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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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4
comfy/text_encoders/qwen_image.py
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@@ -17,12 +17,14 @@ class QwenImageTokenizer(sd1_clip.SD1Tokenizer):
self.llama_template = "<|im_start|>system\nDescribe the image by detailing the color, shape, size, texture, quantity, text, spatial relationships of the objects and background:<|im_end|>\n<|im_start|>user\n{}<|im_end|>\n<|im_start|>assistant\n"
self.llama_template_images = "<|im_start|>system\nDescribe the key features of the input image (color, shape, size, texture, objects, background), then explain how the user's text instruction should alter or modify the image. Generate a new image that meets the user's requirements while maintaining consistency with the original input where appropriate.<|im_end|>\n<|im_start|>user\n<|vision_start|><|image_pad|><|vision_end|>{}<|im_end|>\n<|im_start|>assistant\n"
def tokenize_with_weights(self, text, return_word_ids=False, llama_template=None, images=[], **kwargs):
def tokenize_with_weights(self, text, return_word_ids=False, llama_template=None, images=[], prevent_empty_text=False, **kwargs):
skip_template = False
if text.startswith('<|im_start|>'):
skip_template = True
if text.startswith('<|start_header_id|>'):
skip_template = True
if prevent_empty_text and text == '':
text = ' '
if skip_template:
llama_text = text