Spaces:
Runtime error
Runtime error
File size: 5,448 Bytes
3f7ead4 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 |
import torch
from diffusers import StableDiffusionPipeline
import numpy as np
import abc
import time_utils
import copy
import os
from train_funcs import TRAIN_FUNC_DICT
## get arguments for our script
with_to_k = True
with_augs = True
train_func = "train_closed_form"
### load model
LOW_RESOURCE = True
NUM_DIFFUSION_STEPS = 50
GUIDANCE_SCALE = 7.5
MAX_NUM_WORDS = 77
device = torch.device('cuda:0') if torch.cuda.is_available() else torch.device('cpu')
ldm_stable = StableDiffusionPipeline.from_pretrained("CompVis/stable-diffusion-v1-4").to(device)
tokenizer = ldm_stable.tokenizer
### get layers
ca_layers = []
def append_ca(net_):
if net_.__class__.__name__ == 'CrossAttention':
ca_layers.append(net_)
elif hasattr(net_, 'children'):
for net__ in net_.children():
append_ca(net__)
sub_nets = ldm_stable.unet.named_children()
for net in sub_nets:
if "down" in net[0]:
append_ca(net[1])
elif "up" in net[0]:
append_ca(net[1])
elif "mid" in net[0]:
append_ca(net[1])
### get projection matrices
ca_clip_layers = [l for l in ca_layers if l.to_v.in_features == 768]
projection_matrices = [l.to_v for l in ca_clip_layers]
og_matrices = [copy.deepcopy(l.to_v) for l in ca_clip_layers]
if with_to_k:
projection_matrices = projection_matrices + [l.to_k for l in ca_clip_layers]
og_matrices = og_matrices + [copy.deepcopy(l.to_k) for l in ca_clip_layers]
def edit_model(old_text_, new_text_, lamb=0.1):
#### restart LDM parameters
num_ca_clip_layers = len(ca_clip_layers)
for idx_, l in enumerate(ca_clip_layers):
l.to_v = copy.deepcopy(og_matrices[idx_])
projection_matrices[idx_] = l.to_v
if with_to_k:
l.to_k = copy.deepcopy(og_matrices[num_ca_clip_layers + idx_])
projection_matrices[num_ca_clip_layers + idx_] = l.to_k
try:
#### set up sentences
old_texts = [old_text_]
new_texts = [new_text_]
if with_augs:
base = old_texts[0] if old_texts[0][0:1] != "A" else "a" + old_texts[0][1:]
old_texts.append("A photo of " + base)
old_texts.append("An image of " + base)
old_texts.append("A picture of " + base)
base = new_texts[0] if new_texts[0][0:1] != "A" else "a" + new_texts[0][1:]
new_texts.append("A photo of " + base)
new_texts.append("An image of " + base)
new_texts.append("A picture of " + base)
#### prepare input k* and v*
old_embs, new_embs = [], []
for old_text, new_text in zip(old_texts, new_texts):
text_input = ldm_stable.tokenizer(
[old_text, new_text],
padding="max_length",
max_length=ldm_stable.tokenizer.model_max_length,
truncation=True,
return_tensors="pt",
)
text_embeddings = ldm_stable.text_encoder(text_input.input_ids.to(ldm_stable.device))[0]
old_emb, new_emb = text_embeddings
old_embs.append(old_emb)
new_embs.append(new_emb)
#### indetify corresponding destinations for each token in old_emb
idxs_replaces = []
for old_text, new_text in zip(old_texts, new_texts):
tokens_a = tokenizer(old_text).input_ids
tokens_b = tokenizer(new_text).input_ids
tokens_a = [tokenizer.encode("a ")[1] if tokenizer.decode(t) == 'an' else t for t in tokens_a]
tokens_b = [tokenizer.encode("a ")[1] if tokenizer.decode(t) == 'an' else t for t in tokens_b]
num_orig_tokens = len(tokens_a)
num_new_tokens = len(tokens_b)
idxs_replace = []
j = 0
for i in range(num_orig_tokens):
curr_token = tokens_a[i]
while tokens_b[j] != curr_token:
j += 1
idxs_replace.append(j)
j += 1
while j < 77:
idxs_replace.append(j)
j += 1
while len(idxs_replace) < 77:
idxs_replace.append(76)
idxs_replaces.append(idxs_replace)
#### prepare batch: for each pair of setences, old context and new values
contexts, valuess = [], []
for old_emb, new_emb, idxs_replace in zip(old_embs, new_embs, idxs_replaces):
context = old_emb.detach()
values = []
with torch.no_grad():
for layer in projection_matrices:
values.append(layer(new_emb[idxs_replace]).detach())
contexts.append(context)
valuess.append(values)
#### define training function
train = TRAIN_FUNC_DICT[train_func]
#### train the model
train(ldm_stable, projection_matrices, og_matrices, contexts, valuess, old_texts, new_texts, lamb=lamb)
return f"<b>Current model status:</b> Edited \"{old_text_}\" into \"{new_text_}\""
except:
return "<b>Current model status:</b> An error occured"
def generate_for_text(test_text):
g = torch.Generator(device='cpu')
g.seed()
images = time_utils.text2image_ldm_stable(ldm_stable, [test_text], latent=None, num_inference_steps=NUM_DIFFUSION_STEPS, guidance_scale=GUIDANCE_SCALE, generator=g, low_resource=LOW_RESOURCE)
return time_utils.view_images(images)
|