return model id from TG info node

* base and hd

* image prompt

* reimagine

.gitignore

@@ -0,0 +1 @@
+*.pyc

Readme.md

@@ -1,30 +1,63 @@
 # BRIA ComfyUI API Nodes
 
-## Overview
-This repository contains custom nodes for ComfyUI that allow access to BRIA's API endpoints. You can find our API documentation [here](https://bria-ai-api-docs.redoc.ly/#operation//generation/bria-v2/text-to-image).
+<p align="center" style="background-color:black; padding:10px;">
+  <img src="./images/Bria Logo.svg" alt="BRIA Logo" width="200"/>
+</p>
 
-To use the nodes in the workflow, you need a valid BRIA API token. You can get one [here](https://bria.ai/api/) (with 1000 free calls)
+This repository provides custom nodes for ComfyUI, enabling direct access to **BRIA's API endpoints** for image generation workflows. **API documentation** is available [**here**](https://bria-ai-api-docs.redoc.ly/#operation//generation/bria-v2/text-to-image). 
 
-You can load the workflow, which includes all available nodes, by importing the [workflow.json](workflow.json) file in this repo. 
+An API token is required to use the nodes in your workflows. Get started quickly here
+<a href="https://bria.ai/api/" style="text-decoration:none; vertical-align:middle;">
+  <img src="https://img.shields.io/badge/GET%20YOUR%20TOKEN-1000%20Free%20Calls-blue?style=flat-square" alt="Get Your Token" height="20">
+</a>.
 
-You can also download the following image and import it to comfyui:
+To load a workflow, import the compatible workflow.json files from this [folder](workflows).  
 
-<img src="./images/eraser_workflow.png" alt="Original image" width="500"/>
 
-An illustration of the workflow:
+<!-- Placeholder image of cool workflows. -->
 
- <img src="./images/eraser_workflow_diagram.jpg" alt="Eraser workflow example" width="650"/> <img src="./images/original_image.jpg" alt="Original image" width="150"/>
 
-## Available Nodes
+ <!-- <img src="./images/bria_api_nodes_workflow_diagram.png" alt="all workflows example" width="400"/> <img src="./images/bria_api_nodes_workflow_diagram_2.png" alt="all workflows example" width="400"/> -->
 
-### Eraser
-The **Eraser** node allows users to remove specific objects or areas from an image by providing a mask.
+# Coming soon
 
-This functionality is powered by BRIA's ControlNet inpainting, available on [this model card](https://huggingface.co/briaai/BRIA-2.3-ControlNet-Inpainting) on Hugging Face.
+- [ ] Video Editing
 
-You can also try out BRIA's Eraser demo by visiting our Hugging Face space [here](https://huggingface.co/spaces/briaai/BRIA-Eraser-API).
+# Available Nodes
 
-## Installation
+## Image Generation Nodes
+
+### **Text2Image Base Node**
+This node generates images from text prompts, serving as the foundation for creating visuals based on descriptive input. [[🤗model card](https://huggingface.co/briaai/BRIA-2.3)]
+
+## Tailored Generation Nodes
+These nodes use pre-trained tailored models to generate images that faithfully reproduce specific visual IP elements or guidelines. [[API docs](https://bria-ai-api-docs.redoc.ly/tag/Tailored-Generation)].
+
+### **Tailored Gen**
+This node generates images using a trained tailored model, faithfully reproducing specific visual IP elements or guidelines established during model training.
+
+### **Tailored Model Info**
+This node retrieves the **default settings** and **prompt prefix** of a **trained tailored model**. It provides the necessary information to configure and run the model in the **Tailored Gen node**, ensuring consistency with the model's intended behavior.  
+
+## Image Editing Nodes
+These nodes modify specific parts of images, enabling adjustments, while maintaining the integrity of the rest of the image.[[API docs](https://bria-ai-api-docs.redoc.ly/tag/Image-Editing)]
+
+### **Eraser**
+This node is used to remove specific objects or areas from an image by providing a mask. Powered by BRIA's ControlNet inpainting [[🤗model card](https://huggingface.co/briaai/BRIA-2.3-ControlNet-Inpainting)] [[🤗HF demo](https://huggingface.co/spaces/briaai/BRIA-Eraser-API)].
+
+### **GenFill**
+This node is used to generate objects by prompt in a specific region of an image. This functionality is powered by BRIA's ControlNet Generative Fill. [[🤗model card](https://huggingface.co/briaai/BRIA-2.3-ControlNet-Generative-Fill)] [[🤗HF demo](https://huggingface.co/spaces/briaai/BRIA-Generative-Fill-API)]
+
+## Product Shot Generation Nodes
+These nodes create high-quality product images for eCommerce workflows. [[API docs](https://bria-ai-api-docs.redoc.ly/tag/Product-Shots-Generation)]
+
+### **ShotByText**
+This node is used to modify the background in an image by providing a prompt, This functionality is powered by BRIA's ControlNet Background-Generation.[[🤗ContrlNet model card](https://huggingface.co/briaai/BRIA-2.3-ControlNet-BG-Gen)] [[🤗HF demo](https://huggingface.co/spaces/briaai/Product-Shot-Generation)].
+
+### **ShotByImage**
+This node is used to modify the background in an image by providing a reference image. This functionality is powered by BRIA's ControlNet Background-Generation and BRIA's Image-Prompt. [[🤗ContrlNet model card](https://huggingface.co/briaai/BRIA-2.3-ControlNet-Inpainting)] [[🤗IP-Adapter model card](https://huggingface.co/briaai/Image-Prompt)] [[🤗HF demo](https://huggingface.co/spaces/briaai/Product-Shot-Generation)].
+
+# Installation
 There are two methods to install the BRIA ComfyUI API nodes:
 
 ### Method 1: Using ComfyUI's Custom Node Manager
@@ -43,3 +76,6 @@ There are two methods to install the BRIA ComfyUI API nodes:
    ```
 
 3. Restart ComfyUI and load the workflows.
+
+<!-- ### Campaign generation
+Coming soon -->

__init__.py

@@ -1,11 +1,29 @@
-from .bria_api_node import EraserNode
-
+from .nodes import (EraserNode, GenFillNode, ShotByTextNode, ShotByImageNode, TailoredGenNode,
+                     TailoredModelInfoNode, Text2ImageBaseNode, Text2ImageFastNode, Text2ImageHDNode,
+                     ReimagineNode)
 # Map the node class to a name used internally by ComfyUI
 NODE_CLASS_MAPPINGS = {
     "BriaEraser": EraserNode,  # Return the class, not an instance
+    "BriaGenFill": GenFillNode,
+    "ShotByTextNode": ShotByTextNode,
+    "ShotByImageNode": ShotByImageNode,
+    "BriaTailoredGen": TailoredGenNode,
+    "TailoredModelInfoNode": TailoredModelInfoNode,
+    "Text2ImageBaseNode": Text2ImageBaseNode,
+    "Text2ImageFastNode": Text2ImageFastNode,
+    "Text2ImageHDNode": Text2ImageHDNode,
+    "ReimagineNode": ReimagineNode,
 }
-
 # Map the node display name to the one shown in the ComfyUI node interface
 NODE_DISPLAY_NAME_MAPPINGS = {
     "BriaEraser": "Bria Eraser",
+    "BriaGenFill": "Bria GenFill",
+    "ShotByTextNode": "Bria Shot By Text",
+    "ShotByImageNode": "Bria Shot By Image",
+    "BriaTailoredGen": "Bria Tailored Gen",
+    "TailoredModelInfoNode": "Bria Tailored Model Info",
+    "Text2ImageBaseNode": "Bria Text2Image Base",
+    "Text2ImageFastNode": "Bria Text2Image Fast",
+    "Text2ImageHDNode": "Bria Text2Image HD",
+    "ReimagineNode": "Bria Reimagine",
 }

