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import sys
import os
import itertools
import numpy as np
from tqdm.auto import tqdm
import torch
sys.path.insert(0, os.path.join(os.path.dirname(os.path.realpath(__file__)), "comfy"))
import comfy.sd
import comfy.controlnet
import comfy.model_management
import comfy.sample
from . import tiling
import latent_preview
MAX_RESOLUTION=8192
def recursion_to_list(obj, attr):
current = obj
yield current
while True:
current = getattr(current, attr, None)
if current is not None:
yield current
else:
return
def copy_cond(cond):
return [[c1,c2.copy()] for c1,c2 in cond]
def slice_cond(tile_h, tile_h_len, tile_w, tile_w_len, cond, area):
tile_h_end = tile_h + tile_h_len
tile_w_end = tile_w + tile_w_len
coords = area[0] #h_len, w_len, h, w,
mask = area[1]
if coords is not None:
h_len, w_len, h, w = coords
h_end = h + h_len
w_end = w + w_len
if h < tile_h_end and h_end > tile_h and w < tile_w_end and w_end > tile_w:
new_h = max(0, h - tile_h)
new_w = max(0, w - tile_w)
new_h_end = min(tile_h_end, h_end - tile_h)
new_w_end = min(tile_w_end, w_end - tile_w)
cond[1]['area'] = (new_h_end - new_h, new_w_end - new_w, new_h, new_w)
else:
return (cond, True)
if mask is not None:
new_mask = tiling.get_slice(mask, tile_h,tile_h_len,tile_w,tile_w_len)
if new_mask.sum().cpu() == 0.0 and 'mask' in cond[1]:
return (cond, True)
else:
cond[1]['mask'] = new_mask
return (cond, False)
def slice_gligen(tile_h, tile_h_len, tile_w, tile_w_len, cond, gligen):
tile_h_end = tile_h + tile_h_len
tile_w_end = tile_w + tile_w_len
if gligen is None:
return
gligen_type = gligen[0]
gligen_model = gligen[1]
gligen_areas = gligen[2]
gligen_areas_new = []
for emb, h_len, w_len, h, w in gligen_areas:
h_end = h + h_len
w_end = w + w_len
if h < tile_h_end and h_end > tile_h and w < tile_w_end and w_end > tile_w:
new_h = max(0, h - tile_h)
new_w = max(0, w - tile_w)
new_h_end = min(tile_h_end, h_end - tile_h)
new_w_end = min(tile_w_end, w_end - tile_w)
gligen_areas_new.append((emb, new_h_end - new_h, new_w_end - new_w, new_h, new_w))
if len(gligen_areas_new) == 0:
del cond['gligen']
else:
cond['gligen'] = (gligen_type, gligen_model, gligen_areas_new)
def slice_cnet(h, h_len, w, w_len, model:comfy.controlnet.ControlBase, img):
if img is None:
img = model.cond_hint_original
model.cond_hint = tiling.get_slice(img, h*8, h_len*8, w*8, w_len*8).to(model.control_model.dtype).to(model.device)
def slices_T2I(h, h_len, w, w_len, model:comfy.controlnet.ControlBase, img):
model.control_input = None
if img is None:
img = model.cond_hint_original
model.cond_hint = tiling.get_slice(img, h*8, h_len*8, w*8, w_len*8).float().to(model.device)
# TODO: refactor some of the mess
from PIL import Image
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):
end_at_step = min(end_at_step, steps)
device = comfy.model_management.get_torch_device()
samples = latent_image["samples"]
noise_mask = latent_image["noise_mask"] if "noise_mask" in latent_image else None
force_full_denoise = return_with_leftover_noise == "enable"
if add_noise == "disable":
noise = torch.zeros(samples.size(), dtype=samples.dtype, layout=samples.layout, device="cpu")
else:
skip = latent_image["batch_index"] if "batch_index" in latent_image else None
noise = comfy.sample.prepare_noise(samples, noise_seed, skip)
if noise_mask is not None:
noise_mask = comfy.sample.prepare_mask(noise_mask, noise.shape, device='cpu')
shape = samples.shape
samples = samples.clone()
tile_width = min(shape[-1] * 8, tile_width)
tile_height = min(shape[2] * 8, tile_height)
real_model = None
positive_copy = comfy.sample.convert_cond(positive)
negative_copy = comfy.sample.convert_cond(negative)
