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Basic implementation for the Concept Library tab made by cloning the Home tab.
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ZeroCool940711
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@ -973,52 +973,6 @@ def load_learned_embed_in_clip(learned_embeds_path, text_encoder, tokenizer, tok
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text_encoder.get_input_embeddings().weight.data[token_id] = embeds
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return token
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def concepts_library():
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html_gallery = '''
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<div class="flex gr-gap gr-form-gap row gap-4 w-full flex-wrap" id="main_row">
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'''
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for model in models:
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html_gallery = html_gallery+f'''
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<div class="gr-block gr-box relative w-full overflow-hidden border-solid border border-gray-200 gr-panel">
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<div class="output-markdown gr-prose" style="max-width: 100%;">
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<h3>
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<a href="https://huggingface.co/{model["id"]}" target="_blank">
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<code>{html.escape(model["token"])}</code>
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</a>
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</h3>
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</div>
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<div id="gallery" class="gr-block gr-box relative w-full overflow-hidden border-solid border border-gray-200">
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<div class="wrap svelte-17ttdjv opacity-0"></div>
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<div class="absolute left-0 top-0 py-1 px-2 rounded-br-lg shadow-sm text-xs text-gray-500 flex items-center pointer-events-none bg-white z-20 border-b border-r border-gray-100 dark:bg-gray-900">
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<span class="mr-2 h-[12px] w-[12px] opacity-80">
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<svg xmlns="http://www.w3.org/2000/svg" width="100%" height="100%" viewBox="0 0 24 24" fill="none" stroke="currentColor" stroke-width="1.5" stroke-linecap="round" stroke-linejoin="round" class="feather feather-image">
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<rect x="3" y="3" width="18" height="18" rx="2" ry="2"></rect>
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<circle cx="8.5" cy="8.5" r="1.5"></circle>
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<polyline points="21 15 16 10 5 21"></polyline>
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</svg>
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</span> {model["concept_type"]}
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</div>
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<div class="overflow-y-auto h-full p-2" style="position: relative;">
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<div class="grid gap-2 grid-cols-2 sm:grid-cols-2 md:grid-cols-2 lg:grid-cols-2 xl:grid-cols-2 2xl:grid-cols-2 svelte-1g9btlg pt-6">
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'''
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for image in model["images"]:
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html_gallery = html_gallery + f'''
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<button class="gallery-item svelte-1g9btlg">
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<img alt="" loading="lazy" class="h-full w-full overflow-hidden object-contain" src="file/{image}">
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</button>
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'''
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html_gallery = html_gallery+'''
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</div>
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<iframe style="display: block; position: absolute; top: 0; left: 0; width: 100%; height: 100%; overflow: hidden; border: 0; opacity: 0; pointer-events: none; z-index: -1;" aria-hidden="true" tabindex="-1" src="about:blank"></iframe>
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</div>
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</div>
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</div>
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'''
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html_gallery = html_gallery+'''
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</div>
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'''
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def image_grid(imgs, batch_size, force_n_rows=None, captions=None):
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#print (len(imgs))
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if force_n_rows is not None:
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@ -1377,6 +1331,9 @@ def process_images(
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for files in os.listdir(embedding_path):
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if files.endswith(ext):
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load_learned_embed_in_clip(f"{os.path.join(embedding_path, files)}", text_encoder, tokenizer, f"<{prompt_tokens[0]}>")
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#
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os.makedirs(outpath, exist_ok=True)
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@ -8,46 +8,50 @@ from sd_utils import *
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#other imports
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#from transformers import CLIPTextModel, CLIPTokenizer
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# Temp imports
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# Temp imports
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# end of imports
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#---------------------------------------------------------------------------------------------------------------
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def load_learned_embed_in_clip(learned_embeds_path, text_encoder, tokenizer, token=None):
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loaded_learned_embeds = torch.load(learned_embeds_path, map_location="cpu")
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#def load_learned_embed_in_clip(learned_embeds_path, text_encoder, tokenizer, token=None):
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#loaded_learned_embeds = torch.load(learned_embeds_path, map_location="cpu")
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# separate token and the embeds
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trained_token = list(loaded_learned_embeds.keys())[0]
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embeds = loaded_learned_embeds[trained_token]
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## separate token and the embeds
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#print (loaded_learned_embeds)
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#trained_token = list(loaded_learned_embeds.keys())[0]
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#embeds = loaded_learned_embeds[trained_token]
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# cast to dtype of text_encoder
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dtype = text_encoder.get_input_embeddings().weight.dtype
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embeds.to(dtype)
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## cast to dtype of text_encoder
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#dtype = text_encoder.get_input_embeddings().weight.dtype
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#embeds.to(dtype)
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# add the token in tokenizer
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token = token if token is not None else trained_token
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num_added_tokens = tokenizer.add_tokens(token)
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i = 1
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while(num_added_tokens == 0):
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print(f"The tokenizer already contains the token {token}.")
