update: add a new node for gpt-image-1 image generate
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AhBumm
@@ -20,6 +20,7 @@ NODE_CLASS_MAPPINGS = {
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"BillBum_Modified_Ideogram_API_Node": BillBum_Modified_Ideogram_API_Node,
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"BillBum_NonSysPrompt_VLM_API_Node": BillBum_NonSysPrompt_VLM_API_Node,
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"BillBum_Modified_LLM_ForceStream_Mode": BillBum_Modified_LLM_ForceStream_Mode,
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"BillBum_Modified_GPTImage1_API_Node": BillBum_Modified_GPTImage1_API_Node,
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}
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NODE_DISPLAY_NAME_MAPPINGS = {
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@@ -13,6 +13,8 @@ import requests
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import json
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import re
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import tiktoken
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import math
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from comfy.utils import common_upscale
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META_PROMPT = """
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Given a task description or existing prompt, produce a detailed system prompt to guide a language model in completing the task effectively.
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@@ -75,6 +77,58 @@ def pil2tensor(images: Image.Image | list[Image.Image]) -> torch.Tensor:
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else:
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return torch.cat([single_pil2tensor(img) for img in images], dim=0)
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def downscale_input(image):
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samples = image.movedim(-1,1)
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#downscaling input images to roughly the same size as the outputs
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total = int(1536 * 1024)
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scale_by = math.sqrt(total / (samples.shape[3] * samples.shape[2]))
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if scale_by >= 1:
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return image
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width = round(samples.shape[3] * scale_by)
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height = round(samples.shape[2] * scale_by)
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s = common_upscale(samples, width, height, "lanczos", "disabled")
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s = s.movedim(1,-1)
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return s
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def validate_and_cast_response(response):
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# validate raw JSON response
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data = response.data
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if not data or len(data) == 0:
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raise Exception("No images returned from API endpoint")
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# Initialize list to store image tensors
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image_tensors = []
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# Process each image in the data array
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for image_data in data:
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image_url = image_data.url
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b64_data = image_data.b64_json
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if not image_url and not b64_data:
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raise Exception("No image was generated in the response")
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if b64_data:
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img_data = base64.b64decode(b64_data)
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img = Image.open(io.BytesIO(img_data))
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elif image_url:
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img_response = requests.get(image_url)
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if img_response.status_code != 200:
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raise Exception("Failed to download the image")
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img = Image.open(io.BytesIO(img_response.content))
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img = img.convert("RGBA")
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# Convert to numpy array, normalize to float32 between 0 and 1
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img_array = np.array(img).astype(np.float32) / 255.0
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img_tensor = torch.from_numpy(img_array)
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# Add to list of tensors
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image_tensors.append(img_tensor)
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return torch.stack(image_tensors, dim=0)
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class Text_Concat:
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def __init__(self):
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@@ -1300,6 +1354,198 @@ class BillBum_Modified_Ideogram_API_Node:
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except (KeyError, IndexError) as e:
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raise Exception(f"Unexpected response format: {e}")
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class BillBum_Modified_GPTImage1_API_Node:
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def __init__(self):
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pass
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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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"prompt": ("STRING", {
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"multiline": True,
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"default": "",
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"tooltip": "Text prompt for GPT Image 1",
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}),
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"api_url": ("STRING", {
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"default": "https://api.openai.com/v1/images/generations",
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"tooltip": "Costume API URL",
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}),
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"api_key": ("STRING", {
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"default": "enter your key here...",
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"tooltip": "Costume API key",
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}),
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"model": ("STRING", {
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"default": "gpt-image-1",
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"tooltip": "Model name",
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}),
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},
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"optional": {
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"seed": ("INT", {
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"default": 0,
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"min": 0,
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"max": 2**31-1,
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"step": 1,
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"display": "number",
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"tooltip": "not implemented yet in backend",
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}),
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"quality": ("COMBO", {
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"options": ["low","medium","high"],
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"default": "low",
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"tooltip": "Image quality, affects cost and generation time.",
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}),
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"size": ("COMBO", {
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"options": ["auto", "1024x1024", "1024x1536", "1536x1024"],
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"default": "auto",
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"tooltip": "Image size",
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}),
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"n": ("INT", {
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"default": 1,
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"min": 1,
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"max": 8,
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"step": 1,
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"display": "number",
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"tooltip": "How many images to generate",
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}),
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"image": ("IMAGE", {
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"default": None,
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"tooltip": "Optional reference image for image editing.",
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}),
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"mask": ("MASK", {
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"default": None,
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"tooltip": "Optional reference image for image editing.",
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}),
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}
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}
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RETURN_TYPES = ("IMAGE","INT")
