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ZeroCool940711 2022-09-30 15:20:02 -07:00
parent 20a89a1233
commit 857608c5f6
2 changed files with 133 additions and 120 deletions
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configs/webui/webui_streamlit.yaml
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@ -294,7 +294,7 @@ img2img:
img2txt: img2txt:
batch_size: 100 batch_size: 100
blip_image_eval_size: 512
concepts_library: concepts_library:
concepts_per_page: 12 concepts_per_page: 12

251
scripts/img2txt.py
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@ -12,9 +12,9 @@
# GNU Affero General Public License for more details. # GNU Affero General Public License for more details.
# You should have received a copy of the GNU Affero General Public License # You should have received a copy of the GNU Affero General Public License
# along with this program. If not, see <http://www.gnu.org/licenses/>. # along with this program. If not, see <http://www.gnu.org/licenses/>.
#--------------------------------------------------------------------------------------------------------------------------------------------------- # ---------------------------------------------------------------------------------------------------------------------------------------------------
""" """
CLIP Interrogator made by @pharmapsychotic modified to work with our WebUI. CLIP Interrogator made by @pharmapsychotic modified to work with our WebUI.
@ -31,20 +31,20 @@ Please consider buying him a coffee via [ko-fi](https://ko-fi.com/pharmapsychoti
And if you're looking for more Ai art tools check out my [Ai generative art tools list](https://pharmapsychotic.com/tools.html). And if you're looking for more Ai art tools check out my [Ai generative art tools list](https://pharmapsychotic.com/tools.html).
""" """
#--------------------------------------------------------------------------------------------------------------------------------------------------- # ---------------------------------------------------------------------------------------------------------------------------------------------------
# base webui import and utils. # base webui import and utils.
from ldm.util import default
from sd_utils import * from sd_utils import *
# streamlit imports # streamlit imports
#streamlit components section # streamlit components section
import streamlit_nested_layout import streamlit_nested_layout
#other imports # other imports
import clip, open_clip import clip
import open_clip
import gc import gc
import os import os
import pandas as pd import pandas as pd
@ -56,53 +56,59 @@ from torchvision.transforms.functional import InterpolationMode
from ldm.models.blip import blip_decoder from ldm.models.blip import blip_decoder
# end of imports # end of imports
#--------------------------------------------------------------------------------------------------------------- # ---------------------------------------------------------------------------------------------------------------
device = torch.device('cuda:0' if torch.cuda.is_available() else 'cpu') device = torch.device('cuda:0' if torch.cuda.is_available() else 'cpu')
blip_image_eval_size = 512 blip_image_eval_size = 512
blip_model = None #blip_model_url = 'https://storage.googleapis.com/sfr-vision-language-research/BLIP/models/model*_base_caption.pth'
#blip_model_url = 'https://storage.googleapis.com/sfr-vision-language-research/BLIP/models/model*_base_caption.pth'
def load_blip_model(): def load_blip_model():
print ("Loading BLIP Model") print("Loading BLIP Model")
st.session_state["log_message"].code("Loading BLIP Model", language='') st.session_state["log_message"].code("Loading BLIP Model", language='')
with server_state_lock['blip_model']: if "blip_model" not in server_state:
if "blip_model" not in server_state: with server_state_lock['blip_model']:
blip_model = blip_decoder(pretrained="models/blip/model__base_caption.pth", server_state["blip_model"] = blip_decoder(pretrained="models/blip/model__base_caption.pth",
image_size=blip_image_eval_size, vit='base', med_config="configs/blip/med_config.json") image_size=blip_image_eval_size, vit='base', med_config="configs/blip/med_config.json")
blip_model.eval()
blip_model = blip_model.to(device).half() server_state["blip_model"] = server_state["blip_model"].eval()
print ("BLIP Model Loaded") #if not st.session_state["defaults"].general.optimized:
server_state["blip_model"] = server_state["blip_model"].to(device).half()
print("BLIP Model Loaded")
st.session_state["log_message"].code("BLIP Model Loaded", language='') st.session_state["log_message"].code("BLIP Model Loaded", language='')
