* Upload files for Chroma Implementation
* Remove trailing whitespace
* trim more trailing whitespace..oops
* remove unused imports
* Add supported_inference_dtypes
* Set min_length to 0 and remove attention_mask=True
* Set min_length to 1
* get_mdulations added from blepping and minor changes
* Add lora conversion if statement in lora.py
* Update supported_models.py
* update model_base.py
* add uptream commits
* set modelType.FLOW, will cause beta scheduler to work properly
* Adjust memory usage factor and remove unnecessary code
* fix mistake
* reduce code duplication
* remove unused imports
* refactor for upstream sync
* sync chroma-support with upstream via syncbranch patch
* Update sd.py
* Add Chroma as option for the OptimalStepsScheduler node
* Add basic support for videos as types
This PR adds support for VIDEO as first-class types. In order to avoid
unnecessary costs, VIDEO outputs must implement the `VideoInput` ABC,
but their implementation details can vary. Included are two
implementations of this type which can be returned by other nodes:
* `VideoFromFile` - Created with either a path on disk (as a string) or
a `io.BytesIO` containing the contents of a file in a supported format
(like .mp4). This implementation won't actually load the video unless
necessary. It will also avoid re-encoding when saving if possible.
* `VideoFromComponents` - Created from an image tensor and an optional
audio tensor.
Currently, only h264 encoded videos in .mp4 containers are supported for
saving, but the plan is to add additional encodings/containers in the
near future (particularly .webm).
* Add optimization to avoid parsing entire video
* Improve type declarations to reduce warnings
* Make sure bytesIO objects can be read many times
* Fix a potential issue when saving long videos
* Fix incorrect type annotation
* Add a `LoadVideo` node to make testing easier
* Refactor new types out of the base comfy folder
I've created a new `comfy_api` top-level module. The intention is that
anything within this folder would be covered by semver-style versioning
that would allow custom nodes to rely on them not introducing breaking
changes.
* Fix linting issue
* draft pass at a native comfy implementation of Lotus-D depth and normal est
* fix model_sampling kludges
* fix ruff
---------
Co-authored-by: comfyanonymous <121283862+comfyanonymous@users.noreply.github.com>
This commit relaxes divisibility constraint for single-frame
conditionings. For single frames, the index can be arbitrary, while
multi-frame conditionings (>= 9 frames) must still be aligned to 8
frames.
Co-authored-by: Andrew Kvochko <a.kvochko@lightricks.com>
This patch fixes a bug in LTXVCropGuides when the latent has no
keyframes. Additionally, the first frame is always added as a keyframe.
Co-authored-by: Andrew Kvochko <a.kvochko@lightricks.com>
The frontend part isn't done yet so there is no video preview on the node
or dragging the webm on the interface to load the workflow yet.
This uses a new dependency: PyAV.
* Add 'sigmas' to transformer_options so that downstream code can know about the full scope of current sampling run, fix Hook Keyframes' guarantee_steps=1 inconsistent behavior with sampling split across different Sampling nodes/sampling runs by referencing 'sigmas'
* Cleaned up hooks.py, refactored Hook.should_register and add_hook_patches to use target_dict instead of target so that more information can be provided about the current execution environment if needed
* Refactor WrapperHook into TransformerOptionsHook, as there is no need to separate out Wrappers/Callbacks/Patches into different hook types (all affect transformer_options)
* Refactored HookGroup to also store a dictionary of hooks separated by hook_type, modified necessary code to no longer need to manually separate out hooks by hook_type
* In inner_sample, change "sigmas" to "sampler_sigmas" in transformer_options to not conflict with the "sigmas" that will overwrite "sigmas" in _calc_cond_batch
* Refactored 'registered' to be HookGroup instead of a list of Hooks, made AddModelsHook operational and compliant with should_register result, moved TransformerOptionsHook handling out of ModelPatcher.register_all_hook_patches, support patches in TransformerOptionsHook properly by casting any patches/wrappers/hooks to proper device at sample time
* Made hook clone code sane, made clear ObjectPatchHook and SetInjectionsHook are not yet operational
* Fix performance of hooks when hooks are appended via Cond Pair Set Props nodes by properly caching between positive and negative conds, make hook_patches_backup behave as intended (in the case that something pre-registers WeightHooks on the ModelPatcher instead of registering it at sample time)
* Filter only registered hooks on self.conds in CFGGuider.sample
* Make hook_scope functional for TransformerOptionsHook
* removed 4 whitespace lines to satisfy Ruff,
* Add a get_injections function to ModelPatcher
* Made TransformerOptionsHook contribute to registered hooks properly, added some doc strings and removed a so-far unused variable
* Rename AddModelsHooks to AdditionalModelsHook, rename SetInjectionsHook to InjectionsHook (not yet implemented, but at least getting the naming figured out)
* Clean up a typehint
The 10 step minimum for the AYS scheduler is pointless, it works well at lower steps, like 8 steps, or even 4 steps.
