Spaces:
Runtime error
Runtime error
File size: 5,210 Bytes
22b112d |
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 |
# https://github.com/comfyanonymous/ComfyUI/blob/master/nodes.py
import torch
import ldm_patched.contrib.external
import ldm_patched.modules.utils
def camera_embeddings(elevation, azimuth):
elevation = torch.as_tensor([elevation])
azimuth = torch.as_tensor([azimuth])
embeddings = torch.stack(
[
torch.deg2rad(
(90 - elevation) - (90)
), # Zero123 polar is 90-elevation
torch.sin(torch.deg2rad(azimuth)),
torch.cos(torch.deg2rad(azimuth)),
torch.deg2rad(
90 - torch.full_like(elevation, 0)
),
], dim=-1).unsqueeze(1)
return embeddings
class StableZero123_Conditioning:
@classmethod
def INPUT_TYPES(s):
return {"required": { "clip_vision": ("CLIP_VISION",),
"init_image": ("IMAGE",),
"vae": ("VAE",),
"width": ("INT", {"default": 256, "min": 16, "max": ldm_patched.contrib.external.MAX_RESOLUTION, "step": 8}),
"height": ("INT", {"default": 256, "min": 16, "max": ldm_patched.contrib.external.MAX_RESOLUTION, "step": 8}),
"batch_size": ("INT", {"default": 1, "min": 1, "max": 4096}),
"elevation": ("FLOAT", {"default": 0.0, "min": -180.0, "max": 180.0}),
"azimuth": ("FLOAT", {"default": 0.0, "min": -180.0, "max": 180.0}),
}}
RETURN_TYPES = ("CONDITIONING", "CONDITIONING", "LATENT")
RETURN_NAMES = ("positive", "negative", "latent")
FUNCTION = "encode"
CATEGORY = "conditioning/3d_models"
def encode(self, clip_vision, init_image, vae, width, height, batch_size, elevation, azimuth):
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]
t = vae.encode(encode_pixels)
cam_embeds = camera_embeddings(elevation, azimuth)
cond = torch.cat([pooled, cam_embeds.to(pooled.device).repeat((pooled.shape[0], 1, 1))], dim=-1)
positive = [[cond, {"concat_latent_image": t}]]
negative = [[torch.zeros_like(pooled), {"concat_latent_image": torch.zeros_like(t)}]]
latent = torch.zeros([batch_size, 4, height // 8, width // 8])
return (positive, negative, {"samples":latent})
class StableZero123_Conditioning_Batched:
@classmethod
def INPUT_TYPES(s):
return {"required": { "clip_vision": ("CLIP_VISION",),
"init_image": ("IMAGE",),
"vae": ("VAE",),
"width": ("INT", {"default": 256, "min": 16, "max": ldm_patched.contrib.external.MAX_RESOLUTION, "step": 8}),
"height": ("INT", {"default": 256, "min": 16, "max": ldm_patched.contrib.external.MAX_RESOLUTION, "step": 8}),
"batch_size": ("INT", {"default": 1, "min": 1, "max": 4096}),
"elevation": ("FLOAT", {"default": 0.0, "min": -180.0, "max": 180.0}),
"azimuth": ("FLOAT", {"default": 0.0, "min": -180.0, "max": 180.0}),
"elevation_batch_increment": ("FLOAT", {"default": 0.0, "min": -180.0, "max": 180.0}),
"azimuth_batch_increment": ("FLOAT", {"default": 0.0, "min": -180.0, "max": 180.0}),
}}
RETURN_TYPES = ("CONDITIONING", "CONDITIONING", "LATENT")
RETURN_NAMES = ("positive", "negative", "latent")
FUNCTION = "encode"
CATEGORY = "conditioning/3d_models"
def encode(self, clip_vision, init_image, vae, width, height, batch_size, elevation, azimuth, elevation_batch_increment, azimuth_batch_increment):
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]
t = vae.encode(encode_pixels)
cam_embeds = []
for i in range(batch_size):
cam_embeds.append(camera_embeddings(elevation, azimuth))
elevation += elevation_batch_increment
azimuth += azimuth_batch_increment
cam_embeds = torch.cat(cam_embeds, dim=0)
cond = torch.cat([ldm_patched.modules.utils.repeat_to_batch_size(pooled, batch_size), cam_embeds], dim=-1)
positive = [[cond, {"concat_latent_image": t}]]
negative = [[torch.zeros_like(pooled), {"concat_latent_image": torch.zeros_like(t)}]]
latent = torch.zeros([batch_size, 4, height // 8, width // 8])
return (positive, negative, {"samples":latent, "batch_index": [0] * batch_size})
NODE_CLASS_MAPPINGS = {
"StableZero123_Conditioning": StableZero123_Conditioning,
"StableZero123_Conditioning_Batched": StableZero123_Conditioning_Batched,
}
|