fix
Jaylen Lee
parent
f36c4ad6b1
commit
3beff81526
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@ -189,8 +189,6 @@ The extension enables **large image drawing & upscaling with limited VRAM** via
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### Demofusion available
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ℹ Please set seed in for a better result.
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ℹ Ticking the random jitter usually yields better results.
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ℹ Recommend using higher steps, such as 30 or more, for better results
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@ -201,10 +199,10 @@ The extension enables **large image drawing & upscaling with limited VRAM** via
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ℹ Do not enable it together with tilediffusion. It supports operations such as tilevae, noise inversion, etc.
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ℹ More suitable for realistic styles
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ℹ For parameters such as c1, c2, and c3, please refer to the demofusion. You don't have to adjust it.
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ℹ Euler sampler performs better
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****
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###
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@ -184,8 +184,6 @@
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### Demofusion现已可用
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ℹ 建议设定随机种子以获取更好的图像
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ℹ 勾选随机抖动通常会得到更好的结果
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ℹ推荐使用较高的步数，例如30步以上，往往会有更好的效果
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@ -196,10 +194,10 @@
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ℹ不要同时开启tilediffusion. 但该组件支持tilevae、noise inversion等常用功能
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ℹ更适合写实风格的图像
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ℹc1，c2，c3等参数可以参考demofusion. 你不必调整这些参数
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ℹ Euler采样器表现得更好
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****
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###
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@ -13,13 +13,14 @@ from modules.ui import gr_show
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from tile_methods.abstractdiffusion import AbstractDiffusion
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from tile_methods.demofusion import DemoFusion
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from tile_utils.utils import *
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from modules.sd_samplers_common import InterruptedException
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# import k_diffusion.sampling
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CFG_PATH = os.path.join(scripts.basedir(), 'region_configs')
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BBOX_MAX_NUM = min(getattr(shared.cmd_opts, 'md_max_regions', 8), 16)
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class Script(scripts.Script):
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def __init__(self):
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self.controlnet_script: ModuleType = None
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@ -42,10 +43,8 @@ class Script(scripts.Script):
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with gr.Accordion('DemoFusion', open=False, elem_id=f'MD-{tab}'):
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with gr.Row(variant='compact') as tab_enable:
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enabled = gr.Checkbox(label='Enable DemoFusion(Do not open it with tilediffusion)', value=False, elem_id=uid('enabled'))
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with gr.Row(variant='compact') as tab_default:
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random_jitter = gr.Checkbox(label='Random jitter windows', value=True, elem_id=uid('random-jitter'))
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keep_input_size = gr.Checkbox(label='Keep input image size', value=True, visible=is_img2img, elem_id=uid('keep-input-size'))
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random_jitter = gr.Checkbox(label='Random jitter', value = True, elem_id=uid('random-jitter'))
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keep_input_size = gr.Checkbox(label='Keep input-image size', value=False,visible=is_img2img, elem_id=uid('keep-input-size'))
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gaussian_filter = gr.Checkbox(label='Gaussian filter', value=False, elem_id=uid('gaussian'))
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@ -64,9 +63,9 @@ class Script(scripts.Script):
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batch_size = gr.Slider(minimum=1, maximum=8, step=1, label='Latent window batch size', value=4, elem_id=uid('latent-tile-batch-size'))
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with gr.Row(variant='compact', visible=True) as tab_size:
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c1 = gr.Slider(minimum=0.5, maximum=3, step=0.1, label='c1', value=3, elem_id=f'c1-{tab}')
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c2 = gr.Slider(minimum=0.5, maximum=3, step=0.1, label='c2', value=1, elem_id=f'c2-{tab}')
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c3 = gr.Slider(minimum=0.5, maximum=3, step=0.1, label='c3', value=1, elem_id=f'c3-{tab}')
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c1 = gr.Slider(minimum=0, maximum=5, step=0.1, label='c1', value=3, elem_id=f'c1-{tab}')
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c2 = gr.Slider(minimum=0, maximum=5, step=0.1, label='c2', value=1, elem_id=f'c2-{tab}')
