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HunyuanVideo 1.5 (#10819)

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* init

* update

* Update model.py

* Update model.py

* remove print

* Fix text encoding

* Prevent empty negative prompt

Really doesn't work otherwise

* fp16 works

* I2V

* Update model_base.py

* Update nodes_hunyuan.py

* Better latent rgb factors

* Use the correct sigclip output...

* Support HunyuanVideo1.5 SR model

* whitespaces...

* Proper latent channel count

* SR model fixes

This also still needs timesteps scheduling based on the noise scale, can be used with two samplers too already

* vae_refiner: roll the convolution through temporal

Work in progress.

Roll the convolution through time using 2-latent-frame chunks and a
FIFO queue for the convolution seams.

* Support HunyuanVideo15 latent resampler

* fix

* Some cleanup

Co-Authored-By: comfyanonymous <121283862+comfyanonymous@users.noreply.github.com>

* Proper hyvid15 I2V channels

Co-Authored-By: comfyanonymous <121283862+comfyanonymous@users.noreply.github.com>

* Fix TokenRefiner for fp16

Otherwise x.sum has infs, just in case only casting if input is fp16, I don't know if necessary.

* Bugfix for the HunyuanVideo15 SR model

* vae_refiner: roll the convolution through temporal II

Roll the convolution through time using 2-latent-frame chunks and a
FIFO queue for the convolution seams.

Added support for encoder, lowered to 1 latent frame to save more
VRAM, made work for Hunyuan Image 3.0 (as code shared).

Fixed names, cleaned up code.

* Allow any number of input frames in VAE.

* Better VAE encode mem estimation.

* Lowvram fix.

* Fix hunyuan image 2.1 refiner.

* Fix mistake.

* Name changes.

* Rename.

* Whitespace.

* Fix.

* Fix.

---------

Co-authored-by: kijai <40791699+kijai@users.noreply.github.com>
Co-authored-by: Rattus <rattus128@gmail.com>
This commit is contained in:
comfyanonymous
2025-11-20 19:44:43 -08:00
committed by GitHub
parent 10e90a5757
commit 943b3b615d
15 changed files with 777 additions and 126 deletions
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10
comfy/model_detection.py
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@@ -186,6 +186,16 @@ def detect_unet_config(state_dict, key_prefix, metadata=None):
guidance_keys = list(filter(lambda a: a.startswith("{}guidance_in.".format(key_prefix)), state_dict_keys))
dit_config["guidance_embed"] = len(guidance_keys) > 0
# HunyuanVideo 1.5
if '{}cond_type_embedding.weight'.format(key_prefix) in state_dict_keys:
dit_config["use_cond_type_embedding"] = True
else:
dit_config["use_cond_type_embedding"] = False
if '{}vision_in.proj.0.weight'.format(key_prefix) in state_dict_keys:
dit_config["vision_in_dim"] = state_dict['{}vision_in.proj.0.weight'.format(key_prefix)].shape[0]
else:
dit_config["vision_in_dim"] = None
return dit_config
if '{}double_blocks.0.img_attn.norm.key_norm.scale'.format(key_prefix) in state_dict_keys and ('{}img_in.weight'.format(key_prefix) in state_dict_keys or f"{key_prefix}distilled_guidance_layer.norms.0.scale" in state_dict_keys): #Flux, Chroma or Chroma Radiance (has no img_in.weight)