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https://github.com/jags111/efficiency-nodes-comfyui.git
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Efficiency Nodes V2.0
This commit is contained in:
TSC
523
py/bnk_tiled_samplers.py
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523
py/bnk_tiled_samplers.py
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# https://github.com/BlenderNeko/ComfyUI_TiledKSampler
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import sys
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import os
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import itertools
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import numpy as np
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from tqdm.auto import tqdm
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import torch
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#sys.path.insert(0, os.path.join(os.path.dirname(os.path.realpath(__file__)), "comfy"))
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import comfy.sd
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import comfy.controlnet
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import comfy.model_management
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import comfy.sample
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#from . import tiling
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import latent_preview
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#import torch
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#import itertools
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#import numpy as np
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MAX_RESOLUTION=8192
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def grouper(n, iterable):
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it = iter(iterable)
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while True:
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chunk = list(itertools.islice(it, n))
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if not chunk:
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return
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yield chunk
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def create_batches(n, iterable):
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groups = itertools.groupby(iterable, key=lambda x: (x[1], x[3]))
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for _, x in groups:
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for y in grouper(n, x):
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yield y
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def get_slice(tensor, h, h_len, w, w_len):
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t = tensor.narrow(-2, h, h_len)
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t = t.narrow(-1, w, w_len)
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return t
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def set_slice(tensor1, tensor2, h, h_len, w, w_len, mask=None):
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if mask is not None:
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tensor1[:, :, h:h + h_len, w:w + w_len] = tensor1[:, :, h:h + h_len, w:w + w_len] * (1 - mask) + tensor2 * mask
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else:
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tensor1[:, :, h:h + h_len, w:w + w_len] = tensor2
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def get_tiles_and_masks_simple(steps, latent_shape, tile_height, tile_width):
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latent_size_h = latent_shape[-2]
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latent_size_w = latent_shape[-1]
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tile_size_h = int(tile_height // 8)
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tile_size_w = int(tile_width // 8)
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h = np.arange(0, latent_size_h, tile_size_h)
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w = np.arange(0, latent_size_w, tile_size_w)
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def create_tile(hs, ws, i, j):
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h = int(hs[i])
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w = int(ws[j])
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h_len = min(tile_size_h, latent_size_h - h)
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w_len = min(tile_size_w, latent_size_w - w)
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return (h, h_len, w, w_len, steps, None)
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passes = [
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[[create_tile(h, w, i, j) for i in range(len(h)) for j in range(len(w))]],
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]
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return passes
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def get_tiles_and_masks_padded(steps, latent_shape, tile_height, tile_width):
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batch_size = latent_shape[0]
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latent_size_h = latent_shape[-2]
