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+*.safetensors filter=lfs diff=lfs merge=lfs -text
+*.ckpt filter=lfs diff=lfs merge=lfs -text
+*.pt filter=lfs diff=lfs merge=lfs -text
+*.pth filter=lfs diff=lfs merge=lfs -text

.gitignore

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+__pycache__
+temp/
+*.log

custom_nodes/ComfyUI-AnimateDiff-Evolved/LICENSE

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custom_nodes/ComfyUI-AnimateDiff-Evolved/README.md

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+# AnimateDiff for ComfyUI
+
+Improved [AnimateDiff](https://github.com/guoyww/AnimateDiff/) integration for ComfyUI, as well as advanced sampling options dubbed Evolved Sampling usable outside of AnimateDiff. Please read the AnimateDiff repo README and Wiki for more information about how it works at its core.
+
+AnimateDiff workflows will often make use of these helpful node packs:
+- [ComfyUI_FizzNodes](https://github.com/FizzleDorf/ComfyUI_FizzNodes) for prompt-travel functionality with the BatchPromptSchedule node. Maintained by FizzleDorf.
+- [ComfyUI-Advanced-ControlNet](https://github.com/Kosinkadink/ComfyUI-Advanced-ControlNet) for making ControlNets work with Context Options and controlling which latents should be affected by the ControlNet inputs. Includes SparseCtrl support. Maintained by me.
+- [ComfyUI-VideoHelperSuite](https://github.com/Kosinkadink/ComfyUI-VideoHelperSuite) for loading videos, combining images into videos, and doing various image/latent operations like appending, splitting, duplicating, selecting, or counting. Actively maintained by AustinMroz and I.
+- [comfyui_controlnet_aux](https://github.com/Fannovel16/comfyui_controlnet_aux) for ControlNet preprocessors not present in vanilla ComfyUI. Maintained by Fannovel16.
+- [ComfyUI_IPAdapter_plus](https://github.com/cubiq/ComfyUI_IPAdapter_plus) for IPAdapter support. Maintained by cubiq (matt3o).
+
+# Installation
+
+## If using ComfyUI Manager:
+
+1. Look for ```AnimateDiff Evolved```, and be sure the author is ```Kosinkadink```. Install it.
+![image](https://github.com/Kosinkadink/ComfyUI-AnimateDiff-Evolved/assets/7365912/2c7f29e1-d024-49e1-9eb0-d38070142584)
+
+
+## If installing manually:
+1. Clone this repo into `custom_nodes` folder.
+
+# Model Setup:
+1. Download motion modules. You will need at least 1. Different modules produce different results.
+   - Original models ```mm_sd_v14```, ```mm_sd_v15```, ```mm_sd_v15_v2```, ```v3_sd15_mm```: [HuggingFace](https://huggingface.co/guoyww/animatediff/tree/cd71ae134a27ec6008b968d6419952b0c0494cf2) | [Google Drive](https://drive.google.com/drive/folders/1EqLC65eR1-W-sGD0Im7fkED6c8GkiNFI) | [CivitAI](https://civitai.com/models/108836)
+   - Stabilized finetunes of mm_sd_v14, ```mm-Stabilized_mid``` and ```mm-Stabilized_high```, by **manshoety**: [HuggingFace](https://huggingface.co/manshoety/AD_Stabilized_Motion/tree/main)
+   - Finetunes of mm_sd_v15_v2, ```mm-p_0.5.pth``` and ```mm-p_0.75.pth```, by **manshoety**: [HuggingFace](https://huggingface.co/manshoety/beta_testing_models/tree/main)
+   - Higher resolution finetune,```temporaldiff-v1-animatediff```  by **CiaraRowles**: [HuggingFace](https://huggingface.co/CiaraRowles/TemporalDiff/tree/main)
+   - FP16/safetensor versions of vanilla motion models, hosted by **continue-revolution** (takes up less storage space, but uses up the same amount of VRAM as ComfyUI loads models in fp16 by default): [HuffingFace](https://huggingface.co/conrevo/AnimateDiff-A1111/tree/main)
+2. Place models in one of these locations (you can rename models if you wish):
+   - ```ComfyUI/custom_nodes/ComfyUI-AnimateDiff-Evolved/models```
+   - ```ComfyUI/models/animatediff_models```
+3. Optionally, you can use Motion LoRAs to influence movement of v2-based motion models like mm_sd_v15_v2.
+   - [Google Drive](https://drive.google.com/drive/folders/1EqLC65eR1-W-sGD0Im7fkED6c8GkiNFI?usp=sharing) | [HuggingFace](https://huggingface.co/guoyww/animatediff) | [CivitAI](https://civitai.com/models/108836/animatediff-motion-modules)
+   - Place Motion LoRAs in one of these locations (you can rename Motion LoRAs if you wish): 
+      -  ```ComfyUI/custom_nodes/ComfyUI-AnimateDiff-Evolved/motion_lora```
+      -  ```ComfyUI/models/animatediff_motion_lora```
+4. Get creative! If it works for normal image generation, it (probably) will work for AnimateDiff generations. Latent upscales? Go for it. ControlNets, one or more stacked? You betcha. Masking the conditioning of ControlNets to only affect part of the animation? Sure. Try stuff and you will be surprised by what you can do. Samples with workflows are included below.
+
+NOTE: you can also use custom locations for models/motion loras by making use of the ComfyUI ```extra_model_paths.yaml``` file. The id for motion model folder is ```animatediff_models``` and the id for motion lora folder is ```animatediff_motion_lora```.
+
+
+# Features 
+- Compatible with almost any vanilla or custom KSampler node.
+- ControlNet, SparseCtrl, and IPAdapter support
+- Infinite animation length support via sliding context windows across whole unet (Context Options) and/or within motion module (View Options)
+- Scheduling Context Options to change across different points in the sampling process
+- FreeInit and FreeNoise support (FreeInit is under iteration opts, FreeNoise is in SampleSettings' noise_type dropdown)
+- Mixable Motion LoRAs from [original AnimateDiff repository](https://github.com/guoyww/animatediff/) implemented. Caveat: the original loras really only work on v2-based motion models like ```mm_sd_v15_v2```, ```mm-p_0.5.pth```, and ```mm-p_0.75.pth```.
+     - UPDATE: New motion LoRAs without the v2 limitation can now be trained via the [AnimateDiff-MotionDirector repo](https://github.com/ExponentialML/AnimateDiff-MotionDirector). Shoutout to ExponentialML for implementing MotionDirector for AnimateDiff purposes!
+- Prompt travel using BatchPromptSchedule node from [ComfyUI_FizzNodes](https://github.com/FizzleDorf/ComfyUI_FizzNodes)
+- Scale and Effect multival inputs to control motion amount and motion model influence on generation.
+     - Can be float, list of floats, or masks
+- Custom noise scheduling via Noise Types, Noise Layers, and seed_override/seed_offset/batch_offset in Sample Settings and related nodes
+- AnimateDiff model v1/v2/v3 support
+- Using multiple motion models at once via Gen2 nodes (each supporting 
+- [HotshotXL](https://huggingface.co/hotshotco/Hotshot-XL/tree/main) support (an SDXL motion module arch), ```hsxl_temporal_layers.safetensors```.
+     - NOTE: You will need to use ```autoselect``` or ```linear (HotshotXL/default)``` beta_schedule, the sweetspot for context_length or total frames (when not using context) is 8 frames, and you will need to use an SDXL checkpoint.
+- AnimateDiff-SDXL support, with corresponding model. Currently, a beta version is out, which you can find info about at [AnimateDiff](https://github.com/guoyww/AnimateDiff/).
+     - NOTE: You will need to use ```autoselect``` or ```linear (AnimateDiff-SDXL)``` beta_schedule. Other than that, same rules of thumb apply to AnimateDiff-SDXL as AnimateDiff.
+- [AnimateLCM](https://github.com/G-U-N/AnimateLCM) support
+     - NOTE: You will need to use ```autoselect``` or ```lcm``` or ```lcm[100_ots]``` beta_schedule. To use fully with LCM, be sure to use appropriate LCM lora, use the ```lcm``` sampler_name in KSampler nodes, and lower cfg to somewhere around 1.0 to 2.0. Don't forget to decrease steps (minimum = ~4 steps), since LCM converges faster (less steps). Increase step count to increase detail as desired.
+- AnimateDiff Keyframes to change Scale and Effect at different points in the sampling process.
+- fp8 support; requires newest ComfyUI and torch >= 2.1 (decreases VRAM usage, but changes outputs)
+- Mac M1/M2/M3 support
+- Usage of Context Options and Sample Settings outside of AnimateDiff via Gen2 Use Evolved Sampling node
+
+## Upcoming Features
+- Maskable Motion LoRA
+- Maskable SD LoRA (and perhaps maskable SD Models as well)
+- [PIA](https://github.com/open-mmlab/PIA) support
+- Anything else AnimateDiff-related that comes out
+
+
+# Basic Usage And Nodes
+
+There are two families of nodes that can be used to use AnimateDiff/Evolved Sampling - **Gen1** and **Gen2**. Other than nodes marked specifically for Gen1/Gen2, all other nodes can be used for both Gen1 and Gen2.
+
+Gen1 and Gen2 produce the exact same results (the backend code is identical), the only difference is in how the modes are used. Overall, Gen1 is the simplest way to use basic AnimateDiff features, while Gen2 separates model loading and application from the Evolved Sampling features. This means in practice, Gen2's Use Evolved Sampling node can be used without a model model, letting Context Options and Sample Settings be used without AnimateDiff.
+
+In the following documentation, inputs/outputs will be color coded as follows:
+- 🟩 - required inputs
+- 🟨 - optional inputs
+- 🟦 - start as widgets, can be converted to inputs
+- 🟪 - output
+
+## Gen1/Gen2 Nodes
+
+| ① Gen1 ① | ② Gen2 ② |
+|---|---|
+| - All-in-One node<br/> - If same model is loaded by multiple Gen1 nodes, duplicates RAM usage. | - Separates model loading from application and Evolved Sampling<br/> - Enables no motion model usage while preserving Evolved Sampling features<br/> - Enables multiple motion model usage with Apply AnimateDiff Model (Adv.) Node|
+| ![image](https://github.com/Kosinkadink/ComfyUI-AnimateDiff-Evolved/assets/7365912/a94029fd-5e74-467b-853c-c3ec4cf8a321)| ![image](https://github.com/Kosinkadink/ComfyUI-AnimateDiff-Evolved/assets/7365912/8c050151-6cfb-4350-932d-a105af78a1ec)|
+| ![image](https://github.com/Kosinkadink/ComfyUI-AnimateDiff-Evolved/assets/7365912/c7ae9ef3-b5cd-4800-b249-da2cb73c4c1e)| ![image](https://github.com/Kosinkadink/ComfyUI-AnimateDiff-Evolved/assets/7365912/cffa21f7-0e33-45d1-9950-ad22eb229134) |
+
+
+### Inputs
+- 🟩*model*: StableDiffusion (SD) Model input.
+- 🟦*model_name*: AnimateDiff (AD) model to load and/or apply during the sampling process. Certain motion models work with SD1.5, while others work with SDXL.
+- 🟦*beta_schedule*: Applies selected beta_schedule to SD model; ```autoselect``` will automatically select the recommended beta_schedule for selected motion models - or will use_existing if no motion model selected for Gen2.
+- 🟨*context_options*: Context Options node from the context_opts submenu - should be used when needing to go back the sweetspot of an AnimateDiff model. Works with no motion models as well (Gen2 only).
+- 🟨*sample_settings*: Sample Settings node input - used to apply custom sampling options such as FreeNoise (noise_type), FreeInit (iter_opts), custom seeds, Noise Layers, etc. Works with no motion models as well (Gen2 only).
+- 🟨*motion_lora*: For v2-based models, Motion LoRA will influence the generated movement. Only a few official motion LoRAs were released - soon, I will be working with some community members to create training code to create (and test) new Motion LoRAs that might work with non-v2 models.
+- 🟨*ad_settings*: Modifies motion models during loading process, allowing the Positional Encoders (PEs) to be adjusted to extend a model's sweetspot or modify overall motion.
+- 🟨*ad_keyframes*: Allows scheduling of ```scale_multival``` and ```effect_multival``` inputs across sampling timesteps.
+- 🟨*scale_multival*: Uses a ```Multival``` input (defaults to ```1.0```). Previously called motion_scale, it directly influences the amount of motion generated by the model. With the Multival nodes, it can accept a float, list of floats, and/or mask inputs, allowing different scale to be applied to not only different frames, but different areas of frames (including per-frame).
+- 🟨*effect_multival*: Uses a ```Multival``` input (defaults to ```1.0```). Determines the influence of the motion models on the sampling process. Value of ```0.0``` is equivalent to normal SD output with no AnimateDiff influence. With the Multival nodes, it can accept a float, list of floats, and/or mask inputs, allowing different effect amount to be applied to not only different frames, but different areas of frames (including per-frame).
+
+#### Gen2-Only Inputs
+- 🟨*motion_model*: Input for loaded motion_model.
+- 🟨*m_models*: One (or more) motion models outputted from Apply AnimateDiff Model nodes.
+
+#### Gen2 Adv.-Only Inputs
+- 🟨*prev_m_models*: Previous applied motion models to use alongside this one.
+- 🟨*start_percent*: Determines when connected motion_model should take effect (supercedes any ad_keyframes).
+- 🟨*end_percent*: Determines when connected motion_model should stop taking effect (supercedes any ad_keyframes).
+
+#### Gen1 (Legacy) Inputs
+- 🟦*motion_scale*: legacy version of ```scale_multival```, can only be a float.
+- 🟦*apply_v2_models_properly*: backwards compatible toggle for months-old workflows that used code that did not turn off groupnorm hack for v2 models. **Only affects v2 models, nothing else.** All nodes default this value to ```True``` now.
+
+### Outputs
+- 🟪*MODEL*: Injected SD model with Evolved Sampling/AnimateDiff.
+
+#### Gen2-Only Outputs
+- 🟪*MOTION_MODEL*: Loaded motion model.
+- 🟪*M_MODELS*: One (or more) applied motion models, to be either plugged into Use Evolved Sampling or another Apply AnimateDiff Model (Adv.) node.
+
+
+## Multival Nodes
+
+For Multival inputs, these nodes allow the use of floats, list of floats, and/or masks to use as input. Scaled Mask node allows customization of dark/light areas of masks in terms of what the values correspond to.
+
+| Node | Inputs |
+|---|---|
+| ![image](https://github.com/Kosinkadink/ComfyUI-AnimateDiff-Evolved/assets/7365912/d4c6a63f-703a-402b-989e-ab4d04141c7a) | 🟨*mask_optional*: Mask for float values - black means 0.0, white means 1.0 (multiplied by float_val). <br/> 🟦*float_val*: Float multiplier.|
+| ![image](https://github.com/Kosinkadink/ComfyUI-AnimateDiff-Evolved/assets/7365912/bc100bec-0407-47c8-aebd-f74f2417711e) | 🟩*mask*: Mask for float values. <br/> 🟦*min_float_val*: Minimum value. <br/>🟦*max_float_val*: Maximum value. <br/> 🟦*scaling*: When ```absolute```, black means min_float_val, white means max_float_val. When ```relative```, darkest area in masks (total) means min_float_val, lighest area in massk (total) means max_float_val. |
+
+
+## AnimateDiff Keyframe
+
+Allows scheduling (in terms of timesteps) for scale_multival and effect_multival.
+
+The two settings to determine schedule are ***start_percent*** and ***guarantee_steps***. When multiple keyframes have the same start_percent, they will be executed in the order they are connected, and run for guarantee_steps before moving on to the next node.
+
+| Node |
+|---|
+| ![image](https://github.com/Kosinkadink/ComfyUI-AnimateDiff-Evolved/assets/7365912/dca73cdc-157a-47db-bed2-6ba584dceccd) |
+
+### Inputs
+- 🟨*prev_ad_keyframes*: Chained keyframes to create schedule.
+- 🟨*scale_multival*: Value of scale to use for this keyframe.
+- 🟨*effect_multival*: Value of effect to use for this keyframe.
+- 🟦*start_percent*: Percent of timesteps to start usage of this keyframe. If multiple keyframes have same start_percent, order of execution is determined by their chained order, and will last for guarantee_steps timesteps.
+- 🟦*guarantee_steps*: Minimum amount of steps the keyframe will be used - when set to 0, this keyframe will only be used when no other keyframes are better matches for current timestep.
+- 🟦*inherit_missing*: When set to ```True```, any missing scale_multival or effect_multival inputs will inherit the previous keyframe's values - if the previous keyframe also inherits missing, the last inherited value will be used.
+
+
+## Context Options and View Options
+
+These nodes provide techniques used to extend the lengths of animations to get around the sweetspot limitations of AnimateDiff models (typically 16 frames) and HotshotXL model (8 frames). 
+
+Context Options works by diffusing portions of the animation at a time, including main SD diffusion, ControlNets, IPAdapters, etc., effectively limiting VRAM usage to be equivalent to be context_length latents.
+
+View Options, in contrast, work by portioning the latents seen by the motion model. This does NOT decrease VRAM usage, but in general is more stable and faster than Context Options, since the latents don't have to go through the whole SD unet.
+
+Context Options and View Options can be combined to get the best of both worlds - longer context_length can be used to gain more stable output, at the cost of using more VRAM (since context_length determines how much SD sampling is done at the same time on the GPU). Provided you have the VRAM, you could also use Views Only Context Options to use only View Options (and automatically make context_length equivalent to full latents) to get a speed boost in return for the higher VRAM usage.
+
+There are two types of Context/View Options: ***Standard*** and ***Looped***. ***Standard*** options do not cause looping in the output. ***Looped*** options, as the name implies, causes looping in the output (from end to beginning). Prior to the code rework, the only context available was the looping kind.
+
+***I recommend using Standard Static at first when not wanting looped outputs.***
+
+In the below animations, ***green*** shows the Contexts, and ***red*** shows the Views. TL;DR green is the amount of latents that are loaded into VRAM (and sampled), while red is the amount of latents that get passed into the motion model at a time.
+
+### Context Options◆Standard Static
+| Behavior |
+|---|
+| ![anim__00005](https://github.com/Kosinkadink/ComfyUI-AnimateDiff-Evolved/assets/7365912/b26792d6-0f41-4f07-93aa-e5ee83f4d90e) <br/> (latent count: 64, context_length: 16, context_overlap: 4, total steps: 20)|
+
+| Node | Inputs |
+|---|---|
+| ![image](https://github.com/Kosinkadink/ComfyUI-AnimateDiff-Evolved/assets/7365912/a4a5f38e-3a1b-4328-9537-ad17567aed75) | 🟦*context_length*: Amount of latents to diffuse at once.<br/> 🟦*context_overlap*: Minimum common latents between adjacent windows.<br/> 🟦*fuse_method*: Method for averaging results of windows.<br/> 🟦*use_on_equal_length*: When True, allows context to be used when latent count matches context_length.<br/> 🟦*start_percent*: When multiple Context Options are chained, allows scheduling.<br/> 🟦*guarantee_steps*: When scheduling contexts, determines the *minimum* amount of sampling steps context should be used.<br/> 🟦*context_length*: Amount of latents to diffuse at once.<br/> 🟨*prev_context*: Allows chaining of contexts.<br/> 🟨*view_options*: When context_length > view_length (unless otherwise specified), allows view_options to be used within each context window.|
+
+### Context Options◆Standard Uniform
+| Behavior |
+|---|
+| ![anim__00006](https://github.com/Kosinkadink/ComfyUI-AnimateDiff-Evolved/assets/7365912/69707e3d-f49e-4368-89d5-616af2631594) <br/> (latent count: 64, context_length: 16, context_overlap: 4, context_stride: 1, total steps: 20) |
+| ![anim__00010](https://github.com/Kosinkadink/ComfyUI-AnimateDiff-Evolved/assets/7365912/7fc083b4-406f-4809-94ca-b389784adcab) <br/> (latent count: 64, context_length: 16, context_overlap: 4, context_stride: 2, total steps: 20) |
+
+| Node | Inputs |
+|---|---|
+| ![image](https://github.com/Kosinkadink/ComfyUI-AnimateDiff-Evolved/assets/7365912/c2c8c7ea-66b6-408d-be46-1d805ecd64d1) | 🟦*context_length*: Amount of latents to diffuse at once.<br/> 🟦*context_overlap*: Minimum common latents between adjacent windows.<br/> 🟦*context_stride*: Maximum 2^(stride-1) distance between adjacent latents.<br/> 🟦*fuse_method*: Method for averaging results of windows.<br/> 🟦*use_on_equal_length*: When True, allows context to be used when latent count matches context_length.<br/> 🟦*start_percent*: When multiple Context Options are chained, allows scheduling.<br/> 🟦*guarantee_steps*: When scheduling contexts, determines the *minimum* amount of sampling steps context should be used.<br/> 🟦*context_length*: Amount of latents to diffuse at once.<br/> 🟨*prev_context*: Allows chaining of contexts.<br/> 🟨*view_options*: When context_length > view_length (unless otherwise specified), allows view_options to be used within each context window.|
+
+### Context Options◆Looped Uniform
+| Behavior |
+|---|
+| ![anim__00008](https://github.com/Kosinkadink/ComfyUI-AnimateDiff-Evolved/assets/7365912/d08ac1c9-2cec-4c9e-b257-0a804448d41b) <br/> (latent count: 64, context_length: 16, context_overlap: 4, context_stride: 1, closed_loop: False, total steps: 20) |
+| ![anim__00009](https://github.com/Kosinkadink/ComfyUI-AnimateDiff-Evolved/assets/7365912/61e0311b-b623-423f-bbcb-eb4eb02e9002) <br/> (latent count: 64, context_length: 16, context_overlap: 4, context_stride: 1, closed_loop: True, total steps: 20) |
+
+| Node | Inputs |
+|---|---|
+| ![image](https://github.com/Kosinkadink/ComfyUI-AnimateDiff-Evolved/assets/7365912/c2c8c7ea-66b6-408d-be46-1d805ecd64d1) | 🟦*context_length*: Amount of latents to diffuse at once.<br/> 🟦*context_overlap*: Minimum common latents between adjacent windows.<br/> 🟦*context_stride*: Maximum 2^(stride-1) distance between adjacent latents.<br/> 🟦*closed_loop*: When True, adds additional windows to enhance looping.<br/> 🟦*fuse_method*: Method for averaging results of windows.<br/> 🟦*use_on_equal_length*: When True, allows context to be used when latent count matches context_length - allows loops to be made when latent count == context_length.<br/> 🟦*start_percent*: When multiple Context Options are chained, allows scheduling.<br/> 🟦*guarantee_steps*: When scheduling contexts, determines the *minimum* amount of sampling steps context should be used.<br/> 🟦*context_length*: Amount of latents to diffuse at once.<br/> 🟨*prev_context*: Allows chaining of contexts.<br/> 🟨*view_options*: When context_length > view_length (unless otherwise specified), allows view_options to be used within each context window.|
+
+### Context Options◆Views Only [VRAM⇈]
+| Behavior |
+|---|
+| ![anim__00011](https://github.com/Kosinkadink/ComfyUI-AnimateDiff-Evolved/assets/7365912/f2e422a4-c894-4e89-8f35-1964b89f369d) <br/> (latent count: 64, view_length: 16, view_overlap: 4, View Options◆Standard Static, total steps: 20) |
+
+| Node | Inputs |
+|---|---|
+| ![image](https://github.com/Kosinkadink/ComfyUI-AnimateDiff-Evolved/assets/7365912/8cd6a0a4-ee8a-46c3-b04b-a100f87025b3) | 🟩*view_opts_req*: View_options to be used across all latents. <br/> 🟨*prev_context*: Allows chaining of contexts.<br/> |
+
+
+There are View Options equivalent of these schedules:
+
+### View Options◆Standard Static
+| Behavior |
+|---|
+| ![anim__00012](https://github.com/Kosinkadink/ComfyUI-AnimateDiff-Evolved/assets/7365912/7aee4ccb-b669-42fd-a1b5-2005003d5f8d) <br/> (latent count: 64, view_length: 16, view_overlap: 4, Context Options◆Standard Static, context_length: 32, context_overlap: 8, total steps: 20) |
+
+| Node | Inputs |
+|---|---|
+| ![image](https://github.com/Kosinkadink/ComfyUI-AnimateDiff-Evolved/assets/7365912/4b22c73f-99cb-4781-bd33-e1b3db848207) | 🟦*view_length*: Amount of latents in context to pass into motion model at a time.<br/> 🟦*view_overlap*: Minimum common latents between adjacent windows.<br/> 🟦*fuse_method*: Method for averaging results of windows.<br/> |
+
+### View Options◆Standard Uniform
+| Behavior |
+|---|
+| ![anim__00015](https://github.com/Kosinkadink/ComfyUI-AnimateDiff-Evolved/assets/7365912/faa2cd26-9f94-4fce-90b2-8acec84b444e ) <br/> (latent count: 64, view_length: 16, view_overlap: 4, view_stride: 1, Context Options◆Standard Static, context_length: 32, context_overlap: 8, total steps: 20) |
+
+| Node | Inputs |
+|---|---|
+| ![image](https://github.com/Kosinkadink/ComfyUI-AnimateDiff-Evolved/assets/7365912/bbf017e6-3545-4043-ba41-fcbe2f54496a) | 🟦*view_length*: Amount of latents in context to pass into motion model at a time.<br/> 🟦*view_overlap*: Minimum common latents between adjacent windows.<br/> 🟦*view_stride*: Maximum 2^(stride-1) distance between adjacent latents.<br/> 🟦*fuse_method*: Method for averaging results of windows.<br/> |
+
+### View Options◆Looped Uniform
+| Behavior |
+|---|
+| ![anim__00016](https://github.com/Kosinkadink/ComfyUI-AnimateDiff-Evolved/assets/7365912/8922b44b-cb19-4b2a-8486-2df8a46bf573) <br/> (latent count: 64, view_length: 16, view_overlap: 4, view_stride: 1, closed_loop: False, Context Options◆Standard Static, context_length: 32, context_overlap: 8, total steps: 20) |
+| NOTE: this one is probably not going to come out looking well unless you are using this for a very specific reason. |
+
+| Node | Inputs |
+|---|---|
+| ![image](https://github.com/Kosinkadink/ComfyUI-AnimateDiff-Evolved/assets/7365912/c58fe4d4-81a8-436b-8028-9e81c2ace18a) | 🟦*view_length*: Amount of latents in context to pass into motion model at a time.<br/> 🟦*view_overlap*: Minimum common latents between adjacent windows.<br/> 🟦*view_stride*: Maximum 2^(stride-1) distance between adjacent latents.<br/> 🟦*closed_loop*: When True, adds additional windows to enhance looping.<br/> 🟦*use_on_equal_length*: When True, allows context to be used when latent count matches context_length - allows loops to be made when latent count == context_length.<br/> 🟦*fuse_method*: Method for averaging results of windows.<br/> |
+
+## Sample Settings
+
+The Sample Settings node allows customization of the sampling process beyond what is exposed on most KSampler nodes. With its default values, it will NOT have any effect, and can safely be attached without changing any behavior.
+
+TL;DR To use FreeNoise, select ```FreeNoise``` from the noise_type dropdown. FreeNoise does not decrease performance in any way. To use FreeInit, attach the FreeInit Iteration Options to the iteration_opts input. NOTE: FreeInit, despite it's name, works by resampling the latents ```iterations``` amount of times - this means if you use iteration=2, total sampling time will be exactly twice as slow since it will be performing the sampling twice.
+
+Noise Layers with the inputs of the same name (or very close to same name) have same intended behavior as the ones for Sample Settings - refer to the inputs below.
+
+| Node |
+|---|
+| ![image](https://github.com/Kosinkadink/ComfyUI-AnimateDiff-Evolved/assets/7365912/563a13cf-7aed-4acc-9ce3-1556660a34c2) |
+
+### Inputs
+- 🟨*noise_layers*: Customizable, stackable noise to add to/modify initial noise.
+- 🟨*iteration_opts*: Options for determining if (and how) sampling should be repeated consecutively; if you want to check out FreeInit, this is how to use it.
+- 🟨*seed_override*: Accepts a single int to use a seed instead of the seed passed into the KSampler, or a list of ints (like via FizzNodes' BatchedValueSchedule) to assign individual seeds to each latent in the batch.
+- 🟦*seed_offset*: When not set to 0, adds value to current seed, predictably changing it, whatever the original seed may have been.
+- 🟦*batch_offset*: When not set to 0, will 'offset' the noise as if the first latent was actually the batch_offset-nth latent, shifting all the noises over.
+- 🟦*noise_type*: Selects type of noise to be generated. Values include:
+   - **default**: generates different noise for all latents as usual.
+   - **constant**: generates exact same noise for all latents (based on seed).
+   - **empty**: generates no noise for all latents (as if noise was turned off).
+   - **repeated_context**: repeats noise every context_length (or view_length) amount of latents; stabilizes longer generations, but has very obvious repetition.
+   - **FreeNoise**: repeats noise such that it is repeated every context_length (or view_length), but the overlapped noise between contexts/views is shuffled to make repetition less prevelant while still achieving stabilization.
+- 🟦*seed_gen*: Allows choosing between ComfyUI and Auto1111 methods of noise generation. One is not better than the other (noise distributions are the same), they are just different methods.
+   - **comfy**: Noise is generated for the entire latent batch tensor at once based on the provided seed.
+   - **auto1111**: Noise is generated individually for each latent, with each latent receiving an increasing +1 seed offset (first latent uses seed, second latent uses seed+1, etc.).
+- 🟦*adapt_denoise_steps*: When True, KSamplers with a 'denoise' input will automatically scale down the total steps to run like the default options in Auto1111.
+   - **True**: Steps will decrease with lower denoise, i.e. 20 steps with 0.5 denoise will be 10 total steps executed, but sigmas will be selected that still achieve 0.5 denoise. Trades speed for quality (since less steps are sampled).
+   - **False**: Default behavior; 20 steps with 0.5 denoise will execute 20 steps.
+
+
+## Iteration Options
+
+These options allow KSamplers to re-sample the same latents without needing to chain multiple KSamplers together, and also allows specialized iteration behavior to implement features such as FreeInit.
+
+### Default Iteration Options
+
+Simply re-runs the KSampler, plugging in the output of the previous iteration into the next one. At the dafault iterations=1, it is no different than not having this node plugged in at all.
+
+| Node | Inputs |
+|---|---|
+| ![image](https://github.com/Kosinkadink/ComfyUI-AnimateDiff-Evolved/assets/7365912/23c5e698-6eff-43cc-92e9-488e9b5ca96a) | 🟦*iterations*: Total amount of times KSampler should run back-to-back. <br/> 🟦*iter_batch_offset*: batch_offset to apply on each subsequent iteration. <br/> 🟦*iter_seed_offset*: seed_offset to apply on each subsequent iteration. |
+
+### FreeInit Iteration Options
+
+Implements [FreeInit](https://github.com/TianxingWu/FreeInit), which is the idea that AnimateDiff was trained on latents of existing videos (images with temporal coherence between them) that were then noised rather than from random initial noise, and that when noising existing latents, low-frequency data still remains in the noised latents. It combines the low-frequency noise from existing videos (or, as is the default behavior, the previous iteration) with the high-frequency noise in randomly generated noise to run the subsequent iterations. ***Each iteration is a full sample - 2 iterations means it will take twice as long to run as compared to having 1 iteration/no iteration_opts connected.***
+
+When apply_to_1st_iter is False, the noising/low-freq/high-freq combination will not occur on the first iteration, with the assumption that there are no useful latents passed in to do the noise combining in the first place, thus requiring at least 2 iterations for FreeInit to take effect.
+
+If you have an existing set of latents to use to get low-freq noise from, you may set apply_to_1st_iter to True, and then even if you set iterations=1, FreeInit will still take effect.
+
+| Node |
+|---|
+| ![image](https://github.com/Kosinkadink/ComfyUI-AnimateDiff-Evolved/assets/7365912/21404e4f-ab67-44ed-8bf9-e510bc2571de) |
+
+#### Inputs
+- 🟦*iterations*: Total amount of times KSampler should run back-to-back. Refer to explanation above why it is 2 by default (and when it can be set to 1 instead).
+- 🟦*init_type*: Code implementation for applying FreeInit.
+   - ***FreeInit [sampler sigma]***: likely closest to intended implementation, and gets the sigma for noising from the sampler instead of the model (when possible).
+   - ***FreeInit [model sigma]***: gets sigma for noising from the model; when using Custom KSampler, this is the method that will be used for both FreeInit options.
+   - ***DinkInit_v1***: my initial, flawed implementation of FreeInit before I figured out how to exactly copy the noising behavior. By sheer luck and trial and error, I managed to have it actually sort of work with this method. Mainly for backwards compatibility now, but might produce useful results too.
+
+- 🟦*apply_to_1st_iter*: When set to True, will do FreeInit low-freq/high-freq combo work even on the 1st iteration it runs Refer to explanation in the above FreeInit Iteration Options section for when this can be set to True.
+- 🟦*init_type*: Code implementation for applying FreeInit.
+- 🟦*iter_batch_offset*: batch_offset to apply on each subsequent iteration.
+- 🟦*iter_seed_offset*: seed_offset to apply on each subsequent iteration. Defaults to 1 so that new random noise is used for each iteration.
+
+- 🟦*filter*: Determines low-freq filter to apply to noise. Very technical, look into code/online resources to figure out how the individual filters act.
+- 🟦*d_s*: Spatial parameter of filter (within latents, I think); very technical. Look into code/online resources if you wish to know what exactly it does.
+- 🟦*d_t*: Temporal parameter of filter (across latents, I think); very technical. Look into code/online resources if you wish to know what exactly it does.
+- 🟦*n_butterworth*: Only applies to ```butterworth``` filter; very technical. Look into code/online resources if you wish to know what exactly it does.
+- 🟦*sigma_step*: Noising step to use/emulate when noising latents to then get low-freq noise out of. 999 actually means last (-1), and any number under 999 will mean the distance away from last. Leave at 999 unless you know what you're trying to do with it.
+
+
+## Noise Layers
+
+These nodes allow initial noise to be added onto, weighted, or replaced. In near future, I will add the ability for masks to 'move' the noise relative to the masks' movement instead of just 'cutting and pasting' the noise.
+
+The inputs that are shared with Sample Settings have the same exact effect - only new option is in seed_gen_override, which by default will use same seed_gen as Sample Settings (use existing). You can make a noise layer use a different seed_gen strategy at will, or use a different seed/set of seeds, etc.
+
+The ```mask_optional``` parameter determines where on the initial noise the noise layer should be applied.
+
+| Node | Behavior + Inputs |
+|---|---|
+| ![image](https://github.com/Kosinkadink/ComfyUI-AnimateDiff-Evolved/assets/7365912/66487969-669d-47d3-9742-85ae26606903) | [Add]; Adds noise directly on top. <br/> 🟦*noise_weight*: Multiplier for noise layer before being added on top. |
+| ![image](https://github.com/Kosinkadink/ComfyUI-AnimateDiff-Evolved/assets/7365912/52acb25c-9116-4594-b3fb-01b7b15bb79d) | [Add Weighted]; Adds noise, but takes a weighted average between what is already there and itself. <br/> 🟦*noise_weight*: Weight of new noise in the weighted average with existing noise. <br/> 🟦*balance_multipler*: Scale for how much noise_weight should affect existing noise; 1.0 means normal weighted average, and below 1.0 will lessen the weighted reduction by that amount (i.e. if balance_multiplier is set to 0.5 and noise_weight is 0.25, existing noise will only be reduced by 0.125 instead of 0.25, but new noise will be added with the unmodified 0.25 weight). |
+| ![image](https://github.com/Kosinkadink/ComfyUI-AnimateDiff-Evolved/assets/7365912/4feb586e-9920-4f35-8f92-e2e36fabb2df) | [Replace]; Directly replaces existing noise from layers underneath with itself. |
+
+
+# Samples (download or drag images of the workflows into ComfyUI to instantly load the corresponding workflows!)
+
+NOTE: I've scaled down the gifs to 0.75x size to make them take up less space on the README.
+
+### txt2img
+
+| Result |
+|---|
+| ![readme_00006](https://github.com/Kosinkadink/ComfyUI-AnimateDiff-Evolved/assets/7365912/b615a4aa-db3e-4b24-b88f-b694e52f6364) |
+| Workflow |
+| ![t2i_wf](https://github.com/Kosinkadink/ComfyUI-AnimateDiff-Evolved/assets/7365912/6eb47506-b503-482b-9baf-4c238f30a9c2)   |
+
+### txt2img - (prompt travel)
+
+| Result |
+|---|
+| ![readme_00010](https://github.com/Kosinkadink/ComfyUI-AnimateDiff-Evolved/assets/7365912/c27a2029-2c69-4272-b40f-64408e9e2ea6) |
+| Workflow |
+| ![t2i_prompttravel_wf](https://github.com/Kosinkadink/ComfyUI-AnimateDiff-Evolved/assets/7365912/e5a72ea1-628d-423e-98ed-f20e1bcc5320) |
+
+
+
+### txt2img - 48 frame animation with 16 context_length (Context Options◆Standard Static) + FreeNoise
+
+| Result |
+|---|
+| ![readme_00012](https://github.com/Kosinkadink/ComfyUI-AnimateDiff-Evolved/assets/7365912/684f6e79-d653-482f-899a-1900dc56cd8f) |
+| Workflow |
+| ![t2i_context_freenoise_wf](https://github.com/Kosinkadink/ComfyUI-AnimateDiff-Evolved/assets/7365912/9d0e53fa-49d6-483d-a660-3f41d7451002) |
+
+
+# Old Samples (TODO: update all of these + add new ones when I get sleep)
+
+### txt2img - 32 frame animation with 16 context_length (uniform) - PanLeft and ZoomOut Motion LoRAs
+
+![t2i_context_mlora_wf](https://github.com/Kosinkadink/ComfyUI-AnimateDiff-Evolved/assets/7365912/41ec4141-389c-4ef4-ae3e-a963a0fa841f)
+
+![aaa_readme_00094_](https://github.com/Kosinkadink/ComfyUI-AnimateDiff-Evolved/assets/7365912/14abee9a-5500-4d14-8632-15ac77ba5709)
+
+[aaa_readme_00095_.webm](https://github.com/Kosinkadink/ComfyUI-AnimateDiff-Evolved/assets/7365912/d730ae2e-188c-4a61-8a6d-bd48f60a2d07)
+
+
+### txt2img w/ latent upscale (partial denoise on upscale)
+
+![t2i_lat_ups_wf](https://github.com/Kosinkadink/ComfyUI-AnimateDiff-Evolved/assets/7365912/521991dd-8e39-4fed-9970-514507c75067)
+
+![aaa_readme_up_00001_](https://github.com/Kosinkadink/ComfyUI-AnimateDiff-Evolved/assets/7365912/f4199e25-c839-41ed-8986-fb7dbbe2ac52)
+
+[aaa_readme_up_00002_.webm](https://github.com/Kosinkadink/ComfyUI-AnimateDiff-Evolved/assets/7365912/2f44342f-3fd8-4863-8e3d-360377d608b7)
+
+
+
+### txt2img w/ latent upscale (partial denoise on upscale) - PanLeft and ZoomOut Motion LoRAs
+
+![t2i_mlora_lat_ups_wf](https://github.com/Kosinkadink/ComfyUI-AnimateDiff-Evolved/assets/7365912/f34882de-7dd4-4264-8f59-e24da350be2a)
+
+![aaa_readme_up_00023_](https://github.com/Kosinkadink/ComfyUI-AnimateDiff-Evolved/assets/7365912/e2ca5c0c-b5d9-42de-b877-4ed29db81eb9)
+
+[aaa_readme_up_00024_.webm](https://github.com/Kosinkadink/ComfyUI-AnimateDiff-Evolved/assets/7365912/414c16d8-231c-422f-8dfc-a93d4b68ffcc)
+
+
+
+### txt2img w/ latent upscale (partial denoise on upscale) - 48 frame animation with 16 context_length (uniform)
+
+![t2i_lat_ups_full_wf](https://github.com/Kosinkadink/ComfyUI-AnimateDiff-Evolved/assets/7365912/a1ebc14e-853e-4cda-9cda-9a7553fa3d85)
+
+[aaa_readme_up_00009_.webm](https://github.com/Kosinkadink/ComfyUI-AnimateDiff-Evolved/assets/7365912/f7a45f81-e700-4bfe-9fdd-fbcaa4fa8a4e)
+
+
+
+### txt2img w/ latent upscale (full denoise on upscale)
+
+![t2i_lat_ups_full_wf](https://github.com/Kosinkadink/ComfyUI-AnimateDiff-Evolved/assets/7365912/5058f201-3f52-4c48-ac7e-525c3c8f3df3)
+
+![aaa_readme_up_00010_](https://github.com/Kosinkadink/ComfyUI-AnimateDiff-Evolved/assets/7365912/804610de-18ec-43af-9af2-4a83cf31d16b)
+
+[aaa_readme_up_00012_.webm](https://github.com/Kosinkadink/ComfyUI-AnimateDiff-Evolved/assets/7365912/3eb575cf-92dd-434a-b3db-1a2064ff0033)
+
+
+
+### txt2img w/ latent upscale (full denoise on upscale) - 48 frame animation with 16 context_length (uniform)
+
+![t2i_context_lat_ups_wf](https://github.com/Kosinkadink/ComfyUI-AnimateDiff-Evolved/assets/7365912/7b9ec22b-d4e0-4083-9846-5743ed90583e)
+
+[aaa_readme_up_00014_.webm](https://github.com/Kosinkadink/ComfyUI-AnimateDiff-Evolved/assets/7365912/034aff4c-f814-4b87-b5d1-407b1089af0d)
+
+
+
+### txt2img w/ ControlNet-stabilized latent-upscale (partial denoise on upscale, Scaled Soft ControlNet Weights)
+
+![t2i_lat_ups_softcontrol_wf](https://github.com/Kosinkadink/ComfyUI-AnimateDiff-Evolved/assets/7365912/c769c2bd-5aac-48d0-92b7-d73c422d4863)
+
+![aaa_readme_up_00017_](https://github.com/Kosinkadink/ComfyUI-AnimateDiff-Evolved/assets/7365912/221954cc-95df-4e0c-8ec9-266d0108dad4)
+
+[aaa_readme_up_00019_.webm](https://github.com/Kosinkadink/ComfyUI-AnimateDiff-Evolved/assets/7365912/b562251d-a4fb-4141-94dd-9f8bca9f3ce8)
+
+
+
+### txt2img w/ ControlNet-stabilized latent-upscale (partial denoise on upscale, Scaled Soft ControlNet Weights) 48 frame animation with 16 context_length (uniform)
+
+![t2i_context_lat_ups_softcontrol_wf](https://github.com/Kosinkadink/ComfyUI-AnimateDiff-Evolved/assets/7365912/798567a8-4ef0-4814-aeeb-4f770df8d783)
+
+[aaa_readme_up_00003_.webm](https://github.com/Kosinkadink/ComfyUI-AnimateDiff-Evolved/assets/7365912/0f57c949-0af3-4da4-b7c4-5c1fb1549927)
+
+
+
+### txt2img w/ Initial ControlNet input (using Normal LineArt preprocessor on first txt2img as an example)
+
+![t2i_initcn_wf](https://github.com/Kosinkadink/ComfyUI-AnimateDiff-Evolved/assets/7365912/caa7abdf-7ba0-456c-9fa4-547944ea6e72)
+
+![aaa_readme_cn_00002_](https://github.com/Kosinkadink/ComfyUI-AnimateDiff-Evolved/assets/7365912/055ef87c-50c6-4bb9-b35e-dd97916b47cc)
+
+[aaa_readme_cn_00003_.webm](https://github.com/Kosinkadink/ComfyUI-AnimateDiff-Evolved/assets/7365912/9c9d425d-2378-4af0-8464-2c6c0d1a68bf)
+
+
+
+### txt2img w/ Initial ControlNet input (using Normal LineArt preprocessor on first txt2img 48 frame as an example) 48 frame animation with 16 context_length (uniform)
+
+![t2i_context_initcn_wf](https://github.com/Kosinkadink/ComfyUI-AnimateDiff-Evolved/assets/7365912/f9de2711-dcfd-4fea-8b3b-31e3794fbff9)
+
+![aaa_readme_cn_00005_](https://github.com/Kosinkadink/ComfyUI-AnimateDiff-Evolved/assets/7365912/6bf14361-5b09-4305-b2a7-f7babad4bd14)
+
+[aaa_readme_cn_00006_.webm](https://github.com/Kosinkadink/ComfyUI-AnimateDiff-Evolved/assets/7365912/5d3665b7-c2da-46a1-88d8-ab43ba8eb0c6)
+
+
+
+### txt2img w/ Initial ControlNet input (using OpenPose images) + latent upscale w/ full denoise
+
+![t2i_openpose_upscale_wf](https://github.com/Kosinkadink/ComfyUI-AnimateDiff-Evolved/assets/7365912/306a40c4-0591-496d-a320-c33f0fc4b3d2)
+
+(open_pose images provided courtesy of toyxyz)
+
+![AA_openpose_cn_gif_00001_](https://github.com/Kosinkadink/ComfyUI-AnimateDiff/assets/7365912/23291941-864d-495a-8ba8-d02e05756396)
+
+![aaa_readme_cn_00032_](https://github.com/Kosinkadink/ComfyUI-AnimateDiff-Evolved/assets/7365912/621a2ca6-2f08-4ed1-96ad-8e6635303173)
+
+[aaa_readme_cn_00033_.webm](https://github.com/Kosinkadink/ComfyUI-AnimateDiff-Evolved/assets/7365912/c5df09a5-8c64-4811-9ecf-57ac73d82377)
+
+
+
+### txt2img w/ Initial ControlNet input (using OpenPose images) + latent upscale w/ full denoise, 48 frame animation with 16 context_length (uniform)
+
+![t2i_context_openpose_upscale_wf](https://github.com/Kosinkadink/ComfyUI-AnimateDiff-Evolved/assets/7365912/a931af6f-bf6a-40d3-bd55-1d7bad32e665)
+
+(open_pose images provided courtesy of toyxyz)
+
+![aaa_readme_preview_00002_](https://github.com/Kosinkadink/ComfyUI-AnimateDiff-Evolved/assets/7365912/028a1e9e-37b5-477d-8665-0e8723306d65)
+
+[aaa_readme_cn_00024_.webm](https://github.com/Kosinkadink/ComfyUI-AnimateDiff-Evolved/assets/7365912/8f4c840c-06a2-4c64-b97e-568dd5ff6f46)
+
+
+
+### img2img 
+
+TODO: fill this out with a few useful ways, some using control net tile. I'm sorry there is nothing here right now, I have a lot of code to write. I'll try to fill this section out + Advance ControlNet use piece by piece.
+
+
+
+## Known Issues
+
+### Some motion models have visible watermark on resulting images (especially when using mm_sd_v15)
+
+Training data used by the authors of the AnimateDiff paper contained Shutterstock watermarks. Since mm_sd_v15 was finetuned on finer, less drastic movement, the motion module attempts to replicate the transparency of that watermark and does not get blurred away like mm_sd_v14. Using other motion modules, or combinations of them using Advanced KSamplers should alleviate watermark issues.