else: else:
print ("BLIP Model already loaded") print("BLIP Model already loaded")
st.session_state["log_message"].code("BLIP Model Already Loaded", language='') st.session_state["log_message"].code("BLIP Model Already Loaded", language='')
#return server_state["blip_model"]
return blip_model
def generate_caption(pil_image): def generate_caption(pil_image):
global blip_model
#width, height = pil_image.size
gpu_image = transforms.Compose([ load_blip_model()
transforms.Resize((blip_image_eval_size, blip_image_eval_size), interpolation=InterpolationMode.BICUBIC),
transforms.ToTensor(), gpu_image = transforms.Compose([ # type: ignore
transforms.Normalize((0.48145466, 0.4578275, 0.40821073), (0.26862954, 0.26130258, 0.27577711)) transforms.Resize((blip_image_eval_size, blip_image_eval_size), interpolation=InterpolationMode.BICUBIC), # type: ignore
])(pil_image).unsqueeze(0).to(device).half() transforms.ToTensor(), # type: ignore
transforms.Normalize((0.48145466, 0.4578275, 0.40821073), (0.26862954, 0.26130258, 0.27577711)) # type: ignore
])(pil_image).unsqueeze(0).to(device).half()
with torch.no_grad(): with torch.no_grad():
caption = blip_model.generate(gpu_image, sample=False, num_beams=3, max_length=20, min_length=5) caption = server_state["blip_model"].generate(gpu_image, sample=False, num_beams=3, max_length=20, min_length=5)
#print (caption) #print (caption)
return caption[0] return caption[0]
def load_list(filename): def load_list(filename):
with open(filename, 'r', encoding='utf-8', errors='replace') as f: with open(filename, 'r', encoding='utf-8', errors='replace') as f:
items = [line.strip() for line in f.readlines()] items = [line.strip() for line in f.readlines()]
return items return items
def rank(model, image_features, text_array, top_count=1): def rank(model, image_features, text_array, top_count=1):
top_count = min(top_count, len(text_array)) top_count = min(top_count, len(text_array))
text_tokens = clip.tokenize([text for text in text_array]).cuda() text_tokens = clip.tokenize([text for text in text_array]).cuda()
@ -115,13 +121,15 @@ def rank(model, image_features, text_array, top_count=1):
similarity += (100.0 * image_features[i].unsqueeze(0) @ text_features.T).softmax(dim=-1) similarity += (100.0 * image_features[i].unsqueeze(0) @ text_features.T).softmax(dim=-1)
similarity /= image_features.shape[0] similarity /= image_features.shape[0]
top_probs, top_labels = similarity.cpu().topk(top_count, dim=-1) top_probs, top_labels = similarity.cpu().topk(top_count, dim=-1)
return [(text_array[top_labels[0][i].numpy()], (top_probs[0][i].numpy()*100)) for i in range(top_count)] return [(text_array[top_labels[0][i].numpy()], (top_probs[0][i].numpy()*100)) for i in range(top_count)]
def clear_cuda(): def clear_cuda():
torch.cuda.empty_cache() torch.cuda.empty_cache()
gc.collect() gc.collect()
def batch_rank(model, image_features, text_array, batch_size=st.session_state["defaults"].img2txt.batch_size): def batch_rank(model, image_features, text_array, batch_size=st.session_state["defaults"].img2txt.batch_size):
batch_count = len(text_array) // batch_size batch_count = len(text_array) // batch_size
batches = [text_array[i*batch_size:(i+1)*batch_size] for i in range(batch_count)] batches = [text_array[i*batch_size:(i+1)*batch_size] for i in range(batch_count)]
@ -132,18 +140,19 @@ def batch_rank(model, image_features, text_array, batch_size=st.session_state["d
return ranks return ranks
def interrogate(image, models): def interrogate(image, models):
global blip_model
#server_state["blip_model"] =
load_blip_model()
blip_model = load_blip_model() print("Generating Caption")
print ("Generating Caption")
st.session_state["log_message"].code("Generating Caption", language='') st.session_state["log_message"].code("Generating Caption", language='')
caption = generate_caption(image) caption = generate_caption(image)
if st.session_state["defaults"].general.optimized: if st.session_state["defaults"].general.optimized:
del blip_model del server_state["blip_model"]
clear_cuda() clear_cuda()
print ("Caption Generated") print("Caption Generated")
st.session_state["log_message"].code("Caption Generated", language='') st.session_state["log_message"].code("Caption Generated", language='')
if len(models) == 0: if len(models) == 0:
@ -151,44 +160,48 @@ def interrogate(image, models):
return return
table = [] table = []
bests = [[('',0)]]*5 bests = [[('', 0)]]*5
print ("Ranking Text") print("Ranking Text")
for model_name in models: for model_name in models:
print(f"Interrogating with {model_name}...") print(f"Interrogating with {model_name}...")
