Update Storydiffusion_node.py
update
This commit is contained in:
smthemex
+130
-67
@@ -101,6 +101,9 @@ def narry_list(list_in):
|
||||
list_in[i] = modified_value
|
||||
return list_in
|
||||
|
||||
def remove_punctuation_from_strings(lst):
|
||||
pattern = r"[\W]+$" # 匹配字符串末尾的所有非单词字符
|
||||
return [re.sub(pattern, '', s) for s in lst]
|
||||
|
||||
def tensor_list(list_in):
|
||||
for i in range(len(list_in)):
|
||||
@@ -630,7 +633,7 @@ def process_generation(
|
||||
scheduler_choice,
|
||||
lora,
|
||||
lora_scale,
|
||||
lora_adapter
|
||||
trigger_words
|
||||
): # Corrected font_choice usage
|
||||
if len(general_prompt.splitlines()) >= 3:
|
||||
raise "Support for more than three characters is temporarily unavailable due to VRAM limitations, but this issue will be resolved soon."
|
||||
@@ -674,10 +677,11 @@ def process_generation(
|
||||
##### ########################
|
||||
pipe.scheduler = scheduler_choice.from_config(pipe.scheduler.config)
|
||||
# pipe.scheduler = DDIMScheduler.from_config(pipe.scheduler.config)
|
||||
if lora !="none":
|
||||
pipe.load_lora_weights(lora, adapter_name=trigger_words)
|
||||
pipe.set_adapters(trigger_words)
|
||||
# pipe._lora_scale=lora_scale
|
||||
pipe.enable_freeu(s1=0.6, s2=0.4, b1=1.1, b2=1.2)
|
||||
pipe.load_lora_weights(lora,adapter_name=lora_adapter)
|
||||
pipe.set_adapters(lora_adapter)
|
||||
#pipe._lora_scale=lora_scale
|
||||
cur_model_type = _sd_type + "-" + _model_type
|
||||
pipe.enable_vae_slicing()
|
||||
if device != "mps":
|
||||
@@ -689,6 +693,11 @@ def process_generation(
|
||||
load_chars = load_character_files_on_running(unet, character_files=_char_files)
|
||||
|
||||
prompts = prompt_array.splitlines()
|
||||
add_trigger_words = "," + trigger_words + ";"
|
||||
if lora!="none":
|
||||
prompts=remove_punctuation_from_strings(prompts)
|
||||
prompts = [item + add_trigger_words for item in prompts]
|
||||
#print(prompts)
|
||||
global character_dict, character_index_dict, invert_character_index_dict, ref_indexs_dict, ref_totals
|
||||
|
||||
character_dict, character_list = character_to_dict(general_prompt)
|
||||
@@ -713,7 +722,7 @@ def process_generation(
|
||||
]
|
||||
|
||||
prompts = [
|
||||
prompt.rpartition("#")[0] if "#" in prompt else prompt for prompt in prompts
|
||||
prompt.rpartition("#")[0]+add_trigger_words if "#" in prompt else prompt for prompt in prompts
|
||||
]
|
||||
# print(prompts)
|
||||
# id_prompts = prompts[:id_length]
|
||||
@@ -764,29 +773,53 @@ def process_generation(
|
||||
style_name, current_prompts, negative_prompt
|
||||
)
|
||||
if _model_type == "original":
|
||||
id_images = pipe(
|
||||
cur_positive_prompts,
|
||||
num_inference_steps=_num_steps,
|
||||
guidance_scale=guidance_scale,
|
||||
height=height,
|
||||
width=width,
|
||||
cross_attention_kwargs={"scale": lora_scale},
|
||||
negative_prompt=negative_prompt,
|
||||
generator=generator,
|
||||
).images
|
||||
if lora != "none":
|
||||
id_images = pipe(
|
||||
cur_positive_prompts,
|
||||
num_inference_steps=_num_steps,
|
||||
guidance_scale=guidance_scale,
