Comfy tailored portrait - version (#19 )

tailored portrait (#18 )
Update Readme.md
2025-03-10 12:54:27 +04:00 · 2025-03-10 12:45:44 +04:00 · 2025-02-10 18:11:40 +02:00 · 2025-02-10 18:10:57 +02:00 · 2025-02-10 18:05:56 +02:00 · 2025-02-10 18:04:28 +02:00
33 changed files with 1782 additions and 406 deletions
@@ -0,0 +1 @@
+*.pyc
@@ -1,30 +1,73 @@
 # 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 and editing workflows. **API documentation** is available [**here**](https://docs.bria.ai/). 

-You can load the workflow, which includes all available nodes, by importing the [workflow.json](workflow.json) file in this repo. 
+BRIA's APIs and models are built for commercial use and trained on 100% licensed data and does not contain copyrighted materials, such as fictional characters, logos, trademarks, public figures, harmful content, or privacy-infringing content.

-You can also download the following image and import it to comfyui:
+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>.

-<img src="./images/eraser_workflow.png" alt="Original image" width="500"/>
+For direct API Endpoint use, look for the endpoint in our of our API partners like: [**fal.ai**](https://fal.ai/models?keywords=bria). 
+For source code and weigths access, go to our [**Hugging Face**](https://huggingface.co/briaai) space.

-An illustration of the workflow:
+To load a workflow, import the compatible workflow.json files from this [folder](workflows).  
+<p align="center">
+  <img src="./images/background_workflow.png" width="1200"/>
+</p>

- <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
+<!-- Placeholder image of cool workflows. -->

-### Eraser
-The **Eraser** node allows users to remove specific objects or areas from an image by providing a mask.

-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.
+ <!-- <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"/> -->

-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
+These nodes create high-quality images from text or image prompts, generating photorealistic or artistic results with support for various aspect ratios.
+
+| Node                   | Description                                                        |
+|------------------------|--------------------------------------------------------------------|
+| **Text2Image Base**    | Generates images from text prompts, serving as the foundation for text-based image creation. |
+| **Text2Image Fast**    | Optimized for speed, this node generates images from text prompts with faster results while maintaining quality. |
+| **Text2Image HD**      | Optimized for high-resolution outputs, this node generates detailed and sharp visuals from text prompts. |
+| **Reimagine**          | Guides image generation using both prompts and an input image. Preserve the original structure and depth while introducing new materials, colors, and textures. |
+
+## Tailored Generation Nodes
+These nodes use pre-trained tailored models to generate images that faithfully reproduce specific visual IP elements or guidelines.
+
+| Node                   | Description                                                        |
+|------------------------|--------------------------------------------------------------------|
+| **Tailored Gen**       | Generates images using a trained tailored model, reproducing specific visual IP elements or guidelines. Use the Tailored Model Info node to load the model's default settings. |
+| **Tailored Model Info** | Retrieves the default settings and prompt prefix of a trained tailored model, which can be used to configure the Tailored Gen node. |
+
+## Image Editing Nodes
+These nodes modify specific parts of images, enabling adjustments while maintaining the integrity of the rest of the image.
+
+| Node                   | Description                                                        |
+|------------------------|--------------------------------------------------------------------|
+| **RMBG 2.0 (Remove Background)** | Removes the background from an image, isolating the foreground subject. |
+| **Replace Background**  | Replaces an image’s background with a new one, guided by either a reference image or a prompt. |
+| **Expand Image**        | Expands the dimensions of an image, generating new content to fill the extended areas. |
+| **Eraser**             | Removes specific objects or areas from an image by providing a mask. |
+| **GenFill**            | Generates objects by prompt in a specific region of an image. |
+| **Erase Foreground**    | Removes the foreground from an image, isolating the background. |
+
+## Product Shot Editing Nodes
+These nodes create high-quality product images for eCommerce workflows.
+
+| Node                   | Description                                                        |
+|------------------------|--------------------------------------------------------------------|
+| **ShotByText**         | Modifies an image's background by providing a text prompt. Powered by BRIA's ControlNet Background-Generation. |
+| **ShotByImage**        | Modifies an image's background by providing a reference image. Uses BRIA's ControlNet Background-Generation and Image-Prompt. |
+
+# Installation
 There are two methods to install the BRIA ComfyUI API nodes:

