0\n",
+                        "default": DEFAULT_YAML_PARAMS,
                         "multiline": True,
                         "dynamicPrompts": False,
                         "tooltip": "The text parameter block allows setting custom parameters using YAML (recommended) or JSON. Optional, may be left blank.",
@@ -319,7 +253,6 @@ class SubstepsNode(metaclass=IntegratedNode):
         params_opt=None,
         **kwargs,
     ):
-        MODULES.initialize()
         if substeps_opt is not None:
             chain = substeps_opt.clone()
         else:
@@ -337,23 +270,30 @@ class SubstepsNode(metaclass=IntegratedNode):
         return (chain,)
 
 
-class ParamNode(metaclass=IntegratedNode):
+class Wildcard(str):
+    __slots__ = ()
+
+    def __ne__(self, _unused):
+        return False
+
+
+class ParamNode:
     RETURN_TYPES = ("OCS_PARAMS",)
     CATEGORY = "sampling/custom_sampling/OCS"
     DESCRIPTION = "Overly Complicated Sampling parameter definition node. Used to set parameters like custom noise types that require an input."
-    OUTPUT_TOOLTIPS = (
+    OUTPUT_TYPES = (
         "Can be connected to another OCS Param or OCS MultiParam node or any other OCS node that takes OCS_PARAMS as an input.",
     )
 
     FUNCTION = "go"
 
-    OCS_PARAM_INPUT_TYPES = {
+    WC = Wildcard("*")
+
+    OCS_PARAM_TYPES = {
         "custom_noise": lambda v: hasattr(v, "make_noise_sampler"),
         "merge_sampler": lambda v: isinstance(v, StepSamplerChain),
         "restart_custom_noise": lambda v: hasattr(v, "make_noise_sampler"),
-        "sampler": lambda _v: True,
-        "vae": lambda _v: True,
-        "upscale_model": lambda _v: True,
+        "SAMPLER": lambda _v: True,
     }
 
     @classmethod
@@ -361,16 +301,15 @@ class ParamNode(metaclass=IntegratedNode):
         return {
             "required": {
                 "key": (
-                    tuple(cls.OCS_PARAM_INPUT_TYPES.keys()),
+                    tuple(cls.OCS_PARAM_TYPES.keys()),
                     {
                         "tooltip": "Used to set the type of custom parameter.",
                     },
                 ),
                 "value": (
-                    WILDCARD_PARAM,
+                    cls.WC,
                     {
-                        "tooltip": f"Connect the type of value expected by the key. Allows connecting output from any type of node HOWEVER if it is the wrong type expected by the key you will get an error when you run the workflow.\n{PARAM_INPUT_TYPES_HINT}",
-                        "forceInput": True,
+                        "tooltip": "Connect the type of value expected by the key. Allows connecting output from any type of node HOWEVER if it is the wrong type expected by the key you will get an error when you run the workflow.",
                     },
                 ),
             },
@@ -379,47 +318,28 @@ class ParamNode(metaclass=IntegratedNode):
                     "OCS_PARAMS",
                     {
                         "tooltip": "You may optionally connect the output from other OCS Param or OCS MultiParam nodes here to set multiple parameters.",
-                        "forceInput": True,
                     },
                 ),
                 "parameters": (
                     "STRING",
                     {
-                        "default": "",
-                        "placeholder": "# Additional YAML or JSON parameters",
+                        "default": "# Additional YAML or JSON parameters\n",
                         "multiline": True,
                         "dynamicPrompts": False,
-                        "defaultInput": True,
                         "tooltip": "The text parameter block allows setting custom parameters using YAML (recommended) or JSON. Optional, may be left blank.",
                     },
                 ),
             },
         }
 
-    @classmethod
-    def get_renamed_key(cls, key, params):
-        rename = params.get("rename")
-        if rename is None:
-            return key
-        if not isinstance(rename, str):
-            raise ValueError("Param rename key must be a string if set")
-        rename = rename.strip()
-        if not rename or not all(c == "_" or c.isalnum() for c in rename):
-            raise ValueError(
-                "Param rename keys must consist of one or more alphanumeric or underscore characters"
-            )
-        return f"{key}_{rename}"
-
     def go(self, *, key, value, params_opt=None, parameters=""):
-        MODULES.initialize()
-        if not self.OCS_PARAM_INPUT_TYPES[key](value):
+        if not self.OCS_PARAM_TYPES[key](value):
             raise ValueError(f"CSamplerParam: Bad value type for key {key}")
         if parameters:
             extra_params = yaml.safe_load(parameters)
             if extra_params is not None:
                 if not isinstance(extra_params, dict):
                     raise ValueError("Parameters must be a JSON or YAML object")
-                key = self.get_renamed_key(key, extra_params)
         else:
             extra_params = None
         params = ParamGroup(items={}) if params_opt is None else params_opt.clone()
@@ -429,7 +349,7 @@ class ParamNode(metaclass=IntegratedNode):
         return (params,)
 
 
-class MultiParamNode(ParamNode, metaclass=IntegratedNode):
+class MultiParamNode:
     RETURN_TYPES = ("OCS_PARAMS",)
     CATEGORY = "sampling/custom_sampling/OCS"
     DESCRIPTION = "Overly Complicated Sampling parameter definition node. Used to set parameters like custom noise types that require an input. Like the OCS Param node but allows setting multiple parameters at the same time."
@@ -444,7 +364,7 @@ class MultiParamNode(ParamNode, metaclass=IntegratedNode):
     @classmethod
     def INPUT_TYPES(cls):
         param_keys = (
-            ("", *ParamNode.OCS_PARAM_INPUT_TYPES.keys()),
+            ("", *ParamNode.OCS_PARAM_TYPES.keys()),
             {
                 "tooltip": "Used to set the type of custom parameter.",
             },
@@ -458,30 +378,26 @@ class MultiParamNode(ParamNode, metaclass=IntegratedNode):
                     "OCS_PARAMS",
                     {
                         "tooltip": "You may optionally connect the output from other OCS MultiParam or OCS Param nodes here to set multiple parameters.",
-                        "forceInput": True,
                     },
                 ),
                 "parameters": (
                     "STRING",
                     {
-                        "default": "",
-                        "placeholder": """\
+                        "default": """\
 # Additional YAML or JSON parameters
 # Should be an object with key corresponding to the index of the input
 """,
                         "multiline": True,
                         "dynamicPrompts": False,
-                        "defaultInput": True,
                         "tooltip": "The text parameter block allows setting custom parameters using YAML (recommended) or JSON. Optional, may be left blank.",
                     },
                 ),
             }
             | {
                 f"value_opt_{idx}": (
-                    WILDCARD_PARAM,
+                    ParamNode.WC,
                     {
-                        "tooltip": f"Connect the type of value expected by the corresponding key. Allows connecting output from any type of node HOWEVER if it is the wrong type expected by the corresponding key you will get an error when you run the workflow.\n{PARAM_INPUT_TYPES_HINT}",
-                        "forceInput": True,
+                        "tooltip": "Connect the type of value expected by the corresponding key. Allows connecting output from any type of node HOWEVER if it is the wrong type expected by the corresponding key you will get an error when you run the workflow.",
                     },
                 )
                 for idx in range(1, cls.PARAM_COUNT + 1)
@@ -489,7 +405,6 @@ class MultiParamNode(ParamNode, metaclass=IntegratedNode):
         }
 
     def go(self, *, params_opt=None, parameters="", **kwargs):
-        MODULES.initialize()
         params = ParamGroup(items={}) if params_opt is None else params_opt.clone()
         if parameters:
             extra_params = yaml.safe_load(parameters)
@@ -504,18 +419,17 @@ class MultiParamNode(ParamNode, metaclass=IntegratedNode):
             key, value = kwargs.get(f"key_{idx}"), kwargs.get(f"value_opt_{idx}")
             if not key or value is None:
                 continue
-            if not self.OCS_PARAM_INPUT_TYPES[key](value):
+            if not ParamNode.OCS_PARAM_TYPES[key](value):
                 raise ValueError(f"CSamplerParamGroup: Bad value type for key {key}")
-            extra = extra_params.get(str(idx))
-            key = self.get_renamed_key(key, extra)
             params[key] = value
+            extra = extra_params.get(str(idx))
             if extra is not None:
                 params[f"{key}.params"] = extra
 
         return (params,)
 
 
-class SimpleRestartSchedule(metaclass=IntegratedNode):
+class SimpleRestartSchedule:
     RETURN_TYPES = ("SIGMAS",)
     CATEGORY = "sampling/custom_sampling/OCS"
     DESCRIPTION = "Overly Complicated Sampling simple Restart schedule node. Allows generating a Restart sampling schedule based on a text definition."
@@ -548,9 +462,8 @@ class SimpleRestartSchedule(metaclass=IntegratedNode):
                 "schedule": (
                     "STRING",
                     {
-                        "default": "",
-                        "placeholder": """\
-# YAML or JSON restart schedule. Example:
+                        "default": """\
+# YAML or JSON restart schedule
 # Every 5 steps, jump back 3 steps
 - [5, -3]
 # Jump to schedule item 0
@@ -564,8 +477,7 @@ class SimpleRestartSchedule(metaclass=IntegratedNode):
             },
         }
 
-    def go(self, *, sigmas, start_step=0, schedule=""):
-        MODULES.initialize()
+    def go(self, *, sigmas, start_step=0, schedule="[]"):
         if schedule:
             parsed_schedule = yaml.safe_load(schedule)
             if parsed_schedule is not None:
@@ -578,12 +490,12 @@ class SimpleRestartSchedule(metaclass=IntegratedNode):
         return (Restart.simple_schedule(sigmas, start_step, parsed_schedule),)
 
 
-class ModelSetMaxSigmaNode(metaclass=IntegratedNode):
+class ModelSetMaxSigmaNode:
     RETURN_TYPES = ("MODEL",)
     CATEGORY = "hacks"
     DESCRIPTION = "Allows forcing a model's maximum and minumum sigmas to a specified value. You generally do NOT want to connect this to a sampler node. Connect it to a scheduler node (i.e. BasicScheduler) instead."
     OUTPUT_TOOLTIPS = (
-        "Patched model. Can be connected to a scheduler node (i.e. BasicScheduler). Generally NOT recommended to connect to an actual sampler, the main use case is only for generating sigmas.",
+        "Patched model. Can be connected to a scheduler node (i.e. BasicScheduler). Generally NOT recommended to connect to an actual sampler.",
     )
 
     FUNCTION = "go"
@@ -601,7 +513,7 @@ class ModelSetMaxSigmaNode(metaclass=IntegratedNode):
                 "mode": (
                     ("recalculate", "simple_multiply"),
                     {
-                        "tooltip": "Mode to use when setting sigmas in the patched model. Recalculate should generally be more accurate.",
+                        "tooltip": "Mode use for setting sigmas in the patched model. Recalculate should generally be more accurate.",
                     },
                 ),
                 "sigma_max": (
@@ -612,7 +524,7 @@ class ModelSetMaxSigmaNode(metaclass=IntegratedNode):
                         "max": 10000.0,
                         "step": 0.01,
                         "round": False,
-                        "tooltip": "You can set the maximum sigma here. If you use a positive value, it will be interpreted as the absolute value for the max sigma. If you use a negative value it will be interpreted as a percentage of the current value (where 1.0 signifies 100%). Schedules generated with the patched model should start from sigma_max (or close to it).",
+                        "tooltip": "You can set the maximum sigma here. If you use a negative value, it will be interpreted as the absolute value for the max sigma. If you use a positive value it will be interpreted as a percentage (where 1.0 signified 100%). Schedules generated with the patched model should start from sigma_max (or close to it).",
                     },
                 ),
                 "fake_sigma_min": (
@@ -623,14 +535,13 @@ class ModelSetMaxSigmaNode(metaclass=IntegratedNode):
                         "max": 1000.0,
                         "step": 0.01,
                         "round": False,
-                        "tooltip": "You can set the minimum sigma here. Disabled if set to 0. If you use a positive value, it will be interpreted as the absolute value for the max sigma. If you use a negative value it will be interpreted as a percentage of the current value (where 1.0 signifies 100%). Schedules generated with the patched model should end with [sigma_min, 0]. NOTE: May not work with some schedulers. I recommend leaving this at 0 unless you know you need it (and even then it may not work).",
+                        "tooltip": "You can set the minimum sigma here. Disabled if set to 0. If you use a negative value, it will be interpreted as the absolute value for the max sigma. If you use a positive value it will be interpreted as a percentage (where 1.0 signified 100%). Schedules generated with the patched model should end with [sigma_min, 0]. NOTE: May not work with some schedulers. I recommend leaving this at 0 unless you know you need it (and even then it may not work).",
                     },
                 ),
             }
         }
 
     def go(self, model, mode="recalculate", sigma_max=-1.0, fake_sigma_min=0.0):
-        MODULES.initialize()
         if sigma_max == 0:
             raise ValueError("ModelSetMaxSigma: Invalid sigma_max value")
         if mode not in ("recalculate", "simple_multiply"):
@@ -671,113 +582,12 @@ class ModelSetMaxSigmaNode(metaclass=IntegratedNode):
                 "ModelSetMaxSigma: Invalid fake_min_sigma value, result max <= min"
             )
         model.add_object_patch("model_sampling", ms)
-        tqdm.write(
-            f"OCS: ModelSetMaxSigma: Set model sigmas({mode}): old_max={orig_max_sigma:.04}, old_min={orig_min_sigma:.03}, new_max={new_max_sigma:.04}, new_min={new_min_sigma:.03}"
+        print(
+            f"ModelSetMaxSigma: Set model sigmas({mode}): old_max={orig_max_sigma:.04}, old_min={orig_min_sigma:.03}, new_max={new_max_sigma:.04}, new_min={new_min_sigma:.03}"
         )
         return (model,)
 
 
-class ApplyFilterLatent(metaclass=IntegratedNode):
-    RETURN_TYPES = ("LATENT",)
-    CATEGORY = "sampling/custom_sampling/OCS"
-    DESCRIPTION = "Allows applying an OCS filter to any latent. Define a filter block in yaml_config."
-
-    FUNCTION = "go"
-
-    @classmethod
-    def INPUT_TYPES(cls):
-        return {
-            "required": {
-                "latent": (
-                    "LATENT",
-                    {
-                        "tooltip": "Latent input. Note: This node does not care about masks.",
-                    },
-                ),
-                "seed": (
-                    "INT",
-                    {
-                        "default": 0,
-                        "min": 0,
-                        "max": 0xFFFFFFFFFFFFFFFF,
-                        "tooltip": "Seed to use for generated noise.",
-                    },
-                ),
-                "yaml_config": (
-                    "STRING",
-                    {
-                        "default": "",
-                        "placeholder": """\
-# YAML or JSON filter definition
-""",
-                        "multiline": True,
-                        "dynamicPrompts": False,
-                        "tooltip": "Enter your filter definition here. There is essentially no error handling.",
-                    },
-                ),
-            },
-        }
-
-    @classmethod
-    def get_latent_samples(cls, latent: dict) -> torch.Tensor:
-        samples = latent["samples"]
-        batch_indexes = latent.get("batch_index")
-        if batch_indexes is None:
-            return samples.clone()
-        return samples[tuple(batch_indexes), ...].clone()
-
-    def go(self, *, latent: dict, seed: int, yaml_config: str) -> tuple:
-        MODULES.initialize()
-        torch.manual_seed(seed)
-        samples = self.get_latent_samples(latent)
-        config = yaml.safe_load(yaml_config)
-        if not config:
-            return ({"samples": samples},)
-        if not isinstance(config, dict) or "filter" not in config:
-            raise ValueError(
-                "Bad YAML config type (must be object) or missing filter key in config"
-            )
-        filter_def = config.get("filter")
-        if not isinstance(filter_def, dict):
-            raise ValueError("Bad type for filter definition, must be object")
-        ocs_filter = make_filter(filter_def)
-        new_samples = ocs_filter.apply(samples.to(dtype=torch.float32)).to(samples)
-        return ({"samples": new_samples},)
-
-
-class ApplyFilterImage(ApplyFilterLatent):
-    DESCRIPTION = "Allows applying an OCS filter to any image. Define a filter block in yaml_config."
-    RETURN_TYPES = ("IMAGE",)
-
-    @classmethod
-    def INPUT_TYPES(cls):
-        result = super().INPUT_TYPES()
-        del result["required"]["latent"]
-        result["required"] = {
-            "image": (
-                "IMAGE",
-                {"tooltip": "Image input."},
-            ),
-        } | result["required"]
-        return result
-
-    def go(self, *, image: torch.Tensor, seed: int, yaml_config: str) -> tuple:
-        image = image.clone()
-        if image.ndim == 3:
-            image = image.unsqueeze(0)
-        result = (
-            super()
-            .go(
-                latent={"samples": image.movedim(-1, 1)},
-                seed=seed,
-                yaml_config=yaml_config,
-            )[0]["samples"]
-            .movedim(1, -1)
-            .to(image)
-        )
-        return (result,)
-
-
 __all__ = (
     "SamplerNode",
     "GroupNode",

py/noise.py

@@ -1,10 +1,8 @@
 import gc
-import math
 import random
 
 import scipy
 import torch
-from tqdm import tqdm
 
 from .filtering import Filter, make_filter
 from .utils import scale_noise, fallback
@@ -17,16 +15,15 @@ class ImmiscibleNoise(Filter):
         "size": 0,
         "batching": "channel",
         "maximize": False,
-        "distance_scale": 0.0,
-        "distance_scale_ref": None,
     }
 
     def __call__(self, noise_sampler, x_ref, *, refs=None):
-        if self.size == 0 or self.strength == 0 or not self.check_applies(refs):
+        if not self.check_applies(refs):
             return noise_sampler()
-        size = self.size if self.strength == 1.0 else self.size + 1
         return self.apply(
-            torch.cat(tuple(noise_sampler() for _ in range(size))),
+            torch.cat(tuple(noise_sampler() for _ in range(self.size)))
+            if self.size > 0
+            else noise_sampler(),
             default_ref=x_ref,
             refs=refs,
             output_shape=x_ref.shape,
@@ -35,103 +32,52 @@ class ImmiscibleNoise(Filter):
     def filter(self, latent, ref_latent, *, refs, output_shape):
         if self.size == 0:
             return latent
-        offset = 0 if self.strength == 1.0 else output_shape[0]
         return self.unbatch(
-            self.immiscible(self.batch(latent[offset:]), self.batch(ref_latent)),
-            output_shape,
+            self.immiscible(self.batch(latent), self.batch(ref_latent)), output_shape
         )
 
     def batch(self, latent):
         if self.batching == "batch":
             return latent
         sz = latent.shape
+        if latent.ndim != 4:
+            raise ValueError("Both latent and reference must be four-dimensional")
         if self.batching == "channel":
-            return latent.reshape(sz[0] * sz[1], *sz[2:])
-        if self.batching == "frame":
-            if latent.ndim != 5:
-                raise ValueError(
-                    "Both latent and reference must be five-dimensional for frame mode"
-                )
-            return latent.permute(0, 2, 1, 3, 4).reshape(sz[0] * sz[2], sz[1], *sz[3:])
+            return latent.view(sz[0] * sz[1], *sz[2:])
         if self.batching == "row":
-            return latent.reshape(math.prod(sz[:-1]), sz[-1])
+            return latent.view(sz[0] * sz[1] * sz[2], sz[3])
         if self.batching == "column":
-            if latent.ndim != 4:
-                raise ValueError(
-                    "Both latent and reference must be four-dimensional for column mode"
-                )
             return latent.permute(0, 1, 3, 2).reshape(sz[0] * sz[1] * sz[3], sz[2])
         raise ValueError("Bad Immmiscible noise batching type")
 
     def unbatch(self, latent, sz):
         if self.batching == "column":
-            return latent.reshape(*sz[:2], sz[3], sz[2]).permute(0, 1, 3, 2)
-        if self.batching == "frame":
-            return latent.reshape(sz[0], sz[2], sz[1], *sz[3:]).permute(0, 2, 1, 3, 4)
-        return latent.reshape(*sz)
+            return latent.view(*sz[:2], sz[3], sz[2]).permute(0, 1, 3, 2)
+        return latent.view(*sz)
 
-    # Originally based on implementation from https://github.com/kohya-ss/sd-scripts/pull/1395
-    # Idea for use with inference as well as implementation help from https://github.com/Clybius
-    def immiscible(
-        self,
-        latent: torch.Tensor,
-        ref_latent: torch.Tensor,
-        *,
-        out_latent: torch.Tensor | None = None,
-        return_idxs=False,
-    ):
+    # Based on implementation from https://github.com/kohya-ss/sd-scripts/pull/1395
+    # Idea from https://github.com/Clybius
+    def immiscible(self, latent, ref_latent):
         # "Immiscible Diffusion: Accelerating Diffusion Training with Noise Assignment" (2024) Li et al. arxiv.org/abs/2406.12303
         # Minimize latent-noise pairs over a batch
-        batch = latent.shape[0]
-        ref_latent = ref_latent.detach().clone()
-        if self.distance_scale == 0:
-            ref_latent_expanded = ref_latent.unsqueeze(1).expand(
-                -1, batch, *ref_latent.shape[1:]
-            )
-            latent_expanded = latent.unsqueeze(0).expand(
-                ref_latent.shape[0], *latent.shape
-            )
-            dist = (ref_latent_expanded - latent_expanded) ** 2
-            del ref_latent_expanded, latent_expanded
-            dist = dist.mean(tuple(range(2, dist.dim())))
-        else:
-            dist = torch.linalg.vector_norm(
-                fallback(self.distance_scale_ref, self.distance_scale)
-                * ref_latent.flatten(start_dim=1).unsqueeze(1)
-                - self.distance_scale * latent.flatten(start_dim=1).unsqueeze(0),
-                dim=2,
-            )
-        dist = dist.half()
+        n = latent.shape[0]
+        ref_latent_expanded = (
+            ref_latent.half().unsqueeze(1).expand(-1, n, *ref_latent.shape[1:])
+        )
+        latent_expanded = (
+            latent.half().unsqueeze(0).expand(ref_latent.shape[0], *latent.shape)
+        )
+        dist = (ref_latent_expanded - latent_expanded) ** 2
+        dist = dist.mean(list(range(2, dist.dim()))).cpu()
         try:
             assign_mat = scipy.optimize.linear_sum_assignment(
-                dist.cpu(), maximize=self.maximize
+                dist, maximize=self.maximize
             )
-        except ValueError as exc:
-            tqdm.write(f"OCS: Immiscible: Failed due to exception: {exc}")
-            return (
-                None
-                if return_idxs
-                else fallback(out_latent, latent)[: ref_latent.shape[0]]
-            )
-        return (
-            assign_mat if return_idxs else fallback(out_latent, latent)[assign_mat[1]]
-        )
-
-    def immiscible_simple(
-        self,
-        latent: torch.Tensor,
-        ref_latent: torch.Tensor,
-        *,
-        out_latent: torch.Tensor | None = None,
-    ) -> torch.Tensor:
-        return self.unbatch(
-            self.immiscible(
-                self.batch(latent),
-                self.batch(ref_latent),
-                out_latent=self.batch(out_latent) if out_latent is not None else None,
-            ),
-            ref_latent.shape,
-        )
+        except ValueError as _exc:
+            # print("\nImmiscible: Failed optimization, skipping")
+            return latent[: ref_latent.shape[0]]
+        # print("IMM IDX", assign_mat[1])
+        return latent[assign_mat[1]]
 
 
 class NoiseSamplerCache:
@@ -144,11 +90,10 @@ class NoiseSamplerCache:
         *,
         normalize_noise=True,
         cpu_noise=True,
-        batch_size=1,
-        caching=True,
+        batch_size=32,
+        caching=False,
         cache_reset_interval=9999,
-        set_seed=True,
-        seed_offset=1,
+        set_seed=False,
         scale=1.0,
         normalize_dims=(-3, -2, -1),
         immiscible=None,
@@ -158,7 +103,7 @@ class NoiseSamplerCache:
         self.x = x
         self.mega_x = None
         self.seed = seed
-        self.seed_offset = seed_offset
+        self.seed_offset = 0
         self.min_sigma = min_sigma
         self.max_sigma = max_sigma
         self.cache = {}
@@ -178,11 +123,6 @@ class NoiseSamplerCache:
         if set_seed:
             random.seed(seed)
             torch.manual_seed(seed)
-            if self.seed_offset > 0:
-                for _ in range(self.seed_offset):
-                    _ = torch.randn_like(x)
-        else:
-            self.seed_offset = 0
 
     def reset_cache(self):
         self.cache = {}
@@ -220,14 +160,10 @@ class NoiseSamplerCache:
         size,
         sigma,
         sigma_next,
-        *,
         immiscible=None,
-        sigmas=None,
     ):
         size = min(size, self.batch_size)
-        if immiscible is None:
-            immiscible = self.immiscible
-        cache_key = (nsobj, size, hash(immiscible))
+        cache_key = (nsobj, size)
         if self.caching:
             noise_sampler = self.cache.get(cache_key)
             if noise_sampler:
@@ -237,26 +173,14 @@ class NoiseSamplerCache:
         curr_x = self.mega_x[: self.x.shape[0] * size, ...]
         if nsobj is None:
 
