Dr.Lt.Data
d74c1d0112
Resolves `AttributeError: 'DifferentialDiffusion' object has no attribute 'execute'` on older ComfyUI (<0.3.63) where the V3 schema migration has not landed yet. The prior code called `.execute(model)[0]` directly, breaking anyone still on the legacy `.apply(model)` API. Introduce `modules/impact/utils.apply_differential_diffusion(model)` which uses `hasattr` to pick the correct branch at runtime: - `.execute` -> V3 classmethod (ComfyUI >= 0.3.63) - `.apply` -> legacy instance method (older) - otherwise -> AttributeError with upgrade guidance Replace all 7 direct call sites (animatediff_nodes, core x2, impact_pack x2, segs_nodes, segs_upscaler) with the helper. The existing import guard in segs_upscaler is preserved; the helper's deferred import means ImportError still propagates for very old ComfyUI builds without `comfy_extras.nodes_differential_diffusion`. Fixes GitHub issue #1177. Also add an E2E test harness: - docs/E2E_TEST_STRATEGY.md — isolated-launch workflow with --disable-all-custom-nodes + --whitelist-custom-nodes, Playwright smoke test, workflow execution via /prompt + /history + /view, verification criteria, operational notes. - tests/e2e_dd_compat.py — reference test that runs SEGSDetailer with noise_mask_feather=20 on a real input image, asserting status==success and non-zero pixel delta between input and paste previews (proves the detailer loop and compat shim actually ran).
201 lines
11 KiB
Python
201 lines
11 KiB
Python
from nodes import MAX_RESOLUTION
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import impact.core as core
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from impact.core import SEG
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from impact.segs_nodes import SEGSPaste
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import comfy
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from impact import utils
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import torch
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import nodes
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import logging
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try:
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from comfy_extras import nodes_differential_diffusion
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except Exception:
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logging.warning("\n#############################################\n[Impact Pack] ComfyUI is an outdated version.\n#############################################\n")
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raise Exception("[Impact Pack] ComfyUI is an outdated version.")
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class SEGSDetailerForAnimateDiff:
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@classmethod
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def INPUT_TYPES(cls):
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return {"required": {
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"image_frames": ("IMAGE", ),
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"segs": ("SEGS", ),
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"guide_size": ("FLOAT", {"default": 512, "min": 64, "max": MAX_RESOLUTION, "step": 8}),
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"guide_size_for": ("BOOLEAN", {"default": True, "label_on": "bbox", "label_off": "crop_region"}),
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"max_size": ("FLOAT", {"default": 768, "min": 64, "max": MAX_RESOLUTION, "step": 8}),
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"seed": ("INT", {"default": 0, "min": 0, "max": 0xffffffffffffffff}),
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"steps": ("INT", {"default": 20, "min": 1, "max": 10000}),
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"cfg": ("FLOAT", {"default": 8.0, "min": 0.0, "max": 100.0}),
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"sampler_name": (comfy.samplers.KSampler.SAMPLERS,),
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"scheduler": (core.get_schedulers(),),
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"denoise": ("FLOAT", {"default": 0.5, "min": 0.0001, "max": 1.0, "step": 0.01}),
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"basic_pipe": ("BASIC_PIPE", {"tooltip": "If the `ImpactDummyInput` is connected to the model in the basic_pipe, the inference stage is skipped."}),
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"refiner_ratio": ("FLOAT", {"default": 0.2, "min": 0.0, "max": 1.0}),
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},
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"optional": {
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"refiner_basic_pipe_opt": ("BASIC_PIPE",),
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"noise_mask_feather": ("INT", {"default": 20, "min": 0, "max": 100, "step": 1}),
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"scheduler_func_opt": ("SCHEDULER_FUNC",),
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}
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}
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RETURN_TYPES = ("SEGS", "IMAGE")
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RETURN_NAMES = ("segs", "cnet_images")
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OUTPUT_IS_LIST = (False, True)
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FUNCTION = "doit"
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CATEGORY = "ImpactPack/Detailer"
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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."
