1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
VAE weights loaded.
100%|██████████████████████████████████████████████████████████████████████████████████| 20/20 [00:03<00:00, 5.78it/s]
Loading VAE weights from function argument: G:\StableDiffusion\webui\models\VAE\MoistMix.vae.pt
Applying xformers cross attention optimization.
VAE weights loaded.
100%|██████████████████████████████████████████████████████████████████████████████████| 20/20 [00:03<00:00, 5.82it/s]
Loading weights [b85c60dae0] from D:\0AI_StableDiffusion_Putout\Stable-diffusion\djzHellBeastV21_0.safetensors
Creating model from config: G:\StableDiffusion\webui\repositories\stable-diffusion-stability-ai\configs\stable-diffusion\v2-inference-v.yaml
LatentDiffusion: Running in v-prediction mode
DiffusionWrapper has 865.91 M params.
Loading VAE weights specified in settings: G:\StableDiffusion\webui\models\VAE\Anything-V3.0.vae.pt
Applying xformers cross attention optimization.
Model loaded in 20.4s (find config: 1.6s, load config: 0.2s, create model: 0.3s, apply weights to model: 14.5s, apply half(): 1.4s, load VAE: 0.8s, move model to device: 1.6s).
Loading VAE weights from function argument: G:\StableDiffusion\webui\models\VAE\facebombmixVAE_kl-f8-anime2.ckpt
Applying xformers cross attention optimization.
VAE weights loaded.
0%| | 0/20 [00:00<?, ?it/s]
Loading VAE weights specified in settings: G:\StableDiffusion\webui\models\VAE\Anything-V3.0.vae.pt
Applying xformers cross attention optimization.
VAE weights loaded.
Error completing request
Arguments: ('task(7tfwr25ohxd0s8p)', '((((close up portrait)))),1girl,tmasterpiece,best quality, full head <lora:koyori-000015:1>,def1,smile', ',(easynegative),(ng_deepnegative_v1_75t),(worst quality, low quality:1.4),((watermark)),((signature))', [], 20, 15, False, False, 1, 1, 7, 2132.0, -1.0, 0, 0, 0, False, 512, 512, False, 0.7, 2, 'Latent', 0, 0, 0, [], 3, False, False, 'LoRA', 'None', 0, 0, 'LoRA', 'None', 0, 0, 'LoRA', 'None', 0, 0, 'LoRA', 'None', 0, 0, 'LoRA', 'None', 0, 0, None, 'Refresh models', <scripts.controlnet_ui.controlnet_ui_group.UiControlNetUnit object at 0x0000024371236B60>, <scripts.controlnet_ui.controlnet_ui_group.UiControlNetUnit object at 0x0000024371234D00>, <scripts.controlnet_ui.controlnet_ui_group.UiControlNetUnit object at 0x0000024180B817E0>, False, False, 'positive', 'comma', 0, False, False, '', 10, 'xxmix9realistic_v40.safetensors ,nabimix_v2.safetensors , djzHellBeastV21_0.safetensors ,dream2reality_v10.safetensors, facebombmix_v1Bakedvae.safetensors , ', 19, 'facebombmixVAE_kl-f8-anime2.ckpt, kl-f8-anime.ckpt, kl-f8-anime2.ckpt, vae-ft-mse-840000-ema-pruned.ckpt, Anything-V3.0.vae.pt, MoistMix.vae.pt', 0, '', True, False, False, False, 18, None, None, False, None, None, False, None, None, False, 50) {}
Traceback (most recent call last):
File "G:\StableDiffusion\webui\modules\call_queue.py", line 56, in f
res = list(func(*args, **kwargs))
File "G:\StableDiffusion\webui\modules\call_queue.py", line 37, in f
res = func(*args, **kwargs)
File "G:\StableDiffusion\webui\modules\txt2img.py", line 53, in txt2img
processed = modules.scripts.scripts_txt2img.run(p, *args)
File "G:\StableDiffusion\webui\modules\scripts.py", line 376, in run
processed = script.run(p, *script_args)
File "G:\StableDiffusion\webui\scripts\xyz_grid.py", line 596, in run
processed, sub_grids = draw_xyz_grid(
File "G:\StableDiffusion\webui\scripts\xyz_grid.py", line 265, in draw_xyz_grid
process_cell(x, y, z, ix, iy, iz)
File "G:\StableDiffusion\webui\scripts\xyz_grid.py", line 234, in process_cell
processed: Processed = cell(x, y, z)
File "G:\StableDiffusion\webui\scripts\xyz_grid.py", line 567, in cell
res = process_images(pc)
File "G:\StableDiffusion\webui\modules\processing.py", line 486, in process_images
res = process_images_inner(p)
File "G:\StableDiffusion\webui\extensions\sd-webui-controlnet\scripts\batch_hijack.py", line 42, in processing_process_images_hijack
return getattr(processing, '__controlnet_original_process_images_inner')(p, *args, **kwargs)
File "G:\StableDiffusion\webui\modules\processing.py", line 632, in process_images_inner
samples_ddim = p.sample(conditioning=c, unconditional_conditioning=uc, seeds=seeds, subseeds=subseeds, subseed_strength=p.subseed_strength, prompts=prompts)
File "G:\StableDiffusion\webui\modules\processing.py", line 832, in sample
samples = self.sampler.sample(self, x, conditioning, unconditional_conditioning, image_conditioning=self.txt2img_image_conditioning(x))
File "G:\StableDiffusion\webui\modules\sd_samplers_kdiffusion.py", line 349, in sample
