mirror of
https://github.com/comfyanonymous/ComfyUI.git
synced 2025-01-11 02:15:17 +00:00
Refactor and improve the sag node.
Moved all the sag related code to comfy_extras/nodes_sag.py
This commit is contained in:
comfyanonymous
parent
6761233e9d
commit
ba04a87d10
@ -61,6 +61,9 @@ class ModelPatcher:
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else:
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self.model_options["sampler_cfg_function"] = sampler_cfg_function
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def set_model_sampler_post_cfg_function(self, post_cfg_function):
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self.model_options["sampler_post_cfg_function"] = self.model_options.get("sampler_post_cfg_function", []) + [post_cfg_function]
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def set_model_unet_function_wrapper(self, unet_wrapper_function):
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self.model_options["model_function_wrapper"] = unet_wrapper_function
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@ -70,13 +73,17 @@ class ModelPatcher:
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to["patches"] = {}
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to["patches"][name] = to["patches"].get(name, []) + [patch]
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def set_model_patch_replace(self, patch, name, block_name, number):
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def set_model_patch_replace(self, patch, name, block_name, number, transformer_index=None):
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to = self.model_options["transformer_options"]
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if "patches_replace" not in to:
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to["patches_replace"] = {}
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if name not in to["patches_replace"]:
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to["patches_replace"][name] = {}
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to["patches_replace"][name][(block_name, number)] = patch
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if transformer_index is not None:
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block = (block_name, number, transformer_index)
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else:
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block = (block_name, number)
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to["patches_replace"][name][block] = patch
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def set_model_attn1_patch(self, patch):
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self.set_model_patch(patch, "attn1_patch")
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@ -84,11 +91,11 @@ class ModelPatcher:
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def set_model_attn2_patch(self, patch):
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self.set_model_patch(patch, "attn2_patch")
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def set_model_attn1_replace(self, patch, block_name, number):
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self.set_model_patch_replace(patch, "attn1", block_name, number)
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def set_model_attn1_replace(self, patch, block_name, number, transformer_index=None):
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self.set_model_patch_replace(patch, "attn1", block_name, number, transformer_index)
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def set_model_attn2_replace(self, patch, block_name, number):
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self.set_model_patch_replace(patch, "attn2", block_name, number)
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def set_model_attn2_replace(self, patch, block_name, number, transformer_index=None):
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self.set_model_patch_replace(patch, "attn2", block_name, number, transformer_index)
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def set_model_attn1_output_patch(self, patch):
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self.set_model_patch(patch, "attn1_output_patch")
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@ -1,7 +1,6 @@
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from .k_diffusion import sampling as k_diffusion_sampling
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from .extra_samplers import uni_pc
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import torch
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import torch.nn.functional as F
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import enum
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from comfy import model_management
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import math
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@ -9,310 +8,260 @@ from comfy import model_base
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import comfy.utils
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import comfy.conds
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def get_area_and_mult(conds, x_in, timestep_in):
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area = (x_in.shape[2], x_in.shape[3], 0, 0)
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strength = 1.0
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if 'timestep_start' in conds:
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timestep_start = conds['timestep_start']
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if timestep_in[0] > timestep_start:
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return None
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if 'timestep_end' in conds:
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timestep_end = conds['timestep_end']
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if timestep_in[0] < timestep_end:
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return None
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if 'area' in conds:
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area = conds['area']
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if 'strength' in conds:
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strength = conds['strength']
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input_x = x_in[:,:,area[2]:area[0] + area[2],area[3]:area[1] + area[3]]
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if 'mask' in conds:
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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 conds:
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mask_strength = conds["mask_strength"]
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mask = conds['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 conds:
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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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conditioning = {}
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model_conds = conds["model_conds"]
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for c in model_conds:
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conditioning[c] = model_conds[c].process_cond(batch_size=x_in.shape[0], device=x_in.device, area=area)
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control = None
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if 'control' in conds:
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control = conds['control']
