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Starting
on
A10G
Starting
on
A10G
| import os | |
| import sys | |
| import numpy | |
| import torch | |
| import rembg | |
| import threading | |
| import urllib.request | |
| from PIL import Image | |
| import streamlit as st | |
| import huggingface_hub | |
| img_example_counter = 0 | |
| iret_base = 'resources/examples' | |
| iret = [ | |
| dict(rimageinput=os.path.join(iret_base, x), dispi=os.path.join(iret_base, x)) | |
| for x in sorted(os.listdir(iret_base)) | |
| ] | |
| class SAMAPI: | |
| predictor = None | |
| def get_instance(sam_checkpoint=None): | |
| if SAMAPI.predictor is None: | |
| if sam_checkpoint is None: | |
| sam_checkpoint = "tmp/sam_vit_h_4b8939.pth" | |
| if not os.path.exists(sam_checkpoint): | |
| os.makedirs('tmp', exist_ok=True) | |
| urllib.request.urlretrieve( | |
| "https://dl.fbaipublicfiles.com/segment_anything/sam_vit_h_4b8939.pth", | |
| sam_checkpoint | |
| ) | |
| device = "cuda:0" if torch.cuda.is_available() else "cpu" | |
| model_type = "default" | |
| from segment_anything import sam_model_registry, SamPredictor | |
| sam = sam_model_registry[model_type](checkpoint=sam_checkpoint) | |
| sam.to(device=device) | |
| predictor = SamPredictor(sam) | |
| SAMAPI.predictor = predictor | |
| return SAMAPI.predictor | |
| def segment_api(rgb, mask=None, bbox=None, sam_checkpoint=None): | |
| """ | |
| Parameters | |
| ---------- | |
| rgb : np.ndarray h,w,3 uint8 | |
| mask: np.ndarray h,w bool | |
| Returns | |
| ------- | |
| """ | |
| np = numpy | |
| predictor = SAMAPI.get_instance(sam_checkpoint) | |
| predictor.set_image(rgb) | |
| if mask is None and bbox is None: | |
| box_input = None | |
| else: | |
| # mask to bbox | |
| if bbox is None: | |
| y1, y2, x1, x2 = np.nonzero(mask)[0].min(), np.nonzero(mask)[0].max(), np.nonzero(mask)[1].min(), \ | |
| np.nonzero(mask)[1].max() | |
| else: | |
| x1, y1, x2, y2 = bbox | |
| box_input = np.array([[x1, y1, x2, y2]]) | |
| masks, scores, logits = predictor.predict( | |
| box=box_input, | |
| multimask_output=True, | |
| return_logits=False, | |
| ) | |
| mask = masks[-1] | |
| return mask | |
| def image_examples(samples, ncols, return_key=None, example_text="Examples"): | |
| global img_example_counter | |
| trigger = False | |
| with st.expander(example_text, True): | |
| for i in range(len(samples) // ncols): | |
| cols = st.columns(ncols) | |
| for j in range(ncols): | |
| idx = i * ncols + j | |
| if idx >= len(samples): | |
| continue | |
| entry = samples[idx] | |
| with cols[j]: | |
| st.image(entry['dispi']) | |
| img_example_counter += 1 | |
| with st.columns(5)[2]: | |
| this_trigger = st.button('\+', key='imgexuse%d' % img_example_counter) | |
| trigger = trigger or this_trigger | |
| if this_trigger: | |
| trigger = entry[return_key] | |
| return trigger | |
| def segment_img(img: Image): | |
| output = rembg.remove(img) | |
| mask = numpy.array(output)[:, :, 3] > 0 | |
| sam_mask = SAMAPI.segment_api(numpy.array(img)[:, :, :3], mask) | |
| segmented_img = Image.new("RGBA", img.size, (0, 0, 0, 0)) | |
| segmented_img.paste(img, mask=Image.fromarray(sam_mask)) | |
| return segmented_img | |
| def segment_6imgs(zero123pp_imgs): | |
| imgs = [zero123pp_imgs.crop([0, 0, 320, 320]), | |
| zero123pp_imgs.crop([320, 0, 640, 320]), | |
| zero123pp_imgs.crop([0, 320, 320, 640]), | |
| zero123pp_imgs.crop([320, 320, 640, 640]), | |
| zero123pp_imgs.crop([0, 640, 320, 960]), | |
| zero123pp_imgs.crop([320, 640, 640, 960])] | |
| segmented_imgs = [] | |
| for i, img in enumerate(imgs): | |
| output = rembg.remove(img) | |
| mask = numpy.array(output)[:, :, 3] | |
| mask = SAMAPI.segment_api(numpy.array(img)[:, :, :3], mask) | |
| data = numpy.array(img)[:,:,:3] | |
| data[mask == 0] = [255, 255, 255] | |
| segmented_imgs.append(data) | |
| result = numpy.concatenate([ | |
| numpy.concatenate([segmented_imgs[0], segmented_imgs[1]], axis=1), | |
| numpy.concatenate([segmented_imgs[2], segmented_imgs[3]], axis=1), | |
| numpy.concatenate([segmented_imgs[4], segmented_imgs[5]], axis=1) | |
| ]) | |
| return Image.fromarray(result) | |
| def expand2square(pil_img, background_color): | |
| width, height = pil_img.size | |
| if width == height: | |
| return pil_img | |
| elif width > height: | |
| result = Image.new(pil_img.mode, (width, width), background_color) | |
| result.paste(pil_img, (0, (width - height) // 2)) | |
| return result | |
| else: | |
| result = Image.new(pil_img.mode, (height, height), background_color) | |
| result.paste(pil_img, ((height - width) // 2, 0)) | |