st.session_state["log_message"].code(f"Interrogating with {model_name}...", language='') st.session_state["log_message"].code(f"Interrogating with {model_name}...", language='')
if model_name == 'ViT-H-14':
model, _, preprocess = open_clip.create_model_and_transforms(model_name, pretrained='laion2b_s32b_b79k')
elif model_name == 'ViT-g-14':
model, _, preprocess = open_clip.create_model_and_transforms(model_name, pretrained='laion2b_s12b_b42k')
else:
model, preprocess = clip.load(model_name, device=device)
model.cuda().eval()
images = preprocess(image).unsqueeze(0).cuda()
with torch.no_grad():
image_features = model.encode_image(images).float()
image_features /= image_features.norm(dim=-1, keepdim=True)
if "clip_model" not in server_state:
#with server_state_lock[server_state["clip_model"]]:
if model_name == 'ViT-H-14':
server_state["clip_model"], _, server_state["preprocess"] = open_clip.create_model_and_transforms(model_name, pretrained='laion2b_s32b_b79k')
elif model_name == 'ViT-g-14':
server_state["clip_model"], _, server_state["preprocess"] = open_clip.create_model_and_transforms(model_name, pretrained='laion2b_s12b_b42k')
else:
server_state["clip_model"], server_state["preprocess"] = clip.load(model_name, device=device)
server_state["clip_model"] = server_state["clip_model"].cuda().eval()
images = server_state["preprocess"](image).unsqueeze(0).cuda()
with torch.no_grad():
image_features = server_state["clip_model"].encode_image(images).float()
image_features /= image_features.norm(dim=-1, keepdim=True)
if st.session_state["defaults"].general.optimized: if st.session_state["defaults"].general.optimized:
clear_cuda() clear_cuda()
ranks = [] ranks = []
ranks.append(batch_rank(model, image_features, server_state["mediums"])) ranks.append(batch_rank(server_state["clip_model"], image_features, server_state["mediums"]))
ranks.append(batch_rank(model, image_features, ["by "+artist for artist in server_state["artists"]])) ranks.append(batch_rank(server_state["clip_model"], image_features, ["by "+artist for artist in server_state["artists"]]))
ranks.append(batch_rank(model, image_features, server_state["trending_list"])) ranks.append(batch_rank(server_state["clip_model"], image_features, server_state["trending_list"]))
ranks.append(batch_rank(model, image_features, server_state["movements"])) ranks.append(batch_rank(server_state["clip_model"], image_features, server_state["movements"]))
ranks.append(batch_rank(model, image_features, server_state["flavors"])) ranks.append(batch_rank(server_state["clip_model"], image_features, server_state["flavors"]))
# ranks.append(batch_rank(model, image_features, server_state["genres"])) # ranks.append(batch_rank(server_state["clip_model"], image_features, server_state["genres"]))
# ranks.append(batch_rank(model, image_features, server_state["styles"])) # ranks.append(batch_rank(server_state["clip_model"], image_features, server_state["styles"]))
# ranks.append(batch_rank(model, image_features, server_state["techniques"])) # ranks.append(batch_rank(server_state["clip_model"], image_features, server_state["techniques"]))
# ranks.append(batch_rank(model, image_features, server_state["subjects"])) # ranks.append(batch_rank(server_state["clip_model"], image_features, server_state["subjects"]))
# ranks.append(batch_rank(model, image_features, server_state["colors"])) # ranks.append(batch_rank(server_state["clip_model"], image_features, server_state["colors"]))
# ranks.append(batch_rank(model, image_features, server_state["moods"])) # ranks.append(batch_rank(server_state["clip_model"], image_features, server_state["moods"]))
# ranks.append(batch_rank(model, image_features, server_state["themes"])) # ranks.append(batch_rank(server_state["clip_model"], image_features, server_state["themes"]))
# ranks.append(batch_rank(model, image_features, server_state["keywords"])) # ranks.append(batch_rank(server_state["clip_model"], image_features, server_state["keywords"]))
for i in range(len(ranks)): for i in range(len(ranks)):
