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Merge pull request #14170 from MrCheeze/sd-turbo

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Add support for SD 2.1 Turbo
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AUTOMATIC1111 2023-12-02 09:30:07 +03:00 committed by GitHub
commit 4125552752
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2 changed files with 19 additions and 7 deletions
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9
modules/sd_hijack.py
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@ -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

17
modules/sd_models.py
View File

@ -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