242 lines
10 KiB
Python
242 lines
10 KiB
Python
import copy
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import itertools
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import os
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from pathlib import Path
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import html
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import gc
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import gradio as gr
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import torch
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from PIL import Image
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from torch import optim
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from modules import shared
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from transformers import CLIPModel, CLIPProcessor, CLIPTokenizer
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from tqdm.auto import tqdm, trange
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from modules.shared import opts, device
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def get_all_images_in_folder(folder):
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return [os.path.join(folder, f) for f in os.listdir(folder) if
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os.path.isfile(os.path.join(folder, f)) and check_is_valid_image_file(f)]
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def check_is_valid_image_file(filename):
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return filename.lower().endswith(('.png', '.jpg', '.jpeg', ".gif", ".tiff", ".webp"))
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def batched(dataset, total, n=1):
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for ndx in range(0, total, n):
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yield [dataset.__getitem__(i) for i in range(ndx, min(ndx + n, total))]
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def iter_to_batched(iterable, n=1):
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it = iter(iterable)
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while True:
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chunk = tuple(itertools.islice(it, n))
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if not chunk:
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return
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yield chunk
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def create_ui():
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import modules.ui
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with gr.Group():
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with gr.Accordion("Open for Clip Aesthetic!", open=False):
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with gr.Row():
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aesthetic_weight = gr.Slider(minimum=0, maximum=1, step=0.01, label="Aesthetic weight",
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value=0.9)
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aesthetic_steps = gr.Slider(minimum=0, maximum=50, step=1, label="Aesthetic steps", value=5)
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with gr.Row():
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aesthetic_lr = gr.Textbox(label='Aesthetic learning rate',
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placeholder="Aesthetic learning rate", value="0.0001")
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aesthetic_slerp = gr.Checkbox(label="Slerp interpolation", value=False)
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aesthetic_imgs = gr.Dropdown(sorted(shared.aesthetic_embeddings.keys()),
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label="Aesthetic imgs embedding",
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value="None")
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modules.ui.create_refresh_button(aesthetic_imgs, shared.update_aesthetic_embeddings, lambda: {"choices": sorted(shared.aesthetic_embeddings.keys())}, "refresh_aesthetic_embeddings")
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with gr.Row():
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aesthetic_imgs_text = gr.Textbox(label='Aesthetic text for imgs',
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placeholder="This text is used to rotate the feature space of the imgs embs",
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value="")
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aesthetic_slerp_angle = gr.Slider(label='Slerp angle', minimum=0, maximum=1, step=0.01,
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value=0.1)
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aesthetic_text_negative = gr.Checkbox(label="Is negative text", value=False)
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return aesthetic_weight, aesthetic_steps, aesthetic_lr, aesthetic_slerp, aesthetic_imgs, aesthetic_imgs_text, aesthetic_slerp_angle, aesthetic_text_negative
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aesthetic_clip_model = None
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def aesthetic_clip():
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global aesthetic_clip_model
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if aesthetic_clip_model is None or aesthetic_clip_model.name_or_path != shared.sd_model.cond_stage_model.wrapped.transformer.name_or_path:
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aesthetic_clip_model = CLIPModel.from_pretrained(shared.sd_model.cond_stage_model.wrapped.transformer.name_or_path)
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aesthetic_clip_model.cpu()
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return aesthetic_clip_model
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def generate_imgs_embd(name, folder, batch_size):
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model = aesthetic_clip().to(device)
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processor = CLIPProcessor.from_pretrained(model.name_or_path)
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with torch.no_grad():
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embs = []
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for paths in tqdm(iter_to_batched(get_all_images_in_folder(folder), batch_size),
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desc=f"Generating embeddings for {name}"):
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if shared.state.interrupted:
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break
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inputs = processor(images=[Image.open(path) for path in paths], return_tensors="pt").to(device)
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outputs = model.get_image_features(**inputs).cpu()
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embs.append(torch.clone(outputs))
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inputs.to("cpu")
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del inputs, outputs
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embs = torch.cat(embs, dim=0).mean(dim=0, keepdim=True)
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# The generated embedding will be located here
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path = str(Path(shared.cmd_opts.aesthetic_embeddings_dir) / f"{name}.pt")
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torch.save(embs, path)
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model.cpu()
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del processor
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del embs
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gc.collect()
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torch.cuda.empty_cache()
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res = f"""
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Done generating embedding for {name}!
