xinyun/ComfyUI
1
0
Fork 0
mirror of https://git.datalinker.icu/comfyanonymous/ComfyUI synced 2026-11-11 17:06:36 +08:00
Code Issues Packages Projects Releases Wiki Activity

3dvae tiled_encode and tiled_decode

Browse Source
This commit is contained in:
u 2024-08-30 11:03:26 +08:00
parent c4bc81d120
commit 4a4ac59d62
1 changed files with 44 additions and 10 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

54
comfy_extras/nodes_cogvidex_model.py
View File

@ -556,10 +556,18 @@ class CogVideoEncodePrompt:
class CogVideoImageEncodeSampler:
@classmethod
def INPUT_TYPES(s):
return {"required": {
"vae3d": ("VAE3D",),
"image": ("IMAGE", ),
return {
"required": {
"vae3d": ("VAE3D",),
"image": ("IMAGE", ),
"enable_vae_tiling": ("BOOLEAN", {"default": False}),
},
"optional": {
"tile_sample_min_height": ("INT", {"default": 96, "min": 16, "max": 2048, "step": 8}),
"tile_sample_min_width": ("INT", {"default": 96, "min": 16, "max": 2048, "step": 8}),
"tile_overlap_factor_height": ("FLOAT", {"default": 0.083, "min": 0.0, "max": 1.0, "step": 0.001}),
"tile_overlap_factor_width": ("FLOAT", {"default": 0.083, "min": 0.0, "max": 1.0, "step": 0.001}),
}
}
RETURN_TYPES = ("LATENT",)
@ -568,12 +576,21 @@ class CogVideoImageEncodeSampler:
CATEGORY = "latent"
DESCRIPTION = """3DVAE image Encode to latent"""
def encode(self, vae3d, image):
def encode(self, vae3d, image, enable_vae_tiling, tile_sample_min_height, tile_sample_min_width, tile_overlap_factor_height, tile_overlap_factor_width):
device = mm.get_torch_device()
offload_device = mm.unet_offload_device()
generator = torch.Generator(device=device).manual_seed(0)
vae3d.to(device)
if enable_vae_tiling:
vae3d.enable_tiling(
tile_sample_min_height=tile_sample_min_height,
tile_sample_min_width=tile_sample_min_width,
tile_overlap_factor_height=tile_overlap_factor_height,
tile_overlap_factor_width=tile_overlap_factor_width,
)
else:
vae3d.disable_tiling()
input_image = image.clone() * 2.0 - 1.0
input_image = input_image.to(vae3d.dtype).to(device)
input_image = input_image.unsqueeze(0).permute(0, 4, 1, 2, 3) # B, C, T, H, W
@ -669,10 +686,18 @@ class CogVideoSampler:
class CogVideoSamplerDecodeImages:
@classmethod
def INPUT_TYPES(s):
return {"required": {
"vae3d": ("VAE3D",),
"samples": ("LATENT", ),
return {
"required": {
"vae3d": ("VAE3D",),
"samples": ("LATENT", ),
"enable_vae_tiling": ("BOOLEAN", {"default": False}),
},
"optional": {
"tile_sample_min_height": ("INT", {"default": 96, "min": 16, "max": 2048, "step": 8}),
"tile_sample_min_width": ("INT", {"default": 96, "min": 16, "max": 2048, "step": 8}),
"tile_overlap_factor_height": ("FLOAT", {"default": 0.083, "min": 0.0, "max": 1.0, "step": 0.001}),
"tile_overlap_factor_width": ("FLOAT", {"default": 0.083, "min": 0.0, "max": 1.0, "step": 0.001}),
}
}
RETURN_TYPES = ("BARCHIMAGE",)
@ -681,7 +706,7 @@ class CogVideoSamplerDecodeImages:
CATEGORY = "latent"
DESCRIPTION = """CogVideoX sampler 3DVAE decode images"""
def decode(self, vae3d, samples):
def decode(self, vae3d, samples, enable_vae_tiling, tile_sample_min_height, tile_sample_min_width, tile_overlap_factor_height, tile_overlap_factor_width):
"""
vae3d: VAE3D
samples: dict of samples, torch.Tensor # B, T_chunk, C, H, W
@ -690,7 +715,16 @@ class CogVideoSamplerDecodeImages:
offload_device = mm.unet_offload_device()
latents = samples["samples"]
vae3d.to(device)
if enable_vae_tiling:
vae3d.enable_tiling(
tile_sample_min_height=tile_sample_min_height,
tile_sample_min_width=tile_sample_min_width,
tile_overlap_factor_height=tile_overlap_factor_height,
tile_overlap_factor_width=tile_overlap_factor_width,
)
else:
vae3d.disable_tiling()
latents = latents.to(vae3d.dtype)
latents = latents.permute(0, 2, 1, 3, 4) # [batch_size, num_channels, num_frames, height, width]
latents = 1 / vae3d.config.scaling_factor * latents