Spaces:
Running
on
Zero
Running
on
Zero
xinjie.wang
commited on
Commit
·
6f5d0e5
1
Parent(s):
0398906
update
Browse files- common.py +4 -2
- embodied_gen/models/text_model.py +10 -0
- embodied_gen/scripts/imageto3d.py +1 -1
- embodied_gen/scripts/text2image.py +6 -0
- requirements.txt +5 -3
common.py
CHANGED
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@@ -165,7 +165,7 @@ if os.getenv("GRADIO_APP") == "imageto3d":
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RBG14_REMOVER = BMGG14Remover()
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SAM_PREDICTOR = SAMPredictor(model_type="vit_h", device="cpu")
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PIPELINE = TrellisImageTo3DPipeline.from_pretrained(
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-
"
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)
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# PIPELINE.cuda()
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SEG_CHECKER = ImageSegChecker(GPT_CLIENT)
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@@ -179,7 +179,7 @@ elif os.getenv("GRADIO_APP") == "textto3d":
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RBG_REMOVER = RembgRemover()
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RBG14_REMOVER = BMGG14Remover()
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PIPELINE = TrellisImageTo3DPipeline.from_pretrained(
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-
"
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)
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# PIPELINE.cuda()
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text_model_dir = "weights/Kolors"
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@@ -671,6 +671,7 @@ def text2image_fn(
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image_wh: int | tuple[int, int] = [1024, 1024],
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rmbg_tag: str = "rembg",
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n_sample: int = 3,
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req: gr.Request = None,
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):
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if isinstance(image_wh, int):
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@@ -692,6 +693,7 @@ def text2image_fn(
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ip_image=ip_image,
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image_wh=image_wh,
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infer_step=infer_step,
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)
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for idx in range(len(images)):
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RBG14_REMOVER = BMGG14Remover()
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SAM_PREDICTOR = SAMPredictor(model_type="vit_h", device="cpu")
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PIPELINE = TrellisImageTo3DPipeline.from_pretrained(
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+
"microsoft/TRELLIS-image-large"
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)
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# PIPELINE.cuda()
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SEG_CHECKER = ImageSegChecker(GPT_CLIENT)
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RBG_REMOVER = RembgRemover()
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RBG14_REMOVER = BMGG14Remover()
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PIPELINE = TrellisImageTo3DPipeline.from_pretrained(
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+
"microsoft/TRELLIS-image-large"
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)
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# PIPELINE.cuda()
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text_model_dir = "weights/Kolors"
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image_wh: int | tuple[int, int] = [1024, 1024],
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rmbg_tag: str = "rembg",
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n_sample: int = 3,
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+
seed: int = None,
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req: gr.Request = None,
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):
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if isinstance(image_wh, int):
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ip_image=ip_image,
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image_wh=image_wh,
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infer_step=infer_step,
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+
seed=seed,
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)
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for idx in range(len(images)):
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embodied_gen/models/text_model.py
CHANGED
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@@ -18,6 +18,8 @@
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import logging
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import torch
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from diffusers import (
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AutoencoderKL,
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EulerDiscreteScheduler,
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@@ -138,11 +140,18 @@ def text2img_gen(
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image_wh: tuple[int, int] = [1024, 1024],
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infer_step: int = 50,
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ip_image_size: int = 512,
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) -> list[Image.Image]:
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prompt = "Single " + prompt + ", in the center of the image"
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prompt += ", high quality, high resolution, best quality, white background, 3D style," # noqa
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logger.info(f"Processing prompt: {prompt}")
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kwargs = dict(
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prompt=prompt,
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height=image_wh[1],
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@@ -150,6 +159,7 @@ def text2img_gen(
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num_inference_steps=infer_step,
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guidance_scale=guidance_scale,
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num_images_per_prompt=n_sample,
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)
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if ip_image is not None:
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if isinstance(ip_image, str):
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import logging
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import torch
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+
import numpy as np
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import random
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from diffusers import (
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AutoencoderKL,
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EulerDiscreteScheduler,
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image_wh: tuple[int, int] = [1024, 1024],
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infer_step: int = 50,
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ip_image_size: int = 512,
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+
seed: int = None,
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) -> list[Image.Image]:
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prompt = "Single " + prompt + ", in the center of the image"
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prompt += ", high quality, high resolution, best quality, white background, 3D style," # noqa
