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Consolidated add_hook_patches_as_diffs into add_hook_patches func, fixed fp8 support for model-as-lora feature

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kosinkadink1@gmail.com 2024-09-14 14:09:43 +09:00
parent 9ae758175d
commit 1268d04295
1 changed files with 13 additions and 31 deletions
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44
comfy/model_patcher.py
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@ -575,7 +575,7 @@ class ModelPatcher:
if cached_group.contains(hook):
self.cached_hook_patches.pop(cached_group)
def add_hook_patches(self, hook: comfy.hooks.HookWeight, patches, strength_patch=1.0, strength_model=1.0):
def add_hook_patches(self, hook: comfy.hooks.HookWeight, patches, strength_patch=1.0, strength_model=1.0, is_diff=False):
# NOTE: this mirrors behavior of add_patches func
current_hook_patches: Dict[str,List] = self.hook_patches.get(hook.hook_ref, {})
p = set()
@ -594,41 +594,23 @@ class ModelPatcher:
if key in model_sd:
p.add(k)
current_patches: List[Tuple] = current_hook_patches.get(key, [])
current_patches.append((strength_patch, patches[k], strength_model, offset, function))
if is_diff:
# take difference between desired weight and existing weight to get diff
# TODO: try to implement diff his via strength_path/strength_model diff
model_dtype = comfy.utils.get_attr(self.model, key).dtype
if model_dtype in [torch.float8_e5m2, torch.float8_e4m3fn]:
diff_weight = (patches[k].to(torch.float32)-comfy.utils.get_attr(self.model, key).to(torch.float32)).to(model_dtype)
else:
diff_weight = patches[k]-comfy.utils.get_attr(self.model, key)
current_patches.append((strength_patch, (diff_weight,), strength_model, offset, function))
else:
current_patches.append((strength_patch, patches[k], strength_model, offset, function))
current_hook_patches[key] = current_patches
self.hook_patches[hook.hook_ref] = current_hook_patches
# since should care about these patches too to determine if same model, reroll patches_uuid
self.patches_uuid = uuid.uuid4()
return list(p)
def add_hooked_patches_as_diffs(self, hook: comfy.hooks.HookWeight, patches: Dict, strength_patch=1.0, strength_model=1.0):
# NOTE: this mirrors behavior of add_patches func
current_hooked_patches: Dict[str,List] = self.hooked_patches.get(hook.hook_ref, {})
p = set()
model_sd = self.model.state_dict()
for k in patches:
offset = None
function = None
if isinstance(k, str):
key = k
else:
offset = k[1]
key = k[0]
if len(k) > 2:
function = k[2]
if key in model_sd:
p.add(k)
current_patches: List[Tuple] = current_hooked_patches.get(key, [])
# take difference between desired weight and existing weight to get diff
# TODO: create fix for fp8; cast to torch32 first?
current_patches.append((strength_patch, (patches[k]-comfy.utils.get_attr(self.model, key),), strength_model, offset, function))
current_hooked_patches[key] = current_patches
self.hook_patches[hook.hook_ref] = current_hooked_patches
# since should care about these patches too to determine if same model, reroll patches_uuid
self.patches_uuid = uuid.uuid4()
return list(p)
def get_combined_hook_patches(self, hooks: comfy.hooks.HookWeightGroup):
# combined_patches will contain weights of all relevant hooks, per key
combined_patches = {}
@ -728,7 +710,7 @@ class ModelPatcher:
comfy.utils.copy_to_param(self.model, k, self.hook_backup[k][0].to(device=self.hook_backup[k][1]))
self.hook_backup.clear()
self.current_hooks = None # TODO: should this be clear_cached_hooked_weights instead?
self.current_hooks = None # TODO: should this be clear_cached_hook_weights instead?
def clean_hooks(self):
self.unpatch_hooks()