bria_api_node.py

@@ -1,185 +0,0 @@
-import numpy as np
-import requests
-from PIL import Image
-import io
-import base64
-from torchvision.transforms import ToPILImage, ToTensor
-import torch
-
-# Base class for shared functionality between both nodes
-class BriaAPINode:
-    def __init__(self, api_url):
-        self.api_url = api_url
-    
-    def preprocess_image(self, image):
-        if isinstance(image, torch.Tensor):
-            # Print image shape for debugging
-            if image.dim() == 4:  # (batch_size, height, width, channels)
-                image = image.squeeze(0)  # Remove the batch dimension (1)
-                # Convert to PIL after permuting to (height, width, channels)
-                image = ToPILImage()(image.permute(2, 0, 1))  # (height, width, channels)
-            else:
-                print("Unexpected image dimensions. Expected 4D tensor.")
-        return image
-
-    def preprocess_mask(self, mask):
-        if isinstance(mask, torch.Tensor):
-            # Print mask shape for debugging
-            if mask.dim() == 3:  # (batch_size, height, width)
-                mask = mask.squeeze(0)  # Remove the batch dimension (1)
-                # Convert to PIL (grayscale mask)
-                mask = ToPILImage()(mask)  # No permute needed for grayscale
-            else:
-                print("Unexpected mask dimensions. Expected 3D tensor.")
-        return mask
-    
-    def image_to_base64(self, pil_image):
-        # Convert a PIL image to a base64-encoded string
-        buffered = io.BytesIO()
-        pil_image.save(buffered, format="PNG")  # Save the image to the buffer in PNG format
-        buffered.seek(0)  # Rewind the buffer to the beginning
-        return base64.b64encode(buffered.getvalue()).decode('utf-8')
-
-    def process_request(self, image, mask, api_key):
-        if api_key.strip() == "" or api_key.strip() == "BRIA_API_TOKEN":
-            raise Exception("Please insert a valid API key.")
-
-        # Check if image and mask are tensors, if so, convert to NumPy arrays
-        if isinstance(image, torch.Tensor):
-            image = self.preprocess_image(image)
-        if isinstance(mask, torch.Tensor):
-            mask = self.preprocess_mask(mask)
-
-        # Convert the image and mask directly to Base64 strings
-        image_base64 = self.image_to_base64(image)
-        mask_base64 = self.image_to_base64(mask)
-
-        # Prepare the API request payload
-        payload = {
-            "file": f"{image_base64}",
-            "mask_file": f"{mask_base64}"
-        }
-
-        headers = {
-            "Content-Type": "application/json",
-            "api_token": f"{api_key}"
-        }
-
-        try:
-            response = requests.post(self.api_url, json=payload, headers=headers)
-            # Check for successful response
-            if response.status_code == 200:
-                print('response is 200')
-                # Process the output image from API response
-                response_dict = response.json()
-                image_response = requests.get(response_dict['result_url'])
-                result_image = Image.open(io.BytesIO(image_response.content))
-                result_image = result_image.convert("RGBA")
-                result_image = np.array(result_image).astype(np.float32) / 255.0
-                result_image = torch.from_numpy(result_image)[None,]
-                # image_tensor = image_tensor = ToTensor()(output_image)
-                # image_tensor = image_tensor.permute(1, 2, 0) / 255.0  # Shape now becomes [1, 2200, 1548, 3]
-                # print(f"output tensor shape is: {image_tensor.shape}")
-                return (result_image,)
-            else:
-                raise Exception(f"Error: API request failed with status code {response.status_code}")
-
-        except Exception as e:
-            raise Exception(f"{e}")
-
-# Eraser Node
-class EraserNode(BriaAPINode):
-    @staticmethod
-    def INPUT_TYPES():
-        return {
-            "required": {
-                "image": ("IMAGE",),  # Input image from another node
-                "mask": ("MASK",),  # Binary mask input
-                "api_key": ("STRING", {"default": "BRIA_API_TOKEN"})  # API Key input with a default value
-            }
-        }
-
-    RETURN_TYPES = ("IMAGE",)
-    RETURN_NAMES = ("output_image",)
-    CATEGORY = "API Nodes"
-    FUNCTION = "execute"  # This is the method that will be executed
-
-    def __init__(self):
-        super().__init__("https://engine.prod.bria-api.com/v1/eraser")  # Eraser API URL
-
-    # Define the execute method as expected by ComfyUI
-    def execute(self, image, mask, api_key):
-        return self.process_request(image, mask, api_key)
-
-
-# Generative Fill Node
-class GenerativeFillNode(BriaAPINode):
-    @staticmethod
-    def INPUT_TYPES():
-        return {
-            "required": {
-                "image": ("IMAGE",),  # Input image from another node
-                "mask": ("MASK",),  # Binary mask input
-                "prompt": ("STRING",),
-                "api_key": ("STRING", {"default": "BRIA_API_TOKEN"}), # API Key input with a default value
-            },
-            "optional": {
-                "negative_prompt": ("STRING", {"default": None}),
-            },
-        }
-
-    RETURN_TYPES = ("IMAGE",)
-    RETURN_NAMES = ("output_image",)
-    CATEGORY = "API Nodes"
-    FUNCTION = "execute"  # This is the method that will be executed
-
-    def __init__(self):
-        super().__init__("https://engine.prod.bria-api.com/v1/gen_fill")  # Eraser API URL
-
-    # Define the execute method as expected by ComfyUI
-    def execute(self, image, mask, prompt, negative_prompt, api_key):
-        if api_key.strip() == "" or api_key.strip() == "BRIA_API_TOKEN":
-            raise Exception("Please insert a valid API key.")
-
-        # Check if image and mask are tensors, if so, convert to NumPy arrays
-        if isinstance(image, torch.Tensor):
-            image = self.preprocess_image(image)
-        if isinstance(mask, torch.Tensor):
-            mask = self.preprocess_mask(mask)
-
-        # Convert the image and mask directly to Base64 strings
-        image_base64 = self.image_to_base64(image)
-        mask_base64 = self.image_to_base64(mask)
-
-        # Prepare the API request payload
-        payload = {
-            "file": f"{image_base64}",
-            "mask_file": f"{mask_base64}",
-            "prompt": prompt,
-            "negative_prompt": negative_prompt,
-            "sync": True
-        }
-
-        headers = {
-            "Content-Type": "application/json",
-            "api_token": f"{api_key}"
-        }
-
-        try:
-            response = requests.post(self.api_url, json=payload, headers=headers)
-            # Check for successful response
-            if response.status_code == 200:
-                print('response is 200')
-                # Process the output image from API response
-                response_dict = response.json()
-                image_response = requests.get(response_dict['urls'][0])
-                result_image = Image.open(io.BytesIO(image_response.content))
-                result_image = result_image.convert("RGB")
-                result_image = np.array(result_image).astype(np.float32) / 255.0
-                result_image = torch.from_numpy(result_image)[None,]
-                return (result_image,)
-            else:
-                raise Exception(f"Error: API request failed with status code {response.status_code}")
-
-        except Exception as e:
-            raise Exception(f"{e}")