-            def ns(*_unused, **_unusedkwargs):
-                noise = torch.randn(
-                    curr_x.shape,
-                    dtype=curr_x.dtype,
-                    layout=curr_x.layout,
-                    device="cpu" if self.cpu_noise else curr_x.device,
-                )
-                if noise.device != curr_x.device:
-                    return noise.to(curr_x.device)
-                return noise
+            def ns(_s, _sn, *_unused, **_unusedkwargs):
+                return torch.randn_like(curr_x)
 
         else:
-            if sigmas is not None:
-                sigma_min, sigma_max = sigmas[sigmas > 0].min(), sigmas.max()
-            else:
-                sigma_min, sigma_max = self.min_sigma, self.max_sigma
             ns = nsobj.make_noise_sampler(
                 curr_x,
-                sigma_min,
-                sigma_max,
+                self.min_sigma,
+                self.max_sigma,
                 seed=curr_seed,
                 normalized=False,
                 cpu=self.cpu_noise,
@@ -265,10 +189,10 @@ class NoiseSamplerCache:
         orig_h, orig_w = self.x.shape[-2:]
         remain = 0
         noise = None
+        if immiscible is None:
+            immiscible = self.immiscible
 
         def noise_sampler_(
-            curr_sigma,
-            curr_sigma_next,
             *_unused,
             out_hw=(orig_h, orig_w),
             **_unusedkwargs,
@@ -279,9 +203,7 @@ class NoiseSamplerCache:
                     f"Noise size mismatch: {out_hw} vs {(orig_h, orig_w)}"
                 )
             if remain < 1:
-                curr_sigma = fallback(curr_sigma, sigma)
-                curr_sigma_next = fallback(curr_sigma_next, sigma_next)
-                noise = self.scale_noise(ns(curr_sigma, curr_sigma_next)).view(
+                noise = self.scale_noise(ns(sigma, sigma_next)).view(
                     size,
                     *self.x.shape,
                 )

py/sampling.py

@@ -2,8 +2,8 @@ import torch
 from tqdm.auto import trange
 
 
-from .filtering import FILTER_HANDLERS, FilterRefs
-from .model import OCSModel
+from .filtering import FILTER_HANDLERS
+from .model import ModelCallCache
 from .noise import NoiseSamplerCache
 from .substep_sampling import SamplerState
 from .substep_merging import MERGE_SUBSTEPS_CLASSES
@@ -44,18 +44,14 @@ def composable_sampler(
             return torch.randn_like(x)
 
     restart_params = copts.get("restart", {})
-    restart_enabled = restart_params.get("enabled", True)
-    restart_custom_noise = copts.get("restart_custom_noise")
-    if isinstance(restart_custom_noise, str):
-        restart_custom_noise = copts.get(f"restart_custom_noise_{restart_custom_noise}")
     restart = Restart(
         s_noise=restart_params.get("s_noise", 1.0),
-        custom_noise=restart_custom_noise,
+        custom_noise=copts.get("restart_custom_noise"),
         immiscible=restart_params.get("immiscible", False),
     )
 
     ss = SamplerState(
-        OCSModel(
+        ModelCallCache(
             model,
             x,
             x.new_ones((x.shape[0],)),
@@ -67,8 +63,8 @@ def composable_sampler(
         extra_args,
         noise_sampler=noise_sampler,
         callback=callback,
-        eta=eta if eta != 1.0 else copts.get("eta", 1.0),
-        s_noise=s_noise if s_noise != 1.0 else copts.get("s_noise", 1.0),
+        eta=eta if eta != 1.0 else copts["eta"],
+        s_noise=s_noise if s_noise != 1.0 else copts["s_noise"],
         reta=copts.get("reta", 1.0),
         disable_status=disable,
     )
@@ -79,23 +75,18 @@ def composable_sampler(
     nsc = NoiseSamplerCache(
         x,
         extra_args.get("seed", 42),
-        sigmas[sigmas > 0].min(),
-        sigmas.max(),
+        sigmas[-1],
+        sigmas[0],
         **copts.get("noise", {}),
     )
     ss.noise = nsc
-    sigma_chunks = (
-        tuple(restart.split_sigmas(sigmas)) if restart_enabled else ((0.0, sigmas),)
-    )
+    sigma_chunks = tuple(restart.split_sigmas(sigmas))
     step_count = sum(len(chunk) - 1 for _noise, chunk in sigma_chunks)
     ss.total_steps = step_count
     step = 0
+    restart_snoise = copts.get("restart_s_noise", 1.0)
     with trange(step_count, disable=ss.disable_status) as pbar:
         for noise_scale, chunk_sigmas in sigma_chunks:
-            if step != 0 and noise_scale != 0:
-                prev_refs = FilterRefs({
-                    f"pre_restart_{k}": v for k, v in ss.refs.items()
-                })
             ss.sigmas = chunk_sigmas
             ss.update(0, step=step, substep=0)
             if step != 0:
@@ -108,15 +99,18 @@ def composable_sampler(
             if step != 0 and noise_scale != 0:
                 restart_ns = restart.get_noise_sampler(nsc)
                 x += nsc.scale_noise(
-                    restart_ns(nsc.min_sigma, nsc.max_sigma, refs=prev_refs | ss.refs),
-                    noise_scale,
+                    restart_ns(refs=ss.refs),
+                    noise_scale * restart_snoise,
                 )
                 del restart_ns
-                del prev_refs
             for idx in range(len(chunk_sigmas) - 1):
                 if idx > 0:
                     ss.update(idx, step=step, substep=0)
                     nsc.update_x(x)
+                # print(
+                #     f"STEP {step + 1:>3}: {ss.sigma.item():.03} -> {ss.sigma_next.item():.03} || up={ss.sigma_up.item():.03}, down={ss.sigma_down.item():.03}"
+                # )
+                ss.model.reset_cache()
                 nsc.update_x(x)
                 merge_sampler = find_merge_sampler(merge_samplers, ss)
                 if merge_sampler is None:

py/step_samplers/__init__.py

@@ -1,20 +0,0 @@
-from . import (  # noqa: F401
-    builtins,
-    blep,
-    clybius,
-    extraltodeus,
-    misc,
-    solver_tde,
-    solver_tode,
-    solver_tsde,
-    solver_diffrax,
-)
-
-from . import registry
-
-registry.init()
-
-STEP_SAMPLERS = registry.STEP_SAMPLERS
-STEP_SAMPLER_SIMPLE_NAMES = registry.STEP_SAMPLER_SIMPLE_NAMES
-
-__all__ = ("STEP_SAMPLERS", "STEP_SAMPLER_SIMPLE_NAMES")

py/step_samplers/base.py

@@ -1,536 +0,0 @@
-import contextlib
-import typing
-
-import torch
-
-from . import registry  # noqa: F401
-
-from .. import filtering, noise, utils
-from ..utils import fallback
-
-
-class SamplerResult:
-    CLONE_KEYS = (
-        "denoised_cond",
-        "denoised_uncond",
-        "denoised",
-        "final",
-        "is_rectified_flow",
-        "noise_pred",
-        "noise_sampler",
-        "s_noise",
-        "sampler",
-        "sigma_down",
-        "sigma_next",
-        "sigma_up",
-        "sigma",
-        "step",
-        "substep",
-        "x_",
-    )
-
-    def __init__(
-        self,
-        ss,
-        sampler,
-        x,
-        sigma_up=None,
-        *,
-        split_result=None,
-        sigma=None,
-        sigma_next=None,
-        sigma_down=None,
-        s_noise=None,
-        noise_sampler=None,
-        final=True,
-    ):
-        self.is_rectified_flow = ss.model.is_rectified_flow
-        self.sampler = sampler
-        self.sigma_up = fallback(sigma_up, ss.sigma.new_zeros(1))
-        self.s_noise = fallback(s_noise, sampler.s_noise)
-        self.sigma = fallback(sigma, ss.sigma)
-        self.sigma_next = fallback(sigma_next, ss.sigma_next)
-        self.sigma_down = fallback(sigma_down, self.sigma_next)
-        self.noise_sampler = fallback(noise_sampler, sampler.noise_sampler)
-        self.final = final
-        self.step = ss.step
-        self.substep = ss.substep
-        self.x_ = x
-        if split_result is not None:
-            self.denoised, self.noise_pred = split_result
-        elif x is None:
-            raise ValueError("SamplerResult requires at least one of x, split_result")
-        else:
-            self.denoised = self.noise_pred = None
-            _ = self.extract_pred(ss)
-        self.denoised_uncond = ss.hcur.denoised_uncond
-        self.denoised_cond = ss.hcur.denoised_cond
-
-    def get_noise(self, *, scaled=True, ss=None):
-        if self.sigma_next == 0 or self.noise_scale == 0:
-            return torch.zeros_like(self.x_)
-        return self.noise_sampler(
-            self.sigma,
-            self.sigma_next,
-            out_hw=self.x.shape[-2:],
-            x_ref=self.x,
-            refs=filtering.FilterRefs.from_sr(self) if ss is None else ss.refs,
-        ).mul_(self.noise_scale if scaled else 1.0)
-
-    def extract_pred(self, ss):
-        if self.denoised is None or self.noise_pred is None:
-            self.denoised, self.noise_pred = utils.extract_pred(
-                ss.hcur.x, self.x_, ss.sigma, self.sigma_down
-            )
-        return self.denoised, self.noise_pred
-
-    @property
-    def x(self):
-        if self.x_ is None:
-            self.x_ = self.denoised + self.sigma_down * self.noise_pred
-        return self.x_
-
-    @property
-    def noise_scale(self):
-        return self.sigma_up * self.s_noise
-
-    def noise_x(self, x=None, scale=1.0, *, ss=None):
-        x = fallback(x, self.x)
-        if self.sigma_next == 0 or self.noise_scale == 0:
-            return x
-        noise = self.get_noise(ss=ss).mul_(scale)
-        if not self.is_rectified_flow:
-            return noise.add_(x)
-        x_coeff = (1 - self.sigma_next) / (1 - self.sigma_down)
-        # print(f"\nRF noise: {x_coeff}")
-        return noise.add_(x_coeff * x)
-
-    def clone(self):
-        obj = self.__new__(self.__class__)
-        for k in self.CLONE_KEYS:
-            if hasattr(self, k):
-                setattr(obj, k, getattr(self, k))
-        return obj
-
-
-class StepSamplerContext:
-    def __init__(self, sampler, *args, **kwargs):
-        self.sampler = sampler
-        self.args = args
-        self.kwargs = kwargs
-
-    def __enter__(self):
-        if self.sampler.ss is not None:
-            raise RuntimeError("Cannot reenter prepared sampler in context manager!")
-        self.sampler.prepare(*self.args, **self.kwargs)
-        return self.sampler
-
-    def __exit__(self, *_unused):
-        self.sampler.reset()
-
-
-class SingleStepSampler:
-    name = None
-    self_noise = 0
-    model_calls = 0
-    ancestralize = False
-    sample_sigma_zero = False
-    immiscible = None
-    allow_cfgpp = False
-    allow_alt_cfgpp = False
-    afs_end_step = -1
-    uses_alt_noise = False
-
-    default_eta = 1.0
-
-    def __init__(
-        self,
-        *,
-        noise_sampler=None,
-        substeps=1,
-        s_noise=1.0,
-        eta=None,
-        eta_retry_increment=0,
-        dyn_eta_start=None,
-        dyn_eta_end=None,
-        weight=1.0,
-        pre_filter=None,
-        post_filter=None,
-        immiscible=None,
-        **kwargs,
-    ):
-        self.ss = None
-        self.options = kwargs
-        self.cfgpp = self.allow_cfgpp and self.options.pop("cfgpp", False) is True
-        alt_cfgpp_scale = self.options.pop("alt_cfgpp_scale", 0.0)
-        self.alt_cfgpp_scale = 0.0 if not self.allow_alt_cfgpp else alt_cfgpp_scale
-        self.s_noise = s_noise
-        self.eta = fallback(eta, self.default_eta)
-        self.eta_retry_increment = eta_retry_increment
-        self.dyn_eta_start = dyn_eta_start
-        self.dyn_eta_end = dyn_eta_end
-        self.noise_sampler = noise_sampler
-        self.immiscible = (
-            noise.ImmiscibleNoise(**immiscible)
-            if immiscible not in (False, None)
-            else immiscible
-        )
-        self.weight = weight
-        self.afs_end_step = self.options.pop("afs_end_step", -1)
-        self.substeps = substeps
-        self.pre_filter = (
-            None if pre_filter is None else filtering.make_filter(pre_filter)
-        )
-        self.post_filter = (
-            None if post_filter is None else filtering.make_filter(post_filter)
-        )
-        self.custom_noise = self.options.get("custom_noise")
-        if isinstance(self.custom_noise, str):
-            self.custom_noise = self.options.get(f"custom_noise_{self.custom_noise}")
-        if not self.uses_alt_noise:
-            return
-        self.alt_custom_noise = self.options.get("custom_noise_alt")
-        alt_immiscible = self.options.get("alt_immiscible")
-        self.alt_immiscible = (
-            noise.ImmiscibleNoise(**alt_immiscible)
-            if isinstance(alt_immiscible, dict)
-            else alt_immiscible
-        )
-
-    def __call__(self, x):
-        ss = self.ss
-        orig_x = x
-        if not self.sample_sigma_zero and ss.sigma_next == 0:
-            return (yield from self.denoised_result())
-        if ss.step <= self.afs_end_step:
-            return (yield from self.afs_step(x))
-        if self.pre_filter or self.post_filter:
-            filter_refs = ss.refs | filtering.FilterRefs({"orig_x": orig_x})
-        if self.pre_filter:
-            x = self.pre_filter.apply(x, refs=filter_refs)
-        next_x = None
-        sg = self.step(x)
-        with contextlib.suppress(StopIteration):
-            while True:
-                sr = sg.send(next_x)
-                if sr.final:
-                    if self.ancestralize:
-                        sr = self.ancestralize_result(sr)
-                    curr_x = sr.x
-                    if self.post_filter:
-                        curr_x = self.post_filter.apply(curr_x, refs=filter_refs)
-                    sr.x_ = curr_x
-                    return (yield sr)
-                next_x = sr.noise_x(ss=ss)
-
-    def step(self, x):
-        raise NotImplementedError
-
-    def prepare(self, ss):
-        self.ss = ss
-        self.noise_sampler = ss.noise.make_caching_noise_sampler(
-            self.custom_noise,
-            self.max_noise_samples,
-            ss.sigma,
-            ss.sigma_next,
-            immiscible=fallback(self.immiscible, ss.noise.immiscible),
-        )
-        if not self.uses_alt_noise:
-            return
-        if self.alt_custom_noise is None and self.alt_immiscible is None:
-            self.alt_noise_sampler = self.noise_sampler
-            return
-        self.alt_noise_sampler = ss.noise.make_caching_noise_sampler(
-            fallback(self.alt_custom_noise, self.custom_noise),
-            1,
-            ss.sigma,
-            ss.sigma_next,
-            immiscible=fallback(
-                fallback(self.alt_immiscible, self.immiscible),
-                ss.noise.immiscible,
-            ),
-        )
-
-    def reset(self):
-        self.ss = None
-        self.noise_sampler = None
-        if self.uses_alt_noise:
-            self.alt_noise_sampler = None
-
-    # From https://arxiv.org/abs/2210.05475
-    def afs_step(self, x):
-        sigma, sigma_next = self.ss.sigma, self.ss.sigma_next
-        afs_d = x / ((1 + sigma**2).sqrt())
-        dt = sigma_next - sigma
-        return (yield from self.result(x + afs_d * dt))
-
-    # Euler - based on original ComfyUI implementation
-    def euler_step(
-        self,
-        x,
-        *,
-        sigma_down=None,
-        sigma_up=None,
-        eta=None,
-        sigma=None,
-        sigma_next=None,
-    ):
-        eta = fallback(eta, self.get_dyn_eta())
-        if sigma_down is None or sigma_up is None:
-            if not (sigma_down is None and sigma_up is None):
-                raise ValueError("Must pass both sigma_down and sigma_up or neither")
-            sigma_down, sigma_up = self.get_ancestral_step(
-                eta=eta, sigma=sigma, sigma_next=sigma_next
-            )
-        return (
-            yield from self.split_result(
-                *self.get_split_prediction(), sigma_down=sigma_down, sigma_up=sigma_up
-            )
-        )
-
-    def denoised_result(self, **kwargs):
-        ss = self.ss
-        return (
-            yield SamplerResult(ss, self, ss.denoised, ss.sigma.new_zeros(1), **kwargs)
-        )
-
-    def result(self, x, noise_scale=None, **kwargs):
-        return (yield SamplerResult(self.ss, self, x, noise_scale, **kwargs))
-
-    def split_result(
-        self, denoised=None, noise_pred=None, sigma_up=None, sigma_down=None, **kwargs
-    ):
-        return (
-            yield SamplerResult(
-                ss=self.ss,
-                sampler=self,
-                x=None,
-                sigma_up=sigma_up,
-                sigma_down=sigma_down,
-                split_result=(denoised, noise_pred),
-                **kwargs,
-            )
-        )
-
-    def get_ancestral_step(
-        self, *args, dyn_eta=False, as_dict=False, retry_increment=None, **kwargs
-    ):
-        if dyn_eta:
-            args = (self.get_dyn_eta(), *args)
-        retry_increment = fallback(retry_increment, self.eta_retry_increment)
-        sigma_down, sigma_up = self.ss.get_ancestral_step(
-            *args, retry_increment=retry_increment, **kwargs
-        )
-        if not as_dict:
-            return sigma_down, sigma_up
-        return {"sigma_down": sigma_down, "sigma_up": sigma_up}
-
-    def ancestralize_result(self, sr):
-        ss = self.ss
-        new_sr = sr.clone()
-        if new_sr.sigma_down is not None and new_sr.sigma_down != new_sr.sigma_next:
-            return sr
-        eta = self.get_dyn_eta()
-        if sr.sigma_next == 0 or eta == 0:
-            return sr
-        sd, su = self.get_ancestral_step(eta, sigma=sr.sigma, sigma_next=sr.sigma_next)
-        _ = new_sr.extract_pred(ss)
-        new_sr.x_ = None
-        new_sr.sigma_up = su
-        new_sr.sigma_down = sd
-        return new_sr
-
-    def __str__(self):
-        return f"<SS({self.name}): s_noise={self.s_noise}, eta={self.eta}>"
-
-    def get_dyn_value(self, start, end):
-        if None in (start, end):
-            return 1.0
-        if start == end:
-            return start
-        ss = self.ss
-        main_idx = getattr(ss, "main_idx", ss.idx)
-        main_sigmas = getattr(ss, "main_sigmas", ss.sigmas)
-        step_pct = main_idx / (len(main_sigmas) - 1)
-        dd_diff = end - start
-        return start + dd_diff * step_pct
-
-    def get_dyn_eta(self):
-        return self.eta * self.get_dyn_value(self.dyn_eta_start, self.dyn_eta_end)
-
-    @property
-    def max_noise_samples(self):
-        return (1 + self.self_noise) * self.substeps
-
-    @property
-    def require_uncond(self):
-        return self.cfgpp or self.alt_cfgpp_scale != 0
-
-    def to_d(self, mr, *, use_cfgpp=True, **kwargs):
-        if not use_cfgpp:
-            return mr.to_d(**kwargs)
-        return mr.to_d(alt_cfgpp_scale=self.alt_cfgpp_scale, cfgpp=self.cfgpp, **kwargs)
-
-    def get_split_prediction(self, *, mr=None, sigma=None, **kwargs):
-        mr = fallback(mr, self.ss.hcur)
-        sigma = fallback(sigma, mr.sigma)
-        return mr.get_split_prediction(
-            sigma=sigma,
-            alt_cfgpp_scale=self.alt_cfgpp_scale,
-            cfgpp=self.cfgpp,
-            **kwargs,
-        )
-
-    def call_model(self, *args, **kwargs):
-        ss = self.ss
-        kwargs["require_uncond"] = self.require_uncond or kwargs.get(
-            "require_uncond", False
-        )
-        kwargs["cfg_scale_override"] = kwargs.get(
-            "cfg_scale_override",
-            self.options.get("cfg_scale_override", ss.cfg_scale_override),
-        )
-        return ss.call_model(*args, ss=ss, **kwargs)
-
-    def step_mix(self, x, denoised, uncond, ratio, *, blend=torch.lerp):
-        if self.cfgpp:
-            return denoised + (x - uncond).mul_(ratio)
-        pp = self.alt_cfgpp_scale
-        if pp == 0:
-            return blend(denoised, x, ratio)
-        return blend(denoised * (1 + pp) - uncond * pp, x, ratio)
-
-
-class HistorySingleStepSampler(SingleStepSampler):
-    default_history_limit, max_history = 0, 0
-
-    def __init__(self, *args, history_limit=None, **kwargs):
-        super().__init__(*args, **kwargs)
-        self.history_limit = min(
-            self.max_history,
-            max(
-                0,
-                self.default_history_limit if history_limit is None else history_limit,
-            ),
-        )
-
-    def available_history(self):
-        ss = self.ss
-        available = max(
-            0, min(ss.idx, self.history_limit, self.max_history, len(ss.hist) - 1)
-        )
-        if not available:
-            return available
-        curr_shape = ss.hist[-1].denoised.shape
-        for eff_available in range(available):
-            if ss.hist[-2 - eff_available].denoised.shape != curr_shape:
-                return eff_available
-        return available
-
-
-class ReversibleConfig(typing.NamedTuple):
-    scale: float
-    eta: float
-    dyn_eta_start: float | None = None
-    dyn_eta_end: float | None = None
-    eta_retry_increment: float = 0.0
-    start_step: int = 0
-    end_step: int = 9999
-    use_cfgpp: bool = False
-
-    @classmethod
-    def build(cls, *, default_eta, default_scale, eta=None, scale=None, **kwargs):
-        return cls.__new__(
-            cls,
-            eta=fallback(eta, default_eta),
-            scale=fallback(scale, default_scale),
-            **kwargs,
-        )
-
-    def check(self, step):
-        return self.scale != 0 and self.start_step <= step <= self.end_step
-
-
-class ReversibleSingleStepSampler(HistorySingleStepSampler):
-    default_reversible_scale = 1.0
-    default_reta = 1.0
-
-    def __init__(
-        self,
-        *,
-        reversible_scale=None,
-        reta=None,
-        dyn_reta_start=None,
-        dyn_reta_end=None,
-        reversible_start_step=0,
-        reversible=None,
-        **kwargs,
-    ):
-        super().__init__(**kwargs)
-        if reversible is None:
-            # For backward compatibility.
-            self.reversible = ReversibleConfig.build(
-                default_eta=self.default_reta,
-                default_scale=self.default_reversible_scale,
-                scale=reversible_scale,
-                eta=reta,
-                dyn_eta_start=dyn_reta_start,
-                dyn_eta_end=dyn_reta_end,
-                start_step=reversible_start_step,
-            )
-            return
-        self.reversible = ReversibleConfig.build(
-            default_eta=self.default_reta,
-            default_scale=self.default_reversible_scale,
-            **reversible,
-        )
-
-    def reversible_correction(self):
-        raise NotImplementedError
-
-    def get_dyn_reta(self, *, r=None):
-        r = fallback(r, self.reversible)
-        ss = self.ss
-        if not r.check(ss.step):
-            return 0.0
-        return r.eta * self.get_dyn_value(r.dyn_eta_start, r.dyn_eta_end)
-
-    dyn_reta = property(get_dyn_reta)
-
-    def get_reversible_cfg(self, *, reversible=None):
-        reversible = fallback(reversible, self.reversible)
-        ss = self.ss
-        if not reversible.check(ss.step):
-            return 0.0, 0.0
-        return self.get_dyn_reta(r=reversible), reversible.scale
-
-
-class DPMPPStepMixin:
-    @staticmethod
-    def sigma_fn(t):
-        return t.neg().exp()
-
-    @staticmethod
-    def t_fn(t):
-        return t.log().neg()
-
-
-class MinSigmaStepMixin:
-    @staticmethod
-    def adjust_step(sigma, min_sigma, threshold=5e-04):
-        if min_sigma - sigma > threshold:
-            return sigma.clamp(min=min_sigma)
-        return sigma
-
-    def adjusted_step(self, sn, result, mcc, sigma_up):
-        ss = self.ss
-        if sn == ss.sigma_next:
-            return sigma_up, result
-        # FIXME: Make sure we're noising from the right sigma.
-        result = yield from self.result(
-            result, sigma_up, sigma=ss.sigma, sigma_next=sn, final=False
-        )
-        mr = self.call_model(result, sn, call_index=mcc)
-        dt = ss.sigma_next - sn
-        result = result + self.to_d(mr) * dt
-        return sigma_up.new_zeros(1), result

