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Merge branch 'dev' into lora-refactor

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This commit is contained in:
Vladimir Mandic
2024-11-29 07:50:55 -05:00
committed by GitHub
parent bf170ea08c f2d5307c54
commit d6c1487f9a
74 changed files with 2445 additions and 380 deletions
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modules/processing_diffusers.py
+5 -4
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@@ -77,7 +77,6 @@ def process_base(p: processing.StableDiffusionProcessing):
clip_skip=p.clip_skip,
desc='Base',
)
timer.process.record('args')
shared.state.sampling_steps = base_args.get('prior_num_inference_steps', None) or p.steps or base_args.get('num_inference_steps', None)
if shared.opts.scheduler_eta is not None and shared.opts.scheduler_eta > 0 and shared.opts.scheduler_eta < 1:
p.extra_generation_params["Sampler Eta"] = shared.opts.scheduler_eta
@@ -233,7 +232,8 @@ def process_hires(p: processing.StableDiffusionProcessing, output):
output = shared.sd_model(**hires_args) # pylint: disable=not-callable
if isinstance(output, dict):
output = SimpleNamespace(**output)
shared.history.add(output.images, info=processing.create_infotext(p), ops=p.ops)
if hasattr(output, 'images'):
shared.history.add(output.images, info=processing.create_infotext(p), ops=p.ops)
sd_models_compile.check_deepcache(enable=False)
sd_models_compile.openvino_post_compile(op="base")
except AssertionError as e:
@@ -315,7 +315,8 @@ def process_refine(p: processing.StableDiffusionProcessing, output):
output = shared.sd_refiner(**refiner_args) # pylint: disable=not-callable
if isinstance(output, dict):
output = SimpleNamespace(**output)
shared.history.add(output.images, info=processing.create_infotext(p), ops=p.ops)
if hasattr(output, 'images'):
shared.history.add(output.images, info=processing.create_infotext(p), ops=p.ops)
sd_models_compile.openvino_post_compile(op="refiner")
except AssertionError as e:
shared.log.info(e)
@@ -353,7 +354,7 @@ def process_decode(p: processing.StableDiffusionProcessing, output):
if not hasattr(model, 'vae'):
if hasattr(model, 'pipe') and hasattr(model.pipe, 'vae'):
model = model.pipe
if hasattr(model, "vae") and output.images is not None and len(output.images) > 0:
if (hasattr(model, "vae") or hasattr(model, "vqgan")) and output.images is not None and len(output.images) > 0:
if p.hr_resize_mode > 0 and (p.hr_upscaler != 'None' or p.hr_resize_mode == 5):
width = max(getattr(p, 'width', 0), getattr(p, 'hr_upscale_to_x', 0))
height = max(getattr(p, 'height', 0), getattr(p, 'hr_upscale_to_y', 0))