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patches = None
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if 'gligen' in conds:
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gligen = conds['gligen']
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patches = {}
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gligen_type = gligen[0]
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gligen_model = gligen[1]
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if gligen_type == "position":
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gligen_patch = gligen_model.model.set_position(input_x.shape, gligen[2], input_x.device)
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else:
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gligen_patch = gligen_model.model.set_empty(input_x.shape, input_x.device)
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patches['middle_patch'] = [gligen_patch]
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return (input_x, mult, conditioning, area, control, patches)
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def cond_equal_size(c1, c2):
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if c1 is c2:
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return True
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if c1.keys() != c2.keys():
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return False
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for k in c1:
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if not c1[k].can_concat(c2[k]):
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return False
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return True
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def can_concat_cond(c1, c2):
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if c1[0].shape != c2[0].shape:
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return False
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#control
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if (c1[4] is None) != (c2[4] is None):
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return False
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if c1[4] is not None:
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if c1[4] is not c2[4]:
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return False
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#patches
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if (c1[5] is None) != (c2[5] is None):
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return False
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if (c1[5] is not None):
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if c1[5] is not c2[5]:
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return False
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return cond_equal_size(c1[2], c2[2])
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def cond_cat(c_list):
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c_crossattn = []
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c_concat = []
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c_adm = []
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crossattn_max_len = 0
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temp = {}
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for x in c_list:
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for k in x:
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cur = temp.get(k, [])
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cur.append(x[k])
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temp[k] = cur
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out = {}
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for k in temp:
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conds = temp[k]
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out[k] = conds[0].concat(conds[1:])
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return out
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def calc_cond_uncond_batch(model, cond, uncond, x_in, timestep, model_options):
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out_cond = torch.zeros_like(x_in)
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out_count = torch.ones_like(x_in) * 1e-37
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out_uncond = torch.zeros_like(x_in)
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out_uncond_count = torch.ones_like(x_in) * 1e-37
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COND = 0
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UNCOND = 1
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to_run = []
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for x in cond:
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p = get_area_and_mult(x, x_in, timestep)
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if p is None:
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continue
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to_run += [(p, COND)]
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if uncond is not None:
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for x in uncond:
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p = get_area_and_mult(x, x_in, timestep)
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if p is None:
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continue
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to_run += [(p, UNCOND)]
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while len(to_run) > 0:
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first = to_run[0]
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first_shape = first[0][0].shape
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to_batch_temp = []
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for x in range(len(to_run)):
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if can_concat_cond(to_run[x][0], first[0]):
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to_batch_temp += [x]
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to_batch_temp.reverse()
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to_batch = to_batch_temp[:1]
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free_memory = model_management.get_free_memory(x_in.device)
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for i in range(1, len(to_batch_temp) + 1):
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batch_amount = to_batch_temp[:len(to_batch_temp)//i]
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input_shape = [len(batch_amount) * first_shape[0]] + list(first_shape)[1:]
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if model.memory_required(input_shape) < free_memory:
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to_batch = batch_amount
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break
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input_x = []
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mult = []
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c = []
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cond_or_uncond = []
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area = []
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control = None
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patches = None
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for x in to_batch:
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o = to_run.pop(x)
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p = o[0]