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c3 = gr.Slider(minimum=0, maximum=5, step=0.1, label='c3', value=1, elem_id=f'c3-{tab}')
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with gr.Row(variant='compact') as tab_upscale:
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scale_factor = gr.Slider(minimum=1.0, maximum=8.0, step=1, label='Scale Factor', value=2.0, elem_id=uid('upscaler-factor'))
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@ -223,11 +222,12 @@ class Script(scripts.Script):
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################################################## Phase Initialization ######################################################
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if not image_ori:
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p.current_step = 0
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p.denoising_strength = 1
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# p.sampler = sd_samplers.create_sampler(p.sampler_name, p.sd_model) #NOTE:Wrong but very useful. If corrected, please replace with the content with the following lines
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# latents = p.rng.next()
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p.denoising_strength = 1
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p.sampler = Script.create_sampler_original_md(p.sampler_name, p.sd_model)
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p.sampler = Script.create_sampler_original_md(p.sampler_name, p.sd_model) #scale
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x = p.rng.next()
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latents = p.sampler.sample(p, x, conditioning, unconditional_conditioning, image_conditioning=p.txt2img_image_conditioning(x))
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del x
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@ -285,7 +285,7 @@ class Script(scripts.Script):
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p.noise = noise
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p.x = p.latents.clone()
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p.current_step=-1
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p.current_step=0
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p.latents = p.sampler.sample_img2img(p,p.latents, noise , conditioning, unconditional_conditioning, image_conditioning=p.image_conditioning)
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if self.flag_noise_inverse:
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@ -297,6 +297,17 @@ class Script(scripts.Script):
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p.width = p.width*p.scale_factor
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p.height = p.height*p.scale_factor
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return p.latents
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@staticmethod
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def callback_hijack(self_sampler,d,p):
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p.current_step = d['i']
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if self_sampler.stop_at is not None and p.current_step > self_sampler.stop_at:
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raise InterruptedException
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state.sampling_step = p.current_step
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shared.total_tqdm.update()
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p.current_step += 1
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def create_sampler_hijack(
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@ -313,6 +324,8 @@ class Script(scripts.Script):
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return self.delegate.sampler_raw
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else:
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self.reset()
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sd_samplers_common.Sampler.callback_ori = sd_samplers_common.Sampler.callback_state
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sd_samplers_common.Sampler.callback_state = lambda self_sampler,d:Script.callback_hijack(self_sampler,d,p)
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self.flag_noise_inverse = hasattr(p, "init_images") and len(p.init_images) > 0 and noise_inverse
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flag_noise_inverse = self.flag_noise_inverse
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@ -467,6 +480,9 @@ class Script(scripts.Script):
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if hasattr(Script, "create_random_tensors_original_md"):
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processing.create_random_tensors = Script.create_random_tensors_original_md
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del Script.create_random_tensors_original_md
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if hasattr(sd_samplers_common.Sampler, "callback_ori"):
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sd_samplers_common.Sampler.callback_state = sd_samplers_common.Sampler.callback_ori
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del sd_samplers_common.Sampler.callback_ori
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DemoFusion.unhook()
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self.delegate = None
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@ -17,10 +17,6 @@ class DemoFusion(AbstractDiffusion):
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super().__init__(p, *args, **kwargs)
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assert p.sampler_name != 'UniPC', 'Demofusion is not compatible with UniPC!'
