nodes/kijai-ComfyUI-CogVideoXWrapper
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Update workflows, fix controlnet

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kijai
2024-11-19 15:23:38 +02:00
parent a7646c0d6f
commit 128f89c4d2
16 changed files with 6627 additions and 7832 deletions
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nodes.py
+20 -22
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@@ -595,14 +595,14 @@ class CogVideoSampler:
FUNCTION = "process"
CATEGORY = "CogVideoWrapper"
def process(self, pipeline, positive, negative, steps, cfg, seed, scheduler, num_frames, samples=None,
def process(self, model, positive, negative, steps, cfg, seed, scheduler, num_frames, samples=None,
denoise_strength=1.0, image_cond_latents=None, context_options=None, controlnet=None, tora_trajectory=None, fastercache=None):
mm.soft_empty_cache()
model_name = pipeline.get("model_name", "")
model_name = model.get("model_name", "")
supports_image_conds = True if "I2V" in model_name or "interpolation" in model_name.lower() or "fun" in model_name.lower() else False
if "fun" in model_name.lower() and image_cond_latents is not None:
if "fun" in model_name.lower() and "pose" not in model_name.lower() and image_cond_latents is not None:
assert image_cond_latents["mask"] is not None, "For fun inpaint models use CogVideoImageEncodeFunInP"
fun_mask = image_cond_latents["mask"]
else:
@@ -632,11 +632,11 @@ class CogVideoSampler:
device = mm.get_torch_device()
offload_device = mm.unet_offload_device()
pipe = pipeline["pipe"]
dtype = pipeline["dtype"]
scheduler_config = pipeline["scheduler_config"]
pipe = model["pipe"]
dtype = model["dtype"]
scheduler_config = model["scheduler_config"]
if not pipeline["cpu_offloading"] and pipeline["manual_offloading"]:
if not model["cpu_offloading"] and model["manual_offloading"]:
pipe.transformer.to(device)
generator = torch.Generator(device=torch.device("cpu")).manual_seed(seed)
@@ -683,10 +683,10 @@ class CogVideoSampler:
except:
pass
autocastcondition = not pipeline["onediff"] or not dtype == torch.float32
autocastcondition = not model["onediff"] or not dtype == torch.float32
autocast_context = torch.autocast(mm.get_autocast_device(device), dtype=dtype) if autocastcondition else nullcontext()
with autocast_context:
latents = pipeline["pipe"](
latents = model["pipe"](
num_inference_steps=steps,
height = height,
width = width,
@@ -708,7 +708,7 @@ class CogVideoSampler:
controlnet=controlnet,
tora=tora_trajectory if tora_trajectory is not None else None,
)
if not pipeline["cpu_offloading"] and pipeline["manual_offloading"]:
if not model["cpu_offloading"] and model["manual_offloading"]:
pipe.transformer.to(offload_device)
if fastercache is not None:
@@ -763,18 +763,16 @@ class CogVideoDecode:
@classmethod
def INPUT_TYPES(s):
return {"required": {
"samples": ("LATENT", ),
"vae": ("VAE", {"default": None}),
"enable_vae_tiling": ("BOOLEAN", {"default": True, "tooltip": "Drastically reduces memory use but may introduce seams"}),
},
"optional": {
"tile_sample_min_height": ("INT", {"default": 240, "min": 16, "max": 2048, "step": 8, "tooltip": "Minimum tile height, default is half the height"}),
"tile_sample_min_width": ("INT", {"default": 360, "min": 16, "max": 2048, "step": 8, "tooltip": "Minimum tile width, default is half the width"}),
"tile_overlap_factor_height": ("FLOAT", {"default": 0.2, "min": 0.0, "max": 1.0, "step": 0.001}),
"tile_overlap_factor_width": ("FLOAT", {"default": 0.2, "min": 0.0, "max": 1.0, "step": 0.001}),
"auto_tile_size": ("BOOLEAN", {"default": True, "tooltip": "Auto size based on height and width, default is half the size"}),
}
}
"vae": ("VAE",),
"samples": ("LATENT",),
"enable_vae_tiling": ("BOOLEAN", {"default": True, "tooltip": "Drastically reduces memory use but may introduce seams"}),
"tile_sample_min_height": ("INT", {"default": 240, "min": 16, "max": 2048, "step": 8, "tooltip": "Minimum tile height, default is half the height"}),
"tile_sample_min_width": ("INT", {"default": 360, "min": 16, "max": 2048, "step": 8, "tooltip": "Minimum tile width, default is half the width"}),
"tile_overlap_factor_height": ("FLOAT", {"default": 0.2, "min": 0.0, "max": 1.0, "step": 0.001}),
"tile_overlap_factor_width": ("FLOAT", {"default": 0.2, "min": 0.0, "max": 1.0, "step": 0.001}),
"auto_tile_size": ("BOOLEAN", {"default": True, "tooltip": "Auto size based on height and width, default is half the size"}),
},
}
RETURN_TYPES = ("IMAGE",)
RETURN_NAMES = ("images",)