2277 Commits

Include renames

Author	SHA1	Message	Date
rattusandGitHub	37f711d4a1	mm: Fix cast buffers with intel offloading (#12229) ... Intel has offloading support but there were some nvidia calls in the new cast buffer stuff.	2026-02-02 17:34:46 -05:00
comfyanonymousandGitHub	dd86b15521	Enable embeddings for some qwen 3 models. (#12218)	2026-02-02 03:51:09 -05:00
comfyanonymousandGitHub	021ba20719	Fix issue with parameters on root model object. (#12216)	2026-02-01 20:12:52 -05:00
rattusandGitHub	2b5da3b72e	dynamic_vram: silence pytorch buffer warning (#12210) ... This is log clutter and concerning to users. Its a false alarm.	2026-02-01 20:09:55 -05:00
rattusandGitHub	794d05bdb1	dynamic_vram: respect argument cast dtypes in non-comfy weights (#12209) ... This function has a dtype argument that allows the caller to set the dtype in the cast. TIL Some models override this on weight casts, which means its the highest priority. Priority scheme is: argument > model dtype > state dict dtype	2026-02-01 20:09:21 -05:00
rattusandGitHub	361b9a82a3	fix pinning with model defined dtype (#12208) ... pinned memory was converted back to pinning the CPU side weight without any changes. Fix the pinner to use the CPU weight and not the model defined geometry. This will either save RAM or stop buffer overruns when the types mismatch. Fix the model defined weight caster to use the [ s.weight, s.bias ] interpretation, as xfer_dest might be the flattened pin now. Fix the detection of needing to cast to not be conditional on !pin.	2026-02-01 08:42:32 -08:00
comfyanonymousandGitHub	667a1b8878	Fix some custom nodes breaking. (#12203)	2026-02-01 01:55:18 -05:00
rattusandGitHub	f8acd9c402	Reduce RAM usage, fix VRAM OOMs, and fix Windows shared memory spilling with adaptive model loading (#11845)	2026-02-01 01:01:11 -05:00
comfyanonymousandGitHub	873de5f37a	KV cache implementation for using llama models for text generation. (#12195)	2026-01-31 21:11:11 -05:00
comfyanonymousandGitHub	b8f848bfe3	Fix model not working with any res. (#12186)	2026-01-31 00:12:48 -05:00
comfyanonymousandGitHub	c9b633d84f	Add missing spacial downscale ratios. (#12146)	2026-01-28 20:52:51 -05:00
guillandGitHub	dcff27fe3f	Add support for dev-only nodes. (#12106) ... When a node is declared as dev-only, it doesn't show in the default UI unless the dev mode is enabled in the settings. The intention is to allow nodes related to unit testing to be included in ComfyUI distributions without confusing the average user.	2026-01-27 13:03:29 -08:00
rattusandGitHub	6516ab335d	wan-vae: Switch off feature cache for single frame (#12090) ... The code throughout is None safe to just skip the feature cache saving step if none. Set it none in single frame use so qwen doesn't burn VRAM on the unused cache.	2026-01-26 19:40:19 -05:00
comfyanonymousandGitHub	2129e7d278	Fix mistral 3 tokenizer code failing on latest transformers version and other breakage. (#12095) ... * Fix mistral 3 tokenizer code failing on latest transformers version. * Add requests to the requirements	2026-01-26 11:39:00 -05:00
Kohaku-BlueleafandGitHub	a97c98068f	[Weight-adapter/Trainer] Bypass forward mode in Weight adapter system (#11958) ... * Add API of bypass forward module * bypass implementation * add bypass fwd into nodes list/trainer	2026-01-24 22:56:22 -05:00
comfyanonymousandGitHub	635406e283	Only enable fp16 on z image models that actually support it. (#12065)	2026-01-24 22:32:28 -05:00
comfyanonymousandGitHub	aef4e13588	Make empty latent node work with other models. (#12062)	2026-01-24 19:23:20 -05:00
