nodes/cubiq-ComfyUI_essentials
1
0
Fork 0
Code Issues Pull Requests Packages Projects Releases Wiki Activity

add temporary StableZero123 node with increments

Browse Source
This commit is contained in:
matt3o
2023-12-19 11:22:18 +01:00
parent aa59a9291a
commit d583b5f9e1
1 changed files with 42 additions and 0 deletions
Download Patch File Download Diff File Expand all files Collapse all files
essentials.py
+42
View File
@@ -727,7 +727,47 @@ class ConsoleDebug:
return (None,)
from comfy_extras.nodes_stable3d import camera_embeddings
class StableZero123_Increments:
@classmethod
def INPUT_TYPES(s):
return {"required": { "clip_vision": ("CLIP_VISION",),
"init_image": ("IMAGE",),
"vae": ("VAE",),
"width": ("INT", {"default": 256, "min": 16, "max": MAX_RESOLUTION, "step": 8}),
"height": ("INT", {"default": 256, "min": 16, "max": 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_inc": ("FLOAT", {"default": 0.0, "min": -180.0, "max": 180.0}),
"azimuth_inc": ("FLOAT", {"default": 0.0, "min": -180.0, "max": 180.0}),
}}
RETURN_TYPES = ("CONDITIONING", "CONDITIONING", "LATENT")
RETURN_NAMES = ("positive", "negative", "latent")
FUNCTION = "encode"
CATEGORY = "essentials"
def encode(self, clip_vision, init_image, vae, width, height, batch_size, elevation, azimuth, elevation_inc, azimuth_inc):
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]
t = vae.encode(encode_pixels)
cam_embeds = [camera_embeddings(elevation + i * elevation_inc, azimuth + i * azimuth_inc) for i in range(batch_size)]
cam_embeds = torch.cat(cam_embeds, dim=0)
cond = torch.cat([pooled.repeat((batch_size, 1, 1)), 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})
NODE_CLASS_MAPPINGS = {
"StableZero123_Increments": StableZero123_Increments,
"GetImageSize+": GetImageSize,
"ImageResize+": ImageResize,
@@ -755,6 +795,8 @@ NODE_CLASS_MAPPINGS = {
}
NODE_DISPLAY_NAME_MAPPINGS = {
"StableZero123_Increments": "🔧 StableZero123 with Increments (temporary)",
"GetImageSize+": "🔧 Get Image Size",
"ImageResize+": "🔧 Image Resize",