For example with LCM or DMD2.
Example here: https://i.ibb.co/56CSPMj/image.png
* Add MaHiRo (improved CFG)
long explanation of what it is is [here](https://huggingface.co/spaces/yoinked/blue-arxiv) (2024-1208.1)
note: if the node name has encoding issues (utf 8/whatever), id suggest to replace the face at the end with `(>w<)`
* add it to nodes.py, add description, and make it a post_cfg function
* fix
* revert the sampler_cfg_function thing
* switch cfg to args["denoised"]
* Added hook_patches to ModelPatcher for weights (model)
* Initial changes to calc_cond_batch to eventually support hook_patches
* Added current_patcher property to BaseModel
* Consolidated add_hook_patches_as_diffs into add_hook_patches func, fixed fp8 support for model-as-lora feature
* Added call to initialize_timesteps on hooks in process_conds func, and added call prepare current keyframe on hooks in calc_cond_batch
* Added default_conds support in calc_cond_batch func
* Added initial set of hook-related nodes, added code to register hooks for loras/model-as-loras, small renaming/refactoring
* Made CLIP work with hook patches
* Added initial hook scheduling nodes, small renaming/refactoring
* Fixed MaxSpeed and default conds implementations
* Added support for adding weight hooks that aren't registered on the ModelPatcher at sampling time
* Made Set Clip Hooks node work with hooks from Create Hook nodes, began work on better Create Hook Model As LoRA node
* Initial work on adding 'model_as_lora' lora type to calculate_weight
* Continued work on simpler Create Hook Model As LoRA node, started to implement ModelPatcher callbacks, attachments, and additional_models
* Fix incorrect ref to create_hook_patches_clone after moving function
* Added injections support to ModelPatcher + necessary bookkeeping, added additional_models support in ModelPatcher, conds, and hooks
* Added wrappers to ModelPatcher to facilitate standardized function wrapping
* Started scaffolding for other hook types, refactored get_hooks_from_cond to organize hooks by type
* Fix skip_until_exit logic bug breaking injection after first run of model
* Updated clone_has_same_weights function to account for new ModelPatcher properties, improved AutoPatcherEjector usage in partially_load
* Added WrapperExecutor for non-classbound functions, added calc_cond_batch wrappers
* Refactored callbacks+wrappers to allow storing lists by id
* Added forward_timestep_embed_patch type, added helper functions on ModelPatcher for emb_patch and forward_timestep_embed_patch, added helper functions for removing callbacks/wrappers/additional_models by key, added custom_should_register prop to hooks
* Added get_attachment func on ModelPatcher
* Implement basic MemoryCounter system for determing with cached weights due to hooks should be offloaded in hooks_backup
* Modified ControlNet/T2IAdapter get_control function to receive transformer_options as additional parameter, made the model_options stored in extra_args in inner_sample be a clone of the original model_options instead of same ref
* Added create_model_options_clone func, modified type annotations to use __future__ so that I can use the better type annotations
* Refactored WrapperExecutor code to remove need for WrapperClassExecutor (now gone), added sampler.sample wrapper (pending review, will likely keep but will see what hacks this could currently let me get rid of in ACN/ADE)
* Added Combine versions of Cond/Cond Pair Set Props nodes, renamed Pair Cond to Cond Pair, fixed default conds never applying hooks (due to hooks key typo)
* Renamed Create Hook Model As LoRA nodes to make the test node the main one (more changes pending)
* Added uuid to conds in CFGGuider and uuids to transformer_options to allow uniquely identifying conds in batches during sampling
