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https://github.com/Sygil-Dev/sygil-webui.git
synced 2024-12-15 06:21:34 +03:00
- Bumped the version of diffusers used on the txt2vid tab to be now v0.3.0.
- Added initial file for the textual inversion tab.
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ZeroCool940711
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ef2da42489
@ -22,7 +22,7 @@ dependencies:
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- accelerate==0.12.0
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- albumentations==0.4.3
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- basicsr>=1.3.4.0
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- diffusers==0.2.4
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- diffusers==0.3.0
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- einops==0.3.0
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- facexlib>=0.2.3
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- gradio==3.1.6
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@ -43,6 +43,7 @@ dependencies:
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- streamlit-option-menu==0.3.2
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- streamlit_nested_layout
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- test-tube>=0.7.5
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- tensorboard
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- torch-fidelity==0.3.0
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- torchmetrics==0.6.0
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- transformers==4.19.2
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@ -1,17 +0,0 @@
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# base webui import and utils.
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from webui_streamlit import st
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from sd_utils import *
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# streamlit imports
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#other imports
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# Temp imports
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# end of imports
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#---------------------------------------------------------------------------------------------------------------
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def layout():
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st.write("Textual Inversion")
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53
scripts/textual_inversion.py
Normal file
53
scripts/textual_inversion.py
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@ -0,0 +1,53 @@
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# base webui import and utils.
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from webui_streamlit import st
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from sd_utils import *
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# streamlit imports
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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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# 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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# 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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# 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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# 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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#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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#model_content["token"] = learned_token
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#models.append(model_content)
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def layout():
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st.write("Textual Inversion")
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@ -147,14 +147,13 @@ def diffuse(
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#scale and decode the image latents with vae
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cond_latents_2 = 1 / 0.18215 * cond_latents
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image_2 = pipe.vae.decode(cond_latents_2)
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image = pipe.vae.decode(cond_latents_2)
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# generate output numpy image as uint8
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image_2 = (image_2 / 2 + 0.5).clamp(0, 1)
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image_2 = image_2.cpu().permute(0, 2, 3, 1).numpy()
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image_2 = (image_2[0] * 255).astype(np.uint8)
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image = torch.clamp((image["sample"] + 1.0) / 2.0, min=0.0, max=1.0)
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image = transforms.ToPILImage()(image.squeeze_(0))
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st.session_state["preview_image"].image(image_2)
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st.session_state["preview_image"].image(image)
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step_counter = 0
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@ -186,15 +185,6 @@ def diffuse(
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)
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st.session_state["progress_bar"].progress(percent if percent < 100 else 100)
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# scale and decode the image latents with vae
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cond_latents = 1 / 0.18215 * cond_latents
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image = pipe.vae.decode(cond_latents)
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# generate output numpy image as uint8
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image = (image / 2 + 0.5).clamp(0, 1)
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image = image.cpu().permute(0, 2, 3, 1).numpy()
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image = (image[0] * 255).astype(np.uint8)
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return image
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#
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@ -223,7 +213,8 @@ def txt2vid(
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#-----------------------------------------------
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beta_start = 0.0001,
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beta_end = 0.00012,
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beta_schedule = "scaled_linear"
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beta_schedule = "scaled_linear",
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starting_image=None
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):
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"""
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prompt = ["blueberry spaghetti", "strawberry spaghetti"], # prompt to dream about
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@ -385,7 +376,8 @@ def txt2vid(
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# get the conditional text embeddings based on the prompt
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text_input = st.session_state["pipe"].tokenizer(prompts, padding="max_length", max_length=st.session_state["pipe"].tokenizer.model_max_length, truncation=True, return_tensors="pt")
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cond_embeddings = st.session_state["pipe"].text_encoder(text_input.input_ids.to(torch_device))[0] # shape [1, 77, 768]
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# sample a source
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init1 = torch.randn((1, st.session_state["pipe"].unet.in_channels, height // 8, width // 8), device=torch_device)
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@ -427,15 +419,15 @@ def txt2vid(
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with autocast("cuda"):
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image = diffuse(st.session_state["pipe"], cond_embeddings, init, num_inference_steps, cfg_scale, eta)
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im = Image.fromarray(image)
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#im = Image.fromarray(image)
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outpath = os.path.join(full_path, 'frame%06d.png' % frame_index)
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im.save(outpath, quality=quality)
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image.save(outpath, quality=quality)
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# send the image to the UI to update it
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#st.session_state["preview_image"].image(im)
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#append the frames to the frames list so we can use them later.
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frames.append(np.asarray(im))
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frames.append(np.asarray(image))
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#increase frame_index counter.
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frame_index += 1
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@ -555,6 +547,9 @@ def layout():
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width = st.slider("Width:", min_value=64, max_value=2048, value=st.session_state['defaults'].txt2vid.width, step=64)
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height = st.slider("Height:", min_value=64, max_value=2048, value=st.session_state['defaults'].txt2vid.height, step=64)
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cfg_scale = st.slider("CFG (Classifier Free Guidance Scale):", min_value=1.0, max_value=30.0, value=st.session_state['defaults'].txt2vid.cfg_scale, step=0.5, help="How strongly the image should follow the prompt.")
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#uploaded_images = st.file_uploader("Upload Image", accept_multiple_files=False, type=["png", "jpg", "jpeg"],
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#help="Upload an image which will be used for the image to image generation.")
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seed = st.text_input("Seed:", value=st.session_state['defaults'].txt2vid.seed, help=" The seed to use, if left blank a random seed will be generated.")
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#batch_count = st.slider("Batch count.", min_value=1, max_value=100, value=st.session_state['defaults'].txt2vid.batch_count, step=1, help="How many iterations or batches of images to generate in total.")
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#batch_size = st.slider("Batch size", min_value=1, max_value=250, value=st.session_state['defaults'].txt2vid.batch_size, step=1,
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@ -690,7 +685,7 @@ def layout():
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seeds=seed, quality=100, eta=0.0, width=width,
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height=height, weights_path=custom_model, scheduler=scheduler_name,
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disable_tqdm=False, fp=st.session_state.defaults.general.fp, beta_start=st.session_state["beta_start"], beta_end=st.session_state["beta_end"],
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beta_schedule=beta_scheduler_type)
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beta_schedule=beta_scheduler_type, starting_image=None)
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#message.success('Done!', icon="✅")
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message.success('Render Complete: ' + info + '; Stats: ' + stats, icon="✅")
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