images/Bria Logo.svg

@@ -0,0 +1,21 @@
+<svg xmlns="http://www.w3.org/2000/svg" width="132" height="85.104" viewBox="0 0 132 85.104">
+    <defs>
+        <style>
+            .cls-2{fill:#5300c9}.cls-3{fill:#80f}
+        </style>
+    </defs>
+    <g id="Logo" transform="translate(-471.45 -246.19)">
+        <circle id="Ellipse_1" data-name="Ellipse 1" cx="9.553" cy="9.553" r="9.553" transform="translate(560.271 310.103)" style="fill:#d80067"/>
+        <g id="Group_56" data-name="Group 56" transform="translate(471.45 246.19)">
+            <path id="Path_504" data-name="Path 504" class="cls-2" d="M690.915 569.28h-6.548a.077.077 0 0 0-.078.078v6.042a.077.077 0 0 0 .078.078h6.421c2.237 0 3.46-1.224 3.46-3.164a3.008 3.008 0 0 0-3.333-3.034z" transform="translate(-652.51 -521.037)"/>
+            <path id="Path_505" data-name="Path 505" class="cls-2" d="M684.378 502.736h6a3.179 3.179 0 0 0 3.24-2.044 3.127 3.127 0 0 0-3.039-4.032h-6.2a.077.077 0 0 0-.078.078v5.922a.077.077 0 0 0 .077.076z" transform="translate(-652.518 -459.261)"/>
+            <path id="Path_506" data-name="Path 506" class="cls-2" d="M877.962 498.92h-6.5a.077.077 0 0 0-.078.078v6.639a.077.077 0 0 0 .078.078h6.5c2.489 0 4.093-1.35 4.093-3.418 0-2.027-1.604-3.377-4.093-3.377z" transform="translate(-811.664 -461.183)"/>
+            <path id="Path_507" data-name="Path 507" class="cls-2" d="M552.856 278.056a.077.077 0 0 1 .078-.078h2.244a42.566 42.566 0 1 0-2.322 28.112zm-41.9 28.191h-14.609a.077.077 0 0 1-.078-.078v-28.113a.077.077 0 0 1 .078-.078h14.564c5.19 0 8.565 3.08 8.565 7.721a5.646 5.646 0 0 1-4.641 5.949v.169c2.616.211 5.443 2.237 5.443 6.5 0 4.683-3.164 7.929-9.325 7.929zm30.8 0a.074.074 0 0 1-.058-.027l-8.477-9.652a.08.08 0 0 0-.058-.027h-1.913a.077.077 0 0 0-.078.078v9.55a.077.077 0 0 1-.078.078h-6.807a.077.077 0 0 1-.078-.078v-28.113a.077.077 0 0 1 .078-.078h14.227c6.117 0 10.168 3.712 10.168 9.24 0 4.6-2.818 7.914-7.3 8.959a.075.075 0 0 0-.04.124l8.658 9.817a.077.077 0 0 1-.058.128z" transform="translate(-471.45 -246.19)"/>
+        </g>
+        <g id="Group_57" data-name="Group 57" transform="translate(557.15 259.772)">
+            <path id="Path_508" data-name="Path 508" class="cls-3" d="M1147.2 613.979a.078.078 0 0 0-.072-.049h-11.793a.076.076 0 0 0-.072.049l-1.327 3.446a23.417 23.417 0 0 0 15.136 1.414z" transform="translate(-1120.72 -572.602)"/>
+            <path id="Path_509" data-name="Path 509" class="cls-3" d="M1068.251 337.15a23.451 23.451 0 0 0-22.851 18.206h2.591a.077.077 0 0 1 .078.078v17.094a23.542 23.542 0 0 0 4.712 5.68l9.529-22.8a.078.078 0 0 1 .072-.048h7.028a.079.079 0 0 1 .072.048l10.624 25.421a23.445 23.445 0 0 0-11.854-43.675z" transform="translate(-1045.4 -337.15)"/>
+            <path id="Path_510" data-name="Path 510" class="cls-3" d="M1166.073 521.687a.077.077 0 0 0 .072-.106l-3.515-8.992a.078.078 0 0 0-.145 0l-3.475 8.992a.078.078 0 0 0 .072.106z" transform="translate(-1142.042 -486.351)"/>
+        </g>
+    </g>
+</svg>

nodes/__init__.py

@@ -0,0 +1,10 @@
+from .eraser_node import EraserNode
+from .generative_fill_node import GenFillNode
+from .shot_by_text_node import ShotByTextNode
+from .shot_by_image_node import ShotByImageNode
+from .tailored_gen_node import TailoredGenNode
+from .tailored_model_info_node import TailoredModelInfoNode
+from .text_2_image_base_node import Text2ImageBaseNode
+from .text_2_image_fast_node import Text2ImageFastNode
+from .text_2_image_hd_node import Text2ImageHDNode
+from .reimagine_node import ReimagineNode

nodes/common.py

@@ -0,0 +1,92 @@
+import numpy as np
+from PIL import Image
+import io
+import torch
+import base64
+from torchvision.transforms import ToPILImage
+import requests
+
+def postprocess_image(image):
+    result_image = Image.open(io.BytesIO(image))
+    result_image = result_image.convert("RGB")
+    result_image = np.array(result_image).astype(np.float32) / 255.0
+    result_image = torch.from_numpy(result_image)[None,]
+    return result_image
+
+def image_to_base64(pil_image):
+    # Convert a PIL image to a base64-encoded string
+    buffered = io.BytesIO()
+    pil_image.save(buffered, format="PNG")  # Save the image to the buffer in PNG format
+    buffered.seek(0)  # Rewind the buffer to the beginning
+    return base64.b64encode(buffered.getvalue()).decode('utf-8')
+
+def preprocess_image(image):
+    if isinstance(image, torch.Tensor):
+        # Print image shape for debugging
+        if image.dim() == 4:  # (batch_size, height, width, channels)
+            image = image.squeeze(0)  # Remove the batch dimension (1)
+            # Convert to PIL after permuting to (height, width, channels)
+            image = ToPILImage()(image.permute(2, 0, 1))  # (height, width, channels)
+        else:
+            print("Unexpected image dimensions. Expected 4D tensor.")
+    return image
+
+
+def preprocess_mask(mask):
+    if isinstance(mask, torch.Tensor):
+        # Print mask shape for debugging
+        if mask.dim() == 3:  # (batch_size, height, width)
+            mask = mask.squeeze(0)  # Remove the batch dimension (1)
+            # Convert to PIL (grayscale mask)
+            mask = ToPILImage()(mask)  # No permute needed for grayscale
+        else:
+            print("Unexpected mask dimensions. Expected 3D tensor.")
+    return mask
+
+
+def process_request(api_url, image, mask, api_key):
+    if api_key.strip() == "" or api_key.strip() == "BRIA_API_TOKEN":
+        raise Exception("Please insert a valid API key.")
+
+    # Check if image and mask are tensors, if so, convert to NumPy arrays
+    if isinstance(image, torch.Tensor):
+        image = preprocess_image(image)
+    if isinstance(mask, torch.Tensor):
+        mask = preprocess_mask(mask)
+
+    # Convert the image and mask directly to Base64 strings
+    image_base64 = image_to_base64(image)
+    mask_base64 = image_to_base64(mask)
+
+    # Prepare the API request payload
+    payload = {
+        "file": f"{image_base64}",
+        "mask_file": f"{mask_base64}"
+    }
+
+    headers = {
+        "Content-Type": "application/json",
+        "api_token": f"{api_key}"
+    }
+
+    try:
+        response = requests.post(api_url, json=payload, headers=headers)
+        # Check for successful response
+        if response.status_code == 200:
+            print('response is 200')
+            # Process the output image from API response
+            response_dict = response.json()
+            image_response = requests.get(response_dict['result_url'])
+            result_image = Image.open(io.BytesIO(image_response.content))
+            result_image = result_image.convert("RGBA")
+            result_image = np.array(result_image).astype(np.float32) / 255.0
+            result_image = torch.from_numpy(result_image)[None,]
+            # image_tensor = image_tensor = ToTensor()(output_image)
+            # image_tensor = image_tensor.permute(1, 2, 0) / 255.0  # Shape now becomes [1, 2200, 1548, 3]
+            # print(f"output tensor shape is: {image_tensor.shape}")
+            return (result_image,)
+        else:
+            raise Exception(f"Error: API request failed with status code {response.status_code}")
+
+    except Exception as e:
+        raise Exception(f"{e}")

nodes/eraser_node.py

@@ -0,0 +1,25 @@
+from .common import process_request
+
+class EraserNode():
+    @classmethod
+    def INPUT_TYPES(self):
+        return {
+            "required": {
+                "image": ("IMAGE",),  # Input image from another node
+                "mask": ("MASK",),  # Binary mask input
+                "api_key": ("STRING", {"default": "BRIA_API_TOKEN"})  # API Key input with a default value
+            }
+        }
+
+    RETURN_TYPES = ("IMAGE",)
+    RETURN_NAMES = ("output_image",)
+    CATEGORY = "API Nodes"
+    FUNCTION = "execute"  # This is the method that will be executed
+
+    def __init__(self):
+        self.api_url = "https://engine.prod.bria-api.com/v1/eraser"  # Eraser API URL
+
+    # Define the execute method as expected by ComfyUI
+    def execute(self, image, mask, api_key):
+        return process_request(self.api_url, image, mask, api_key)
+    

