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3dvae tiled_encode and tiled_decode

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u 2024-08-30 11:03:26 +08:00
parent c4bc81d120
commit 4a4ac59d62
1 changed files with 44 additions and 10 deletions
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50
comfy_extras/nodes_cogvidex_model.py
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@ -556,10 +556,18 @@ class CogVideoEncodePrompt:
class CogVideoImageEncodeSampler: class CogVideoImageEncodeSampler:
@classmethod @classmethod
def INPUT_TYPES(s): def INPUT_TYPES(s):
return {"required": { return {
"vae3d": ("VAE3D",), "required": {
"image": ("IMAGE", ), "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",) RETURN_TYPES = ("LATENT",)
@ -568,11 +576,20 @@ class CogVideoImageEncodeSampler:
CATEGORY = "latent" CATEGORY = "latent"
DESCRIPTION = """3DVAE image Encode to 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() 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)
vae3d.to(device) 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 = image.clone() * 2.0 - 1.0
input_image = input_image.to(vae3d.dtype).to(device) input_image = input_image.to(vae3d.dtype).to(device)
@ -669,10 +686,18 @@ class CogVideoSampler:
class CogVideoSamplerDecodeImages: class CogVideoSamplerDecodeImages:
@classmethod @classmethod
def INPUT_TYPES(s): def INPUT_TYPES(s):
return {"required": { return {
"vae3d": ("VAE3D",), "required": {
"samples": ("LATENT", ), "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",) RETURN_TYPES = ("BARCHIMAGE",)
@ -681,7 +706,7 @@ class CogVideoSamplerDecodeImages:
CATEGORY = "latent" CATEGORY = "latent"
DESCRIPTION = """CogVideoX sampler 3DVAE decode images""" 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 vae3d: VAE3D
samples: dict of samples, torch.Tensor # B, T_chunk, C, H, W samples: dict of samples, torch.Tensor # B, T_chunk, C, H, W
@ -690,6 +715,15 @@ class CogVideoSamplerDecodeImages:
offload_device = mm.unet_offload_device() offload_device = mm.unet_offload_device()
latents = samples["samples"] latents = samples["samples"]
vae3d.to(device) 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.to(vae3d.dtype)
latents = latents.permute(0, 2, 1, 3, 4) # [batch_size, num_channels, num_frames, height, width] latents = latents.permute(0, 2, 1, 3, 4) # [batch_size, num_channels, num_frames, height, width]