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OpenVINO Lora support

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Disty0
2023-09-16 16:10:02 +03:00
parent b13494a142
commit 793b5f4921
6 changed files with 188 additions and 98 deletions
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modules/processing_diffusers.py
+10 -10
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@@ -260,9 +260,9 @@ def process_diffusers(p: StableDiffusionProcessing, seeds, prompts, negative_pro
if shared.opts.cuda_compile_backend == "openvino_fx":
compile_height = p.height if not hires else p.hr_upscale_to_y
compile_width = p.width if not hires else p.hr_upscale_to_x
if (not hasattr(shared.sd_model, "compiled_model_state") or (not shared.sd_model.compiled_model_state.first_pass
and (shared.sd_model.compiled_model_state.height != compile_height or shared.sd_model.compiled_model_state.width != compile_width
or shared.sd_model.compiled_model_state.batch_size != p.batch_size))):
if (shared.compiled_model_state is None or (not shared.compiled_model_state.first_pass
and (shared.compiled_model_state.height != compile_height or shared.compiled_model_state.width != compile_width
or shared.compiled_model_state.batch_size != p.batch_size))):
shared.log.info("OpenVINO: Resolution change detected")
shared.log.info("OpenVINO: Recompiling base model")
sd_models.unload_model_weights(op='model')
@@ -271,15 +271,17 @@ def process_diffusers(p: StableDiffusionProcessing, seeds, prompts, negative_pro
shared.log.info("OpenVINO: Recompiling refiner")
sd_models.unload_model_weights(op='refiner')
sd_models.reload_model_weights(op='refiner')
shared.sd_model.compiled_model_state.height = compile_height
shared.sd_model.compiled_model_state.width = compile_width
shared.sd_model.compiled_model_state.batch_size = p.batch_size
shared.sd_model.compiled_model_state.first_pass = False
shared.compiled_model_state.height = compile_height
shared.compiled_model_state.width = compile_width
shared.compiled_model_state.batch_size = p.batch_size
shared.compiled_model_state.first_pass = False
else:
pass #Can be implemented for TensorRT or Olive
else:
pass #Do nothing if compile is disabled
recompile_model()
is_karras_compatible = shared.sd_model.__class__.__init__.__annotations__.get("scheduler", None) == diffusers.schedulers.scheduling_utils.KarrasDiffusionSchedulers
if (not hasattr(shared.sd_model.scheduler, 'name')) or (shared.sd_model.scheduler.name != p.sampler_name) and (p.sampler_name != 'Default') and is_karras_compatible:
sampler = sd_samplers.all_samplers_map.get(p.sampler_name, None)
@@ -316,8 +318,6 @@ def process_diffusers(p: StableDiffusionProcessing, seeds, prompts, negative_pro
unload_diffusers_lora()
return results
recompile_model()
if shared.opts.diffusers_move_base and not shared.sd_model.has_accelerate:
shared.sd_model.to(devices.device)
@@ -379,12 +379,12 @@ def process_diffusers(p: StableDiffusionProcessing, seeds, prompts, negative_pro
output.images = hires_resize(latents=output.images)
if latent_scale_mode is not None or p.hr_force:
p.ops.append('hires')
recompile_model(hires=True)
if (not hasattr(shared.sd_model.scheduler, 'name')) or (shared.sd_model.scheduler.name != p.latent_sampler) and (p.latent_sampler != 'Default') and is_karras_compatible:
sampler = sd_samplers.all_samplers_map.get(p.latent_sampler, None)
if sampler is None:
sampler = sd_samplers.all_samplers_map.get("UniPC")
sd_samplers.create_sampler(sampler.name, shared.sd_model) # TODO(Patrick): For wrapped pipelines this is currently a no-op
recompile_model(hires=True)
sd_models.set_diffuser_pipe(shared.sd_model, sd_models.DiffusersTaskType.IMAGE_2_IMAGE)
hires_args = set_pipeline_args(
model=shared.sd_model,