py/step_samplers/blep.py

@@ -1,546 +0,0 @@
-import typing
-
-import inspect
-import torch
-
-import comfy
-
-from .. import filtering
-from .. import expression as expr
-from ..utils import fallback
-from .base import (
-    StepSamplerContext,
-    SingleStepSampler,
-    registry,
-)
-
-
-try:
-    import pytorch_wavelets as ptwav
-
-    HAVE_WAVELETS = True
-except ImportError:
-    HAVE_WAVELETS = False
-
-
-class DynamicStep(SingleStepSampler):
-    name = "dynamic"
-    sample_sigma_zero = True
-
-    def __init__(self, *args, **kwargs):
-        super().__init__(*args, **kwargs)
-        dynamic = self.options.get("dynamic")
-        if dynamic is None:
-            raise ValueError(
-                "Dynamic sampler type requires specifying dynamic block in text parameters"
-            )
-        if isinstance(dynamic, str):
-            dynamic = ({"expression": dynamic},)
-        elif not isinstance(dynamic, (tuple, list)):
-            raise ValueError(
-                "Bad type for dynamic block: must be string or list of objects"
-            )
-        elif len(dynamic) == 0:
-            raise ValueError("Dynamic block as a list cannot be empty")
-        dynresult = []
-        for idx, item in enumerate(dynamic):
-            if not isinstance(item, dict):
-                raise ValueError(
-                    f"Bad item in dynamic block at index {idx}: must be a dict"
-                )
-            dyn_when = item.get("when")
-            if isinstance(dyn_when, str):
-                dyn_when = expr.Expression(dyn_when)
-            elif dyn_when is not None:
-                raise ValueError(
-                    f"Unexpected type for when key in dynamic block at index {idx}, must be string or null/unset"
-                )
-            dyn_params = item.get("expression")
-            if not isinstance(dyn_params, str):
-                raise ValueError(
-                    f"Missing or incorrectly typed expression key for dynamic block at index {idx}: must be a string"
-                )
-            dynresult.append((dyn_when, expr.Expression(dyn_params)))
-        self.dynamic = tuple(dynresult)
-
-    def step(self, x):
-        sampler_params = None
-        handlers = filtering.FILTER_HANDLERS.clone(constants=self.ss.refs)
-        for idx, (dyn_when, dyn_params) in enumerate(self.dynamic):
-            if dyn_when is not None and not bool(dyn_when.eval(handlers)):
-                continue
-            sampler_params = dyn_params.eval(handlers)
-            if sampler_params is not None:
-                break
-        if sampler_params is None:
-            raise RuntimeError(
-                "Dynamic sampler could not find matching sampler: all expressions failed to return a result"
-            )
-        if not isinstance(sampler_params, dict):
-            raise TypeError(
-                f"Dynamic sampler expression must evaluate to a dict, got type {type(sampler_params)}"
-            )
-        if bool(sampler_params.get("dynamic_inherit")):
-            copy_keys = (
-                "s_noise",
-                "eta",
-                "pre_filter",
-                "post_filter",
-                "immiscible",
-            )
-            opts = {k: getattr(self, k) for k in copy_keys}
-        else:
-            opts = {}
-        opts["custom_noise"] = self.custom_noise
-        opts |= sampler_params
-        opts |= {k: v for k, v in self.options.items() if k.startswith("custom_noise_")}
-        # print("\n\nDYN OPTS", opts)
-        step_method = opts.get("step_method", "default")
-        sampler_class = registry.STEP_SAMPLER_SIMPLE_NAMES.get(step_method)
-        if sampler_class is None:
-            raise ValueError(f"Unknown step method {step_method} in dynamic sampler")
-        sampler = sampler_class(**opts)
-        with StepSamplerContext(sampler, self.ss) as sampler:
-            yield from sampler.step(x)
-
-
-class AdapterStep(SingleStepSampler):
-    name = "adapter"
-    model_calls = 2
-    immiscible = False
-
-    def __init__(self, *args, **kwargs):
-        super().__init__(*args, **kwargs)
-        self.external_sampler = self.options.pop(
-            "SAMPLER", comfy.samplers.sampler_object("euler")
-        )
-        sig = inspect.signature(self.external_sampler.sampler_function)
-        self.external_sampler_options = {
-            k: v
-            for k, v in self.options.pop("external_sampler", {}).items()
-            if k in sig.parameters
-        }
-        self.external_sampler_uses_noise = "noise_sampler" in sig.parameters
-        self.ancestralize = self.options.pop("ancestralize", self.ancestralize) is True
-
-    def step(self, x):
-        ss = self.ss
-        sigmas = ss.sigmas[ss.idx : ss.idx + 2]
-        kwargs = {
-            "callback": None,
-            "disable": True,
-            "extra_args": {"seed": ss.noise.seed + ss.noise.seed_offset},
-        } | self.external_sampler_options
-        if self.external_sampler_uses_noise:
-            kwargs["noise_sampler"] = ss.noise.make_caching_noise_sampler(
-                self.options.get("custom_noise"),
-                1,
-                sigmas[-1],
-                sigmas[0],
-                immiscible=fallback(self.immiscible, ss.noise.immiscible),
-            )
-
-        mcc = 1
-
-        def model_wrapper(x_, sigma_, *args, **kwargs):
-            nonlocal mcc
-            if torch.equal(x_, x) and sigma_ == ss.sigma:
-                return ss.hcur.denoised.clone()
-            mr = self.call_model(x_, sigma_, *args, call_index=mcc, **kwargs)
-            mcc += 1
-            return mr.denoised.clone()
-
-        result = self.external_sampler.sampler_function(
-            model_wrapper, x.clone(), sigmas, **kwargs
-        )
-        yield from self.result(result, ss.sigma.new_zeros(1))
-
-
-class CycleSingleStepSampler(SingleStepSampler):
-    default_eta = 0.0
-
-    def __init__(self, *, cycle_pct=0.25, cycle_adjust_scales=True, **kwargs):
-        super().__init__(**kwargs)
-        if cycle_pct < 0:
-            raise ValueError("cycle_pct must be positive")
-        self.cycle_pct = cycle_pct
-        self.cycle_adjust_scales = cycle_adjust_scales
-
-    def get_cycle_scales(self, sigma_next):
-        keep_scale = 1 - self.cycle_pct
-        if not self.cycle_adjust_scales:
-            return keep_scale, self.cycle_pct
-        add_scale = ((sigma_next**2.0 - (keep_scale * sigma_next) ** 2.0) ** 0.5) * (
-            0.95 + 0.25 * self.cycle_pct
-        )
-        # print(f">> keep={keep_scale}, add={add_scale}")
-        return keep_scale, add_scale
-
-
-class EulerCycleStep(CycleSingleStepSampler):
-    name = "blep_euler_cycle"
-    allow_alt_cfgpp = True
-    allow_cfgpp = True
-
-    def step(self, x):
-        sigma_next = self.ss.sigma_next
-        denoised_pred, d = self.get_split_prediction()
-        keep_scale, add_scale = self.get_cycle_scales(sigma_next)
-        return (
-            yield from self.split_result(
-                denoised_pred, d * keep_scale, sigma_up=add_scale, sigma_down=sigma_next
-            )
-        )
-
-
-class TrapezoidalCycleStep(CycleSingleStepSampler):
-    name = "blep_trapezoidal_cycle"
-    model_calls = 1
-    allow_alt_cfgpp = False
-
-    def __init__(self, *args, **kwargs):
-        super().__init__(*args, **kwargs)
-        blend_mode = self.options.get("blend_mode", "lerp").strip()
-        self.blend = (
-            filtering.BLENDING_MODES[blend_mode] if blend_mode != "lerp" else torch.lerp
-        )
-
-    def step(self, x):
-        ss = self.ss
-        sigma, sigma_next = ss.sigma, ss.sigma_next
-        ratio = sigma_next / sigma
-        dratio = 1 - (sigma / sigma_next) * 0.5
-
-        # Denoised sample at the next sigma
-        mr_next = self.call_model(
-            self.blend(ss.denoised, x, ratio),
-            ss.sigma_next,
-            call_index=1,
-        )
-
-        keep_scale, add_scale = self.get_cycle_scales(ss.sigma_next)
-
-        denoised_prime = self.blend(mr_next.denoised, ss.denoised, dratio)
-        noise_pred = (x - denoised_prime).mul_(ratio * keep_scale)
-
-        yield from self.result(
-            denoised_prime.add_(noise_pred), add_scale, sigma_down=sigma_next
-        )
-
-
-class BASConfig(typing.NamedTuple):
-    batch_multiplier: int = 2
-    start_step: int = 0
-    end_step: int = 3
-    s_noise: float = 1.0
-    eta: float = 0.0
-    eta_retry_increment: float = 0
-    denoised_factors: list | tuple | None = None
-    denoised_factors_scale: float = 1.0
-    denoised_multiplier: float = 1.0
-    renoise_mode: str = "restart"
-    fromstep_factor: float = 1.0
-    tostep_factor: float = 1.0
-    tostep_source: str = "dt"
-
-
-# Batch augmented sampler
-class BASStep(SingleStepSampler):
-    name = "blep_bas"
-    model_calls = -1
-    uses_alt_noise = True
-
-    def __init__(self, **kwargs):
-        super().__init__(**kwargs)
-        bas = self.bas = BASConfig(**self.options.get("bas", {}))
-        if bas.renoise_mode not in {"restart", "restart_noneta", "simple"}:
-            raise ValueError("Bad BAS renoise mode")
-        if bas.tostep_source not in {"dt", "sigma", "sigma_next"}:
-            raise ValueError("Bad BAS tostep_source")
-        blend_mode = self.options.get("blend_mode", "lerp").strip()
-        self.blend = (
-            filtering.BLENDING_MODES[blend_mode] if blend_mode != "lerp" else torch.lerp
-        )
-
-    def step(self, x):
-        ss = self.ss
-        bas = self.bas
-        sigma, sigma_next = ss.sigma, ss.sigma_next
-        eta = self.get_dyn_eta()
-        sigma_down, sigma_up = self.get_ancestral_step(eta)
-        denoised = ss.denoised
-        ratio = sigma_down / sigma
-        if (
-            ss.step >= bas.end_step
-            or ss.step < bas.start_step
-            or bas.batch_multiplier < 1
-        ):
-            return (yield from self.result(self.blend(denoised, x, ratio), sigma_up))
-        bsigma = sigma * bas.fromstep_factor
-        if bas.tostep_source == "sigma":
-            bsigma_next = sigma * bas.tostep_factor
-        elif bas.tostep_source == "sigma_next":
-            bsigma_next = sigma_next * bas.tostep_factor
-        elif bas.tostep_source == "dt":
-            bsigma_next = bsigma + (sigma_next - bsigma) * bas.tostep_factor
-        else:
-            raise RuntimeError("Impossible BAS tostep_source")
-        if bsigma <= bsigma_next:
-            raise ValueError("BAS: Bad configuration, got sigma <= sigma_next")
-        bsigma_down, bsigma_up = self.get_ancestral_step(
-            bas.eta,
-            sigma=bsigma,
-            sigma_next=bsigma_next,
-            retry_increment=bas.eta_retry_increment,
-        )
-        bratio = bsigma_down / bsigma
-        x_new = self.blend(denoised, x, bratio)
-        batch_factor = bas.batch_multiplier
-        if bas.denoised_factors is None:
-            dn_factors = (bas.denoised_factors_scale / (batch_factor + 1),) * (
-                batch_factor + 1
-            )
-            dn_sum = bas.denoised_factors_scale
-        else:
-            dn_factors = tuple(bas.denoised_factors)[: batch_factor + 1]
-            tooshort = (batch_factor + 1) - len(dn_factors)
-            if tooshort > 0:
-                dn_factors = dn_factors + (dn_factors[-1],) * tooshort
-            if len(dn_factors) != batch_factor + 1:
-                raise ValueError("Bad length for bas_denoised_factors")
-            dn_sum = sum(dn_factors)
-            if bas.denoised_factors_scale != 0:
-                dn_factors = tuple(
-                    (f / dn_sum) * bas.denoised_factors_scale for f in dn_factors
-                )
-        # print(
-        #     f"\nBAS STEP: step {bsigma} -> {bsigma_next} : down={bsigma_down}, up={bsigma_up}, bratio={bratio}, dn_factors={dn_factors}"
-        # )
-        if dn_sum == 0:
-            raise ValueError("bas_denoised_factors must sum to a non-zero quantity")
-        batch_size = x.shape[0]
-        expanded_batch = batch_size * batch_factor
-        x_expanded = x.new_zeros(expanded_batch, *x.shape[1:])
-        renoise_mode = bas.renoise_mode
-        if renoise_mode == "restart":
-            noise_factor = (bsigma**2 - bsigma_down**2) ** 0.5
-        elif renoise_mode == "restart_noneta":
-            noise_factor = bsigma_up + (bsigma**2 - bsigma_next**2) ** 0.5
-        else:
-            noise_factor = bsigma_up + (bsigma - bsigma_next)
-        for bidx in range(batch_factor):
-            # print(
-            #     f"NOISE ITER {bidx} -- {bidx * batch_size} -> {bidx * batch_size + batch_size}"
-            # )
-            x_expanded[
-                bidx * batch_size : bidx * batch_size + batch_size
-            ] = yield from self.result(
-                x_new,
-                noise_factor,
-                sigma=bsigma,
-                sigma_down=bsigma_down,
-                s_noise=bas.s_noise,
-                noise_sampler=self.alt_noise_sampler,
-                final=False,
-            )
-        s_in = x.new_ones(expanded_batch)
-        del x_new
-        mr_expanded = self.call_model(x_expanded, bsigma, s_in=s_in, call_index=1)
-        denoised_expanded = mr_expanded.denoised
-        denoised_new = torch.zeros_like(denoised)
-        for obidx in range(batch_size):
-            for bidx in range(-1, batch_factor):
-                # print(f"DN ITER {obidx} <{bidx}> = dn_exp[{bidx * batch_size + obidx}]")
-                dn_curr = (
-                    denoised[obidx]
-                    if bidx == -1
-                    else denoised_expanded[bidx * batch_size + obidx]
-                )
-                denoised_new[obidx] += dn_curr * dn_factors[bidx + 1]
-        denoised_new *= bas.denoised_multiplier
-        result = self.blend(denoised_new, x, ratio)
-        yield from self.result(result, sigma_up)
-
-
-def scale_wavelets(waves, factor_yl, factor_yh=None):
-    factor_yh = fallback(factor_yh, factor_yl)
-    if factor_yl == 1 and factor_yh == 1:
-        return waves
-    return (waves[0] * factor_yl, tuple(t * factor_yh for t in waves[1]))
-
-
-def blend_wavelets(a, b, *, factor_yl, factor_yh, blend_yl, blend_yh=None):
-    blend_yh = fallback(blend_yh, blend_yl)
-    if not isinstance(factor_yl, torch.Tensor):
-        factor_yl = a[0].new_full((1,), factor_yl)
-    if not isinstance(factor_yh, torch.Tensor):
-        factor_yh = a[0].new_full((1,), factor_yh)
-    return (
-        blend_yl(a[0], b[0], factor_yl),
-        tuple(blend_yh(ta, tb, factor_yh) for ta, tb in zip(a[1], b[1])),
-    )
-
-
-class WeoonConfig(typing.NamedTuple):
-    start_step: int = 0
-    end_step: int = 9999
-    eta: float = 0.0
-    eta_retry_increment: float = 0.0
-    s_noise: float = 1.0
-    # One of dwt, dwt1d, dtcwt
-    wavelet_mode: str = "dwt"
-    padding: str = "periodization"
-    inv_padding: str | None = None
-    level: int = 3
-    wave: str = "db4"
-    inv_wave: str | None = None
-    dtcwt_qshift: str = "qshift_a"
-    dtcwt_biort: str = "near_sym_a"
-    dtcwt_inv_qshift: str | None = None
-    dtcwt_inv_biort: str | None = None
-    downstep_scale: float = 1.0
-    yl_strength: float = 1.0
-    yh_strength: float = 0.5
-    wavelet_blend_mode: str = "lerp"
-    wavelet_blend_mode_yh: str | None = None
-    denoised_yl_multiplier: float = 1.0
-    denoised_yh_multiplier: float = 1.0
-    denoised_down_yl_multiplier: float = 1.0
-    denoised_down_yh_multiplier: float = 1.0
-    flatten_start_dim: int = 2
-
-
-class WeoonStep(SingleStepSampler):
-    name = "blep_weoon"
-    model_calls = 1
-    uses_alt_noise = True
-
-    def __init__(self, **kwargs):
-        if not HAVE_WAVELETS:
-            raise RuntimeError(
-                "Wavelet sampling requires the pytorch_wavelets package installed in your environment",
-            )
-        super().__init__(**kwargs)
-        w = self.weoon = WeoonConfig(**self.options.get("weoon", {}))
-        blend_mode = self.options.get("blend_mode", "lerp").strip()
-        self.blend = (
-            filtering.BLENDING_MODES[blend_mode] if blend_mode != "lerp" else torch.lerp
-        )
-        self.wavelet_blend = (
-            filtering.BLENDING_MODES[w.wavelet_blend_mode]
-            if w.wavelet_blend_mode != "lerp"
-            else torch.lerp
-        )
-        if w.wavelet_blend_mode_yh is None:
-            self.wavelet_blend_yh = self.wavelet_blend
-        else:
-            self.wavelet_blend_yh = (
-                filtering.BLENDING_MODES[w.wavelet_blend_mode_yh]
-                if w.wavelet_blend_mode_yh != "lerp"
-                else torch.lerp
-            )
-        if not (0 <= w.flatten_start_dim <= 2):
-            raise ValueError("Bad flatten_start_dim in Weoon sampler")
-        if w.wavelet_mode == "dtcwt":
-            self.wavelet_forward = ptwav.DTCWTForward(
-                J=w.level, mode=w.padding, biort=w.dtcwt_biort, qshift=w.dtcwt_qshift
-            )
-            self.wavelet_inverse = ptwav.DTCWTInverse(
-                mode=fallback(w.inv_padding, w.padding),
-                biort=fallback(w.dtcwt_inv_biort, w.dtcwt_biort),
-                qshift=fallback(w.dtcwt_inv_qshift, w.dtcwt_qshift),
-            )
-        elif w.wavelet_mode == "dwt":
-            self.wavelet_forward = ptwav.DWTForward(
-                J=w.level, wave=w.wave, mode=w.padding
-            )
-            self.wavelet_inverse = ptwav.DWTInverse(
-                wave=fallback(w.inv_wave, w.wave),
-                mode=fallback(w.inv_padding, w.padding),
-            )
-        elif w.wavelet_mode == "dwt1d":
-            self.wavelet_forward = ptwav.DWT1DForward(
-                J=w.level, wave=w.wave, mode=w.padding
-            )
-            self.wavelet_inverse = ptwav.DWT1DInverse(
-                wave=fallback(w.inv_wave, w.wave),
-                mode=fallback(w.inv_padding, w.padding),
-            )
-
-    def maybe_flatten(self, tensor: torch.Tensor) -> torch.Tensor:
-        w = self.weoon
-        need_flatten = w.wavelet_mode == "dwt1d"
-        if not need_flatten:
-            return tensor
-        start_dim = w.flatten_start_dim
-        tensor = tensor.flatten(start_dim=start_dim)
-        if start_dim == 0:
-            return tensor[None, None, ...]
-        if start_dim == 1:
-            return tensor[:, None, ...]
-        return tensor
-
-    def step(self, x):
-        w = self.weoon
-        ss = self.ss
-        sigma, sigma_next = ss.sigma, ss.sigma_next
-        sigma_down, sigma_up = self.get_ancestral_step(self.get_dyn_eta())
-        ratio = sigma_down / sigma
-        if not w.start_step <= ss.step <= w.end_step:
-            return (yield from self.result(self.blend(ss.denoised, x, ratio), sigma_up))
-        self.wavelet_forward.to(x)
-        self.wavelet_inverse.to(x)
-        dt = sigma_next - sigma
-        wsigma_next = (sigma + dt * w.downstep_scale).clamp_(0)
-        wsigma_down, wsigma_up = self.get_ancestral_step(
-            w.eta,
-            sigma=sigma,
-            sigma_next=wsigma_next,
-            retry_increment=w.eta_retry_increment,
-        )
-        wratio = wsigma_down / sigma
-        x_down = self.blend(ss.denoised, x, wratio)
-        if wsigma_up != 0:
-            x_down = yield from self.result(
-                x_down,
-                wsigma_up,
-                sigma_next=wsigma_next,
-                sigma_down=wsigma_down,
-                s_noise=w.s_noise,
-                noise_sampler=self.alt_noise_sampler,
-                final=False,
-            )
-        mr_down = self.call_model(x_down, wsigma_next, call_index=1)
-        coeffs = scale_wavelets(
-            self.wavelet_forward(self.maybe_flatten(ss.denoised)),
-            factor_yl=w.denoised_yl_multiplier,
-            factor_yh=w.denoised_yh_multiplier,
-        )
-        coeffs_down = scale_wavelets(
-            self.wavelet_forward(self.maybe_flatten(mr_down.denoised)),
-            factor_yl=w.denoised_down_yl_multiplier,
-            factor_yh=w.denoised_down_yh_multiplier,
-        )
-        coeffs_out = blend_wavelets(
-            coeffs,
-            coeffs_down,
-            factor_yl=w.yl_strength,
-            factor_yh=w.yh_strength,
-            blend_yl=self.wavelet_blend,
-            blend_yh=self.wavelet_blend_yh,
-        )
-        denoised_new = self.wavelet_inverse(coeffs_out)
-        if denoised_new.shape != x.shape:
-            denoised_new = denoised_new.reshape(*x.shape)
-        x = self.blend(denoised_new, x, ratio)
-        yield from self.result(x, sigma_up)
-
-
-registry.add(
-    BASStep,
-    DynamicStep,
-    AdapterStep,
-    EulerCycleStep,
-    TrapezoidalCycleStep,
-    WeoonStep,
-)

