390 lines
15 KiB
Python
390 lines
15 KiB
Python
import torch
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import comfy.model_base
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import comfy.ldm.modules.diffusionmodules.openaimodel
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import comfy.samplers
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from comfy.samplers import model_management, lcm, math
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from comfy.ldm.modules.diffusionmodules.openaimodel import timestep_embedding, forward_timestep_embed
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from modules.filters import gaussian_filter_2d
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sharpness = 2.0
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def sampling_function_patched(model_function, x, timestep, uncond, cond, cond_scale, cond_concat=None, model_options={},
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seed=None):
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def get_area_and_mult(cond, x_in, cond_concat_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 cond[1]:
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timestep_start = cond[1]['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 cond[1]:
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timestep_end = cond[1]['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 cond[1]:
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area = cond[1]['area']
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if 'strength' in cond[1]:
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strength = cond[1]['strength']
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adm_cond = None
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if 'adm_encoded' in cond[1]:
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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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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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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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cropped = []
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for x in cond_concat_in:
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cr = x[:, :, area[2]:area[0] + area[2], area[3]:area[1] + area[3]]
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cropped.append(cr)
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conditionning['c_concat'] = torch.cat(cropped, dim=1)
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if adm_cond is not None:
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conditionning['c_adm'] = adm_cond
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control = None
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if 'control' in cond[1]:
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control = cond[1]['control']
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patches = None
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if 'gligen' in cond[1]:
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gligen = cond[1]['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.set_position(input_x.shape, gligen[2], input_x.device)
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else:
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gligen_patch = gligen_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, conditionning, 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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if 'c_crossattn' in c1:
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s1 = c1['c_crossattn'].shape
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s2 = c2['c_crossattn'].shape
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if s1 != s2:
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if s1[0] != s2[0] or s1[2] != s2[2]: # these 2 cases should not happen
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return False
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mult_min = lcm(s1[1], s2[1])
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diff = mult_min // min(s1[1], s2[1])
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if diff > 4: # arbitrary limit on the padding because it's probably going to impact performance negatively if it's too much
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return False
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if 'c_concat' in c1:
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if c1['c_concat'].shape != c2['c_concat'].shape:
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return False
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if 'c_adm' in c1:
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if c1['c_adm'].shape != c2['c_adm'].shape:
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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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for x in c_list:
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if 'c_crossattn' in x:
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c = x['c_crossattn']
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if crossattn_max_len == 0:
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crossattn_max_len = c.shape[1]
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else:
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crossattn_max_len = lcm(crossattn_max_len, c.shape[1])
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c_crossattn.append(c)
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if 'c_concat' in x:
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c_concat.append(x['c_concat'])
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if 'c_adm' in x:
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c_adm.append(x['c_adm'])
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out = {}
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c_crossattn_out = []
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for c in c_crossattn:
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if c.shape[1] < crossattn_max_len:
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c = c.repeat(1, crossattn_max_len // c.shape[1], 1) # padding with repeat doesn't change result
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c_crossattn_out.append(c)
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if len(c_crossattn_out) > 0:
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out['c_crossattn'] = [torch.cat(c_crossattn_out)]
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if len(c_concat) > 0:
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out['c_concat'] = [torch.cat(c_concat)]
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if len(c_adm) > 0:
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out['c_adm'] = torch.cat(c_adm)
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return out
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def calc_cond_uncond_batch(model_function, cond, uncond, x_in, timestep, max_total_area, cond_concat_in,
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model_options):
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out_cond = torch.zeros_like(x_in)
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out_count = torch.ones_like(x_in) / 100000.0
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out_uncond = torch.zeros_like(x_in)
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out_uncond_count = torch.ones_like(x_in) / 100000.0
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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, cond_concat_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, cond_concat_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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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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if (len(batch_amount) * first_shape[0] * first_shape[2] * first_shape[3] < max_total_area):
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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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c['transformer_options'] = transformer_options
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transformer_options['uc_mask'] = torch.Tensor(cond_or_uncond).to(input_x).float()[:, None, None, None]
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if 'model_function_wrapper' in model_options:
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output = model_options['model_function_wrapper'](model_function,
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{"input": input_x, "timestep": timestep_, "c": c,
