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import spaces |
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import sys |
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import os |
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import torch |
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torch.jit.script = lambda f: f |
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import timm |
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sys.path.append('./') |
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import gradio as gr |
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import random |
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import numpy as np |
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from gradio_demo.character_template import character_man, lorapath_man |
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from gradio_demo.character_template import character_woman, lorapath_woman |
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from gradio_demo.character_template import styles, lorapath_styles |
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|
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import os |
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from typing import Tuple, List |
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import copy |
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import argparse |
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from diffusers.utils import load_image |
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import cv2 |
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from PIL import Image, ImageOps |
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from transformers import DPTFeatureExtractor, DPTForDepthEstimation |
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from controlnet_aux import OpenposeDetector |
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from controlnet_aux.open_pose.body import Body |
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from inference.models import YOLOWorld |
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from src.efficientvit.models.efficientvit.sam import EfficientViTSamPredictor |
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from src.efficientvit.sam_model_zoo import create_sam_model |
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import supervision as sv |
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try: |
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from groundingdino.models import build_model |
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from groundingdino.util import box_ops |
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from groundingdino.util.slconfig import SLConfig |
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from groundingdino.util.utils import clean_state_dict, get_phrases_from_posmap |
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from groundingdino.util.inference import annotate, predict |
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from segment_anything import build_sam, SamPredictor |
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import groundingdino.datasets.transforms as T |
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except: |
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print("groundingdino can not be load") |
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|
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from src.pipelines.lora_pipeline import LoraMultiConceptPipeline |
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from src.prompt_attention.p2p_attention import AttentionReplace |
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from diffusers import ControlNetModel, StableDiffusionXLPipeline |
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from src.pipelines.lora_pipeline import revise_regionally_controlnet_forward |
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from download import OMG_download |
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CHARACTER_MAN_NAMES = list(character_man.keys()) |
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CHARACTER_WOMAN_NAMES = list(character_woman.keys()) |
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STYLE_NAMES = list(styles.keys()) |
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MAX_SEED = np.iinfo(np.int32).max |
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title = r""" |
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<h1 align="center"> OMG + LoRAs </h1> |
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""" |
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description = r""" |
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<b>Official 🤗 Gradio demo</b> for <a href='https://github.com/kongzhecn/OMG/' target='_blank'><b>OMG: Occlusion-friendly Personalized Multi-concept Generation In Diffusion Models</b></a>.<be><br> |
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<br> |
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<a href='https://kongzhecn.github.io/omg-project/' target='_blank'><b>[Project]</b></a> <a href='https://github.com/kongzhecn/OMG/' target='_blank'><b>[Code]</b></a> <a href='https://arxiv.org/abs/2403.10983/' target='_blank'><b>[Arxiv]</b></a> <br> |
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<br> |
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❗️<b>Related demos<b>:<a href='https://huggingface.co/spaces/Fucius/OMG-InstantID/' target='_blank'><b> OMG + InstantID</b></a>❗️<br> |
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<br> |
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How to use:<br> |
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1. Select two characters. |
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2. Enter a text prompt as done in normal text-to-image models. |