py/step_samplers/builtins.py

@@ -1,541 +0,0 @@
-import torch
-
-import comfy
-from comfy.k_diffusion.sampling import get_ancestral_step
-
-from .base import (
-    SingleStepSampler,
-    DPMPPStepMixin,
-    HistorySingleStepSampler,
-    ReversibleSingleStepSampler,
-    registry,
-)
-
-
-class EulerStep(SingleStepSampler):
-    name = "euler"
-    allow_cfgpp = True
-    allow_alt_cfgpp = True
-    step = SingleStepSampler.euler_step
-
-
-class DPMPP2MStep(HistorySingleStepSampler, DPMPPStepMixin):
-    name = "dpmpp_2m"
-    default_history_limit, max_history = 1, 1
-    ancestralize = True
-    default_eta = 0.0
-
-    def step(self, x):
-        ss = self.ss
-        s, sn = ss.sigma, ss.sigma_next
-        t, t_next = self.t_fn(s), self.t_fn(sn)
-        h = t_next - t
-        st, st_next = self.sigma_fn(t), self.sigma_fn(t_next)
-        if self.available_history() > 0:
-            h_last = t - self.t_fn(ss.sigma_prev)
-            r = h_last / h
-            denoised, old_denoised = ss.denoised, ss.hprev.denoised
-            denoised_d = (1 + 1 / (2 * r)) * denoised - (1 / (2 * r)) * old_denoised
-        else:
-            denoised_d = ss.denoised
-        yield from self.result((st_next / st) * x - (-h).expm1() * denoised_d)
-
-
-class DPMPP2MSDEStep(ReversibleSingleStepSampler):
-    name = "dpmpp_2m_sde"
-    default_history_limit, max_history = 1, 1
-    default_reversible_scale = 0.0
-
-    def __init__(self, *, solver_type="midpoint", **kwargs):
-        super().__init__(**kwargs)
-        solver_type = solver_type.lower().strip()
-        if solver_type not in ("midpoint", "heun"):
-            raise ValueError("Bad solver_type: must be one of midpoint, heun")
-        self.solver_type = solver_type
-
-    def step(self, x):
-        ss = self.ss
-        sigma, sigma_next = ss.sigma, ss.sigma_next
-        denoised = ss.denoised
-        # DPM-Solver++(2M) SDE
-        t, s = -sigma.log(), -sigma_next.log()
-        h = s - t
-        eta_h = self.get_dyn_eta() * h
-        ratio = sigma_next / sigma
-        x = ((ratio * (-eta_h).exp()) * x).add_((-h - eta_h).expm1().neg() * denoised)
-        noise_strength = sigma_next * (-2 * eta_h).expm1().neg().sqrt()
-        if self.available_history() == 0:
-            return (yield from self.result(x, noise_strength))
-        sigma_prev, old_denoised = ss.hprev.sigma, ss.hprev.denoised
-        h_last = (-sigma.log()) - (-sigma_prev.log())
-        r = h_last / h
-        if self.solver_type == "midpoint":
-            multiplier = 0.5 * (-h - eta_h).expm1().neg()
-        else:
-            multiplier = (-h - eta_h).expm1().neg() / (-h - eta_h) + 1
-        reta, reversible_scale = self.get_reversible_cfg()
-        if reversible_scale != 0:
-            multiplier *= 0.5
-        x += (denoised - old_denoised).mul_((1 / r) * multiplier)
-        if reversible_scale != 0:
-            reta_h = reta * h
-            if self.solver_type == "midpoint":
-                rmultiplier = 0.5 * (-h - reta_h).expm1().neg()
-            else:
-                rmultiplier = (-h - reta_h).expm1().neg() / (-h - reta_h) + 1
-            rmultiplier = ((1 / r) * (rmultiplier**2 / 2)) * reversible_scale
-            x -= (old_denoised - denoised).mul_(rmultiplier)
-        yield from self.result(x, noise_strength)
-
-
-class DPMPP3MSDEStep(HistorySingleStepSampler):
-    name = "dpmpp_3m_sde"
-    default_history_limit, max_history = 2, 2
-
-    def step(self, x):
-        ss = self.ss
-        denoised = ss.denoised
-        t, s = -ss.sigma.log(), -ss.sigma_next.log()
-        h = s - t
-        eta = self.get_dyn_eta()
-        h_eta = h * (eta + 1)
-        x = torch.exp(-h_eta) * x + (-h_eta).expm1().neg() * denoised
-        noise_strength = ss.sigma_next * (-2 * h * eta).expm1().neg().sqrt()
-        ah = self.available_history()
-        if ah == 0:
-            return (yield from self.result(x, noise_strength))
-        hist = ss.hist
-        h_1 = (-ss.sigma.log()) - (-ss.sigma_prev.log())
-        denoised_1 = hist[-2].denoised
-        if ah == 1:
-            r = h_1 / h
-            d = (denoised - denoised_1) / r
-            phi_2 = h_eta.neg().expm1() / h_eta + 1
-            x = x + phi_2 * d
-        else:  # 2+ history items available
-            h_2 = (-ss.sigma_prev.log()) - (-ss.sigmas[ss.idx - 2].log())
-            denoised_2 = hist[-3].denoised
-            r0 = h_1 / h
-            r1 = h_2 / h
-            d1_0 = (denoised - denoised_1) / r0
-            d1_1 = (denoised_1 - denoised_2) / r1
-            d1 = d1_0 + (d1_0 - d1_1) * r0 / (r0 + r1)
-            d2 = (d1_0 - d1_1) / (r0 + r1)
-            phi_2 = h_eta.neg().expm1() / h_eta + 1
-            phi_3 = phi_2 / h_eta - 0.5
-            x = x + phi_2 * d1 - phi_3 * d2
-        yield from self.result(x, noise_strength)
-
-    def step_(self, x):
-        sr = next(super().step(x))
-        if self.available_history() < 2:
-            yield sr
-            return
-        ss = self.ss
-        sigma, sigma_next = ss.sigma, ss.sigma_next
-        hprev = ss.hist[-2]
-        hprevprev = ss.hist[-3]
-        t, s = -sigma.log(), -sigma_next.log()
-        h = s - t
-        eta = self.get_dyn_eta()
-        h_eta = h * (eta + 1)
-        h_2 = (-ss.sigma_prev.log()) - (-hprevprev.sigma.log())
-        denoised = ss.denoised
-        denoised_1 = hprev.denoised
-        denoised_2 = hprevprev.denoised
-        h_1 = (-ss.sigma.log()) - (-hprev.sigma.log())
-        r0 = h_1 / h
-        r1 = h_2 / h
-        d1_0 = (denoised - denoised_1).div_(r0)
-        d1_1 = (denoised_1 - denoised_2).div_(r1)
-        d1 = d1_0 + (d1_0 - d1_1) * r0 / (r0 + r1)
-        d2 = (d1_0 - d1_1).div_(r0 + r1)
-        phi_2 = h_eta.neg().expm1() / h_eta + 1
-        phi_3 = phi_2 / h_eta - 0.5
-        sr.x_ += phi_2 * d1 - phi_3 * d2
-        yield sr
-        # x = x + phi_2 * d1 - phi_3 * d2
-        # yield from self.result(sr.x + phi_2 * d1 - phi_3 * d2, sr.sigma_up)
-
-
-# Alt CFG++ approach referenced from https://github.com/comfyanonymous/ComfyUI/pull/3871 - thanks!
-class DPMPP2SStep(SingleStepSampler, DPMPPStepMixin):
-    name = "dpmpp_2s"
-    model_calls = 1
-    allow_alt_cfgpp = True
-
-    def step(self, x):
-        ss = self.ss
-        t_fn, sigma_fn = self.t_fn, self.sigma_fn
-        sigma_down, sigma_up = self.get_ancestral_step(self.get_dyn_eta())
-        # DPM-Solver++(2S)
-        t, t_next = t_fn(ss.sigma), t_fn(sigma_down)
-        r = 1 / 2
-        h = t_next - t
-        s = t + r * h
-        eff_x = (
-            x
-            if self.alt_cfgpp_scale == 0 or ss.hcur.denoised_uncond is None
-            else x + (ss.denoised - ss.hcur.denoised_uncond) * self.alt_cfgpp_scale
-        )
-        x_2 = (sigma_fn(s) / sigma_fn(t)) * eff_x - (-h * r).expm1() * ss.denoised
-        denoised_2 = self.call_model(x_2, sigma_fn(s), call_index=1).denoised
-        x = (sigma_fn(t_next) / sigma_fn(t)) * eff_x - (-h).expm1() * denoised_2
-        yield from self.result(x, sigma_up, sigma_down=sigma_down)
-
-
-class DPMPPSDEStep(SingleStepSampler, DPMPPStepMixin):
-    name = "dpmpp_sde"
-    self_noise = 1
-    model_calls = 1
-    allow_alt_cfgpp = True  # Implementation may not be correct.
-    uses_alt_noise = True
-
-    def __init__(self, *args, r=1 / 2, **kwargs):
-        super().__init__(*args, **kwargs)
-        self.r = r
-
-    def step(self, x):
-        ss = self.ss
-        t_fn, sigma_fn = self.t_fn, self.sigma_fn
-        r, eta = self.r, self.get_dyn_eta()
-        # DPM-Solver++
-        t, t_next = t_fn(ss.sigma), t_fn(ss.sigma_next)
-        h = t_next - t
-        s = t + h * r
-        fac = 1 / (2 * r)
-
-        # Step 1
-        sd, su = get_ancestral_step(sigma_fn(t), sigma_fn(s), eta)
-        s_ = t_fn(sd)
-        eff_x = (
-            x
-            if self.alt_cfgpp_scale == 0 or ss.hcur.denoised_uncond is None
-            else x + (ss.denoised - ss.hcur.denoised_uncond) * self.alt_cfgpp_scale
-        )
-        x_2 = (sigma_fn(s_) / sigma_fn(t)) * eff_x - (t - s_).expm1() * ss.denoised
-        x_2 = yield from self.result(
-            x_2,
-            su,
-            sigma=sigma_fn(t),
-            sigma_next=sigma_fn(s),
-            noise_sampler=self.alt_noise_sampler,
-            final=False,
-        )
-        denoised_2 = self.call_model(x_2, sigma_fn(s), call_index=1).denoised
-
-        # Step 2
-        sd, su = get_ancestral_step(sigma_fn(t), sigma_fn(t_next), eta)
-        t_next_ = t_fn(sd)
-        denoised_d = (1 - fac) * ss.denoised + fac * denoised_2
-        x = (sigma_fn(t_next_) / sigma_fn(t)) * eff_x - (
-            t - t_next_
-        ).expm1() * denoised_d
-        yield from self.result(x, su, sigma_down=sd)
-
-
-# Adapted from https://github.com/zju-pi/diff-sampler/blob/main/diff-solvers-main/solvers.py
-# under Apache 2 license
-class IPNDMStep(HistorySingleStepSampler):
-    name = "ipndm"
-    ancestralize = True
-    default_history_limit, max_history = 1, 3
-    allow_alt_cfgpp = True
-    default_eta = 0.0
-
-    IPNDM_MULTIPLIERS = (
-        ((1,), 1),
-        ((3, -1), 2),
-        ((23, -16, 5), 12),
-        ((55, -59, 37, -9), 24),
-    )
-
-    def step(self, x):
-        ss = self.ss
-        order = self.available_history() + 1
-        if order > 1:
-            hd = tuple(self.to_d(ss.hist[-hidx]) for hidx in range(order, 1, -1))
-        (dm, *hms), divisor = self.IPNDM_MULTIPLIERS[order - 1]
-        noise = dm * self.to_d(ss.hcur)
-        for hidx, hm in enumerate(hms, start=1):
-            noise += hm * hd[-hidx]
-        noise /= divisor
-        yield from self.result(x + ss.dt * noise)
-
-
-# Adapted from https://github.com/zju-pi/diff-sampler/blob/main/diff-solvers-main/solvers.py
-# under Apache 2 license
-class IPNDMVStep(HistorySingleStepSampler):
-    name = "ipndm_v"
-    ancestralize = True
-    default_history_limit, max_history = 1, 3
-    allow_alt_cfgpp = True
-    default_eta = 0.0
-
-    def step(self, x):
-        ss = self.ss
-        dt = ss.dt
-        d = self.to_d(ss.hcur)
-        order = self.available_history() + 1
-        if order > 1:
-            hd = tuple(self.to_d(ss.hist[-hidx]) for hidx in range(order, 1, -1))
-            hns = (
-                ss.sigmas[ss.idx - (order - 2) : ss.idx + 1]
-                - ss.sigmas[ss.idx - (order - 1) : ss.idx]
-            )
-        if order == 1:
-            noise = d
-        elif order == 2:
-            coeff1 = (2 + (dt / hns[-1])) / 2
-            coeff2 = -(dt / hns[-1]) / 2
-            noise = coeff1 * d + coeff2 * hd[-1]
-        elif order == 3:
-            temp = (
-                1
-                - dt
-                / (3 * (dt + hns[-1]))
-                * (dt * (dt + hns[-1]))
-                / (hns[-1] * (hns[-1] + hns[-2]))
-            ) / 2
-            coeff1 = (2 + (dt / hns[-1])) / 2 + temp
-            coeff2 = -(dt / hns[-1]) / 2 - (1 + hns[-1] / hns[-2]) * temp
-            coeff3 = temp * hns[-1] / hns[-2]
-            noise = coeff1 * d + coeff2 * hd[-1] + coeff3 * hd[-2]
-        else:
-            temp1 = (
-                1
-                - dt
-                / (3 * (dt + hns[-1]))
-                * (dt * (dt + hns[-1]))
-                / (hns[-1] * (hns[-1] + hns[-2]))
-            ) / 2
-            temp2 = (
-                (
-                    (1 - dt / (3 * (dt + hns[-1]))) / 2
-                    + (1 - dt / (2 * (dt + hns[-1])))
-                    * dt
-                    / (6 * (dt + hns[-1] + hns[-2]))
-                )
-                * (dt * (dt + hns[-1]) * (dt + hns[-1] + hns[-2]))
-                / (hns[-1] * (hns[-1] + hns[-2]) * (hns[-1] + hns[-2] + hns[-3]))
-            )
-            coeff1 = (2 + (dt / hns[-1])) / 2 + temp1 + temp2
-            coeff2 = (
-                -(dt / hns[-1]) / 2
-                - (1 + hns[-1] / hns[-2]) * temp1
-                - (
-                    1
-                    + (hns[-1] / hns[-2])
-                    + (hns[-1] * (hns[-1] + hns[-2]) / (hns[-2] * (hns[-2] + hns[-3])))
-                )
-                * temp2
-            )
-            coeff3 = (
-                temp1 * hns[-1] / hns[-2]
-                + (
-                    (hns[-1] / hns[-2])
-                    + (hns[-1] * (hns[-1] + hns[-2]) / (hns[-2] * (hns[-2] + hns[-3])))
-                    * (1 + hns[-2] / hns[-3])
-                )
-                * temp2
-            )
-            coeff4 = (
-                -temp2
-                * (hns[-1] * (hns[-1] + hns[-2]) / (hns[-2] * (hns[-2] + hns[-3])))
-                * hns[-1]
-                / hns[-2]
-            )
-            noise = coeff1 * d + coeff2 * hd[-1] + coeff3 * hd[-2] + coeff4 * hd[-3]
-        yield from self.result(x + ss.dt * noise)
-
-
-class DEISStep(HistorySingleStepSampler):
-    name = "deis"
-    ancestralize = True
-    default_history_limit, max_history = 1, 3
-    allow_alt_cfgpp = True
-    default_eta = 0.0
-
-    def __init__(self, *args, deis_mode="tab", **kwargs):
-        super().__init__(*args, **kwargs)
-        self.deis_mode = deis_mode
-        self.deis_coeffs_key = None
-        self.deis_coeffs = None
-
-    def get_deis_coeffs(self):
-        ss = self.ss
-        key = (
-            self.history_limit,
-            len(ss.sigmas),
-            ss.sigmas[0].item(),
-            ss.sigmas[-1].item(),
-        )
-        if self.deis_coeffs_key == key:
-            return self.deis_coeffs
-        self.deis_coeffs_key = key
-        self.deis_coeffs = comfy.k_diffusion.deis.get_deis_coeff_list(
-            ss.sigmas, self.history_limit + 1, deis_mode=self.deis_mode
-        )
-        return self.deis_coeffs
-
-    def step(self, x):
-        ss = self.ss
-        dt = ss.dt
-        d = self.to_d(ss.hcur)
-        order = self.available_history() + 1
-        if order < 2:
-            noise = dt * d  # Euler
-        else:
-            c = self.get_deis_coeffs()[ss.idx]
-            hd = tuple(self.to_d(ss.hist[-hidx]) for hidx in range(order, 1, -1))
-            noise = c[0] * d
-            for i in range(1, order):
-                noise += c[i] * hd[-i]
-        yield from self.result(x + noise)
-
-
-# https://openreview.net/pdf?id=o2ND9v0CeK
-# Implementation referenced from ComfyUI
-class GradientEstimationStep(HistorySingleStepSampler):
-    name = "gradient_estimation"
-    ancestralize = False
-    default_history_limit, max_history = 1, 1
-    default_eta = 0.0
-
-    def __init__(self, *args, ge_gamma=2.0, **kwargs):
-        super().__init__(*args, **kwargs)
-        self.ge_gamma = ge_gamma
-
-    def step(self, x):
-        ss = self.ss
-        sigma_down, sigma_up = self.get_ancestral_step(self.get_dyn_eta())
-        dt = sigma_down - ss.sigma
-        d = self.to_d(ss.hcur)
-        if self.available_history() < 1:
-            noise_pred = dt * d  # Euler
-        else:
-            gamma = self.ge_gamma
-            noise_pred = dt * (gamma * d + (1 - gamma) * ss.hist[-2].d)
-        yield from self.result(x + noise_pred, sigma_up, sigma_down=sigma_down)
-
-
-class HeunPP2Step(SingleStepSampler):
-    name = "heunpp2"
-    ancestralize = True
-    model_calls = 2
-    allow_alt_cfgpp = True
-
-    def __init__(self, *args, max_order=3, **kwargs):
-        super().__init__(*args, **kwargs)
-        self.max_order = max(1, min(self.model_calls + 1, max_order))
-
-    def step(self, x):
-        ss = self.ss
-        steps_remain = max(0, len(ss.sigmas) - (ss.idx + 2))
-        order = min(self.max_order, steps_remain + 1)
-        sn = ss.sigma_next
-        if order == 1:
-            return (yield from self.euler_step(x))
-        d = self.to_d(ss.hcur)
-        dt = ss.dt
-        w = order * ss.sigma
-        w2 = sn / w
-        x_2 = x + d * dt
-        d_2 = self.to_d(self.call_model(x_2, sn, call_index=1))
-        if order == 2:
-            # Heun's method (ish)
-            w1 = 1 - w2
-            d_prime = d * w1 + d_2 * w2
-        else:
-            # Heun++ (ish)
-            snn = ss.sigmas[ss.idx + 2]
-            dt_2 = snn - sn
-            x_3 = x_2 + d_2 * dt_2
-            d_3 = self.to_d(self.call_model(x_3, snn, call_index=2))
-            w3 = snn / w
-            w1 = 1 - w2 - w3
-            d_prime = w1 * d + w2 * d_2 + w3 * d_3
-        yield from self.result(x + d_prime * dt)
-
-
-# Referenced from ComfyUI implementation
-class DPM2Step(SingleStepSampler):
-    name = "dpm_2"
-    model_calls = 1
-
-    def step(self, x):
-        ss = self.ss
-        sigma, sigma_next = ss.sigma, ss.sigma_next
-        eta = self.get_dyn_eta()
-        sigma_down, sigma_up = self.get_ancestral_step(eta)
-        sigma_mid = sigma.log().lerp(sigma_next.log(), 0.5).exp()
-        dt_1, dt_2 = sigma_mid - sigma, sigma_down - sigma
-        d = self.to_d(ss.hcur)
-        mr_2 = self.call_model(x + d * dt_1, sigma_mid, call_index=1)
-        d_2 = self.to_d(mr_2)
-        yield from self.result(x + d_2 * dt_2, sigma_up)
-
-
-# Referenced from ComfyUI implementation
-class RESMultistepStep(HistorySingleStepSampler, DPMPPStepMixin):
-    name = "res_multistep"
-    default_history_limit, max_history = 1, 1
-    default_eta = 0.0
-
-    def __init__(self, *args, **kwargs):
-        super().__init__(*args, **kwargs)
-
-    @staticmethod
-    def phi1_fn(t: torch.Tensor) -> torch.Tensor:
-        return t.expm1() / t
-
-    @classmethod
-    def phi2_fn(cls, t: torch.Tensor) -> torch.Tensor:
-        return (cls.phi1_fn(t) - 1.0) / t
-
-    def step(self, x):
-        ss = self.ss
-        sigma = ss.sigma
-        eta = self.get_dyn_eta()
-        sigma_down, sigma_up = self.get_ancestral_step(eta)
-        if self.available_history() == 0:
-            dt = sigma_down - sigma
-            d = self.to_d(ss.hcur)
-            return (yield from self.result(x + dt * d, sigma_up, sigma_down=sigma_down))
-        prev_mr = ss.hist[-2]
-        prev_sigma_down = self.get_ancestral_step(
-            sigma=prev_mr.sigma, sigma_next=sigma, eta=eta
-        )[0]
-        # Second order multistep method in https://arxiv.org/pdf/2308.02157
-        t, t_old, t_next, t_prev = (
-            self.t_fn(sigma),
-            self.t_fn(prev_sigma_down),
-            self.t_fn(sigma_down),
-            self.t_fn(prev_mr.sigma),
-        )
-        h = t_next - t
-        h_s = self.sigma_fn(h)
-        c2 = (t_prev - t_old) / h
-
-        phi1_val, phi2_val = self.phi1_fn(-h), self.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)
-        result = h_s * x + h * (b1 * ss.denoised + b2 * prev_mr.denoised)
-        yield from self.result(result, sigma_up, sigma_down=sigma_down)
-
-
-registry.add(
-    DEISStep,
-    DPMPP2MSDEStep,
-    DPMPP2MStep,
-    DPMPP3MSDEStep,
-    DPMPPSDEStep,
-    EulerStep,
-    HeunPP2Step,
-    IPNDMStep,
-    IPNDMVStep,
-    GradientEstimationStep,
-    DPM2Step,
-    DPMPP2SStep,
-    RESMultistepStep,
-)

