xinyun/ComfyUI-CogVideoXWrapper
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Update nodes.py

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kijai 2024-11-12 00:53:55 +02:00
parent db697fea11
commit c8772c3aa0
1 changed files with 6 additions and 4 deletions
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nodes.py
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@ -370,7 +370,7 @@ class CogVideoImageEncode:
vae.enable_slicing() vae.enable_slicing()
model_name = pipeline.get("model_name", "") model_name = pipeline.get("model_name", "")
if "1.5" in model_name or "1_5" in model_name: if ("1.5" in model_name or "1_5" in model_name) and image.shape[0] == 1:
vae_scaling_factor = 1 / vae.config.scaling_factor vae_scaling_factor = 1 / vae.config.scaling_factor
else: else:
vae_scaling_factor = vae.config.scaling_factor vae_scaling_factor = vae.config.scaling_factor
@ -428,12 +428,13 @@ class CogVideoImageEncode:
elif hasattr(latents, "latents"): elif hasattr(latents, "latents"):
latents = latents.latents latents = latents.latents
latents = vae_scaling_factor * latents
latents = latents.permute(0, 2, 1, 3, 4) # B, T_chunk, C, H, W latents = latents.permute(0, 2, 1, 3, 4) # B, T_chunk, C, H, W
latents_list.append(latents) latents_list.append(latents)
# Concatenate all the chunks along the temporal dimension # Concatenate all the chunks along the temporal dimension
final_latents = torch.cat(latents_list, dim=1) final_latents = torch.cat(latents_list, dim=1)
final_latents = final_latents * vae_scaling_factor
log.info(f"Encoded latents shape: {final_latents.shape}") log.info(f"Encoded latents shape: {final_latents.shape}")
if not pipeline["cpu_offloading"]: if not pipeline["cpu_offloading"]:
vae.to(offload_device) vae.to(offload_device)
@ -810,9 +811,9 @@ class CogVideoSampler:
}), }),
}, },
"optional": { "optional": {
"samples": ("LATENT", ), "samples": ("LATENT", {"tooltip": "init Latents to use for video2video process"} ),
"denoise_strength": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.01}), "denoise_strength": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.01}),
"image_cond_latents": ("LATENT", ), "image_cond_latents": ("LATENT",{"tooltip": "Latent to use for image2video conditioning"} ),
"context_options": ("COGCONTEXT", ), "context_options": ("COGCONTEXT", ),
"controlnet": ("COGVIDECONTROLNET",), "controlnet": ("COGVIDECONTROLNET",),
"tora_trajectory": ("TORAFEATURES", ), "tora_trajectory": ("TORAFEATURES", ),
@ -841,6 +842,7 @@ class CogVideoSampler:
context_options is not None context_options is not None
), "1.0 I2V model can only do 49 frames" ), "1.0 I2V model can only do 49 frames"
if image_cond_latents is not None: if image_cond_latents is not None:
assert image_cond_latents.shape[0] == 1, "Image condition latents must be a single latent"
assert "I2V" in pipeline.get("model_name", ""), "Image condition latents only supported for I2V models" assert "I2V" in pipeline.get("model_name", ""), "Image condition latents only supported for I2V models"
else: else:
assert "I2V" not in pipeline.get("model_name", ""), "Image condition latents required for I2V models" assert "I2V" not in pipeline.get("model_name", ""), "Image condition latents required for I2V models"