File size: 5,226 Bytes
77fbc00 |
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 |
# https://github.com/comfyanonymous/ComfyUI/blob/master/nodes.py
import ldm_patched.contrib.external
import torch
import ldm_patched.modules.utils
import ldm_patched.modules.sd
import ldm_patched.utils.path_utils
import ldm_patched.contrib.external_model_merging
class ImageOnlyCheckpointLoader:
@classmethod
def INPUT_TYPES(s):
return {"required": { "ckpt_name": (ldm_patched.utils.path_utils.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 = ldm_patched.utils.path_utils.get_full_path("checkpoints", ckpt_name)
out = ldm_patched.modules.sd.load_checkpoint_guess_config(ckpt_path, output_vae=True, output_clip=False, output_clipvision=True, embedding_directory=ldm_patched.utils.path_utils.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": ldm_patched.contrib.external.MAX_RESOLUTION, "step": 8}),
"height": ("INT", {"default": 576, "min": 16, "max": ldm_patched.contrib.external.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 = ldm_patched.modules.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 ImageOnlyCheckpointSave(ldm_patched.contrib.external_model_merging.CheckpointSave):
CATEGORY = "_for_testing"
@classmethod
def INPUT_TYPES(s):
return {"required": { "model": ("MODEL",),
"clip_vision": ("CLIP_VISION",),
"vae": ("VAE",),
"filename_prefix": ("STRING", {"default": "checkpoints/ldm_patched"}),},
"hidden": {"prompt": "PROMPT", "extra_pnginfo": "EXTRA_PNGINFO"},}
def save(self, model, clip_vision, vae, filename_prefix, prompt=None, extra_pnginfo=None):
ldm_patched.contrib.external_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,
"ImageOnlyCheckpointSave": ImageOnlyCheckpointSave,
}
NODE_DISPLAY_NAME_MAPPINGS = {
"ImageOnlyCheckpointLoader": "Image Only Checkpoint Loader (img2vid model)",
}
|