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token = f"{token[:-1]}-{i}>"
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print(f"Attempting to add the token {token}.")
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num_added_tokens = tokenizer.add_tokens(token)
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i+=1
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## add the token in tokenizer
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#token = token if token is not None else trained_token
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#num_added_tokens = tokenizer.add_tokens(token)
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#i = 1
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#while(num_added_tokens == 0):
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#print(f"The tokenizer already contains the token {token}.")
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#token = f"{token[:-1]}-{i}>"
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#print(f"Attempting to add the token {token}.")
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#num_added_tokens = tokenizer.add_tokens(token)
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#i+=1
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# resize the token embeddings
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text_encoder.resize_token_embeddings(len(tokenizer))
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## resize the token embeddings
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#text_encoder.resize_token_embeddings(len(tokenizer))
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# get the id for the token and assign the embeds
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token_id = tokenizer.convert_tokens_to_ids(token)
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text_encoder.get_input_embeddings().weight.data[token_id] = embeds
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return token
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## get the id for the token and assign the embeds
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#token_id = tokenizer.convert_tokens_to_ids(token)
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#text_encoder.get_input_embeddings().weight.data[token_id] = embeds
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#return token
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#def token_loader()
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learned_token = load_learned_embed_in_clip(f"models/custom/embeddings/Custom Ami.pt", pipe.text_encoder, pipe.tokenizer, "*")
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##def token_loader()
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#learned_token = load_learned_embed_in_clip(f"models/custom/embeddings/Custom Ami.pt", st.session_state.pipe.text_encoder, st.session_state.pipe.tokenizer, "*")
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#model_content["token"] = learned_token
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#models.append(model_content)
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model_id = "./models/custom/embeddings/"
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def layout():
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st.write("Textual Inversion")
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st.write("Textual Inversion")
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@ -4,6 +4,8 @@ from sd_utils import *
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# streamlit imports
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from streamlit import StopException
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from streamlit.runtime.in_memory_file_manager import in_memory_file_manager
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from streamlit.elements import image as STImage
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#other imports
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import os
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@ -12,8 +14,6 @@ from io import BytesIO
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from ldm.models.diffusion.ddim import DDIMSampler
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from ldm.models.diffusion.plms import PLMSSampler
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from streamlit.runtime.in_memory_file_manager import in_memory_file_manager
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from streamlit.elements import image as STImage
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# Temp imports
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@ -185,12 +185,7 @@ def layout():
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st.session_state["update_preview_frequency"] = st.text_input("Update Image Preview Frequency", value=st.session_state['defaults'].txt2img.update_preview_frequency,
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help="Frequency in steps at which the the preview image is updated. By default the frequency \
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is set to 1 step.")
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#
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#if st.session_state.defaults.general.use_sd_concepts_library:
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#with st.expander("Concept Library"):
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#st.write("test")
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with col2:
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preview_tab, gallery_tab = st.tabs(["Preview", "Gallery"])
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@ -100,8 +100,8 @@ def layout():
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iconName=['dashboard','model_training' ,'cloud_download', 'settings'], default_choice=0)
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if tabs =='Stable Diffusion':
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txt2img_tab, img2img_tab, txt2vid_tab, postprocessing_tab = st.tabs(["Text-to-Image Unified", "Image-to-Image Unified",
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"Text-to-Video","Post-Processing"])
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txt2img_tab, img2img_tab, txt2vid_tab, postprocessing_tab, concept_library_tab = st.tabs(["Text-to-Image Unified", "Image-to-Image Unified",
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"Text-to-Video","Post-Processing", "Concept Library"])
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#with home_tab:
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#from home import layout
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#layout()
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@ -117,7 +117,10 @@ def layout():
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with txt2vid_tab:
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from txt2vid import layout
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layout()
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with concept_library_tab:
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from sd_concept_library import layout
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layout()
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#
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elif tabs == 'Model Manager':
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