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@staticmethod
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def do_detail(image_frames, segs, guide_size, guide_size_for, max_size, seed, steps, cfg, sampler_name, scheduler,
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denoise, basic_pipe, refiner_ratio=None, refiner_basic_pipe_opt=None, noise_mask_feather=0, scheduler_func_opt=None):
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model, clip, vae, positive, negative = basic_pipe
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if refiner_basic_pipe_opt is None:
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refiner_model, refiner_clip, refiner_positive, refiner_negative = None, None, None, None
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else:
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refiner_model, refiner_clip, _, refiner_positive, refiner_negative = refiner_basic_pipe_opt
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segs = core.segs_scale_match(segs, image_frames.shape)
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new_segs = []
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cnet_image_list = []
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if not (isinstance(model, str) and model == "DUMMY") and noise_mask_feather > 0 and 'denoise_mask_function' not in model.model_options:
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model = utils.apply_differential_diffusion(model)
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for seg in segs[1]:
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cropped_image_frames = None
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for image in image_frames:
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image = image.unsqueeze(0)
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cropped_image = seg.cropped_image if seg.cropped_image is not None else utils.crop_tensor4(image, seg.crop_region)
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cropped_image = utils.to_tensor(cropped_image)
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if cropped_image_frames is None:
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cropped_image_frames = cropped_image
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else:
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cropped_image_frames = torch.concat((cropped_image_frames, cropped_image), dim=0)
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cropped_image_frames = cropped_image_frames.cpu().numpy()
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# It is assumed that AnimateDiff does not support conditioning masks based on test results, but it will be added for future consideration.
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cropped_positive = [
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[condition, {
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k: core.crop_condition_mask(v, cropped_image_frames, seg.crop_region) if k == "mask" else v
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for k, v in details.items()
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}]
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for condition, details in positive
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]
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cropped_negative = [
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[condition, {
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k: core.crop_condition_mask(v, cropped_image_frames, seg.crop_region) if k == "mask" else v
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for k, v in details.items()
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}]
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for condition, details in negative
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]
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if not (isinstance(model, str) and model == "DUMMY"):
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enhanced_image_tensor, cnet_images = core.enhance_detail_for_animatediff(cropped_image_frames, model, clip, vae, guide_size, guide_size_for, max_size,
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seg.bbox, seed, steps, cfg, sampler_name, scheduler,
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cropped_positive, cropped_negative, denoise, seg.cropped_mask,
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refiner_ratio=refiner_ratio, refiner_model=refiner_model,
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refiner_clip=refiner_clip, refiner_positive=refiner_positive,
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refiner_negative=refiner_negative, control_net_wrapper=seg.control_net_wrapper,
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noise_mask_feather=noise_mask_feather, scheduler_func=scheduler_func_opt)
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else:
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enhanced_image_tensor = cropped_image_frames
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cnet_images = None
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if cnet_images is not None:
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cnet_image_list.extend(cnet_images)
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if enhanced_image_tensor is None:
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new_cropped_image = cropped_image_frames
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else:
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new_cropped_image = enhanced_image_tensor.cpu().numpy()
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new_seg = SEG(new_cropped_image, seg.cropped_mask, seg.confidence, seg.crop_region, seg.bbox, seg.label, None)
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new_segs.append(new_seg)
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return (segs[0], new_segs), cnet_image_list
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def doit(self, image_frames, segs, guide_size, guide_size_for, max_size, seed, steps, cfg, sampler_name, scheduler,
