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feat: Detailer for AD - supports noise_mask_feather and DifferentialDiffusion

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fix: SEGSPaste - device mismatch error on --gpu-only
This commit is contained in:
Dr.Lt.Data
2024-06-01 01:47:18 +09:00
parent c31b22537b
commit b1b68426f9
6 changed files with 26 additions and 22 deletions
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modules/impact/animatediff_nodes.py
+12 -14
View File
@@ -21,12 +21,11 @@ class SEGSDetailerForAnimateDiff:
"scheduler": (core.SCHEDULERS,),
"denoise": ("FLOAT", {"default": 0.5, "min": 0.0001, "max": 1.0, "step": 0.01}),
"basic_pipe": ("BASIC_PIPE",),
"refiner_ratio": ("FLOAT", {"default": 0.2, "min": 0.0, "max": 1.0})
"refiner_ratio": ("FLOAT", {"default": 0.2, "min": 0.0, "max": 1.0}),
},
"optional": {
"refiner_basic_pipe_opt": ("BASIC_PIPE",),
# TODO: "inpaint_model": ("BOOLEAN", {"default": False, "label_on": "enabled", "label_off": "disabled"}),
# TODO: "noise_mask_feather": ("INT", {"default": 20, "min": 0, "max": 100, "step": 1}),
"noise_mask_feather": ("INT", {"default": 20, "min": 0, "max": 100, "step": 1}),
}
}
@@ -40,7 +39,7 @@ class SEGSDetailerForAnimateDiff:
@staticmethod
def do_detail(image_frames, segs, guide_size, guide_size_for, max_size, seed, steps, cfg, sampler_name, scheduler,
denoise, basic_pipe, refiner_ratio=None, refiner_basic_pipe_opt=None, inpaint_model=False, noise_mask_feather=0):
denoise, basic_pipe, refiner_ratio=None, refiner_basic_pipe_opt=None, noise_mask_feather=0):
model, clip, vae, positive, negative = basic_pipe
if refiner_basic_pipe_opt is None:
@@ -90,7 +89,7 @@ class SEGSDetailerForAnimateDiff:
refiner_ratio=refiner_ratio, refiner_model=refiner_model,
refiner_clip=refiner_clip, refiner_positive=refiner_positive,
refiner_negative=refiner_negative, control_net_wrapper=seg.control_net_wrapper,
inpaint_model=inpaint_model, noise_mask_feather=noise_mask_feather)
noise_mask_feather=noise_mask_feather)
if cnet_images is not None:
cnet_image_list.extend(cnet_images)
@@ -109,7 +108,7 @@ class SEGSDetailerForAnimateDiff:
segs, cnet_images = SEGSDetailerForAnimateDiff.do_detail(image_frames, segs, guide_size, guide_size_for, max_size, seed, steps, cfg, sampler_name,
scheduler, denoise, basic_pipe, refiner_ratio, refiner_basic_pipe_opt,
inpaint_model=inpaint_model, noise_mask_feather=noise_mask_feather)
noise_mask_feather=noise_mask_feather)
if len(cnet_images) == 0:
cnet_images = [empty_pil_tensor()]
@@ -135,13 +134,12 @@ class DetailerForEachPipeForAnimateDiff:
"feather": ("INT", {"default": 5, "min": 0, "max": 100, "step": 1}),
"basic_pipe": ("BASIC_PIPE", ),
"refiner_ratio": ("FLOAT", {"default": 0.2, "min": 0.0, "max": 1.0}),
},
},
"optional": {
"detailer_hook": ("DETAILER_HOOK",),
"refiner_basic_pipe_opt": ("BASIC_PIPE",),
# "inpaint_model": ("BOOLEAN", {"default": False, "label_on": "enabled", "label_off": "disabled"}),
# "noise_mask_feather": ("INT", {"default": 20, "min": 0, "max": 100, "step": 1}),
}
"detailer_hook": ("DETAILER_HOOK",),
"refiner_basic_pipe_opt": ("BASIC_PIPE",),
"noise_mask_feather": ("INT", {"default": 20, "min": 0, "max": 100, "step": 1}),
}
}
RETURN_TYPES = ("IMAGE", "SEGS", "BASIC_PIPE", "IMAGE")
@@ -154,7 +152,7 @@ class DetailerForEachPipeForAnimateDiff:
@staticmethod
def doit(image_frames, segs, guide_size, guide_size_for, max_size, seed, steps, cfg, sampler_name, scheduler,
