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| version = "v3.0" | |
| from PIL import Image | |
| import torch | |
| import models | |
| from models import load_sd | |
| import utils | |
| from models import pipelines, sam | |
| from utils import parse, latents | |
| from shared import model_dict, sam_model_dict | |
| verbose = False | |
| vae, tokenizer, text_encoder, unet, scheduler, dtype = model_dict.vae, model_dict.tokenizer, model_dict.text_encoder, model_dict.unet, model_dict.scheduler, 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 | |
| num_inference_steps = 20 # Number of denoising steps | |
| 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, gligen_scheduled_sampling_beta=0.3, | |
| verbose=False, 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 | |
| ) | |
| 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, | |
| so_center_box = False, fg_blending_ratio = 0.1, 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] | |
| if True: | |
| so_negative_prompt = "artifacts, blurry, smooth texture, bad quality, distortions, unrealistic, distorted image, bad proportions, duplicate, two, many, group, occlusion, occluded, side, border, collate" | |
| overall_negative_prompt = "artifacts, blurry, smooth texture, bad quality, distortions, unrealistic, distorted image, bad proportions, duplicate" | |
| else: | |
| so_negative_prompt = "" | |
| overall_negative_prompt = "" | |
| 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, 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 | |
| ) | |
| 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 | |