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Basic Genmo Mochi video model support.

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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 commit is contained in:
comfyanonymous
2024-10-26 06:54:00 -04:00
parent c3ffbae067
commit 5cbb01bc2f
18 changed files with 1677 additions and 24 deletions
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16
comfy/model_base.py
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@@ -24,6 +24,7 @@ from comfy.ldm.cascade.stage_b import StageB
from comfy.ldm.modules.encoders.noise_aug_modules import CLIPEmbeddingNoiseAugmentation
from comfy.ldm.modules.diffusionmodules.upscaling import ImageConcatWithNoiseAugmentation
from comfy.ldm.modules.diffusionmodules.mmdit import OpenAISignatureMMDITWrapper
import comfy.ldm.genmo.joint_model.asymm_models_joint
import comfy.ldm.aura.mmdit
import comfy.ldm.hydit.models
import comfy.ldm.audio.dit
@@ -718,3 +719,18 @@ class Flux(BaseModel):
out['c_crossattn'] = comfy.conds.CONDRegular(cross_attn)
out['guidance'] = comfy.conds.CONDRegular(torch.FloatTensor([kwargs.get("guidance", 3.5)]))
return out
class GenmoMochi(BaseModel):
def __init__(self, model_config, model_type=ModelType.FLOW, device=None):
super().__init__(model_config, model_type, device=device, unet_model=comfy.ldm.genmo.joint_model.asymm_models_joint.AsymmDiTJoint)
def extra_conds(self, **kwargs):
out = super().extra_conds(**kwargs)
attention_mask = kwargs.get("attention_mask", None)
if attention_mask is not None:
out['attention_mask'] = comfy.conds.CONDRegular(attention_mask)
out['num_tokens'] = comfy.conds.CONDConstant(max(1, torch.sum(attention_mask).item()))
cross_attn = kwargs.get("cross_attn", None)
if cross_attn is not None:
out['c_crossattn'] = comfy.conds.CONDRegular(cross_attn)
return out