py/step_samplers/clybius.py

@@ -1,755 +0,0 @@
-# Samplers based on Clybius' designs, mostly from https://github.com/Clybius/ComfyUI-Extra-Samplers/
-
-import math
-import torch
-
-from comfy.k_diffusion.sampling import get_ancestral_step, to_d
-
-from .base import SingleStepSampler, ReversibleConfig, ReversibleSingleStepSampler
-from .builtins import DPMPP2MSDEStep
-from . import res_support
-from . import registry
-
-from .. import filtering
-from .. import utils
-
-
-# Based on original implementation from https://github.com/Clybius/ComfyUI-Extra-Samplers
-# Apparently the only difference between Heun and Trapezoidal is the first step using ETA or not.
-class ReversibleHeunStep(ReversibleSingleStepSampler):
-    name = "reversible_heun"
-    model_calls = 1
-    allow_alt_cfgpp = True
-    allow_cfgpp = True
-    trapezoidal_mode = False
-
-    def __init__(self, *args, **kwargs):
-        super().__init__(*args, **kwargs)
-        blend_mode = self.options.get("blend_mode", "lerp").strip()
-        self.blend = (
-            filtering.BLENDING_MODES[blend_mode] if blend_mode != "lerp" else torch.lerp
-        )
-
-    def reversible_correction(self, d, d_next, dt_reversible):
-        if dt_reversible == 0 or self.reversible.scale == 0:
-            return None
-        return d_next.sub_(d).div_(4).mul_(dt_reversible**2).mul_(self.reversible.scale)
-
-    def step_internal(self, x, *, history_mode=False):
-        ss = self.ss
-        history_mode = history_mode and self.available_history() > 0
-        if not history_mode:
-            mr_1, mr_2 = ss.hcur, None
-        else:
-            mr_1, mr_2 = ss.hprev, ss.hcur
-        sigma = ss.sigma
-        denoised, uncond = mr_1.denoised, mr_1.denoised_uncond
-        sigma_down, sigma_up = self.get_ancestral_step(self.get_dyn_eta())
-        ratio = sigma_down / sigma
-        dratio = 1 - (sigma / sigma_down) * 0.5
-        if mr_2 is None:
-            x_2 = self.step_mix(x, denoised, uncond, ratio, blend=self.blend)
-            mr_2 = self.call_model(x_2, sigma_down, call_index=1)
-            del x_2
-        denoised_2, uncond_2 = mr_2.denoised, mr_2.denoised_uncond
-        denoised_prime = self.blend(denoised_2, denoised, dratio)
-        if self.cfgpp:
-            denoised_prime += denoised * 0.5
-            uncond_prime = (uncond_2 * (1 - dratio)).add_(uncond)
-        elif self.alt_cfgpp_scale != 0:
-            uncond_prime = self.blend(uncond_2, uncond, dratio)
-        else:
-            uncond_prime = uncond
-        x = self.step_mix(x, denoised_prime, uncond_prime, ratio, blend=self.blend)
-        if self.reversible.scale != 0:
-            correction = self.reversible_correction(
-                d=self.to_d(mr_1, use_cfgpp=self.reversible.use_cfgpp),
-                d_next=self.to_d(mr_2, use_cfgpp=self.reversible.use_cfgpp),
-                dt_reversible=self.get_ancestral_step(self.dyn_reta)[0] - sigma,
-            )
-            if correction is not None:
-                x -= correction
-        yield from self.result(x, sigma_up)
-
-    def step(self, x):
-        return self.step_internal(x, history_mode=False)
-
-
-# Based on original implementation from https://github.com/Clybius/ComfyUI-Extra-Samplers
-class ReversibleHeun1SStep(ReversibleHeunStep):
-    name = "reversible_heun_1s"
-    model_calls = (0, 1)
-    default_history_limit, max_history = 1, 1
-    allow_alt_cfgpp = True
-    allow_cfgpp = True
-
-    def step(self, x):
-        return self.step_internal(x, history_mode=True)
-
-
-# Based on original implementation from https://github.com/Clybius/ComfyUI-Extra-Samplers
-class RESStep(SingleStepSampler):
-    name = "res"
-    model_calls = 1
-    allow_alt_cfgpp = True  # May not be implemented correctly.
-
-    def __init__(self, *, res_simple_phi=False, res_c2=0.5, **kwargs):
-        super().__init__(**kwargs)
-        self.simple_phi = res_simple_phi
-        self.c2 = res_c2
-
-    def step(self, x):
-        ss = self.ss
-        eta = self.get_dyn_eta()
-        sigma_down, sigma_up = self.get_ancestral_step(eta)
-        denoised = ss.denoised
-        lam_next = sigma_down.log().neg() if eta != 0 else ss.sigma_next.log().neg()
-        lam = ss.sigma.log().neg()
-
-        h = lam_next - lam
-        a2_1, b1, b2 = res_support._de_second_order(
-            h=h, c2=self.c2, simple_phi_calc=self.simple_phi
-        )
-
-        c2_h = 0.5 * h
-
-        eff_x = (
-            x
-            if self.alt_cfgpp_scale == 0 or ss.hcur.denoised_uncond is None
-            else x + (ss.denoised - ss.hcur.denoised_uncond) * self.alt_cfgpp_scale
-        )
-        x_2 = math.exp(-c2_h) * eff_x + a2_1 * h * denoised
-        lam_2 = lam + c2_h
-        sigma_2 = lam_2.neg().exp()
-
-        denoised2 = self.call_model(x_2, sigma_2, call_index=1).denoised
-
-        x = math.exp(-h) * eff_x + h * (b1 * denoised + b2 * denoised2)
-        yield from self.result(x, sigma_up, sigma_down=sigma_down)
-
-
-class TrapezoidalStep(ReversibleHeunStep):
-    reversible = False
-    trapezoidal_mode = True
-
-
-# Based on original implementation from https://github.com/Clybius/ComfyUI-Extra-Samplers
-class TrapezoidalStep_(SingleStepSampler):
-    name = "trapezoidal"
-    model_calls = 1
-    allow_alt_cfgpp = True
-
-    def step(self, x):
-        ss = self.ss
-        sigma_next = ss.sigma_next
-        sigma_down, sigma_up = self.get_ancestral_step(self.get_dyn_eta())
-
-        # Predict the sample at the next sigma using Euler step
-        euler_sr = next(self.euler_step(x, sigma_down=sigma_next, sigma_up=0))
-        d = euler_sr.noise_pred
-
-        # Denoised sample at the next sigma
-        mr_next = self.call_model(euler_sr.x, euler_sr.sigma_down, call_index=1)
-
-        denoised_pred_next, d_next = self.get_split_prediction(mr=mr_next)
-        yield from self.split_result(
-            denoised_pred_next,
-            (d + d_next) * 0.5,
-            sigma_up=sigma_up,
-            sigma_down=sigma_down,
-        )
-
-    def step_(self, x):
-        ss = self.ss
-        sigma_down, sigma_up = self.get_ancestral_step(self.get_dyn_eta())
-
-        # Calculate the derivative using the model
-        d_i = self.to_d(ss.hcur)
-
-        # Predict the sample at the next sigma using Euler step
-        x_pred = x + d_i * ss.dt
-
-        # Denoised sample at the next sigma
-        mr_next = self.call_model(x_pred, ss.sigma_next, call_index=1)
-
-        # Calculate the derivative at the next sigma
-        d_next = self.to_d(mr_next)
-        dt_2 = sigma_down - ss.sigma
-
-        # Update the sample using the Trapezoidal rule
-        x = x + dt_2 * (d_i + d_next) / 2
-        yield from self.result(x, sigma_up, sigma_down=sigma_down)
-
-
-# Based on original implementation from https://github.com/Clybius/ComfyUI-Extra-Samplers
-class BogackiStep(ReversibleSingleStepSampler):
-    name = "bogacki"
-    reversible = False
-    model_calls = 2
-    allow_alt_cfgpp = True
-
-    def __init__(self, *args, **kwargs):
-        super().__init__(*args, **kwargs)
-        if not self.reversible:
-            self.reversible.scale = 0
-
-    def step(self, x):
-        ss = self.ss
-        s = ss.sigma
-        sd, su = self.get_ancestral_step(self.get_dyn_eta())
-        reta, reversible_scale = self.get_reversible_cfg()
-        sdr, _sur = self.get_ancestral_step(reta)
-        dt, dtr = sd - s, sdr - s
-
-        # Calculate the derivative using the model
-        d = self.to_d(ss.hcur)
-
-        # Bogacki-Shampine steps
-        k1 = d * dt
-        k2 = self.to_d(self.call_model(x + k1 / 2, s + dt / 2, call_index=1)) * dt
-        k3 = (
-            self.to_d(
-                self.call_model(x + 3 * k1 / 4 + k2 / 4, s + 3 * dt / 4, call_index=2)
-            )
-            * dt
-        )
-
-        # Reversible correction term (inspired by Reversible Heun)
-        correction = dtr**2 * (k3 - k2) / 6
-
-        # Update the sample
-        x = (x + 2 * k1 / 9 + k2 / 3 + 4 * k3 / 9) - correction * reversible_scale
-        yield from self.result(x, su, sigma_down=sd)
-
-
-class ReversibleBogackiStep(BogackiStep):
-    name = "reversible_bogacki"
-    reversible = True
-
-
-# Based on original implementation from https://github.com/Clybius/ComfyUI-Extra-Samplers
-class RK4Step(SingleStepSampler):
-    name = "rk4"
-    model_calls = 3
-    allow_alt_cfgpp = True
-
-    def step(self, x):
-        ss = self.ss
-        sigma_down, sigma_up = self.get_ancestral_step(self.get_dyn_eta())
-        sigma = ss.sigma
-        d = self.to_d(ss.hcur)
-        dt = sigma_down - sigma
-
-        # Runge-Kutta steps
-        k1 = d * dt
-        k2 = self.to_d(self.call_model(x + k1 / 2, sigma + dt / 2, call_index=1)) * dt
-        k3 = self.to_d(self.call_model(x + k2 / 2, sigma + dt / 2, call_index=2)) * dt
-        k4 = self.to_d(self.call_model(x + k3, sigma + dt, call_index=3)) * dt
-
-        # Update the sample
-        x = x + (k1 + 2 * k2 + 2 * k3 + k4) / 6
-        yield from self.result(x, sigma_up, sigma_down=sigma_down)
-
-
-# Based on original implementation from https://github.com/Clybius/ComfyUI-Extra-Samplers
-class RKF45Step(SingleStepSampler):
-    name = "rkf45"
-    model_calls = 5
-    allow_alt_cfgpp = True
-
-    def step(self, x):
-        ss = self.ss
-        sigma_down, sigma_up = self.get_ancestral_step(self.get_dyn_eta())
-        sigma = ss.sigma
-        d = self.to_d(ss.hcur)
-        dt = sigma_down - sigma
-
-        # Runge-Kutta steps
-        sigma_progression = (
-            sigma + dt / 4,
-            sigma + 3 * dt / 8,
-            sigma + 12 * dt / 13,
-            sigma + dt,
-        )
-
-        call_progression = (
-            lambda k1: x + k1 / 4,
-            lambda k1, k2: x + 3 * k1 / 32 + 9 * k2 / 32,
-            lambda k1, k2, k3: x
-            + 1932 * k1 / 2197
-            - 7200 * k2 / 2197
-            + 7296 * k3 / 2197,
-            lambda k1, k2, k3, k4: x
-            + 439 * k1 / 216
-            - 8 * k2
-            + 3680 * k3 / 513
-            - 845 * k4 / 4104,
-        )
-
-        k = [d * dt]
-        for idx, (ksigma, kfun) in enumerate(zip(sigma_progression, call_progression)):
-            curr_x = kfun(*k)
-            k.append(self.to_d(self.call_model(curr_x, ksigma)) * dt)
-        del curr_x
-        x = x + 25 * k[0] / 216 + 1408 * k[2] / 2565 + 2197 * k[3] / 4104 - k[4] / 5
-        yield from self.result(x, sigma_up, sigma_down=sigma_down)
-
-
-# Based on original implementation from https://github.com/Clybius/ComfyUI-Extra-Samplers
-class RKDynamicStep(SingleStepSampler):
-    name = "rk_dynamic"
-    model_calls = (0, 3)
-    allow_alt_cfgpp = True
-
-    rk_weights = (
-        (1,),
-        (0.5, 0.5),
-        (1 / 6, 2 / 3, 1 / 6),
-        (1 / 8, 3 / 8, 3 / 8, 1 / 8),
-    )
-
-    rk_error_orders = ((0.0375, 4), (0.075, 3), (0.15, 2))
-
-    def __init__(self, *args, max_order=4, **kwargs):
-        super().__init__(*args, **kwargs)
-        self.max_order = max(0, min(max_order, 4))
-
-    def get_rk_error_order(self, error):
-        for threshold, order in self.rk_error_orders:
-            if error < threshold:
-                return order
-        return 1
-
-    def step(self, x):
-        ss = self.ss
-        order = self.max_order
-
-        sigma_down, sigma_up = self.get_ancestral_step(self.get_dyn_eta())
-        sigma = ss.sigma
-        d = self.to_d(ss.hcur)
-        dt = sigma_down - sigma
-
-        error = ss.hcur.get_error(ss.hprev) if len(ss.hist) > 1 else 0.0
-        if order < 1:
-            order = self.get_rk_error_order(error)
-
-        k = [d * dt]
-        curr_weight = self.rk_weights[order - 1]
-
-        # print(
-        #     f"\nRK: weight={curr_weight!r}, histlen={len(ss.hist)}, order={order} ({self.max_order}), err={error:.6}\n"
-        # )
-        for j in range(1, order):
-            # Calculate intermediate k values based on the current order
-            k_sum = sum(curr_weight[i] * k[i] for i in range(j))
-            mr = self.call_model(x + k_sum, sigma + dt * sum(curr_weight[:j]))
-            k.append(self.to_d(mr) * dt)
-            del mr
-
-        # Update the sample using the weighted sum of k values
-        x = x + sum(curr_weight[j] * k[j] for j in range(order))
-
-        yield from self.result(x, sigma_up, sigma_down=sigma_down)
-
-
-# Based on original implementation from https://github.com/Clybius/ComfyUI-Extra-Samplers
-class EulerDancingStep(SingleStepSampler):
-    name = "clybius_euler_dancing"
-    self_noise = 1
-
-    def __init__(
-        self,
-        *,
-        deta=1.0,
-        ds_noise=None,
-        leap=2,
-        dyn_deta_start=None,
-        dyn_deta_end=None,
-        dyn_deta_mode="lerp",
-        **kwargs,
-    ):
-        super().__init__(**kwargs)
-        self.deta = deta
-        self.ds_noise = ds_noise if ds_noise is not None else self.s_noise
-        self.leap = leap
-        self.dyn_deta_start = dyn_deta_start
-        self.dyn_deta_end = dyn_deta_end
-        if dyn_deta_mode not in ("lerp", "lerp_alt", "deta"):
-            raise ValueError("Bad dyn_deta_mode")
-        self.dyn_deta_mode = dyn_deta_mode
-
-    def step(self, x):
-        ss = self.ss
-        eta = self.eta
-        deta = self.deta
-        leap_sigmas = ss.sigmas[ss.idx :]
-        leap_sigmas = leap_sigmas[: utils.find_first_unsorted(leap_sigmas)]
-        zero_idx = (leap_sigmas <= 0).nonzero().flatten()[:1]
-        max_leap = (zero_idx.item() if len(zero_idx) else len(leap_sigmas)) - 1
-        is_danceable = max_leap > 1 and ss.sigma_next != 0
-        curr_leap = max(1, min(self.leap, max_leap))
-        sigma_leap = leap_sigmas[curr_leap] if is_danceable else ss.sigma_next
-        del leap_sigmas
-        sigma_down, sigma_up = get_ancestral_step(ss.sigma, sigma_leap, eta)
-        print("???", sigma_down, sigma_up)
-        d = to_d(x, ss.sigma, ss.denoised)
-        # Euler method
-        dt = sigma_down - ss.sigma
-        x = x + d * dt
-        if curr_leap == 1:
-            return (yield from self.result(x, sigma_up))
-        noise_strength = self.ds_noise * sigma_up
-        if noise_strength != 0:
-            x = yield from self.result(x, sigma_up, sigma_next=sigma_leap, final=False)
-
-            # x = x + self.noise_sampler(ss.sigma, sigma_leap).mul_(
-            #     self.ds_noise * sigma_up
-            # )
-        # sigma_down2, sigma_up2 = get_ancestral_step(sigma_leap, ss.sigma, eta=deta)
-        # _sigma_down2, sigma_up2 = get_ancestral_step(sigma_leap, ss.sigma, eta=deta)
-        # sigma_up2 = ss.sigma_next + (ss.sigma - ss.sigma_next) * 0.5
-        sigma_up2 = get_ancestral_step(ss.sigma_next, sigma_leap, eta=deta)[1] + (
-            ss.sigma_next * 0.5
-        )
-        sigma_down2, _sigma_up2 = get_ancestral_step(
-            ss.sigma_next, sigma_leap, eta=deta
-        )
-        print(">>>", sigma_down2, sigma_up2, "--", ss.sigma, "->", sigma_leap)
-        # sigma_down2, sigma_up2 = get_ancestral_step(ss.sigma_next, sigma_leap, eta=deta)
-        d_2 = to_d(x, sigma_leap, ss.denoised)
-        dt_2 = sigma_down2 - sigma_leap
-        x = x + d_2 * dt_2
-        yield from self.result(x, sigma_up2, sigma_down=sigma_down2)
-
-    # def _step(self, x, ss):
-    #     eta = self.get_dyn_eta(ss)
-    #     leap_sigmas = ss.sigmas[ss.idx :]
-    #     leap_sigmas = leap_sigmas[: utils.find_first_unsorted(leap_sigmas)]
-    #     zero_idx = (leap_sigmas <= 0).nonzero().flatten()[:1]
-    #     max_leap = (zero_idx.item() if len(zero_idx) else len(leap_sigmas)) - 1
-    #     is_danceable = max_leap > 1 and ss.sigma_next != 0
-    #     curr_leap = max(1, min(self.leap, max_leap))
-    #     sigma_leap = leap_sigmas[curr_leap] if is_danceable else ss.sigma_next
-    #     # DANCE 35 6 tensor(10.0947, device='cuda:0') -- tensor([21.9220,
-    #     # print("DANCE", max_leap, curr_leap, sigma_leap, "--", leap_sigmas)
-    #     del leap_sigmas
-    #     sigma_down, sigma_up = get_ancestral_step(ss.sigma, sigma_leap, eta)
-    #     d = to_d(x, ss.sigma, ss.denoised)
-    #     # Euler method
-    #     dt = sigma_down - ss.sigma
-    #     x = x + d * dt
-    #     if curr_leap == 1:
-    #         return x, sigma_up
-    #     dance_scale = self.get_dyn_value(ss, self.dyn_deta_start, self.dyn_deta_end)
-    #     if curr_leap == 1 or not is_danceable or abs(dance_scale) < 1e-04:
-    #         print("NODANCE", dance_scale, self.deta, is_danceable, ss.sigma_next)
-    #         yield SamplerResult(ss, self, x, sigma_up)
-    #     print(
-    #         "DANCE", dance_scale, self.deta, self.dyn_deta_mode, self.ds_noise, sigma_up
-    #     )
-    #     sigma_down_normal, sigma_up_normal = get_ancestral_step(
-    #         ss.sigma, ss.sigma_next, eta
-    #     )
-    #     if self.dyn_deta_mode == "lerp":
-    #         dt_normal = sigma_down_normal - ss.sigma
-    #         x_normal = x + d * dt_normal
-    #     else:
-    #         x_normal = x
-    #     sigma_down2, sigma_up2 = get_ancestral_step(
-    #         sigma_leap,
-    #         ss.sigma_next,
-    #         eta=self.deta * (1.0 if self.dyn_deta_mode != "deta" else dance_scale),
-    #     )
-    #     print(
-    #         "-->",
-    #         sigma_down2,
-    #         sigma_up2,
-    #         "--",
-    #         self.deta * (1.0 if self.dyn_deta_mode != "deta" else dance_scale),
-    #     )
-    #     x = x + self.noise_sampler(ss.sigma, sigma_leap).mul_(self.ds_noise * sigma_up)
-    #     d_2 = to_d(x, sigma_leap, ss.denoised)
-    #     dt_2 = sigma_down2 - sigma_leap
-    #     result = x + d_2 * dt_2
-    #     # SIGMA: norm_up=9.062416076660156, up=10.703859329223633, up2=19.376544952392578, str=21.955078125
-    #     noise_strength = sigma_up2 + ((sigma_up - sigma_up_normal) ** 5.0)
-    #     noise_strength = sigma_up2 + ((sigma_up2 - sigma_up) * 0.5)
-    #     # noise_strength = sigma_up2 + (
-    #     #     (sigma_up2 - sigma_up) ** (1.0 - (sigma_up_normal / sigma_up2))
-    #     # )
-    #     noise_diff = (
-    #         sigma_up - sigma_up_normal
-    #         if sigma_up > sigma_up_normal
-    #         else sigma_up_normal - sigma_up
-    #     )
-    #     noise_div = (
-    #         sigma_up / sigma_up_normal
-    #         if sigma_up > sigma_up_normal
-    #         else sigma_up_normal / sigma_up
-    #     )
-    #     noise_diff = sigma_up2 - sigma_up_normal
-    #     noise_div = sigma_up2 / sigma_up_normal
-    #     noise_div = ss.sigma / sigma_leap
-
-    #     # noise_strength = sigma_up2 + (noise_diff * noise_div)
-    #     # noise_strength = sigma_up2 + ((noise_diff * 0.5) ** 2.0)
-    #     # noise_strength = sigma_up2 + ((1.0 - noise_diff) ** 0.5)
-    #     # noise_strength = sigma_up2 + (((sigma_up2 - sigma_up) * 0.5) ** 2.0)
-    #     # noise_strength = sigma_up2 + (((sigma_up2 - sigma_up_normal) * 0.5) ** 1.5)
-    #     # noise_strength = sigma_up2 + (
-    #     #     (noise_diff * 0.1875) ** (1.0 / (noise_div - 0.0))
-    #     # )
-    #     # noise_strength = sigma_up2 + (
-    #     #     (noise_diff * 0.125) ** (1.0 / (noise_div * 1.25))
-    #     # )
-    #     # noise_strength = sigma_up2 + ((noise_diff * 0.2) ** (1.0 / (noise_div * 1.0)))
-    #     noise_strength = sigma_up2 + (noise_diff * 0.9 * max(0.0, noise_div - 0.8))
-    #     noise_strength = sigma_up2 + (
-    #         (noise_diff / (curr_leap * 0.4))
-    #         * ((noise_div - (curr_leap / 2.0)).clamp(min=0, max=1.5) * 1.0)
-    #     )
-    #     # (1.0 / (noise_div * 1.25)))
-    #     # noise_strength = sigma_up2 + ((noise_diff * 0.5) ** noise_div)
-    #     print(
-    #         f"SIGMA: norm_up={sigma_up_normal}, up={sigma_up}, up2={sigma_up2}, str={noise_strength}",
-    #         # noise_diff,
-    #         noise_div,
-    #     )
-    #     return result, noise_strength
-
-    #     noise_diff = sigma_up2 - sigma_up * dance_scale
-    #     noise_scale = sigma_up2 + noise_diff * (0.025 * curr_leap)
-    #     # noise_scale = sigma_up2 * self.ds_noise
-    #     if self.dyn_deta_mode == "deta" or dance_scale == 1.0:
-    #         return result, noise_scale
-    #     result = torch.lerp(x_normal, result, dance_scale)
-    #     # FIXME: Broken for noise samplers that care about s/sn
-    #     return result, noise_scale
-
-    # def step(self, x, ss):
-    #     eta = self.get_dyn_eta(ss)
-    #     leap_sigmas = ss.sigmas[ss.idx :]
-    #     leap_sigmas = leap_sigmas[: find_first_unsorted(leap_sigmas)]
-    #     zero_idx = (leap_sigmas <= 0).nonzero().flatten()[:1]
-    #     max_leap = (zero_idx.item() if len(zero_idx) else len(leap_sigmas)) - 1
-    #     is_danceable = max_leap > 1 and ss.sigma_next != 0
-    #     curr_leap = max(1, min(self.leap, max_leap))
-    #     sigma_leap = leap_sigmas[curr_leap] if is_danceable else ss.sigma_next
-    #     # print("DANCE", max_leap, curr_leap, sigma_leap, "--", leap_sigmas)
-    #     del leap_sigmas
-    #     sigma_down, sigma_up = get_ancestral_step(ss.sigma, sigma_leap, eta)
-    #     d = to_d(x, ss.sigma, ss.denoised)
-    #     # Euler method
-    #     dt = sigma_down - ss.sigma
-    #     x = x + d * dt
-    #     if curr_leap == 1:
-    #         return x, sigma_up
-    #     dance_scale = self.get_dyn_value(ss, self.dyn_deta_start, self.dyn_deta_end)
-    #     if not is_danceable or abs(dance_scale) < 1e-04:
-    #         print("NODANCE", dance_scale, self.deta)
-    #         return x, sigma_up
-    #     print("NODANCE", dance_scale, self.deta)
-    #     sigma_down_normal, _sigma_up_normal = get_ancestral_step(
-    #         ss.sigma, ss.sigma_next, eta
-    #     )
-    #     if self.dyn_deta_mode == "lerp":
-    #         dt_normal = sigma_down_normal - ss.sigma
-    #         x_normal = x + d * dt_normal
-    #     else:
-    #         x_normal = x
-    #     x = x + self.noise_sampler(ss.sigma, sigma_leap).mul_(self.s_noise * sigma_up)
-    #     sigma_down2, sigma_up2 = get_ancestral_step(
-    #         sigma_leap,
-    #         ss.sigma_next,
-    #         eta=self.deta * (1.0 if self.dyn_deta_mode != "deta" else dance_scale),
-    #     )
-    #     d_2 = to_d(x, sigma_leap, ss.denoised)
-    #     dt_2 = sigma_down2 - sigma_leap
-    #     result = x + d_2 * dt_2
-    #     noise_diff = sigma_up2 - sigma_up * dance_scale
-    #     noise_scale = sigma_up2 + noise_diff * (0.025 * curr_leap)
-    #     if self.dyn_deta_mode == "deta" or dance_scale == 1.0:
-    #         return result, noise_scale
-    #     result = torch.lerp(x_normal, result, dance_scale)
-    #     # FIXME: Broken for noise samplers that care about s/sn
-    #     return result, noise_scale
-
-
-# Based on implementation from https://github.com/Clybius/ComfyUI-Extra-Samplers
-# Which was originally written by Katherine Crowson
-class TTMJVPStep(SingleStepSampler):
-    name = "ttm_jvp"
-    model_calls = 1
-
-    def __init__(self, *args, alternate_phi_2_calc=True, **kwargs):
-        super().__init__(*args, **kwargs)
-        self.alternate_phi_2_calc = alternate_phi_2_calc
-
-    def step(self, x):
-        ss = self.ss
-        eta = self.get_dyn_eta()
-        sigma, sigma_next = ss.sigma, ss.sigma_next
-        # 2nd order truncated Taylor method
-        t, s = -sigma.log(), -sigma_next.log()
-        h = s - t
-        h_eta = h * (eta + 1)
-
-        eps = to_d(x, sigma, ss.denoised)
-        denoised_prime = self.call_model(
-            x, sigma, tangents=(eps * -sigma, -sigma), call_index=1
-        ).jdenoised
-
-        phi_1 = -torch.expm1(-h_eta)
-        if self.alternate_phi_2_calc:
-            phi_2 = torch.expm1(-h) + h  # seems to work better with eta > 0
-        else:
-            phi_2 = torch.expm1(-h_eta) + h_eta
-        x = torch.exp(-h_eta) * x + phi_1 * ss.denoised + phi_2 * denoised_prime
-
-        noise_scale = (
-            sigma_next * torch.sqrt(-torch.expm1(-2 * h * eta))
-            if eta
-            else ss.sigma.new_zeros(1)
-        )
-        yield from self.result(x, noise_scale)
-
-
-class HeunStep(ReversibleSingleStepSampler):
-    name = "heun"
-    model_calls = 1
-    default_history_limit, max_history = 0, 0
-    allow_alt_cfgpp = True
-
-    def reversible_correction(self, d_from, d_to):
-        reta, reversible_scale = self.get_reversible_cfg()
-        if reversible_scale == 0:
-            return 0
-        sdr = self.get_ancestral_step(reta)[0]
-        dtr = sdr - self.ss.sigma
-        return (dtr**2 * (d_to - d_from) / 4) * self.reversible.scale
-
-    def step(self, x):
-        ss = self.ss
-        s = ss.sigma
-        sd, su = self.get_ancestral_step(self.get_dyn_eta())
-        dt = sd - s
-        hcur = ss.hcur
-        d = self.to_d(hcur)
-        x_next = hcur.denoised + d * sd
-        d_next = self.to_d(self.call_model(x_next, sd, call_index=1))
-        result = hcur.denoised + d * s
-        result += (dt * (d + d_next)) * 0.5
-        result -= self.reversible_correction(d, d_next)
-        yield from self.result(result, su, sigma_down=sd)
-
-
-class Heun1SStep(HeunStep):
-    name = "heun_1s"
-    model_calls = (0, 1)
-    allow_alt_cfgpp = True
-    default_history_limit, max_history = 1, 1
-
-    def step(self, x):
-        ss = self.ss
-        s = ss.sigma
-        if self.available_history() == 0:
-            return (yield from super().step(x))
-        hcur, hprev = ss.hcur, ss.hprev
-        d_prev = self.to_d(hprev)
-        sd, su = self.get_ancestral_step(self.get_dyn_eta())
-        dt = sd - s
-        d = self.to_d(hcur)
-        result = hcur.denoised + hcur.sigma * self.to_d(hcur)
-        result += (dt * (d_prev + d)) * 0.5
-        result -= self.reversible_correction(d_prev, d)
-        yield from self.result(result, su, sigma_down=sd)
-
-
-class ClybiusSENSStep(DPMPP2MSDEStep):
-    name = "clybius_sens"
-    default_history_limit, max_history = 2, 2
-    allow_alt_cfgpp = False
-    default_reta = 1.0
-    default_reversible_scale = 1.0
-
-    def __init__(self, *, tsde_reversible=None, **kwargs):
-        super().__init__(**kwargs)
-        self.tsde_reversible = ReversibleConfig.build(
-            default_eta=self.default_reta,
-            default_scale=self.default_reversible_scale,
-            **utils.fallback(tsde_reversible, {}),
-        )
-
-    def step(self, x):
-        ss = self.ss
-        sigma, sigma_next = ss.sigma, ss.sigma_next
-        denoised = ss.denoised
-        # DPM-Solver++(2M) SDE
-        t, s = -sigma.log(), -sigma_next.log()
-        h = s - t
-        eta_h = self.get_dyn_eta() * h
-        ratio = sigma_next / sigma
-        x = ((ratio * (-eta_h).exp()) * x).add_((-h - eta_h).expm1().neg() * denoised)
-        noise_strength = sigma_next * (-2 * eta_h).expm1().neg().sqrt()
-        if self.available_history() == 0:
-            return (yield from self.result(x, noise_strength))
-        sigma_prev, old_denoised = ss.hprev.sigma, ss.hprev.denoised
-        h_last = (-sigma.log()) - (-sigma_prev.log())
-        r = h_last / h
-        if self.solver_type == "midpoint":
-            multiplier = 0.5 * (-h - eta_h).expm1().neg()
-        else:
-            multiplier = (-h - eta_h).expm1().neg() / (-h - eta_h) + 1
-        reta, reversible_scale = self.get_reversible_cfg()
-        if reversible_scale != 0:
-            multiplier *= 0.5
-        x += (denoised - old_denoised).mul_((1 / r) * multiplier)
-        if reversible_scale != 0:
-            reta_h = reta * h
-            if self.solver_type == "midpoint":
-                rmultiplier = 0.5 * (-h - reta_h).expm1().neg()
-            else:
-                rmultiplier = (-h - reta_h).expm1().neg() / (-h - reta_h) + 1
-            rmultiplier = ((1 / r) * (rmultiplier**2 / 2)) * reversible_scale
-            x -= (old_denoised - denoised).mul_(rmultiplier)
-        if self.available_history() > 1:
-            tsde_reta, tsde_reversible_scale = self.get_reversible_cfg(
-                reversible=self.tsde_reversible
-            )
-            tsde_reta_h = tsde_reta * h
-            sigma_prev_2 = ss.hist[-3].sigma
-            h_last_2 = (-sigma_prev.log()) - (-sigma_prev_2.log())
-            r = h_last_2 / h
-            old_denoised_2 = ss.hist[-3].denoised
-            d = (old_denoised - old_denoised_2).div_(r)
-            d_2 = (old_denoised - denoised).div_(r)
-
-            d_rev = (denoised - old_denoised).div_(r)
-            d_2_rev = (old_denoised_2 - old_denoised).div_(r)
-
-            rphi = tsde_reta_h.neg().expm1() / tsde_reta_h + 1
-            tsde_adjustment = rphi * (d + d_2) / 2
-            if tsde_reversible_scale != 0:
-                tsde_adjustment -= (rphi**2 * (d_rev + d_2_rev) / 2).mul_(
-                    tsde_reversible_scale
-                )
-            x += tsde_adjustment
-        yield from self.result(x, noise_strength)
-
-
-registry.add(
-    BogackiStep,
-    ClybiusSENSStep,
-    EulerDancingStep,
-    ReversibleBogackiStep,
-    ReversibleHeunStep,
-    ReversibleHeun1SStep,
-    RESStep,
-    TTMJVPStep,
-    TrapezoidalStep,
-    RK4Step,
-    RKDynamicStep,
-    RKF45Step,
-    Heun1SStep,
-    HeunStep,
-)