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latent_size_w = latent_shape[-1]
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tile_size_h = int(tile_height // 8)
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tile_size_h = int((tile_size_h // 4) * 4)
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tile_size_w = int(tile_width // 8)
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tile_size_w = int((tile_size_w // 4) * 4)
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# masks
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mask_h = [0, tile_size_h // 4, tile_size_h - tile_size_h // 4, tile_size_h]
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mask_w = [0, tile_size_w // 4, tile_size_w - tile_size_w // 4, tile_size_w]
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masks = [[] for _ in range(3)]
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for i in range(3):
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for j in range(3):
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mask = torch.zeros((batch_size, 1, tile_size_h, tile_size_w), dtype=torch.float32, device='cpu')
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mask[:, :, mask_h[i]:mask_h[i + 1], mask_w[j]:mask_w[j + 1]] = 1.0
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masks[i].append(mask)
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def create_mask(h_ind, w_ind, h_ind_max, w_ind_max, mask_h, mask_w, h_len, w_len):
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mask = masks[1][1]
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if not (h_ind == 0 or h_ind == h_ind_max or w_ind == 0 or w_ind == w_ind_max):
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return get_slice(mask, 0, h_len, 0, w_len)
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mask = mask.clone()
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if h_ind == 0 and mask_h:
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mask += masks[0][1]
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if h_ind == h_ind_max and mask_h:
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mask += masks[2][1]
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if w_ind == 0 and mask_w:
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mask += masks[1][0]
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if w_ind == w_ind_max and mask_w:
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mask += masks[1][2]
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if h_ind == 0 and w_ind == 0 and mask_h and mask_w:
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mask += masks[0][0]
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if h_ind == 0 and w_ind == w_ind_max and mask_h and mask_w:
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mask += masks[0][2]
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if h_ind == h_ind_max and w_ind == 0 and mask_h and mask_w:
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mask += masks[2][0]
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if h_ind == h_ind_max and w_ind == w_ind_max and mask_h and mask_w:
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mask += masks[2][2]
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return get_slice(mask, 0, h_len, 0, w_len)
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h = np.arange(0, latent_size_h, tile_size_h)
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h_shift = np.arange(tile_size_h // 2, latent_size_h - tile_size_h // 2, tile_size_h)
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w = np.arange(0, latent_size_w, tile_size_w)
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w_shift = np.arange(tile_size_w // 2, latent_size_w - tile_size_h // 2, tile_size_w)
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def create_tile(hs, ws, mask_h, mask_w, i, j):
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h = int(hs[i])
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w = int(ws[j])
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h_len = min(tile_size_h, latent_size_h - h)
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w_len = min(tile_size_w, latent_size_w - w)
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mask = create_mask(i, j, len(hs) - 1, len(ws) - 1, mask_h, mask_w, h_len, w_len)
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return (h, h_len, w, w_len, steps, mask)
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passes = [
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[[create_tile(h, w, True, True, i, j) for i in range(len(h)) for j in range(len(w))]],
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[[create_tile(h_shift, w, False, True, i, j) for i in range(len(h_shift)) for j in range(len(w))]],
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[[create_tile(h, w_shift, True, False, i, j) for i in range(len(h)) for j in range(len(w_shift))]],
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[[create_tile(h_shift, w_shift, False, False, i, j) for i in range(len(h_shift)) for j in range(len(w_shift))]],
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]
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return passes