custom_nodes/ComfyUI-AnimateDiff-Evolved/__init__.py

@@ -0,0 +1,10 @@
+import folder_paths
+from .animatediff.logger import logger
+from .animatediff.utils_model import get_available_motion_models, Folders
+from .animatediff.nodes import NODE_CLASS_MAPPINGS, NODE_DISPLAY_NAME_MAPPINGS
+
+if len(get_available_motion_models()) == 0:
+    logger.error(f"No motion models found. Please download one and place in: {folder_paths.get_folder_paths(Folders.ANIMATEDIFF_MODELS)}")
+
+WEB_DIRECTORY = "./web"
+__all__ = ["NODE_CLASS_MAPPINGS", "NODE_DISPLAY_NAME_MAPPINGS", "WEB_DIRECTORY"]

custom_nodes/ComfyUI-AnimateDiff-Evolved/animatediff/ad_settings.py

@@ -0,0 +1,143 @@
+from torch import Tensor
+
+from .utils_motion import normalize_min_max
+
+
+class AnimateDiffSettings:
+    def __init__(self,
+                 adjust_pe: 'AdjustPEGroup'=None,
+                 pe_strength: float=1.0,
+                 attn_strength: float=1.0,
+                 attn_q_strength: float=1.0,
+                 attn_k_strength: float=1.0,
+                 attn_v_strength: float=1.0,
+                 attn_out_weight_strength: float=1.0,
+                 attn_out_bias_strength: float=1.0,
+                 other_strength: float=1.0,
+                 attn_scale: float=1.0,
+                 mask_attn_scale: Tensor=None,
+                 mask_attn_scale_min: float=1.0,
+                 mask_attn_scale_max: float=1.0,
+                 ):
+        # PE-interpolation settings
+        self.adjust_pe = adjust_pe if adjust_pe is not None else AdjustPEGroup()
+        # general strengths
+        self.pe_strength = pe_strength
+        self.attn_strength = attn_strength
+        self.other_strength = other_strength
+        # specific attn strengths
+        self.attn_q_strength = attn_q_strength
+        self.attn_k_strength = attn_k_strength
+        self.attn_v_strength = attn_v_strength
+        self.attn_out_weight_strength = attn_out_weight_strength
+        self.attn_out_bias_strength = attn_out_bias_strength
+        # attention scale settings - DEPRECATED
+        self.attn_scale = attn_scale
+        # attention scale mask settings - DEPRECATED
+        self.mask_attn_scale = mask_attn_scale.clone() if mask_attn_scale is not None else mask_attn_scale
+        self.mask_attn_scale_min = mask_attn_scale_min
+        self.mask_attn_scale_max = mask_attn_scale_max
+        self._prepare_mask_attn_scale()
+    
+    def _prepare_mask_attn_scale(self):
+        if self.mask_attn_scale is not None:
+            self.mask_attn_scale = normalize_min_max(self.mask_attn_scale, self.mask_attn_scale_min, self.mask_attn_scale_max)
+
+    def has_mask_attn_scale(self) -> bool:
+        return self.mask_attn_scale is not None
+
+    def has_pe_strength(self) -> bool:
+        return self.pe_strength != 1.0
+    
+    def has_attn_strength(self) -> bool:
+        return self.attn_strength != 1.0
+    
+    def has_other_strength(self) -> bool:
+        return self.other_strength != 1.0
+
+    def has_anything_to_apply(self) -> bool:
+        return self.adjust_pe.has_anything_to_apply() \
+            or self.has_pe_strength() \
+            or self.has_attn_strength() \
+            or self.has_other_strength() \
+            or self.has_any_attn_sub_strength()
+
+    def has_any_attn_sub_strength(self) -> bool:
+        return self.has_attn_q_strength() \
+            or self.has_attn_k_strength() \
+            or self.has_attn_v_strength() \
+            or self.has_attn_out_weight_strength() \
+            or self.has_attn_out_bias_strength()
+
+    def has_attn_q_strength(self) -> bool:
+        return self.attn_q_strength != 1.0
+
+    def has_attn_k_strength(self) -> bool:
+        return self.attn_k_strength != 1.0
+
+    def has_attn_v_strength(self) -> bool:
+        return self.attn_v_strength != 1.0
+
+    def has_attn_out_weight_strength(self) -> bool:
+        return self.attn_out_weight_strength != 1.0
+
+    def has_attn_out_bias_strength(self) -> bool:
+        return self.attn_out_bias_strength != 1.0
+
+
+class AdjustPE:
+    def __init__(self,
+                 cap_initial_pe_length: int=0, interpolate_pe_to_length: int=0,
+                 initial_pe_idx_offset: int=0, final_pe_idx_offset: int=0,
+                 motion_pe_stretch: int=0, print_adjustment=False):
+        # PE-interpolation settings
+        self.cap_initial_pe_length = cap_initial_pe_length
+        self.interpolate_pe_to_length = interpolate_pe_to_length
+        self.initial_pe_idx_offset = initial_pe_idx_offset
+        self.final_pe_idx_offset = final_pe_idx_offset
+        self.motion_pe_stretch = motion_pe_stretch
+        self.print_adjustment = print_adjustment
+
+    def has_cap_initial_pe_length(self) -> bool:
+        return self.cap_initial_pe_length > 0
+    
+    def has_interpolate_pe_to_length(self) -> bool:
+        return self.interpolate_pe_to_length > 0
+    
+    def has_initial_pe_idx_offset(self) -> bool:
+        return self.initial_pe_idx_offset > 0
+    
+    def has_final_pe_idx_offset(self) -> bool:
+        return self.final_pe_idx_offset > 0
+
+    def has_motion_pe_stretch(self) -> bool:
+        return self.motion_pe_stretch > 0
+    
+    def has_anything_to_apply(self) -> bool:
+        return self.has_cap_initial_pe_length() \
+            or self.has_interpolate_pe_to_length() \
+            or self.has_initial_pe_idx_offset() \
+            or self.has_final_pe_idx_offset() \
+            or self.has_motion_pe_stretch()
+
+
+class AdjustPEGroup:
+    def __init__(self, initial: AdjustPE=None):
+        self.adjusts: list[AdjustPE] = []
+        if initial is not None:
+            self.add(initial)
+
+    def add(self, adjust_pe: AdjustPE):
+        self.adjusts.append(adjust_pe)
+    
+    def has_anything_to_apply(self):
+        for adjust in self.adjusts:
+            if adjust.has_anything_to_apply():
+                return True
+        return False
+
+    def clone(self):
+        new_group = AdjustPEGroup()
+        for adjust in self.adjusts:
+            new_group.add(adjust)
+        return new_group

custom_nodes/ComfyUI-AnimateDiff-Evolved/animatediff/context.py

@@ -0,0 +1,389 @@
+from typing import Callable, Optional, Union
+
+import numpy as np
+from torch import Tensor
+
+from comfy.model_base import BaseModel
+
+from .utils_motion import get_sorted_list_via_attr
+
+class ContextFuseMethod:
+    FLAT = "flat"
+    PYRAMID = "pyramid"
+    RELATIVE = "relative"
+
+    LIST = [PYRAMID, FLAT]
+    LIST_STATIC = [PYRAMID, RELATIVE, FLAT]
+
+
+class ContextType:
+    UNIFORM_WINDOW = "uniform window"
+
+
+class ContextOptions:
+    def __init__(self, context_length: int=None, context_stride: int=None, context_overlap: int=None,
+                 context_schedule: str=None, closed_loop: bool=False, fuse_method: str=ContextFuseMethod.FLAT,
+                 use_on_equal_length: bool=False, view_options: 'ContextOptions'=None,
+                 start_percent=0.0, guarantee_steps=1):
+        # permanent settings
+        self.context_length = context_length
+        self.context_stride = context_stride
+        self.context_overlap = context_overlap
+        self.context_schedule = context_schedule
+        self.closed_loop = closed_loop
+        self.fuse_method = fuse_method
+        self.sync_context_to_pe = False  # this feature is likely bad and stay unused, so I might remove this
+        self.use_on_equal_length = use_on_equal_length
+        self.view_options = view_options.clone() if view_options else view_options
+        # scheduling
+        self.start_percent = float(start_percent)
+        self.start_t = 999999999.9
+        self.guarantee_steps = guarantee_steps
+        # temporary vars
+        self._step: int = 0
+    
+    @property
+    def step(self):
+        return self._step
+    @step.setter
+    def step(self, value: int):
+        self._step = value
+        if self.view_options:
+            self.view_options.step = value
+
+    def clone(self):
+        n = ContextOptions(context_length=self.context_length, context_stride=self.context_stride,
+                                  context_overlap=self.context_overlap, context_schedule=self.context_schedule,
+                                  closed_loop=self.closed_loop, fuse_method=self.fuse_method,
+                                  use_on_equal_length=self.use_on_equal_length, view_options=self.view_options,
+                                  start_percent=self.start_percent, guarantee_steps=self.guarantee_steps)
+        n.start_t = self.start_t
+        return n
+
+
+class ContextOptionsGroup:
+    def __init__(self):
+        self.contexts: list[ContextOptions] = []
+        self._current_context: ContextOptions = None
+        self._current_used_steps: int = 0
+        self._current_index: int = 0
+        self.step = 0
+
+    def reset(self):
+        self._current_context = None
+        self._current_used_steps = 0
+        self._current_index = 0
+        self.step = 0
+        self._set_first_as_current()
+
+    @classmethod
+    def default(cls):
+        def_context = ContextOptions()
+        new_group = ContextOptionsGroup()
+        new_group.add(def_context)
+        return new_group
+
+    def add(self, context: ContextOptions):
+        # add to end of list, then sort
+        self.contexts.append(context)
+        self.contexts = get_sorted_list_via_attr(self.contexts, "start_percent")
+        self._set_first_as_current()
+
+    def add_to_start(self, context: ContextOptions):
+        # add to start of list, then sort
+        self.contexts.insert(0, context)
+        self.contexts = get_sorted_list_via_attr(self.contexts, "start_percent")
+        self._set_first_as_current()
+
+    def has_index(self, index: int) -> int:
+        return index >=0 and index < len(self.contexts)
+
+    def is_empty(self) -> bool:
+        return len(self.contexts) == 0
+    
+    def clone(self):
+        cloned = ContextOptionsGroup()
+        for context in self.contexts:
+            cloned.contexts.append(context)
+        cloned._set_first_as_current()
+        return cloned
+
+    def initialize_timesteps(self, model: BaseModel):
+        for context in self.contexts:
+            context.start_t = model.model_sampling.percent_to_sigma(context.start_percent)
+
+    def prepare_current_context(self, t: Tensor):
+        curr_t: float = t[0]
+        prev_index = self._current_index
+        # if met guaranteed steps, look for next context in case need to switch
+        if self._current_used_steps >= self._current_context.guarantee_steps:
+            # if has next index, loop through and see if need to switch
+            if self.has_index(self._current_index+1):
+                for i in range(self._current_index+1, len(self.contexts)):
+                    eval_c  = self.contexts[i]
+                    # check if start_t is greater or equal to curr_t
+                    # NOTE: t is in terms of sigmas, not percent, so bigger number = earlier step in sampling
+                    if eval_c.start_t >= curr_t:
+                        self._current_index = i
+                        self._current_context = eval_c
+                        self._current_used_steps = 0
+                        # if guarantee_steps greater than zero, stop searching for other keyframes
+                        if self._current_context.guarantee_steps > 0:
+                            break
+                    # if eval_c is outside the percent range, stop looking further
+                    else:
+                        break
+        # update steps current context is used
+        self._current_used_steps += 1
+
+    def _set_first_as_current(self):
+        if len(self.contexts) > 0:
+            self._current_context = self.contexts[0]
+
+    # properties shadow those of ContextOptions
+    @property
+    def context_length(self):
+        return self._current_context.context_length
+    
+    @property
+    def context_overlap(self):
+        return self._current_context.context_overlap
+    
+    @property
+    def context_stride(self):
+        return self._current_context.context_stride
+    
+    @property
+    def context_schedule(self):
+        return self._current_context.context_schedule
+    
+    @property
+    def closed_loop(self):
+        return self._current_context.closed_loop
+    
+    @property
+    def fuse_method(self):
+        return self._current_context.fuse_method
+    
+    @property
+    def use_on_equal_length(self):
+        return self._current_context.use_on_equal_length
+    
+    @property
+    def view_options(self):
+        return self._current_context.view_options
+
+
+class ContextSchedules:
+    UNIFORM_LOOPED = "looped_uniform"
+    UNIFORM_STANDARD = "standard_uniform"
+    STATIC_STANDARD = "standard_static"
+    BATCHED = "batched"
+    VIEW_AS_CONTEXT = "view_as_context"
+
+    LEGACY_UNIFORM_LOOPED = "uniform"
+    LEGACY_UNIFORM_SCHEDULE_LIST = [LEGACY_UNIFORM_LOOPED]
+
+
+# from https://github.com/neggles/animatediff-cli/blob/main/src/animatediff/pipelines/context.py
+def create_windows_uniform_looped(num_frames: int, opts: Union[ContextOptionsGroup, ContextOptions]):
+    windows = []
+    if num_frames < opts.context_length:
+        windows.append(list(range(num_frames)))
+        return windows
+    
+    context_stride = min(opts.context_stride, int(np.ceil(np.log2(num_frames / opts.context_length))) + 1)
+    # obtain uniform windows as normal, looping and all
+    for context_step in 1 << np.arange(context_stride):
+        pad = int(round(num_frames * ordered_halving(opts.step)))
+        for j in range(
+            int(ordered_halving(opts.step) * context_step) + pad,
+            num_frames + pad + (0 if opts.closed_loop else -opts.context_overlap),
+            (opts.context_length * context_step - opts.context_overlap),
+        ):
+            windows.append([e % num_frames for e in range(j, j + opts.context_length * context_step, context_step)])
+
+    return windows
+
+
+def create_windows_uniform_standard(num_frames: int, opts: Union[ContextOptionsGroup, ContextOptions]):
+    # unlike looped, uniform_straight does NOT allow windows that loop back to the beginning;
+    # instead, they get shifted to the corresponding end of the frames.
+    # in the case that a window (shifted or not) is identical to the previous one, it gets skipped.
+    windows = []
+    if num_frames <= opts.context_length:
+        windows.append(list(range(num_frames)))
+        return windows
+    
+    context_stride = min(opts.context_stride, int(np.ceil(np.log2(num_frames / opts.context_length))) + 1)
+    # first, obtain uniform windows as normal, looping and all
+    for context_step in 1 << np.arange(context_stride):
+        pad = int(round(num_frames * ordered_halving(opts.step)))
+        for j in range(
+            int(ordered_halving(opts.step) * context_step) + pad,
+            num_frames + pad + (-opts.context_overlap),
+            (opts.context_length * context_step - opts.context_overlap),
+        ):
+            windows.append([e % num_frames for e in range(j, j + opts.context_length * context_step, context_step)])
+    
+    # now that windows are created, shift any windows that loop, and delete duplicate windows
+    delete_idxs = []
+    win_i = 0
+    while win_i < len(windows):
+        # if window is rolls over itself, need to shift it
+        is_roll, roll_idx = does_window_roll_over(windows[win_i], num_frames)
+        if is_roll:
+            roll_val = windows[win_i][roll_idx]  # roll_val might not be 0 for windows of higher strides
+            shift_window_to_end(windows[win_i], num_frames=num_frames)
+            # check if next window (cyclical) is missing roll_val
+            if roll_val not in windows[(win_i+1) % len(windows)]:
+                # need to insert new window here - just insert window starting at roll_val
+                windows.insert(win_i+1, list(range(roll_val, roll_val + opts.context_length)))
+        # delete window if it's not unique
+        for pre_i in range(0, win_i):
+            if windows[win_i] == windows[pre_i]:
+                delete_idxs.append(win_i)
+                break
+        win_i += 1
+
+    # reverse delete_idxs so that they will be deleted in an order that doesn't break idx correlation
+    delete_idxs.reverse()
+    for i in delete_idxs:
+        windows.pop(i)
+
+    return windows
+
+
+def create_windows_static_standard(num_frames: int, opts: Union[ContextOptionsGroup, ContextOptions]):
+    windows = []
+    if num_frames <= opts.context_length:
+        windows.append(list(range(num_frames)))
+        return windows
+    # always return the same set of windows
+    delta = opts.context_length - opts.context_overlap
+    for start_idx in range(0, num_frames, delta):
+        # if past the end of frames, move start_idx back to allow same context_length
+        ending = start_idx + opts.context_length
+        if ending >= num_frames:
+            final_delta = ending - num_frames
+            final_start_idx = start_idx - final_delta
+            windows.append(list(range(final_start_idx, final_start_idx + opts.context_length)))
+            break
+        windows.append(list(range(start_idx, start_idx + opts.context_length)))
+    return windows
+
+
+def create_windows_batched(num_frames: int, opts: Union[ContextOptionsGroup, ContextOptions]):
+    windows = []
+    if num_frames <= opts.context_length:
+        windows.append(list(range(num_frames)))
+        return windows
+    # always return the same set of windows;
+    # no overlap, just cut up based on context_length;
+    # last window size will be different if num_frames % opts.context_length != 0
+    for start_idx in range(0, num_frames, opts.context_length):
+        windows.append(list(range(start_idx, min(start_idx + opts.context_length, num_frames))))
+    return windows
+
+
+def create_windows_default(num_frames: int, opts: Union[ContextOptionsGroup, ContextOptions]):
+    return [list(range(num_frames))]
+
+
+def get_context_windows(num_frames: int, opts: Union[ContextOptionsGroup, ContextOptions]):
+    context_func = CONTEXT_MAPPING.get(opts.context_schedule, None)
+    if not context_func:
+        raise ValueError(f"Unknown context_schedule '{opts.context_schedule}'.")
+    return context_func(num_frames, opts)
+
+
+CONTEXT_MAPPING = {
+    ContextSchedules.UNIFORM_LOOPED: create_windows_uniform_looped,
+    ContextSchedules.UNIFORM_STANDARD: create_windows_uniform_standard,
+    ContextSchedules.STATIC_STANDARD: create_windows_static_standard,
+    ContextSchedules.BATCHED: create_windows_batched,
+    ContextSchedules.VIEW_AS_CONTEXT: create_windows_default,  # just return all to allow Views to do all the work
+}
+
+
+def get_context_weights(num_frames: int, fuse_method: str):
+    weights_func = FUSE_MAPPING.get(fuse_method, None)
+    if not weights_func:
+        raise ValueError(f"Unknown fuse_method '{fuse_method}'.")
+    return weights_func(num_frames)
+
+
+def create_weights_flat(length: int, **kwargs) -> list[float]:
+    # weight is the same for all
+    return [1.0] * length
+
+
+def create_weights_pyramid(length: int, **kwargs) -> list[float]:
+    # weight is based on the distance away from the edge of the context window;
+    # based on weighted average concept in FreeNoise paper
+    if length % 2 == 0:
+        max_weight = length // 2
+        weight_sequence = list(range(1, max_weight + 1, 1)) + list(range(max_weight, 0, -1))
+    else:
+        max_weight = (length + 1) // 2
+        weight_sequence = list(range(1, max_weight, 1)) + [max_weight] + list(range(max_weight - 1, 0, -1))
+    return weight_sequence
+
+
+FUSE_MAPPING = {
+    ContextFuseMethod.FLAT: create_weights_flat,
+    ContextFuseMethod.PYRAMID: create_weights_pyramid,
+    ContextFuseMethod.RELATIVE: create_weights_pyramid,
+}
+
+
+# Returns fraction that has denominator that is a power of 2
+def ordered_halving(val):
+    # get binary value, padded with 0s for 64 bits
+    bin_str = f"{val:064b}"
+    # flip binary value, padding included
+    bin_flip = bin_str[::-1]
+    # convert binary to int
+    as_int = int(bin_flip, 2)
+    # divide by 1 << 64, equivalent to 2**64, or 18446744073709551616,
+    # or b10000000000000000000000000000000000000000000000000000000000000000 (1 with 64 zero's)
+    return as_int / (1 << 64)
+
+
+def get_missing_indexes(windows: list[list[int]], num_frames: int) -> list[int]:
+    all_indexes = list(range(num_frames))
+    for w in windows:
+        for val in w:
+            try:
+                all_indexes.remove(val)
+            except ValueError:
+                pass
+    return all_indexes
+
+
+def does_window_roll_over(window: list[int], num_frames: int) -> tuple[bool, int]:
+    prev_val = -1
+    for i, val in enumerate(window):
+        val = val % num_frames
+        if val < prev_val:
+            return True, i
+        prev_val = val
+    return False, -1
+
+
+def shift_window_to_start(window: list[int], num_frames: int):
+    start_val = window[0]
+    for i in range(len(window)):
+        # 1) subtract each element by start_val to move vals relative to the start of all frames
+        # 2) add num_frames and take modulus to get adjusted vals
+        window[i] = ((window[i] - start_val) + num_frames) % num_frames
+
+
+def shift_window_to_end(window: list[int], num_frames: int):
+    # 1) shift window to start
+    shift_window_to_start(window, num_frames)
+    end_val = window[-1]
+    end_delta = num_frames - end_val - 1
+    for i in range(len(window)):
+        # 2) add end_delta to each val to slide windows to end
+        window[i] = window[i] + end_delta

custom_nodes/ComfyUI-AnimateDiff-Evolved/animatediff/freeinit.py

@@ -0,0 +1,162 @@
+# S-Lab License 1.0
+
+# Copyright 2023 S-Lab
+# Redistribution and use for non-commercial purpose in source and binary forms, with or without modification, are permitted provided that the following conditions are met:
+# 1. Redistributions of source code must retain the above copyright notice, this list of conditions and the following disclaimer.
+# 2. Redistributions in binary form must reproduce the above copyright notice, this list of conditions and the following disclaimer in the documentation and/or other materials provided with the distribution.
+# 3. Neither the name of the copyright holder nor the names of its contributors may be used to endorse or promote products derived from this software without specific prior written permission.
+# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
+# 4. In the event that redistribution and/or use for commercial purpose in source or binary forms, with or without modification is required, please contact the contributor(s) of the work.
+
+# Code has been modified from https://github.com/TianxingWu/FreeInit
+
+import torch
+import torch.fft as fft
+import math
+
+
+class FreeInitFilter:
+    GAUSSIAN = "gaussian"
+    IDEAL = "ideal"
+    BOX = "box"
+    BUTTERWORTH = "butterworth"
+
+    LIST = [GAUSSIAN, BUTTERWORTH, IDEAL, BOX]
+
+
+def freq_mix_3d(x, noise, LPF):
+    """
+    Noise reinitialization.
+
+    Args:
+        x: diffused latent
+        noise: randomly sampled noise
+        LPF: low pass filter
+    """
+    # FFT
+    x_freq = fft.fftn(x, dim=(-4, -2, -1))
+    x_freq = fft.fftshift(x_freq, dim=(-4, -2, -1))
+    noise_freq = fft.fftn(noise, dim=(-4, -2, -1))
+    noise_freq = fft.fftshift(noise_freq, dim=(-4, -2, -1))
+
+    # frequency mix
+    HPF = 1 - LPF
+    x_freq_low = x_freq * LPF
+    noise_freq_high = noise_freq * HPF
+    x_freq_mixed = x_freq_low + noise_freq_high # mix in freq domain
+
+    # IFFT
+    x_freq_mixed = fft.ifftshift(x_freq_mixed, dim=(-4, -2, -1))
+    x_mixed = fft.ifftn(x_freq_mixed, dim=(-4, -2, -1)).real
+
+    return x_mixed
+
+
+def get_freq_filter(shape, device, filter_type, n, d_s, d_t):
+    """
+    Form the frequency filter for noise reinitialization.
+
+    Args:
+        shape: shape of latent (T, C, H, W)
+        filter_type: type of the freq filter
+        n: (only for butterworth) order of the filter, larger n ~ ideal, smaller n ~ gaussian
+        d_s: normalized stop frequency for spatial dimensions (0.0-1.0)
+        d_t: normalized stop frequency for temporal dimension (0.0-1.0)
+    """
+    if filter_type == FreeInitFilter.GAUSSIAN:
+        return gaussian_low_pass_filter(shape=shape, d_s=d_s, d_t=d_t).to(device)
+    elif filter_type == FreeInitFilter.IDEAL:
+        return ideal_low_pass_filter(shape=shape, d_s=d_s, d_t=d_t).to(device)
+    elif filter_type == FreeInitFilter.BOX:
+        return box_low_pass_filter(shape=shape, d_s=d_s, d_t=d_t).to(device)
+    elif filter_type == FreeInitFilter.BUTTERWORTH:
+        return butterworth_low_pass_filter(shape=shape, n=n, d_s=d_s, d_t=d_t).to(device)
+    else:
+        raise NotImplementedError
+
+def gaussian_low_pass_filter(shape, d_s=0.25, d_t=0.25):
+    """
+    Compute the gaussian low pass filter mask.
+
+    Args:
+        shape: shape of the filter (volume)
+        d_s: normalized stop frequency for spatial dimensions (0.0-1.0)
+        d_t: normalized stop frequency for temporal dimension (0.0-1.0)
+    """
+    T, H, W = shape[-4], shape[-2], shape[-1]
+    mask = torch.zeros(shape)
+    if d_s==0 or d_t==0:
+        return mask
+    for t in range(T):
+        for h in range(H):
+            for w in range(W):
+                d_square = (((d_s/d_t)*(2*t/T-1))**2 + (2*h/H-1)**2 + (2*w/W-1)**2)
+                mask[t, ..., h,w] = math.exp(-1/(2*d_s**2) * d_square)
+    return mask
+
+
+def butterworth_low_pass_filter(shape, n=4, d_s=0.25, d_t=0.25):
+    """
+    Compute the butterworth low pass filter mask.
+
+    Args:
+        shape: shape of the filter (volume)
+        n: order of the filter, larger n ~ ideal, smaller n ~ gaussian
+        d_s: normalized stop frequency for spatial dimensions (0.0-1.0)
+        d_t: normalized stop frequency for temporal dimension (0.0-1.0)
+    """
+    T, H, W = shape[-4], shape[-2], shape[-1]
+    mask = torch.zeros(shape)
+    if d_s==0 or d_t==0:
+        return mask
+    for t in range(T):
+        for h in range(H):
+            for w in range(W):
+                d_square = (((d_s/d_t)*(2*t/T-1))**2 + (2*h/H-1)**2 + (2*w/W-1)**2)
+                mask[t, ..., h,w] = 1 / (1 + (d_square / d_s**2)**n)
+    return mask
+
+
+def ideal_low_pass_filter(shape, d_s=0.25, d_t=0.25):
+    """
+    Compute the ideal low pass filter mask.
+
+    Args:
+        shape: shape of the filter (volume)
+        d_s: normalized stop frequency for spatial dimensions (0.0-1.0)
+        d_t: normalized stop frequency for temporal dimension (0.0-1.0)
+    """
+    T, H, W = shape[-4], shape[-2], shape[-1]
+    mask = torch.zeros(shape)
+    if d_s==0 or d_t==0:
+        return mask
+    for t in range(T):
+        for h in range(H):
+            for w in range(W):
+                d_square = (((d_s/d_t)*(2*t/T-1))**2 + (2*h/H-1)**2 + (2*w/W-1)**2)
+                mask[t, ...,h,w] =  1 if d_square <= d_s*2 else 0
+    return mask
+
+
+def box_low_pass_filter(shape, d_s=0.25, d_t=0.25):
+    """
+    Compute the ideal low pass filter mask (approximated version).
+
+    Args:
+        shape: shape of the filter (volume)
+        d_s: normalized stop frequency for spatial dimensions (0.0-1.0)
+        d_t: normalized stop frequency for temporal dimension (0.0-1.0)
+    """
+    T, H, W = shape[-4], shape[-2], shape[-1]
+    mask = torch.zeros(shape)
+    if d_s==0 or d_t==0:
+        return mask
+
+    threshold_s = round(int(H // 2) * d_s)
+    threshold_t = round(T // 2 * d_t)
+
+    cframe, crow, ccol = T // 2, H // 2, W //2
+    mask[cframe - threshold_t:cframe + threshold_t, ..., crow - threshold_s:crow + threshold_s, ccol - threshold_s:ccol + threshold_s] = 1.0
+
+    return mask
+

custom_nodes/ComfyUI-AnimateDiff-Evolved/animatediff/logger.py

@@ -0,0 +1,36 @@
+import copy
+import logging
+import sys
+
+
+class ColoredFormatter(logging.Formatter):
+    COLORS = {
+        "DEBUG": "\033[0;36m",  # CYAN
+        "INFO": "\033[0;32m",  # GREEN
+        "WARNING": "\033[0;33m",  # YELLOW
+        "ERROR": "\033[0;31m",  # RED
+        "CRITICAL": "\033[0;37;41m",  # WHITE ON RED
+        "RESET": "\033[0m",  # RESET COLOR
+    }
+
+    def format(self, record):
+        colored_record = copy.copy(record)
+        levelname = colored_record.levelname
+        seq = self.COLORS.get(levelname, self.COLORS["RESET"])
+        colored_record.levelname = f"{seq}{levelname}{self.COLORS['RESET']}"
+        return super().format(colored_record)
+
+
+# Create a new logger
+logger = logging.getLogger("AnimateDiffEvo")
+logger.propagate = False
+
+# Add handler if we don't have one.
+if not logger.handlers:
+    handler = logging.StreamHandler(sys.stdout)
+    handler.setFormatter(ColoredFormatter("[%(name)s] - %(levelname)s - %(message)s"))
+    logger.addHandler(handler)
+
+# Configure logger
+loglevel = logging.INFO
+logger.setLevel(loglevel)

custom_nodes/ComfyUI-AnimateDiff-Evolved/animatediff/model_injection.py

@@ -0,0 +1,581 @@
+import copy
+from typing import Union
+
+from einops import rearrange
+from torch import Tensor
+import torch.nn.functional as F
+import torch
+
+import comfy.model_management
+import comfy.utils
+from comfy.model_patcher import ModelPatcher
+from comfy.model_base import BaseModel
+
+from .ad_settings import AnimateDiffSettings
+from .context import ContextOptions, ContextOptions, ContextOptionsGroup
+from .motion_module_ad import AnimateDiffModel, AnimateDiffFormat, has_mid_block, normalize_ad_state_dict
+from .logger import logger
+from .utils_motion import ADKeyframe, ADKeyframeGroup, MotionCompatibilityError, get_combined_multival, normalize_min_max
+from .motion_lora import MotionLoraInfo, MotionLoraList
+from .utils_model import get_motion_lora_path, get_motion_model_path, get_sd_model_type
+from .sample_settings import SampleSettings, SeedNoiseGeneration
+
+
+# some motion_model casts here might fail if model becomes metatensor or is not castable;
+# should not really matter if it fails, so ignore raised Exceptions
+class ModelPatcherAndInjector(ModelPatcher):
+    def __init__(self, m: ModelPatcher):
+        # replicate ModelPatcher.clone() to initialize ModelPatcherAndInjector
+        super().__init__(m.model, m.load_device, m.offload_device, m.size, m.current_device, weight_inplace_update=m.weight_inplace_update)
+        self.patches = {}
+        for k in m.patches:
+            self.patches[k] = m.patches[k][:]
+
+        self.object_patches = m.object_patches.copy()
+        self.model_options = copy.deepcopy(m.model_options)
+        self.model_keys = m.model_keys
+
+        # injection stuff
+        self.motion_injection_params: InjectionParams = None
+        self.sample_settings: SampleSettings = SampleSettings()
+        self.motion_models: MotionModelGroup = None
+    
+    def model_patches_to(self, device):
+        super().model_patches_to(device)
+        if self.motion_models is not None:
+            for motion_model in self.motion_models.models:
+                try:
+                    motion_model.model.to(device)
+                except Exception:
+                    pass
+
+    def patch_model(self, device_to=None):
+        # first, perform model patching
+        patched_model = super().patch_model(device_to)
+        # finally, perform motion model injection
+        self.inject_model(device_to=device_to)
+        return patched_model
+
+    def unpatch_model(self, device_to=None):
+        # first, eject motion model from unet
+        self.eject_model(device_to=device_to)
+        # finally, do normal model unpatching
+        return super().unpatch_model(device_to)
+
+    def inject_model(self, device_to=None):
+        if self.motion_models is not None:
+            for motion_model in self.motion_models.models:
+                motion_model.model.inject(self)
+                try:
+                    motion_model.model.to(device_to)
+                except Exception:
+                    pass
+
+    def eject_model(self, device_to=None):
+        if self.motion_models is not None:
+            for motion_model in self.motion_models.models:
+                motion_model.model.eject(self)
+                try:
+                    motion_model.model.to(device_to)
+                except Exception:
+                    pass
+
+    def clone(self):
+        cloned = ModelPatcherAndInjector(self)
+        cloned.motion_models = self.motion_models.clone() if self.motion_models else self.motion_models
+        cloned.sample_settings = self.sample_settings
+        cloned.motion_injection_params = self.motion_injection_params.clone() if self.motion_injection_params else self.motion_injection_params
+        return cloned
+
+
+class MotionModelPatcher(ModelPatcher):
+    # Mostly here so that type hints work in IDEs
+    def __init__(self, *args, **kwargs):
+        super().__init__(*args, **kwargs)
+        self.model: AnimateDiffModel = self.model
+        self.timestep_percent_range = (0.0, 1.0)
+        self.timestep_range: tuple[float, float] = None
+        self.keyframes: ADKeyframeGroup = ADKeyframeGroup()
+
+        self.scale_multival = None
+        self.effect_multival = None
+        # temporary variables
+        self.current_used_steps = 0
+        self.current_keyframe: ADKeyframe = None
+        self.current_index = -1
+        self.current_scale: Union[float, Tensor] = None
+        self.current_effect: Union[float, Tensor] = None
+        self.combined_scale: Union[float, Tensor] = None
+        self.combined_effect: Union[float, Tensor] = None
+        self.was_within_range = False
+
+    def patch_model(self, *args, **kwargs):
+        # patch as normal, but prepare_weights so that lowvram meta device works properly
+        patched_model = super().patch_model(*args, **kwargs)
+        self.prepare_weights()
+        return patched_model
+
+    def prepare_weights(self):
+        # in case lowvram is active and meta device is used, need to convert weights
+        # otherwise, will get exceptions thrown related to meta device
+        # TODO: with new comfy lowvram system, this is unnecessary
+        state_dict = self.model.state_dict()
+        for key in state_dict:
+            weight = comfy.model_management.resolve_lowvram_weight(state_dict[key], self.model, key)
+            try:
+                comfy.utils.set_attr(self.model, key, weight)
+            except Exception:
+                pass
+
+    def pre_run(self, model: ModelPatcherAndInjector):
+        self.cleanup()
+        self.model.reset()
+        # just in case, prepare_weights before every run
+        self.prepare_weights()
+        self.model.set_scale(self.scale_multival)
+        self.model.set_effect(self.effect_multival)
+
+    def initialize_timesteps(self, model: BaseModel):
+        self.timestep_range = (model.model_sampling.percent_to_sigma(self.timestep_percent_range[0]),
+                               model.model_sampling.percent_to_sigma(self.timestep_percent_range[1]))
+        if self.keyframes is not None:
+            for keyframe in self.keyframes.keyframes:
+                keyframe.start_t = model.model_sampling.percent_to_sigma(keyframe.start_percent)
+
+    def prepare_current_keyframe(self, t: Tensor):
+        curr_t: float = t[0]
+        prev_index = self.current_index
+        # if met guaranteed steps, look for next keyframe in case need to switch
+        if self.current_keyframe is None or self.current_used_steps >= self.current_keyframe.guarantee_steps:
+            # if has next index, loop through and see if need to switch
+            if self.keyframes.has_index(self.current_index+1):
+                for i in range(self.current_index+1, len(self.keyframes)):
+                    eval_kf = self.keyframes[i]
+                    # check if start_t is greater or equal to curr_t
+                    # NOTE: t is in terms of sigmas, not percent, so bigger number = earlier step in sampling
+                    if eval_kf.start_t >= curr_t:
+                        self.current_index = i
+                        self.current_keyframe = eval_kf
+                        self.current_used_steps = 0
+                        # keep track of scale and effect multivals, accounting for inherit_missing
+                        if self.current_keyframe.has_scale():
+                            self.current_scale = self.current_keyframe.scale_multival
+                        elif not self.current_keyframe.inherit_missing:
+                            self.current_scale = None
+                        if self.current_keyframe.has_effect():
+                            self.current_effect = self.current_keyframe.effect_multival
+                        elif not self.current_keyframe.inherit_missing:
+                            self.current_effect = None
+                        # if guarantee_steps greater than zero, stop searching for other keyframes
+                        if self.current_keyframe.guarantee_steps > 0:
+                            break
+                    # if eval_kf is outside the percent range, stop looking further
+                    else:
+                        break
+        # if index changed, apply new combined values
+        if prev_index != self.current_index:
+            # combine model's scale and effect with keyframe's scale and effect
+            self.combined_scale = get_combined_multival(self.scale_multival, self.current_scale)
+            self.combined_effect = get_combined_multival(self.effect_multival, self.current_effect)
+            # apply scale and effect
+            self.model.set_scale(self.combined_scale)
+            self.model.set_effect(self.combined_effect)
+        # apply effect - if not within range, set effect to 0, effectively turning model off
+        if curr_t > self.timestep_range[0] or curr_t < self.timestep_range[1]:
+            self.model.set_effect(0.0)
+            self.was_within_range = False
+        else:
+            # if was not in range last step, apply effect to toggle AD status
+            if not self.was_within_range:
+                self.model.set_effect(self.combined_effect)
+                self.was_within_range = True
+        # update steps current keyframe is used
+        self.current_used_steps += 1
+
+    def cleanup(self):
+        if self.model is not None:
+            self.model.cleanup()
+        self.current_used_steps = 0
+        self.current_keyframe = None
+        self.current_index = -1
+        self.current_scale = None
+        self.current_effect = None
+        self.combined_scale = None
+        self.combined_effect = None
+        self.was_within_range = False
+
+    def clone(self):
+        # normal ModelPatcher clone actions
+        n = MotionModelPatcher(self.model, self.load_device, self.offload_device, self.size, self.current_device, weight_inplace_update=self.weight_inplace_update)
+        n.patches = {}
+        for k in self.patches:
+            n.patches[k] = self.patches[k][:]
+
+        n.object_patches = self.object_patches.copy()
+        n.model_options = copy.deepcopy(self.model_options)
+        n.model_keys = self.model_keys
+        # extra cloned params
+        n.timestep_percent_range = self.timestep_percent_range
+        n.timestep_range = self.timestep_range
+        n.keyframes = self.keyframes.clone()
+        n.scale_multival = self.scale_multival
+        n.effect_multival = self.effect_multival
+        return n
+
+
+class MotionModelGroup:
+    def __init__(self, init_motion_model: MotionModelPatcher=None):
+        self.models: list[MotionModelPatcher] = []
+        if init_motion_model is not None:
+            self.add(init_motion_model)
+
+    def add(self, mm: MotionModelPatcher):
+        # add to end of list
+        self.models.append(mm)
+
+    def add_to_start(self, mm: MotionModelPatcher):
+        self.models.insert(0, mm)
+
+    def __getitem__(self, index) -> MotionModelPatcher:
+        return self.models[index]
+    
+    def is_empty(self) -> bool:
+        return len(self.models) == 0
+    
+    def clone(self) -> 'MotionModelGroup':
+        cloned = MotionModelGroup()
+        for mm in self.models:
+            cloned.add(mm)
+        return cloned
+    
+    def set_sub_idxs(self, sub_idxs: list[int]):
+        for motion_model in self.models:
+            motion_model.model.set_sub_idxs(sub_idxs=sub_idxs)
+    
+    def set_view_options(self, view_options: ContextOptions):
+        for motion_model in self.models:
+            motion_model.model.set_view_options(view_options)
+
+    def set_video_length(self, video_length: int, full_length: int):
+        for motion_model in self.models:
+            motion_model.model.set_video_length(video_length=video_length, full_length=full_length)
+    
+    def initialize_timesteps(self, model: BaseModel):
+        for motion_model in self.models:
+            motion_model.initialize_timesteps(model)
+
+    def pre_run(self, model: ModelPatcherAndInjector):
+        for motion_model in self.models:
+            motion_model.pre_run(model)
+    
+    def prepare_current_keyframe(self, t: Tensor):
+        for motion_model in self.models:
+            motion_model.prepare_current_keyframe(t=t)
+
+    def get_name_string(self, show_version=False):
+        identifiers = []
+        for motion_model in self.models:
+            id = motion_model.model.mm_info.mm_name
+            if show_version:
+                id += f":{motion_model.model.mm_info.mm_version}"
+            identifiers.append(id)
+        return ", ".join(identifiers)
+
+
+def get_vanilla_model_patcher(m: ModelPatcher) -> ModelPatcher:
+    model = ModelPatcher(m.model, m.load_device, m.offload_device, m.size, m.current_device, weight_inplace_update=m.weight_inplace_update)
+    model.patches = {}
+    for k in m.patches:
+        model.patches[k] = m.patches[k][:]
+
+    model.object_patches = m.object_patches.copy()
+    model.model_options = copy.deepcopy(m.model_options)
+    model.model_keys = m.model_keys
+    return model
+
+# adapted from https://github.com/guoyww/AnimateDiff/blob/main/animatediff/utils/convert_lora_safetensor_to_diffusers.py
+# Example LoRA keys:
+#   down_blocks.0.motion_modules.0.temporal_transformer.transformer_blocks.0.attention_blocks.0.processor.to_q_lora.down.weight
+#   down_blocks.0.motion_modules.0.temporal_transformer.transformer_blocks.0.attention_blocks.0.processor.to_q_lora.up.weight
+#
+# Example model keys: 
+#   down_blocks.0.motion_modules.0.temporal_transformer.transformer_blocks.0.attention_blocks.0.to_q.weight
+#
+def load_motion_lora_as_patches(motion_model: MotionModelPatcher, lora: MotionLoraInfo) -> None:
+    def get_version(has_midblock: bool):
+        return "v2" if has_midblock else "v1"
+
+    lora_path = get_motion_lora_path(lora.name)
+    logger.info(f"Loading motion LoRA {lora.name}")
+    state_dict = comfy.utils.load_torch_file(lora_path)
+
+    # remove all non-temporal keys (in case model has extra stuff in it)
+    for key in list(state_dict.keys()):
+        if "temporal" not in key:
+            del state_dict[key]
+    if len(state_dict) == 0:
+        raise ValueError(f"'{lora.name}' contains no temporal keys; it is not a valid motion LoRA!")
+
+    model_has_midblock = motion_model.model.mid_block != None
+    lora_has_midblock = has_mid_block(state_dict)
+    logger.info(f"Applying a {get_version(lora_has_midblock)} LoRA ({lora.name}) to a { motion_model.model.mm_info.mm_version} motion model.")
+
+    patches = {}
+    # convert lora state dict to one that matches motion_module keys and tensors
+    for key in state_dict:
+        # if motion_module doesn't have a midblock, skip mid_block entries
+        if not model_has_midblock:
+            if "mid_block" in key: continue
+        # only process lora down key (we will process up at the same time as down)
+        if "up." in key: continue
+
+        # get up key version of down key
+        up_key = key.replace(".down.", ".up.")
+
+        # adapt key to match motion_module key format - remove 'processor.', '_lora', 'down.', and 'up.'
+        model_key = key.replace("processor.", "").replace("_lora", "").replace("down.", "").replace("up.", "")
+        # motion_module keys have a '0.' after all 'to_out.' weight keys
+        model_key = model_key.replace("to_out.", "to_out.0.")
+        
+        weight_down = state_dict[key]
+        weight_up = state_dict[up_key]
+        # actual weights obtained by matrix multiplication of up and down weights
+        # save as a tuple, so that (Motion)ModelPatcher's calculate_weight function detects len==1, applying it correctly
+        patches[model_key] = (torch.mm(weight_up, weight_down),)
+    del state_dict
+    # add patches to motion ModelPatcher
+    motion_model.add_patches(patches=patches, strength_patch=lora.strength)
+
+
+def load_motion_module_gen1(model_name: str, model: ModelPatcher, motion_lora: MotionLoraList = None, motion_model_settings: AnimateDiffSettings = None) -> MotionModelPatcher:
+    model_path = get_motion_model_path(model_name)
+    logger.info(f"Loading motion module {model_name}")
+    mm_state_dict = comfy.utils.load_torch_file(model_path, safe_load=True)
+    # TODO: check for empty state dict?
+    # get normalized state_dict and motion model info
+    mm_state_dict, mm_info = normalize_ad_state_dict(mm_state_dict=mm_state_dict, mm_name=model_name)
+    # check that motion model is compatible with sd model
+    model_sd_type = get_sd_model_type(model)
+    if model_sd_type != mm_info.sd_type:
+        raise MotionCompatibilityError(f"Motion module '{mm_info.mm_name}' is intended for {mm_info.sd_type} models, " \
+                                       + f"but the provided model is type {model_sd_type}.")
+    # apply motion model settings
+    mm_state_dict = apply_mm_settings(model_dict=mm_state_dict, mm_settings=motion_model_settings)
+    # initialize AnimateDiffModelWrapper
+    ad_wrapper = AnimateDiffModel(mm_state_dict=mm_state_dict, mm_info=mm_info)
+    ad_wrapper.to(model.model_dtype())
+    ad_wrapper.to(model.offload_device)
+    is_animatelcm = mm_info.mm_format==AnimateDiffFormat.ANIMATELCM
+    load_result = ad_wrapper.load_state_dict(mm_state_dict, strict=not is_animatelcm)
+    # TODO: report load_result of motion_module loading?
+    # wrap motion_module into a ModelPatcher, to allow motion lora patches
+    motion_model = MotionModelPatcher(model=ad_wrapper, load_device=model.load_device, offload_device=model.offload_device)
+    # load motion_lora, if present
+    if motion_lora is not None:
+        for lora in motion_lora.loras:
+            load_motion_lora_as_patches(motion_model, lora)
+    return motion_model
+
+
+def load_motion_module_gen2(model_name: str, motion_model_settings: AnimateDiffSettings = None) -> MotionModelPatcher:
+    model_path = get_motion_model_path(model_name)
+    logger.info(f"Loading motion module {model_name} via Gen2")
+    mm_state_dict = comfy.utils.load_torch_file(model_path, safe_load=True)
+    # TODO: check for empty state dict?
+    # get normalized state_dict and motion model info (converts alternate AD models like HotshotXL into AD keys)
+    mm_state_dict, mm_info = normalize_ad_state_dict(mm_state_dict=mm_state_dict, mm_name=model_name)
+    # apply motion model settings
+    mm_state_dict = apply_mm_settings(model_dict=mm_state_dict, mm_settings=motion_model_settings)
+    # initialize AnimateDiffModelWrapper
+    ad_wrapper = AnimateDiffModel(mm_state_dict=mm_state_dict, mm_info=mm_info)
+    ad_wrapper.to(comfy.model_management.unet_dtype())
+    ad_wrapper.to(comfy.model_management.unet_offload_device())
+    is_animatelcm = mm_info.mm_format==AnimateDiffFormat.ANIMATELCM
+    load_result = ad_wrapper.load_state_dict(mm_state_dict, strict=not is_animatelcm)
+    # TODO: manually check load_results for AnimateLCM models
+    if is_animatelcm:
+        pass
+    # TODO: report load_result of motion_module loading?
+    # wrap motion_module into a ModelPatcher, to allow motion lora patches
+    motion_model = MotionModelPatcher(model=ad_wrapper, load_device=comfy.model_management.get_torch_device(),
+                                      offload_device=comfy.model_management.unet_offload_device())
+    return motion_model
+
+
+def create_fresh_motion_module(motion_model: MotionModelPatcher) -> MotionModelPatcher:
+    ad_wrapper = AnimateDiffModel(mm_state_dict=motion_model.model.state_dict(), mm_info=motion_model.model.mm_info)
+    ad_wrapper.to(comfy.model_management.unet_dtype())
+    ad_wrapper.to(comfy.model_management.unet_offload_device())
+    ad_wrapper.load_state_dict(motion_model.model.state_dict())
+    return MotionModelPatcher(model=ad_wrapper, load_device=comfy.model_management.get_torch_device(),
+                                      offload_device=comfy.model_management.unet_offload_device())
+
+
+def validate_model_compatibility_gen2(model: ModelPatcher, motion_model: MotionModelPatcher):
+    # check that motion model is compatible with sd model
+    model_sd_type = get_sd_model_type(model)
+    mm_info = motion_model.model.mm_info
+    if model_sd_type != mm_info.sd_type:
+        raise MotionCompatibilityError(f"Motion module '{mm_info.mm_name}' is intended for {mm_info.sd_type} models, " \
+                                       + f"but the provided model is type {model_sd_type}.")
+
+
+def interpolate_pe_to_length(model_dict: dict[str, Tensor], key: str, new_length: int):
+    pe_shape = model_dict[key].shape
+    temp_pe = rearrange(model_dict[key], "(t b) f d -> t b f d", t=1)
+    temp_pe = F.interpolate(temp_pe, size=(new_length, pe_shape[-1]), mode="bilinear")
+    temp_pe = rearrange(temp_pe, "t b f d -> (t b) f d", t=1)
+    model_dict[key] = temp_pe
+    del temp_pe
+
+
+def interpolate_pe_to_length_diffs(model_dict: dict[str, Tensor], key: str, new_length: int):
+    # TODO: fill out and try out
+    pe_shape = model_dict[key].shape
+    temp_pe = rearrange(model_dict[key], "(t b) f d -> t b f d", t=1)
+    temp_pe = F.interpolate(temp_pe, size=(new_length, pe_shape[-1]), mode="bilinear")
+    temp_pe = rearrange(temp_pe, "t b f d -> (t b) f d", t=1)
+    model_dict[key] = temp_pe
+    del temp_pe
+
+
+def interpolate_pe_to_length_pingpong(model_dict: dict[str, Tensor], key: str, new_length: int):
+    if model_dict[key].shape[1] < new_length:
+        temp_pe = model_dict[key]
+        flipped_temp_pe = torch.flip(temp_pe[:, 1:-1, :], [1])
+        use_flipped = True
+        preview_pe = None
+        while model_dict[key].shape[1] < new_length:
+            preview_pe = model_dict[key]
+            model_dict[key] = torch.cat([model_dict[key], flipped_temp_pe if use_flipped else temp_pe], dim=1)
+            use_flipped = not use_flipped
+        del temp_pe
+        del flipped_temp_pe
+        del preview_pe
+    model_dict[key] = model_dict[key][:, :new_length]
+
+
+def freeze_mask_of_pe(model_dict: dict[str, Tensor], key: str):
+    pe_portion = model_dict[key].shape[2] // 64
+    first_pe = model_dict[key][:,:1,:]
+    model_dict[key][:,:,pe_portion:] = first_pe[:,:,pe_portion:]
+    del first_pe
+
+
+def freeze_mask_of_attn(model_dict: dict[str, Tensor], key: str):
+    attn_portion = model_dict[key].shape[0] // 2
+    model_dict[key][:attn_portion,:attn_portion] *= 1.5
+
+
+def apply_mm_settings(model_dict: dict[str, Tensor], mm_settings: AnimateDiffSettings) -> dict[str, Tensor]:
+    if mm_settings is None:
+        return model_dict
+    if not mm_settings.has_anything_to_apply():
+        return model_dict
+    # first, handle PE Adjustments
+    for adjust in mm_settings.adjust_pe.adjusts:
+        if adjust.has_anything_to_apply():
+            already_printed = False
+            for key in model_dict:
+                if "attention_blocks" in key and "pos_encoder" in key:
+                    # apply simple motion pe stretch, if needed
+                    if adjust.has_motion_pe_stretch():
+                        original_length = model_dict[key].shape[1]
+                        new_pe_length = original_length + adjust.motion_pe_stretch
+                        interpolate_pe_to_length(model_dict, key, new_length=new_pe_length)
+                        if adjust.print_adjustment and not already_printed:
+                            logger.info(f"[Adjust PE]: PE Stretch from {original_length} to {new_pe_length}.")
+                    # apply pe_idx_offset, if needed
+                    if adjust.has_initial_pe_idx_offset():
+                        original_length = model_dict[key].shape[1]
+                        model_dict[key] = model_dict[key][:, adjust.initial_pe_idx_offset:]
+                        if adjust.print_adjustment and not already_printed:
+                            logger.info(f"[Adjust PE]: Offsetting PEs by {adjust.initial_pe_idx_offset}; PE length to shortens from {original_length} to {model_dict[key].shape[1]}.")
+                    # apply has_cap_initial_pe_length, if needed
+                    if adjust.has_cap_initial_pe_length():
+                        original_length = model_dict[key].shape[1]
+                        model_dict[key] = model_dict[key][:, :adjust.cap_initial_pe_length]
+                        if adjust.print_adjustment and not already_printed:
+                            logger.info(f"[Adjust PE]: Capping PEs (initial) from {original_length} to {model_dict[key].shape[1]}.")
+                    # apply interpolate_pe_to_length, if needed
+                    if adjust.has_interpolate_pe_to_length():
+                        original_length = model_dict[key].shape[1]
+                        interpolate_pe_to_length(model_dict, key, new_length=adjust.interpolate_pe_to_length)
+                        if adjust.print_adjustment and not already_printed:
+                            logger.info(f"[Adjust PE]: Interpolating PE length from {original_length} to {model_dict[key].shape[1]}.")
+                    # apply final_pe_idx_offset, if needed
+                    if adjust.has_final_pe_idx_offset():
+                        original_length = model_dict[key].shape[1]
+                        model_dict[key] = model_dict[key][:, adjust.final_pe_idx_offset:]
+                        if adjust.print_adjustment and not already_printed:
+                            logger.info(f"[Adjust PE]: Capping PEs (final) from {original_length} to {model_dict[key].shape[1]}.")
+                    already_printed = True
+    # finally, apply any weight changes
+    for key in model_dict:
+        if "attention_blocks" in key:
+            if "pos_encoder" in key and mm_settings.adjust_pe.has_anything_to_apply():
+                # apply pe_strength, if needed
+                if mm_settings.has_pe_strength():
+                    model_dict[key] *= mm_settings.pe_strength
+            else:
+                # apply attn_strenth, if needed
+                if mm_settings.has_attn_strength():
+                    model_dict[key] *= mm_settings.attn_strength
+                # apply specific attn_strengths, if needed
+                if mm_settings.has_any_attn_sub_strength():
+                    if "to_q" in key and mm_settings.has_attn_q_strength():
+                        model_dict[key] *= mm_settings.attn_q_strength
+                    elif "to_k" in key and mm_settings.has_attn_k_strength():
+                        model_dict[key] *= mm_settings.attn_k_strength
+                    elif "to_v" in key and mm_settings.has_attn_v_strength():
+                        model_dict[key] *= mm_settings.attn_v_strength
+                    elif "to_out" in key:
+                        if key.strip().endswith("weight") and mm_settings.has_attn_out_weight_strength():
+                            model_dict[key] *= mm_settings.attn_out_weight_strength
+                        elif key.strip().endswith("bias") and mm_settings.has_attn_out_bias_strength():
+                            model_dict[key] *= mm_settings.attn_out_bias_strength
+        # apply other strength, if needed
+        elif mm_settings.has_other_strength():
+            model_dict[key] *= mm_settings.other_strength
+    return model_dict
+
+
+class InjectionParams:
+    def __init__(self, unlimited_area_hack: bool=False, apply_mm_groupnorm_hack: bool=True, model_name: str="",
+                 apply_v2_properly: bool=True) -> None:
+        self.full_length = None
+        self.unlimited_area_hack = unlimited_area_hack
+        self.apply_mm_groupnorm_hack = apply_mm_groupnorm_hack
+        self.model_name = model_name
+        self.apply_v2_properly = apply_v2_properly
+        self.context_options: ContextOptionsGroup = ContextOptionsGroup.default()
+        self.motion_model_settings = AnimateDiffSettings() # Gen1
+        self.sub_idxs = None  # value should NOT be included in clone, so it will auto reset
+    
+    def set_noise_extra_args(self, noise_extra_args: dict):
+        noise_extra_args["context_options"] = self.context_options.clone()
+
+    def set_context(self, context_options: ContextOptionsGroup):
+        self.context_options = context_options.clone() if context_options else ContextOptionsGroup.default()
+    
+    def is_using_sliding_context(self) -> bool:
+        return self.context_options.context_length is not None
+
+    def set_motion_model_settings(self, motion_model_settings: AnimateDiffSettings): # Gen1
+        if motion_model_settings is None:
+            self.motion_model_settings = AnimateDiffSettings()
+        else:
+            self.motion_model_settings = motion_model_settings
+
+    def reset_context(self):
+        self.context_options = ContextOptionsGroup.default()
+    
+    def clone(self) -> 'InjectionParams':
+        new_params = InjectionParams(
+            self.unlimited_area_hack, self.apply_mm_groupnorm_hack,
+            self.model_name, apply_v2_properly=self.apply_v2_properly,
+            )
+        new_params.full_length = self.full_length
+        new_params.set_context(self.context_options)
+        new_params.set_motion_model_settings(self.motion_model_settings) # Gen1
+        return new_params

custom_nodes/ComfyUI-AnimateDiff-Evolved/animatediff/motion_lora.py

@@ -0,0 +1,25 @@
+class MotionLoraInfo:
+    def __init__(self, name: str, strength: float = 1.0, hash: str=""):
+        self.name = name
+        self.strength = strength
+        self.hash = ""
+    
+    def set_hash(self, hash: str):
+        self.hash = hash
+    
+    def clone(self):
+        return MotionLoraInfo(self.name, self.strength, self.hash)
+
+
+class MotionLoraList:
+    def __init__(self):
+        self.loras: list[MotionLoraInfo] = []
+    
+    def add_lora(self, lora: MotionLoraInfo):
+        self.loras.append(lora)
+    
+    def clone(self):
+        new_list = MotionLoraList()
+        for lora in self.loras:
+            new_list.add_lora(lora.clone())
+        return new_list