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denoise, basic_pipe, refiner_ratio=None, refiner_basic_pipe_opt=None, inpaint_model=False, noise_mask_feather=0, scheduler_func_opt=None):
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segs, cnet_images = SEGSDetailerForAnimateDiff.do_detail(image_frames, segs, guide_size, guide_size_for, max_size, seed, steps, cfg, sampler_name,
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scheduler, denoise, basic_pipe, refiner_ratio, refiner_basic_pipe_opt,
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noise_mask_feather=noise_mask_feather, scheduler_func_opt=scheduler_func_opt)
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if len(cnet_images) == 0:
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cnet_images = [utils.empty_pil_tensor()]
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return (segs, cnet_images)
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class DetailerForEachPipeForAnimateDiff:
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@classmethod
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def INPUT_TYPES(cls):
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return {"required": {
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"image_frames": ("IMAGE", ),
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"segs": ("SEGS", ),
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"guide_size": ("FLOAT", {"default": 512, "min": 64, "max": nodes.MAX_RESOLUTION, "step": 8}),
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"guide_size_for": ("BOOLEAN", {"default": True, "label_on": "bbox", "label_off": "crop_region"}),
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"max_size": ("FLOAT", {"default": 1024, "min": 64, "max": nodes.MAX_RESOLUTION, "step": 8}),
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"seed": ("INT", {"default": 0, "min": 0, "max": 0xffffffffffffffff}),
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"steps": ("INT", {"default": 20, "min": 1, "max": 10000}),
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"cfg": ("FLOAT", {"default": 8.0, "min": 0.0, "max": 100.0}),
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"sampler_name": (comfy.samplers.KSampler.SAMPLERS,),
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"scheduler": (core.get_schedulers(),),
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"denoise": ("FLOAT", {"default": 0.5, "min": 0.0001, "max": 1.0, "step": 0.01}),
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"feather": ("INT", {"default": 5, "min": 0, "max": 100, "step": 1}),
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"basic_pipe": ("BASIC_PIPE", {"tooltip": "If the `ImpactDummyInput` is connected to the model in the basic_pipe, the inference stage is skipped."}),
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"refiner_ratio": ("FLOAT", {"default": 0.2, "min": 0.0, "max": 1.0}),
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},
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"optional": {
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"detailer_hook": ("DETAILER_HOOK",),
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"refiner_basic_pipe_opt": ("BASIC_PIPE",),
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"noise_mask_feather": ("INT", {"default": 20, "min": 0, "max": 100, "step": 1}),
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"scheduler_func_opt": ("SCHEDULER_FUNC",),
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}
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}
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RETURN_TYPES = ("IMAGE", "SEGS", "BASIC_PIPE", "IMAGE")
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RETURN_NAMES = ("image", "segs", "basic_pipe", "cnet_images")
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OUTPUT_IS_LIST = (False, False, False, True)
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FUNCTION = "doit"
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CATEGORY = "ImpactPack/Detailer"
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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."
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@staticmethod
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def doit(image_frames, segs, guide_size, guide_size_for, max_size, seed, steps, cfg, sampler_name, scheduler,
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denoise, feather, basic_pipe, refiner_ratio=None, detailer_hook=None, refiner_basic_pipe_opt=None,
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noise_mask_feather=0, scheduler_func_opt=None):
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enhanced_segs = []
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cnet_image_list = []
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for sub_seg in segs[1]:
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single_seg = segs[0], [sub_seg]
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enhanced_seg, cnet_images = SEGSDetailerForAnimateDiff().do_detail(image_frames, single_seg, guide_size, guide_size_for, max_size, seed, steps, cfg, sampler_name, scheduler,
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denoise, basic_pipe, refiner_ratio, refiner_basic_pipe_opt, noise_mask_feather, scheduler_func_opt=scheduler_func_opt)
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image_frames = SEGSPaste.doit(image_frames, enhanced_seg, feather, alpha=255)[0]
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if cnet_images is not None:
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cnet_image_list.extend(cnet_images)
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if detailer_hook is not None:
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image_frames = detailer_hook.post_paste(image_frames)
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enhanced_segs += enhanced_seg[1]
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new_segs = segs[0], enhanced_segs
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return image_frames, new_segs, basic_pipe, cnet_image_list
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