lllyasviel
GitHub
parent
7e1b551cc2
commit
96d15b4933
@ -1 +1 @@
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version = '1.0.27'
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version = '1.0.28'
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@ -1,33 +0,0 @@
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import torch
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import comfy.model_base
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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_negative_adm():
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comfy.model_base.SDXL.encode_adm = sdxl_encode_adm_patched
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@ -53,8 +53,8 @@ def worker():
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results = []
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seed = image_seed
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if not isinstance(seed, int) or seed < 0 or seed > 65535:
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seed = random.randint(1, 65535)
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if not isinstance(seed, int) or seed < 0 or seed > 1024*1024*1024:
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seed = random.randint(1, 1024*1024*1024)
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all_steps = steps * image_number
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@ -11,10 +11,10 @@ from comfy.sd import load_checkpoint_guess_config
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from nodes import VAEDecode, EmptyLatentImage, CLIPTextEncode
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from comfy.sample import prepare_mask, broadcast_cond, load_additional_models, cleanup_additional_models
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from modules.samplers_advanced import KSampler, KSamplerWithRefiner
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from modules.adm_patch import patch_negative_adm
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from modules.patch import patch_all
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patch_negative_adm()
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patch_all()
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opCLIPTextEncode = CLIPTextEncode()
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opEmptyLatentImage = EmptyLatentImage()
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opVAEDecode = VAEDecode()
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32
modules/filters.py
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32
modules/filters.py
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@ -0,0 +1,32 @@
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import torch
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import torch.nn as nn
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import torch.nn.functional as F
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import numpy as np
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def gaussian_kernel(kernel_size, sigma):
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kernel = np.fromfunction(
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lambda x, y: (1 / (2 * np.pi * sigma ** 2)) *
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np.exp(-((x - (kernel_size - 1) / 2) ** 2 + (y - (kernel_size - 1) / 2) ** 2) / (2 * sigma ** 2)),
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(kernel_size, kernel_size)
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)
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return kernel / np.sum(kernel)
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class GaussianBlur(nn.Module):
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def __init__(self, channels, kernel_size, sigma):
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super(GaussianBlur, self).__init__()
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self.channels = channels
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self.kernel_size = kernel_size
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self.sigma = sigma
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self.padding = kernel_size // 2 # Ensure output size matches input size
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self.register_buffer('kernel', torch.tensor(gaussian_kernel(kernel_size, sigma), dtype=torch.float32))
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self.kernel = self.kernel.view(1, 1, kernel_size, kernel_size)
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self.kernel = self.kernel.expand(self.channels, -1, -1, -1) # Repeat the kernel for each input channel
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def forward(self, x):
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x = F.conv2d(x, self.kernel.to(x), padding=self.padding, groups=self.channels)
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return x
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gaussian_filter_2d = GaussianBlur(4, 7, 0.8)
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387
modules/patch.py
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387
modules/patch.py
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@ -0,0 +1,387 @@
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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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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)
|
||||
h = forward_timestep_embed(self.middle_block, h, emb, context, transformer_options)
|
||||
if control is not None and 'middle' in control and len(control['middle']) > 0:
|
||||
h += control['middle'].pop()
|
||||
|
||||
for id, module in enumerate(self.output_blocks):
|
||||
transformer_options["block"] = ("output", id)
|
||||
hsp = hs.pop()
|
||||
if control is not None and 'output' in control and len(control['output']) > 0:
|
||||
ctrl = control['output'].pop()
|
||||
if ctrl is not None:
|
||||
hsp += ctrl
|
||||
|
||||
h = torch.cat([h, hsp], dim=1)
|
||||
del hsp
|
||||
if len(hs) > 0:
|
||||
output_shape = hs[-1].shape
|
||||
else:
|
||||
output_shape = None
|
||||