custom_nodes/ComfyUI-AnimateDiff-Evolved/animatediff/motion_module_ad.py

@@ -0,0 +1,971 @@
+import math
+from typing import Iterable, Tuple, Union
+import re
+
+import torch
+from einops import rearrange, repeat
+from torch import Tensor, nn
+
+from comfy.ldm.modules.attention import FeedForward, SpatialTransformer
+from comfy.model_patcher import ModelPatcher
+from comfy.ldm.modules.diffusionmodules import openaimodel
+from comfy.ldm.modules.diffusionmodules.openaimodel import SpatialTransformer
+from comfy.controlnet import broadcast_image_to
+from comfy.utils import repeat_to_batch_size
+import comfy.ops
+import comfy.model_management
+
+from .context import ContextFuseMethod, ContextOptions, get_context_weights, get_context_windows
+from .utils_motion import CrossAttentionMM, MotionCompatibilityError, extend_to_batch_size, prepare_mask_batch
+from .utils_model import BetaSchedules, ModelTypeSD
+from .logger import logger
+
+
+def zero_module(module):
+    # Zero out the parameters of a module and return it.
+    for p in module.parameters():
+        p.detach().zero_()
+    return module
+
+
+class AnimateDiffFormat:
+    ANIMATEDIFF = "AnimateDiff"
+    HOTSHOTXL = "HotshotXL"
+    ANIMATELCM = "AnimateLCM"
+
+
+class AnimateDiffVersion:
+    V1 = "v1"
+    V2 = "v2"
+    V3 = "v3"
+
+
+class AnimateDiffInfo:
+    def __init__(self, sd_type: str, mm_format: str, mm_version: str, mm_name: str):
+        self.sd_type = sd_type
+        self.mm_format = mm_format
+        self.mm_version = mm_version
+        self.mm_name = mm_name
+    
+    def get_string(self):
+        return f"{self.mm_name}:{self.mm_version}:{self.mm_format}:{self.sd_type}"
+
+
+def is_hotshotxl(mm_state_dict: dict[str, Tensor]) -> bool:
+    # use pos_encoder naming to determine if hotshotxl model
+    for key in mm_state_dict.keys():
+        if key.endswith("pos_encoder.positional_encoding"):
+            return True
+    return False
+
+
+def is_animatelcm(mm_state_dict: dict[str, Tensor]) -> bool:
+    # use lack of ANY pos_encoder keys to determine if animatelcm model
+    for key in mm_state_dict.keys():
+        if "pos_encoder" in key:
+            return False
+    return True
+
+
+def get_down_block_max(mm_state_dict: dict[str, Tensor]) -> int:
+    # keep track of biggest down_block count in module
+    biggest_block = 0
+    for key in mm_state_dict.keys():
+        if "down_blocks" in key:
+            try:
+                block_int = key.split(".")[1]
+                block_num = int(block_int)
+                if block_num > biggest_block:
+                    biggest_block = block_num
+            except ValueError:
+                pass
+    return biggest_block
+
+
+def has_mid_block(mm_state_dict: dict[str, Tensor]):
+    # check if keys contain mid_block
+    for key in mm_state_dict.keys():
+        if key.startswith("mid_block."):
+            return True
+    return False
+
+
+def get_position_encoding_max_len(mm_state_dict: dict[str, Tensor], mm_name: str, mm_format: str) -> Union[int, None]:
+    # use pos_encoder.pe entries to determine max length - [1, {max_length}, {320|640|1280}]
+    for key in mm_state_dict.keys():
+        if key.endswith("pos_encoder.pe"):
+            return mm_state_dict[key].size(1) # get middle dim
+    # AnimateLCM models should have no pos_encoder entries, and assumed to be 64
+    if mm_format == AnimateDiffFormat.ANIMATELCM:
+        return 64
+    raise MotionCompatibilityError(f"No pos_encoder.pe found in mm_state_dict - {mm_name} is not a valid AnimateDiff motion module!")
+
+
+_regex_hotshotxl_module_num = re.compile(r'temporal_attentions\.(\d+)\.')
+def find_hotshot_module_num(key: str) -> Union[int, None]:
+    found = _regex_hotshotxl_module_num.search(key)
+    if found:
+        return int(found.group(1))
+    return None
+
+
+def normalize_ad_state_dict(mm_state_dict: dict[str, Tensor], mm_name: str) -> Tuple[dict[str, Tensor], AnimateDiffInfo]:
+    # from pathlib import Path
+    # with open(Path(__file__).parent.parent.parent / f"keys_{mm_name}.txt", "w") as afile:
+    #     for key, value in mm_state_dict.items():
+    #         afile.write(f"{key}:\t{value.shape}\n")
+    
+    # remove all non-temporal keys (in case model has extra stuff in it)
+    for key in list(mm_state_dict.keys()):
+        if "temporal" not in key:
+            del mm_state_dict[key]
+    # determine what SD model the motion module is intended for
+    sd_type: str = None
+    down_block_max = get_down_block_max(mm_state_dict)
+    if down_block_max == 3:
+        sd_type = ModelTypeSD.SD1_5
+    elif down_block_max == 2:
+        sd_type = ModelTypeSD.SDXL
+    else:
+        raise ValueError(f"'{mm_name}' is not a valid SD1.5 nor SDXL motion module - contained {down_block_max} downblocks.")
+    # determine the model's format
+    mm_format = AnimateDiffFormat.ANIMATEDIFF
+    if is_hotshotxl(mm_state_dict):
+        mm_format = AnimateDiffFormat.HOTSHOTXL
+    if is_animatelcm(mm_state_dict):
+        mm_format = AnimateDiffFormat.ANIMATELCM
+    # determine the model's version
+    mm_version = AnimateDiffVersion.V1
+    if has_mid_block(mm_state_dict):
+        mm_version = AnimateDiffVersion.V2
+    elif sd_type==ModelTypeSD.SD1_5 and get_position_encoding_max_len(mm_state_dict, mm_name, mm_format)==32:
+        mm_version = AnimateDiffVersion.V3
+    info = AnimateDiffInfo(sd_type=sd_type, mm_format=mm_format, mm_version=mm_version, mm_name=mm_name)
+    # convert to AnimateDiff format, if needed
+    if mm_format == AnimateDiffFormat.HOTSHOTXL:
+        # HotshotXL is AD-based architecture applied to SDXL instead of SD1.5
+        # By renaming the keys, no code needs to be adapted at all
+        #
+        # reformat temporal_attentions:
+        # HSXL: temporal_attentions.#.
+        #   AD: motion_modules.#.temporal_transformer.
+        # HSXL: pos_encoder.positional_encoding
+        #   AD: pos_encoder.pe
+        for key in list(mm_state_dict.keys()):
+            module_num = find_hotshot_module_num(key)
+            if module_num is not None:
+                new_key = key.replace(f"temporal_attentions.{module_num}",
+                                      f"motion_modules.{module_num}.temporal_transformer", 1)
+                new_key = new_key.replace("pos_encoder.positional_encoding", "pos_encoder.pe")
+                mm_state_dict[new_key] = mm_state_dict[key]
+                del mm_state_dict[key]
+    # return adjusted mm_state_dict and info
+    return mm_state_dict, info
+
+
+class BlockType:
+    UP = "up"
+    DOWN = "down"
+    MID = "mid"
+
+
+class AnimateDiffModel(nn.Module):
+    def __init__(self, mm_state_dict: dict[str, Tensor], mm_info: AnimateDiffInfo):
+        super().__init__()
+        self.mm_info = mm_info
+        self.down_blocks: Iterable[MotionModule] = nn.ModuleList([])
+        self.up_blocks: Iterable[MotionModule] = nn.ModuleList([])
+        self.mid_block: Union[MotionModule, None] = None
+        self.encoding_max_len = get_position_encoding_max_len(mm_state_dict, mm_info.mm_name, mm_info.mm_format)
+        self.has_position_encoding = self.encoding_max_len is not None
+        # determine ops to use (to support fp8 properly)
+        if comfy.model_management.unet_manual_cast(comfy.model_management.unet_dtype(), comfy.model_management.get_torch_device()) is None:
+            ops = comfy.ops.disable_weight_init
+        else:
+            ops = comfy.ops.manual_cast
+        # SDXL has 3 up/down blocks, SD1.5 has 4 up/down blocks
+        if mm_info.sd_type == ModelTypeSD.SDXL:
+            layer_channels = (320, 640, 1280)
+        else:
+            layer_channels = (320, 640, 1280, 1280)
+        # fill out down/up blocks and middle block, if present
+        for c in layer_channels:
+            self.down_blocks.append(MotionModule(c, temporal_position_encoding=self.has_position_encoding,
+                                                 temporal_position_encoding_max_len=self.encoding_max_len, block_type=BlockType.DOWN, ops=ops))
+        for c in reversed(layer_channels):
+            self.up_blocks.append(MotionModule(c, temporal_position_encoding=self.has_position_encoding,
+                                               temporal_position_encoding_max_len=self.encoding_max_len, block_type=BlockType.UP, ops=ops))
+        if has_mid_block(mm_state_dict):
+            self.mid_block = MotionModule(1280, temporal_position_encoding=self.has_position_encoding,
+                                          temporal_position_encoding_max_len=self.encoding_max_len, block_type=BlockType.MID, ops=ops)
+        self.AD_video_length: int = 24
+
+    def get_device_debug(self):
+        return self.down_blocks[0].motion_modules[0].temporal_transformer.proj_in.weight.device
+
+    def is_length_valid_for_encoding_max_len(self, length: int):
+        if self.encoding_max_len is None:
+            return True
+        return length <= self.encoding_max_len
+
+    def get_best_beta_schedule(self, log=False) -> str:
+        to_return = None
+        if self.mm_info.sd_type == ModelTypeSD.SD1_5:
+            if self.mm_info.mm_format == AnimateDiffFormat.ANIMATELCM:
+                to_return = BetaSchedules.LCM  # while LCM_100 is the intended schedule, I find LCM to have much less flicker
+            else:
+                to_return = BetaSchedules.SQRT_LINEAR
+        elif self.mm_info.sd_type == ModelTypeSD.SDXL:
+            if self.mm_info.mm_format == AnimateDiffFormat.HOTSHOTXL:
+                to_return = BetaSchedules.LINEAR
+            else:
+                to_return = BetaSchedules.LINEAR_ADXL
+        if to_return is not None:
+            if log: logger.info(f"[Autoselect]: '{to_return}' beta_schedule for {self.mm_info.get_string()}")
+        else:
+            to_return = BetaSchedules.USE_EXISTING
+            if log: logger.info(f"[Autoselect]: could not find beta_schedule for {self.mm_info.get_string()}, defaulting to '{to_return}'")
+        return to_return
+
+    def cleanup(self):
+        pass
+
+    def inject(self, model: ModelPatcher):
+        unet: openaimodel.UNetModel = model.model.diffusion_model
+        # inject input (down) blocks
+        # SD15 mm contains 4 downblocks, each with 2 TemporalTransformers - 8 in total
+        # SDXL mm contains 3 downblocks, each with 2 TemporalTransformers - 6 in total
+        self._inject(unet.input_blocks, self.down_blocks)
+        # inject output (up) blocks
+        # SD15 mm contains 4 upblocks, each with 3 TemporalTransformers - 12 in total
+        # SDXL mm contains 3 upblocks, each with 3 TemporalTransformers - 9 in total
+        self._inject(unet.output_blocks, self.up_blocks)
+        # inject mid block, if needed (encapsulate in list to make structure compatible)
+        if self.mid_block is not None:
+            self._inject([unet.middle_block], [self.mid_block])
+        del unet
+
+    def _inject(self, unet_blocks: nn.ModuleList, mm_blocks: nn.ModuleList):
+        # Rules for injection:
+        # For each component list in a unet block:
+        #     if SpatialTransformer exists in list, place next block after last occurrence
+        #     elif ResBlock exists in list, place next block after first occurrence
+        #     else don't place block
+        injection_count = 0
+        unet_idx = 0
+        # details about blocks passed in
+        per_block = len(mm_blocks[0].motion_modules)
+        injection_goal = len(mm_blocks) * per_block
+        # only stop injecting when modules exhausted
+        while injection_count < injection_goal:
+            # figure out which VanillaTemporalModule from mm to inject
+            mm_blk_idx, mm_vtm_idx = injection_count // per_block, injection_count % per_block
+            # figure out layout of unet block components
+            st_idx = -1 # SpatialTransformer index
+            res_idx = -1 # first ResBlock index
+            # first, figure out indeces of relevant blocks
+            for idx, component in enumerate(unet_blocks[unet_idx]):
+                if type(component) == SpatialTransformer:
+                    st_idx = idx
+                elif type(component).__name__ == "ResBlock" and res_idx < 0:
+                    res_idx = idx
+            # if SpatialTransformer exists, inject right after
+            if st_idx >= 0:
+                #logger.info(f"AD: injecting after ST({st_idx})")
+                unet_blocks[unet_idx].insert(st_idx+1, mm_blocks[mm_blk_idx].motion_modules[mm_vtm_idx])
+                injection_count += 1
+            # otherwise, if only ResBlock exists, inject right after
+            elif res_idx >= 0:
+                #logger.info(f"AD: injecting after Res({res_idx})")
+                unet_blocks[unet_idx].insert(res_idx+1, mm_blocks[mm_blk_idx].motion_modules[mm_vtm_idx])
+                injection_count += 1
+            # increment unet_idx
+            unet_idx += 1
+
+    def eject(self, model: ModelPatcher):
+        unet: openaimodel.UNetModel = model.model.diffusion_model
+        # remove from input blocks (downblocks)
+        self._eject(unet.input_blocks)
+        # remove from output blocks (upblocks)
+        self._eject(unet.output_blocks)
+        # remove from middle block (encapsulate in list to make compatible)
+        self._eject([unet.middle_block])
+        del unet
+
+    def _eject(self, unet_blocks: nn.ModuleList):
+        # eject all VanillaTemporalModule objects from all blocks
+        for block in unet_blocks:
+            idx_to_pop = []
+            for idx, component in enumerate(block):
+                if type(component) == VanillaTemporalModule:
+                    idx_to_pop.append(idx)
+            # pop in backwards order, as to not disturb what the indeces refer to
+            for idx in sorted(idx_to_pop, reverse=True):
+                block.pop(idx)
+
+    def set_video_length(self, video_length: int, full_length: int):
+        self.AD_video_length = video_length
+        for block in self.down_blocks:
+            block.set_video_length(video_length, full_length)
+        for block in self.up_blocks:
+            block.set_video_length(video_length, full_length)
+        if self.mid_block is not None:
+            self.mid_block.set_video_length(video_length, full_length)
+    
+    def set_scale(self, multival: Union[float, Tensor]):
+        if multival is None:
+            multival = 1.0
+        if type(multival) == Tensor:
+            self._set_scale_multiplier(1.0)
+            self._set_scale_mask(multival)
+        else:
+            self._set_scale_multiplier(multival)
+            self._set_scale_mask(None)
+    
+    def set_effect(self, multival: Union[float, Tensor]):
+        for block in self.down_blocks:
+            block.set_effect(multival)
+        for block in self.up_blocks:
+            block.set_effect(multival)
+        if self.mid_block is not None:
+            self.mid_block.set_effect(multival)
+
+    def set_sub_idxs(self, sub_idxs: list[int]):
+        for block in self.down_blocks:
+            block.set_sub_idxs(sub_idxs)
+        for block in self.up_blocks:
+            block.set_sub_idxs(sub_idxs)
+        if self.mid_block is not None:
+            self.mid_block.set_sub_idxs(sub_idxs)
+
+    def set_view_options(self, view_options: ContextOptions):
+        for block in self.down_blocks:
+            block.set_view_options(view_options)
+        for block in self.up_blocks:
+            block.set_view_options(view_options)
+        if self.mid_block is not None:
+            self.mid_block.set_view_options(view_options)
+
+    def reset(self):
+        self._reset_sub_idxs()
+        self._reset_scale_multiplier()
+        self._reset_temp_vars()
+
+    def _set_scale_multiplier(self, multiplier: Union[float, None]):
+        for block in self.down_blocks:
+            block.set_scale_multiplier(multiplier)
+        for block in self.up_blocks:
+            block.set_scale_multiplier(multiplier)
+        if self.mid_block is not None:
+            self.mid_block.set_scale_multiplier(multiplier)
+
+    def _set_scale_mask(self, mask: Tensor):
+        for block in self.down_blocks:
+            block.set_scale_mask(mask)
+        for block in self.up_blocks:
+            block.set_scale_mask(mask)
+        if self.mid_block is not None:
+            self.mid_block.set_scale_mask(mask)
+    
+    def _reset_temp_vars(self):
+        for block in self.down_blocks:
+            block.reset_temp_vars()
+        for block in self.up_blocks:
+            block.reset_temp_vars()
+        if self.mid_block is not None:
+            self.mid_block.reset_temp_vars()
+
+    def _reset_scale_multiplier(self):
+        self._set_scale_multiplier(None)
+
+    def _reset_sub_idxs(self):
+        self.set_sub_idxs(None)
+
+
+class MotionModule(nn.Module):
+    def __init__(self,
+            in_channels,
+            temporal_position_encoding=True,
+            temporal_position_encoding_max_len=24,
+            block_type: str=BlockType.DOWN,
+            ops=comfy.ops.disable_weight_init
+        ):
+        super().__init__()
+        if block_type == BlockType.MID:
+            # mid blocks contain only a single VanillaTemporalModule
+            self.motion_modules: Iterable[VanillaTemporalModule] = nn.ModuleList([get_motion_module(in_channels, temporal_position_encoding, temporal_position_encoding_max_len, ops=ops)])
+        else:
+            # down blocks contain two VanillaTemporalModules
+            self.motion_modules: Iterable[VanillaTemporalModule] = nn.ModuleList(
+                [
+                    get_motion_module(in_channels, temporal_position_encoding, temporal_position_encoding_max_len, ops=ops),
+                    get_motion_module(in_channels, temporal_position_encoding, temporal_position_encoding_max_len, ops=ops)
+                ]
+            )
+            # up blocks contain one additional VanillaTemporalModule
+            if block_type == BlockType.UP: 
+                self.motion_modules.append(get_motion_module(in_channels, temporal_position_encoding, temporal_position_encoding_max_len, ops=ops))
+    
+    def set_video_length(self, video_length: int, full_length: int):
+        for motion_module in self.motion_modules:
+            motion_module.set_video_length(video_length, full_length)
+    
+    def set_scale_multiplier(self, multiplier: Union[float, None]):
+        for motion_module in self.motion_modules:
+            motion_module.set_scale_multiplier(multiplier)
+    
+    def set_scale_mask(self, mask: Tensor):
+        for motion_module in self.motion_modules:
+            motion_module.set_scale_mask(mask)
+    
+    def set_effect(self, multival: Union[float, Tensor]):
+        for motion_module in self.motion_modules:
+            motion_module.set_effect(multival)
+    
+    def set_sub_idxs(self, sub_idxs: list[int]):
+        for motion_module in self.motion_modules:
+            motion_module.set_sub_idxs(sub_idxs)
+
+    def set_view_options(self, view_options: ContextOptions):
+        for motion_module in self.motion_modules:
+            motion_module.set_view_options(view_options=view_options)
+
+    def reset_temp_vars(self):
+        for motion_module in self.motion_modules:
+            motion_module.reset_temp_vars()
+
+
+def get_motion_module(in_channels, temporal_position_encoding, temporal_position_encoding_max_len, ops=comfy.ops.disable_weight_init):
+    return VanillaTemporalModule(in_channels=in_channels, temporal_position_encoding=temporal_position_encoding, temporal_position_encoding_max_len=temporal_position_encoding_max_len, ops=ops)
+
+
+class VanillaTemporalModule(nn.Module):
+    def __init__(
+        self,
+        in_channels,
+        num_attention_heads=8,
+        num_transformer_block=1,
+        attention_block_types=("Temporal_Self", "Temporal_Self"),
+        cross_frame_attention_mode=None,
+        temporal_position_encoding=True,
+        temporal_position_encoding_max_len=24,
+        temporal_attention_dim_div=1,
+        zero_initialize=True,
+        ops=comfy.ops.disable_weight_init,
+    ):
+        super().__init__()
+
+        self.video_length = 16
+        self.full_length = 16
+        self.sub_idxs = None
+        self.view_options = None
+
+        self.effect = None
+        self.temp_effect_mask: Tensor = None
+        self.prev_input_tensor_batch = 0
+
+        self.temporal_transformer = TemporalTransformer3DModel(
+            in_channels=in_channels,
+            num_attention_heads=num_attention_heads,
+            attention_head_dim=in_channels
+            // num_attention_heads
+            // temporal_attention_dim_div,
+            num_layers=num_transformer_block,
+            attention_block_types=attention_block_types,
+            cross_frame_attention_mode=cross_frame_attention_mode,
+            temporal_position_encoding=temporal_position_encoding,
+            temporal_position_encoding_max_len=temporal_position_encoding_max_len,
+            ops=ops
+        )
+
+        if zero_initialize:
+            self.temporal_transformer.proj_out = zero_module(
+                self.temporal_transformer.proj_out
+            )
+
+    def set_video_length(self, video_length: int, full_length: int):
+        self.video_length = video_length
+        self.full_length = full_length
+        self.temporal_transformer.set_video_length(video_length, full_length)
+    
+    def set_scale_multiplier(self, multiplier: Union[float, None]):
+        self.temporal_transformer.set_scale_multiplier(multiplier)
+
+    def set_scale_mask(self, mask: Tensor):
+        self.temporal_transformer.set_scale_mask(mask)
+
+    def set_effect(self, multival: Union[float, Tensor]):
+        if type(multival) == Tensor:
+            self.effect = multival
+        elif multival is not None and math.isclose(multival, 1.0):
+            self.effect = None
+        else:
+            self.effect = multival
+        self.temp_effect_mask = None
+
+    def set_sub_idxs(self, sub_idxs: list[int]):
+        self.sub_idxs = sub_idxs
+        self.temporal_transformer.set_sub_idxs(sub_idxs)
+
+    def set_view_options(self, view_options: ContextOptions):
+        self.view_options = view_options
+
+    def reset_temp_vars(self):
+        self.set_effect(None)
+        self.set_view_options(None)
+        self.temporal_transformer.reset_temp_vars()
+
+    def get_effect_mask(self, input_tensor: Tensor):
+        batch, channel, height, width = input_tensor.shape
+        batched_number = batch // self.video_length
+        full_batched_idxs = list(range(self.video_length))*batched_number
+        # if there is a cached temp_effect_mask and it is valid for current input, return it
+        if batch == self.prev_input_tensor_batch and self.temp_effect_mask is not None:
+            if self.sub_idxs is not None:
+                return self.temp_effect_mask[self.sub_idxs*batched_number]
+            return self.temp_effect_mask[full_batched_idxs]
+        # clear any existing mask
+        del self.temp_effect_mask
+        self.temp_effect_mask = None
+        # recalculate temp mask
+        self.prev_input_tensor_batch = batch
+        # make sure mask matches expected dimensions
+        mask = prepare_mask_batch(self.effect, shape=(self.full_length, 1, height, width))
+        # make sure mask is as long as full_length - clone last element of list if too short
+        self.temp_effect_mask = extend_to_batch_size(mask, self.full_length).to(
+            dtype=input_tensor.dtype, device=input_tensor.device)
+        # return finalized mask
+        if self.sub_idxs is not None:
+            return self.temp_effect_mask[self.sub_idxs*batched_number]
+        return self.temp_effect_mask[full_batched_idxs]
+
+    def forward(self, input_tensor: Tensor, encoder_hidden_states=None, attention_mask=None):
+        if self.effect is None:
+            return self.temporal_transformer(input_tensor, encoder_hidden_states, attention_mask, self.view_options)
+        # return weighted average of input_tensor and AD output
+        if type(self.effect) != Tensor:
+            effect = self.effect
+            # do nothing if effect is 0
+            if math.isclose(effect, 0.0):
+                return input_tensor
+        else:
+            effect = self.get_effect_mask(input_tensor)
+        return input_tensor*(1.0-effect) + self.temporal_transformer(input_tensor, encoder_hidden_states, attention_mask, self.view_options)*effect
+
+
+class TemporalTransformer3DModel(nn.Module):
+    def __init__(
+        self,
+        in_channels,
+        num_attention_heads,
+        attention_head_dim,
+        num_layers,
+        attention_block_types=(
+            "Temporal_Self",
+            "Temporal_Self",
+        ),
+        dropout=0.0,
+        norm_num_groups=32,
+        cross_attention_dim=768,
+        activation_fn="geglu",
+        attention_bias=False,
+        upcast_attention=False,
+        cross_frame_attention_mode=None,
+        temporal_position_encoding=False,
+        temporal_position_encoding_max_len=24,
+        ops=comfy.ops.disable_weight_init,
+    ):
+        super().__init__()
+        self.video_length = 16
+        self.full_length = 16
+        self.raw_scale_mask: Union[Tensor, None] = None
+        self.temp_scale_mask: Union[Tensor, None] = None
+        self.sub_idxs: Union[list[int], None] = None
+        self.prev_hidden_states_batch = 0
+
+
+        inner_dim = num_attention_heads * attention_head_dim
+
+        self.norm = ops.GroupNorm(
+            num_groups=norm_num_groups, num_channels=in_channels, eps=1e-6, affine=True
+        )
+        self.proj_in = ops.Linear(in_channels, inner_dim)
+
+        self.transformer_blocks: Iterable[TemporalTransformerBlock] = nn.ModuleList(
+            [
+                TemporalTransformerBlock(
+                    dim=inner_dim,
+                    num_attention_heads=num_attention_heads,
+                    attention_head_dim=attention_head_dim,
+                    attention_block_types=attention_block_types,
+                    dropout=dropout,
+                    norm_num_groups=norm_num_groups,
+                    cross_attention_dim=cross_attention_dim,
+                    activation_fn=activation_fn,
+                    attention_bias=attention_bias,
+                    upcast_attention=upcast_attention,
+                    cross_frame_attention_mode=cross_frame_attention_mode,
+                    temporal_position_encoding=temporal_position_encoding,
+                    temporal_position_encoding_max_len=temporal_position_encoding_max_len,
+                    ops=ops,
+                )
+                for d in range(num_layers)
+            ]
+        )
+        self.proj_out = ops.Linear(inner_dim, in_channels)
+
+    def set_video_length(self, video_length: int, full_length: int):
+        self.video_length = video_length
+        self.full_length = full_length
+    
+    def set_scale_multiplier(self, multiplier: Union[float, None]):
+        for block in self.transformer_blocks:
+            block.set_scale_multiplier(multiplier)
+
+    def set_scale_mask(self, mask: Tensor):
+        self.raw_scale_mask = mask
+        self.temp_scale_mask = None
+
+    def set_sub_idxs(self, sub_idxs: list[int]):
+        self.sub_idxs = sub_idxs
+        for block in self.transformer_blocks:
+            block.set_sub_idxs(sub_idxs)
+
+    def reset_temp_vars(self):
+        del self.temp_scale_mask
+        self.temp_scale_mask = None
+        self.prev_hidden_states_batch = 0
+
+    def get_scale_mask(self, hidden_states: Tensor) -> Union[Tensor, None]:
+        # if no raw mask, return None
+        if self.raw_scale_mask is None:
+            return None
+        shape = hidden_states.shape
+        batch, channel, height, width = shape
+        # if temp mask already calculated, return it
+        if self.temp_scale_mask != None:
+            # check if hidden_states batch matches
+            if batch == self.prev_hidden_states_batch:
+                if self.sub_idxs is not None:
+                    return self.temp_scale_mask[:, self.sub_idxs, :]
+                return self.temp_scale_mask
+            # if does not match, reset cached temp_scale_mask and recalculate it
+            del self.temp_scale_mask
+            self.temp_scale_mask = None
+        # otherwise, calculate temp mask
+        self.prev_hidden_states_batch = batch
+        mask = prepare_mask_batch(self.raw_scale_mask, shape=(self.full_length, 1, height, width))
+        mask = repeat_to_batch_size(mask, self.full_length)
+        # if mask not the same amount length as full length, make it match
+        if self.full_length != mask.shape[0]:
+            mask = broadcast_image_to(mask, self.full_length, 1)
+        # reshape mask to attention K shape (h*w, latent_count, 1)
+        batch, channel, height, width = mask.shape
+        # first, perform same operations as on hidden_states,
+        # turning (b, c, h, w) -> (b, h*w, c)
+        mask = mask.permute(0, 2, 3, 1).reshape(batch, height*width, channel)
+        # then, make it the same shape as attention's k, (h*w, b, c)
+        mask = mask.permute(1, 0, 2)
+        # make masks match the expected length of h*w
+        batched_number = shape[0] // self.video_length
+        if batched_number > 1:
+            mask = torch.cat([mask] * batched_number, dim=0)
+        # cache mask and set to proper device
+        self.temp_scale_mask = mask
+        # move temp_scale_mask to proper dtype + device
+        self.temp_scale_mask = self.temp_scale_mask.to(dtype=hidden_states.dtype, device=hidden_states.device)
+        # return subset of masks, if needed
+        if self.sub_idxs is not None:
+            return self.temp_scale_mask[:, self.sub_idxs, :]
+        return self.temp_scale_mask
+
+    def forward(self, hidden_states, encoder_hidden_states=None, attention_mask=None, view_options: ContextOptions=None):
+        batch, channel, height, width = hidden_states.shape
+        residual = hidden_states
+        scale_mask = self.get_scale_mask(hidden_states)
+        # add some casts for fp8 purposes - does not affect speed otherwise
+        hidden_states = self.norm(hidden_states).to(hidden_states.dtype)
+        inner_dim = hidden_states.shape[1]
+        hidden_states = hidden_states.permute(0, 2, 3, 1).reshape(
+            batch, height * width, inner_dim
+        )
+        hidden_states = self.proj_in(hidden_states).to(hidden_states.dtype)
+
+        # Transformer Blocks
+        for block in self.transformer_blocks:
+            hidden_states = block(
+                hidden_states,
+                encoder_hidden_states=encoder_hidden_states,
+                attention_mask=attention_mask,
+                video_length=self.video_length,
+                scale_mask=scale_mask,
+                view_options=view_options
+            )
+
+        # output
+        hidden_states = self.proj_out(hidden_states)
+        hidden_states = (
+            hidden_states.reshape(batch, height, width, inner_dim)
+            .permute(0, 3, 1, 2)
+            .contiguous()
+        )
+
+        output = hidden_states + residual
+
+        return output
+
+
+class TemporalTransformerBlock(nn.Module):
+    def __init__(
+        self,
+        dim,
+        num_attention_heads,
+        attention_head_dim,
+        attention_block_types=(
+            "Temporal_Self",
+            "Temporal_Self",
+        ),
+        dropout=0.0,
+        norm_num_groups=32,
+        cross_attention_dim=768,
+        activation_fn="geglu",
+        attention_bias=False,
+        upcast_attention=False,
+        cross_frame_attention_mode=None,
+        temporal_position_encoding=False,
+        temporal_position_encoding_max_len=24,
+        ops=comfy.ops.disable_weight_init,
+    ):
+        super().__init__()
+
+        attention_blocks = []
+        norms = []
+
+        for block_name in attention_block_types:
+            attention_blocks.append(
+                VersatileAttention(
+                    attention_mode=block_name.split("_")[0],
+                    context_dim=cross_attention_dim # called context_dim for ComfyUI impl
+                    if block_name.endswith("_Cross")
+                    else None,
+                    query_dim=dim,
+                    heads=num_attention_heads,
+                    dim_head=attention_head_dim,
+                    dropout=dropout,
+                    #bias=attention_bias, # remove for Comfy CrossAttention
+                    #upcast_attention=upcast_attention, # remove for Comfy CrossAttention
+                    cross_frame_attention_mode=cross_frame_attention_mode,
+                    temporal_position_encoding=temporal_position_encoding,
+                    temporal_position_encoding_max_len=temporal_position_encoding_max_len,
+                    ops=ops,
+                )
+            )
+            norms.append(ops.LayerNorm(dim))
+
+        self.attention_blocks: Iterable[VersatileAttention] = nn.ModuleList(attention_blocks)
+        self.norms = nn.ModuleList(norms)
+
+        self.ff = FeedForward(dim, dropout=dropout, glu=(activation_fn == "geglu"), operations=ops)
+        self.ff_norm = ops.LayerNorm(dim)
+
+    def set_scale_multiplier(self, multiplier: Union[float, None]):
+        for block in self.attention_blocks:
+            block.set_scale_multiplier(multiplier)
+
+    def set_sub_idxs(self, sub_idxs: list[int]):
+        for block in self.attention_blocks:
+            block.set_sub_idxs(sub_idxs)
+
+    def forward(
+        self,
+        hidden_states: Tensor,
+        encoder_hidden_states: Tensor=None,
+        attention_mask: Tensor=None,
+        video_length: int=None,
+        scale_mask: Tensor=None,
+        view_options: ContextOptions=None,
+    ):
+        # make view_options None if context_length > video_length, or if equal and equal not allowed
+        if view_options:
+            if view_options.context_length > video_length:
+                view_options = None
+            elif view_options.context_length == video_length and not view_options.use_on_equal_length:
+                view_options = None
+        if not view_options:
+            for attention_block, norm in zip(self.attention_blocks, self.norms):
+                norm_hidden_states = norm(hidden_states).to(hidden_states.dtype)
+                hidden_states = (
+                    attention_block(
+                        norm_hidden_states,
+                        encoder_hidden_states=encoder_hidden_states
+                        if attention_block.is_cross_attention
+                        else None,
+                        attention_mask=attention_mask,
+                        video_length=video_length,
+                        scale_mask=scale_mask
+                    ) + hidden_states
+                )
+        else:
+            # views idea gotten from diffusers AnimateDiff FreeNoise implementation:
+            # https://github.com/arthur-qiu/FreeNoise-AnimateDiff/blob/main/animatediff/models/motion_module.py
+            # apply sliding context windows (views)
+            views = get_context_windows(num_frames=video_length, opts=view_options)
+            hidden_states = rearrange(hidden_states, "(b f) d c -> b f d c", f=video_length)
+            value_final = torch.zeros_like(hidden_states)
+            count_final = torch.zeros_like(hidden_states)
+            # bias_final = [0.0] * video_length
+            batched_conds = hidden_states.size(1) // video_length
+            for sub_idxs in views:
+                sub_hidden_states = rearrange(hidden_states[:, sub_idxs], "b f d c -> (b f) d c")
+                for attention_block, norm in zip(self.attention_blocks, self.norms):
+                    norm_hidden_states = norm(sub_hidden_states).to(sub_hidden_states.dtype)
+                    sub_hidden_states = (
+                        attention_block(
+                            norm_hidden_states,
+                            encoder_hidden_states=encoder_hidden_states # do these need to be changed for sub_idxs too?
+                            if attention_block.is_cross_attention
+                            else None,
+                            attention_mask=attention_mask,
+                            video_length=len(sub_idxs),
+                            scale_mask=scale_mask[:, sub_idxs, :] if scale_mask is not None else scale_mask
+                        ) + sub_hidden_states
+                    )
+                sub_hidden_states = rearrange(sub_hidden_states, "(b f) d c -> b f d c", f=len(sub_idxs))
+
+                # if view_options.fuse_method == ContextFuseMethod.RELATIVE:
+                #     for pos, idx in enumerate(sub_idxs):
+                #         # bias is the influence of a specific index in relation to the whole context window
+                #         bias = 1 - abs(idx - (sub_idxs[0] + sub_idxs[-1]) / 2) / ((sub_idxs[-1] - sub_idxs[0] + 1e-2) / 2)
+                #         bias = max(1e-2, bias)
+                #         # take weighted averate relative to total bias of current idx
+                #         bias_total = bias_final[idx]
+                #         prev_weight = torch.tensor([bias_total / (bias_total + bias)],
+                #                                    dtype=value_final.dtype, device=value_final.device).unsqueeze(0).unsqueeze(-1).unsqueeze(-1)
+                #         #prev_weight = torch.cat([prev_weight]*value_final.shape[1], dim=1)
+                #         new_weight = torch.tensor([bias / (bias_total + bias)],
+                #                                    dtype=value_final.dtype, device=value_final.device).unsqueeze(0).unsqueeze(-1).unsqueeze(-1)
+                #         #new_weight = torch.cat([new_weight]*value_final.shape[1], dim=1)
+                #         test = value_final[:, idx:idx+1, :, :]
+                #         value_final[:, idx:idx+1, :, :] = value_final[:, idx:idx+1, :, :] * prev_weight + sub_hidden_states[:, pos:pos+1, : ,:] * new_weight
+                #         bias_final[idx] = bias_total + bias
+                # else:
+                weights = get_context_weights(len(sub_idxs), view_options.fuse_method) * batched_conds
+                weights_tensor = torch.Tensor(weights).to(device=hidden_states.device).unsqueeze(0).unsqueeze(-1).unsqueeze(-1)
+                value_final[:, sub_idxs] += sub_hidden_states * weights_tensor
+                count_final[:, sub_idxs] += weights_tensor
+            
+            # get weighted average of sub_hidden_states, if fuse method requires it
+            # if view_options.fuse_method != ContextFuseMethod.RELATIVE:
+            hidden_states = value_final / count_final
+            hidden_states = rearrange(hidden_states, "b f d c -> (b f) d c")
+            del value_final
+            del count_final
+            # del bias_final
+
+        hidden_states = self.ff(self.ff_norm(hidden_states)) + hidden_states
+
+        output = hidden_states
+        return output
+
+
+class PositionalEncoding(nn.Module):
+    def __init__(self, d_model, dropout=0.0, max_len=24):
+        super().__init__()
+        self.dropout = nn.Dropout(p=dropout)
+        position = torch.arange(max_len).unsqueeze(1)
+        div_term = torch.exp(
+            torch.arange(0, d_model, 2) * (-math.log(10000.0) / d_model)
+        )
+        pe = torch.zeros(1, max_len, d_model)
+        pe[0, :, 0::2] = torch.sin(position * div_term)
+        pe[0, :, 1::2] = torch.cos(position * div_term)
+        self.register_buffer("pe", pe)
+        self.sub_idxs = None
+
+    def set_sub_idxs(self, sub_idxs: list[int]):
+        self.sub_idxs = sub_idxs
+
+    def forward(self, x):
+        #if self.sub_idxs is not None:
+        #    x = x + self.pe[:, self.sub_idxs]
+        #else:
+        x = x + self.pe[:, : x.size(1)]
+        return self.dropout(x)
+
+
+class VersatileAttention(CrossAttentionMM):
+    def __init__(
+        self,
+        attention_mode=None,
+        cross_frame_attention_mode=None,
+        temporal_position_encoding=False,
+        temporal_position_encoding_max_len=24,
+        ops=comfy.ops.disable_weight_init,
+        *args,
+        **kwargs,
+    ):
+        super().__init__(operations=ops, *args, **kwargs)
+        assert attention_mode == "Temporal"
+
+        self.attention_mode = attention_mode
+        self.is_cross_attention = kwargs["context_dim"] is not None
+
+        self.pos_encoder = (
+            PositionalEncoding(
+                kwargs["query_dim"],
+                dropout=0.0,
+                max_len=temporal_position_encoding_max_len,
+            )
+            if (temporal_position_encoding and attention_mode == "Temporal")
+            else None
+        )
+
+    def extra_repr(self):
+        return f"(Module Info) Attention_Mode: {self.attention_mode}, Is_Cross_Attention: {self.is_cross_attention}"
+
+    def set_scale_multiplier(self, multiplier: Union[float, None]):
+        if multiplier is None or math.isclose(multiplier, 1.0):
+            self.scale = 1.0
+        else:
+            self.scale = multiplier
+
+    def set_sub_idxs(self, sub_idxs: list[int]):
+        if self.pos_encoder != None:
+            self.pos_encoder.set_sub_idxs(sub_idxs)
+
+    def forward(
+        self,
+        hidden_states: Tensor,
+        encoder_hidden_states=None,
+        attention_mask=None,
+        video_length=None,
+        scale_mask=None,
+    ):
+        if self.attention_mode != "Temporal":
+            raise NotImplementedError
+
+        d = hidden_states.shape[1]
+        hidden_states = rearrange(
+            hidden_states, "(b f) d c -> (b d) f c", f=video_length
+        )
+
+        if self.pos_encoder is not None:
+           hidden_states = self.pos_encoder(hidden_states).to(hidden_states.dtype)
+
+        encoder_hidden_states = (
+            repeat(encoder_hidden_states, "b n c -> (b d) n c", d=d)
+            if encoder_hidden_states is not None
+            else encoder_hidden_states
+        )
+
+        hidden_states = super().forward(
+            hidden_states,
+            encoder_hidden_states,
+            value=None,
+            mask=attention_mask,
+            scale_mask=scale_mask,
+        )
+
+        hidden_states = rearrange(hidden_states, "(b d) f c -> (b f) d c", d=d)
+
+        return hidden_states

custom_nodes/ComfyUI-AnimateDiff-Evolved/animatediff/nodes.py

@@ -0,0 +1,149 @@
+import comfy.sample as comfy_sample
+
+from .sampling import motion_sample_factory
+
+from .nodes_gen1 import (AnimateDiffLoaderGen1, LegacyAnimateDiffLoaderWithContext, AnimateDiffModelSettings,
+                         AnimateDiffModelSettingsSimple, AnimateDiffModelSettingsAdvanced, AnimateDiffModelSettingsAdvancedAttnStrengths)
+from .nodes_gen2 import UseEvolvedSamplingNode, ApplyAnimateDiffModelNode, ApplyAnimateDiffModelBasicNode, LoadAnimateDiffModelNode, ADKeyframeNode
+from .nodes_multival import MultivalDynamicNode, MultivalScaledMaskNode
+from .nodes_sample import (FreeInitOptionsNode, NoiseLayerAddWeightedNode, SampleSettingsNode, NoiseLayerAddNode, NoiseLayerReplaceNode, IterationOptionsNode,
+                           CustomCFGNode, CustomCFGKeyframeNode)
+from .nodes_sigma_schedule import (SigmaScheduleNode, RawSigmaScheduleNode, WeightedAverageSigmaScheduleNode, InterpolatedWeightedAverageSigmaScheduleNode, SplitAndCombineSigmaScheduleNode)
+from .nodes_context import (LegacyLoopedUniformContextOptionsNode, LoopedUniformContextOptionsNode, LoopedUniformViewOptionsNode, StandardUniformContextOptionsNode, StandardStaticContextOptionsNode, BatchedContextOptionsNode,
+                            StandardStaticViewOptionsNode, StandardUniformViewOptionsNode, ViewAsContextOptionsNode)
+from .nodes_ad_settings import AnimateDiffSettingsNode, ManualAdjustPENode, SweetspotStretchPENode, FullStretchPENode
+from .nodes_extras import AnimateDiffUnload, EmptyLatentImageLarge, CheckpointLoaderSimpleWithNoiseSelect
+from .nodes_deprecated import AnimateDiffLoader_Deprecated, AnimateDiffLoaderAdvanced_Deprecated, AnimateDiffCombine_Deprecated
+from .nodes_lora import AnimateDiffLoraLoader, MaskedLoraLoader
+
+from .logger import logger
+
+# override comfy_sample.sample with animatediff-support version
+comfy_sample.sample = motion_sample_factory(comfy_sample.sample)
+comfy_sample.sample_custom = motion_sample_factory(comfy_sample.sample_custom, is_custom=True)
+
+
+NODE_CLASS_MAPPINGS = {
+    # Unencapsulated
+    "ADE_AnimateDiffLoRALoader": AnimateDiffLoraLoader,
+    "ADE_AnimateDiffSamplingSettings": SampleSettingsNode,
+    "ADE_AnimateDiffKeyframe": ADKeyframeNode,
+    # Multival Nodes
+    "ADE_MultivalDynamic": MultivalDynamicNode,
+    "ADE_MultivalScaledMask": MultivalScaledMaskNode,
+    # Context Opts
+    "ADE_StandardStaticContextOptions": StandardStaticContextOptionsNode,
+    "ADE_StandardUniformContextOptions": StandardUniformContextOptionsNode,
+    "ADE_LoopedUniformContextOptions": LoopedUniformContextOptionsNode,
+    "ADE_ViewsOnlyContextOptions": ViewAsContextOptionsNode,
+    "ADE_BatchedContextOptions": BatchedContextOptionsNode,
+    "ADE_AnimateDiffUniformContextOptions": LegacyLoopedUniformContextOptionsNode, # Legacy
+    # View Opts
+    "ADE_StandardStaticViewOptions": StandardStaticViewOptionsNode,
+    "ADE_StandardUniformViewOptions": StandardUniformViewOptionsNode,
+    "ADE_LoopedUniformViewOptions": LoopedUniformViewOptionsNode,
+    # Iteration Opts
+    "ADE_IterationOptsDefault": IterationOptionsNode,
+    "ADE_IterationOptsFreeInit": FreeInitOptionsNode,
+    # Noise Layer Nodes
+    "ADE_NoiseLayerAdd": NoiseLayerAddNode,
+    "ADE_NoiseLayerAddWeighted": NoiseLayerAddWeightedNode,
+    "ADE_NoiseLayerReplace": NoiseLayerReplaceNode,
+    # AnimateDiff Settings
+    "ADE_AnimateDiffSettings": AnimateDiffSettingsNode,
+    "ADE_AdjustPESweetspotStretch": SweetspotStretchPENode,
+    "ADE_AdjustPEFullStretch": FullStretchPENode,
+    "ADE_AdjustPEManual": ManualAdjustPENode,
+    # Sample Settings
+    "ADE_CustomCFG": CustomCFGNode,
+    "ADE_CustomCFGKeyframe": CustomCFGKeyframeNode,
+    "ADE_SigmaSchedule": SigmaScheduleNode,
+    "ADE_RawSigmaSchedule": RawSigmaScheduleNode,
+    "ADE_SigmaScheduleWeightedAverage": WeightedAverageSigmaScheduleNode,
+    "ADE_SigmaScheduleWeightedAverageInterp": InterpolatedWeightedAverageSigmaScheduleNode,
+    "ADE_SigmaScheduleSplitAndCombine": SplitAndCombineSigmaScheduleNode,
+    # Extras Nodes
+    "ADE_AnimateDiffUnload": AnimateDiffUnload,
+    "ADE_EmptyLatentImageLarge": EmptyLatentImageLarge,
+    "CheckpointLoaderSimpleWithNoiseSelect": CheckpointLoaderSimpleWithNoiseSelect,
+    # Gen1 Nodes
+    "ADE_AnimateDiffLoaderGen1": AnimateDiffLoaderGen1,
+    "ADE_AnimateDiffLoaderWithContext": LegacyAnimateDiffLoaderWithContext,
+    "ADE_AnimateDiffModelSettings_Release": AnimateDiffModelSettings,
+    "ADE_AnimateDiffModelSettingsSimple": AnimateDiffModelSettingsSimple,
+    "ADE_AnimateDiffModelSettings": AnimateDiffModelSettingsAdvanced,
+    "ADE_AnimateDiffModelSettingsAdvancedAttnStrengths": AnimateDiffModelSettingsAdvancedAttnStrengths,
+    # Gen2 Nodes
+    "ADE_UseEvolvedSampling": UseEvolvedSamplingNode,
+    "ADE_ApplyAnimateDiffModelSimple": ApplyAnimateDiffModelBasicNode,
+    "ADE_ApplyAnimateDiffModel": ApplyAnimateDiffModelNode,
+    "ADE_LoadAnimateDiffModel": LoadAnimateDiffModelNode,
+    # MaskedLoraLoader
+    #"ADE_MaskedLoadLora": MaskedLoraLoader,
+    # Deprecated Nodes
+    "AnimateDiffLoaderV1": AnimateDiffLoader_Deprecated,
+    "ADE_AnimateDiffLoaderV1Advanced": AnimateDiffLoaderAdvanced_Deprecated,
+    "ADE_AnimateDiffCombine": AnimateDiffCombine_Deprecated,
+}
+NODE_DISPLAY_NAME_MAPPINGS = {
+    # Unencapsulated
+    "ADE_AnimateDiffLoRALoader": "Load AnimateDiff LoRA 🎭🅐🅓",
+    "ADE_AnimateDiffSamplingSettings": "Sample Settings 🎭🅐🅓",
+    "ADE_AnimateDiffKeyframe": "AnimateDiff Keyframe 🎭🅐🅓",
+    # Multival Nodes
+    "ADE_MultivalDynamic": "Multival Dynamic 🎭🅐🅓",
+    "ADE_MultivalScaledMask": "Multival Scaled Mask 🎭🅐🅓",
+    # Context Opts
+    "ADE_StandardStaticContextOptions": "Context Options◆Standard Static 🎭🅐🅓",
+    "ADE_StandardUniformContextOptions": "Context Options◆Standard Uniform 🎭🅐🅓",
+    "ADE_LoopedUniformContextOptions": "Context Options◆Looped Uniform 🎭🅐🅓",
+    "ADE_ViewsOnlyContextOptions": "Context Options◆Views Only [VRAM⇈] 🎭🅐🅓",
+    "ADE_BatchedContextOptions": "Context Options◆Batched [Non-AD] 🎭🅐🅓",
+    "ADE_AnimateDiffUniformContextOptions": "Context Options◆Looped Uniform 🎭🅐🅓", # Legacy
+    # View Opts
+    "ADE_StandardStaticViewOptions": "View Options◆Standard Static 🎭🅐🅓",
+    "ADE_StandardUniformViewOptions": "View Options◆Standard Uniform 🎭🅐🅓",
+    "ADE_LoopedUniformViewOptions": "View Options◆Looped Uniform 🎭🅐🅓",
+    # Iteration Opts
+    "ADE_IterationOptsDefault": "Default Iteration Options 🎭🅐🅓",
+    "ADE_IterationOptsFreeInit": "FreeInit Iteration Options 🎭🅐🅓",
+    # Noise Layer Nodes
+    "ADE_NoiseLayerAdd": "Noise Layer [Add] 🎭🅐🅓",
+    "ADE_NoiseLayerAddWeighted": "Noise Layer [Add Weighted] 🎭🅐🅓",
+    "ADE_NoiseLayerReplace": "Noise Layer [Replace] 🎭🅐🅓",
+    # AnimateDiff Settings
+    "ADE_AnimateDiffSettings": "AnimateDiff Settings 🎭🅐🅓",
+    "ADE_AdjustPESweetspotStretch": "Adjust PE [Sweetspot Stretch] 🎭🅐🅓",
+    "ADE_AdjustPEFullStretch": "Adjust PE [Full Stretch] 🎭🅐🅓",
+    "ADE_AdjustPEManual": "Adjust PE [Manual] 🎭🅐🅓",
+    # Sample Settings
+    "ADE_CustomCFG": "Custom CFG 🎭🅐🅓",
+    "ADE_CustomCFGKeyframe": "Custom CFG Keyframe 🎭🅐🅓",
+    "ADE_SigmaSchedule": "Create Sigma Schedule 🎭🅐🅓",
+    "ADE_RawSigmaSchedule": "Create Raw Sigma Schedule 🎭🅐🅓",
+    "ADE_SigmaScheduleWeightedAverage": "Sigma Schedule Weighted Mean 🎭🅐🅓",
+    "ADE_SigmaScheduleWeightedAverageInterp": "Sigma Schedule Interpolated Mean 🎭🅐🅓",
+    "ADE_SigmaScheduleSplitAndCombine": "Sigma Schedule Split Combine 🎭🅐🅓",
+    # Extras Nodes
+    "ADE_AnimateDiffUnload": "AnimateDiff Unload 🎭🅐🅓",
+    "ADE_EmptyLatentImageLarge": "Empty Latent Image (Big Batch) 🎭🅐🅓",
+    "CheckpointLoaderSimpleWithNoiseSelect": "Load Checkpoint w/ Noise Select 🎭🅐🅓",
+    # Gen1 Nodes
+    "ADE_AnimateDiffLoaderGen1": "AnimateDiff Loader 🎭🅐🅓①",
+    "ADE_AnimateDiffLoaderWithContext": "AnimateDiff Loader [Legacy] 🎭🅐🅓①",
+    "ADE_AnimateDiffModelSettings_Release": "[DEPR] Motion Model Settings 🎭🅐🅓①",
+    "ADE_AnimateDiffModelSettingsSimple": "[DEPR] Motion Model Settings (Simple) 🎭🅐🅓①",
+    "ADE_AnimateDiffModelSettings": "[DEPR] Motion Model Settings (Advanced) 🎭🅐🅓①",
+    "ADE_AnimateDiffModelSettingsAdvancedAttnStrengths": "[DEPR] Motion Model Settings (Adv. Attn) 🎭🅐🅓①",
+    # Gen2 Nodes
+    "ADE_UseEvolvedSampling": "Use Evolved Sampling 🎭🅐🅓②",
+    "ADE_ApplyAnimateDiffModelSimple": "Apply AnimateDiff Model 🎭🅐🅓②",
+    "ADE_ApplyAnimateDiffModel": "Apply AnimateDiff Model (Adv.) 🎭🅐🅓②",
+    "ADE_LoadAnimateDiffModel": "Load AnimateDiff Model 🎭🅐🅓②",
+    # MaskedLoraLoader
+    #"ADE_MaskedLoadLora": "Load LoRA (Masked) 🎭🅐🅓",
+    # Deprecated Nodes
+    "AnimateDiffLoaderV1": "AnimateDiff Loader [DEPRECATED] 🎭🅐🅓",
+    "ADE_AnimateDiffLoaderV1Advanced": "AnimateDiff Loader (Advanced) [DEPRECATED] 🎭🅐🅓",
+    "ADE_AnimateDiffCombine": "AnimateDiff Combine [DEPRECATED, Use Video Combine (VHS) Instead!] 🎭🅐🅓",
+}

custom_nodes/ComfyUI-AnimateDiff-Evolved/animatediff/nodes_ad_settings.py

@@ -0,0 +1,107 @@
+from .ad_settings import AdjustPE, AdjustPEGroup, AnimateDiffSettings
+from .utils_model import BIGMAX
+
+
+class AnimateDiffSettingsNode:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {
+            "optional": {
+                "pe_adjust": ("PE_ADJUST",),
+            }
+        }
+    
+    RETURN_TYPES = ("AD_SETTINGS",)
+    CATEGORY = "Animate Diff 🎭🅐🅓/ad settings"
+    FUNCTION = "get_ad_settings"
+
+    def get_ad_settings(self, pe_adjust: AdjustPEGroup=None):
+        return (AnimateDiffSettings(adjust_pe=pe_adjust),)
+
+
+class ManualAdjustPENode:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {
+            "required": {
+                "cap_initial_pe_length": ("INT", {"default": 0, "min": 0, "step": 1}),
+                "interpolate_pe_to_length": ("INT", {"default": 0, "min": 0, "step": 1}),
+                "initial_pe_idx_offset": ("INT", {"default": 0, "min": 0, "step": 1}),
+                "final_pe_idx_offset": ("INT", {"default": 0, "min": 0, "step": 1}),
+                "print_adjustment": ("BOOLEAN", {"default": False}),
+            },
+            "optional": {
+                "prev_pe_adjust": ("PE_ADJUST",),
+            }
+        }
+    
+    RETURN_TYPES = ("PE_ADJUST",)
+    CATEGORY = "Animate Diff 🎭🅐🅓/ad settings/pe adjust"
+    FUNCTION = "get_pe_adjust"
+
+    def get_pe_adjust(self, cap_initial_pe_length: int, interpolate_pe_to_length: int, 
+                      initial_pe_idx_offset: int, final_pe_idx_offset: int, print_adjustment: bool,
+                      prev_pe_adjust: AdjustPEGroup=None):
+        if prev_pe_adjust is None:
+            prev_pe_adjust = AdjustPEGroup()
+        prev_pe_adjust = prev_pe_adjust.clone()
+        adjust = AdjustPE(cap_initial_pe_length=cap_initial_pe_length, interpolate_pe_to_length=interpolate_pe_to_length,
+                          initial_pe_idx_offset=initial_pe_idx_offset, final_pe_idx_offset=final_pe_idx_offset,
+                          print_adjustment=print_adjustment)
+        prev_pe_adjust.add(adjust)
+        return (prev_pe_adjust,)
+
+
+class SweetspotStretchPENode:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {
+            "required": {
+                "sweetspot": ("INT", {"default": 16, "min": 0, "max": BIGMAX},),
+                "new_sweetspot": ("INT", {"default": 16, "min": 0, "max": BIGMAX},),
+                "print_adjustment": ("BOOLEAN", {"default": False}),
+            },
+            "optional": {
+                "prev_pe_adjust": ("PE_ADJUST",),
+            }
+        }
+    
+    RETURN_TYPES = ("PE_ADJUST",)
+    CATEGORY = "Animate Diff 🎭🅐🅓/ad settings/pe adjust"
+    FUNCTION = "get_pe_adjust"
+
+    def get_pe_adjust(self, sweetspot: int, new_sweetspot: int, print_adjustment: bool, prev_pe_adjust: AdjustPEGroup=None):
+        if prev_pe_adjust is None:
+            prev_pe_adjust = AdjustPEGroup()
+        prev_pe_adjust = prev_pe_adjust.clone()
+        adjust = AdjustPE(cap_initial_pe_length=sweetspot, interpolate_pe_to_length=new_sweetspot,
+                          print_adjustment=print_adjustment)
+        prev_pe_adjust.add(adjust)
+        return (prev_pe_adjust,)
+
+
+class FullStretchPENode:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {
+            "required": {
+                "pe_stretch": ("INT", {"default": 0, "min": 0, "max": BIGMAX},),
+                "print_adjustment": ("BOOLEAN", {"default": False}),
+            },
+            "optional": {
+                "prev_pe_adjust": ("PE_ADJUST",),
+            }
+        }
+    
+    RETURN_TYPES = ("PE_ADJUST",)
+    CATEGORY = "Animate Diff 🎭🅐🅓/ad settings/pe adjust"
+    FUNCTION = "get_pe_adjust"
+
+    def get_pe_adjust(self, pe_stretch: int, print_adjustment: bool, prev_pe_adjust: AdjustPEGroup=None):
+        if prev_pe_adjust is None:
+            prev_pe_adjust = AdjustPEGroup()
+        prev_pe_adjust = prev_pe_adjust.clone()
+        adjust = AdjustPE(motion_pe_stretch=pe_stretch,
+                          print_adjustment=print_adjustment)
+        prev_pe_adjust.add(adjust)
+        return (prev_pe_adjust,)