 ### Method 1: Using ComfyUI's Custom Node Manager
@@ -43,3 +86,6 @@ There are two methods to install the BRIA ComfyUI API nodes:
   ```

 3. Restart ComfyUI and load the workflows.
+
+<!-- ### Campaign generation
+Coming soon -->
@@ -1,11 +1,39 @@
-from .bria_api_node import EraserNode
-
+from nodes.tailored_portrait_node import TailoredPortraitNode
+from .nodes import (EraserNode, GenFillNode, ImageExpansionNode, ReplaceBgNode, RmbgNode, RemoveForegroundNode, 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,
+    "ImageExpansionNode": ImageExpansionNode,
+    "ReplaceBgNode": ReplaceBgNode,
+    "RmbgNode": RmbgNode,
+    "RemoveForegroundNode": RemoveForegroundNode,
+    "ShotByTextNode": ShotByTextNode,
+    "ShotByImageNode": ShotByImageNode,
+    "BriaTailoredGen": TailoredGenNode,
+    "TailoredModelInfoNode": TailoredModelInfoNode,
+    "TailoredPortraitNode": TailoredPortraitNode,
+    "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",
+    "ImageExpansionNode": "Bria Image Expansion",
+    "ReplaceBgNode": "Bria Replace Background",
+    "RmbgNode": "Bria RMBG",
+    "RemoveForegroundNode": "Bria Remove Foreground",
+    "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",
 }
@@ -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}")
@@ -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"/>
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+        <g id="Group_57" data-name="Group 57" transform="translate(557.15 259.772)">
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+            <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>
@@ -0,0 +1,15 @@
+from .eraser_node import EraserNode
+from .generative_fill_node import GenFillNode
+from .image_expansion_node import ImageExpansionNode
+from .replace_bg_node import ReplaceBgNode
+from .rmbg_node import RmbgNode
+from .remove_foreground_node import RemoveForegroundNode
+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 .tailored_portrait_node import TailoredPortraitNode
+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
@@ -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}")
@@ -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)
+    
@@ -0,0 +1,80 @@
+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
+            },
+            "optional": {
+                "seed": ("INT", {"default": 123456})
+            }
+        }
+
+    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, seed):
+        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,
+            "seed": seed,
+        }
+
+        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}")
@@ -0,0 +1,101 @@
+import numpy as np
+import requests
+from PIL import Image
+import io
+import torch
+
+from .common import image_to_base64, preprocess_image
+
+
+class ImageExpansionNode():
+    @classmethod
+    def INPUT_TYPES(self):
+        return {
+            "required": {
+                "image": ("IMAGE",),  # Input image from another node
+                "original_image_size": ("STRING",),
+                "original_image_location": ("STRING",),
+                "api_key": ("STRING", {"default": "BRIA_API_TOKEN"}), # API Key input with a default value
+            },
+            
+            "optional": {
+                "canvas_size": ("STRING", {"default": "1000, 1000"}),
+                "prompt": ("STRING", {"default": ""}),
+                "seed": ("INT", {"default": 681794}),
+                "negative_prompt": ("STRING", {"default": "Ugly, mutated"}),
+                "content_moderation": ("BOOLEAN", {"default": False}), 
+            } 
+        }
+
+    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/image_expansion"  # Image Expansion API URL
+
+    # Define the execute method as expected by ComfyUI
+    def execute(self, image, 
+                original_image_size,
+                original_image_location,
+                canvas_size,
+                prompt,
+                seed,
+                negative_prompt,
+                content_moderation,
+                api_key):
+        if api_key.strip() == "" or api_key.strip() == "BRIA_API_TOKEN":
+            raise Exception("Please insert a valid API key.")
+        
+        original_image_size = [int(x.strip()) for x in original_image_size.split(",")]
+        original_image_location = [int(x.strip()) for x in original_image_location.split(",")]
+        canvas_size = [int(x.strip()) for x in canvas_size.split(",")]
+        
+        if prompt == "":
+            prompt = None
+        if negative_prompt == "":
+            negative_prompt = " " # hack to avoid error in triton which expects non-empty string
+
+        # Check if image and mask are tensors, if so, convert to NumPy arrays
+        if isinstance(image, torch.Tensor):
+            image = preprocess_image(image)
+
+        # Convert the image directly to Base64 string
+        image_base64 = image_to_base64(image)
+
+        # Prepare the API request payload
+        payload = {
+            "file": f"{image_base64}",
+            "original_image_size": original_image_size,
+            "original_image_location": original_image_location,
+            "canvas_size": canvas_size,