nodes/generative_fill_node.py

@@ -0,0 +1,76 @@
+import numpy as np
+import requests
+from PIL import Image
+import io
+import torch
+
+from .common import image_to_base64, preprocess_image, preprocess_mask
+
+
+class GenFillNode():
+    @classmethod
+    def INPUT_TYPES(self):
+        return {
+            "required": {
+                "image": ("IMAGE",),  # Input image from another node
+                "mask": ("MASK",),  # Binary mask input
+                "prompt": ("STRING",),
+                "api_key": ("STRING", {"default": "BRIA_API_TOKEN"}), # API Key input with a default value
+            },
+        }
+
+    RETURN_TYPES = ("IMAGE",)
+    RETURN_NAMES = ("output_image",)
+    CATEGORY = "API Nodes"
+    FUNCTION = "execute"  # This is the method that will be executed
+
+    def __init__(self):
+        self.api_url = "https://engine.prod.bria-api.com/v1/gen_fill"  # Eraser API URL
+
+    # Define the execute method as expected by ComfyUI
+    def execute(self, image, mask, prompt, api_key):
+        if api_key.strip() == "" or api_key.strip() == "BRIA_API_TOKEN":
+            raise Exception("Please insert a valid API key.")
+
+        # Check if image and mask are tensors, if so, convert to NumPy arrays
+        if isinstance(image, torch.Tensor):
+            image = preprocess_image(image)
+        if isinstance(mask, torch.Tensor):
+            mask = preprocess_mask(mask)
+
+        # Convert the image and mask directly to Base64 strings
+        image_base64 = image_to_base64(image)
+        mask_base64 = image_to_base64(mask)
+
+        # Prepare the API request payload
+        payload = {
+            "file": f"{image_base64}",
+            "mask_file": f"{mask_base64}",
+            "prompt": prompt,
+            "negative_prompt": "blurry",
+            "sync": True
+        }
+
+        headers = {
+            "Content-Type": "application/json",
+            "api_token": f"{api_key}"
+        }
+
+        try:
+            response = requests.post(self.api_url, json=payload, headers=headers)
+            # Check for successful response
+            if response.status_code == 200:
+                print('response is 200')
+                # Process the output image from API response
+                response_dict = response.json()
+                image_response = requests.get(response_dict['urls'][0])
+                result_image = Image.open(io.BytesIO(image_response.content))
+                result_image = result_image.convert("RGB")
+                result_image = np.array(result_image).astype(np.float32) / 255.0
+                result_image = torch.from_numpy(result_image)[None,]
+                return (result_image,)
+            else:
+                raise Exception(f"Error: API request failed with status code {response.status_code}")
+
+        except Exception as e:
+            raise Exception(f"{e}")

nodes/reimagine_node.py

@@ -0,0 +1,67 @@
+import requests
+
+from .common import postprocess_image, preprocess_image, image_to_base64
+
+
+class ReimagineNode():
+    @classmethod
+    def INPUT_TYPES(self):
+        return {
+            "required": {
+                "api_key": ("STRING", ),
+                "prompt": ("STRING",),
+            },
+            "optional": {
+                "seed": ("INT", {"default": -1}),
+                "steps_num": ("INT", {"default": 12}), # if used with tailored, possibly get this from the tailored model info node
+                "structure_ref_influence": ("FLOAT", {"default": 0.75}),
+                "fast": ("INT", {"default": 0}), # if used with tailored, possibly get this from the tailored model info node
+                "structure_image": ("IMAGE", ),
+                "tailored_model_id": ("STRING", ),
+                "tailored_model_influence": ("FLOAT", {"default": 0.5}),
+                "tailored_generation_prefix": ("STRING",), # if used with tailored, possibly get this from the tailored model info node
+            }
+        }
+
+    RETURN_TYPES = ("IMAGE",)
+    RETURN_NAMES = ("output_image",)
+    CATEGORY = "API Nodes"
+    FUNCTION = "execute"  # This is the method that will be executed
+
+    def __init__(self):
+        self.api_url = "https://engine.prod.bria-api.com/v1/reimagine" #"http://0.0.0.0:5000/v1/reimagine"
+
+    def execute(
+            self, api_key, prompt, seed, 
+            steps_num, fast, structure_ref_influence, structure_image=None,
+            tailored_model_id=None, tailored_model_influence=None, tailored_generation_prefix=None,
+        ):
+        fast = bool(fast)
+        payload = {
+            "prompt": tailored_generation_prefix + prompt,
+            "num_results": 1,
+            "sync": True,
+            "seed": seed,
+            "steps_num": steps_num,
+            "include_generation_prefix": False,
+        }
+        if structure_image is not None:
+            structure_image = preprocess_image(structure_image)
+            structure_image = image_to_base64(structure_image)
+            payload["structure_image_file"] = structure_image
+            payload["structure_ref_influence"] = structure_ref_influence
+        if tailored_model_id is not None and tailored_model_id != "":
+            payload["tailored_model_id"] = tailored_model_id
+            payload["tailored_model_influence"] = tailored_model_influence
+        response = requests.post(
+            self.api_url,
+            json=payload,
+            headers={"api_token": api_key}
+        )
+        if response.status_code == 200:
+                response_dict = response.json()
+                image_response = requests.get(response_dict['result'][0]["urls"][0])
+                result_image = postprocess_image(image_response.content)
+                return (result_image,)
+        else:
+            raise Exception(f"Error: API request failed with status code {response.status_code} and text {response.text}")

nodes/shot_by_image_node.py

@@ -0,0 +1,68 @@
+import requests
+import torch
+
+from .common import postprocess_image, preprocess_image, image_to_base64
+
+class ShotByImageNode():
+    @classmethod
+    def INPUT_TYPES(self):
+        return {
+            "required": {
+                "image": ("IMAGE",),  # Input image from another node
+                "ref_image": ("IMAGE",),  # ref image from another node
+                "enhance_ref_image": ("INT", {"default": 1}),
+                "api_key": ("STRING", {"default": "BRIA_API_TOKEN"})  # API Key input with a default value
+            }
+        }
+
+    RETURN_TYPES = ("IMAGE",)
+    RETURN_NAMES = ("output_image",)
+    CATEGORY = "API Nodes"
+    FUNCTION = "execute"  # This is the method that will be executed
+
+    def __init__(self):
+        self.api_url = "https://engine.prod.bria-api.com/v1/product/lifestyle_shot_by_image"  # Eraser API URL
+
+    # Define the execute method as expected by ComfyUI
+    def execute(self, image, ref_image, api_key, enhance_ref_image, ):
+        if api_key.strip() == "" or api_key.strip() == "BRIA_API_TOKEN":
+            raise Exception("Please insert a valid API key.")
+
+        # Check if image and mask are tensors, if so, convert to NumPy arrays
+        if isinstance(image, torch.Tensor):
+            image = preprocess_image(image)
+        if isinstance(ref_image, torch.Tensor):
+            ref_image = preprocess_image(ref_image)
+
+        # Convert the image and mask directly to Base64 strings                    
+        image_base64 = image_to_base64(image)
+        ref_image_base64 = image_to_base64(ref_image)
+        enhance_ref_image = bool(enhance_ref_image)
+
+        payload = {
+            "file": image_base64,
+            "ref_image_file": ref_image_base64,
+            "enhance_ref_image": enhance_ref_image,
+            "placement_type": "original",
+            "original_quality": True, 
+            "sync": True
+        }
+        headers = {
+            "Content-Type": "application/json",
+            "api_token": f"{api_key}"
+        }        
+        try:
+            response = requests.post(self.api_url, json=payload, headers=headers)
+            # Check for successful response
+            if response.status_code == 200:
+                print('response is 200')
+                # Process the output image from API response
+                response_dict = response.json()
+                image_response = requests.get(response_dict['result'][0][0])
+                result_image = postprocess_image(image_response.content)
+                return (result_image,)
+            else:
+                raise Exception(f"Error: API request failed with status code {response.status_code}")
+
+        except Exception as e:
+            raise Exception(f"{e}")        