custom_nodes/ComfyUI-AnimateDiff-Evolved/animatediff/nodes_context.py

@@ -0,0 +1,347 @@
+from .context import ContextFuseMethod, ContextOptions, ContextOptionsGroup, ContextSchedules
+from .utils_model import BIGMAX
+
+
+LENGTH_MAX = 128   # keep an eye on these max values;
+STRIDE_MAX = 32    # would need to be updated
+OVERLAP_MAX = 128  # if new motion modules come out
+
+
+class LoopedUniformContextOptionsNode:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {
+            "required": {
+                "context_length": ("INT", {"default": 16, "min": 1, "max": LENGTH_MAX}),
+                "context_stride": ("INT", {"default": 1, "min": 1, "max": STRIDE_MAX}),
+                "context_overlap": ("INT", {"default": 4, "min": 0, "max": OVERLAP_MAX}),
+                "closed_loop": ("BOOLEAN", {"default": False},),
+                #"sync_context_to_pe": ("BOOLEAN", {"default": False},),
+            },
+            "optional": {
+                "fuse_method": (ContextFuseMethod.LIST,),
+                "use_on_equal_length": ("BOOLEAN", {"default": False},),
+                "start_percent": ("FLOAT", {"default": 0.0, "min": 0.0, "max": 1.0, "step": 0.001}),
+                "guarantee_steps": ("INT", {"default": 1, "min": 0, "max": BIGMAX}),
+                "prev_context": ("CONTEXT_OPTIONS",),
+                "view_opts": ("VIEW_OPTS",),
+            }
+        }
+    
+    RETURN_TYPES = ("CONTEXT_OPTIONS",)
+    RETURN_NAMES = ("CONTEXT_OPTS",)
+    CATEGORY = "Animate Diff 🎭🅐🅓/context opts"
+    FUNCTION = "create_options"
+
+    def create_options(self, context_length: int, context_stride: int, context_overlap: int, closed_loop: bool,
+                       fuse_method: str=ContextFuseMethod.FLAT, use_on_equal_length=False, start_percent: float=0.0, guarantee_steps: int=1,
+                       view_opts: ContextOptions=None, prev_context: ContextOptionsGroup=None):
+        if prev_context is None:
+            prev_context = ContextOptionsGroup()
+        prev_context = prev_context.clone()
+
+        context_options = ContextOptions(
+            context_length=context_length,
+            context_stride=context_stride,
+            context_overlap=context_overlap,
+            context_schedule=ContextSchedules.UNIFORM_LOOPED,
+            closed_loop=closed_loop,
+            fuse_method=fuse_method,
+            use_on_equal_length=use_on_equal_length,
+            start_percent=start_percent,
+            guarantee_steps=guarantee_steps,
+            view_options=view_opts,
+            )
+        #context_options.set_sync_context_to_pe(sync_context_to_pe)
+        prev_context.add(context_options)
+        return (prev_context,)
+
+
+# This Legacy version exists to maintain compatiblity with old workflows
+class LegacyLoopedUniformContextOptionsNode:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {
+            "required": {
+                "context_length": ("INT", {"default": 16, "min": 1, "max": LENGTH_MAX}),
+                "context_stride": ("INT", {"default": 1, "min": 1, "max": STRIDE_MAX}),
+                "context_overlap": ("INT", {"default": 4, "min": 0, "max": OVERLAP_MAX}),
+                "context_schedule": (ContextSchedules.LEGACY_UNIFORM_SCHEDULE_LIST,),
+                "closed_loop": ("BOOLEAN", {"default": False},),
+                #"sync_context_to_pe": ("BOOLEAN", {"default": False},),
+            },
+            "optional": {
+                "fuse_method": (ContextFuseMethod.LIST, {"default": ContextFuseMethod.FLAT}),
+                "use_on_equal_length": ("BOOLEAN", {"default": False},),
+                "start_percent": ("FLOAT", {"default": 0.0, "min": 0.0, "max": 1.0, "step": 0.001}),
+                "guarantee_steps": ("INT", {"default": 1, "min": 0, "max": BIGMAX}),
+                "prev_context": ("CONTEXT_OPTIONS",),
+                "view_opts": ("VIEW_OPTS",),
+            }
+        }
+    
+    RETURN_TYPES = ("CONTEXT_OPTIONS",)
+    RETURN_NAMES = ("CONTEXT_OPTS",)
+    CATEGORY = ""  # No Category, so will not appear in menu
+    FUNCTION = "create_options"
+
+    def create_options(self, fuse_method: str=ContextFuseMethod.FLAT, context_schedule: str=None, **kwargs):
+        return LoopedUniformContextOptionsNode.create_options(self, fuse_method=fuse_method, **kwargs)
+
+
+class StandardUniformContextOptionsNode:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {
+            "required": {
+                "context_length": ("INT", {"default": 16, "min": 1, "max": LENGTH_MAX}),
+                "context_stride": ("INT", {"default": 1, "min": 1, "max": STRIDE_MAX}),
+                "context_overlap": ("INT", {"default": 4, "min": 0, "max": OVERLAP_MAX}),
+            },
+            "optional": {
+                "fuse_method": (ContextFuseMethod.LIST,),
+                "use_on_equal_length": ("BOOLEAN", {"default": False},),
+                "start_percent": ("FLOAT", {"default": 0.0, "min": 0.0, "max": 1.0, "step": 0.001}),
+                "guarantee_steps": ("INT", {"default": 1, "min": 0, "max": BIGMAX}),
+                "prev_context": ("CONTEXT_OPTIONS",),
+                "view_opts": ("VIEW_OPTS",),
+            }
+        }
+    
+    RETURN_TYPES = ("CONTEXT_OPTIONS",)
+    RETURN_NAMES = ("CONTEXT_OPTS",)
+    CATEGORY = "Animate Diff 🎭🅐🅓/context opts"
+    FUNCTION = "create_options"
+
+    def create_options(self, context_length: int, context_stride: int, context_overlap: int,
+                       fuse_method: str=ContextFuseMethod.PYRAMID, use_on_equal_length=False, start_percent: float=0.0, guarantee_steps: int=1,
+                       view_opts: ContextOptions=None, prev_context: ContextOptionsGroup=None):
+        if prev_context is None:
+            prev_context = ContextOptionsGroup()
+        prev_context = prev_context.clone()
+
+        context_options = ContextOptions(
+            context_length=context_length,
+            context_stride=context_stride,
+            context_overlap=context_overlap,
+            context_schedule=ContextSchedules.UNIFORM_STANDARD,
+            closed_loop=False,
+            fuse_method=fuse_method,
+            use_on_equal_length=use_on_equal_length,
+            start_percent=start_percent,
+            guarantee_steps=guarantee_steps,
+            view_options=view_opts,
+            )
+        prev_context.add(context_options)
+        return (prev_context,)
+
+
+class StandardStaticContextOptionsNode:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {
+            "required": {
+                "context_length": ("INT", {"default": 16, "min": 1, "max": LENGTH_MAX}),
+                "context_overlap": ("INT", {"default": 4, "min": 0, "max": OVERLAP_MAX}),
+            },
+            "optional": {
+                "fuse_method": (ContextFuseMethod.LIST_STATIC,),
+                "use_on_equal_length": ("BOOLEAN", {"default": False},),
+                "start_percent": ("FLOAT", {"default": 0.0, "min": 0.0, "max": 1.0, "step": 0.001}),
+                "guarantee_steps": ("INT", {"default": 1, "min": 0, "max": BIGMAX}),
+                "prev_context": ("CONTEXT_OPTIONS",),
+                "view_opts": ("VIEW_OPTS",),
+            }
+        }
+    
+    RETURN_TYPES = ("CONTEXT_OPTIONS",)
+    RETURN_NAMES = ("CONTEXT_OPTS",)
+    CATEGORY = "Animate Diff 🎭🅐🅓/context opts"
+    FUNCTION = "create_options"
+
+    def create_options(self, context_length: int, context_overlap: int,
+                       fuse_method: str=ContextFuseMethod.PYRAMID, use_on_equal_length=False, start_percent: float=0.0, guarantee_steps: int=1,
+                       view_opts: ContextOptions=None, prev_context: ContextOptionsGroup=None):
+        if prev_context is None:
+            prev_context = ContextOptionsGroup()
+        prev_context = prev_context.clone()
+        
+        context_options = ContextOptions(
+            context_length=context_length,
+            context_stride=None,
+            context_overlap=context_overlap,
+            context_schedule=ContextSchedules.STATIC_STANDARD,
+            fuse_method=fuse_method,
+            use_on_equal_length=use_on_equal_length,
+            start_percent=start_percent,
+            guarantee_steps=guarantee_steps,
+            view_options=view_opts,
+            )
+        prev_context.add(context_options)
+        return (prev_context,)
+
+
+class BatchedContextOptionsNode:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {
+            "required": {
+                "context_length": ("INT", {"default": 16, "min": 1, "max": LENGTH_MAX}),
+            },
+            "optional": {
+                "start_percent": ("FLOAT", {"default": 0.0, "min": 0.0, "max": 1.0, "step": 0.001}),
+                "guarantee_steps": ("INT", {"default": 1, "min": 0, "max": BIGMAX}),
+                "prev_context": ("CONTEXT_OPTIONS",),
+            }
+        }
+    
+    RETURN_TYPES = ("CONTEXT_OPTIONS",)
+    RETURN_NAMES = ("CONTEXT_OPTS",)
+    CATEGORY = "Animate Diff 🎭🅐🅓/context opts"
+    FUNCTION = "create_options"
+
+    def create_options(self, context_length: int, start_percent: float=0.0, guarantee_steps: int=1,
+                       prev_context: ContextOptionsGroup=None):
+        if prev_context is None:
+            prev_context = ContextOptionsGroup()
+        prev_context = prev_context.clone()
+        
+        context_options = ContextOptions(
+            context_length=context_length,
+            context_overlap=0,
+            context_schedule=ContextSchedules.BATCHED,
+            start_percent=start_percent,
+            guarantee_steps=guarantee_steps,
+            )
+        prev_context.add(context_options)
+        return (prev_context,)
+
+
+class ViewAsContextOptionsNode:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {
+            "required": {
+                "view_opts_req": ("VIEW_OPTS",),
+            },
+            "optional": {
+                "start_percent": ("FLOAT", {"default": 0.0, "min": 0.0, "max": 1.0, "step": 0.001}),
+                "guarantee_steps": ("INT", {"default": 1, "min": 0, "max": BIGMAX}),
+                "prev_context": ("CONTEXT_OPTIONS",),
+            }
+        }
+    
+    RETURN_TYPES = ("CONTEXT_OPTIONS",)
+    RETURN_NAMES = ("CONTEXT_OPTS",)
+    CATEGORY = "Animate Diff 🎭🅐🅓/context opts"
+    FUNCTION = "create_options"
+
+    def create_options(self, view_opts_req: ContextOptions, start_percent: float=0.0, guarantee_steps: int=1,
+                       prev_context: ContextOptionsGroup=None):
+        if prev_context is None:
+            prev_context = ContextOptionsGroup()
+        prev_context = prev_context.clone()
+        context_options = ContextOptions(
+            context_schedule=ContextSchedules.VIEW_AS_CONTEXT,
+            start_percent=start_percent,
+            guarantee_steps=guarantee_steps,
+            view_options=view_opts_req,
+            use_on_equal_length=True
+        )
+        prev_context.add(context_options)
+        return (prev_context,)
+
+
+#########################
+# View Options
+class StandardStaticViewOptionsNode:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {
+            "required": {
+                "view_length": ("INT", {"default": 16, "min": 1, "max": LENGTH_MAX}),
+                "view_overlap": ("INT", {"default": 4, "min": 0, "max": OVERLAP_MAX}),
+            },
+            "optional": {
+                "fuse_method": (ContextFuseMethod.LIST,),
+            }
+        }
+    
+    RETURN_TYPES = ("VIEW_OPTS",)
+    CATEGORY = "Animate Diff 🎭🅐🅓/context opts/view opts"
+    FUNCTION = "create_options"
+
+    def create_options(self, view_length: int, view_overlap: int,
+                       fuse_method: str=ContextFuseMethod.FLAT,):
+        view_options = ContextOptions(
+            context_length=view_length,
+            context_stride=None,
+            context_overlap=view_overlap,
+            context_schedule=ContextSchedules.STATIC_STANDARD,
+            fuse_method=fuse_method,
+            )
+        return (view_options,)
+
+
+class StandardUniformViewOptionsNode:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {
+            "required": {
+                "view_length": ("INT", {"default": 16, "min": 1, "max": LENGTH_MAX}),
+                "view_stride": ("INT", {"default": 1, "min": 1, "max": STRIDE_MAX}),
+                "view_overlap": ("INT", {"default": 4, "min": 0, "max": OVERLAP_MAX}),
+            },
+            "optional": {
+                "fuse_method": (ContextFuseMethod.LIST,),
+            }
+        }
+    
+    RETURN_TYPES = ("VIEW_OPTS",)
+    CATEGORY = "Animate Diff 🎭🅐🅓/context opts/view opts"
+    FUNCTION = "create_options"
+
+    def create_options(self, view_length: int, view_overlap: int, view_stride: int,
+                       fuse_method: str=ContextFuseMethod.PYRAMID,):
+        view_options = ContextOptions(
+            context_length=view_length,
+            context_stride=view_stride,
+            context_overlap=view_overlap,
+            context_schedule=ContextSchedules.UNIFORM_STANDARD,
+            fuse_method=fuse_method,
+            )
+        return (view_options,)
+
+
+class LoopedUniformViewOptionsNode:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {
+            "required": {
+                "view_length": ("INT", {"default": 16, "min": 1, "max": LENGTH_MAX}),
+                "view_stride": ("INT", {"default": 1, "min": 1, "max": STRIDE_MAX}),
+                "view_overlap": ("INT", {"default": 4, "min": 0, "max": OVERLAP_MAX}),
+                "closed_loop": ("BOOLEAN", {"default": False},),
+            },
+            "optional": {
+                "fuse_method": (ContextFuseMethod.LIST,),
+                "use_on_equal_length": ("BOOLEAN", {"default": False},),
+            }
+        }
+    
+    RETURN_TYPES = ("VIEW_OPTS",)
+    CATEGORY = "Animate Diff 🎭🅐🅓/context opts/view opts"
+    FUNCTION = "create_options"
+
+    def create_options(self, view_length: int, view_overlap: int, view_stride: int, closed_loop: bool,
+                       fuse_method: str=ContextFuseMethod.PYRAMID, use_on_equal_length=False):
+        view_options = ContextOptions(
+            context_length=view_length,
+            context_stride=view_stride,
+            context_overlap=view_overlap,
+            context_schedule=ContextSchedules.UNIFORM_LOOPED,
+            closed_loop=closed_loop,
+            fuse_method=fuse_method,
+            use_on_equal_length=use_on_equal_length,
+            )
+        return (view_options,)

custom_nodes/ComfyUI-AnimateDiff-Evolved/animatediff/nodes_deprecated.py

@@ -0,0 +1,277 @@
+import json
+import os
+import shutil
+import subprocess
+from typing import Dict, List
+
+import numpy as np
+import torch
+from PIL import Image
+from PIL.PngImagePlugin import PngInfo
+
+import folder_paths
+from comfy.model_patcher import ModelPatcher
+
+from .context import ContextOptionsGroup, ContextOptions, ContextSchedules
+from .logger import logger
+from .utils_model import Folders, BetaSchedules, get_available_motion_models
+from .model_injection import ModelPatcherAndInjector, InjectionParams, MotionModelGroup, load_motion_module_gen1
+
+
+class AnimateDiffLoader_Deprecated:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {
+            "required": {
+                "model": ("MODEL",),
+                "latents": ("LATENT",),
+                "model_name": (get_available_motion_models(),),
+                "unlimited_area_hack": ("BOOLEAN", {"default": False},),
+                "beta_schedule": (BetaSchedules.get_alias_list_with_first_element(BetaSchedules.SQRT_LINEAR),),
+            },
+        }
+
+    RETURN_TYPES = ("MODEL", "LATENT")
+    CATEGORY = ""
+    FUNCTION = "load_mm_and_inject_params"
+
+    def load_mm_and_inject_params(
+        self,
+        model: ModelPatcher,
+        latents: Dict[str, torch.Tensor],
+        model_name: str, unlimited_area_hack: bool, beta_schedule: str,
+    ):
+        # load motion module
+        motion_model = load_motion_module_gen1(model_name, model)
+        # get total frames
+        init_frames_len = len(latents["samples"])  # deprecated - no longer used for anything lol
+        # set injection params
+        params = InjectionParams(
+                unlimited_area_hack=unlimited_area_hack,
+                apply_mm_groupnorm_hack=True,
+                model_name=model_name,
+                apply_v2_properly=False,
+        )
+        # inject for use in sampling code
+        model = ModelPatcherAndInjector(model)
+        model.motion_models = MotionModelGroup(motion_model)
+        model.motion_injection_params = params
+
+        # save model sampling from BetaSchedule as object patch
+        # if autoselect, get suggested beta_schedule from motion model
+        if beta_schedule == BetaSchedules.AUTOSELECT and not model.motion_models.is_empty():
+            beta_schedule = model.motion_models[0].model.get_best_beta_schedule(log=True)
+        new_model_sampling = BetaSchedules.to_model_sampling(beta_schedule, model)
+        if new_model_sampling is not None:
+            model.add_object_patch("model_sampling", new_model_sampling)
+
+        del motion_model
+        return (model, latents)
+
+
+class AnimateDiffLoaderAdvanced_Deprecated:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {
+            "required": {
+                "model": ("MODEL",),
+                "latents": ("LATENT",),
+                "model_name": (get_available_motion_models(),),
+                "unlimited_area_hack": ("BOOLEAN", {"default": False},),
+                "context_length": ("INT", {"default": 16, "min": 0, "max": 1000}),
+                "context_stride": ("INT", {"default": 1, "min": 1, "max": 1000}),
+                "context_overlap": ("INT", {"default": 4, "min": 0, "max": 1000}),
+                "context_schedule": (ContextSchedules.LEGACY_UNIFORM_SCHEDULE_LIST,),
+                "closed_loop": ("BOOLEAN", {"default": False},),
+                "beta_schedule": (BetaSchedules.get_alias_list_with_first_element(BetaSchedules.SQRT_LINEAR),),
+            },
+        }
+
+    RETURN_TYPES = ("MODEL", "LATENT")
+    CATEGORY = ""
+    FUNCTION = "load_mm_and_inject_params"
+
+    def load_mm_and_inject_params(self,
+            model: ModelPatcher,
+            latents: Dict[str, torch.Tensor],
+            model_name: str, unlimited_area_hack: bool,
+            context_length: int, context_stride: int, context_overlap: int, context_schedule: str, closed_loop: bool,
+            beta_schedule: str,
+        ):
+        # load motion module
+        motion_model = load_motion_module_gen1(model_name, model)
+        # get total frames
+        init_frames_len = len(latents["samples"])  # deprecated - no longer used for anything lol
+        # set injection params
+        params = InjectionParams(
+                unlimited_area_hack=unlimited_area_hack,
+                apply_mm_groupnorm_hack=True,
+                model_name=model_name,
+                apply_v2_properly=False,
+        )
+        context_group = ContextOptionsGroup()
+        context_group.add(
+            ContextOptions(
+                context_length=context_length,
+                context_stride=context_stride,
+                context_overlap=context_overlap,
+                context_schedule=context_schedule,
+                closed_loop=closed_loop,
+                )
+            )
+        # set context settings
+        params.set_context(context_options=context_group)
+        # inject for use in sampling code
+        model = ModelPatcherAndInjector(model)
+        model.motion_models = MotionModelGroup(motion_model)
+        model.motion_injection_params = params
+
+        # save model sampling from BetaSchedule as object patch
+        # if autoselect, get suggested beta_schedule from motion model
+        if beta_schedule == BetaSchedules.AUTOSELECT and not model.motion_models.is_empty():
+            beta_schedule = model.motion_models[0].model.get_best_beta_schedule(log=True)
+        new_model_sampling = BetaSchedules.to_model_sampling(beta_schedule, model)
+        if new_model_sampling is not None:
+            model.add_object_patch("model_sampling", new_model_sampling)
+
+        del motion_model
+        return (model, latents)
+    
+
+class AnimateDiffCombine_Deprecated:
+    ffmpeg_warning_already_shown = False
+    @classmethod
+    def INPUT_TYPES(s):
+        ffmpeg_path = shutil.which("ffmpeg")
+        #Hide ffmpeg formats if ffmpeg isn't available
+        if ffmpeg_path is not None:
+            ffmpeg_formats = ["video/"+x[:-5] for x in folder_paths.get_filename_list(Folders.VIDEO_FORMATS)]
+        else:
+            ffmpeg_formats = []
+            if not s.ffmpeg_warning_already_shown:
+                # Deprecated node are now hidden, so no need to show warning unless node is used.
+                # logger.warning("This warning can be ignored, you should not be using the deprecated AnimateDiff Combine node anyway. If you are, use Video Combine from ComfyUI-VideoHelperSuite instead. ffmpeg could not be found. Outputs that require it have been disabled")
+                s.ffmpeg_warning_already_shown = True
+        return {
+            "required": {
+                "images": ("IMAGE",),
+                "frame_rate": (
+                    "INT",
+                    {"default": 8, "min": 1, "max": 24, "step": 1},
+                ),
+                "loop_count": ("INT", {"default": 0, "min": 0, "max": 100, "step": 1}),
+                "filename_prefix": ("STRING", {"default": "AnimateDiff"}),
+                "format": (["image/gif", "image/webp"] + ffmpeg_formats,),
+                "pingpong": ("BOOLEAN", {"default": False}),
+                "save_image": ("BOOLEAN", {"default": True}),
+            },
+            "hidden": {
+                "prompt": "PROMPT",
+                "extra_pnginfo": "EXTRA_PNGINFO",
+            },
+        }
+
+    RETURN_TYPES = ("GIF",)
+    OUTPUT_NODE = True
+    CATEGORY = ""
+    FUNCTION = "generate_gif"
+
+    def generate_gif(
+        self,
+        images,
+        frame_rate: int,
+        loop_count: int,
+        filename_prefix="AnimateDiff",
+        format="image/gif",
+        pingpong=False,
+        save_image=True,
+        prompt=None,
+        extra_pnginfo=None,
+    ):
+        logger.warning("Do not use AnimateDiff Combine node, it is deprecated. Use Video Combine node from ComfyUI-VideoHelperSuite instead. Video nodes from VideoHelperSuite are actively maintained, more feature-rich, and also automatically attempts to get ffmpeg.")
+        # convert images to numpy
+        frames: List[Image.Image] = []
+        for image in images:
+            img = 255.0 * image.cpu().numpy()
+            img = Image.fromarray(np.clip(img, 0, 255).astype(np.uint8))
+            frames.append(img)
+            
+        # get output information
+        output_dir = (
+            folder_paths.get_output_directory()
+            if save_image
+            else folder_paths.get_temp_directory()
+        )
+        (
+            full_output_folder,
+            filename,
+            counter,
+            subfolder,
+            _,
+        ) = folder_paths.get_save_image_path(filename_prefix, output_dir)
+
+        metadata = PngInfo()
+        if prompt is not None:
+            metadata.add_text("prompt", json.dumps(prompt))
+        if extra_pnginfo is not None:
+            for x in extra_pnginfo:
+                metadata.add_text(x, json.dumps(extra_pnginfo[x]))
+
+        # save first frame as png to keep metadata
+        file = f"{filename}_{counter:05}_.png"
+        file_path = os.path.join(full_output_folder, file)
+        frames[0].save(
+            file_path,
+            pnginfo=metadata,
+            compress_level=4,
+        )
+        if pingpong:
+            frames = frames + frames[-2:0:-1]
+        
+        format_type, format_ext = format.split("/")
+        file = f"{filename}_{counter:05}_.{format_ext}"
+        file_path = os.path.join(full_output_folder, file)
+        if format_type == "image":
+            # Use pillow directly to save an animated image
+            frames[0].save(
+                file_path,
+                format=format_ext.upper(),
+                save_all=True,
+                append_images=frames[1:],
+                duration=round(1000 / frame_rate),
+                loop=loop_count,
+                compress_level=4,
+            )
+        else:
+            # Use ffmpeg to save a video
+            ffmpeg_path = shutil.which("ffmpeg")
+            if ffmpeg_path is None:
+                #Should never be reachable
+                raise ProcessLookupError("Could not find ffmpeg")
+
+            video_format_path = folder_paths.get_full_path("video_formats", format_ext + ".json")
+            with open(video_format_path, 'r') as stream:
+                video_format = json.load(stream)
+            file = f"{filename}_{counter:05}_.{video_format['extension']}"
+            file_path = os.path.join(full_output_folder, file)
+            dimensions = f"{frames[0].width}x{frames[0].height}"
+            args = [ffmpeg_path, "-v", "error", "-f", "rawvideo", "-pix_fmt", "rgb24",
+                    "-s", dimensions, "-r", str(frame_rate), "-i", "-"] \
+                    + video_format['main_pass'] + [file_path]
+
+            env=os.environ.copy()
+            if  "environment" in video_format:
+                env.update(video_format["environment"])
+            with subprocess.Popen(args, stdin=subprocess.PIPE, env=env) as proc:
+                for frame in frames:
+                    proc.stdin.write(frame.tobytes())
+
+        previews = [
+            {
+                "filename": file,
+                "subfolder": subfolder,
+                "type": "output" if save_image else "temp",
+                "format": format,
+            }
+        ]
+        return {"ui": {"gifs": previews}}

custom_nodes/ComfyUI-AnimateDiff-Evolved/animatediff/nodes_extras.py

@@ -0,0 +1,78 @@
+import torch
+
+import folder_paths
+import nodes as comfy_nodes
+from comfy.model_patcher import ModelPatcher
+from comfy.sd import load_checkpoint_guess_config
+
+from .logger import logger
+from .utils_model import BetaSchedules
+from .model_injection import get_vanilla_model_patcher
+
+
+class AnimateDiffUnload:
+    def __init__(self) -> None:
+        pass
+
+    @classmethod
+    def INPUT_TYPES(s):
+        return {"required": {"model": ("MODEL",)}}
+
+    RETURN_TYPES = ("MODEL",)
+    CATEGORY = "Animate Diff 🎭🅐🅓/extras"
+    FUNCTION = "unload_motion_modules"
+
+    def unload_motion_modules(self, model: ModelPatcher):
+        # return model clone with ejected params
+        #model = eject_params_from_model(model)
+        model = get_vanilla_model_patcher(model)
+        return (model.clone(),)
+
+
+class CheckpointLoaderSimpleWithNoiseSelect:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {
+            "required": {
+                "ckpt_name": (folder_paths.get_filename_list("checkpoints"), ),
+                "beta_schedule": (BetaSchedules.ALIAS_LIST, {"default": BetaSchedules.USE_EXISTING}, )
+            },
+            "optional": {
+                "use_custom_scale_factor": ("BOOLEAN", {"default": False}),
+                "scale_factor": ("FLOAT", {"default": 0.18215, "min": 0.0, "max": 1.0, "step": 0.00001})
+            }
+        }
+    RETURN_TYPES = ("MODEL", "CLIP", "VAE")
+    FUNCTION = "load_checkpoint"
+
+    CATEGORY = "Animate Diff 🎭🅐🅓/extras"
+
+    def load_checkpoint(self, ckpt_name, beta_schedule, output_vae=True, output_clip=True, use_custom_scale_factor=False, scale_factor=0.18215):
+        ckpt_path = folder_paths.get_full_path("checkpoints", ckpt_name)
+        out = load_checkpoint_guess_config(ckpt_path, output_vae=True, output_clip=True, embedding_directory=folder_paths.get_folder_paths("embeddings"))
+        # register chosen beta schedule on model - convert to beta_schedule name recognized by ComfyUI
+        new_model_sampling = BetaSchedules.to_model_sampling(beta_schedule, out[0])
+        if new_model_sampling is not None:
+            out[0].model.model_sampling = new_model_sampling
+        if use_custom_scale_factor:
+            out[0].model.latent_format.scale_factor = scale_factor
+        return out
+
+
+class EmptyLatentImageLarge:
+    def __init__(self, device="cpu"):
+        self.device = device
+
+    @classmethod
+    def INPUT_TYPES(s):
+        return {"required": { "width": ("INT", {"default": 512, "min": 64, "max": comfy_nodes.MAX_RESOLUTION, "step": 8}),
+                              "height": ("INT", {"default": 512, "min": 64, "max": comfy_nodes.MAX_RESOLUTION, "step": 8}),
+                              "batch_size": ("INT", {"default": 1, "min": 1, "max": 262144})}}
+    RETURN_TYPES = ("LATENT",)
+    FUNCTION = "generate"
+
+    CATEGORY = "Animate Diff 🎭🅐🅓/extras"
+
+    def generate(self, width, height, batch_size=1):
+        latent = torch.zeros([batch_size, 4, height // 8, width // 8])
+        return ({"samples":latent}, )

custom_nodes/ComfyUI-AnimateDiff-Evolved/animatediff/nodes_gen1.py

@@ -0,0 +1,340 @@
+from pathlib import Path
+import torch
+
+import comfy.sample as comfy_sample
+from comfy.model_patcher import ModelPatcher
+
+from .ad_settings import AdjustPEGroup, AnimateDiffSettings, AdjustPE
+from .context import ContextOptions, ContextOptionsGroup, ContextSchedules
+from .logger import logger
+from .utils_model import BetaSchedules, get_available_motion_loras, get_available_motion_models, get_motion_lora_path
+from .utils_motion import ADKeyframeGroup, get_combined_multival
+from .motion_lora import MotionLoraInfo, MotionLoraList
+from .model_injection import InjectionParams, ModelPatcherAndInjector, MotionModelGroup, load_motion_lora_as_patches, load_motion_module_gen1, load_motion_module_gen2, validate_model_compatibility_gen2
+from .sample_settings import SampleSettings, SeedNoiseGeneration
+from .sampling import motion_sample_factory
+
+
+class AnimateDiffLoaderGen1:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {
+            "required": {
+                "model": ("MODEL",),
+                "model_name": (get_available_motion_models(),),
+                "beta_schedule": (BetaSchedules.ALIAS_LIST, {"default": BetaSchedules.AUTOSELECT}),
+                #"apply_mm_groupnorm_hack": ("BOOLEAN", {"default": True}),
+            },
+            "optional": {
+                "context_options": ("CONTEXT_OPTIONS",),
+                "motion_lora": ("MOTION_LORA",),
+                "ad_settings": ("AD_SETTINGS",),
+                "ad_keyframes": ("AD_KEYFRAMES",),
+                "sample_settings": ("SAMPLE_SETTINGS",),
+                "scale_multival": ("MULTIVAL",),
+                "effect_multival": ("MULTIVAL",),
+            }
+        }
+
+    RETURN_TYPES = ("MODEL",)
+    CATEGORY = "Animate Diff 🎭🅐🅓/① Gen1 nodes ①"
+    FUNCTION = "load_mm_and_inject_params"
+
+    def load_mm_and_inject_params(self,
+        model: ModelPatcher,
+        model_name: str, beta_schedule: str,# apply_mm_groupnorm_hack: bool,
+        context_options: ContextOptionsGroup=None, motion_lora: MotionLoraList=None, ad_settings: AnimateDiffSettings=None,
+        sample_settings: SampleSettings=None, scale_multival=None, effect_multival=None, ad_keyframes: ADKeyframeGroup=None,
+    ):
+        # load motion module and motion settings, if included
+        motion_model = load_motion_module_gen2(model_name=model_name, motion_model_settings=ad_settings)
+        # confirm that it is compatible with SD model
+        validate_model_compatibility_gen2(model=model, motion_model=motion_model)
+        # apply motion model to loaded_mm
+        if motion_lora is not None:
+            for lora in motion_lora.loras:
+                load_motion_lora_as_patches(motion_model, lora)
+        motion_model.scale_multival = scale_multival
+        motion_model.effect_multival = effect_multival
+        motion_model.keyframes = ad_keyframes.clone() if ad_keyframes else ADKeyframeGroup()
+        
+        # create injection params
+        params = InjectionParams(unlimited_area_hack=False, model_name=motion_model.model.mm_info.mm_name)
+        # apply context options
+        if context_options:
+            params.set_context(context_options)
+
+        # set motion_scale and motion_model_settings
+        if not ad_settings:
+            ad_settings = AnimateDiffSettings()
+        ad_settings.attn_scale = 1.0
+        params.set_motion_model_settings(ad_settings)
+
+        # backwards compatibility to support old way of masking scale
+        if params.motion_model_settings.mask_attn_scale is not None:
+            motion_model.scale_multival = get_combined_multival(scale_multival, (params.motion_model_settings.mask_attn_scale * params.motion_model_settings.attn_scale))
+        
+        # need to use a ModelPatcher that supports injection of motion modules into unet
+        # need to use a ModelPatcher that supports injection of motion modules into unet
+        model = ModelPatcherAndInjector(model)
+        model.motion_models = MotionModelGroup(motion_model)
+        model.sample_settings = sample_settings if sample_settings is not None else SampleSettings()
+        model.motion_injection_params = params
+        
+        if model.sample_settings.custom_cfg is not None:
+            logger.info("[Sample Settings] custom_cfg is set; will override any KSampler cfg values or patches.")
+
+        if model.sample_settings.sigma_schedule is not None:
+            logger.info("[Sample Settings] sigma_schedule is set; will override beta_schedule.")
+            model.add_object_patch("model_sampling", model.sample_settings.sigma_schedule.clone().model_sampling)
+        else:
+            # save model sampling from BetaSchedule as object patch
+            # if autoselect, get suggested beta_schedule from motion model
+            if beta_schedule == BetaSchedules.AUTOSELECT and not model.motion_models.is_empty():
+                beta_schedule = model.motion_models[0].model.get_best_beta_schedule(log=True)
+            new_model_sampling = BetaSchedules.to_model_sampling(beta_schedule, model)
+            if new_model_sampling is not None:
+                model.add_object_patch("model_sampling", new_model_sampling)
+
+        del motion_model
+        return (model,)
+
+
+class LegacyAnimateDiffLoaderWithContext:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {
+            "required": {
+                "model": ("MODEL",),
+                "model_name": (get_available_motion_models(),),
+                "beta_schedule": (BetaSchedules.ALIAS_LIST, {"default": BetaSchedules.AUTOSELECT}),
+                #"apply_mm_groupnorm_hack": ("BOOLEAN", {"default": True}),
+            },
+            "optional": {
+                "context_options": ("CONTEXT_OPTIONS",),
+                "motion_lora": ("MOTION_LORA",),
+                "ad_settings": ("AD_SETTINGS",),
+                "sample_settings": ("SAMPLE_SETTINGS",),
+                "motion_scale": ("FLOAT", {"default": 1.0, "min": 0.0, "step": 0.001}),
+                "apply_v2_models_properly": ("BOOLEAN", {"default": True}),
+                "ad_keyframes": ("AD_KEYFRAMES",),
+            }
+        }
+    
+    RETURN_TYPES = ("MODEL",)
+    CATEGORY = "Animate Diff 🎭🅐🅓/① Gen1 nodes ①"
+    FUNCTION = "load_mm_and_inject_params"
+
+
+    def load_mm_and_inject_params(self,
+        model: ModelPatcher,
+        model_name: str, beta_schedule: str,# apply_mm_groupnorm_hack: bool,
+        context_options: ContextOptionsGroup=None, motion_lora: MotionLoraList=None, ad_settings: AnimateDiffSettings=None, motion_model_settings: AnimateDiffSettings=None,
+        sample_settings: SampleSettings=None, motion_scale: float=1.0, apply_v2_models_properly: bool=False, ad_keyframes: ADKeyframeGroup=None,
+    ):
+        if ad_settings is not None:
+            motion_model_settings = ad_settings
+        # load motion module
+        motion_model = load_motion_module_gen1(model_name, model, motion_lora=motion_lora, motion_model_settings=motion_model_settings)
+        # set injection params
+        params = InjectionParams(
+                unlimited_area_hack=False,
+                model_name=model_name,
+                apply_v2_properly=apply_v2_models_properly,
+        )
+        if context_options:
+            params.set_context(context_options)
+        # set motion_scale and motion_model_settings
+        if not motion_model_settings:
+            motion_model_settings = AnimateDiffSettings()
+        motion_model_settings.attn_scale = motion_scale
+        params.set_motion_model_settings(motion_model_settings)
+
+        if params.motion_model_settings.mask_attn_scale is not None:
+            motion_model.scale_multival = params.motion_model_settings.mask_attn_scale * params.motion_model_settings.attn_scale
+        else:
+            motion_model.scale_multival = params.motion_model_settings.attn_scale
+
+        motion_model.keyframes = ad_keyframes.clone() if ad_keyframes else ADKeyframeGroup()
+
+        model = ModelPatcherAndInjector(model)
+        model.motion_models = MotionModelGroup(motion_model)
+        model.sample_settings = sample_settings if sample_settings is not None else SampleSettings()
+        model.motion_injection_params = params
+
+        # save model sampling from BetaSchedule as object patch
+        # if autoselect, get suggested beta_schedule from motion model
+        if beta_schedule == BetaSchedules.AUTOSELECT and not model.motion_models.is_empty():
+            beta_schedule = model.motion_models[0].model.get_best_beta_schedule(log=True)
+        new_model_sampling = BetaSchedules.to_model_sampling(beta_schedule, model)
+        if new_model_sampling is not None:
+            model.add_object_patch("model_sampling", new_model_sampling)
+
+        del motion_model
+        return (model,)
+
+
+class AnimateDiffModelSettings:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {
+            "required": {
+                "min_motion_scale": ("FLOAT", {"default": 1.0, "min": 0.0, "step": 0.001}),
+                "max_motion_scale": ("FLOAT", {"default": 1.0, "min": 0.0, "step": 0.001}),
+            },
+            "optional": {
+                "mask_motion_scale": ("MASK",),
+            }
+        }
+    
+    RETURN_TYPES = ("AD_SETTINGS",)
+    CATEGORY = ""  #"Animate Diff 🎭🅐🅓/① Gen1 nodes ①/motion settings"
+    FUNCTION = "get_motion_model_settings"
+
+    def get_motion_model_settings(self, mask_motion_scale: torch.Tensor=None, min_motion_scale: float=1.0, max_motion_scale: float=1.0):
+        motion_model_settings = AnimateDiffSettings(
+            mask_attn_scale=mask_motion_scale,
+            mask_attn_scale_min=min_motion_scale,
+            mask_attn_scale_max=max_motion_scale,
+            )
+
+        return (motion_model_settings,)
+
+
+class AnimateDiffModelSettingsSimple:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {
+            "required": {
+                "motion_pe_stretch": ("INT", {"default": 0, "min": 0, "step": 1}),
+            },
+            "optional": {
+                "mask_motion_scale": ("MASK",),
+                "min_motion_scale": ("FLOAT", {"default": 1.0, "min": 0.0, "step": 0.001}),
+                "max_motion_scale": ("FLOAT", {"default": 1.0, "min": 0.0, "step": 0.001}),
+            }
+        }
+    
+    RETURN_TYPES = ("AD_SETTINGS",)
+    CATEGORY = ""  #"Animate Diff 🎭🅐🅓/① Gen1 nodes ①/motion settings/experimental"
+    FUNCTION = "get_motion_model_settings"
+
+    def get_motion_model_settings(self, motion_pe_stretch: int,
+                                  mask_motion_scale: torch.Tensor=None, min_motion_scale: float=1.0, max_motion_scale: float=1.0):
+        adjust_pe = AdjustPEGroup(AdjustPE(motion_pe_stretch=motion_pe_stretch))
+        motion_model_settings = AnimateDiffSettings(
+            adjust_pe=adjust_pe,
+            mask_attn_scale=mask_motion_scale,
+            mask_attn_scale_min=min_motion_scale,
+            mask_attn_scale_max=max_motion_scale,
+            )
+
+        return (motion_model_settings,)
+
+
+class AnimateDiffModelSettingsAdvanced:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {
+            "required": {
+                "pe_strength": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.0001}),
+                "attn_strength": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.0001}),
+                "other_strength": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.0001}),
+                "motion_pe_stretch": ("INT", {"default": 0, "min": 0, "step": 1}),
+                "cap_initial_pe_length": ("INT", {"default": 0, "min": 0, "step": 1}),
+                "interpolate_pe_to_length": ("INT", {"default": 0, "min": 0, "step": 1}),
+                "initial_pe_idx_offset": ("INT", {"default": 0, "min": 0, "step": 1}),
+                "final_pe_idx_offset": ("INT", {"default": 0, "min": 0, "step": 1}),
+            },
+            "optional": {
+                "mask_motion_scale": ("MASK",),
+                "min_motion_scale": ("FLOAT", {"default": 1.0, "min": 0.0, "step": 0.001}),
+                "max_motion_scale": ("FLOAT", {"default": 1.0, "min": 0.0, "step": 0.001}),
+            }
+        }
+    
+    RETURN_TYPES = ("AD_SETTINGS",)
+    CATEGORY = ""  #"Animate Diff 🎭🅐🅓/① Gen1 nodes ①/motion settings/experimental"
+    FUNCTION = "get_motion_model_settings"
+
+    def get_motion_model_settings(self, pe_strength: float, attn_strength: float, other_strength: float,
+                                  motion_pe_stretch: int,
+                                  cap_initial_pe_length: int, interpolate_pe_to_length: int,
+                                  initial_pe_idx_offset: int, final_pe_idx_offset: int,
+                                  mask_motion_scale: torch.Tensor=None, min_motion_scale: float=1.0, max_motion_scale: float=1.0):
+        adjust_pe = AdjustPEGroup(AdjustPE(motion_pe_stretch=motion_pe_stretch,
+                             cap_initial_pe_length=cap_initial_pe_length, interpolate_pe_to_length=interpolate_pe_to_length,
+                             initial_pe_idx_offset=initial_pe_idx_offset, final_pe_idx_offset=final_pe_idx_offset))
+        motion_model_settings = AnimateDiffSettings(
+            adjust_pe=adjust_pe,
+            pe_strength=pe_strength,
+            attn_strength=attn_strength,
+            other_strength=other_strength,
+            mask_attn_scale=mask_motion_scale,
+            mask_attn_scale_min=min_motion_scale,
+            mask_attn_scale_max=max_motion_scale,
+            )
+
+        return (motion_model_settings,)
+
+
+class AnimateDiffModelSettingsAdvancedAttnStrengths:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {
+            "required": {
+                "pe_strength": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.0001}),
+                "attn_strength": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.0001}),
+                "attn_q_strength": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.0001}),
+                "attn_k_strength": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.0001}),
+                "attn_v_strength": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.0001}),
+                "attn_out_weight_strength": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.0001}),
+                "attn_out_bias_strength": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.0001}),
+                "other_strength": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.0001}),
+                "motion_pe_stretch": ("INT", {"default": 0, "min": 0, "step": 1}),
+                "cap_initial_pe_length": ("INT", {"default": 0, "min": 0, "step": 1}),
+                "interpolate_pe_to_length": ("INT", {"default": 0, "min": 0, "step": 1}),
+                "initial_pe_idx_offset": ("INT", {"default": 0, "min": 0, "step": 1}),
+                "final_pe_idx_offset": ("INT", {"default": 0, "min": 0, "step": 1}),
+            },
+            "optional": {
+                "mask_motion_scale": ("MASK",),
+                "min_motion_scale": ("FLOAT", {"default": 1.0, "min": 0.0, "step": 0.001}),
+                "max_motion_scale": ("FLOAT", {"default": 1.0, "min": 0.0, "step": 0.001}),
+            }
+        }
+    
+    RETURN_TYPES = ("AD_SETTINGS",)
+    CATEGORY = ""  #"Animate Diff 🎭🅐🅓/① Gen1 nodes ①/motion settings/experimental"
+    FUNCTION = "get_motion_model_settings"
+
+    def get_motion_model_settings(self, pe_strength: float, attn_strength: float,
+                                  attn_q_strength: float,
+                                  attn_k_strength: float,
+                                  attn_v_strength: float,
+                                  attn_out_weight_strength: float,
+                                  attn_out_bias_strength: float,
+                                  other_strength: float,
+                                  motion_pe_stretch: int,
+                                  cap_initial_pe_length: int, interpolate_pe_to_length: int,
+                                  initial_pe_idx_offset: int, final_pe_idx_offset: int,
+                                  mask_motion_scale: torch.Tensor=None, min_motion_scale: float=1.0, max_motion_scale: float=1.0):
+        adjust_pe = AdjustPEGroup(AdjustPE(motion_pe_stretch=motion_pe_stretch,
+                             cap_initial_pe_length=cap_initial_pe_length, interpolate_pe_to_length=interpolate_pe_to_length,
+                             initial_pe_idx_offset=initial_pe_idx_offset, final_pe_idx_offset=final_pe_idx_offset))
+        motion_model_settings = AnimateDiffSettings(
+            adjust_pe=adjust_pe,
+            pe_strength=pe_strength,
+            attn_strength=attn_strength,
+            attn_q_strength=attn_q_strength,
+            attn_k_strength=attn_k_strength,
+            attn_v_strength=attn_v_strength,
+            attn_out_weight_strength=attn_out_weight_strength,
+            attn_out_bias_strength=attn_out_bias_strength,
+            other_strength=other_strength,
+            mask_attn_scale=mask_motion_scale,
+            mask_attn_scale_min=min_motion_scale,
+            mask_attn_scale_max=max_motion_scale,
+            )
+
+        return (motion_model_settings,)

custom_nodes/ComfyUI-AnimateDiff-Evolved/animatediff/nodes_gen2.py

@@ -0,0 +1,212 @@
+from pathlib import Path
+import torch
+
+import comfy.sample as comfy_sample
+from comfy.model_patcher import ModelPatcher
+
+from .ad_settings import AnimateDiffSettings
+from .context import ContextOptions, ContextOptionsGroup, ContextSchedules
+from .logger import logger
+from .utils_model import BIGMAX, BetaSchedules, get_available_motion_loras, get_available_motion_models, get_motion_lora_path
+from .utils_motion import ADKeyframeGroup, ADKeyframe
+from .motion_lora import MotionLoraInfo, MotionLoraList
+from .model_injection import (InjectionParams, ModelPatcherAndInjector, MotionModelGroup, MotionModelPatcher, create_fresh_motion_module,
+                              load_motion_module_gen1, load_motion_module_gen2, load_motion_lora_as_patches, validate_model_compatibility_gen2)
+from .sample_settings import SampleSettings, SeedNoiseGeneration
+from .sampling import motion_sample_factory
+
+
+class UseEvolvedSamplingNode:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {
+            "required": {
+                "model": ("MODEL",),
+                "beta_schedule": (BetaSchedules.ALIAS_LIST, {"default": BetaSchedules.AUTOSELECT}),
+            },
+            "optional": {
+                "m_models": ("M_MODELS",),
+                "context_options": ("CONTEXT_OPTIONS",),
+                "sample_settings": ("SAMPLE_SETTINGS",),
+                #"beta_schedule_override": ("BETA_SCHEDULE",),
+            }
+        }
+    
+    RETURN_TYPES = ("MODEL",)
+    CATEGORY = "Animate Diff 🎭🅐🅓/② Gen2 nodes ②"
+    FUNCTION = "use_evolved_sampling"
+
+    def use_evolved_sampling(self, model: ModelPatcher, beta_schedule: str, m_models: MotionModelGroup=None, context_options: ContextOptionsGroup=None,
+                             sample_settings: SampleSettings=None, beta_schedule_override=None):
+        if m_models is not None:
+            m_models = m_models.clone()
+            # for each motion model, confirm that it is compatible with SD model
+            for motion_model in m_models.models:
+                validate_model_compatibility_gen2(model=model, motion_model=motion_model)
+            # create injection params
+            model_name_list = [motion_model.model.mm_info.mm_name for motion_model in m_models.models]
+            model_names = ",".join(model_name_list)
+            # TODO: check if any apply_v2_properly is set to False
+            params = InjectionParams(unlimited_area_hack=False, model_name=model_names)
+        else:
+            params = InjectionParams()
+        # apply context options
+        if context_options:
+            params.set_context(context_options)
+        # need to use a ModelPatcher that supports injection of motion modules into unet
+        model = ModelPatcherAndInjector(model)
+        model.motion_models = m_models
+        model.sample_settings = sample_settings if sample_settings is not None else SampleSettings()
+        model.motion_injection_params = params
+
+        if model.sample_settings.custom_cfg is not None:
+            logger.info("[Sample Settings] custom_cfg is set; will override any KSampler cfg values or patches.")
+
+        if model.sample_settings.sigma_schedule is not None:
+            logger.info("[Sample Settings] sigma_schedule is set; will override beta_schedule.")
+            model.add_object_patch("model_sampling", model.sample_settings.sigma_schedule.clone().model_sampling)
+        else:
+            # save model_sampling from BetaSchedule as object patch
+            # if autoselect, get suggested beta_schedule from motion model
+            if beta_schedule == BetaSchedules.AUTOSELECT and not model.motion_models.is_empty():
+                beta_schedule = model.motion_models[0].model.get_best_beta_schedule(log=True)
+            new_model_sampling = BetaSchedules.to_model_sampling(beta_schedule, model)
+            if new_model_sampling is not None:
+                model.add_object_patch("model_sampling", new_model_sampling)
+        
+        del m_models
+        return (model,)
+
+
+class ApplyAnimateDiffModelNode:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {
+            "required": {
+                "motion_model": ("MOTION_MODEL_ADE",),
+                "start_percent": ("FLOAT", {"default": 0.0, "min": 0.0, "max": 1.0, "step": 0.001}),
+                "end_percent": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.001}),
+            },
+            "optional": {
+                "motion_lora": ("MOTION_LORA",),
+                "scale_multival": ("MULTIVAL",),
+                "effect_multival": ("MULTIVAL",),
+                "ad_keyframes": ("AD_KEYFRAMES",),
+                "prev_m_models": ("M_MODELS",),
+            }
+        }
+    
+    RETURN_TYPES = ("M_MODELS",)
+    CATEGORY = "Animate Diff 🎭🅐🅓/② Gen2 nodes ②"
+    FUNCTION = "apply_motion_model"
+
+    def apply_motion_model(self, motion_model: MotionModelPatcher, start_percent: float=0.0, end_percent: float=1.0,
+                           motion_lora: MotionLoraList=None, ad_keyframes: ADKeyframeGroup=None,
+                           scale_multival=None, effect_multival=None,
+                           prev_m_models: MotionModelGroup=None,):
+        # set up motion models list
+        if prev_m_models is None:
+            prev_m_models = MotionModelGroup()
+        prev_m_models = prev_m_models.clone()
+        motion_model = motion_model.clone()
+        # check if internal motion model already present in previous model - create new if so
+        for prev_model in prev_m_models.models:
+            if motion_model.model is prev_model.model:
+                # need to create new internal model based on same state_dict
+                motion_model = create_fresh_motion_module(motion_model)
+        # apply motion model to loaded_mm
+        if motion_lora is not None:
+            for lora in motion_lora.loras:
+                load_motion_lora_as_patches(motion_model, lora)
+        motion_model.scale_multival = scale_multival
+        motion_model.effect_multival = effect_multival
+        motion_model.keyframes = ad_keyframes.clone() if ad_keyframes else ADKeyframeGroup()
+        motion_model.timestep_percent_range = (start_percent, end_percent)
+        # add to beginning, so that after injection, it will be the earliest of prev_m_models to be run
+        prev_m_models.add_to_start(mm=motion_model)
+        return (prev_m_models,)
+        
+
+class ApplyAnimateDiffModelBasicNode:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {
+            "required": {
+                "motion_model": ("MOTION_MODEL_ADE",),
+            },
+            "optional": {
+                "motion_lora": ("MOTION_LORA",),
+                "scale_multival": ("MULTIVAL",),
+                "effect_multival": ("MULTIVAL",),
+                "ad_keyframes": ("AD_KEYFRAMES",),
+            }
+        }
+    
+    RETURN_TYPES = ("M_MODELS",)
+    CATEGORY = "Animate Diff 🎭🅐🅓/② Gen2 nodes ②"
+    FUNCTION = "apply_motion_model"
+
+    def apply_motion_model(self,
+                           motion_model: MotionModelPatcher, motion_lora: MotionLoraList=None,
+                           scale_multival=None, effect_multival=None, ad_keyframes=None):
+        # just a subset of normal ApplyAnimateDiffModelNode inputs
+        return ApplyAnimateDiffModelNode.apply_motion_model(self, motion_model, motion_lora=motion_lora,
+                                                            scale_multival=scale_multival, effect_multival=effect_multival,
+                                                            ad_keyframes=ad_keyframes)
+
+
+class LoadAnimateDiffModelNode:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {
+            "required": {
+                "model_name": (get_available_motion_models(),),
+            },
+            "optional": {
+                "ad_settings": ("AD_SETTINGS",),
+            }
+        }
+
+    RETURN_TYPES = ("MOTION_MODEL_ADE",)
+    RETURN_NAMES = ("MOTION_MODEL",)
+    CATEGORY = "Animate Diff 🎭🅐🅓/② Gen2 nodes ②"
+    FUNCTION = "load_motion_model"
+
+    def load_motion_model(self, model_name: str, ad_settings: AnimateDiffSettings=None):
+        # load motion module and motion settings, if included
+        motion_model = load_motion_module_gen2(model_name=model_name, motion_model_settings=ad_settings)
+        return (motion_model,)
+
+
+class ADKeyframeNode:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {
+            "required": {
+                "start_percent": ("FLOAT", {"default": 0.0, "min": 0.0, "max": 1.0, "step": 0.001}, ),
+            },
+            "optional": {
+                "prev_ad_keyframes": ("AD_KEYFRAMES", ),
+                "scale_multival": ("MULTIVAL",),
+                "effect_multival": ("MULTIVAL",),
+                "inherit_missing": ("BOOLEAN", {"default": True}, ),
+                "guarantee_steps": ("INT", {"default": 1, "min": 0, "max": BIGMAX}),
+            }
+        }
+    
+    RETURN_TYPES = ("AD_KEYFRAMES", )
+    FUNCTION = "load_keyframe"
+
+    CATEGORY = "Animate Diff 🎭🅐🅓"
+
+    def load_keyframe(self,
+                      start_percent: float, prev_ad_keyframes=None,
+                      scale_multival: [float, torch.Tensor]=None, effect_multival: [float, torch.Tensor]=None,
+                      inherit_missing: bool=True, guarantee_steps: int=1):
+        if not prev_ad_keyframes:
+            prev_ad_keyframes = ADKeyframeGroup()
+        prev_ad_keyframes = prev_ad_keyframes.clone()
+        keyframe = ADKeyframe(start_percent=start_percent, scale_multival=scale_multival, effect_multival=effect_multival,
+                              inherit_missing=inherit_missing, guarantee_steps=guarantee_steps)
+        prev_ad_keyframes.add(keyframe)
+        return (prev_ad_keyframes,)

custom_nodes/ComfyUI-AnimateDiff-Evolved/animatediff/nodes_lora.py

@@ -0,0 +1,90 @@
+from pathlib import Path
+
+import folder_paths
+import comfy.utils
+import comfy.sd
+
+from .logger import logger
+from .utils_model import get_available_motion_loras, get_motion_lora_path
+from .motion_lora import MotionLoraInfo, MotionLoraList
+
+
+class AnimateDiffLoraLoader:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {
+            "required": {
+                "lora_name": (get_available_motion_loras(),),
+                "strength": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.001}),
+            },
+            "optional": {
+                "prev_motion_lora": ("MOTION_LORA",),
+            }
+        }
+    
+    RETURN_TYPES = ("MOTION_LORA",)
+    CATEGORY = "Animate Diff 🎭🅐🅓"
+    FUNCTION = "load_motion_lora"
+
+    def load_motion_lora(self, lora_name: str, strength: float, prev_motion_lora: MotionLoraList=None):
+        if prev_motion_lora is None:
+            prev_motion_lora = MotionLoraList()
+        else:
+            prev_motion_lora = prev_motion_lora.clone()
+        # check if motion lora with name exists
+        lora_path = get_motion_lora_path(lora_name)
+        if not Path(lora_path).is_file():
+            raise FileNotFoundError(f"Motion lora with name '{lora_name}' not found.")
+        # create motion lora info to be loaded in AnimateDiff Loader
+        lora_info = MotionLoraInfo(name=lora_name, strength=strength)
+        prev_motion_lora.add_lora(lora_info)
+
+        return (prev_motion_lora,)
+
+
+class MaskedLoraLoader:
+    def __init__(self):
+        self.loaded_lora = None
+
+    @classmethod
+    def INPUT_TYPES(s):
+        return {"required": { "model": ("MODEL",),
+                              "clip": ("CLIP", ),
+                              "lora_name": (folder_paths.get_filename_list("loras"), ),
+                              "strength_model": ("FLOAT", {"default": 1.0, "min": -20.0, "max": 20.0, "step": 0.01}),
+                              "strength_clip": ("FLOAT", {"default": 1.0, "min": -20.0, "max": 20.0, "step": 0.01}),
+                              }}
+    #RETURN_TYPES = ()
+    RETURN_TYPES = ("MODEL", "CLIP")
+    FUNCTION = "load_lora"
+
+    CATEGORY = "loaders"
+
+    def load_lora(self, model, clip, lora_name, strength_model, strength_clip):
+        if strength_model == 0 and strength_clip == 0:
+            return (model, clip)
+
+        lora_path = folder_paths.get_full_path("loras", lora_name)
+        lora = None
+        if self.loaded_lora is not None:
+            if self.loaded_lora[0] == lora_path:
+                lora = self.loaded_lora[1]
+            else:
+                temp = self.loaded_lora
+                self.loaded_lora = None
+                del temp
+
+        if lora is None:
+            lora = comfy.utils.load_torch_file(lora_path, safe_load=True)
+            self.loaded_lora = (lora_path, lora)
+
+        from pathlib import Path
+        with open(Path(__file__).parent.parent.parent / "sd_lora_keys.txt", "w") as lfile:
+            for key in lora:
+                lfile.write(f"{key}:\t{lora[key].size()}\n")
+
+        #model_lora, clip_lora = comfy.sd.load_lora_for_models(model, clip, lora, strength_model, strength_clip)
+        #return (model_lora, clip_lora)
+        return (model, clip)
+        
+            

