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update changelog and cleanup lora

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Signed-off-by: Vladimir Mandic <mandic00@live.com>
This commit is contained in:
Vladimir Mandic
2024-10-17 08:32:08 -04:00
parent 6705e60b03
commit cbc52fe5c3
2 changed files with 11 additions and 24 deletions
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CHANGELOG.md
+4 -3
View File
@@ -1,8 +1,8 @@
# Change Log for SD.Next
## Update for 2024-10-16
## Update for 2024-10-17
### Highlights for 2024-10-16
### Highlights for 2024-10-17
- **Reprocess**: New workflow options that allow you to generate at lower quality and then
reprocess at higher quality for select images only or generate without hires/refine and then reprocess with hires/refine
@@ -30,7 +30,7 @@ And there are also other goodies like multiple *XYZ grid* improvements, addition
[README](https://github.com/vladmandic/automatic/blob/master/README.md) | [CHANGELOG](https://github.com/vladmandic/automatic/blob/master/CHANGELOG.md) | [WiKi](https://github.com/vladmandic/automatic/wiki) | [Discord](https://discord.com/invite/sd-next-federal-batch-inspectors-1101998836328697867)
### Details for 2024-10-16
### Details for 2024-10-17
- **reprocess**
- new top-level button: reprocess latent from your history of generated image(s)
@@ -245,6 +245,7 @@ And there are also other goodies like multiple *XYZ grid* improvements, addition
- **upscaling**
- interruptible operations
- **refactor**
- general lora apply/unapply process
- modularize main process loop
- massive log cleanup
- full lint pass
extensions-builtin/Lora/extra_networks_lora.py
+7 -21
View File
@@ -11,26 +11,17 @@ def get_stepwise(param, step, steps):
def sorted_positions(raw_steps):
steps = [[float(s.strip()) for s in re.split("[@~]", x)]
for x in re.split("[,;]", str(raw_steps))]
# If we just got a single number, just return it
if len(steps[0]) == 1:
if len(steps[0]) == 1: # If we just got a single number, just return it
return steps[0][0]
# Add implicit 1s to any steps which don't have a weight
steps = [[s[0], s[1] if len(s) == 2 else 1] for s in steps]
# Sort by index
steps.sort(key=lambda k: k[1])
steps = [[s[0], s[1] if len(s) == 2 else 1] for s in steps] # Add implicit 1s to any steps which don't have a weight
steps.sort(key=lambda k: k[1]) # Sort by index
steps = [list(v) for v in zip(*steps)]
return steps
def calculate_weight(m, step, max_steps, step_offset=2):
if isinstance(m, list):
if m[1][-1] <= 1.0:
if max_steps > 0:
step = (step) / (max_steps - step_offset)
else:
step = 1.0
step = (step) / (max_steps - step_offset) if max_steps > 0 else 1.0
v = np.interp(step, m[1], m[0])
return v
else:
@@ -130,14 +121,13 @@ class ExtraNetworkLora(extra_networks.ExtraNetwork):
networks.originals.apply() # apply patches
self.active = True
self.model = shared.opts.sd_model_checkpoint
t1 = time.time()
names, te_multipliers, unet_multipliers, dyn_dims = self.parse(p, params_list, step)
networks.load_networks(names, te_multipliers, unet_multipliers, dyn_dims)
t2 = time.time()
t1 = time.time()
if len(networks.loaded_networks) > 0 and step == 0:
self.infotext(p)
self.prompt(p)
shared.log.info(f'Load network: type=LoRA apply={[n.name for n in networks.loaded_networks]} patch={t1-t0:.2f} te={te_multipliers} unet={unet_multipliers} dims={dyn_dims} load={t2-t1:.2f}')
shared.log.info(f'Load network: type=LoRA apply={[n.name for n in networks.loaded_networks]} te={te_multipliers} unet={unet_multipliers} dims={dyn_dims} load={t1-t0:.2f}')
def deactivate(self, p):
t0 = time.time()
@@ -153,11 +143,7 @@ class ExtraNetworkLora(extra_networks.ExtraNetwork):
t1 = time.time()
networks.timer['restore'] += t1 - t0
if self.active and networks.debug:
shared.log.debug(f"LoRA end: load={networks.timer['load']:.2f} apply={networks.timer['apply']:.2f} restore={networks.timer['restore']:.2f}")
# if self.active and getattr(networks, "originals", None ) is not None:
# networks.originals.undo() # remove patches
# if networks.debug:
# shared.log.debug("LoRA deactivate")
shared.log.debug(f"Network end: type=LoRA load={networks.timer['load']:.2f} apply={networks.timer['apply']:.2f} restore={networks.timer['restore']:.2f}")
if self.errors:
p.comment("Networks with errors: " + ", ".join(f"{k} ({v})" for k, v in self.errors.items()))
for k, v in self.errors.items():