llm-grounded-diffusion / generation.py
Tony Lian
Use fast schedule for per-box generation to speed up
d871568
raw
history blame
10.9 kB
version = "v3.0"
import torch
import numpy as np
import models
import utils
from models import pipelines, sam
from utils import parse, latents
from shared import model_dict, sam_model_dict, DEFAULT_SO_NEGATIVE_PROMPT, DEFAULT_OVERALL_NEGATIVE_PROMPT
import gc
verbose = False
# Accelerates per-box generation
use_fast_schedule = True
vae, tokenizer, text_encoder, unet, dtype = model_dict.vae, model_dict.tokenizer, model_dict.text_encoder, model_dict.unet, model_dict.dtype
model_dict.update(sam_model_dict)
# Hyperparams
height = 512 # default height of Stable Diffusion
width = 512 # default width of Stable Diffusion
H, W = height // 8, width // 8 # size of the latent
guidance_scale = 7.5 # Scale for classifier-free guidance
# batch size that is not 1 is not supported
overall_batch_size = 1
# discourage masks with confidence below
discourage_mask_below_confidence = 0.85
# discourage masks with iou (with coarse binarized attention mask) below
discourage_mask_below_coarse_iou = 0.25
run_ind = None
def generate_single_object_with_box_batch(prompts, bboxes, phrases, words, input_latents_list, input_embeddings,
sam_refine_kwargs, num_inference_steps, gligen_scheduled_sampling_beta=0.3,
verbose=False, scheduler_key=None, visualize=True, batch_size=None, **kwargs):
# batch_size=None: does not limit the batch size (pass all input together)
# prompts and words are not used since we don't have cross-attention control in this function
input_latents = torch.cat(input_latents_list, dim=0)
# We need to "unsqueeze" to tell that we have only one box and phrase in each batch item
bboxes, phrases = [[item] for item in bboxes], [[item] for item in phrases]
input_len = len(bboxes)
assert len(bboxes) == len(phrases), f"{len(bboxes)} != {len(phrases)}"
if batch_size is None:
batch_size = input_len
run_times = int(np.ceil(input_len / batch_size))
mask_selected_list, single_object_pil_images_box_ann, latents_all = [], [], []
for batch_idx in range(run_times):
input_latents_batch, bboxes_batch, phrases_batch = input_latents[batch_idx * batch_size:(batch_idx + 1) * batch_size], \
bboxes[batch_idx * batch_size:(batch_idx + 1) * batch_size], phrases[batch_idx * batch_size:(batch_idx + 1) * batch_size]
input_embeddings_batch = input_embeddings[0], input_embeddings[1][batch_idx * batch_size:(batch_idx + 1) * batch_size]
_, single_object_images_batch, single_object_pil_images_box_ann_batch, latents_all_batch = pipelines.generate_gligen(
model_dict, input_latents_batch, input_embeddings_batch, num_inference_steps, bboxes_batch, phrases_batch, gligen_scheduled_sampling_beta=gligen_scheduled_sampling_beta,
guidance_scale=guidance_scale, return_saved_cross_attn=False,
return_box_vis=True, save_all_latents=True, batched_condition=True, scheduler_key=scheduler_key, **kwargs
)
gc.collect()
torch.cuda.empty_cache()
# `sam_refine_boxes` also calls `empty_cache` so we don't need to explicitly empty the cache again.
mask_selected, _ = sam.sam_refine_boxes(sam_input_images=single_object_images_batch, boxes=bboxes_batch, model_dict=model_dict, verbose=verbose, **sam_refine_kwargs)
mask_selected_list.append(np.array(mask_selected)[:, 0])
single_object_pil_images_box_ann.append(single_object_pil_images_box_ann_batch)
latents_all.append(latents_all_batch)
single_object_pil_images_box_ann, latents_all = sum(single_object_pil_images_box_ann, []), torch.cat(latents_all, dim=1)
# mask_selected_list: List(batch)[List(image)[List(box)[Array of shape (64, 64)]]]
mask_selected = np.concatenate(mask_selected_list, axis=0)
mask_selected = mask_selected.reshape((-1, *mask_selected.shape[-2:]))
assert mask_selected.shape[0] == input_latents.shape[0], f"{mask_selected.shape[0]} != {input_latents.shape[0]}"
print(mask_selected.shape)
mask_selected_tensor = torch.tensor(mask_selected)
latents_all = latents_all.transpose(0,1)[:,:,None,...]
gc.collect()
torch.cuda.empty_cache()
return latents_all, mask_selected_tensor, single_object_pil_images_box_ann
def get_masked_latents_all_list(so_prompt_phrase_word_box_list, input_latents_list, so_input_embeddings, verbose=False, **kwargs):
latents_all_list, mask_tensor_list = [], []
if not so_prompt_phrase_word_box_list:
return latents_all_list, mask_tensor_list
prompts, bboxes, phrases, words = [], [], [], []
for prompt, phrase, word, box in so_prompt_phrase_word_box_list:
prompts.append(prompt)
bboxes.append(box)
phrases.append(phrase)
words.append(word)
latents_all_list, mask_tensor_list, so_img_list = generate_single_object_with_box_batch(prompts, bboxes, phrases, words, input_latents_list, input_embeddings=so_input_embeddings, verbose=verbose, **kwargs)
return latents_all_list, mask_tensor_list, so_img_list
# Note: need to keep the supervision, especially the box corrdinates, corresponds to each other in single object and overall.
