From 42082e8a3239c1c32cd9e2a03a20b610af857b51 Mon Sep 17 00:00:00 2001 From: devdn Date: Tue, 28 Mar 2023 18:18:28 -0400 Subject: performance increase --- modules/sd_samplers_kdiffusion.py | 22 +++++++++++++++++----- 1 file changed, 17 insertions(+), 5 deletions(-) (limited to 'modules/sd_samplers_kdiffusion.py') diff --git a/modules/sd_samplers_kdiffusion.py b/modules/sd_samplers_kdiffusion.py index e9f08518..6a54ce32 100644 --- a/modules/sd_samplers_kdiffusion.py +++ b/modules/sd_samplers_kdiffusion.py @@ -76,7 +76,7 @@ class CFGDenoiser(torch.nn.Module): return denoised - def forward(self, x, sigma, uncond, cond, cond_scale, image_cond): + def forward(self, x, sigma, uncond, cond, cond_scale, s_min_uncond, image_cond): if state.interrupted or state.skipped: raise sd_samplers_common.InterruptedException @@ -116,6 +116,12 @@ class CFGDenoiser(torch.nn.Module): tensor = denoiser_params.text_cond uncond = denoiser_params.text_uncond + sigma_thresh = s_min_uncond + if(torch.dot(sigma,sigma) < sigma.shape[0] * (sigma_thresh*sigma_thresh) and not is_edit_model): + uncond = torch.zeros([0,0,uncond.shape[2]]) + x_in=x_in[:x_in.shape[0]//2] + sigma_in=sigma_in[:sigma_in.shape[0]//2] + if tensor.shape[1] == uncond.shape[1]: if not is_edit_model: cond_in = torch.cat([tensor, uncond]) @@ -144,7 +150,8 @@ class CFGDenoiser(torch.nn.Module): x_out[a:b] = self.inner_model(x_in[a:b], sigma_in[a:b], cond=make_condition_dict(c_crossattn, image_cond_in[a:b])) - x_out[-uncond.shape[0]:] = self.inner_model(x_in[-uncond.shape[0]:], sigma_in[-uncond.shape[0]:], cond=make_condition_dict([uncond], image_cond_in[-uncond.shape[0]:])) + if uncond.shape[0]: + x_out[-uncond.shape[0]:] = self.inner_model(x_in[-uncond.shape[0]:], sigma_in[-uncond.shape[0]:], cond=make_condition_dict([uncond], image_cond_in[-uncond.shape[0]:])) denoised_params = CFGDenoisedParams(x_out, state.sampling_step, state.sampling_steps) cfg_denoised_callback(denoised_params) @@ -157,7 +164,10 @@ class CFGDenoiser(torch.nn.Module): sd_samplers_common.store_latent(x_out[-uncond.shape[0]:]) if not is_edit_model: - denoised = self.combine_denoised(x_out, conds_list, uncond, cond_scale) + if uncond.shape[0]: + denoised = self.combine_denoised(x_out, conds_list, uncond, cond_scale) + else: + denoised = x_out else: denoised = self.combine_denoised_for_edit_model(x_out, cond_scale) @@ -165,7 +175,6 @@ class CFGDenoiser(torch.nn.Module): denoised = self.init_latent * self.mask + self.nmask * denoised self.step += 1 - return denoised @@ -244,6 +253,7 @@ class KDiffusionSampler: self.model_wrap_cfg.step = 0 self.model_wrap_cfg.image_cfg_scale = getattr(p, 'image_cfg_scale', None) self.eta = p.eta if p.eta is not None else opts.eta_ancestral + self.s_min_uncond = getattr(p, 's_min_uncond', 0.0) k_diffusion.sampling.torch = TorchHijack(self.sampler_noises if self.sampler_noises is not None else []) @@ -326,6 +336,7 @@ class KDiffusionSampler: 'image_cond': image_conditioning, 'uncond': unconditional_conditioning, 'cond_scale': p.cfg_scale, + 's_min_uncond': self.s_min_uncond } samples = self.launch_sampling(t_enc + 1, lambda: self.func(self.model_wrap_cfg, xi, extra_args=extra_args, disable=False, callback=self.callback_state, **extra_params_kwargs)) @@ -359,7 +370,8 @@ class KDiffusionSampler: 'cond': conditioning, 'image_cond': image_conditioning, 'uncond': unconditional_conditioning, - 'cond_scale': p.cfg_scale + 'cond_scale': p.cfg_scale, + 's_min_uncond': self.s_min_uncond }, disable=False, callback=self.callback_state, **extra_params_kwargs)) return samples -- cgit v1.2.3 From 44e8e9c36807d4a71c2fc84129ebcf5ba4f77f21 Mon Sep 17 00:00:00 2001 From: devdn Date: Thu, 30 Mar 2023 00:54:28 -0400 Subject: fix live preview & alternate uncond guidance for better quality --- modules/sd_samplers_kdiffusion.py | 14 ++++++++------ 1 file changed, 8 insertions(+), 6 deletions(-) (limited to 'modules/sd_samplers_kdiffusion.py') diff --git a/modules/sd_samplers_kdiffusion.py b/modules/sd_samplers_kdiffusion.py index 6a54ce32..17d24df4 100644 --- a/modules/sd_samplers_kdiffusion.py +++ b/modules/sd_samplers_kdiffusion.py @@ -116,11 +116,13 @@ class CFGDenoiser(torch.nn.Module): tensor = denoiser_params.text_cond uncond = denoiser_params.text_uncond - sigma_thresh = s_min_uncond - if(torch.dot(sigma,sigma) < sigma.shape[0] * (sigma_thresh*sigma_thresh) and not is_edit_model): - uncond = torch.zeros([0,0,uncond.shape[2]]) - x_in=x_in[:x_in.shape[0]//2] - sigma_in=sigma_in[:sigma_in.shape[0]//2] + if self.step % 2 and s_min_uncond > 0 and not is_edit_model: + # alternating uncond allows for higher thresholds without the quality loss normally expected from raising it + sigma_threshold = s_min_uncond + if(torch.dot(sigma,sigma) < sigma.shape[0] * (sigma_threshold*sigma_threshold) ): + uncond = torch.zeros([0,0,uncond.shape[2]]) + x_in=x_in[:x_in.shape[0]//2] + sigma_in=sigma_in[:sigma_in.shape[0]//2] if tensor.shape[1] == uncond.shape[1]: if not is_edit_model: @@ -159,7 +161,7 @@ class CFGDenoiser(torch.nn.Module): devices.test_for_nans(x_out, "unet") if opts.live_preview_content == "Prompt": - sd_samplers_common.store_latent(x_out[0:uncond.shape[0]]) + sd_samplers_common.store_latent(x_out[0:x_out.shape[0]-uncond.shape[0]]) elif opts.live_preview_content == "Negative prompt": sd_samplers_common.store_latent(x_out[-uncond.shape[0]:]) -- cgit v1.2.3 From d40e44ade479f7bba30d5317381cbc58c861775b Mon Sep 17 00:00:00 2001 From: Deciare <1689220+deciare@users.noreply.github.com> Date: Tue, 18 Apr 2023 23:18:58 -0400 Subject: Option to use CPU for random number generation. Makes a given manual seed generate the same images across different platforms, independently of the GPU architecture in use. Fixes #9613. --- modules/devices.py | 8 ++++++-- modules/sd_samplers_common.py | 9 +++++++++ modules/sd_samplers_kdiffusion.py | 2 +- modules/shared.py | 1 + 4 files changed, 17 insertions(+), 3 deletions(-) (limited to 'modules/sd_samplers_kdiffusion.py') diff --git a/modules/devices.py b/modules/devices.py index 52c3e7cd..3bc86a6a 100644 --- a/modules/devices.py +++ b/modules/devices.py @@ -92,14 +92,18 @@ def cond_cast_float(input): def randn(seed, shape): + from modules.shared import opts + torch.manual_seed(seed) - if device.type == 'mps': + if opts.use_cpu_randn or device.type == 'mps': return torch.randn(shape, device=cpu).to(device) return torch.randn(shape, device=device) def randn_without_seed(shape): - if device.type == 'mps': + from modules.shared import opts + + if opts.use_cpu_randn or device.type == 'mps': return torch.randn(shape, device=cpu).to(device) return torch.randn(shape, device=device) diff --git a/modules/sd_samplers_common.py b/modules/sd_samplers_common.py index a1aac7cf..e6a372d5 100644 --- a/modules/sd_samplers_common.py +++ b/modules/sd_samplers_common.py @@ -60,3 +60,12 @@ def store_latent(decoded): class InterruptedException(BaseException): pass + +if opts.use_cpu_randn: + import torchsde._brownian.brownian_interval + + def torchsde_randn(size, dtype, device, seed): + generator = torch.Generator(devices.cpu).manual_seed(int(seed)) + return torch.randn(size, dtype=dtype, device=devices.cpu, generator=generator).to(device) + + torchsde._brownian.brownian_interval._randn = torchsde_randn diff --git a/modules/sd_samplers_kdiffusion.py b/modules/sd_samplers_kdiffusion.py index e9f08518..13f4567a 100644 --- a/modules/sd_samplers_kdiffusion.py +++ b/modules/sd_samplers_kdiffusion.py @@ -190,7 +190,7 @@ class TorchHijack: if noise.shape == x.shape: return noise - if x.device.type == 'mps': + if opts.use_cpu_randn or x.device.type == 'mps': return torch.randn_like(x, device=devices.cpu).to(x.device) else: return torch.randn_like(x) diff --git a/modules/shared.py b/modules/shared.py index 5fd0eecb..59b037d5 100644 --- a/modules/shared.py +++ b/modules/shared.py @@ -331,6 +331,7 @@ options_templates.update(options_section(('sd', "Stable Diffusion"), { "comma_padding_backtrack": OptionInfo(20, "Increase coherency by padding from the last comma within n tokens when using more than 75 tokens", gr.Slider, {"minimum": 0, "maximum": 74, "step": 1 }), "CLIP_stop_at_last_layers": OptionInfo(1, "Clip skip", gr.Slider, {"minimum": 1, "maximum": 12, "step": 1}), "upcast_attn": OptionInfo(False, "Upcast cross attention layer to float32"), + "use_cpu_randn": OptionInfo(False, "Use CPU for random number generation to make manual seeds generate the same image across platforms. This may change existing seeds."), })) options_templates.update(options_section(('compatibility', "Compatibility"), { -- cgit v1.2.3 From 5fe0dd79beaa5ef737ff85254ee9870f60ae9464 Mon Sep 17 00:00:00 2001 From: AUTOMATIC <16777216c@gmail.com> Date: Sat, 29 Apr 2023 11:29:37 +0300 Subject: rename CPU RNG to RNG source in settings, add infotext and parameters copypaste support to RNG source --- modules/devices.py | 4 ++-- modules/generation_parameters_copypaste.py | 5 +++++ modules/processing.py | 3 ++- modules/sd_samplers_common.py | 3 ++- modules/sd_samplers_kdiffusion.py | 2 +- modules/shared.py | 2 +- 6 files changed, 13 insertions(+), 6 deletions(-) (limited to 'modules/sd_samplers_kdiffusion.py') diff --git a/modules/devices.py b/modules/devices.py index 3bc86a6a..c705a3cb 100644 --- a/modules/devices.py +++ b/modules/devices.py @@ -95,7 +95,7 @@ def randn(seed, shape): from modules.shared import opts torch.manual_seed(seed) - if opts.use_cpu_randn or device.type == 'mps': + if opts.randn_source == "CPU" or device.type == 'mps': return torch.randn(shape, device=cpu).to(device) return torch.randn(shape, device=device) @@ -103,7 +103,7 @@ def randn(seed, shape): def randn_without_seed(shape): from modules.shared import opts - if opts.use_cpu_randn or device.type == 'mps': + if opts.randn_source == "CPU" or device.type == 'mps': return torch.randn(shape, device=cpu).to(device) return torch.randn(shape, device=device) diff --git a/modules/generation_parameters_copypaste.py b/modules/generation_parameters_copypaste.py index 6df76858..e7269363 100644 --- a/modules/generation_parameters_copypaste.py +++ b/modules/generation_parameters_copypaste.py @@ -284,6 +284,10 @@ Steps: 20, Sampler: Euler a, CFG scale: 7, Seed: 965400086, Size: 512x512, Model restore_old_hires_fix_params(res) + # Missing RNG means the default was set, which is GPU RNG + if "RNG" not in res: + res["RNG"] = "GPU" + return res @@ -304,6 +308,7 @@ infotext_to_setting_name_mapping = [ ('UniPC skip type', 'uni_pc_skip_type'), ('UniPC order', 'uni_pc_order'), ('UniPC lower order final', 'uni_pc_lower_order_final'), + ('RNG', 'randn_source'), ] diff --git a/modules/processing.py b/modules/processing.py index 5556afc5..7bac154d 100644 --- a/modules/processing.py +++ b/modules/processing.py @@ -477,7 +477,8 @@ def create_infotext(p, all_prompts, all_seeds, all_subseeds, comments=None, iter "Conditional mask weight": getattr(p, "inpainting_mask_weight", shared.opts.inpainting_mask_weight) if p.is_using_inpainting_conditioning else None, "Clip skip": None if clip_skip <= 1 else clip_skip, "ENSD": None if opts.eta_noise_seed_delta == 0 else opts.eta_noise_seed_delta, - "Init image hash": getattr(p, 'init_img_hash', None) + "Init image hash": getattr(p, 'init_img_hash', None), + "RNG": (opts.randn_source if opts.randn_source != "GPU" else None) } generation_params.update(p.extra_generation_params) diff --git a/modules/sd_samplers_common.py b/modules/sd_samplers_common.py index e6a372d5..bc074238 100644 --- a/modules/sd_samplers_common.py +++ b/modules/sd_samplers_common.py @@ -61,7 +61,8 @@ def store_latent(decoded): class InterruptedException(BaseException): pass -if opts.use_cpu_randn: + +if opts.randn_source == "CPU": import torchsde._brownian.brownian_interval def torchsde_randn(size, dtype, device, seed): diff --git a/modules/sd_samplers_kdiffusion.py b/modules/sd_samplers_kdiffusion.py index 13f4567a..a547d1b5 100644 --- a/modules/sd_samplers_kdiffusion.py +++ b/modules/sd_samplers_kdiffusion.py @@ -190,7 +190,7 @@ class TorchHijack: if noise.shape == x.shape: return noise - if opts.use_cpu_randn or x.device.type == 'mps': + if opts.randn_source == "CPU" or x.device.type == 'mps': return torch.randn_like(x, device=devices.cpu).to(x.device) else: return torch.randn_like(x) diff --git a/modules/shared.py b/modules/shared.py index b5b401fe..73704889 100644 --- a/modules/shared.py +++ b/modules/shared.py @@ -334,7 +334,7 @@ options_templates.update(options_section(('sd', "Stable Diffusion"), { "comma_padding_backtrack": OptionInfo(20, "Increase coherency by padding from the last comma within n tokens when using more than 75 tokens", gr.Slider, {"minimum": 0, "maximum": 74, "step": 1 }), "CLIP_stop_at_last_layers": OptionInfo(1, "Clip skip", gr.Slider, {"minimum": 1, "maximum": 12, "step": 1}), "upcast_attn": OptionInfo(False, "Upcast cross attention layer to float32"), - "use_cpu_randn": OptionInfo(False, "Use CPU for random number generation to make manual seeds generate the same image across platforms. This may change existing seeds."), + "randn_source": OptionInfo("GPU", "Random number generator source. Changes seeds drastically. Use CPU to produce the same picture across different vidocard vendors.", gr.Radio, {"choices": ["GPU", "CPU"]}), })) options_templates.update(options_section(('compatibility', "Compatibility"), { -- cgit v1.2.3 From 1d11e896984c883f6a0debb3abaef945595cbc70 Mon Sep 17 00:00:00 2001 From: AUTOMATIC <16777216c@gmail.com> Date: Sat, 29 Apr 2023 15:57:09 +0300 Subject: rework Negative Guidance minimum sigma to work with AND, add infotext and copypaste parameters support --- javascript/hints.js | 3 ++- modules/generation_parameters_copypaste.py | 1 + modules/processing.py | 3 ++- modules/sd_samplers_kdiffusion.py | 43 +++++++++++++++++------------- 4 files changed, 30 insertions(+), 20 deletions(-) (limited to 'modules/sd_samplers_kdiffusion.py') diff --git a/javascript/hints.js b/javascript/hints.js index c6bae360..44d418da 100644 --- a/javascript/hints.js +++ b/javascript/hints.js @@ -111,7 +111,8 @@ titles = { "Resize height to": "Resizes image to this height. If 0, height is inferred from either of two nearby sliders.", "Multiplier for extra networks": "When adding extra network such as Hypernetwork or Lora to prompt, use this multiplier for it.", "Discard weights with matching name": "Regular expression; if weights's name matches it, the weights is not written to the resulting checkpoint. Use ^model_ema to discard EMA weights.", - "Extra networks tab order": "Comma-separated list of tab names; tabs listed here will appear in the extra networks UI first and in order lsited." + "Extra networks tab order": "Comma-separated list of tab names; tabs listed here will appear in the extra networks UI first and in order lsited.", + "Negative Guidance minimum sigma": "Skip negative prompt for steps where image is already mostly denoised; the higher this value, the more skips there will be; provides increased performance in exchange for minor quality reduction." } diff --git a/modules/generation_parameters_copypaste.py b/modules/generation_parameters_copypaste.py index e7269363..99f1a0d3 100644 --- a/modules/generation_parameters_copypaste.py +++ b/modules/generation_parameters_copypaste.py @@ -309,6 +309,7 @@ infotext_to_setting_name_mapping = [ ('UniPC order', 'uni_pc_order'), ('UniPC lower order final', 'uni_pc_lower_order_final'), ('RNG', 'randn_source'), + ('NGMS', 's_min_uncond'), ] diff --git a/modules/processing.py b/modules/processing.py index 04a06290..c50784f4 100644 --- a/modules/processing.py +++ b/modules/processing.py @@ -480,7 +480,8 @@ def create_infotext(p, all_prompts, all_seeds, all_subseeds, comments=None, iter "Clip skip": None if clip_skip <= 1 else clip_skip, "ENSD": None if opts.eta_noise_seed_delta == 0 else opts.eta_noise_seed_delta, "Init image hash": getattr(p, 'init_img_hash', None), - "RNG": (opts.randn_source if opts.randn_source != "GPU" else None) + "RNG": opts.randn_source if opts.randn_source != "GPU" else None, + "NGMS": None if p.s_min_uncond == 0 else p.s_min_uncond, } generation_params.update(p.extra_generation_params) diff --git a/modules/sd_samplers_kdiffusion.py b/modules/sd_samplers_kdiffusion.py index d42d5fcf..f8aaac59 100644 --- a/modules/sd_samplers_kdiffusion.py +++ b/modules/sd_samplers_kdiffusion.py @@ -115,20 +115,21 @@ class CFGDenoiser(torch.nn.Module): sigma_in = denoiser_params.sigma tensor = denoiser_params.text_cond uncond = denoiser_params.text_uncond + skip_uncond = False - if self.step % 2 and s_min_uncond > 0 and not is_edit_model: - # alternating uncond allows for higher thresholds without the quality loss normally expected from raising it - sigma_threshold = s_min_uncond - if(torch.dot(sigma,sigma) < sigma.shape[0] * (sigma_threshold*sigma_threshold) ): - uncond = torch.zeros([0,0,uncond.shape[2]]) - x_in=x_in[:x_in.shape[0]//2] - sigma_in=sigma_in[:sigma_in.shape[0]//2] + # alternating uncond allows for higher thresholds without the quality loss normally expected from raising it + if self.step % 2 and s_min_uncond > 0 and sigma[0] < s_min_uncond and not is_edit_model: + skip_uncond = True + x_in = x_in[:-batch_size] + sigma_in = sigma_in[:-batch_size] - if tensor.shape[1] == uncond.shape[1]: - if not is_edit_model: - cond_in = torch.cat([tensor, uncond]) - else: + if tensor.shape[1] == uncond.shape[1] or skip_uncond: + if is_edit_model: cond_in = torch.cat([tensor, uncond, uncond]) + elif skip_uncond: + cond_in = tensor + else: + cond_in = torch.cat([tensor, uncond]) if shared.batch_cond_uncond: x_out = self.inner_model(x_in, sigma_in, cond=make_condition_dict([cond_in], image_cond_in)) @@ -152,9 +153,15 @@ class CFGDenoiser(torch.nn.Module): x_out[a:b] = self.inner_model(x_in[a:b], sigma_in[a:b], cond=make_condition_dict(c_crossattn, image_cond_in[a:b])) - if uncond.shape[0]: + if not skip_uncond: x_out[-uncond.shape[0]:] = self.inner_model(x_in[-uncond.shape[0]:], sigma_in[-uncond.shape[0]:], cond=make_condition_dict([uncond], image_cond_in[-uncond.shape[0]:])) + if skip_uncond: + #x_out = torch.cat([x_out, x_out[0:batch_size]]) # we skipped uncond denoising, so we put cond-denoised image to where the uncond-denoised image should be + denoised_image_indexes = [x[0][0] for x in conds_list] + fake_uncond = torch.cat([x_out[i:i+1] for i in denoised_image_indexes]) + x_out = torch.cat([x_out, fake_uncond]) + denoised_params = CFGDenoisedParams(x_out, state.sampling_step, state.sampling_steps) cfg_denoised_callback(denoised_params) @@ -165,13 +172,12 @@ class CFGDenoiser(torch.nn.Module): elif opts.live_preview_content == "Negative prompt": sd_samplers_common.store_latent(x_out[-uncond.shape[0]:]) - if not is_edit_model: - if uncond.shape[0]: - denoised = self.combine_denoised(x_out, conds_list, uncond, cond_scale) - else: - denoised = x_out - else: + if is_edit_model: denoised = self.combine_denoised_for_edit_model(x_out, cond_scale) + elif skip_uncond: + denoised = self.combine_denoised(x_out, conds_list, uncond, 1.0) + else: + denoised = self.combine_denoised(x_out, conds_list, uncond, cond_scale) if self.mask is not None: denoised = self.init_latent * self.mask + self.nmask * denoised @@ -221,6 +227,7 @@ class KDiffusionSampler: self.eta = None self.config = None self.last_latent = None + self.s_min_uncond = None self.conditioning_key = sd_model.model.conditioning_key -- cgit v1.2.3 From 737b73a820584b8035fcc37fe35993bec867f326 Mon Sep 17 00:00:00 2001 From: AUTOMATIC <16777216c@gmail.com> Date: Sat, 29 Apr 2023 16:05:20 +0300 Subject: some extra lines I forgot to add for previous commit --- modules/sd_samplers_kdiffusion.py | 5 ++--- 1 file changed, 2 insertions(+), 3 deletions(-) (limited to 'modules/sd_samplers_kdiffusion.py') diff --git a/modules/sd_samplers_kdiffusion.py b/modules/sd_samplers_kdiffusion.py index f8aaac59..136aa8e5 100644 --- a/modules/sd_samplers_kdiffusion.py +++ b/modules/sd_samplers_kdiffusion.py @@ -156,11 +156,10 @@ class CFGDenoiser(torch.nn.Module): if not skip_uncond: x_out[-uncond.shape[0]:] = self.inner_model(x_in[-uncond.shape[0]:], sigma_in[-uncond.shape[0]:], cond=make_condition_dict([uncond], image_cond_in[-uncond.shape[0]:])) + denoised_image_indexes = [x[0][0] for x in conds_list] if skip_uncond: - #x_out = torch.cat([x_out, x_out[0:batch_size]]) # we skipped uncond denoising, so we put cond-denoised image to where the uncond-denoised image should be - denoised_image_indexes = [x[0][0] for x in conds_list] fake_uncond = torch.cat([x_out[i:i+1] for i in denoised_image_indexes]) - x_out = torch.cat([x_out, fake_uncond]) + x_out = torch.cat([x_out, fake_uncond]) # we skipped uncond denoising, so we put cond-denoised image to where the uncond-denoised image should be denoised_params = CFGDenoisedParams(x_out, state.sampling_step, state.sampling_steps) cfg_denoised_callback(denoised_params) -- cgit v1.2.3 From 8863b31d83b527d041ca45e23b9af99e2346081a Mon Sep 17 00:00:00 2001 From: AUTOMATIC <16777216c@gmail.com> Date: Sat, 29 Apr 2023 16:06:20 +0300 Subject: use correct images for previews when using AND (see #9491) --- modules/sd_samplers_kdiffusion.