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3. Click the <b>Submit</b> button to start customizing. |
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4. Enjoy the generated image😊! |
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""" |
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article = r""" |
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--- |
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📝 **Citation** |
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<br> |
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If our work is helpful for your research or applications, please cite us via: |
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```bibtex |
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@article{kong2024omg, |
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title={OMG: Occlusion-friendly Personalized Multi-concept Generation in Diffusion Models}, |
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author={Kong, Zhe and Zhang, Yong and Yang, Tianyu and Wang, Tao and Zhang, Kaihao and Wu, Bizhu and Chen, Guanying and Liu, Wei and Luo, Wenhan}, |
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journal={arXiv preprint arXiv:2403.10983}, |
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year={2024} |
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} |
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``` |
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""" |
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tips = r""" |
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### Usage tips of OMG |
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1. Input text prompts to describe a man and a woman |
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""" |
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|
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css = ''' |
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.gradio-container {width: 85% !important} |
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''' |
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|
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def sample_image(pipe, |
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input_prompt, |
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input_neg_prompt=None, |
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generator=None, |
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concept_models=None, |
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num_inference_steps=50, |
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guidance_scale=7.5, |
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controller=None, |
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stage=None, |
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region_masks=None, |
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lora_list = None, |
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styleL=None, |
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**extra_kargs |
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): |
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|
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spatial_condition = extra_kargs.pop('spatial_condition') |
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if spatial_condition is not None: |
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spatial_condition_input = [spatial_condition] * len(input_prompt) |
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else: |
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spatial_condition_input = None |
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|
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images = pipe( |
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prompt=input_prompt, |
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concept_models=concept_models, |
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negative_prompt=input_neg_prompt, |
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generator=generator, |
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guidance_scale=guidance_scale, |
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num_inference_steps=num_inference_steps, |
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cross_attention_kwargs={"scale": 0.8}, |
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controller=controller, |
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stage=stage, |
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region_masks=region_masks, |
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lora_list=lora_list, |
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styleL=styleL, |
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image=spatial_condition_input, |
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**extra_kargs).images |
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return images |
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|
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def load_image_yoloworld(image_source) -> Tuple[np.array, torch.Tensor]: |
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image = np.asarray(image_source) |
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return image |
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|
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def load_image_dino(image_source) -> Tuple[np.array, torch.Tensor]: |
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transform = T.Compose( |
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[ |
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T.RandomResize([800], max_size=1333), |
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T.ToTensor(), |
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T.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225]), |
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] |
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) |