custom_nodes/ComfyUI-AnimateDiff-Evolved/animatediff/nodes_multival.py

@@ -0,0 +1,136 @@
+from collections.abc import Iterable
+from typing import Union
+
+import torch
+from torch import Tensor
+
+from .utils_motion import linear_conversion, normalize_min_max, extend_to_batch_size
+
+
+class ScaleType:
+    ABSOLUTE = "absolute"
+    RELATIVE = "relative"
+    LIST = [ABSOLUTE, RELATIVE]
+
+
+class MultivalDynamicNode:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {
+            "required": {
+                "float_val": ("FLOAT", {"default": 1.0, "min": 0.0, "step": 0.001},),
+            },
+            "optional": {
+                "mask_optional": ("MASK",)
+            }
+        }
+    
+    RETURN_TYPES = ("MULTIVAL",)
+    CATEGORY = "Animate Diff 🎭🅐🅓/multival"
+    FUNCTION = "create_multival"
+
+    def create_multival(self, float_val: Union[float, list[float]]=1.0, mask_optional: Tensor=None):
+        # first, normalize inputs
+        # if float_val is iterable, treat as a list and assume inputs are floats
+        float_is_iterable = False
+        if isinstance(float_val, Iterable):
+            float_is_iterable = True
+            float_val = list(float_val)
+            # if mask present, make sure float_val list can be applied to list - match lengths
+            if mask_optional is not None:
+                if len(float_val) < mask_optional.shape[0]:
+                    # copies last entry enough times to match mask shape
+                    float_val = float_val + float_val[-1]*(mask_optional.shape[0]-len(float_val))
+                if mask_optional.shape[0] < len(float_val):
+                    mask_optional = extend_to_batch_size(mask_optional, len(float_val))
+                float_val = float_val[:mask_optional.shape[0]]
+            float_val: Tensor = torch.tensor(float_val).unsqueeze(-1).unsqueeze(-1)
+        # now that inputs are normalized, figure out what value to actually return
+        if mask_optional is not None:
+            mask_optional = mask_optional.clone()
+            if float_is_iterable:
+                mask_optional = mask_optional[:] * float_val.to(mask_optional.dtype).to(mask_optional.device)
+            else:
+                mask_optional = mask_optional * float_val
+            return (mask_optional,)
+        else:
+            if not float_is_iterable:
+                return (float_val,)
+            # create a dummy mask of b,h,w=float_len,1,1 (sigle pixel)
+            # purpose is for float input to work with mask code, without special cases
+            float_len = float_val.shape[0] if float_is_iterable else 1
+            shape = (float_len,1,1)
+            mask_optional = torch.ones(shape)
+            mask_optional = mask_optional[:] * float_val.to(mask_optional.dtype).to(mask_optional.device)
+            return (mask_optional,)
+
+
+class MultivalScaledMaskNode:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {
+            "required": {
+                "min_float_val": ("FLOAT", {"default": 0.0, "min": 0.0, "step": 0.001}),
+                "max_float_val": ("FLOAT", {"default": 1.0, "min": 0.0, "step": 0.001}),
+                "mask": ("MASK",),
+            },
+            "optional": {
+                "scaling": (ScaleType.LIST,),
+            }
+        }
+
+    RETURN_TYPES = ("MULTIVAL",)
+    CATEGORY = "Animate Diff 🎭🅐🅓/multival"
+    FUNCTION = "create_multival"
+
+    def create_multival(self, min_float_val: float, max_float_val: float, mask: Tensor, scaling: str=ScaleType.ABSOLUTE):
+        # TODO: allow min_float_val and max_float_val to be list[float]
+        if isinstance(min_float_val, Iterable):
+            raise ValueError(f"min_float_val must be type float (no lists allowed here), not {type(min_float_val).__name__}.")
+        if isinstance(max_float_val, Iterable):
+            raise ValueError(f"max_float_val must be type float (no lists allowed here), not {type(max_float_val).__name__}.")
+        
+        if scaling == ScaleType.ABSOLUTE:
+            mask = linear_conversion(mask.clone(), new_min=min_float_val, new_max=max_float_val)
+        elif scaling == ScaleType.RELATIVE:
+            mask = normalize_min_max(mask.clone(), new_min=min_float_val, new_max=max_float_val)
+        else:
+            raise ValueError(f"scaling '{scaling}' not recognized.")
+        return MultivalDynamicNode.create_multival(self, mask_optional=mask)
+
+
+class MultivalDynamicFloatInputNode:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {
+            "required": {
+                "float_val": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.001, "forceInput": True},),
+            },
+            "optional": {
+                "mask_optional": ("MASK",)
+            }
+        }
+    
+    RETURN_TYPES = ("MULTIVAL",)
+    CATEGORY = "Animate Diff 🎭🅐🅓/multival"
+    FUNCTION = "create_multival"
+
+    def create_multival(self, float_val: Union[float, list[float]]=None, mask_optional: Tensor=None):
+        return MultivalDynamicNode.create_multival(self, float_val=float_val, mask_optional=mask_optional)
+
+
+class MultivalFloatNode:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {
+            "required": {
+                "float_val": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.001},),
+            },
+        }
+    
+    RETURN_TYPES = ("MULTIVAL",)
+    CATEGORY = "Animate Diff 🎭🅐🅓/multival"
+    FUNCTION = "create_multival"
+
+    def create_multival(self, float_val: Union[float, list[float]]=None):
+        return MultivalDynamicNode.create_multival(self, float_val=float_val)

custom_nodes/ComfyUI-AnimateDiff-Evolved/animatediff/nodes_sample.py

@@ -0,0 +1,255 @@
+from typing import Union
+from torch import Tensor
+
+from .freeinit import FreeInitFilter
+from .sample_settings import (FreeInitOptions, IterationOptions,
+                              NoiseLayerAdd, NoiseLayerAddWeighted, NoiseLayerGroup, NoiseLayerReplace, NoiseLayerType,
+                              SeedNoiseGeneration, SampleSettings, CustomCFGKeyframeGroup, CustomCFGKeyframe)
+from .utils_model import BIGMIN, BIGMAX, SigmaSchedule
+
+
+class SampleSettingsNode:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {
+            "required": {
+                "batch_offset": ("INT", {"default": 0, "min": 0, "max": BIGMAX}),
+                "noise_type": (NoiseLayerType.LIST,),
+                "seed_gen": (SeedNoiseGeneration.LIST,),
+                "seed_offset": ("INT", {"default": 0, "min": BIGMIN, "max": BIGMAX}),
+            },
+            "optional": {
+                "noise_layers": ("NOISE_LAYERS",),
+                "iteration_opts": ("ITERATION_OPTS",),
+                "seed_override": ("INT", {"default": 0, "min": 0, "max": 0xffffffffffffffff, "forceInput": True}),
+                "adapt_denoise_steps": ("BOOLEAN", {"default": False},),
+                "custom_cfg": ("CUSTOM_CFG",),
+                "sigma_schedule": ("SIGMA_SCHEDULE",),
+            }
+        }
+
+    RETURN_TYPES = ("SAMPLE_SETTINGS",)
+    RETURN_NAMES = ("settings",)
+    CATEGORY = "Animate Diff 🎭🅐🅓"
+    FUNCTION = "create_settings"
+
+    def create_settings(self, batch_offset: int, noise_type: str, seed_gen: str, seed_offset: int, noise_layers: NoiseLayerGroup=None,
+                        iteration_opts: IterationOptions=None, seed_override: int=None, adapt_denoise_steps=False,
+                        custom_cfg: CustomCFGKeyframeGroup=None, sigma_schedule: SigmaSchedule=None):
+        sampling_settings = SampleSettings(batch_offset=batch_offset, noise_type=noise_type, seed_gen=seed_gen, seed_offset=seed_offset, noise_layers=noise_layers,
+                                           iteration_opts=iteration_opts, seed_override=seed_override, adapt_denoise_steps=adapt_denoise_steps,
+                                           custom_cfg=custom_cfg, sigma_schedule=sigma_schedule)
+        return (sampling_settings,)
+
+
+class NoiseLayerReplaceNode:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {
+            "required": {
+                "batch_offset": ("INT", {"default": 0, "min": 0, "max": BIGMAX}),
+                "noise_type": (NoiseLayerType.LIST,),
+                "seed_gen_override": (SeedNoiseGeneration.LIST_WITH_OVERRIDE,),
+                "seed_offset": ("INT", {"default": 0, "min": BIGMIN, "max": BIGMAX}),
+            },
+            "optional": {
+                "prev_noise_layers": ("NOISE_LAYERS",),
+                "mask_optional": ("MASK",),
+                "seed_override": ("INT", {"default": 0, "min": 0, "max": 0xffffffffffffffff, "forceInput": True}),
+            }
+        }
+
+    RETURN_TYPES = ("NOISE_LAYERS",)
+    CATEGORY = "Animate Diff 🎭🅐🅓/noise layers"
+    FUNCTION = "create_layers"
+
+    def create_layers(self, batch_offset: int, noise_type: str, seed_gen_override: str, seed_offset: int,
+                      prev_noise_layers: NoiseLayerGroup=None, mask_optional: Tensor=None, seed_override: int=None,):
+        # prepare prev_noise_layers
+        if prev_noise_layers is None:
+            prev_noise_layers = NoiseLayerGroup()
+        prev_noise_layers = prev_noise_layers.clone()
+        # create layer
+        layer = NoiseLayerReplace(noise_type=noise_type, batch_offset=batch_offset, seed_gen_override=seed_gen_override, seed_offset=seed_offset,
+                                  seed_override=seed_override, mask=mask_optional)
+        prev_noise_layers.add_to_start(layer)
+        return (prev_noise_layers,)
+
+
+class NoiseLayerAddNode:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {
+            "required": {
+                "batch_offset": ("INT", {"default": 0, "min": 0, "max": BIGMAX}),
+                "noise_type": (NoiseLayerType.LIST,),
+                "seed_gen_override": (SeedNoiseGeneration.LIST_WITH_OVERRIDE,),
+                "seed_offset": ("INT", {"default": 0, "min": BIGMIN, "max": BIGMAX}),
+                "noise_weight": ("FLOAT", {"default": 0.5, "min": 0.0, "max": 10.0, "step": 0.001}),
+            },
+            "optional": {
+                "prev_noise_layers": ("NOISE_LAYERS",),
+                "mask_optional": ("MASK",),
+                "seed_override": ("INT", {"default": 0, "min": 0, "max": 0xffffffffffffffff, "forceInput": True}),
+            }
+        }
+
+    RETURN_TYPES = ("NOISE_LAYERS",)
+    CATEGORY = "Animate Diff 🎭🅐🅓/noise layers"
+    FUNCTION = "create_layers"
+
+    def create_layers(self, batch_offset: int, noise_type: str, seed_gen_override: str, seed_offset: int,
+                      noise_weight: float,
+                      prev_noise_layers: NoiseLayerGroup=None, mask_optional: Tensor=None, seed_override: int=None,):
+        # prepare prev_noise_layers
+        if prev_noise_layers is None:
+            prev_noise_layers = NoiseLayerGroup()
+        prev_noise_layers = prev_noise_layers.clone()
+        # create layer
+        layer = NoiseLayerAdd(noise_type=noise_type, batch_offset=batch_offset, seed_gen_override=seed_gen_override, seed_offset=seed_offset,
+                              seed_override=seed_override, mask=mask_optional,
+                              noise_weight=noise_weight)
+        prev_noise_layers.add_to_start(layer)
+        return (prev_noise_layers,)
+
+
+class NoiseLayerAddWeightedNode:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {
+            "required": {
+                "batch_offset": ("INT", {"default": 0, "min": 0, "max": BIGMAX}),
+                "noise_type": (NoiseLayerType.LIST,),
+                "seed_gen_override": (SeedNoiseGeneration.LIST_WITH_OVERRIDE,),
+                "seed_offset": ("INT", {"default": 0, "min": BIGMIN, "max": BIGMAX}),
+                "noise_weight": ("FLOAT", {"default": 0.5, "min": 0.0, "max": 10.0, "step": 0.001}),
+                "balance_multiplier": ("FLOAT", {"default": 1.0, "min": 0.0, "step": 0.001}),
+            },
+            "optional": {
+                "prev_noise_layers": ("NOISE_LAYERS",),
+                "mask_optional": ("MASK",),
+                "seed_override": ("INT", {"default": 0, "min": 0, "max": 0xffffffffffffffff, "forceInput": True}),
+            }
+        }
+
+    RETURN_TYPES = ("NOISE_LAYERS",)
+    CATEGORY = "Animate Diff 🎭🅐🅓/noise layers"
+    FUNCTION = "create_layers"
+
+    def create_layers(self, batch_offset: int, noise_type: str, seed_gen_override: str, seed_offset: int,
+                      noise_weight: float, balance_multiplier: float,
+                      prev_noise_layers: NoiseLayerGroup=None, mask_optional: Tensor=None, seed_override: int=None,):
+        # prepare prev_noise_layers
+        if prev_noise_layers is None:
+            prev_noise_layers = NoiseLayerGroup()
+        prev_noise_layers = prev_noise_layers.clone()
+        # create layer
+        layer = NoiseLayerAddWeighted(noise_type=noise_type, batch_offset=batch_offset, seed_gen_override=seed_gen_override, seed_offset=seed_offset,
+                              seed_override=seed_override, mask=mask_optional,
+                              noise_weight=noise_weight, balance_multiplier=balance_multiplier)
+        prev_noise_layers.add_to_start(layer)
+        return (prev_noise_layers,)
+
+
+class IterationOptionsNode:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {
+            "required": {
+                "iterations": ("INT", {"default": 1, "min": 1}),
+            },
+            "optional": {
+                "iter_batch_offset": ("INT", {"default": 0, "min": 0, "max": BIGMAX}),
+                "iter_seed_offset": ("INT", {"default": 0, "min": BIGMIN, "max": BIGMAX}),
+            }
+        }
+
+    RETURN_TYPES = ("ITERATION_OPTS",)
+    CATEGORY = "Animate Diff 🎭🅐🅓/iteration opts"
+    FUNCTION = "create_iter_opts"
+
+    def create_iter_opts(self, iterations: int, iter_batch_offset: int=0, iter_seed_offset: int=0):
+        iter_opts = IterationOptions(iterations=iterations, iter_batch_offset=iter_batch_offset, iter_seed_offset=iter_seed_offset)
+        return (iter_opts,)
+
+
+class FreeInitOptionsNode:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {
+            "required": {
+                "iterations": ("INT", {"default": 2, "min": 1}),
+                "filter": (FreeInitFilter.LIST,),
+                "d_s": ("FLOAT", {"default": 0.25, "min": 0.0, "max": 1.0, "step": 0.001}),
+                "d_t": ("FLOAT", {"default": 0.25, "min": 0.0, "max": 1.0, "step": 0.001}),
+                "n_butterworth": ("INT", {"default": 4, "min": 1, "max": 100},),
+                "sigma_step": ("INT", {"default": 999, "min": 1, "max": 999}),
+                "apply_to_1st_iter": ("BOOLEAN", {"default": False}),
+                "init_type": (FreeInitOptions.LIST,)
+            },
+            "optional": {
+                "iter_batch_offset": ("INT", {"default": 0, "min": 0, "max": BIGMAX}),
+                "iter_seed_offset": ("INT", {"default": 1, "min": BIGMIN, "max": BIGMAX}),
+            }
+        }
+
+    RETURN_TYPES = ("ITERATION_OPTS",)
+    CATEGORY = "Animate Diff 🎭🅐🅓/iteration opts"
+    FUNCTION = "create_iter_opts"
+
+    def create_iter_opts(self, iterations: int, filter: str, d_s: float, d_t: float, n_butterworth: int,
+                         sigma_step: int, apply_to_1st_iter: bool, init_type: str,
+                         iter_batch_offset: int=0, iter_seed_offset: int=1):
+        # init_type does nothing for now, not until I add more methods of applying low+high freq noise
+        iter_opts = FreeInitOptions(iterations=iterations, step=sigma_step, apply_to_1st_iter=apply_to_1st_iter,
+                                    filter=filter, d_s=d_s, d_t=d_t, n=n_butterworth, init_type=init_type,
+                                    iter_batch_offset=iter_batch_offset, iter_seed_offset=iter_seed_offset)
+        return (iter_opts,)
+
+
+class CustomCFGNode:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {
+            "required": {
+                "cfg_multival": ("MULTIVAL",),
+            }
+        }
+
+    RETURN_TYPES = ("CUSTOM_CFG",)
+    CATEGORY = "Animate Diff 🎭🅐🅓/sample settings"
+    FUNCTION = "create_custom_cfg"
+
+    def create_custom_cfg(self, cfg_multival: Union[float, Tensor]):
+        keyframe = CustomCFGKeyframe(cfg_multival=cfg_multival)
+        cfg_custom = CustomCFGKeyframeGroup()
+        cfg_custom.add(keyframe)
+        return (cfg_custom,)
+
+
+class CustomCFGKeyframeNode:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {
+            "required": {
+                "cfg_multival": ("MULTIVAL",),
+                "start_percent": ("FLOAT", {"default": 0.0, "min": 0.0, "max": 1.0, "step": 0.001}),
+                "guarantee_steps": ("INT", {"default": 1, "min": 0, "max": BIGMAX}),
+            },
+            "optional": {
+                "prev_custom_cfg": ("CUSTOM_CFG",),
+            }
+        }
+
+    RETURN_TYPES = ("CUSTOM_CFG",)
+    CATEGORY = "Animate Diff 🎭🅐🅓/sample settings"
+    FUNCTION = "create_custom_cfg"
+
+    def create_custom_cfg(self, cfg_multival: Union[float, Tensor], start_percent: float=0.0, guarantee_steps: int=1,
+                          prev_custom_cfg: CustomCFGKeyframeGroup=None):
+        if not prev_custom_cfg:
+            prev_custom_cfg = CustomCFGKeyframeGroup()
+        prev_custom_cfg = prev_custom_cfg.clone()
+        keyframe = CustomCFGKeyframe(cfg_multival=cfg_multival, start_percent=start_percent, guarantee_steps=guarantee_steps)
+        prev_custom_cfg.add(keyframe)
+        return (prev_custom_cfg,)

custom_nodes/ComfyUI-AnimateDiff-Evolved/animatediff/nodes_sigma_schedule.py

@@ -0,0 +1,141 @@
+import torch
+
+from .utils_model import BetaSchedules, SigmaSchedule, ModelSamplingType, ModelSamplingConfig, InterpolationMethod
+
+
+def validate_sigma_schedule_compatibility(schedule_A: SigmaSchedule, schedule_B: SigmaSchedule,
+                                          name_a: str="sigma_schedule_A", name_b: str="sigma_schedule_B"):
+    if schedule_A.total_sigmas() != schedule_B.total_sigmas():
+            raise Exception(f"Weighted Average cannot be taken of Sigma Schedules that do not have the same amount of sigmas; " +
+                            f"{name_a} has {schedule_A.total_sigmas()} sigmas (lcm={schedule_A.is_lcm()}), " +
+                            f"{name_b} has {schedule_B.total_sigmas()} sigmas (lcm={schedule_B.is_lcm()}).")
+
+
+class SigmaScheduleNode:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {
+            "required": {
+                "beta_schedule": (BetaSchedules.ALIAS_ACTIVE_LIST,),
+            }
+        }
+    
+    RETURN_TYPES = ("SIGMA_SCHEDULE",)
+    CATEGORY = "Animate Diff 🎭🅐🅓/sample settings/sigma schedule"
+    FUNCTION = "get_sigma_schedule"
+
+    def get_sigma_schedule(self, beta_schedule: str):
+        model_type = ModelSamplingType.from_alias(ModelSamplingType.EPS)
+        new_model_sampling = BetaSchedules._to_model_sampling(alias=beta_schedule,
+                                                              model_type=model_type)
+        return (SigmaSchedule(model_sampling=new_model_sampling, model_type=model_type),)
+
+
+class RawSigmaScheduleNode:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {
+            "required": {
+                "raw_beta_schedule": (BetaSchedules.RAW_BETA_SCHEDULE_LIST,),
+                "linear_start": ("FLOAT", {"default": 0.00085, "min": 0.0, "max": 1.0, "step": 0.000001}),
+                "linear_end": ("FLOAT", {"default": 0.012, "min": 0.0, "max": 1.0, "step": 0.000001}),
+                #"cosine_s": ("FLOAT", {"default": 8e-3, "min": 0.0, "max": 1.0, "step": 0.000001}),
+                "sampling": (ModelSamplingType._FULL_LIST,),
+                "lcm_original_timesteps": ("INT", {"default": 50, "min": 1, "max": 1000}),
+                "lcm_zsnr": ("BOOLEAN", {"default": False}),
+            }
+        }
+    
+    RETURN_TYPES = ("SIGMA_SCHEDULE",)
+    CATEGORY = "Animate Diff 🎭🅐🅓/sample settings/sigma schedule"
+    FUNCTION = "get_sigma_schedule"
+
+    def get_sigma_schedule(self, raw_beta_schedule: str, linear_start: float, linear_end: float,# cosine_s: float,
+                           sampling: str, lcm_original_timesteps: int, lcm_zsnr: bool):
+        new_config = ModelSamplingConfig(beta_schedule=raw_beta_schedule, linear_start=linear_start, linear_end=linear_end)
+        if sampling != ModelSamplingType.LCM:
+            lcm_original_timesteps=None
+            lcm_zsnr=False
+        model_type = ModelSamplingType.from_alias(sampling)    
+        new_model_sampling = BetaSchedules._to_model_sampling(alias=BetaSchedules.AUTOSELECT, model_type=model_type, config_override=new_config, original_timesteps=lcm_original_timesteps)
+        if lcm_zsnr:
+            SigmaSchedule.apply_zsnr(new_model_sampling=new_model_sampling)
+        return (SigmaSchedule(model_sampling=new_model_sampling, model_type=model_type),)
+
+
+class WeightedAverageSigmaScheduleNode:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {
+            "required": {
+                "schedule_A": ("SIGMA_SCHEDULE",),
+                "schedule_B": ("SIGMA_SCHEDULE",),
+                "weight_A": ("FLOAT", {"default": 0.5, "min": 0.0, "max": 1.0, "step": 0.001}),
+            }
+        }
+    
+    RETURN_TYPES = ("SIGMA_SCHEDULE",)
+    CATEGORY = "Animate Diff 🎭🅐🅓/sample settings/sigma schedule"
+    FUNCTION = "get_sigma_schedule"
+
+    def get_sigma_schedule(self, schedule_A: SigmaSchedule, schedule_B: SigmaSchedule, weight_A: float):
+        validate_sigma_schedule_compatibility(schedule_A, schedule_B)
+        new_sigmas = schedule_A.model_sampling.sigmas * weight_A + schedule_B.model_sampling.sigmas * (1-weight_A)
+        combo_schedule = schedule_A.clone()
+        combo_schedule.model_sampling.set_sigmas(new_sigmas)
+        return (combo_schedule,)
+
+
+class InterpolatedWeightedAverageSigmaScheduleNode:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {
+            "required": {
+                "schedule_A": ("SIGMA_SCHEDULE",),
+                "schedule_B": ("SIGMA_SCHEDULE",),
+                "weight_A_Start": ("FLOAT", {"default": 0.5, "min": 0.0, "max": 1.0, "step": 0.001}),
+                "weight_A_End": ("FLOAT", {"default": 0.5, "min": 0.0, "max": 1.0, "step": 0.001}),
+                "interpolation": (InterpolationMethod._LIST,),
+            }
+        }
+    
+    RETURN_TYPES = ("SIGMA_SCHEDULE",)
+    CATEGORY = "Animate Diff 🎭🅐🅓/sample settings/sigma schedule"
+    FUNCTION = "get_sigma_schedule"
+
+    def get_sigma_schedule(self, schedule_A: SigmaSchedule, schedule_B: SigmaSchedule,
+                           weight_A_Start: float, weight_A_End: float, interpolation: str):
+        validate_sigma_schedule_compatibility(schedule_A, schedule_B)
+        # get reverse weights, since sigmas are currently reversed
+        weights = InterpolationMethod.get_weights(num_from=weight_A_Start, num_to=weight_A_End,
+                                                  length=schedule_A.total_sigmas(), method=interpolation, reverse=True)
+        weights = weights.to(schedule_A.model_sampling.sigmas.dtype).to(schedule_A.model_sampling.sigmas.device)
+        new_sigmas = schedule_A.model_sampling.sigmas * weights + schedule_B.model_sampling.sigmas * (1.0-weights)
+        combo_schedule = schedule_A.clone()
+        combo_schedule.model_sampling.set_sigmas(new_sigmas)
+        return (combo_schedule,)
+
+
+class SplitAndCombineSigmaScheduleNode:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {
+            "required": {
+                "schedule_Start": ("SIGMA_SCHEDULE",),
+                "schedule_End": ("SIGMA_SCHEDULE",),
+                "idx_split_percent": ("FLOAT", {"default": 0.5, "min": 0.0, "max": 1.0, "step": 0.001})
+            }
+        }
+    
+    RETURN_TYPES = ("SIGMA_SCHEDULE",)
+    CATEGORY = "Animate Diff 🎭🅐🅓/sample settings/sigma schedule"
+    FUNCTION = "get_sigma_schedule"
+
+    def get_sigma_schedule(self, schedule_Start: SigmaSchedule, schedule_End: SigmaSchedule, idx_split_percent: float):
+        validate_sigma_schedule_compatibility(schedule_Start, schedule_End)
+        # first, calculate index to act as split; get diff from 1.0 since sigmas are flipped at this stage
+        idx = int((1.0-idx_split_percent) * schedule_Start.total_sigmas())
+        new_sigmas = torch.cat([schedule_End.model_sampling.sigmas[:idx], schedule_Start.model_sampling.sigmas[idx:]], dim=0)
+        new_schedule = schedule_Start.clone()
+        new_schedule.model_sampling.set_sigmas(new_sigmas)
+        return (new_schedule,)

custom_nodes/ComfyUI-AnimateDiff-Evolved/animatediff/sample_settings.py

@@ -0,0 +1,555 @@
+from collections.abc import Iterable
+from typing import Union
+import torch
+from torch import Tensor
+
+import comfy.sample
+import comfy.samplers
+from comfy.model_patcher import ModelPatcher
+from comfy.model_base import BaseModel
+
+from . import freeinit
+from .context import ContextOptions, ContextOptionsGroup
+from .utils_model import SigmaSchedule
+from .utils_motion import extend_to_batch_size, get_sorted_list_via_attr, prepare_mask_batch
+from .logger import logger
+
+
+def prepare_mask_ad(noise_mask, shape, device):
+    """ensures noise mask is of proper dimensions"""
+    noise_mask = torch.nn.functional.interpolate(noise_mask.reshape((-1, 1, noise_mask.shape[-2], noise_mask.shape[-1])), size=(shape[2], shape[3]), mode="bilinear")
+    #noise_mask = noise_mask.round()
+    noise_mask = torch.cat([noise_mask] * shape[1], dim=1)
+    noise_mask = comfy.utils.repeat_to_batch_size(noise_mask, shape[0])
+    noise_mask = noise_mask.to(device)
+    return noise_mask
+
+
+class NoiseLayerType:
+    DEFAULT = "default"
+    CONSTANT = "constant"
+    EMPTY = "empty"
+    REPEATED_CONTEXT = "repeated_context"
+    FREENOISE = "FreeNoise"
+
+    LIST = [DEFAULT, CONSTANT, EMPTY, REPEATED_CONTEXT, FREENOISE]
+
+
+class NoiseApplication:
+    ADD = "add"
+    ADD_WEIGHTED = "add_weighted"
+    REPLACE = "replace"
+    
+    LIST = [ADD, ADD_WEIGHTED, REPLACE]
+
+
+class NoiseNormalize:
+    DISABLE = "disable"
+    NORMAL = "normal"
+
+    LIST = [DISABLE, NORMAL]
+
+
+class SampleSettings:
+    def __init__(self, batch_offset: int=0, noise_type: str=None, seed_gen: str=None, seed_offset: int=0, noise_layers: 'NoiseLayerGroup'=None,
+                 iteration_opts=None, seed_override:int=None, negative_cond_flipflop=False, adapt_denoise_steps: bool=False,
+                 custom_cfg: 'CustomCFGKeyframeGroup'=None, sigma_schedule: SigmaSchedule=None):
+        self.batch_offset = batch_offset
+        self.noise_type = noise_type if noise_type is not None else NoiseLayerType.DEFAULT
+        self.seed_gen = seed_gen if seed_gen is not None else SeedNoiseGeneration.COMFY
+        self.noise_layers = noise_layers if noise_layers else NoiseLayerGroup()
+        self.iteration_opts = iteration_opts if iteration_opts else IterationOptions()
+        self.seed_offset = seed_offset
+        self.seed_override = seed_override
+        self.negative_cond_flipflop = negative_cond_flipflop
+        self.adapt_denoise_steps = adapt_denoise_steps
+        self.custom_cfg = custom_cfg.clone() if custom_cfg else custom_cfg
+        self.sigma_schedule = sigma_schedule
+    
+    def prepare_noise(self, seed: int, latents: Tensor, noise: Tensor, extra_seed_offset=0, extra_args:dict={}, force_create_noise=True):
+        if self.seed_override is not None:
+            seed = self.seed_override
+        # if seed is iterable, attempt to do per-latent noises
+        if isinstance(seed, Iterable):
+            noise = SeedNoiseGeneration.create_noise_individual_seeds(seeds=seed, latents=latents, seed_offset=self.seed_offset+extra_seed_offset, extra_args=extra_args)
+            seed = seed[0]+self.seed_offset
+        else:
+            seed += self.seed_offset
+            # replace initial noise if not batch_offset 0 or Comfy seed_gen or not NoiseType default
+            if self.batch_offset != 0 or self.seed_offset != 0 or self.noise_type != NoiseLayerType.DEFAULT or self.seed_gen != SeedNoiseGeneration.COMFY or force_create_noise:
+                noise = SeedNoiseGeneration.create_noise(seed=seed+extra_seed_offset, latents=latents, existing_seed_gen=self.seed_gen, seed_gen=self.seed_gen,
+                                                        noise_type=self.noise_type, batch_offset=self.batch_offset, extra_args=extra_args)
+        # apply noise layers
+        for noise_layer in self.noise_layers.layers:
+            # first, generate new noise matching seed gen override
+            layer_noise = noise_layer.create_layer_noise(existing_seed_gen=self.seed_gen, seed=seed, latents=latents,
+                                                         extra_seed_offset=extra_seed_offset, extra_args=extra_args)
+            # next, get noise after applying layer
+            noise = noise_layer.apply_layer_noise(new_noise=layer_noise, old_noise=noise)
+        # noise prepared now
+        return noise
+    
+    def pre_run(self, model: ModelPatcher):
+        if self.custom_cfg is not None:
+            self.custom_cfg.reset()
+    
+    def cleanup(self):
+        if self.custom_cfg is not None:
+            self.custom_cfg.reset()
+
+    def clone(self):
+        return SampleSettings(batch_offset=self.batch_offset, noise_type=self.noise_type, seed_gen=self.seed_gen, seed_offset=self.seed_offset,
+                           noise_layers=self.noise_layers.clone(), iteration_opts=self.iteration_opts, seed_override=self.seed_override,
+                           negative_cond_flipflop=self.negative_cond_flipflop, adapt_denoise_steps=self.adapt_denoise_steps, custom_cfg=self.custom_cfg, sigma_schedule=self.sigma_schedule)
+
+
+class NoiseLayer:
+    def __init__(self, noise_type: str, batch_offset: int, seed_gen_override: str, seed_offset: int, seed_override: int=None, mask: Tensor=None):
+        self.application: str = NoiseApplication.REPLACE
+        self.noise_type = noise_type
+        self.batch_offset = batch_offset
+        self.seed_gen_override = seed_gen_override
+        self.seed_offset = seed_offset
+        self.seed_override = seed_override
+        self.mask = mask
+    
+    def create_layer_noise(self, existing_seed_gen: str, seed: int, latents: Tensor, extra_seed_offset=0, extra_args:dict={}) -> Tensor:
+        if self.seed_override is not None:
+            seed = self.seed_override
+         # if seed is iterable, attempt to do per-latent noises
+        if isinstance(seed, Iterable):
+            return SeedNoiseGeneration.create_noise_individual_seeds(seeds=seed, latents=latents, seed_offset=self.seed_offset+extra_seed_offset, extra_args=extra_args)
+        seed += self.seed_offset + extra_seed_offset
+        return SeedNoiseGeneration.create_noise(seed=seed, latents=latents, existing_seed_gen=existing_seed_gen, seed_gen=self.seed_gen_override,
+                                                noise_type=self.noise_type, batch_offset=self.batch_offset, extra_args=extra_args)
+
+    def apply_layer_noise(self, new_noise: Tensor, old_noise: Tensor) -> Tensor:
+        return old_noise
+    
+    def get_noise_mask(self, noise: Tensor) -> Tensor:
+        if self.mask is None:
+            return 1
+        noise_mask = self.mask.reshape((-1, 1, self.mask.shape[-2], self.mask.shape[-1]))
+        return prepare_mask_ad(noise_mask, noise.shape, noise.device)
+
+
+class NoiseLayerReplace(NoiseLayer):
+    def __init__(self, noise_type: str, batch_offset: int, seed_gen_override: str, seed_offset: int, seed_override: int=None, mask: Tensor=None):
+        super().__init__(noise_type, batch_offset, seed_gen_override, seed_offset, seed_override, mask)
+        self.application = NoiseApplication.REPLACE
+
+    def apply_layer_noise(self, new_noise: Tensor, old_noise: Tensor) -> Tensor:
+        noise_mask = self.get_noise_mask(old_noise)
+        return (1-noise_mask)*old_noise + noise_mask*new_noise
+
+
+class NoiseLayerAdd(NoiseLayer):
+    def __init__(self, noise_type: str, batch_offset: int, seed_gen_override: str, seed_offset: int, seed_override: int=None, mask: Tensor=None,
+                 noise_weight=1.0):
+        super().__init__(noise_type, batch_offset, seed_gen_override, seed_offset, seed_override, mask)
+        self.noise_weight = noise_weight
+        self.application = NoiseApplication.ADD
+
+    def apply_layer_noise(self, new_noise: Tensor, old_noise: Tensor) -> Tensor:
+        noise_mask = self.get_noise_mask(old_noise)
+        return (1-noise_mask)*old_noise + noise_mask*(old_noise + new_noise * self.noise_weight)
+
+
+class NoiseLayerAddWeighted(NoiseLayerAdd):
+    def __init__(self, noise_type: str, batch_offset: int, seed_gen_override: str, seed_offset: int, seed_override: int=None, mask: Tensor=None,
+                 noise_weight=1.0, balance_multiplier=1.0):
+        super().__init__(noise_type, batch_offset, seed_gen_override, seed_offset, seed_override, mask, noise_weight)
+        self.balance_multiplier = balance_multiplier
+        self.application = NoiseApplication.ADD_WEIGHTED
+
+    def apply_layer_noise(self, new_noise: Tensor, old_noise: Tensor) -> Tensor:
+        noise_mask = self.get_noise_mask(old_noise)
+        return (1-noise_mask)*old_noise + noise_mask*(old_noise * (1.0-(self.noise_weight*self.balance_multiplier)) + new_noise * self.noise_weight)
+
+
+class NoiseLayerGroup:
+    def __init__(self):
+        self.layers: list[NoiseLayer] = []
+    
+    def add(self, layer: NoiseLayer) -> None:
+        # add to the end of list
+        self.layers.append(layer)
+
+    def add_to_start(self, layer: NoiseLayer) -> None:
+        # add to the beginning of list
+        self.layers.insert(0, layer)
+
+    def __getitem__(self, index) -> NoiseLayer:
+        return self.layers[index]
+    
+    def is_empty(self) -> bool:
+        return len(self.layers) == 0
+    
+    def clone(self) -> 'NoiseLayerGroup':
+        cloned = NoiseLayerGroup()
+        for layer in self.layers:
+            cloned.add(layer)
+        return cloned
+
+class SeedNoiseGeneration:
+    COMFY = "comfy"
+    AUTO1111 = "auto1111"
+    AUTO1111GPU = "auto1111 [gpu]" # TODO: implement this
+    USE_EXISTING = "use existing"
+
+    LIST = [COMFY, AUTO1111]
+    LIST_WITH_OVERRIDE = [USE_EXISTING, COMFY, AUTO1111]
+
+    @classmethod
+    def create_noise(cls, seed: int, latents: Tensor, existing_seed_gen: str=COMFY, seed_gen: str=USE_EXISTING, noise_type: str=NoiseLayerType.DEFAULT, batch_offset: int=0, extra_args: dict={}):
+        # determine if should use existing type
+        if seed_gen == cls.USE_EXISTING:
+            seed_gen = existing_seed_gen
+        if seed_gen == cls.COMFY:
+            return cls.create_noise_comfy(seed, latents, noise_type, batch_offset, extra_args)
+        elif seed_gen in [cls.AUTO1111, cls.AUTO1111GPU]:
+            return cls.create_noise_auto1111(seed, latents, noise_type, batch_offset, extra_args)
+        raise ValueError(f"Noise seed_gen {seed_gen} is not recognized.")
+
+    @staticmethod
+    def create_noise_comfy(seed: int, latents: Tensor, noise_type: str=NoiseLayerType.DEFAULT, batch_offset: int=0, extra_args: dict={}):
+        common_noise = SeedNoiseGeneration._create_common_noise(seed, latents, noise_type, batch_offset, extra_args)
+        if common_noise is not None:
+            return common_noise
+        if noise_type == NoiseLayerType.CONSTANT:
+            generator = torch.manual_seed(seed)
+            length = latents.shape[0]
+            single_shape = (1 + batch_offset, latents.shape[1], latents.shape[2], latents.shape[3])
+            single_noise = torch.randn(single_shape, dtype=latents.dtype, layout=latents.layout, generator=generator, device="cpu")
+            return torch.cat([single_noise[batch_offset:]] * length, dim=0)
+        # comfy creates noise with a single seed for the entire shape of the latents batched tensor
+        generator = torch.manual_seed(seed)
+        offset_shape = (latents.shape[0] + batch_offset, latents.shape[1], latents.shape[2], latents.shape[3])
+        final_noise = torch.randn(offset_shape, dtype=latents.dtype, layout=latents.layout, generator=generator, device="cpu")
+        final_noise = final_noise[batch_offset:]
+        # convert to derivative noise type, if needed
+        derivative_noise = SeedNoiseGeneration._create_derivative_noise(final_noise, noise_type=noise_type, seed=seed, extra_args=extra_args)
+        if derivative_noise is not None:
+            return derivative_noise
+        return final_noise
+    
+    @staticmethod
+    def create_noise_auto1111(seed: int, latents: Tensor, noise_type: str=NoiseLayerType.DEFAULT, batch_offset: int=0, extra_args: dict={}):
+        common_noise = SeedNoiseGeneration._create_common_noise(seed, latents, noise_type, batch_offset, extra_args)
+        if common_noise is not None:
+            return common_noise
+        if noise_type == NoiseLayerType.CONSTANT:
+            generator = torch.manual_seed(seed+batch_offset)
+            length = latents.shape[0]
+            single_shape = (1, latents.shape[1], latents.shape[2], latents.shape[3])
+            single_noise = torch.randn(single_shape, dtype=latents.dtype, layout=latents.layout, generator=generator, device="cpu")
+            return torch.cat([single_noise] * length, dim=0)
+        # auto1111 applies growing seeds for a batch
+        length = latents.shape[0]
+        single_shape = (1, latents.shape[1], latents.shape[2], latents.shape[3])
+        all_noises = []
+        # i starts at 0
+        for i in range(length):
+            generator = torch.manual_seed(seed+i+batch_offset)
+            all_noises.append(torch.randn(single_shape, dtype=latents.dtype, layout=latents.layout, generator=generator, device="cpu"))
+        final_noise = torch.cat(all_noises, dim=0)
+        # convert to derivative noise type, if needed
+        derivative_noise = SeedNoiseGeneration._create_derivative_noise(final_noise, noise_type=noise_type, seed=seed, extra_args=extra_args)
+        if derivative_noise is not None:
+            return derivative_noise
+        return final_noise
+    
+    @staticmethod
+    def create_noise_individual_seeds(seeds: list[int], latents: Tensor, seed_offset: int=0, extra_args: dict={}):
+        length = latents.shape[0]
+        if len(seeds) < length:
+            raise ValueError(f"{len(seeds)} seeds in seed_override were provided, but at least {length} are required to work with the current latents.")
+        seeds = seeds[:length]
+        single_shape = (1, latents.shape[1], latents.shape[2], latents.shape[3])
+        all_noises = []
+        for seed in seeds:
+            generator = torch.manual_seed(seed+seed_offset)
+            all_noises.append(torch.randn(single_shape, dtype=latents.dtype, layout=latents.layout, generator=generator, device="cpu"))
+        return torch.cat(all_noises, dim=0)
+
+    @staticmethod
+    def _create_common_noise(seed: int, latents: Tensor, noise_type: str=NoiseLayerType.DEFAULT, batch_offset: int=0, extra_args: dict={}):
+        if noise_type == NoiseLayerType.EMPTY:
+            return torch.zeros_like(latents)
+        return None
+    
+    @staticmethod
+    def _create_derivative_noise(noise: Tensor, noise_type: str, seed: int, extra_args: dict):
+        derivative_func = DERIVATIVE_NOISE_FUNC_MAP.get(noise_type, None)
+        if derivative_func is None:
+            return None
+        return derivative_func(noise=noise, seed=seed, extra_args=extra_args)
+
+    @staticmethod
+    def _convert_to_repeated_context(noise: Tensor, extra_args: dict, **kwargs):
+        # if no context_length, return unmodified noise
+        opts: ContextOptionsGroup = extra_args["context_options"]
+        context_length: int = opts.context_length if not opts.view_options else opts.view_options.context_length
+        if context_length is None:
+            return noise
+        length = noise.shape[0]
+        noise = noise[:context_length]
+        cat_count = (length // context_length) + 1
+        return torch.cat([noise] * cat_count, dim=0)[:length]
+
+    @staticmethod
+    def _convert_to_freenoise(noise: Tensor, seed: int, extra_args: dict, **kwargs):
+        # if no context_length, return unmodified noise
+        opts: ContextOptionsGroup = extra_args["context_options"]
+        context_length: int = opts.context_length if not opts.view_options else opts.view_options.context_length
+        context_overlap: int = opts.context_overlap if not opts.view_options else opts.view_options.context_overlap
+        video_length: int = noise.shape[0]
+        if context_length is None:
+            return noise
+        delta = context_length - context_overlap
+        generator = torch.manual_seed(seed)
+
+        for start_idx in range(0, video_length-context_length, delta):
+            # start_idx corresponds to the beginning of a context window
+            # goal: place shuffled in the delta region right after the end of the context window
+            #       if space after context window is not enough to place the noise, adjust and finish
+            place_idx = start_idx + context_length
+            # if place_idx is outside the valid indexes, we are already finished
+            if place_idx >= video_length:
+                break
+            end_idx = place_idx - 1
+            # if there is not enough room to copy delta amount of indexes, copy limited amount and finish
+            if end_idx + delta >= video_length:
+                final_delta = video_length - place_idx
+                # generate list of indexes in final delta region
+                list_idx = torch.Tensor(list(range(start_idx,start_idx+final_delta))).to(torch.long)
+                # shuffle list
+                list_idx = list_idx[torch.randperm(final_delta, generator=generator)]
+                # apply shuffled indexes
+                noise[place_idx:place_idx+final_delta] = noise[list_idx]
+                break
+            # otherwise, do normal behavior
+            # generate list of indexes in delta region
+            list_idx = torch.Tensor(list(range(start_idx,start_idx+delta))).to(torch.long)
+            # shuffle list
+            list_idx = list_idx[torch.randperm(delta, generator=generator)]
+            # apply shuffled indexes
+            noise[place_idx:place_idx+delta] = noise[list_idx]
+        return noise
+
+
+DERIVATIVE_NOISE_FUNC_MAP = {
+    NoiseLayerType.REPEATED_CONTEXT: SeedNoiseGeneration._convert_to_repeated_context,
+    NoiseLayerType.FREENOISE: SeedNoiseGeneration._convert_to_freenoise,
+    }
+
+
+class IterationOptions:
+    SAMPLER = "sampler"
+
+    def __init__(self, iterations: int=1, cache_init_noise=False, cache_init_latents=False,
+                 iter_batch_offset: int=0, iter_seed_offset: int=0):
+        self.iterations = iterations
+        self.cache_init_noise = cache_init_noise
+        self.cache_init_latents = cache_init_latents
+        self.iter_batch_offset = iter_batch_offset
+        self.iter_seed_offset = iter_seed_offset
+        self.need_sampler = False
+
+    def get_sigma(self, model: ModelPatcher, step: int):
+        model_sampling = model.model.model_sampling
+        if "model_sampling" in model.object_patches:
+            model_sampling = model.object_patches["model_sampling"]
+        return model_sampling.sigmas[step]
+
+    def initialize(self, latents: Tensor):
+        pass
+
+    def preprocess_latents(self, curr_i: int, model: ModelPatcher, latents: Tensor, noise: Tensor,
+                           seed: int, sample_settings: SampleSettings, noise_extra_args: dict, **kwargs):
+        if curr_i == 0 or (self.iter_batch_offset == 0 and self.iter_seed_offset == 0):
+            return latents, noise
+        temp_sample_settings = sample_settings.clone()
+        temp_sample_settings.batch_offset += self.iter_batch_offset * curr_i
+        temp_sample_settings.seed_offset += self.iter_seed_offset * curr_i
+        return latents, temp_sample_settings.prepare_noise(seed=seed, latents=latents, noise=None,
+                                                    extra_args=noise_extra_args, force_create_noise=True)
+
+
+class FreeInitOptions(IterationOptions):
+    FREEINIT_SAMPLER = "FreeInit [sampler sigma]"
+    FREEINIT_MODEL = "FreeInit [model sigma]"
+    DINKINIT_V1 = "DinkInit_v1"
+
+    LIST = [FREEINIT_SAMPLER, FREEINIT_MODEL, DINKINIT_V1]
+
+    def __init__(self, iterations: int, step: int=999, apply_to_1st_iter: bool=False,
+                 filter=freeinit.FreeInitFilter.GAUSSIAN, d_s=0.25, d_t=0.25, n=4, init_type=FREEINIT_SAMPLER,
+                 iter_batch_offset: int=0, iter_seed_offset: int=1):
+        super().__init__(iterations=iterations, cache_init_noise=True, cache_init_latents=True,
+                         iter_batch_offset=iter_batch_offset, iter_seed_offset=iter_seed_offset)
+        self.apply_to_1st_iter = apply_to_1st_iter
+        self.step = step
+        self.filter = filter
+        self.d_s = d_s
+        self.d_t = d_t
+        self.n = n
+        self.freq_filter = None
+        self.freq_filter2 = None
+        self.need_sampler = True if init_type in [self.FREEINIT_SAMPLER] else False
+        self.init_type = init_type
+
+    def initialize(self, latents: Tensor):
+        self.freq_filter = freeinit.get_freq_filter(latents.shape, device=latents.device, filter_type=self.filter,
+                                           n=self.n, d_s=self.d_s, d_t=self.d_t)
+    
+    def preprocess_latents(self, curr_i: int, model: ModelPatcher, latents: Tensor, noise: Tensor, cached_latents: Tensor, cached_noise: Tensor,
+                           seed:int, sample_settings: SampleSettings, noise_extra_args: dict, sampler: comfy.samplers.KSampler=None, **kwargs):
+        # if first iter and should not apply, do nothing
+        if curr_i == 0 and not self.apply_to_1st_iter:
+            return latents, noise
+        # otherwise, do FreeInit stuff
+        if self.init_type in [self.FREEINIT_SAMPLER, self.FREEINIT_MODEL]:
+            # NOTE: This should be very close (if not exactly) to how FreeInit is intended to initialize noise the latents.
+            #       The trick is that FreeInit is dependent on the behavior of diffuser's DDIMScheduler.add_noise function.
+            #       The typical noising method of latents + noise * sigma will NOT work.
+            # 1. apply initial noise with appropriate step sigma, normalized against scale_factor
+            if sampler is not None:
+                sigma = sampler.sigmas[999-self.step].to(latents.device) / (model.model.latent_format.scale_factor)
+            else:
+                sigma = self.get_sigma(model, self.step-1000).to(latents.device) / (model.model.latent_format.scale_factor)
+            alpha_cumprod = 1 / ((sigma * sigma) + 1)
+            sqrt_alpha_prod = alpha_cumprod ** 0.5
+            sqrt_one_minus_alpha_prod = (1 - alpha_cumprod) ** 0.5
+            noised_latents = latents * sqrt_alpha_prod + noise * sqrt_one_minus_alpha_prod
+            # 2. create random noise z_rand for high frequency
+            temp_sample_settings = sample_settings.clone()
+            temp_sample_settings.batch_offset += self.iter_batch_offset * curr_i
+            temp_sample_settings.seed_offset += self.iter_seed_offset * curr_i
+            z_rand = temp_sample_settings.prepare_noise(seed=seed, latents=latents, noise=None,
+                                                    extra_args=noise_extra_args, force_create_noise=True)
+            # 3. noise reinitialization - combines low freq. noise from noised_latents and high freq. noise from z_rand
+            noised_latents = freeinit.freq_mix_3d(x=noised_latents, noise=z_rand.to(dtype=latents.dtype, device=latents.device), LPF=self.freq_filter)
+            return cached_latents, noised_latents
+        elif self.init_type == self.DINKINIT_V1:
+            # NOTE: This was my first attempt at implementing FreeInit; it sorta works due to my alpha_cumprod shenanigans,
+            #       but completely by accident.
+            # 1. apply initial noise with appropriate step sigma
+            sigma = self.get_sigma(model, self.step-1000).to(latents.device)
+            alpha_cumprod = 1 / ((sigma * sigma) + 1) #1 / ((sigma * sigma)) # 1 / ((sigma * sigma) + 1)
+            noised_latents = (latents + (cached_noise * sigma)) * alpha_cumprod
+            # 2. create random noise z_rand for high frequency
+            temp_sample_settings = sample_settings.clone()
+            temp_sample_settings.batch_offset += self.iter_batch_offset * curr_i
+            temp_sample_settings.seed_offset += self.iter_seed_offset * curr_i
+            z_rand = temp_sample_settings.prepare_noise(seed=seed, latents=latents, noise=None,
+                                                    extra_args=noise_extra_args, force_create_noise=True)
+            ####z_rand = torch.randn_like(latents, dtype=latents.dtype, device=latents.device)
+            # 3. noise reinitialization - combines low freq. noise from noised_latents and high freq. noise from z_rand
+            noised_latents = freeinit.freq_mix_3d(x=noised_latents, noise=z_rand.to(dtype=latents.dtype, device=latents.device), LPF=self.freq_filter)
+            return cached_latents, noised_latents
+        else:
+            raise ValueError(f"FreeInit init_type '{self.init_type}' is not recognized.")
+
+
+class CustomCFGKeyframe:
+    def __init__(self, cfg_multival: Union[float, Tensor], start_percent=0.0, guarantee_steps=1):
+        self.cfg_multival = cfg_multival
+        # scheduling
+        self.start_percent = float(start_percent)
+        self.start_t = 999999999.9
+        self.guarantee_steps = guarantee_steps
+    
+    def clone(self):
+        c = CustomCFGKeyframe(cfg_multival=self.cfg_multival,
+                              start_percent=self.start_percent, guarantee_steps=self.guarantee_steps)
+        c.start_t = self.start_t
+        return c
+
+
+class CustomCFGKeyframeGroup:
+    def __init__(self):
+        self.keyframes: list[CustomCFGKeyframe] = []
+        self._current_keyframe: CustomCFGKeyframe = None
+        self._current_used_steps: int = 0
+        self._current_index: int = 0
+    
+    def reset(self):
+        self._current_keyframe = None
+        self._current_used_steps = 0
+        self._current_index = 0
+        self._set_first_as_current()
+
+    def add(self, keyframe: CustomCFGKeyframe):
+        # add to end of list, then sort
+        self.keyframes.append(keyframe)
+        self.keyframes = get_sorted_list_via_attr(self.keyframes, "start_percent")
+        self._set_first_as_current()
+
+    def _set_first_as_current(self):
+        if len(self.keyframes) > 0:
+            self._current_keyframe = self.keyframes[0]
+        else:
+            self._current_keyframe = None
+
+    def has_index(self, index: int) -> int:
+        return index >=0 and index < len(self.keyframes)
+
+    def is_empty(self) -> bool:
+        return len(self.keyframes) == 0
+    
+    def clone(self):
+        cloned = CustomCFGKeyframeGroup()
+        for keyframe in self.keyframes:
+            cloned.keyframes.append(keyframe)
+        cloned._set_first_as_current()
+        return cloned
+    
+    def initialize_timesteps(self, model: BaseModel):
+        for keyframe in self.keyframes:
+            keyframe.start_t = model.model_sampling.percent_to_sigma(keyframe.start_percent)
+    
+    def prepare_current_keyframe(self, t: Tensor):
+        curr_t: float = t[0]
+        prev_index = self._current_index
+        # if met guaranteed steps, look for next keyframe in case need to switch
+        if self._current_used_steps >= self._current_keyframe.guarantee_steps:
+            # if has next index, loop through and see if need t oswitch
+            if self.has_index(self._current_index+1):
+                for i in range(self._current_index+1, len(self.keyframes)):
+                    eval_c = self.keyframes[i]
+                    # check if start_t is greater or equal to curr_t
+                    # NOTE: t is in terms of sigmas, not percent, so bigger number = earlier step in sampling
+                    if eval_c.start_t >= curr_t:
+                        self._current_index = i
+                        self._current_keyframe = eval_c
+                        self._current_used_steps = 0
+                        # if guarantee_steps greater than zero, stop searching for other keyframes
+                        if self._current_keyframe.guarantee_steps > 0:
+                            break
+                    # if eval_c is outside the percent range, stop looking further
+                    else: break
+        # update steps current context is used
+        self._current_used_steps += 1
+
+    def patch_model(self, model: ModelPatcher) -> ModelPatcher:
+        def evolved_custom_cfg(args):
+            cond: Tensor = args["cond"]
+            uncond: Tensor = args["uncond"]
+            # cond scale is based purely off of CustomCFG - cond_scale input in sampler is ignored!
+            cond_scale = self.cfg_multival
+            if isinstance(cond_scale, Tensor):
+                cond_scale = prepare_mask_batch(cond_scale.to(cond.dtype).to(cond.device), cond.shape)
+                cond_scale = extend_to_batch_size(cond_scale, cond.shape[0])
+            return uncond + (cond - uncond) * cond_scale
+
+        model = model.clone()
+        model.set_model_sampler_cfg_function(evolved_custom_cfg)
+        return model
+
+    # properties shadow those of CustomCFGKeyframe
+    @property
+    def cfg_multival(self):
+        if self._current_keyframe != None:
+            return self._current_keyframe.cfg_multival
+        return None

