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https://git.datalinker.icu/kijai/ComfyUI-KJNodes.git
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Merge branch 'main' into develop
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kijai
commit
891daeb438
3
config.json
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3
config.json
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@ -0,0 +1,3 @@
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{
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"sai_api_key": "your_api_key_here"
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}
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271
nodes.py
271
nodes.py
@ -4519,6 +4519,7 @@ class ImageAndMaskPreview(SaveImage):
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"required": {
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"mask_opacity": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.01}),
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"mask_color": ("STRING", {"default": "255, 255, 255"}),
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"pass_through": ("BOOLEAN", {"default": False}),
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},
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"optional": {
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"image": ("IMAGE",),
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@ -4526,15 +4527,20 @@ class ImageAndMaskPreview(SaveImage):
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},
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"hidden": {"prompt": "PROMPT", "extra_pnginfo": "EXTRA_PNGINFO"},
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}
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RETURN_TYPES = ("IMAGE",)
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RETURN_NAMES = ("composite",)
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FUNCTION = "execute"
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CATEGORY = "KJNodes"
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DESCRIPTION = """
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Preview an image or a mask, when both inputs are used
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composites the mask on top of the image.
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with pass_through on the preview is disabled and the
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composite is returned from the composite slot instead,
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this allows for the preview to be passed for video combine
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nodes for example.
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"""
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def execute(self, mask_opacity, mask_color, filename_prefix="ComfyUI", image=None, mask=None, prompt=None, extra_pnginfo=None):
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def execute(self, mask_opacity, mask_color, pass_through, filename_prefix="ComfyUI", image=None, mask=None, prompt=None, extra_pnginfo=None):
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if mask is not None and image is None:
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preview = mask.reshape((-1, 1, mask.shape[-2], mask.shape[-1])).movedim(1, -1).expand(-1, -1, -1, 3)
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elif mask is None and image is not None:
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@ -4550,7 +4556,9 @@ composites the mask on top of the image.
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mask_image[:, :, :, 2] = color_list[2] // 255 # Blue channel
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preview, = ImageCompositeMasked.composite(self, image, mask_image, 0, 0, True, mask_adjusted)
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return self.save_images(preview, filename_prefix, prompt, extra_pnginfo)
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if pass_through:
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return (preview, )
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return(self.save_images(preview, filename_prefix, prompt, extra_pnginfo))
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class SplineEditor:
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@ -4605,7 +4613,256 @@ class SplineEditor:
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print(masks_out.shape)
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return (masks_out, coordinates, normalized_y_values,)
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class StabilityAPI_SD3:
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@classmethod
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def INPUT_TYPES(cls):
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return {
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"required": {
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"prompt": ("STRING", {"multiline": True}),
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"n_prompt": ("STRING", {"multiline": True}),
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"seed": ("INT", {"default": 123,"min": 0, "max": 4294967294, "step": 1}),
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"model": (
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[
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'sd3',
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'sd3-turbo',
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],
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{
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"default": 'sd3'
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}),
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"aspect_ratio": (
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[
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'1:1',
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'16:9',
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'21:9',
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'2:3',
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'3:2',
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'4:5',
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'5:4',
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'9:16',
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'9:21',
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],
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{
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"default": '1:1'
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}),
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"output_format": (
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[
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'png',
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'jpeg',
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],
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{
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"default": 'jpeg'
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}),
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},
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"optional": {
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"api_key": ("STRING", {"multiline": True}),
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"image": ("IMAGE",),
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"img2img_strength": ("FLOAT", {"default": 0.5, "min": 0.0, "max": 1.0, "step": 0.01}),
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"disable_metadata": ("BOOLEAN", {"default": True}),
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},
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}
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RETURN_TYPES = ("IMAGE",)
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FUNCTION = "apicall"
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CATEGORY = "KJNodes/experimental"
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DESCRIPTION = """
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## Calls StabilityAI API
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Although you may have multiple keys in your account,
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you should use the same key for all requests to this API.
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Get your API key here: https://platform.stability.ai/account/keys
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Recommended to set the key in the config.json -file under this
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node packs folder.
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# WARNING:
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Otherwise the API key may get saved in the image metadata even
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with "disable_metadata" on if the workflow includes save nodes
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separate from this node.