modelPatches, inference_memory = comfy.sample.get_additional_models(positive_copy, negative_copy, model.model_dtype())
comfy.model_management.load_models_gpu([model] + modelPatches, model.memory_required(noise.shape) + inference_memory)
real_model = model.model
sampler = comfy.samplers.KSampler(real_model, steps=steps, device=device, sampler=sampler_name, scheduler=scheduler, denoise=denoise, model_options=model.model_options)
if tiling_strategy != 'padded':
if noise_mask is not None:
samples += sampler.sigmas[start_at_step].cpu() * noise_mask * model.model.process_latent_out(noise)
else:
samples += sampler.sigmas[start_at_step].cpu() * model.model.process_latent_out(noise)
# cnets
cnets = [c['control'] for (_, c) in positive + negative if 'control' in c]
# unroll recursion
cnets = list(set([x for m in cnets for x in recursion_to_list(m, "previous_controlnet")]))
# filter down to only cnets
cnets = [x for x in cnets if isinstance(x, comfy.controlnet.ControlNet)]
cnet_imgs = [
torch.nn.functional.interpolate(m.cond_hint_original, (shape[-2] * 8, shape[-1] * 8), mode='nearest-exact').to('cpu')
if m.cond_hint_original.shape[-2] != shape[-2] * 8 or m.cond_hint_original.shape[-1] != shape[-1] * 8 else None
for m in cnets]
# T2I
T2Is = [c['control'] for (_, c) in positive + negative if 'control' in c]
# unroll recursion
T2Is = [x for m in T2Is for x in recursion_to_list(m, "previous_controlnet")]
# filter down to only T2I
T2Is = [x for x in T2Is if isinstance(x, comfy.controlnet.T2IAdapter)]
T2I_imgs = [
torch.nn.functional.interpolate(m.cond_hint_original, (shape[-2] * 8, shape[-1] * 8), mode='nearest-exact').to('cpu')
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
for m in T2Is
]
T2I_imgs = [
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
for m, img in zip(T2Is, T2I_imgs)
]
#cond area and mask
spatial_conds_pos = [
(c[1]['area'] if 'area' in c[1] else None,
comfy.sample.prepare_mask(c[1]['mask'], shape, device) if 'mask' in c[1] else None)
for c in positive
]
spatial_conds_neg = [
(c[1]['area'] if 'area' in c[1] else None,
comfy.sample.prepare_mask(c[1]['mask'], shape, device) if 'mask' in c[1] else None)
for c in negative
]
#gligen
gligen_pos = [
c[1]['gligen'] if 'gligen' in c[1] else None
for c in positive
]
gligen_neg = [
c[1]['gligen'] if 'gligen' in c[1] else None
for c in negative
]
gen = torch.manual_seed(noise_seed)
if tiling_strategy == 'random' or tiling_strategy == 'random strict':
tiles = tiling.get_tiles_and_masks_rgrid(end_at_step - start_at_step, samples.shape, tile_height, tile_width, gen)
elif tiling_strategy == 'padded':
tiles = tiling.get_tiles_and_masks_padded(end_at_step - start_at_step, samples.shape, tile_height, tile_width)
else:
tiles = tiling.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 = tiling.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 = tiling.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 = tiling.get_slice(samples, tile_h, tile_h_len, tile_w, tile_w_len).to(device)
if tiling_strategy == 'padded':
tiled_noise = tiling.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 = tiling.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 = [c.copy() for c in positive_copy]#copy_cond(positive_copy)
neg = [c.copy() for c in negative_copy]#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":
tiling.set_slice(samples_next, tile_result, tile_h, tile_h_len, tile_w, tile_w_len, tiled_mask)
else:
tiling.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')
NODE_CLASS_MAPPINGS = {
"BNK_TiledKSamplerAdvanced": TiledKSamplerAdvanced,
"BNK_TiledKSampler": TiledKSampler,
}
NODE_DISPLAY_NAME_MAPPINGS = {
"BNK_TiledKSamplerAdvanced": "TiledK Sampler (Advanced)",
"BNK_TiledKSampler": "Tiled KSampler",
} |