+            "prompt": prompt,
+            "negative_prompt": negative_prompt,
+            "seed": seed,
+            "content_moderation": content_moderation
+        }
+
+        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("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}")
@@ -0,0 +1,69 @@
+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
+                "content_moderation": ("INT", {"default": 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/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,
+            content_moderation=0,
+        ):        
+        payload = {
+            "prompt": tailored_generation_prefix + prompt,
+            "num_results": 1,
+            "sync": True,
+            "seed": seed,
+            "steps_num": steps_num,
+            "include_generation_prefix": False,
+            "content_moderation": content_moderation,
+        }
+        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}")
@@ -0,0 +1,70 @@
+import numpy as np
+import requests
+from PIL import Image
+import io
+import torch
+
+from .common import preprocess_image, image_to_base64
+
+
+class RemoveForegroundNode():
+    @classmethod
+    def INPUT_TYPES(self):
+        return {
+            "required": {
+                "image": ("IMAGE",),  # Input image from another node
+                "api_key": ("STRING", {"default": "BRIA_API_TOKEN"}), # API Key input with a default value
+            },
+            "optional": {
+                "content_moderation": ("BOOLEAN", {"default": False}), 
+            }
+        }
+
+    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.internal.prod.bria-api.com/v1/erase_foreground"  # remove foreground API URL
+
+    # Define the execute method as expected by ComfyUI
+    def execute(self, image, content_moderation, api_key):
+        if api_key.strip() == "" or api_key.strip() == "BRIA_API_TOKEN":
+            raise Exception("Please insert a valid API key.")
+
+        # Check if image is tensor, if so, convert to NumPy array
+        if isinstance(image, torch.Tensor):
+            image = preprocess_image(image)
+
+        # Prepare the API request payload
+        # temporary save the image to /tmp
+        # temp_img_path = "/tmp/temp_img.jpeg"
+        # image.save(temp_img_path, format="JPEG")
+        
+#         files=[('file',('temp_img.jpeg', open(temp_img_path, 'rb'),'image/jpeg'))
+#   ]
+        payload = {"file": image_to_base64(image), "content_moderation": content_moderation}
+
+        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 = 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}")
@@ -0,0 +1,105 @@
+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 ReplaceBgNode():
+    @classmethod
+    def INPUT_TYPES(self):
+        return {
+            "required": {
+                "image": ("IMAGE",),  # Input image from another node
+                "api_key": ("STRING", {"default": "BRIA_API_TOKEN"}), # API Key input with a default value
+            },
+            "optional": {
+                "fast": ("BOOLEAN", {"default": True}), 
+                "bg_prompt": ("STRING",),
+                "ref_image": ("IMAGE",), # Input ref image from another node
+                "refine_prompt": ("BOOLEAN", {"default": True}), 
+                "enhance_ref_image": ("BOOLEAN", {"default": True}), 
+                "original_quality": ("BOOLEAN", {"default": False}), 
+                "force_rmbg": ("BOOLEAN", {"default": False}), 
+                "negative_prompt": ("STRING", {"default": None}), 
+                "seed": ("INT", {"default": 681794}),
+                "content_moderation": ("BOOLEAN", {"default": False}), 
+            }
+        }
+
+    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/background/replace"  # Replace BG API URL
+
+    # Define the execute method as expected by ComfyUI
+    def execute(self, image, fast,
+                refine_prompt,
+                enhance_ref_image,
+                original_quality,
+                force_rmbg,
+                negative_prompt,
+                seed,
+                api_key,
+                content_moderation,
+                bg_prompt=None,
+                ref_image=None,):
+        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)
+
+        # Convert the image and mask directly to Base64 strings
+        image_base64 = image_to_base64(image)
+        ref_image_file = None # initialization, will be updated if it is supplied
+        if ref_image is not None:
+            ref_image = preprocess_image(ref_image)
+            ref_image_file = image_to_base64(ref_image)
+
+        # Prepare the API request payload
+        payload = {
+            "file": f"{image_base64}",
+            "fast": fast,
+            "bg_prompt": bg_prompt,
+            "ref_image_file": ref_image_file,
+            "refine_prompt": refine_prompt,
+            "enhance_ref_image": enhance_ref_image,
+            "original_quality": original_quality,
+            "force_rmbg": force_rmbg,
+            "negative_prompt": negative_prompt,
+            "seed": seed,
+            "sync": True,
+            "num_results": 1,