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Aesthetic embedding saved to {html.escape(path)}
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"""
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shared.update_aesthetic_embeddings()
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return gr.Dropdown.update(choices=sorted(shared.aesthetic_embeddings.keys()), label="Imgs embedding",
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value="None"), \
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gr.Dropdown.update(choices=sorted(shared.aesthetic_embeddings.keys()),
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label="Imgs embedding",
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value="None"), res, ""
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def slerp(low, high, val):
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low_norm = low / torch.norm(low, dim=1, keepdim=True)
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high_norm = high / torch.norm(high, dim=1, keepdim=True)
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omega = torch.acos((low_norm * high_norm).sum(1))
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so = torch.sin(omega)
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res = (torch.sin((1.0 - val) * omega) / so).unsqueeze(1) * low + (torch.sin(val * omega) / so).unsqueeze(1) * high
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return res
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class AestheticCLIP:
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def __init__(self):
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self.skip = False
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self.aesthetic_steps = 0
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self.aesthetic_weight = 0
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self.aesthetic_lr = 0
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self.slerp = False
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self.aesthetic_text_negative = ""
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self.aesthetic_slerp_angle = 0
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self.aesthetic_imgs_text = ""
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self.image_embs_name = None
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self.image_embs = None
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self.load_image_embs(None)
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def set_aesthetic_params(self, p, aesthetic_lr=0, aesthetic_weight=0, aesthetic_steps=0, image_embs_name=None,
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aesthetic_slerp=True, aesthetic_imgs_text="",
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aesthetic_slerp_angle=0.15,
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aesthetic_text_negative=False):
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self.aesthetic_imgs_text = aesthetic_imgs_text
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self.aesthetic_slerp_angle = aesthetic_slerp_angle
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self.aesthetic_text_negative = aesthetic_text_negative
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self.slerp = aesthetic_slerp
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self.aesthetic_lr = aesthetic_lr
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self.aesthetic_weight = aesthetic_weight
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self.aesthetic_steps = aesthetic_steps
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self.load_image_embs(image_embs_name)
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if self.image_embs_name is not None:
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p.extra_generation_params.update({
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"Aesthetic LR": aesthetic_lr,
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"Aesthetic weight": aesthetic_weight,
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"Aesthetic steps": aesthetic_steps,
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"Aesthetic embedding": self.image_embs_name,
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"Aesthetic slerp": aesthetic_slerp,
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"Aesthetic text": aesthetic_imgs_text,
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"Aesthetic text negative": aesthetic_text_negative,
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"Aesthetic slerp angle": aesthetic_slerp_angle,
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})
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def set_skip(self, skip):
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self.skip = skip
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def load_image_embs(self, image_embs_name):
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if image_embs_name is None or len(image_embs_name) == 0 or image_embs_name == "None":
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image_embs_name = None
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self.image_embs_name = None
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if image_embs_name is not None and self.image_embs_name != image_embs_name:
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self.image_embs_name = image_embs_name
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self.image_embs = torch.load(shared.aesthetic_embeddings[self.image_embs_name], map_location=device)
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self.image_embs /= self.image_embs.norm(dim=-1, keepdim=True)
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self.image_embs.requires_grad_(False)
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def __call__(self, z, remade_batch_tokens):
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if not self.skip and self.aesthetic_steps != 0 and self.aesthetic_lr != 0 and self.aesthetic_weight != 0 and self.image_embs_name is not None:
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tokenizer = shared.sd_model.cond_stage_model.tokenizer
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if not opts.use_old_emphasis_implementation:
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remade_batch_tokens = [
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[tokenizer.bos_token_id] + x[:75] + [tokenizer.eos_token_id] for x in
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remade_batch_tokens]
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tokens = torch.asarray(remade_batch_tokens).to(device)
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model = copy.deepcopy(aesthetic_clip()).to(device)
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model.requires_grad_(True)
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if self.aesthetic_imgs_text is not None and len(self.aesthetic_imgs_text) > 0:
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text_embs_2 = model.get_text_features(
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**tokenizer([self.aesthetic_imgs_text], padding=True, return_tensors="pt").to(device))
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if self.aesthetic_text_negative:
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text_embs_2 = self.image_embs - text_embs_2
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text_embs_2 /= text_embs_2.norm(dim=-1, keepdim=True)
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img_embs = slerp(self.image_embs, text_embs_2, self.aesthetic_slerp_angle)
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else:
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img_embs = self.image_embs
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with torch.enable_grad():
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# We optimize the model to maximize the similarity
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optimizer = optim.Adam(
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model.text_model.parameters(), lr=self.aesthetic_lr
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)
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for _ in trange(self.aesthetic_steps, desc="Aesthetic optimization"):
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text_embs = model.get_text_features(input_ids=tokens)
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text_embs = text_embs / text_embs.norm(dim=-1, keepdim=True)
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sim = text_embs @ img_embs.T
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loss = -sim
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optimizer.zero_grad()
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loss.mean().backward()
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optimizer.step()
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zn = model.text_model(input_ids=tokens, output_hidden_states=-opts.CLIP_stop_at_last_layers)
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if opts.CLIP_stop_at_last_layers > 1:
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zn = zn.hidden_states[-opts.CLIP_stop_at_last_layers]
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zn = model.text_model.final_layer_norm(zn)
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else:
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zn = zn.last_hidden_state
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model.cpu()
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del model
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gc.collect()
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torch.cuda.empty_cache()
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zn = torch.concat([zn[77 * i:77 * (i + 1)] for i in range(max(z.shape[1] // 77, 1))], 1)
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if self.slerp:
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z = slerp(z, zn, self.aesthetic_weight)
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else:
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z = z * (1 - self.aesthetic_weight) + zn * self.aesthetic_weight
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return z
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