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def mask_at_boundary(h, h_len, w, w_len, tile_size_h, tile_size_w, latent_size_h, latent_size_w, mask, device='cpu'):
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tile_size_h = int(tile_size_h // 8)
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tile_size_w = int(tile_size_w // 8)
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if (h_len == tile_size_h or h_len == latent_size_h) and (w_len == tile_size_w or w_len == latent_size_w):
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return h, h_len, w, w_len, mask
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h_offset = min(0, latent_size_h - (h + tile_size_h))
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w_offset = min(0, latent_size_w - (w + tile_size_w))
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new_mask = torch.zeros((1, 1, tile_size_h, tile_size_w), dtype=torch.float32, device=device)
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new_mask[:, :, -h_offset:h_len if h_offset == 0 else tile_size_h,
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-w_offset:w_len if w_offset == 0 else tile_size_w] = 1.0 if mask is None else mask
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return h + h_offset, tile_size_h, w + w_offset, tile_size_w, new_mask
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def get_tiles_and_masks_rgrid(steps, latent_shape, tile_height, tile_width, generator):
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def calc_coords(latent_size, tile_size, jitter):
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tile_coords = int((latent_size + jitter - 1) // tile_size + 1)
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tile_coords = [np.clip(tile_size * c - jitter, 0, latent_size) for c in range(tile_coords + 1)]
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tile_coords = [(c1, c2 - c1) for c1, c2 in zip(tile_coords, tile_coords[1:])]
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return tile_coords
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# calc stuff
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batch_size = latent_shape[0]
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latent_size_h = latent_shape[-2]
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latent_size_w = latent_shape[-1]
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tile_size_h = int(tile_height // 8)
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tile_size_w = int(tile_width // 8)
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tiles_all = []
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for s in range(steps):
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rands = torch.rand((2,), dtype=torch.float32, generator=generator, device='cpu').numpy()
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jitter_w1 = int(rands[0] * tile_size_w)
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jitter_w2 = int(((rands[0] + .5) % 1.0) * tile_size_w)
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jitter_h1 = int(rands[1] * tile_size_h)
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jitter_h2 = int(((rands[1] + .5) % 1.0) * tile_size_h)
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# calc number of tiles
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tiles_h = [
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calc_coords(latent_size_h, tile_size_h, jitter_h1),
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calc_coords(latent_size_h, tile_size_h, jitter_h2)
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]
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tiles_w = [
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calc_coords(latent_size_w, tile_size_w, jitter_w1),
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calc_coords(latent_size_w, tile_size_w, jitter_w2)
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]
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tiles = []
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if s % 2 == 0:
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for i, h in enumerate(tiles_h[0]):
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for w in tiles_w[i % 2]:
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tiles.append((int(h[0]), int(h[1]), int(w[0]), int(w[1]), 1, None))
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else:
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for i, w in enumerate(tiles_w[0]):
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for h in tiles_h[i % 2]:
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tiles.append((int(h[0]), int(h[1]), int(w[0]), int(w[1]), 1, None))
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tiles_all.append(tiles)
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return [tiles_all]
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#######################
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def recursion_to_list(obj, attr):
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current = obj
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yield current
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while True:
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current = getattr(current, attr, None)
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if current is not None:
