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import gradio as gr
import numpy as np
import torch
import PIL
from PIL import Image
import os
import sys
import rembg
import time
import json
import cv2
from datetime import datetime
from einops import repeat, rearrange
from omegaconf import OmegaConf
from typing import Dict, Optional, Tuple, List
from dataclasses import dataclass
from .utils import *
from huggingface_hub import hf_hub_download
parent_dir = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))
class GenMVImage(object):
def __init__(self, device):
self.seed = 1024
self.guidance_scale = 7.5
self.step = 50
self.pipelines = {}
self.device = device
def gen_image_from_crm(self, image):
from .third_party.CRM.pipelines import TwoStagePipeline
specs = json.load(open(f"{parent_dir}/apps/third_party/CRM/configs/specs_objaverse_total.json"))
stage1_config = OmegaConf.load(f"{parent_dir}/apps/third_party/CRM/configs/nf7_v3_SNR_rd_size_stroke.yaml").config
stage1_sampler_config = stage1_config.sampler
stage1_model_config = stage1_config.models
stage1_model_config.resume = hf_hub_download(repo_id="Zhengyi/CRM", filename="pixel-diffusion.pth", repo_type="model")
stage1_model_config.config = f"{parent_dir}/apps/third_party/CRM/" + stage1_model_config.config
if "crm" in self.pipelines.keys():
pipeline = self.pipelines['crm']
else:
self.pipelines['crm'] = TwoStagePipeline(
stage1_model_config,
stage1_sampler_config,
device=self.device,
dtype=torch.float16
)
pipeline = self.pipelines['crm']
pipeline.set_seed(self.seed)
rt_dict = pipeline(image, scale=self.guidance_scale, step=self.step)
mv_imgs = rt_dict["stage1_images"]
return mv_imgs[5], mv_imgs[3], mv_imgs[2], mv_imgs[0]
def gen_image_from_mvdream(self, image, text):
from .third_party.mvdream_diffusers.pipeline_mvdream import MVDreamPipeline
if image is None:
if "mvdream" in self.pipelines.keys():
pipe_MVDream = self.pipelines['mvdream']
else:
self.pipelines['mvdream'] = MVDreamPipeline.from_pretrained(
"ashawkey/mvdream-sd2.1-diffusers", # remote weights
torch_dtype=torch.float16,
trust_remote_code=True,
)
self.pipelines['mvdream'] = self.pipelines['mvdream'].to(self.device)
pipe_MVDream = self.pipelines['mvdream']
mv_imgs = pipe_MVDream(
text,
negative_prompt="ugly, deformed, disfigured, poor details, bad anatomy",
num_inference_steps=self.step,
guidance_scale=self.guidance_scale,
generator = torch.Generator(self.device).manual_seed(self.seed)
)
else:
image = np.array(image)
image = image.astype(np.float32) / 255.0
image = image[..., :3] * image[..., 3:4] + (1 - image[..., 3:4])
if "imagedream" in self.pipelines.keys():
pipe_imagedream = self.pipelines['imagedream']
else:
self.pipelines['imagedream'] = MVDreamPipeline.from_pretrained(
"ashawkey/imagedream-ipmv-diffusers", # remote weights
torch_dtype=torch.float16,
trust_remote_code=True,
)
self.pipelines['imagedream'] = self.pipelines['imagedream'].to(self.device)
pipe_imagedream = self.pipelines['imagedream']
mv_imgs = pipe_imagedream(
text,
image,
negative_prompt="ugly, deformed, disfigured, poor details, bad anatomy",
num_inference_steps=self.step,
guidance_scale=self.guidance_scale,
generator = torch.Generator(self.device).manual_seed(self.seed)
)
return mv_imgs[1], mv_imgs[2], mv_imgs[3], mv_imgs[0]
def gen_image_from_wonder3d(self, image, crop_size):
sys.path.append(f"{parent_dir}/apps/third_party/Wonder3D")
from .third_party.Wonder3D.mvdiffusion.pipelines.pipeline_mvdiffusion_image import MVDiffusionImagePipeline
weight_dtype = torch.float16
batch = prepare_data(image, crop_size)
if "wonder3d" in self.pipelines.keys():
pipeline = self.pipelines['wonder3d']
else:
self.pipelines['wonder3d'] = MVDiffusionImagePipeline.from_pretrained(
'flamehaze1115/wonder3d-v1.0',
custom_pipeline=f'{parent_dir}/apps/third_party/Wonder3D/mvdiffusion/pipelines/pipeline_mvdiffusion_image.py',
torch_dtype=weight_dtype
)
self.pipelines['wonder3d'].unet.enable_xformers_memory_efficient_attention()
self.pipelines['wonder3d'].to(self.device)
self.pipelines['wonder3d'].set_progress_bar_config(disable=True)
pipeline = self.pipelines['wonder3d']
generator = torch.Generator(device=pipeline.unet.device).manual_seed(self.seed)
# repeat (2B, Nv, 3, H, W)
imgs_in = torch.cat([batch['imgs_in']] * 2, dim=0).to(weight_dtype)
# (2B, Nv, Nce)
camera_embeddings = torch.cat([batch['camera_embeddings']] * 2, dim=0).to(weight_dtype)
task_embeddings = torch.cat([batch['normal_task_embeddings'], batch['color_task_embeddings']], dim=0).to(weight_dtype)
camera_embeddings = torch.cat([camera_embeddings, task_embeddings], dim=-1).to(weight_dtype)
# (B*Nv, 3, H, W)
imgs_in = rearrange(imgs_in, "Nv C H W -> (Nv) C H W")
# (B*Nv, Nce)
out = pipeline(
imgs_in,
# camera_embeddings,
generator=generator,
guidance_scale=self.guidance_scale,
num_inference_steps=self.step,
output_type='pt',
num_images_per_prompt=1,
**{'eta': 1.0},
).images
bsz = out.shape[0] // 2
normals_pred = out[:bsz]
images_pred = out[bsz:]
normals_pred = [save_image(normals_pred[i]) for i in range(bsz)]
images_pred = [save_image(images_pred[i]) for i in range(bsz)]
mv_imgs = images_pred
return mv_imgs[0], mv_imgs[2], mv_imgs[4], mv_imgs[5]
def run(self, mvimg_model, text, image, crop_size, seed, guidance_scale, step):
self.seed = seed
self.guidance_scale = guidance_scale
self.step = step
if mvimg_model.upper() == "CRM":
return self.gen_image_from_crm(image)
elif mvimg_model.upper() == "IMAGEDREAM":
return self.gen_image_from_mvdream(image, text)
elif mvimg_model.upper() == "WONDER3D":
return self.gen_image_from_wonder3d(image, crop_size) |