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) (limited to 'modules/sd_samplers_kdiffusion.py') diff --git a/modules/sd_samplers_kdiffusion.py b/modules/sd_samplers_kdiffusion.py index 136aa8e5..eb98e599 100644 --- a/modules/sd_samplers_kdiffusion.py +++ b/modules/sd_samplers_kdiffusion.py @@ -167,7 +167,7 @@ class CFGDenoiser(torch.nn.Module): devices.test_for_nans(x_out, "unet") if opts.live_preview_content == "Prompt": - sd_samplers_common.store_latent(x_out[0:x_out.shape[0]-uncond.shape[0]]) + sd_samplers_common.store_latent(torch.cat([x_out[i:i+1] for i in denoised_image_indexes])) elif opts.live_preview_content == "Negative prompt": sd_samplers_common.store_latent(x_out[-uncond.shape[0]:]) -- cgit v1.2.3 From 3ba6c3c83c0983a025c7bddc08bb7f49481b3cbb Mon Sep 17 00:00:00 2001 From: Aarni Koskela Date: Tue, 9 May 2023 22:17:58 +0300 Subject: Fix up string formatting/concatenation to f-strings where feasible --- modules/api/api.py | 22 ++++++------ modules/call_queue.py | 5 +-- modules/esrgan_model.py | 11 +++--- modules/esrgan_model_arch.py | 16 ++++----- modules/extra_networks_hypernet.py | 3 +- modules/generation_parameters_copypaste.py | 4 +-- modules/hashes.py | 4 +-- modules/images.py | 8 ++--- modules/interrogate.py | 4 +-- modules/models/diffusion/ddpm_edit.py | 4 +-- modules/models/diffusion/uni_pc/uni_pc.py | 4 +-- modules/ngrok.py | 4 +-- modules/paths.py | 2 +- modules/processing.py | 13 ++++++-- modules/progress.py | 3 +- modules/realesrgan_model.py | 8 ++--- modules/scripts.py | 5 +-- modules/sd_hijack_clip_old.py | 3 +- modules/sd_hijack_unet.py | 2 +- modules/sd_models.py | 4 +-- modules/sd_models_config.py | 2 +- modules/sd_samplers_kdiffusion.py | 2 +- modules/sd_vae.py | 2 +- modules/styles.py | 2 +- modules/textual_inversion/autocrop.py | 6 ++-- modules/textual_inversion/dataset.py | 2 +- modules/textual_inversion/preprocess.py | 6 ++-- modules/textual_inversion/textual_inversion.py | 12 +++---- modules/ui.py | 46 +++++++++++++------------- modules/ui_extensions.py | 3 +- modules/ui_extra_networks.py | 4 ++- scripts/custom_code.py | 2 +- scripts/loopback.py | 2 +- scripts/xyz_grid.py | 2 +- 34 files changed, 121 insertions(+), 101 deletions(-) (limited to 'modules/sd_samplers_kdiffusion.py') diff --git a/modules/api/api.py b/modules/api/api.py index cdbdce32..9bb95dfd 100644 --- a/modules/api/api.py +++ b/modules/api/api.py @@ -570,20 +570,20 @@ class Api: filename = create_embedding(**args) # create empty embedding sd_hijack.model_hijack.embedding_db.load_textual_inversion_embeddings() # reload embeddings so new one can be immediately used shared.state.end() - return CreateResponse(info = "create embedding filename: {filename}".format(filename = filename)) + return CreateResponse(info=f"create embedding filename: {filename}") except AssertionError as e: shared.state.end() - return TrainResponse(info = "create embedding error: {error}".format(error = e)) + return TrainResponse(info=f"create embedding error: {e}") def create_hypernetwork(self, args: dict): try: shared.state.begin() filename = create_hypernetwork(**args) # create empty embedding shared.state.end() - return CreateResponse(info = "create hypernetwork filename: {filename}".format(filename = filename)) + return CreateResponse(info=f"create hypernetwork filename: {filename}") except AssertionError as e: shared.state.end() - return TrainResponse(info = "create hypernetwork error: {error}".format(error = e)) + return TrainResponse(info=f"create hypernetwork error: {e}") def preprocess(self, args: dict): try: @@ -593,13 +593,13 @@ class Api: return PreprocessResponse(info = 'preprocess complete') except KeyError as e: shared.state.end() - return PreprocessResponse(info = "preprocess error: invalid token: {error}".format(error = e)) + return PreprocessResponse(info=f"preprocess error: invalid token: {e}") except AssertionError as e: shared.state.end() - return PreprocessResponse(info = "preprocess error: {error}".format(error = e)) + return PreprocessResponse(info=f"preprocess error: {e}") except FileNotFoundError as e: shared.state.end() - return PreprocessResponse(info = 'preprocess error: {error}'.format(error = e)) + return PreprocessResponse(info=f'preprocess error: {e}') def train_embedding(self, args: dict): try: @@ -617,10 +617,10 @@ class Api: if not apply_optimizations: sd_hijack.apply_optimizations() shared.state.end() - return TrainResponse(info = "train embedding complete: filename: {filename} error: {error}".format(filename = filename, error = error)) + return TrainResponse(info=f"train embedding complete: filename: {filename} error: {error}") except AssertionError as msg: shared.state.end() - return TrainResponse(info = "train embedding error: {msg}".format(msg = msg)) + return TrainResponse(info=f"train embedding error: {msg}") def train_hypernetwork(self, args: dict): try: @@ -641,10 +641,10 @@ class Api: if not apply_optimizations: sd_hijack.apply_optimizations() shared.state.end() - return TrainResponse(info="train embedding complete: filename: {filename} error: {error}".format(filename=filename, error=error)) + return TrainResponse(info=f"train embedding complete: filename: {filename} error: {error}") except AssertionError as msg: shared.state.end() - return TrainResponse(info="train embedding error: {error}".format(error=error)) + return TrainResponse(info=f"train embedding error: {error}") def get_memory(self): try: diff --git a/modules/call_queue.py b/modules/call_queue.py index 1829f3a6..447bb764 100644 --- a/modules/call_queue.py +++ b/modules/call_queue.py @@ -60,7 +60,7 @@ def wrap_gradio_call(func, extra_outputs=None, add_stats=False): max_debug_str_len = 131072 # (1024*1024)/8 print("Error completing request", file=sys.stderr) - argStr = f"Arguments: {str(args)} {str(kwargs)}" + argStr = f"Arguments: {args} {kwargs}" print(argStr[:max_debug_str_len], file=sys.stderr) if len(argStr) > max_debug_str_len: print(f"(Argument list truncated at {max_debug_str_len}/{len(argStr)} characters)", file=sys.stderr) @@ -73,7 +73,8 @@ def wrap_gradio_call(func, extra_outputs=None, add_stats=False): if extra_outputs_array is None: extra_outputs_array = [None, ''] - res = extra_outputs_array + [f"
{html.escape(type(e).__name__+': '+str(e))}
"] + error_message = f'{type(e).__name__}: {e}' + res = extra_outputs_array + [f"
{html.escape(error_message)}
"] shared.state.skipped = False shared.state.interrupted = False diff --git a/modules/esrgan_model.py b/modules/esrgan_model.py index 9a9c38f1..f4369257 100644 --- a/modules/esrgan_model.py +++ b/modules/esrgan_model.py @@ -156,13 +156,16 @@ class UpscalerESRGAN(Upscaler): def load_model(self, path: str): if "http" in path: - filename = load_file_from_url(url=self.model_url, model_dir=self.model_path, - file_name="%s.pth" % self.model_name, - progress=True) + filename = load_file_from_url( + url=self.model_url, + model_dir=self.model_path, + file_name=f"{self.model_name}.pth", + progress=True, + ) else: filename = path if not os.path.exists(filename) or filename is None: - print("Unable to load %s from %s" % (self.model_path, filename)) + print(f"Unable to load {self.model_path} from {filename}") return None state_dict = torch.load(filename, map_location='cpu' if devices.device_esrgan.type == 'mps' else None) diff --git a/modules/esrgan_model_arch.py b/modules/esrgan_model_arch.py index 1b52b0f5..6071fea7 100644 --- a/modules/esrgan_model_arch.py +++ b/modules/esrgan_model_arch.py @@ -38,7 +38,7 @@ class RRDBNet(nn.Module): elif upsample_mode == 'pixelshuffle': upsample_block = pixelshuffle_block else: - raise NotImplementedError('upsample mode [{:s}] is not found'.format(upsample_mode)) + raise NotImplementedError(f'upsample mode [{upsample_mode}] is not found') if upscale == 3: upsampler = upsample_block(nf, nf, 3, act_type=act_type, convtype=convtype) else: @@ -261,10 +261,10 @@ class Upsample(nn.Module): def extra_repr(self): if self.scale_factor is not None: - info = 'scale_factor=' + str(self.scale_factor) + info = f'scale_factor={self.scale_factor}' else: - info = 'size=' + str(self.size) - info += ', mode=' + self.mode + info = f'size={self.size}' + info += f', mode={self.mode}' return info @@ -350,7 +350,7 @@ def act(act_type, inplace=True, neg_slope=0.2, n_prelu=1, beta=1.0): elif act_type == 'sigmoid': # [0, 1] range output layer = nn.Sigmoid() else: - raise NotImplementedError('activation layer [{:s}] is not found'.format(act_type)) + raise NotImplementedError(f'activation layer [{act_type}] is not found') return layer @@ -372,7 +372,7 @@ def norm(norm_type, nc): elif norm_type == 'none': def norm_layer(x): return Identity() else: - raise NotImplementedError('normalization layer [{:s}] is not found'.format(norm_type)) + raise NotImplementedError(f'normalization layer [{norm_type}] is not found') return layer @@ -388,7 +388,7 @@ def pad(pad_type, padding): elif pad_type == 'zero': layer = nn.ZeroPad2d(padding) else: - raise NotImplementedError('padding layer [{:s}] is not implemented'.format(pad_type)) + raise NotImplementedError(f'padding layer [{pad_type}] is not implemented') return layer @@ -432,7 +432,7 @@ def conv_block(in_nc, out_nc, kernel_size, stride=1, dilation=1, groups=1, bias= pad_type='zero', norm_type=None, act_type='relu', mode='CNA', convtype='Conv2D', spectral_norm=False): """ Conv layer with padding, normalization, activation """ - assert mode in ['CNA', 'NAC', 'CNAC'], 'Wrong conv mode [{:s}]'.format(mode) + assert mode in ['CNA', 'NAC', 'CNAC'], f'Wrong conv mode [{mode}]' padding = get_valid_padding(kernel_size, dilation) p = pad(pad_type, padding) if pad_type and pad_type != 'zero' else None padding = padding if pad_type == 'zero' else 0 diff --git a/modules/extra_networks_hypernet.py b/modules/extra_networks_hypernet.py index 33d100dd..04f27c9f 100644 --- a/modules/extra_networks_hypernet.py +++ b/modules/extra_networks_hypernet.py @@ -10,7 +10,8 @@ class ExtraNetworkHypernet(extra_networks.ExtraNetwork): additional = shared.opts.sd_hypernetwork if additional != "None" and additional in shared.hypernetworks and len([x for x in params_list if x.items[0] == additional]) == 0: - p.all_prompts = [x + f"" for x in p.all_prompts] + hypernet_prompt_text = f"" + p.all_prompts = [f"{prompt}{hypernet_prompt_text}" for prompt in p.all_prompts] params_list.append(extra_networks.ExtraNetworkParams(items=[additional, shared.opts.extra_networks_default_multiplier])) names = [] diff --git a/modules/generation_parameters_copypaste.py b/modules/generation_parameters_copypaste.py index 78248ed2..fe8b18b2 100644 --- a/modules/generation_parameters_copypaste.py +++ b/modules/generation_parameters_copypaste.py @@ -269,8 +269,8 @@ Steps: 20, Sampler: Euler a, CFG scale: 7, Seed: 965400086, Size: 512x512, Model v = v[1:-1] if v[0] == '"' and v[-1] == '"' else v m = re_imagesize.match(v) if m is not None: - res[k+"-1"] = m.group(1) - res[k+"-2"] = m.group(2) + res[f"{k}-1"] = m.group(1) + res[f"{k}-2"] = m.group(2) else: res[k] = v diff --git a/modules/hashes.py b/modules/hashes.py index 83272a07..032120f4 100644 --- a/modules/hashes.py +++ b/modules/hashes.py @@ -13,7 +13,7 @@ cache_data = None def dump_cache(): - with filelock.FileLock(cache_filename+".lock"): + with filelock.FileLock(f"{cache_filename}.lock"): with open(cache_filename, "w", encoding="utf8") as file: json.dump(cache_data, file, indent=4) @@ -22,7 +22,7 @@ def cache(subsection): global cache_data if cache_data is None: - with filelock.FileLock(cache_filename+".lock"): + with filelock.FileLock(f"{cache_filename}.lock"): if not os.path.isfile(cache_filename): cache_data = {} else: diff --git a/modules/images.py b/modules/images.py index 6ceb7c7c..a41965ab 100644 --- a/modules/images.py +++ b/modules/images.py @@ -467,7 +467,7 @@ def get_next_sequence_number(path, basename): """ result = -1 if basename != '': - basename = basename + "-" + basename = f"{basename}-" prefix_length = len(basename) for p in os.listdir(path): @@ -536,7 +536,7 @@ def save_image(image, path, basename, seed=None, prompt=None, extension='png', i add_number = opts.save_images_add_number or file_decoration == '' if file_decoration != "" and add_number: - file_decoration = "-" + file_decoration + file_decoration = f"-{file_decoration}" file_decoration = namegen.apply(file_decoration) + suffix @@ -566,7 +566,7 @@ def save_image(image, path, basename, seed=None, prompt=None, extension='png', i def _atomically_save_image(image_to_save, filename_without_extension, extension): # save image with .tmp extension to avoid race condition when another process detects new image in the directory - temp_file_path = filename_without_extension + ".tmp" + temp_file_path = f"{filename_without_extension}.tmp" image_format = Image.registered_extensions()[extension] if extension.lower() == '.png': @@ -626,7 +626,7 @@ def save_image(image, path, basename, seed=None, prompt=None, extension='png', i if opts.save_txt and info is not None: txt_fullfn = f"{fullfn_without_extension}.txt" with open(txt_fullfn, "w", encoding="utf8") as file: - file.write(info + "\n") + file.write(f"{info}\n") else: txt_fullfn = None diff --git a/modules/interrogate.py b/modules/interrogate.py index e1665708..9f7d657f 100644 --- a/modules/interrogate.py +++ b/modules/interrogate.py @@ -28,7 +28,7 @@ def category_types(): def download_default_clip_interrogate_categories(content_dir): print("Downloading CLIP categories...") - tmpdir = content_dir + "_tmp" + tmpdir = f"{content_dir}_tmp" category_types = ["artists", "flavors", "mediums", "movements"] try: @@ -214,7 +214,7 @@ class InterrogateModels: if shared.opts.interrogate_return_ranks: res += f", ({match}:{score/100:.3f})" else: - res += ", " + match + res += f", {match}" except Exception: print("Error interrogating", file=sys.stderr) diff --git a/modules/models/diffusion/ddpm_edit.py b/modules/models/diffusion/ddpm_edit.py index f3d49c44..f880bc3c 100644 --- a/modules/models/diffusion/ddpm_edit.py +++ b/modules/models/diffusion/ddpm_edit.py @@ -223,7 +223,7 @@ class DDPM(pl.LightningModule): for k in keys: for ik in ignore_keys: if k.startswith(ik): - print("Deleting key {} from state_dict.".format(k)) + print(f"Deleting key {k} from state_dict.") del sd[k] missing, unexpected = self.load_state_dict(sd, strict=False) if not only_model else self.model.load_state_dict( sd, strict=False) @@ -386,7 +386,7 @@ class DDPM(pl.LightningModule): _, loss_dict_no_ema = self.shared_step(batch) with self.ema_scope(): _, loss_dict_ema = self.shared_step(batch) - loss_dict_ema = {key + '_ema': loss_dict_ema[key] for key in loss_dict_ema} + loss_dict_ema = {f"{key}_ema": loss_dict_ema[key] for key in loss_dict_ema} self.log_dict(loss_dict_no_ema, prog_bar=False, logger=True, on_step=False, on_epoch=True) self.log_dict(loss_dict_ema, prog_bar=False, logger=True, on_step=False, on_epoch=True) diff --git a/modules/models/diffusion/uni_pc/uni_pc.py b/modules/models/diffusion/uni_pc/uni_pc.py index eb5f4e76..11b330bc 100644 --- a/modules/models/diffusion/uni_pc/uni_pc.py +++ b/modules/models/diffusion/uni_pc/uni_pc.py @@ -94,7 +94,7 @@ class NoiseScheduleVP: """ if schedule not in ['discrete', 'linear', 'cosine']: - raise ValueError("Unsupported noise schedule {}. The schedule needs to be 'discrete' or 'linear' or 'cosine'".format(schedule)) + raise ValueError(f"Unsupported noise schedule {schedule}. The schedule needs to be 'discrete' or 'linear' or 'cosine'") self.schedule = schedule if schedule == 'discrete': @@ -469,7 +469,7 @@ class UniPC: t = torch.linspace(t_T**(1. / t_order), t_0**(1. / t_order), N + 1).pow(t_order).to(device) return t else: - raise ValueError("Unsupported skip_type {}, need to be 'logSNR' or 'time_uniform' or 'time_quadratic'".format(skip_type)) + raise ValueError(f"Unsupported skip_type {skip_type}, need to be 'logSNR' or 'time_uniform' or 'time_quadratic'") def get_orders_and_timesteps_for_singlestep_solver(self, steps, order, skip_type, t_T, t_0, device): """ diff --git a/modules/ngrok.py b/modules/ngrok.py index 1ad7989b..7a7b4b26 100644 --- a/modules/ngrok.py +++ b/modules/ngrok.py @@ -7,8 +7,8 @@ def connect(token, port, region): else: if ':' in token: # token = authtoken:username:password - account = token.split(':')[1] + ':' + token.split(':')[-1] - token = token.split(':')[0] + token, username, password = token.split(':', 2) + account = f"{username}:{password}" config = conf.PyngrokConfig( auth_token=token, region=region diff --git a/modules/paths.py b/modules/paths.py index 0e1e00e7..acf1894b 100644 --- a/modules/paths.py +++ b/modules/paths.py @@ -16,7 +16,7 @@ for possible_sd_path in possible_sd_paths: sd_path = os.path.abspath(possible_sd_path) break -assert sd_path is not None, "Couldn't find Stable Diffusion in any of: " + str(possible_sd_paths) +assert sd_path is not None, f"Couldn't find Stable Diffusion in any of: {possible_sd_paths}" path_dirs = [ (sd_path, 'ldm', 'Stable Diffusion', []), diff --git a/modules/processing.py b/modules/processing.py index e786791a..1a76e552 100644 --- a/modules/processing.py +++ b/modules/processing.py @@ -500,7 +500,7 @@ def create_infotext(p, all_prompts, all_seeds, all_subseeds, comments=None, iter generation_params_text = ", ".join([k if k == v else f'{k}: {generation_parameters_copypaste.quote(v)}' for k, v in generation_params.items() if v is not None]) - negative_prompt_text = "\nNegative prompt: " + p.all_negative_prompts[index] if p.all_negative_prompts[index] else "" + negative_prompt_text = f"\nNegative prompt: {p.all_negative_prompts[index]}" if p.all_negative_prompts[index] else "" return f"{all_prompts[index]}{negative_prompt_text}\n{generation_params_text}".strip() @@ -780,7 +780,16 @@ def process_images_inner(p: StableDiffusionProcessing) -> Processed: devices.torch_gc() - res = Processed(p, output_images, p.all_seeds[0], infotext(), comments="".join(["\n\n" + x for x in comments]), subseed=p.all_subseeds[0], index_of_first_image=index_of_first_image, infotexts=infotexts) + res = Processed( + p, + images_list=output_images, + seed=p.all_seeds[0], + info=infotext(), + comments="".join(f"\n\n{comment}" for comment in comments), + subseed=p.all_subseeds[0], + index_of_first_image=index_of_first_image, + infotexts=infotexts, + ) if p.scripts is not None: p.scripts.postprocess(p, res) diff --git a/modules/progress.py b/modules/progress.py index 5655346b..948e6f00 100644 --- a/modules/progress.py +++ b/modules/progress.py @@ -96,7 +96,8 @@ def progressapi(req: ProgressRequest): if image is not None: buffered = io.BytesIO() image.save(buffered, format="png") - live_preview = 'data:image/png;base64,' + base64.b64encode(buffered.getvalue()).decode("ascii") + base64_image = base64.b64encode(buffered.getvalue()).decode('ascii') + live_preview = f"data:image/png;base64,{base64_image}" id_live_preview = shared.state.id_live_preview else: live_preview = None diff --git a/modules/realesrgan_model.py b/modules/realesrgan_model.py index d6079433..efd7fca5 100644 --- a/modules/realesrgan_model.py +++ b/modules/realesrgan_model.py @@ -28,9 +28,9 @@ class UpscalerRealESRGAN(Upscaler): for scaler in scalers: if scaler.local_data_path.startswith("http"): filename = modelloader.friendly_name(scaler.local_data_path) - local = next(iter([local_model for local_model in local_model_paths if local_model.endswith(filename + '.pth')]), None) - if local: - scaler.local_data_path = local + local_model_candidates = [local_model for local_model in local_model_paths if local_model.endswith(f"{filename}.pth")] + if local_model_candidates: + scaler.local_data_path = local_model_candidates[0] if scaler.name in opts.realesrgan_enabled_models: self.scalers.append(scaler) @@ -47,7 +47,7 @@ class UpscalerRealESRGAN(Upscaler): info = self.load_model(path) if not os.path.exists(info.local_data_path): - print("Unable to load RealESRGAN model: %s" % info.name) + print(f"Unable to load RealESRGAN model: {info.name}") return img upsampler = RealESRGANer( diff --git a/modules/scripts.py b/modules/scripts.py index 4d0bbd66..d945b89f 100644 --- a/modules/scripts.py +++ b/modules/scripts.py @@ -163,7 +163,8 @@ class Script: """helper function to generate id for a HTML element, constructs final id out of script name, tab and user-supplied item_id""" need_tabname = self.show(True) == self.show(False) - tabname = ('img2img' if self.is_img2img else 'txt2txt') + "_" if need_tabname else "" + tabkind = 'img2img' if self.is_img2img else 'txt2txt' + tabname = f"{tabkind}_" if need_tabname else "" title = re.sub(r'[^a-z_0-9]', '', re.sub(r'\s', '_', self.title().lower())) return f'script_{tabname}{title}_{item_id}' @@ -526,7 +527,7 @@ def add_classes_to_gradio_component(comp): this adds gradio-* to the component for css styling (ie gradio-button to gr.Button), as well as some others """ - comp.elem_classes = ["gradio-" + comp.get_block_name(), *(comp.elem_classes or [])] + comp.elem_classes = [f"gradio-{comp.get_block_name()}", *(comp.elem_classes or [])] if getattr(comp, 'multiselect', False): comp.elem_classes.append('multiselect') diff --git a/modules/sd_hijack_clip_old.py b/modules/sd_hijack_clip_old.py index 6d9fbbe6..a3476e95 100644 --- a/modules/sd_hijack_clip_old.py +++ b/modules/sd_hijack_clip_old.py @@ -75,7 +75,8 @@ def forward_old(self: sd_hijack_clip.FrozenCLIPEmbedderWithCustomWordsBase, text self.hijack.comments += hijack_comments if len(used_custom_terms) > 0: - self.hijack.comments.append("Used embeddings: " + ", ".join([f'{word} [{checksum}]' for word, checksum in used_custom_terms])) + embedding_names = ", ".join(f"{word} [{checksum}]" for word, checksum in used_custom_terms) + self.hijack.comments.append(f"Used embeddings: {embedding_names}") self.hijack.fixes = hijack_fixes return self.process_tokens(remade_batch_tokens, batch_multipliers) diff --git a/modules/sd_hijack_unet.py b/modules/sd_hijack_unet.py index 15858263..ca1daf45 100644 --- a/modules/sd_hijack_unet.py +++ b/modules/sd_hijack_unet.py @@ -18,7 +18,7 @@ class TorchHijackForUnet: if hasattr(torch, item): return getattr(torch, item) - raise AttributeError("'{}' object has no attribute '{}'".format(type(self).__name__, item)) + raise AttributeError(f"'{type(self).__name__}' object has no attribute '{item}'") def cat(self, tensors, *args, **kwargs): if len(tensors) == 2: diff --git a/modules/sd_models.py b/modules/sd_models.py index 59adc7cc..36f643e1 100644 --- a/modules/sd_models.py +++ b/modules/sd_models.py @@ -47,7 +47,7 @@ class CheckpointInfo: self.model_name = os.path.splitext(name.replace("/", "_").replace("\\", "_"))[0] self.hash = model_hash(filename) - self.sha256 = hashes.sha256_from_cache(self.filename, "checkpoint/" + name) + self.sha256 = hashes.sha256_from_cache(self.filename, f"checkpoint/{name}") self.shorthash = self.sha256[0:10] if self.sha256 else None self.title = name if self.shorthash is None else f'{name} [{self.shorthash}]' @@ -69,7 +69,7 @@ class CheckpointInfo: checkpoint_alisases[id] = self def calculate_shorthash(self): - self.sha256 = hashes.sha256(self.filename, "checkpoint/" + self.name) + self.sha256 = hashes.sha256(self.filename, f"checkpoint/{self.name}") if self.sha256 is None: return diff --git a/modules/sd_models_config.py b/modules/sd_models_config.py index 9398f528..7a79925a 100644 --- a/modules/sd_models_config.py +++ b/modules/sd_models_config.py @@ -111,7 +111,7 @@ def find_checkpoint_config_near_filename(info): if info is None: return None - config = os.path.splitext(info.filename)[0] + ".yaml" + config = f"{os.path.splitext(info.filename)[0]}.yaml" if os.path.exists(config): return config diff --git a/modules/sd_samplers_kdiffusion.py b/modules/sd_samplers_kdiffusion.py index eb98e599..0fc9f456 100644 --- a/modules/sd_samplers_kdiffusion.py +++ b/modules/sd_samplers_kdiffusion.py @@ -198,7 +198,7 @@ class TorchHijack: if hasattr(torch, item): return getattr(torch, item) - raise AttributeError("'{}' object has no attribute '{}'".format(type(self).__name__, item)) + raise AttributeError(f"'{type(self).