update nodes for hyprlab api

__init__.py

@@ -6,6 +6,7 @@ from .nodes4tuzi import (
     LoadVideoFromUrlVHS,
     LoadVideoFromUrlComfyIO,
     )
+from .nodes4hypr import HyprLab_Image_API_Node
 
 # Exporting the node classes for ComfyUI to discover
 NODE_CLASS_MAPPINGS = {
@@ -31,6 +32,7 @@ NODE_CLASS_MAPPINGS = {
     "reg_tuzi_chat_response": RegTuziChatResponse,
     "load_video_from_url": LoadVideoFromUrlVHS,
     "load_video_from_url_comfy_core": LoadVideoFromUrlComfyIO,
+    "hyprlab_image_api_node": HyprLab_Image_API_Node,
 }
 
 NODE_DISPLAY_NAME_MAPPINGS = {
@@ -56,4 +58,5 @@ NODE_DISPLAY_NAME_MAPPINGS = {
     "BillBum_Modified_LLM_ForceStream_Mode": "LLM StreamResponse Node (Old)",
     "BillBum_Modified_GPTImage1_API_Node": "Custom GPTImage1 API Node",
     "BillBum_Modified_Flux_API_with_imgInput": "Custom Flux API Node",
+    "hyprlab_image_api_node": "HyprLab ImageGen API Node",
 }

billbum_modified.py

@@ -161,7 +161,7 @@ class Input_Text:
     def INPUT_TYPES(s):
         return {
             "required": {
-                "text": ("STRING", {"multiline": True},),
+                "text": ("STRING", {"dynamicPrompts": True, "multiline": True},),
             },
         }
     

nodes4hypr.py

@@ -0,0 +1,141 @@
+import torch
+import numpy as np
+from PIL import Image
+import io
+import base64
+import requests
+import random
+import tenacity
+import math
+from comfy.utils import common_upscale
+
+
+## ====== Utility Functions ======
+def tensor2pil(image):
+    return Image.fromarray(np.clip(255. * image.cpu().numpy().squeeze(), 0, 255).astype(np.uint8))
+
+def pil2tensor(image):
+    return torch.from_numpy(np.array(image).astype(np.float32) / 255.0).unsqueeze(0)
+
+def downscale_input(image):
+
+    samples = image.movedim(-1,1)
+    total = int(1536 * 1024)
+    scale_by = math.sqrt(total / (samples.shape[3] * samples.shape[2]))
+    if scale_by >= 1:
+        return image
+    width = round(samples.shape[3] * scale_by)
+    height = round(samples.shape[2] * scale_by)
+    s = common_upscale(samples, width, height, "lanczos", "disabled")
+    s = s.movedim(1,-1)
+    return s
+
+
+## ====== HyprLab API Nodes ======
+class HyprLab_Image_API_Node:
+    def __init__(self):
+        pass
+
+    @classmethod
+    def INPUT_TYPES(cls):
+        return {
+            "required": {
+                "prompt": ("STRING", {"forceInput": True, "dynamicPrompts": True, "tooltip": "The main text prompt"}),
+                "seed": ("INT", {"default": 0, "min": 0, "max": 0xffffffffffffffff}),
+                "model": ("STRING", {"default": "nano-banana-pro"}),
+                "api_url": ("STRING", {"multiline": False, "default": "https://api.hyprlab.io/v1/images/generations"}),
+                "api_key": ("STRING", {"multiline": False, "default": "YOUR_API_KEY_HERE"}),
+                "resolution": (["1K", "2K", "4K"], {"default": "1K"}),
+                "aspect_ratio": ([
+                    "match_input_image", "1:1", "9:16", "16:9", "3:4", 
+                    "4:3", "3:2", "2:3", "5:4", "4:5", "21:9"
+                ], {"default": "1:1"}),
+            },
+            "optional": {
+                "image_input": ("IMAGE", {"default": None, "tooltip": "Optional input images to guide generation"}),
+            }
+        }
+
+
+    RETURN_TYPES = ("IMAGE",)
+    RETURN_NAMES = ("IMAGE",)
+    FUNCTION = "generate_image"
+    CATEGORY = "BillBum_API/Image_API"
+
+
+    @staticmethod
+    def _encode_images_to_base64(images):
+        if images is None:
+            return []
+        if images.dim() == 3:
+            images = images.unsqueeze(0)
+        
+        images = downscale_input(images)
+        encoded_images = []
+        for idx in range(images.shape[0]):
+            tensor_image = images[idx].clamp(0.0, 1.0)
+            
+            pil_image = tensor2pil(tensor_image)
+            buffer = io.BytesIO()
+            pil_image.save(buffer, format="PNG")
+            encoded = base64.b64encode(buffer.getvalue()).decode("utf-8")
+            
+            encoded_images.append(f"data:image/png;base64,{encoded}")
+        return encoded_images
+
+    @tenacity.retry(wait=tenacity.wait_exponential(multiplier=1.25, min=5, max=30), stop=tenacity.stop_after_attempt(3))
+    def generate_image(self, prompt, seed, model, api_url, api_key, resolution, aspect_ratio, image_input=None):
+        
+        headers = {
+            "Content-Type": "application/json",
+            "Authorization": f"Bearer {api_key}"
+        }
+        
+        payload = {
+            "model": model,
+            "prompt": prompt,
+            "resolution": resolution,
+            "aspect_ratio": aspect_ratio,
+            "output_format": "png",
+            "response_format": "b64_json",
+            "seed": seed
+        }
+
+        if image_input is not None:
+            encoded_imgs = self._encode_images_to_base64(image_input)
+            if encoded_imgs:
+                payload["image_input"] = encoded_imgs
+
+        try:
+            response = requests.post(api_url, headers=headers, json=payload, timeout=60)
+            response.raise_for_status()
+            response_data = response.json()
+            
+            images_output = []
+            
+            data_list = response_data.get("data", [])
+            if not data_list and "b64_json" in response_data:
+                data_list = [response_data]
+            
+            for item in data_list:
+                b64_str = item.get("b64_json")
+                if b64_str:
+                    img_data = base64.b64decode(b64_str)
+                    img = Image.open(io.BytesIO(img_data))
+                    
+                    if img.mode != "RGBA":
+                        img = img.convert("RGBA")
+                    
+                    images_output.append(pil2tensor(img))
+            
+            if not images_output:
+                print(f"API Response: {response_data}")
+                raise ValueError("API did not return any valid images.")
+
+            return (torch.cat(images_output, dim=0),)
+
+        except Exception as e:
+            if isinstance(e, requests.exceptions.RequestException) and e.response is not None:
+                print(f"API Error Response: {e.response.text}")
+            raise ValueError(f"HyprBanana API Error: {e}")
+

pyproject.toml

@@ -1,7 +1,7 @@
 [project]
 name = "comfyui_billbum_api_nodes"
 description = "API call node for Third-party platforms both official and local. Support VLMs LLMs Dalle3 Flux-Pro(Support kontext, banana etc... now!! and new Support gpt-image-1!!). And some little tools: img to b64 url, b64 url to img, b64 url to b64 data, reg text to word and ',' only, etc."
-version = "1.1.6"
+version = "1.1.7"
 license = {file = "LICENSE"}
 dependencies = ["tenacity", "openai", "pillow", "requests", "torch", "numpy", "tiktoken"]