nodes/shot_by_text_node.py

@@ -0,0 +1,64 @@
+import requests
+import torch
+
+from .common import postprocess_image, preprocess_image, image_to_base64
+
+class ShotByTextNode():
+    @classmethod
+    def INPUT_TYPES(self):
+        return {
+            "required": {
+                "image": ("IMAGE",),  # Input image from another node
+                "scene_description": ("STRING",),
+                "optimize_description": ("INT", {"default": 1}),
+                "api_key": ("STRING", {"default": "BRIA_API_TOKEN"})  # API Key input with a default value
+            }
+        }
+
+    RETURN_TYPES = ("IMAGE",)
+    RETURN_NAMES = ("output_image",)
+    CATEGORY = "API Nodes"
+    FUNCTION = "execute"  # This is the method that will be executed
+
+    def __init__(self):
+        self.api_url = "https://engine.prod.bria-api.com/v1/product/lifestyle_shot_by_text"  # Eraser API URL
+        
+    # Define the execute method as expected by ComfyUI
+    def execute(self, image, api_key, scene_description, optimize_description, ):
+        if api_key.strip() == "" or api_key.strip() == "BRIA_API_TOKEN":
+            raise Exception("Please insert a valid API key.")
+
+        # Check if image and mask are tensors, if so, convert to NumPy arrays
+        if isinstance(image, torch.Tensor):
+            image = preprocess_image(image)
+
+        optimize_description = bool(optimize_description)
+        image_base64 = image_to_base64(image)
+        payload = {
+            "file": image_base64,
+            "scene_description": scene_description,
+            "optimize_description": optimize_description,
+            "placement_type": "original",
+            "original_quality": True,
+            "sync": True
+        }
+        headers = {
+            "Content-Type": "application/json",
+            "api_token": f"{api_key}"
+        }        
+        try:
+            response = requests.post(self.api_url, json=payload, headers=headers)
+            # Check for successful response
+            if response.status_code == 200:
+                print('response is 200')
+                # Process the output image from API response
+                response_dict = response.json()
+                image_response = requests.get(response_dict['result'][0][0])
+                result_image = postprocess_image(image_response.content)
+                return (result_image,)
+            else:
+                raise Exception(f"Error: API request failed with status code {response.status_code}")
+
+        except Exception as e:
+            raise Exception(f"{e}")                
+

nodes/tailored_gen_node.py

@@ -0,0 +1,82 @@
+import requests
+
+from .common import postprocess_image, preprocess_image, image_to_base64
+
+
+class TailoredGenNode():
+    @classmethod
+    def INPUT_TYPES(self):
+        return {
+            "required": {
+                "model_id": ("STRING",),
+                "api_key": ("STRING", ),
+            },
+            "optional": {
+                "prompt": ("STRING",),
+                "generation_prefix": ("STRING",), # possibly get this from the tailored model info node
+                "aspect_ratio": (["1:1", "2:3", "3:2", "3:4", "4:3", "4:5", "5:4", "9:16", "16:9"], {"default": "4:3"}),
+                "seed": ("INT", {"default": -1}),
+                "model_influence": ("FLOAT", {"default": 1.0}),
+                "negative_prompt": ("STRING", {"default": ""}),
+                "fast": ("INT", {"default": 1}), # possibly get this from the tailored model info node
+                "steps_num": ("INT", {"default": 8}), # possibly get this from the tailored model info node
+                "guidance_method_1": (["controlnet_canny", "controlnet_depth", "controlnet_recoloring", "controlnet_color_grid"], {"default": "controlnet_canny"}),
+                "guidance_method_1_scale": ("FLOAT", {"default": 1.0}),
+                "guidance_method_1_image": ("IMAGE", ),
+                "guidance_method_2": (["controlnet_canny", "controlnet_depth", "controlnet_recoloring", "controlnet_color_grid"], {"default": "controlnet_canny"}),
+                "guidance_method_2_scale": ("FLOAT", {"default": 1.0}),
+                "guidance_method_2_image": ("IMAGE", ),
+            }
+        }
+
+    RETURN_TYPES = ("IMAGE",)
+    RETURN_NAMES = ("output_image",)
+    CATEGORY = "API Nodes"
+    FUNCTION = "execute"  # This is the method that will be executed
+
+    def __init__(self):
+        self.api_url = "https://engine.prod.bria-api.com/v1/text-to-image/tailored/" #"http://0.0.0.0:5000/v1/text-to-image/tailored/"
+
+    def execute(
+            self, model_id, api_key, prompt, generation_prefix, aspect_ratio, 
+            seed, model_influence, negative_prompt, fast, steps_num,
+            guidance_method_1=None, guidance_method_1_scale=None, guidance_method_1_image=None,
+            guidance_method_2=None, guidance_method_2_scale=None, guidance_method_2_image=None,
+        ):
+        fast = bool(fast)
+        payload = {
+            "prompt": generation_prefix + prompt,
+            "num_results": 1,
+            "aspect_ratio": aspect_ratio,
+            "sync": True,
+            "seed": seed,
+            "model_influence": model_influence,
+            "negative_prompt": negative_prompt,
+            "fast": fast,
+            "steps_num": steps_num,
+            "include_generation_prefix": False,
+        }
+        if guidance_method_1_image is not None:
+            guidance_method_1_image = preprocess_image(guidance_method_1_image)
+            guidance_method_1_image = image_to_base64(guidance_method_1_image)
+            payload["guidance_method_1"] = guidance_method_1
+            payload["guidance_method_1_scale"] = guidance_method_1_scale
+            payload["guidance_method_1_image_file"] = guidance_method_1_image
+        if guidance_method_2_image is not None:
+            guidance_method_2_image = preprocess_image(guidance_method_2_image)
+            guidance_method_2_image = image_to_base64(guidance_method_2_image)
+            payload["guidance_method_2"] = guidance_method_2
+            payload["guidance_method_2_scale"] = guidance_method_2_scale
+            payload["guidance_method_2_image_file"] = guidance_method_2_image
+        response = requests.post(
+            self.api_url + model_id,
+            json=payload,
+            headers={"api_token": api_key}
+        )
+        if response.status_code == 200:
+                response_dict = response.json()
+                image_response = requests.get(response_dict['result'][0]["urls"][0])
+                result_image = postprocess_image(image_response.content)
+                return (result_image,)
+        else:
+            raise Exception(f"Error: API request failed with status code {response.status_code} and text {response.text}")

nodes/tailored_model_info_node.py

@@ -0,0 +1,35 @@
+import requests
+
+
+class TailoredModelInfoNode():
+    @classmethod
+    def INPUT_TYPES(self):
+        return {
+            "required": {
+                "model_id": ("STRING",),
+                "api_key": ("STRING", )
+            }
+        }
+
+    RETURN_TYPES = ("STRING", "STRING","INT", "INT", )
+    RETURN_NAMES = ("generation_prefix", "model_id", "default_fast",  "default_steps_num", )
+    CATEGORY = "API Nodes"
+    FUNCTION = "execute"  # This is the method that will be executed
+
+    def __init__(self):
+        self.api_url = "https://engine.prod.bria-api.com/v1/tailored-gen/models/"
+
+    # Define the execute method as expected by ComfyUI
+    def execute(self, model_id, api_key):
+        response = requests.get(
+            self.api_url + model_id,
+            headers={"api_token": api_key}
+        )
+        if response.status_code == 200:
+            generation_prefix = response.json()["generation_prefix"]
+            training_version = response.json()["training_version"]
+            default_fast = 1 if training_version == "light" else 0
+            default_steps_num = 8 if training_version == "light" else 30
+            return (generation_prefix, model_id, default_fast, default_steps_num,)
+        else:
+            raise Exception(f"Error: API request failed with status code {response.status_code} and text {response.text}")

