xinyun/ComfyUI-CogVideoXWrapper
1
0
Fork 0
mirror of https://git.datalinker.icu/kijai/ComfyUI-CogVideoXWrapper.git synced 2025-12-14 23:44:26 +08:00
Code Issues Packages Projects Releases Wiki Activity

Add strength parameter for image encode

Browse Source
This commit is contained in:
kijai 2024-11-20 01:32:05 +02:00
parent ecd067260c
commit b9688f3cd2
1 changed files with 3 additions and 2 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

5
nodes.py
View File

@ -220,6 +220,7 @@ class CogVideoImageEncode:
"end_image": ("IMAGE", ), "end_image": ("IMAGE", ),
"enable_tiling": ("BOOLEAN", {"default": False, "tooltip": "Enable tiling for the VAE to reduce memory usage"}), "enable_tiling": ("BOOLEAN", {"default": False, "tooltip": "Enable tiling for the VAE to reduce memory usage"}),
"noise_aug_strength": ("FLOAT", {"default": 0.0, "min": 0.0, "max": 1.0, "step": 0.001, "tooltip": "Augment image with noise"}), "noise_aug_strength": ("FLOAT", {"default": 0.0, "min": 0.0, "max": 1.0, "step": 0.001, "tooltip": "Augment image with noise"}),
"strength": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.01}),
}, },
} }
@ -228,7 +229,7 @@ class CogVideoImageEncode:
FUNCTION = "encode" FUNCTION = "encode"
CATEGORY = "CogVideoWrapper" CATEGORY = "CogVideoWrapper"
def encode(self, vae, start_image, end_image=None, enable_tiling=False, noise_aug_strength=0.0): def encode(self, vae, start_image, end_image=None, enable_tiling=False, noise_aug_strength=0.0, strength=1.0):
device = mm.get_torch_device() device = mm.get_torch_device()
offload_device = mm.unet_offload_device() offload_device = mm.unet_offload_device()
generator = torch.Generator(device=device).manual_seed(0) generator = torch.Generator(device=device).manual_seed(0)
@ -271,7 +272,7 @@ class CogVideoImageEncode:
else: else:
final_latents = start_latents final_latents = start_latents
final_latents = final_latents * vae_scaling_factor final_latents = final_latents * vae_scaling_factor * strength
log.info(f"Encoded latents shape: {final_latents.shape}") log.info(f"Encoded latents shape: {final_latents.shape}")
vae.to(offload_device) vae.to(offload_device)