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yield current
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else:
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return
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def copy_cond(cond):
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return [[c1,c2.copy()] for c1,c2 in cond]
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def slice_cond(tile_h, tile_h_len, tile_w, tile_w_len, cond, area):
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tile_h_end = tile_h + tile_h_len
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tile_w_end = tile_w + tile_w_len
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coords = area[0] #h_len, w_len, h, w,
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mask = area[1]
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if coords is not None:
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h_len, w_len, h, w = coords
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h_end = h + h_len
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w_end = w + w_len
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if h < tile_h_end and h_end > tile_h and w < tile_w_end and w_end > tile_w:
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new_h = max(0, h - tile_h)
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new_w = max(0, w - tile_w)
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new_h_end = min(tile_h_end, h_end - tile_h)
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new_w_end = min(tile_w_end, w_end - tile_w)
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cond[1]['area'] = (new_h_end - new_h, new_w_end - new_w, new_h, new_w)
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else:
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return (cond, True)
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if mask is not None:
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new_mask = get_slice(mask, tile_h,tile_h_len,tile_w,tile_w_len)
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if new_mask.sum().cpu() == 0.0 and 'mask' in cond[1]:
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return (cond, True)
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else:
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cond[1]['mask'] = new_mask
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return (cond, False)
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def slice_gligen(tile_h, tile_h_len, tile_w, tile_w_len, cond, gligen):
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tile_h_end = tile_h + tile_h_len
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tile_w_end = tile_w + tile_w_len
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if gligen is None:
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return
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gligen_type = gligen[0]
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gligen_model = gligen[1]
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gligen_areas = gligen[2]
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gligen_areas_new = []
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for emb, h_len, w_len, h, w in gligen_areas:
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h_end = h + h_len
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w_end = w + w_len
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if h < tile_h_end and h_end > tile_h and w < tile_w_end and w_end > tile_w:
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new_h = max(0, h - tile_h)
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new_w = max(0, w - tile_w)
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new_h_end = min(tile_h_end, h_end - tile_h)
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new_w_end = min(tile_w_end, w_end - tile_w)
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gligen_areas_new.append((emb, new_h_end - new_h, new_w_end - new_w, new_h, new_w))
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if len(gligen_areas_new) == 0:
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del cond['gligen']
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else:
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cond['gligen'] = (gligen_type, gligen_model, gligen_areas_new)
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def slice_cnet(h, h_len, w, w_len, model:comfy.controlnet.ControlBase, img):
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if img is None:
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img = model.cond_hint_original
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model.cond_hint = get_slice(img, h*8, h_len*8, w*8, w_len*8).to(model.control_model.dtype).to(model.device)
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def slices_T2I(h, h_len, w, w_len, model:comfy.controlnet.ControlBase, img):
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model.control_input = None
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if img is None:
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img = model.cond_hint_original
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model.cond_hint = get_slice(img, h*8, h_len*8, w*8, w_len*8).float().to(model.device)