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sd3 requires 6.5 credits per generation
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sd3-turbo requires 4 credits per generation
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If no image is provided, mode is set to text-to-image
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"""
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def apicall(self, prompt, n_prompt, model, seed, aspect_ratio, output_format,
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img2img_strength=0.5, image=None, disable_metadata=True, api_key=""):
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from comfy.cli_args import args
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if disable_metadata:
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args.disable_metadata = True
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else:
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args.disable_metadata = False
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import requests
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from io import BytesIO
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from torchvision import transforms
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data = {
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"mode": "text-to-image",
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"prompt": prompt,
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"model": model,
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"seed": seed,
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"output_format": output_format
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}
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if image is not None:
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image = image.permute(0, 3, 1, 2).squeeze(0)
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to_pil = transforms.ToPILImage()
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pil_image = to_pil(image)
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# Save the PIL Image to a BytesIO object
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buffer = BytesIO()
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pil_image.save(buffer, format='PNG')
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buffer.seek(0)
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files = {"image": ("image.png", buffer, "image/png")}
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data["mode"] = "image-to-image"
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data["image"] = pil_image
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data["strength"] = img2img_strength
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else:
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data["aspect_ratio"] = aspect_ratio,
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files = {"none": ''}
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if model != "sd3-turbo":
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data["negative_prompt"] = n_prompt
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headers={
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"accept": "image/*"
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}
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if api_key != "":
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headers["authorization"] = api_key
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else:
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config_file_path = os.path.join(script_directory,"config.json")
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with open(config_file_path, 'r') as file:
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config = json.load(file)
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api_key_from_config = config.get("sai_api_key")
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headers["authorization"] = api_key_from_config
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response = requests.post(
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f"https://api.stability.ai/v2beta/stable-image/generate/sd3",
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headers=headers,
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files = files,
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data = data,
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)
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if response.status_code == 200:
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# Convert the response content to a PIL Image
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image = Image.open(BytesIO(response.content))
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# Convert the PIL Image to a PyTorch tensor
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transform = transforms.ToTensor()
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tensor_image = transform(image)
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tensor_image = tensor_image.unsqueeze(0)
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tensor_image = tensor_image.permute(0, 2, 3, 1).cpu().float()
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return (tensor_image,)
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else:
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try:
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# Attempt to parse the response as JSON
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error_data = response.json()
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raise Exception(f"Server error: {error_data}")
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except json.JSONDecodeError:
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# If the response is not valid JSON, raise a different exception
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raise Exception(f"Server error: {response.text}")
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class MaskOrImageToWeight:
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@classmethod
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def INPUT_TYPES(s):
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return {
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"required": {
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"output_type": (
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[
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'list',
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'list of lists',
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'pandas series',
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],
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{
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"default": 'list'
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}),
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},
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"optional": {
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"images": ("IMAGE",),
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"masks": ("MASK",),
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},
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}
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RETURN_TYPES = ("FLOAT",)
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FUNCTION = "execute"
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CATEGORY = "KJNodes"
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DESCRIPTION = """
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Gets the mean value of mask or image
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and returns it as a float value.
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"""
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def execute(self, output_type, images=None, masks=None):
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mean_values = []
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if masks is not None and images is None:
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for mask in masks:
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mean_values.append(mask.mean().item())
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print(mean_values)
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elif masks is None and images is not None:
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for image in images:
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mean_values.append(image.mean().item())
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elif masks is not None and images is not None:
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raise Exception("MaskOrImageToWeight: Use either mask or image input only.")
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# Convert mean_values to the specified output_type
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if output_type == 'list':
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return mean_values,
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elif output_type == 'list of lists':
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return [[value] for value in mean_values],
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elif output_type == 'pandas series':
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try:
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import pandas as pd
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except:
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raise Exception("MaskOrImageToWeight: pandas is not installed. Please install pandas to use this output_type")
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return pd.Series(mean_values),
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else:
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raise ValueError(f"Unsupported output_type: {output_type}")
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class FloatToMask:
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@classmethod
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def INPUT_TYPES(s):
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return {
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"required": {
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"input_values": ("FLOAT", {"forceInput": True, "default": 0}),
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"width": ("INT", {"default": 100, "min": 1}),
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"height": ("INT", {"default": 100, "min": 1}),
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},
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}
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RETURN_TYPES = ("MASK",)
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FUNCTION = "execute"
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CATEGORY = "KJNodes"
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DESCRIPTION = """
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Generates a batch of masks based on the input float values.
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The batch size is determined by the length of the input float values.
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Each mask is generated with the specified width and height.
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"""
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def execute(self, input_values, width, height):
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import pandas as pd
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# Ensure input_values is a list
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if isinstance(input_values, (float, int)):
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input_values = [input_values]
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elif isinstance(input_values, pd.Series):
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input_values = input_values.tolist()
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elif isinstance(input_values, list) and all(isinstance(item, list) for item in input_values):
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input_values = [item for sublist in input_values for item in sublist]
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# Generate a batch of masks based on the input_values
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masks = []
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for value in input_values:
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# Assuming value is a float between 0 and 1 representing the mask's intensity
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mask = torch.ones((height, width), dtype=torch.float32) * value
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masks.append(mask)
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masks_out = torch.stack(masks, dim=0)
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print(masks_out.shape)
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return(masks_out,)
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NODE_CLASS_MAPPINGS = {
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"INTConstant": INTConstant,
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"FloatConstant": FloatConstant,
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@ -4685,7 +4942,10 @@ NODE_CLASS_MAPPINGS = {
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"Sleep": Sleep,
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"ImagePadForOutpaintMasked": ImagePadForOutpaintMasked,
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"SplineEditor": SplineEditor,
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"ImageAndMaskPreview": ImageAndMaskPreview
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"ImageAndMaskPreview": ImageAndMaskPreview,
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"StabilityAPI_SD3": StabilityAPI_SD3,
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"MaskOrImageToWeight": MaskOrImageToWeight,
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"FloatToMask": FloatToMask
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}
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NODE_DISPLAY_NAME_MAPPINGS = {
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"INTConstant": "INT Constant",
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@ -4767,4 +5027,7 @@ NODE_DISPLAY_NAME_MAPPINGS = {
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"ImagePadForOutpaintMasked": "Pad Image For Outpaint Masked",
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"SplineEditor": "Spline Editor",
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"ImageAndMaskPreview": "Image & Mask Preview",
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"StabilityAPI_SD3": "Stability API SD3",
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"MaskOrImageToWeight": "Mask Or Image To Weight",
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"FloatToMask": "Float To Mask",
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}
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