278 lines
11 KiB
Python
278 lines
11 KiB
Python
import k_diffusion.sampling
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import k_diffusion.external
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import torch
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import contextlib
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import model_management
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class CFGDenoiser(torch.nn.Module):
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def __init__(self, model):
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super().__init__()
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self.inner_model = model
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def forward(self, x, sigma, uncond, cond, cond_scale):
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if len(uncond[0]) == len(cond[0]) and x.shape[0] * x.shape[2] * x.shape[3] < (96 * 96): #TODO check memory instead
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x_in = torch.cat([x] * 2)
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sigma_in = torch.cat([sigma] * 2)
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cond_in = torch.cat([uncond, cond])
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uncond, cond = self.inner_model(x_in, sigma_in, cond=cond_in).chunk(2)
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else:
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cond = self.inner_model(x, sigma, cond=cond)
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uncond = self.inner_model(x, sigma, cond=uncond)
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return uncond + (cond - uncond) * cond_scale
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class CFGDenoiserComplex(torch.nn.Module):
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def __init__(self, model):
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super().__init__()
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self.inner_model = model
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def forward(self, x, sigma, uncond, cond, cond_scale):
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def get_area_and_mult(cond, x_in, sigma):
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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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min_sigma = 0.0
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max_sigma = 999.0
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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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if 'min_sigma' in cond[1]:
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min_sigma = cond[1]['min_sigma']
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if 'max_sigma' in cond[1]:
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max_sigma = cond[1]['max_sigma']
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if sigma < min_sigma or sigma > max_sigma:
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return None
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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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rr = 8
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if area[2] != 0:
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for t in range(rr):
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mult[:,:,area[2]+t:area[2]+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] + area[2] - 1 - t:area[0] + area[2] - 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[:,:,:,area[3]+t:area[3]+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] + area[3] - 1 - t:area[1] + area[3] - t] *= ((1.0/rr) * (t + 1))
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return (input_x, mult, cond[0], area)
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def calc_cond_uncond_batch(cond, uncond, x_in, sigma, max_total_area):
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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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sigma_cmp = sigma[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, sigma_cmp)
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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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for x in uncond:
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p = get_area_and_mult(x, x_in, sigma_cmp)
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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 = []
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for x in range(len(to_run)):
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if to_run[x][0][0].shape == first_shape:
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if to_run[x][0][2].shape == first[0][2].shape:
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to_batch += [x]
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if (len(to_batch) * first_shape[0] * first_shape[2] * first_shape[3] >= max_total_area):
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break
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to_batch.reverse()
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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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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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batch_chunks = len(cond_or_uncond)
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input_x = torch.cat(input_x)
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c = torch.cat(c)
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sigma_ = torch.cat([sigma] * batch_chunks)
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output = self.inner_model(input_x, sigma_, cond=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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max_total_area = model_management.maximum_batch_area()
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cond, uncond = calc_cond_uncond_batch(cond, uncond, x, sigma, max_total_area)
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return uncond + (cond - uncond) * cond_scale
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def simple_scheduler(model, steps):
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sigs = []
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ss = len(model.sigmas) / steps
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for x in range(steps):
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sigs += [float(model.sigmas[-(1 + int(x * ss))])]
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sigs += [0.0]
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return torch.FloatTensor(sigs)
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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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c_area = c[1]['area']
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smallest = None
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for x in conds:
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if 'area' in x[1]:
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a = x[1]['area']
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if c_area[2] >= a[2] and c_area[3] >= a[3]:
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if a[0] + a[2] >= c_area[0] + c_area[2]:
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if a[1] + a[3] >= c_area[1] + c_area[3]:
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if smallest is None:
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smallest = x
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elif 'area' not in smallest[1]:
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smallest = x
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else:
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if smallest[1]['area'][0] * smallest[1]['area'][1] > a[0] * a[1]:
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smallest = x
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else:
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if smallest is None:
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smallest = x