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image = np.asarray(image_source) |
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image_transformed, _ = transform(image_source, None) |
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return image, image_transformed |
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|
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def predict_mask(segmentmodel, sam, image, TEXT_PROMPT, segmentType, confidence = 0.2, threshold = 0.5): |
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if segmentType=='GroundingDINO': |
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image_source, image = load_image_dino(image) |
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boxes, logits, phrases = predict( |
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model=segmentmodel, |
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image=image, |
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caption=TEXT_PROMPT, |
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box_threshold=0.3, |
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text_threshold=0.25 |
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) |
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sam.set_image(image_source) |
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H, W, _ = image_source.shape |
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boxes_xyxy = box_ops.box_cxcywh_to_xyxy(boxes) * torch.Tensor([W, H, W, H]) |
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|
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transformed_boxes = sam.transform.apply_boxes_torch(boxes_xyxy, image_source.shape[:2]).cuda() |
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masks, _, _ = sam.predict_torch( |
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point_coords=None, |
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point_labels=None, |
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boxes=transformed_boxes, |
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multimask_output=False, |
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) |
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masks=masks[0].squeeze(0) |
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else: |
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image_source = load_image_yoloworld(image) |
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segmentmodel.set_classes([TEXT_PROMPT]) |
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results = segmentmodel.infer(image_source, confidence=confidence) |
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detections = sv.Detections.from_inference(results).with_nms( |
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class_agnostic=True, threshold=threshold |
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) |
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masks = None |
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if len(detections) != 0: |
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print(TEXT_PROMPT + " detected!") |
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sam.set_image(image_source, image_format="RGB") |
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masks, _, _ = sam.predict(box=detections.xyxy[0], multimask_output=False) |
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masks = torch.from_numpy(masks.squeeze()) |
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return masks |
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|
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def prepare_text(prompt, region_prompts): |
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''' |
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Args: |
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prompt_entity: [subject1]-*-[attribute1]-*-[Location1]|[subject2]-*-[attribute2]-*-[Location2]|[global text] |
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Returns: |
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full_prompt: subject1, attribute1 and subject2, attribute2, global text |
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context_prompt: subject1 and subject2, global text |
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entity_collection: [(subject1, attribute1), Location1] |
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''' |
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region_collection = [] |
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|
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regions = region_prompts.split('|') |
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|
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for region in regions: |
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if region == '': |
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break |
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prompt_region, neg_prompt_region = region.split('-*-') |
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prompt_region = prompt_region.replace('[', '').replace(']', '') |
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neg_prompt_region = neg_prompt_region.replace('[', '').replace(']', '') |
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region_collection.append((prompt_region, neg_prompt_region)) |
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return (prompt, region_collection) |
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|
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|
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def build_model_sd(pretrained_model, controlnet_path, device, prompts): |
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controlnet = ControlNetModel.from_pretrained(controlnet_path, torch_dtype=torch.float16).to(device) |
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pipe = LoraMultiConceptPipeline.from_pretrained( |
