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