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Update Storydiffusion_node.py

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smthemex
2025-04-28 10:17:14 +08:00
committed by GitHub
parent 6588a19259
commit 68b6fc9779
1 changed files with 19 additions and 34 deletions
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Storydiffusion_node.py
+19 -34
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@@ -172,18 +172,7 @@ class StoryDiffusion_Apply:
from comfy.clip_vision import load as clip_load
CLIP_VISION=clip_load(clip_vision_path).model
# if infer_mode=="instant_character":
# from comfy.clip_vision import load as clip_load
# if CLIP_VISION is None:
# raise "instant_character need a clipvison dino g model"
# if clip_vision_path is not None:
# CLIP_VISION_2=clip_load(clip_vision_path).model
# elif clip_vision_path is None and clip_vision_path_dual is not None:
# CLIP_VISION_2=clip_load(clip_vision_path_dual).model
# else:
# raise "instant_character need a clipvison siglip model"
if infer_mode in ["story_maker" ,"story_and_maker"] and not CLIP_VISION and ipadapter_ckpt_path is None:
raise "story_maker need a clipvison H model,mask.bin"
@@ -587,8 +576,8 @@ class StoryDiffusion_CLIPTextEncode:
for key ,prompts,role_image,target_phrase in zip(role_list,inf_list_split,image_list,target_phrases):
emb_dict_real_list,latent_dict_list=[],[]
for p,n in zip(prompts,[neg_text]*len(prompts)):
seed=random.randint(0, MAX_SEED)
emb_dict_real,latent_dict=realcustom_clip_emb(text_model,vision_model,switch.get("vae_config"),switch.get("vae_downsample_factor"),seed,p,n,role_image,target_phrase,
emb_dict_real,latent_dict=realcustom_clip_emb(text_model,vision_model,switch.get("vae_config"),switch.get("vae_downsample_factor"),p,n,role_image,target_phrase,
width,height,device,samples_per_prompt,guidance_weight)
emb_dict_real_list.append(emb_dict_real)
latent_dict_list.append(latent_dict)
@@ -607,32 +596,25 @@ class StoryDiffusion_CLIPTextEncode:
only_role_emb,noise_x,inp_neg_list={},{},{}
for key ,prompts in zip(role_list,inf_list_split):
ip_emb,noise_,inp_n=[],[],[]
ip_emb,inp_n=[],[]
for p,n in zip(prompts,[neg_text]*len(prompts)):
seed_random = random.randint(0, MAX_SEED) #pulid 和uno的emb需要随机数
inp,inp_neg,x=get_emb_flux_pulid(t5_,clip_,if_repo,seed_random,p,n,width,height,num_steps=20,guidance=3.5,device=device)
inp,inp_neg=get_emb_flux_pulid(t5_,clip_,if_repo,p,n,width,height,num_steps=20,guidance=3.5,device=device)
ip_emb.append(inp)
inp_n.append(inp_neg)
noise_.append(x)
only_role_emb[key]=ip_emb
noise_x[key]=noise_
inp_neg_list[key]=inp_n
elif infer_mode == "uno":
only_role_emb={}
from .UNO.uno.flux.sampling import prepare_multi_ip_wrapper
from .UNO.uno.flux.sampling import get_noise
for key ,prompts in zip(role_list,inf_list_split):
ip_emb=[]
for p,x_1 in zip(prompts,x_1_refs_dict[key]):
seed_random = random.randint(0, MAX_SEED) #pulid 和uno的emb需要随机数
uno_x = get_noise(1, height, width, device=device,dtype=torch.bfloat16, seed=seed_random)
inp = prepare_multi_ip_wrapper(clip,img=uno_x,prompt=p, ref_imgs=x_1, pe=uno_pe)
inp = prepare_multi_ip_wrapper(clip,prompt=p, ref_imgs=x_1, pe=uno_pe,device=device,h=height,w=width)
ip_emb.append(inp)
only_role_emb[key]=ip_emb
elif infer_mode=="kolor_face":
from .model_loader_utils import glm_single_encode
from .kolors.models.tokenization_chatglm import ChatGLMTokenizer
@@ -758,10 +740,8 @@ class StoryDiffusion_CLIPTextEncode:
prompts_dual=[apply_style_positive(add_style,i+pos_text)[0] for i in prompts_dual] #[' The figurine play whith The pig in the garden,best 8k,RAW']
daul_emb=[]
for dual_t,x_1 in zip(prompts_dual,x_1_refs_dual): # dual_t:The figurine play whith The pig in the garden best 8k,RAW
seed_random = random.randint(0, MAX_SEED)
uno_x = get_noise(1, height, width, device=device,dtype=torch.bfloat16, seed=seed_random)
inp = prepare_multi_ip_wrapper(clip,img=uno_x,prompt=dual_t, ref_imgs=x_1, pe=uno_pe)
for dual_t,x_1 in zip(prompts_dual,x_1_refs_dual): # dual_t:The figurine play whith The pig in the garden best 8k,RAW
inp = prepare_multi_ip_wrapper(clip,prompt=dual_t, ref_imgs=x_1, pe=uno_pe,device=device,h=height,w=width)
daul_emb.append(inp)
else:
daul_emb=None
@@ -1422,7 +1402,7 @@ class StoryDiffusion_KSampler:
control_image=cn_img[index] if isinstance(cn_img , list) else input_id_img_s_dict[key][0],
guidance_scale=cfg,
num_steps=steps,
seed=seed,
seed=random.randint(0, seed),
infusenet_conditioning_scale=1.0,
infusenet_guidance_start=0,
infusenet_guidance_end=1.0,
@@ -1443,10 +1423,9 @@ class StoryDiffusion_KSampler:
num_steps=steps,
start_step=2,
guidance=cfg,
seed=seed,
seed=random.randint(0, seed),
inp=emb,
inp_neg=negative[0][key][index],
x=negative[1][key][index], #seed 上一个节点调用
id_embeddings=input_id_emb_s_dict[key][0],
uncond_id_embeddings=input_id_emb_un_dict[key][0],
) # torch.Size([1, 4, 64, 64])
@@ -1466,6 +1445,8 @@ class StoryDiffusion_KSampler:
guidance=cfg,
num_steps=steps,
inp_cond=emb,
seed=random.randint(0, seed),
) # torch.Size([1, 4, 64, 64])
samples_list.append(samples)
@@ -1478,6 +1459,7 @@ class StoryDiffusion_KSampler:
guidance=cfg,
num_steps=steps,
inp_cond=emb,
seed=random.randint(0, seed)
) # torch.Size([1, 4, 64, 64])
samples_list.insert(index, samples)
@@ -1499,6 +1481,9 @@ class StoryDiffusion_KSampler:
mask_scope=0.20,
mask_strategy="max_norm", #"min_max_per_channel", "max_norm"
guidance_weight=cfg,
height=height,
width=width,
seed=random.randint(0, seed),
device=device,
)
samples_list.append(samples)
@@ -1517,11 +1502,11 @@ class StoryDiffusion_KSampler:
guidance_scale=cfg,
subject_image=True,
subject_scale=0.9,
generator=torch.manual_seed(seed),
generator=torch.manual_seed(random.randint(0, seed)),
text_ids=emb_list[2],
subject_image_embeds_dict=id_emb,
)[0] # torch.Size([1, 4, 64, 64])
print(samples.shape)
#print(samples.shape)
samples_list.append(samples)
out = {}
out["samples"] = torch.cat(samples_list, dim=0)