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consistory_run.py
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# Copyright (C) 2024 NVIDIA Corporation. All rights reserved.
#
# This work is licensed under the LICENSE file
# located at the root directory.
import torch
from diffusers import DDIMScheduler
from consistory_unet_sdxl import ConsistorySDXLUNet2DConditionModel
from consistory_pipeline import ConsistoryExtendAttnSDXLPipeline
from consistory_utils import FeatureInjector, AnchorCache
from utils.general_utils import *
import gc
from utils.ptp_utils import view_images
LATENT_RESOLUTIONS = [32, 64]
def load_pipeline(gpu_id=0):
float_type = torch.float16
sd_id = "stabilityai/stable-diffusion-xl-base-1.0"
device = torch.device(f'cuda:{gpu_id}') if torch.cuda.is_available() else torch.device('cpu')
unet = ConsistorySDXLUNet2DConditionModel.from_pretrained(sd_id, subfolder="unet", torch_dtype=float_type)
scheduler = DDIMScheduler.from_pretrained(sd_id, subfolder="scheduler")
story_pipeline = ConsistoryExtendAttnSDXLPipeline.from_pretrained(
sd_id, unet=unet, torch_dtype=float_type, variant="fp16", use_safetensors=True, scheduler=scheduler
).to(device)
story_pipeline.enable_freeu(s1=0.6, s2=0.4, b1=1.1, b2=1.2)
return story_pipeline
def create_anchor_mapping(bsz, anchor_indices=[0]):
anchor_mapping = torch.eye(bsz, dtype=torch.bool)
for anchor_idx in anchor_indices:
anchor_mapping[:, anchor_idx] = True
return anchor_mapping
def create_token_indices(prompts, batch_size, concept_token, tokenizer):
if isinstance(concept_token, str):
concept_token = [concept_token]
concept_token_id = [tokenizer.encode(x, add_special_tokens=False)[0] for x in concept_token]
tokens = tokenizer.batch_encode_plus(prompts, padding=True, return_tensors='pt')['input_ids']
token_indices = torch.full((len(concept_token), batch_size), -1, dtype=torch.int64)
for i, token_id in enumerate(concept_token_id):
batch_loc, token_loc = torch.where(tokens == token_id)
token_indices[i, batch_loc] = token_loc
return token_indices
def create_latents(story_pipeline, seed, batch_size, same_latent, device, float_type):
# if seed is int
if isinstance(seed, int):
g = torch.Generator('cuda').manual_seed(seed)
shape = (batch_size, story_pipeline.unet.config.in_channels, 128, 128)
latents = randn_tensor(shape, generator=g, device=device, dtype=float_type)
elif isinstance(seed, list):
shape = (batch_size, story_pipeline.unet.config.in_channels, 128, 128)
latents = torch.empty(shape, device=device, dtype=float_type)
for i, seed_i in enumerate(seed):
g = torch.Generator('cuda').manual_seed(seed_i)
curr_latent = randn_tensor(shape, generator=g, device=device, dtype=float_type)
latents[i] = curr_latent[i]
if same_latent:
latents = latents[:1].repeat(batch_size, 1, 1, 1)
return latents, g
# Batch inference
def run_batch_generation(story_pipeline, prompts, concept_token,
seed=40, n_steps=50, mask_dropout=0.5,
same_latent=False, share_queries=True,
perform_sdsa=True, perform_injection=True,
downscale_rate=4, n_achors=2):
device = story_pipeline.device
tokenizer = story_pipeline.tokenizer
float_type = story_pipeline.dtype
unet = story_pipeline.unet
batch_size = len(prompts)
token_indices = create_token_indices(prompts, batch_size, concept_token, tokenizer)
anchor_mappings = create_anchor_mapping(batch_size, anchor_indices=list(range(n_achors)))
default_attention_store_kwargs = {
'token_indices': token_indices,
'mask_dropout': mask_dropout,
'extended_mapping': anchor_mappings
}
default_extended_attn_kwargs = {'extend_kv_unet_parts': ['up']}
query_store_kwargs= {'t_range': [0,n_steps//10], 'strength_start': 0.9, 'strength_end': 0.81836735}
latents, g = create_latents(story_pipeline, seed, batch_size, same_latent, device, float_type)
# ------------------ #
# Extended attention First Run #
if perform_sdsa:
extended_attn_kwargs = {**default_extended_attn_kwargs, 't_range': [(1, n_steps)]}
else:
extended_attn_kwargs = {**default_extended_attn_kwargs, 't_range': []}
print(extended_attn_kwargs['t_range'])
out = story_pipeline(prompt=prompts, generator=g, latents=latents,