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# TODO: refactor some of the mess
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from PIL import Image
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def sample_common(model, add_noise, noise_seed, tile_width, tile_height, tiling_strategy, steps, cfg, sampler_name, scheduler, positive, negative, latent_image, start_at_step, end_at_step, return_with_leftover_noise, denoise=1.0, preview=False):
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end_at_step = min(end_at_step, steps)
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device = comfy.model_management.get_torch_device()
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samples = latent_image["samples"]
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noise_mask = latent_image["noise_mask"] if "noise_mask" in latent_image else None
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force_full_denoise = return_with_leftover_noise == "enable"
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if add_noise == "disable":
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noise = torch.zeros(samples.size(), dtype=samples.dtype, layout=samples.layout, device="cpu")
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else:
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skip = latent_image["batch_index"] if "batch_index" in latent_image else None
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noise = comfy.sample.prepare_noise(samples, noise_seed, skip)
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if noise_mask is not None:
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noise_mask = comfy.sample.prepare_mask(noise_mask, noise.shape, device='cpu')
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shape = samples.shape
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samples = samples.clone()
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tile_width = min(shape[-1] * 8, tile_width)
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tile_height = min(shape[2] * 8, tile_height)
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real_model = None
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modelPatches, inference_memory = comfy.sample.get_additional_models(positive, negative, model.model_dtype())
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comfy.model_management.load_models_gpu([model] + modelPatches, comfy.model_management.batch_area_memory(noise.shape[0] * noise.shape[2] * noise.shape[3]) + inference_memory)
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real_model = model.model
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sampler = comfy.samplers.KSampler(real_model, steps=steps, device=device, sampler=sampler_name, scheduler=scheduler, denoise=denoise, model_options=model.model_options)
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if tiling_strategy != 'padded':
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if noise_mask is not None:
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samples += sampler.sigmas[start_at_step].cpu() * noise_mask * model.model.process_latent_out(noise).cpu()
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else:
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samples += sampler.sigmas[start_at_step].cpu() * model.model.process_latent_out(noise).cpu()
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#cnets
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cnets = comfy.sample.get_models_from_cond(positive, 'control') + comfy.sample.get_models_from_cond(negative, 'control')
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cnets = [m for m in cnets if isinstance(m, comfy.controlnet.ControlNet)]
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cnets = list(set([x for m in cnets for x in recursion_to_list(m, "previous_controlnet")]))
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cnet_imgs = [
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torch.nn.functional.interpolate(m.cond_hint_original, (shape[-2] * 8, shape[-1] * 8), mode='nearest-exact').to('cpu')
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if m.cond_hint_original.shape[-2] != shape[-2] * 8 or m.cond_hint_original.shape[-1] != shape[-1] * 8 else None
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for m in cnets]
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#T2I
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T2Is = comfy.sample.get_models_from_cond(positive, 'control') + comfy.sample.get_models_from_cond(negative, 'control')
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T2Is = [m for m in T2Is if isinstance(m, comfy.controlnet.T2IAdapter)]
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T2Is = [x for m in T2Is for x in recursion_to_list(m, "previous_controlnet")]
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T2I_imgs = [
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torch.nn.functional.interpolate(m.cond_hint_original, (shape[-2] * 8, shape[-1] * 8), mode='nearest-exact').to('cpu')
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if m.cond_hint_original.shape[-2] != shape[-2] * 8 or m.cond_hint_original.shape[-1] != shape[-1] * 8 or (m.channels_in == 1 and m.cond_hint_original.shape[1] != 1) else None