denoise, feather, basic_pipe, refiner_ratio=None, detailer_hook=None, refiner_basic_pipe_opt=None,
inpaint_model=False, noise_mask_feather=0):
noise_mask_feather=0):
enhanced_segs = []
cnet_image_list = []
@@ -162,7 +160,7 @@ class DetailerForEachPipeForAnimateDiff:
for sub_seg in segs[1]:
single_seg = segs[0], [sub_seg]
enhanced_seg, cnet_images = SEGSDetailerForAnimateDiff().do_detail(image_frames, single_seg, guide_size, guide_size_for, max_size, seed, steps, cfg, sampler_name, scheduler,
denoise, basic_pipe, refiner_ratio, refiner_basic_pipe_opt, inpaint_model, noise_mask_feather)
denoise, basic_pipe, refiner_ratio, refiner_basic_pipe_opt, noise_mask_feather)
image_frames = SEGSPaste.doit(image_frames, enhanced_seg, feather, alpha=255)[0]
modules/impact/config.py
+1 -1
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@@ -2,7 +2,7 @@ import configparser
import os
version_code = [5, 8, 1]
version_code = [5, 9]
version = f"V{version_code[0]}.{version_code[1]}" + (f'.{version_code[2]}' if len(version_code) > 2 else '')
dependency_version = 20
modules/impact/core.py
+4 -1
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@@ -305,11 +305,14 @@ def enhance_detail_for_animatediff(image_frames, model, clip, vae, guide_size, g
wildcard_opt=None, wildcard_opt_concat_mode=None,
detailer_hook=None,
refiner_ratio=None, refiner_model=None, refiner_clip=None, refiner_positive=None,
refiner_negative=None, control_net_wrapper=None, inpaint_model=False, noise_mask_feather=0):
refiner_negative=None, control_net_wrapper=None, noise_mask_feather=0):
if noise_mask is not None:
noise_mask = utils.tensor_gaussian_blur_mask(noise_mask, noise_mask_feather)
noise_mask = noise_mask.squeeze(3)
if noise_mask_feather > 0:
model = nodes_differential_diffusion.DifferentialDiffusion().apply(model)[0]
if wildcard_opt is not None and wildcard_opt != "":
model, _, wildcard_positive = wildcards.process_with_loras(wildcard_opt, model, clip)
modules/impact/segs_nodes.py
+6 -3
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@@ -10,6 +10,7 @@ from .core import SEG
import impact.utils as utils
from . import defs
from . import segs_upscaler
from comfy.cli_args import args
import math
@@ -196,9 +197,8 @@ class SEGSPaste:
x, y, *_ = seg.crop_region
# ensure same device
mask.cpu()
image_i.cpu()
ref_image.cpu()
mask = mask.to(image_i.device)
ref_image = ref_image.to(image_i.device)
tensor_paste(image_i, ref_image, (x, y), mask)
@@ -207,6 +207,9 @@ class SEGSPaste:
else:
result = torch.concat((result, image_i), dim=0)
if not args.highvram and not args.gpu_only:
result = result.cpu()
return (result, )
modules/impact/segs_upscaler.py
+2 -2
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@@ -1,6 +1,7 @@
from impact.utils import *
from impact import impact_sampling
from comfy import model_management
from comfy.cli_args import args
import nodes
try:
@@ -17,7 +18,7 @@ except Exception:
def upscale_with_model(upscale_model, image):
device = model_management.get_torch_device()
upscale_model.to(device)
in_img = image.movedim(-1,-3).to(device)
in_img = image.movedim(-1, -3).to(device)
free_memory = model_management.get_free_memory(device)
tile = 512
@@ -35,7 +36,6 @@ def upscale_with_model(upscale_model, image):
if tile < 128:
raise e
upscale_model.cpu()
s = torch.clamp(s.movedim(-3, -1), min=0, max=1.0)
return s