def run(
spec, bg_seed = 1, overall_prompt_override="", fg_seed_start = 20, frozen_step_ratio=0.4, gligen_scheduled_sampling_beta = 0.3, num_inference_steps = 20,
so_center_box = False, fg_blending_ratio = 0.1, scheduler_key='dpm_scheduler', so_negative_prompt = DEFAULT_SO_NEGATIVE_PROMPT, overall_negative_prompt = DEFAULT_OVERALL_NEGATIVE_PROMPT, so_horizontal_center_only = True,
align_with_overall_bboxes = False, horizontal_shift_only = True, use_autocast = False, so_batch_size = None
):
"""
so_center_box: using centered box in single object generation
so_horizontal_center_only: move to the center horizontally only
align_with_overall_bboxes: Align the center of the mask, latents, and cross-attention with the center of the box in overall bboxes
horizontal_shift_only: only shift horizontally for the alignment of mask, latents, and cross-attention
"""
print("generation:", spec, bg_seed, fg_seed_start, frozen_step_ratio, gligen_scheduled_sampling_beta)
frozen_step_ratio = min(max(frozen_step_ratio, 0.), 1.)
frozen_steps = int(num_inference_steps * frozen_step_ratio)
if True:
so_prompt_phrase_word_box_list, overall_prompt, overall_phrases_words_bboxes = parse.convert_spec(spec, height, width, verbose=verbose)
if overall_prompt_override and overall_prompt_override.strip():
overall_prompt = overall_prompt_override.strip()
overall_phrases, overall_words, overall_bboxes = [item[0] for item in overall_phrases_words_bboxes], [item[1] for item in overall_phrases_words_bboxes], [item[2] for item in overall_phrases_words_bboxes]
# The so box is centered but the overall boxes are not (since we need to place to the right place).
if so_center_box:
so_prompt_phrase_word_box_list = [(prompt, phrase, word, utils.get_centered_box(bbox, horizontal_center_only=so_horizontal_center_only)) for prompt, phrase, word, bbox in so_prompt_phrase_word_box_list]
if verbose:
print(f"centered so_prompt_phrase_word_box_list: {so_prompt_phrase_word_box_list}")
so_boxes = [item[-1] for item in so_prompt_phrase_word_box_list]
sam_refine_kwargs = dict(
discourage_mask_below_confidence=discourage_mask_below_confidence, discourage_mask_below_coarse_iou=discourage_mask_below_coarse_iou,
height=height, width=width, H=H, W=W
)
# Note that so and overall use different negative prompts
with torch.autocast("cuda", enabled=use_autocast):
so_prompts = [item[0] for item in so_prompt_phrase_word_box_list]
if so_prompts:
so_input_embeddings = models.encode_prompts(prompts=so_prompts, tokenizer=tokenizer, text_encoder=text_encoder, negative_prompt=so_negative_prompt, one_uncond_input_only=True)
else:
so_input_embeddings = []
overall_input_embeddings = models.encode_prompts(prompts=[overall_prompt], tokenizer=tokenizer, negative_prompt=overall_negative_prompt, text_encoder=text_encoder)
input_latents_list, latents_bg = latents.get_input_latents_list(
model_dict, bg_seed=bg_seed, fg_seed_start=fg_seed_start,
so_boxes=so_boxes, fg_blending_ratio=fg_blending_ratio, height=height, width=width, verbose=False
)
latents_all_list, mask_tensor_list, so_img_list = get_masked_latents_all_list(
so_prompt_phrase_word_box_list, input_latents_list,
gligen_scheduled_sampling_beta=gligen_scheduled_sampling_beta,
sam_refine_kwargs=sam_refine_kwargs, so_input_embeddings=so_input_embeddings, num_inference_steps=num_inference_steps, scheduler_key=scheduler_key, verbose=verbose, batch_size=so_batch_size,
fast_after_steps=frozen_steps if use_fast_schedule else None, fast_rate=2
)
composed_latents, foreground_indices, offset_list = latents.compose_latents_with_alignment(
model_dict, latents_all_list, mask_tensor_list, num_inference_steps,
overall_batch_size, height, width, latents_bg=latents_bg,
align_with_overall_bboxes=align_with_overall_bboxes, overall_bboxes=overall_bboxes,
horizontal_shift_only=horizontal_shift_only, use_fast_schedule=use_fast_schedule, fast_after_steps=frozen_steps
)
overall_bboxes_flattened, overall_phrases_flattened = [], []
for overall_bboxes_item, overall_phrase in zip(overall_bboxes, overall_phrases):
for overall_bbox in overall_bboxes_item:
overall_bboxes_flattened.append(overall_bbox)
overall_phrases_flattened.append(overall_phrase)
# Generate with composed latents
# Foreground should be frozen
frozen_mask = foreground_indices != 0
regen_latents, images = pipelines.generate_gligen(
model_dict, composed_latents, overall_input_embeddings, num_inference_steps,
overall_bboxes_flattened, overall_phrases_flattened, guidance_scale=guidance_scale,
gligen_scheduled_sampling_beta=gligen_scheduled_sampling_beta,
frozen_steps=frozen_steps, frozen_mask=frozen_mask, scheduler_key=scheduler_key
)
print(f"Generation with spatial guidance from input latents and first {frozen_steps} steps frozen (directly from the composed latents input)")
print("Generation from composed latents (with semantic guidance)")
# display(Image.fromarray(images[0]), "img", run_ind)
gc.collect()
torch.cuda.empty_cache()
return images[0], so_img_list