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pretrained_model, controlnet=controlnet, torch_dtype=torch.float16, variant="fp16").to(device) |
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controller = AttentionReplace(prompts, 50, cross_replace_steps={"default_": 1.}, self_replace_steps=0.4, tokenizer=pipe.tokenizer, device=device, dtype=torch.float16, width=1024//32, height=1024//32) |
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revise_regionally_controlnet_forward(pipe.unet, controller) |
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pipe_concept = StableDiffusionXLPipeline.from_pretrained(pretrained_model, torch_dtype=torch.float16, |
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variant="fp16").to(device) |
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return pipe, controller, pipe_concept |
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|
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def build_model_lora(pipe_concept, lora_paths, style_path, condition, args, pipe): |
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pipe_list = [] |
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if condition == "Human pose": |
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controlnet = ControlNetModel.from_pretrained(args.openpose_checkpoint, torch_dtype=torch.float16).to(device) |
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pipe.controlnet = controlnet |
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elif condition == "Canny Edge": |
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controlnet = ControlNetModel.from_pretrained(args.canny_checkpoint, torch_dtype=torch.float16, variant="fp16").to(device) |
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pipe.controlnet = controlnet |
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elif condition == "Depth": |
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controlnet = ControlNetModel.from_pretrained(args.depth_checkpoint, torch_dtype=torch.float16).to(device) |
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pipe.controlnet = controlnet |
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|
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if style_path is not None and os.path.exists(style_path): |
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pipe_concept.load_lora_weights(style_path, weight_name="pytorch_lora_weights.safetensors", adapter_name='style') |
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pipe.load_lora_weights(style_path, weight_name="pytorch_lora_weights.safetensors", adapter_name='style') |
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for lora_path in lora_paths.split('|'): |
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adapter_name = lora_path.split('/')[-1].split('.')[0] |
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pipe_concept.load_lora_weights(lora_path, weight_name="pytorch_lora_weights.safetensors", adapter_name=adapter_name) |
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pipe_concept.enable_xformers_memory_efficient_attention() |
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pipe_list.append(adapter_name) |
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return pipe_list |
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|
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def build_yolo_segment_model(sam_path, device): |
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yolo_world = YOLOWorld(model_id="yolo_world/l") |
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sam = EfficientViTSamPredictor( |
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create_sam_model(name='xl1', weight_url=sam_path).to(device).eval() |
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) |
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return yolo_world, sam |
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|
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def load_model_hf(repo_id, filename, ckpt_config_filename, device='cpu'): |
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args = SLConfig.fromfile(ckpt_config_filename) |
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model = build_model(args) |
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args.device = device |
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checkpoint = torch.load(os.path.join(repo_id, filename), map_location='cpu') |
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log = model.load_state_dict(clean_state_dict(checkpoint['model']), strict=False) |
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print("Model loaded from {} \n => {}".format(filename, log)) |
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_ = model.eval() |
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return model |
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|
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def build_dino_segment_model(ckpt_repo_id, sam_checkpoint): |
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ckpt_filenmae = "groundingdino_swinb_cogcoor.pth" |
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ckpt_config_filename = os.path.join(ckpt_repo_id, "GroundingDINO_SwinB.cfg.py") |
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groundingdino_model = load_model_hf(ckpt_repo_id, ckpt_filenmae, ckpt_config_filename) |
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sam = build_sam(checkpoint=sam_checkpoint) |
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sam.cuda() |
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sam_predictor = SamPredictor(sam) |
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return groundingdino_model, sam_predictor |
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|
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def resize_and_center_crop(image, output_size=(1024, 576)): |
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width, height = image.size |