py/step_samplers/extraltodeus.py

@@ -1,131 +0,0 @@
-# Samplers based on design from https://github.com/Extraltodeus/
-
-import typing
-
-import torch
-import tqdm
-
-from .base import SingleStepSampler
-from . import registry
-
-
-class DistanceConfig(typing.NamedTuple):
-    resample: int = 3
-    resample_end: int = 1
-    eta: float = 0.0
-    s_noise: float = 1.0
-    alt_cfgpp_scale: float = 0.0
-    first_eta_step: int = 0
-    last_eta_step: int = -1
-    custom_noise_name: str = "alt"
-    immiscible: dict | bool | None = None
-
-
-# Based on https://github.com/Extraltodeus/DistanceSampler
-class DistanceStep(SingleStepSampler):
-    name = "extraltodeus_distance"
-    allow_alt_cfgpp = True
-    model_calls = -1
-    uses_alt_noise = True
-
-    def __init__(self, *args, **kwargs):
-        super().__init__(*args, **kwargs)
-        self.distance = DistanceConfig(**self.options.get("distance", {}))
-
-    @property
-    def require_uncond(self):
-        return super().require_uncond or self.distance.alt_cfgpp_scale != 0
-
-    def distance_resample_steps(self):
-        ss = self.ss
-        resample, resample_end = self.distance.resample, self.distance.resample_end
-        if resample == -1:
-            current_resample = min(10, (ss.sigmas.shape[0] - ss.idx) // 2)
-        else:
-            current_resample = resample
-        if resample_end < 0:
-            return current_resample
-        sigma = ss.sigma
-        s_min = (ss.sigmas if ss.sigmas[-1] > 0 else ss.sigmas[:-1]).min()
-        s_max = ss.sigmas.max()
-        res_mul = max(0, min(1, ((sigma - s_min) / (s_max - s_min)) ** 0.5))
-        return max(
-            min(current_resample, resample_end),
-            min(
-                max(current_resample, resample_end),
-                int(current_resample * res_mul + resample_end * (1 - res_mul)),
-            ),
-        )
-
-    @staticmethod
-    def distance_weights(t, p):
-        batch = t.shape[0]
-        d = torch.stack(
-            tuple((t - t[idx]).abs().sum(dim=0) for idx in range(batch)),
-            dim=0,
-        )
-        d_min, d_max = d.min(), d.max()
-        d = torch.nan_to_num(
-            (1 - (d - d_min) / (d_max - d_min)).pow(p),
-            nan=1,
-            neginf=1,
-            posinf=1,
-        )
-        d /= d.sum(dim=0)
-        return d.mul_(t).sum(dim=0)
-
-    def step(self, x):
-        resample_steps = self.distance_resample_steps()
-        if resample_steps < 1:
-            return (yield from self.euler_step(x))
-        distance = self.distance
-        ss = self.ss
-        sigma_down, sigma_up = self.get_ancestral_step(self.get_dyn_eta())
-        rsigma_down, rsigma_up = self.get_ancestral_step(eta=distance.eta)
-        rsigma_up *= distance.s_noise
-        sigma, sigma_next = ss.sigma, ss.sigma_next
-        zero_up = sigma * 0
-        d = self.to_d(ss.hcur)
-        can_ancestral = not torch.equal(rsigma_down, sigma_next)
-        start_eta_idx, end_eta_idx = (
-            max(0, resample_steps + v if v < 0 else v)
-            for v in (
-                distance.first_eta_step,
-                distance.last_eta_step,
-            )
-        )
-        dt = sigma_down - sigma
-        d = self.to_d(ss.hcur)
-        x_n = [d]
-        for re_step in tqdm.trange(
-            resample_steps, desc="distance_resample", disable=ss.disable_status
-        ):
-            if can_ancestral and start_eta_idx <= re_step <= end_eta_idx:
-                curr_sigma_down, curr_sigma_up = rsigma_down, rsigma_up
-            else:
-                curr_sigma_down, curr_sigma_up = sigma_next, zero_up
-            rdt = curr_sigma_down - sigma
-            x_new = x + d * rdt
-            if curr_sigma_up != 0:
-                x_new = yield from self.result(
-                    x_new,
-                    curr_sigma_up,
-                    sigma=sigma,
-                    sigma_down=curr_sigma_down,
-                    noise_sampler=self.alt_noise_sampler,
-                    final=False,
-                )
-            sr = self.call_model(x_new, sigma_next, call_index=re_step + 1)
-            new_d = sr.to_d(
-                sigma=curr_sigma_down, alt_cfgpp_scale=distance.alt_cfgpp_scale
-            )
-            x_n.append(new_d)
-            if re_step == 0:
-                d = (new_d + d) / 2
-            else:
-                d = self.distance_weights(torch.stack(x_n), re_step + 2)
-                x_n.append(d)
-        yield from self.result(x + d * dt, sigma_up, sigma_down=sigma_down)
-
-
-registry.add(DistanceStep)

py/step_samplers/misc.py

@@ -1,29 +0,0 @@
-from .base import SingleStepSampler, registry
-
-
-# Referenced from https://github.com/ace-step/ACE-Step/
-class PingPongStep(SingleStepSampler):
-    name = "pingpong"
-    default_eta = 0.0
-
-    def __init__(self, *args, **kwargs):
-        super().__init__(*args, **kwargs)
-        pingpong_options = self.options.pop("pingpong", {})
-        self.pingpong_start_step = pingpong_options.get("start_step", 0)
-        self.pingpong_end_step = pingpong_options.get("end_step", 0)
-
-    def step(self, x):
-        ss = self.ss
-        use_pingpong = self.pingpong_start_step <= ss.step <= self.pingpong_end_step
-        if not use_pingpong:
-            return (yield from self.euler_step(x, eta=0.0))
-        sn = ss.sigma_next
-        denoised = (
-            ss.denoised * (1.0 - sn) if ss.model.is_rectified_flow else ss.denoised
-        )
-        yield from self.result(denoised, sn, sigma_down=sn)
-
-
-registry.add(
-    PingPongStep,
-)

py/step_samplers/registry.py

@@ -1,39 +0,0 @@
-SAMPLER_LIST = []
-
-STEP_SAMPLERS = {}
-STEP_SAMPLER_SIMPLE_NAMES = {}
-
-
-def add(*objs):
-    global SAMPLER_LIST
-    SAMPLER_LIST += objs
-
-
-def init():
-    global STEP_SAMPLERS, STEP_SAMPLER_SIMPLE_NAMES
-    STEP_SAMPLER_SIMPLE_NAMES.clear()
-    STEP_SAMPLERS.clear()
-    euler = None
-    temp = []
-    for c in SAMPLER_LIST:
-        mc = c.model_calls
-        if mc == 0:
-            prettymc = ""
-        elif isinstance(mc, tuple):
-            prettymc = f" ({mc[0]}-{mc[-1]})"
-        elif mc < 0:
-            prettymc = " (variable)"
-        else:
-            prettymc = f" ({mc})"
-        if c.name == "euler":
-            euler = c
-        temp.append((f"{c.name}{prettymc}", c))
-    temp.sort(key=lambda item: item[1].name)
-    if euler is None:
-        raise RuntimeError(
-            "Impossible: euler sampler not found when building sampler registry"
-        )
-    STEP_SAMPLERS["default (euler)"] = euler
-    STEP_SAMPLERS |= {k: v for k, v in temp}
-    STEP_SAMPLER_SIMPLE_NAMES["default"] = euler
-    STEP_SAMPLER_SIMPLE_NAMES |= {v.name: v for _k, v in temp}

py/step_samplers/solver_base.py

@@ -1,49 +0,0 @@
-from .base import SingleStepSampler, MinSigmaStepMixin
-
-
-class DESolverStep(SingleStepSampler, MinSigmaStepMixin):
-    de_default_solver = None
-    sample_sigma_zero = True
-    default_eta = 0.0
-
-    def __init__(
-        self,
-        *args,
-        de_solver=None,
-        de_max_nfe=100,
-        de_rtol=-2.5,
-        de_atol=-3.5,
-        de_fixup_hack=0.025,
-        de_split=1,
-        de_min_sigma=0.0292,
-        **kwargs,
-    ):
-        self.check_solver_support()
-        super().__init__(*args, **kwargs)
-        de_solver = self.de_default_solver if de_solver is None else de_solver
-        self.de_solver_name = de_solver
-        self.de_max_nfe = de_max_nfe
-        self.de_rtol = 10**de_rtol
-        self.de_atol = 10**de_atol
-        self.de_fixup_hack = de_fixup_hack
-        self.de_split = de_split
-        self.de_min_sigma = de_min_sigma if de_min_sigma is not None else 0.0
-
-    def check_solver_support(self):
-        raise NotImplementedError
-
-    def de_get_step(self, x):
-        eta = self.get_dyn_eta()
-        ss = self.ss
-        s, sn = ss.sigma, ss.sigma_next
-        sn = self.adjust_step(sn, self.de_min_sigma)
-        sigma_down, sigma_up = self.get_ancestral_step(eta, sigma_next=sn)
-        if self.de_fixup_hack != 0:
-            sigma_down = (sigma_down - (s - sigma_down) * self.de_fixup_hack).clamp(
-                min=0
-            )
-        return s, sn, sigma_down, sigma_up
-
-    @staticmethod
-    def reverse_time(t, t0, t1):
-        return t1 + (t0 - t)

py/step_samplers/solver_diffrax.py

@@ -1,304 +0,0 @@
-import contextlib
-import os
-import typing
-import warnings
-
-import numpy
-import torch
-import tqdm
-
-import comfy
-
-from . import registry
-from .solver_base import DESolverStep
-
-HAVE_DIFFRAX = False
-
-
-if not os.environ.get("COMFYUI_OCS_NO_DIFFRAX_SOLVER"):
-    with contextlib.suppress(ImportError):
-        import diffrax
-        import jax
-
-        if not os.environ.get("COMFYUI_OCS_NO_DISABLE_JAX_PREALLOCATE"):
-            os.environ["XLA_PYTHON_CLIENT_PREALLOCATE"] = "false"
-        # jax.config.update("jax_enable_x64", True)
-
-        HAVE_DIFFRAX = True
-
-
-if HAVE_DIFFRAX:
-
-    class RevVirtualBrownianTree(diffrax.VirtualBrownianTree):
-        def evaluate(self, t0, t1, *args, **kwargs):
-            if t1 is not None:
-                return super().evaluate(t1, t0, *args, **kwargs)
-            return super().evaluate(t0, t1, *args, **kwargs)
-
-    class StepCallbackTqdmProgressMeter(diffrax.TqdmProgressMeter):
-        step_callback: typing.Callable = None
-
-        def _init_bar(self, *args, **kwargs):
-            if self.step_callback is None:
-                return super()._init_bar(*args, **kwargs)
-            bar_format = "{percentage:.2f}%{step_callback}|{bar}| [{elapsed}<{remaining}, {rate_fmt}{postfix}]"
-            step_callback = self.step_callback
-
-            class WrapTqdm(tqdm.tqdm):
-                @property
-                def format_dict(self):
-                    d = super().format_dict
-                    d.update(step_callback=step_callback())
-                    return d
-
-            return WrapTqdm(total=100, unit="%", bar_format=bar_format)
-
-
-class DiffraxStep(DESolverStep):
-    name = "diffrax"
-    model_calls = -1
-    allow_alt_cfgpp = True
-    de_default_solver = "dopri5"
-    default_eta = 0.0
-
-    def __init__(
-        self,
-        *args,
-        de_split=1,
-        de_initial_step=0.25,
-        de_ctl_pcoeff=0.3,
-        de_ctl_icoeff=0.9,
-        de_ctl_dcoeff=0.2,
-        diffrax_adaptive=False,
-        diffrax_fake_pure_callback=True,
-        diffrax_g_multiplier=0.0,
-        diffrax_half_solver=False,
-        diffrax_batch_channels=False,
-        diffrax_levy_area_approx="brownian_increment",
-        diffrax_error_order=None,
-        diffrax_sde_mode=False,
-        diffrax_g_reverse_time=False,
-        diffrax_g_time_scaling=False,
-        diffrax_g_split_time_mode=False,
-        **kwargs,
-    ):
-        super().__init__(*args, **kwargs)
-        solvers = dict(
-            euler=diffrax.Euler,
-            heun=diffrax.Heun,
-            midpoint=diffrax.Midpoint,
-            ralston=diffrax.Ralston,
-            bosh3=diffrax.Bosh3,
-            tsit5=diffrax.Tsit5,
-            dopri5=diffrax.Dopri5,
-            dopri8=diffrax.Dopri8,
-            implicit_euler=diffrax.ImplicitEuler,
-            # kvaerno3=diffrax.Kvaerno3,
-            # kvaerno4=diffrax.Kvaerno4,
-            # kvaerno5=diffrax.Kvaerno5,
-            semi_implicit_euler=diffrax.SemiImplicitEuler,
-            reversible_heun=diffrax.ReversibleHeun,
-            leapfrog_midpoint=diffrax.LeapfrogMidpoint,
-            euler_heun=diffrax.EulerHeun,
-            ito_milstein=diffrax.ItoMilstein,
-            stratonovich_milstein=diffrax.StratonovichMilstein,
-            sea=diffrax.SEA,
-            sra1=diffrax.SRA1,
-            shark=diffrax.ShARK,
-            general_shark=diffrax.GeneralShARK,
-            slow_rk=diffrax.SlowRK,
-            spark=diffrax.SPaRK,
-        )
-        levy_areas = dict(
-            brownian_increment=diffrax.BrownianIncrement,
-            space_time=diffrax.SpaceTimeLevyArea,
-            space_time_time=diffrax.SpaceTimeTimeLevyArea,
-        )
-        # jax.config.update("jax_disable_jit", True)
-        self.de_solver_method = solvers[self.de_solver_name]()
-        if diffrax_half_solver:
-            self.de_solver_method = diffrax.HalfSolver(self.de_solver_method)
-        self.de_ctl_pcoeff = de_ctl_pcoeff
-        self.de_ctl_icoeff = de_ctl_icoeff
-        self.de_ctl_dcoeff = de_ctl_dcoeff
-        self.de_initial_step = de_initial_step
-        self.de_adaptive = diffrax_adaptive
-        self.de_split = de_split
-        self.de_fake_pure_callback = diffrax_fake_pure_callback
-        self.de_g_multiplier = diffrax_g_multiplier
-        self.de_batch_channels = diffrax_batch_channels
-        self.de_levy_area_approx = levy_areas[diffrax_levy_area_approx]
-        self.de_error_order = diffrax_error_order
-        self.de_sde_mode = diffrax_sde_mode
-        self.de_g_reverse_time = diffrax_g_reverse_time
-        self.de_g_time_scaling = diffrax_g_time_scaling
-        self.de_g_split_time_mode = diffrax_g_split_time_mode
-
-    # As slow and safe as possible.
-    @staticmethod
-    def t2j(t):
-        return jax.block_until_ready(
-            jax.numpy.array(numpy.array(t.detach().cpu().contiguous()))
-        )
-
-    @staticmethod
-    def j2t(t):
-        return torch.from_numpy(numpy.array(jax.block_until_ready(t))).contiguous()
-
-    def check_solver_support(self):
-        if not HAVE_DIFFRAX:
-            raise RuntimeError(
-                "Diffrax sampler requires diffrax and jax installed in venv."
-            )
-
-    def step(self, x):
-        s, sn, sigma_down, sigma_up = self.de_get_step(x)
-        if self.de_min_sigma is not None and s <= self.de_min_sigma:
-            return (yield from self.euler_step(x))
-        ss = self.ss
-        bidx = 0
-        mcc = 0
-        _b, c, h, w = x.shape
-        interrupted = None
-        t0, t1 = sigma_down.item(), s.item()
-
-        def odefn_(t_orig, y_flat, args=()):
-            nonlocal mcc, interrupted
-            t = self.reverse_time(self.j2t(t_orig).to(s), t0, t1)
-            if t <= 1e-05:
-                return jax.numpy.zeros_like(y_flat)
-            if mcc >= self.de_max_nfe:
-                raise RuntimeError("DiffraxStep: Model call limit exceeded")
-            y = self.j2t(y_flat.reshape(1, c, h, w)).to(x)
-            t32 = t.to(s).clamp(min=1e-05)
-            flat_shape = y_flat.shape
-            del y_flat
-
-            if not args and mcc == 0 and torch.all(t == s):
-                mr_cached = True
-                mr = ss.hcur
-                mcc = 1
-            else:
-                mr_cached = False
-                try:
-                    if not args:
-                        mr = self.call_model(y, t32, call_index=mcc, s_in=t.new_ones(1))
-                    else:
-                        print("TANGENTS")
-                        mr = self.call_model(
-                            y,
-                            t32,
-                            call_index=mcc,
-                            tangents=args,
-                            s_in=t.new_ones(1),
-                        )
-                except comfy.model_management.InterruptProcessingException as exc:
-                    interrupted = exc
-                    raise
-                mcc += 1
-            result = self.to_d(mr)[bidx if mr_cached else 0].reshape(*flat_shape)
-            return self.t2j(-result)
-
-        if not self.de_fake_pure_callback:
-
-            def odefn(t, y_flat, args):
-                return jax.experimental.io_callback(
-                    odefn_, y_flat, t, y_flat, ordered=True
-                )
-
-        else:
-
-            def odefn(t, y_flat, args):
-                return jax.pure_callback(odefn_, y_flat, t, y_flat)
-
-        def g(t, y, _args):
-            if self.de_g_split_time_mode:
-                val = jax.lax.cond(
-                    t < t0 + (t1 - t0) * 0.5,
-                    lambda: self.de_g_multiplier,
-                    lambda: -self.de_g_multiplier,
-                )
-            else:
-                val = self.de_g_multiplier
-            if self.de_g_time_scaling:
-                val *= self.reverse_time(t, t0, t1) if self.de_g_reverse_time else t
-            if not self.de_batch_channels:
-                return val
-            return jax.numpy.float32(val).broadcast((y.shape[0],))
-
-        def progress_callback():
-            return f" ({mcc:>3}/{self.de_max_nfe:>3}) {self.de_solver_name}"
-
-        term = diffrax.ODETerm(odefn)
-        method = self.de_solver_method
-        if self.de_adaptive:
-            controller = diffrax.PIDController(
-                atol=self.de_atol,
-                rtol=self.de_rtol,
-                dtmin=1e-05,
-                pcoeff=self.de_ctl_pcoeff,
-                icoeff=self.de_ctl_icoeff,
-                dcoeff=self.de_ctl_dcoeff,
-                error_order=self.de_error_order,
-            )
-        else:
-            controller = diffrax.ConstantStepSize()
-
-        if not self.de_adaptive:
-            dt0 = (t1 - t0) / self.de_split
-        else:
-            dt0 = (t1 - t0) * self.de_initial_step
-        if self.de_sde_mode:
-            bm = diffrax.VirtualBrownianTree(
-                t0=ss.sigmas.min().item(),
-                t1=ss.sigmas.max().item(),
-                tol=1e-06,
-                levy_area=self.de_levy_area_approx,
-                shape=(c,) if self.de_batch_channels else (),
-                key=jax.random.PRNGKey(ss.noise.seed + ss.noise.seed_offset),
-            )
-            term = diffrax.MultiTerm(term, diffrax.ControlTerm(g, bm))
-        results = []
-        for batch in tqdm.trange(
-            1,
-            x.shape[0] + 1,
-            desc="batch",
-            leave=False,
-            disable=x.shape[0] == 1 or ss.disable_status,
-        ):
-            bidx = batch - 1
-            mcc = 0
-            if self.de_batch_channels:
-                y_flat = x[bidx].flatten(start_dim=1)
-            else:
-                y_flat = x[bidx].unsqueeze(0).flatten(start_dim=1)
-            y_flat = self.t2j(y_flat)
-            with warnings.catch_warnings():
-                warnings.simplefilter(action="ignore", category=FutureWarning)
-                try:
-                    solution = diffrax.diffeqsolve(
-                        terms=term,
-                        solver=method,
-                        t0=t0,
-                        t1=t1,
-                        dt0=dt0,
-                        y0=y_flat,
-                        saveat=diffrax.SaveAt(t1=True),
-                        stepsize_controller=controller,
-                        progress_meter=StepCallbackTqdmProgressMeter(
-                            step_callback=progress_callback,
-                            refresh_steps=1,
-                        ),
-                    )
-                except Exception:
-                    if interrupted is not None:
-                        raise interrupted
-                    raise
-            results.append(self.j2t(solution.ys).view(1, *x.shape[1:]))
-            del solution
-        result = torch.cat(results).to(x)
-        sigma_up, result = yield from self.adjusted_step(sn, result, mcc, sigma_up)
-        yield from self.result(result, sigma_up, sigma_down=sigma_down)
-
-
-registry.add(DiffraxStep)

py/step_samplers/solver_tde.py

@@ -1,118 +0,0 @@
-import contextlib
-
-import torch
-import tqdm
-
-from . import registry
-from .solver_base import DESolverStep
-
-HAVE_TDE = False
-with contextlib.suppress(ImportError):
-    import torchdiffeq as tde
-
-    HAVE_TDE = True
-
-
-class TDEStep(DESolverStep):
-    name = "tde"
-    model_calls = -1
-    allow_alt_cfgpp = True
-    allow_cfgpp = False
-    de_default_solver = "rk4"
-    default_eta = 0.0
-
-    def __init__(
-        self,
-        *args,
-        de_split=1,
-        **kwargs,
-    ):
-        super().__init__(*args, **kwargs)
-        self.de_split = de_split
-
-    def check_solver_support(self):
-        if not HAVE_TDE:
-            raise RuntimeError(
-                "TDE sampler requires torchdiffeq installed in venv. Example: pip install torchdiffeq"
-            )
-
-    def step(self, x):
-        s, sn, sigma_down, sigma_up = self.de_get_step(x)
-        if self.de_min_sigma is not None and s <= self.de_min_sigma:
-            return (yield from self.euler_step(x))
-        ss = self.ss
-        delta = (s - sigma_down).item()
-        mcc = 0
-        bidx = 0
-        pbar = None
-
-        def odefn(t, y):
-            nonlocal mcc
-            if t < 1e-05:
-                return torch.zeros_like(y)
-            if mcc >= self.de_max_nfe:
-                raise RuntimeError("TDEStep: Model call limit exceeded")
-
-            pct = (s - t) / delta
-            pbar.n = round(min(999, pct.item() * 999))
-            pbar.update(0)
-            pbar.set_description(
-                f"{self.de_solver_name}({mcc}/{self.de_max_nfe})", refresh=True
-            )
-
-            if t == ss.sigma and torch.equal(x[bidx], y):
-                mr_cached = True
-                mr = ss.hcur
-                mcc = 1
-            else:
-                mr_cached = False
-                mr = self.call_model(
-                    y.unsqueeze(0), t, call_index=mcc, s_in=t.new_ones(1)
-                )
-                mcc += 1
-            return self.to_d(mr)[bidx if mr_cached else 0]
-
-        result = torch.zeros_like(x)
-        t = sigma_down.new_zeros(self.de_split + 1)
-        torch.linspace(ss.sigma, sigma_down, t.shape[0], out=t)
-
-        for batch in tqdm.trange(
-            1,
-            x.shape[0] + 1,
-            desc="batch",
-            leave=False,
-            disable=x.shape[0] == 1 or ss.disable_status,
-        ):
-            bidx = batch - 1
-            mcc = 0
-            if pbar is not None:
-                pbar.close()
-            pbar = tqdm.tqdm(
-                total=1000,
-                desc=self.de_solver_name,
-                leave=True,
-                disable=ss.disable_status,
-            )
-            solution = tde.odeint(
-                odefn,
-                x[bidx],
-                t,
-                rtol=self.de_rtol,
-                atol=self.de_atol,
-                method=self.de_solver_name,
-                options={
-                    "min_step": 1e-05,
-                    "dtype": torch.float64,
-                },
-            )[-1]
-            result[bidx] = solution
-
-        sigma_up, result = yield from self.adjusted_step(sn, result, mcc, sigma_up)
-        if pbar is not None:
-            pbar.n = pbar.total
-            pbar.update(0)
-            pbar.close()
-        yield from self.result(result, sigma_up, sigma_down=sigma_down)
-
-
-registry.add(TDEStep)