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"cond_or_uncond": cond_or_uncond}).chunk(batch_chunks)
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else:
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output = model_function(input_x, timestep_, **c).chunk(batch_chunks)
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del input_x
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model_management.throw_exception_if_processing_interrupted()
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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[
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o] * \
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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[
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o] * \
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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]] += \
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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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max_total_area = model_management.maximum_batch_area()
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if math.isclose(cond_scale, 1.0):
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uncond = None
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cond, uncond = calc_cond_uncond_batch(model_function, cond, uncond, x, timestep, max_total_area, cond_concat,
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model_options)
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if "sampler_cfg_function" in model_options:
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args = {"cond": cond, "uncond": uncond, "cond_scale": cond_scale, "timestep": timestep}
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return model_options["sampler_cfg_function"](args)
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else:
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return uncond + (cond - uncond) * cond_scale
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def unet_forward_patched(self, x, timesteps=None, context=None, y=None, control=None, transformer_options={}, **kwargs):
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uc_mask = transformer_options['uc_mask']
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transformer_options["original_shape"] = list(x.shape)
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transformer_options["current_index"] = 0
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hs = []
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t_emb = timestep_embedding(timesteps, self.model_channels, repeat_only=False).to(self.dtype)
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emb = self.time_embed(t_emb)
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if self.num_classes is not None:
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assert y.shape[0] == x.shape[0]
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emb = emb + self.label_emb(y)
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h = x.type(self.dtype)
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for id, module in enumerate(self.input_blocks):
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transformer_options["block"] = ("input", id)
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h = forward_timestep_embed(module, h, emb, context, transformer_options)
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if control is not None and 'input' in control and len(control['input']) > 0:
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ctrl = control['input'].pop()
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if ctrl is not None:
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h += ctrl
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hs.append(h)
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transformer_options["block"] = ("middle", 0)
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h = forward_timestep_embed(self.middle_block, h, emb, context, transformer_options)
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if control is not None and 'middle' in control and len(control['middle']) > 0:
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h += control['middle'].pop()
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for id, module in enumerate(self.output_blocks):
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transformer_options["block"] = ("output", id)
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hsp = hs.pop()
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if control is not None and 'output' in control and len(control['output']) > 0:
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ctrl = control['output'].pop()
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if ctrl is not None:
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hsp += ctrl
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h = torch.cat([h, hsp], dim=1)
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del hsp
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if len(hs) > 0:
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output_shape = hs[-1].shape
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else:
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output_shape = None
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h = forward_timestep_embed(module, h, emb, context, transformer_options, output_shape)
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h = h.type(x.dtype)
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x0 = self.out(h)
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alpha = 1.0 - (timesteps / 999.0)[:, None, None, None].clone()
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alpha *= 0.001 * sharpness
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degraded_x0 = gaussian_filter_2d(x0) * alpha + x0 * (1.0 - alpha)
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x0 = x0 * uc_mask + degraded_x0 * (1.0 - uc_mask)
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return x0
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def sdxl_encode_adm_patched(self, **kwargs):
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clip_pooled = kwargs["pooled_output"]
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width = kwargs.get("width", 768)
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height = kwargs.get("height", 768)
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crop_w = kwargs.get("crop_w", 0)
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crop_h = kwargs.get("crop_h", 0)
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target_width = kwargs.get("target_width", width)
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target_height = kwargs.get("target_height", height)
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if kwargs.get("prompt_type", "") == "negative":
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width *= 0.8
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height *= 0.8
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elif kwargs.get("prompt_type", "") == "positive":
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width *= 1.5
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height *= 1.5
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out = []
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out.append(self.embedder(torch.Tensor([height])))
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out.append(self.embedder(torch.Tensor([width])))
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out.append(self.embedder(torch.Tensor([crop_h])))
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out.append(self.embedder(torch.Tensor([crop_w])))
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out.append(self.embedder(torch.Tensor([target_height])))
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out.append(self.embedder(torch.Tensor([target_width])))
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flat = torch.flatten(torch.cat(out))[None, ]
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return torch.cat((clip_pooled.to(flat.device), flat), dim=1)
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def patch_all():
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comfy.samplers.sampling_function = sampling_function_patched
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comfy.model_base.SDXL.encode_adm = sdxl_encode_adm_patched
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comfy.ldm.modules.diffusionmodules.openaimodel.UNetModel.forward = unet_forward_patched
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