rattusandGitHub	4e6a1b66a9	speed up and reduce VRAM of QWEN VAE and WAN (less so) (#12036) ... * ops: introduce autopad for conv3d This works around pytorch missing ability to causal pad as part of the kernel and avoids massive weight duplications for padding. * wan-vae: rework causal padding This currently uses F.pad which takes a full deep copy and is liable to be the VRAM peak. Instead, kick spatial padding back to the op and consolidate the temporal padding with the cat for the cache. * wan-vae: implement zero pad fast path The WAN VAE is also QWEN where it is used single-image. These convolutions are however zero padded 3d convolutions, which means the VAE is actually just 2D down the last element of the conv weight in the temporal dimension. Fast path this, to avoid adding zeros that then just evaporate in convoluton math but cost computation.	2026-01-23 19:56:14 -05:00
comfyanonymousandGitHub	9cf299a9f9	Make regular empty latent node work properly on flux 2 variants. (#12050)	2026-01-23 19:50:48 -05:00
ComfyUI WikiandGitHub	e89b22993a	Support ModelScope-Trainer/DiffSynth LoRA format for Flux.2 Klein models (#12042)	2026-01-23 15:27:49 -05:00
Jukka SeppänenandGitHub	55bd606e92	LTX2: Refactor forward function for better VRAM efficiency and fix spatial inpainting (#12046) ... * Disable timestep embed compression when inpainting Spatial inpainting not compatible with the compression * Reduce crossattn peak VRAM * LTX2: Refactor forward function for better VRAM efficiency	2026-01-23 15:26:38 -05:00
Omri MaromandGitHub	d7f3241bf6	qwen_image: propagate attention mask. (#11966)	2026-01-22 20:02:31 -05:00
comfyanonymousandGitHub	09a2e67151	Support loading flux 2 klein checkpoints saved with SaveCheckpoint. (#12033)	2026-01-22 18:20:48 -05:00
rattusandGitHub	0fd1b78736	Reduce LTX2 VAE VRAM consumption (#12028) ... * causal_video_ae: Remove attention ResNet This attention_head_dim argument does not exist on this constructor so this is dead code. Remove as generic attention mid VAE conflicts with temporal roll. * ltx-vae: consoldate causal/non-causal code paths * ltx-vae: add cache rolling adder * ltx-vae: use cached adder for resnet * ltx-vae: Implement rolling VAE Implement a temporal rolling VAE for the LTX2 VAE. Usually when doing temporal rolling VAEs you can just chunk on time relying on causality and cache behind you as you go. The LTX VAE is however non-causal. So go whole hog and implement per layer run ahead and backpressure between the decoder layers using recursive state beween the layers. Operations are ammended with temporal_cache_state{} which they can use to hold any state then need for partial execution. Convolutions cache their inputs behind the up to N-1 frames, and skip connections need to cache the mismatch between convolution input and output that happens due to missing future (non-causal) input. Each call to run_up() processes a layer accross a range on input that may or may not be complete. It goes depth first to process as much as possible to try and digest frames to the final output ASAP. If layers run out of input due to convolution losses, they simply return without action effectively applying back-pressure to the earlier layers. As the earlier layers do more work and caller deeper, the partial states are reconciled and output continues to digest depth first as much as possible. Chunking is done using a size quota rather than a fixed frame length and any layer can initiate chunking, and multiple layers can chunk at different granulatiries. This remove the old limitation of always having to process 1 latent frame to entirety and having to hold 8 full decoded frames as the VRAM peak.	2026-01-22 16:54:18 -05:00
