From 762265eab58cdb8f2d6398769bab43d8b8db0075 Mon Sep 17 00:00:00 2001 From: AUTOMATIC <16777216c@gmail.com> Date: Wed, 10 May 2023 07:52:45 +0300 Subject: autofixes from ruff --- modules/sd_hijack.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) (limited to 'modules/sd_hijack.py') diff --git a/modules/sd_hijack.py b/modules/sd_hijack.py index f4bb0266..d8135211 100644 --- a/modules/sd_hijack.py +++ b/modules/sd_hijack.py @@ -118,7 +118,7 @@ def weighted_forward(sd_model, x, c, w, *args, **kwargs): try: #Delete temporary weights if appended del sd_model._custom_loss_weight - except AttributeError as e: + except AttributeError: pass #If we have an old loss function, reset the loss function to the original one @@ -133,7 +133,7 @@ def apply_weighted_forward(sd_model): def undo_weighted_forward(sd_model): try: del sd_model.weighted_forward - except AttributeError as e: + except AttributeError: pass -- 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 --- modules/sd_hijack.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) (limited to 'modules/sd_hijack.py') 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 -- 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 --- modules/sd_hijack.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) (limited to 'modules/sd_hijack.py') 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.") -- 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) --- modules/sd_hijack.py | 12 ++++++------ 1 file changed, 6 insertions(+), 6 deletions(-) (limited to 'modules/sd_hijack.py') 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 -- cgit v1.2.3 From 1a43524018ea3e64b93be2abc2a49b6159515442 Mon Sep 17 00:00:00 2001 From: AUTOMATIC <16777216c@gmail.com> Date: Sun, 14 May 2023 13:27:50 +0300 Subject: fix model loading twice in some situations --- modules/sd_hijack.py | 3 +++ 1 file changed, 3 insertions(+) (limited to 'modules/sd_hijack.py') diff --git a/modules/sd_hijack.py b/modules/sd_hijack.py index 7e50f1ab..14e7f799 100644 --- a/modules/sd_hijack.py +++ b/modules/sd_hijack.py @@ -216,6 +216,9 @@ class StableDiffusionModelHijack: self.comments = [] def get_prompt_lengths(self, text): + if self.clip is None: + return "-", "-" + _, token_count = self.clip.process_texts([text]) return token_count, self.clip.get_target_prompt_token_count(token_count) -- cgit v1.2.3 From 2582a0fd3b3e91c5fba9e5e561cbdf5fee835063 Mon Sep 17 00:00:00 2001 From: AUTOMATIC <16777216c@gmail.com> Date: Thu, 18 May 2023 22:48:28 +0300 Subject: make it possible for scripts to add cross attention optimizations add UI selection for cross attention optimization --- modules/sd_hijack.py | 90 ++++++++++++++++++++++++++++------------------------ 1 file changed, 49 insertions(+), 41 deletions(-) (limited to 'modules/sd_hijack.py') diff --git a/modules/sd_hijack.py b/modules/sd_hijack.py index 14e7f799..39193be8 100644 --- a/modules/sd_hijack.py +++ b/modules/sd_hijack.py @@ -3,8 +3,9 @@ from torch.nn.functional import silu from types import MethodType import modules.textual_inversion.textual_inversion -from modules import devices, sd_hijack_optimizations, shared +from modules import devices, sd_hijack_optimizations, shared, script_callbacks, errors from modules.hypernetworks import hypernetwork +from modules.sd_hijack_optimizations import diffusionmodules_model_AttnBlock_forward from modules.shared import cmd_opts from modules import sd_hijack_clip, sd_hijack_open_clip, sd_hijack_unet, sd_hijack_xlmr, xlmr @@ -28,57 +29,56 @@ ldm.modules.attention.BasicTransformerBlock.ATTENTION_MODES["softmax-xformers"] ldm.modules.attention.print = lambda *args: None ldm.modules.diffusionmodules.model.print = lambda *args: None +optimizers = [] +current_optimizer: sd_hijack_optimizations.SdOptimization = None + + +def list_optimizers(): + new_optimizers = script_callbacks.list_optimizers_callback() + + new_optimizers = [x for x in new_optimizers if x.is_available()] + + new_optimizers = sorted(new_optimizers, key=lambda x: x.priority(), reverse=True) + + optimizers.clear() + optimizers.extend(new_optimizers) + def apply_optimizations(): + global current_optimizer + undo_optimizations() ldm.modules.diffusionmodules.model.nonlinearity = silu ldm.modules.diffusionmodules.openaimodel.th = sd_hijack_unet.th - optimization_method = None + if current_optimizer is not None: + current_optimizer.undo() + current_optimizer = None + + selection = shared.opts.cross_attention_optimization + if selection == "Automatic" and len(optimizers) > 0: + matching_optimizer = next(iter([x for x in optimizers if x.cmd_opt and getattr(shared.cmd_opts, x.cmd_opt, False)]), optimizers[0]) + else: + matching_optimizer = next(iter([x for x in optimizers if x.title() == selection]), 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 - - 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.") - ldm.modules.attention.CrossAttention.forward = sd_hijack_optimizations.xformers_attention_forward - ldm.modules.diffusionmodules.model.AttnBlock.forward = sd_hijack_optimizations.xformers_attnblock_forward - optimization_method = 'xformers' - elif cmd_opts.opt_sdp_no_mem_attention and can_use_sdp: - print("Applying scaled dot product cross attention optimization (without memory efficient attention).") - ldm.modules.attention.CrossAttention.forward = sd_hijack_optimizations.scaled_dot_product_no_mem_attention_forward - ldm.modules.diffusionmodules.model.AttnBlock.forward = sd_hijack_optimizations.sdp_no_mem_attnblock_forward - optimization_method = 'sdp-no-mem' - elif cmd_opts.opt_sdp_attention and can_use_sdp: - print("Applying scaled dot product cross attention optimization.") - ldm.modules.attention.CrossAttention.forward = sd_hijack_optimizations.scaled_dot_product_attention_forward - ldm.modules.diffusionmodules.model.AttnBlock.forward = sd_hijack_optimizations.sdp_attnblock_forward - optimization_method = 'sdp' - elif cmd_opts.opt_sub_quad_attention: - print("Applying sub-quadratic cross attention optimization.") - ldm.modules.attention.CrossAttention.forward = sd_hijack_optimizations.sub_quad_attention_forward - ldm.modules.diffusionmodules.model.AttnBlock.forward = sd_hijack_optimizations.sub_quad_attnblock_forward - optimization_method = 'sub-quadratic' - elif cmd_opts.opt_split_attention_v1: - print("Applying v1 cross attention optimization.") - ldm.modules.attention.CrossAttention.forward = sd_hijack_optimizations.split_cross_attention_forward_v1 - optimization_method = 'V1' - elif not cmd_opts.disable_opt_split_attention and (cmd_opts.opt_split_attention_invokeai or not cmd_opts.opt_split_attention and not torch.cuda.is_available()): - print("Applying cross attention optimization (InvokeAI).") - ldm.modules.attention.CrossAttention.forward = sd_hijack_optimizations.split_cross_attention_forward_invokeAI - optimization_method = 'InvokeAI' - elif not cmd_opts.disable_opt_split_attention and (cmd_opts.opt_split_attention or torch.cuda.is_available()): - print("Applying cross attention optimization (Doggettx).") - ldm.modules.attention.CrossAttention.forward = sd_hijack_optimizations.split_cross_attention_forward - ldm.modules.diffusionmodules.model.AttnBlock.forward = sd_hijack_optimizations.cross_attention_attnblock_forward - optimization_method = 'Doggettx' - - return optimization_method + if selection == "None": + matching_optimizer = None + elif matching_optimizer is None: + matching_optimizer = optimizers[0] + + if matching_optimizer is not None: + print(f"Applying optimization: {matching_optimizer.name}") + matching_optimizer.apply() + current_optimizer = matching_optimizer + return current_optimizer.name + else: + return '' def undo_optimizations(): - ldm.modules.attention.CrossAttention.forward = hypernetwork.attention_CrossAttention_forward ldm.modules.diffusionmodules.model.nonlinearity = diffusionmodules_model_nonlinearity + ldm.modules.attention.CrossAttention.forward = hypernetwork.attention_CrossAttention_forward ldm.modules.diffusionmodules.model.AttnBlock.forward = diffusionmodules_model_AttnBlock_forward @@ -169,7 +169,11 @@ class StableDiffusionModelHijack: if m.cond_stage_key == "edit": sd_hijack_unet.hijack_ddpm_edit() - self.optimization_method = apply_optimizations() + try: + self.optimization_method = apply_optimizations() + except