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https://github.com/comfyanonymous/ComfyUI.git
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Merge branch 'comfyanonymous:master' into feature/maskpainting
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ltdrdata
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@ -23,21 +23,36 @@ def sampling_function(model_function, x, timestep, uncond, cond, cond_scale, con
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adm_cond = cond[1]['adm_encoded']
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input_x = x_in[:,:,area[2]:area[0] + area[2],area[3]:area[1] + area[3]]
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mult = torch.ones_like(input_x) * strength
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if 'mask' in cond[1]:
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# Scale the mask to the size of the input
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# The mask should have been resized as we began the sampling process
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mask_strength = 1.0
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if "mask_strength" in cond[1]:
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mask_strength = cond[1]["mask_strength"]
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mask = cond[1]['mask']
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assert(mask.shape[1] == x_in.shape[2])
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assert(mask.shape[2] == x_in.shape[3])
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mask = mask[:,area[2]:area[0] + area[2],area[3]:area[1] + area[3]] * mask_strength
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mask = mask.unsqueeze(1).repeat(input_x.shape[0] // mask.shape[0], input_x.shape[1], 1, 1)
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else:
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mask = torch.ones_like(input_x)
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mult = mask * strength
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if 'mask' not in cond[1]:
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rr = 8
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if area[2] != 0:
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for t in range(rr):
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mult[:,:,t:1+t,:] *= ((1.0/rr) * (t + 1))
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if (area[0] + area[2]) < x_in.shape[2]:
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for t in range(rr):
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mult[:,:,area[0] - 1 - t:area[0] - t,:] *= ((1.0/rr) * (t + 1))
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if area[3] != 0:
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for t in range(rr):
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mult[:,:,:,t:1+t] *= ((1.0/rr) * (t + 1))
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if (area[1] + area[3]) < x_in.shape[3]:
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for t in range(rr):
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mult[:,:,:,area[1] - 1 - t:area[1] - t] *= ((1.0/rr) * (t + 1))
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rr = 8
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if area[2] != 0:
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for t in range(rr):
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mult[:,:,t:1+t,:] *= ((1.0/rr) * (t + 1))
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if (area[0] + area[2]) < x_in.shape[2]:
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for t in range(rr):
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mult[:,:,area[0] - 1 - t:area[0] - t,:] *= ((1.0/rr) * (t + 1))
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if area[3] != 0:
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for t in range(rr):
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mult[:,:,:,t:1+t] *= ((1.0/rr) * (t + 1))
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if (area[1] + area[3]) < x_in.shape[3]:
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for t in range(rr):
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mult[:,:,:,area[1] - 1 - t:area[1] - t] *= ((1.0/rr) * (t + 1))
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conditionning = {}
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conditionning['c_crossattn'] = cond[0]
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if cond_concat_in is not None and len(cond_concat_in) > 0:
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@ -301,6 +316,71 @@ def blank_inpaint_image_like(latent_image):
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blank_image[:,3] *= 0.1380
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return blank_image
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def get_mask_aabb(masks):
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if masks.numel() == 0:
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return torch.zeros((0, 4), device=masks.device, dtype=torch.int)
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b = masks.shape[0]
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bounding_boxes = torch.zeros((b, 4), device=masks.device, dtype=torch.int)
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is_empty = torch.zeros((b), device=masks.device, dtype=torch.bool)
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for i in range(b):
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mask = masks[i]
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if mask.numel() == 0:
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continue
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if torch.max(mask != 0) == False:
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is_empty[i] = True
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continue
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y, x = torch.where(mask)
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bounding_boxes[i, 0] = torch.min(x)
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bounding_boxes[i, 1] = torch.min(y)
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bounding_boxes[i, 2] = torch.max(x)
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bounding_boxes[i, 3] = torch.max(y)
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return bounding_boxes, is_empty
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def resolve_cond_masks(conditions, h, w, device):
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# We need to decide on an area outside the sampling loop in order to properly generate opposite areas of equal sizes.