|
||||
height=height,
|
||||
width=width,
|
||||
cross_attention_kwargs={"scale": lora_scale},
|
||||
negative_prompt=negative_prompt,
|
||||
generator=generator,
|
||||
).images
|
||||
else:
|
||||
id_images = pipe(
|
||||
cur_positive_prompts,
|
||||
num_inference_steps=_num_steps,
|
||||
guidance_scale=guidance_scale,
|
||||
height=height,
|
||||
width=width,
|
||||
negative_prompt=negative_prompt,
|
||||
generator=generator,
|
||||
).images
|
||||
elif _model_type == "Photomaker":
|
||||
id_images = pipe(
|
||||
cur_positive_prompts,
|
||||
input_id_images=input_id_images_dict[character_key],
|
||||
num_inference_steps=_num_steps,
|
||||
guidance_scale=guidance_scale,
|
||||
start_merge_step=start_merge_step,
|
||||
height=height,
|
||||
width=width,
|
||||
cross_attention_kwargs={"scale": lora_scale},
|
||||
negative_prompt=negative_prompt,
|
||||
generator=generator,
|
||||
).images
|
||||
if lora != "none":
|
||||
id_images = pipe(
|
||||
cur_positive_prompts,
|
||||
input_id_images=input_id_images_dict[character_key],
|
||||
num_inference_steps=_num_steps,
|
||||
guidance_scale=guidance_scale,
|
||||
start_merge_step=start_merge_step,
|
||||
height=height,
|
||||
width=width,
|
||||
cross_attention_kwargs={"scale": lora_scale},
|
||||
negative_prompt=negative_prompt,
|
||||
generator=generator,
|
||||
).images
|
||||
else:
|
||||
id_images = pipe(
|
||||
cur_positive_prompts,
|
||||
input_id_images=input_id_images_dict[character_key],
|
||||
num_inference_steps=_num_steps,
|
||||
guidance_scale=guidance_scale,
|
||||
start_merge_step=start_merge_step,
|
||||
height=height,
|
||||
width=width,
|
||||
negative_prompt=negative_prompt,
|
||||
generator=generator,
|
||||
).images
|
||||
else:
|
||||
raise NotImplementedError(
|
||||
"You should choice between original and Photomaker!",
|
||||
@@ -821,34 +854,63 @@ def process_generation(
|
||||
real_prompt = apply_style_positive(style_name, real_prompt)
|
||||
#print(real_prompt)
|
||||
if _model_type == "original":
|
||||
results_dict[real_prompts_ind] = pipe(
|
||||
real_prompt,
|
||||
num_inference_steps=_num_steps,
|
||||
guidance_scale=guidance_scale,
|
||||
height=height,
|
||||
width=width,
|
||||
cross_attention_kwargs={"scale": lora_scale},
|
||||
negative_prompt=negative_prompt,
|
||||
generator=generator,
|
||||
).images[0]
|
||||
if lora != "none":
|
||||
results_dict[real_prompts_ind] = pipe(
|
||||
real_prompt,
|
||||
num_inference_steps=_num_steps,
|
||||
guidance_scale=guidance_scale,
|
||||
height=height,
|
||||
width=width,
|
||||
cross_attention_kwargs={"scale": lora_scale},
|
||||
negative_prompt=negative_prompt,
|
||||
generator=generator,
|
||||
).images[0]
|
||||
else:
|
||||
results_dict[real_prompts_ind] = pipe(
|
||||
real_prompt,
|
||||
num_inference_steps=_num_steps,
|
||||
guidance_scale=guidance_scale,
|
||||
height=height,
|
||||
width=width,
|
||||
negative_prompt=negative_prompt,
|
||||
generator=generator,
|
||||
).images[0]
|
||||
elif _model_type == "Photomaker":
|
||||
results_dict[real_prompts_ind] = pipe(
|
||||
