jinx/ComfyUI
1
Fork 0
mirror of https://github.com/comfyanonymous/ComfyUI.git synced 2025-08-02 23:14:49 +08:00
Code Issues Activity

Make nodes map over input lists (#579)

Browse Source 
* allow nodes to map over lists

* make work with IS_CHANGED and VALIDATE_INPUTS

* give list outputs distinct socket shape

* add rebatch node

* add batch index logic

* add repeat latent batch

* deal with noise mask edge cases in latentfrombatch
This commit is contained in:
BlenderNeko
2023-05-13 17:15:45 +02:00
committed by GitHub
parent 997dd1b131
commit 1201d2eae5
6 changed files with 250 additions and 26 deletions
Download Patch File Download Diff File Expand all files Collapse all files

17
comfy/sample.py
View File

@@ -2,17 +2,26 @@ import torch
import comfy.model_management
import comfy.samplers
import math
import numpy as np
def prepare_noise(latent_image, seed, skip=0):
def prepare_noise(latent_image, seed, noise_inds=None):
"""
creates random noise given a latent image and a seed.
optional arg skip can be used to skip and discard x number of noise generations for a given seed
"""
generator = torch.manual_seed(seed)
for _ in range(skip):
if noise_inds is None:
return torch.randn(latent_image.size(), dtype=latent_image.dtype, layout=latent_image.layout, generator=generator, device="cpu")
unique_inds, inverse = np.unique(noise_inds, return_inverse=True)
noises = []
for i in range(unique_inds[-1]+1):
noise = torch.randn([1] + list(latent_image.size())[1:], dtype=latent_image.dtype, layout=latent_image.layout, generator=generator, device="cpu")
noise = torch.randn(latent_image.size(), dtype=latent_image.dtype, layout=latent_image.layout, generator=generator, device="cpu")
return noise
if i in unique_inds:
noises.append(noise)
noises = [noises[i] for i in inverse]
noises = torch.cat(noises, axis=0)
return noises
def prepare_mask(noise_mask, shape, device):
"""ensures noise mask is of proper dimensions"""