confidence_sum = 0 confidence_sum = 0
@ -204,27 +217,28 @@ def interrogate(image, models):
table.append(row) table.append(row)
if st.session_state["defaults"].general.optimized: if st.session_state["defaults"].general.optimized:
del model del server_state["clip_model"]
gc.collect() gc.collect()
#for i in range(len(st.session_state["uploaded_image"])): # for i in range(len(st.session_state["uploaded_image"])):
st.session_state["prediction_table"][st.session_state["processed_image_count"]].dataframe(pd.DataFrame( st.session_state["prediction_table"][st.session_state["processed_image_count"]].dataframe(pd.DataFrame(
table, columns=["Model", "Medium", "Artist", "Trending", "Movement", "Flavors"])) table, columns=["Model", "Medium", "Artist", "Trending", "Movement", "Flavors"]))
flaves = ', '.join([f"{x[0]}" for x in bests[4]]) flaves = ', '.join([f"{x[0]}" for x in bests[4]])
medium = bests[0][0][0] medium = bests[0][0][0]
if caption.startswith(medium): if caption.startswith(medium):
st.session_state["text_result"][st.session_state["processed_image_count"]].code( st.session_state["text_result"][st.session_state["processed_image_count"]].code(
f"\n\n{caption} {bests[1][0][0]}, {bests[2][0][0]}, {bests[3][0][0]}, {flaves}", language="") f"\n\n{caption} {bests[1][0][0]}, {bests[2][0][0]}, {bests[3][0][0]}, {flaves}", language="")
else: else:
st.session_state["text_result"][st.session_state["processed_image_count"]].code( st.session_state["text_result"][st.session_state["processed_image_count"]].code(
f"\n\n{caption}, {medium} {bests[1][0][0]}, {bests[2][0][0]}, {bests[3][0][0]}, {flaves}", language="") f"\n\n{caption}, {medium} {bests[1][0][0]}, {bests[2][0][0]}, {bests[3][0][0]}, {flaves}", language="")
# #
print ("Finished Interrogating.") print("Finished Interrogating.")
st.session_state["log_message"].code("Finished Interrogating.", language="") st.session_state["log_message"].code("Finished Interrogating.", language="")
# #
def img2txt(): def img2txt():
data_path = "data/" data_path = "data/"
@ -251,11 +265,11 @@ def img2txt():
models.append('ViT-B/32') models.append('ViT-B/32')
if st.session_state['ViTB16']: if st.session_state['ViTB16']:
models.append('ViT-B/16') models.append('ViT-B/16')
if st.session_state["ViTL14"]: if st.session_state["ViTL14"]:
models.append('ViT-L/14') models.append('ViT-L/14')
if st.session_state["ViT-H-14"]: if st.session_state["ViT-H-14"]:
models.append('ViT-H-14') models.append('ViT-H-14')
if st.session_state["ViT-g-14"]: if st.session_state["ViT-g-14"]:
models.append('ViT-g-14') models.append('ViT-g-14')
if st.session_state["ViTL14_336px"]: if st.session_state["ViTL14_336px"]:
models.append('ViT-L/14@336px') models.append('ViT-L/14@336px')
@ -270,33 +284,35 @@ def img2txt():
if st.session_state["RN50x64"]: if st.session_state["RN50x64"]:
models.append('RN50x64') models.append('RN50x64')
#if str(image_path_or_url).startswith('http://') or str(image_path_or_url).startswith('https://'): # if str(image_path_or_url).startswith('http://') or str(image_path_or_url).startswith('https://'):
#image = Image.open(requests.get(image_path_or_url, stream=True).raw).convert('RGB') #image = Image.open(requests.get(image_path_or_url, stream=True).raw).convert('RGB')
#else: # else:
#image = Image.open(image_path_or_url).convert('RGB') #image = Image.open(image_path_or_url).convert('RGB')
#thumb = st.session_state["uploaded_image"].image.copy() #thumb = st.session_state["uploaded_image"].image.copy()
#thumb.thumbnail([blip_image_eval_size, blip_image_eval_size]) #thumb.thumbnail([blip_image_eval_size, blip_image_eval_size])
#display(thumb) # display(thumb)
st.session_state["processed_image_count"] = 0 st.session_state["processed_image_count"] = 0
for i in range(len(st.session_state["uploaded_image"])): for i in range(len(st.session_state["uploaded_image"])):
interrogate(st.session_state["uploaded_image"][i].pil_image, models=models) interrogate(st.session_state["uploaded_image"][i].pil_image, models=models)