__name__}' object has no attribute '{item}'") def randn_like(self, x): if self.sampler_noises: diff --git a/modules/sd_vae.py b/modules/sd_vae.py index 9b00f76e..521e485a 100644 --- a/modules/sd_vae.py +++ b/modules/sd_vae.py @@ -89,7 +89,7 @@ def refresh_vae_list(): def find_vae_near_checkpoint(checkpoint_file): checkpoint_path = os.path.splitext(checkpoint_file)[0] - for vae_location in [checkpoint_path + ".vae.pt", checkpoint_path + ".vae.ckpt", checkpoint_path + ".vae.safetensors"]: + for vae_location in [f"{checkpoint_path}.vae.pt", f"{checkpoint_path}.vae.ckpt", f"{checkpoint_path}.vae.safetensors"]: if os.path.isfile(vae_location): return vae_location diff --git a/modules/styles.py b/modules/styles.py index 9ed85991..11642075 100644 --- a/modules/styles.py +++ b/modules/styles.py @@ -74,7 +74,7 @@ class StyleDatabase: def save_styles(self, path: str) -> None: # Always keep a backup file around if os.path.exists(path): - shutil.copy(path, path + ".bak") + shutil.copy(path, f"{path}.bak") fd = os.open(path, os.O_RDWR|os.O_CREAT) with os.fdopen(fd, "w", encoding="utf-8-sig", newline='') as file: diff --git a/modules/textual_inversion/autocrop.py b/modules/textual_inversion/autocrop.py index 68e1103c..ba1bdcd4 100644 --- a/modules/textual_inversion/autocrop.py +++ b/modules/textual_inversion/autocrop.py @@ -111,7 +111,7 @@ def focal_point(im, settings): if corner_centroid is not None: color = BLUE box = corner_centroid.bounding(max_size * corner_centroid.weight) - d.text((box[0], box[1]-15), "Edge: %.02f" % corner_centroid.weight, fill=color) + d.text((box[0], box[1]-15), f"Edge: {corner_centroid.weight:.02f}", fill=color) d.ellipse(box, outline=color) if len(corner_points) > 1: for f in corner_points: @@ -119,7 +119,7 @@ def focal_point(im, settings): if entropy_centroid is not None: color = "#ff0" box = entropy_centroid.bounding(max_size * entropy_centroid.weight) - d.text((box[0], box[1]-15), "Entropy: %.02f" % entropy_centroid.weight, fill=color) + d.text((box[0], box[1]-15), f"Entropy: {entropy_centroid.weight:.02f}", fill=color) d.ellipse(box, outline=color) if len(entropy_points) > 1: for f in entropy_points: @@ -127,7 +127,7 @@ def focal_point(im, settings): if face_centroid is not None: color = RED box = face_centroid.bounding(max_size * face_centroid.weight) - d.text((box[0], box[1]-15), "Face: %.02f" % face_centroid.weight, fill=color) + d.text((box[0], box[1]-15), f"Face: {face_centroid.weight:.02f}", fill=color) d.ellipse(box, outline=color) if len(face_points) > 1: for f in face_points: diff --git a/modules/textual_inversion/dataset.py b/modules/textual_inversion/dataset.py index af9fbcf2..41610e03 100644 --- a/modules/textual_inversion/dataset.py +++ b/modules/textual_inversion/dataset.py @@ -72,7 +72,7 @@ class PersonalizedBase(Dataset): except Exception: continue - text_filename = os.path.splitext(path)[0] + ".txt" + text_filename = f"{os.path.splitext(path)[0]}.txt" filename = os.path.basename(path) if os.path.exists(text_filename): diff --git a/modules/textual_inversion/preprocess.py b/modules/textual_inversion/preprocess.py index 4a29151d..da0bcb26 100644 --- a/modules/textual_inversion/preprocess.py +++ b/modules/textual_inversion/preprocess.py @@ -63,9 +63,9 @@ def save_pic_with_caption(image, index, params: PreprocessParams, existing_capti image.save(os.path.join(params.dstdir, f"{basename}.png")) if params.preprocess_txt_action == 'prepend' and existing_caption: - caption = existing_caption + ' ' + caption + caption = f"{existing_caption} {caption}" elif params.preprocess_txt_action == 'append' and existing_caption: - caption = caption + ' ' + existing_caption + caption = f"{caption} {existing_caption}" elif params.preprocess_txt_action == 'copy' and existing_caption: caption = existing_caption @@ -174,7 +174,7 @@ def preprocess_work(process_src, process_dst, process_width, process_height, pre params.src = filename existing_caption = None - existing_caption_filename = os.path.splitext(filename)[0] + '.txt' + existing_caption_filename = f"{os.path.splitext(filename)[0]}.txt" if os.path.exists(existing_caption_filename): with open(existing_caption_filename, 'r', encoding="utf8") as file: existing_caption = file.read() diff --git a/modules/textual_inversion/textual_inversion.py b/modules/textual_inversion/textual_inversion.py index 379df243..4368eb63 100644 --- a/modules/textual_inversion/textual_inversion.py +++ b/modules/textual_inversion/textual_inversion.py @@ -69,7 +69,7 @@ class Embedding: 'hash': self.checksum(), 'optimizer_state_dict': self.optimizer_state_dict, } - torch.save(optimizer_saved_dict, filename + '.optim') + torch.save(optimizer_saved_dict, f"{filename}.optim") def checksum(self): if self.cached_checksum is not None: @@ -437,8 +437,8 @@ def train_embedding(id_task, embedding_name, learn_rate, batch_size, gradient_st optimizer = torch.optim.AdamW([embedding.vec], lr=scheduler.learn_rate, weight_decay=0.0) if shared.opts.save_optimizer_state: optimizer_state_dict = None - if os.path.exists(filename + '.optim'): - optimizer_saved_dict = torch.load(filename + '.optim', map_location='cpu') + if os.path.exists(f"{filename}.optim"): + optimizer_saved_dict = torch.load(f"{filename}.optim", map_location='cpu') if embedding.checksum() == optimizer_saved_dict.get('hash', None): optimizer_state_dict = optimizer_saved_dict.get('optimizer_state_dict', None) @@ -599,7 +599,7 @@ def train_embedding(id_task, embedding_name, learn_rate, batch_size, gradient_st data = torch.load(last_saved_file) info.add_text("sd-ti-embedding", embedding_to_b64(data)) - title = "<{}>".format(data.get('name', '???')) + title = f"<{data.get('name', '???')}>" try: vectorSize = list(data['string_to_param'].values())[0].shape[0] @@ -608,8 +608,8 @@ def train_embedding(id_task, embedding_name, learn_rate, batch_size, gradient_st checkpoint = sd_models.select_checkpoint() footer_left = checkpoint.model_name - footer_mid = '[{}]'.format(checkpoint.shorthash) - footer_right = '{}v {}s'.format(vectorSize, steps_done) + footer_mid = f'[{checkpoint.shorthash}]' + footer_right = f'{vectorSize}v {steps_done}s' captioned_image = caption_image_overlay(image, title, footer_left, footer_mid, footer_right) captioned_image = insert_image_data_embed(captioned_image, data) diff --git a/modules/ui.py b/modules/ui.py index 34b2aaff..d02f6e82 100644 --- a/modules/ui.py +++ b/modules/ui.py @@ -101,7 +101,7 @@ def visit(x, func, path=""): for c in x.children: visit(c, func, path) elif x.label is not None: - func(path + "/" + str(x.label), x) + func(f"{path}/{x.label}", x) def add_style(name: str, prompt: str, negative_prompt: str): @@ -166,7 +166,7 @@ def process_interrogate(interrogation_function, mode, ii_input_dir, ii_output_di img = Image.open(image) filename = os.path.basename(image) left, _ = os.path.splitext(filename) - print(interrogation_function(img), file=open(os.path.join(ii_output_dir, left + ".txt"), 'a')) + print(interrogation_function(img), file=open(os.path.join(ii_output_dir, f"{left}.txt"), 'a')) return [gr.update(), None] @@ -182,29 +182,29 @@ def interrogate_deepbooru(image): def create_seed_inputs(target_interface): - with FormRow(elem_id=target_interface + '_seed_row', variant="compact"): - seed = (gr.Textbox if cmd_opts.use_textbox_seed else gr.Number)(label='Seed', value=-1, elem_id=target_interface + '_seed') + with FormRow(elem_id=f"{target_interface}_seed_row", variant="compact"): + seed = (gr.Textbox if cmd_opts.use_textbox_seed else gr.Number)(label='Seed', value=-1, elem_id=f"{target_interface}_seed") seed.style(container=False) - random_seed = ToolButton(random_symbol, elem_id=target_interface + '_random_seed', label='Random seed') - reuse_seed = ToolButton(reuse_symbol, elem_id=target_interface + '_reuse_seed', label='Reuse seed') + random_seed = ToolButton(random_symbol, elem_id=f"{target_interface}_random_seed", label='Random seed') + reuse_seed = ToolButton(reuse_symbol, elem_id=f"{target_interface}_reuse_seed", label='Reuse seed') - seed_checkbox = gr.Checkbox(label='Extra', elem_id=target_interface + '_subseed_show', value=False) + seed_checkbox = gr.Checkbox(label='Extra', elem_id=f"{target_interface}_subseed_show", value=False) # Components to show/hide based on the 'Extra' checkbox seed_extras = [] - with FormRow(visible=False, elem_id=target_interface + '_subseed_row') as seed_extra_row_1: + with FormRow(visible=False, elem_id=f"{target_interface}_subseed_row") as seed_extra_row_1: seed_extras.append(seed_extra_row_1) - subseed = gr.Number(label='Variation seed', value=-1, elem_id=target_interface + '_subseed') + subseed = gr.Number(label='Variation seed', value=-1, elem_id=f"{target_interface}_subseed") subseed.style(container=False) - random_subseed = ToolButton(random_symbol, elem_id=target_interface + '_random_subseed') - reuse_subseed = ToolButton(reuse_symbol, elem_id=target_interface + '_reuse_subseed') - subseed_strength = gr.Slider(label='Variation strength', value=0.0, minimum=0, maximum=1, step=0.01, elem_id=target_interface + '_subseed_strength') + random_subseed = ToolButton(random_symbol, elem_id=f"{target_interface}_random_subseed") + reuse_subseed = ToolButton(reuse_symbol, elem_id=f"{target_interface}_reuse_subseed") + subseed_strength = gr.Slider(label='Variation strength', value=0.0, minimum=0, maximum=1, step=0.01, elem_id=f"{target_interface}_subseed_strength") with FormRow(visible=False) as seed_extra_row_2: seed_extras.append(seed_extra_row_2) - seed_resize_from_w = gr.Slider(minimum=0, maximum=2048, step=8, label="Resize seed from width", value=0, elem_id=target_interface + '_seed_resize_from_w') - seed_resize_from_h = gr.Slider(minimum=0, maximum=2048, step=8, label="Resize seed from height", value=0, elem_id=target_interface + '_seed_resize_from_h') + seed_resize_from_w = gr.Slider(minimum=0, maximum=2048, step=8, label="Resize seed from width", value=0, elem_id=f"{target_interface}_seed_resize_from_w") + seed_resize_from_h = gr.Slider(minimum=0, maximum=2048, step=8, label="Resize seed from height", value=0, elem_id=f"{target_interface}_seed_resize_from_h") random_seed.click(fn=lambda: -1, show_progress=False, inputs=[], outputs=[seed]) random_subseed.click(fn=lambda: -1, show_progress=False, inputs=[], outputs=[subseed]) @@ -765,7 +765,7 @@ def create_ui(): ) button.click( fn=lambda: None, - _js="switch_to_"+name.replace(" ", "_"), + _js=f"switch_to_{name.replace(' ', '_')}", inputs=[], outputs=[], ) @@ -1462,18 +1462,18 @@ def create_ui(): elif t == bool: comp = gr.Checkbox else: - raise Exception(f'bad options item type: {str(t)} for key {key}') + raise Exception(f'bad options item type: {t} for key {key}') - elem_id = "setting_"+key + elem_id = f"setting_{key}" if info.refresh is not None: if is_quicksettings: res = comp(label=info.label, value=fun(), elem_id=elem_id, **(args or {})) - create_refresh_button(res, info.refresh, info.component_args, "refresh_" + key) + create_refresh_button(res, info.refresh, info.component_args, f"refresh_{key}") else: with FormRow(): res = comp(label=info.label, value=fun(), elem_id=elem_id, **(args or {})) - create_refresh_button(res, info.refresh, info.component_args, "refresh_" + key) + create_refresh_button(res, info.refresh, info.component_args, f"refresh_{key}") else: res = comp(label=info.label, value=fun(), elem_id=elem_id, **(args or {})) @@ -1545,7 +1545,7 @@ def create_ui(): current_tab.__exit__() gr.Group() - current_tab = gr.TabItem(elem_id="settings_{}".format(elem_id), label=text) + current_tab = gr.TabItem(elem_id=f"settings_{elem_id}", label=text) current_tab.__enter__() current_row = gr.Column(variant='compact') current_row.__enter__() @@ -1664,7 +1664,7 @@ def create_ui(): for interface, label, ifid in interfaces: if label in shared.opts.hidden_tabs: continue - with gr.TabItem(label, id=ifid, elem_id='tab_' + ifid): + with gr.TabItem(label, id=ifid, elem_id=f"tab_{ifid}"): interface.render() if os.path.exists(os.path.join(script_path, "notification.mp3")): @@ -1771,10 +1771,10 @@ def create_ui(): def loadsave(path, x): def apply_field(obj, field, condition=None, init_field=None): - key = path + "/" + field + key = f"{path}/{field}" if getattr(obj, 'custom_script_source', None) is not None: - key = 'customscript/' + obj.custom_script_source + '/' + key + key = f"customscript/{obj.custom_script_source}/{key}" if getattr(obj, 'do_not_save_to_config', False): return diff --git a/modules/ui_extensions.py b/modules/ui_extensions.py index 99ac8756..d9faf85a 100644 --- a/modules/ui_extensions.py +++ b/modules/ui_extensions.py @@ -61,7 +61,8 @@ def save_config_state(name): if not name: name = "Config" current_config_state["name"] = name - filename = os.path.join(config_states_dir, datetime.now().strftime("%Y_%m_%d-%H_%M_%S") + "_" + name + ".json") + timestamp = datetime.now().strftime('%Y_%m_%d-%H_%M_%S') + filename = os.path.join(config_states_dir, f"{timestamp}_{name}.json") print(f"Saving backup of webui/extension state to {filename}.") with open(filename, "w", encoding="utf-8") as f: json.dump(current_config_state, f) diff --git a/modules/ui_extra_networks.py b/modules/ui_extra_networks.py index 86c05a55..8c3dea56 100644 --- a/modules/ui_extra_networks.py +++ b/modules/ui_extra_networks.py @@ -69,7 +69,9 @@ class ExtraNetworksPage: pass def link_preview(self, filename): - return "./sd_extra_networks/thumb?filename=" + urllib.parse.quote(filename.replace('\\', '/')) + "&mtime=" + str(os.path.getmtime(filename)) + quoted_filename = urllib.parse.quote(filename.replace('\\', '/')) + mtime = os.path.getmtime(filename) + return f"./sd_extra_networks/thumb?filename={quoted_filename}&mtime={mtime}" def search_terms_from_path(self, filename, possible_directories=None): abspath = os.path.abspath(filename) diff --git a/scripts/custom_code.py b/scripts/custom_code.py index 4071d86d..f36a3675 100644 --- a/scripts/custom_code.py +++ b/scripts/custom_code.py @@ -77,7 +77,7 @@ return process_images(p) module.display = display indent = " " * indent_level - indented = code.replace('\n', '\n' + indent) + indented = code.replace('\n', f"\n{indent}") body = f"""def __webuitemp__(): {indent}{indented} __webuitemp__()""" diff --git a/scripts/loopback.py b/scripts/loopback.py index d3065fe6..ad6609be 100644 --- a/scripts/loopback.py +++ b/scripts/loopback.py @@ -84,7 +84,7 @@ class Script(scripts.Script): p.color_corrections = initial_color_corrections if append_interrogation != "None": - p.prompt = original_prompt + ", " if original_prompt != "" else "" + p.prompt = f"{original_prompt}, " if original_prompt else "" if append_interrogation == "CLIP": p.prompt += shared.interrogator.interrogate(p.init_images[0]) elif append_interrogation == "DeepBooru": diff --git a/scripts/xyz_grid.py b/scripts/xyz_grid.py index 01d97791..a725d74a 100644 --- a/scripts/xyz_grid.py +++ b/scripts/xyz_grid.py @@ -439,7 +439,7 @@ class Script(scripts.Script): z_type.change(fn=select_axis, inputs=[z_type,z_values_dropdown], outputs=[fill_z_button,z_values,z_values_dropdown]) def get_dropdown_update_from_params(axis,params): - val_key = axis + " Values" + val_key = f"{axis} Values" vals = params.get(val_key,"") valslist = [x.strip() for x in chain.from_iterable(csv.reader(StringIO(vals))) if x] return gr.update(value = valslist) -- cgit v1.2.3 From f741a98baccae100fcfb40c017b5c35c5cba1b0c Mon Sep 17 00:00:00 2001 From: AUTOMATIC <16777216c@gmail.com> Date: Wed, 10 May 2023 08:43:42 +0300 Subject: imports cleanup for ruff --- extensions-builtin/Lora/lora.py | 1 - extensions-builtin/ScuNET/scripts/scunet_model.py | 1 - extensions-builtin/SwinIR/scripts/swinir_model.py | 3 +-- modules/codeformer/codeformer_arch.py | 4 +--- modules/codeformer/vqgan_arch.py | 2 -- modules/codeformer_model.py | 4 +--- modules/config_states.py | 2 +- modules/esrgan_model.py | 2 +- modules/esrgan_model_arch.py | 1 - modules/extensions.py | 1 - modules/generation_parameters_copypaste.py | 4 ---- modules/hypernetworks/hypernetwork.py | 3 +-- modules/hypernetworks/ui.py | 2 -- modules/images.py | 2 +- modules/img2img.py | 5 +---- modules/mac_specific.py | 1 - modules/modelloader.py | 1 - modules/models/diffusion/uni_pc/uni_pc.py | 1 - modules/processing.py | 5 ++--- modules/sd_hijack.py | 2 +- modules/sd_hijack_inpainting.py | 6 ------ modules/sd_hijack_ip2p.py | 5 +---- modules/sd_hijack_xlmr.py | 2 -- modules/sd_models.py | 2 +- modules/sd_models_config.py | 1 - modules/sd_samplers_kdiffusion.py | 1 - modules/sd_vae.py | 3 --- modules/shared.py | 3 --- modules/styles.py | 9 --------- modules/textual_inversion/autocrop.py | 4 +--- modules/textual_inversion/image_embedding.py | 2 +- modules/textual_inversion/preprocess.py | 4 ---- modules/textual_inversion/textual_inversion.py | 1 - modules/txt2img.py | 9 +++------ modules/ui.py | 5 ++--- modules/ui_extra_networks.py | 1 - modules/ui_postprocessing.py | 2 +- modules/upscaler.py | 2 -- modules/xlmr.py | 2 +- pyproject.toml | 11 +++++++---- scripts/custom_code.py | 2 +- scripts/outpainting_mk_2.py | 4 ++-- scripts/poor_mans_outpainting.py | 4 ++-- scripts/prompt_matrix.py | 7 ++----- scripts/prompts_from_file.py | 5 +---- scripts/sd_upscale.py | 4 ++-- scripts/xyz_grid.py | 6 ++---- webui.py | 2 +- 48 files changed, 42 insertions(+), 114 deletions(-) (limited to 'modules/sd_samplers_kdiffusion.py') diff --git a/extensions-builtin/Lora/lora.py b/extensions-builtin/Lora/lora.py index ba1293df..0ab43229 100644 --- a/extensions-builtin/Lora/lora.py +++ b/extensions-builtin/Lora/lora.py @@ -1,4 +1,3 @@ -import glob import os import re import torch diff --git a/extensions-builtin/ScuNET/scripts/scunet_model.py b/extensions-builtin/ScuNET/scripts/scunet_model.py index c7fd5739..aa2fdb3a 100644 --- a/extensions-builtin/ScuNET/scripts/scunet_model.py +++ b/extensions-builtin/ScuNET/scripts/scunet_model.py @@ -13,7 +13,6 @@ import modules.upscaler from modules import devices, modelloader from scunet_model_arch import SCUNet as net from modules.shared import opts -from modules import images class UpscalerScuNET(modules.upscaler.Upscaler): diff --git a/extensions-builtin/SwinIR/scripts/swinir_model.py b/extensions-builtin/SwinIR/scripts/swinir_model.py index d77c3a92..55dd94ab 100644 --- a/extensions-builtin/SwinIR/scripts/swinir_model.py +++ b/extensions-builtin/SwinIR/scripts/swinir_model.py @@ -1,4 +1,3 @@ -import contextlib import os import numpy as np @@ -8,7 +7,7 @@ from basicsr.utils.download_util import load_file_from_url from tqdm import tqdm from modules import modelloader, devices, script_callbacks, shared -from modules.shared import cmd_opts, opts, state +from modules.shared import opts, state from swinir_model_arch import SwinIR as net from swinir_model_arch_v2 import Swin2SR as net2 from modules.upscaler import Upscaler, UpscalerData diff --git a/modules/codeformer/codeformer_arch.py b/modules/codeformer/codeformer_arch.py index f1a7cf09..00c407de 100644 --- a/modules/codeformer/codeformer_arch.py +++ b/modules/codeformer/codeformer_arch.py @@ -1,14 +1,12 @@ # this file is copied from CodeFormer repository. Please see comment in modules/codeformer_model.py import math -import numpy as np import torch from torch import nn, Tensor import torch.nn.functional as F -from typing import Optional, List +from typing import Optional from modules.codeformer.vqgan_arch import VQAutoEncoder, ResBlock -from basicsr.utils import get_root_logger from basicsr.utils.registry import ARCH_REGISTRY def calc_mean_std(feat, eps=1e-5): diff --git a/modules/codeformer/vqgan_arch.py b/modules/codeformer/vqgan_arch.py index e7293683..820e6b12 100644 --- a/modules/codeformer/vqgan_arch.py +++ b/modules/codeformer/vqgan_arch.py @@ -5,11 +5,9 @@ VQGAN code, adapted from the original created by the Unleashing Transformers aut https://github.com/samb-t/unleashing-transformers/blob/master/models/vqgan.py ''' -import numpy as np import torch import torch.nn as nn import torch.nn.functional as F -import copy from basicsr.utils import get_root_logger from basicsr.utils.registry import ARCH_REGISTRY diff --git a/modules/codeformer_model.py b/modules/codeformer_model.py index 8d84bbc9..8e56cb89 100644 --- a/modules/codeformer_model.py +++ b/modules/codeformer_model.py @@ -33,11 +33,9 @@ def setup_model(dirname): try: from torchvision.transforms.functional import normalize from modules.codeformer.codeformer_arch import CodeFormer - from basicsr.utils.download_util import load_file_from_url - from basicsr.utils import imwrite, img2tensor, tensor2img + from basicsr.utils import img2tensor, tensor2img from facelib.utils.face_restoration_helper import FaceRestoreHelper from facelib.detection.retinaface import retinaface - from modules.shared import cmd_opts net_class = CodeFormer diff --git a/modules/config_states.py b/modules/config_states.py index 2ea00929..8f1ff428 100644 --- a/modules/config_states.py +++ b/modules/config_states.py @@ -14,7 +14,7 @@ from collections import OrderedDict import git from modules import shared, extensions -from modules.paths_internal import extensions_dir, extensions_builtin_dir, script_path, config_states_dir +from modules.paths_internal import script_path, config_states_dir all_config_states = OrderedDict() diff --git a/modules/esrgan_model.py b/modules/esrgan_model.py index f4369257..85aa6934 100644 --- a/modules/esrgan_model.py +++ b/modules/esrgan_model.py @@ -6,7 +6,7 @@ from PIL import Image from basicsr.utils.download_util import load_file_from_url import modules.esrgan_model_arch as arch -from modules import shared, modelloader, images, devices +from modules import modelloader, images, devices from modules.upscaler import Upscaler, UpscalerData from modules.shared import opts diff --git a/modules/esrgan_model_arch.py b/modules/esrgan_model_arch.py index 7f8bc7c0..4de9dd8d 100644 --- a/modules/esrgan_model_arch.py +++ b/modules/esrgan_model_arch.py @@ -2,7 +2,6 @@ from collections import OrderedDict import math -import functools import torch import torch.nn as nn import torch.nn.functional as F diff --git a/modules/extensions.py b/modules/extensions.py index 34d9d654..829f8cd9 100644 --- a/modules/extensions.py +++ b/modules/extensions.py @@ -3,7 +3,6 @@ import sys import traceback import time -from datetime import datetime import git from modules import shared diff --git a/modules/generation_parameters_copypaste.py b/modules/generation_parameters_copypaste.py index fe8b18b2..f1c59c46 100644 --- a/modules/generation_parameters_copypaste.py +++ b/modules/generation_parameters_copypaste.py @@ -1,15 +1,11 @@ import base64 -import html import io -import math import os import re -from pathlib import Path import gradio as gr from modules.paths import data_path from modules import