Exception as e: + errors.display(e, "applying cross attention optimization") + undo_optimizations() self.clip = m.cond_stage_model @@ -223,6 +227,10 @@ class StableDiffusionModelHijack: return token_count, self.clip.get_target_prompt_token_count(token_count) + def redo_hijack(self, m): + self.undo_hijack(m) + self.hijack(m) + class EmbeddingsWithFixes(torch.nn.Module): def __init__(self, wrapped, embeddings): -- cgit v1.2.3 From 8a3d232839930376898634f65bd6c16f3a41e5b4 Mon Sep 17 00:00:00 2001 From: AUTOMATIC <16777216c@gmail.com> Date: Fri, 19 May 2023 00:03:27 +0300 Subject: fix linter issues --- modules/sd_hijack.py | 1 - 1 file changed, 1 deletion(-) (limited to 'modules/sd_hijack.py') diff --git a/modules/sd_hijack.py b/modules/sd_hijack.py index 39193be8..75f1c540 100644 --- a/modules/sd_hijack.py +++ b/modules/sd_hijack.py @@ -5,7 +5,6 @@ from types import MethodType import modules.textual_inversion.textual_inversion from modules import devices, sd_hijack_optimizations, shared, script_callbacks, errors from modules.hypernetworks import hypernetwork -from modules.sd_hijack_optimizations import diffusionmodules_model_AttnBlock_forward from modules.shared import cmd_opts from modules import sd_hijack_clip, sd_hijack_open_clip, sd_hijack_unet, sd_hijack_xlmr, xlmr -- cgit v1.2.3 From 2140bd1c108dd17bbf8601b10da7865ed1ac1607 Mon Sep 17 00:00:00 2001 From: AUTOMATIC <16777216c@gmail.com> Date: Fri, 19 May 2023 10:05:07 +0300 Subject: make it actually work after suggestions --- modules/sd_hijack.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) (limited to 'modules/sd_hijack.py') diff --git a/modules/sd_hijack.py b/modules/sd_hijack.py index 75f1c540..08d31080 100644 --- a/modules/sd_hijack.py +++ b/modules/sd_hijack.py @@ -37,7 +37,7 @@ def list_optimizers(): new_optimizers = [x for x in new_optimizers if x.is_available()] - new_optimizers = sorted(new_optimizers, key=lambda x: x.priority(), reverse=True) + new_optimizers = sorted(new_optimizers, key=lambda x: x.priority, reverse=True) optimizers.clear() optimizers.extend(new_optimizers) -- cgit v1.2.3 From a6e653be26cc05f4438145fa0082816e9fbbf5fc Mon Sep 17 00:00:00 2001 From: AUTOMATIC <16777216c@gmail.com> Date: Tue, 23 May 2023 18:02:09 +0300 Subject: possible fix for empty list of optimizations #10605 --- modules/sd_hijack.py | 21 +++++++++++++++------ 1 file changed, 15 insertions(+), 6 deletions(-) (limited to 'modules/sd_hijack.py') diff --git a/modules/sd_hijack.py b/modules/sd_hijack.py index 08d31080..f93df0a6 100644 --- a/modules/sd_hijack.py +++ b/modules/sd_hijack.py @@ -48,6 +48,11 @@ def apply_optimizations(): undo_optimizations() + if len(optimizers) == 0: + # a script can access the model very early, and optimizations would not be filled by then + current_optimizer = None + return '' + ldm.modules.diffusionmodules.model.nonlinearity = silu ldm.modules.diffusionmodules.openaimodel.th = sd_hijack_unet.th @@ -67,8 +72,9 @@ def apply_optimizations(): matching_optimizer = optimizers[0] if matching_optimizer is not None: - print(f"Applying optimization: {matching_optimizer.name}") + print(f"Applying optimization: {matching_optimizer.name}... ", end='') matching_optimizer.apply() + print("done.") current_optimizer = matching_optimizer return current_optimizer.name else: @@ -149,6 +155,13 @@ class StableDiffusionModelHijack: def __init__(self): self.embedding_db.add_embedding_dir(cmd_opts.embeddings_dir) + def apply_optimizations(self): + try: + self.optimization_method = apply_optimizations() + except Exception as e: + errors.display(e, "applying cross attention optimization") + undo_optimizations() + def hijack(self, m): if type(m.cond_stage_model) == xlmr.BertSeriesModelWithTransformation: model_embeddings = m.cond_stage_model.roberta.embeddings @@ -168,11 +181,7 @@ class StableDiffusionModelHijack: if m.cond_stage_key == "edit": sd_hijack_unet.hijack_ddpm_edit() - try: - self.optimization_method = apply_optimizations() - except Exception as e: - errors.display(e, "applying cross attention optimization") - undo_optimizations() + self.apply_optimizations() self.clip = m.cond_stage_model -- cgit v1.2.3