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from nodes import MAX_RESOLUTION
from impact.utils import *
import impact.core as core
from impact.core import SEG
from impact.segs_nodes import SEGSPaste
try:
from comfy_extras import nodes_differential_diffusion
except Exception:
print(f"\n#############################################\n[Impact Pack] ComfyUI is an outdated version.\n#############################################\n")
raise Exception("[Impact Pack] ComfyUI is an outdated version.")
class SEGSDetailerForAnimateDiff:
@classmethod
def INPUT_TYPES(cls):
return {"required": {
"image_frames": ("IMAGE", ),
"segs": ("SEGS", ),
"guide_size": ("FLOAT", {"default": 512, "min": 64, "max": MAX_RESOLUTION, "step": 8}),
"guide_size_for": ("BOOLEAN", {"default": True, "label_on": "bbox", "label_off": "crop_region"}),
"max_size": ("FLOAT", {"default": 768, "min": 64, "max": MAX_RESOLUTION, "step": 8}),
"seed": ("INT", {"default": 0, "min": 0, "max": 0xffffffffffffffff}),
"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": (core.SCHEDULERS,),
"denoise": ("FLOAT", {"default": 0.5, "min": 0.0001, "max": 1.0, "step": 0.01}),
"basic_pipe": ("BASIC_PIPE", {"tooltip": "If the `ImpactDummyInput` is connected to the model in the basic_pipe, the inference stage is skipped."}),
"refiner_ratio": ("FLOAT", {"default": 0.2, "min": 0.0, "max": 1.0}),
},
"optional": {
"refiner_basic_pipe_opt": ("BASIC_PIPE",),
"noise_mask_feather": ("INT", {"default": 20, "min": 0, "max": 100, "step": 1}),
"scheduler_func_opt": ("SCHEDULER_FUNC",),
}
}
RETURN_TYPES = ("SEGS", "IMAGE")
RETURN_NAMES = ("segs", "cnet_images")
OUTPUT_IS_LIST = (False, True)
FUNCTION = "doit"
CATEGORY = "ImpactPack/Detailer"
DESCRIPTION = "This node enhances details by inpainting each region within the detected area bundle (SEGS) after enlarging them based on the guide size.\nThis node is applied specifically to SEGS rather than the entire image. To apply it to the entire image, use the 'SEGS Paste' node.\nAs a specialized detailer node for improving video details, such as in AnimateDiff, this node can handle cases where the masks contained in SEGS serve as batch masks spanning multiple frames."
@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, noise_mask_feather=0, scheduler_func_opt=None):
model, clip, vae, positive, negative = basic_pipe
if refiner_basic_pipe_opt is None:
refiner_model, refiner_clip, refiner_positive, refiner_negative = None, None, None, None
else:
refiner_model, refiner_clip, _, refiner_positive, refiner_negative = refiner_basic_pipe_opt
segs = core.segs_scale_match(segs, image_frames.shape)
new_segs = []
cnet_image_list = []
if not (isinstance(model, str) and model == "DUMMY") and noise_mask_feather > 0 and 'denoise_mask_function' not in model.model_options:
model = nodes_differential_diffusion.DifferentialDiffusion().apply(model)[0]
for seg in segs[1]:
cropped_image_frames = None
for image in image_frames:
image = image.unsqueeze(0)
cropped_image = seg.cropped_image if seg.cropped_image is not None else crop_tensor4(image, seg.crop_region)
cropped_image = to_tensor(cropped_image)
if cropped_image_frames is None:
cropped_image_frames = cropped_image
else:
cropped_image_frames = torch.concat((cropped_image_frames, cropped_image), dim=0)
cropped_image_frames = cropped_image_frames.cpu().numpy()
# It is assumed that AnimateDiff does not support conditioning masks based on test results, but it will be added for future consideration.