shared, ui_tempdir, script_callbacks -import tempfile from PIL import Image re_param_code = r'\s*([\w ]+):\s*("(?:\\"[^,]|\\"|\\|[^\"])+"|[^,]*)(?:,|$)' diff --git a/modules/hypernetworks/hypernetwork.py b/modules/hypernetworks/hypernetwork.py index 1fc49537..9fe749b7 100644 --- a/modules/hypernetworks/hypernetwork.py +++ b/modules/hypernetworks/hypernetwork.py @@ -1,4 +1,3 @@ -import csv import datetime import glob import html @@ -18,7 +17,7 @@ from modules.textual_inversion.learn_schedule import LearnRateScheduler from torch import einsum from torch.nn.init import normal_, xavier_normal_, xavier_uniform_, kaiming_normal_, kaiming_uniform_, zeros_ -from collections import defaultdict, deque +from collections import deque from statistics import stdev, mean diff --git a/modules/hypernetworks/ui.py b/modules/hypernetworks/ui.py index 76599f5a..be168736 100644 --- a/modules/hypernetworks/ui.py +++ b/modules/hypernetworks/ui.py @@ -1,6 +1,4 @@ import html -import os -import re import gradio as gr import modules.hypernetworks.hypernetwork diff --git a/modules/images.py b/modules/images.py index 5eb6d855..7392cb8b 100644 --- a/modules/images.py +++ b/modules/images.py @@ -19,7 +19,7 @@ import json import hashlib from modules import sd_samplers, shared, script_callbacks, errors -from modules.shared import opts, cmd_opts +from modules.shared import opts LANCZOS = (Image.Resampling.LANCZOS if hasattr(Image, 'Resampling') else Image.LANCZOS) diff --git a/modules/img2img.py b/modules/img2img.py index 32b1ecd6..d704bf90 100644 --- a/modules/img2img.py +++ b/modules/img2img.py @@ -1,12 +1,9 @@ -import math import os -import sys -import traceback import numpy as np from PIL import Image, ImageOps, ImageFilter, ImageEnhance, ImageChops, UnidentifiedImageError -from modules import devices, sd_samplers +from modules import sd_samplers from modules.generation_parameters_copypaste import create_override_settings_dict from modules.processing import Processed, StableDiffusionProcessingImg2Img, process_images from modules.shared import opts, state diff --git a/modules/mac_specific.py b/modules/mac_specific.py index 40ce2101..5c2f92a1 100644 --- a/modules/mac_specific.py +++ b/modules/mac_specific.py @@ -1,6 +1,5 @@ import torch import platform -from modules import paths from modules.sd_hijack_utils import CondFunc from packaging import version diff --git a/modules/modelloader.py b/modules/modelloader.py index cf685000..92ada694 100644 --- a/modules/modelloader.py +++ b/modules/modelloader.py @@ -1,4 +1,3 @@ -import glob import os import shutil import importlib diff --git a/modules/models/diffusion/uni_pc/uni_pc.py b/modules/models/diffusion/uni_pc/uni_pc.py index 11b330bc..a4c4ef4e 100644 --- a/modules/models/diffusion/uni_pc/uni_pc.py +++ b/modules/models/diffusion/uni_pc/uni_pc.py @@ -1,5 +1,4 @@ import torch -import torch.nn.functional as F import math from tqdm.auto import trange diff --git a/modules/processing.py b/modules/processing.py index 6f5233c1..c3932d6b 100644 --- a/modules/processing.py +++ b/modules/processing.py @@ -2,7 +2,6 @@ import json import math import os import sys -import warnings import hashlib import torch @@ -11,10 +10,10 @@ from PIL import Image, ImageFilter, ImageOps import random import cv2 from skimage import exposure -from typing import Any, Dict, List, Optional +from typing import Any, Dict, List import modules.sd_hijack -from modules import devices, prompt_parser, masking, sd_samplers, lowvram, generation_parameters_copypaste, script_callbacks, extra_networks, sd_vae_approx, scripts +from modules import devices, prompt_parser, masking, sd_samplers, lowvram, generation_parameters_copypaste, extra_networks, sd_vae_approx, scripts from modules.sd_hijack import model_hijack from modules.shared import opts, cmd_opts, state import modules.shared as shared diff --git a/modules/sd_hijack.py b/modules/sd_hijack.py index d8135211..81573b78 100644 --- a/modules/sd_hijack.py +++ b/modules/sd_hijack.py @@ -3,7 +3,7 @@ from torch.nn.functional import silu from types import MethodType import modules.textual_inversion.textual_inversion -from modules import devices, sd_hijack_optimizations, shared, sd_hijack_checkpoint +from modules import devices, sd_hijack_optimizations, shared from modules.hypernetworks import hypernetwork from modules.shared import cmd_opts from modules import sd_hijack_clip, sd_hijack_open_clip, sd_hijack_unet, sd_hijack_xlmr, xlmr diff --git a/modules/sd_hijack_inpainting.py b/modules/sd_hijack_inpainting.py index 55a2ce4d..344d75c8 100644 --- a/modules/sd_hijack_inpainting.py +++ b/modules/sd_hijack_inpainting.py @@ -1,15 +1,9 @@ -import os import torch -from einops import repeat -from omegaconf import ListConfig - import ldm.models.diffusion.ddpm import ldm.models.diffusion.ddim import ldm.models.diffusion.plms -from ldm.models.diffusion.ddpm import LatentDiffusion -from ldm.models.diffusion.plms import PLMSSampler from ldm.models.diffusion.ddim import DDIMSampler, noise_like from ldm.models.diffusion.sampling_util import norm_thresholding diff --git a/modules/sd_hijack_ip2p.py b/modules/sd_hijack_ip2p.py index 41ed54a2..6fe6b6ff 100644 --- a/modules/sd_hijack_ip2p.py +++ b/modules/sd_hijack_ip2p.py @@ -1,8 +1,5 @@ -import collections import os.path -import sys -import gc -import time + def should_hijack_ip2p(checkpoint_info): from modules import sd_models_config diff --git a/modules/sd_hijack_xlmr.py b/modules/sd_hijack_xlmr.py index 4ac51c38..28528329 100644 --- a/modules/sd_hijack_xlmr.py +++ b/modules/sd_hijack_xlmr.py @@ -1,8 +1,6 @@ -import open_clip.tokenizer import torch from modules import sd_hijack_clip, devices -from modules.shared import opts class FrozenXLMREmbedderWithCustomWords(sd_hijack_clip.FrozenCLIPEmbedderWithCustomWords): diff --git a/modules/sd_models.py b/modules/sd_models.py index 11c1a344..1c09c709 100644 --- a/modules/sd_models.py +++ b/modules/sd_models.py @@ -565,7 +565,7 @@ def reload_model_weights(sd_model=None, info=None): def unload_model_weights(sd_model=None, info=None): - from modules import lowvram, devices, sd_hijack + from modules import devices, sd_hijack timer = Timer() if model_data.sd_model: diff --git a/modules/sd_models_config.py b/modules/sd_models_config.py index 7a79925a..9bfe1237 100644 --- a/modules/sd_models_config.py +++ b/modules/sd_models_config.py @@ -1,4 +1,3 @@ -import re import os import torch diff --git a/modules/sd_samplers_kdiffusion.py b/modules/sd_samplers_kdiffusion.py index 0fc9f456..3b8e9622 100644 --- a/modules/sd_samplers_kdiffusion.py +++ b/modules/sd_samplers_kdiffusion.py @@ -1,7 +1,6 @@ from collections import deque import torch import inspect -import einops import k_diffusion.sampling from modules import prompt_parser, devices, sd_samplers_common diff --git a/modules/sd_vae.py b/modules/sd_vae.py index 521e485a..b7176125 100644 --- a/modules/sd_vae.py +++ b/modules/sd_vae.py @@ -1,8 +1,5 @@ -import torch -import safetensors.torch import os import collections -from collections import namedtuple from modules import paths, shared, devices, script_callbacks, sd_models import glob from copy import deepcopy diff --git a/modules/shared.py b/modules/shared.py index 4631965b..44cd2c0c 100644 --- a/modules/shared.py +++ b/modules/shared.py @@ -1,12 +1,9 @@ -import argparse import datetime import json import os import sys import time -import requests -from PIL import Image import gradio as gr import tqdm diff --git a/modules/styles.py b/modules/styles.py index 11642075..c22769cf 100644 --- a/modules/styles.py +++ b/modules/styles.py @@ -1,18 +1,9 @@ -# We need this so Python doesn't complain about the unknown StableDiffusionProcessing-typehint at runtime -from __future__ import annotations - import csv import os import os.path import typing -import collections.abc as abc -import tempfile import shutil -if typing.TYPE_CHECKING: - # Only import this when code is being type-checked, it doesn't have any effect at runtime - from .processing import StableDiffusionProcessing - class PromptStyle(typing.NamedTuple): name: str diff --git a/modules/textual_inversion/autocrop.py b/modules/textual_inversion/autocrop.py index d7d8d2e3..7770d22f 100644 --- a/modules/textual_inversion/autocrop.py +++ b/modules/textual_inversion/autocrop.py @@ -1,10 +1,8 @@ import cv2 import requests import os -from collections import defaultdict -from math import log, sqrt import numpy as np -from PIL import Image, ImageDraw +from PIL import ImageDraw GREEN = "#0F0" BLUE = "#00F" diff --git a/modules/textual_inversion/image_embedding.py b/modules/textual_inversion/image_embedding.py index 5593f88c..ee0e850a 100644 --- a/modules/textual_inversion/image_embedding.py +++ b/modules/textual_inversion/image_embedding.py @@ -2,7 +2,7 @@ import base64 import json import numpy as np import zlib -from PIL import Image, PngImagePlugin, ImageDraw, ImageFont +from PIL import Image, ImageDraw, ImageFont from fonts.ttf import Roboto import torch from modules.shared import opts diff --git a/modules/textual_inversion/preprocess.py b/modules/textual_inversion/preprocess.py index da0bcb26..d0cad09e 100644 --- a/modules/textual_inversion/preprocess.py +++ b/modules/textual_inversion/preprocess.py @@ -1,13 +1,9 @@ import os from PIL import Image, ImageOps import math -import platform -import sys import tqdm -import time from modules import paths, shared, images, deepbooru -from modules.shared import opts, cmd_opts from modules.textual_inversion import autocrop diff --git a/modules/textual_inversion/textual_inversion.py b/modules/textual_inversion/textual_inversion.py index f753b75f..9ed9ba45 100644 --- a/modules/textual_inversion/textual_inversion.py +++ b/modules/textual_inversion/textual_inversion.py @@ -1,7 +1,6 @@ import os import sys import traceback -import inspect from collections import namedtuple import torch diff --git a/modules/txt2img.py b/modules/txt2img.py index 16841d0f..f022381c 100644 --- a/modules/txt2img.py +++ b/modules/txt2img.py @@ -1,18 +1,15 @@ import modules.scripts -from modules import sd_samplers +from modules import sd_samplers, processing from modules.generation_parameters_copypaste import create_override_settings_dict -from modules.processing import StableDiffusionProcessing, Processed, StableDiffusionProcessingTxt2Img, \ - StableDiffusionProcessingImg2Img, process_images from modules.shared import opts, cmd_opts import modules.shared as shared -import modules.processing as processing from modules.ui import plaintext_to_html def txt2img(id_task: str, prompt: str, negative_prompt: str, prompt_styles, steps: int, sampler_index: int, restore_faces: bool, tiling: bool, n_iter: int, batch_size: int, cfg_scale: float, seed: int, subseed: int, subseed_strength: float, seed_resize_from_h: int, seed_resize_from_w: int, seed_enable_extras: bool, height: int, width: int, enable_hr: bool, denoising_strength: float, hr_scale: float, hr_upscaler: str, hr_second_pass_steps: int, hr_resize_x: int, hr_resize_y: int, override_settings_texts, *args): override_settings = create_override_settings_dict(override_settings_texts) - p = StableDiffusionProcessingTxt2Img( + p = processing.StableDiffusionProcessingTxt2Img( sd_model=shared.sd_model, outpath_samples=opts.outdir_samples or opts.outdir_txt2img_samples, outpath_grids=opts.outdir_grids or opts.outdir_txt2img_grids, @@ -53,7 +50,7 @@ def txt2img(id_task: str, prompt: str, negative_prompt: str, prompt_styles, step processed = modules.scripts.scripts_txt2img.run(p, *args) if processed is None: - processed = process_images(p) + processed = processing.process_images(p) p.close() diff --git a/modules/ui.py b/modules/ui.py index 6beda76f..f7e57593 100644 --- a/modules/ui.py +++ b/modules/ui.py @@ -14,10 +14,10 @@ from PIL import Image, PngImagePlugin from modules.call_queue import wrap_gradio_gpu_call, wrap_queued_call, wrap_gradio_call from modules import sd_hijack, sd_models, localization, script_callbacks, ui_extensions, deepbooru, sd_vae, extra_networks, postprocessing, ui_components, ui_common, ui_postprocessing, progress -from modules.ui_components import FormRow, FormColumn, FormGroup, ToolButton, FormHTML +from modules.ui_components import FormRow, FormGroup, ToolButton, FormHTML from modules.paths import script_path, data_path -from modules.shared import opts, cmd_opts, restricted_opts +from modules.shared import opts, cmd_opts import modules.codeformer_model import modules.generation_parameters_copypaste as parameters_copypaste @@ -28,7 +28,6 @@ import modules.shared as shared import modules.styles import modules.textual_inversion.ui from modules import prompt_parser -from modules.images import save_image from modules.sd_hijack import model_hijack from modules.sd_samplers import samplers, samplers_for_img2img from modules.textual_inversion import textual_inversion diff --git a/modules/ui_extra_networks.py b/modules/ui_extra_networks.py index 49e06289..800e467a 100644 --- a/modules/ui_extra_networks.py +++ b/modules/ui_extra_networks.py @@ -1,4 +1,3 @@ -import glob import os.path import urllib.parse from pathlib import Path diff --git a/modules/ui_postprocessing.py b/modules/ui_postprocessing.py index f25639e5..c7dc1154 100644 --- a/modules/ui_postprocessing.py +++ b/modules/ui_postprocessing.py @@ -1,5 +1,5 @@ import gradio as gr -from modules import scripts_postprocessing, scripts, shared, gfpgan_model, codeformer_model, ui_common, postprocessing, call_queue +from modules import scripts, shared, ui_common, postprocessing, call_queue import modules.generation_parameters_copypaste as parameters_copypaste diff --git a/modules/upscaler.py b/modules/upscaler.py index 0ad4fe99..777593b0 100644 --- a/modules/upscaler.py +++ b/modules/upscaler.py @@ -2,8 +2,6 @@ import os from abc import abstractmethod import PIL -import numpy as np -import torch from PIL import Image import modules.shared diff --git a/modules/xlmr.py b/modules/xlmr.py index beab3fdf..e056c3f6 100644 --- a/modules/xlmr.py +++ b/modules/xlmr.py @@ -1,4 +1,4 @@ -from transformers import BertPreTrainedModel,BertModel,BertConfig +from transformers import BertPreTrainedModel, BertConfig import torch.nn as nn import torch from transformers.models.xlm_roberta.configuration_xlm_roberta import XLMRobertaConfig diff --git a/pyproject.toml b/pyproject.toml index 1e164abc..9caa9ba2 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -1,10 +1,13 @@ [tool.ruff] +exclude = ["extensions"] + ignore = [ "E501", - "E731", - "E402", # Module level import not at top of file - "F401" # Module imported but unused + + "F401", # Module imported but unused ] -exclude = ["extensions"] + +[tool.ruff.per-file-ignores] +"webui.py" = ["E402"] # Module level import not at top of file \ No newline at end of file diff --git a/scripts/custom_code.py b/scripts/custom_code.py index f36a3675..cc6f0d49 100644 --- a/scripts/custom_code.py +++ b/scripts/custom_code.py @@ -4,7 +4,7 @@ import ast import copy from modules.processing import Processed -from modules.shared import opts, cmd_opts, state +from modules.shared import cmd_opts def convertExpr2Expression(expr): diff --git a/scripts/outpainting_mk_2.py b/scripts/outpainting_mk_2.py index b10fed6c..665dbe89 100644 --- a/scripts/outpainting_mk_2.py +++ b/scripts/outpainting_mk_2.py @@ -7,9 +7,9 @@ import modules.scripts as scripts import gradio as gr from PIL import Image, ImageDraw -from modules import images, processing, devices +from modules import images from modules.processing import Processed, process_images -from modules.shared import opts, cmd_opts, state +from modules.shared import opts, state # this function is taken from https://github.com/parlance-zz/g-diffuser-bot diff --git a/scripts/poor_mans_outpainting.py b/scripts/poor_mans_outpainting.py index ddcbd2d3..c0bbecc1 100644 --- a/scripts/poor_mans_outpainting.py +++ b/scripts/poor_mans_outpainting.py @@ -4,9 +4,9 @@ import modules.scripts as scripts import gradio as gr from PIL import Image, ImageDraw -from modules import images, processing, devices +from modules import images, devices from modules.processing import Processed, process_images -from modules.shared import opts, cmd_opts, state +from modules.shared import opts, state class Script(scripts.Script): diff --git a/scripts/prompt_matrix.py b/scripts/prompt_matrix.py index e9b11517..fb06beab 100644 --- a/scripts/prompt_matrix.py +++ b/scripts/prompt_matrix.py @@ -1,14 +1,11 @@ import math -from collections import namedtuple -from copy import copy -import random import modules.scripts as scripts import gradio as gr from modules import images -from modules.processing import process_images, Processed -from modules.shared import opts, cmd_opts, state +from modules.processing import process_images +from modules.shared import opts, state import modules.sd_samplers diff --git a/scripts/prompts_from_file.py b/scripts/prompts_from_file.py index 76dc5778..149bc85f 100644 --- a/scripts/prompts_from_file.py +++ b/scripts/prompts_from_file.py @@ -1,6 +1,4 @@ import copy -import math -import os import random import sys import traceback @@ -11,8 +9,7 @@ import gradio as gr from modules import sd_samplers from modules.processing import Processed, process_images -from PIL import Image -from modules.shared import opts, cmd_opts, state +from modules.shared import state def process_string_tag(tag): diff --git a/scripts/sd_upscale.py b/scripts/sd_upscale.py index 332d76d9..d873a09c 100644 --- a/scripts/sd_upscale.py +++ b/scripts/sd_upscale.py @@ -4,9 +4,9 @@ import modules.scripts as scripts import gradio as gr from PIL import Image -from modules import processing, shared, sd_samplers, images, devices +from modules import processing, shared, images, devices from modules.processing import Processed -from modules.shared import opts, cmd_opts, state +from modules.shared import opts, state class Script(scripts.Script): diff --git a/scripts/xyz_grid.py b/scripts/xyz_grid.py index 2ff42ef8..332e0ecd 100644 --- a/scripts/xyz_grid.py +++ b/scripts/xyz_grid.py @@ -10,15 +10,13 @@ import numpy as np import modules.scripts as scripts import gradio as gr -from modules import images, paths, sd_samplers, processing, sd_models, sd_vae +from modules import images, sd_samplers, processing, sd_models, sd_vae from modules.processing import process_images, Processed, StableDiffusionProcessingTxt2Img -from modules.shared import opts, cmd_opts, state +from modules.shared import opts, state import modules.shared as shared import modules.sd_samplers import modules.sd_models import modules.sd_vae -import glob -import os import re from modules.ui_components import ToolButton diff --git a/webui.py b/webui.py index ec3d2aba..48277075 100644 --- a/webui.py +++ b/webui.py @@ -43,7 +43,7 @@ if ".dev" in torch.__version__ or "+git" in torch.__version__: torch.__long_version__ = torch.__version__ torch.__version__ = re.search(r'[\d.]+[\d]', torch.__version__).group(0) -from modules import shared, devices, sd_samplers, upscaler, extensions, localization, ui_tempdir, ui_extra_networks, config_states +from modules import shared, sd_samplers, upscaler, extensions, localization, ui_tempdir, ui_extra_networks, config_states import modules.codeformer_model as codeformer import modules.face_restoration import modules.gfpgan_model as gfpgan -- cgit v1.2.3 From 028d3f6425d85f122027c127fba8bcbf4f66ee75 Mon Sep 17 00:00:00 2001 From: AUTOMATIC <16777216c@gmail.com> Date: Wed, 10 May 2023 11:05:02 +0300 Subject: ruff auto fixes --- extensions-builtin/LDSR/sd_hijack_autoencoder.py | 4 ++-- extensions-builtin/LDSR/sd_hijack_ddpm_v1.py | 12 ++++++------ extensions-builtin/Lora/lora.py | 12 ++++++------ extensions-builtin/Lora/scripts/lora_script.py | 2 +- modules/config_states.py | 2 +- modules/deepbooru.py | 2 +- modules/devices.py | 2 +- modules/hypernetworks/hypernetwork.py | 2 +- modules/hypernetworks/ui.py | 4 ++-- modules/interrogate.py | 2 +- modules/modelloader.py | 2 +- modules/models/diffusion/ddpm_edit.py | 4 ++-- modules/scripts_auto_postprocessing.py | 2 +- modules/sd_hijack.py | 2 +- modules/sd_hijack_optimizations.py | 14 +++++++------- modules/sd_samplers_compvis.py | 2 +- modules/sd_samplers_kdiffusion.py | 2 +- modules/shared.py | 6 +++--- modules/textual_inversion/textual_inversion.py | 2 +- modules/ui.py | 8 ++++---- modules/ui_extra_networks.py | 4 ++-- modules/ui_tempdir.py | 2 +- 22 files changed, 47 insertions(+), 47 deletions(-) (limited to 'modules/sd_samplers_kdiffusion.py') diff --git a/extensions-builtin/LDSR/sd_hijack_autoencoder.py b/extensions-builtin/LDSR/sd_hijack_autoencoder.py index 6303fed5..f457ca93 100644 --- a/extensions-builtin/LDSR/sd_hijack_autoencoder.py +++ b/extensions-builtin/LDSR/sd_hijack_autoencoder.py @@ -288,5 +288,5 @@ class VQModelInterface(VQModel): dec = self.decoder(quant) return dec -setattr(ldm.models.autoencoder, "VQModel", VQModel) -setattr(ldm.models.autoencoder, "VQModelInterface", VQModelInterface) +ldm.models.autoencoder.VQModel = VQModel +ldm.models.autoencoder.VQModelInterface = VQModelInterface diff --git a/extensions-builtin/LDSR/sd_hijack_ddpm_v1.py b/extensions-builtin/LDSR/sd_hijack_ddpm_v1.py index 4d3f6c56..d8fc30e3 100644 --- a/extensions-builtin/LDSR/sd_hijack_ddpm_v1.py +++ b/extensions-builtin/LDSR/sd_hijack_ddpm_v1.py @@ -1116,7 +1116,7 @@ class LatentDiffusionV1(DDPMV1): if cond is not None: if isinstance(cond, dict): cond = {key: cond[key][:batch_size] if not isinstance(cond[key], list) else - list(map(lambda x: x[:batch_size], cond[key])) for key in cond} + [x[:batch_size] for x in cond[key]] for key in cond} else: cond = [c[:batch_size] for c in cond] if isinstance(cond, list) else cond[:batch_size] @@ -1215,7 +1215,7 @@ class LatentDiffusionV1(DDPMV1): if cond is not None: if isinstance(cond, dict): cond = {key: cond[key][:batch_size] if not isinstance(cond[key], list) else - list(map(lambda x: x[:batch_size], cond[key])) for key in cond} + [x[:batch_size] for x in cond[key]] for key in cond} else: cond = [c[:batch_size] for c in cond] if isinstance(cond, list) else cond[:batch_size] return self.p_sample_loop(cond, @@ -1437,7 +1437,7 @@ class Layout2ImgDiffusionV1(LatentDiffusionV1): logs['bbox_image'] = cond_img return logs -setattr(ldm.models.diffusion.ddpm, "DDPMV1", DDPMV1) -setattr(ldm.models.diffusion.ddpm, "LatentDiffusionV1", LatentDiffusionV1) -setattr(ldm.models.diffusion.ddpm, "DiffusionWrapperV1", DiffusionWrapperV1) -setattr(ldm.models.diffusion.ddpm, "Layout2ImgDiffusionV1", Layout2ImgDiffusionV1) +ldm.models.diffusion.ddpm.DDPMV1 = DDPMV1 +ldm.models.diffusion.ddpm.LatentDiffusionV1 = LatentDiffusionV1 +ldm.models.diffusion.ddpm.DiffusionWrapperV1 = DiffusionWrapperV1 +ldm.models.diffusion.ddpm.Layout2ImgDiffusionV1 = Layout2ImgDiffusionV1 diff --git a/extensions-builtin/Lora/lora.py b/extensions-builtin/Lora/lora.py index 0ab43229..9795540f 100644 --- a/extensions-builtin/Lora/lora.py +++ b/extensions-builtin/Lora/lora.py @@ -172,7 +172,7 @@ def load_lora(name, filename): else: print(f'Lora layer {key_diffusers} matched a layer with unsupported type: {type(sd_module).