samples = self.launch_sampling(steps, lambda: self.func(self.model_wrap_cfg, x, extra_args={
File "G:\StableDiffusion\webui\modules\sd_samplers_kdiffusion.py", line 225, in launch_sampling
return func()
File "G:\StableDiffusion\webui\modules\sd_samplers_kdiffusion.py", line 349, in <lambda>
samples = self.launch_sampling(steps, lambda: self.func(self.model_wrap_cfg, x, extra_args={
File "G:\StableDiffusion\system\python\lib\site-packages\torch\autograd\grad_mode.py", line 27, in decorate_context
return func(*args, **kwargs)
File "G:\StableDiffusion\webui\repositories\k-diffusion\k_diffusion\sampling.py", line 594, in sample_dpmpp_2m
denoised = model(x, sigmas[i] * s_in, **extra_args)
File "G:\StableDiffusion\system\python\lib\site-packages\torch\nn\modules\module.py", line 1194, in _call_impl
return forward_call(*input, **kwargs)
File "G:\StableDiffusion\webui\modules\sd_samplers_kdiffusion.py", line 117, in forward
x_out = self.inner_model(x_in, sigma_in, cond={"c_crossattn": [cond_in], "c_concat": [image_cond_in]})
File "G:\StableDiffusion\system\python\lib\site-packages\torch\nn\modules\module.py", line 1194, in _call_impl
return forward_call(*input, **kwargs)
File "G:\StableDiffusion\webui\repositories\k-diffusion\k_diffusion\external.py", line 167, in forward
return self.get_v(input * c_in, self.sigma_to_t(sigma), **kwargs) * c_out + input * c_skip
File "G:\StableDiffusion\webui\repositories\k-diffusion\k_diffusion\external.py", line 177, in get_v
return self.inner_model.apply_model(x, t, cond)
File "G:\StableDiffusion\webui\modules\sd_hijack_utils.py", line 17, in <lambda>
setattr(resolved_obj, func_path[-1], lambda *args, **kwargs: self(*args, **kwargs))
File "G:\StableDiffusion\webui\modules\sd_hijack_utils.py", line 28, in __call__
return self.__orig_func(*args, **kwargs)
File "G:\StableDiffusion\webui\repositories\stable-diffusion-stability-ai\ldm\models\diffusion\ddpm.py", line 858, in apply_model
x_recon = self.model(x_noisy, t, **cond)
File "G:\StableDiffusion\system\python\lib\site-packages\torch\nn\modules\module.py", line 1194, in _call_impl
return forward_call(*input, **kwargs)
File "G:\StableDiffusion\webui\repositories\stable-diffusion-stability-ai\ldm\models\diffusion\ddpm.py", line 1329, in forward
out = self.diffusion_model(x, t, context=cc)
File "G:\StableDiffusion\system\python\lib\site-packages\torch\nn\modules\module.py", line 1194, in _call_impl
return forward_call(*input, **kwargs)
File "G:\StableDiffusion\webui\repositories\stable-diffusion-stability-ai\ldm\modules\diffusionmodules\openaimodel.py", line 776, in forward
h = module(h, emb, context)
File "G:\StableDiffusion\system\python\lib\site-packages\torch\nn\modules\module.py", line 1194, in _call_impl
return forward_call(*input, **kwargs)
File "G:\StableDiffusion\webui\repositories\stable-diffusion-stability-ai\ldm\modules\diffusionmodules\openaimodel.py", line 84, in forward
x = layer(x, context)
File "G:\StableDiffusion\system\python\lib\site-packages\torch\nn\modules\module.py", line 1194, in _call_impl
return forward_call(*input, **kwargs)
File "G:\StableDiffusion\webui\repositories\stable-diffusion-stability-ai\ldm\modules\attention.py", line 322, in forward
x = self.proj_in(x)
File "G:\StableDiffusion\system\python\lib\site-packages\torch\nn\modules\module.py", line 1194, in _call_impl
return forward_call(*input, **kwargs)
File "G:\StableDiffusion\webui\extensions-builtin\Lora\lora.py", line 178, in lora_Linear_forward
return lora_forward(self, input, torch.nn.Linear_forward_before_lora(self, input))
File "G:\StableDiffusion\webui\extensions-builtin\Lora\lora.py", line 172, in lora_forward
res = res + module.up(module.down(input)) * lora.multiplier * (module.alpha / module.up.weight.shape[1] if module.alpha else 1.0)
File "G:\StableDiffusion\system\python\lib\site-packages\torch\nn\modules\module.py", line 1194, in _call_impl
return forward_call(*input, **kwargs)
File "G:\StableDiffusion\webui\extensions-builtin\Lora\lora.py", line 182, in lora_Conv2d_forward
return lora_forward(self, input, torch.nn.Conv2d_forward_before_lora(self, input))
File "G:\StableDiffusion\system\python\lib\site-packages\torch\nn\modules\conv.py", line 463, in forward
return self._conv_forward(input, self.weight, self.bias)
File "G:\StableDiffusion\system\python\lib\site-packages\torch\nn\modules\conv.py", line 459, in _conv_forward
return F.conv2d(input, weight, bias, self.stride,
RuntimeError: Given groups=1, weight of size [16, 320, 1, 1], expected input[1, 2, 4096, 320] to have 320 channels, but got 2 channels instead