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# While we're doing this, we can also resolve the mask device and scaling for performance reasons
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for i in range(len(conditions)):
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c = conditions[i]
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if 'mask' in c[1]:
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mask = c[1]['mask']
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mask = mask.to(device=device)
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modified = c[1].copy()
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if len(mask.shape) == 2:
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mask = mask.unsqueeze(0)
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if mask.shape[2] != h or mask.shape[3] != w:
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mask = torch.nn.functional.interpolate(mask.unsqueeze(1), size=(h, w), mode='bilinear', align_corners=False).squeeze(1)
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if modified.get("set_area_to_bounds", False):
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bounds = torch.max(torch.abs(mask),dim=0).values.unsqueeze(0)
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boxes, is_empty = get_mask_aabb(bounds)
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if is_empty[0]:
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# Use the minimum possible size for efficiency reasons. (Since the mask is all-0, this becomes a noop anyway)
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modified['area'] = (8, 8, 0, 0)
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else:
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box = boxes[0]
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H, W, Y, X = (box[3] - box[1] + 1, box[2] - box[0] + 1, box[1], box[0])
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# Make sure the height and width are divisible by 8
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if X % 8 != 0:
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newx = X // 8 * 8
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W = W + (X - newx)
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X = newx
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if Y % 8 != 0:
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newy = Y // 8 * 8
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H = H + (Y - newy)
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Y = newy
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if H % 8 != 0:
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H = H + (8 - (H % 8))
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if W % 8 != 0:
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W = W + (8 - (W % 8))
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area = (int(H), int(W), int(Y), int(X))
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modified['area'] = area
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modified['mask'] = mask
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conditions[i] = [c[0], modified]
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def create_cond_with_same_area_if_none(conds, c):
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if 'area' not in c[1]:
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return
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@ -461,7 +541,6 @@ class KSampler:
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sigmas = self.calculate_sigmas(new_steps).to(self.device)
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self.sigmas = sigmas[-(steps + 1):]
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def sample(self, noise, positive, negative, cfg, latent_image=None, start_step=None, last_step=None, force_full_denoise=False, denoise_mask=None, sigmas=None, callback=None):
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if sigmas is None:
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sigmas = self.sigmas
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@ -484,6 +563,10 @@ class KSampler:
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positive = positive[:]
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negative = negative[:]
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resolve_cond_masks(positive, noise.shape[2], noise.shape[3], self.device)
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resolve_cond_masks(negative, noise.shape[2], noise.shape[3], self.device)
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#make sure each cond area has an opposite one with the same area
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for c in positive:
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create_cond_with_same_area_if_none(negative, c)
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32
nodes.py
32
nodes.py
@ -80,11 +80,41 @@ class ConditioningSetArea:
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n = [t[0], t[1].copy()]
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n[1]['area'] = (height // 8, width // 8, y // 8, x // 8)
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n[1]['strength'] = strength
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n[1]['set_area_to_bounds'] = False
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n[1]['min_sigma'] = min_sigma
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n[1]['max_sigma'] = max_sigma
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c.append(n)
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return (c, )
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class ConditioningSetMask:
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@classmethod
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def INPUT_TYPES(s):
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return {"required": {"conditioning": ("CONDITIONING", ),
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"mask": ("MASK", ),
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"strength": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.01}),
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"set_cond_area": (["default", "mask bounds"],),
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}}
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RETURN_TYPES = ("CONDITIONING",)
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FUNCTION = "append"
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CATEGORY = "conditioning"
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def append(self, conditioning, mask, set_cond_area, strength):
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c = []
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set_area_to_bounds = False
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if set_cond_area != "default":
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set_area_to_bounds = True
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if len(mask.shape) < 3:
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mask = mask.unsqueeze(0)
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for t in conditioning:
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n = [t[0], t[1].copy()]
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_, h, w = mask.shape
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n[1]['mask'] = mask
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n[1]['set_area_to_bounds'] = set_area_to_bounds
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n[1]['mask_strength'] = strength
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c.append(n)
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return (c, )
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class VAEDecode:
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def __init__(self, device="cpu"):
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self.device = device
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@ -1115,6 +1145,7 @@ NODE_CLASS_MAPPINGS = {
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"ImagePadForOutpaint": ImagePadForOutpaint,
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"ConditioningCombine": ConditioningCombine,
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"ConditioningSetArea": ConditioningSetArea,
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"ConditioningSetMask": ConditioningSetMask,
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"KSamplerAdvanced": KSamplerAdvanced,