attention_store_kwargs=default_attention_store_kwargs,
extended_attn_kwargs=extended_attn_kwargs,
share_queries=share_queries,
query_store_kwargs=query_store_kwargs,
num_inference_steps=n_steps)
last_masks = story_pipeline.attention_store.last_mask
dift_features = unet.latent_store.dift_features['261_0'][batch_size:]
dift_features = torch.stack([gaussian_smooth(x, kernel_size=3, sigma=1) for x in dift_features], dim=0)
nn_map, nn_distances = cyclic_nn_map(dift_features, last_masks, LATENT_RESOLUTIONS, device)
torch.cuda.empty_cache()
gc.collect()
# ------------------ #
# Extended attention with nn_map #
if perform_injection:
feature_injector = FeatureInjector(nn_map, nn_distances, last_masks, inject_range_alpha=[(n_steps//10, n_steps//3,0.8)],
swap_strategy='min', inject_unet_parts=['up', 'down'], dist_thr='dynamic')
out = story_pipeline(prompt=prompts, generator=g, latents=latents,
attention_store_kwargs=default_attention_store_kwargs,
extended_attn_kwargs=extended_attn_kwargs,
share_queries=share_queries,
query_store_kwargs=query_store_kwargs,
feature_injector=feature_injector,
num_inference_steps=n_steps)
img_all = view_images([np.array(x) for x in out.images], display_image=False, downscale_rate=downscale_rate)
# display_attn_maps(story_pipeline.attention_store.last_mask, out.images)
torch.cuda.empty_cache()
gc.collect()
else:
img_all = view_images([np.array(x) for x in out.images], display_image=False, downscale_rate=downscale_rate)
return out.images, img_all
# Anchors
def run_anchor_generation(story_pipeline, prompts, concept_token,
seed=40, n_steps=50, mask_dropout=0.5,
same_latent=False, share_queries=True,
perform_sdsa=True, perform_injection=True,
downscale_rate=4, cache_cpu_offloading=False):
device = story_pipeline.device
tokenizer = story_pipeline.tokenizer
float_type = story_pipeline.dtype
unet = story_pipeline.unet
batch_size = len(prompts)
token_indices = create_token_indices(prompts, batch_size, concept_token, tokenizer)
default_attention_store_kwargs = {
'token_indices': token_indices,
'mask_dropout': mask_dropout
}
default_extended_attn_kwargs = {'extend_kv_unet_parts': ['up']}
query_store_kwargs={'t_range': [0,n_steps//10], 'strength_start': 0.9, 'strength_end': 0.81836735}
latents, g = create_latents(story_pipeline, seed, batch_size, same_latent, device, float_type)
anchor_cache_first_stage = AnchorCache()
anchor_cache_second_stage = AnchorCache()
# ------------------ #
# Extended attention First Run #
if perform_sdsa:
extended_attn_kwargs = {**default_extended_attn_kwargs, 't_range': [(1, n_steps)]}
else:
extended_attn_kwargs = {**default_extended_attn_kwargs, 't_range': []}
print(extended_attn_kwargs['t_range'])
out = story_pipeline(prompt=prompts, generator=g, latents=latents,
attention_store_kwargs=default_attention_store_kwargs,
extended_attn_kwargs=extended_attn_kwargs,
share_queries=share_queries,
query_store_kwargs=query_store_kwargs,
anchors_cache=anchor_cache_first_stage,
num_inference_steps=n_steps)
last_masks = story_pipeline.attention_store.last_mask
dift_features = unet.latent_store.dift_features['261_0'][batch_size:]
dift_features = torch.stack([gaussian_smooth(x, kernel_size=3, sigma=1) for x in dift_features], dim=0)
anchor_cache_first_stage.dift_cache = dift_features
anchor_cache_first_stage.anchors_last_mask = last_masks
if cache_cpu_offloading:
anchor_cache_first_stage.to_device(torch.device('cpu'))
nn_map, nn_distances = cyclic_nn_map(dift_features, last_masks, LATENT_RESOLUTIONS, device)
torch.cuda.empty_cache()
gc.collect()
# ------------------ #
# Extended attention with nn_map #
if perform_injection:
feature_injector = FeatureInjector(nn_map, nn_distances, last_masks, inject_range_alpha=[(n_steps//10, n_steps//3,0.8)],
swap_strategy='min', inject_unet_parts=['up', 'down'], dist_thr='dynamic')
out = story_pipeline(prompt=prompts, generator=g, latents=latents,
attention_store_kwargs=default_attention_store_kwargs,
extended_attn_kwargs=extended_attn_kwargs,
share_queries=share_queries,
query_store_kwargs=query_store_kwargs,
feature_injector=feature_injector,
anchors_cache=anchor_cache_second_stage,