__name__}') continue - assert False, f'Lora layer {key_diffusers} matched a layer with unsupported type: {type(sd_module).__name__}' + raise AssertionError(f"Lora layer {key_diffusers} matched a layer with unsupported type: {type(sd_module).__name__}") with torch.no_grad(): module.weight.copy_(weight) @@ -184,7 +184,7 @@ def load_lora(name, filename): elif lora_key == "lora_down.weight": lora_module.down = module else: - assert False, f'Bad Lora layer name: {key_diffusers} - must end in lora_up.weight, lora_down.weight or alpha' + raise AssertionError(f"Bad Lora layer name: {key_diffusers} - must end in lora_up.weight, lora_down.weight or alpha") if len(keys_failed_to_match) > 0: print(f"Failed to match keys when loading Lora {filename}: {keys_failed_to_match}") @@ -202,7 +202,7 @@ def load_loras(names, multipliers=None): loaded_loras.clear() loras_on_disk = [available_lora_aliases.get(name, None) for name in names] - if any([x is None for x in loras_on_disk]): + if any(x is None for x in loras_on_disk): list_available_loras() loras_on_disk = [available_lora_aliases.get(name, None) for name in names] @@ -309,7 +309,7 @@ def lora_apply_weights(self: Union[torch.nn.Conv2d, torch.nn.Linear, torch.nn.Mu print(f'failed to calculate lora weights for layer {lora_layer_name}') - setattr(self, "lora_current_names", wanted_names) + self.lora_current_names = wanted_names def lora_forward(module, input, original_forward): @@ -343,8 +343,8 @@ def lora_forward(module, input, original_forward): def lora_reset_cached_weight(self: Union[torch.nn.Conv2d, torch.nn.Linear]): - setattr(self, "lora_current_names", ()) - setattr(self, "lora_weights_backup", None) + self.lora_current_names = () + self.lora_weights_backup = None def lora_Linear_forward(self, input): diff --git a/extensions-builtin/Lora/scripts/lora_script.py b/extensions-builtin/Lora/scripts/lora_script.py index 7db971fd..b70e2de7 100644 --- a/extensions-builtin/Lora/scripts/lora_script.py +++ b/extensions-builtin/Lora/scripts/lora_script.py @@ -53,7 +53,7 @@ script_callbacks.on_infotext_pasted(lora.infotext_pasted) shared.options_templates.update(shared.options_section(('extra_networks', "Extra Networks"), { - "sd_lora": shared.OptionInfo("None", "Add Lora to prompt", gr.Dropdown, lambda: {"choices": ["None"] + [x for x in lora.available_loras]}, refresh=lora.list_available_loras), + "sd_lora": shared.OptionInfo("None", "Add Lora to prompt", gr.Dropdown, lambda: {"choices": ["None"] + list(lora.available_loras)}, refresh=lora.list_available_loras), })) diff --git a/modules/config_states.py b/modules/config_states.py index 8f1ff428..75da862a 100644 --- a/modules/config_states.py +++ b/modules/config_states.py @@ -35,7 +35,7 @@ def list_config_states(): j["filepath"] = path config_states.append(j) - config_states = list(sorted(config_states, key=lambda cs: cs["created_at"], reverse=True)) + config_states = sorted(config_states, key=lambda cs: cs["created_at"], reverse=True) for cs in config_states: timestamp = time.asctime(time.gmtime(cs["created_at"])) diff --git a/modules/deepbooru.py b/modules/deepbooru.py index 1c4554a2..547e1b4c 100644 --- a/modules/deepbooru.py +++ b/modules/deepbooru.py @@ -78,7 +78,7 @@ class DeepDanbooru: res = [] - filtertags = set([x.strip().replace(' ', '_') for x in shared.opts.deepbooru_filter_tags.split(",")]) + filtertags = {x.strip().replace(' ', '_') for x in shared.opts.deepbooru_filter_tags.split(",")} for tag in [x for x in tags if x not in filtertags]: probability = probability_dict[tag] diff --git a/modules/devices.py b/modules/devices.py index c705a3cb..d8a34a0f 100644 --- a/modules/devices.py +++ b/modules/devices.py @@ -65,7 +65,7 @@ def enable_tf32(): # enabling benchmark option seems to enable a range of cards to do fp16 when they otherwise can't # see https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/4407 - if any([torch.cuda.get_device_capability(devid) == (7, 5) for devid in range(0, torch.cuda.device_count())]): + if any(torch.cuda.get_device_capability(devid) == (7, 5) for devid in range(0, torch.cuda.device_count())): torch.backends.cudnn.benchmark = True torch.backends.cuda.matmul.allow_tf32 = True diff --git a/modules/hypernetworks/hypernetwork.py b/modules/hypernetworks/hypernetwork.py index 9fe749b7..6ef0bfdf 100644 --- a/modules/hypernetworks/hypernetwork.py +++ b/modules/hypernetworks/hypernetwork.py @@ -403,7 +403,7 @@ def attention_CrossAttention_forward(self, x, context=None, mask=None): k = self.to_k(context_k) v = self.to_v(context_v) - q, k, v = map(lambda t: rearrange(t, 'b n (h d) -> (b h) n d', h=h), (q, k, v)) + q, k, v = (rearrange(t, 'b n (h d) -> (b h) n d', h=h) for t in (q, k, v)) sim = einsum('b i d, b j d -> b i j', q, k) * self.scale diff --git a/modules/hypernetworks/ui.py b/modules/hypernetworks/ui.py index be168736..e3f9eb13 100644 --- a/modules/hypernetworks/ui.py +++ b/modules/hypernetworks/ui.py @@ -5,13 +5,13 @@ import modules.hypernetworks.hypernetwork from modules import devices, sd_hijack, shared not_available = ["hardswish", "multiheadattention"] -keys = list(x for x in modules.hypernetworks.hypernetwork.HypernetworkModule.activation_dict.keys() if x not in not_available) +keys = [x for x in modules.hypernetworks.hypernetwork.HypernetworkModule.activation_dict.keys() if x not in not_available] def create_hypernetwork(name, enable_sizes, overwrite_old, layer_structure=None, activation_func=None, weight_init=None, add_layer_norm=False, use_dropout=False, dropout_structure=None): filename = modules.hypernetworks.hypernetwork.create_hypernetwork(name, enable_sizes, overwrite_old, layer_structure, activation_func, weight_init, add_layer_norm, use_dropout, dropout_structure) - return gr.Dropdown.update(choices=sorted([x for x in shared.hypernetworks.keys()])), f"Created: {filename}", "" + return gr.Dropdown.update(choices=sorted(shared.hypernetworks.keys())), f"Created: {filename}", "" def train_hypernetwork(*args): diff --git a/modules/interrogate.py b/modules/interrogate.py index 22df9216..a1c8e537 100644 --- a/modules/interrogate.py +++ b/modules/interrogate.py @@ -159,7 +159,7 @@ class InterrogateModels: text_array = text_array[0:int(shared.opts.interrogate_clip_dict_limit)] top_count = min(top_count, len(text_array)) - text_tokens = clip.tokenize([text for text in text_array], truncate=True).to(devices.device_interrogate) + text_tokens = clip.tokenize(list(text_array), truncate=True).to(devices.device_interrogate) text_features = self.clip_model.encode_text(text_tokens).type(self.dtype) text_features /= text_features.norm(dim=-1, keepdim=True) diff --git a/modules/modelloader.py b/modules/modelloader.py index 92ada694..25612bf8 100644 --- a/modules/modelloader.py +++ b/modules/modelloader.py @@ -39,7 +39,7 @@ def load_models(model_path: str, model_url: str = None, command_path: str = None if os.path.islink(full_path) and not os.path.exists(full_path): print(f"Skipping broken symlink: {full_path}") continue - if ext_blacklist is not None and any([full_path.endswith(x) for x in ext_blacklist]): + if ext_blacklist is not None and any(full_path.endswith(x) for x in ext_blacklist): continue if full_path not in output: output.append(full_path) diff --git a/modules/models/diffusion/ddpm_edit.py b/modules/models/diffusion/ddpm_edit.py index 611c2b69..09432117 100644 --- a/modules/models/diffusion/ddpm_edit.py +++ b/modules/models/diffusion/ddpm_edit.py @@ -1130,7 +1130,7 @@ class LatentDiffusion(DDPM): if cond is not None: if isinstance(cond, dict): cond = {key: cond[key][:batch_size] if not isinstance(cond[key], list) else - list(map(lambda x: x[:batch_size], cond[key])) for key in cond} + [x[:batch_size] for x in cond[key]] for key in cond} else: cond = [c[:batch_size] for c in cond] if isinstance(cond, list) else cond[:batch_size] @@ -1229,7 +1229,7 @@ class LatentDiffusion(DDPM): if cond is not None: if isinstance(cond, dict): cond = {key: cond[key][:batch_size] if not isinstance(cond[key], list) else - list(map(lambda x: x[:batch_size], cond[key])) for key in cond} + [x[:batch_size] for x in cond[key]] for key in cond} else: cond = [c[:batch_size] for c in cond] if isinstance(cond, list) else cond[:batch_size] return self.p_sample_loop(cond, diff --git a/modules/scripts_auto_postprocessing.py b/modules/scripts_auto_postprocessing.py index 30d6d658..d63078de 100644 --- a/modules/scripts_auto_postprocessing.py +++ b/modules/scripts_auto_postprocessing.py @@ -17,7 +17,7 @@ class ScriptPostprocessingForMainUI(scripts.Script): return self.postprocessing_controls.values() def postprocess_image(self, p, script_pp, *args): - args_dict = {k: v for k, v in zip(self.postprocessing_controls, args)} + args_dict = dict(zip(self.postprocessing_controls, args)) pp = scripts_postprocessing.PostprocessedImage(script_pp.image) pp.info = {} diff --git a/modules/sd_hijack.py b/modules/sd_hijack.py index 81573b78..e374aeb8 100644 --- a/modules/sd_hijack.py +++ b/modules/sd_hijack.py @@ -37,7 +37,7 @@ def apply_optimizations(): optimization_method = None - can_use_sdp = hasattr(torch.nn.functional, "scaled_dot_product_attention") and callable(getattr(torch.nn.functional, "scaled_dot_product_attention")) # not everyone has torch 2.x to use sdp + can_use_sdp = hasattr(torch.nn.functional, "scaled_dot_product_attention") and callable(torch.nn.functional.scaled_dot_product_attention) # not everyone has torch 2.x to use sdp if cmd_opts.force_enable_xformers or (cmd_opts.xformers and shared.xformers_available and torch.version.cuda and (6, 0) <= torch.cuda.get_device_capability(shared.device) <= (9, 0)): print("Applying xformers cross attention optimization.") diff --git a/modules/sd_hijack_optimizations.py b/modules/sd_hijack_optimizations.py index b623d53d..a174bbe1 100644 --- a/modules/sd_hijack_optimizations.py +++ b/modules/sd_hijack_optimizations.py @@ -49,7 +49,7 @@ def split_cross_attention_forward_v1(self, x, context=None, mask=None): v_in = self.to_v(context_v) del context, context_k, context_v, x - q, k, v = map(lambda t: rearrange(t, 'b n (h d) -> (b h) n d', h=h), (q_in, k_in, v_in)) + q, k, v = (rearrange(t, 'b n (h d) -> (b h) n d', h=h) for t in (q_in, k_in, v_in)) del q_in, k_in, v_in dtype = q.dtype @@ -98,7 +98,7 @@ def split_cross_attention_forward(self, x, context=None, mask=None): del context, x - q, k, v = map(lambda t: rearrange(t, 'b n (h d) -> (b h) n d', h=h), (q_in, k_in, v_in)) + q, k, v = (rearrange(t, 'b n (h d) -> (b h) n d', h=h) for t in (q_in, k_in, v_in)) del q_in, k_in, v_in r1 = torch.zeros(q.shape[0], q.shape[1], v.shape[2], device=q.device, dtype=q.dtype) @@ -229,7 +229,7 @@ def split_cross_attention_forward_invokeAI(self, x, context=None, mask=None): with devices.without_autocast(disable=not shared.opts.upcast_attn): k = k * self.scale - q, k, v = map(lambda t: rearrange(t, 'b n (h d) -> (b h) n d', h=h), (q, k, v)) + q, k, v = (rearrange(t, 'b n (h d) -> (b h) n d', h=h) for t in (q, k, v)) r = einsum_op(q, k, v) r = r.to(dtype) return self.to_out(rearrange(r, '(b h) n d -> b n (h d)', h=h)) @@ -334,7 +334,7 @@ def xformers_attention_forward(self, x, context=None, mask=None): k_in = self.to_k(context_k) v_in = self.to_v(context_v) - q, k, v = map(lambda t: rearrange(t, 'b n (h d) -> b n h d', h=h), (q_in, k_in, v_in)) + q, k, v = (rearrange(t, 'b n (h d) -> b n h d', h=h) for t in (q_in, k_in, v_in)) del q_in, k_in, v_in dtype = q.dtype @@ -460,7 +460,7 @@ def xformers_attnblock_forward(self, x): k = self.k(h_) v = self.v(h_) b, c, h, w = q.shape - q, k, v = map(lambda t: rearrange(t, 'b c h w -> b (h w) c'), (q, k, v)) + q, k, v = (rearrange(t, 'b c h w -> b (h w) c') for t in (q, k, v)) dtype = q.dtype if shared.opts.upcast_attn: q, k = q.float(), k.float() @@ -482,7 +482,7 @@ def sdp_attnblock_forward(self, x): k = self.k(h_) v = self.v(h_) b, c, h, w = q.shape - q, k, v = map(lambda t: rearrange(t, 'b c h w -> b (h w) c'), (q, k, v)) + q, k, v = (rearrange(t, 'b c h w -> b (h w) c') for t in (q, k, v)) dtype = q.dtype if shared.opts.upcast_attn: q, k = q.float(), k.float() @@ -506,7 +506,7 @@ def sub_quad_attnblock_forward(self, x): k = self.k(h_) v = self.v(h_) b, c, h, w = q.shape - q, k, v = map(lambda t: rearrange(t, 'b c h w -> b (h w) c'), (q, k, v)) + q, k, v = (rearrange(t, 'b c h w -> b (h w) c') for t in (q, k, v)) q = q.contiguous() k = k.contiguous() v = v.contiguous() diff --git a/modules/sd_samplers_compvis.py b/modules/sd_samplers_compvis.py index bfcc5574..7427648f 100644 --- a/modules/sd_samplers_compvis.py +++ b/modules/sd_samplers_compvis.py @@ -83,7 +83,7 @@ class VanillaStableDiffusionSampler: conds_list, tensor = prompt_parser.reconstruct_multicond_batch(cond, self.step) unconditional_conditioning = prompt_parser.reconstruct_cond_batch(unconditional_conditioning, self.step) - assert all([len(conds) == 1 for conds in conds_list]), 'composition via AND is not supported for DDIM/PLMS samplers' + assert all(len(conds) == 1 for conds in conds_list), 'composition via AND is not supported for DDIM/PLMS samplers' cond = tensor # for DDIM, shapes must match, we can't just process cond and uncond independently; diff --git a/modules/sd_samplers_kdiffusion.py b/modules/sd_samplers_kdiffusion.py index 3b8e9622..2f733cf5 100644 --- a/modules/sd_samplers_kdiffusion.py +++ b/modules/sd_samplers_kdiffusion.py @@ -86,7 +86,7 @@ class CFGDenoiser(torch.nn.Module): conds_list, tensor = prompt_parser.reconstruct_multicond_batch(cond, self.step) uncond = prompt_parser.reconstruct_cond_batch(uncond, self.step) - assert not is_edit_model or all([len(conds) == 1 for conds in conds_list]), "AND is not supported for InstructPix2Pix checkpoint (unless using Image CFG scale = 1.0)" + assert not is_edit_model or all(len(conds) == 1 for conds in conds_list), "AND is not supported for InstructPix2Pix checkpoint (unless using Image CFG scale = 1.0)" batch_size = len(conds_list) repeats = [len(conds_list[i]) for i in range(batch_size)] diff --git a/modules/shared.py b/modules/shared.py index 7d70f041..e2691585 100644 --- a/modules/shared.py +++ b/modules/shared.py @@ -381,7 +381,7 @@ options_templates.update(options_section(('extra_networks', "Extra Networks"), { "extra_networks_card_width": OptionInfo(0, "Card width for Extra Networks (px)"), "extra_networks_card_height": OptionInfo(0, "Card height for Extra Networks (px)"), "extra_networks_add_text_separator": OptionInfo(" ", "Extra text to add before <...> when adding extra network to prompt"), - "sd_hypernetwork": OptionInfo("None", "Add hypernetwork to prompt", gr.Dropdown, lambda: {"choices": ["None"] + [x for x in hypernetworks.keys()]}, refresh=reload_hypernetworks), + "sd_hypernetwork": OptionInfo("None", "Add hypernetwork to prompt", gr.Dropdown, lambda: {"choices": ["None"] + list(hypernetworks.keys())}, refresh=reload_hypernetworks), })) options_templates.update(options_section(('ui', "User interface"), { @@ -403,7 +403,7 @@ options_templates.update(options_section(('ui', "User interface"), { "keyedit_precision_extra": OptionInfo(0.05, "Ctrl+up/down precision when editing ", gr.Slider, {"minimum": 0.01, "maximum": 0.2, "step": 0.001}), "keyedit_delimiters": OptionInfo(".,\\/!?%^*;:{}=`~()", "Ctrl+up/down word delimiters"), "quicksettings_list": OptionInfo(["sd_model_checkpoint"], "Quicksettings list", ui_components.DropdownMulti, lambda: {"choices": list(opts.data_labels.keys())}), - "hidden_tabs": OptionInfo([], "Hidden UI tabs (requires restart)", ui_components.DropdownMulti, lambda: {"choices": [x for x in tab_names]}), + "hidden_tabs": OptionInfo([], "Hidden UI tabs (requires restart)", ui_components.DropdownMulti, lambda: {"choices": list(tab_names)}), "ui_reorder": OptionInfo(", ".join(ui_reorder_categories), "txt2img/img2img UI item order"), "ui_extra_networks_tab_reorder": OptionInfo("", "Extra networks tab order"), "localization": OptionInfo("None", "Localization (requires restart)", gr.Dropdown, lambda: {"choices": ["None"] + list(localization.localizations.keys())}, refresh=lambda: localization.list_localizations(cmd_opts.localizations_dir)), @@ -583,7 +583,7 @@ class Options: if item.section not in section_ids: section_ids[item.section] = len(section_ids) - self.data_labels = {k: v for k, v in sorted(settings_items, key=lambda x: section_ids[x[1].section])} + self.data_labels = dict(sorted(settings_items, key=lambda x: section_ids[x[1].section])) def cast_value(self, key, value): """casts an arbitrary to the same type as this setting's value with key diff --git a/modules/textual_inversion/textual_inversion.py b/modules/textual_inversion/textual_inversion.py index 9ed9ba45..c37bb2ad 100644 --- a/modules/textual_inversion/textual_inversion.py +++ b/modules/textual_inversion/textual_inversion.py @@ -167,7 +167,7 @@ class EmbeddingDatabase: if 'string_to_param' in data: param_dict = data['string_to_param'] if hasattr(param_dict, '_parameters'): - param_dict = getattr(param_dict, '_parameters') # fix for torch 1.12.1 loading saved file from torch 1.11 + param_dict = param_dict._parameters # fix for torch 1.12.1 loading saved file from torch 1.11 assert len(param_dict) == 1, 'embedding file has multiple terms in it' emb = next(iter(param_dict.items()))[1] # diffuser concepts diff --git a/modules/ui.py b/modules/ui.py index 782b569d..84d661b2 100644 --- a/modules/ui.py +++ b/modules/ui.py @@ -1222,7 +1222,7 @@ def create_ui(): ) def get_textual_inversion_template_names(): - return sorted([x for x in textual_inversion.textual_inversion_templates]) + return sorted(textual_inversion.textual_inversion_templates) with gr.Tab(label="Train", id="train"): gr.HTML(value="

Train an embedding or Hypernetwork; you must specify a directory with a set of 1:1 ratio images [wiki]

") @@ -1230,8 +1230,8 @@ def create_ui(): train_embedding_name = gr.Dropdown(label='Embedding', elem_id="train_embedding", choices=sorted(sd_hijack.model_hijack.embedding_db.word_embeddings.keys())) create_refresh_button(train_embedding_name, sd_hijack.model_hijack.embedding_db.load_textual_inversion_embeddings, lambda: {"choices": sorted(sd_hijack.model_hijack.embedding_db.word_embeddings.keys())}, "refresh_train_embedding_name") - train_hypernetwork_name = gr.Dropdown(label='Hypernetwork', elem_id="train_hypernetwork", choices=[x for x in shared.hypernetworks.keys()]) - create_refresh_button(train_hypernetwork_name, shared.reload_hypernetworks, lambda: {"choices": sorted([x for x in shared.hypernetworks.keys()])}, "refresh_train_hypernetwork_name") + train_hypernetwork_name = gr.Dropdown(label='Hypernetwork', elem_id="train_hypernetwork", choices=list(shared.hypernetworks.keys())) + create_refresh_button(train_hypernetwork_name, shared.reload_hypernetworks, lambda: {"choices": sorted(shared.hypernetworks.keys())}, "refresh_train_hypernetwork_name") with FormRow(): embedding_learn_rate = gr.Textbox(label='Embedding Learning rate', placeholder="Embedding Learning rate", value="0.005", elem_id="train_embedding_learn_rate") @@ -1808,7 +1808,7 @@ def create_ui(): if type(x) == gr.Dropdown: def check_dropdown(val): if getattr(x, 'multiselect', False): - return all([value in x.choices for value in val]) + return all(value in x.choices for value in val) else: return val in x.choices diff --git a/modules/ui_extra_networks.py b/modules/ui_extra_networks.py index 800e467a..ab585917 100644 --- a/modules/ui_extra_networks.py +++ b/modules/ui_extra_networks.py @@ -26,7 +26,7 @@ def register_page(page): def fetch_file(filename: str = ""): from starlette.responses import FileResponse - if not any([Path(x).absolute() in Path(filename).absolute().parents for x in allowed_dirs]): + if not any(Path(x).absolute() in Path(filename).absolute().parents for x in allowed_dirs): raise ValueError(f"File cannot be fetched: {filename}. Must be in one of directories registered by extra pages.") ext = os.path.splitext(filename)[1].lower() @@ -326,7 +326,7 @@ def setup_ui(ui, gallery): is_allowed = False for extra_page in ui.stored_extra_pages: - if any([path_is_parent(x, filename) for x in extra_page.allowed_directories_for_previews()]): + if any(path_is_parent(x, filename) for x in extra_page.allowed_directories_for_previews()): is_allowed = True break diff --git a/modules/ui_tempdir.py b/modules/ui_tempdir.py index 46fa9cb0..cac73c51 100644 --- a/modules/ui_tempdir.py +++ b/modules/ui_tempdir.py @@ -23,7 +23,7 @@ def register_tmp_file(gradio, filename): def check_tmp_file(gradio, filename): if hasattr(gradio, 'temp_file_sets'): - return any([filename in fileset for fileset in gradio.temp_file_sets]) + return any(filename in fileset for fileset in gradio.temp_file_sets) if hasattr(gradio, 'temp_dirs'): return any(Path(temp_dir).resolve() in Path(filename).resolve().parents for temp_dir in gradio.temp_dirs) -- cgit v1.2.3 From 49a55b410b66b7dd9be9335d8a2e3a71e4f8b15c Mon Sep 17 00:00:00 2001 From: Aarni Koskela Date: Thu, 11 May 2023 18:28:15 +0300 Subject: Autofix Ruff W (not W605) (mostly whitespace) --- extensions-builtin/LDSR/ldsr_model_arch.py | 4 +- extensions-builtin/LDSR/sd_hijack_ddpm_v1.py | 6 +-- extensions-builtin/ScuNET/scunet_model_arch.py | 2 +- extensions-builtin/SwinIR/scripts/swinir_model.py | 2 +- extensions-builtin/SwinIR/swinir_model_arch.py | 2 +- extensions-builtin/SwinIR/swinir_model_arch_v2.py | 52 +++++++++++------------ launch.py | 2 +- modules/api/api.py | 4 +- modules/api/models.py | 2 +- modules/cmd_args.py | 2 +- modules/codeformer/codeformer_arch.py | 14 +++--- modules/codeformer/vqgan_arch.py | 38 ++++++++--------- modules/esrgan_model_arch.py | 4 +- modules/extras.py | 2 +- modules/hypernetworks/hypernetwork.py | 12 +++--- modules/images.py | 2 +- modules/mac_specific.py | 4 +- modules/masking.py | 2 +- modules/ngrok.py | 4 +- modules/processing.py | 2 +- modules/script_callbacks.py | 14 +++--- modules/sd_hijack.py | 12 +++--- modules/sd_hijack_optimizations.py | 32 +++++++------- modules/sd_models.py | 4 +- modules/sd_samplers_kdiffusion.py | 18 ++++---- modules/sub_quadratic_attention.py | 2 +- modules/textual_inversion/dataset.py | 4 +- modules/textual_inversion/preprocess.py | 2 +- modules/textual_inversion/textual_inversion.py | 16 +++---- modules/ui.py | 18 ++++---- modules/ui_extensions.py | 6 +-- modules/xlmr.py | 6 +-- pyproject.toml | 5 ++- scripts/img2imgalt.py | 14 +++--- scripts/loopback.py | 8 ++-- scripts/poor_mans_outpainting.py | 2 +- scripts/prompt_matrix.py | 2 +- scripts/prompts_from_file.py | 4 +- scripts/sd_upscale.py | 2 +- 39 files changed, 167 insertions(+), 166 deletions(-) (limited to 'modules/sd_samplers_kdiffusion.py') diff --git a/extensions-builtin/LDSR/ldsr_model_arch.py b/extensions-builtin/LDSR/ldsr_model_arch.py index 2173de79..7f450086 100644 --- a/extensions-builtin/LDSR/ldsr_model_arch.py +++ b/extensions-builtin/LDSR/ldsr_model_arch.py @@ -130,11 +130,11 @@ class LDSR: im_og = im_og.resize((width_downsampled_pre, height_downsampled_pre), Image.LANCZOS) else: print(f"Down sample rate is 1 from {target_scale} / 4 (Not downsampling)") - + # pad width and height to multiples of 64, pads with the edge values of image to avoid artifacts pad_w, pad_h = np.max(((2, 2), np.ceil(np.array(im_og.size) / 64).astype(int)), axis=0) * 64 - im_og.size im_padded = Image.fromarray(np.pad(np.array(im_og), ((0, pad_h), (0, pad_w), (0, 0)), mode='edge')) - + logs = self.run(model["model"], im_padded, diffusion_steps, eta) sample = logs["sample"] diff --git a/extensions-builtin/LDSR/sd_hijack_ddpm_v1.py b/extensions-builtin/LDSR/sd_hijack_ddpm_v1.py index 57c02d12..631a08ef 100644 --- a/extensions-builtin/LDSR/sd_hijack_ddpm_v1.py +++ b/extensions-builtin/LDSR/sd_hijack_ddpm_v1.py @@ -460,7 +460,7 @@ class LatentDiffusionV1(DDPMV1): self.instantiate_cond_stage(cond_stage_config) self.cond_stage_forward = cond_stage_forward self.clip_denoised = False - self.bbox_tokenizer = None + self.bbox_tokenizer = None self.restarted_from_ckpt = False if ckpt_path is not None: @@ -792,7 +792,7 @@ class LatentDiffusionV1(DDPMV1): z = z.view((z.shape[0], -1, ks[0], ks[1], z.shape[-1])) # (bn, nc, ks[0], ks[1], L ) # 2. apply model loop over last dim - if isinstance(self.first_stage_model, VQModelInterface): + if isinstance(self.first_stage_model, VQModelInterface): output_list = [self.first_stage_model.decode(z[:, :, :, :, i], force_not_quantize=predict_cids or force_not_quantize) for i in range(z.shape[-1])] @@ -890,7 +890,7 @@ class LatentDiffusionV1(DDPMV1): if hasattr(self, "split_input_params"): assert len(cond) == 1 # todo can only deal with one conditioning atm - assert not return_ids + assert not return_ids ks = self.split_input_params["ks"] # eg. (128, 128) stride = self.split_input_params["stride"] # eg. (64, 64) diff --git a/extensions-builtin/ScuNET/scunet_model_arch.py b/extensions-builtin/ScuNET/scunet_model_arch.py index 8028918a..b51a8806 100644 --- a/extensions-builtin/ScuNET/scunet_model_arch.py +++ b/extensions-builtin/ScuNET/scunet_model_arch.py @@ -265,4 +265,4 @@ class SCUNet(nn.Module): nn.init.constant_(m.bias, 0) elif isinstance(m, nn.LayerNorm): nn.init.constant_(m.bias, 0) - nn.init.constant_(m.weight, 1.0) \ No newline at end of file + nn.init.constant_(m.weight, 1.0) diff --git a/extensions-builtin/SwinIR/scripts/swinir_model.py b/extensions-builtin/SwinIR/scripts/swinir_model.py index 55dd94ab..0ba50487 100644 --- a/extensions-builtin/SwinIR/scripts/swinir_model.py +++ b/extensions-builtin/SwinIR/scripts/swinir_model.py @@ -150,7 +150,7 @@ def inference(img, model, tile, tile_overlap, window_size, scale): for w_idx in w_idx_list: if state.interrupted or state.skipped: break - + in_patch = img[..., h_idx: h_idx + tile, w_idx: w_idx + tile] out_patch = model(in_patch) out_patch_mask = torch.ones_like(out_patch) diff --git a/extensions-builtin/SwinIR/swinir_model_arch.py b/extensions-builtin/SwinIR/swinir_model_arch.py index 73e37cfa..93b93274 100644 --- a/extensions-builtin/SwinIR/swinir_model_arch.py +++ b/extensions-builtin/SwinIR/swinir_model_arch.py @@ -805,7 +805,7 @@ class SwinIR(nn.Module): def forward(self, x): H, W = x.shape[2:] x = self.check_image_size(x) - + self.mean = self.mean.type_as(x) x = (x - self.mean) * self.img_range diff --git a/extensions-builtin/SwinIR/swinir_model_arch_v2.py b/extensions-builtin/SwinIR/swinir_model_arch_v2.py index 3ca9be78..dad22cca 100644 --- a/extensions-builtin/SwinIR/swinir_model_arch_v2.py +++ b/extensions-builtin/SwinIR/swinir_model_arch_v2.py @@ -241,7 +241,7 @@ class SwinTransformerBlock(nn.Module): attn_mask = None self.register_buffer("attn_mask", attn_mask) - + def calculate_mask(self, x_size): # calculate attention mask for SW-MSA H, W = x_size @@ -263,7 +263,7 @@ class SwinTransformerBlock(nn.Module): attn_mask = mask_windows.unsqueeze(1) - mask_windows.unsqueeze(2) attn_mask = attn_mask.masked_fill(attn_mask != 0, float(-100.0)).masked_fill(attn_mask == 0, float(0.0)) - return attn_mask + return attn_mask def forward(self, x, x_size): H, W = x_size @@ -288,7 +288,7 @@ class SwinTransformerBlock(nn.Module): attn_windows = self.attn(x_windows, mask=self.attn_mask) # nW*B, window_size*window_size, C else: attn_windows = self.attn(x_windows, mask=self.calculate_mask(x_size).to(x.device)) - + # merge windows attn_windows = attn_windows.view(-1, self.window_size, self.window_size, C) shifted_x = window_reverse(attn_windows, self.window_size, H, W) # B H' W' C @@ -369,7 +369,7 @@ class PatchMerging(nn.Module): H, W = self.input_resolution flops = (H // 2) * (W // 2) * 4 * self.dim * 2 * self.dim flops += H * W * self.dim // 2 - return flops + return flops class BasicLayer(nn.Module): """ A basic Swin Transformer layer for one stage. @@ -447,7 +447,7 @@ class BasicLayer(nn.Module): nn.init.constant_(blk.norm1.weight, 0) nn.init.constant_(blk.norm2.bias, 0) nn.init.constant_(blk.norm2.weight, 0) - + class PatchEmbed(nn.Module): r""" Image to Patch Embedding Args: @@ -492,7 +492,7 @@ class PatchEmbed(nn.Module): flops = Ho * Wo * self.embed_dim * self.in_chans * (self.patch_size[0] * self.patch_size[1]) if self.norm is not None: flops += Ho * Wo * self.embed_dim - return flops + return flops class RSTB(nn.Module): """Residual Swin Transformer Block (RSTB). @@ -531,7 +531,7 @@ class RSTB(nn.Module): num_heads=num_heads, window_size=window_size, mlp_ratio=mlp_ratio, - qkv_bias=qkv_bias, + qkv_bias=qkv_bias, drop=drop, attn_drop=attn_drop, drop_path=drop_path, norm_layer=norm_layer, @@ -622,7 +622,7 @@ class Upsample(nn.Sequential): else: raise ValueError(f'scale {scale} is not supported. ' 'Supported scales: 2^n and 3.') super(Upsample, self).__init__(*m) - + class Upsample_hf(nn.Sequential): """Upsample module. @@ -642,7 +642,7 @@ class Upsample_hf(nn.Sequential): m.append(nn.PixelShuffle(3)) else: raise ValueError(f'scale {scale} is not supported. ' 'Supported scales: 2^n and 3.') - super(Upsample_hf, self).__init__(*m) + super(Upsample_hf, self).__init__(*m) class UpsampleOneStep(nn.Sequential): @@ -667,8 +667,8 @@ class UpsampleOneStep(nn.Sequential): H, W = self.input_resolution flops = H * W * self.num_feat * 3 * 9 return flops - - + + class Swin2SR(nn.Module): r""" Swin2SR @@ -699,7 +699,7 @@ class Swin2SR(nn.Module): def __init__(self, img_size=64, patch_size=1, in_chans=3, embed_dim=96, depths=(6, 6, 6, 6), num_heads=(6, 6, 6, 6), - window_size=7, mlp_ratio=4., qkv_bias=True, + window_size=7, mlp_ratio=4., qkv_bias=True, drop_rate=0., attn_drop_rate=0., drop_path_rate=0.1, norm_layer=nn.LayerNorm, ape=False, patch_norm=True, use_checkpoint=False, upscale=2, img_range=1., upsampler='', resi_connection='1conv', @@ -764,7 +764,7 @@ class Swin2SR(nn.Module): num_heads=num_heads[i_layer], window_size=window_size, mlp_ratio=self.mlp_ratio, - qkv_bias=qkv_bias, + qkv_bias=qkv_bias, drop=drop_rate, attn_drop=attn_drop_rate, drop_path=dpr[sum(depths[:i_layer]):sum(depths[:i_layer + 1])], # no impact on SR results norm_layer=norm_layer, @@ -776,7 +776,7 @@ class Swin2SR(nn.Module): ) self.layers.append(layer) - + if self.upsampler == 'pixelshuffle_hf': self.layers_hf = nn.ModuleList() for i_layer in range(self.num_layers): @@ -787,7 +787,7 @@ class Swin2SR(nn.Module): num_heads=num_heads[i_layer], window_size=window_size, mlp_ratio=self.mlp_ratio, - qkv_bias=qkv_bias, + qkv_bias=qkv_bias, drop=drop_rate, attn_drop=attn_drop_rate, drop_path=dpr[sum(depths[:i_layer]):sum(depths[:i_layer + 1])], # no impact on SR results norm_layer=norm_layer, @@ -799,7 +799,7 @@ class Swin2SR(nn.Module): ) self.layers_hf.append(layer) - + self.norm = norm_layer(self.num_features) # build the last conv layer in deep feature extraction @@ -829,10 +829,10 @@ class Swin2SR(nn.Module): self.conv_aux = nn.Conv2d(num_feat, num_out_ch, 3, 1, 1) self.conv_after_aux = nn.Sequential( nn.Conv2d(3, num_feat, 3, 1, 1), - nn.LeakyReLU(inplace=True)) + nn.LeakyReLU(inplace=True)) self.upsample = Upsample(upscale, num_feat) self.conv_last = nn.Conv2d(num_feat, num_out_ch, 3, 1, 1) - + elif self.upsampler == 'pixelshuffle_hf': self.conv_before_upsample = nn.Sequential(nn.Conv2d(embed_dim, num_feat, 3, 1, 1), nn.LeakyReLU(inplace=True)) @@ -846,7 +846,7 @@ class Swin2SR(nn.Module): nn.Conv2d(embed_dim, num_feat, 3, 1, 1), nn.LeakyReLU(inplace=True)) self.conv_last_hf = nn.Conv2d(num_feat, num_out_ch, 3, 1, 1) - + elif self.upsampler == 'pixelshuffledirect': # for lightweight SR (to save parameters) self.upsample = UpsampleOneStep(upscale, embed_dim, num_out_ch, @@ -905,7 +905,7 @@ class Swin2SR(nn.Module): x = self.patch_unembed(x, x_size) return x - + def forward_features_hf(self, x): x_size = (x.shape[2], x.shape[3]) x = self.patch_embed(x) @@ -919,7 +919,7 @@ class Swin2SR(nn.Module): x = self.norm(x) # B L C x = self.patch_unembed(x, x_size) - return x + return x def forward(self, x): H, W = x.shape[2:] @@ -951,7 +951,7 @@ class Swin2SR(nn.Module): x = self.conv_after_body(self.forward_features(x)) + x x_before = self.conv_before_upsample(x) x_out = self.conv_last(self.upsample(x_before)) - + x_hf = self.conv_first_hf(x_before) x_hf = self.conv_after_body_hf(self.forward_features_hf(x_hf)) + x_hf x_hf = self.conv_before_upsample_hf(x_hf) @@ -977,15 +977,15 @@ class Swin2SR(nn.Module): x_first = self.conv_first(x) res = self.conv_after_body(self.forward_features(x_first)) + x_first x = x + self.conv_last(res) - + x = x / self.img_range + self.mean if self.upsampler == "pixelshuffle_aux": return x[:, :, :H*self.upscale, :W*self.upscale], aux - + elif self.upsampler == "pixelshuffle_hf": x_out = x_out / self.img_range + self.mean return x_out[:, :, :H*self.upscale, :W*self.upscale], x[:, :, :H*self.upscale, :W*self.upscale], x_hf[:, :, :H*self.upscale, :W*self.upscale] - + else: return x[:, :, :H*self.upscale, :W*self.upscale] @@ -1014,4 +1014,4 @@ if __name__ == '__main__': x = torch.randn((1, 3, height, width)) x = model(x) - print(x.shape) \ No newline at end of file + print(x.shape) diff --git a/launch.py b/launch.py index 670af87c..62b33f14 100644 --- a/launch.py +++ b/launch.py @@ -327,7 +327,7 @@ def prepare_environment(): if args.update_all_extensions: git_pull_recursive(extensions_dir) - + if "--exit" in sys.argv: print("Exiting because of --exit argument") exit(0) diff --git a/modules/api/api.py b/modules/api/api.py index 594fa655..165985c3 100644 --- a/modules/api/api.py +++ b/modules/api/api.py @@ -227,7 +227,7 @@ class Api: script_idx = script_name_to_index(script_name, script_runner.selectable_scripts) script = script_runner.selectable_scripts[script_idx] return script, script_idx - + def get_scripts_list(self): t2ilist = [str(title.lower()) for title in scripts.scripts_txt2img.titles] i2ilist = [str(title.lower()) for title in scripts.scripts_img2img.titles] @@ -237,7 +237,7 @@ class Api: def get_script(self, script_name, script_runner): if script_name is None or script_name == "": return None, None - + script_idx = script_name_to_index(script_name, script_runner.scripts) return script_runner.scripts[script_idx] diff --git a/modules/api/models.py b/modules/api/models.py index 4d291076..006ccdb7 100644 --- a/modules/api/models.py +++ b/modules/api/models.py @@ -289,4 +289,4 @@ class MemoryResponse(BaseModel): class ScriptsList(BaseModel): txt2img: list = Field(default=None,title="Txt2img", description="Titles of scripts (txt2img)") - img2img: list = Field(default=None,title="Img2img", description="Titles of scripts (img2img)") \ No newline at end of file + img2img: list = Field(default=None,title="Img2img", description="Titles of scripts (img2img)") diff --git a/modules/cmd_args.py b/modules/cmd_args.py index e01ca655..f4a4ab36 100644 --- a/modules/cmd_args.py +++ b/modules/cmd_args.py @@ -102,4 +102,4 @@ parser.add_argument("--no-gradio-queue", action='store_true', help="Disables gra parser.add_argument("--skip-version-check", action='store_true', help="Do not check versions of torch and xformers") parser.add_argument("--no-hashing", action='store_true', help="disable sha256 hashing of checkpoints to help loading performance", default=False) parser.add_argument("--no-download-sd-model", action='store_true', help="don't download SD1.5 model even if no model is found in --ckpt-dir", default=False) -parser.add_argument('--subpath', type=str, help='customize the subpath for gradio, use with reverse proxy') \ No newline at end of file +parser.add_argument('--subpath', type=str, help='customize the subpath for gradio, use with reverse proxy') diff --git a/modules/codeformer/codeformer_arch.py b/modules/codeformer/codeformer_arch.py index 45c70f84..12db6814 100644 --- a/modules/codeformer/codeformer_arch.py +++ b/modules/codeformer/codeformer_arch.py @@ -119,7 +119,7 @@ class TransformerSALayer(nn.Module): tgt_mask: Optional[Tensor] = None, tgt_key_padding_mask: Optional[Tensor] = None, query_pos: Optional[Tensor] = None): - + # self attention tgt2 = self.norm1(tgt) q = k = self.with_pos_embed(tgt2, query_pos) @@ -159,7 +159,7 @@ class Fuse_sft_block(nn.Module): @ARCH_REGISTRY.register() class CodeFormer(VQAutoEncoder): - def __init__(self, dim_embd=512, n_head=8, n_layers=9, + def __init__(self, dim_embd=512, n_head=8, n_layers=9, codebook_size=1024, latent_size=256, connect_list=('32', '64', '128', '256'), fix_modules=('quantize', 'generator')): @@ -179,14 +179,14 @@ class CodeFormer(VQAutoEncoder): self.feat_emb = nn.Linear(256, self.dim_embd) # transformer - self.ft_layers = nn.Sequential(*[TransformerSALayer(embed_dim=dim_embd, nhead=n_head, dim_mlp=self.dim_mlp, dropout=0.0) + self.ft_layers = nn.Sequential(*[TransformerSALayer(embed_dim=dim_embd, nhead=n_head, dim_mlp=self.dim_mlp, dropout=0.0) for _ in range(self.n_layers)]) # logits_predict head self.idx_pred_layer = nn.Sequential( nn.LayerNorm(dim_embd), nn.Linear(dim_embd, codebook_size, bias=False)) - + self.channels = { '16': 512, '32': 256, @@ -221,7 +221,7 @@ class CodeFormer(VQAutoEncoder): enc_feat_dict = {} out_list = [self.fuse_encoder_block[f_size] for f_size in self.connect_list] for i, block in enumerate(self.encoder.blocks): - x = block(x) + x = block(x) if i in out_list: enc_feat_dict[str(x.shape[-1])] = x.clone() @@ -266,11 +266,11 @@ class CodeFormer(VQAutoEncoder): fuse_list = [self.fuse_generator_block[f_size] for f_size in self.connect_list] for i, block in enumerate(self.generator.blocks): - x = block(x) + x = block(x) if i in fuse_list: # fuse after i-th block f_size = str(x.shape[-1]) if w>0: x = self.fuse_convs_dict[f_size](enc_feat_dict[f_size].detach(), x, w) out = x # logits doesn't need softmax before cross_entropy loss - return out, logits, lq_feat \ No newline at end of file + return out, logits, lq_feat diff --git a/modules/codeformer/vqgan_arch.py b/modules/codeformer/vqgan_arch.py index b24a0394..09ee6660 100644 --- a/modules/codeformer/vqgan_arch.py +++ b/modules/codeformer/vqgan_arch.py @@ -13,7 +13,7 @@ from basicsr.utils.registry import ARCH_REGISTRY def normalize(in_channels): return torch.nn.GroupNorm(num_groups=32, num_channels=in_channels, eps=1e-6, affine=True) - + @torch.jit.script def swish(x): @@ -210,15 +210,15 @@ class AttnBlock(nn.Module): # compute attention b, c, h, w = q.shape q = q.reshape(b, c, h*w) - q = q.permute(0, 2, 1) + q = q.permute(0, 2, 1) k = k.reshape(b, c, h*w) - w_ = torch.bmm(q, k) + w_ = torch.bmm(q, k) w_ = w_ * (int(c)**(-0.5)) w_ = F.softmax(w_, dim=2) # attend to values v = v.reshape(b, c, h*w) - w_ = w_.permute(0, 2, 1) + w_ = w_.permute(0, 2, 1) h_ = torch.bmm(v, w_) h_ = h_.reshape(b, c, h, w) @@ -270,18 +270,18 @@ class Encoder(nn.Module): def forward(self, x): for block in self.blocks: x = block(x) - + return x class Generator(nn.Module): def __init__(self, nf, emb_dim, ch_mult, res_blocks, img_size, attn_resolutions): super().__init__() - self.nf = nf - self.ch_mult = ch_mult + self.nf = nf + self.ch_mult = ch_mult self.num_resolutions = len(self.ch_mult) self.num_res_blocks = res_blocks - self.resolution = img_size + self.resolution = img_size self.attn_resolutions = attn_resolutions self.in_channels = emb_dim self.out_channels = 3 @@ -315,24 +315,24 @@ class Generator(nn.Module): blocks.append(nn.Conv2d(block_in_ch, self.out_channels, kernel_size=3, stride=1, padding=1)) self.blocks = nn.ModuleList(blocks) - + def forward(self, x): for block in self.blocks: x = block(x) - + return x - + @ARCH_REGISTRY.register() class VQAutoEncoder(nn.Module): def __init__(self, img_size, nf, ch_mult, quantizer="nearest", res_blocks=2, attn_resolutions=None, codebook_size=1024, emb_dim=256, beta=0.25, gumbel_straight_through=False, gumbel_kl_weight=1e-8, model_path=None): super().__init__() logger = get_root_logger() - self.in_channels = 3 - self.nf = nf - self.n_blocks = res_blocks + self.in_channels = 3 + self.nf = nf + self.n_blocks = res_blocks self.codebook_size = codebook_size self.embed_dim = emb_dim self.ch_mult = ch_mult @@ -363,11 +363,11 @@ class VQAutoEncoder(nn.Module): self.kl_weight ) self.generator = Generator( - self.nf, + self.nf, self.embed_dim, - self.ch_mult, - self.n_blocks, - self.resolution, + self.ch_mult, + self.n_blocks, + self.resolution, self.attn_resolutions ) @@ -432,4 +432,4 @@ class VQGANDiscriminator(nn.Module): raise ValueError('Wrong params!') def forward(self, x): - return self.main(x) \ No newline at end of file + return self.main(x) diff --git a/modules/esrgan_model_arch.py b/modules/esrgan_model_arch.py index 4de9dd8d..2b9888ba 100644 --- a/modules/esrgan_model_arch.py +++ b/modules/esrgan_model_arch.py @@ -105,7 +105,7 @@ class ResidualDenseBlock_5C(nn.Module): Modified options that can be used: - "Partial Convolution based Padding" arXiv:1811.11718 - "Spectral normalization" arXiv:1802.05957 - - "ICASSP 2020 - ESRGAN+ : Further Improving ESRGAN" N. C. + - "ICASSP 2020 - ESRGAN+ : Further Improving ESRGAN" N. C. {Rakotonirina} and A. {Rasoanaivo} """ @@ -170,7 +170,7 @@ class GaussianNoise(nn.Module): scale = self.sigma * x.detach() if self.is_relative_detach else self.sigma * x sampled_noise = self.noise.repeat(*x.size()).normal_() * scale x = x + sampled_noise - return x + return x def conv1x1(in_planes, out_planes, stride=1): return nn.Conv2d(in_planes, out_planes, kernel_size=1, stride=stride, bias=False) diff --git a/modules/extras.py b/modules/extras.py index eb4f0b42..bdf9b3b7 100644 --- a/modules/extras.py +++ b/modules/extras.py @@ -199,7 +199,7 @@ def run_modelmerger(id_task, primary_model_name, secondary_model_name, tertiary_ result_is_inpainting_model = True else: theta_0[key] = theta_func2(a, b, multiplier) - + theta_0[key] = to_half(theta_0[key], save_as_half) shared.state.sampling_step += 1 diff --git a/modules/hypernetworks/hypernetwork.py b/modules/hypernetworks/hypernetwork.py index 38ef074f..570b5603 100644 --- a/modules/hypernetworks/hypernetwork.py +++ b/modules/hypernetworks/hypernetwork.py @@ -540,7 +540,7 @@ def train_hypernetwork(id_task, hypernetwork_name, learn_rate, batch_size, gradi return hypernetwork, filename scheduler = LearnRateScheduler(learn_rate, steps, initial_step) - + clip_grad = torch.nn.utils.clip_grad_value_ if clip_grad_mode == "value" else torch.nn.utils.clip_grad_norm_ if clip_grad_mode == "norm" else None if clip_grad: clip_grad_sched = LearnRateScheduler(clip_grad_value, steps, initial_step, verbose=False) @@ -593,7 +593,7 @@ def train_hypernetwork(id_task, hypernetwork_name, learn_rate, batch_size, gradi print(e) scaler = torch.cuda.amp.GradScaler() - + batch_size = ds.batch_size gradient_step = ds.gradient_step # n steps = batch_size * gradient_step * n image processed @@ -636,7 +636,7 @@ def train_hypernetwork(id_task, hypernetwork_name, learn_rate, batch_size, gradi if clip_grad: clip_grad_sched.step(hypernetwork.step) - + with devices.autocast(): x = batch.latent_sample.to(devices.device, non_blocking=pin_memory) if use_weight: @@ -657,14 +657,14 @@ def train_hypernetwork(id_task, hypernetwork_name, learn_rate, batch_size, gradi _loss_step += loss.item() scaler.scale(loss).backward() - + # go back until we reach gradient accumulation steps if (j + 1) % gradient_step != 0: continue loss_logging.append(_loss_step) if clip_grad: clip_grad(weights, clip_grad_sched.learn_rate) - + scaler.step(optimizer) scaler.update() hypernetwork.step += 1 @@ -674,7 +674,7 @@ def train_hypernetwork(id_task, hypernetwork_name, learn_rate, batch_size, gradi _loss_step = 0 steps_done = hypernetwork.step + 1 - + epoch_num = hypernetwork.step // steps_per_epoch epoch_step = hypernetwork.step % steps_per_epoch diff --git a/modules/images.py b/modules/images.py index 3b8b62d9..b2de3662 100644 --- a/modules/images.py +++ b/modules/images.py @@ -367,7 +367,7 @@ class FilenameGenerator: self.seed = seed self.prompt = prompt self.image = image - + def hasprompt(self, *args): lower = self.prompt.lower() if self.p is None or self.prompt is None: diff --git a/modules/mac_specific.py b/modules/mac_specific.py index 5c2f92a1..d74c6b95 100644 --- a/modules/mac_specific.py +++ b/modules/mac_specific.py @@ -42,7 +42,7 @@ if has_mps: # MPS workaround for https://github.com/pytorch/pytorch/issues/79383 CondFunc('torch.Tensor.to', lambda orig_func, self, *args, **kwargs: orig_func(self.contiguous(), *args, **kwargs), lambda _, self, *args, **kwargs: self.device.type != 'mps' and (args and isinstance(args[0], torch.device) and args[0].type == 'mps' or isinstance(kwargs.get('device'), torch.device) and kwargs['device'].type == 'mps')) - # MPS workaround for https://github.com/pytorch/pytorch/issues/80800 + # MPS workaround for https://github.com/pytorch/pytorch/issues/80800 CondFunc('torch.nn.functional.layer_norm', lambda orig_func, *args, **kwargs: orig_func(*([args[0].contiguous()] + list(args[1:])), **kwargs), lambda _, *args, **kwargs: args and isinstance(args[0], torch.Tensor) and args[0].device.type == 'mps') # MPS workaround for https://github.com/pytorch/pytorch/issues/90532 @@ -60,4 +60,4 @@ if has_mps: # MPS