* Fixed models not being unloaded properly due to current_patcher reference; the current ComfyUI model cleanup code requires that nothing else has a reference to the ModelPatcher instances
* Fixed default conds not respecting hook keyframes, made keyframes not reset cache when strength is unchanged, fixed Cond Set Default Combine throwing error, fixed model-as-lora throwing error during calculate_weight after a recent ComfyUI update, small refactoring/scaffolding changes for hooks
* Changed CreateHookModelAsLoraTest to be the new CreateHookModelAsLora, rename old ones as 'direct' and will be removed prior to merge
* Added initial support within CLIP Text Encode (Prompt) node for scheduling weight hook CLIP strength via clip_start_percent/clip_end_percent on conds, added schedule_clip toggle to Set CLIP Hooks node, small cleanup/fixes
* Fix range check in get_hooks_for_clip_schedule so that proper keyframes get assigned to corresponding ranges
* Optimized CLIP hook scheduling to treat same strength as same keyframe
* Less fragile memory management.
* Make encode_from_tokens_scheduled call cleaner, rollback change in model_patcher.py for hook_patches_backup dict
* Fix issue.
* Remove useless function.
* Prevent and detect some types of memory leaks.
* Run garbage collector when switching workflow if needed.
* Moved WrappersMP/CallbacksMP/WrapperExecutor to patcher_extension.py
* Refactored code to store wrappers and callbacks in transformer_options, added apply_model and diffusion_model.forward wrappers
* Fix issue.
* Refactored hooks in calc_cond_batch to be part of get_area_and_mult tuple, added extra_hooks to ControlBase to allow custom controlnets w/ hooks, small cleanup and renaming
* Fixed inconsistency of results when schedule_clip is set to False, small renaming/typo fixing, added initial support for ControlNet extra_hooks to work in tandem with normal cond hooks, initial work on calc_cond_batch merging all subdicts in returned transformer_options
* Modified callbacks and wrappers so that unregistered types can be used, allowing custom_nodes to have their own unique callbacks/wrappers if desired
* Updated different hook types to reflect actual progress of implementation, initial scaffolding for working WrapperHook functionality
* Fixed existing weight hook_patches (pre-registered) not working properly for CLIP
* Removed Register/Direct hook nodes since they were present only for testing, removed diff-related weight hook calculation as improved_memory removes unload_model_clones and using sample time registered hooks is less hacky
* Added clip scheduling support to all other native ComfyUI text encoding nodes (sdxl, flux, hunyuan, sd3)
* Made WrapperHook functional, added another wrapper/callback getter, added ON_DETACH callback to ModelPatcher
* Made opt_hooks append by default instead of replace, renamed comfy.hooks set functions to be more accurate
* Added apply_to_conds to Set CLIP Hooks, modified relevant code to allow text encoding to automatically apply hooks to output conds when apply_to_conds is set to True
* Fix cached_hook_patches not respecting target_device/memory_counter results
* Fixed issue with setting weights from hooks instead of copying them, added additional memory_counter check when caching hook patches
* Remove unnecessary torch.no_grad calls for hook patches
* Increased MemoryCounter minimum memory to leave free by *2 until a better way to get inference memory estimate of currently loaded models exists
* For encode_from_tokens_scheduled, allow start_percent and end_percent in add_dict to limit which scheduled conds get encoded for optimization purposes
* Removed a .to call on results of calculate_weight in patch_hook_weight_to_device that was screwing up the intermediate results for fp8 prior to being passed into stochastic_rounding call
* Made encode_from_tokens_scheduled work when no hooks are set on patcher
* Small cleanup of comments
