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| 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 | |
| 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): | |
| # 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 | |
| ) | |
| 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 | |
| ) | |
| 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) | |
| gc.collect() | |
| torch.cuda.empty_cache() | |
| return images[0], so_img_list | |