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stable-diffusion-webui-composable-lora
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verbose for debug

original
opparco 2023-02-25 02:43:26 +09:00
parent 5c1ca9c57c
commit 6e7efd6560
1 changed files with 16 additions and 14 deletions
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30
composable_lora.py
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@ -32,12 +32,13 @@ def reset_counters():
def debug(*values: object, lora_layer_name: str): def debug(*values: object, lora_layer_name: str):
if lora_layer_name.startswith("transformer_"): if verbose:
if lora_layer_name.endswith("_11_mlp_fc2"): if lora_layer_name.startswith("transformer_"):
print(*values) if lora_layer_name.endswith("_11_mlp_fc2"):
elif lora_layer_name.startswith("diffusion_model_"): print(*values)
if lora_layer_name.endswith("_11_1_proj_out"): elif lora_layer_name.startswith("diffusion_model_"):
print(*values) if lora_layer_name.endswith("_11_1_proj_out"):
print(*values)
def lora_forward(compvis_module, input, res): def lora_forward(compvis_module, input, res):
@ -80,12 +81,12 @@ def lora_forward(compvis_module, input, res):
loras = prompt_loras[text_model_encoder_counter] loras = prompt_loras[text_model_encoder_counter]
multiplier = loras.get(lora.name, 0.0) multiplier = loras.get(lora.name, 0.0)
if multiplier != 0.0: if multiplier != 0.0:
# debug(f"c #{text_model_encoder_counter} lora.name={lora.name} mul={multiplier}", lora_layer_name=lora_layer_name) debug(f"c #{text_model_encoder_counter} lora.name={lora.name} mul={multiplier}", lora_layer_name=lora_layer_name)
res += multiplier * alpha * patch res += multiplier * alpha * patch
else: else:
# uc # uc
if opt_uc_text_model_encoder and lora.multiplier != 0.0: if opt_uc_text_model_encoder and lora.multiplier != 0.0:
# debug(f"uc #{text_model_encoder_counter} lora.name={lora.name} lora.mul={lora.multiplier}", lora_layer_name=lora_layer_name) debug(f"uc #{text_model_encoder_counter} lora.name={lora.name} lora.mul={lora.multiplier}", lora_layer_name=lora_layer_name)
res += lora.multiplier * alpha * patch res += lora.multiplier * alpha * patch
if get_learned_conditioning_prompt_schedules_counter != 1 and lora_layer_name.endswith("_11_mlp_fc2"): # last lora_layer_name of text_model_encoder if get_learned_conditioning_prompt_schedules_counter != 1 and lora_layer_name.endswith("_11_mlp_fc2"): # last lora_layer_name of text_model_encoder
@ -103,13 +104,13 @@ def lora_forward(compvis_module, input, res):
for p, loras in enumerate(prompt_loras): for p, loras in enumerate(prompt_loras):
multiplier = loras.get(lora.name, 0.0) multiplier = loras.get(lora.name, 0.0)
if multiplier != 0.0: if multiplier != 0.0:
# debug(f"tensor #{b}.{p} lora.name={lora.name} mul={multiplier}", lora_layer_name=lora_layer_name) debug(f"tensor #{b}.{p} lora.name={lora.name} mul={multiplier}", lora_layer_name=lora_layer_name)
res[tensor_off] += multiplier * alpha * patch[tensor_off] res[tensor_off] += multiplier * alpha * patch[tensor_off]
tensor_off += 1 tensor_off += 1
# uc # uc
if opt_uc_diffusion_model and lora.multiplier != 0.0: if opt_uc_diffusion_model and lora.multiplier != 0.0:
# debug(f"uncond lora.name={lora.name} lora.mul={lora.multiplier}", lora_layer_name=lora_layer_name) debug(f"uncond lora.name={lora.name} lora.mul={lora.multiplier}", lora_layer_name=lora_layer_name)
res[uncond_off] += lora.multiplier * alpha * patch[uncond_off] res[uncond_off] += lora.multiplier * alpha * patch[uncond_off]
uncond_off += 1 uncond_off += 1
else: # "diffusion_model_" else: # "diffusion_model_"
@ -119,12 +120,12 @@ def lora_forward(compvis_module, input, res):
loras = prompt_loras[diffusion_model_counter] loras = prompt_loras[diffusion_model_counter]
multiplier = loras.get(lora.name, 0.0) multiplier = loras.get(lora.name, 0.0)
if multiplier != 0.0: if multiplier != 0.0:
# debug(f"c #{diffusion_model_counter} lora.name={lora.name} mul={multiplier}", lora_layer_name=lora_layer_name) debug(f"c #{diffusion_model_counter} lora.name={lora.name} mul={multiplier}", lora_layer_name=lora_layer_name)
res += multiplier * alpha * patch res += multiplier * alpha * patch
else: else:
# uc # uc
if opt_uc_diffusion_model and lora.multiplier != 0.0: if opt_uc_diffusion_model and lora.multiplier != 0.0:
# debug(f"uc {lora_layer_name} lora.name={lora.name} lora.mul={lora.multiplier}", lora_layer_name=lora_layer_name) debug(f"uc {lora_layer_name} lora.name={lora.name} lora.mul={lora.multiplier}", lora_layer_name=lora_layer_name)
res += lora.multiplier * alpha * patch res += lora.multiplier * alpha * patch
if lora_layer_name.endswith("_11_1_proj_out"): # last lora_layer_name of diffusion_model if lora_layer_name.endswith("_11_1_proj_out"): # last lora_layer_name of diffusion_model
@ -135,7 +136,7 @@ def lora_forward(compvis_module, input, res):
else: else:
# default # default
if lora.multiplier != 0.0: if lora.multiplier != 0.0:
# debug(f"DEFAULT {lora_layer_name} lora.name={lora.name} lora.mul={lora.multiplier}", lora_layer_name=lora_layer_name) debug(f"DEFAULT {lora_layer_name} lora.name={lora.name} lora.mul={lora.multiplier}", lora_layer_name=lora_layer_name)
res += lora.multiplier * alpha * patch res += lora.multiplier * alpha * patch
return res return res
@ -154,7 +155,7 @@ def lora_get_learned_conditioning_prompt_schedules(prompts, steps):
# #
# order: uc c # order: uc c
# #
# print(f"### get_learned_conditioning_prompt_schedules")
prompt_schedules = prompt_parser.get_learned_conditioning_prompt_schedules_before_lora(prompts, steps) prompt_schedules = prompt_parser.get_learned_conditioning_prompt_schedules_before_lora(prompts, steps)
get_learned_conditioning_prompt_schedules_counter += 1 get_learned_conditioning_prompt_schedules_counter += 1
@ -165,6 +166,7 @@ def lora_get_learned_conditioning_prompt_schedules(prompts, steps):
enabled = False enabled = False
opt_uc_text_model_encoder = False opt_uc_text_model_encoder = False
opt_uc_diffusion_model = False opt_uc_diffusion_model = False
verbose = False
num_batches: int = 0 num_batches: int = 0
prompt_loras: List[Dict[str, float]] = [] prompt_loras: List[Dict[str, float]] = []