real_prompt,
|
||||
input_id_images=(
|
||||
input_id_images_dict[cur_character[0]]
|
||||
if real_prompts_ind not in nc_indexs
|
||||
else input_id_images_dict[character_list[0]]
|
||||
),
|
||||
num_inference_steps=_num_steps,
|
||||
guidance_scale=guidance_scale,
|
||||
start_merge_step=start_merge_step,
|
||||
height=height,
|
||||
width=width,
|
||||
cross_attention_kwargs={"scale": lora_scale},
|
||||
negative_prompt=negative_prompt,
|
||||
generator=generator,
|
||||
nc_flag=True if real_prompts_ind in nc_indexs else False,
|
||||
).images[0]
|
||||
if lora != "none":
|
||||
results_dict[real_prompts_ind] = pipe(
|
||||
real_prompt,
|
||||
input_id_images=(
|
||||
input_id_images_dict[cur_character[0]]
|
||||
if real_prompts_ind not in nc_indexs
|
||||
else input_id_images_dict[character_list[0]]
|
||||
),
|
||||
num_inference_steps=_num_steps,
|
||||
guidance_scale=guidance_scale,
|
||||
start_merge_step=start_merge_step,
|
||||
height=height,
|
||||
width=width,
|
||||
cross_attention_kwargs={"scale": lora_scale},
|
||||
negative_prompt=negative_prompt,
|
||||
generator=generator,
|
||||
nc_flag=True if real_prompts_ind in nc_indexs else False,
|
||||
).images[0]
|
||||
else:
|
||||
results_dict[real_prompts_ind] = pipe(
|
||||
real_prompt,
|
||||
input_id_images=(
|
||||
input_id_images_dict[cur_character[0]]
|
||||
if real_prompts_ind not in nc_indexs
|
||||
else input_id_images_dict[character_list[0]]
|
||||
),
|
||||
num_inference_steps=_num_steps,
|
||||
guidance_scale=guidance_scale,
|
||||
start_merge_step=start_merge_step,
|
||||
height=height,
|
||||
width=width,
|
||||
negative_prompt=negative_prompt,
|
||||
generator=generator,
|
||||
nc_flag=True if real_prompts_ind in nc_indexs else False,
|
||||
).images[0]
|
||||
else:
|
||||
raise NotImplementedError(
|
||||
"You should choice between original and Photomaker!",
|
||||
@@ -870,9 +932,9 @@ class Storydiffusion_Text2Img:
|
||||
"sd_type": (yaml_list,),
|
||||
"ckpt_name": (folder_paths.get_filename_list("checkpoints"),),
|
||||
"photomake_model": (["none"] + folder_paths.get_filename_list("photomaker"),),
|
||||
"lora": (folder_paths.get_filename_list("loras"),),
|
||||
"lora": (["none"]+folder_paths.get_filename_list("loras"),),
|
||||
"lora_scale": ("FLOAT", {"default": 0.8, "min": 0.1, "max": 5.0, "step": 0.1}),
|
||||
"lora_adapter": ("STRING", {"default": ""}),
|
||||
"trigger_words": ("STRING", {"default": "best quality"}),
|
||||
"character_prompt": ("STRING", {"multiline": True,
|
||||
"default": "[Bob] A man, wearing a black sui,\n"
|
||||
"[Alice]a woman, wearing a white shirt."}),
|
||||
@@ -912,7 +974,7 @@ class Storydiffusion_Text2Img:
|
||||
FUNCTION = "text2image"
|
||||
CATEGORY = "Storydiffusion"
|
||||
|
||||
def text2image(self, sd_type, ckpt_name,lora,lora_scale,lora_adapter, steps, img_style, photomake_model, scheduler, cfg, seed,
|
||||
def text2image(self, sd_type, ckpt_name,lora,lora_scale,trigger_words, steps, img_style, photomake_model, scheduler, cfg, seed,
|
||||
sa32_degree,
|
||||