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aspect_ratio = width / height |
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new_height = output_size[1] |
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new_width = int(aspect_ratio * new_height) |
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|
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resized_image = image.resize((new_width, new_height), Image.LANCZOS) |
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|
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if new_width < output_size[0] or new_height < output_size[1]: |
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padding_color = "gray" |
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resized_image = ImageOps.expand(resized_image, |
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((output_size[0] - new_width) // 2, |
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(output_size[1] - new_height) // 2, |
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(output_size[0] - new_width + 1) // 2, |
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(output_size[1] - new_height + 1) // 2), |
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fill=padding_color) |
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|
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left = (resized_image.width - output_size[0]) / 2 |
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top = (resized_image.height - output_size[1]) / 2 |
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right = (resized_image.width + output_size[0]) / 2 |
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bottom = (resized_image.height + output_size[1]) / 2 |
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|
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cropped_image = resized_image.crop((left, top, right, bottom)) |
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|
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return cropped_image |
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|
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def main(device, segment_type): |
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pipe, controller, pipe_concept = build_model_sd(args.pretrained_sdxl_model, args.openpose_checkpoint, device, prompts_tmp) |
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|
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if segment_type == 'GroundingDINO': |
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detect_model, sam = build_dino_segment_model(args.dino_checkpoint, args.sam_checkpoint) |
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else: |
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detect_model, sam = build_yolo_segment_model(args.efficientViT_checkpoint, device) |
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|
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resolution_list = ["1440*728", |
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"1344*768", |
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"1216*832", |
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"1152*896", |
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"1024*1024", |
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"896*1152", |
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"832*1216", |
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"768*1344", |
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"728*1440"] |
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ratio_list = [1440 / 728, 1344 / 768, 1216 / 832, 1152 / 896, 1024 / 1024, 896 / 1152, 832 / 1216, 768 / 1344, |
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728 / 1440] |
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condition_list = ["None", |
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"Human pose", |
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"Canny Edge", |
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"Depth"] |
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|
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depth_estimator = DPTForDepthEstimation.from_pretrained(args.dpt_checkpoint).to("cuda") |
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feature_extractor = DPTFeatureExtractor.from_pretrained(args.dpt_checkpoint) |
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body_model = Body(args.pose_detector_checkpoint) |
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openpose = OpenposeDetector(body_model) |
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|
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def remove_tips(): |
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return gr.update(visible=False) |
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|
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def randomize_seed_fn(seed: int, randomize_seed: bool) -> int: |
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if randomize_seed: |
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seed = random.randint(0, MAX_SEED) |
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return seed |
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|
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def get_humanpose(img): |
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openpose_image = openpose(img) |
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return openpose_image |
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|
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def get_cannyedge(image): |
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image = np.array(image) |
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image = cv2.Canny(image, 100, 200) |
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image = image[:, :, None] |
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image = np.concatenate([image, image, image], axis=2) |