Jukka SeppänenandGitHub	16b9aabd52	Support Multi/InfiniteTalk (#10179) ... * re-init * Update model_multitalk.py * whitespace... * Update model_multitalk.py * remove print * this is redundant * remove import * Restore preview functionality * Move block_idx to transformer_options * Remove LoopingSamplerCustomAdvanced * Remove looping functionality, keep extension functionality * Update model_multitalk.py * Handle ref_attn_mask with separate patch to avoid having to always return q and k from self_attn * Chunk attention map calculation for multiple speakers to reduce peak VRAM usage * Update model_multitalk.py * Add ModelPatch type back * Fix for latest upstream * Use DynamicCombo for cleaner node Basically just so that single_speaker mode hides mask inputs and 2nd audio input * Update nodes_wan.py	2026-01-21 23:09:48 -05:00
Jukka SeppänenandGitHub	245f6139b6	More targeted embedding_connector loading for LTX2 text encoder (#11992) ... Reduces errors	2026-01-21 23:05:06 -05:00
Jukka SeppänenandGitHub	3365ad18a5	Support LTX2 tiny vae (taeltx_2) (#11929)	2026-01-21 23:03:51 -05:00
comfyanonymousandGitHub	abe2ec26a6	Support the Anima model. (#12012)	2026-01-21 19:44:28 -05:00
MarkuryandGitHub	0fc15700be	Add LyCoris LoKr MLP layer support for Flux2 (#11997)	2026-01-20 23:18:33 -05:00
comfyanonymousandGitHub	e755268e7b	Config for Qwen 3 0.6B model. (#11998)	2026-01-20 23:08:31 -05:00
MyloandGitHub	c4a14df9a3	Dynamically detect chroma radiance patch size (#11991)	2026-01-20 18:46:11 -05:00
Ivan ZorinandGitHub	965d0ed509	fix: remove normalization of audio in LTX Mel spectrogram creation (#11990) ... For LTX Audio VAE, remove normalization of audio during MEL spectrogram creation. This aligs inference with training and prevents loud audio from being attenuated.	2026-01-20 18:44:28 -05:00
comfyanonymousandGitHub	8ccc0c94fa	Make omni stuff work on regular z image for easier testing. (#11985)	2026-01-20 00:32:00 -05:00
comfyanonymousandGitHub	2108167f9f	Support zimage omni base model. (#11979)	2026-01-19 23:17:38 -05:00
comfyanonymousandGitHub	70c91b8248	Fix #11963 (#11982)	2026-01-19 22:32:40 -05:00
rkfgandGitHub	0da5a0fe58	Convert mono audio to fake stereo for LTXV VAE encoding (#11965)	2026-01-19 22:12:02 -05:00
comfyanonymousandGitHub	e0eacb0688	Simpler way to implement the #11980 loras. (#11981)	2026-01-19 22:00:36 -05:00
comfyanonymousandGitHub	7ac999bf30	Add image sizes to clip vision outputs. (#11923)	2026-01-16 23:02:28 -05:00
comfyanonymousandGitHub	4c816d5c69	Adjust memory usage factor calculation for flux2 klein. (#11900)	2026-01-15 20:06:40 -05:00
comfyanonymousandGitHub	3b832231bb	Flux2 Klein support. (#11890)	2026-01-15 10:33:15 -05:00
Jukka SeppänenandGitHub	be518db5a7	Remove extraneous clip missing warnings when loading LTX2 embeddings_connector weights (#11874)	2026-01-14 17:54:04 -05:00
rattusandGitHub	80441eb15e	utils: fix lanczos grayscale upscaling (#11873)	2026-01-14 17:53:16 -05:00
comfyanonymousandGitHub	6165c38cb5	Optimize nvfp4 lora applying. (#11866) ... This changes results a bit but it also speeds up things a lot.	2026-01-14 00:49:38 -05:00
SilverandGitHub	712cca36a1	feat: throttle ProgressBar updates to reduce WebSocket flooding (#11504)	2026-01-13 22:41:44 -05:00
comfyanonymousandGitHub	eff2b9d412	Optimize nvfp4 lora applying. (#11856)	2026-01-13 19:37:19 -05:00
comfyanonymousandGitHub	15b312de7a	Optimize nvfp4 lora applying. (#11854)	2026-01-13 19:23:58 -05:00
comfyanonymousandGitHub	1dcbd9efaf	Bump ltxav mem estimation a bit. (#11842)	2026-01-13 01:42:07 -05:00
comfyanonymousandGitHub	117e7a5853	Refactor to try to lower mem usage. (#11840)	2026-01-12 21:01:52 -08:00
comfyanonymousandGitHub	b3c0e4de57	Make loras work on nvfp4 models. (#11837) ... The initial applying is a bit slow but will probably be sped up in the future.	2026-01-12 22:33:54 -05:00
Jukka SeppänenandGitHub	fd5c0755af	Reduce LTX2 VRAM use by more efficient timestep embed handling (#11829)	2026-01-12 17:28:59 -05:00