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update diffusers and extra networks

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This commit is contained in:
Vladimir Mandic
2023-07-04 09:28:48 -04:00
parent 145b990c73
commit b216a35ddd
11 changed files with 91 additions and 31 deletions
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modules/sd_models.py
+31 -6
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@@ -223,7 +223,8 @@ def select_checkpoint(model=True):
checkpoint_info = next(iter(checkpoints_list.values()))
if model_checkpoint is not None:
shared.log.warning(f"Selected checkpoint not found: {model_checkpoint}")
shared.log.warning(f"Loading fallback checkpoint: {checkpoint_info.title}")
# shared.log.warning(f"Loading fallback checkpoint: {checkpoint_info.title}")
shared.opts.data['sd_checkpoint'] = checkpoint_info.title
shared.log.debug(f'Select checkpoint: {checkpoint_info.title if checkpoint_info is not None else None}')
return checkpoint_info
@@ -579,15 +580,39 @@ def load_diffuser(checkpoint_info=None, already_loaded_state_dict=None, timer=No
prior = diffusers.DiffusionPipeline.from_pretrained(prior_id, **diffusers_load_config)
sd_model = PriorPipeline(prior=prior, main=sd_model) # wrap sd_model
if shared.cmd_opts.medvram:
sd_model.enable_model_cpu_offload()
if shared.cmd_opts.lowvram:
sd_model.enable_sequential_cpu_offload()
if hasattr(sd_model, "enable_sequential_cpu_offload"):
if shared.cmd_opts.lowvram or shared.opts.diffusers_seq_cpu_offload:
sd_model.enable_sequential_cpu_offload()
shared.log.debug('Diffusers: enable sequenctial CPU offload')
if hasattr(sd_model, "enable_model_cpu_offload"):
if shared.cmd_opts.medvram or shared.opts.diffusers_model_cpu_offload:
shared.log.debug('Diffusers: enable model CPU offload')
sd_model.enable_model_cpu_offload()
if hasattr(sd_model, "enable_vae_slicing"):
if shared.opts.diffusers_vae_slicing:
shared.log.debug('Diffusers: enable VAE slicing')
sd_model.enable_vae_slicing()
else:
sd_model.disable_vae_slicing()
if hasattr(sd_model, "enable_vae_tiling"):
if shared.opts.diffusers_vae_tiling:
shared.log.debug('Diffusers: enable VAE tiling')
sd_model.enable_vae_tiling()
else:
sd_model.disable_vae_tiling()
if hasattr(sd_model, "enable_attention_slicing"):
if shared.opts.diffusers_attention_slicing:
shared.log.debug('Diffusers: enable attention slicing')
sd_model.enable_attention_slicing()
else:
sd_model.disable_attention_slicing()
if shared.opts.cross_attention_optimization == "xFormers":
sd_model.enable_xformers_memory_efficient_attention()
if shared.opts.opt_channelslast:
shared.log.debug('Diffusers: enable channels last')
sd_model.unet.to(memory_format=torch.channels_last)
if shared.opts.cuda_compile and torch.cuda.is_available():
sd_model.to(devices.device)
sd_model.unet.to(memory_format=torch.channels_last)
import torch._dynamo as dynamo # pylint: disable=unused-import
torch._dynamo.config.verbose = shared.opts.cuda_compile_verbose # pylint: disable=protected-access
torch._dynamo.config.suppress_errors = shared.opts.cuda_compile_errors # pylint: disable=protected-access