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if smallest is None:
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return
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if 'area' in smallest[1]:
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if smallest[1]['area'] == c_area:
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return
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n = c[1].copy()
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conds += [[smallest[0], n]]
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class KSampler:
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SCHEDULERS = ["karras", "normal", "simple"]
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SAMPLERS = ["sample_euler", "sample_euler_ancestral", "sample_heun", "sample_dpm_2", "sample_dpm_2_ancestral",
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"sample_lms", "sample_dpm_fast", "sample_dpm_adaptive", "sample_dpmpp_2s_ancestral", "sample_dpmpp_sde",
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"sample_dpmpp_2m"]
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def __init__(self, model, steps, device, sampler=None, scheduler=None, denoise=None):
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self.model = model
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if self.model.parameterization == "v":
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self.model_wrap = k_diffusion.external.CompVisVDenoiser(self.model, quantize=True)
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else:
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self.model_wrap = k_diffusion.external.CompVisDenoiser(self.model, quantize=True)
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self.model_k = CFGDenoiserComplex(self.model_wrap)
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self.device = device
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if scheduler not in self.SCHEDULERS:
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scheduler = self.SCHEDULERS[0]
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if sampler not in self.SAMPLERS:
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sampler = self.SAMPLERS[0]
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self.scheduler = scheduler
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self.sampler = sampler
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self.sigma_min=float(self.model_wrap.sigmas[0])
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self.sigma_max=float(self.model_wrap.sigmas[-1])
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self.set_steps(steps, denoise)
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def _calculate_sigmas(self, steps):
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sigmas = None
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discard_penultimate_sigma = False
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if self.sampler in ['sample_dpm_2', 'sample_dpm_2_ancestral']:
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steps += 1
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discard_penultimate_sigma = True
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if self.scheduler == "karras":
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sigmas = k_diffusion.sampling.get_sigmas_karras(n=steps, sigma_min=self.sigma_min, sigma_max=self.sigma_max, device=self.device)
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elif self.scheduler == "normal":
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sigmas = self.model_wrap.get_sigmas(steps).to(self.device)
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elif self.scheduler == "simple":
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sigmas = simple_scheduler(self.model_wrap, steps).to(self.device)
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else:
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print("error invalid scheduler", self.scheduler)
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if discard_penultimate_sigma:
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sigmas = torch.cat([sigmas[:-2], sigmas[-1:]])
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return sigmas
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def set_steps(self, steps, denoise=None):
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self.steps = steps
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if denoise is None:
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self.sigmas = self._calculate_sigmas(steps)
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else:
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new_steps = int(steps/denoise)
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sigmas = self._calculate_sigmas(new_steps)
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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):
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sigmas = self.sigmas
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sigma_min = self.sigma_min
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if last_step is not None and last_step < (len(sigmas) - 1):
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sigma_min = sigmas[last_step]
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sigmas = sigmas[:last_step + 1]
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if force_full_denoise:
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sigmas[-1] = 0
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if start_step is not None:
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if start_step < (len(sigmas) - 1):
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sigmas = sigmas[start_step:]
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else:
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if latent_image is not None:
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return latent_image
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else:
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return torch.zeros_like(noise)
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noise *= sigmas[0]
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if latent_image is not None:
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noise += latent_image
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positive = positive[:]
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negative = negative[:]
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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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for c in negative:
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create_cond_with_same_area_if_none(positive, c)
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if self.model.model.diffusion_model.dtype == torch.float16:
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precision_scope = torch.autocast
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else:
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precision_scope = contextlib.nullcontext
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with precision_scope(self.device):
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if self.sampler == "sample_dpm_fast":
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samples = k_diffusion.sampling.sample_dpm_fast(self.model_k, noise, sigma_min, sigmas[0], self.steps, extra_args={"cond":positive, "uncond":negative, "cond_scale": cfg})
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elif self.sampler == "sample_dpm_adaptive":
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samples = k_diffusion.sampling.sample_dpm_adaptive(self.model_k, noise, sigma_min, sigmas[0], extra_args={"cond":positive, "uncond":negative, "cond_scale": cfg})
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else:
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samples = getattr(k_diffusion.sampling, self.sampler)(self.model_k, noise, sigmas, extra_args={"cond":positive, "uncond":negative, "cond_scale": cfg})
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return samples.to(torch.float32)
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