mirror of
https://github.com/sd-webui/stable-diffusion-webui.git
synced 2024-12-15 07:12:58 +03:00
407 lines
19 KiB
Python
407 lines
19 KiB
Python
# This file is part of stable-diffusion-webui (https://github.com/sd-webui/stable-diffusion-webui/).
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# Copyright 2022 sd-webui team.
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# This program is free software: you can redistribute it and/or modify
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# it under the terms of the GNU Affero General Public License as published by
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# the Free Software Foundation, either version 3 of the License, or
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# (at your option) any later version.
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# This program is distributed in the hope that it will be useful,
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# but WITHOUT ANY WARRANTY; without even the implied warranty of
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# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
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# GNU Affero General Public License for more details.
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# You should have received a copy of the GNU Affero General Public License
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# along with this program. If not, see <http://www.gnu.org/licenses/>.
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# ---------------------------------------------------------------------------------------------------------------------------------------------------
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"""
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CLIP Interrogator made by @pharmapsychotic modified to work with our WebUI.
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# CLIP Interrogator by @pharmapsychotic
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Twitter: https://twitter.com/pharmapsychotic
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Github: https://github.com/pharmapsychotic/clip-interrogator
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Description:
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What do the different OpenAI CLIP models see in an image? What might be a good text prompt to create similar images using CLIP guided diffusion
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or another text to image model? The CLIP Interrogator is here to get you answers!
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Please consider buying him a coffee via [ko-fi](https://ko-fi.com/pharmapsychotic) or following him on [twitter](https://twitter.com/pharmapsychotic).
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And if you're looking for more Ai art tools check out my [Ai generative art tools list](https://pharmapsychotic.com/tools.html).
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"""
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# ---------------------------------------------------------------------------------------------------------------------------------------------------
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# base webui import and utils.
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from sd_utils import *
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# streamlit imports
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# streamlit components section
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import streamlit_nested_layout
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# other imports
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import clip
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import open_clip
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import gc
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import os
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import pandas as pd
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#import requests
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import torch
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from PIL import Image
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from torchvision import transforms
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from torchvision.transforms.functional import InterpolationMode
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from ldm.models.blip import blip_decoder
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# end of imports
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# ---------------------------------------------------------------------------------------------------------------
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device = torch.device('cuda:0' if torch.cuda.is_available() else 'cpu')
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blip_image_eval_size = 512
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#blip_model_url = 'https://storage.googleapis.com/sfr-vision-language-research/BLIP/models/model*_base_caption.pth'
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def load_blip_model():
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print("Loading BLIP Model")
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st.session_state["log_message"].code("Loading BLIP Model", language='')
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if "blip_model" not in server_state:
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with server_state_lock['blip_model']:
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server_state["blip_model"] = blip_decoder(pretrained="models/blip/model__base_caption.pth",
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image_size=blip_image_eval_size, vit='base', med_config="configs/blip/med_config.json")
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server_state["blip_model"] = server_state["blip_model"].eval()
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#if not st.session_state["defaults"].general.optimized:
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server_state["blip_model"] = server_state["blip_model"].to(device).half()
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print("BLIP Model Loaded")
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st.session_state["log_message"].code("BLIP Model Loaded", language='')
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else:
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print("BLIP Model already loaded")
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st.session_state["log_message"].code("BLIP Model Already Loaded", language='')
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#return server_state["blip_model"]
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def generate_caption(pil_image):
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load_blip_model()
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gpu_image = transforms.Compose([ # type: ignore
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transforms.Resize((blip_image_eval_size, blip_image_eval_size), interpolation=InterpolationMode.BICUBIC), # type: ignore
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transforms.ToTensor(), # type: ignore
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transforms.Normalize((0.48145466, 0.4578275, 0.40821073), (0.26862954, 0.26130258, 0.27577711)) # type: ignore
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])(pil_image).unsqueeze(0).to(device).half()
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with torch.no_grad():
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caption = server_state["blip_model"].generate(gpu_image, sample=False, num_beams=3, max_length=20, min_length=5)
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#print (caption)
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return caption[0]
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def load_list(filename):
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with open(filename, 'r', encoding='utf-8', errors='replace') as f:
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items = [line.strip() for line in f.readlines()]
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return items
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def rank(model, image_features, text_array, top_count=1):
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top_count = min(top_count, len(text_array))
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text_tokens = clip.tokenize([text for text in text_array]).cuda()
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with torch.no_grad():
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text_features = model.encode_text(text_tokens).float()
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text_features /= text_features.norm(dim=-1, keepdim=True)
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similarity = torch.zeros((1, len(text_array))).to(device)
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for i in range(image_features.shape[0]):
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similarity += (100.0 * image_features[i].unsqueeze(0) @ text_features.T).softmax(dim=-1)