num_inference_steps=n_steps)
img_all = view_images([np.array(x) for x in out.images], display_image=False, downscale_rate=downscale_rate)
# display_attn_maps(story_pipeline.attention_store.last_mask, out.images)
anchor_cache_second_stage.dift_cache = dift_features
anchor_cache_second_stage.anchors_last_mask = last_masks
if cache_cpu_offloading:
anchor_cache_second_stage.to_device(torch.device('cpu'))
torch.cuda.empty_cache()
gc.collect()
else:
img_all = view_images([np.array(x) for x in out.images], display_image=False, downscale_rate=downscale_rate)
return out.images, img_all, anchor_cache_first_stage, anchor_cache_second_stage
def run_extra_generation(story_pipeline, prompts, concept_token,
anchor_cache_first_stage, anchor_cache_second_stage,
seed=40, n_steps=50, mask_dropout=0.5,
same_latent=False, share_queries=True,
perform_sdsa=True, perform_injection=True,
downscale_rate=4, cache_cpu_offloading=False):
device = story_pipeline.device
tokenizer = story_pipeline.tokenizer
float_type = story_pipeline.dtype
unet = story_pipeline.unet
batch_size = len(prompts)
token_indices = create_token_indices(prompts, batch_size, concept_token, tokenizer)
default_attention_store_kwargs = {
'token_indices': token_indices,
'mask_dropout': mask_dropout
}
default_extended_attn_kwargs = {'extend_kv_unet_parts': ['up']}
query_store_kwargs={'t_range': [0,n_steps//10], 'strength_start': 0.9, 'strength_end': 0.81836735}
extra_batch_size = batch_size + 2
if isinstance(seed, list):
seed = [seed[0], seed[0], *seed]
latents, g = create_latents(story_pipeline, seed, extra_batch_size, same_latent, device, float_type)
latents = latents[2:]
anchor_cache_first_stage.set_mode_inject()
anchor_cache_second_stage.set_mode_inject()
# ------------------ #
# Extended attention First Run #
if cache_cpu_offloading:
anchor_cache_first_stage.to_device(device)
if perform_sdsa:
extended_attn_kwargs = {**default_extended_attn_kwargs, 't_range': [(1, n_steps)]}
else:
extended_attn_kwargs = {**default_extended_attn_kwargs, 't_range': []}
print(extended_attn_kwargs['t_range'])
out = story_pipeline(prompt=prompts, generator=g, latents=latents,
attention_store_kwargs=default_attention_store_kwargs,
extended_attn_kwargs=extended_attn_kwargs,
share_queries=share_queries,
query_store_kwargs=query_store_kwargs,
anchors_cache=anchor_cache_first_stage,
num_inference_steps=n_steps)
last_masks = story_pipeline.attention_store.last_mask
dift_features = unet.latent_store.dift_features['261_0'][batch_size:]
dift_features = torch.stack([gaussian_smooth(x, kernel_size=3, sigma=1) for x in dift_features], dim=0)
anchor_dift_features = anchor_cache_first_stage.dift_cache
anchor_last_masks = anchor_cache_first_stage.anchors_last_mask
nn_map, nn_distances = anchor_nn_map(dift_features, anchor_dift_features, last_masks, anchor_last_masks, LATENT_RESOLUTIONS, device)
if cache_cpu_offloading:
anchor_cache_first_stage.to_device(torch.device('cpu'))
torch.cuda.empty_cache()
gc.collect()
# ------------------ #
# Extended attention with nn_map #
if perform_injection:
if cache_cpu_offloading:
anchor_cache_second_stage.to_device(device)
feature_injector = FeatureInjector(nn_map, nn_distances, last_masks, inject_range_alpha=[(n_steps//10, n_steps//3,0.8)],
swap_strategy='min', inject_unet_parts=['up', 'down'], dist_thr='dynamic')
out = story_pipeline(prompt=prompts, generator=g, latents=latents,
attention_store_kwargs=default_attention_store_kwargs,
extended_attn_kwargs=extended_attn_kwargs,
share_queries=share_queries,
query_store_kwargs=query_store_kwargs,
feature_injector=feature_injector,
anchors_cache=anchor_cache_second_stage,
num_inference_steps=n_steps)
img_all = view_images([np.array(x) for x in out.images], display_image=False, downscale_rate=downscale_rate)
# display_attn_maps(story_pipeline.attention_store.last_mask, out.images)
if cache_cpu_offloading:
anchor_cache_second_stage.to_device(torch.device('cpu'))
torch.cuda.empty_cache()
gc.collect()
else:
img_all = view_images([np.array(x) for x in out.images], display_image=False, downscale_rate=downscale_rate)
return out.images, img_all