llm-grounded-diffusion / generation.py
Tony Lian
Allow using different schedulers and negative prompts
ec7f11c
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8.3 kB
version = "v3.0"
import torch
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
verbose = False
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
so_batch_size = 1
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(prompt, box, phrase, word, input_latents, input_embeddings,
sam_refine_kwargs, num_inference_steps, gligen_scheduled_sampling_beta=0.3,
verbose=False, scheduler_key=None, visualize=True):
bboxes, phrases, words = [box], [phrase], [word]
latents, single_object_images, single_object_pil_images_box_ann, latents_all = pipelines.generate_gligen(
model_dict, input_latents, input_embeddings, num_inference_steps, bboxes, phrases, 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, scheduler_key=scheduler_key
)
mask_selected, conf_score_selected = sam.sam_refine_box(sam_input_image=single_object_images[0], box=box, model_dict=model_dict, verbose=verbose, **sam_refine_kwargs)
mask_selected_tensor = torch.tensor(mask_selected)
return latents_all, mask_selected_tensor, single_object_pil_images_box_ann[0]
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, so_img_list = [], [], []
if not so_prompt_phrase_word_box_list:
return latents_all_list, mask_tensor_list
so_uncond_embeddings, so_cond_embeddings = so_input_embeddings
for idx, ((prompt, phrase, word, box), input_latents) in enumerate(zip(so_prompt_phrase_word_box_list, input_latents_list)):
so_current_cond_embeddings = so_cond_embeddings[idx:idx+1]
so_current_text_embeddings = torch.cat([so_uncond_embeddings, so_current_cond_embeddings], dim=0)
so_current_input_embeddings = so_current_text_embeddings, so_uncond_embeddings, so_current_cond_embeddings
latents_all, mask_tensor, so_img = generate_single_object_with_box(prompt, box, phrase, word, input_latents, input_embeddings=so_current_input_embeddings, verbose=verbose, **kwargs)
latents_all_list.append(latents_all)
mask_tensor_list.append(mask_tensor)
so_img_list.append(so_img)
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, 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
):
"""
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)
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
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
)
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
)
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)
return images[0], so_img_list