85 lines
2.9 KiB
Python
85 lines
2.9 KiB
Python
import torch
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from .utils.config import Config
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from .tools.inferences import inference_unianimate_entrance
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from . import run_align_pose
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# from tools import *
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class UniAnimateImage:
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@classmethod
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def INPUT_TYPES(cls):
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return {
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"required": {
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"seed": ("INT", {"default": 11, "min": 0, "max": 1000000000, "step": 1}),
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"steps": ("INT", {"default": 30, "min": 25, "max": 50, "step": 1}),
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"useFirstFrame": ("BOOLEAN", { "default": False }),
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"reference_image": ("IMAGE",), # single image
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"ref_pose": ("IMAGE",), # single image
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"pose_sequence": ("IMAGE",), # Batch of pose images
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"frame_interval": ("INT", {"default": 1, "min": 1, "max": 8, "step": 1}),
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"max_frames": ("INT", {"default": 32, "min": 1, "max": 64, "step": 1}),
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"resolution_x": ("INT", {"default": 512, "min": 512, "max": 768, "step": 256}),
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}
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}
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RETURN_TYPES = ("IMAGE", "MASK")
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FUNCTION = "process"
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CATEGORY = "image"
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def process(self, seed, steps, useFirstFrame, reference_image, ref_pose, pose_sequence, frame_interval, max_frames, resolution_x):
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cfg_update = Config(load=True)
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resolution_y = 768
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if resolution_x == 768:
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resolution_y = 1216
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resolution = [resolution_x, resolution_y]
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print("Ready for inference.")
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# print(f"image is: {reference_image}")
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frames = inference_unianimate_entrance(seed, steps, useFirstFrame, reference_image, ref_pose, pose_sequence, frame_interval, max_frames, resolution, cfg_update=cfg_update.cfg_dict)
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mask_template = torch.zeros((1, resolution_y, resolution_x), dtype=torch.float32)
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masks = [mask_template.clone() for _ in range(len(pose_sequence))]
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masks = torch.cat(masks, dim=0)
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return (frames, masks)
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class Gen_align_pose:
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@classmethod
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def INPUT_TYPES(cls):
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return {
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"required": {
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"reference_image": ("IMAGE",), # single image
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"video": ("IMAGE",), # video
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}
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}
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RETURN_TYPES = ("IMAGE", "IMAGE")
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FUNCTION = "process"
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CATEGORY = "image"
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def process(self, reference_image, video):
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if torch.cuda.is_available():
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print(f"CUDA version: {torch.version.cuda}")
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print(f"CUDNN version: {torch.backends.cudnn.version()}")
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print(f"Device name: {torch.cuda.get_device_name(0)}")
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else:
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print("CUDA is not available")
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poses, refPose = run_align_pose.mp_main(reference_image, video)
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return (refPose, poses)
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NODE_CLASS_MAPPINGS = {
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"UniAnimateImage" : UniAnimateImage,
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"Gen_align_pose" : Gen_align_pose,
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}
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NODE_DISPLAY_NAME_MAPPINGS = {
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"UniAnimateImage" :"Animate image with UniAnimate",
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"Gen_align_pose" :"Align & Generate poses for UniAnimate",
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}
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