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logger.info(f"Processing prompt: {prompt}")
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+
if seed is not None:
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generator = torch.Generator(pipeline.device).manual_seed(seed)
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torch.manual_seed(seed)
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np.random.seed(seed)
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random.seed(seed)
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+
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kwargs = dict(
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prompt=prompt,
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height=image_wh[1],
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num_inference_steps=infer_step,
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guidance_scale=guidance_scale,
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num_images_per_prompt=n_sample,
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+
generator=generator,
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)
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if ip_image is not None:
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if isinstance(ip_image, str):
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embodied_gen/scripts/imageto3d.py
CHANGED
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@@ -70,7 +70,7 @@ IMAGESR_MODEL = ImageRealESRGAN(outscale=4)
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RBG_REMOVER = RembgRemover()
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RBG14_REMOVER = BMGG14Remover()
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SAM_PREDICTOR = SAMPredictor(model_type="vit_h", device="cpu")
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-
PIPELINE = TrellisImageTo3DPipeline.from_pretrained("
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PIPELINE.cuda()
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SEG_CHECKER = ImageSegChecker(GPT_CLIENT)
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GEO_CHECKER = MeshGeoChecker(GPT_CLIENT)
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RBG_REMOVER = RembgRemover()
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RBG14_REMOVER = BMGG14Remover()
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SAM_PREDICTOR = SAMPredictor(model_type="vit_h", device="cpu")
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+
PIPELINE = TrellisImageTo3DPipeline.from_pretrained("microsoft/TRELLIS-image-large")
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PIPELINE.cuda()
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SEG_CHECKER = ImageSegChecker(GPT_CLIENT)
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GEO_CHECKER = MeshGeoChecker(GPT_CLIENT)
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embodied_gen/scripts/text2image.py
CHANGED
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@@ -82,6 +82,11 @@ def parse_args():
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type=int,
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default=50,
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)
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args = parser.parse_args()
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return args
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@@ -143,6 +148,7 @@ def entrypoint(
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ip_image=ip_img_path,
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image_wh=[args.resolution, args.resolution],
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infer_step=args.infer_step,
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)
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save_paths = []
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type=int,
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default=50,
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)
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parser.add_argument(
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"--seed",
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type=int,
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default=0,
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)
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args = parser.parse_args()
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return args
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ip_image=ip_img_path,
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image_wh=[args.resolution, args.resolution],
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infer_step=args.infer_step,
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+
seed=args.seed,
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)
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save_paths = []
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requirements.txt
CHANGED
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@@ -5,6 +5,7 @@ torchvision==0.19.0
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xformers==0.0.27.post2
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pytorch-lightning==2.4.0
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spconv-cu120==2.3.6
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triton
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dataclasses_json
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easydict
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@@ -37,6 +38,7 @@ kolors@git+https://github.com/Kwai-Kolors/Kolors.git#egg=038818d
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segment-anything@git+https://github.com/facebookresearch/segment-anything.git#egg=dca509f
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https://github.com/nerfstudio-project/gsplat/releases/download/v1.5.0/gsplat-1.5.0+pt24cu121-cp310-cp310-linux_x86_64.whl
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https://github.com/Dao-AILab/flash-attention/releases/download/v2.7.0.post2/flash_attn-2.7.0.post2+cu12torch2.4cxx11abiFALSE-cp310-cp310-linux_x86_64.whl
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-
https://
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-
https://
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https://huggingface.co/
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xformers==0.0.27.post2
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pytorch-lightning==2.4.0
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spconv-cu120==2.3.6
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+
numpy==1.26.4
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triton
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dataclasses_json
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easydict
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segment-anything@git+https://github.com/facebookresearch/segment-anything.git#egg=dca509f
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https://github.com/nerfstudio-project/gsplat/releases/download/v1.5.0/gsplat-1.5.0+pt24cu121-cp310-cp310-linux_x86_64.whl
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https://github.com/Dao-AILab/flash-attention/releases/download/v2.7.0.post2/flash_attn-2.7.0.post2+cu12torch2.4cxx11abiFALSE-cp310-cp310-linux_x86_64.whl
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+
kaolin@git+https://github.com/NVIDIAGameWorks/kaolin.git@v0.16.0
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# nvdiffrast@git+https://github.com/NVlabs/nvdiffrast.git#egg=729261d
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https://huggingface.co/xinjjj/RoboAssetGen/resolve/main/wheel_cu121/nvdiffrast-0.3.3-cp310-cp310-linux_x86_64.whl
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+
https://huggingface.co/xinjjj/RoboAssetGen/resolve/main/wheel_cu121/diff_gaussian_rasterization-0.0.0-cp310-cp310-linux_x86_64.whl
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