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def add_one(self):
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self.p.current_step += 1
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return
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def hook(self):
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steps, self.t_enc = sd_samplers_common.setup_img2img_steps(self.p, None)
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@ -177,13 +173,13 @@ class DemoFusion(AbstractDiffusion):
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blurred_latents = F.conv2d(latents, kernel, padding=kernel_size//2, groups=channels)
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return blurred_latents
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''' ↓↓↓ kernel hijacks ↓↓↓ '''
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@torch.no_grad()
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@keep_signature
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def forward_one_step(self, x_in, sigma, **kwarg):
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self.add_one()
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if self.is_kdiff:
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self.xi = self.p.x + self.p.noise * self.p.sigmas[self.p.current_step]
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else:
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@ -197,23 +193,24 @@ class DemoFusion(AbstractDiffusion):
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self.c1 = self.cosine_factor ** self.p.cosine_scale_1
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self.x_in_tmp = x_in*(1 - self.c1) + self.xi * self.c1
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x_in_tmp = x_in*(1 - self.c1) + self.xi * self.c1
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if self.p.random_jitter:
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jitter_range = self.jitter_range
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else:
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jitter_range = 0
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self.x_in_tmp_ = F.pad(self.x_in_tmp,(jitter_range, jitter_range, jitter_range, jitter_range),'constant',value=0)
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_,_,H,W = self.x_in_tmp.shape
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x_in_tmp_ = F.pad(x_in_tmp,(jitter_range, jitter_range, jitter_range, jitter_range),'constant',value=0)
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_,_,H,W = x_in_tmp.shape
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std_, mean_ = self.x_in_tmp.std(), self.x_in_tmp.mean()
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std_, mean_ = x_in_tmp.std(), x_in_tmp.mean()
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c3 = 0.99 * self.cosine_factor ** self.p.cosine_scale_3 + 1e-2
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latents_gaussian = self.gaussian_filter(self.x_in_tmp, kernel_size=(2*self.p.current_scale_num-1), sigma=0.8*c3)
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self.latents_gaussian = (latents_gaussian - latents_gaussian.mean()) / latents_gaussian.std() * std_ + mean_
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latents_gaussian = self.gaussian_filter(x_in_tmp, kernel_size=(2*self.p.current_scale_num-1), sigma=0.8*c3)
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latents_gaussian = (latents_gaussian - latents_gaussian.mean()) / latents_gaussian.std() * std_ + mean_
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self.jitter_range = jitter_range
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self.sampler.model_wrap_cfg.inner_model.forward = self.sample_one_step_local
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self.repeat_3 = False
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x_local = self.sampler.model_wrap_cfg.forward_ori(self.x_in_tmp_,sigma, **kwarg)
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x_local = self.sampler.model_wrap_cfg.forward_ori(x_in_tmp_,sigma, **kwarg)
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self.sampler.model_wrap_cfg.inner_model.forward = self.sampler_forward
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x_local = x_local[:,:,jitter_range:jitter_range+H,jitter_range:jitter_range+W]
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@ -229,9 +226,9 @@ class DemoFusion(AbstractDiffusion):
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######
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if self.gaussian_filter:
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x_global_i = self.sampler.model_wrap_cfg.forward_ori(self.latents_gaussian[:,:,h::self.p.current_scale_num,w::self.p.current_scale_num],sigma, **kwarg)
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x_global_i = self.sampler.model_wrap_cfg.forward_ori(latents_gaussian[:,:,h::self.p.current_scale_num,w::self.p.current_scale_num],sigma, **kwarg)
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else:
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x_global_i = self.sampler.model_wrap_cfg.forward_ori(self.x_in_tmp[:,:,h::self.p.current_scale_num,w::self.p.current_scale_num],sigma, **kwarg) # x_in_tmp could be changed to latents_gaussian
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x_global_i = self.sampler.model_wrap_cfg.forward_ori(x_in_tmp[:,:,h::self.p.current_scale_num,w::self.p.current_scale_num],sigma, **kwarg) # x_in_tmp could be changed to latents_gaussian
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x_global[:,:,h::self.p.current_scale_num,w::self.p.current_scale_num] += x_global_i
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######
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@ -241,9 +238,9 @@ class DemoFusion(AbstractDiffusion):
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# self.x_out_list = []
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# self.x_out_idx = -1
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# self.flag = 1
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# self.sampler.model_wrap_cfg.forward_ori(self.latents_gaussian[:,:,h::self.p.current_scale_num,w::self.p.current_scale_num],sigma,**kwarg)
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# self.sampler.model_wrap_cfg.forward_ori(x_in_tmp[:,:,h::self.p.current_scale_num,w::self.p.current_scale_num],sigma,**kwarg)
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# self.flag = 0
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# x_global_i = self.sampler.model_wrap_cfg.forward_ori(self.x_in_tmp[:,:,h::self.p.current_scale_num,w::self.p.current_scale_num],sigma,**kwarg)
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# x_global_i = self.sampler.model_wrap_cfg.forward_ori(x_in_tmp[:,:,h::self.p.current_scale_num,w::self.p.current_scale_num],sigma,**kwarg)
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# x_global[:,:,h::self.p.current_scale_num,w::self.p.current_scale_num] += x_global_i
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self.p.sd_model.apply_model = self.p.sd_model.apply_model_ori
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