* Turn off hook patch caching when only 1 hook present in sampling, replace some current_hook = None with calls to self.patch_hooks(None) instead to avoid a potential edge case
* On Cond/Cond Pair nodes, removed opt_ prefix from optional inputs
* Allow both FLOATS and FLOAT for floats_strength input
* Revert change, does not work
* Made patch_hook_weight_to_device respect set_func and convert_func
* Make discard_model_sampling True by default
* Add changes manually from 'master' so merge conflict resolution goes more smoothly
* Cleaned up text encode nodes with just a single clip.encode_from_tokens_scheduled call
* Make sure encode_from_tokens_scheduled will respect use_clip_schedule on clip
* Made nodes in nodes_hooks be marked as experimental (beta)
* Add get_nested_additional_models for cases where additional_models could have their own additional_models, and add robustness for circular additional_models references
* Made finalize_default_conds area math consistent with other sampling code
* Changed 'opt_hooks' input of Cond/Cond Pair Set Default Combine nodes to 'hooks'
* Remove a couple old TODO's and a no longer necessary workaround
This one should work for skipping the single layers of models like Flux
and Auraflow.
If you want to see how these models work and how many double/single layers
they have see the "ModelMerge*" nodes for the specific model.
To use:
"Load CLIP" node with t5xxl + type mochi
"Load Diffusion Model" node with the mochi dit file.
"Load VAE" with the mochi vae file.
EmptyMochiLatentVideo node for the latent.
euler + linear_quadratic in the KSampler node.
This is a port of the ModelSamplerTonemapNoiseTest from the experiments
repo.
To replicate that node use LatentOperationTonemapReinhard and
LatentApplyOperationCFG together.
It probably only works on Linux.
For maximum speed on Flux with Nvidia 40 series/ada and newer try using
this node with fp8_e4m3fn and the --fast argument.
text_encoder_diff should be connected to a CLIPMergeSubtract node.
model_diff and text_encoder_diff are optional inputs so you can create
model only loras, text encoder only loras or a lora that contains both.
* add support for HunYuanDit ControlNet
* fix hunyuandit controlnet
* fix typo in hunyuandit controlnet
* fix typo in hunyuandit controlnet
* fix code format style
* add control_weight support for HunyuanDit Controlnet
* use control_weights in HunyuanDit Controlnet
* fix typo
* Lower SAG step for finer control
Since the introduction of cfg++ which uses very low cfg value, a step of 0.1 in SAG might be too high for finer control. Even SAG of 0.1 can be too high when cfg is only 0.6, so I change the step to 0.01.
* Lower PAG step as well.
* Update nodes_sag.py
There were two bugs in PorterDuffImageComposite.
The first one is the fact that it uses the mask input directly as alpha, missing the conversion (`1-a`). The fix is similar to c16f5744.
The second one is that all color composition formulas assume alpha premultiplied values, while the input is not premultiplied.
This change fixes both of these issue.
I will not add this dependency to the default ones because models in the
spandrel_extra_arches package are non commercial and therefore not
compatible with free software licenses like the one ComfyUI uses.
If you don't mind this you can install it manually yourself.
This sampler is an LCM sampler that upscales the latent during sampling.
It can be used to generate at a higher resolution with an LCM model very
quickly.
To try it use it with a basic 5 step LCM workflow with scale_ratio 1.5 or
2.0
* Implement Differential Diffusion
* Cleanup.
* Fix.
* Masks should be applied at full strength.
* Fix colors.
* Register the node.
* Cleaner code.
* Fix issue with getting unipc sampler.
* Adjust thresholds.
* Switch to linear thresholds.
* Only calculate nearest_idx on valid thresholds.