custom_nodes/ComfyUI-AnimateDiff-Evolved/animatediff/sampling.py

@@ -0,0 +1,528 @@
+from typing import Callable
+
+import math
+import torch
+from torch import Tensor
+from torch.nn.functional import group_norm
+from einops import rearrange
+
+import comfy.ldm.modules.attention as attention
+from comfy.ldm.modules.diffusionmodules import openaimodel
+import comfy.model_management as model_management
+import comfy.samplers
+import comfy.sample
+import comfy.utils
+from comfy.controlnet import ControlBase
+import comfy.ops
+
+from .context import ContextFuseMethod, ContextSchedules, get_context_weights, get_context_windows
+from .sample_settings import IterationOptions, SampleSettings, SeedNoiseGeneration, prepare_mask_ad
+from .utils_model import ModelTypeSD, wrap_function_to_inject_xformers_bug_info
+from .model_injection import InjectionParams, ModelPatcherAndInjector, MotionModelGroup, MotionModelPatcher
+from .motion_module_ad import AnimateDiffFormat, AnimateDiffInfo, AnimateDiffVersion, VanillaTemporalModule
+from .logger import logger
+
+
+##################################################################################
+######################################################################
+# Global variable to use to more conveniently hack variable access into samplers
+class AnimateDiffHelper_GlobalState:
+    def __init__(self):
+        self.motion_models: MotionModelGroup = None
+        self.params: InjectionParams = None
+        self.sample_settings: SampleSettings = None
+        self.reset()
+    
+    def initialize(self, model):
+        # this function is to be run in sampling func
+        if not self.initialized:
+            self.initialized = True
+            if self.motion_models is not None:
+                self.motion_models.initialize_timesteps(model)
+            if self.params.context_options is not None:
+                self.params.context_options.initialize_timesteps(model)
+            if self.sample_settings.custom_cfg is not None:
+                self.sample_settings.custom_cfg.initialize_timesteps(model)
+
+    def reset(self):
+        self.initialized = False
+        self.start_step: int = 0
+        self.last_step: int = 0
+        self.current_step: int = 0
+        self.total_steps: int = 0
+        if self.motion_models is not None:
+            del self.motion_models
+            self.motion_models = None
+        if self.params is not None:
+            del self.params
+            self.params = None
+        if self.sample_settings is not None:
+            del self.sample_settings
+            self.sample_settings = None
+    
+    def update_with_inject_params(self, params: InjectionParams):
+        self.params = params
+
+    def is_using_sliding_context(self):
+        return self.params is not None and self.params.is_using_sliding_context()
+    
+    def create_exposed_params(self):
+        # This dict will be exposed to be used by other extensions
+        # DO NOT change any of the key names
+        # or I will find you 👁.👁
+        return {
+            "full_length": self.params.full_length,
+            "context_length": self.params.context_options.context_length,
+            "sub_idxs": self.params.sub_idxs,
+        }
+
+ADGS = AnimateDiffHelper_GlobalState()
+######################################################################
+##################################################################################
+
+
+##################################################################################
+#### Code Injection ##################################################
+
+# refer to forward_timestep_embed in comfy/ldm/modules/diffusionmodules/openaimodel.py
+def forward_timestep_embed_factory() -> Callable:
+    def forward_timestep_embed(ts, x, emb, context=None, transformer_options={}, output_shape=None, time_context=None, num_video_frames=None, image_only_indicator=None):
+        for layer in ts:
+            if isinstance(layer, openaimodel.VideoResBlock):
+                x = layer(x, emb, num_video_frames, image_only_indicator)
+            elif isinstance(layer, openaimodel.TimestepBlock):
+                x = layer(x, emb)
+            elif isinstance(layer, VanillaTemporalModule):
+                x = layer(x, context)
+            elif isinstance(layer, attention.SpatialVideoTransformer):
+                x = layer(x, context, time_context, num_video_frames, image_only_indicator, transformer_options)
+                if "transformer_index" in transformer_options:
+                    transformer_options["transformer_index"] += 1
+                if "current_index" in transformer_options: # keep this for backward compat, for now
+                    transformer_options["current_index"] += 1
+            elif isinstance(layer, attention.SpatialTransformer):
+                x = layer(x, context, transformer_options)
+                if "transformer_index" in transformer_options:
+                    transformer_options["transformer_index"] += 1
+                if "current_index" in transformer_options:  # keep this for backward compat, for now
+                    transformer_options["current_index"] += 1
+            elif isinstance(layer, openaimodel.Upsample):
+                x = layer(x, output_shape=output_shape)
+            else:
+                x = layer(x)
+        return x
+    return forward_timestep_embed
+
+
+def unlimited_memory_required(*args, **kwargs):
+    return 0
+
+
+def groupnorm_mm_factory(params: InjectionParams, manual_cast=False):
+    def groupnorm_mm_forward(self, input: Tensor) -> Tensor:
+        # axes_factor normalizes batch based on total conds and unconds passed in batch;
+        # the conds and unconds per batch can change based on VRAM optimizations that may kick in
+        if not params.is_using_sliding_context():
+            batched_conds = input.size(0)//params.full_length
+        else:
+            batched_conds = input.size(0)//params.context_options.context_length
+
+        input = rearrange(input, "(b f) c h w -> b c f h w", b=batched_conds)
+        if manual_cast:
+            weight, bias = comfy.ops.cast_bias_weight(self, input)
+        else:
+            weight, bias = self.weight, self.bias
+        input = group_norm(input, self.num_groups, weight, bias, self.eps)
+        input = rearrange(input, "b c f h w -> (b f) c h w", b=batched_conds)
+        return input
+    return groupnorm_mm_forward
+
+
+def get_additional_models_factory(orig_get_additional_models: Callable, motion_models: MotionModelGroup):
+    def get_additional_models_with_motion(*args, **kwargs):
+        models, inference_memory = orig_get_additional_models(*args, **kwargs)
+        if motion_models is not None:
+            for motion_model in motion_models.models:
+                models.append(motion_model)
+        # TODO: account for inference memory as well?
+        return models, inference_memory
+    return get_additional_models_with_motion
+######################################################################
+##################################################################################
+
+
+def apply_params_to_motion_models(motion_models: MotionModelGroup, params: InjectionParams):
+    params = params.clone()
+    for context in params.context_options.contexts:
+        if context.context_schedule == ContextSchedules.VIEW_AS_CONTEXT:
+            context.context_length = params.full_length
+    # TODO: check (and message) should be different based on use_on_equal_length setting
+    if params.context_options.context_length:
+        pass
+
+    allow_equal = params.context_options.use_on_equal_length
+    if params.context_options.context_length:
+        enough_latents = params.full_length >= params.context_options.context_length if allow_equal else params.full_length > params.context_options.context_length
+    else:
+        enough_latents = False
+    if params.context_options.context_length and enough_latents:
+        logger.info(f"Sliding context window activated - latents passed in ({params.full_length}) greater than context_length {params.context_options.context_length}.")
+    else:
+        logger.info(f"Regular AnimateDiff activated - latents passed in ({params.full_length}) less or equal to context_length {params.context_options.context_length}.")
+        params.reset_context()
+    if motion_models is not None:
+        # if no context_length, treat video length as intended AD frame window
+        if not params.context_options.context_length:
+            for motion_model in motion_models.models:
+                if not motion_model.model.is_length_valid_for_encoding_max_len(params.full_length):
+                    raise ValueError(f"Without a context window, AnimateDiff model {motion_model.model.mm_info.mm_name} has upper limit of {motion_model.model.encoding_max_len} frames, but received {params.full_length} latents.")
+            motion_models.set_video_length(params.full_length, params.full_length)
+        # otherwise, treat context_length as intended AD frame window
+        else:
+            for motion_model in motion_models.models:
+                view_options = params.context_options.view_options
+                context_length = view_options.context_length if view_options else params.context_options.context_length
+                if not motion_model.model.is_length_valid_for_encoding_max_len(context_length):
+                    raise ValueError(f"AnimateDiff model {motion_model.model.mm_info.mm_name} has upper limit of {motion_model.model.encoding_max_len} frames for a context window, but received context length of {params.context_options.context_length}.")
+            motion_models.set_video_length(params.context_options.context_length, params.full_length)
+        # inject model
+        module_str = "modules" if len(motion_models.models) > 1 else "module"
+        logger.info(f"Using motion {module_str} {motion_models.get_name_string(show_version=True)}.")
+    return params
+
+
+class FunctionInjectionHolder:
+    def __init__(self):
+        pass
+    
+    def inject_functions(self, model: ModelPatcherAndInjector, params: InjectionParams):
+        # Save Original Functions
+        self.orig_forward_timestep_embed = openaimodel.forward_timestep_embed # needed to account for VanillaTemporalModule
+        self.orig_memory_required = model.model.memory_required # allows for "unlimited area hack" to prevent halving of conds/unconds
+        self.orig_groupnorm_forward = torch.nn.GroupNorm.forward # used to normalize latents to remove "flickering" of colors/brightness between frames
+        self.orig_groupnorm_manual_cast_forward = comfy.ops.manual_cast.GroupNorm.forward_comfy_cast_weights
+        self.orig_sampling_function = comfy.samplers.sampling_function # used to support sliding context windows in samplers
+        self.orig_prepare_mask = comfy.sample.prepare_mask
+        self.orig_get_additional_models = comfy.sample.get_additional_models
+        # Inject Functions
+        openaimodel.forward_timestep_embed = forward_timestep_embed_factory()
+        if params.unlimited_area_hack:
+            model.model.memory_required = unlimited_memory_required
+        if model.motion_models is not None:
+            # only apply groupnorm hack if not [v3 or ([not Hotshot] and SD1.5 and v2 and apply_v2_properly)]
+            info: AnimateDiffInfo = model.motion_models[0].model.mm_info
+            if not (info.mm_version == AnimateDiffVersion.V3 or
+                    (info.mm_format not in [AnimateDiffFormat.HOTSHOTXL] and info.sd_type == ModelTypeSD.SD1_5 and info.mm_version == AnimateDiffVersion.V2 and params.apply_v2_properly)):
+                torch.nn.GroupNorm.forward = groupnorm_mm_factory(params)
+                comfy.ops.manual_cast.GroupNorm.forward_comfy_cast_weights = groupnorm_mm_factory(params, manual_cast=True)
+                # if mps device (Apple Silicon), disable batched conds to avoid black images with groupnorm hack
+                try:
+                    if model.load_device.type == "mps":
+                        model.model.memory_required = unlimited_memory_required
+                except Exception:
+                    pass
+            del info
+        comfy.samplers.sampling_function = evolved_sampling_function
+        comfy.sample.prepare_mask = prepare_mask_ad
+        comfy.sample.get_additional_models = get_additional_models_factory(self.orig_get_additional_models, model.motion_models)
+
+    def restore_functions(self, model: ModelPatcherAndInjector):
+        # Restoration
+        try:
+            model.model.memory_required = self.orig_memory_required
+            openaimodel.forward_timestep_embed = self.orig_forward_timestep_embed
+            torch.nn.GroupNorm.forward = self.orig_groupnorm_forward
+            comfy.ops.manual_cast.GroupNorm.forward_comfy_cast_weights = self.orig_groupnorm_manual_cast_forward
+            comfy.samplers.sampling_function = self.orig_sampling_function
+            comfy.sample.prepare_mask = self.orig_prepare_mask
+            comfy.sample.get_additional_models = self.orig_get_additional_models
+        except AttributeError:
+            logger.error("Encountered AttributeError while attempting to restore functions - likely, an error occured while trying " + \
+                         "to save original functions before injection, and a more specific error was thrown by ComfyUI.")
+
+
+def motion_sample_factory(orig_comfy_sample: Callable, is_custom: bool=False) -> Callable:
+    def motion_sample(model: ModelPatcherAndInjector, noise: Tensor, *args, **kwargs):
+        # check if model is intended for injecting
+        if type(model) != ModelPatcherAndInjector:
+            return orig_comfy_sample(model, noise, *args, **kwargs)
+        # otherwise, injection time
+        latents = None
+        cached_latents = None
+        cached_noise = None
+        function_injections = FunctionInjectionHolder()
+        try:
+            if model.sample_settings.custom_cfg is not None:
+                model = model.sample_settings.custom_cfg.patch_model(model)
+            # clone params from model
+            params = model.motion_injection_params.clone()
+            # get amount of latents passed in, and store in params
+            latents: Tensor = args[-1]
+            params.full_length = latents.size(0)
+            # reset global state
+            ADGS.reset()
+
+            # apply custom noise, if needed
+            disable_noise = kwargs.get("disable_noise") or False
+            seed = kwargs["seed"]
+
+            # apply params to motion model
+            params = apply_params_to_motion_models(model.motion_models, params)
+
+            # store and inject functions
+            function_injections.inject_functions(model, params)
+
+            # prepare noise_extra_args for noise generation purposes
+            noise_extra_args = {"disable_noise": disable_noise}
+            params.set_noise_extra_args(noise_extra_args)
+            # if noise is not disabled, do noise stuff
+            if not disable_noise:
+                noise = model.sample_settings.prepare_noise(seed, latents, noise, extra_args=noise_extra_args, force_create_noise=False)
+
+            # callback setup
+            original_callback = kwargs.get("callback", None)
+            def ad_callback(step, x0, x, total_steps):
+                if original_callback is not None:
+                    original_callback(step, x0, x, total_steps)
+                # update GLOBALSTATE for next iteration
+                ADGS.current_step = ADGS.start_step + step + 1
+            kwargs["callback"] = ad_callback
+            ADGS.motion_models = model.motion_models
+            ADGS.sample_settings = model.sample_settings
+
+            # apply adapt_denoise_steps
+            args = list(args)
+            if model.sample_settings.adapt_denoise_steps and not is_custom:
+                # only applicable when denoise and steps are provided (from simple KSampler nodes)
+                denoise = kwargs.get("denoise", None)
+                steps = args[0]
+                if denoise is not None and type(steps) == int:
+                    args[0] = max(int(denoise * steps), 1)
+
+
+            iter_opts = IterationOptions()
+            if model.sample_settings is not None:
+                iter_opts = model.sample_settings.iteration_opts
+            iter_opts.initialize(latents)
+            # cache initial noise and latents, if needed
+            if iter_opts.cache_init_latents:
+                cached_latents = latents.clone()
+            if iter_opts.cache_init_noise:
+                cached_noise = noise.clone()
+            # prepare iter opts preprocess kwargs, if needed
+            iter_kwargs = {}
+            if iter_opts.need_sampler:
+                # -5 for sampler_name (not custom) and sampler (custom)
+                model_management.load_model_gpu(model)
+                if is_custom:
+                    iter_kwargs[IterationOptions.SAMPLER] = None #args[-5]
+                else:
+                    iter_kwargs[IterationOptions.SAMPLER] = comfy.samplers.KSampler(
+                        model.model, steps=999, #steps=args[-7],
+                        device=model.current_device, sampler=args[-5],
+                        scheduler=args[-4], denoise=kwargs.get("denoise", None),
+                        model_options=model.model_options)
+
+            for curr_i in range(iter_opts.iterations):
+                # handle GLOBALSTATE vars and step tally
+                ADGS.update_with_inject_params(params)
+                ADGS.start_step = kwargs.get("start_step") or 0
+                ADGS.current_step = ADGS.start_step
+                ADGS.last_step = kwargs.get("last_step") or 0
+                if iter_opts.iterations > 1:
+                    logger.info(f"Iteration {curr_i+1}/{iter_opts.iterations}")
+                # perform any iter_opts preprocessing on latents
+                latents, noise = iter_opts.preprocess_latents(curr_i=curr_i, model=model, latents=latents, noise=noise,
+                                                              cached_latents=cached_latents, cached_noise=cached_noise,
+                                                              seed=seed,
+                                                              sample_settings=model.sample_settings, noise_extra_args=noise_extra_args,
+                                                              **iter_kwargs)
+                args[-1] = latents
+
+                if model.motion_models is not None:
+                    model.motion_models.pre_run(model)
+                if model.sample_settings is not None:
+                    model.sample_settings.pre_run(model)
+                latents = wrap_function_to_inject_xformers_bug_info(orig_comfy_sample)(model, noise, *args, **kwargs)
+            return latents
+        finally:
+            del latents
+            del noise
+            del cached_latents
+            del cached_noise
+            # reset global state
+            ADGS.reset()
+            # restore injected functions
+            function_injections.restore_functions(model)
+            del function_injections
+    return motion_sample
+
+
+def evolved_sampling_function(model, x, timestep, uncond, cond, cond_scale, model_options: dict={}, seed=None):
+    ADGS.initialize(model)
+    if ADGS.motion_models is not None:
+        ADGS.motion_models.prepare_current_keyframe(t=timestep)
+    if ADGS.params.context_options is not None:
+        ADGS.params.context_options.prepare_current_context(t=timestep)
+    if ADGS.sample_settings.custom_cfg is not None:
+        ADGS.sample_settings.custom_cfg.prepare_current_keyframe(t=timestep)
+
+    # never use cfg1 optimization if using custom_cfg (since can have timesteps and such)
+    if ADGS.sample_settings.custom_cfg is None and math.isclose(cond_scale, 1.0) and model_options.get("disable_cfg1_optimization", False) == False:
+        uncond_ = None
+    else:
+        uncond_ = uncond
+
+    # add AD/evolved-sampling params to model_options (transformer_options)
+    model_options = model_options.copy()
+    if "tranformer_options" not in model_options:
+        model_options["tranformer_options"] = {}
+    model_options["transformer_options"]["ad_params"] = ADGS.create_exposed_params()
+
+    if not ADGS.is_using_sliding_context():
+        cond_pred, uncond_pred = comfy.samplers.calc_cond_uncond_batch(model, cond, uncond_, x, timestep, model_options)
+    else:
+        cond_pred, uncond_pred = sliding_calc_cond_uncond_batch(model, cond, uncond_, x, timestep, model_options)
+
+    if "sampler_cfg_function" in model_options:
+        args = {"cond": x - cond_pred, "uncond": x - uncond_pred, "cond_scale": cond_scale, "timestep": timestep, "input": x, "sigma": timestep,
+                "cond_denoised": cond_pred, "uncond_denoised": uncond_pred, "model": model, "model_options": model_options}
+        cfg_result = x - model_options["sampler_cfg_function"](args)
+    else:
+        cfg_result = uncond_pred + (cond_pred - uncond_pred) * cond_scale
+
+    for fn in model_options.get("sampler_post_cfg_function", []):
+        args = {"denoised": cfg_result, "cond": cond, "uncond": uncond, "model": model, "uncond_denoised": uncond_pred, "cond_denoised": cond_pred,
+                "sigma": timestep, "model_options": model_options, "input": x}
+        cfg_result = fn(args)
+
+    return cfg_result
+
+
+# sliding_calc_cond_uncond_batch inspired by ashen's initial hack for 16-frame sliding context:
+# https://github.com/comfyanonymous/ComfyUI/compare/master...ashen-sensored:ComfyUI:master
+def sliding_calc_cond_uncond_batch(model, cond, uncond, x_in: Tensor, timestep, model_options):
+    def prepare_control_objects(control: ControlBase, full_idxs: list[int]):
+        if control.previous_controlnet is not None:
+            prepare_control_objects(control.previous_controlnet, full_idxs)
+        control.sub_idxs = full_idxs
+        control.full_latent_length = ADGS.params.full_length
+        control.context_length = ADGS.params.context_options.context_length
+    
+    def get_resized_cond(cond_in, full_idxs) -> list:
+        # reuse or resize cond items to match context requirements
+        resized_cond = []
+        # cond object is a list containing a dict - outer list is irrelevant, so just loop through it
+        for actual_cond in cond_in:
+            resized_actual_cond = actual_cond.copy()
+            # now we are in the inner dict - "pooled_output" is a tensor, "control" is a ControlBase object, "model_conds" is dictionary
+            for key in actual_cond:
+                try:
+                    cond_item = actual_cond[key]
+                    if isinstance(cond_item, Tensor):
+                        # check that tensor is the expected length - x.size(0)
+                        if cond_item.size(0) == x_in.size(0):
+                            # if so, it's subsetting time - tell controls the expected indeces so they can handle them
+                            actual_cond_item = cond_item[full_idxs]
+                            resized_actual_cond[key] = actual_cond_item
+                        else:
+                            resized_actual_cond[key] = cond_item
+                    # look for control
+                    elif key == "control":
+                        control_item = cond_item
+                        if hasattr(control_item, "sub_idxs"):
+                            prepare_control_objects(control_item, full_idxs)
+                        else:
+                            raise ValueError(f"Control type {type(control_item).__name__} may not support required features for sliding context window; \
+                                                use Control objects from Kosinkadink/ComfyUI-Advanced-ControlNet nodes, or make sure Advanced-ControlNet is updated.")
+                        resized_actual_cond[key] = control_item
+                        del control_item
+                    elif isinstance(cond_item, dict):
+                        new_cond_item = cond_item.copy()
+                        # when in dictionary, look for tensors and CONDCrossAttn [comfy/conds.py] (has cond attr that is a tensor)
+                        for cond_key, cond_value in new_cond_item.items():
+                            if isinstance(cond_value, Tensor):
+                                if cond_value.size(0) == x_in.size(0):
+                                    new_cond_item[cond_key] = cond_value[full_idxs]
+                            # if has cond that is a Tensor, check if needs to be subset
+                            elif hasattr(cond_value, "cond") and isinstance(cond_value.cond, Tensor):
+                                if cond_value.cond.size(0) == x_in.size(0):
+                                    new_cond_item[cond_key] = cond_value._copy_with(cond_value.cond[full_idxs])
+                        resized_actual_cond[key] = new_cond_item
+                    else:
+                        resized_actual_cond[key] = cond_item
+                finally:
+                    del cond_item  # just in case to prevent VRAM issues
+            resized_cond.append(resized_actual_cond)
+        return resized_cond
+
+    # get context windows
+    ADGS.params.context_options.step = ADGS.current_step
+    context_windows = get_context_windows(ADGS.params.full_length, ADGS.params.context_options)
+    # figure out how input is split
+    batched_conds = x_in.size(0)//ADGS.params.full_length
+
+    if ADGS.motion_models is not None:
+        ADGS.motion_models.set_view_options(ADGS.params.context_options.view_options)
+
+    # prepare final cond, uncond, and out_count
+    cond_final = torch.zeros_like(x_in)
+    uncond_final = torch.zeros_like(x_in)
+    out_count_final = torch.zeros((x_in.shape[0], 1, 1, 1), device=x_in.device)
+    bias_final = [0.0] * x_in.shape[0]
+
+    # perform calc_cond_uncond_batch per context window
+    for ctx_idxs in context_windows:
+        ADGS.params.sub_idxs = ctx_idxs
+        if ADGS.motion_models is not None:
+            ADGS.motion_models.set_sub_idxs(ctx_idxs)
+            ADGS.motion_models.set_video_length(len(ctx_idxs), ADGS.params.full_length)
+        # update exposed params
+        model_options["transformer_options"]["ad_params"]["sub_idxs"] = ctx_idxs
+        model_options["transformer_options"]["ad_params"]["context_length"] = len(ctx_idxs)
+        # account for all portions of input frames
+        full_idxs = []
+        for n in range(batched_conds):
+            for ind in ctx_idxs:
+                full_idxs.append((ADGS.params.full_length*n)+ind)
+        # get subsections of x, timestep, cond, uncond, cond_concat
+        sub_x = x_in[full_idxs]
+        sub_timestep = timestep[full_idxs]
+        sub_cond = get_resized_cond(cond, full_idxs) if cond is not None else None
+        sub_uncond = get_resized_cond(uncond, full_idxs) if uncond is not None else None
+
+        sub_cond_out, sub_uncond_out = comfy.samplers.calc_cond_uncond_batch(model, sub_cond, sub_uncond, sub_x, sub_timestep, model_options)
+
+        if ADGS.params.context_options.fuse_method == ContextFuseMethod.RELATIVE:
+            full_length = ADGS.params.full_length
+            for pos, idx in enumerate(ctx_idxs):
+                # bias is the influence of a specific index in relation to the whole context window
+                bias = 1 - abs(idx - (ctx_idxs[0] + ctx_idxs[-1]) / 2) / ((ctx_idxs[-1] - ctx_idxs[0] + 1e-2) / 2)
+                bias = max(1e-2, bias)
+                # take weighted average relative to total bias of current idx
+                # and account for batched_conds
+                for n in range(batched_conds):
+                    bias_total = bias_final[(full_length*n)+idx]
+                    prev_weight = (bias_total / (bias_total + bias))
+                    new_weight = (bias / (bias_total + bias))
+                    cond_final[(full_length*n)+idx] = cond_final[(full_length*n)+idx] * prev_weight + sub_cond_out[(full_length*n)+pos] * new_weight
+                    uncond_final[(full_length*n)+idx] = uncond_final[(full_length*n)+idx] * prev_weight + sub_uncond_out[(full_length*n)+pos] * new_weight
+                    bias_final[(full_length*n)+idx] = bias_total + bias
+        else:
+            # add conds and counts based on weights of fuse method
+            weights = get_context_weights(len(ctx_idxs), ADGS.params.context_options.fuse_method) * batched_conds
+            weights_tensor = torch.Tensor(weights).to(device=x_in.device).unsqueeze(-1).unsqueeze(-1).unsqueeze(-1)
+            cond_final[full_idxs] += sub_cond_out * weights_tensor
+            uncond_final[full_idxs] += sub_uncond_out * weights_tensor
+            out_count_final[full_idxs] += weights_tensor
+
+    if ADGS.params.context_options.fuse_method == ContextFuseMethod.RELATIVE:
+        # already normalized, so return as is
+        del out_count_final
+        return cond_final, uncond_final
+    else:
+        # normalize cond and uncond via division by context usage counts
+        cond_final /= out_count_final
+        uncond_final /= out_count_final
+        del out_count_final
+        return cond_final, uncond_final

custom_nodes/ComfyUI-AnimateDiff-Evolved/animatediff/utils_model.py

@@ -0,0 +1,417 @@
+import hashlib
+from pathlib import Path
+from typing import Callable, Union
+from collections.abc import Iterable
+from time import time
+import copy
+
+import torch
+import numpy as np
+
+import folder_paths
+from comfy.model_base import SD21UNCLIP, SDXL, BaseModel, SDXLRefiner, SVD_img2vid, model_sampling, ModelType
+from comfy.model_management import xformers_enabled
+from comfy.model_patcher import ModelPatcher
+
+import comfy.model_sampling
+import comfy_extras.nodes_model_advanced
+
+
+BIGMIN = -(2**53-1)
+BIGMAX = (2**53-1)
+
+
+class ModelSamplingConfig:
+    def __init__(self, beta_schedule: str, linear_start: float=None, linear_end: float=None):
+        self.sampling_settings = {"beta_schedule": beta_schedule}
+        if linear_start is not None:
+            self.sampling_settings["linear_start"] = linear_start
+        if linear_end is not None:
+            self.sampling_settings["linear_end"] = linear_end
+        self.beta_schedule = beta_schedule  # keeping this for backwards compatibility
+
+
+class ModelSamplingType:
+    EPS = "eps"
+    V_PREDICTION = "v_prediction"
+    LCM = "lcm"
+
+    _NON_LCM_LIST = [EPS, V_PREDICTION]
+    _FULL_LIST = [EPS, V_PREDICTION, LCM]
+
+    MAP = {
+        EPS: ModelType.EPS,
+        V_PREDICTION: ModelType.V_PREDICTION,
+        LCM: comfy_extras.nodes_model_advanced.LCM,
+    }
+
+    @classmethod
+    def from_alias(cls, alias: str):
+        return cls.MAP[alias]
+
+
+def factory_model_sampling_discrete_distilled(original_timesteps=50):
+    class ModelSamplingDiscreteDistilledEvolved(comfy_extras.nodes_model_advanced.ModelSamplingDiscreteDistilled):
+        def __init__(self, *args, **kwargs):
+            self.original_timesteps = original_timesteps  # normal LCM has 50
+            super().__init__(*args, **kwargs)
+    return ModelSamplingDiscreteDistilledEvolved
+
+
+# based on code in comfy_extras/nodes_model_advanced.py
+def evolved_model_sampling(model_config: ModelSamplingConfig, model_type: ModelType, alias: str, original_timesteps: int=None):
+    # if LCM, need to handle manually
+    if BetaSchedules.is_lcm(alias) or original_timesteps is not None:
+        sampling_type = comfy_extras.nodes_model_advanced.LCM
+        if original_timesteps is not None:
+            sampling_base = factory_model_sampling_discrete_distilled(original_timesteps=original_timesteps)
+        elif alias == BetaSchedules.LCM_100:
+            sampling_base = factory_model_sampling_discrete_distilled(original_timesteps=100)
+        elif alias == BetaSchedules.LCM_25:
+            sampling_base = factory_model_sampling_discrete_distilled(original_timesteps=25)
+        else:
+            sampling_base = comfy_extras.nodes_model_advanced.ModelSamplingDiscreteDistilled
+        class ModelSamplingAdvancedEvolved(sampling_base, sampling_type):
+            pass
+        # NOTE: if I want to support zsnr, this is where I would add that code
+        return ModelSamplingAdvancedEvolved(model_config)
+    # otherwise, use vanilla model_sampling function
+    return model_sampling(model_config, model_type)
+
+
+class BetaSchedules:
+    AUTOSELECT = "autoselect"
+    SQRT_LINEAR = "sqrt_linear (AnimateDiff)"
+    LINEAR_ADXL = "linear (AnimateDiff-SDXL)"
+    LINEAR = "linear (HotshotXL/default)"
+    AVG_LINEAR_SQRT_LINEAR = "avg(sqrt_linear,linear)"
+    LCM_AVG_LINEAR_SQRT_LINEAR = "lcm avg(sqrt_linear,linear)"
+    LCM = "lcm"
+    LCM_100 = "lcm[100_ots]"
+    LCM_25 = "lcm[25_ots]"
+    LCM_SQRT_LINEAR = "lcm >> sqrt_linear"
+    USE_EXISTING = "use existing"
+    SQRT = "sqrt"
+    COSINE = "cosine"
+    SQUAREDCOS_CAP_V2 = "squaredcos_cap_v2"
+    RAW_LINEAR = "linear"
+    RAW_SQRT_LINEAR = "sqrt_linear"
+
+    RAW_BETA_SCHEDULE_LIST = [RAW_LINEAR, RAW_SQRT_LINEAR, SQRT, COSINE, SQUAREDCOS_CAP_V2]
+
+    ALIAS_LCM_LIST = [LCM, LCM_100, LCM_25, LCM_SQRT_LINEAR]
+
+    ALIAS_ACTIVE_LIST = [SQRT_LINEAR, LINEAR_ADXL, LINEAR, AVG_LINEAR_SQRT_LINEAR, LCM_AVG_LINEAR_SQRT_LINEAR, LCM, LCM_100, LCM_SQRT_LINEAR, # LCM_25 is purposely omitted
+                  SQRT, COSINE, SQUAREDCOS_CAP_V2]
+
+    ALIAS_LIST = [AUTOSELECT, USE_EXISTING] + ALIAS_ACTIVE_LIST
+
+    
+
+    ALIAS_MAP = {
+        SQRT_LINEAR: "sqrt_linear",
+        LINEAR_ADXL: "linear", # also linear, but has different linear_end (0.020)
+        LINEAR: "linear",
+        LCM_100: "linear",  # distilled, 100 original timesteps
+        LCM_25: "linear",  # distilled, 25 original timesteps
+        LCM: "linear",  # distilled
+        LCM_SQRT_LINEAR: "sqrt_linear", # distilled, sqrt_linear
+        SQRT: "sqrt",
+        COSINE: "cosine",
+        SQUAREDCOS_CAP_V2: "squaredcos_cap_v2",
+        RAW_LINEAR: "linear",
+        RAW_SQRT_LINEAR: "sqrt_linear"
+    }
+
+    @classmethod
+    def is_lcm(cls, alias: str):
+        return alias in cls.ALIAS_LCM_LIST
+
+    @classmethod
+    def to_name(cls, alias: str):
+        return cls.ALIAS_MAP[alias]
+    
+    @classmethod
+    def to_config(cls, alias: str) -> ModelSamplingConfig:
+        linear_start = None
+        linear_end = None
+        if alias == cls.LINEAR_ADXL:
+            # uses linear_end=0.020
+            linear_end = 0.020
+        return ModelSamplingConfig(cls.to_name(alias), linear_start=linear_start, linear_end=linear_end)
+    
+    @classmethod
+    def _to_model_sampling(cls, alias: str, model_type: ModelType, config_override: ModelSamplingConfig=None, original_timesteps: int=None):
+        if alias == cls.USE_EXISTING:
+            return None
+        elif config_override != None:
+            return evolved_model_sampling(config_override, model_type=model_type, alias=alias, original_timesteps=original_timesteps)
+        elif alias == cls.AVG_LINEAR_SQRT_LINEAR:
+            ms_linear = evolved_model_sampling(cls.to_config(cls.LINEAR), model_type=model_type, alias=cls.LINEAR)
+            ms_sqrt_linear = evolved_model_sampling(cls.to_config(cls.SQRT_LINEAR), model_type=model_type, alias=cls.SQRT_LINEAR)
+            avg_sigmas = (ms_linear.sigmas + ms_sqrt_linear.sigmas) / 2
+            ms_linear.set_sigmas(avg_sigmas)
+            return ms_linear
+        elif alias == cls.LCM_AVG_LINEAR_SQRT_LINEAR:
+            ms_linear = evolved_model_sampling(cls.to_config(cls.LCM), model_type=model_type, alias=cls.LCM)
+            ms_sqrt_linear = evolved_model_sampling(cls.to_config(cls.LCM_SQRT_LINEAR), model_type=model_type, alias=cls.LCM_SQRT_LINEAR)
+            avg_sigmas = (ms_linear.sigmas + ms_sqrt_linear.sigmas) / 2
+            ms_linear.set_sigmas(avg_sigmas)
+            return ms_linear
+            # average out the sigmas
+        ms_obj = evolved_model_sampling(cls.to_config(alias), model_type=model_type, alias=alias, original_timesteps=original_timesteps)
+        return ms_obj
+
+    @classmethod
+    def to_model_sampling(cls, alias: str, model: ModelPatcher):
+        return cls._to_model_sampling(alias=alias, model_type=model.model.model_type)
+
+    @staticmethod
+    def get_alias_list_with_first_element(first_element: str):
+        new_list = BetaSchedules.ALIAS_LIST.copy()
+        element_index = new_list.index(first_element)
+        new_list[0], new_list[element_index] = new_list[element_index], new_list[0]
+        return new_list
+
+
+class SigmaSchedule:
+    def __init__(self, model_sampling: comfy.model_sampling.ModelSamplingDiscrete, model_type: ModelType):
+        self.model_sampling = model_sampling
+        #self.config = config
+        self.model_type = model_type
+        self.original_timesteps = getattr(self.model_sampling, "original_timesteps", None)
+    
+    def is_lcm(self):
+        return self.original_timesteps is not None
+
+    def total_sigmas(self):
+        return len(self.model_sampling.sigmas)
+    
+    def clone(self) -> 'SigmaSchedule':
+        new_model_sampling = copy.deepcopy(self.model_sampling)
+        #new_config = copy.deepcopy(self.config)
+        return SigmaSchedule(model_sampling=new_model_sampling, model_type=self.model_type)
+
+    # def clone(self):
+    #     pass
+
+    @staticmethod
+    def apply_zsnr(new_model_sampling: comfy.model_sampling.ModelSamplingDiscrete):
+        new_model_sampling.set_sigmas(comfy_extras.nodes_model_advanced.rescale_zero_terminal_snr_sigmas(new_model_sampling.sigmas))
+
+    # def get_lcmified(self, original_timesteps=50, zsnr=False) -> 'SigmaSchedule':
+    #     new_model_sampling = evolved_model_sampling(model_config=self.config, model_type=self.model_type, alias=None, original_timesteps=original_timesteps)
+    #     if zsnr:
+    #         new_model_sampling.set_sigmas(comfy_extras.nodes_model_advanced.rescale_zero_terminal_snr_sigmas(new_model_sampling.sigmas))
+    #     return SigmaSchedule(model_sampling=new_model_sampling, config=self.config, model_type=self.model_type, is_lcm=True)
+        
+
+class InterpolationMethod:
+    LINEAR = "linear"
+    EASE_IN = "ease_in"
+    EASE_OUT = "ease_out"
+    EASE_IN_OUT = "ease_in_out"
+
+    _LIST = [LINEAR, EASE_IN, EASE_OUT, EASE_IN_OUT]
+
+    @classmethod
+    def get_weights(cls, num_from: float, num_to: float, length: int, method: str, reverse=False):
+        diff = num_to - num_from
+        if method == cls.LINEAR:
+            weights = torch.linspace(num_from, num_to, length)
+        elif method == cls.EASE_IN:
+            index = torch.linspace(0, 1, length)
+            weights = diff * np.power(index, 2) + num_from
+        elif method == cls.EASE_OUT:
+            index = torch.linspace(0, 1, length)
+            weights = diff * (1 - np.power(1 - index, 2)) + num_from
+        elif method == cls.EASE_IN_OUT:
+            index = torch.linspace(0, 1, length)
+            weights = diff * ((1 - np.cos(index * np.pi)) / 2) + num_from
+        else:
+            raise ValueError(f"Unrecognized interpolation method '{method}'.")
+        if reverse:
+            weights = weights.flip(dims=(0,))
+        return weights
+
+
+class Folders:
+    ANIMATEDIFF_MODELS = "animatediff_models"
+    MOTION_LORA = "animatediff_motion_lora"
+    VIDEO_FORMATS = "animatediff_video_formats"
+
+
+def add_extension_to_folder_path(folder_name: str, extensions: Union[str, list[str]]):
+    if folder_name in folder_paths.folder_names_and_paths:
+        if isinstance(extensions, str):
+            folder_paths.folder_names_and_paths[folder_name][1].add(extensions)
+        elif isinstance(extensions, Iterable):
+            for ext in extensions:
+                folder_paths.folder_names_and_paths[folder_name][1].add(ext) 
+
+
+def try_mkdir(full_path: str):
+    try:
+        Path(full_path).mkdir()
+    except Exception:
+        pass
+
+
+# register motion models folder(s)
+folder_paths.add_model_folder_path(Folders.ANIMATEDIFF_MODELS, str(Path(__file__).parent.parent / "models"))
+folder_paths.add_model_folder_path(Folders.ANIMATEDIFF_MODELS, str(Path(folder_paths.models_dir) / Folders.ANIMATEDIFF_MODELS))
+add_extension_to_folder_path(Folders.ANIMATEDIFF_MODELS, folder_paths.supported_pt_extensions)
+try_mkdir(str(Path(folder_paths.models_dir) / Folders.ANIMATEDIFF_MODELS))
+
+# register motion LoRA folder(s)
+folder_paths.add_model_folder_path(Folders.MOTION_LORA, str(Path(__file__).parent.parent / "motion_lora"))
+folder_paths.add_model_folder_path(Folders.MOTION_LORA, str(Path(folder_paths.models_dir) / Folders.MOTION_LORA))
+add_extension_to_folder_path(Folders.MOTION_LORA, folder_paths.supported_pt_extensions)
+try_mkdir(str(Path(folder_paths.models_dir) / Folders.MOTION_LORA))
+
+# register video_formats folder
+folder_paths.add_model_folder_path(Folders.VIDEO_FORMATS, str(Path(__file__).parent.parent / "video_formats"))
+add_extension_to_folder_path(Folders.VIDEO_FORMATS, ".json")
+
+
+def get_available_motion_models():
+    return folder_paths.get_filename_list(Folders.ANIMATEDIFF_MODELS)
+
+
+def get_motion_model_path(model_name: str):
+    return folder_paths.get_full_path(Folders.ANIMATEDIFF_MODELS, model_name)
+
+
+def get_available_motion_loras():
+    return folder_paths.get_filename_list(Folders.MOTION_LORA)
+
+
+def get_motion_lora_path(lora_name: str):
+    return folder_paths.get_full_path(Folders.MOTION_LORA, lora_name)
+
+
+# modified from https://stackoverflow.com/questions/22058048/hashing-a-file-in-python
+def calculate_file_hash(filename: str, hash_every_n: int = 50):
+    h = hashlib.sha256()
+    b = bytearray(1024*1024)
+    mv = memoryview(b)
+    with open(filename, 'rb', buffering=0) as f:
+        i = 0
+        # don't hash entire file, only portions of it
+        while n := f.readinto(mv):
+            if i%hash_every_n == 0:
+                h.update(mv[:n])
+            i += 1
+    return h.hexdigest()
+
+
+def calculate_model_hash(model: ModelPatcher):
+    unet = model.model.diff
+    t = unet.input_blocks[1]
+    m = hashlib.sha256()
+    for buf in t.buffers():
+        m.update(buf.cpu().numpy().view(np.uint8))
+    return m.hexdigest()
+
+
+class ModelTypeSD:
+    SD1_5 = "SD1.5"
+    SD2_1 = "SD2.1"
+    SDXL = "SDXL"
+    SDXL_REFINER = "SDXL_Refiner"
+    SVD = "SVD"
+
+
+def get_sd_model_type(model: ModelPatcher) -> str:
+    if model is None:
+        return None
+    elif type(model.model) == BaseModel:
+        return ModelTypeSD.SD1_5
+    elif type(model.model) == SDXL:
+        return ModelTypeSD.SDXL
+    elif type(model.model) == SD21UNCLIP:
+        return ModelTypeSD.SD2_1
+    elif type(model.model) == SDXLRefiner:
+        return ModelTypeSD.SDXL_REFINER
+    elif type(model.model) == SVD_img2vid:
+        return ModelTypeSD.SVD
+    else:
+        return str(type(model.model).__name__)
+
+def is_checkpoint_sd1_5(model: ModelPatcher):
+    return False if model is None else type(model.model) == BaseModel
+
+def is_checkpoint_sdxl(model: ModelPatcher):
+    return False if model is None else type(model.model) == SDXL
+
+
+def raise_if_not_checkpoint_sd1_5(model: ModelPatcher):
+    if not is_checkpoint_sd1_5(model):
+        raise ValueError(f"For AnimateDiff, SD Checkpoint (model) is expected to be SD1.5-based (BaseModel), but was: {type(model.model).__name__}")
+
+
+# TODO: remove this filth when xformers bug gets fixed in future xformers version
+def wrap_function_to_inject_xformers_bug_info(function_to_wrap: Callable) -> Callable:
+    if not xformers_enabled:
+        return function_to_wrap
+    else:
+        def wrapped_function(*args, **kwargs):
+            try:
+                return function_to_wrap(*args, **kwargs)
+            except RuntimeError as e:
+                if str(e).startswith("CUDA error: invalid configuration argument"):
+                    raise RuntimeError(f"An xformers bug was encountered in AnimateDiff - this is unexpected, \
+                                       report this to Kosinkadink/ComfyUI-AnimateDiff-Evolved repo as an issue, \
+                                       and a workaround for now is to run ComfyUI with the --disable-xformers argument.")
+                raise
+        return wrapped_function
+
+
+class Timer(object):
+    __slots__ = ("start_time", "end_time")
+
+    def __init__(self) -> None:
+        self.start_time = 0.0
+        self.end_time = 0.0
+
+    def start(self) -> None:
+        self.start_time = time()
+
+    def update(self) -> None:
+        self.start()
+
+    def stop(self) -> float:
+        self.end_time = time()
+        return self.get_time_diff()
+
+    def get_time_diff(self) -> float:
+        return self.end_time - self.start_time
+
+    def get_time_current(self) -> float:
+        return time() - self.start_time
+
+
+# TODO: possibly add configuration file in future when needed?
+# # Load config settings
+# ADE_DIR = Path(__file__).parent.parent
+# ADE_CONFIG_FILE = ADE_DIR / "ade_config.json"
+
+# class ADE_Settings:
+#     USE_XFORMERS_IN_VERSATILE_ATTENTION = "use_xformers_in_VersatileAttention"
+
+# # Create ADE config if not present
+# ABS_CONFIG = {
+#     ADE_Settings.USE_XFORMERS_IN_VERSATILE_ATTENTION: True
+# }
+# if not ADE_CONFIG_FILE.exists():
+#     with ADE_CONFIG_FILE.open("w") as f:
+#         json.dumps(ABS_CONFIG, indent=4)
+# # otherwise, load it and use values
+# else:
+#     loaded_values: dict = None
+#     with ADE_CONFIG_FILE.open("r") as f:
+#         loaded_values = json.load(f)
+#     if loaded_values is not None:
+#         for key, value in loaded_values.items():
+#             if key in ABS_CONFIG:
+#                 ABS_CONFIG[key] = value

custom_nodes/ComfyUI-AnimateDiff-Evolved/animatediff/utils_motion.py

@@ -0,0 +1,230 @@
+from typing import Union
+import torch
+import torch.nn.functional as F
+from torch import Tensor, nn
+
+import comfy.model_management as model_management
+import comfy.ops
+import comfy.utils
+from comfy.cli_args import args
+from comfy.ldm.modules.attention import attention_basic, attention_pytorch, attention_split, attention_sub_quad, default
+
+from .logger import logger
+
+
+# until xformers bug is fixed, do not use xformers for VersatileAttention! TODO: change this when fix is out
+# logic for choosing optimized_attention method taken from comfy/ldm/modules/attention.py
+optimized_attention_mm = attention_basic
+if model_management.xformers_enabled():
+    pass
+    #optimized_attention_mm = attention_xformers
+if model_management.pytorch_attention_enabled():
+    optimized_attention_mm = attention_pytorch
+else:
+    if args.use_split_cross_attention:
+        optimized_attention_mm = attention_split
+    else:
+        optimized_attention_mm = attention_sub_quad
+
+
+class CrossAttentionMM(nn.Module):
+    def __init__(self, query_dim, context_dim=None, heads=8, dim_head=64, dropout=0., dtype=None, device=None,
+                 operations=comfy.ops.disable_weight_init):
+        super().__init__()
+        inner_dim = dim_head * heads
+        context_dim = default(context_dim, query_dim)
+
+        self.heads = heads
+        self.dim_head = dim_head
+        self.scale = None
+        self.default_scale = dim_head ** -0.5
+
+        self.to_q = operations.Linear(query_dim, inner_dim, bias=False, dtype=dtype, device=device)
+        self.to_k = operations.Linear(context_dim, inner_dim, bias=False, dtype=dtype, device=device)
+        self.to_v = operations.Linear(context_dim, inner_dim, bias=False, dtype=dtype, device=device)
+
+        self.to_out = nn.Sequential(operations.Linear(inner_dim, query_dim, dtype=dtype, device=device), nn.Dropout(dropout))
+
+    def forward(self, x, context=None, value=None, mask=None, scale_mask=None):
+        q = self.to_q(x)
+        context = default(context, x)
+        k: Tensor = self.to_k(context)
+        if value is not None:
+            v = self.to_v(value)
+            del value
+        else:
+            v = self.to_v(context)
+
+        # apply custom scale by multiplying k by scale factor
+        if self.scale is not None:
+            k *= self.scale
+        
+        # apply scale mask, if present
+        if scale_mask is not None:
+            k *= scale_mask
+
+        out = optimized_attention_mm(q, k, v, self.heads, mask)
+        return self.to_out(out)
+
+# TODO: set up comfy.ops style classes for groupnorm and other functions
+class GroupNormAD(torch.nn.GroupNorm):
+    def __init__(self, num_groups: int, num_channels: int, eps: float = 1e-5, affine: bool = True,
+                 device=None, dtype=None) -> None:
+        super().__init__(num_groups=num_groups, num_channels=num_channels, eps=eps, affine=affine, device=device, dtype=dtype)
+    
+    def forward(self, input: Tensor) -> Tensor:
+        return F.group_norm(
+             input, self.num_groups, self.weight, self.bias, self.eps)
+
+
+# applies min-max normalization, from:
+# https://stackoverflow.com/questions/68791508/min-max-normalization-of-a-tensor-in-pytorch
+def normalize_min_max(x: Tensor, new_min = 0.0, new_max = 1.0):
+    return linear_conversion(x, x_min=x.min(), x_max=x.max(), new_min=new_min, new_max=new_max)
+
+
+def linear_conversion(x, x_min=0.0, x_max=1.0, new_min=0.0, new_max=1.0):
+    x_min = float(x_min)
+    x_max = float(x_max)
+    new_min = float(new_min)
+    new_max = float(new_max)
+    return (((x - x_min)/(x_max - x_min)) * (new_max - new_min)) + new_min
+
+
+# adapted from comfy/sample.py
+def prepare_mask_batch(mask: Tensor, shape: Tensor, multiplier: int=1, match_dim1=False):
+    mask = mask.clone()
+    mask = torch.nn.functional.interpolate(mask.reshape((-1, 1, mask.shape[-2], mask.shape[-1])), size=(shape[2]*multiplier, shape[3]*multiplier), mode="bilinear")
+    if match_dim1:
+        mask = torch.cat([mask] * shape[1], dim=1)
+    return mask
+
+
+def extend_to_batch_size(tensor: Tensor, batch_size: int):
+    if tensor.shape[0] > batch_size:
+        return tensor[:batch_size]
+    elif tensor.shape[0] < batch_size:
+        remainder = batch_size-tensor.shape[0]
+        return torch.cat([tensor] + [tensor[-1:]]*remainder, dim=0)
+    return tensor
+
+
+def get_sorted_list_via_attr(objects: list, attr: str) -> list:
+    if not objects:
+        return objects
+    elif len(objects) <= 1:
+        return [x for x in objects]
+    # now that we know we have to sort, do it following these rules:
+    # a) if objects have same value of attribute, maintain their relative order
+    # b) perform sorting of the groups of objects with same attributes
+    unique_attrs = {}
+    for o in objects:
+        val_attr = getattr(o, attr)
+        attr_list = unique_attrs.get(val_attr, list())
+        attr_list.append(o)
+        if val_attr not in unique_attrs:
+            unique_attrs[val_attr] = attr_list
+    # now that we have the unique attr values grouped together in relative order, sort them by key
+    sorted_attrs = dict(sorted(unique_attrs.items()))
+    # now flatten out the dict into a list to return
+    sorted_list = []
+    for object_list in sorted_attrs.values():
+        sorted_list.extend(object_list)
+    return sorted_list
+
+
+class MotionCompatibilityError(ValueError):
+    pass
+
+
+def get_combined_multival(multivalA: Union[float, Tensor], multivalB: Union[float, Tensor]) -> Union[float, Tensor]:
+    # if one is None, use the other
+    if multivalA == None:
+        return multivalB
+    elif multivalB == None:
+        return multivalA 
+    # both have a value - combine them based on type
+    # if both are Tensors, make dims match before multiplying
+    if type(multivalA) == Tensor and type(multivalB) == Tensor:
+        areaA = multivalA.shape[1]*multivalA.shape[2]
+        areaB = multivalB.shape[1]*multivalB.shape[2]
+        # match height/width to mask with larger area
+        leader,follower = multivalA,multivalB if areaA >= areaB else multivalB,multivalA
+        batch_size = multivalA.shape[0] if multivalA.shape[0] >= multivalB.shape[0] else multivalB.shape[0]
+        # make follower same dimensions as leader
+        follower = torch.unsqueeze(follower, 1)
+        follower = comfy.utils.common_upscale(follower, leader.shape[2], leader.shape[1], "bilinear", "center")
+        follower = torch.squeeze(follower, 1)
+        # make sure batch size will match
+        leader = extend_to_batch_size(leader, batch_size)
+        follower = extend_to_batch_size(follower, batch_size)
+        return leader * follower
+    # otherwise, just multiply them together - one of them is a float
+    return multivalA * multivalB
+
+
+class ADKeyframe:
+    def __init__(self,
+                 start_percent: float = 0.0,
+                 scale_multival: Union[float, Tensor]=None,
+                 effect_multival: Union[float, Tensor]=None,
+                 inherit_missing: bool=True,
+                 guarantee_steps: int=1,
+                 default: bool=False,
+                 ):
+        self.start_percent = start_percent
+        self.start_t = 999999999.9
+        self.scale_multival = scale_multival
+        self.effect_multival = effect_multival
+        self.inherit_missing = inherit_missing
+        self.guarantee_steps = guarantee_steps
+        self.default = default
+    
+    def has_scale(self):
+        return self.scale_multival is not None
+    
+    def has_effect(self):
+        return self.effect_multival is not None
+
+
+class ADKeyframeGroup:
+    def __init__(self):
+        self.keyframes: list[ADKeyframe] = []
+        self.keyframes.append(ADKeyframe(guarantee_steps=1, default=True))
+    
+    def add(self, keyframe: ADKeyframe):
+        # remove any default keyframes that match start_percent of new keyframe
+        default_to_delete = []
+        for i in range(len(self.keyframes)):
+            if self.keyframes[i].default and self.keyframes[i].start_percent == keyframe.start_percent:
+                default_to_delete.append(i)
+        for i in reversed(default_to_delete):
+            self.keyframes.pop(i)
+        # add to end of list, then sort
+        self.keyframes.append(keyframe)
+        self.keyframes = get_sorted_list_via_attr(self.keyframes, "start_percent")
+    
+    def get_index(self, index: int) -> Union[ADKeyframe, None]:
+        try:
+            return self.keyframes[index]
+        except IndexError:
+            return None
+    
+    def has_index(self, index: int) -> int:
+        return index >=0 and index < len(self.keyframes)
+
+    def __getitem__(self, index) -> ADKeyframe:
+        return self.keyframes[index]
+
+    def __len__(self) -> int:
+        return len(self.keyframes)
+
+    def is_empty(self) -> bool:
+        return len(self.keyframes) == 0
+
+    def clone(self) -> 'ADKeyframeGroup':
+        cloned = ADKeyframeGroup()
+        for tk in self.keyframes:
+            if not tk.default:
+                cloned.add(tk)
+        return cloned

custom_nodes/ComfyUI-AnimateDiff-Evolved/models/mm_sd_v15_v2.ckpt

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:69ed0f5fef82b110aca51bcab73b21104242bc65d6ab4b8b2a2a94d31cad1bf0
+size 1817888431

custom_nodes/ComfyUI-AnimateDiff-Evolved/motion_lora/v2_lora_ZoomIn.ckpt

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:70ce8b9057b173b9249c48aca5d66c8aa1d8aaa040fda394e50e37f3e278195e
+size 77474499

custom_nodes/ComfyUI-AnimateDiff-Evolved/video_formats/av1-webm.json

@@ -0,0 +1,10 @@
+{
+    "main_pass":
+    [
+        "-n", "-c:v", "libsvtav1",
+        "-pix_fmt", "yuv420p10le",
+        "-crf", "23"
+    ],
+     "extension": "webm",
+     "environment": {"SVT_LOG": "1"}
+}

custom_nodes/ComfyUI-AnimateDiff-Evolved/video_formats/h264-mp4.json

@@ -0,0 +1,9 @@
+{
+    "main_pass":
+    [
+        "-n", "-c:v", "libx264",
+        "-pix_fmt", "yuv420p",
+        "-crf", "19"
+    ],
+     "extension": "mp4"
+}

custom_nodes/ComfyUI-AnimateDiff-Evolved/video_formats/h265-mp4.json

@@ -0,0 +1,11 @@
+{
+    "main_pass":
+    [
+        "-n", "-c:v", "libx265",
+        "-pix_fmt", "yuv420p10le",
+        "-preset", "medium",
+        "-crf", "22",
+        "-x265-params", "log-level=quiet"
+    ],
+     "extension": "mp4"
+}

custom_nodes/ComfyUI-AnimateDiff-Evolved/video_formats/webm.json

@@ -0,0 +1,9 @@
+{
+    "main_pass":
+    [
+        "-n",
+        "-pix_fmt", "yuv420p",
+        "-crf", "23"
+    ],
+     "extension": "webm"
+}

custom_nodes/ComfyUI-AnimateDiff-Evolved/web/js/gif_preview.js

@@ -0,0 +1,142 @@
+import { app } from '../../../scripts/app.js'
+import { api } from '../../../scripts/api.js'
+
+function offsetDOMWidget(
+    widget,
+    ctx,
+    node,
+    widgetWidth,
+    widgetY,
+    height
+  ) {
+    const margin = 10
+    const elRect = ctx.canvas.getBoundingClientRect()
+    const transform = new DOMMatrix()
+      .scaleSelf(
+        elRect.width / ctx.canvas.width,
+        elRect.height / ctx.canvas.height
+      )
+      .multiplySelf(ctx.getTransform())
+      .translateSelf(0, widgetY + margin)
+  
+    const scale = new DOMMatrix().scaleSelf(transform.a, transform.d)
+    Object.assign(widget.inputEl.style, {
+      transformOrigin: '0 0',
+      transform: scale,
+      left: `${transform.e}px`,
+      top: `${transform.d + transform.f}px`,
+      width: `${widgetWidth}px`,
+      height: `${(height || widget.parent?.inputHeight || 32) - margin}px`,
+      position: 'absolute',
+      background: !node.color ? '' : node.color,
+      color: !node.color ? '' : 'white',
+      zIndex: 5, //app.graph._nodes.indexOf(node),
+    })
+  }
+
+  export const hasWidgets = (node) => {
+    if (!node.widgets || !node.widgets?.[Symbol.iterator]) {
+      return false
+    }
+    return true
+  }
+
+  export const cleanupNode = (node) => {
+    if (!hasWidgets(node)) {
+      return
+    }
+  
+    for (const w of node.widgets) {
+      if (w.canvas) {
+        w.canvas.remove()
+      }
+      if (w.inputEl) {
+        w.inputEl.remove()
+      }
+      // calls the widget remove callback
+      w.onRemoved?.()
+    }
+  }
+
+const CreatePreviewElement = (name, val, format) => {
+  const [type] = format.split('/')  
+  const w = {
+      name,
+      type,
+      value: val,
+      draw: function (ctx, node, widgetWidth, widgetY, height) {
+        const [cw, ch] = this.computeSize(widgetWidth)
+        offsetDOMWidget(this, ctx, node, widgetWidth, widgetY, ch)
+      },
+      computeSize: function (_) {
+        const ratio = this.inputRatio || 1
+        const width = Math.max(220, this.parent.size[0])
+        return [width, (width / ratio + 10)]
+      },
+      onRemoved: function () {
+        if (this.inputEl) {
+          this.inputEl.remove()
+        }
+      },
+    }
+
+    w.inputEl = document.createElement(type === 'video' ? 'video' : 'img')
+    w.inputEl.src = w.value
+    if (type === 'video') {
+      w.inputEl.setAttribute('type', 'video/webm');
+      w.inputEl.autoplay = true
+      w.inputEl.loop = true
+      w.inputEl.controls = false;
+    }
+    w.inputEl.onload = function () {
+      w.inputRatio = w.inputEl.naturalWidth / w.inputEl.naturalHeight
+    }
+    document.body.appendChild(w.inputEl)
+    return w
+  }
+
+const gif_preview = {
+    name: 'AnimateDiff.gif_preview',
+    async beforeRegisterNodeDef(nodeType, nodeData, app) {
+        switch (nodeData.name) {
+            case 'ADE_AnimateDiffCombine':{
+                const onExecuted = nodeType.prototype.onExecuted
+                nodeType.prototype.onExecuted = function (message) {
+                const prefix = 'ad_gif_preview_'
+                const r = onExecuted ? onExecuted.apply(this, message) : undefined
+
+                if (this.widgets) {
+                    const pos = this.widgets.findIndex((w) => w.name === `${prefix}_0`)
+                    if (pos !== -1) {
+                      for (let i = pos; i < this.widgets.length; i++) {
+                        this.widgets[i].onRemoved?.()
+                    }
+                    this.widgets.length = pos
+                    }
+                    if (message?.gifs) {
+                      message.gifs.forEach((params, i) => {
+                        const previewUrl = api.apiURL(
+                          '/view?' + new URLSearchParams(params).toString()
+                        )
+                  const w = this.addCustomWidget(
+                    CreatePreviewElement(`${prefix}_${i}`, previewUrl, params.format || 'image/gif')
+                  )
+                  w.parent = this
+                  })
+                 }
+                  const onRemoved = this.onRemoved
+                  this.onRemoved = () => {
+                    cleanupNode(this)
+                    return onRemoved?.()
+                  }
+                }
+                this.setSize([this.size[0], this.computeSize([this.size[0], this.size[1]])[1]])
+                return r
+                }
+                break
+              }
+        }
+    }
+}
+
+app.registerExtension(gif_preview)

custom_nodes/ComfyUI-Impact-Pack/LICENSE.txt

@@ -0,0 +1,674 @@
+                    GNU GENERAL PUBLIC LICENSE
+                       Version 3, 29 June 2007
+
+ Copyright (C) 2007 Free Software Foundation, Inc. <https://fsf.org/>
+ Everyone is permitted to copy and distribute verbatim copies
+ of this license document, but changing it is not allowed.
+
+                            Preamble
+
+  The GNU General Public License is a free, copyleft license for
+software and other kinds of works.
+
+  The licenses for most software and other practical works are designed
+to take away your freedom to share and change the works.  By contrast,
+the GNU General Public License is intended to guarantee your freedom to
+share and change all versions of a program--to make sure it remains free
+software for all its users.  We, the Free Software Foundation, use the
+GNU General Public License for most of our software; it applies also to
+any other work released this way by its authors.  You can apply it to
+your programs, too.
+
+  When we speak of free software, we are referring to freedom, not
+price.  Our General Public Licenses are designed to make sure that you
+have the freedom to distribute copies of free software (and charge for
+them if you wish), that you receive source code or can get it if you
+want it, that you can change the software or use pieces of it in new
+free programs, and that you know you can do these things.
+
+  To protect your rights, we need to prevent others from denying you
+these rights or asking you to surrender the rights.  Therefore, you have
+certain responsibilities if you distribute copies of the software, or if
+you modify it: responsibilities to respect the freedom of others.
+
+  For example, if you distribute copies of such a program, whether
+gratis or for a fee, you must pass on to the recipients the same
+freedoms that you received.  You must make sure that they, too, receive
+or can get the source code.  And you must show them these terms so they
+know their rights.
+
+  Developers that use the GNU GPL protect your rights with two steps:
+(1) assert copyright on the software, and (2) offer you this License
+giving you legal permission to copy, distribute and/or modify it.
+
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+authors' sake, the GPL requires that modified versions be marked as
+changed, so that their problems will not be attributed erroneously to
+authors of previous versions.
+
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+stand ready to extend this provision to those domains in future versions
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+
+  Finally, every program is threatened constantly by software patents.
+States should not allow patents to restrict development and use of
+software on general-purpose computers, but in those that do, we wish to
+avoid the special danger that patents applied to a free program could
+make it effectively proprietary.  To prevent this, the GPL assures that
+patents cannot be used to render the program non-free.
+
+  The precise terms and conditions for copying, distribution and
+modification follow.
+
+                       TERMS AND CONDITIONS
+
+  0. Definitions.
+
+  "This License" refers to version 3 of the GNU General Public License.
+
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custom_nodes/ComfyUI-Impact-Pack/README.md