# increase counter. # increase counter.
st.session_state["processed_image_count"] += 1 st.session_state["processed_image_count"] += 1
# #
def layout(): def layout():
#set_page_title("Image-to-Text - Stable Diffusion WebUI") #set_page_title("Image-to-Text - Stable Diffusion WebUI")
#st.info("Under Construction. :construction_worker:") #st.info("Under Construction. :construction_worker:")
with st.form("img2txt-inputs"): with st.form("img2txt-inputs"):
st.session_state["generation_mode"] = "img2txt" st.session_state["generation_mode"] = "img2txt"
#st.write("---") # st.write("---")
# creating the page layout using columns # creating the page layout using columns
col1, col2 = st.columns([1,4], gap="large") col1, col2 = st.columns([1, 4], gap="large")
with col1: with col1:
#url = st.text_area("Input Text","") #url = st.text_area("Input Text","")
@ -304,68 +320,66 @@ def layout():
#st.subheader("Input Image") #st.subheader("Input Image")
st.session_state["uploaded_image"] = st.file_uploader('Input Image', type=['png', 'jpg', 'jpeg'], accept_multiple_files=True) st.session_state["uploaded_image"] = st.file_uploader('Input Image', type=['png', 'jpg', 'jpeg'], accept_multiple_files=True)
st.subheader("CLIP models") st.subheader("CLIP models")
with st.expander("Stable Diffusion", expanded=True): with st.expander("Stable Diffusion", expanded=True):
st.session_state["ViTL14"] = st.checkbox("ViTL14", value=True, help="For StableDiffusion you can just use ViTL14.") st.session_state["ViTL14"] = st.checkbox("ViTL14", value=True, help="For StableDiffusion you can just use ViTL14.")
with st.expander("Others"): with st.expander("Others"):
st.info("For DiscoDiffusion and JAX enable all the same models here as you intend to use when generating your images.") st.info("For DiscoDiffusion and JAX enable all the same models here as you intend to use when generating your images.")
st.session_state["ViT-H-14"] = st.checkbox("ViT-H-14", value=False, help="ViT-H-14 model.") st.session_state["ViT-H-14"] = st.checkbox("ViT-H-14", value=False, help="ViT-H-14 model.")
st.session_state["ViT-g-14"] = st.checkbox("ViT-g-14", value=False, help="ViT-g-14 model.") st.session_state["ViT-g-14"] = st.checkbox("ViT-g-14", value=False, help="ViT-g-14 model.")
st.session_state["ViTL14_336px"] = st.checkbox("ViTL14_336px", value=False, help="ViTL14_336px model.") st.session_state["ViTL14_336px"] = st.checkbox("ViTL14_336px", value=False, help="ViTL14_336px model.")
st.session_state["ViTB16"] = st.checkbox("ViTB16", value=False, help="ViTB16 model.") st.session_state["ViTB16"] = st.checkbox("ViTB16", value=False, help="ViTB16 model.")
st.session_state["ViTB32"] = st.checkbox("ViTB32", value=False, help="ViTB32 model.") st.session_state["ViTB32"] = st.checkbox("ViTB32", value=False, help="ViTB32 model.")
st.session_state["RN50"] = st.checkbox("RN50", value=False, help="RN50 model.") st.session_state["RN50"] = st.checkbox("RN50", value=False, help="RN50 model.")
st.session_state["RN50x4"] = st.checkbox("RN50x4", value=False, help="RN50x4 model.") st.session_state["RN50x4"] = st.checkbox("RN50x4", value=False, help="RN50x4 model.")
st.session_state["RN50x16"] = st.checkbox("RN50x16", value=False, help="RN50x16 model.") st.session_state["RN50x16"] = st.checkbox("RN50x16", value=False, help="RN50x16 model.")
st.session_state["RN50x64"] = st.checkbox("RN50x64", value=False, help="RN50x64 model.") st.session_state["RN50x64"] = st.checkbox("RN50x64", value=False, help="RN50x64 model.")
st.session_state["RN101"] = st.checkbox("RN101", value=False, help="RN101 model.") st.session_state["RN101"] = st.checkbox("RN101", value=False, help="RN101 model.")
