Merge pull request #14170 from MrCheeze/sd-turbo (41255527) · Commits · github_fork / Stable Diffusion Webui

modules/sd_hijack.py

+6 −3

Original line number	Diff line number	Diff line
		@@ -38,9 +38,6 @@ ldm.models.diffusion.ddpm.print = shared.ldm_print
		optimizers = []
		current_optimizer: sd_hijack_optimizations.SdOptimization = None

		ldm_original_forward = patches.patch(__file__, ldm.modules.diffusionmodules.openaimodel.UNetModel, "forward", sd_unet.UNetModel_forward)
		sgm_original_forward = patches.patch(__file__, sgm.modules.diffusionmodules.openaimodel.UNetModel, "forward", sd_unet.UNetModel_forward)

		def list_optimizers():
		new_optimizers = script_callbacks.list_optimizers_callback()

		@@ -258,6 +255,9 @@ class StableDiffusionModelHijack:

		import modules.models.diffusion.ddpm_edit

		ldm_original_forward = patches.patch(__file__, ldm.modules.diffusionmodules.openaimodel.UNetModel, "forward", sd_unet.UNetModel_forward)
		sgm_original_forward = patches.patch(__file__, sgm.modules.diffusionmodules.openaimodel.UNetModel, "forward", sd_unet.UNetModel_forward)

		if isinstance(m, ldm.models.diffusion.ddpm.LatentDiffusion):
		sd_unet.original_forward = ldm_original_forward
		elif isinstance(m, modules.models.diffusion.ddpm_edit.LatentDiffusion):
		@@ -303,6 +303,9 @@ class StableDiffusionModelHijack:
		self.layers = None
		self.clip = None

		patches.undo(__file__, ldm.modules.diffusionmodules.openaimodel.UNetModel, "forward")
		patches.undo(__file__, sgm.modules.diffusionmodules.openaimodel.UNetModel, "forward")

		sd_unet.original_forward = None

+13 −4

Original line number	Diff line number	Diff line
		@@ -230,15 +230,19 @@ def select_checkpoint():
		return checkpoint_info


		checkpoint_dict_replacements = {
		checkpoint_dict_replacements_sd1 = {
		'cond_stage_model.transformer.embeddings.': 'cond_stage_model.transformer.text_model.embeddings.',
		'cond_stage_model.transformer.encoder.': 'cond_stage_model.transformer.text_model.encoder.',
		'cond_stage_model.transformer.final_layer_norm.': 'cond_stage_model.transformer.text_model.final_layer_norm.',
		}

		checkpoint_dict_replacements_sd2_turbo = { # Converts SD 2.1 Turbo from SGM to LDM format.
		'conditioner.embedders.0.': 'cond_stage_model.',
		}


		def transform_checkpoint_dict_key(k):
		for text, replacement in checkpoint_dict_replacements.items():
		def transform_checkpoint_dict_key(k, replacements):
		for text, replacement in replacements.items():
		if k.startswith(text):
		k = replacement + k[len(text):]

		@@ -249,9 +253,14 @@ def get_state_dict_from_checkpoint(pl_sd):
		pl_sd = pl_sd.pop("state_dict", pl_sd)
		pl_sd.pop("state_dict", None)

		is_sd2_turbo = 'conditioner.embedders.0.model.ln_final.weight' in pl_sd and pl_sd['conditioner.embedders.0.model.ln_final.weight'].size()[0] == 1024

		sd = {}
		for k, v in pl_sd.items():
		new_key = transform_checkpoint_dict_key(k)
		if is_sd2_turbo:
		new_key = transform_checkpoint_dict_key(k, checkpoint_dict_replacements_sd2_turbo)
		else:
		new_key = transform_checkpoint_dict_key(k, checkpoint_dict_replacements_sd1)

		if new_key is not None:
		sd[new_key] = v