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similarity /= image_features.shape[0]
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top_probs, top_labels = similarity.cpu().topk(top_count, dim=-1)
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return [(text_array[top_labels[0][i].numpy()], (top_probs[0][i].numpy()*100)) for i in range(top_count)]
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def clear_cuda():
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torch.cuda.empty_cache()
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gc.collect()
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def batch_rank(model, image_features, text_array, batch_size=st.session_state["defaults"].img2txt.batch_size):
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batch_count = len(text_array) // batch_size
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batches = [text_array[i*batch_size:(i+1)*batch_size] for i in range(batch_count)]
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batches.append(text_array[batch_count*batch_size:])
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ranks = []
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for batch in batches:
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ranks += rank(model, image_features, batch)
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return ranks
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def interrogate(image, models):
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#server_state["blip_model"] =
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load_blip_model()
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print("Generating Caption")
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st.session_state["log_message"].code("Generating Caption", language='')
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caption = generate_caption(image)
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if st.session_state["defaults"].general.optimized:
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del server_state["blip_model"]
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clear_cuda()
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print("Caption Generated")
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st.session_state["log_message"].code("Caption Generated", language='')
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if len(models) == 0:
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print(f"\n\n{caption}")
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return
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table = []
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bests = [[('', 0)]]*5
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print("Ranking Text")
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for model_name in models:
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print(f"Interrogating with {model_name}...")
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st.session_state["log_message"].code(f"Interrogating with {model_name}...", language='')
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if "clip_model" not in server_state:
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#with server_state_lock[server_state["clip_model"]]:
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if model_name == 'ViT-H-14':
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server_state["clip_model"], _, server_state["preprocess"] = open_clip.create_model_and_transforms(model_name, pretrained='laion2b_s32b_b79k')
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elif model_name == 'ViT-g-14':
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server_state["clip_model"], _, server_state["preprocess"] = open_clip.create_model_and_transforms(model_name, pretrained='laion2b_s12b_b42k')
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else:
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server_state["clip_model"], server_state["preprocess"] = clip.load(model_name, device=device)
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server_state["clip_model"] = server_state["clip_model"].cuda().eval()
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images = server_state["preprocess"](image).unsqueeze(0).cuda()
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with torch.no_grad():
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image_features = server_state["clip_model"].encode_image(images).float()
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image_features /= image_features.norm(dim=-1, keepdim=True)
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if st.session_state["defaults"].general.optimized:
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clear_cuda()
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ranks = []
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ranks.append(batch_rank(server_state["clip_model"], image_features, server_state["mediums"]))
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ranks.append(batch_rank(server_state["clip_model"], image_features, ["by "+artist for artist in server_state["artists"]]))
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ranks.append(batch_rank(server_state["clip_model"], image_features, server_state["trending_list"]))
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ranks.append(batch_rank(server_state["clip_model"], image_features, server_state["movements"]))
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ranks.append(batch_rank(server_state["clip_model"], image_features, server_state["flavors"]))
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# ranks.append(batch_rank(server_state["clip_model"], image_features, server_state["genres"]))
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# ranks.append(batch_rank(server_state["clip_model"], image_features, server_state["styles"]))
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# ranks.append(batch_rank(server_state["clip_model"], image_features, server_state["techniques"]))
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# ranks.append(batch_rank(server_state["clip_model"], image_features, server_state["subjects"]))
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# ranks.append(batch_rank(server_state["clip_model"], image_features, server_state["colors"]))
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# ranks.append(batch_rank(server_state["clip_model"], image_features, server_state["moods"]))
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# ranks.append(batch_rank(server_state["clip_model"], image_features, server_state["themes"]))
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# ranks.append(batch_rank(server_state["clip_model"], image_features, server_state["keywords"]))
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for i in range(len(ranks)):
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confidence_sum = 0
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for ci in range(len(ranks[i])):
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confidence_sum += ranks[i][ci][1]
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if confidence_sum > sum(bests[i][t][1] for t in range(len(bests[i]))):
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bests[i] = ranks[i]
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row = [model_name]
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for r in ranks:
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row.append(', '.join([f"{x[0]} ({x[1]:0.1f}%)" for x in r]))
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table.append(row)
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if st.session_state["defaults"].general.optimized:
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del server_state["clip_model"]
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gc.collect()
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# for i in range(len(st.session_state["uploaded_image"])):
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st.session_state["prediction_table"][st.session_state["processed_image_count"]].dataframe(pd.DataFrame(
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table, columns=["Model", "Medium", "Artist", "Trending", "Movement", "Flavors"]))
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flaves = ', '.join([f"{x[0]}" for x in bests[4]])
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medium = bests[0][0][0]
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if caption.startswith(medium):
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st.session_state["text_result"][st.session_state["processed_image_count"]].code(
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f"\n\n{caption} {bests[1][0][0]}, {bests[2][0][0]}, {bests[3][0][0]}, {flaves}", language="")
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else:
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st.session_state["text_result"][st.session_state["processed_image_count"]].code(
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f"\n\n{caption}, {medium} {bests[1][0][0]}, {bests[2][0][0]}, {bests[3][0][0]}, {flaves}", language="")
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#
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print("Finished Interrogating.")