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input_x += [p[0]]
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mult += [p[1]]
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c += [p[2]]
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area += [p[3]]
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cond_or_uncond += [o[1]]
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control = p[4]
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patches = p[5]
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batch_chunks = len(cond_or_uncond)
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input_x = torch.cat(input_x)
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c = cond_cat(c)
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timestep_ = torch.cat([timestep] * batch_chunks)
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if control is not None:
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c['control'] = control.get_control(input_x, timestep_, c, len(cond_or_uncond))
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transformer_options = {}
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if 'transformer_options' in model_options:
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transformer_options = model_options['transformer_options'].copy()
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if patches is not None:
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if "patches" in transformer_options:
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cur_patches = transformer_options["patches"].copy()
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for p in patches:
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if p in cur_patches:
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cur_patches[p] = cur_patches[p] + patches[p]
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else:
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cur_patches[p] = patches[p]
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else:
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transformer_options["patches"] = patches
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transformer_options["cond_or_uncond"] = cond_or_uncond[:]
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transformer_options["sigmas"] = timestep
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c['transformer_options'] = transformer_options
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if 'model_function_wrapper' in model_options:
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output = model_options['model_function_wrapper'](model.apply_model, {"input": input_x, "timestep": timestep_, "c": c, "cond_or_uncond": cond_or_uncond}).chunk(batch_chunks)
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else:
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output = model.apply_model(input_x, timestep_, **c).chunk(batch_chunks)
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del input_x
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for o in range(batch_chunks):
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if cond_or_uncond[o] == COND:
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out_cond[:,:,area[o][2]:area[o][0] + area[o][2],area[o][3]:area[o][1] + area[o][3]] += output[o] * mult[o]
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out_count[:,:,area[o][2]:area[o][0] + area[o][2],area[o][3]:area[o][1] + area[o][3]] += mult[o]
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else:
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out_uncond[:,:,area[o][2]:area[o][0] + area[o][2],area[o][3]:area[o][1] + area[o][3]] += output[o] * mult[o]
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out_uncond_count[:,:,area[o][2]:area[o][0] + area[o][2],area[o][3]:area[o][1] + area[o][3]] += mult[o]
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del mult
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out_cond /= out_count
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del out_count
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out_uncond /= out_uncond_count
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del out_uncond_count
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return out_cond, out_uncond
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#The main sampling function shared by all the samplers
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#Returns denoised
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def sampling_function(model, x, timestep, uncond, cond, cond_scale, model_options={}, seed=None):
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def get_area_and_mult(conds, x_in, timestep_in):
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area = (x_in.shape[2], x_in.shape[3], 0, 0)
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strength = 1.0
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if math.isclose(cond_scale, 1.0):
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uncond_ = None
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else:
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uncond_ = uncond
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if 'timestep_start' in conds:
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timestep_start = conds['timestep_start']
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if timestep_in[0] > timestep_start:
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return None
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if 'timestep_end' in conds:
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timestep_end = conds['timestep_end']
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if timestep_in[0] < timestep_end:
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return None
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if 'area' in conds:
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area = conds['area']
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if 'strength' in conds:
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strength = conds['strength']
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input_x = x_in[:,:,area[2]:area[0] + area[2],area[3]:area[1] + area[3]]
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if 'mask' in conds:
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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 conds:
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mask_strength = conds["mask_strength"]
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mask = conds['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 conds:
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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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conditioning = {}
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model_conds = conds["model_conds"]
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for c in model_conds:
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conditioning[c] = model_conds[c].process_cond(batch_size=x_in.shape[0], device=x_in.device, area=area)
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control = None
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if 'control' in conds:
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control = conds['control']
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patches = None
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if 'gligen' in conds:
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gligen = conds['gligen']
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patches = {}
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gligen_type = gligen[0]
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gligen_model = gligen[1]
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if gligen_type == "position":
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gligen_patch = gligen_model.model.set_position(input_x.shape, gligen[2], input_x.device)
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else:
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gligen_patch = gligen_model.model.set_empty(input_x.shape, input_x.device)
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patches['middle_patch'] = [gligen_patch]
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return (input_x, mult, conditioning, area, control, patches)
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def cond_equal_size(c1, c2):
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if c1 is c2:
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return True
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if c1.keys() != c2.keys():
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return False
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for k in c1:
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if not c1[k].can_concat(c2[k]):
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return False
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return True
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def can_concat_cond(c1, c2):
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if c1[0].shape != c2[0].shape:
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return False
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#control
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if (c1[4] is None) != (c2[4] is None):
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return False
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if c1[4] is not None:
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if c1[4] is not c2[4]:
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return False
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#patches
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if (c1[5] is None) != (c2[5] is None):
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return False
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if (c1[5] is not None):
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if c1[5] is not c2[5]:
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return False
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return cond_equal_size(c1[2], c2[2])
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def cond_cat(c_list):
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c_crossattn = []
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c_concat = []
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c_adm = []
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crossattn_max_len = 0
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temp = {}
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for x in c_list:
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for k in x:
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cur = temp.get(k, [])
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cur.append(x[k])
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temp[k] = cur
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out = {}
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for k in temp:
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conds = temp[k]
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out[k] = conds[0].concat(conds[1:])
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return out
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def calc_cond_uncond_batch(model, cond, uncond, x_in, timestep, model_options):
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out_cond = torch.zeros_like(x_in)
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out_count = torch.ones_like(x_in) * 1e-37
|
||||
|
||||
out_uncond = torch.zeros_like(x_in)
|
||||
out_uncond_count = torch.ones_like(x_in) * 1e-37
|
||||
|
||||
COND = 0
|
||||
UNCOND = 1
|
||||
|
||||
to_run = []
|
||||
for x in cond:
|
||||
p = get_area_and_mult(x, x_in, timestep)
|
||||
if p is None:
|
||||
continue
|
||||
|
||||
to_run += [(p, COND)]
|
||||
if uncond is not None:
|
||||
for x in uncond:
|
||||
p = get_area_and_mult(x, x_in, timestep)
|
||||
if p is None:
|
||||
continue
|
||||
|
||||
to_run += [(p, UNCOND)]
|
||||
|
||||
while len(to_run) > 0:
|
||||
first = to_run[0]
|
||||
first_shape = first[0][0].shape
|
||||
to_batch_temp = []
|
||||
for x in range(len(to_run)):
|
||||
if can_concat_cond(to_run[x][0], first[0]):
|
||||
to_batch_temp += [x]
|
||||
|
||||
to_batch_temp.reverse()
|
||||
to_batch = to_batch_temp[:1]
|
||||
|
||||
free_memory = model_management.get_free_memory(x_in.device)
|
||||
for i in range(1, len(to_batch_temp) + 1):
|
||||
batch_amount = to_batch_temp[:len(to_batch_temp)//i]
|
||||
input_shape = [len(batch_amount) * first_shape[0]] + list(first_shape)[1:]
|
||||
if model.memory_required(input_shape) < free_memory:
|
||||
to_batch = batch_amount
|
||||
break
|
||||
|
||||
input_x = []
|
||||
mult = []
|
||||
c = []
|
||||
cond_or_uncond = []
|
||||
area = []
|
||||
control = None
|
||||
patches = None
|
||||
for x in to_batch:
|
||||
o = to_run.pop(x)
|
||||
p = o[0]
|
||||
input_x += [p[0]]
|
||||
mult += [p[1]]
|
||||
c += [p[2]]
|
||||
area += [p[3]]
|
||||
cond_or_uncond += [o[1]]
|
||||
control = p[4]
|
||||
patches = p[5]
|
||||
|
||||
batch_chunks = len(cond_or_uncond)
|
||||
input_x = torch.cat(input_x)
|
||||
c = cond_cat(c)
|
||||
timestep_ = torch.cat([timestep] * batch_chunks)
|
||||
|
||||
if control is not None:
|
||||
c['control'] = control.get_control(input_x, timestep_, c, len(cond_or_uncond))
|
||||
|
||||
transformer_options = {}
|
||||
if 'transformer_options' in model_options:
|
||||
transformer_options = model_options['transformer_options'].copy()
|
||||
|
||||
if patches is not None:
|
||||
if "patches" in transformer_options:
|
||||
cur_patches = transformer_options["patches"].copy()
|
||||
for p in patches:
|
||||
if p in cur_patches:
|
||||
cur_patches[p] = cur_patches[p] + patches[p]
|
||||
else:
|
||||
cur_patches[p] = patches[p]
|
||||
else:
|
||||
transformer_options["patches"] = patches
|
||||
|
||||
transformer_options["cond_or_uncond"] = cond_or_uncond[:]
|
||||
transformer_options["sigmas"] = timestep
|
||||
|
||||
c['transformer_options'] = transformer_options
|
||||
|
||||
if 'model_function_wrapper' in model_options:
|
||||
output = model_options['model_function_wrapper'](model.apply_model, {"input": input_x, "timestep": timestep_, "c": c, "cond_or_uncond": cond_or_uncond}).chunk(batch_chunks)
|
||||
else:
|
||||
output = model.apply_model(input_x, timestep_, **c).chunk(batch_chunks)
|
||||
del input_x
|
||||
|
||||
for o in range(batch_chunks):
|
||||
if cond_or_uncond[o] == COND:
|
||||
out_cond[:,:,area[o][2]:area[o][0] + area[o][2],area[o][3]:area[o][1] + area[o][3]] += output[o] * mult[o]
|
||||
out_count[:,:,area[o][2]:area[o][0] + area[o][2],area[o][3]:area[o][1] + area[o][3]] += mult[o]
|
||||
else:
|
||||
out_uncond[:,:,area[o][2]:area[o][0] + area[o][2],area[o][3]:area[o][1] + area[o][3]] += output[o] * mult[o]
|
||||
out_uncond_count[:,:,area[o][2]:area[o][0] + area[o][2],area[o][3]:area[o][1] + area[o][3]] += mult[o]