py/step_samplers/solver_tode.py

@@ -1,132 +0,0 @@
-import contextlib
-
-import torch
-import tqdm
-
-from . import registry
-from .solver_base import DESolverStep
-
-
-HAVE_TODE = False
-with contextlib.suppress(ImportError, RuntimeError):
-    import torchode as tode
-
-    HAVE_TODE = True
-
-
-class TODEStep(DESolverStep):
-    name = "tode"
-    model_calls = -1
-    allow_alt_cfgpp = True
-    de_default_solver = "dopri5"
-    default_eta = 0.0
-
-    def __init__(
-        self,
-        *args,
-        de_initial_step=0.25,
-        tode_compile=False,
-        de_ctl_pcoeff=0.3,
-        de_ctl_icoeff=0.9,
-        de_ctl_dcoeff=0.2,
-        **kwargs,
-    ):
-        if not HAVE_TODE:
-            raise RuntimeError(
-                "TODE sampler requires torchode installed in venv. Example: pip install torchode"
-            )
-        super().__init__(*args, **kwargs)
-        self.de_solver_method = tode.interface.METHODS[self.de_solver_name]
-        self.de_ctl_pcoeff = de_ctl_pcoeff
-        self.de_ctl_icoeff = de_ctl_icoeff
-        self.de_ctl_dcoeff = de_ctl_dcoeff
-        self.de_compile = tode_compile
-        self.de_initial_step = de_initial_step
-
-    def check_solver_support(self):
-        if not HAVE_TODE:
-            raise RuntimeError(
-                "TODE sampler requires torchode installed in venv. Example: pip install torchode"
-            )
-
-    def step(self, x):
-        s, sn, sigma_down, sigma_up = self.de_get_step(x)
-        if self.de_min_sigma is not None and s <= self.de_min_sigma:
-            return (yield from self.euler_step(x))
-        ss = self.ss
-        delta = (ss.sigma - sigma_down).item()
-        mcc = 0
-        pbar = None
-        b, c, h, w = x.shape
-
-        def odefn(t, y_flat):
-            nonlocal mcc
-            if torch.all(t <= 1e-05).item():
-                return torch.zeros_like(y_flat)
-            if mcc >= self.de_max_nfe:
-                raise RuntimeError("TDEStep: Model call limit exceeded")
-
-            pct = (s - t) / delta
-            pbar.n = round(pct.min().item() * 999)
-            pbar.update(0)
-            pbar.set_description(
-                f"{self.de_solver_name}({mcc}/{self.de_max_nfe})", refresh=True
-            )
-            y = y_flat.reshape(-1, c, h, w)
-            t32 = t.to(torch.float32)
-            del y_flat
-
-            if mcc == 0 and torch.all(t == s):
-                mr = ss.hcur
-                mcc = 1
-            else:
-                mr = self.call_model(y, t32.clamp(min=1e-05), call_index=mcc)
-                mcc += 1
-            result = self.to_d(mr).flatten(start_dim=1)
-            for bi in range(t.shape[0]):
-                if t[bi] <= 1e-05:
-                    result[bi, :] = 0
-            return result
-
-        t = torch.stack((s, sigma_down)).to(torch.float64).repeat(b, 1)
-
-        pbar = tqdm.tqdm(
-            total=1000, desc=self.de_solver_name, leave=True, disable=ss.disable_status
-        )
-
-        term = tode.ODETerm(odefn)
-        method = self.de_solver_method(term=term)
-        controller = tode.PIDController(
-            term=term,
-            atol=self.de_atol,
-            rtol=self.de_rtol,
-            dt_min=1e-05,
-            pcoeff=self.de_ctl_pcoeff,
-            icoeff=self.de_ctl_icoeff,
-            dcoeff=self.de_ctl_dcoeff,
-        )
-        solver_ = tode.AutoDiffAdjoint(method, controller)
-        solver = solver_ if not self.de_compile else torch.compile(solver_)
-        problem = tode.InitialValueProblem(
-            y0=x.flatten(start_dim=1), t_start=t[:, 0], t_end=t[:, -1]
-        )
-        dt0 = (
-            (t[:, -1] - t[:, 0]) * self.de_initial_step
-            if self.de_initial_step
-            else None
-        )
-        solution = solver.solve(problem, dt0=dt0)
-
-        # print("\nSOLUTION", solution.stats, solution.ys.shape)
-        result = solution.ys[:, -1].reshape(-1, c, h, w)
-        del solution
-
-        sigma_up, result = yield from self.adjusted_step(sn, result, mcc, sigma_up)
-        if pbar is not None:
-            pbar.n = pbar.total
-            pbar.update(0)
-            pbar.close()
-        yield from self.result(result, sigma_up, sigma_down=sigma_down)
-
-
-registry.add(TODEStep)

py/step_samplers/solver_tsde.py

@@ -1,192 +0,0 @@
-import contextlib
-
-import torch
-import tqdm
-
-from . import registry
-from .solver_base import DESolverStep
-
-HAVE_TSDE = False
-with contextlib.suppress(ImportError):
-    import torchsde
-
-    HAVE_TSDE = True
-
-
-class TSDEStep(DESolverStep):
-    name = "tsde"
-    model_calls = -1
-    allow_alt_cfgpp = True
-    de_default_solver = "reversible_heun"
-    default_eta = 0.0
-
-    def __init__(
-        self,
-        *args,
-        de_initial_step=0.25,
-        de_split=1,
-        de_adaptive=False,
-        tsde_noise_type="scalar",
-        tsde_sde_type="stratonovich",
-        tsde_levy_area_approx="none",
-        tsde_noise_channels=1,
-        tsde_g_multiplier=0.05,
-        tsde_g_reverse_time=True,
-        tsde_g_derp_mode=False,
-        tsde_batch_channels=True,
-        **kwargs,
-    ):
-        super().__init__(*args, **kwargs)
-        self.de_initial_step = de_initial_step
-        self.de_adaptive = de_adaptive
-        self.de_split = de_split
-        self.de_noise_type = tsde_noise_type
-        self.de_sde_type = tsde_sde_type
-        self.de_levy_area_approx = tsde_levy_area_approx
-        self.de_g_multiplier = tsde_g_multiplier
-        self.de_noise_channels = tsde_noise_channels
-        self.de_g_reverse_time = tsde_g_reverse_time
-        self.de_g_derp_mode = tsde_g_derp_mode
-        self.de_batch_channels = tsde_batch_channels
-
-    def check_solver_support(self):
-        if not HAVE_TSDE:
-            raise RuntimeError(
-                "TSDE sampler requires torchsde installed in venv. Example: pip install torchsde"
-            )
-
-    def step(self, x):
-        s, sn, sigma_down, sigma_up = self.de_get_step(x)
-        if self.de_min_sigma is not None and s <= self.de_min_sigma:
-            return (yield from self.euler_step(x))
-        ss = self.ss
-        delta = (ss.sigma - sigma_down).item()
-        bidx = 0
-        mcc = 0
-        pbar = None
-        _b, c, h, w = x.shape
-        outer_self = self
-
-        class SDE(torch.nn.Module):
-            noise_type = outer_self.de_noise_type
-            sde_type = outer_self.de_sde_type
-
-            @torch.no_grad()
-            def f(self, t_rev, y_flat):
-                nonlocal mcc
-                t = s - (t_rev - sigma_down)
-                # print(f"\nf at t_rev={t_rev}, t={t} :: {y_flat.shape}")
-                if torch.all(t <= 1e-05).item():
-                    return torch.zeros_like(y_flat)
-                if mcc >= outer_self.de_max_nfe:
-                    raise RuntimeError("TSDEStep: Model call limit exceeded")
-
-                pct = (s - t) / delta
-                pbar.n = round(pct.min().item() * 999)
-                pbar.update(0)
-                pbar.set_description(
-                    f"{outer_self.de_solver_name}({mcc}/{outer_self.de_max_nfe})",
-                    refresh=True,
-                )
-                flat_shape = y_flat.shape
-                y = y_flat.view(1, c, h, w)
-                t32 = t.to(torch.float32)
-                del y_flat
-
-                if mcc == 0 and torch.all(t == s):
-                    mr_cached = True
-                    mr = ss.hcur
-                    mcc = 1
-                else:
-                    mr_cached = False
-                    mr = outer_self.call_model(
-                        y, t32.clamp(min=1e-05), call_index=mcc, s_in=t.new_ones(1)
-                    )
-                    mcc += 1
-                return -outer_self.to_d(mr)[bidx if mr_cached else 0].view(*flat_shape)
-
-            @torch.no_grad()
-            def g(self, t_rev, y_flat):
-                t = (s - sigma_down) - (t_rev - sigma_down)
-                pct = t / (s - sigma_down)
-                if outer_self.de_g_reverse_time:
-                    pct = 1.0 - pct
-                multiplier = outer_self.de_g_multiplier
-                if outer_self.de_g_derp_mode and mcc % 2 == 0:
-                    multiplier *= -1
-                val = t * pct * multiplier
-                if self.noise_type == "diagonal":
-                    out = val.repeat(*y_flat.shape)
-                elif self.noise_type == "scalar":
-                    out = val.repeat(*y_flat.shape, 1)
-                else:
-                    out = val.repeat(*y_flat.shape, outer_self.de_noise_channels)
-                return out
-
-        t = torch.stack((sigma_down, s)).to(torch.float)
-
-        pbar = tqdm.tqdm(
-            total=1000, desc=self.de_solver_name, leave=True, disable=ss.disable_status
-        )
-
-        dt0 = (
-            delta * self.de_initial_step if self.de_adaptive else delta / self.de_split
-        )
-        results = []
-        for batch in tqdm.trange(
-            1,
-            x.shape[0] + 1,
-            desc="batch",
-            leave=False,
-            disable=x.shape[0] == 1 or ss.disable_status,
-        ):
-            bidx = batch - 1
-            mcc = 0
-            sde = SDE()
-            if self.de_batch_channels:
-                y_flat = x[bidx].flatten(start_dim=1)
-            else:
-                y_flat = x[bidx].unsqueeze(0).flatten(start_dim=1)
-            if sde.noise_type == "diagonal":
-                bm_size = (y_flat.shape[0], y_flat.shape[1])
-            elif sde.noise_type == "scalar":
-                bm_size = (y_flat.shape[0], 1)
-            else:
-                bm_size = (y_flat.shape[0], self.de_noise_channels)
-            bm = torchsde.BrownianInterval(
-                dtype=x.dtype,
-                device=x.device,
-                t0=-s,
-                t1=s,
-                entropy=ss.noise.seed,
-                levy_area_approximation=self.de_levy_area_approx,
-                tol=1e-06,
-                size=bm_size,
-            )
-
-            ys = torchsde.sdeint(
-                sde,
-                y_flat,
-                t,
-                method=self.de_solver_name,
-                adaptive=self.de_adaptive,
-                atol=self.de_atol,
-                rtol=self.de_rtol,
-                dt=dt0,
-                bm=bm,
-            )
-            del y_flat
-            results.append(ys[-1].view(1, c, h, w))
-            del ys
-        result = torch.cat(results)
-        del results
-
-        sigma_up, result = yield from self.adjusted_step(sn, result, mcc, sigma_up)
-        if pbar is not None:
-            pbar.n = pbar.total
-            pbar.update(0)
-            pbar.close()
-        yield from self.result(result, sigma_up, sigma_down=sigma_down)
-
-
-registry.add(TSDEStep)

py/substep_merging.py

@@ -6,12 +6,9 @@ import tqdm
 from . import expression as expr
 from . import utils
 
-from .filtering import make_filter, FilterRefs, FILTER_HANDLERS
-from .noise import ImmiscibleNoise
+from .filtering import make_filter, FilterRefs
 from .restart import Restart
 from .step_samplers import STEP_SAMPLERS
-from .step_samplers.base import StepSamplerContext
-from .substep_sampling import StepSamplerChain
 from .utils import check_time, fallback
 
 
@@ -23,7 +20,6 @@ class MergeSubstepsSampler:
             STEP_SAMPLERS[sitem["step_method"]](**sitem) for sitem in group.items
         )
         options = group.options.copy()
-        self.group = group
         self.time_mode = group.time_mode
         self.time_start = group.time_start
         self.time_end = group.time_end
@@ -37,10 +33,6 @@ class MergeSubstepsSampler:
         self.pre_filter = None if pre_filter is None else make_filter(pre_filter)
         self.post_filter = None if post_filter is None else make_filter(post_filter)
         self.preview_mode = options.pop("preview_mode", "denoised")
-        self.require_uncond = any(sampler.require_uncond for sampler in samplers)
-        self.cfg_scale_override = options.pop("cfg_scale_override", None)
-        self.afs_start_step = options.pop("afs_start_step", 0)
-        self.afs_end_step = options.pop("afs_end_step", -1)
         self.options = options
 
     def check_match(self, handlers: None | object, *, ss: None | object = None):
@@ -61,51 +53,35 @@ class MergeSubstepsSampler:
         return operator.truth(self.when.eval(handlers))
 
     def step_input(self, x, *, ss=None):
-        ss = fallback(ss, self.ss)
-        ss.noise.update_x(x)
         if self.pre_filter is None:
             return x
-        x = self.pre_filter.apply(x, refs=fallback(ss, self.ss).refs)
-        ss.noise.update_x(x)
-        return x
+        ss = fallback(ss, self.ss)
+        return self.pre_filter.apply(x, refs=fallback(ss, self.ss).refs)
 
     def step_output(self, x, *, orig_x=None, ss=None):
-        ss = fallback(ss, self.ss)
-        ss.noise.update_x(x)
         if self.post_filter is None:
             return x
+        ss = fallback(ss, self.ss)
         refs = ss.refs if orig_x is None else ss.refs | FilterRefs({"orig_x": orig_x})
-        x = self.post_filter.apply(x, refs=refs)
-        ss.noise.update_x(x)
-        return x
+        return self.post_filter.apply(x, refs=refs)
 
     def __call__(self, x):
         orig_x = x
         x = self.step_input(x)
-        if self.afs_start_step <= self.ss.step <= self.afs_end_step:
-            x = self.afs_step(x)
-        else:
-            x = self.step(x)
+        x = self.step(x)
         return self.step_output(x, orig_x=orig_x)
 
-    # From https://arxiv.org/abs/2210.05475
-    def afs_step(self, x):
-        sigma, sigma_next = self.ss.sigma, self.ss.sigma_next
-        afs_d = x / ((1 + sigma**2).sqrt())
-        dt = sigma_next - sigma
-        return x + afs_d * dt
-
     def step(self, x):
         raise NotImplementedError
 
-    def substep(self, x, sampler):
-        sg = sampler(x)
+    def substep(self, x, sampler, ss=None):
+        sg = sampler(x, fallback(ss, self.ss))
         yield from utils.step_generator(sg, get_next=lambda sr: sr.x)
 
-    def simple_substep(self, x, sampler):
-        for sr in self.substep(x, sampler):
+    def simple_substep(self, x, sampler, ss=None):
+        for sr in self.substep(x, sampler, ss=ss):
             if not sr.final:
-                raise RuntimeError("Unexpected non-final sampler result in substep!")
+                sr.noise_x(ss=fallback(ss, self.ss))
         return sr
 
     def merge_steps(self, x, result=None, *, noise=None, ss=None, denoised=True):
@@ -128,17 +104,6 @@ class MergeSubstepsSampler:
         preview_mode = fallback(preview_mode, self.preview_mode)
         return ss.callback(hi=mr, preview_mode=preview_mode)
 
-    def call_model(self, x, ss=None, sigma=None, **kwargs):
-        ss = fallback(ss, self.ss)
-        sigma = fallback(sigma, ss.sigma)
-        return ss.call_model(
-            x,
-            sigma,
-            ss=ss,
-            cfg_scale_override=self.cfg_scale_override,
-            require_uncond=self.require_uncond,
-        )
-
 
 class SimpleSubstepsSampler(MergeSubstepsSampler):
     name = "simple"
@@ -153,16 +118,26 @@ class SimpleSubstepsSampler(MergeSubstepsSampler):
 
     def step(self, x):
         ss, ssampler = self.ss, self.samplers[0]
-        ss.hist.push(self.call_model(x))
+        custom_noise = ssampler.options.get(
+            "custom_noise", self.options.get("custom_noise")
+        )
+        noise_sampler = ss.noise.make_caching_noise_sampler(
+            custom_noise,
+            1,
+            ss.sigma,
+            ss.sigma_next,
+            immiscible=fallback(ssampler.immiscible, ss.noise.immiscible),
+        )
+        ssampler.noise_sampler = noise_sampler
+        ss.hist.push(ss.model(x, ss.sigma, ss=ss))
         ss.refs = FilterRefs.from_ss(ss, have_current=True)
         self.callback()
-        with StepSamplerContext(ssampler, ss) as ssampler:
-            sr = self.simple_substep(x, ssampler)
-        return self.merge_steps(sr.noise_x(ss=ss))
+        sr = self.simple_substep(x, ssampler)
+        return self.merge_steps(sr.x, noise=sr.get_noise(ss=ss))
 
 
-class SupremeAvgMergeSubstepsSampler(MergeSubstepsSampler):
-    name = "supreme_avg"
+class NormalMergeSubstepsSampler(MergeSubstepsSampler):
+    name = "normal"
 
     def step(self, x):
         ss = self.ss
@@ -173,21 +148,31 @@ class SupremeAvgMergeSubstepsSampler(MergeSubstepsSampler):
         noise_total = 0.0
         substep = 0
         pbar = tqdm.tqdm(total=self.substeps, initial=1, disable=ss.disable_status)
-        ss.hist.push(self.call_model(x))
+        ss.hist.push(ss.model(x, ss.sigma, ss=ss))
         ss.refs = FilterRefs.from_ss(ss, have_current=True)
         self.callback()
-        for ssampler_ in self.samplers:
-            with StepSamplerContext(ssampler_, ss) as ssampler:
-                for subidx in range(ssampler.substeps):
-                    pbar.set_description(f"{ssampler.name}: {substep + 1}/{substeps}")
-                    sr = self.simple_substep(x, ssampler)
-                    z_avg += renoise_weight * sr.x
-                    if sr.noise_scale != 0 and ss.sigma_next != 0:
-                        noise_total += renoise_weight * sr.noise_scale
-                        noise += renoise_weight * sr.get_noise(ss=ss)
-                    substep += 1
-                    ss.substep = substep
-                    pbar.update(1)
+        for ssampler in self.samplers:
+            custom_noise = ssampler.options.get(
+                "custom_noise", self.options.get("custom_noise")
+            )
+            noise_sampler = ss.noise.make_caching_noise_sampler(
+                custom_noise,
+                ssampler.max_noise_samples(),
+                ss.sigma,
+                ss.sigma_next,
+                immiscible=fallback(ssampler.immiscible, ss.noise.immiscible),
+            )
+            ssampler.noise_sampler = noise_sampler
+            for subidx in range(ssampler.substeps):
+                pbar.set_description(f"{ssampler.name}: {substep + 1}/{substeps}")
+                sr = self.simple_substep(x, ssampler)
+                z_avg += renoise_weight * sr.x
+                if sr.noise_scale != 0 and ss.sigma_next != 0:
+                    noise_total += renoise_weight * sr.noise_scale
+                    noise += renoise_weight * sr.get_noise(ss=ss)
+                substep += 1
+                ss.substep = substep
+                pbar.update(1)
 
         noise = ss.noise.scale_noise(
             noise,
@@ -242,7 +227,7 @@ class SupremeAvgMergeSubstepsSampler(MergeSubstepsSampler):
 #             noise_sampler = ss.noise.make_caching_noise_sampler(
 #                 custom_noise,
 #                 ssampler.substeps
-#                 + (0 if ss.sigma_next == 0 else ssampler.max_noise_samples),
+#                 + (0 if ss.sigma_next == 0 else ssampler.max_noise_samples()),
 #                 ss.sigma,
 #                 ss.sigma_next,
 #             )
@@ -304,7 +289,7 @@ class SupremeAvgMergeSubstepsSampler(MergeSubstepsSampler):
 #             )
 #             noise_sampler = ss.noise.make_caching_noise_sampler(
 #                 custom_noise,
-#                 ssampler.max_noise_samples + ssampler.substeps,
+#                 ssampler.max_noise_samples() + ssampler.substeps,
 #                 ss.sigma,
 #                 ss.sigma_next,
 #             )
@@ -347,7 +332,7 @@ class SupremeAvgMergeSubstepsSampler(MergeSubstepsSampler):
 #         final = merge_ss.sigma_next == 0
 #         noise_sampler = merge_ss.noise.make_caching_noise_sampler(
 #             msampler.options.get("custom_noise", self.options.get("custom_noise")),
-#             msampler.max_noise_samples + int(not final),
+#             msampler.max_noise_samples() + int(not final),
 #             merge_ss.sigma,
 #             merge_ss.sigma_next,
 #         )
@@ -408,24 +393,34 @@ class DivideMergeSubstepsSampler(MergeSubstepsSampler):
         subss.main_sigmas = ss.sigmas
         substep = 0
         pbar = tqdm.tqdm(total=self.substeps, initial=0, disable=ss.disable_status)
-        for ssampler_ in self.samplers:
-            with StepSamplerContext(ssampler_, subss) as ssampler:
-                for subidx in range(ssampler.substeps):
-                    subss.update(substep, substep=substep)
-                    pbar.set_description(
-                        f"substep({ssampler.name}): {subss.sigma.item():.03} -> {subss.sigma_next.item():.03}"
-                    )
-                    subss.hist.push(self.call_model(x, ss=subss))
-                    subss.refs = FilterRefs.from_ss(subss, have_current=True)
-                    if substep == 0:
-                        self.callback(ss=subss)
-                    sr = self.simple_substep(x, ssampler)
-                    x = sr.x
-                    noise_strength = sr.noise_scale
-                    if noise_strength != 0 and subss.sigma_next != 0:
-                        x = sr.noise_x(ss=subss)
-                    substep += 1
-                    pbar.update(1)
+        for ssampler in self.samplers:
+            custom_noise = ssampler.options.get(
+                "custom_noise", self.options.get("custom_noise")
+            )
+            noise_sampler = ss.noise.make_caching_noise_sampler(
+                custom_noise,
+                ssampler.max_noise_samples(),
+                ss.sigma,
+                ss.sigma_next,
+                immiscible=fallback(ssampler.immiscible, ss.noise.immiscible),
+            )
+            ssampler.noise_sampler = noise_sampler
+            for subidx in range(ssampler.substeps):
+                subss.update(substep, substep=substep)
+                pbar.set_description(
+                    f"substep({ssampler.name}): {subss.sigma.item():.03} -> {subss.sigma_next.item():.03}"
+                )
+                subss.hist.push(subss.model(x, subss.sigma, ss=subss))
+                subss.refs = FilterRefs.from_ss(subss, have_current=True)
+                if substep == 0:
+                    self.callback(ss=subss)
+                sr = self.simple_substep(x, ssampler, ss=subss)
+                x = sr.x
+                noise_strength = sr.noise_scale
+                if noise_strength != 0 and subss.sigma_next != 0:
+                    x = sr.noise_x(ss=subss)
+                substep += 1
+                pbar.update(1)
         pbar.update(0)
         return x
 
@@ -442,14 +437,9 @@ class OvershootMergeSubstepsSampler(MergeSubstepsSampler):
         super().__init__(ss, group, **kwargs)
         self.overshoot_expand_steps = self.options.pop("overshoot_expand_steps", 1)
         restart = self.options.pop("restart", {})
-        restart_custom_noise = self.options.get("restart_custom_noise")
-        if isinstance(restart_custom_noise, str):
-            restart_custom_noise = self.options.get(
-                f"restart_custom_noise_{restart_custom_noise}"
-            )
         self.restart = Restart(
             s_noise=restart.get("s_noise", 1.0),
-            custom_noise=restart_custom_noise,
+            custom_noise=self.options.pop("restart_custom_noise", None),
             immiscible=restart.get("immiscible", False),
         )
 
@@ -480,280 +470,57 @@ class OvershootMergeSubstepsSampler(MergeSubstepsSampler):
         pbar = tqdm.tqdm(total=self.substeps, initial=0, disable=ss.disable_status)
         max_idx = len(subss.sigmas) - 2
         last_down = None
-        for ssampler_ in self.samplers:
-            with StepSamplerContext(ssampler_, subss) as ssampler:
-                for subidx in range(ssampler.substeps):
-                    subss.update(subss.idx + substep, substep=substep)
-                    pbar.set_description(
-                        f"substep({ssampler.name}): {subss.sigma.item():.03} -> {subss.sigma_next.item():.03}"
-                    )
-                    subss.hist.push(self.call_model(x, ss=subss))
-                    subss.refs = FilterRefs.from_ss(subss, have_current=True)
-                    if substep == 0:
-                        ss.hist.push(subss.hcur)
-                        self.callback(ss=subss)
-                    sr = self.simple_substep(x, ssampler)
-                    x = sr.x
-                    noise_strength = sr.noise_scale
-                    if noise_strength != 0 and subss.sigma_next != 0:
-                        x = sr.noise_x(ss=subss)
-                    substep += 1
-                    pbar.update(1)
-                    last_down = subss.sigma_next.item()
-                    if subss.idx + substep >= max_idx:
-                        break
-                if subss.idx >= max_idx:
+        for ssampler in self.samplers:
+            custom_noise = ssampler.options.get(
+                "custom_noise", self.options.get("custom_noise")
+            )
+            noise_sampler = ss.noise.make_caching_noise_sampler(
+                custom_noise,
+                ssampler.max_noise_samples(),
+                ss.sigma,
+                ss.sigma_next,
+                immiscible=fallback(ssampler.immiscible, ss.noise.immiscible),
+            )
+            ssampler.noise_sampler = noise_sampler
+            for subidx in range(ssampler.substeps):
+                subss.update(subss.idx + substep, substep=substep)
+                pbar.set_description(
+                    f"substep({ssampler.name}): {subss.sigma.item():.03} -> {subss.sigma_next.item():.03}"
+                )
+                subss.hist.push(subss.model(x, subss.sigma, ss=subss))
+                subss.refs = FilterRefs.from_ss(subss, have_current=True)
+                if substep == 0:
+                    ss.hist.push(subss.hcur)
+                    self.callback(ss=subss)
+                sr = self.simple_substep(x, ssampler, ss=subss)
+                x = sr.x
+                noise_strength = sr.noise_scale
+                if noise_strength != 0 and subss.sigma_next != 0:
+                    x = sr.noise_x(ss=subss)
+                substep += 1
+                pbar.update(1)
+                last_down = subss.sigma_next.item()
+                if subss.idx + substep >= max_idx:
                     break
+            if subss.idx >= max_idx:
+                break
         if last_down is not None and last_down < ss.sigma_next:
             restart_ns = self.restart.get_noise_sampler(ss.noise)
             x += ss.noise.scale_noise(
-                restart_ns(last_down, ss.sigma_next, refs=ss.refs),
+                restart_ns(refs=ss.refs),
                 self.restart.get_noise_scale(last_down, ss.sigma_next),
             )
         pbar.update(0)
         return x
 