nodes/text_2_image_base_node.py

@@ -0,0 +1,92 @@
+import requests
+
+from .common import postprocess_image, preprocess_image, image_to_base64
+
+
+class Text2ImageBaseNode():
+    @classmethod
+    def INPUT_TYPES(self):
+        return {
+            "required": {
+                "api_key": ("STRING", ),
+            },
+            "optional": {
+                "prompt": ("STRING",),
+                "aspect_ratio": (["1:1", "2:3", "3:2", "3:4", "4:3", "4:5", "5:4", "9:16", "16:9"], {"default": "4:3"}),
+                "seed": ("INT", {"default": -1}),
+                "negative_prompt": ("STRING", {"default": ""}),
+                "steps_num": ("INT", {"default": 30}), 
+                "prompt_enhancement": ("INT", {"default": 0}),
+                "text_guidance_scale": ("INT", {"default": 5}),
+                "medium": (["photography", "art", "none"], {"default": "none"}),
+                "guidance_method_1": (["controlnet_canny", "controlnet_depth", "controlnet_recoloring", "controlnet_color_grid"], {"default": "controlnet_canny"}),
+                "guidance_method_1_scale": ("FLOAT", {"default": 1.0}),
+                "guidance_method_1_image": ("IMAGE", ),
+                "guidance_method_2": (["controlnet_canny", "controlnet_depth", "controlnet_recoloring", "controlnet_color_grid"], {"default": "controlnet_canny"}),
+                "guidance_method_2_scale": ("FLOAT", {"default": 1.0}),
+                "guidance_method_2_image": ("IMAGE", ),
+                "image_prompt_mode": (["regular", "style_only"], {"default": "regular"}),
+                "image_prompt_image": ("IMAGE", ),
+                "image_prompt_scale": ("FLOAT", {"default": 1.0}),
+            }
+        }
+
+    RETURN_TYPES = ("IMAGE",)
+    RETURN_NAMES = ("output_image",)
+    CATEGORY = "API Nodes"
+    FUNCTION = "execute"  # This is the method that will be executed
+
+    def __init__(self):
+        self.api_url = "https://engine.prod.bria-api.com/v1/text-to-image/base/2.3" #"http://0.0.0.0:5000/v1/text-to-image/base/2.3"
+
+    def execute(
+            self, api_key, prompt, aspect_ratio, seed, negative_prompt, 
+            steps_num, prompt_enhancement, text_guidance_scale, medium,
+            guidance_method_1=None, guidance_method_1_scale=None, guidance_method_1_image=None,
+            guidance_method_2=None, guidance_method_2_scale=None, guidance_method_2_image=None,
+            image_prompt_mode=None, image_prompt_image=None, image_prompt_scale=None,
+        ):
+        prompt_enhancement = bool(prompt_enhancement)
+        payload = {
+            "prompt": prompt,
+            "num_results": 1,
+            "aspect_ratio": aspect_ratio,
+            "sync": True,
+            "seed": seed,
+            "negative_prompt": negative_prompt,
+            "steps_num": steps_num,
+            "text_guidance_scale": text_guidance_scale,
+            "prompt_enhancement": prompt_enhancement,
+        }
+        if medium != "none":
+            payload["medium"] = medium
+        if guidance_method_1_image is not None:
+            guidance_method_1_image = preprocess_image(guidance_method_1_image)
+            guidance_method_1_image = image_to_base64(guidance_method_1_image)
+            payload["guidance_method_1"] = guidance_method_1
+            payload["guidance_method_1_scale"] = guidance_method_1_scale
+            payload["guidance_method_1_image_file"] = guidance_method_1_image
+        if guidance_method_2_image is not None:
+            guidance_method_2_image = preprocess_image(guidance_method_2_image)
+            guidance_method_2_image = image_to_base64(guidance_method_2_image)
+            payload["guidance_method_2"] = guidance_method_2
+            payload["guidance_method_2_scale"] = guidance_method_2_scale
+            payload["guidance_method_2_image_file"] = guidance_method_2_image
+        if image_prompt_image is not None:
+            image_prompt_image = preprocess_image(image_prompt_image)
+            image_prompt_image = image_to_base64(image_prompt_image)
+            payload["image_prompt_mode"] = image_prompt_mode
+            payload["image_prompt_file"] = image_prompt_image
+            payload["image_prompt_scale"] = image_prompt_scale
+        response = requests.post(
+            self.api_url,
+            json=payload,
+            headers={"api_token": api_key}
+        )
+        if response.status_code == 200:
+                response_dict = response.json()
+                image_response = requests.get(response_dict['result'][0]["urls"][0])
+                result_image = postprocess_image(image_response.content)
+                return (result_image,)
+        else:
+            raise Exception(f"Error: API request failed with status code {response.status_code} and text {response.text}")

nodes/text_2_image_fast_node.py

@@ -0,0 +1,85 @@
+import requests
+
+from .common import postprocess_image, preprocess_image, image_to_base64
+
+
+class Text2ImageFastNode():
+    @classmethod
+    def INPUT_TYPES(self):
+        return {
+            "required": {
+                "api_key": ("STRING", ),
+            },
+            "optional": {
+                "prompt": ("STRING",),
+                "aspect_ratio": (["1:1", "2:3", "3:2", "3:4", "4:3", "4:5", "5:4", "9:16", "16:9"], {"default": "4:3"}),
+                "seed": ("INT", {"default": -1}),
+                "steps_num": ("INT", {"default": 8}), 
+                "prompt_enhancement": ("INT", {"default": 0}),
+                "guidance_method_1": (["controlnet_canny", "controlnet_depth", "controlnet_recoloring", "controlnet_color_grid"], {"default": "controlnet_canny"}),
+                "guidance_method_1_scale": ("FLOAT", {"default": 1.0}),
+                "guidance_method_1_image": ("IMAGE", ),
+                "guidance_method_2": (["controlnet_canny", "controlnet_depth", "controlnet_recoloring", "controlnet_color_grid"], {"default": "controlnet_canny"}),
+                "guidance_method_2_scale": ("FLOAT", {"default": 1.0}),
+                "guidance_method_2_image": ("IMAGE", ),
+                "image_prompt_mode": (["regular", "style_only"], {"default": "regular"}),
+                "image_prompt_image": ("IMAGE", ),
+                "image_prompt_scale": ("FLOAT", {"default": 1.0}),
+            }
+        }
+
+    RETURN_TYPES = ("IMAGE",)
+    RETURN_NAMES = ("output_image",)
+    CATEGORY = "API Nodes"
+    FUNCTION = "execute"
+
+    def __init__(self):
+        self.api_url = "https://engine.prod.bria-api.com/v1/text-to-image/fast/2.3" #"http://0.0.0.0:5000/v1/text-to-image/fast/2.3"
+
+    def execute(
+            self, api_key, prompt, aspect_ratio, seed, 
+            steps_num, prompt_enhancement,
+            guidance_method_1=None, guidance_method_1_scale=None, guidance_method_1_image=None,
+            guidance_method_2=None, guidance_method_2_scale=None, guidance_method_2_image=None,
+            image_prompt_mode=None, image_prompt_image=None, image_prompt_scale=None,
+        ):
+        prompt_enhancement = bool(prompt_enhancement)
+        payload = {
+            "prompt": prompt,
+            "num_results": 1,
+            "aspect_ratio": aspect_ratio,
+            "sync": True,
+            "seed": seed,
+            "steps_num": steps_num,
+            "prompt_enhancement": prompt_enhancement,
+        }
+        if guidance_method_1_image is not None:
+            guidance_method_1_image = preprocess_image(guidance_method_1_image)
+            guidance_method_1_image = image_to_base64(guidance_method_1_image)
+            payload["guidance_method_1"] = guidance_method_1
+            payload["guidance_method_1_scale"] = guidance_method_1_scale
+            payload["guidance_method_1_image_file"] = guidance_method_1_image
+        if guidance_method_2_image is not None:
+            guidance_method_2_image = preprocess_image(guidance_method_2_image)
+            guidance_method_2_image = image_to_base64(guidance_method_2_image)
+            payload["guidance_method_2"] = guidance_method_2
+            payload["guidance_method_2_scale"] = guidance_method_2_scale
+            payload["guidance_method_2_image_file"] = guidance_method_2_image
+        if image_prompt_image is not None:
+            image_prompt_image = preprocess_image(image_prompt_image)
+            image_prompt_image = image_to_base64(image_prompt_image)
+            payload["image_prompt_mode"] = image_prompt_mode
+            payload["image_prompt_file"] = image_prompt_image
+            payload["image_prompt_scale"] = image_prompt_scale
+        response = requests.post(
+            self.api_url,
+            json=payload,
+            headers={"api_token": api_key}
+        )
+        if response.status_code == 200:
+                response_dict = response.json()
+                image_response = requests.get(response_dict['result'][0]["urls"][0])
+                result_image = postprocess_image(image_response.content)
+                return (result_image,)
+        else:
+            raise Exception(f"Error: API request failed with status code {response.status_code} and text {response.text}")