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canny_image = Image.fromarray(image) |
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return canny_image |
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|
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def get_depth(image): |
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image = feature_extractor(images=image, return_tensors="pt").pixel_values.to("cuda") |
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with torch.no_grad(), torch.autocast("cuda"): |
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depth_map = depth_estimator(image).predicted_depth |
|
|
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depth_map = torch.nn.functional.interpolate( |
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depth_map.unsqueeze(1), |
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size=(1024, 1024), |
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mode="bicubic", |
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align_corners=False, |
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) |
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depth_min = torch.amin(depth_map, dim=[1, 2, 3], keepdim=True) |
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depth_max = torch.amax(depth_map, dim=[1, 2, 3], keepdim=True) |
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depth_map = (depth_map - depth_min) / (depth_max - depth_min) |
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image = torch.cat([depth_map] * 3, dim=1) |
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image = image.permute(0, 2, 3, 1).cpu().numpy()[0] |
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image = Image.fromarray((image * 255.0).clip(0, 255).astype(np.uint8)) |
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return image |
|
|
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@spaces.GPU(duration=180) |
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def generate_image(prompt1, negative_prompt, man, woman, resolution, local_prompt1, local_prompt2, seed, condition, condition_img1, style): |
|
|
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path1 = lorapath_man[man] |
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path2 = lorapath_woman[woman] |
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pipe_concept.unload_lora_weights() |
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pipe.unload_lora_weights() |
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pipe_list = build_model_lora(pipe_concept, path1 + "|" + path2, lorapath_styles[style], condition, args, pipe) |
|
|
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if lorapath_styles[style] is not None and os.path.exists(lorapath_styles[style]): |
|
styleL = True |
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else: |
|
styleL = False |
|
|
|
input_list = [prompt1] |
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condition_list = [condition_img1] |
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output_list = [] |
|
|
|
width, height = int(resolution.split("*")[0]), int(resolution.split("*")[1]) |
|
|
|
kwargs = { |
|
'height': height, |
|
'width': width, |
|
} |
|
|
|
for prompt, condition_img in zip(input_list, condition_list): |
|
if prompt!='': |
|
input_prompt = [] |
|
p = '{prompt}, 35mm photograph, film, professional, 4k, highly detailed.' |
|
if styleL: |
|
p = styles[style] + p |
|
input_prompt.append([p.replace("{prompt}", prompt), p.replace("{prompt}", prompt)]) |
|
if styleL: |
|
input_prompt.append([(styles[style] + local_prompt1, character_man.get(man)[1]), |
|
(styles[style] + local_prompt2, character_woman.get(woman)[1])]) |
|
else: |
|
input_prompt.append([(local_prompt1, character_man.get(man)[1]), |
|
(local_prompt2, character_woman.get(woman)[1])]) |
|
|
|
if condition == 'Human pose' and condition_img is not None: |
|
index = ratio_list.index( |
|
min(ratio_list, key=lambda x: abs(x - condition_img.shape[1] / condition_img.shape[0]))) |
|
resolution = resolution_list[index] |
|
width, height = int(resolution.split("*")[0]), int(resolution.split("*")[1]) |
|
kwargs['height'] = height |
|
kwargs['width'] = width |
|
condition_img = resize_and_center_crop(Image.fromarray(condition_img), (width, height)) |
|
spatial_condition = get_humanpose(condition_img) |
|
elif condition == 'Canny Edge' and condition_img is not None: |
|
index = ratio_list.index( |
|
min(ratio_list, key=lambda x: abs(x - condition_img.shape[1] / condition_img.shape[0]))) |
|
resolution = resolution_list[index] |
|
width, height = int(resolution.split("*")[0]), int(resolution.split("*")[1]) |
|
kwargs['height'] = height |
|
kwargs['width'] = width |
|
condition_img = resize_and_center_crop(Image.fromarray(condition_img), (width, height)) |
|
spatial_condition = get_cannyedge(condition_img) |
|
elif condition == 'Depth' and condition_img is not None: |
|
index = ratio_list.index( |
|
min(ratio_list, key=lambda x: abs(x - condition_img.shape[1] / condition_img.shape[0]))) |
|
resolution = resolution_list[index] |
|
width, height = int(resolution.split("*")[0]), int(resolution.split("*")[1]) |
|
kwargs['height'] = height |
|
kwargs['width'] = width |
|
condition_img = resize_and_center_crop(Image.fromarray(condition_img), (width, height)) |
|
spatial_condition = get_depth(condition_img) |
|
else: |
|
spatial_condition = None |
|
|
|
kwargs['spatial_condition'] = spatial_condition |
|
controller.reset() |
|
image = sample_image( |
|
pipe, |
|
input_prompt=input_prompt, |
|
concept_models=pipe_concept, |
|
input_neg_prompt=[negative_prompt] * len(input_prompt), |
|
generator=torch.Generator(device).manual_seed(seed), |
|
controller=controller, |
|
stage=1, |
|
lora_list=pipe_list, |
|
styleL=styleL, |
|
**kwargs) |
|
|
|
controller.reset() |
|
if pipe.tokenizer("man")["input_ids"][1] in pipe.tokenizer(args.prompt)["input_ids"][1:-1]: |
|