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st.session_state["log_message"].code("Finished Interrogating.", language="")
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#
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def img2txt():
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data_path = "data/"
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server_state["artists"] = load_list(os.path.join(data_path, 'img2txt', 'artists.txt'))
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server_state["flavors"] = load_list(os.path.join(data_path, 'img2txt', 'flavors.txt'))
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server_state["mediums"] = load_list(os.path.join(data_path, 'img2txt', 'mediums.txt'))
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server_state["movements"] = load_list(os.path.join(data_path, 'img2txt', 'movements.txt'))
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server_state["sites"] = load_list(os.path.join(data_path, 'img2txt', 'sites.txt'))
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# server_state["genres"] = load_list(os.path.join(data_path, 'img2txt', 'genres.txt'))
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# server_state["styles"] = load_list(os.path.join(data_path, 'img2txt', 'styles.txt'))
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# server_state["techniques"] = load_list(os.path.join(data_path, 'img2txt', 'techniques.txt'))
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# server_state["subjects"] = load_list(os.path.join(data_path, 'img2txt', 'subjects.txt'))
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server_state["trending_list"] = [site for site in server_state["sites"]]
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server_state["trending_list"].extend(["trending on "+site for site in server_state["sites"]])
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server_state["trending_list"].extend(["featured on "+site for site in server_state["sites"]])
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server_state["trending_list"].extend([site+" contest winner" for site in server_state["sites"]])
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#image_path_or_url = "https://i.redd.it/e2e8gimigjq91.jpg"
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models = []
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if st.session_state["ViTB32"]:
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models.append('ViT-B/32')
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if st.session_state['ViTB16']:
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models.append('ViT-B/16')
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if st.session_state["ViTL14"]:
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models.append('ViT-L/14')
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if st.session_state["ViT-H-14"]:
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models.append('ViT-H-14')
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if st.session_state["ViT-g-14"]:
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models.append('ViT-g-14')
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if st.session_state["ViTL14_336px"]:
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models.append('ViT-L/14@336px')
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if st.session_state["RN101"]:
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models.append('RN101')
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if st.session_state["RN50"]:
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models.append('RN50')
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if st.session_state["RN50x4"]:
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models.append('RN50x4')
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if st.session_state["RN50x16"]:
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models.append('RN50x16')
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if st.session_state["RN50x64"]:
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models.append('RN50x64')
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# if str(image_path_or_url).startswith('http://') or str(image_path_or_url).startswith('https://'):
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#image = Image.open(requests.get(image_path_or_url, stream=True).raw).convert('RGB')
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# else:
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#image = Image.open(image_path_or_url).convert('RGB')
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#thumb = st.session_state["uploaded_image"].image.copy()
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#thumb.thumbnail([blip_image_eval_size, blip_image_eval_size])
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# display(thumb)
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st.session_state["processed_image_count"] = 0
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for i in range(len(st.session_state["uploaded_image"])):
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interrogate(st.session_state["uploaded_image"][i].pil_image, models=models)
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# increase counter.
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st.session_state["processed_image_count"] += 1
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#
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def layout():
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#set_page_title("Image-to-Text - Stable Diffusion WebUI")
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#st.info("Under Construction. :construction_worker:")
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with st.form("img2txt-inputs"):
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st.session_state["generation_mode"] = "img2txt"
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# st.write("---")
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# creating the page layout using columns
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col1, col2 = st.columns([1, 4], gap="large")
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with col1:
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#url = st.text_area("Input Text","")
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#url = st.text_input("Input Text","", placeholder="A corgi wearing a top hat as an oil painting.")