 
-class LookaheadMergeSubstepsSampler(MergeSubstepsSampler):
-    name = "lookahead"
-
-    def __init__(self, ss, group, **kwargs):
-        super().__init__(ss, group, **kwargs)
-        lookahead = self.options.pop("lookahead", {}).copy()
-        self.lookahead_eta = lookahead.pop("eta", 0.0)
-        self.lookahead_s_noise = lookahead.pop("s_noise", 1.0)
-        self.lookahead_dt_factor = lookahead.pop("dt_factor", 1.0)
-        immiscible = lookahead.get("immiscible", False)
-        self.immiscible = (
-            ImmiscibleNoise(**immiscible) if immiscible is not False else False
-        )
-
-        self.custom_noise = self.options.get("custom_noise")
-        if isinstance(self.custom_noise, str):
-            self.custom_noise = self.options.get(f"custom_noise_{self.custom_noise}")
-
-    def step(self, x):
-        orig_x = x.clone()
-        ss = self.ss
-        subss = self.ss.clone_edit(idx=ss.idx, sigmas=ss.sigmas)
-        substep = 0
-        max_idx = len(ss.sigmas) - 1
-        eff_substeps = min(max_idx - ss.idx, self.substeps)
-        pbar = tqdm.tqdm(total=eff_substeps, initial=0, disable=ss.disable_status)
-        for ssampler_ in self.samplers:
-            substeps_remain = eff_substeps - substep
-            if substeps_remain == 0:
-                break
-            with StepSamplerContext(ssampler_, subss) as ssampler:
-                for subidx in range(min(substeps_remain, ssampler.substeps)):
-                    subss.update(ss.idx + substep, substep=substep)
-                    pbar.set_description(
-                        f"substep({ssampler.name}): {subss.sigma.item():.03} -> {subss.sigma_next.item():.03}"
-                    )
-                    subss.hist.push(self.call_model(x, ss=subss))
-                    subss.refs = FilterRefs.from_ss(subss, have_current=True)
-                    if substep == 0:
-                        self.callback(ss=subss)
-                    sr = self.simple_substep(x, ssampler)
-                    x = sr.x
-                    noise_strength = sr.noise_scale
-                    if noise_strength != 0 and subss.sigma_next != 0:
-                        x = sr.noise_x(ss=subss)
-                    substep += 1
-                    pbar.update(1)
-                    if substeps_remain == 1:
-                        break
-        pbar.update(0)
-        sigma_down, sigma_up = ss.get_ancestral_step(
-            eta=self.lookahead_eta, sigma=ss.sigma, sigma_next=ss.sigma_next
-        )
-        if sr.sigma_next == sigma_down:
-            return x
-        dt = (
-            torch.sqrt(1.0 + (ss.sigma - sigma_down) ** 2) * 0.05
-            + (ss.sigma - sigma_down) * 0.95
-        ) * self.lookahead_dt_factor
-        denoised = sr.denoised
-        d = (orig_x - denoised) / ss.sigma
-        x = orig_x + d * -dt
-        if sigma_down == 0 or sigma_up == 0:
-            return x
-        noise_sampler = ss.noise.make_caching_noise_sampler(
-            self.custom_noise,
-            1,
-            ss.sigma,
-            ss.sigma_next,
-            immiscible=fallback(self.immiscible, ss.noise.immiscible),
-        )
-        # FIXME: This sigma, sigma_next is probably wrong.
-        x += ss.noise.scale_noise(
-            noise_sampler(ss.sigma, ss.sigma_next, refs=ss.refs),
-            sigma_up * self.lookahead_s_noise,
-        )
-        return x
-
-
-class PingpongMergeSubstepsSampler(MergeSubstepsSampler):
-    name = "pingpong"
-
-    def __init__(self, ss, group, **kwargs):
-        super().__init__(ss, group, **kwargs)
-        pingpong = self.options.pop("pingpong", {}).copy()
-        self.pingpong_s_noise = pingpong.pop("s_noise", 1.0)
-        immiscible = pingpong.get("immiscible", False)
-        self.immiscible = (
-            ImmiscibleNoise(**immiscible) if immiscible is not False else False
-        )
-
-        self.custom_noise = self.options.get("custom_noise")
-        if isinstance(self.custom_noise, str):
-            self.custom_noise = self.options.get(f"custom_noise_{self.custom_noise}")
-
-    def step(self, x):
-        orig_x = x.clone()
-        ss = self.ss
-        subss = self.ss.clone_edit(idx=ss.idx, sigmas=ss.sigmas)
-        substep = 0
-        max_idx = len(ss.sigmas) - 1
-        eff_substeps = min(max_idx - ss.idx, self.substeps)
-        pbar = tqdm.tqdm(total=eff_substeps, initial=0, disable=ss.disable_status)
-        for ssampler_ in self.samplers:
-            substeps_remain = eff_substeps - substep
-            if substeps_remain == 0:
-                break
-            with StepSamplerContext(ssampler_, subss) as ssampler:
-                for subidx in range(min(substeps_remain, ssampler.substeps)):
-                    subss.update(ss.idx + substep, substep=substep)
-                    pbar.set_description(
-                        f"substep({ssampler.name}): {subss.sigma.item():.03} -> {subss.sigma_next.item():.03}"
-                    )
-                    subss.hist.push(self.call_model(x, ss=subss))
-                    subss.refs = FilterRefs.from_ss(subss, have_current=True)
-                    if substep == 0:
-                        self.callback(ss=subss)
-                    sr = self.simple_substep(x, ssampler)
-                    x = sr.x
-                    noise_strength = sr.noise_scale
-                    if noise_strength != 0 and subss.sigma_next != 0:
-                        x = sr.noise_x(ss=subss)
-                    substep += 1
-                    pbar.update(1)
-                    if substeps_remain == 1:
-                        break
-        pbar.update(0)
-        if sr.sigma_next == 0:
-            return x
-        sigma, sigma_next = ss.sigma, ss.sigma_next
-        alpha = subss.sigma_next / sigma
-        synth_denoised = (x - alpha * orig_x) / (1 - alpha)
-        noise_sampler = ss.noise.make_caching_noise_sampler(
-            self.custom_noise,
-            1,
-            sigma,
-            sigma_next,
-            immiscible=fallback(self.immiscible, ss.noise.immiscible),
-        )
-        noise_refs = ss.refs | FilterRefs({
-            "orig_x": orig_x,
-            "x": x,
-            "denoised": synth_denoised,
-        })
-        noise = (
-            noise_sampler(sigma, sigma_next, refs=noise_refs) * self.pingpong_s_noise
-        )
-        if ss.model.is_rectified_flow:
-            return torch.lerp(synth_denoised, noise, sigma_next)
-        return synth_denoised + noise * sigma_next
-
-
-class DynamicMergeSubstepsSampler(MergeSubstepsSampler):
-    name = "dynamic"
-
-    def __init__(self, ss, group, **kwargs):
-        super().__init__(ss, group, **kwargs)
-        dynamic = self.options.get("dynamic")
-        if dynamic is None:
-            raise ValueError(
-                "Dynamic group type requires specifying dynamic block in text parameters"
-            )
-        if isinstance(dynamic, str):
-            dynamic = ({"expression": dynamic},)
-        elif not isinstance(dynamic, (tuple, list)):
-            raise ValueError(
-                "Bad type for dynamic block: must be string or list of objects"
-            )
-        elif len(dynamic) == 0:
-            raise ValueError("Dynamic block as a list cannot be empty")
-        dynresult = []
-        for idx, item in enumerate(dynamic):
-            if not isinstance(item, dict):
-                raise ValueError(
-                    f"Bad item in dynamic block at index {idx}: must be a dict"
-                )
-            dyn_when = item.get("when")
-            if isinstance(dyn_when, str):
-                dyn_when = expr.Expression(dyn_when)
-            elif dyn_when is not None:
-                raise ValueError(
-                    f"Unexpected type for when key in dynamic block at index {idx}, must be string or null/unset"
-                )
-            dyn_params = item.get("expression")
-            if not isinstance(dyn_params, str):
-                raise ValueError(
-                    f"Missing or incorrectly typed expression key for dynamic block at index {idx}: must be a string"
-                )
-            dynresult.append((dyn_when, expr.Expression(dyn_params)))
-        self.dynamic = tuple(dynresult)
-
-    def step(self, x):
-        group_params = None
-        handlers = FILTER_HANDLERS.clone(constants=self.ss.refs)
-        for idx, (dyn_when, dyn_params) in enumerate(self.dynamic):
-            if dyn_when is not None and not bool(dyn_when.eval(handlers)):
-                continue
-            group_params = dyn_params.eval(handlers)
-            if group_params is not None:
-                break
-        if group_params is None:
-            raise RuntimeError(
-                "Dynamic group could not find matching group: all expressions failed to return a result"
-            )
-        if not isinstance(group_params, dict):
-            raise TypeError(
-                f"Dynamic group expression must evaluate to a dict, got type {type(group_params)}"
-            )
-        if bool(group_params.get("dynamic_inherit")):
-            copy_keys = ("preview_mode",)
-            opts = {k: getattr(self, k) for k in copy_keys}
-        else:
-            opts = {}
-        opts |= {
-            k: v
-            for k, v in self.options.items()
-            if k.startswith("custom_noise") or k.startswith("restart_custom_noise")
-        }
-        opts |= group_params
-        # print("\n\nDYN GROUP OPTS", opts)
-        merge_method = opts.pop("merge_method", "simple").strip()
-        if merge_method == "default":
-            merge_method = "simple"
-        group_class = MERGE_SUBSTEPS_CLASSES.get(merge_method)
-        if group_class is None:
-            raise ValueError(f"Unknown merge method {merge_method} in dynamic group")
-        group = StepSamplerChain(
-            merge_method=merge_method, items=self.group.items, **opts
-        )
-        sampler = group_class(self.ss, group)
-        return sampler.step(x)
-
-
 MERGE_SUBSTEPS_CLASSES = {
     "default (simple)": SimpleSubstepsSampler,
-    "supreme_avg": SupremeAvgMergeSubstepsSampler,
+    "normal": NormalMergeSubstepsSampler,
     "divide": DivideMergeSubstepsSampler,
     "overshoot": OvershootMergeSubstepsSampler,
+    # "average": AverageMergeSubstepsSampler,
+    # "sample": SampleMergeSubstepsSampler,
+    # "sample_uncached": SampleUncachedMergeSubstepsSampler,
     "simple": SimpleSubstepsSampler,
-    "lookahead": LookaheadMergeSubstepsSampler,
-    "pingpong": PingpongMergeSubstepsSampler,
-    "dynamic": DynamicMergeSubstepsSampler,
 }

py/substep_sampling.py

@@ -82,7 +82,6 @@ class StepSamplerGroups(CommonOptionsItems):
 
 class SamplerState:
     CLONE_KEYS = (
-        "cfg_scale_override",
         "model",
         "hist",
         "extra_args",
@@ -124,7 +123,6 @@ class SamplerState:
         s_noise=1.0,
         disable_status=False,
         history_size=4,
-        cfg_scale_override=None,
     ):
         self.model = model
         self.hist = History(max(1, history_size))
@@ -140,7 +138,6 @@ class SamplerState:
         self.step = 0
         self.substep = 0
         self.total_steps = len(sigmas) - 1
-        self.cfg_scale_override = cfg_scale_override
         self.update(idx)  # Sets idx, sigma_prev, sigma, sigma_down, refs
 
     @property
@@ -155,14 +152,6 @@ class SamplerState:
     def denoised(self):
         return self.hcur.denoised
 
-    @property
-    def denoised_uncond(self):
-        return self.hcur.denoised_uncond
-
-    @property
-    def denoised_cond(self):
-        return self.hcur.denoised_cond
-
     @property
     def dt(self):
         return self.sigma_next - self.sigma
@@ -185,56 +174,16 @@ class SamplerState:
             self.substep = substep
         self.refs = FilterRefs.from_ss(self)
 
-    def get_ancestral_step(
-        self, eta=1.0, sigma=None, sigma_next=None, retry_increment=0
-    ):
-        if self.model.is_rectified_flow:
-            return self.get_ancestral_step_rf(
-                eta=eta,
-                sigma=sigma,
-                sigma_next=sigma_next,
-                retry_increment=retry_increment,
+    def get_ancestral_step(self, eta=1.0, sigma=None, sigma_next=None):
+        sigma = self.sigma if sigma is None else sigma
+        sigma_next = self.sigma_next if sigma_next is None else sigma_next
+        sd, su = (
+            v if isinstance(v, torch.Tensor) else sigma.new_full((1,), v)
+            for v in get_ancestral_step(
+                sigma, sigma_next, eta=eta if sigma_next != 0 else 0
             )
-        sigma = fallback(sigma, self.sigma)
-        sigma_next = fallback(sigma_next, self.sigma_next)
-        if eta <= 0 or sigma_next <= 0:
-            return sigma_next, sigma_next.new_zeros(1)
-        while eta > 0:
-            sd, su = (
-                v if isinstance(v, torch.Tensor) else sigma.new_full((1,), v)
-                for v in get_ancestral_step(
-                    sigma, sigma_next, eta=eta if sigma_next != 0 else 0
-                )
-            )
-            if sd > 0 and su > 0:
-                return sd, su
-            if retry_increment <= 0:
-                break
-            # print(f"\nETA {eta} failed, retrying with {eta - retry_increment}")
-            eta -= retry_increment
-        return sigma_next, sigma_next.new_zeros(1)
-
-    # Referenced from Comfy dpmpp_2s_ancestral_RF
-    def get_ancestral_step_rf(
-        self, eta=1.0, sigma=None, sigma_next=None, retry_increment=0
-    ):
-        sigma = fallback(sigma, self.sigma)
-        sigma_next = fallback(sigma_next, self.sigma_next)
-        if eta <= 0 or sigma_next <= 0:
-            return sigma_next, sigma_next.new_zeros(1)
-        while eta > 0:
-            sigma_down = sigma_next * (1 + (sigma_next / sigma - 1) * eta)
-            alpha_ip1, alpha_down = 1 - sigma_next, 1 - sigma_down
-            sigma_up = (
-                sigma_next**2 - sigma_down**2 * alpha_ip1**2 / alpha_down**2
-            ) ** 0.5
-            if sigma_down > 0 and sigma_up > 0:
-                return sigma_down, sigma_up
-            if retry_increment <= 0:
-                break
-            eta -= retry_increment
-        return sigma_next, sigma_next.new_zeros(1)
-        # print(f"\nRF ancestral: down={sigma_down}, up={sigma_up}")
+        )
+        return sd, su
 
     def clone_edit(self, **kwargs):
         obj = self.__class__.__new__(self.__class__)
@@ -253,16 +202,6 @@ class SamplerState:
             preview = fallback(hi.denoised_uncond, hi.denoised)
         elif preview_mode == "raw":
             preview = hi.x
-        elif (
-            preview_mode == "diff"
-            and hi.denoised_uncond is not None
-            and hi.denoised_cond is not None
-        ):
-            preview = (
-                hi.denoised_uncond * 0.25 + (hi.denoised_uncond - hi.denoised_cond) * 16
-            )
-        elif preview_mode == "noisy":
-            preview = (hi.x - hi.denoised) * 0.1 + hi.denoised
         else:
             preview = hi.denoised
         return self.callback_({
@@ -276,7 +215,3 @@ class SamplerState:
     def reset(self):
         self.hist.reset()
         self.denoised = None
-
-    def call_model(self, *args, **kwargs):
-        cfg_scale_override = kwargs.pop("cfg_scale_override", self.cfg_scale_override)
-        return self.model(*args, cfg_scale_override=cfg_scale_override, **kwargs)

py/unsafe_expression_whitelists.py

@@ -1,287 +0,0 @@
-TORCH_FUNCTION_WHITELIST = frozenset((
-    "abs",
-    "absolute",
-    "acos",
-    "acosh",
-    "add",
-    "addbmm",
-    "addcdiv",
-    "addcmul",
-    "addmm",
-    "addmv",
-    "addr",
-    "adjoint",
-    "all",
-    "allclose",
-    "amax",
-    "amin",
-    "aminmax",
-    "angle",
-    "any",
-    "arccos",
-    "arccosh",
-    "arcsin",
-    "arcsinh",
-    "arctan",
-    "arctan2",
-    "arctanh",
-    "argmax",
-    "argmin",
-    "argsort",
-    "argwhere",
-    "as_strided",
-    "asin",
-    "asinh",
-    "atan",
-    "atan2",
-    "atanh",
-    "baddbmm",
-    "bernoulli",
-    "bincount",
-    "bitwise_and",
-    "bitwise_left_shift",
-    "bitwise_not",
-    "bitwise_or",
-    "bitwise_right_shift",
-    "bitwise_xor",
-    "bmm",
-    "broadcast_to",
-    "ceil",
-    "cholesky",
-    "cholesky_inverse",
-    "cholesky_solve",
-    "chunk",
-    "clamp",
-    "clip",
-    "clone",
-    "conj",
-    "conj_physical",
-    "contiguous",
-    "copysign",
-    "corrcoef",
-    "cos",
-    "cosh",
-    "count_nonzero",
-    "cov",
-    "cross",
-    "cummax",
-    "cummin",
-    "cumprod",
-    "cumsum",
-    "deg2rad",
-    "det",
-    "detach",
-    "diag",
-    "diag_embed",
-    "diagflat",
-    "diagonal",
-    "diagonal_scatter",
-    "diff",
-    "digamma",
-    "dim",
-    "dist",
-    "div",
-    "divide",
-    "dot",
-    "dsplit",
-    "eq",
-    "equal",
-    "erf",
-    "erfc",
-    "erfinv",
-    "exp",
-    "expand",
-    "expand_as",
-    "expm1",
-    "fix",
-    "flatten",
-    "flip",
-    "fliplr",
-    "flipud",
-    "float_power",
-    "floor",
-    "floor_divide",
-    "fmax",
-    "fmin",
-    "fmod",
-    "frac",
-    "frexp",
-    "gather",
-    "gcd",
-    "ge",
-    "geqrf",
-    "ger",
-    "greater",
-    "greater_equal",
-    "gt",
-    "hardshrink",
-    "heaviside",
-    "histc",
-    "hsplit",
-    "hypot",
-    "i0",
-    "igamma",
-    "igammac",
-    "index_add",
-    "index_copy",
-    "index_fill",
-    "index_put",
-    "index_reduce",
-    "index_select",
-    "inner",
-    "inverse",
-    "isclose",
-    "isfinite",
-    "isinf",
-    "isnan",
-    "isneginf",
-    "isposinf",
-    "kthvalue",
-    "lcm()",
-    "ldexp",
-    "le",
-    "lerp",
-    "less",
-    "less_equal",
-    "lgamma",
-    "log",
-    "log10",
-    "log1p",
-    "log2",
-    "logaddexp",
-    "logaddexp2",
-    "logcumsumexp",
-    "logdet",
-    "logical_and",
-    "logical_not",
-    "logical_or",
-    "logical_xor",
-    "logit",
-    "logsumexp",
-    "lt",
-    "lu",
-    "lu_solve",
-    "masked_fill",
-    "masked_scatter",
-    "masked_select",
-    "matmul",
-    "matrix_exp",
-    "max",
-    "maximum",
-    "mean",
-    "median",
-    "min",
-    "minimum",
-    "mm",
-    "mode",
-    "moveaxis",
-    "movedim",
-    "msort",
-    "mul",
-    "multinomial",
-    "multiply",
-    "mv",
-    "mvlgamma",
-    "nan_to_num",
-    "nanmean",
-    "nanmedian",
-    "nanquantile",
-    "nansum",
-    "narrow",
-    "narrow_copy",
-    "ne",
-    "neg",
-    "negative",
-    "new_empty",
-    "new_full",
-    "new_ones",
-    "new_zeros",
-    "nextafter",
-    "nonzero",
-    "norm",
-    "not_equal",
-    "numel",
-    "orgqr",
-    "ormqr",
-    "outer",
-    "permute",
-    "polygamma",
-    "positive",
-    "pow",
-    "prod",
-    "qr",
-    "quantile",
-    "rad2deg",
-    "ravel",
-    "reciprocal",
-    "remainder",
-    "renorm",
-    "repeat",
-    "repeat_interleave",
-    "reshape",
-    "reshape_as",
-    "resolve_conj",
-    "resolve_neg",
-    "roll",
-    "rot90",
-    "round",
-    "rsqrt",
-    "scatter",
-    "scatter_add",
-    "scatter_reduce",
-    "select",
-    "select_scatter",
-    "sgn",
-    "sigmoid",
-    "sign",
-    "signbit",
-    "sin",
-    "sinc",
-    "sinh",
-    "slice_scatter",
-    "slogdet",
-    "smm",
-    "softmax",
-    "sort",
-    "sparse_mask",
-    "split",
-    "sqrt",
-    "square",
-    "squeeze",
-    "sspaddmm",
-    "std",
-    "stft",
-    "sub",
-    "subtract",
-    "sum",
-    "sum_to_size",
-    "svd",
-    "swapaxes",
-    "swapdims",
-    "t",
-    "take",
-    "take_along_dim",
-    "tan",
-    "tanh",
-    "tensor_split",
-    "tile",
-    "topk",
-    "transpose",
-    "triangular_solve",
-    "tril",
-    "triu",
-    "true_divide",
-    "trunc",
-    "unflatten",
-    "unfold",
-    "unique",
-    "unique_consecutive",
-    "unsqueeze",
-    "var",
-    "vdot",
-    "view",
-    "view_as",
-    "vsplit",
-    "where",
-    "xlogy",
-))

py/utils.py

@@ -4,6 +4,9 @@ import torch
 
 from comfy.k_diffusion.sampling import to_d
 
+from . import latent
+
+
 # def scale_noise_(
 #     noise,
 #     factor=1.0,
@@ -51,118 +54,6 @@ def scale_noise(
     return noise.sub_(noise.mean(dim=normalize_dims, keepdim=True)).mul_(factor)
 
 
-def _quantile_norm_scaledown(
-    noise: torch.Tensor,
-    nq: torch.Tensor,
-    **_kwargs: dict,
-) -> torch.Tensor:
-    mv = noise.abs().max().detach().item()
-    return noise if mv == 0 else torch.where(noise.abs() > nq, noise * (nq / mv), noise)
-
-
-quantile_handlers = {
-    "clamp": lambda noise, nq, **_kwargs: noise.clamp(-nq, nq),
-    "scale_down": _quantile_norm_scaledown,
-    "tanh": lambda noise, nq, **_kwargs: noise.tanh().mul_(nq.abs()),
-    "tanh_outliers": lambda noise, nq, **_kwargs: torch.where(
-        noise.abs() > nq,
-        noise.tanh().mul_(nq.abs()),
-        noise,
-    ),
-    "sigmoid": lambda noise, nq, **_kwargs: noise.sigmoid()
-    .mul_(nq.abs())
-    .copysign(noise),
-    "sigmoid_outliers": lambda noise, nq, **_kwargs: torch.where(
-        noise.abs() > nq,
-        noise.sigmoid().mul_(nq.abs()).copysign(noise),
-        noise,
-    ),
-    "tenth": lambda noise, nq, **_kwargs: torch.where(
-        noise.abs() > nq,
-        noise * 0.1,
-        noise,
-    ),
-    "half": lambda noise, nq, **_kwargs: torch.where(
-        noise.abs() > nq,
-        noise * 0.5,
-        noise,
-    ),
-    "zero": lambda noise, nq, **_kwargs: torch.where(noise.abs() > nq, 0, noise),
-    "reverse_zero": lambda noise, nq, **_kwargs: torch.where(
-        noise.abs() >= nq,
-        noise,
-        0,
-    ),
-}
-
-
-# Initial version based on Studentt distribution normalizatino from https://github.com/Clybius/ComfyUI-Extra-Samplers/
-def quantile_normalize(
-    noise: torch.Tensor,
-    *,
-    quantile: float = 0.75,
-    dim: int | None = 1,
-    flatten: bool = True,
-    nq_fac: float = 1.0,
-    pow_fac: float = 0.5,
-    strategy: str = "clamp",
-    strategy_handler=None,
-) -> torch.Tensor:
-    if quantile is None or quantile <= 0 or quantile >= 1:
-        return noise
-    orig_shape = noise.shape
-    if isinstance(quantile, (tuple, list)):
-        quantile = torch.tensor(
-            quantile,
-            device=noise.device,
-            dtype=noise.dtype,
-        )
-    qdim = dim
-    if noise.ndim > 1 and flatten:
-        if qdim is not None and qdim >= noise.ndim:
-            qdim = 1 if noise.ndim > 2 else None
-        if qdim is None:
-            flatdim = 0
-        elif -1 < qdim < 2:  # 0, 1
-            flatdim = qdim + 1
-        elif 1 < qdim < 4:  # 2, 3
-            noise = noise.movedim(qdim, 1)
-            tempshape = noise.shape
-            flatdim = 2
-        else:
-            raise ValueError(
-                "Cannot handling quantile normalization flattening dims > 3",
-            )
-    else:
-        flatdim = None
-    nq = torch.quantile(
-        (noise if flatdim is None else noise.flatten(start_dim=flatdim)).abs(),
-        quantile,
-        dim=-1,
-    )
-    nq_shape = tuple(nq.shape) + (1,) * (noise.ndim - nq.ndim)
-    nq = nq.mul_(nq_fac).reshape(*nq_shape)
-    handler = (
-        quantile_handlers.get(strategy)
-        if strategy_handler is None
-        else strategy_handler
-    )
-    if handler is None:
-        raise ValueError("Unknown strategy")
-    noise = handler(
-        noise,
-        nq,
-        dim=dim,
-        flatten=flatten,
-    )
-    noise = noise.abs().pow(pow_fac).copysign(noise)
-    if flatdim is not None and qdim in {2, 3}:
-        return (
-            noise.reshape(tempshape).movedim(1, qdim).reshape(orig_shape).contiguous()
-        )
-    return noise
-
-
 # def scale_noise(
 #     noise,
 #     factor=1.0,