| return result | |
| def check_dependencies(): | |
| reqs = [] | |
| try: | |
| import diffusers | |
| except ImportError: | |
| import traceback | |
| traceback.print_exc() | |
| print("Error: `diffusers` not found.", file=sys.stderr) | |
| reqs.append("diffusers==0.20.2") | |
| else: | |
| if not diffusers.__version__.startswith("0.20"): | |
| print( | |
| f"Warning: You are using an unsupported version of diffusers ({diffusers.__version__}), which may lead to performance issues.", | |
| file=sys.stderr | |
| ) | |
| print("Recommended version is `diffusers==0.20.2`.", file=sys.stderr) | |
| try: | |
| import transformers | |
| except ImportError: | |
| import traceback | |
| traceback.print_exc() | |
| print("Error: `transformers` not found.", file=sys.stderr) | |
| reqs.append("transformers==4.29.2") | |
| if torch.__version__ < '2.0': | |
| try: | |
| import xformers | |
| except ImportError: | |
| print("Warning: You are using PyTorch 1.x without a working `xformers` installation.", file=sys.stderr) | |
| print("You may see a significant memory overhead when running the model.", file=sys.stderr) | |
| if len(reqs): | |
| print(f"Info: Fix all dependency errors with `pip install {' '.join(reqs)}`.") | |
| def load_zero123plus_pipeline(): | |
| if 'HF_TOKEN' in os.environ: | |
| huggingface_hub.login(os.environ['HF_TOKEN']) | |
| from diffusers import DiffusionPipeline, EulerAncestralDiscreteScheduler | |
| pipeline = DiffusionPipeline.from_pretrained( | |
| "sudo-ai/zero123plus-v1.1", custom_pipeline="sudo-ai/zero123plus-pipeline", | |
| torch_dtype=torch.float16 | |
| ) | |
| # Feel free to tune the scheduler | |
| pipeline.scheduler = EulerAncestralDiscreteScheduler.from_config( | |
| pipeline.scheduler.config, timestep_spacing='trailing' | |
| ) | |
| if torch.cuda.is_available(): | |
| pipeline.to('cuda:0') | |
| sys.main_lock = threading.Lock() | |
| return pipeline | |
| check_dependencies() | |
| pipeline = load_zero123plus_pipeline() | |
| SAMAPI.get_instance() | |
| torch.set_grad_enabled(False) | |
| st.title("Zero123++ Demo") | |
| # st.caption("For faster inference without waiting in queue, you may clone the space and run it yourself.") | |
| prog = st.progress(0.0, "Idle") | |
| pic = st.file_uploader("Upload an Image", key='imageinput', type=['png', 'jpg', 'webp']) | |
| left, right = st.columns(2) | |
| with left: | |
| rem_input_bg = st.checkbox("Remove Input Background") | |
| with right: | |
| rem_output_bg = st.checkbox("Remove Output Background") | |
| num_inference_steps = st.slider("Number of Inference Steps", 15, 100, 75) | |
| st.caption("Diffusion Steps. For general real or synthetic objects, around 28 is enough. For objects with delicate details such as faces (either realistic or illustration), you may need 75 or more steps.") | |
| cfg_scale = st.slider("Classifier Free Guidance Scale", 1.0, 10.0, 4.0) | |
| seed = st.text_input("Seed", "42") | |
| submit = False | |
| if st.button("Submit"): | |
| submit = True | |
| results_container = st.container() | |
| sample_got = image_examples(iret, 4, 'rimageinput') | |
| if sample_got: | |
| pic = sample_got | |
| with results_container: | |
| if sample_got or submit: | |
| prog.progress(0.03, "Waiting in Queue...") | |
| with sys.main_lock: | |
| seed = int(seed) | |
| torch.manual_seed(seed) | |
| img = Image.open(pic) | |
| if max(img.size) > 1280: | |
| w, h = img.size | |
| w = round(1280 / max(img.size) * w) | |
| h = round(1280 / max(img.size) * h) | |
| img = img.resize((w, h)) | |
| left, right = st.columns(2) | |
| with left: | |
| st.image(img) | |
| st.caption("Input Image") | |
| prog.progress(0.1, "Preparing Inputs") | |
| if rem_input_bg: | |
| with right: | |
| img = segment_img(img) | |
| st.image(img) | |
| st.caption("Input (Background Removed)") | |
| img = expand2square(img, (127, 127, 127, 0)) | |
| pipeline.set_progress_bar_config(disable=True) | |
| result = pipeline( | |
| img, | |
| num_inference_steps=num_inference_steps, | |
| guidance_scale=cfg_scale, | |
| generator=torch.Generator(pipeline.device).manual_seed(seed), | |
| callback=lambda i, t, latents: prog.progress(0.1 + 0.8 * i / num_inference_steps, "Diffusion Step %d" % i) | |
| ).images[0] | |
| prog.progress(0.9, "Post Processing") | |
| left, right = st.columns(2) | |
| with left: | |
| st.image(result) | |
| st.caption("Result") | |
| if rem_output_bg: | |
| result = segment_6imgs(result) | |
| with right: | |
| st.image(result) | |
| st.caption("Result (Background Removed)") | |
| prog.progress(1.0, "Idle") | |