288 lines
11 KiB
Python
288 lines
11 KiB
Python
import torch
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from torch.nn.functional import silu
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from types import MethodType
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import modules.textual_inversion.textual_inversion
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from modules import devices, sd_hijack_optimizations, shared, script_callbacks, errors
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from modules.hypernetworks import hypernetwork
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from modules.shared import cmd_opts
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from modules import sd_hijack_clip, sd_hijack_open_clip, sd_hijack_unet, sd_hijack_xlmr, xlmr
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import ldm.modules.attention
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import ldm.modules.diffusionmodules.model
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import ldm.modules.diffusionmodules.openaimodel
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import ldm.models.diffusion.ddim
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import ldm.models.diffusion.plms
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import ldm.modules.encoders.modules
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attention_CrossAttention_forward = ldm.modules.attention.CrossAttention.forward
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diffusionmodules_model_nonlinearity = ldm.modules.diffusionmodules.model.nonlinearity
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diffusionmodules_model_AttnBlock_forward = ldm.modules.diffusionmodules.model.AttnBlock.forward
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# new memory efficient cross attention blocks do not support hypernets and we already
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# have memory efficient cross attention anyway, so this disables SD2.0's memory efficient cross attention
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ldm.modules.attention.MemoryEfficientCrossAttention = ldm.modules.attention.CrossAttention
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ldm.modules.attention.BasicTransformerBlock.ATTENTION_MODES["softmax-xformers"] = ldm.modules.attention.CrossAttention
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# silence new console spam from SD2
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ldm.modules.attention.print = lambda *args: None
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ldm.modules.diffusionmodules.model.print = lambda *args: None
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optimizers = []
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current_optimizer: sd_hijack_optimizations.SdOptimization = None
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def list_optimizers():
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new_optimizers = script_callbacks.list_optimizers_callback()
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new_optimizers = [x for x in new_optimizers if x.is_available()]
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new_optimizers = sorted(new_optimizers, key=lambda x: x.priority, reverse=True)
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optimizers.clear()
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optimizers.extend(new_optimizers)
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print(optimizers)
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def apply_optimizations():
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global current_optimizer
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undo_optimizations()
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ldm.modules.diffusionmodules.model.nonlinearity = silu
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ldm.modules.diffusionmodules.openaimodel.th = sd_hijack_unet.th
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if current_optimizer is not None:
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current_optimizer.undo()
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current_optimizer = None
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selection = shared.opts.cross_attention_optimization
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if selection == "Automatic" and len(optimizers) > 0:
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matching_optimizer = next(iter([x for x in optimizers if x.cmd_opt and getattr(shared.cmd_opts, x.cmd_opt, False)]), optimizers[0])
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else:
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matching_optimizer = next(iter([x for x in optimizers if x.title() == selection]), None)
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if selection == "None":
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matching_optimizer = None
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elif matching_optimizer is None:
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matching_optimizer = optimizers[0]
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if matching_optimizer is not None:
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print(f"Applying optimization: {matching_optimizer.name}")
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matching_optimizer.apply()
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current_optimizer = matching_optimizer
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return current_optimizer.name
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else:
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return ''
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def undo_optimizations():
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ldm.modules.diffusionmodules.model.nonlinearity = diffusionmodules_model_nonlinearity
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ldm.modules.attention.CrossAttention.forward = hypernetwork.attention_CrossAttention_forward
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ldm.modules.diffusionmodules.model.AttnBlock.forward = diffusionmodules_model_AttnBlock_forward
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def fix_checkpoint():
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"""checkpoints are now added and removed in embedding/hypernet code, since torch doesn't want
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checkpoints to be added when not training (there's a warning)"""
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pass
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def weighted_loss(sd_model, pred, target, mean=True):
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#Calculate the weight normally, but ignore the mean
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loss = sd_model._old_get_loss(pred, target, mean=False)
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#Check if we have weights available
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weight = getattr(sd_model, '_custom_loss_weight', None)
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if weight is not None:
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loss *= weight
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#Return the loss, as mean if specified
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return loss.mean() if mean else loss
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def weighted_forward(sd_model, x, c, w, *args, **kwargs):
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try:
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#Temporarily append weights to a place accessible during loss calc
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sd_model._custom_loss_weight = w
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#Replace 'get_loss' with a weight-aware one. Otherwise we need to reimplement 'forward' completely
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#Keep 'get_loss', but don't overwrite the previous old_get_loss if it's already set
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if not hasattr(sd_model, '_old_get_loss'):
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sd_model._old_get_loss = sd_model.get_loss
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sd_model.get_loss = MethodType(weighted_loss, sd_model)
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#Run the standard forward function, but with the patched 'get_loss'
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return sd_model.forward(x, c, *args, **kwargs)
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finally:
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try:
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#Delete temporary weights if appended
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del sd_model._custom_loss_weight
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except AttributeError:
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pass
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#If we have an old loss function, reset the loss function to the original one
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if hasattr(sd_model, '_old_get_loss'):
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sd_model.get_loss = sd_model._old_get_loss
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del sd_model._old_get_loss
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def apply_weighted_forward(sd_model):
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#Add new function 'weighted_forward' that can be called to calc weighted loss
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sd_model.weighted_forward = MethodType(weighted_forward, sd_model)
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def undo_weighted_forward(sd_model):
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try:
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del sd_model.weighted_forward
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except AttributeError:
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pass
