init
This commit is contained in:
smthem
+59
-3
@@ -137,7 +137,7 @@ class StoryDiffusion_Apply:
|
||||
{
|
||||
"model": ("MODEL",),
|
||||
"vae": ("VAE",),
|
||||
"infer_mode": (["story", "classic","flux_pulid","infiniteyou","uno","realcustom","instant_character","dreamo","bagel_edit","story_maker","story_and_maker","consistory","kolor_face","msdiffusion" ],),
|
||||
"infer_mode": (["story", "classic","flux_pulid","infiniteyou","uno","realcustom","instant_character","dreamo","flux_omi","bagel_edit","story_maker","story_and_maker","consistory","kolor_face","msdiffusion" ],),
|
||||
"photomake_ckpt": (["none"] + [i for i in folder_paths.get_filename_list("photomaker") if "v1" in i or "v2" in i],),
|
||||
"ipadapter_ckpt": (["none"] + folder_paths.get_filename_list("photomaker"),),
|
||||
"quantize_mode": ([ "fp8", "nf4","fp16", ],),
|
||||
@@ -276,6 +276,12 @@ class StoryDiffusion_Apply:
|
||||
raise "EasyFunction_Lite node extra_repo must fill bagel repo"
|
||||
max_mem_per_gpu=str(int(total_vram/1000))+"GIB"
|
||||
model = load_bagel_model(repo,quantize_mode,max_mem_per_gpu)
|
||||
elif infer_mode == "flux_omi":
|
||||
from .model_loader_utils import Loader_Flux_Diffuser
|
||||
if not isinstance(model,dict) :
|
||||
raise " must link EasyFunction_Lite node "
|
||||
model = Loader_Flux_Diffuser(model,ipadapter_ckpt_path,vae,quantize_mode)
|
||||
|
||||
else: # can not choice a mode
|
||||
print("infer use comfyui classic mode")
|
||||
|
||||
@@ -491,6 +497,8 @@ class StoryDiffusion_CLIPTextEncode:
|
||||
pass
|
||||
elif infer_mode == "bagel_edit":
|
||||
image_emb=input_id_images_dict
|
||||
elif infer_mode == "flux_omi":
|
||||
image_emb=input_id_images_dict
|
||||
elif infer_mode == "dreamo":
|
||||
from .model_loader_utils import Dreamo_image_encoder
|
||||
from huggingface_hub import hf_hub_download
|
||||
@@ -738,7 +746,7 @@ class StoryDiffusion_CLIPTextEncode:
|
||||
else:
|
||||
if infer_mode=="classic": #TODO 逆序的角色会出现iD不匹配，受影响的有story文生图
|
||||
only_role_emb= cf_clip(only_role_list, clip, infer_mode,role_list) #story模式需要拆分prompt，所以这里需要传入role_list
|
||||
elif infer_mode=="dreamo" or infer_mode=="bagel_edit":
|
||||
elif infer_mode=="dreamo" or infer_mode=="bagel_edit" or infer_mode=="flux_omi":
|
||||
pass # TODO 暂时不支持dreamo
|
||||
else:
|
||||
only_role_emb= cf_clip(inf_list_split, clip, infer_mode,role_list) #story,story_maker,story_and_maker,msdiffusion,infinite
|
||||
@@ -757,7 +765,7 @@ class StoryDiffusion_CLIPTextEncode:
|
||||
else:
|
||||
if infer_mode!="kolor_face":
|
||||
nc_emb=cf_clip(nc_txt_list, clip, infer_mode,role_list,input_split=False)
|
||||
elif infer_mode=="dreamo" or infer_mode=="bagel_edit":
|
||||
elif infer_mode=="dreamo" or infer_mode=="bagel_edit" or infer_mode=="flux_omi":
|
||||
pass # TODO 暂时不支持dreamo
|
||||
else:
|
||||
nc_emb,_= glm_single_encode(chatglm3_model, nc_txt_list,role_list, neg_text, 1,nc_mode=True)
|
||||
@@ -866,6 +874,12 @@ class StoryDiffusion_CLIPTextEncode:
|
||||
only_role_emb[key]=prompts
|
||||
postive_dict= {"role": only_role_emb, "nc": None, "daul": None}
|
||||
negative = neg_text # TODO
|
||||
elif infer_mode=="flux_omi":
|
||||
only_role_emb={}
|
||||
for key,prompts in zip(role_list,inf_list_split):
|
||||
only_role_emb[key]=prompts
|
||||
postive_dict= {"role": only_role_emb, "nc": None, "daul": None}
|
||||
negative = neg_text # TODO
|
||||
elif infer_mode=="realcustom":
|
||||
postive_dict= {"role": only_role_emb, "nc": None, "daul": daul_emb}
|
||||
negative=[letent_real]
|
||||
@@ -1733,6 +1747,30 @@ class StoryDiffusion_KSampler:
|
||||
samples_list=[VAE.encode(phi2narry(pixels)[:,:,:,:3]) for pixels in samples_list] # TODO pil to tensor
|
||||
out["samples"] = torch.cat(samples_list, dim=0)
|
||||
return (out,)
|
||||
elif infer_mode =="flux_omi":
|
||||
samples_list = []
|
||||
for key in role_list:
|
||||
for prompt in only_role_emb[key]:
|
||||
samples=model(
|
||||
prompt=prompt,
|
||||
height=height,
|
||||
width=width,
|
||||
guidance_scale=cfg if cfg==3.5 else 3.5,
|
||||
num_inference_steps=steps,
|
||||
max_sequence_length=512,
|
||||
generator=torch.Generator("cpu").manual_seed(seed),
|
||||
spatial_images=[image_embeds[key]],
|
||||
subject_images=[], #TODO TRY ON
|
||||
cond_size=512,
|
||||
).images
|
||||
#print(samples.shape)
|
||||
samples_list.append(samples)
|
||||
out = {}
|
||||
out["samples"] = torch.cat(samples_list, dim=0)
|
||||
# Clear cache after generation
|
||||
from .OmniConsistency.infer import clear_cache
|
||||
clear_cache(model.transformer)
|
||||
return (out,)
|
||||
else: #none:
|
||||
return
|
||||
|
||||
@@ -1792,6 +1830,24 @@ class StoryDiffusion_Lora_Control:
|
||||
if "blur" in lora_path:
|
||||
loras.append([lora_path, 'anti_blur', 1.0])
|
||||
model.load_loras(loras)
|
||||
elif infer_mode=="flux_omi":
|
||||
if loras != "none":
|
||||
lora_path = folder_paths.get_full_path("loras", loras)
|
||||
else:
|
||||
raise Exception("InfiniteYou need to select a lora")
|
||||
|
||||
multi_lora=[lora_path]
|
||||
if switch.get("lora_ckpt_path") is not None:
|
||||
multi_lora.append(switch.get("lora_ckpt_path"))
|
||||
if switch.get("lora_dual_path") is not None:
|
||||
multi_lora.append(switch.get("lora_dual_path"))
|
||||
if switch.get("lora_ckpt_path") is not None and switch.get("lora_dual_path") is not None:
|
||||
from .OmniConsistency.src_inference.lora_helper import set_multi_lora
|
||||
lora_weights = [1]*len(multi_lora)
|
||||
set_multi_lora(model.transformer, multi_lora, lora_weights=[lora_weights], cond_size=512)
|
||||
else:
|
||||
model.load_lora_weights(lora_path)
|
||||
|
||||
switch["controlnet_scale"]=controlnet_scale
|
||||
switch["controlnet"]=controlnet
|
||||
switch["use_lora"]=use_lora
|
||||
|
||||
Reference in New Issue
Block a user