workaround for https://github.com/pytorch/pytorch/issues/92311 if platform.processor() == 'i386': for funcName in ['torch.argmax', 'torch.Tensor.argmax']: - CondFunc(funcName, lambda _, input, *args, **kwargs: torch.max(input.float() if input.dtype == torch.int64 else input, *args, **kwargs)[1], lambda _, input, *args, **kwargs: input.device.type == 'mps') \ No newline at end of file + CondFunc(funcName, lambda _, input, *args, **kwargs: torch.max(input.float() if input.dtype == torch.int64 else input, *args, **kwargs)[1], lambda _, input, *args, **kwargs: input.device.type == 'mps') diff --git a/modules/masking.py b/modules/masking.py index a5c4d2da..be9f84c7 100644 --- a/modules/masking.py +++ b/modules/masking.py @@ -4,7 +4,7 @@ from PIL import Image, ImageFilter, ImageOps def get_crop_region(mask, pad=0): """finds a rectangular region that contains all masked ares in an image. Returns (x1, y1, x2, y2) coordinates of the rectangle. For example, if a user has painted the top-right part of a 512x512 image", the result may be (256, 0, 512, 256)""" - + h, w = mask.shape crop_left = 0 diff --git a/modules/ngrok.py b/modules/ngrok.py index 7a7b4b26..67a74e85 100644 --- a/modules/ngrok.py +++ b/modules/ngrok.py @@ -13,7 +13,7 @@ def connect(token, port, region): config = conf.PyngrokConfig( auth_token=token, region=region ) - + # Guard for existing tunnels existing = ngrok.get_tunnels(pyngrok_config=config) if existing: @@ -24,7 +24,7 @@ def connect(token, port, region): print(f'ngrok has already been connected to localhost:{port}! URL: {public_url}\n' 'You can use this link after the launch is complete.') return - + try: if account is None: public_url = ngrok.connect(port, pyngrok_config=config, bind_tls=True).public_url diff --git a/modules/processing.py b/modules/processing.py index c3932d6b..f902b9df 100644 --- a/modules/processing.py +++ b/modules/processing.py @@ -164,7 +164,7 @@ class StableDiffusionProcessing: self.all_subseeds = None self.iteration = 0 self.is_hr_pass = False - + @property def sd_model(self): diff --git a/modules/script_callbacks.py b/modules/script_callbacks.py index 17109732..7d9dd736 100644 --- a/modules/script_callbacks.py +++ b/modules/script_callbacks.py @@ -32,22 +32,22 @@ class CFGDenoiserParams: def __init__(self, x, image_cond, sigma, sampling_step, total_sampling_steps, text_cond, text_uncond): self.x = x """Latent image representation in the process of being denoised""" - + self.image_cond = image_cond """Conditioning image""" - + self.sigma = sigma """Current sigma noise step value""" - + self.sampling_step = sampling_step """Current Sampling step number""" - + self.total_sampling_steps = total_sampling_steps """Total number of sampling steps planned""" - + self.text_cond = text_cond """ Encoder hidden states of text conditioning from prompt""" - + self.text_uncond = text_uncond """ Encoder hidden states of text conditioning from negative prompt""" @@ -240,7 +240,7 @@ def add_callback(callbacks, fun): callbacks.append(ScriptCallback(filename, fun)) - + def remove_current_script_callbacks(): stack = [x for x in inspect.stack() if x.filename != __file__] filename = stack[0].filename if len(stack) > 0 else 'unknown file' diff --git a/modules/sd_hijack.py b/modules/sd_hijack.py index e374aeb8..7e50f1ab 100644 --- a/modules/sd_hijack.py +++ b/modules/sd_hijack.py @@ -34,7 +34,7 @@ def apply_optimizations(): ldm.modules.diffusionmodules.model.nonlinearity = silu ldm.modules.diffusionmodules.openaimodel.th = sd_hijack_unet.th - + optimization_method = None can_use_sdp = hasattr(torch.nn.functional, "scaled_dot_product_attention") and callable(torch.nn.functional.scaled_dot_product_attention) # not everyone has torch 2.x to use sdp @@ -92,12 +92,12 @@ def fix_checkpoint(): def weighted_loss(sd_model, pred, target, mean=True): #Calculate the weight normally, but ignore the mean loss = sd_model._old_get_loss(pred, target, mean=False) - + #Check if we have weights available weight = getattr(sd_model, '_custom_loss_weight', None) if weight is not None: loss *= weight - + #Return the loss, as mean if specified return loss.mean() if mean else loss @@ -105,7 +105,7 @@ def weighted_forward(sd_model, x, c, w, *args, **kwargs): try: #Temporarily append weights to a place accessible during loss calc sd_model._custom_loss_weight = w - + #Replace 'get_loss' with a weight-aware one. Otherwise we need to reimplement 'forward' completely #Keep 'get_loss', but don't overwrite the previous old_get_loss if it's already set if not hasattr(sd_model, '_old_get_loss'): @@ -120,7 +120,7 @@ def weighted_forward(sd_model, x, c, w, *args, **kwargs): del sd_model._custom_loss_weight except AttributeError: pass - + #If we have an old loss function, reset the loss function to the original one if hasattr(sd_model, '_old_get_loss'): sd_model.get_loss = sd_model._old_get_loss @@ -184,7 +184,7 @@ class StableDiffusionModelHijack: def undo_hijack(self, m): if type(m.cond_stage_model) == xlmr.BertSeriesModelWithTransformation: - m.cond_stage_model = m.cond_stage_model.wrapped + m.cond_stage_model = m.cond_stage_model.wrapped elif type(m.cond_stage_model) == sd_hijack_clip.FrozenCLIPEmbedderWithCustomWords: m.cond_stage_model = m.cond_stage_model.wrapped diff --git a/modules/sd_hijack_optimizations.py b/modules/sd_hijack_optimizations.py index a174bbe1..f00fe55c 100644 --- a/modules/sd_hijack_optimizations.py +++ b/modules/sd_hijack_optimizations.py @@ -62,10 +62,10 @@ def split_cross_attention_forward_v1(self, x, context=None, mask=None): end = i + 2 s1 = einsum('b i d, b j d -> b i j', q[i:end], k[i:end]) s1 *= self.scale - + s2 = s1.softmax(dim=-1) del s1 - + r1[i:end] = einsum('b i j, b j d -> b i d', s2, v[i:end]) del s2 del q, k, v @@ -95,43 +95,43 @@ def split_cross_attention_forward(self, x, context=None, mask=None): with devices.without_autocast(disable=not shared.opts.upcast_attn): k_in = k_in * self.scale - + del context, x - + q, k, v = (rearrange(t, 'b n (h d) -> (b h) n d', h=h) for t in (q_in, k_in, v_in)) del q_in, k_in, v_in - + r1 = torch.zeros(q.shape[0], q.shape[1], v.shape[2], device=q.device, dtype=q.dtype) - + mem_free_total = get_available_vram() - + gb = 1024 ** 3 tensor_size = q.shape[0] * q.shape[1] * k.shape[1] * q.element_size() modifier = 3 if q.element_size() == 2 else 2.5 mem_required = tensor_size * modifier steps = 1 - + if mem_required > mem_free_total: steps = 2 ** (math.ceil(math.log(mem_required / mem_free_total, 2))) # print(f"Expected tensor size:{tensor_size/gb:0.1f}GB, cuda free:{mem_free_cuda/gb:0.1f}GB " # f"torch free:{mem_free_torch/gb:0.1f} total:{mem_free_total/gb:0.1f} steps:{steps}") - + if steps > 64: max_res = math.floor(math.sqrt(math.sqrt(mem_free_total / 2.5)) / 8) * 64 raise RuntimeError(f'Not enough memory, use lower resolution (max approx. {max_res}x{max_res}). ' f'Need: {mem_required / 64 / gb:0.1f}GB free, Have:{mem_free_total / gb:0.1f}GB free') - + slice_size = q.shape[1] // steps if (q.shape[1] % steps) == 0 else q.shape[1] for i in range(0, q.shape[1], slice_size): end = i + slice_size s1 = einsum('b i d, b j d -> b i j', q[:, i:end], k) - + s2 = s1.softmax(dim=-1, dtype=q.dtype) del s1 - + r1[:, i:end] = einsum('b i j, b j d -> b i d', s2, v) del s2 - + del q, k, v r1 = r1.to(dtype) @@ -228,7 +228,7 @@ def split_cross_attention_forward_invokeAI(self, x, context=None, mask=None): with devices.without_autocast(disable=not shared.opts.upcast_attn): k = k * self.scale - + q, k, v = (rearrange(t, 'b n (h d) -> (b h) n d', h=h) for t in (q, k, v)) r = einsum_op(q, k, v) r = r.to(dtype) @@ -369,7 +369,7 @@ def scaled_dot_product_attention_forward(self, x, context=None, mask=None): q = q_in.view(batch_size, -1, h, head_dim).transpose(1, 2) k = k_in.view(batch_size, -1, h, head_dim).transpose(1, 2) v = v_in.view(batch_size, -1, h, head_dim).transpose(1, 2) - + del q_in, k_in, v_in dtype = q.dtype @@ -451,7 +451,7 @@ def cross_attention_attnblock_forward(self, x): h3 += x return h3 - + def xformers_attnblock_forward(self, x): try: h_ = x diff --git a/modules/sd_models.py b/modules/sd_models.py index d1e946a5..3316d021 100644 --- a/modules/sd_models.py +++ b/modules/sd_models.py @@ -165,7 +165,7 @@ def model_hash(filename): def select_checkpoint(): model_checkpoint = shared.opts.sd_model_checkpoint - + checkpoint_info = checkpoint_alisases.get(model_checkpoint, None) if checkpoint_info is not None: return checkpoint_info @@ -372,7 +372,7 @@ def enable_midas_autodownload(): if not os.path.exists(path): if not os.path.exists(midas_path): mkdir(midas_path) - + print(f"Downloading midas model weights for {model_type} to {path}") request.urlretrieve(midas_urls[model_type], path) print(f"{model_type} downloaded") diff --git a/modules/sd_samplers_kdiffusion.py b/modules/sd_samplers_kdiffusion.py index 2f733cf5..e9e41818 100644 --- a/modules/sd_samplers_kdiffusion.py +++ b/modules/sd_samplers_kdiffusion.py @@ -93,10 +93,10 @@ class CFGDenoiser(torch.nn.Module): if shared.sd_model.model.conditioning_key == "crossattn-adm": image_uncond = torch.zeros_like(image_cond) - make_condition_dict = lambda c_crossattn, c_adm: {"c_crossattn": c_crossattn, "c_adm": c_adm} + make_condition_dict = lambda c_crossattn, c_adm: {"c_crossattn": c_crossattn, "c_adm": c_adm} else: image_uncond = image_cond - make_condition_dict = lambda c_crossattn, c_concat: {"c_crossattn": c_crossattn, "c_concat": [c_concat]} + make_condition_dict = lambda c_crossattn, c_concat: {"c_crossattn": c_crossattn, "c_concat": [c_concat]} if not is_edit_model: x_in = torch.cat([torch.stack([x[i] for _ in range(n)]) for i, n in enumerate(repeats)] + [x]) @@ -316,7 +316,7 @@ class KDiffusionSampler: sigma_sched = sigmas[steps - t_enc - 1:] xi = x + noise * sigma_sched[0] - + extra_params_kwargs = self.initialize(p) parameters = inspect.signature(self.func).parameters @@ -339,9 +339,9 @@ class KDiffusionSampler: self.model_wrap_cfg.init_latent = x self.last_latent = x extra_args={ - 'cond': conditioning, - 'image_cond': image_conditioning, - 'uncond': unconditional_conditioning, + 'cond': conditioning, + 'image_cond': image_conditioning, + 'uncond': unconditional_conditioning, 'cond_scale': p.cfg_scale, 's_min_uncond': self.s_min_uncond } @@ -374,9 +374,9 @@ class KDiffusionSampler: self.last_latent = x samples = self.launch_sampling(steps, lambda: self.func(self.model_wrap_cfg, x, extra_args={ - 'cond': conditioning, - 'image_cond': image_conditioning, - 'uncond': unconditional_conditioning, + 'cond': conditioning, + 'image_cond': image_conditioning, + 'uncond': unconditional_conditioning, 'cond_scale': p.cfg_scale, 's_min_uncond': self.s_min_uncond }, disable=False, callback=self.callback_state, **extra_params_kwargs)) diff --git a/modules/sub_quadratic_attention.py b/modules/sub_quadratic_attention.py index cc38debd..497568eb 100644 --- a/modules/sub_quadratic_attention.py +++ b/modules/sub_quadratic_attention.py @@ -179,7 +179,7 @@ def efficient_dot_product_attention( chunk_idx, min(query_chunk_size, q_tokens) ) - + summarize_chunk: SummarizeChunk = partial(_summarize_chunk, scale=scale) summarize_chunk: SummarizeChunk = partial(checkpoint, summarize_chunk) if use_checkpoint else summarize_chunk compute_query_chunk_attn: ComputeQueryChunkAttn = partial( diff --git a/modules/textual_inversion/dataset.py b/modules/textual_inversion/dataset.py index 41610e03..b9621fc9 100644 --- a/modules/textual_inversion/dataset.py +++ b/modules/textual_inversion/dataset.py @@ -118,7 +118,7 @@ class PersonalizedBase(Dataset): weight = torch.ones(latent_sample.shape) else: weight = None - + if latent_sampling_method == "random": entry = DatasetEntry(filename=path, filename_text=filename_text, latent_dist=latent_dist, weight=weight) else: @@ -243,4 +243,4 @@ class BatchLoaderRandom(BatchLoader): return self def collate_wrapper_random(batch): - return BatchLoaderRandom(batch) \ No newline at end of file + return BatchLoaderRandom(batch) diff --git a/modules/textual_inversion/preprocess.py b/modules/textual_inversion/preprocess.py index d0cad09e..a009d8e8 100644 --- a/modules/textual_inversion/preprocess.py +++ b/modules/textual_inversion/preprocess.py @@ -125,7 +125,7 @@ def multicrop_pic(image: Image, mindim, maxdim, minarea, maxarea, objective, thr default=None ) return wh and center_crop(image, *wh) - + def preprocess_work(process_src, process_dst, process_width, process_height, preprocess_txt_action, process_keep_original_size, process_flip, process_split, process_caption, process_caption_deepbooru=False, split_threshold=0.5, overlap_ratio=0.2, process_focal_crop=False, process_focal_crop_face_weight=0.9, process_focal_crop_entropy_weight=0.3, process_focal_crop_edges_weight=0.5, process_focal_crop_debug=False, process_multicrop=None, process_multicrop_mindim=None, process_multicrop_maxdim=None, process_multicrop_minarea=None, process_multicrop_maxarea=None, process_multicrop_objective=None, process_multicrop_threshold=None): width = process_width diff --git a/modules/textual_inversion/textual_inversion.py b/modules/textual_inversion/textual_inversion.py index 9e1b2b9a..d489ed1e 100644 --- a/modules/textual_inversion/textual_inversion.py +++ b/modules/textual_inversion/textual_inversion.py @@ -323,16 +323,16 @@ def tensorboard_add(tensorboard_writer, loss, global_step, step, learn_rate, epo tensorboard_add_scaler(tensorboard_writer, f"Learn rate/train/epoch-{epoch_num}", learn_rate, step) def tensorboard_add_scaler(tensorboard_writer, tag, value, step): - tensorboard_writer.add_scalar(tag=tag, + tensorboard_writer.add_scalar(tag=tag, scalar_value=value, global_step=step) def tensorboard_add_image(tensorboard_writer, tag, pil_image, step): # Convert a pil image to a torch tensor img_tensor = torch.as_tensor(np.array(pil_image, copy=True)) - img_tensor = img_tensor.view(pil_image.size[1], pil_image.size[0], + img_tensor = img_tensor.view(pil_image.size[1], pil_image.size[0], len(pil_image.getbands())) img_tensor = img_tensor.permute((2, 0, 1)) - + tensorboard_writer.add_image(tag, img_tensor, global_step=step) def validate_train_inputs(model_name, learn_rate, batch_size, gradient_step, data_root, template_file, template_filename, steps, save_model_every, create_image_every, log_directory, name="embedding"): @@ -402,7 +402,7 @@ def train_embedding(id_task, embedding_name, learn_rate, batch_size, gradient_st if initial_step >= steps: shared.state.textinfo = "Model has already been trained beyond specified max steps" return embedding, filename - + scheduler = LearnRateScheduler(learn_rate, steps, initial_step) clip_grad = torch.nn.utils.clip_grad_value_ if clip_grad_mode == "value" else \ torch.nn.utils.clip_grad_norm_ if clip_grad_mode == "norm" else \ @@ -412,7 +412,7 @@ def train_embedding(id_task, embedding_name, learn_rate, batch_size, gradient_st # dataset loading may take a while, so input validations and early returns should be done before this shared.state.textinfo = f"Preparing dataset from {html.escape(data_root)}..." old_parallel_processing_allowed = shared.parallel_processing_allowed - + if shared.opts.training_enable_tensorboard: tensorboard_writer = tensorboard_setup(log_directory) @@ -439,7 +439,7 @@ def train_embedding(id_task, embedding_name, learn_rate, batch_size, gradient_st optimizer_saved_dict = torch.load(f"{filename}.optim", map_location='cpu') if embedding.checksum() == optimizer_saved_dict.get('hash', None): optimizer_state_dict = optimizer_saved_dict.get('optimizer_state_dict', None) - + if optimizer_state_dict is not None: optimizer.load_state_dict(optimizer_state_dict) print("Loaded existing optimizer from checkpoint") @@ -485,7 +485,7 @@ def train_embedding(id_task, embedding_name, learn_rate, batch_size, gradient_st if clip_grad: clip_grad_sched.step(embedding.step) - + with devices.autocast(): x = batch.latent_sample.to(devices.device, non_blocking=pin_memory) if use_weight: @@ -513,7 +513,7 @@ def train_embedding(id_task, embedding_name, learn_rate, batch_size, gradient_st # go back until we reach gradient accumulation steps if (j + 1) % gradient_step != 0: continue - + if clip_grad: clip_grad(embedding.vec, clip_grad_sched.learn_rate) diff --git a/modules/ui.py b/modules/ui.py index 1efb656a..ff82fff6 100644 --- a/modules/ui.py +++ b/modules/ui.py @@ -1171,7 +1171,7 @@ def create_ui(): process_focal_crop_entropy_weight = gr.Slider(label='Focal point entropy weight', value=0.15, minimum=0.0, maximum=1.0, step=0.05, elem_id="train_process_focal_crop_entropy_weight") process_focal_crop_edges_weight = gr.Slider(label='Focal point edges weight', value=0.5, minimum=0.0, maximum=1.0, step=0.05, elem_id="train_process_focal_crop_edges_weight") process_focal_crop_debug = gr.Checkbox(label='Create debug image', elem_id="train_process_focal_crop_debug") - + with gr.Column(visible=False) as process_multicrop_col: gr.Markdown('Each image is center-cropped with an automatically chosen width and height.') with gr.Row(): @@ -1183,7 +1183,7 @@ def create_ui(): with gr.Row(): process_multicrop_objective = gr.Radio(["Maximize area", "Minimize error"], value="Maximize area", label="Resizing objective", elem_id="train_process_multicrop_objective") process_multicrop_threshold = gr.Slider(minimum=0, maximum=1, step=0.01, label="Error threshold", value=0.1, elem_id="train_process_multicrop_threshold") - + with gr.Row(): with gr.Column(scale=3): gr.HTML(value="") @@ -1226,7 +1226,7 @@ def create_ui(): with FormRow(): embedding_learn_rate = gr.Textbox(label='Embedding Learning rate', placeholder="Embedding Learning rate", value="0.005", elem_id="train_embedding_learn_rate") hypernetwork_learn_rate = gr.Textbox(label='Hypernetwork Learning rate', placeholder="Hypernetwork Learning rate", value="0.00001", elem_id="train_hypernetwork_learn_rate") - + with FormRow(): clip_grad_mode = gr.Dropdown(value="disabled", label="Gradient Clipping", choices=["disabled", "value", "norm"]) clip_grad_value = gr.Textbox(placeholder="Gradient clip value", value="0.1", show_label=False) @@ -1565,7 +1565,7 @@ def create_ui(): gr.HTML(shared.html("licenses.html"), elem_id="licenses") gr.Button(value="Show all pages", elem_id="settings_show_all_pages") - + def unload_sd_weights(): modules.sd_models.unload_model_weights() @@ -1841,15 +1841,15 @@ def versions_html(): return f""" version: {tag} - •  + • python: {python_version} - •  + • torch: {getattr(torch, '__long_version__',torch.__version__)} - •  + • xformers: {xformers_version} - •  + • gradio: {gr.__version__} - •  + • checkpoint: N/A """ diff --git a/modules/ui_extensions.py b/modules/ui_extensions.py index ed70abe5..af497733 100644 --- a/modules/ui_extensions.py +++ b/modules/ui_extensions.py @@ -467,7 +467,7 @@ def refresh_available_extensions_from_data(hide_tags, sort_column, filter_text=" {html.escape(description)}

Added: {html.escape(added)}

{install_code} - + """ for tag in [x for x in extension_tags if x not in tags]: @@ -535,9 +535,9 @@ def create_ui(): hide_tags = gr.CheckboxGroup(value=["ads", "localization", "installed"], label="Hide extensions with tags", choices=["script", "ads", "localization", "installed"]) sort_column = gr.Radio(value="newest first", label="Order", choices=["newest first", "oldest first", "a-z", "z-a", "internal order", ], type="index") - with gr.Row(): + with gr.Row(): search_extensions_text = gr.Text(label="Search").style(container=False) - + install_result = gr.HTML() available_extensions_table = gr.HTML() diff --git a/modules/xlmr.py b/modules/xlmr.py index e056c3f6..a407a3ca 100644 --- a/modules/xlmr.py +++ b/modules/xlmr.py @@ -28,7 +28,7 @@ class BertSeriesModelWithTransformation(BertPreTrainedModel): config_class = BertSeriesConfig def __init__(self, config=None, **kargs): - # modify initialization for autoloading + # modify initialization for autoloading if config is None: config = XLMRobertaConfig() config.attention_probs_dropout_prob= 0.1 @@ -74,7 +74,7 @@ class BertSeriesModelWithTransformation(BertPreTrainedModel): text["attention_mask"] = torch.tensor( text['attention_mask']).to(device) features = self(**text) - return features['projection_state'] + return features['projection_state'] def forward( self, @@ -134,4 +134,4 @@ class BertSeriesModelWithTransformation(BertPreTrainedModel): class RobertaSeriesModelWithTransformation(BertSeriesModelWithTransformation): base_model_prefix = 'roberta' - config_class= RobertaSeriesConfig \ No newline at end of file + config_class= RobertaSeriesConfig diff --git a/pyproject.toml b/pyproject.toml index c88907be..d4a1bbf4 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -6,6 +6,7 @@ extend-select = [ "B", "C", "I", + "W", ] exclude = [ @@ -20,7 +21,7 @@ ignore = [ "I001", # Import block is un-sorted or un-formatted "C901", # Function is too complex "C408", # Rewrite as a literal - + "W605", # invalid escape sequence, messes with some docstrings ] [tool.ruff.per-file-ignores] @@ -28,4 +29,4 @@ ignore = [ [tool.ruff.flake8-bugbear] # Allow default arguments like, e.g., `data: List[str] = fastapi.Query(None)`. -extend-immutable-calls = ["fastapi.Depends", "fastapi.security.HTTPBasic"] \ No newline at end of file +extend-immutable-calls = ["fastapi.Depends", "fastapi.security.HTTPBasic"] diff --git a/scripts/img2imgalt.py b/scripts/img2imgalt.py index bb00fb3f..1e833fa8 100644 --- a/scripts/img2imgalt.py +++ b/scripts/img2imgalt.py @@ -149,9 +149,9 @@ class Script(scripts.Script): sigma_adjustment = gr.Checkbox(label="Sigma adjustment for finding noise for image", value=False, elem_id=self.elem_id("sigma_adjustment")) return [ - info, + info, override_sampler, - override_prompt, original_prompt, original_negative_prompt, + override_prompt, original_prompt, original_negative_prompt, override_steps, st, override_strength, cfg, randomness, sigma_adjustment, @@ -191,17 +191,17 @@ class Script(scripts.Script): self.cache = Cached(rec_noise, cfg, st, lat, original_prompt, original_negative_prompt, sigma_adjustment) rand_noise = processing.create_random_tensors(p.init_latent.shape[1:], seeds=seeds, subseeds=subseeds, subseed_strength=p.subseed_strength, seed_resize_from_h=p.seed_resize_from_h, seed_resize_from_w=p.seed_resize_from_w, p=p) - + combined_noise = ((1 - randomness) * rec_noise + randomness * rand_noise) / ((randomness**2 + (1-randomness)**2) ** 0.5) - + sampler = sd_samplers.create_sampler(p.sampler_name, p.sd_model) sigmas = sampler.model_wrap.get_sigmas(p.steps) - + noise_dt = combined_noise - (p.init_latent / sigmas[0]) - + p.seed = p.seed + 1 - + return sampler.sample_img2img(p, p.init_latent, noise_dt, conditioning, unconditional_conditioning, image_conditioning=p.image_conditioning) p.sample = sample_extra diff --git a/scripts/loopback.py b/scripts/loopback.py index ad6609be..2d5feaf9 100644 --- a/scripts/loopback.py +++ b/scripts/loopback.py @@ -14,7 +14,7 @@ class Script(scripts.Script): def show(self, is_img2img): return is_img2img - def ui(self, is_img2img): + def ui(self, is_img2img): loops = gr.Slider(minimum=1, maximum=32, step=1, label='Loops', value=4, elem_id=self.elem_id("loops")) final_denoising_strength = gr.Slider(minimum=0, maximum=1, step=0.01, label='Final denoising strength', value=0.5, elem_id=self.elem_id("final_denoising_strength")) denoising_curve = gr.Dropdown(label="Denoising strength curve", choices=["Aggressive", "Linear", "Lazy"], value="Linear") @@ -104,7 +104,7 @@ class Script(scripts.Script): p.seed = processed.seed + 1 p.denoising_strength = calculate_denoising_strength(i + 1) - + if state.skipped: break @@ -121,7 +121,7 @@ class Script(scripts.Script): all_images.append(last_image) p.inpainting_fill = original_inpainting_fill - + if state.interrupted: break @@ -132,7 +132,7 @@ class Script(scripts.Script): if opts.return_grid: grids.append(grid) - + all_images = grids + all_images processed = Processed(p, all_images, initial_seed, initial_info) diff --git a/scripts/poor_mans_outpainting.py b/scripts/poor_mans_outpainting.py index c0bbecc1..ea0632b6 100644 --- a/scripts/poor_mans_outpainting.py +++ b/scripts/poor_mans_outpainting.py @@ -19,7 +19,7 @@ class Script(scripts.Script): def ui(self, is_img2img): if not is_img2img: return None - + pixels = gr.Slider(label="Pixels to expand", minimum=8, maximum=256, step=8, value=128, elem_id=self.elem_id("pixels")) mask_blur = gr.Slider(label='Mask blur', minimum=0, maximum=64, step=1, value=4, elem_id=self.elem_id("mask_blur")) inpainting_fill = gr.Radio(label='Masked content', choices=['fill', 'original', 'latent noise', 'latent nothing'], value='fill', type="index", elem_id=self.elem_id("inpainting_fill")) diff --git a/scripts/prompt_matrix.py b/scripts/prompt_matrix.py index fb06beab..88324fe6 100644 --- a/scripts/prompt_matrix.py +++ b/scripts/prompt_matrix.py @@ -96,7 +96,7 @@ class Script(scripts.Script): p.prompt_for_display = positive_prompt processed = process_images(p) - grid = images.image_grid(processed.images, p.batch_size, rows=1 << ((len(prompt_matrix_parts) - 1) // 2)) + grid = images.image_grid(processed.images, p.batch_size, rows=1 << ((len(prompt_matrix_parts) - 1) // 2)) grid = images.draw_prompt_matrix(grid, processed.images[0].width, processed.images[0].height, prompt_matrix_parts, margin_size) processed.images.insert(0, grid) processed.index_of_first_image = 1 diff --git a/scripts/prompts_from_file.py b/scripts/prompts_from_file.py index 9607077a..2378816f 100644 --- a/scripts/prompts_from_file.py +++ b/scripts/prompts_from_file.py @@ -109,7 +109,7 @@ class Script(scripts.Script): def title(self): return "Prompts from file or textbox" - def ui(self, is_img2img): + def ui(self, is_img2img): checkbox_iterate = gr.Checkbox(label="Iterate seed every line", value=False, elem_id=self.elem_id("checkbox_iterate")) checkbox_iterate_batch = gr.Checkbox(label="Use same random seed for all lines", value=False, elem_id=self.elem_id("checkbox_iterate_batch")) @@ -166,7 +166,7 @@ class Script(scripts.Script): proc = process_images(copy_p) images += proc.images - + if checkbox_iterate: p.seed = p.seed + (p.batch_size * p.n_iter) all_prompts += proc.all_prompts diff --git a/scripts/sd_upscale.py b/scripts/sd_upscale.py index 0b1d3096..e614c23b 100644 --- a/scripts/sd_upscale.py +++ b/scripts/sd_upscale.py @@ -16,7 +16,7 @@ class Script(scripts.Script): def show(self, is_img2img): return is_img2img - def ui(self, is_img2img): + def ui(self, is_img2img): info = gr.HTML("

Will upscale the image by the selected scale factor; use width and height sliders to set tile size

") overlap = gr.Slider(minimum=0, maximum=256, step=16, label='Tile overlap', value=64, elem_id=self.elem_id("overlap")) scale_factor = gr.Slider(minimum=1.0, maximum=4.0, step=0.05, label='Scale Factor', value=2.0, elem_id=self.elem_id("scale_factor")) -- cgit v1.2.3 From 3078001439d25b66ef5627c9e3d431aa23bbed73 Mon Sep 17 00:00:00 2001 From: catboxanon <122327233+catboxanon@users.noreply.github.com> Date: Sun, 14 May 2023 01:49:41 +0000 Subject: Add/modify CFG callbacks Required by self-attn guidance extension https://github.com/ashen-sensored/sd_webui_SAG --- modules/script_callbacks.py | 35 +++++++++++++++++++++++++++++++++++ modules/sd_samplers_kdiffusion.py | 8 +++++++- 2 files changed, 42 insertions(+), 1 deletion(-) (limited to 'modules/sd_samplers_kdiffusion.py') diff --git a/modules/script_callbacks.py b/modules/script_callbacks.py index 7d9dd736..e83c6ecf 100644 --- a/modules/script_callbacks.py +++ b/modules/script_callbacks.py @@ -53,6 +53,21 @@ class CFGDenoiserParams: class CFGDenoisedParams: + def __init__(self, x, sampling_step, total_sampling_steps, inner_model): + self.x = x + """Latent image representation in the process of being denoised""" + + self.sampling_step = sampling_step + """Current Sampling step number""" + + self.total_sampling_steps = total_sampling_steps + """Total number of sampling steps planned""" + + self.inner_model = inner_model + """Inner model reference that is being used for denoising""" + + +class AfterCFGCallbackParams: def __init__(self, x, sampling_step, total_sampling_steps): self.x = x """Latent image representation in the process of being denoised""" @@ -63,6 +78,9 @@ class CFGDenoisedParams: self.total_sampling_steps = total_sampling_steps """Total number of sampling steps planned""" + self.output_altered = False + """A flag for CFGDenoiser that indicates whether the output has been altered by the callback""" + class UiTrainTabParams: def __init__(self, txt2img_preview_params): @@ -87,6 +105,7 @@ callback_map = dict( callbacks_image_saved=[], callbacks_cfg_denoiser=[], callbacks_cfg_denoised=[], + callbacks_cfg_after_cfg=[], callbacks_before_component=[], callbacks_after_component=[], callbacks_image_grid=[], @@ -186,6 +205,14 @@ def cfg_denoised_callback(params: CFGDenoisedParams): report_exception(c, 'cfg_denoised_callback') +def cfg_after_cfg_callback(params: AfterCFGCallbackParams): + for c in callback_map['callbacks_cfg_after_cfg']: + try: + c.callback(params) + except Exception: + report_exception(c, 'cfg_after_cfg_callback') + + def before_component_callback(component, **kwargs): for c in callback_map['callbacks_before_component']: try: @@ -332,6 +359,14 @@ def on_cfg_denoised(callback): add_callback(callback_map['callbacks_cfg_denoised'], callback) +def on_cfg_after_cfg(callback): + """register a function to be called in the kdiffussion cfg_denoiser method after cfg calculations has completed. + The callback is called with one argument: + - params: CFGDenoisedParams - parameters to be passed to the inner model and sampling state details. + """ + add_callback(callback_map['callbacks_cfg_after_cfg'], callback) + + def on_before_component(callback): """register a function to be called before a component is created. The callback is called with arguments: diff --git a/modules/sd_samplers_kdiffusion.py b/modules/sd_samplers_kdiffusion.py index e9e41818..55f0d3a3 100644 --- a/modules/sd_samplers_kdiffusion.py +++ b/modules/sd_samplers_kdiffusion.py @@ -8,6 +8,7 @@ from modules.shared import opts, state import modules.shared as shared from modules.script_callbacks import CFGDenoiserParams, cfg_denoiser_callback from modules.script_callbacks import CFGDenoisedParams, cfg_denoised_callback +from modules.script_callbacks import AfterCFGCallbackParams, cfg_after_cfg_callback samplers_k_diffusion = [ ('Euler a', 'sample_euler_ancestral', ['k_euler_a', 'k_euler_ancestral'], {}), @@ -160,7 +161,7 @@ class CFGDenoiser(torch.nn.Module): fake_uncond = torch.cat([x_out[i:i+1] for i in denoised_image_indexes]) x_out = torch.cat([x_out, fake_uncond]) # we skipped uncond denoising, so we put cond-denoised image to where the uncond-denoised image should be - denoised_params = CFGDenoisedParams(x_out, state.sampling_step, state.sampling_steps) + denoised_params = CFGDenoisedParams(x_out, state.sampling_step, state.sampling_steps, self.inner_model) cfg_denoised_callback(denoised_params) devices.test_for_nans(x_out, "unet") @@ -180,6 +181,11 @@ class CFGDenoiser(torch.nn.Module): if self.mask is not None: denoised = self.init_latent * self.mask + self.nmask * denoised + after_cfg_callback_params = AfterCFGCallbackParams(denoised, state.sampling_step, state.sampling_steps) + cfg_after_cfg_callback(after_cfg_callback_params) + if after_cfg_callback_params.output_altered: + denoised = after_cfg_callback_params.x + self.step += 1 return denoised -- cgit v1.2.3 From 005849331e82cded96f6f3e5ff828037c672c38d Mon Sep 17 00:00:00 2001 From: AUTOMATIC <16777216c@gmail.com> Date: Sun, 14 May 2023 08:15:22 +0300 Subject: remove output_altered flag from AfterCFGCallbackParams --- modules/script_callbacks.py | 3 --- modules/sd_samplers_kdiffusion.py | 3 +-- 2 files changed, 1 insertion(+), 5 deletions(-) (limited to 'modules/sd_samplers_kdiffusion.py') diff --git a/modules/script_callbacks.py b/modules/script_callbacks.py index 57dfd457..3c21a362 100644 --- a/modules/script_callbacks.py +++ b/modules/script_callbacks.py @@ -78,9 +78,6 @@ class AfterCFGCallbackParams: self.total_sampling_steps = total_sampling_steps """Total number of sampling steps planned""" - self.output_altered = False - """A flag for CFGDenoiser indicating whether the output has been altered by the callback""" - class UiTrainTabParams: def __init__(self, txt2img_preview_params): diff --git a/modules/sd_samplers_kdiffusion.py b/modules/sd_samplers_kdiffusion.py index 55f0d3a3..61f23ad7 100644 --- a/modules/sd_samplers_kdiffusion.py +++ b/modules/sd_samplers_kdiffusion.py @@ -183,8 +183,7 @@ class CFGDenoiser(torch.nn.Module): after_cfg_callback_params = AfterCFGCallbackParams(denoised, state.sampling_step, state.sampling_steps) cfg_after_cfg_callback(after_cfg_callback_params) - if after_cfg_callback_params.output_altered: - denoised = after_cfg_callback_params.x + denoised = after_cfg_callback_params.x self.step += 1 return denoised -- cgit v1.2.3 From cdac5ace1456ba779d5a0171ff8757f31955bfee Mon Sep 17 00:00:00 2001 From: AUTOMATIC <16777216c@gmail.com> Date: Tue, 16 May 2023 11:54:02 +0300 Subject: suppress ENSD infotext for samplers that don't use it --- modules/processing.py | 11 +++++++---- modules/sd_samplers.py | 8 +++++++- modules/sd_samplers_common.py | 21 ++++++++++++++++++++- modules/sd_samplers_compvis.py | 8 ++++++-- modules/sd_samplers_kdiffusion.py | 16 ++++++++-------- 5 files changed, 48 insertions(+), 16 deletions(-) (limited to 'modules/sd_samplers_kdiffusion.py') diff --git a/modules/processing.py b/modules/processing.py index 94fe2625..15806f78 100644 --- a/modules/processing.py +++ b/modules/processing.py @@ -13,7 +13,7 @@ from skimage import exposure from typing import Any, Dict, List import modules.sd_hijack -from modules import devices, prompt_parser, masking, sd_samplers, lowvram, generation_parameters_copypaste, extra_networks, sd_vae_approx, scripts +from modules import devices, prompt_parser, masking, sd_samplers, lowvram, generation_parameters_copypaste, extra_networks, sd_vae_approx, scripts, sd_samplers_common from modules.sd_hijack import model_hijack from modules.shared import opts, cmd_opts, state import modules.shared as shared @@ -480,6 +480,10 @@ def create_infotext(p, all_prompts, all_seeds, all_subseeds, comments=None, iter clip_skip = getattr(p, 'clip_skip', opts.CLIP_stop_at_last_layers) enable_hr = getattr(p, 'enable_hr', False) + uses_ensd = opts.eta_noise_seed_delta != 0 + if uses_ensd: + uses_ensd = sd_samplers_common.is_sampler_using_eta_noise_seed_delta(p) + generation_params = { "Steps": p.steps, "Sampler": p.sampler_name, @@ -496,17 +500,16 @@ def create_infotext(p, all_prompts, all_seeds, all_subseeds, comments=None, iter "Denoising strength": getattr(p, 'denoising_strength', None), "Conditional mask weight": getattr(p, "inpainting_mask_weight", shared.opts.inpainting_mask_weight) if p.is_using_inpainting_conditioning else None, "Clip skip": None if clip_skip <= 1 else clip_skip, - "ENSD": None if opts.eta_noise_seed_delta == 0 else opts.eta_noise_seed_delta, + "ENSD": opts.eta_noise_seed_delta if uses_ensd else None, "Token merging ratio": None if opts.token_merging_ratio == 0 else opts.token_merging_ratio, "Token merging ratio hr": None if not enable_hr or opts.token_merging_ratio_hr == 0 else opts.token_merging_ratio_hr, "Init image hash": getattr(p, 'init_img_hash', None), "RNG": opts.randn_source if opts.randn_source != "GPU" else None, "NGMS": None if p.s_min_uncond == 0 else p.s_min_uncond, + **p.extra_generation_params, "Version": program_version() if opts.add_version_to_infotext else None, } - generation_params.update(p.extra_generation_params) - generation_params_text = ", ".join([k if k == v else f'{k}: {generation_parameters_copypaste.quote(v)}' for k, v in generation_params.items() if v is not None]) negative_prompt_text = f"\nNegative prompt: {p.all_negative_prompts[index]}" if p.all_negative_prompts[index] else "" diff --git a/modules/sd_samplers.py b/modules/sd_samplers.py index 4f1bf21d..f22aad8f 100644 --- a/modules/sd_samplers.py +++ b/modules/sd_samplers.py @@ -14,12 +14,18 @@ samplers_for_img2img = [] samplers_map = {} -def create_sampler(name, model): +def find_sampler_config(name): if name is not None: config = all_samplers_map.get(name, None) else: config = all_samplers[0] + return config + + +def create_sampler(name, model): + config = find_sampler_config(name) + assert config is not None, f'bad sampler name: {name}' sampler = config.constructor(model) diff --git a/modules/sd_samplers_common.py b/modules/sd_samplers_common.py index bc074238..92880caf 100644 --- a/modules/sd_samplers_common.py +++ b/modules/sd_samplers_common.py @@ -2,7 +2,7 @@ from collections import namedtuple import numpy as np import torch from PIL import Image -from modules import devices, processing, images, sd_vae_approx +from modules import devices, processing, images, sd_vae_approx, sd_samplers from modules.shared import opts, state import modules.shared as shared @@ -58,6 +58,25 @@ def store_latent(decoded): shared.state.assign_current_image(sample_to_image(decoded)) +def is_sampler_using_eta_noise_seed_delta(p): + """returns whether sampler from config will use eta noise seed delta for image creation""" + + sampler_config = sd_samplers.find_sampler_config(p.sampler_name) + + eta = p.eta + + if eta is None and p.sampler is not None: + eta = p.sampler.eta + + if eta is None and sampler_config is not None: + eta = 0 if sampler_config.options.get("default_eta_is_0", False) else 1.0 + + if eta == 0: + return False + + return sampler_config.options.get("uses_ensd", False) + + class InterruptedException(BaseException): pass diff --git a/modules/sd_samplers_compvis.py b/modules/sd_samplers_compvis.py index b1ee3be7..bdae8b40 100644 --- a/modules/sd_samplers_compvis.py +++ b/modules/sd_samplers_compvis.py @@ -11,7 +11,7 @@ import modules.models.diffusion.uni_pc samplers_data_compvis = [ - sd_samplers_common.SamplerData('DDIM', lambda model: VanillaStableDiffusionSampler(ldm.models.diffusion.ddim.DDIMSampler, model), [], {}), + sd_samplers_common.SamplerData('DDIM', lambda model: VanillaStableDiffusionSampler(ldm.models.diffusion.ddim.DDIMSampler, model), [], {"default_eta_is_0": True, "uses_ensd": True}), sd_samplers_common.SamplerData('PLMS', lambda model: VanillaStableDiffusionSampler(ldm.models.diffusion.plms.PLMSSampler, model), [], {}), sd_samplers_common.SamplerData('UniPC', lambda model: VanillaStableDiffusionSampler(modules.models.diffusion.uni_pc.UniPCSampler, model), [], {}), ] @@ -134,7 +134,11 @@ class VanillaStableDiffusionSampler: self.update_step(x) def initialize(self, p): - self.eta = p.eta if p.eta is not None else shared.opts.eta_ddim + if self.is_ddim: + self.eta = p.eta if p.eta is not None else shared.opts.eta_ddim + else: + self.eta = 0.0 + if self.eta != 0.0: p.extra_generation_params["Eta DDIM"] = self.eta diff --git a/modules/sd_samplers_kdiffusion.py b/modules/sd_samplers_kdiffusion.py index 61f23ad7..5455561a 100644 --- a/modules/sd_samplers_kdiffusion.py +++ b/modules/sd_samplers_kdiffusion.py @@ -11,21 +11,21 @@ from modules.script_callbacks import CFGDenoisedParams, cfg_denoised_callback from modules.script_callbacks import AfterCFGCallbackParams, cfg_after_cfg_callback samplers_k_diffusion = [ - ('Euler a', 'sample_euler_ancestral', ['k_euler_a', 'k_euler_ancestral'], {}), + ('Euler a', 'sample_euler_ancestral', ['k_euler_a', 'k_euler_ancestral'], {"uses_ensd": True}), ('Euler', 'sample_euler', ['k_euler'], {}), ('LMS', 'sample_lms', ['k_lms'], {}), ('Heun', 'sample_heun', ['k_heun'], {}), ('DPM2', 'sample_dpm_2', ['k_dpm_2'], {'discard_next_to_last_sigma': True}), - ('DPM2 a', 'sample_dpm_2_ancestral', ['k_dpm_2_a'], {'discard_next_to_last_sigma': True}), - ('DPM++ 2S a', 'sample_dpmpp_2s_ancestral', ['k_dpmpp_2s_a'], {}), + ('DPM2 a', 'sample_dpm_2_ancestral', ['k_dpm_2_a'], {'discard_next_to_last_sigma': True, "uses_ensd": True}), + ('DPM++ 2S a', 'sample_dpmpp_2s_ancestral', ['k_dpmpp_2s_a'], {"uses_ensd": True}), ('DPM++ 2M', 'sample_dpmpp_2m', ['k_dpmpp_2m'], {}), ('DPM++ SDE', 'sample_dpmpp_sde', ['k_dpmpp_sde'], {}), - ('DPM fast', 'sample_dpm_fast', ['k_dpm_fast'], {}), - ('DPM adaptive', 'sample_dpm_adaptive', ['k_dpm_ad'], {}), + ('DPM fast', 'sample_dpm_fast', ['k_dpm_fast'], {"uses_ensd": True}), + ('DPM adaptive', 'sample_dpm_adaptive', ['k_dpm_ad'], {"uses_ensd": True}), ('LMS Karras', 'sample_lms', ['k_lms_ka'], {'scheduler': 'karras'}), - ('DPM2 Karras', 'sample_dpm_2', ['k_dpm_2_ka'], {'scheduler': 'karras', 'discard_next_to_last_sigma': True}), - ('DPM2 a Karras', 'sample_dpm_2_ancestral', ['k_dpm_2_a_ka'], {'scheduler': 'karras', 'discard_next_to_last_sigma': True}), - ('DPM++ 2S a Karras', 'sample_dpmpp_2s_ancestral', ['k_dpmpp_2s_a_ka'], {'scheduler': 'karras'}), + ('DPM2 Karras', 'sample_dpm_2', ['k_dpm_2_ka'], {'scheduler': 'karras', 'discard_next_to_last_sigma': True, "uses_ensd": True}), + ('DPM2 a Karras', 'sample_dpm_2_ancestral', ['k_dpm_2_a_ka'], {'scheduler': 'karras', 'discard_next_to_last_sigma': True, "uses_ensd": True}), + ('DPM++ 2S a Karras', 'sample_dpmpp_2s_ancestral', ['k_dpmpp_2s_a_ka'], {'scheduler': 'karras', "uses_ensd": True}), ('DPM++ 2M Karras', 'sample_dpmpp_2m', ['k_dpmpp_2m_ka'], {'scheduler': 'karras'}), ('DPM++ SDE Karras', 'sample_dpmpp_sde', ['k_dpmpp_sde_ka'], {'scheduler': 'karras'}), ] -- cgit v1.2.3 From a61cbef02c7652f96d333b28e01f5230e225224e Mon Sep 17 00:00:00 2001 From: AUTOMATIC <16777216c@gmail.com> Date: Tue, 16 May 2023 12:36:15 +0300 Subject: add second_order field to sampler config --- modules/processing.py | 8 ++------ modules/sd_samplers_kdiffusion.py | 14 +++++++------- 2 files changed, 9 insertions(+), 13 deletions(-) (limited to 'modules/sd_samplers_kdiffusion.py') diff --git a/modules/processing.py b/modules/processing.py index 15806f78..678c4468 100644 --- a/modules/processing.py +++ b/modules/processing.py @@ -681,12 +681,8 @@ def process_images_inner(p: StableDiffusionProcessing) -> Processed: processed = Processed(p, [], p.seed, "") file.write(processed.infotext(p, 0)) - step_multiplier = 1 - if not shared.opts.dont_fix_second_order_samplers_schedule: - try: - step_multiplier = 2 if sd_samplers.all_samplers_map.get(p.sampler_name).aliases[0] in ['k_dpmpp_2s_a', 'k_dpmpp_2s_a_ka', 'k_dpmpp_sde', 'k_dpmpp_sde_ka', 'k_dpm_2', 'k_dpm_2_a', 'k_heun'] else 1 - except Exception: - pass + sampler_config = sd_samplers.find_sampler_config(p.sampler_name) + step_multiplier = 2 if sampler_config and sampler_config.options.get("second_order", False) else 1 uc = get_conds_with_caching(prompt_parser.get_learned_conditioning, negative_prompts, p.steps * step_multiplier, cached_uc) c = get_conds_with_caching(prompt_parser.get_multicond_learned_conditioning, prompts, p.steps * step_multiplier, cached_c) diff --git a/modules/sd_samplers_kdiffusion.py b/modules/sd_samplers_kdiffusion.py index 5455561a..552c6c64 100644 --- a/modules/sd_samplers_kdiffusion.py +++ b/modules/sd_samplers_kdiffusion.py @@ -14,20 +14,20 @@ samplers_k_diffusion = [ ('Euler a', 'sample_euler_ancestral', ['k_euler_a', 'k_euler_ancestral'], {"uses_ensd": True}), ('Euler', 'sample_euler', ['k_euler'], {}), ('LMS', 'sample_lms', ['k_lms'], {}), - ('Heun', 'sample_heun', ['k_heun'], {}), + ('Heun', 'sample_heun', ['k_heun'], {"second_order": True}), ('DPM2', 'sample_dpm_2', ['k_dpm_2'], {'discard_next_to_last_sigma': True}), ('DPM2 a', 'sample_dpm_2_ancestral', ['k_dpm_2_a'], {'discard_next_to_last_sigma': True, "uses_ensd": True}), - ('DPM++ 2S a', 'sample_dpmpp_2s_ancestral', ['k_dpmpp_2s_a'], {"uses_ensd": True}), + ('DPM++ 2S a', 'sample_dpmpp_2s_ancestral', ['k_dpmpp_2s_a'], {"uses_ensd": True, "second_order": True}), ('DPM++ 2M', 'sample_dpmpp_2m', ['k_dpmpp_2m'], {}), - ('DPM++ SDE', 'sample_dpmpp_sde', ['k_dpmpp_sde'], {}), + ('DPM++ SDE', 'sample_dpmpp_sde', ['k_dpmpp_sde'], {"second_order": True}), ('DPM fast', 'sample_dpm_fast', ['k_dpm_fast'], {"uses_ensd": True}), ('DPM adaptive', 'sample_dpm_adaptive', ['k_dpm_ad'], {"uses_ensd": True}), ('LMS Karras', 'sample_lms', ['k_lms_ka'], {'scheduler': 'karras'}), - ('DPM2 Karras', 'sample_dpm_2', ['k_dpm_2_ka'], {'scheduler': 'karras', 'discard_next_to_last_sigma': True, "uses_ensd": True}), - ('DPM2 a Karras', 'sample_dpm_2_ancestral', ['k_dpm_2_a_ka'], {'scheduler': 'karras', 'discard_next_to_last_sigma': True, "uses_ensd": True}), - ('DPM++ 2S a Karras', 'sample_dpmpp_2s_ancestral', ['k_dpmpp_2s_a_ka'], {'scheduler': 'karras', "uses_ensd": True}), + ('DPM2 Karras', 'sample_dpm_2', ['k_dpm_2_ka'], {'scheduler': 'karras', 'discard_next_to_last_sigma': True, "uses_ensd": True, "second_order": True}), + ('DPM2 a Karras', 'sample_dpm_2_ancestral', ['k_dpm_2_a_ka'], {'scheduler': 'karras', 'discard_next_to_last_sigma': True, "uses_ensd": True, "second_order": True}), + ('DPM++ 2S a Karras', 'sample_dpmpp_2s_ancestral', ['k_dpmpp_2s_a_ka'], {'scheduler': 'karras', "uses_ensd": True, "second_order": True}), ('DPM++ 2M Karras', 'sample_dpmpp_2m', ['k_dpmpp_2m_ka'], {'scheduler': 'karras'}), - ('DPM++ SDE Karras', 'sample_dpmpp_sde', ['k_dpmpp_sde_ka'], {'scheduler': 'karras'}), + ('DPM++ SDE Karras', 'sample_dpmpp_sde', ['k_dpmpp_sde_ka'], {'scheduler': 'karras', "second_order": True}), ] samplers_data_k_diffusion = [ -- cgit v1.2.3