sa64_degree, character_id_number, character_prompt, negative_prompt, scene_prompt,
|
||||
img_height, img_width):
|
||||
@@ -926,10 +988,10 @@ class Storydiffusion_Text2Img:
|
||||
|
||||
ckpt_path = folder_paths.get_full_path("checkpoints", ckpt_name)
|
||||
ckpt_path = get_instance_path(ckpt_path)
|
||||
if lora!="none":
|
||||
lora_path=folder_paths.get_full_path("loras", lora)
|
||||
lora = get_instance_path(lora_path)
|
||||
|
||||
lora_path=folder_paths.get_full_path("loras", lora)
|
||||
lora = get_instance_path(lora_path)
|
||||
# print(ckpt_path)
|
||||
|
||||
if photomake_model == "none":
|
||||
photomaker_path = hf_hub_download(
|
||||
@@ -1073,7 +1135,7 @@ class Storydiffusion_Text2Img:
|
||||
G_width,
|
||||
_char_files,
|
||||
photomaker_path,
|
||||
scheduler_choice,lora,lora_scale,lora_adapter)
|
||||
scheduler_choice,lora,lora_scale,trigger_words)
|
||||
|
||||
for value in gen:
|
||||
pass_value = value
|
||||
@@ -1095,9 +1157,9 @@ class Storydiffusion_Img2Img:
|
||||
"sd_type": (yaml_list,),
|
||||
"ckpt_name": (folder_paths.get_filename_list("checkpoints"),),
|
||||
"photomake_model": (["none"] + folder_paths.get_filename_list("photomaker"),),
|
||||
"lora": (folder_paths.get_filename_list("loras"),),
|
||||
"lora": (["none"]+folder_paths.get_filename_list("loras"),),
|
||||
"lora_scale":("FLOAT", {"default": 0.8, "min": 0.1, "max": 5.0, "step": 0.1}),
|
||||
"lora_adapter": ("STRING", {"default": ""}),
|
||||
"trigger_words": ("STRING", {"default": "best quality"}),
|
||||
"character_prompt": ("STRING", {"multiline": True,
|
||||
"default": "[Taylor]a woman img, wearing a white T-shirt, blue loose hair,"}),
|
||||
"scene_prompt": ("STRING", {"multiline": True,
|
||||
@@ -1133,7 +1195,7 @@ class Storydiffusion_Img2Img:
|
||||
FUNCTION = "img2image"
|
||||
CATEGORY = "Storydiffusion"
|
||||
|
||||
def img2image(self, image, sd_type, ckpt_name,lora,lora_scale,lora_adapter, photomake_model, character_prompt, scene_prompt, character_id_number,
|
||||
def img2image(self, image, sd_type, ckpt_name,lora,lora_scale,trigger_words, photomake_model, character_prompt, scene_prompt, character_id_number,
|
||||
negative_prompt, scheduler, img_style,
|
||||
sa32_degree, sa64_degree, seed, steps, cfg, ip_adapter_strength, style_strength_ratio, img_height,
|
||||
img_width, ):
|
||||
@@ -1161,8 +1223,9 @@ class Storydiffusion_Img2Img:
|
||||
ckpt_path = get_instance_path(ckpt_path)
|
||||
style_name = img_style
|
||||
|
||||
lora_path = folder_paths.get_full_path("loras", lora)
|
||||
lora = get_instance_path(lora_path)
|
||||
if lora != "none":
|
||||
lora_path = folder_paths.get_full_path("loras", lora)
|
||||
lora = get_instance_path(lora_path)
|
||||
|
||||
if photomake_model == "none":
|
||||
photomaker_path = hf_hub_download(
|
||||
@@ -1304,7 +1367,7 @@ class Storydiffusion_Img2Img:
|
||||
_char_files,
|
||||
photomaker_path,
|
||||
scheduler_choice,
|
||||
lora,lora_scale,lora_adapter)
|
||||
lora,lora_scale,trigger_words)
|
||||
|
||||
for value in gen:
|
||||
pass_value = value
|
||||
|
||||
Reference in New Issue
Block a user