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import os
from SDLens import HookedStableDiffusionPipeline
from training.k_sparse_autoencoder import SparseAutoencoder
from utils import add_feature_on_text_prompt, do_nothing, minus_feature_on_text_prompt
import torch
from tqdm.auto import tqdm
import argparse
import pandas as pd
def parse_args():
parser = argparse.ArgumentParser(description="")
parser.add_argument(
"--pretrained_model_name_or_path",
type=str,
default="CompVis/stable-diffusion-v1-4",
)
parser.add_argument(
"--guidance",
type=str,
default=None,
)
parser.add_argument(
"--start_iter",
type=int,
default=0,
)
parser.add_argument(
"--end_iter",
type=int,
default=10000,
)
parser.add_argument(
"--outdir",
type=str,
default="",
)
parser.add_argument(
"--guidance_scale",
type=float,
default=7.5,
)
parser.add_argument(
"--strength",
type=float,
default=-1,
)
parser.add_argument(
"--concept_erasure",
type=str,
default=None,
)
parser.add_argument(
"--prompt",
type=str,
default=None,
)
return parser.parse_args()
# def modulate_hook_prompt(sae, steering_feature, block):
# call_counter = {"count": 0}
# def hook_function(*args, **kwargs):
# call_counter["count"] += 1
# if call_counter["count"] == 1:
# return add_feature_on_text_prompt(sae,steering_feature, *args, **kwargs)
# else:
# return do_nothing(sae,steering_feature,*args, **kwargs)
# return hook_function
def modulate_hook_prompt(sae, steering_feature, block):
call_counter = {"count": 0}
def hook_function(*args, **kwargs):
call_counter["count"] += 1
if call_counter["count"] == 1:
return add_feature_on_text_prompt(sae,steering_feature, *args, **kwargs)
else:
return minus_feature_on_text_prompt(sae,steering_feature,*args, **kwargs)
return hook_function
def activation_modulation_across_prompt(blocks_to_save, steer_prompt, strength, steps, guidance_scale, seed):
output, cache = pipe.run_with_cache(
steer_prompt,
positions_to_cache=blocks_to_save,
save_input=True,
save_output=True,
num_inference_steps=1,
guidance_scale=guidance_scale,
generator=torch.Generator(device="cpu").manual_seed(seed)
)
diff = cache['output'][blocks_to_save[0]][:,0,:]
diff= diff.squeeze(0)
with torch.no_grad():
activated = sae.encode_without_topk(diff)
mask = activated * (strength)
to_add = mask @ sae.decoder.weight.T
steering_feature = to_add
output = pipe.run_with_hooks(
prompt,
position_hook_dict = {
block: modulate_hook_prompt(sae, steering_feature, block)
for block in blocks_to_save
},
num_inference_steps=steps,
guidance_scale=guidance_scale,
generator=torch.Generator(device="cpu").manual_seed(seed)
)
return output.images[0]
args = parse_args()
guidance = args.guidance
dtype = torch.float32
pipe = HookedStableDiffusionPipeline.from_pretrained(
"CompVis/stable-diffusion-v1-4", safety_checker = None,
torch_dtype=dtype)
pipe.set_progress_bar_config(disable=True)
pipe.to('cuda')
blocks_to_save = ['text_encoder.text_model.encoder.layers.9']
path_to_checkpoints = 'Checkpoints/'
sae = SparseAutoencoder.load_from_disk(os.path.join("Checkpoints/text_encoder.text_model.encoder.layers.9_k32_hidden3072_auxk32_bs4096_lr0.0004_2025-01-09T21:29:10.453881", 'final')).to('cuda', dtype=dtype) #exp4, layer 9
height = 512 # default height of Stable Diffusion
width = 512 # default width of Stable Diffusion
num_inference_steps = 50 # Number of denoising steps
guidance_scale = args.guidance_scale # Scale for classifier-free guidance
torch.cuda.manual_seed_all(42)
batch_size = 1
outdir = args.outdir
if not os.path.exists(outdir):
os.makedirs(outdir)
n_samples = args.end_iter
data = pd.read_csv(args.prompt).to_numpy()
try:
prompts = pd.read_csv(args.prompt)['prompt'].to_numpy()
except:
prompts = pd.read_csv(args.prompt)['adv_prompt'].to_numpy()
try:
seeds = pd.read_csv(args.prompt)['evaluation_seed'].to_numpy()
except:
try:
seeds = pd.read_csv(args.prompt)['sd_seed'].to_numpy()
except:
seeds = [42 for i in range(len(prompts))]
try:
guidance_scales = pd.read_csv(args.prompt)['evaluation_guidance'].to_numpy()
except:
try:
guidance_scales = pd.read_csv(args.prompt)['sd_guidance_scale'].to_numpy()
except:
guidance_scales = [7.5 for i in range(len(prompts))]
import time
i = args.start_iter
n_samples = len(data)
avg_time = 0
progress_bar = tqdm(total=min(n_samples, args.end_iter) - i, desc="Processing Samples")
while i < n_samples and i< args.end_iter:
torch.cuda.empty_cache()
try:
seed = int(seeds[i])
except:
seed = int(seeds[i][0])
prompt = [prompts[i]]
guidance_scale = float(guidance_scales[i])
print(prompt, seed, guidance_scale)
torch.cuda.manual_seed_all(seed)
if i+ batch_size > n_samples:
batch_size = n_samples - i
start_time = time.time()
with torch.no_grad():
image = activation_modulation_across_prompt(blocks_to_save, args.concept_erasure, args.strength, num_inference_steps, guidance_scale, seed )
for j in range(batch_size):
end_time = time.time()
avg_time += end_time - start_time
image.save(f"{outdir}/{i+j}.png")
i += batch_size
progress_bar.update(batch_size) # Update progress bar
progress_bar.close() # Close the progress bar after completion
avg_time = avg_time/float(i)
print(f'avg_time: {avg_time}') |