Spaces:
Running
on
L40S
Running
on
L40S
File size: 6,007 Bytes
4450790 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 |
import nodes
import torch
import comfy.utils
import comfy.sd
import folder_paths
import comfy_extras.nodes_model_merging
class ImageOnlyCheckpointLoader:
@classmethod
def INPUT_TYPES(s):
return {"required": { "ckpt_name": (folder_paths.get_filename_list("checkpoints"), ),
}}
RETURN_TYPES = ("MODEL", "CLIP_VISION", "VAE")
FUNCTION = "load_checkpoint"
CATEGORY = "loaders/video_models"
def load_checkpoint(self, ckpt_name, output_vae=True, output_clip=True):
ckpt_path = folder_paths.get_full_path_or_raise("checkpoints", ckpt_name)
out = comfy.sd.load_checkpoint_guess_config(ckpt_path, output_vae=True, output_clip=False, output_clipvision=True, embedding_directory=folder_paths.get_folder_paths("embeddings"))
return (out[0], out[3], out[2])
class SVD_img2vid_Conditioning:
@classmethod
def INPUT_TYPES(s):
return {"required": { "clip_vision": ("CLIP_VISION",),
"init_image": ("IMAGE",),
"vae": ("VAE",),
"width": ("INT", {"default": 1024, "min": 16, "max": nodes.MAX_RESOLUTION, "step": 8}),
"height": ("INT", {"default": 576, "min": 16, "max": nodes.MAX_RESOLUTION, "step": 8}),
"video_frames": ("INT", {"default": 14, "min": 1, "max": 4096}),
"motion_bucket_id": ("INT", {"default": 127, "min": 1, "max": 1023}),
"fps": ("INT", {"default": 6, "min": 1, "max": 1024}),
"augmentation_level": ("FLOAT", {"default": 0.0, "min": 0.0, "max": 10.0, "step": 0.01})
}}
RETURN_TYPES = ("CONDITIONING", "CONDITIONING", "LATENT")
RETURN_NAMES = ("positive", "negative", "latent")
FUNCTION = "encode"
CATEGORY = "conditioning/video_models"
def encode(self, clip_vision, init_image, vae, width, height, video_frames, motion_bucket_id, fps, augmentation_level):
output = clip_vision.encode_image(init_image)
pooled = output.image_embeds.unsqueeze(0)
pixels = comfy.utils.common_upscale(init_image.movedim(-1,1), width, height, "bilinear", "center").movedim(1,-1)
encode_pixels = pixels[:,:,:,:3]
if augmentation_level > 0:
encode_pixels += torch.randn_like(pixels) * augmentation_level
t = vae.encode(encode_pixels)
positive = [[pooled, {"motion_bucket_id": motion_bucket_id, "fps": fps, "augmentation_level": augmentation_level, "concat_latent_image": t}]]
negative = [[torch.zeros_like(pooled), {"motion_bucket_id": motion_bucket_id, "fps": fps, "augmentation_level": augmentation_level, "concat_latent_image": torch.zeros_like(t)}]]
latent = torch.zeros([video_frames, 4, height // 8, width // 8])
return (positive, negative, {"samples":latent})
class VideoLinearCFGGuidance:
@classmethod
def INPUT_TYPES(s):
return {"required": { "model": ("MODEL",),
"min_cfg": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 100.0, "step":0.5, "round": 0.01}),
}}
RETURN_TYPES = ("MODEL",)
FUNCTION = "patch"
CATEGORY = "sampling/video_models"
def patch(self, model, min_cfg):
def linear_cfg(args):
cond = args["cond"]
uncond = args["uncond"]
cond_scale = args["cond_scale"]
scale = torch.linspace(min_cfg, cond_scale, cond.shape[0], device=cond.device).reshape((cond.shape[0], 1, 1, 1))
return uncond + scale * (cond - uncond)
m = model.clone()
m.set_model_sampler_cfg_function(linear_cfg)
return (m, )
class VideoTriangleCFGGuidance:
@classmethod
def INPUT_TYPES(s):
return {"required": { "model": ("MODEL",),
"min_cfg": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 100.0, "step":0.5, "round": 0.01}),
}}
RETURN_TYPES = ("MODEL",)
FUNCTION = "patch"
CATEGORY = "sampling/video_models"
def patch(self, model, min_cfg):
def linear_cfg(args):
cond = args["cond"]
uncond = args["uncond"]
cond_scale = args["cond_scale"]
period = 1.0
values = torch.linspace(0, 1, cond.shape[0], device=cond.device)
values = 2 * (values / period - torch.floor(values / period + 0.5)).abs()
scale = (values * (cond_scale - min_cfg) + min_cfg).reshape((cond.shape[0], 1, 1, 1))
return uncond + scale * (cond - uncond)
m = model.clone()
m.set_model_sampler_cfg_function(linear_cfg)
return (m, )
class ImageOnlyCheckpointSave(comfy_extras.nodes_model_merging.CheckpointSave):
CATEGORY = "advanced/model_merging"
@classmethod
def INPUT_TYPES(s):
return {"required": { "model": ("MODEL",),
"clip_vision": ("CLIP_VISION",),
"vae": ("VAE",),
"filename_prefix": ("STRING", {"default": "checkpoints/ComfyUI"}),},
"hidden": {"prompt": "PROMPT", "extra_pnginfo": "EXTRA_PNGINFO"},}
def save(self, model, clip_vision, vae, filename_prefix, prompt=None, extra_pnginfo=None):
comfy_extras.nodes_model_merging.save_checkpoint(model, clip_vision=clip_vision, vae=vae, filename_prefix=filename_prefix, output_dir=self.output_dir, prompt=prompt, extra_pnginfo=extra_pnginfo)
return {}
NODE_CLASS_MAPPINGS = {
"ImageOnlyCheckpointLoader": ImageOnlyCheckpointLoader,
"SVD_img2vid_Conditioning": SVD_img2vid_Conditioning,
"VideoLinearCFGGuidance": VideoLinearCFGGuidance,
"VideoTriangleCFGGuidance": VideoTriangleCFGGuidance,
"ImageOnlyCheckpointSave": ImageOnlyCheckpointSave,
}
NODE_DISPLAY_NAME_MAPPINGS = {
"ImageOnlyCheckpointLoader": "Image Only Checkpoint Loader (img2vid model)",
}
|