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import PIL
import requests
from io import BytesIO
from torchvision.transforms import ToTensor
from deepfloyd_if.modules import IFStageI, IFStageII, StableStageIII
from deepfloyd_if.modules.t5 import T5Embedder
from deepfloyd_if.pipelines import inpainting
def download_image(url):
response = requests.get(url)
return PIL.Image.open(BytesIO(response.content)).convert("RGB")
img_url = "https://raw.githubusercontent.com/Fantasy-Studio/Paint-by-Example/main/examples/image/example_1.png"
mask_url = "https://raw.githubusercontent.com/Fantasy-Studio/Paint-by-Example/main/examples/mask/example_1.png"
init_image = download_image(img_url).resize((512, 512))
mask_image = download_image(mask_url).resize((512, 512))
# convert mask_image to torch.Tensor to avoid bug
mask_image = ToTensor()(mask_image).unsqueeze(0) # (1, 3, 512, 512)
# Run locally
device = 'cuda:5'
cache_dir = "/comp_robot/rentianhe/weights/IF/"
if_I = IFStageI('IF-I-L-v1.0', device=device, cache_dir=cache_dir)
if_II = IFStageII('IF-II-L-v1.0', device=device, cache_dir=cache_dir)
if_III = StableStageIII('stable-diffusion-x4-upscaler', device=device, cache_dir=cache_dir)
t5 = T5Embedder(device=device, cache_dir=cache_dir)
result = inpainting(
t5=t5, if_I=if_I,
if_II=if_II,
if_III=if_III,
support_pil_img=init_image,
inpainting_mask=mask_image,
prompt=[
'A Panda'
],
seed=42,
if_I_kwargs={
"guidance_scale": 7.0,
"sample_timestep_respacing": "10,10,10,10,10,0,0,0,0,0",
'support_noise_less_qsample_steps': 0,
},
if_II_kwargs={
"guidance_scale": 4.0,
'aug_level': 0.0,
"sample_timestep_respacing": '100',
},
if_III_kwargs={
"guidance_scale": 9.0,
"noise_level": 20,
"sample_timestep_respacing": "75",
},
)
if_I.show(result['I'], 2, 3)
if_I.show(result['II'], 2, 6)
if_I.show(result['III'], 2, 14)
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