@@ -0,0 +1,454 @@
+[![Youtube Badge](https://img.shields.io/badge/Youtube-FF0000?style=for-the-badge&logo=Youtube&logoColor=white&link=https://www.youtube.com/watch?v=AccoxDZIg3Y&list=PL_Ej2RDzjQLGfEeizq4GISeY3FtVyFmGP)](https://www.youtube.com/watch?v=AccoxDZIg3Y&list=PL_Ej2RDzjQLGfEeizq4GISeY3FtVyFmGP)
+
+# ComfyUI-Impact-Pack
+
+**Custom nodes pack for ComfyUI**
+This custom node helps to conveniently enhance images through Detector, Detailer, Upscaler, Pipe, and more.
+
+
+## NOTICE
+* V4.77: Compatibility patch applied. Requires ComfyUI version (Oct. 8th) or later.
+* V4.73.3: ControlNetApply (SEGS) supports AnimateDiff
+* V4.20.1: Due to the feature update in `RegionalSampler`, the parameter order has changed, causing malfunctions in previously created `RegionalSamplers`. Please adjust the parameters accordingly.
+* V4.12: `MASKS` is changed to `MASK`.
+* V4.7.2 isn't compatible with old version of `ControlNet Auxiliary Preprocessor`. If you will use `MediaPipe FaceMesh to SEGS` update to latest version(Sep. 17th).  
+* Selection weight syntax is changed(: -> ::) since V3.16. ([tutorial](https://github.com/ltdrdata/ComfyUI-extension-tutorials/blob/Main/ComfyUI-Impact-Pack/tutorial/ImpactWildcardProcessor.md))
+* Starting from V3.6, requires latest version(Aug 8, 9ccc965) of ComfyUI.
+* **In versions below V3.3.1, there was an issue with the image quality generated after using the UltralyticsDetectorProvider. Please make sure to upgrade to a newer version.**
+* Starting from V3.0, nodes related to `mmdet` are optional nodes that are activated only based on the configuration settings.
+  - Through ComfyUI-Impact-Subpack, you can utilize UltralyticsDetectorProvider to access various detection models.
+* Between versions 2.22 and 2.21, there is partial compatibility loss regarding the Detailer workflow. If you continue to use the existing workflow, errors may occur during execution. An additional output called "enhanced_alpha_list" has been added to Detailer-related nodes.
+* The permission error related to cv2 that occurred during the installation of Impact Pack has been patched in version 2.21.4. However, please note that the latest versions of ComfyUI and ComfyUI-Manager are required.
+* The "PreviewBridge" feature may not function correctly on ComfyUI versions released before July 1, 2023.
+* Attempting to load the "ComfyUI-Impact-Pack" on ComfyUI versions released before June 27, 2023, will result in a failure.
+* With the addition of wildcard support in FaceDetailer, the structure of DETAILER_PIPE-related nodes and Detailer nodes has changed. There may be malfunctions when using the existing workflow.
+
+
+## Custom Nodes
+* [Detectors](https://github.com/ltdrdata/ComfyUI-extension-tutorials/blob/Main/ComfyUI-Impact-Pack/tutorial/detectors.md)
+  * SAMLoader - Loads the SAM model.
+  * UltralyticsDetectorProvider - Loads the Ultralystics model to provide SEGM_DETECTOR, BBOX_DETECTOR.
+    - Unlike `MMDetDetectorProvider`, for segm models, `BBOX_DETECTOR` is also provided.
+    - The various models available in UltralyticsDetectorProvider can be downloaded through **ComfyUI-Manager**.
+  * ONNXDetectorProvider - Loads the ONNX model to provide BBOX_DETECTOR.
+  * CLIPSegDetectorProvider - Wrapper for CLIPSeg to provide BBOX_DETECTOR.
+    * You need to install the ComfyUI-CLIPSeg node extension.
+  * SEGM Detector (combined) - Detects segmentation and returns a mask from the input image.
+  * BBOX Detector (combined) - Detects bounding boxes and returns a mask from the input image.
+  * SAMDetector (combined) - Utilizes the SAM technology to extract the segment at the location indicated by the input SEGS on the input image and outputs it as a unified mask.
+  * SAMDetector (Segmented) - It is similar to `SAMDetector (combined)`, but it separates and outputs the detected segments. Multiple segments can be found for the same detected area, and currently, a policy is in place to group them arbitrarily in sets of three. This aspect is expected to be improved in the future.
+    * As a result, it outputs the `combined_mask`, which is a unified mask, and `batch_masks`, which are multiple masks grouped together in batch form.
+    * While `batch_masks` may not be completely separated, it provides functionality to perform some level of segmentation.
+  * Simple Detector (SEGS) - Operating primarily with `BBOX_DETECTOR`, and with the additional provision of `SAM_MODEL` or `SEGM_DETECTOR`, this node internally generates improved SEGS through mask operations on both *bbox* and *silhouette*. It serves as a convenient tool to simplify a somewhat intricate workflow.
+
+* ControlNet
+  * ControlNetApply (SEGS) - To apply ControlNet in SEGS, you need to use the Preprocessor Provider node from the Inspire Pack to utilize this node.
+    * `segs_preprocessor` and `control_image` can be selectively applied. If an `control_image` is given, `segs_preprocessor` will be ignored.
+    * If set to `control_image`, you can preview the cropped cnet image through `SEGSPreview (CNET Image)`. Images generated by `segs_preprocessor` should be verified through the `cnet_images` output of each Detailer.
+    * The `segs_preprocessor` operates by applying preprocessing on-the-fly based on the cropped image during the detailing process, while `control_image` will be cropped and used as input to `ControlNetApply (SEGS)`.
+  * ControlNetClear (SEGS) - Clear applied ControlNet in SEGS 
+
+* Bitwise(SEGS & SEGS) - Performs a 'bitwise and' operation between two SEGS.
+* Bitwise(SEGS - SEGS) - Subtracts one SEGS from another.
+* Bitwise(SEGS & MASK) - Performs a bitwise AND operation between SEGS and MASK.
+* Bitwise(SEGS & MASKS ForEach) - Performs a bitwise AND operation between SEGS and MASKS.
+  * Please note that this operation is performed with batches of MASKS, not just a single MASK.
+* Bitwise(MASK & MASK) - Performs a 'bitwise and' operation between two masks.
+* Bitwise(MASK - MASK) - Subtracts one mask from another.
+* Bitwise(MASK + MASK) - Combine two masks.
+* SEGM Detector (SEGS) - Detects segmentation and returns SEGS from the input image.
+* BBOX Detector (SEGS) - Detects bounding boxes and returns SEGS from the input image.
+
+* Detailer
+  * Detailer (SEGS) - Refines the image based on SEGS.
+  * DetailerDebug (SEGS) - Refines the image based on SEGS. Additionally, it provides the ability to monitor the cropped image and the refined image of the cropped image.
+    * To prevent regeneration caused by the seed that does not change every time when using 'external_seed', please disable the 'seed random generate' option in the 'Detailer...' node.
+  * MASK to SEGS - Generates SEGS based on the mask.
+    * MASK to SEGS For AnimateDiff - Generates SEGS based on the mask for AnimateDiff.
+  * MediaPipe FaceMesh to SEGS - Separate each landmark from the mediapipe facemesh image to create labeled SEGS.
+    * Usually, the size of images created through the MediaPipe facemesh preprocessor is downscaled. It resizes the MediaPipe facemesh image to the original size given as reference_image_opt for matching sizes during processing. 
+  * ToBinaryMask - Separates the mask generated with alpha values between 0 and 255 into 0 and 255. The non-zero parts are always set to 255.
+  * Masks to Mask List - This node converts the MASKS in batch form to a list of individual masks.
+  * Mask List to Masks - This node converts the MASK list to MASK batch form.
+  * EmptySEGS - Provides an empty SEGS.
+  * MaskPainter - Provides a feature to draw masks.
+  * FaceDetailer - Easily detects faces and improves them.
+  * FaceDetailer (pipe) - Easily detects faces and improves them (for multipass).
+  * MaskDetailer (pipe) - This is a simple inpaint node that applies the Detailer to the mask area.
+
+* `FromDetailer (SDXL/pipe), BasicPipe -> DetailerPipe (SDXL), Edit DetailerPipe (SDXL)` - These are pipe functions used in Detailer for utilizing the refiner model of SDXL.
+
+* SEGS Manipulation nodes 
+  * SEGSDetailer - Performs detailed work on SEGS without pasting it back onto the original image.
+  * SEGSPaste - Pastes the results of SEGS onto the original image.
+    * If `ref_image_opt` is present, the images contained within SEGS are ignored. Instead, the image within `ref_image_opt` corresponding to the crop area of SEGS is taken and pasted. The size of the image in `ref_image_opt` should be the same as the original image size.
+    * This node can be used in conjunction with the processing results of AnimateDiff.
+  * SEGSPreview - Provides a preview of SEGS.
+     * This option is used to preview the improved image through `SEGSDetailer` before merging it into the original. Prior to going through ```SEGSDetailer```, SEGS only contains mask information without image information. If fallback_image_opt is connected to the original image, SEGS without image information will generate a preview using the original image. However, if SEGS already contains image information, fallback_image_opt will be ignored.
+     * This node can be used in conjunction with the processing results of AnimateDiff.
+  * SEGSPreview (CNET Image) - Show images configured with `ControlNetApply (SEGS)` for debugging purposes.
+  * SEGSToImageList - Convert SEGS To Image List
+  * SEGSToMaskList - Convert SEGS To Mask List
+  * SEGS Filter (label) - This node filters SEGS based on the label of the detected areas. 
+  * SEGS Filter (ordered) - This node sorts SEGS based on size and position and retrieves SEGs within a certain range. 
+  * SEGS Filter (range) - This node retrieves only SEGs from SEGS that have a size and position within a certain range.
+  * SEGS Assign (label) - Assign labels sequentially to SEGS. This node is useful when used with `[LAB]` of FaceDetailer.
+  * SEGSConcat - Concatenate segs1 and segs2. If source shape of segs1 and segs2 are different from segs2 will be ignored.
+  * Picker (SEGS) - Among the input SEGS, you can select a specific SEG through a dialog. If no SEG is selected, it outputs an empty SEGS. Increasing the batch_size of SEGSDetailer can be used for the purpose of selecting from the candidates.
+  * Set Default Image For SEGS - Set a default image for SEGS. SEGS with images set this way do not need to have a fallback image set. When override is set to false, the original image is preserved.
+  * Remove Image from SEGS - Remove the image set for the SEGS that has been configured by "Set Default Image for SEGS" or SEGSDetailer. When the image for the SEGS is removed, the Detailer node will operate based on the currently processed image instead of the SEGS. 
+  * Make Tile SEGS - [experimental] Create SEGS in the form of tiles from an image to facilitate experiments for Tiled Upscale using the Detailer.
+    * The `filter_in_segs_opt` and `filter_out_segs_opt` are optional inputs. If these inputs are provided, when creating the tiles, the mask for each tile is generated by overlapping with the mask of `filter_in_segs_opt` and excluding the overlap with the mask of `filter_out_segs_opt`. Tiles with an empty mask will not be created as SEGS.
+  * Dilate Mask (SEGS) - Dilate/Erosion Mask in SEGS
+  * Gaussian Blur Mask (SEGS) - Apply Gaussian Blur to Mask in SEGS
+  * SEGS_ELT Manipulation - experimental nodes
+    * DecomposeSEGS - Decompose SEGS to allow for detailed manipulation.
+    * AssembleSEGS - Reassemble the decomposed SEGS.
+    * From SEG_ELT - Extract detailed information from SEG_ELT.
+    * Edit SEG_ELT - Modify some of the information in SEG_ELT.
+    * Dilate SEG_ELT - Dilate the mask of SEG_ELT.
+
+* Mask Manipulation
+  * Dilate Mask - Dilate Mask. 
+    * Support erosion for negative value.
+  * Gaussian Blur Mask - Apply Gaussian Blur to Mask. You can utilize this for mask feathering.
+ 
+* Pipe nodes
+   * ToDetailerPipe, FromDetailerPipe - These nodes are used to bundle multiple inputs used in the detailer, such as models and vae, ..., into a single DETAILER_PIPE or extract the elements that are bundled in the DETAILER_PIPE.
+   * ToBasicPipe, FromBasicPipe - These nodes are used to bundle model, clip, vae, positive conditioning, and negative conditioning into a single BASIC_PIPE, or extract each element from the BASIC_PIPE.
+   * EditBasicPipe, EditDetailerPipe - These nodes are used to replace some elements in BASIC_PIPE or DETAILER_PIPE.
+   * FromDetailerPipe_v2, FromBasicPipe_v2 - It has the same functionality as `FromDetailerPipe` and `FromBasicPipe`, but it has an additional output that directly exports the input pipe. It is useful when editing EditBasicPipe and EditDetailerPipe.
+* Latent Scale (on Pixel Space) - This node converts latent to pixel space, upscales it, and then converts it back to latent.
+   * If upscale_model_opt is provided, it uses the model to upscale the pixel and then downscales it using the interpolation method provided in scale_method to the target resolution.
+* PixelKSampleUpscalerProvider - An upscaler is provided that converts latent to pixels using VAEDecode, performs upscaling, converts back to latent using VAEEncode, and then performs k-sampling. This upscaler can be attached to nodes such as 'Iterative Upscale' for use.
+  * Similar to 'Latent Scale (on Pixel Space)', if upscale_model_opt is provided, it performs pixel upscaling using the model.
+* PixelTiledKSampleUpscalerProvider - It is similar to PixelKSampleUpscalerProvider, but it uses ComfyUI_TiledKSampler and Tiled VAE Decoder/Encoder to avoid GPU VRAM issues at high resolutions.
+  * You need to install the [BlenderNeko/ComfyUI_TiledKSampler](https://github.com/BlenderNeko/ComfyUI_TiledKSampler) node extension.
+
+* PK_HOOK
+  * DenoiseScheduleHookProvider - IterativeUpscale provides a hook that gradually changes the denoise to target_denoise as the iterative-step progresses.
+  * CfgScheduleHookProvider - IterativeUpscale provides a hook that gradually changes the cfg to target_cfg as the iterative-step progresses.
+  * StepsScheduleHookProvider - IterativeUpscale provides a hook that gradually changes the sampling-steps to target_steps as the iterative-step progresses.
+  * NoiseInjectionHookProvider - During each iteration of IterativeUpscale, noise is injected into the latent space while varying the strength according to a schedule.
+    * You need to install the [BlenderNeko/ComfyUI_Noise](https://github.com/BlenderNeko/ComfyUI_Noise) node extension.
+    * The seed serves as the initial value required for generating noise, and it increments by 1 with each iteration as the process unfolds.
+    * The source determines the types of CPU noise and GPU noise to be configured.
+    * Currently, there is only a simple schedule available, where the strength of the noise varies from start_strength to end_strength during the progression of each iteration.
+  * UnsamplerHookProvider - Apply Unsampler during each iteration. To use this node, ComfyUI_Noise must be installed.
+  * PixelKSampleHookCombine - This is used to connect two PK_HOOKs. hook1 is executed first and then hook2 is executed.
+    * If you want to simultaneously change cfg and denoise, you can combine the PK_HOOKs of CfgScheduleHookProvider and PixelKSampleHookCombine.
+ 
+* DETAILER_HOOK
+  * NoiseInjectionDetailerHookProvider - The `detailer_hook` is a hook in the `Detailer` that injects noise during the processing of each SEGS.
+  * UnsamplerDetailerHookProvider - Apply Unsampler during each cycle. To use this node, ComfyUI_Noise must be installed.
+  * DenoiseSchedulerDetailerHookProvider - During the progress of the cycle, the detailer's denoise is altered up to the `target_denoise`. 
+  * CoreMLDetailerHookProvider - CoreML supports only 512x512, 512x768, 768x512, 768x768 size sampling. CoreMLDetailerHookProvider precisely fixes the upscale of the crop_region to this size. When using this hook, it will always be selected size, regardless of the guide_size. However, if the guide_size is too small, skipping will occur.
+  * DetailerHookCombine - This is used to connect two DETAILER_HOOKs. Similar to PixelKSampleHookCombine.
+  * SEGSOrderedFilterDetailerHook, SEGSRangeFilterDetailerHook, SEGSLabelFilterDetailerHook - There are a wrapper node that provides SEGSFilter nodes to be applied in FaceDetailer or Detector by creating DETAILER_HOOK.
+  * PreviewDetailerHOok - Connecting this hook node helps provide assistance for viewing previews whenever SEGS Detailing tasks are completed. When working with a large number of SEGS, such as Make Tile SEGS, it allows for monitoring the situation as improvements progress incrementally.
+    * Since this is the hook applied when pasting onto the original image, it has no effect on nodes like `SEGSDetailer`.
+
+* Iterative Upscale (Latent/on Pixel Space) - The upscaler takes the input upscaler and splits the scale_factor into steps, then iteratively performs upscaling. 
+This takes latent as input and outputs latent as the result.
+* Iterative Upscale (Image) - The upscaler takes the input upscaler and splits the scale_factor into steps, then iteratively performs upscaling. This takes image as input and outputs image as the result.
+  * Internally, this node uses 'Iterative Upscale (Latent)'.
+
+* TwoSamplersForMask - This node can apply two samplers depending on the mask area. The base_sampler is applied to the area where the mask is 0, while the mask_sampler is applied to the area where the mask is 1.
+  * Note: The latent encoded through VAEEncodeForInpaint cannot be used.
+* KSamplerProvider - This is a wrapper that enables KSampler to be used in TwoSamplersForMask TwoSamplersForMaskUpscalerProvider.
+* TiledKSamplerProvider - ComfyUI_TiledKSampler is a wrapper that provides KSAMPLER.
+  * You need to install the [BlenderNeko/ComfyUI_TiledKSampler](https://github.com/BlenderNeko/ComfyUI_TiledKSampler) node extension.
+  
+* TwoAdvancedSamplersForMask - TwoSamplersForMask is similar to TwoAdvancedSamplersForMask, but they differ in their operation. TwoSamplersForMask performs sampling in the mask area only after all the samples in the base area are finished. On the other hand, TwoAdvancedSamplersForMask performs sampling in both the base area and the mask area sequentially at each step.
+* KSamplerAdvancedProvider - This is a wrapper that enables KSampler to be used in TwoAdvancedSamplersForMask, RegionalSampler.
+  * sigma_factor: By multiplying the denoise schedule by the sigma_factor, you can adjust the amount of denoising based on the configured denoise.
+
+* TwoSamplersForMaskUpscalerProvider - This is an Upscaler that extends TwoSamplersForMask to be used in Iterative Upscale.
+  * TwoSamplersForMaskUpscalerProviderPipe - pipe version of TwoSamplersForMaskUpscalerProvider.
+
+* Image Utils
+  * PreviewBridge (image) - This custom node can be used with a bridge for image when using the MaskEditor feature of Clipspace.
+  * PreviewBridge (latent) - This custom node can be used with a bridge for latent image when using the MaskEditor feature of Clipspace.
+    * If a latent with a mask is provided as input, it displays the mask. Additionally, the mask output provides the mask set in the latent.
+    * If a latent without a mask is provided as input, it outputs the original latent as is, but the mask output provides an output with the entire region set as a mask.
+    * When set mask through MaskEditor, a mask is applied to the latent, and the output includes the stored mask. The same mask is also output as the mask output.
+    * When connected to `vae_opt`, it takes higher priority than the `preview_method`.
+  * ImageSender, ImageReceiver - The images generated in ImageSender are automatically sent to the ImageReceiver with the same link_id.
+  * LatentSender, LatentReceiver - The latent generated in LatentSender are automatically sent to the LatentReceiver with the same link_id.
+    * Furthermore, LatentSender is implemented with PreviewLatent, which stores the latent in payload form within the image thumbnail.
+    * Due to the current structure of ComfyUI, it is unable to distinguish between SDXL latent and SD1.5/SD2.1 latent. Therefore, it generates thumbnails by decoding them using the SD1.5 method.
+
+* Switch nodes
+  * Switch (image,mask), Switch (latent), Switch (SEGS) - Among multiple inputs, it selects the input designated by the selector and outputs it. The first input must be provided, while the others are optional. However, if the input specified by the selector is not connected, an error may occur.
+  * Switch (Any) - This is a Switch node that takes an arbitrary number of inputs and produces a single output. Its type is determined when connected to any node, and connecting inputs increases the available slots for connections.
+  * Inversed Switch (Any) - In contrast to `Switch (Any)`, it takes a single input and outputs one of many. Due to ComfyUI's functional limitations, the value of `select` must be determined at the time of queuing a prompt, and while it can serve as a `Primitive Node` or `ImpactInt`, it cannot function properly when connected through other nodes. 
+  * Guide
+    * When the `Switch (Any)` and `Inversed Switch (Any)` selects are transformed into primitives, it's important to be cautious because the select range is not appropriately constrained, potentially leading to unintended behavior.   
+    * `Switch (image,mask)`, `Switch (latent)`, `Switch (SEGS)`, `Switch (Any)` supports `sel_mode` param. The `sel_mode` sets the moment at which the `select` parameter is determined. `select_on_prompt` determines the `select` at the time of queuing the prompt, while `select_on_execution` determines it during the execution of the workflow. While `select_on_execution` offers more flexibility, it can potentially trigger workflow execution errors due to running nodes that may be impossible to execute within the limitations of ComfyUI. `select_on_prompt` bypasses this constraint by treating any inputs not selected as if they were disconnected. However, please note that when using `select_on_prompt`, the `select` can only be used with widgets or `Primitive Nodes` determined at the queue prompt.
+    * There is an issue when connecting the built-in reroute node with the switch's input/output slots. it can lead to forced disconnections during workflow loading. Therefore, it is advisable not to use reroute for making connections in such cases. However, there are no issues when using the reroute node in Pythongossss.
+
+* [Wildcards](https://github.com/ltdrdata/ComfyUI-extension-tutorials/blob/Main/ComfyUI-Impact-Pack/tutorial/ImpactWildcard.md) - These are nodes that supports syntax in the form of `__wildcard-name__` and dynamic prompt syntax like `{a|b|c}`.
+  * Wildcard files can be used by placing `.txt` or `.yaml` files under either `ComfyUI-Impact-Pack/wildcards` or `ComfyUI-Impact-Pack/custom_wildcards` paths.
+    * You can download and use [Wildcard YAML](https://civitai.com/models/138970/billions-of-wildcards-all-in-one) files in this format.
+    * After the first execution, you can change the custom wildcards path in the `custom_wildcards` entry within the `ComfyUI-Impact-Pack/impact-pack.ini` file created.
+  * ImpactWildcardProcessor - The text is generated by processing the wildcard in the Text. If the mode is set to "populate", a dynamic prompt is generated with each execution and the input is filled in the second textbox. If the mode is set to "fixed", the content of the second textbox remains unchanged.
+    * When an image is generated with the "fixed" mode, the prompt used for that particular generation is stored in the metadata.
+  * ImpactWildcardEncode - Similar to ImpactWildcardProcessor, this provides the loading functionality of LoRAs (e.g. `<lora:some_awesome_lora:0.7:1.2>`). Populated prompts are encoded using the clip after all the lora loading is done.
+    * If the `Inspire Pack` is installed, you can use **Lora Block Weight** in the form of `LBW=lbw spec;`
+    * `<lora:chunli:1.0:1.0:LBW=B11:0,0,0,0,0,0,0,0,0,0,A,0,0,0,0,0,0;A=0.;>`, `<lora:chunli:1.0:1.0:LBW=0,0,0,0,0,0,0,0,0,0,A,B,0,0,0,0,0;A=0.5;B=0.2;>`, `<lora:chunli:1.0:1.0:LBW=SD-MIDD;>`
+
+* Regional Sampling - These nodes offer the capability to divide regions and perform partial sampling using a mask. Unlike TwoSamplersForMask, sampling for each region is applied during each step.
+  * RegionalPrompt - This node combines a **mask** for specifying regions and the **sampler** to apply to each region to create `REGIONAL_PROMPTS`.
+  * CombineRegionalPrompts - Combine multiple `REGIONAL_PROMPTS` to create a single `REGIONAL_PROMPTS`.
+  * RegionalSampler - This node performs sampling using a base sampler and regional prompts. Sampling by the base sampler is executed at each step, while sampling for each region is performed through the sampler bound to each region.
+    * overlap_factor - Specifies the amount of overlap for each region to blend well with the area outside the mask.
+    * restore_latent - When sampling each region, restore the areas outside the mask to the base latent, preventing additional noise from being introduced outside the mask during region sampling.
+  * RegionalSamplerAdvanced - This is the Advanced version of the RegionalSampler. You can control it using `step` instead of `denoise`.
+  * NOTE: The `sde` sampler and `uni_pc` sampler introduce additional noise during each step of the sampling process. To mitigate this, when sampling each region, the `uni_pc` sampler applies additional `dpmpp_fast`, and the sde sampler applies the `dpmpp_2m` sampler as an additional measure.
+
+* KSampler (pipe), KSampler (advanced/pipe)
+
+* Image batch To Image List - Convert Image batch to Image List
+- You can use images generated in a multi batch to handle them
+* Make Image List - Convert multiple images into a single image list
+* Make Image Batch - Convert multiple images into a single image batch
+- The input of images can be scaled up as needed
+
+* String Selector - It selects and returns a portion of the string. When `multiline` mode is disabled, it simply returns the string of the line pointed to by the selector. When `multiline` mode is enabled, it divides the string based on lines that start with `#` and returns them. If the `select` value is larger than the number of items, it will start counting from the first line again and return accordingly.
+* Combine Conditionings - It takes multiple conditionings as input and combines them into a single conditioning.
+* Concat Conditionings - It takes multiple conditionings as input and concat them into a single conditioning.
+
+* Logics (experimental) - These nodes are experimental nodes designed to implement the logic for loops and dynamic switching.
+  * ImpactCompare, ImpactConditionalBranch, ImpactConditionalBranchSelMode, ImpactInt, ImpactValueSender, ImpactValueReceiver, ImpactImageInfo, ImpactMinMax, ImpactNeg, ImpactConditionalStopIteration
+  * ImpactIsNotEmptySEGS - This node returns `true` only if the input SEGS is not empty. 
+  * Queue Trigger - When this node is executed, it adds a new queue to assist with repetitive tasks. It will only execute if the signal's status changes.
+  * Queue Trigger (Countdown) - Like the Queue Trigger, it adds a queue, but only adds it if it's greater than 1, and decrements the count by one each time it runs.
+  * Sleep - Waits for the specified time (in seconds).
+  * Set Widget Value - This node sets one of the optional inputs to the specified node's widget. An error may occur if the types do not match.
+  * Set Mute State - This node changes the mute state of a specific node.
+  * Control Bridge - This node modifies the state of the connected control nodes based on the `mode` and `behavior` . If there are nodes that require a change, the current execution is paused, the mute status is updated, and a new prompt queue is inserted. 
+    * When the `mode` is `active`, it makes the connected control nodes active regardless of the behavior. 
+    * When the `mode` is `Bypass/Mute`, it changes the state of the connected nodes based on whether the behavior is `Bypass` or `Mute`.
+    * **Limitation**: Due to these characteristics, it does not function correctly when the batch count exceeds 1. Additionally, it does not guarantee proper operation when the seed is randomized or when the state of nodes is altered by actions such as `Queue Trigger`, `Set Widget Value`, `Set Mute`, before the Control Bridge.
+    * When utilizing this node, please structure the workflow in such a way that `Queue Trigger`, `Set Widget Value`, `Set Mute State`, and similar actions are executed at the end of the workflow.
+    * If you want to change the value of the seed at each iteration, please ensure that Set Widget Value is executed at the end of the workflow instead of using randomization.
+      * It is not a problem if the seed changes due to randomization as long as it occurs after the Control Bridge section.
+  * Remote Boolean (on prompt), Remote Int (on prompt) - At the start of the prompt, this node forcibly sets the `widget_value` of `node_id`. It is disregarded if the target widget type is different.
+  * You can find the `node_id` by checking through [ComfyUI-Manager](https://github.com/ltdrdata/ComfyUI-Manager) using the format `Badge: #ID Nickname`.
+  * Experimental set of nodes for implementing loop functionality (tutorial to be prepared later / [example workflow](test/loop-test.json)).
+
+* HuggingFace - These nodes provide functionalities based on HuggingFace repository models.
+  * `HF Transformers Classifier Provider` - This is a node that provides a classifier based on HuggingFace's transformers models.
+    * The 'repo id' parameter should contain HuggingFace's repo id. When `preset_repo_id` is set to `Manual repo id`, use the manually entered repo id in `manual_repo_id`.
+    * e.g. 'rizvandwiki/gender-classification-2' is a repository that provides a model for gender classification.
+  * `SEGS Classify` - This node utilizes the `TRANSFORMERS_CLASSIFIER` loaded with 'HF Transformers Classifier Provider' to classify `SEGS`.
+    * The 'expr' allows for forms like `label > number`, and in the case of `preset_expr` being `Manual expr`, it uses the expression entered in `manual_expr`.
+    * For example, in the case of `male <= 0.4`, if the score of the `male` label in the classification result is less than or equal to 0.4, it is categorized as `filtered_SEGS`, otherwise, it is categorized as `remained_SEGS`.
+      * For supported labels, please refer to the `config.json` of the respective HuggingFace repository.
+    * `#Female` and `#Male` are symbols that group multiple labels such as `Female, women, woman, ...`, for convenience, rather than being single labels.
+
+## MMDet nodes
+* MMDetDetectorProvider - Loads the MMDet model to provide BBOX_DETECTOR and SEGM_DETECTOR.
+* To use the existing MMDetDetectorProvider, you need to enable the MMDet usage configuration.
+
+
+## Feature
+* Interactive SAM Detector (Clipspace) - When you right-click on a node that has 'MASK' and 'IMAGE' outputs, a context menu will open. From this menu, you can either open a dialog to create a SAM Mask using 'Open in SAM Detector', or copy the content (likely mask data) using 'Copy (Clipspace)' and generate a mask using 'Impact SAM Detector' from the clipspace menu, and then paste it using 'Paste (Clipspace)'.
+* Providing a feature to detect errors that occur when mixing models and clips from checkpoints such as `SDXL Base`, `SDXL Refiner`, `SD1.x`, `SD2.x` during sample execution, and reporting appropriate errors.
+
+## Deprecated
+* The following nodes have been kept only for compatibility with existing workflows, and are no longer supported. Please replace them with new nodes.
+   * ONNX Detector (SEGS) - BBOX Detector (SEGS)
+   * MMDetLoader -> MMDetDetectorProvider
+   * SegsMaskCombine -> SEGS to MASK (combined)
+   * BboxDetectorForEach -> BBOX Detector (SEGS)
+   * SegmDetectorForEach -> SEGM Detector (SEGS)
+   * BboxDetectorCombined -> BBOX Detector (combined)
+   * SegmDetectorCombined -> SEGM Detector (combined)
+   * MaskPainter -> PreviewBridge
+* To use the existing deprecated legacy nodes, you need to enable the MMDet usage configuration.
+
+
+## Ultralytics models
+* huggingface.co/Bingsu/[adetailer](https://github.com/ultralytics/assets/releases/) - You can download face, people detection models, and clothing detection models.
+* ultralytics/[assets](https://github.com/ultralytics/assets/releases/) - You can download various types of detection models other than faces or people.
+* civitai/[adetailer](https://civitai.com/search/models?sortBy=models_v5&query=adetailer) - You can download various types detection models....Many models are associated with NSFW content.
+
+## How to activate 'MMDet usage'
+* Upon the initial execution, an `impact-pack.ini` file will be generated in the custom_nodes/ComfyUI-Impact-Pack directory.
+```
+[default]
+dependency_version = 2
+mmdet_skip = True
+```
+* Change `mmdet_skip = True` to `mmdet_skip = False`
+```
+[default]
+dependency_version = 2
+mmdet_skip = False
+```
+* Restart ComfyUI
+
+
+## Installation
+
+1. `cd custom_nodes`
+1. `git clone https://github.com/ltdrdata/ComfyUI-Impact-Pack.git`
+3. `cd ComfyUI-Impact-Pack`
+4. (optional) `git submodule update --init --recursive`
+   * Impact Pack will automatically download subpack during its initial launch.
+5. (optional) `python install.py`
+   * Impact Pack will automatically install its dependencies during its initial launch.
+   * For the portable version, you should execute the command `..\..\..\python_embeded\python.exe install.py` to run the installation script.
+
+
+6. Restart ComfyUI
+
+* NOTE: If an error occurs during the installation process, please refer to [Troubleshooting Page](troubleshooting/TROUBLESHOOTING.md) for assistance. 
+* You can use this colab notebook [colab notebook](https://colab.research.google.com/github/ltdrdata/ComfyUI-Impact-Pack/blob/Main/notebook/comfyui_colab_impact_pack.ipynb) to launch it. This notebook automatically downloads the impact pack to the custom_nodes directory, installs the tested dependencies, and runs it.
+
+## Package Dependencies (If you need to manual setup.)
+
+* pip install
+   * openmim
+   * segment-anything
+   * ultralytics
+   * scikit-image
+   * piexif
+   * (optional) pycocotools
+   * (optional) onnxruntime
+   
+* mim install (optional)
+   * mmcv==2.0.0, mmdet==3.0.0, mmengine==0.7.2
+
+* linux packages (ubuntu)
+  * libgl1-mesa-glx
+  * libglib2.0-0
+
+
+## Config example
+* Once you run the Impact Pack for the first time, an `impact-pack.ini` file will be automatically generated in the Impact Pack directory. You can modify this configuration file to customize the default behavior.
+  * `dependency_version` - don't touch this
+  * `mmdet_skip` - disable MMDet based nodes and legacy nodes if `True`
+  * `sam_editor_cpu` - use cpu for `SAM editor` instead of gpu
+  * sam_editor_model: Specify the SAM model for the SAM editor.
+    * You can download various SAM models using ComfyUI-Manager.
+    * Path to SAM model: `ComfyUI/models/sams`
+```
+[default]
+dependency_version = 9
+mmdet_skip = True
+sam_editor_cpu = False
+sam_editor_model = sam_vit_b_01ec64.pth
+```
+
+
+## Other Materials (auto-download on initial startup)
+
+* ComfyUI/models/mmdets/bbox <= https://huggingface.co/dustysys/ddetailer/resolve/main/mmdet/bbox/mmdet_anime-face_yolov3.pth
+* ComfyUI/models/mmdets/bbox <= https://raw.githubusercontent.com/Bing-su/dddetailer/master/config/mmdet_anime-face_yolov3.py
+* ComfyUI/models/sams <= https://dl.fbaipublicfiles.com/segment_anything/sam_vit_b_01ec64.pth
+
+## Troubleshooting page
+* [Troubleshooting Page](troubleshooting/TROUBLESHOOTING.md)
+
+
+## How to use (DDetailer feature)
+
+#### 1. Basic auto face detection and refine exapmle.
+![simple](https://github.com/ltdrdata/ComfyUI-extension-tutorials/raw/Main/ComfyUI-Impact-Pack/images/simple.png)
+* The face that has been damaged due to low resolution is restored with high resolution by generating and synthesizing it, in order to restore the details.
+* The FaceDetailer node is a combination of a Detector node for face detection and a Detailer node for image enhancement. See the [Advanced Tutorial](https://github.com/ltdrdata/ComfyUI-extension-tutorials/raw/Main/ComfyUI-Impact-Pack/tutorial/advanced.md) for a more detailed explanation.
+* Pass the MMDetLoader 's bbox model and the detection model loaded by SAMLoader to FaceDetailer . Since it performs the function of KSampler for image enhancement, it overlaps with KSampler's options.
+* The MASK output of FaceDetailer provides a visualization of where the detected and enhanced areas are.
+
+![simple-orig](https://github.com/ltdrdata/ComfyUI-extension-tutorials/raw/Main/ComfyUI-Impact-Pack/images/simple-original.png) ![simple-refined](https://github.com/ltdrdata/ComfyUI-extension-tutorials/raw/Main/ComfyUI-Impact-Pack/images/simple-refined.png)
+* You can see that the face in the image on the left has increased detail as in the image on the right.
+
+#### 2. 2Pass refine (restore a severely damaged face)
+![2pass-workflow-example](https://github.com/ltdrdata/ComfyUI-extension-tutorials/raw/Main/ComfyUI-Impact-Pack/images/2pass-simple.png)
+* Although two FaceDetailers can be attached together for a 2-pass configuration, various common inputs used in KSampler can be passed through DETAILER_PIPE, so FaceDetailerPipe can be used to configure easily.
+* In 1pass, only rough outline recovery is required, so restore with a reasonable resolution and low options. However, if you increase the dilation at this time, not only the face but also the surrounding parts are included in the recovery range, so it is useful when you need to reshape the face other than the facial part.
+
+![2pass-example-original](https://github.com/ltdrdata/ComfyUI-extension-tutorials/raw/Main/ComfyUI-Impact-Pack/images/2pass-original.png) ![2pass-example-middle](https://github.com/ltdrdata/ComfyUI-extension-tutorials/raw/Main/ComfyUI-Impact-Pack/images/2pass-1pass.png) ![2pass-example-result](https://github.com/ltdrdata/ComfyUI-extension-tutorials/raw/Main/ComfyUI-Impact-Pack/images/2pass-2pass.png)
+* In the first stage, the severely damaged face is restored to some extent, and in the second stage, the details are restored
+
+#### 3. Face Bbox(bounding box) + Person silhouette segmentation (prevent distortion of the background.)
+![combination-workflow-example](https://github.com/ltdrdata/ComfyUI-extension-tutorials/raw/Main/ComfyUI-Impact-Pack/images/combination.jpg)
+![combination-example-original](https://github.com/ltdrdata/ComfyUI-extension-tutorials/raw/Main/ComfyUI-Impact-Pack/images/combination-original.png) ![combination-example-refined](https://github.com/ltdrdata/ComfyUI-extension-tutorials/raw/Main/ComfyUI-Impact-Pack/images/combination-refined.png)
+
+* Facial synthesis that emphasizes details is delicately aligned with the contours of the face, and it can be observed that it does not affect the image outside of the face.
+
+* The BBoxDetectorForEach node is used to detect faces, and the SAMDetectorCombined node is used to find the segment related to the detected face. By using the Segs & Mask node with the two masks obtained in this way, an accurate mask that intersects based on segs can be generated. If this generated mask is input to the DetailerForEach node, only the target area can be created in high resolution from the image and then composited.
+
+#### 4. Iterative Upscale
+![upscale-workflow-example](https://github.com/ltdrdata/ComfyUI-extension-tutorials/raw/Main/ComfyUI-Impact-Pack/images/upscale-workflow.png)
+ 
+* The IterativeUpscale node is a node that enlarges an image/latent by a scale_factor. In this process, the upscale is carried out progressively by dividing it into steps.
+* IterativeUpscale takes an Upscaler as an input, similar to a plugin, and uses it during each iteration. PixelKSampleUpscalerProvider is an Upscaler that converts the latent representation to pixel space and applies ksampling.
+  * The upscale_model_opt is an optional parameter that determines whether to use the upscale function of the model base if available. Using the upscale function of the model base can significantly reduce the number of iterative steps required. If an x2 upscaler is used, the image/latent is first upscaled by a factor of 2 and then downscaled to the target scale at each step before further processing is done.
+
+* The following image is an image of 304x512 pixels and the same image scaled up to three times its original size using IterativeUpscale.
+
+![combination-example-original](https://github.com/ltdrdata/ComfyUI-extension-tutorials/raw/Main/ComfyUI-Impact-Pack/images/upscale-original.png) ![combination-example-refined](https://github.com/ltdrdata/ComfyUI-extension-tutorials/raw/Main/ComfyUI-Impact-Pack/images/upscale-3x.png)
+
+
+#### 5. Interactive SAM Detector (Clipspace)
+
+* When you right-click on the node that outputs 'MASK' and 'IMAGE', a menu called "Open in SAM Detector" appears, as shown in the following picture. Clicking on the menu opens a dialog in SAM's functionality, allowing you to generate a segment mask.
+![samdetector-menu](https://github.com/ltdrdata/ComfyUI-extension-tutorials/raw/Main/ComfyUI-Impact-Pack/images/SAMDetector-menu.png)
+
+* By clicking the left mouse button on a coordinate, a positive prompt in blue color is entered, indicating the area that should be included. Clicking the right mouse button on a coordinate enters a negative prompt in red color, indicating the area that should be excluded. Positive prompts represent the areas that should be included, while negative prompts represent the areas that should be excluded.
+* You can remove the points that were added by using the "undo" button. After selecting the points, pressing the "detect" button generates the mask. Additionally, you can adjust the fidelity slider to determine the extent to which the mask belongs to the confidence region.
+
+![samdetector-dialog](https://github.com/ltdrdata/ComfyUI-extension-tutorials/raw/Main/ComfyUI-Impact-Pack/images/SAMDetector-dialog.jpg)
+
+* If you opened the dialog through "Open in SAM Detector" from the node, you can directly apply the changes by clicking the "Save to node" button. However, if you opened the dialog through the "clipspace" menu, you can save it to clipspace by clicking the "Save" button.
+
+![samdetector-result](https://github.com/ltdrdata/ComfyUI-extension-tutorials/raw/Main/ComfyUI-Impact-Pack/images/SAMDetector-result.jpg)
+
+* When you execute using the reflected mask in the node, you can observe that the image and mask are displayed separately.
+
+
+## Others Tutorials
+* [ComfyUI-extension-tutorials/ComfyUI-Impact-Pack](https://github.com/ltdrdata/ComfyUI-extension-tutorials/tree/Main/ComfyUI-Impact-Pack) - You can find various tutorials and workflows on this page.
+* [Advanced Tutorial](https://github.com/ltdrdata/ComfyUI-extension-tutorials/blob/Main/ComfyUI-Impact-Pack/tutorial/advanced.md)
+* [SAM Application](https://github.com/ltdrdata/ComfyUI-extension-tutorials/blob/Main/ComfyUI-Impact-Pack/tutorial/sam.md)
+* [PreviewBridge](https://github.com/ltdrdata/ComfyUI-extension-tutorials/blob/Main/ComfyUI-Impact-Pack/tutorial/previewbridge.md)
+* [Mask Pointer](https://github.com/ltdrdata/ComfyUI-extension-tutorials/blob/Main/ComfyUI-Impact-Pack/tutorial/maskpointer.md)
+* [ONNX Tutorial](https://github.com/ltdrdata/ComfyUI-extension-tutorials/blob/Main/ComfyUI-Impact-Pack/tutorial/ONNX.md)
+* [CLIPSeg Tutorial](https://github.com/ltdrdata/ComfyUI-extension-tutorials/blob/Main/ComfyUI-Impact-Pack/tutorial/clipseg.md)
+* [Extreme Highresolution Upscale](https://github.com/ltdrdata/ComfyUI-extension-tutorials/blob/Main/ComfyUI-Impact-Pack/tutorial/extreme-upscale.md)
+* [TwoSamplersForMask](https://github.com/ltdrdata/ComfyUI-extension-tutorials/blob/Main/ComfyUI-Impact-Pack/tutorial/TwoSamplers.md)
+* [TwoAdvancedSamplersForMask](https://github.com/ltdrdata/ComfyUI-extension-tutorials/blob/Main/ComfyUI-Impact-Pack/tutorial/TwoAdvancedSamplers.md)
+* [Advanced Iterative Upscale: PK_HOOK](https://github.com/ltdrdata/ComfyUI-extension-tutorials/blob/Main/ComfyUI-Impact-Pack/tutorial/pk_hook.md)
+* [Advanced Iterative Upscale: TwoSamplersForMask Upscale Provider](https://github.com/ltdrdata/ComfyUI-extension-tutorials/blob/Main/ComfyUI-Impact-Pack/tutorial/TwoSamplersUpscale.md)
+* [Interactive SAM + PreviewBridge](https://github.com/ltdrdata/ComfyUI-extension-tutorials/blob/Main/ComfyUI-Impact-Pack/tutorial/sam_with_preview_bridge.md)
+* [ImageSender/ImageReceiver/LatentSender/LatentReceiver](https://github.com/ltdrdata/ComfyUI-extension-tutorials/blob/Main/ComfyUI-Impact-Pack/tutorial/sender_receiver.md)
+* [ImpactWildcardProcessor](https://github.com/ltdrdata/ComfyUI-extension-tutorials/blob/Main/ComfyUI-Impact-Pack/tutorial/ImpactWildcardProcessor.md)
+
+
+## Credits
+
+ComfyUI/[ComfyUI](https://github.com/comfyanonymous/ComfyUI) - A powerful and modular stable diffusion GUI.
+
+dustysys/[ddetailer](https://github.com/dustysys/ddetailer) - DDetailer for Stable-diffusion-webUI extension.
+
+Bing-su/[dddetailer](https://github.com/Bing-su/dddetailer) - The anime-face-detector used in ddetailer has been updated to be compatible with mmdet 3.0.0, and we have also applied a patch to the pycocotools dependency for Windows environment in ddetailer.
+
+facebook/[segment-anything](https://github.com/facebookresearch/segment-anything) - Segmentation Anything!
+
+hysts/[anime-face-detector](https://github.com/hysts/anime-face-detector) - Creator of `anime-face_yolov3`, which has impressive performance on a variety of art styles.
+
+open-mmlab/[mmdetection](https://github.com/open-mmlab/mmdetection) - Object detection toolset. `dd-person_mask2former` was trained via transfer learning using their [R-50 Mask2Former instance segmentation model](https://github.com/open-mmlab/mmdetection/tree/master/configs/mask2former#instance-segmentation) as a base.
+
+biegert/[ComfyUI-CLIPSeg](https://github.com/biegert/ComfyUI-CLIPSeg) - This is a custom node that enables the use of CLIPSeg technology, which can find segments through prompts, in ComfyUI.
+
+BlenderNeok/[ComfyUI-TiledKSampler](https://github.com/BlenderNeko/ComfyUI_TiledKSampler) - 
+The tile sampler allows high-resolution sampling even in places with low GPU VRAM.
+
+BlenderNeok/[ComfyUI_Noise](https://github.com/BlenderNeko/ComfyUI_Noise) - The noise injection feature relies on this function.
+
+WASasquatch/[was-node-suite-comfyui](https://github.com/WASasquatch/was-node-suite-comfyui) - A powerful custom node extensions of ComfyUI.
+
+Trung0246/[ComfyUI-0246](https://github.com/Trung0246/ComfyUI-0246) - Nice bypass hack!