# #
#st.subheader("Logs:") # st.subheader("Logs:")
st.session_state["log_message"] = st.empty() st.session_state["log_message"] = st.empty()
st.session_state["log_message"].code('', language="") st.session_state["log_message"].code('', language="")
with col2: with col2:
st.subheader("Image") st.subheader("Image")
refresh = st.form_submit_button("Refresh", help='Refresh the image preview to show your uploaded image instead of the default placeholder.') refresh = st.form_submit_button("Refresh", help='Refresh the image preview to show your uploaded image instead of the default placeholder.')
if st.session_state["uploaded_image"]: if st.session_state["uploaded_image"]:
#print (type(st.session_state["uploaded_image"])) #print (type(st.session_state["uploaded_image"]))
#if len(st.session_state["uploaded_image"]) == 1: # if len(st.session_state["uploaded_image"]) == 1:
st.session_state["input_image_preview"] = [] st.session_state["input_image_preview"] = []
st.session_state["input_image_preview_container"] = [] st.session_state["input_image_preview_container"] = []
st.session_state["prediction_table"] = [] st.session_state["prediction_table"] = []
st.session_state["text_result"] = [] st.session_state["text_result"] = []
for i in range(len(st.session_state["uploaded_image"])): for i in range(len(st.session_state["uploaded_image"])):
st.session_state["input_image_preview_container"].append(i) st.session_state["input_image_preview_container"].append(i)
st.session_state["input_image_preview_container"][i]= st.empty() st.session_state["input_image_preview_container"][i] = st.empty()
with st.session_state["input_image_preview_container"][i].container(): with st.session_state["input_image_preview_container"][i].container():
col1_output, col2_output = st.columns([2,10], gap="medium") col1_output, col2_output = st.columns([2, 10], gap="medium")
with col1_output: with col1_output:
st.session_state["input_image_preview"].append(i) st.session_state["input_image_preview"].append(i)
st.session_state["input_image_preview"][i]= st.empty() st.session_state["input_image_preview"][i] = st.empty()
st.session_state["uploaded_image"][i].pil_image = Image.open(st.session_state["uploaded_image"][i]).convert('RGB') st.session_state["uploaded_image"][i].pil_image = Image.open(st.session_state["uploaded_image"][i]).convert('RGB')
st.session_state["input_image_preview"][i].image(st.session_state["uploaded_image"][i].pil_image, use_column_width=True, clamp=True) st.session_state["input_image_preview"][i].image(st.session_state["uploaded_image"][i].pil_image, use_column_width=True, clamp=True)
with st.session_state["input_image_preview_container"][i].container(): with st.session_state["input_image_preview_container"][i].container():
with col2_output: with col2_output:
st.session_state["prediction_table"].append(i) st.session_state["prediction_table"].append(i)
st.session_state["prediction_table"][i] = st.empty() st.session_state["prediction_table"][i] = st.empty()
st.session_state["prediction_table"][i].table() st.session_state["prediction_table"][i].table()
st.session_state["text_result"].append(i) st.session_state["text_result"].append(i)
st.session_state["text_result"][i]= st.empty() st.session_state["text_result"][i] = st.empty()
st.session_state["text_result"][i].code("", language="") st.session_state["text_result"][i].code("", language="")
else: else:
#st.session_state["input_image_preview"].code('', language="") #st.session_state["input_image_preview"].code('', language="")
@ -373,13 +387,13 @@ def layout():
# #
# Every form must have a submit button, the extra blank spaces is a temp way to align it with the input field. Needs to be done in CSS or some other way. # Every form must have a submit button, the extra blank spaces is a temp way to align it with the input field. Needs to be done in CSS or some other way.
#generate_col1.title("") # generate_col1.title("")
#generate_col1.title("") # generate_col1.title("")
generate_button = st.form_submit_button("Generate!") generate_button = st.form_submit_button("Generate!")
if generate_button: if generate_button:
# if model, pipe, RealESRGAN or GFPGAN is in st.session_state remove the model and pipe form session_state so that they are reloaded. # if model, pipe, RealESRGAN or GFPGAN is in st.session_state remove the model and pipe form session_state so that they are reloaded.
if "model" in st.session_state and st.session_state["defaults"].general.optimized: if "model" in st.session_state and st.session_state["defaults"].general.optimized:
del st.session_state["model"] del st.session_state["model"]
if "pipe" in st.session_state and st.session_state["defaults"].general.optimized: if "pipe" in st.session_state and st.session_state["defaults"].general.optimized:
del st.session_state["pipe"] del st.session_state["pipe"]
@ -387,7 +401,6 @@ def layout():
del st.session_state["RealESRGAN"] del st.session_state["RealESRGAN"]
if "GFPGAN" in st.session_state and st.session_state["defaults"].general.optimized: if "GFPGAN" in st.session_state and st.session_state["defaults"].general.optimized:
del st.session_state["GFPGAN"] del st.session_state["GFPGAN"]
# run clip interrogator # run clip interrogator
img2txt() img2txt()