mask1 = predict_mask(detect_model, sam, image[0], 'man', args.segment_type, confidence=0.10, |
|
threshold=0.5) |
|
else: |
|
mask1 = None |
|
|
|
if pipe.tokenizer("woman")["input_ids"][1] in pipe.tokenizer(args.prompt)["input_ids"][1:-1]: |
|
mask2 = predict_mask(detect_model, sam, image[0], 'woman', args.segment_type, confidence=0.10, |
|
threshold=0.5) |
|
else: |
|
mask2 = None |
|
|
|
if mask1 is None and mask2 is None: |
|
output_list.append(image[1]) |
|
else: |
|
image = sample_image( |
|
pipe, |
|
input_prompt=input_prompt, |
|
concept_models=pipe_concept, |
|
input_neg_prompt=[negative_prompt] * len(input_prompt), |
|
generator=torch.Generator(device).manual_seed(seed), |
|
controller=controller, |
|
stage=2, |
|
region_masks=[mask1, mask2], |
|
lora_list=pipe_list, |
|
styleL=styleL, |
|
**kwargs) |
|
output_list.append(image[1]) |
|
else: |
|
output_list.append(None) |
|
output_list.append(spatial_condition) |
|
return output_list |
|
|
|
|
|
|
|
|
|
def get_local_value_man(input): |
|
return character_man[input][0] |
|
|
|
def get_local_value_woman(input): |
|
return character_woman[input][0] |
|
|
|
with gr.Blocks(css=css) as demo: |
|
|
|
gr.Markdown(title) |
|
gr.Markdown(description) |
|
|
|
with gr.Row(): |
|
gallery = gr.Image(label="Generated Images", height=512, width=512) |
|
gen_condition = gr.Image(label="Spatial Condition", height=512, width=512) |
|
usage_tips = gr.Markdown(label="Usage tips of OMG", value=tips, visible=False) |
|
|
|
with gr.Row(): |
|
condition_img1 = gr.Image(label="Input an RGB image for condition", height=128, width=128) |
|
|
|
|
|
with gr.Row(): |
|
man = gr.Dropdown(label="Character 1 selection", choices=CHARACTER_MAN_NAMES, value="Chris Evans (identifier: Chris Evans)") |
|
woman = gr.Dropdown(label="Character 2 selection", choices=CHARACTER_WOMAN_NAMES, value="Taylor Swift (identifier: TaylorSwift)") |
|
resolution = gr.Dropdown(label="Image Resolution (width*height)", choices=resolution_list, value="1024*1024") |
|
condition = gr.Dropdown(label="Input condition type", choices=condition_list, value="None") |
|
style = gr.Dropdown(label="style", choices=STYLE_NAMES, value="None") |
|
|
|
with gr.Row(): |
|
local_prompt1 = gr.Textbox(label="Character1_prompt", |
|
info="Describe the Character 1, this prompt should include the identifier of character 1", |
|
value="Close-up photo of the Chris Evans, 35mm photograph, film, professional, 4k, highly detailed.") |
|
local_prompt2 = gr.Textbox(label="Character2_prompt", |
|
info="Describe the Character 2, this prompt should include the identifier of character2", |
|
value="Close-up photo of the TaylorSwift, 35mm photograph, film, professional, 4k, highly detailed.") |
|
|
|
man.change(get_local_value_man, man, local_prompt1) |
|
woman.change(get_local_value_woman, woman, local_prompt2) |
|
|
|
|
|
with gr.Column(): |
|
prompt = gr.Textbox(label="Prompt 1", |
|
info="Give a simple prompt to describe the first image content", |
|
placeholder="Required", |
|
value="close-up shot, photography, a man and a woman on the street, facing the camera smiling") |
|
|
|
|
|
with gr.Accordion(open=False, label="Advanced Options"): |
|
seed = gr.Slider( |
|
label="Seed", |
|
minimum=0, |
|
maximum=MAX_SEED, |
|
step=1, |
|
value=42, |
|
) |
|
negative_prompt = gr.Textbox(label="Negative Prompt", |
|
placeholder="noisy, blurry, soft, deformed, ugly", |
|
value="noisy, blurry, soft, deformed, ugly") |
|
randomize_seed = gr.Checkbox(label="Randomize seed", value=True) |
|
|
|
submit = gr.Button("Submit", variant="primary") |
|
|
|
submit.click( |
|
fn=remove_tips, |
|
outputs=usage_tips, |
|
).then( |
|
fn=randomize_seed_fn, |
|
inputs=[seed, randomize_seed], |
|
outputs=seed, |
|
queue=False, |
|
api_name=False, |
|
).then( |
|
fn=generate_image, |
|
inputs=[prompt, negative_prompt, man, woman, resolution, local_prompt1, local_prompt2, seed, condition, condition_img1, style], |
|
outputs=[gallery, gen_condition] |
|
) |
|
gr.Markdown(article) |
|
demo.launch(share=True) |
|
|
|
|
|
|
|
def parse_args(): |
|
parser = argparse.ArgumentParser('', add_help=False) |
|
parser.add_argument('--pretrained_sdxl_model', default='Fucius/stable-diffusion-xl-base-1.0', type=str) |
|
parser.add_argument('--openpose_checkpoint', default='thibaud/controlnet-openpose-sdxl-1.0', type=str) |
|
parser.add_argument('--canny_checkpoint', default='diffusers/controlnet-canny-sdxl-1.0', type=str) |
|
parser.add_argument('--depth_checkpoint', default='diffusers/controlnet-depth-sdxl-1.0', type=str) |
|
parser.add_argument('--efficientViT_checkpoint', default='./checkpoint/sam/efficientvit_sam_xl1.pt', type=str) |
|
parser.add_argument('--dino_checkpoint', default='./checkpoint/GroundingDINO', type=str) |
|
parser.add_argument('--sam_checkpoint', default='./checkpoint/sam/sam_vit_h_4b8939.pth', type=str) |
|
parser.add_argument('--dpt_checkpoint', default='Intel/dpt-hybrid-midas', type=str) |
|
parser.add_argument('--pose_detector_checkpoint', default='./checkpoint/ControlNet/annotator/ckpts/body_pose_model.pth', type=str) |
|
parser.add_argument('--prompt', default='Close-up photo of the cool man and beautiful woman in surprised expressions as they accidentally discover a mysterious island while on vacation by the sea, 35mm photograph, film, professional, 4k, highly detailed.', type=str) |
|
parser.add_argument('--negative_prompt', default='noisy, blurry, soft, deformed, ugly', type=str) |
|
parser.add_argument('--seed', default=22, type=int) |
|
parser.add_argument('--suffix', default='', type=str) |
|
parser.add_argument('--segment_type', default='yoloworld', help='GroundingDINO or yoloworld', type=str) |
|
return parser.parse_args() |
|
|
|
if __name__ == '__main__': |
|
args = parse_args() |
|
|
|
prompts = [args.prompt]*2 |
|
prompts_tmp = copy.deepcopy(prompts) |
|
device = torch.device('cuda') if torch.cuda.is_available() else torch.device('cpu') |
|
download = OMG_download() |
|
main(device, args.segment_type) |