h = forward_timestep_embed(module, h, emb, context, transformer_options, output_shape)
|
||||
h = h.type(x.dtype)
|
||||
x0 = self.out(h)
|
||||
|
||||
alpha = 1.0 - ((timesteps / 999.0)[:, None, None, None].clone() ** 2.0)
|
||||
alpha *= 0.01
|
||||
degraded_x0 = gaussian_filter_2d(x0) * alpha + x0 * (1.0 - alpha)
|
||||
|
||||
x0 = x0 * uc_mask + degraded_x0 * (1.0 - uc_mask)
|
||||
|
||||
return x0
|
||||
|
||||
|
||||
def sdxl_encode_adm_patched(self, **kwargs):
|
||||
clip_pooled = kwargs["pooled_output"]
|
||||
width = kwargs.get("width", 768)
|
||||
height = kwargs.get("height", 768)
|
||||
crop_w = kwargs.get("crop_w", 0)
|
||||
crop_h = kwargs.get("crop_h", 0)
|
||||
target_width = kwargs.get("target_width", width)
|
||||
target_height = kwargs.get("target_height", height)
|
||||
|
||||
if kwargs.get("prompt_type", "") == "negative":
|
||||
width *= 0.8
|
||||
height *= 0.8
|
||||
elif kwargs.get("prompt_type", "") == "positive":
|
||||
width *= 1.5
|
||||
height *= 1.5
|
||||
|
||||
out = []
|
||||
out.append(self.embedder(torch.Tensor([height])))
|
||||
out.append(self.embedder(torch.Tensor([width])))
|
||||
out.append(self.embedder(torch.Tensor([crop_h])))
|
||||
out.append(self.embedder(torch.Tensor([crop_w])))
|
||||
out.append(self.embedder(torch.Tensor([target_height])))
|
||||
out.append(self.embedder(torch.Tensor([target_width])))
|
||||
flat = torch.flatten(torch.cat(out))[None, ]
|
||||
return torch.cat((clip_pooled.to(flat.device), flat), dim=1)
|
||||
|
||||
|
||||
def patch_all():
|
||||
comfy.samplers.sampling_function = sampling_function_patched
|
||||
comfy.model_base.SDXL.encode_adm = sdxl_encode_adm_patched
|
||||
comfy.ldm.modules.diffusionmodules.openaimodel.UNetModel.forward = unet_forward_patched
|
||||
13
readme.md
13
readme.md
@ -96,12 +96,13 @@ Note that some of these tricks are currently (2023 Aug 11) impossible to reprodu
|
||||
|
||||
1. Native refiner swap inside one single k-sampler. The advantage is that now the refiner model can reuse the base model's momentum (or ODE's history parameters) collected from k-sampling to achieve more coherent sampling. In Automatic1111's high-res fix and ComfyUI's node system, the base model and refiner use two independent k-samplers, which means the momentum is largely wasted, and the sampling continuity is broken. Fooocus uses its own advanced k-diffusion sampling that ensures seamless, native, and continuous swap in a refiner setup. (Update Aug 13: Actually I discussed this with Automatic1111 several days ago and it seems that the “native refiner swap inside one single k-sampler” is [merged]( https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12371) into the dev branch of webui. Great!)
|
||||
2. Negative ADM guidance. Because the highest resolution level of XL Base does not have cross attentions, the positive and negative signals for XL's highest resolution level cannot receive enough contrasts during the CFG sampling, causing the results look a bit plastic or overly smooth in certain cases. Fortunately, since the XL's highest resolution level is still conditioned on image aspect ratios (ADM), we can modify the adm on the positive/negative side to compensate for the lack of CFG contrast in the highest resolution level.
|
||||
3. We modified the style templates a bit and added the "cinematic-default".
|
||||
4. We tested the "sd_xl_offset_example-lora_1.0.safetensors" and it seems that when the lora weight is below 0.5, the results are always better than XL without lora.
|
||||
5. The parameters of samplers are carefully tuned.
|
||||
6. Because XL uses positional encoding for generation resolution, images generated by several fixed resolutions look a bit better than that from arbitrary resolutions (because the positional encoding is not very good at handling int numbers that are unseen during training). This suggests that the resolutions in UI may be hard coded for best results.
|
||||
7. Separated prompts for two different text encoders seem unnecessary. Separated prompts for base model and refiner may work but the effects are random, and we refrain from implement this.
|
||||
8. DPM family seems well-suited for XL, since XL sometimes generates overly smooth texture but DPM family sometimes generate overly dense detail in texture. Their joint effect looks neutral and appealing to human perception.
|
||||
3. We implemented a carefully tuned variation of the Section 5.1 of ["Improving Sample Quality of Diffusion Models Using Self-Attention Guidance"](https://arxiv.org/pdf/2210.00939.pdf). The weight is set to very low, but this is Fooocus's final guarantee to make sure than the XL will never yield overly smooth or plastic appearance. This can almostly eliminate all cases that XL still occasionally produce overly smooth results even with negative ADM guidance.
|
||||
4. We modified the style templates a bit and added the "cinematic-default".
|
||||
5. We tested the "sd_xl_offset_example-lora_1.0.safetensors" and it seems that when the lora weight is below 0.5, the results are always better than XL without lora.
|
||||
6. The parameters of samplers are carefully tuned.
|
||||
7. Because XL uses positional encoding for generation resolution, images generated by several fixed resolutions look a bit better than that from arbitrary resolutions (because the positional encoding is not very good at handling int numbers that are unseen during training). This suggests that the resolutions in UI may be hard coded for best results.
|
||||
8. Separated prompts for two different text encoders seem unnecessary. Separated prompts for base model and refiner may work but the effects are random, and we refrain from implement this.
|
||||
9. DPM family seems well-suited for XL, since XL sometimes generates overly smooth texture but DPM family sometimes generate overly dense detail in texture. Their joint effect looks neutral and appealing to human perception.
|
||||
|
||||
## Thanks
|
||||
|
||||
|
||||
@ -1,3 +1,7 @@
|
||||
### 1.0.28
|
||||
|
||||
* SAG implemented
|
||||
|
||||
### 1.0.27
|
||||
|
||||
* Fix small problem in textbox css
|
||||
|
||||
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Reference in New Issue
Block a user