nodes/text_2_image_hd_node.py

@@ -0,0 +1,62 @@
+import requests
+
+from .common import postprocess_image
+
+
+class Text2ImageHDNode():
+    @classmethod
+    def INPUT_TYPES(self):
+        return {
+            "required": {
+                "api_key": ("STRING", ),
+            },
+            "optional": {
+                "prompt": ("STRING",),
+                "aspect_ratio": (["1:1", "2:3", "3:2", "3:4", "4:3", "4:5", "5:4", "9:16", "16:9"], {"default": "4:3"}),
+                "seed": ("INT", {"default": -1}),
+                "negative_prompt": ("STRING", {"default": ""}),
+                "steps_num": ("INT", {"default": 30}), 
+                "prompt_enhancement": ("INT", {"default": 0}),
+                "text_guidance_scale": ("INT", {"default": 5}),
+                "medium": (["photography", "art", "none"], {"default": "none"}),
+            }
+        }
+
+    RETURN_TYPES = ("IMAGE",)
+    RETURN_NAMES = ("output_image",)
+    CATEGORY = "API Nodes"
+    FUNCTION = "execute"
+
+    def __init__(self):
+        self.api_url = "https://engine.prod.bria-api.com/v1/text-to-image/hd/2.3" #"http://0.0.0.0:5000/v1/text-to-image/hd/2.3"
+
+    def execute(
+            self, api_key, prompt, aspect_ratio, seed, negative_prompt, 
+            steps_num, prompt_enhancement, text_guidance_scale, medium,
+        ):
+        prompt_enhancement = bool(prompt_enhancement)
+        payload = {
+            "prompt": prompt,
+            "num_results": 1,
+            "aspect_ratio": aspect_ratio,
+            "sync": True,
+            "seed": seed,
+            "negative_prompt": negative_prompt,
+            "steps_num": steps_num,
+            "text_guidance_scale": text_guidance_scale,
+            "prompt_enhancement": prompt_enhancement,
+        }
+        if medium != "none":
+            payload["medium"] = medium
+        response = requests.post(
+            self.api_url,
+            json=payload,
+            headers={"api_token": api_key}
+        )
+        if response.status_code == 200:
+                response_dict = response.json()
+                image_response = requests.get(response_dict['result'][0]["urls"][0])
+                result_image = postprocess_image(image_response.content)
+                return (result_image,)
+        else:
+            raise Exception(f"Error: API request failed with status code {response.status_code} and text {response.text}")

pyproject.toml

@@ -1,7 +1,7 @@
 [project]
 name = "comfyui-bria-api"
 description = "Custom nodes for ComfyUI using BRIA's API."
-version = "1.0.0"
+version = "2.0.0"
 license = {file = "LICENSE"}
 
 [project.urls]

workflows/Product shot generation_workflow.json

@@ -0,0 +1,296 @@
+{
+  "last_node_id": 42,
+  "last_link_id": 65,
+  "nodes": [
+    {
+      "id": 42,
+      "type": "LoadImage",
+      "pos": {
+        "0": 591,
+        "1": 593
+      },
+      "size": {
+        "0": 315,
+        "1": 314
+      },
+      "flags": {},
+      "order": 0,
+      "mode": 0,
+      "inputs": [],
+      "outputs": [
+        {
+          "name": "IMAGE",
+          "type": "IMAGE",
+          "links": [
+            64,
+            65
+          ],
+          "slot_index": 0
+        },
+        {
+          "name": "MASK",
+          "type": "MASK",
+          "links": null
+        }
+      ],
+      "properties": {
+        "Node name for S&R": "LoadImage"
+      },
+      "widgets_values": [
+        "A_bottle_of_perfume.png",
+        "image"
+      ]
+    },
+    {
+      "id": 39,
+      "type": "LoadImage",
+      "pos": {
+        "0": 600,
+        "1": 988
+      },
+      "size": {
+        "0": 315,
+        "1": 314
+      },
+      "flags": {},
+      "order": 1,
+      "mode": 0,
+      "inputs": [],
+      "outputs": [
+        {
+          "name": "IMAGE",
+          "type": "IMAGE",
+          "links": [
+            59
+          ],
+          "slot_index": 0
+        },
+        {
+          "name": "MASK",
+          "type": "MASK",
+          "links": null
+        }
+      ],
+      "properties": {
+        "Node name for S&R": "LoadImage"
+      },
+      "widgets_values": [
+        "A_red_studio_with_a_shelf__close_up.png",
+        "image"
+      ]
+    },
+    {
+      "id": 15,
+      "type": "Note",
+      "pos": {
+        "0": 995.4524536132812,
+        "1": 601.5353393554688
+      },
+      "size": {
+        "0": 306.28387451171875,
+        "1": 58
+      },
+      "flags": {},
+      "order": 2,
+      "mode": 0,
+      "inputs": [],
+      "outputs": [],
+      "properties": {},
+      "widgets_values": [
+        "You can get your BRIA API token at:\nhttps://bria.ai/api/"
+      ],
+      "color": "#432",
+      "bgcolor": "#653"
+    },
+    {
+      "id": 40,
+      "type": "PreviewImage",
+      "pos": {
+        "0": 1408,
+        "1": 623
+      },
+      "size": [
+        210,
+        246
+      ],
+      "flags": {},
+      "order": 5,
+      "mode": 0,
+      "inputs": [
+        {
+          "name": "images",
+          "type": "IMAGE",
+          "link": 62
+        }
+      ],
+      "outputs": [],
+      "properties": {
+        "Node name for S&R": "PreviewImage"
+      }
+    },
+    {
+      "id": 41,
+      "type": "PreviewImage",
+      "pos": {
+        "0": 1408,
+        "1": 951
+      },
+      "size": [
+        210,
+        246
+      ],
+      "flags": {},
+      "order": 6,
+      "mode": 0,
+      "inputs": [
+        {
+          "name": "images",
+          "type": "IMAGE",
+          "link": 63
+        }
+      ],
+      "outputs": [],
+      "properties": {
+        "Node name for S&R": "PreviewImage"
+      }
+    },
+    {
+      "id": 36,
+      "type": "ShotByTextNode",
+      "pos": {
+        "0": 996,
+        "1": 736
+      },
+      "size": {
+        "0": 315,
+        "1": 106
+      },
+      "flags": {},
+      "order": 3,
+      "mode": 0,
+      "inputs": [
+        {
+          "name": "image",
+          "type": "IMAGE",
+          "link": 64
+        }
+      ],
+      "outputs": [
+        {
+          "name": "output_image",
+          "type": "IMAGE",
+          "links": [
+            62
+          ],
+          "slot_index": 0
+        }
+      ],
+      "properties": {
+        "Node name for S&R": "ShotByTextNode"
+      },
+      "widgets_values": [
+        "a beautiful sunset",
+        1,
+        ""
+      ]
+    },
+    {
+      "id": 37,
+      "type": "ShotByImageNode",
+      "pos": {
+        "0": 999,
+        "1": 932
+      },
+      "size": {
+        "0": 315,
+        "1": 102
+      },
+      "flags": {},
+      "order": 4,
+      "mode": 0,
+      "inputs": [
+        {
+          "name": "image",
+          "type": "IMAGE",
+          "link": 65
+        },
+        {
+          "name": "ref_image",
+          "type": "IMAGE",
+          "link": 59
+        }
+      ],
+      "outputs": [
+        {
+          "name": "output_image",
+          "type": "IMAGE",
+          "links": [
+            63
+          ],
+          "slot_index": 0
+        }
+      ],
+      "properties": {
+        "Node name for S&R": "ShotByImageNode"
+      },
+      "widgets_values": [
+        0,
+        ""
+      ]
+    }
+  ],
+  "links": [
+    [
+      59,
+      39,
+      0,
+      37,
+      1,
+      "IMAGE"
+    ],
+    [
+      62,
+      36,
+      0,
+      40,
+      0,
+      "IMAGE"
+    ],
+    [
+      63,
+      37,
+      0,
+      41,
+      0,
+      "IMAGE"
+    ],
+    [
+      64,
+      42,
+      0,
+      36,
+      0,
+      "IMAGE"
+    ],
+    [
+      65,
+      42,
+      0,
+      37,
+      0,
+      "IMAGE"
+    ]
+  ],
+  "groups": [],
+  "config": {},
+  "extra": {
+    "ds": {
+      "scale": 0.9849732675807669,
+      "offset": [
+        -339.6686422794803,
+        -496.4354678014682
+      ]
+    }
+  },
+  "version": 0.4
+}