|
||||
del mult
|
||||
|
||||
out_cond /= out_count
|
||||
del out_count
|
||||
out_uncond /= out_uncond_count
|
||||
del out_uncond_count
|
||||
return out_cond, out_uncond
|
||||
|
||||
|
||||
# if we're doing SAG, we still need to do uncond guidance, even though the cond and uncond will cancel out.
|
||||
if math.isclose(cond_scale, 1.0) and "sag" not in model_options:
|
||||
uncond = None
|
||||
|
||||
cond_pred, uncond_pred = calc_cond_uncond_batch(model, cond, uncond, x, timestep, model_options)
|
||||
cond_pred, uncond_pred = calc_cond_uncond_batch(model, cond, uncond_, x, timestep, model_options)
|
||||
cfg_result = uncond_pred + (cond_pred - uncond_pred) * cond_scale
|
||||
if "sampler_cfg_function" in model_options:
|
||||
args = {"cond": x - cond_pred, "uncond": x - uncond_pred, "cond_scale": cond_scale, "timestep": timestep, "input": x, "sigma": timestep}
|
||||
cfg_result = x - model_options["sampler_cfg_function"](args)
|
||||
|
||||
if "sag" in model_options:
|
||||
assert uncond is not None, "SAG requires uncond guidance"
|
||||
sag_scale = model_options["sag_scale"]
|
||||
sag_sigma = model_options["sag_sigma"]
|
||||
sag_threshold = model_options.get("sag_threshold", 1.0)
|
||||
for fn in model_options.get("sampler_post_cfg_function", []):
|
||||
args = {"denoised": cfg_result, "cond": cond, "uncond": uncond, "model": model, "uncond_denoised": uncond_pred, "cond_denoised": cond_pred,
|
||||
"sigma": timestep, "model_options": model_options, "input": x}
|
||||
cfg_result = fn(args)
|
||||
|
||||
# these methods are added by the sag patcher
|
||||
uncond_attn = model.get_attn_scores()
|
||||
mid_shape = model.get_mid_block_shape()
|
||||
# create the adversarially blurred image
|
||||
degraded = create_blur_map(uncond_pred, uncond_attn, mid_shape, sag_sigma, sag_threshold)
|
||||
degraded_noised = degraded + x - uncond_pred
|
||||
# call into the UNet
|
||||
(sag, _) = calc_cond_uncond_batch(model, uncond, None, degraded_noised, timestep, model_options)
|
||||
cfg_result += (degraded - sag) * sag_scale
|
||||
return cfg_result
|
||||
|
||||
def create_blur_map(x0, attn, mid_shape, sigma=3.0, threshold=1.0):
|
||||
# reshape and GAP the attention map
|
||||
_, hw1, hw2 = attn.shape
|
||||
b, _, lh, lw = x0.shape
|
||||
attn = attn.reshape(b, -1, hw1, hw2)
|
||||
# Global Average Pool
|
||||
mask = attn.mean(1, keepdim=False).sum(1, keepdim=False) > threshold
|
||||
# Reshape
|
||||
mask = (
|
||||
mask.reshape(b, *mid_shape)
|
||||
.unsqueeze(1)
|
||||
.type(attn.dtype)
|
||||
)
|
||||
# Upsample
|
||||
mask = F.interpolate(mask, (lh, lw))
|
||||
|
||||
blurred = gaussian_blur_2d(x0, kernel_size=9, sigma=sigma)
|
||||
blurred = blurred * mask + x0 * (1 - mask)
|
||||
return blurred
|
||||
|
||||
def gaussian_blur_2d(img, kernel_size, sigma):
|
||||
ksize_half = (kernel_size - 1) * 0.5
|
||||
|
||||