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for m in T2Is
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]
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T2I_imgs = [
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torch.mean(img, 1, keepdim=True) if img is not None and m.channels_in == 1 and m.cond_hint_original.shape[1] else img
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for m, img in zip(T2Is, T2I_imgs)
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]
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#cond area and mask
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spatial_conds_pos = [
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(c[1]['area'] if 'area' in c[1] else None,
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comfy.sample.prepare_mask(c[1]['mask'], shape, device) if 'mask' in c[1] else None)
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for c in positive
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]
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spatial_conds_neg = [
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(c[1]['area'] if 'area' in c[1] else None,
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comfy.sample.prepare_mask(c[1]['mask'], shape, device) if 'mask' in c[1] else None)
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for c in negative
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]
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#gligen
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gligen_pos = [
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c[1]['gligen'] if 'gligen' in c[1] else None
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for c in positive
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]
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gligen_neg = [
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c[1]['gligen'] if 'gligen' in c[1] else None
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for c in negative
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]
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positive_copy = comfy.sample.broadcast_cond(positive, shape[0], device)
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negative_copy = comfy.sample.broadcast_cond(negative, shape[0], device)
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gen = torch.manual_seed(noise_seed)
|
||||
if tiling_strategy == 'random' or tiling_strategy == 'random strict':
|
||||
tiles = get_tiles_and_masks_rgrid(end_at_step - start_at_step, samples.shape, tile_height, tile_width, gen)
|
||||
elif tiling_strategy == 'padded':
|
||||
tiles = get_tiles_and_masks_padded(end_at_step - start_at_step, samples.shape, tile_height, tile_width)
|
||||
else:
|
||||
tiles = get_tiles_and_masks_simple(end_at_step - start_at_step, samples.shape, tile_height, tile_width)
|
||||
|
||||
total_steps = sum([num_steps for img_pass in tiles for steps_list in img_pass for _,_,_,_,num_steps,_ in steps_list])
|
||||
current_step = [0]
|
||||
|
||||
preview_format = "JPEG"
|
||||
if preview_format not in ["JPEG", "PNG"]:
|
||||
preview_format = "JPEG"
|
||||
previewer = None
|
||||
if preview:
|
||||
previewer = latent_preview.get_previewer(device, model.model.latent_format)
|
||||
|
||||
|
||||
with tqdm(total=total_steps) as pbar_tqdm:
|
||||
pbar = comfy.utils.ProgressBar(total_steps)
|
||||
|
||||
def callback(step, x0, x, total_steps):
|
||||
current_step[0] += 1
|
||||
preview_bytes = None
|
||||
if previewer:
|
||||
preview_bytes = previewer.decode_latent_to_preview_image(preview_format, x0)
|
||||
pbar.update_absolute(current_step[0], preview=preview_bytes)
|
||||
pbar_tqdm.update(1)
|
||||
|
||||
if tiling_strategy == "random strict":
|
||||
samples_next = samples.clone()
|
||||
for img_pass in tiles:
|
||||
for i in range(len(img_pass)):
|
||||
for tile_h, tile_h_len, tile_w, tile_w_len, tile_steps, tile_mask in img_pass[i]:
|
||||
tiled_mask = None
|
||||
if noise_mask is not None:
|
||||
tiled_mask = get_slice(noise_mask, tile_h, tile_h_len, tile_w, tile_w_len).to(device)
|
||||
if tile_mask is not None:
|
||||
if tiled_mask is not None:
|
||||
tiled_mask *= tile_mask.to(device)
|
||||
else:
|
||||
tiled_mask = tile_mask.to(device)
|
||||
|
||||
if tiling_strategy == 'padded' or tiling_strategy == 'random strict':
|
||||
tile_h, tile_h_len, tile_w, tile_w_len, tiled_mask = mask_at_boundary( tile_h, tile_h_len, tile_w, tile_w_len,
|
||||
tile_height, tile_width, samples.shape[-2], samples.shape[-1],
|
||||
tiled_mask, device)
|
||||
|
||||
|
||||
if tiled_mask is not None and tiled_mask.sum().cpu() == 0.0:
|
||||
continue
|
||||
|
||||
tiled_latent = get_slice(samples, tile_h, tile_h_len, tile_w, tile_w_len).to(device)
|
||||
|
||||
if tiling_strategy == 'padded':
|
||||
tiled_noise = get_slice(noise, tile_h, tile_h_len, tile_w, tile_w_len).to(device)
|
||||
else:
|
||||
if tiled_mask is None or noise_mask is None:
|
||||
tiled_noise = torch.zeros_like(tiled_latent)
|
||||
else:
|
||||
tiled_noise = get_slice(noise, tile_h, tile_h_len, tile_w, tile_w_len).to(device) * (1 - tiled_mask)
|
||||
|
||||
#TODO: all other condition based stuff like area sets and GLIGEN should also happen here
|
||||