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"SetLatentNoiseMask": SetLatentNoiseMask,
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"LatentComposite": LatentComposite,
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@ -1164,6 +1195,7 @@ NODE_DISPLAY_NAME_MAPPINGS = {
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"CLIPSetLastLayer": "CLIP Set Last Layer",
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"ConditioningCombine": "Conditioning (Combine)",
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"ConditioningSetArea": "Conditioning (Set Area)",
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"ConditioningSetMask": "Conditioning (Set Mask)",
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"ControlNetApply": "Apply ControlNet",
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# Latent
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"VAEEncodeForInpaint": "VAE Encode (for Inpainting)",
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@ -6,6 +6,7 @@ app.registerExtension({
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name: "Comfy.SlotDefaults",
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suggestionsNumber: null,
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init() {
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LiteGraph.search_filter_enabled = true;
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LiteGraph.middle_click_slot_add_default_node = true;
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this.suggestionsNumber = app.ui.settings.addSetting({
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id: "Comfy.NodeSuggestions.number",
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@ -43,6 +44,14 @@ app.registerExtension({
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}
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if (this.slot_types_default_out[type].includes(nodeId)) continue;
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this.slot_types_default_out[type].push(nodeId);
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// Input types have to be stored as lower case
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// Store each node that can handle this input type
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const lowerType = type.toLocaleLowerCase();
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if (!(lowerType in LiteGraph.registered_slot_in_types)) {
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LiteGraph.registered_slot_in_types[lowerType] = { nodes: [] };
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}
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LiteGraph.registered_slot_in_types[lowerType].nodes.push(nodeType.comfyClass);
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}
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var outputs = nodeData["output"];
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@ -53,6 +62,16 @@ app.registerExtension({
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}
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this.slot_types_default_in[type].push(nodeId);
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// Store each node that can handle this output type
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if (!(type in LiteGraph.registered_slot_out_types)) {
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LiteGraph.registered_slot_out_types[type] = { nodes: [] };
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}
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LiteGraph.registered_slot_out_types[type].nodes.push(nodeType.comfyClass);
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if(!LiteGraph.slot_types_out.includes(type)) {
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LiteGraph.slot_types_out.push(type);
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}
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}
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var maxNum = this.suggestionsNumber.value;
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this.setDefaults(maxNum);
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@ -5880,10 +5880,10 @@ LGraphNode.prototype.executeAction = function(action)
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node.resizable !== false &&
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isInsideRectangle( e.canvasX,
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e.canvasY,
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node.pos[0] + node.size[0] - 5,
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node.pos[1] + node.size[1] - 5,
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10,
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10
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node.pos[0] + node.size[0] - 15,
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node.pos[1] + node.size[1] - 15,
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20,
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20
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)
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) {
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this.graph.beforeChange();
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@ -6428,10 +6428,10 @@ LGraphNode.prototype.executeAction = function(action)
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isInsideRectangle(
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e.canvasX,
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e.canvasY,
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node.pos[0] + node.size[0] - 5,
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node.pos[1] + node.size[1] - 5,
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5,
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5
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node.pos[0] + node.size[0] - 15,
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node.pos[1] + node.size[1] - 15,
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15,
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15
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)
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) {
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this.canvas.style.cursor = "se-resize";
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@ -120,7 +120,7 @@ body {
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.comfy-menu > button,
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.comfy-menu-btns button,
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.comfy-menu .comfy-list button,
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.comfy-modal button{
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.comfy-modal button {
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color: var(--input-text);
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background-color: var(--comfy-input-bg);
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border-radius: 8px;
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@ -129,6 +129,15 @@ body {
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margin-top: 2px;
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}
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.comfy-menu > button:hover,
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.comfy-menu-btns button:hover,
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.comfy-menu .comfy-list button:hover,
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.comfy-modal button:hover,
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.comfy-settings-btn:hover {
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filter: brightness(1.2);
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cursor: pointer;
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}
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.comfy-menu span.drag-handle {
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width: 10px;
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height: 20px;
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@ -284,4 +293,7 @@ button.comfy-queue-btn {
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top: 0;
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right: 2px;
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}
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.litecontextmenu {
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z-index: 9999 !important;
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}
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