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class StableDiffusionModelHijack:
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fixes = None
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comments = []
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layers = None
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circular_enabled = False
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clip = None
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optimization_method = None
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embedding_db = modules.textual_inversion.textual_inversion.EmbeddingDatabase()
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def __init__(self):
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self.embedding_db.add_embedding_dir(cmd_opts.embeddings_dir)
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def hijack(self, m):
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if type(m.cond_stage_model) == xlmr.BertSeriesModelWithTransformation:
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model_embeddings = m.cond_stage_model.roberta.embeddings
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model_embeddings.token_embedding = EmbeddingsWithFixes(model_embeddings.word_embeddings, self)
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m.cond_stage_model = sd_hijack_xlmr.FrozenXLMREmbedderWithCustomWords(m.cond_stage_model, self)
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elif type(m.cond_stage_model) == ldm.modules.encoders.modules.FrozenCLIPEmbedder:
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model_embeddings = m.cond_stage_model.transformer.text_model.embeddings
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model_embeddings.token_embedding = EmbeddingsWithFixes(model_embeddings.token_embedding, self)
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m.cond_stage_model = sd_hijack_clip.FrozenCLIPEmbedderWithCustomWords(m.cond_stage_model, self)
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elif type(m.cond_stage_model) == ldm.modules.encoders.modules.FrozenOpenCLIPEmbedder:
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m.cond_stage_model.model.token_embedding = EmbeddingsWithFixes(m.cond_stage_model.model.token_embedding, self)
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m.cond_stage_model = sd_hijack_open_clip.FrozenOpenCLIPEmbedderWithCustomWords(m.cond_stage_model, self)
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apply_weighted_forward(m)
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if m.cond_stage_key == "edit":
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sd_hijack_unet.hijack_ddpm_edit()
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try:
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self.optimization_method = apply_optimizations()
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except Exception as e:
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errors.display(e, "applying cross attention optimization")
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undo_optimizations()
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self.clip = m.cond_stage_model
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def flatten(el):
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flattened = [flatten(children) for children in el.children()]
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res = [el]
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for c in flattened:
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res += c
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return res
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self.layers = flatten(m)
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def undo_hijack(self, m):
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if type(m.cond_stage_model) == xlmr.BertSeriesModelWithTransformation:
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m.cond_stage_model = m.cond_stage_model.wrapped
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elif type(m.cond_stage_model) == sd_hijack_clip.FrozenCLIPEmbedderWithCustomWords:
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m.cond_stage_model = m.cond_stage_model.wrapped
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model_embeddings = m.cond_stage_model.transformer.text_model.embeddings
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if type(model_embeddings.token_embedding) == EmbeddingsWithFixes:
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model_embeddings.token_embedding = model_embeddings.token_embedding.wrapped
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elif type(m.cond_stage_model) == sd_hijack_open_clip.FrozenOpenCLIPEmbedderWithCustomWords:
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m.cond_stage_model.wrapped.model.token_embedding = m.cond_stage_model.wrapped.model.token_embedding.wrapped
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m.cond_stage_model = m.cond_stage_model.wrapped
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undo_optimizations()
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undo_weighted_forward(m)
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self.apply_circular(False)
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self.layers = None
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self.clip = None
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def apply_circular(self, enable):
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if self.circular_enabled == enable:
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return
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self.circular_enabled = enable
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for layer in [layer for layer in self.layers if type(layer) == torch.nn.Conv2d]:
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layer.padding_mode = 'circular' if enable else 'zeros'
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def clear_comments(self):
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self.comments = []
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def get_prompt_lengths(self, text):
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if self.clip is None:
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return "-", "-"
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_, token_count = self.clip.process_texts([text])
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return token_count, self.clip.get_target_prompt_token_count(token_count)
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def redo_hijack(self, m):
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self.undo_hijack(m)
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self.hijack(m)
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class EmbeddingsWithFixes(torch.nn.Module):
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def __init__(self, wrapped, embeddings):
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super().__init__()
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self.wrapped = wrapped
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self.embeddings = embeddings
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def forward(self, input_ids):
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batch_fixes = self.embeddings.fixes
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self.embeddings.fixes = None
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inputs_embeds = self.wrapped(input_ids)
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if batch_fixes is None or len(batch_fixes) == 0 or max([len(x) for x in batch_fixes]) == 0:
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return inputs_embeds
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vecs = []
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for fixes, tensor in zip(batch_fixes, inputs_embeds):
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for offset, embedding in fixes:
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emb = devices.cond_cast_unet(embedding.vec)
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emb_len = min(tensor.shape[0] - offset - 1, emb.shape[0])
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tensor = torch.cat([tensor[0:offset + 1], emb[0:emb_len], tensor[offset + 1 + emb_len:]])
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vecs.append(tensor)
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return torch.stack(vecs)
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def add_circular_option_to_conv_2d():
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conv2d_constructor = torch.nn.Conv2d.__init__
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def conv2d_constructor_circular(self, *args, **kwargs):
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return conv2d_constructor(self, *args, padding_mode='circular', **kwargs)
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torch.nn.Conv2d.__init__ = conv2d_constructor_circular
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model_hijack = StableDiffusionModelHijack()
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def register_buffer(self, name, attr):
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"""
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Fix register buffer bug for Mac OS.
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"""
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if type(attr) == torch.Tensor:
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if attr.device != devices.device:
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attr = attr.to(device=devices.device, dtype=(torch.float32 if devices.device.type == 'mps' else None))
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setattr(self, name, attr)
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ldm.models.diffusion.ddim.DDIMSampler.register_buffer = register_buffer
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ldm.models.diffusion.plms.PLMSSampler.register_buffer = register_buffer
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