x = torch.linspace(-ksize_half, ksize_half, steps=kernel_size)
|
||||
|
||||
pdf = torch.exp(-0.5 * (x / sigma).pow(2))
|
||||
|
||||
x_kernel = pdf / pdf.sum()
|
||||
x_kernel = x_kernel.to(device=img.device, dtype=img.dtype)
|
||||
|
||||
kernel2d = torch.mm(x_kernel[:, None], x_kernel[None, :])
|
||||
kernel2d = kernel2d.expand(img.shape[-3], 1, kernel2d.shape[0], kernel2d.shape[1])
|
||||
|
||||
padding = [kernel_size // 2, kernel_size // 2, kernel_size // 2, kernel_size // 2]
|
||||
|
||||
img = F.pad(img, padding, mode="reflect")
|
||||
img = F.conv2d(img, kernel2d, groups=img.shape[-3])
|
||||
return img
|
||||
|
||||
class CFGNoisePredictor(torch.nn.Module):
|
||||
def __init__(self, model):
|
||||
super().__init__()
|
||||
|
||||
@ -1,8 +1,12 @@
|
||||
import torch
|
||||
from torch import einsum
|
||||
import torch.nn.functional as F
|
||||
import math
|
||||
|
||||
from einops import rearrange, repeat
|
||||
import os
|
||||
from comfy.ldm.modules.attention import optimized_attention, _ATTN_PRECISION
|
||||
import comfy.samplers
|
||||
|
||||
# from comfy/ldm/modules/attention.py
|
||||
# but modified to return attention scores as well as output
|
||||
@ -49,7 +53,49 @@ def attention_basic_with_sim(q, k, v, heads, mask=None):
|
||||
)
|
||||
return (out, sim)
|
||||
|
||||
class SagNode:
|
||||
def create_blur_map(x0, attn, sigma=3.0, threshold=1.0):
|
||||
# reshape and GAP the attention map
|
||||
_, hw1, hw2 = attn.shape
|
||||
b, _, lh, lw = x0.shape
|
||||
attn = attn.reshape(b, -1, hw1, hw2)
|
||||
# Global Average Pool
|
||||
mask = attn.mean(1, keepdim=False).sum(1, keepdim=False) > threshold
|
||||
ratio = round(math.sqrt(lh * lw / hw1))
|
||||
mid_shape = [math.ceil(lh / ratio), math.ceil(lw / ratio)]
|
||||
|
||||
# Reshape
|
||||
mask = (
|
||||
mask.reshape(b, *mid_shape)
|
||||
.unsqueeze(1)
|
||||
.type(attn.dtype)
|
||||
)
|
||||
# Upsample
|
||||
mask = F.interpolate(mask, (lh, lw))
|
||||
|
||||
blurred = gaussian_blur_2d(x0, kernel_size=9, sigma=sigma)
|
||||
blurred = blurred * mask + x0 * (1 - mask)
|
||||
return blurred
|
||||
|
||||
def gaussian_blur_2d(img, kernel_size, sigma):
|
||||
ksize_half = (kernel_size - 1) * 0.5
|
||||
|
||||
x = torch.linspace(-ksize_half, ksize_half, steps=kernel_size)
|
||||
|
||||
pdf = torch.exp(-0.5 * (x / sigma).pow(2))
|
||||
|
||||
x_kernel = pdf / pdf.sum()
|
||||
x_kernel = x_kernel.to(device=img.device, dtype=img.dtype)
|
||||
|
||||
kernel2d = torch.mm(x_kernel[:, None], x_kernel[None, :])
|
||||
kernel2d = kernel2d.expand(img.shape[-3], 1, kernel2d.shape[0], kernel2d.shape[1])
|
||||
|
||||
padding = [kernel_size // 2, kernel_size // 2, kernel_size // 2, kernel_size // 2]
|
||||
|
||||
img = F.pad(img, padding, mode="reflect")
|
||||
img = F.conv2d(img, kernel2d, groups=img.shape[-3])
|
||||
return img
|
||||
|
||||
class SelfAttentionGuidance:
|
||||
@classmethod
|
||||
def INPUT_TYPES(s):
