generation.py

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, 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
):
    """    
    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

    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