|
||||
#cnets
|
||||
for m, img in zip(cnets, cnet_imgs):
|
||||
slice_cnet(tile_h, tile_h_len, tile_w, tile_w_len, m, img)
|
||||
|
||||
#T2I
|
||||
for m, img in zip(T2Is, T2I_imgs):
|
||||
slices_T2I(tile_h, tile_h_len, tile_w, tile_w_len, m, img)
|
||||
|
||||
pos = copy_cond(positive_copy)
|
||||
neg = copy_cond(negative_copy)
|
||||
|
||||
#cond areas
|
||||
pos = [slice_cond(tile_h, tile_h_len, tile_w, tile_w_len, c, area) for c, area in zip(pos, spatial_conds_pos)]
|
||||
pos = [c for c, ignore in pos if not ignore]
|
||||
neg = [slice_cond(tile_h, tile_h_len, tile_w, tile_w_len, c, area) for c, area in zip(neg, spatial_conds_neg)]
|
||||
neg = [c for c, ignore in neg if not ignore]
|
||||
|
||||
#gligen
|
||||
for (_, cond), gligen in zip(pos, gligen_pos):
|
||||
slice_gligen(tile_h, tile_h_len, tile_w, tile_w_len, cond, gligen)
|
||||
for (_, cond), gligen in zip(neg, gligen_neg):
|
||||
slice_gligen(tile_h, tile_h_len, tile_w, tile_w_len, cond, gligen)
|
||||
|
||||
tile_result = sampler.sample(tiled_noise, pos, neg, cfg=cfg, latent_image=tiled_latent, start_step=start_at_step + i * tile_steps, last_step=start_at_step + i*tile_steps + tile_steps, force_full_denoise=force_full_denoise and i+1 == end_at_step - start_at_step, denoise_mask=tiled_mask, callback=callback, disable_pbar=True, seed=noise_seed)
|
||||
tile_result = tile_result.cpu()
|
||||
if tiled_mask is not None:
|
||||
tiled_mask = tiled_mask.cpu()
|
||||
if tiling_strategy == "random strict":
|
||||
set_slice(samples_next, tile_result, tile_h, tile_h_len, tile_w, tile_w_len, tiled_mask)
|
||||
else:
|
||||
set_slice(samples, tile_result, tile_h, tile_h_len, tile_w, tile_w_len, tiled_mask)
|
||||
if tiling_strategy == "random strict":
|
||||
samples = samples_next.clone()
|
||||
|
||||
|
||||
comfy.sample.cleanup_additional_models(modelPatches)
|
||||
|
||||
out = latent_image.copy()
|
||||
out["samples"] = samples.cpu()
|
||||
return (out, )
|
||||
|
||||
class TiledKSampler:
|
||||
@classmethod
|
||||
def INPUT_TYPES(s):
|
||||
return {"required":
|
||||
{"model": ("MODEL",),
|
||||
"seed": ("INT", {"default": 0, "min": 0, "max": 0xffffffffffffffff}),
|
||||
"tile_width": ("INT", {"default": 512, "min": 256, "max": MAX_RESOLUTION, "step": 64}),
|
||||
"tile_height": ("INT", {"default": 512, "min": 256, "max": MAX_RESOLUTION, "step": 64}),
|
||||
"tiling_strategy": (["random", "random strict", "padded", 'simple'], ),
|
||||
"steps": ("INT", {"default": 20, "min": 1, "max": 10000}),
|
||||
"cfg": ("FLOAT", {"default": 8.0, "min": 0.0, "max": 100.0}),
|
||||
"sampler_name": (comfy.samplers.KSampler.SAMPLERS, ),
|
||||
"scheduler": (comfy.samplers.KSampler.SCHEDULERS, ),
|
||||
"positive": ("CONDITIONING", ),
|
||||
"negative": ("CONDITIONING", ),
|
||||
"latent_image": ("LATENT", ),
|
||||
"denoise": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.01}),
|
||||
}}
|
||||
|
||||
RETURN_TYPES = ("LATENT",)
|
||||
FUNCTION = "sample"
|
||||
|
||||
CATEGORY = "sampling"
|
||||
|
||||
def sample(self, model, seed, tile_width, tile_height, tiling_strategy, steps, cfg, sampler_name, scheduler, positive, negative, latent_image, denoise):
|
||||
steps_total = int(steps / denoise)
|
||||
return sample_common(model, 'enable', seed, tile_width, tile_height, tiling_strategy, steps_total, cfg, sampler_name, scheduler, positive, negative, latent_image, steps_total-steps, steps_total, 'disable', denoise=1.0, preview=True)
|
||||
|
||||
class TiledKSamplerAdvanced:
|
||||
@classmethod
|
||||
def INPUT_TYPES(s):
|
||||
return {"required":
|
||||
{"model": ("MODEL",),
|
||||
"add_noise": (["enable", "disable"], ),
|
||||
"noise_seed": ("INT", {"default": 0, "min": 0, "max": 0xffffffffffffffff}),
|
||||
"tile_width": ("INT", {"default": 512, "min": 256, "max": MAX_RESOLUTION, "step": 64}),
|
||||
"tile_height": ("INT", {"default": 512, "min": 256, "max": MAX_RESOLUTION, "step": 64}),
|
||||
"tiling_strategy": (["random", "random strict", "padded", 'simple'], ),
|
||||
"steps": ("INT", {"default": 20, "min": 1, "max": 10000}),
|
||||
"cfg": ("FLOAT", {"default": 8.0, "min": 0.0, "max": 100.0}),
|
||||
"sampler_name": (comfy.samplers.KSampler.SAMPLERS, ),
|
||||
"scheduler": (comfy.samplers.KSampler.SCHEDULERS, ),
|
||||
"positive": ("CONDITIONING", ),
|
||||
"negative": ("CONDITIONING", ),
|
||||
"latent_image": ("LATENT", ),
|
||||
"start_at_step": ("INT", {"default": 0, "min": 0, "max": 10000}),
|
||||
"end_at_step": ("INT", {"default": 10000, "min": 0, "max": 10000}),
|
||||
"return_with_leftover_noise": (["disable", "enable"], ),
|
||||
"preview": (["disable", "enable"], ),
|
||||
}}
|
||||
|
||||
RETURN_TYPES = ("LATENT",)
|
||||
FUNCTION = "sample"
|
||||
|
||||
CATEGORY = "sampling"
|
||||
|
||||
def sample(self, model, add_noise, noise_seed, tile_width, tile_height, tiling_strategy, steps, cfg, sampler_name, scheduler, positive, negative, latent_image, start_at_step, end_at_step, return_with_leftover_noise, preview, denoise=1.0):
|
||||
return sample_common(model, add_noise, noise_seed, tile_width, tile_height, tiling_strategy, steps, cfg, sampler_name, scheduler, positive, negative, latent_image, start_at_step, end_at_step, return_with_leftover_noise, denoise=1.0, preview= preview == 'enable')
|
||||
Reference in New Issue
Block a user