class RecraftImageInpaintingNode:
"""
Modify image based on prompt and mask.
"""
RETURN_TYPES = (IO.IMAGE,)
DESCRIPTION = cleandoc(__doc__ or "") # Handle potential None value
FUNCTION = "api_call"
API_NODE = True
CATEGORY = "api node/image/Recraft"
@classmethod
def INPUT_TYPES(s):
return {
"required": {
"image": (IO.IMAGE, ),
"mask": (IO.MASK, ),
"prompt": (
IO.STRING,
{
"multiline": True,
"default": "",
"tooltip": "Prompt for the image generation.",
},
),
"n": (
IO.INT,
{
"default": 1,
"min": 1,
"max": 6,
"tooltip": "The number of images to generate.",
},
),
"seed": (
IO.INT,
{
"default": 0,
"min": 0,
"max": 0xFFFFFFFFFFFFFFFF,
"control_after_generate": True,
"tooltip": "Seed to determine if node should re-run; actual results are nondeterministic regardless of seed.",
},
),
},
"optional": {
"recraft_style": (RecraftIO.STYLEV3,),
"negative_prompt": (
IO.STRING,
{
"default": "",
"forceInput": True,
"tooltip": "An optional text description of undesired elements on an image.",
},
),
},
"hidden": {
"auth_token": "AUTH_TOKEN_COMFY_ORG",
},
}
def api_call(
self,
image: torch.Tensor,
mask: torch.Tensor,
prompt: str,
n: int,
seed,
auth_token=None,
recraft_style: RecraftStyle = None,
negative_prompt: str = None,
**kwargs,
):
default_style = RecraftStyle(RecraftStyleV3.realistic_image)
if recraft_style is None:
recraft_style = default_style
if not negative_prompt:
negative_prompt = None
request = RecraftImageGenerationRequest(
prompt=prompt,
negative_prompt=negative_prompt,
model=RecraftModel.recraftv3,
n=n,
style=recraft_style.style,
substyle=recraft_style.substyle,
style_id=recraft_style.style_id,
random_seed=seed,
)
# prepare mask tensor
_, H, W, _ = image.shape
mask = mask.unsqueeze(-1)
mask = mask.movedim(-1,1)
mask = common_upscale(mask, width=W, height=H, upscale_method="nearest-exact", crop="disabled")
mask = mask.movedim(1,-1)
mask = (mask > 0.5).float()
images = []
total = image.shape[0]
pbar = ProgressBar(total)
for i in range(total):
sub_bytes = handle_recraft_file_request(
image=image[i],
mask=mask[i:i+1],
path="/proxy/recraft/images/inpaint",
request=request,
auth_token=auth_token,
)
images.append(torch.cat([bytesio_to_image_tensor(x) for x in sub_bytes], dim=0))
pbar.update(1)
images_tensor = torch.cat(images, dim=0)
return (images_tensor, )