cropped_positive = [
[condition, {
k: core.crop_condition_mask(v, cropped_image_frames, seg.crop_region) if k == "mask" else v
for k, v in details.items()
}]
for condition, details in positive
]
cropped_negative = [
[condition, {
k: core.crop_condition_mask(v, cropped_image_frames, seg.crop_region) if k == "mask" else v
for k, v in details.items()
}]
for condition, details in negative
]
if not (isinstance(model, str) and model == "DUMMY"):
enhanced_image_tensor, cnet_images = core.enhance_detail_for_animatediff(cropped_image_frames, model, clip, vae, guide_size, guide_size_for, max_size,
seg.bbox, seed, steps, cfg, sampler_name, scheduler,
cropped_positive, cropped_negative, denoise, seg.cropped_mask,
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,
noise_mask_feather=noise_mask_feather, scheduler_func=scheduler_func_opt)
else:
enhanced_image_tensor = cropped_image_frames
cnet_images = None
if cnet_images is not None:
cnet_image_list.extend(cnet_images)
if enhanced_image_tensor is None:
new_cropped_image = cropped_image_frames
else:
new_cropped_image = enhanced_image_tensor.cpu().numpy()
new_seg = SEG(new_cropped_image, seg.cropped_mask, seg.confidence, seg.crop_region, seg.bbox, seg.label, None)
new_segs.append(new_seg)
return (segs[0], new_segs), cnet_image_list
def doit(self, 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, scheduler_func_opt=None):
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,
noise_mask_feather=noise_mask_feather, scheduler_func_opt=scheduler_func_opt)
if len(cnet_images) == 0:
cnet_images = [empty_pil_tensor()]
return (segs, cnet_images)
class DetailerForEachPipeForAnimateDiff:
@classmethod
def INPUT_TYPES(cls):
return {"required": {
"image_frames": ("IMAGE", ),
"segs": ("SEGS", ),
"guide_size": ("FLOAT", {"default": 512, "min": 64, "max": nodes.MAX_RESOLUTION, "step": 8}),
"guide_size_for": ("BOOLEAN", {"default": True, "label_on": "bbox", "label_off": "crop_region"}),
"max_size": ("FLOAT", {"default": 1024, "min": 64, "max": nodes.MAX_RESOLUTION, "step": 8}),
"seed": ("INT", {"default": 0, "min": 0, "max": 0xffffffffffffffff}),
"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": (core.SCHEDULERS,),
"denoise": ("FLOAT", {"default": 0.5, "min": 0.0001, "max": 1.0, "step": 0.01}),
"feather": ("INT", {"default": 5, "min": 0, "max": 100, "step": 1}),
"basic_pipe": ("BASIC_PIPE", {"tooltip": "If the `ImpactDummyInput` is connected to the model in the basic_pipe, the inference stage is skipped."}),
"refiner_ratio": ("FLOAT", {"default": 0.2, "min": 0.0, "max": 1.0}),
},
"optional": {
"detailer_hook": ("DETAILER_HOOK",),
"refiner_basic_pipe_opt": ("BASIC_PIPE",),
"noise_mask_feather": ("INT", {"default": 20, "min": 0, "max": 100, "step": 1}),
"scheduler_func_opt": ("SCHEDULER_FUNC",),
}
}
RETURN_TYPES = ("IMAGE", "SEGS", "BASIC_PIPE", "IMAGE")
RETURN_NAMES = ("image", "segs", "basic_pipe", "cnet_images")
OUTPUT_IS_LIST = (False, False, False, True)
FUNCTION = "doit"
CATEGORY = "ImpactPack/Detailer"
DESCRIPTION = "This node enhances details by inpainting each region within the detected area bundle (SEGS) after enlarging them based on the guide size.\nThis node is a specialized detailer node for enhancing video details, such as in AnimateDiff. It can handle cases where the masks contained in SEGS serve as batch masks spanning multiple frames."
@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,
noise_mask_feather=0, scheduler_func_opt=None):
enhanced_segs = []
cnet_image_list = []
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, noise_mask_feather, scheduler_func_opt=scheduler_func_opt)
image_frames = SEGSPaste.doit(image_frames, enhanced_seg, feather, alpha=255)[0]
if cnet_images is not None:
cnet_image_list.extend(cnet_images)
if detailer_hook is not None:
image_frames = detailer_hook.post_paste(image_frames)
enhanced_segs += enhanced_seg[1]
new_segs = segs[0], enhanced_segs
return image_frames, new_segs, basic_pipe, cnet_image_list
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