+            "content_moderation": content_moderation            
+        }   
+
+        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]) # first indexing for batched, second for url
+                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}")
@@ -0,0 +1,67 @@
+import numpy as np
+import requests
+from PIL import Image
+import io
+import torch
+
+from .common import preprocess_image
+from io import BytesIO
+
+class RmbgNode():
+    @classmethod
+    def INPUT_TYPES(self):
+        return {
+            "required": {
+                "image": ("IMAGE",),  # Input image from another node
+                "api_key": ("STRING", {"default": "BRIA_API_TOKEN"}), # API Key input with a default value
+            },
+            "optional": {
+                "content_moderation": ("BOOLEAN", {"default": False}), 
+            }               
+        }
+
+    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/background/remove"  # RMBG API URL
+
+    # Define the execute method as expected by ComfyUI
+    def execute(self, image, content_moderation, api_key):
+        if api_key.strip() == "" or api_key.strip() == "BRIA_API_TOKEN":
+            raise Exception("Please insert a valid API key.")
+
+        # Check if image is tensor, if so, convert to NumPy array
+        if isinstance(image, torch.Tensor):
+            image = preprocess_image(image)
+
+        # Prepare the API request payload
+        image_buffer = BytesIO()
+        image.save(image_buffer, format="JPEG")
+
+        # Get binary data from buffer
+        image_buffer.seek(0)  # Move cursor to the start of the buffer
+        binary_data = image_buffer.read()
+
+        files=[('file',('temp_img.jpeg',  BytesIO(binary_data),'image/jpeg'))]
+        payload = {"content_moderation": content_moderation}   
+
+        try:
+            response = requests.post(self.api_url, data=payload, headers={"api_token": api_key}, files=files)
+            # 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 = 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}")
@@ -0,0 +1,72 @@
+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
+            },
+            "optional": {
+                "content_moderation": ("BOOLEAN", {"default": False}), 
+            }        
+        }
+
+    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, content_moderation):
+        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,
+            "content_moderation": content_moderation
+        }
+        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}")        
@@ -0,0 +1,69 @@
+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
+            },
+            "optional": {
+                "content_moderation": ("BOOLEAN", {"default": False}), 
+            }   
+        }
+
+    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, content_moderation):
+        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,
+            "content_moderation": content_moderation
+
+        }
+        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}")                
+
@@ -0,0 +1,84 @@
+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", ),
+                "content_moderation": ("INT", {"default": 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/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,
+            content_moderation=0,
+        ):
+        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,
+            "content_moderation": content_moderation,
+        }
+        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}")
@@ -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}")
@@ -0,0 +1,72 @@
+import numpy as np
+import requests
+from PIL import Image
+import io
+import torch
+
+from .common import image_to_base64
+
+class TailoredPortraitNode():
+    @classmethod
+    def INPUT_TYPES(self):
+        return {
+            "required": {
+                "image": ("IMAGE",),  # Input image from another node
+                "tailored_model_id": ("INT",),
+                "api_key": ("STRING", {"default": "BRIA_API_TOKEN"}), # API Key input with a default value
+            },
+            "optional": {
+                "seed": ("INT", {"default": 123456}),
+                "tailored_model_influence": ("FLOAT", {"default": 0.9}),
+                "id_strength": ("FLOAT", {"default": 0.7}),
+            }
+        }
+
+    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/tailored-gen/restyle_portrait"  # Eraser API URL
+
+    # Define the execute method as expected by ComfyUI
+    def execute(self, image, tailored_model_id, api_key, seed, tailored_model_influence, id_strength):
+        if api_key.strip() == "" or api_key.strip() == "BRIA_API_TOKEN":
+            raise Exception("Please insert a valid API key.")
+
+        # Convert the image and mask directly to Base64 strings
+        image_base64 = image_to_base64(image)
+
+        # Prepare the API request payload
+        payload = {
+            "id_image_file": f"{image_base64}",
+            "tailored_model_id": tailored_model_id,
+            "tailored_model_influence": tailored_model_influence,
+            "id_strength": id_strength,
+            "seed": seed
+        }