custom_nodes/ComfyUI-Impact-Pack/__init__.py

@@ -0,0 +1,502 @@
+"""
+@author: Dr.Lt.Data
+@title: Impact Pack
+@nickname: Impact Pack
+@description: This extension offers various detector nodes and detailer nodes that allow you to configure a workflow that automatically enhances facial details. And provide iterative upscaler.
+"""
+
+import shutil
+import folder_paths
+import os
+import sys
+import traceback
+
+comfy_path = os.path.dirname(folder_paths.__file__)
+impact_path = os.path.join(os.path.dirname(__file__))
+subpack_path = os.path.join(os.path.dirname(__file__), "impact_subpack")
+modules_path = os.path.join(os.path.dirname(__file__), "modules")
+wildcards_path = os.path.join(os.path.dirname(__file__), "wildcards")
+custom_wildcards_path = os.path.join(os.path.dirname(__file__), "custom_wildcards")
+
+sys.path.append(modules_path)
+
+import impact.config
+import impact.sample_error_enhancer
+print(f"### Loading: ComfyUI-Impact-Pack ({impact.config.version})")
+
+
+def do_install():
+    import importlib
+    spec = importlib.util.spec_from_file_location('impact_install', os.path.join(os.path.dirname(__file__), 'install.py'))
+    impact_install = importlib.util.module_from_spec(spec)
+    spec.loader.exec_module(impact_install)
+
+
+# ensure dependency
+if not os.path.exists(os.path.join(subpack_path, ".git")) and os.path.exists(subpack_path):
+    print(f"### CompfyUI-Impact-Pack: corrupted subpack detected.")
+    shutil.rmtree(subpack_path)
+
+if impact.config.get_config()['dependency_version'] < impact.config.dependency_version or not os.path.exists(subpack_path):
+    print(f"### ComfyUI-Impact-Pack: Updating dependencies [{impact.config.get_config()['dependency_version']} -> {impact.config.dependency_version}]")
+    do_install()
+
+sys.path.append(subpack_path)
+
+# Core
+# recheck dependencies for colab
+try:
+    import impact.subpack_nodes  # This import must be done before cv2.
+
+    import folder_paths
+    import torch
+    import cv2
+    import numpy as np
+    import comfy.samplers
+    import comfy.sd
+    import warnings
+    from PIL import Image, ImageFilter
+    from skimage.measure import label, regionprops
+    from collections import namedtuple
+    import piexif
+
+    if not impact.config.get_config()['mmdet_skip']:
+        import mmcv
+        from mmdet.apis import (inference_detector, init_detector)
+        from mmdet.evaluation import get_classes
+except:
+    import importlib
+    print("### ComfyUI-Impact-Pack: Reinstall dependencies (several dependencies are missing.)")
+    do_install()
+
+import impact.impact_server  # to load server api
+
+def setup_js():
+    import nodes
+    js_dest_path = os.path.join(comfy_path, "web", "extensions", "impact-pack")
+
+    if hasattr(nodes, "EXTENSION_WEB_DIRS"):
+        if os.path.exists(js_dest_path):
+            shutil.rmtree(js_dest_path)
+    else:
+        print(f"[WARN] ComfyUI-Impact-Pack: Your ComfyUI version is outdated. Please update to the latest version.")
+        # setup js
+        if not os.path.exists(js_dest_path):
+            os.makedirs(js_dest_path)
+
+        js_src_path = os.path.join(impact_path, "js", "impact-pack.js")
+        shutil.copy(js_src_path, js_dest_path)
+
+        js_src_path = os.path.join(impact_path, "js", "impact-sam-editor.js")
+        shutil.copy(js_src_path, js_dest_path)
+
+        js_src_path = os.path.join(impact_path, "js", "comboBoolMigration.js")
+        shutil.copy(js_src_path, js_dest_path)
+
+
+setup_js()
+
+from impact.impact_pack import *
+from impact.detectors import *
+from impact.pipe import *
+from impact.logics import *
+from impact.util_nodes import *
+from impact.segs_nodes import *
+from impact.special_samplers import *
+from impact.hf_nodes import *
+from impact.bridge_nodes import *
+from impact.hook_nodes import *
+from impact.animatediff_nodes import *
+
+import threading
+
+wildcard_path = impact.config.get_config()['custom_wildcards']
+
+
+def wildcard_load():
+    with wildcards.wildcard_lock:
+        impact.wildcards.read_wildcard_dict(wildcards_path)
+
+        try:
+            impact.wildcards.read_wildcard_dict(impact.config.get_config()['custom_wildcards'])
+        except Exception as e:
+            print(f"[Impact Pack] Failed to load custom wildcards directory.")
+
+        print(f"[Impact Pack] Wildcards loading done.")
+
+
+threading.Thread(target=wildcard_load).start()
+
+
+NODE_CLASS_MAPPINGS = {
+    "SAMLoader": SAMLoader,
+    "CLIPSegDetectorProvider": CLIPSegDetectorProvider,
+    "ONNXDetectorProvider": ONNXDetectorProvider,
+
+    "BitwiseAndMaskForEach": BitwiseAndMaskForEach,
+    "SubtractMaskForEach": SubtractMaskForEach,
+
+    "DetailerForEach": DetailerForEach,
+    "DetailerForEachDebug": DetailerForEachTest,
+    "DetailerForEachPipe": DetailerForEachPipe,
+    "DetailerForEachDebugPipe": DetailerForEachTestPipe,
+    "DetailerForEachPipeForAnimateDiff": DetailerForEachPipeForAnimateDiff,
+
+    "SAMDetectorCombined": SAMDetectorCombined,
+    "SAMDetectorSegmented": SAMDetectorSegmented,
+
+    "FaceDetailer": FaceDetailer,
+    "FaceDetailerPipe": FaceDetailerPipe,
+    "MaskDetailerPipe": MaskDetailerPipe,
+
+    "ToDetailerPipe": ToDetailerPipe,
+    "ToDetailerPipeSDXL": ToDetailerPipeSDXL,
+    "FromDetailerPipe": FromDetailerPipe,
+    "FromDetailerPipe_v2": FromDetailerPipe_v2,
+    "FromDetailerPipeSDXL": FromDetailerPipe_SDXL,
+    "ToBasicPipe": ToBasicPipe,
+    "FromBasicPipe": FromBasicPipe,
+    "FromBasicPipe_v2": FromBasicPipe_v2,
+    "BasicPipeToDetailerPipe": BasicPipeToDetailerPipe,
+    "BasicPipeToDetailerPipeSDXL": BasicPipeToDetailerPipeSDXL,
+    "DetailerPipeToBasicPipe": DetailerPipeToBasicPipe,
+    "EditBasicPipe": EditBasicPipe,
+    "EditDetailerPipe": EditDetailerPipe,
+    "EditDetailerPipeSDXL": EditDetailerPipeSDXL,
+
+    "LatentPixelScale": LatentPixelScale,
+    "PixelKSampleUpscalerProvider": PixelKSampleUpscalerProvider,
+    "PixelKSampleUpscalerProviderPipe": PixelKSampleUpscalerProviderPipe,
+    "IterativeLatentUpscale": IterativeLatentUpscale,
+    "IterativeImageUpscale": IterativeImageUpscale,
+    "PixelTiledKSampleUpscalerProvider": PixelTiledKSampleUpscalerProvider,
+    "PixelTiledKSampleUpscalerProviderPipe": PixelTiledKSampleUpscalerProviderPipe,
+    "TwoSamplersForMaskUpscalerProvider": TwoSamplersForMaskUpscalerProvider,
+    "TwoSamplersForMaskUpscalerProviderPipe": TwoSamplersForMaskUpscalerProviderPipe,
+
+    "PixelKSampleHookCombine": PixelKSampleHookCombine,
+    "DenoiseScheduleHookProvider": DenoiseScheduleHookProvider,
+    "StepsScheduleHookProvider": StepsScheduleHookProvider,
+    "CfgScheduleHookProvider": CfgScheduleHookProvider,
+    "NoiseInjectionHookProvider": NoiseInjectionHookProvider,
+    "UnsamplerHookProvider": UnsamplerHookProvider,
+    "CoreMLDetailerHookProvider": CoreMLDetailerHookProvider,
+    "PreviewDetailerHookProvider": PreviewDetailerHookProvider,
+
+    "DetailerHookCombine": DetailerHookCombine,
+    "NoiseInjectionDetailerHookProvider": NoiseInjectionDetailerHookProvider,
+    "UnsamplerDetailerHookProvider": UnsamplerDetailerHookProvider,
+    "DenoiseSchedulerDetailerHookProvider": DenoiseSchedulerDetailerHookProvider,
+    "SEGSOrderedFilterDetailerHookProvider": SEGSOrderedFilterDetailerHookProvider,
+    "SEGSRangeFilterDetailerHookProvider": SEGSRangeFilterDetailerHookProvider,
+    "SEGSLabelFilterDetailerHookProvider": SEGSLabelFilterDetailerHookProvider,
+
+    "BitwiseAndMask": BitwiseAndMask,
+    "SubtractMask": SubtractMask,
+    "AddMask": AddMask,
+    "ImpactSegsAndMask": SegsBitwiseAndMask,
+    "ImpactSegsAndMaskForEach": SegsBitwiseAndMaskForEach,
+    "EmptySegs": EmptySEGS,
+
+    "MediaPipeFaceMeshToSEGS": MediaPipeFaceMeshToSEGS,
+    "MaskToSEGS": MaskToSEGS,
+    "MaskToSEGS_for_AnimateDiff": MaskToSEGS_for_AnimateDiff,
+    "ToBinaryMask": ToBinaryMask,
+    "MasksToMaskList": MasksToMaskList,
+    "MaskListToMaskBatch": MaskListToMaskBatch,
+    "ImageListToImageBatch": ImageListToImageBatch,
+    "SetDefaultImageForSEGS": DefaultImageForSEGS,
+    "RemoveImageFromSEGS": RemoveImageFromSEGS,
+
+    "BboxDetectorSEGS": BboxDetectorForEach,
+    "SegmDetectorSEGS": SegmDetectorForEach,
+    "ONNXDetectorSEGS": BboxDetectorForEach,
+    "ImpactSimpleDetectorSEGS_for_AD": SimpleDetectorForAnimateDiff,
+    "ImpactSimpleDetectorSEGS": SimpleDetectorForEach,
+    "ImpactSimpleDetectorSEGSPipe": SimpleDetectorForEachPipe,
+    "ImpactControlNetApplySEGS": ControlNetApplySEGS,
+    "ImpactControlNetApplyAdvancedSEGS": ControlNetApplyAdvancedSEGS,
+    "ImpactControlNetClearSEGS": ControlNetClearSEGS,
+
+    "ImpactDecomposeSEGS": DecomposeSEGS,
+    "ImpactAssembleSEGS": AssembleSEGS,
+    "ImpactFrom_SEG_ELT": From_SEG_ELT,
+    "ImpactEdit_SEG_ELT": Edit_SEG_ELT,
+    "ImpactDilate_Mask_SEG_ELT": Dilate_SEG_ELT,
+    "ImpactDilateMask": DilateMask,
+    "ImpactGaussianBlurMask": GaussianBlurMask,
+    "ImpactDilateMaskInSEGS": DilateMaskInSEGS,
+    "ImpactGaussianBlurMaskInSEGS": GaussianBlurMaskInSEGS,
+    "ImpactScaleBy_BBOX_SEG_ELT": SEG_ELT_BBOX_ScaleBy,
+
+    "BboxDetectorCombined_v2": BboxDetectorCombined,
+    "SegmDetectorCombined_v2": SegmDetectorCombined,
+    "SegsToCombinedMask": SegsToCombinedMask,
+
+    "KSamplerProvider": KSamplerProvider,
+    "TwoSamplersForMask": TwoSamplersForMask,
+    "TiledKSamplerProvider": TiledKSamplerProvider,
+
+    "KSamplerAdvancedProvider": KSamplerAdvancedProvider,
+    "TwoAdvancedSamplersForMask": TwoAdvancedSamplersForMask,
+
+    "PreviewBridge": PreviewBridge,
+    "PreviewBridgeLatent": PreviewBridgeLatent,
+    "ImageSender": ImageSender,
+    "ImageReceiver": ImageReceiver,
+    "LatentSender": LatentSender,
+    "LatentReceiver": LatentReceiver,
+    "ImageMaskSwitch": ImageMaskSwitch,
+    "LatentSwitch": GeneralSwitch,
+    "SEGSSwitch": GeneralSwitch,
+    "ImpactSwitch": GeneralSwitch,
+    "ImpactInversedSwitch": GeneralInversedSwitch,
+
+    "ImpactWildcardProcessor": ImpactWildcardProcessor,
+    "ImpactWildcardEncode": ImpactWildcardEncode,
+
+    "SEGSDetailer": SEGSDetailer,
+    "SEGSPaste": SEGSPaste,
+    "SEGSPreview": SEGSPreview,
+    "SEGSPreviewCNet": SEGSPreviewCNet,
+    "SEGSToImageList": SEGSToImageList,
+    "ImpactSEGSToMaskList": SEGSToMaskList,
+    "ImpactSEGSToMaskBatch": SEGSToMaskBatch,
+    "ImpactSEGSConcat": SEGSConcat,
+    "ImpactSEGSPicker": SEGSPicker,
+    "ImpactMakeTileSEGS": MakeTileSEGS,
+
+    "SEGSDetailerForAnimateDiff": SEGSDetailerForAnimateDiff,
+
+    "ImpactKSamplerBasicPipe": KSamplerBasicPipe,
+    "ImpactKSamplerAdvancedBasicPipe": KSamplerAdvancedBasicPipe,
+
+    "ReencodeLatent": ReencodeLatent,
+    "ReencodeLatentPipe": ReencodeLatentPipe,
+
+    "ImpactImageBatchToImageList": ImageBatchToImageList,
+    "ImpactMakeImageList": MakeImageList,
+    "ImpactMakeImageBatch": MakeImageBatch,
+
+    "RegionalSampler": RegionalSampler,
+    "RegionalSamplerAdvanced": RegionalSamplerAdvanced,
+    "CombineRegionalPrompts": CombineRegionalPrompts,
+    "RegionalPrompt": RegionalPrompt,
+
+    "ImpactCombineConditionings": CombineConditionings,
+    "ImpactConcatConditionings": ConcatConditionings,
+
+    "ImpactSEGSLabelAssign": SEGSLabelAssign,
+    "ImpactSEGSLabelFilter": SEGSLabelFilter,
+    "ImpactSEGSRangeFilter": SEGSRangeFilter,
+    "ImpactSEGSOrderedFilter": SEGSOrderedFilter,
+
+    "ImpactCompare": ImpactCompare,
+    "ImpactConditionalBranch": ImpactConditionalBranch,
+    "ImpactConditionalBranchSelMode": ImpactConditionalBranchSelMode,
+    "ImpactIfNone": ImpactIfNone,
+    "ImpactConvertDataType": ImpactConvertDataType,
+    "ImpactLogicalOperators": ImpactLogicalOperators,
+    "ImpactInt": ImpactInt,
+    "ImpactFloat": ImpactFloat,
+    "ImpactValueSender": ImpactValueSender,
+    "ImpactValueReceiver": ImpactValueReceiver,
+    "ImpactImageInfo": ImpactImageInfo,
+    "ImpactLatentInfo": ImpactLatentInfo,
+    "ImpactMinMax": ImpactMinMax,
+    "ImpactNeg": ImpactNeg,
+    "ImpactConditionalStopIteration": ImpactConditionalStopIteration,
+    "ImpactStringSelector": StringSelector,
+
+    "RemoveNoiseMask": RemoveNoiseMask,
+
+    "ImpactLogger": ImpactLogger,
+    "ImpactDummyInput": ImpactDummyInput,
+
+    "ImpactQueueTrigger": ImpactQueueTrigger,
+    "ImpactQueueTriggerCountdown": ImpactQueueTriggerCountdown,
+    "ImpactSetWidgetValue": ImpactSetWidgetValue,
+    "ImpactNodeSetMuteState": ImpactNodeSetMuteState,
+    "ImpactControlBridge": ImpactControlBridge,
+    "ImpactIsNotEmptySEGS": ImpactNotEmptySEGS,
+    "ImpactSleep": ImpactSleep,
+    "ImpactRemoteBoolean": ImpactRemoteBoolean,
+    "ImpactRemoteInt": ImpactRemoteInt,
+
+    "ImpactHFTransformersClassifierProvider": HF_TransformersClassifierProvider,
+    "ImpactSEGSClassify": SEGS_Classify
+}
+
+
+NODE_DISPLAY_NAME_MAPPINGS = {
+    "SAMLoader": "SAMLoader (Impact)",
+
+    "BboxDetectorSEGS": "BBOX Detector (SEGS)",
+    "SegmDetectorSEGS": "SEGM Detector (SEGS)",
+    "ONNXDetectorSEGS": "ONNX Detector (SEGS/legacy) - use BBOXDetector",
+    "ImpactSimpleDetectorSEGS_for_AD": "Simple Detector for AnimateDiff (SEGS)",
+    "ImpactSimpleDetectorSEGS": "Simple Detector (SEGS)",
+    "ImpactSimpleDetectorSEGSPipe": "Simple Detector (SEGS/pipe)",
+    "ImpactControlNetApplySEGS": "ControlNetApply (SEGS)",
+    "ImpactControlNetApplyAdvancedSEGS": "ControlNetApplyAdvanced (SEGS)",
+
+    "BboxDetectorCombined_v2": "BBOX Detector (combined)",
+    "SegmDetectorCombined_v2": "SEGM Detector (combined)",
+    "SegsToCombinedMask": "SEGS to MASK (combined)",
+    "MediaPipeFaceMeshToSEGS": "MediaPipe FaceMesh to SEGS",
+    "MaskToSEGS": "MASK to SEGS",
+    "MaskToSEGS_for_AnimateDiff": "MASK to SEGS for AnimateDiff",
+    "BitwiseAndMaskForEach": "Bitwise(SEGS & SEGS)",
+    "SubtractMaskForEach": "Bitwise(SEGS - SEGS)",
+    "ImpactSegsAndMask": "Bitwise(SEGS & MASK)",
+    "ImpactSegsAndMaskForEach": "Bitwise(SEGS & MASKS ForEach)",
+    "BitwiseAndMask": "Bitwise(MASK & MASK)",
+    "SubtractMask": "Bitwise(MASK - MASK)",
+    "AddMask": "Bitwise(MASK + MASK)",
+    "DetailerForEach": "Detailer (SEGS)",
+    "DetailerForEachPipe": "Detailer (SEGS/pipe)",
+    "DetailerForEachDebug": "DetailerDebug (SEGS)",
+    "DetailerForEachDebugPipe": "DetailerDebug (SEGS/pipe)",
+    "SEGSDetailerForAnimateDiff": "SEGSDetailer For AnimateDiff (SEGS/pipe)",
+    "DetailerForEachPipeForAnimateDiff": "Detailer For AnimateDiff (SEGS/pipe)",
+
+    "SAMDetectorCombined": "SAMDetector (combined)",
+    "SAMDetectorSegmented": "SAMDetector (segmented)",
+    "FaceDetailerPipe": "FaceDetailer (pipe)",
+    "MaskDetailerPipe": "MaskDetailer (pipe)",
+
+    "FromDetailerPipeSDXL": "FromDetailer (SDXL/pipe)",
+    "BasicPipeToDetailerPipeSDXL": "BasicPipe -> DetailerPipe (SDXL)",
+    "EditDetailerPipeSDXL": "Edit DetailerPipe (SDXL)",
+
+    "BasicPipeToDetailerPipe": "BasicPipe -> DetailerPipe",
+    "DetailerPipeToBasicPipe": "DetailerPipe -> BasicPipe",
+    "EditBasicPipe": "Edit BasicPipe",
+    "EditDetailerPipe": "Edit DetailerPipe",
+
+    "LatentPixelScale": "Latent Scale (on Pixel Space)",
+    "IterativeLatentUpscale": "Iterative Upscale (Latent/on Pixel Space)",
+    "IterativeImageUpscale": "Iterative Upscale (Image)",
+
+    "TwoSamplersForMaskUpscalerProvider": "TwoSamplersForMask Upscaler Provider",
+    "TwoSamplersForMaskUpscalerProviderPipe": "TwoSamplersForMask Upscaler Provider (pipe)",
+
+    "ReencodeLatent": "Reencode Latent",
+    "ReencodeLatentPipe": "Reencode Latent (pipe)",
+
+    "ImpactKSamplerBasicPipe": "KSampler (pipe)",
+    "ImpactKSamplerAdvancedBasicPipe": "KSampler (Advanced/pipe)",
+    "ImpactSEGSLabelAssign": "SEGS Assign (label)",
+    "ImpactSEGSLabelFilter": "SEGS Filter (label)",
+    "ImpactSEGSRangeFilter": "SEGS Filter (range)",
+    "ImpactSEGSOrderedFilter": "SEGS Filter (ordered)",
+    "ImpactSEGSConcat": "SEGS Concat",
+    "ImpactSEGSToMaskList": "SEGS to Mask List",
+    "ImpactSEGSToMaskBatch": "SEGS to Mask Batch",
+    "ImpactSEGSPicker": "Picker (SEGS)",
+    "ImpactMakeTileSEGS": "Make Tile SEGS",
+
+    "ImpactDecomposeSEGS": "Decompose (SEGS)",
+    "ImpactAssembleSEGS": "Assemble (SEGS)",
+    "ImpactFrom_SEG_ELT": "From SEG_ELT",
+    "ImpactEdit_SEG_ELT": "Edit SEG_ELT",
+    "ImpactDilate_Mask_SEG_ELT": "Dilate Mask (SEG_ELT)",
+    "ImpactScaleBy_BBOX_SEG_ELT": "ScaleBy BBOX (SEG_ELT)",
+    "ImpactDilateMask": "Dilate Mask",
+    "ImpactGaussianBlurMask": "Gaussian Blur Mask",
+    "ImpactDilateMaskInSEGS": "Dilate Mask (SEGS)",
+    "ImpactGaussianBlurMaskInSEGS": "Gaussian Blur Mask (SEGS)",
+
+    "PreviewBridge": "Preview Bridge (Image)",
+    "PreviewBridgeLatent": "Preview Bridge (Latent)",
+    "ImageSender": "Image Sender",
+    "ImageReceiver": "Image Receiver",
+    "ImageMaskSwitch": "Switch (images, mask)",
+    "ImpactSwitch": "Switch (Any)",
+    "ImpactInversedSwitch": "Inversed Switch (Any)",
+
+    "MasksToMaskList": "Masks to Mask List",
+    "MaskListToMaskBatch": "Mask List to Masks",
+    "ImpactImageBatchToImageList": "Image batch to Image List",
+    "ImageListToImageBatch": "Image List to Image Batch",
+    "ImpactMakeImageList": "Make Image List",
+    "ImpactMakeImageBatch": "Make Image Batch",
+    "ImpactStringSelector": "String Selector",
+    "ImpactIsNotEmptySEGS": "SEGS isn't Empty",
+    "SetDefaultImageForSEGS": "Set Default Image for SEGS",
+    "RemoveImageFromSEGS": "Remove Image from SEGS",
+
+    "RemoveNoiseMask": "Remove Noise Mask",
+
+    "ImpactCombineConditionings": "Combine Conditionings",
+    "ImpactConcatConditionings": "Concat Conditionings",
+
+    "ImpactQueueTrigger": "Queue Trigger",
+    "ImpactQueueTriggerCountdown": "Queue Trigger (Countdown)",
+    "ImpactSetWidgetValue": "Set Widget Value",
+    "ImpactNodeSetMuteState": "Set Mute State",
+    "ImpactControlBridge": "Control Bridge",
+    "ImpactSleep": "Sleep",
+    "ImpactRemoteBoolean": "Remote Boolean (on prompt)",
+    "ImpactRemoteInt": "Remote Int (on prompt)",
+
+    "ImpactHFTransformersClassifierProvider": "HF Transformers Classifier Provider",
+    "ImpactSEGSClassify": "SEGS Classify",
+
+    "LatentSwitch": "Switch (latent/legacy)",
+    "SEGSSwitch": "Switch (SEGS/legacy)",
+
+    "SEGSPreviewCNet": "SEGSPreview (CNET Image)"
+}
+
+if not impact.config.get_config()['mmdet_skip']:
+    from impact.mmdet_nodes import *
+    import impact.legacy_nodes
+    NODE_CLASS_MAPPINGS.update({
+        "MMDetDetectorProvider": MMDetDetectorProvider,
+        "MMDetLoader": impact.legacy_nodes.MMDetLoader,
+        "MaskPainter": impact.legacy_nodes.MaskPainter,
+        "SegsMaskCombine": impact.legacy_nodes.SegsMaskCombine,
+        "BboxDetectorForEach": impact.legacy_nodes.BboxDetectorForEach,
+        "SegmDetectorForEach": impact.legacy_nodes.SegmDetectorForEach,
+        "BboxDetectorCombined": impact.legacy_nodes.BboxDetectorCombined,
+        "SegmDetectorCombined": impact.legacy_nodes.SegmDetectorCombined,
+    })
+
+    NODE_DISPLAY_NAME_MAPPINGS.update({
+        "MaskPainter": "MaskPainter (Deprecated)",
+        "MMDetLoader": "MMDetLoader (Legacy)",
+        "SegsMaskCombine": "SegsMaskCombine (Legacy)",
+        "BboxDetectorForEach": "BboxDetectorForEach (Legacy)",
+        "SegmDetectorForEach": "SegmDetectorForEach (Legacy)",
+        "BboxDetectorCombined": "BboxDetectorCombined (Legacy)",
+        "SegmDetectorCombined": "SegmDetectorCombined (Legacy)",
+    })
+
+try:
+    import impact.subpack_nodes
+
+    NODE_CLASS_MAPPINGS.update(impact.subpack_nodes.NODE_CLASS_MAPPINGS)
+    NODE_DISPLAY_NAME_MAPPINGS.update(impact.subpack_nodes.NODE_DISPLAY_NAME_MAPPINGS)
+except Exception as e:
+    print("### ComfyUI-Impact-Pack: (IMPORT FAILED) Subpack\n")
+    print("  The module at the `custom_nodes/ComfyUI-Impact-Pack/impact_subpack` path appears to be incomplete.")
+    print("  Recommended to delete the path and restart ComfyUI.")
+    print("  If the issue persists, please report it to https://github.com/ltdrdata/ComfyUI-Impact-Pack/issues.")
+    print("\n---------------------------------")
+    traceback.print_exc()
+    print("---------------------------------\n")
+
+WEB_DIRECTORY = "js"
+__all__ = ['NODE_CLASS_MAPPINGS', 'NODE_DISPLAY_NAME_MAPPINGS']
+
+
+try:
+    import cm_global
+    cm_global.register_extension('ComfyUI-Impact-Pack',
+                                 {'version': config.version_code,
+                                  'name': 'Impact Pack',
+                                  'nodes': set(NODE_CLASS_MAPPINGS.keys()),
+                                  'description': 'This extension provides inpainting functionality based on the detector and detailer, along with convenient workflow features like wildcards and logics.', })
+except:
+    pass

custom_nodes/ComfyUI-Impact-Pack/disable.py

@@ -0,0 +1,38 @@
+import os
+import sys
+import time
+import platform
+import shutil
+import subprocess
+
+comfy_path = '../..'
+
+def rmtree(path):
+    retry_count = 3
+
+    while True:
+        try:
+            retry_count -= 1
+
+            if platform.system() == "Windows":
+                subprocess.check_call(['attrib', '-R', path + '\\*', '/S'])
+
+            shutil.rmtree(path)
+
+            return True
+
+        except Exception as ex:
+            print(f"ex: {ex}")
+            time.sleep(3)
+
+            if retry_count < 0:
+                raise ex
+
+            print(f"Uninstall retry({retry_count})")
+
+js_dest_path = os.path.join(comfy_path, "web", "extensions", "impact-pack")
+
+if os.path.exists(js_dest_path):
+    rmtree(js_dest_path)
+        
+

custom_nodes/ComfyUI-Impact-Pack/impact-pack.ini

@@ -0,0 +1,8 @@
+[default]
+dependency_version = 20
+mmdet_skip = True
+sam_editor_cpu = False
+sam_editor_model = sam_vit_b_01ec64.pth
+custom_wildcards = /home/tiger/Magic-ComfyUI/custom_nodes/ComfyUI-Impact-Pack/custom_wildcards
+disable_gpu_opencv = True
+

custom_nodes/ComfyUI-Impact-Pack/impact_subpack/LICENSE

@@ -0,0 +1,661 @@
+                    GNU AFFERO GENERAL PUBLIC LICENSE
+                       Version 3, 19 November 2007
+
+ Copyright (C) 2007 Free Software Foundation, Inc. <https://fsf.org/>
+ Everyone is permitted to copy and distribute verbatim copies
+ of this license document, but changing it is not allowed.
+
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+or that patent license was granted, prior to 28 March 2007.
+
+  Nothing in this License shall be construed as excluding or limiting
+any implied license or other defenses to infringement that may
+otherwise be available to you under applicable patent law.
+
+  12. No Surrender of Others' Freedom.
+
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+not convey it at all.  For example, if you agree to terms that obligate you
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+
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+
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+
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+
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+PARTIES OR A FAILURE OF THE PROGRAM TO OPERATE WITH ANY OTHER PROGRAMS),
+EVEN IF SUCH HOLDER OR OTHER PARTY HAS BEEN ADVISED OF THE POSSIBILITY OF
+SUCH DAMAGES.
+
+  17. Interpretation of Sections 15 and 16.
+
+  If the disclaimer of warranty and limitation of liability provided
+above cannot be given local legal effect according to their terms,
+reviewing courts shall apply local law that most closely approximates
+an absolute waiver of all civil liability in connection with the
+Program, unless a warranty or assumption of liability accompanies a
+copy of the Program in return for a fee.
+
+                     END OF TERMS AND CONDITIONS
+
+            How to Apply These Terms to Your New Programs
+
+  If you develop a new program, and you want it to be of the greatest
+possible use to the public, the best way to achieve this is to make it
+free software which everyone can redistribute and change under these terms.
+
+  To do so, attach the following notices to the program.  It is safest
+to attach them to the start of each source file to most effectively
+state the exclusion of warranty; and each file should have at least
+the "copyright" line and a pointer to where the full notice is found.
+
+    <one line to give the program's name and a brief idea of what it does.>
+    Copyright (C) <year>  <name of author>
+
+    This program is free software: you can redistribute it and/or modify
+    it under the terms of the GNU Affero General Public License as published
+    by the Free Software Foundation, either version 3 of the License, or
+    (at your option) any later version.
+
+    This program is distributed in the hope that it will be useful,
+    but WITHOUT ANY WARRANTY; without even the implied warranty of
+    MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
+    GNU Affero General Public License for more details.
+
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+    along with this program.  If not, see <https://www.gnu.org/licenses/>.
+
+Also add information on how to contact you by electronic and paper mail.
+
+  If your software can interact with users remotely through a computer
+network, you should also make sure that it provides a way for users to
+get its source.  For example, if your program is a web application, its
+interface could display a "Source" link that leads users to an archive
+of the code.  There are many ways you could offer source, and different
+solutions will be better for different programs; see section 13 for the
+specific requirements.
+
+  You should also get your employer (if you work as a programmer) or school,
+if any, to sign a "copyright disclaimer" for the program, if necessary.
+For more information on this, and how to apply and follow the GNU AGPL, see
+<https://www.gnu.org/licenses/>.

custom_nodes/ComfyUI-Impact-Pack/impact_subpack/README.md

@@ -0,0 +1,18 @@
+# ComfyUI-Impact-Subpack
+This extension serves as a complement to the Impact Pack, offering features that are not deemed suitable for inclusion by default in the ComfyUI Impact Pack
+
+The nodes in this repository cannot be used standalone and depend on [ComfyUI-Impact-Pack](https://github.com/ltdrdata/ComfyUI-Impact-Pack).
+
+## Nodes
+* UltralyticsDetectorProvider - This node provides an object detection detector based on Ultralystics.
+    * By using this Detector Provider, you can replace the existing mmdet-based detector.
+
+
+## Credits
+
+ComfyUI/[ComfyUI](https://github.com/comfyanonymous/ComfyUI) - A powerful and modular stable diffusion GUI.
+
+Bing-su/[adetailer](https://github.com/Bing-su/adetailer/) - This repo sitoryprovides an object detection model and features based on Ultralystics.
+
+huggingface/Bingsu/[adetailer](https://huggingface.co/Bingsu/adetailer/tree/main) - This repository offers various models based on Ultralystics.
+* You can download other models supported by the UltralyticsDetectorProvider from here.

custom_nodes/ComfyUI-Impact-Pack/impact_subpack/impact/subcore.py

@@ -0,0 +1,213 @@
+from pathlib import Path
+from PIL import Image
+
+import impact.core as core
+import cv2
+import numpy as np
+from torchvision.transforms.functional import to_pil_image
+import torch
+
+try:
+    from ultralytics import YOLO
+except Exception as e:
+    print(e)
+    print(f"\n!!!!!\n\n[ComfyUI-Impact-Subpack] If this error occurs, please check the following link:\n\thttps://github.com/ltdrdata/ComfyUI-Impact-Pack/blob/Main/troubleshooting/TROUBLESHOOTING.md\n\n!!!!!\n")
+    raise e
+
+
+def load_yolo(model_path: str):
+    try:
+        return YOLO(model_path)
+    except ModuleNotFoundError:
+        # https://github.com/ultralytics/ultralytics/issues/3856
+        YOLO("yolov8n.pt")
+        return YOLO(model_path)
+
+
+def inference_bbox(
+    model,
+    image: Image.Image,
+    confidence: float = 0.3,
+    device: str = "",
+):
+    pred = model(image, conf=confidence, device=device)
+
+    bboxes = pred[0].boxes.xyxy.cpu().numpy()
+    cv2_image = np.array(image)
+    if len(cv2_image.shape) == 3:
+        cv2_image = cv2_image[:, :, ::-1].copy()  # Convert RGB to BGR for cv2 processing
+    else:
+        # Handle the grayscale image here
+        # For example, you might want to convert it to a 3-channel grayscale image for consistency:
+        cv2_image = cv2.cvtColor(cv2_image, cv2.COLOR_GRAY2BGR)
+    cv2_gray = cv2.cvtColor(cv2_image, cv2.COLOR_BGR2GRAY)
+
+    segms = []
+    for x0, y0, x1, y1 in bboxes:
+        cv2_mask = np.zeros(cv2_gray.shape, np.uint8)
+        cv2.rectangle(cv2_mask, (int(x0), int(y0)), (int(x1), int(y1)), 255, -1)
+        cv2_mask_bool = cv2_mask.astype(bool)
+        segms.append(cv2_mask_bool)
+
+    n, m = bboxes.shape
+    if n == 0:
+        return [[], [], [], []]
+
+    results = [[], [], [], []]
+    for i in range(len(bboxes)):
+        results[0].append(pred[0].names[int(pred[0].boxes[i].cls.item())])
+        results[1].append(bboxes[i])
+        results[2].append(segms[i])
+        results[3].append(pred[0].boxes[i].conf.cpu().numpy())
+
+    return results
+
+
+def inference_segm(
+    model,
+    image: Image.Image,
+    confidence: float = 0.3,
+    device: str = "",
+):
+    pred = model(image, conf=confidence, device=device)
+
+    bboxes = pred[0].boxes.xyxy.cpu().numpy()
+    n, m = bboxes.shape
+    if n == 0:
+        return [[], [], [], []]
+
+    # NOTE: masks.data will be None when n == 0
+    segms = pred[0].masks.data.cpu().numpy()
+
+    results = [[], [], [], []]
+    for i in range(len(bboxes)):
+        results[0].append(pred[0].names[int(pred[0].boxes[i].cls.item())])
+        results[1].append(bboxes[i])
+
+        mask = torch.from_numpy(segms[i])
+        scaled_mask = torch.nn.functional.interpolate(mask.unsqueeze(0).unsqueeze(0), size=(image.size[1], image.size[0]),
+                                                      mode='bilinear', align_corners=False)
+        scaled_mask = scaled_mask.squeeze().squeeze()
+
+        results[2].append(scaled_mask.numpy())
+        results[3].append(pred[0].boxes[i].conf.cpu().numpy())
+
+    return results
+
+
+class UltraBBoxDetector:
+    bbox_model = None
+
+    def __init__(self, bbox_model):
+        self.bbox_model = bbox_model
+
+    def detect(self, image, threshold, dilation, crop_factor, drop_size=1, detailer_hook=None):
+        drop_size = max(drop_size, 1)
+        detected_results = inference_bbox(self.bbox_model, core.tensor2pil(image), threshold)
+        segmasks = core.create_segmasks(detected_results)
+
+        if dilation > 0:
+            segmasks = core.dilate_masks(segmasks, dilation)
+
+        items = []
+        h = image.shape[1]
+        w = image.shape[2]
+
+        for x, label in zip(segmasks, detected_results[0]):
+            item_bbox = x[0]
+            item_mask = x[1]
+
+            y1, x1, y2, x2 = item_bbox
+
+            if x2 - x1 > drop_size and y2 - y1 > drop_size:  # minimum dimension must be (2,2) to avoid squeeze issue
+                crop_region = core.make_crop_region(w, h, item_bbox, crop_factor)
+
+                if detailer_hook is not None:
+                    crop_region = detailer_hook.post_crop_region(w, h, item_bbox, crop_region)
+
+                cropped_image = core.crop_image(image, crop_region)
+                cropped_mask = core.crop_ndarray2(item_mask, crop_region)
+                confidence = x[2]
+                # bbox_size = (item_bbox[2]-item_bbox[0],item_bbox[3]-item_bbox[1]) # (w,h)
+
+                item = core.SEG(cropped_image, cropped_mask, confidence, crop_region, item_bbox, label, None)
+
+                items.append(item)
+
+        shape = image.shape[1], image.shape[2]
+        segs = shape, items
+
+        if detailer_hook is not None and hasattr(detailer_hook, "post_detection"):
+            segs = detailer_hook.post_detection(segs)
+
+        return segs
+
+    def detect_combined(self, image, threshold, dilation):
+        detected_results = inference_bbox(self.bbox_model, core.tensor2pil(image), threshold)
+        segmasks = core.create_segmasks(detected_results)
+        if dilation > 0:
+            segmasks = core.dilate_masks(segmasks, dilation)
+
+        return core.combine_masks(segmasks)
+
+    def setAux(self, x):
+        pass
+
+
+class UltraSegmDetector:
+    bbox_model = None
+
+    def __init__(self, bbox_model):
+        self.bbox_model = bbox_model
+
+    def detect(self, image, threshold, dilation, crop_factor, drop_size=1, detailer_hook=None):
+        drop_size = max(drop_size, 1)
+        detected_results = inference_segm(self.bbox_model, core.tensor2pil(image), threshold)
+        segmasks = core.create_segmasks(detected_results)
+
+        if dilation > 0:
+            segmasks = core.dilate_masks(segmasks, dilation)
+
+        items = []
+        h = image.shape[1]
+        w = image.shape[2]
+
+        for x, label in zip(segmasks, detected_results[0]):
+            item_bbox = x[0]
+            item_mask = x[1]
+
+            y1, x1, y2, x2 = item_bbox
+
+            if x2 - x1 > drop_size and y2 - y1 > drop_size:  # minimum dimension must be (2,2) to avoid squeeze issue
+                crop_region = core.make_crop_region(w, h, item_bbox, crop_factor)
+
+                if detailer_hook is not None:
+                    crop_region = detailer_hook.post_crop_region(w, h, item_bbox, crop_region)
+
+                cropped_image = core.crop_image(image, crop_region)
+                cropped_mask = core.crop_ndarray2(item_mask, crop_region)
+                confidence = x[2]
+                # bbox_size = (item_bbox[2]-item_bbox[0],item_bbox[3]-item_bbox[1]) # (w,h)
+
+                item = core.SEG(cropped_image, cropped_mask, confidence, crop_region, item_bbox, label, None)
+
+                items.append(item)
+
+        shape = image.shape[1], image.shape[2]
+        segs = shape, items
+
+        if detailer_hook is not None and hasattr(detailer_hook, "post_detection"):
+            segs = detailer_hook.post_detection(segs)
+
+        return segs
+
+    def detect_combined(self, image, threshold, dilation):
+        detected_results = inference_segm(self.bbox_model, core.tensor2pil(image), threshold)
+        segmasks = core.create_segmasks(detected_results)
+        if dilation > 0:
+            segmasks = core.dilate_masks(segmasks, dilation)
+
+        return core.combine_masks(segmasks)
+
+    def setAux(self, x):
+        pass

custom_nodes/ComfyUI-Impact-Pack/impact_subpack/impact/subpack_nodes.py

@@ -0,0 +1,45 @@
+import os
+import folder_paths
+import impact.core as core
+import impact.subcore as subcore
+from impact.utils import add_folder_path_and_extensions
+
+version_code = 20
+
+print(f"### Loading: ComfyUI-Impact-Pack (Subpack: V0.4)")
+
+model_path = folder_paths.models_dir
+add_folder_path_and_extensions("ultralytics_bbox", [os.path.join(model_path, "ultralytics", "bbox")], folder_paths.supported_pt_extensions)
+add_folder_path_and_extensions("ultralytics_segm", [os.path.join(model_path, "ultralytics", "segm")], folder_paths.supported_pt_extensions)
+add_folder_path_and_extensions("ultralytics", [os.path.join(model_path, "ultralytics")], folder_paths.supported_pt_extensions)
+
+
+class UltralyticsDetectorProvider:
+    @classmethod
+    def INPUT_TYPES(s):
+        bboxs = ["bbox/"+x for x in folder_paths.get_filename_list("ultralytics_bbox")]
+        segms = ["segm/"+x for x in folder_paths.get_filename_list("ultralytics_segm")]
+        return {"required": {"model_name": (bboxs + segms, )}}
+    RETURN_TYPES = ("BBOX_DETECTOR", "SEGM_DETECTOR")
+    FUNCTION = "doit"
+
+    CATEGORY = "ImpactPack"
+
+    def doit(self, model_name):
+        model_path = folder_paths.get_full_path("ultralytics", model_name)
+        model = subcore.load_yolo(model_path)
+
+        if model_name.startswith("bbox"):
+            return subcore.UltraBBoxDetector(model), core.NO_SEGM_DETECTOR()
+        else:
+            return subcore.UltraBBoxDetector(model), subcore.UltraSegmDetector(model)
+
+
+NODE_CLASS_MAPPINGS = {
+    "UltralyticsDetectorProvider": UltralyticsDetectorProvider
+}
+
+
+NODE_DISPLAY_NAME_MAPPINGS = {
+
+}

custom_nodes/ComfyUI-Impact-Pack/impact_subpack/install.py

@@ -0,0 +1,32 @@
+import os
+import sys
+from torchvision.datasets.utils import download_url
+
+subpack_path = os.path.join(os.path.dirname(__file__))
+comfy_path = os.path.join(subpack_path, '..', '..', '..')
+
+sys.path.append(comfy_path)
+
+import folder_paths
+model_path = folder_paths.models_dir
+ultralytics_bbox_path = os.path.join(model_path, "ultralytics", "bbox")
+ultralytics_segm_path = os.path.join(model_path, "ultralytics", "segm")
+
+if not os.path.exists(os.path.join(subpack_path, '..', '..', 'skip_download_model')):
+    if not os.path.exists(ultralytics_bbox_path):
+        os.makedirs(ultralytics_bbox_path)
+
+    if not os.path.exists(ultralytics_segm_path):
+        os.makedirs(ultralytics_segm_path)
+
+    if not os.path.exists(os.path.join(ultralytics_bbox_path, "face_yolov8m.pt")):
+        download_url("https://huggingface.co/Bingsu/adetailer/resolve/main/face_yolov8m.pt",
+                     ultralytics_bbox_path)
+
+    if not os.path.exists(os.path.join(ultralytics_bbox_path, "hand_yolov8s.pt")):
+        download_url("https://huggingface.co/Bingsu/adetailer/resolve/main/hand_yolov8s.pt",
+                     ultralytics_bbox_path)
+
+    if not os.path.exists(os.path.join(ultralytics_segm_path, "person_yolov8m-seg.pt")):
+        download_url("https://huggingface.co/Bingsu/adetailer/resolve/main/person_yolov8m-seg.pt",
+                     ultralytics_segm_path)

custom_nodes/ComfyUI-Impact-Pack/impact_subpack/requirements.txt

@@ -0,0 +1 @@
+ultralytics!=8.0.177

custom_nodes/ComfyUI-Impact-Pack/install.py

@@ -0,0 +1,285 @@
+import os
+import shutil
+import sys
+import subprocess
+import threading
+import locale
+import traceback
+import re
+
+
+if sys.argv[0] == 'install.py':
+    sys.path.append('.')   # for portable version
+
+
+impact_path = os.path.join(os.path.dirname(__file__), "modules")
+old_subpack_path = os.path.join(os.path.dirname(__file__), "subpack")
+subpack_path = os.path.join(os.path.dirname(__file__), "impact_subpack")
+subpack_repo = "https://github.com/ltdrdata/ComfyUI-Impact-Subpack"
+comfy_path = os.path.abspath(os.path.join(os.path.dirname(__file__), '..', '..'))
+
+
+sys.path.append(impact_path)
+sys.path.append(comfy_path)
+
+
+# ---
+def handle_stream(stream, is_stdout):
+    stream.reconfigure(encoding=locale.getpreferredencoding(), errors='replace')
+
+    for msg in stream:
+        if is_stdout:
+            print(msg, end="", file=sys.stdout)
+        else: 
+            print(msg, end="", file=sys.stderr)
+            
+
+def process_wrap(cmd_str, cwd=None, handler=None):
+    print(f"[Impact Pack] EXECUTE: {cmd_str} in '{cwd}'")
+    process = subprocess.Popen(cmd_str, cwd=cwd, stdout=subprocess.PIPE, stderr=subprocess.PIPE, text=True, bufsize=1)
+
+    if handler is None:
+        handler = handle_stream
+
+    stdout_thread = threading.Thread(target=handler, args=(process.stdout, True))
+    stderr_thread = threading.Thread(target=handler, args=(process.stderr, False))
+
+    stdout_thread.start()
+    stderr_thread.start()
+
+    stdout_thread.join()
+    stderr_thread.join()
+
+    return process.wait()
+# ---
+
+
+pip_list = None
+
+
+def get_installed_packages():
+    global pip_list
+
+    if pip_list is None:
+        try:
+            result = subprocess.check_output([sys.executable, '-m', 'pip', 'list'], universal_newlines=True)
+            pip_list = set([line.split()[0].lower() for line in result.split('\n') if line.strip()])
+        except subprocess.CalledProcessError as e:
+            print(f"[ComfyUI-Manager] Failed to retrieve the information of installed pip packages.")
+            return set()
+    
+    return pip_list
+    
+
+def is_installed(name):
+    name = name.strip()
+    pattern = r'([^<>!=]+)([<>!=]=?)'
+    match = re.search(pattern, name)
+    
+    if match:
+        name = match.group(1)
+        
+    result = name.lower() in get_installed_packages()
+    return result
+    
+
+def is_requirements_installed(file_path):
+    print(f"req_path: {file_path}")
+    if os.path.exists(file_path):
+        with open(file_path, 'r') as file:
+            lines = file.readlines()
+            for line in lines:
+                if not is_installed(line):
+                    return False
+                    
+    return True
+
+try:
+    import platform
+    import folder_paths
+    from torchvision.datasets.utils import download_url
+    import impact.config
+
+
+    print("### ComfyUI-Impact-Pack: Check dependencies")
+
+    if "python_embeded" in sys.executable or "python_embedded" in sys.executable:
+        pip_install = [sys.executable, '-s', '-m', 'pip', 'install']
+        mim_install = [sys.executable, '-s', '-m', 'mim', 'install']
+    else:
+        pip_install = [sys.executable, '-m', 'pip', 'install']
+        mim_install = [sys.executable, '-m', 'mim', 'install']
+
+
+    def ensure_subpack():
+        import git
+        if os.path.exists(subpack_path):
+            try:
+                repo = git.Repo(subpack_path)
+                repo.remotes.origin.pull()
+            except:
+                traceback.print_exc()
+                if platform.system() == 'Windows':
+                    print(f"[ComfyUI-Impact-Pack] Please turn off ComfyUI and remove '{subpack_path}' and restart ComfyUI.")
+                else:
+                    shutil.rmtree(subpack_path)
+                    git.Repo.clone_from(subpack_repo, subpack_path)
+        else:
+            git.Repo.clone_from(subpack_repo, subpack_path)
+
+        if os.path.exists(old_subpack_path):
+            shutil.rmtree(old_subpack_path)
+
+
+    def remove_olds():
+        global comfy_path
+
+        comfy_path = os.path.dirname(folder_paths.__file__)
+        custom_nodes_path = os.path.join(comfy_path, "custom_nodes")
+        old_ini_path = os.path.join(custom_nodes_path, "impact-pack.ini")
+        old_py_path = os.path.join(custom_nodes_path, "comfyui-impact-pack.py")
+
+        if os.path.exists(impact.config.old_config_path):
+            impact.config.get_config()['mmdet_skip'] = False
+            os.remove(impact.config.old_config_path)
+
+        if os.path.exists(old_ini_path):
+            print(f"Delete legacy file: {old_ini_path}")
+            os.remove(old_ini_path)
+
+        if os.path.exists(old_py_path):
+            print(f"Delete legacy file: {old_py_path}")
+            os.remove(old_py_path)
+
+
+    def ensure_pip_packages_first():
+        subpack_req = os.path.join(subpack_path, "requirements.txt")
+        if os.path.exists(subpack_req) and not is_requirements_installed(subpack_req):
+            process_wrap(pip_install + ['-r', 'requirements.txt'], cwd=subpack_path)
+
+        if not impact.config.get_config()['mmdet_skip']:
+            process_wrap(pip_install + ['openmim'])
+
+            try:
+                import pycocotools
+            except Exception:
+                if platform.system() not in ["Windows"] or platform.machine() not in ["AMD64", "x86_64"]:
+                    print(f"Your system is {platform.system()}; !! You need to install 'libpython3-dev' for this step. !!")
+
+                    process_wrap(pip_install + ['pycocotools'])
+                else:
+                    pycocotools = {
+                        (3, 8): "https://github.com/Bing-su/dddetailer/releases/download/pycocotools/pycocotools-2.0.6-cp38-cp38-win_amd64.whl",
+                        (3, 9): "https://github.com/Bing-su/dddetailer/releases/download/pycocotools/pycocotools-2.0.6-cp39-cp39-win_amd64.whl",
+                        (3, 10): "https://github.com/Bing-su/dddetailer/releases/download/pycocotools/pycocotools-2.0.6-cp310-cp310-win_amd64.whl",
+                        (3, 11): "https://github.com/Bing-su/dddetailer/releases/download/pycocotools/pycocotools-2.0.6-cp311-cp311-win_amd64.whl",
+                    }
+
+                    version = sys.version_info[:2]
+                    url = pycocotools[version]
+                    process_wrap(pip_install + [url])
+
+
+    def ensure_pip_packages_last():
+        my_path = os.path.dirname(__file__)
+        requirements_path = os.path.join(my_path, "requirements.txt")
+        
+        if not is_requirements_installed(requirements_path):
+            process_wrap(pip_install + ['-r', requirements_path])
+            
+        # fallback
+        try:
+            import segment_anything
+            from skimage.measure import label, regionprops
+            import piexif
+        except Exception:
+            process_wrap(pip_install + ['-r', requirements_path])
+
+        # !! cv2 importing test must be very last !!
+        try:
+            import cv2
+        except Exception:
+            try:
+                if not is_installed('opencv-python'):
+                    process_wrap(pip_install + ['opencv-python'])
+                if not is_installed('opencv-python-headless'):
+                    process_wrap(pip_install + ['opencv-python-headless'])
+            except:
+                print(f"[ERROR] ComfyUI-Impact-Pack: failed to install 'opencv-python'. Please, install manually.")
+
+    def ensure_mmdet_package():
+        try:
+            import mmcv
+            import mmdet
+            from mmdet.evaluation import get_classes
+        except Exception:
+            process_wrap(pip_install + ['opendatalab==0.0.9'])
+            process_wrap(pip_install + ['-U', 'openmim'])
+            process_wrap(mim_install + ['mmcv>=2.0.0rc4, <2.1.0'])
+            process_wrap(mim_install + ['mmdet==3.0.0'])
+            process_wrap(mim_install + ['mmengine==0.7.4'])
+
+
+    def install():
+        remove_olds()
+
+        subpack_install_script = os.path.join(subpack_path, "install.py")
+
+        print(f"### ComfyUI-Impact-Pack: Updating subpack")
+        try:
+            import git
+        except Exception:
+            if not is_installed('GitPython'):
+                process_wrap(pip_install + ['GitPython'])
+
+        ensure_subpack()  # The installation of the subpack must take place before ensure_pip. cv2 triggers a permission error.
+
+        if os.path.exists(subpack_install_script):
+            process_wrap([sys.executable, 'install.py'], cwd=subpack_path)
+            if not is_requirements_installed(os.path.join(subpack_path, 'requirements.txt')):
+                process_wrap(pip_install + ['-r', 'requirements.txt'], cwd=subpack_path)
+        else:
+            print(f"### ComfyUI-Impact-Pack: (Install Failed) Subpack\nFile not found: `{subpack_install_script}`")
+
+        ensure_pip_packages_first()
+
+        if not impact.config.get_config()['mmdet_skip']:
+            ensure_mmdet_package()
+
+        ensure_pip_packages_last()
+
+        # Download model
+        print("### ComfyUI-Impact-Pack: Check basic models")
+
+        model_path = folder_paths.models_dir
+
+        bbox_path = os.path.join(model_path, "mmdets", "bbox")
+        sam_path = os.path.join(model_path, "sams")
+        onnx_path = os.path.join(model_path, "onnx")
+
+        if not os.path.exists(os.path.join(os.path.dirname(__file__), '..', 'skip_download_model')):
+            if not os.path.exists(bbox_path):
+                os.makedirs(bbox_path)
+
+            if not impact.config.get_config()['mmdet_skip']:
+                if not os.path.exists(os.path.join(bbox_path, "mmdet_anime-face_yolov3.pth")):
+                    download_url("https://huggingface.co/dustysys/ddetailer/resolve/main/mmdet/bbox/mmdet_anime-face_yolov3.pth", bbox_path)
+
+                if not os.path.exists(os.path.join(bbox_path, "mmdet_anime-face_yolov3.py")):
+                    download_url("https://raw.githubusercontent.com/Bing-su/dddetailer/master/config/mmdet_anime-face_yolov3.py", bbox_path)
+
+            if not os.path.exists(os.path.join(sam_path, "sam_vit_b_01ec64.pth")):
+                download_url("https://dl.fbaipublicfiles.com/segment_anything/sam_vit_b_01ec64.pth", sam_path)
+
+        if not os.path.exists(onnx_path):
+            print(f"### ComfyUI-Impact-Pack: onnx model directory created ({onnx_path})")
+            os.mkdir(onnx_path)
+
+        impact.config.write_config()
+
+
+    install()
+
+except Exception as e:
+    print("[ERROR] ComfyUI-Impact-Pack: Dependency installation has failed. Please install manually.")
+    traceback.print_exc()

custom_nodes/ComfyUI-Impact-Pack/js/comboBoolMigration.js

@@ -0,0 +1,35 @@
+import { ComfyApp, app } from "../../scripts/app.js";
+
+let conflict_check = undefined;
+
+app.registerExtension({
+	name: "Comfy.impact.comboBoolMigration",
+
+	nodeCreated(node, app) {
+		for(let i in node.widgets) {
+			let widget = node.widgets[i];
+
+			if(conflict_check == undefined) {
+				conflict_check = !!app.extensions.find((ext) => ext.name === "Comfy.comboBoolMigration");
+			}
+
+			if(conflict_check)
+				return;
+
+			if(widget.type == "toggle") {
+				let value = widget.value;
+
+				var v = Object.getOwnPropertyDescriptor(widget, 'value');
+				if(!v) {
+					Object.defineProperty(widget, "value", {
+						set: (value) => {
+								delete widget.value;
+								widget.value = value == true || value == widget.options.on;
+							},
+						get: () => { return value; }
+					});
+				}
+			}
+		}
+	}
+});