workflows/genfill_workflow.json

@@ -0,0 +1,204 @@
+{
+  "last_node_id": 35,
+  "last_link_id": 55,
+  "nodes": [
+    {
+      "id": 33,
+      "type": "PreviewImage",
+      "pos": {
+        "0": 1420,
+        "1": 574
+      },
+      "size": {
+        "0": 433.29193115234375,
+        "1": 357.1255187988281
+      },
+      "flags": {},
+      "order": 4,
+      "mode": 0,
+      "inputs": [
+        {
+          "name": "images",
+          "type": "IMAGE",
+          "link": 54
+        }
+      ],
+      "outputs": [],
+      "properties": {
+        "Node name for S&R": "PreviewImage"
+      }
+    },
+    {
+      "id": 30,
+      "type": "LoadImage",
+      "pos": {
+        "0": 479,
+        "1": 572
+      },
+      "size": {
+        "0": 395.7845153808594,
+        "1": 352.8512268066406
+      },
+      "flags": {},
+      "order": 0,
+      "mode": 0,
+      "inputs": [],
+      "outputs": [
+        {
+          "name": "IMAGE",
+          "type": "IMAGE",
+          "links": [
+            53
+          ],
+          "slot_index": 0,
+          "shape": 3
+        },
+        {
+          "name": "MASK",
+          "type": "MASK",
+          "links": [
+            55
+          ],
+          "slot_index": 1,
+          "shape": 3
+        }
+      ],
+      "properties": {
+        "Node name for S&R": "LoadImage"
+      },
+      "widgets_values": [
+        "clipspace/clipspace-mask-1438488.400000006.png [input]",
+        "image"
+      ]
+    },
+    {
+      "id": 14,
+      "type": "Note",
+      "pos": {
+        "0": 478,
+        "1": 444
+      },
+      "size": {
+        "0": 396.80859375,
+        "1": 61.8046875
+      },
+      "flags": {},
+      "order": 1,
+      "mode": 0,
+      "inputs": [],
+      "outputs": [],
+      "properties": {},
+      "widgets_values": [
+        "Right click, and choose \"Open in Mask Editor\" to draw a mask of areas you want to erase."
+      ],
+      "color": "#432",
+      "bgcolor": "#653"
+    },
+    {
+      "id": 15,
+      "type": "Note",
+      "pos": {
+        "0": 983,
+        "1": 440
+      },
+      "size": {
+        "0": 306.28387451171875,
+        "1": 58
+      },
+      "flags": {},
+      "order": 2,
+      "mode": 0,
+      "inputs": [],
+      "outputs": [],
+      "properties": {},
+      "widgets_values": [
+        "You can get your BRIA API token at:\nhttps://bria.ai/api/"
+      ],
+      "color": "#432",
+      "bgcolor": "#653"
+    },
+    {
+      "id": 34,
+      "type": "BriaGenFill",
+      "pos": {
+        "0": 992,
+        "1": 572
+      },
+      "size": {
+        "0": 315,
+        "1": 102
+      },
+      "flags": {},
+      "order": 3,
+      "mode": 0,
+      "inputs": [
+        {
+          "name": "image",
+          "type": "IMAGE",
+          "link": 53
+        },
+        {
+          "name": "mask",
+          "type": "MASK",
+          "link": 55
+        }
+      ],
+      "outputs": [
+        {
+          "name": "output_image",
+          "type": "IMAGE",
+          "links": [
+            54
+          ],
+          "shape": 3,
+          "slot_index": 0
+        }
+      ],
+      "properties": {
+        "Node name for S&R": "BriaGenFill"
+      },
+      "widgets_values": [
+        "a beautiful paint brush",
+        "BRIA_API_TOKEN"
+      ]
+    }
+  ],
+  "links": [
+    [
+      53,
+      30,
+      0,
+      34,
+      0,
+      "IMAGE"
+    ],
+    [
+      54,
+      34,
+      0,
+      33,
+      0,
+      "IMAGE"
+    ],
+    [
+      55,
+      30,
+      1,
+      34,
+      1,
+      "MASK"
+    ]
+  ],
+  "groups": [],
+  "config": {},
+  "extra": {
+    "ds": {
+      "scale": 0.8264462809917364,
+      "offset": [
+        -266.96526103236687,
+        114.47857424738714
+      ]
+    }
+  },
+  "version": 0.4
+}

workflows/tailored_workflow.json

@@ -0,0 +1 @@
+{"last_node_id":21,"last_link_id":49,"nodes":[{"id":2,"type":"TailoredModelInfoNode","pos":[480.2208557128906,641.4290161132812],"size":[315,122],"flags":{},"order":0,"mode":0,"inputs":[],"outputs":[{"name":"generation_prefix","type":"STRING","links":[2],"slot_index":0,"localized_name":"generation_prefix"},{"name":"default_fast","type":"INT","links":[46],"slot_index":1,"localized_name":"default_fast"},{"name":"default_steps_num","type":"INT","links":[40],"slot_index":2,"localized_name":"default_steps_num"}],"properties":{"Node name for S&R":"TailoredModelInfoNode"},"widgets_values":["",""]},{"id":3,"type":"PreviewImage","pos":[1728.9140625,688.2759399414062],"size":[210,246],"flags":{},"order":6,"mode":0,"inputs":[{"name":"images","type":"IMAGE","link":41,"localized_name":"images"}],"outputs":[],"properties":{"Node name for S&R":"PreviewImage"},"widgets_values":[]},{"id":11,"type":"LoadImage","pos":[693.87060546875,831.6130981445312],"size":[315,314],"flags":{},"order":1,"mode":0,"inputs":[],"outputs":[{"name":"IMAGE","type":"IMAGE","links":[48],"slot_index":0,"localized_name":"IMAGE"},{"name":"MASK","type":"MASK","links":null,"localized_name":"MASK"}],"properties":{"Node name for S&R":"LoadImage"},"widgets_values":["example.png","image"]},{"id":5,"type":"JjkShowText","pos":[847.2867431640625,591.890625],"size":[315,76],"flags":{},"order":4,"mode":0,"inputs":[{"name":"text","type":"STRING","link":2,"widget":{"name":"text"}}],"outputs":[{"name":"text","type":"STRING","links":[49],"slot_index":0,"shape":6,"localized_name":"text"}],"properties":{"Node name for S&R":"JjkShowText"},"widgets_values":["A photo of a character named Sami, a siamese cat with blue eyes, "]},{"id":15,"type":"BriaTailoredGen","pos":[1207.595947265625,645.6781005859375],"size":[456,438],"flags":{},"order":5,"mode":0,"inputs":[{"name":"guidance_method_1_image","type":"IMAGE","link":48,"shape":7,"localized_name":"guidance_method_1_image"},{"name":"guidance_method_2_image","type":"IMAGE","link":null,"shape":7,"localized_name":"guidance_method_2_image"},{"name":"generation_prefix","type":"STRING","link":49,"widget":{"name":"generation_prefix"},"shape":7},{"name":"fast","type":"INT","link":46,"widget":{"name":"fast"},"shape":7},{"name":"steps_num","type":"INT","link":40,"widget":{"name":"steps_num"},"shape":7}],"outputs":[{"name":"output_image","type":"IMAGE","links":[41],"slot_index":0,"localized_name":"output_image"}],"properties":{"Node name for S&R":"BriaTailoredGen"},"widgets_values":["","","a cat","","4:3",-1,"randomize",1,"","",1,"controlnet_canny",1,"controlnet_canny",1]},{"id":21,"type":"Note","pos":[1215.2469482421875,522.4407348632812],"size":[449.75360107421875,58],"flags":{},"order":3,"mode":0,"inputs":[],"outputs":[],"properties":{},"widgets_values":["You can get your BRIA API token at: https://bria.ai/api/"],"color":"#432","bgcolor":"#653"},{"id":19,"type":"Note","pos":[484.5993957519531,486.4328918457031],"size":[306.0655212402344,89.87609100341797],"flags":{},"order":2,"mode":0,"inputs":[],"outputs":[],"properties":{},"widgets_values":["This node is used to retrieve default settings and prompt prefixes for the chosen tailored model."],"color":"#432","bgcolor":"#653"}],"links":[[2,2,0,5,0,"STRING"],[40,2,2,15,4,"INT"],[41,15,0,3,0,"IMAGE"],[46,2,1,15,3,"INT"],[48,11,0,15,0,"IMAGE"],[49,5,0,15,2,"STRING"]],"groups":[],"config":{},"extra":{"ds":{"scale":0.7627768444385483,"offset":[-122.90473166350671,-300.2018923615813]},"node_versions":{"comfyui-bria-api":"c72754d15b53a13ee0c0419d70401232c56b7fdb","comfy-core":"v0.3.8-1-gc441048","ComfyUI-Jjk-Nodes":"b3c99bb78a99551776b5eab1a820e1cd58f84f31"}},"version":0.4}