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RETURN_NAMES = ("image","seed")
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FUNCTION = "api_call_image1"
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CATEGORY = "BillBum_API"
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def api_call_image1(self, prompt, api_url, api_key, model, seed=0, quality="low", image=None, mask=None, n=1, size="1024x1024"):
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if image is not None:
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batch_size = image.shape[0]
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img_binaries = []
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mask_binary = None
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for i in range(batch_size):
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single_image = image[i:i+1]
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scaled_image = downscale_input(single_image).squeeze()
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image_np = (scaled_image.numpy() * 255).astype(np.uint8)
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img = Image.fromarray(image_np)
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img_byte_arr = io.BytesIO()
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img.save(img_byte_arr, format='PNG')
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img_byte_arr.seek(0)
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img_binary = img_byte_arr
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img_binary.name = f"image_{i}.png"
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img_binaries.append(img_binary)
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client = OpenAI(
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base_url=api_url,
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api_key=api_key
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)
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if batch_size == 1:
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if mask is not None:
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if image.shape[0] != 1:
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raise Exception("Cannot use a mask with multiple image")
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if image is None:
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raise Exception("Cannot use a mask without an input image")
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if mask.shape[1:] != image.shape[1:-1]:
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raise Exception("Mask and Image must be the same size")
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batch, height, width = mask.shape
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rgba_mask = torch.zeros(height, width, 4, device="cpu")
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rgba_mask[:,:,3] = (1-mask.squeeze().cpu())
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scaled_mask = downscale_input(rgba_mask.unsqueeze(0)).squeeze()
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mask_np = (scaled_mask.numpy() * 255).astype(np.uint8)
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mask_img = Image.fromarray(mask_np)
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mask_img_byte_arr = io.BytesIO()
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mask_img.save(mask_img_byte_arr, format='PNG')
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mask_img_byte_arr.seek(0)
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mask_binary = mask_img_byte_arr
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mask_binary.name = "mask.png"
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result = client.images.edit(
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model=model,
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image=img_binaries[0],
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mask=mask_binary,
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prompt=prompt,
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quality=quality,
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n=n,
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size=size,
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)
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else:
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result = client.images.edit(
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model=model,
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image=img_binaries[0],
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prompt=prompt,
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quality=quality,
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n=n,
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size=size,
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)
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else:
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if mask is not None:
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if image.shape[0] != 1:
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raise Exception("Cannot use a mask with multiple image")
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if image is None:
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raise Exception("Cannot use a mask without an input image")
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if mask.shape[1:] != image.shape[1:-1]:
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raise Exception("Mask and Image must be the same size")
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batch, height, width = mask.shape
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rgba_mask = torch.zeros(height, width, 4, device="cpu")
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rgba_mask[:,:,3] = (1-mask.squeeze().cpu())
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scaled_mask = downscale_input(rgba_mask.unsqueeze(0)).squeeze()
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mask_np = (scaled_mask.numpy() * 255).astype(np.uint8)
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mask_img = Image.fromarray(mask_np)
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mask_img_byte_arr = io.BytesIO()
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mask_img.save(mask_img_byte_arr, format='PNG')
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mask_img_byte_arr.seek(0)
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mask_binary = mask_img_byte_arr
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mask_binary.name = "mask.png"
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result = client.images.edit(
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model=model,
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image=img_binaries,
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mask=mask_binary,
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prompt=prompt,
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quality=quality,
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n=n,
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size=size,
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)
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else:
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result = client.images.edit(
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model=model,
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image=img_binaries,
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prompt=prompt,
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quality=quality,
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n=n,
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size=size,
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)
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else:
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client = OpenAI(
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base_url=api_url,
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api_key=api_key
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)
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result = client.images.generate(
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model=model,
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prompt=prompt,
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quality=quality,
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n=n,
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size=size,
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)
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img_tensor = validate_and_cast_response(result)
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return (img_tensor,seed)
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# Ensure these mappings are correctly integrated into your ComfyUI environment
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NODE_CLASS_MAPPINGS = {
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}
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+2
-2
@@ -1,7 +1,7 @@
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[project]
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name = "comfyui_billbum_nodes"
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description = "API call node for Third-party platforms both official and local. Support VLMs LLMs Dalle3 Flux-Pro SD3 etc. And some little tools: img to b64 url, b64 url to img, b64 url to b64 data, reg text to word and ',' only, etc."
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version = "1.0.9"
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description = "API call node for Third-party platforms both official and local. Support VLMs LLMs Dalle3 Flux-Pro etc(new Support gpt-image-1 api!!!). And some little tools: img to b64 url, b64 url to img, b64 url to b64 data, reg text to word and ',' only, etc."
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version = "1.1.0"
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license = {file = "LICENSE"}
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dependencies = ["tenacity", "openai", "pillow", "requests", "together", "torch", "numpy", "tiktoken"]
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