|
||||
return {"required": { "model": ("MODEL",),
|
||||
@ -63,15 +109,9 @@ class SagNode:
|
||||
|
||||
def patch(self, model, scale, blur_sigma):
|
||||
m = model.clone()
|
||||
# set extra options on the model
|
||||
m.model_options["sag"] = True
|
||||
m.model_options["sag_scale"] = scale
|
||||
m.model_options["sag_sigma"] = blur_sigma
|
||||
|
||||
|
||||
attn_scores = None
|
||||
mid_block_shape = None
|
||||
m.model.get_attn_scores = lambda: attn_scores
|
||||
m.model.get_mid_block_shape = lambda: mid_block_shape
|
||||
|
||||
# TODO: make this work properly with chunked batches
|
||||
# currently, we can only save the attn from one UNet call
|
||||
@ -92,24 +132,41 @@ class SagNode:
|
||||
else:
|
||||
return optimized_attention(q, k, v, heads=heads)
|
||||
|
||||
def post_cfg_function(args):
|
||||
nonlocal attn_scores
|
||||
nonlocal mid_block_shape
|
||||
uncond_attn = attn_scores
|
||||
|
||||
sag_scale = scale
|
||||
sag_sigma = blur_sigma
|
||||
sag_threshold = 1.0
|
||||
model = args["model"]
|
||||
uncond_pred = args["uncond_denoised"]
|
||||
uncond = args["uncond"]
|
||||
cfg_result = args["denoised"]
|
||||
sigma = args["sigma"]
|
||||
model_options = args["model_options"]
|
||||
x = args["input"]
|
||||
|
||||
# create the adversarially blurred image
|
||||
degraded = create_blur_map(uncond_pred, uncond_attn, sag_sigma, sag_threshold)
|
||||
degraded_noised = degraded + x - uncond_pred
|
||||
# call into the UNet
|
||||
(sag, _) = comfy.samplers.calc_cond_uncond_batch(model, uncond, None, degraded_noised, sigma, model_options)
|
||||
return cfg_result + (degraded - sag) * sag_scale
|
||||
|
||||
m.set_model_sampler_post_cfg_function(post_cfg_function)
|
||||
|
||||
# from diffusers:
|
||||
# unet.mid_block.attentions[0].transformer_blocks[0].attn1.patch
|
||||
def set_model_patch_replace(patch, name, key):
|
||||
to = m.model_options["transformer_options"]
|
||||
if "patches_replace" not in to:
|
||||
to["patches_replace"] = {}
|
||||
if name not in to["patches_replace"]:
|
||||
to["patches_replace"][name] = {}
|
||||
to["patches_replace"][name][key] = patch
|
||||
set_model_patch_replace(attn_and_record, "attn1", ("middle", 0, 0))
|
||||
# from diffusers:
|
||||
# unet.mid_block.attentions[0].register_forward_hook()
|
||||
def forward_hook(m, inp, out):
|
||||
nonlocal mid_block_shape
|
||||
mid_block_shape = out[0].shape[-2:]
|
||||
m.model.diffusion_model.middle_block[0].register_forward_hook(forward_hook)
|
||||
m.set_model_attn1_replace(attn_and_record, "middle", 0, 0)
|
||||
|
||||
return (m, )
|
||||
|
||||
NODE_CLASS_MAPPINGS = {
|
||||
"Self-Attention Guidance": SagNode,
|
||||
"SelfAttentionGuidance": SelfAttentionGuidance,
|
||||
}
|
||||
|
||||
NODE_DISPLAY_NAME_MAPPINGS = {
|
||||
"SelfAttentionGuidance": "Self-Attention Guidance",
|
||||
}
|
||||
|
||||
Loading…
Reference in New Issue
Block a user