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#st.subheader("Input Image")
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st.session_state["uploaded_image"] = st.file_uploader('Input Image', type=['png', 'jpg', 'jpeg'], accept_multiple_files=True)
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st.subheader("CLIP models")
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with st.expander("Stable Diffusion", expanded=True):
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st.session_state["ViTL14"] = st.checkbox("ViTL14", value=True, help="For StableDiffusion you can just use ViTL14.")
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with st.expander("Others"):
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st.info("For DiscoDiffusion and JAX enable all the same models here as you intend to use when generating your images.")
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st.session_state["ViT-H-14"] = st.checkbox("ViT-H-14", value=False, help="ViT-H-14 model.")
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st.session_state["ViT-g-14"] = st.checkbox("ViT-g-14", value=False, help="ViT-g-14 model.")
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st.session_state["ViTL14_336px"] = st.checkbox("ViTL14_336px", value=False, help="ViTL14_336px model.")
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st.session_state["ViTB16"] = st.checkbox("ViTB16", value=False, help="ViTB16 model.")
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st.session_state["ViTB32"] = st.checkbox("ViTB32", value=False, help="ViTB32 model.")
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st.session_state["RN50"] = st.checkbox("RN50", value=False, help="RN50 model.")
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st.session_state["RN50x4"] = st.checkbox("RN50x4", value=False, help="RN50x4 model.")
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st.session_state["RN50x16"] = st.checkbox("RN50x16", value=False, help="RN50x16 model.")
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st.session_state["RN50x64"] = st.checkbox("RN50x64", value=False, help="RN50x64 model.")
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st.session_state["RN101"] = st.checkbox("RN101", value=False, help="RN101 model.")
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#
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# st.subheader("Logs:")
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st.session_state["log_message"] = st.empty()
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st.session_state["log_message"].code('', language="")
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with col2:
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st.subheader("Image")
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refresh = st.form_submit_button("Refresh", help='Refresh the image preview to show your uploaded image instead of the default placeholder.')
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if st.session_state["uploaded_image"]:
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#print (type(st.session_state["uploaded_image"]))
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# if len(st.session_state["uploaded_image"]) == 1:
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st.session_state["input_image_preview"] = []
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st.session_state["input_image_preview_container"] = []
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st.session_state["prediction_table"] = []
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st.session_state["text_result"] = []
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for i in range(len(st.session_state["uploaded_image"])):
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st.session_state["input_image_preview_container"].append(i)
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st.session_state["input_image_preview_container"][i] = st.empty()
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with st.session_state["input_image_preview_container"][i].container():
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col1_output, col2_output = st.columns([2, 10], gap="medium")
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with col1_output:
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st.session_state["input_image_preview"].append(i)
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st.session_state["input_image_preview"][i] = st.empty()
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st.session_state["uploaded_image"][i].pil_image = Image.open(st.session_state["uploaded_image"][i]).convert('RGB')
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st.session_state["input_image_preview"][i].image(st.session_state["uploaded_image"][i].pil_image, use_column_width=True, clamp=True)
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with st.session_state["input_image_preview_container"][i].container():
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with col2_output:
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st.session_state["prediction_table"].append(i)
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st.session_state["prediction_table"][i] = st.empty()
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st.session_state["prediction_table"][i].table()
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st.session_state["text_result"].append(i)
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st.session_state["text_result"][i] = st.empty()
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st.session_state["text_result"][i].code("", language="")
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else:
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#st.session_state["input_image_preview"].code('', language="")
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st.image("images/streamlit/img2txt_placeholder.png", clamp=True)
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#
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# 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.
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# generate_col1.title("")
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# generate_col1.title("")
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generate_button = st.form_submit_button("Generate!")
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if generate_button:
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# if model, pipe, RealESRGAN or GFPGAN is in st.session_state remove the model and pipe form session_state so that they are reloaded.
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if "model" in st.session_state and st.session_state["defaults"].general.optimized:
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del st.session_state["model"]
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if "pipe" in st.session_state and st.session_state["defaults"].general.optimized:
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del st.session_state["pipe"]
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if "RealESRGAN" in st.session_state and st.session_state["defaults"].general.optimized:
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del st.session_state["RealESRGAN"]
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if "GFPGAN" in st.session_state and st.session_state["defaults"].general.optimized:
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del st.session_state["GFPGAN"]
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|
|
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# run clip interrogator
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img2txt()
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