+
+        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['image_res'])
+                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}")
@@ -0,0 +1,94 @@
+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}),
+                "content_moderation": ("INT", {"default": 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,
+            content_moderation=0,
+        ):
+        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,
+            "content_moderation": content_moderation,
+        }
+        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}")
@@ -0,0 +1,87 @@
+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}),
+                "content_moderation": ("INT", {"default": 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,
+            content_moderation=0,
+        ):
+        payload = {
+            "prompt": prompt,
+            "num_results": 1,
+            "aspect_ratio": aspect_ratio,
+            "sync": True,
+            "seed": seed,
+            "steps_num": steps_num,
+            "prompt_enhancement": prompt_enhancement,
+            "content_moderation": content_moderation,
+        }
+        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}")
@@ -0,0 +1,63 @@
+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"}),
+                "content_moderation": ("INT", {"default": 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/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, content_moderation=0,
+        ):
+        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,
+            "content_moderation": content_moderation,
+        }
+        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}")
@@ -1,7 +1,7 @@
 [project]
 name = "comfyui-bria-api"
 description = "Custom nodes for ComfyUI using BRIA's API."
-version = "1.0.0"
+version = "2.0.3"
 license = {file = "LICENSE"}

 [project.urls]
@@ -1,203 +0,0 @@
-{
-  "last_node_id": 28,
-  "last_link_id": 42,
-  "nodes": [
-    {
-      "id": 15,
-      "type": "Note",
-      "pos": {
-        "0": 1021,
-        "1": 280
-      },
-      "size": {
-        "0": 311.8914794921875,
-        "1": 153.69827270507812
-      },
-      "flags": {},
-      "order": 0,
-      "mode": 0,
-      "inputs": [],
-      "outputs": [],
-      "properties": {},
-      "widgets_values": [
-        "The default BRIA API key for ComfyUI (BRIA_ComfyUI_Key) offers 10,000 API calls for the entire community. \n\nGet your own token at:\nhttps://bria.ai/api/"
-      ],
-      "color": "#432",
-      "bgcolor": "#653"
-    },
-    {
-      "id": 13,
-      "type": "PreviewImage",
-      "pos": {
-        "0": 1410,
-        "1": 160
-      },
-      "size": {
-        "0": 474.7605895996094,
-        "1": 303.117919921875
-      },
-      "flags": {},
-      "order": 4,
-      "mode": 0,
-      "inputs": [
-        {
-          "name": "images",
-          "type": "IMAGE",
-          "link": 42
-        }
-      ],
-      "outputs": [],
-      "properties": {
-        "Node name for S&R": "PreviewImage"
-      }
-    },
-    {
-      "id": 21,
-      "type": "LoadImage",
-      "pos": {
-        "0": 477,
-        "1": 156
-      },
-      "size": {
-        "0": 408.4602355957031,
-        "1": 333.19830322265625
-      },
-      "flags": {},
-      "order": 1,
-      "mode": 0,
-      "inputs": [],
-      "outputs": [
-        {
-          "name": "IMAGE",
-          "type": "IMAGE",
-          "links": [
-            40
-          ],
-          "slot_index": 0,
-          "shape": 3
-        },
-        {
-          "name": "MASK",
-          "type": "MASK",
-          "links": [
-            41
-          ],
-          "slot_index": 1,
-          "shape": 3
-        }
-      ],
-      "properties": {
-        "Node name for S&R": "LoadImage"
-      },
-      "widgets_values": [
-        "clipspace/clipspace-mask-82245.69999998808.png [input]",
-        "image"
-      ]
-    },
-    {
-      "id": 28,
-      "type": "BriaEraser",
-      "pos": {
-        "0": 1022,
-        "1": 159
-      },
-      "size": {
-        "0": 315,
-        "1": 78
-      },
-      "flags": {},
-      "order": 3,
-      "mode": 0,
-      "inputs": [
-        {
-          "name": "image",
-          "type": "IMAGE",
-          "link": 40
-        },
-        {
-          "name": "mask",
-          "type": "MASK",
-          "link": 41
-        }
-      ],
-      "outputs": [
-        {
-          "name": "output_image",
-          "type": "IMAGE",
-          "links": [
-            42
-          ],
-          "slot_index": 0,
-          "shape": 3
-        }
-      ],
-      "properties": {
-        "Node name for S&R": "BriaEraser"
-      },
-      "widgets_values": [
-        "BRIA_ComfyUI_Key"
-      ]
-    },
-    {
-      "id": 14,
-      "type": "Note",
-      "pos": {
-        "0": 483,
-        "1": 39
-      },
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+    "VHS_latentpreviewrate": 0
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+}
Author	SHA1	Message	Date
xenia-kra	21267d0995	Comfy tailored portrait - version (#19 )	2025-03-10 12:54:27 +04:00
xenia-kra	07827ef34f	tailored portrait (#18 )	2025-03-10 12:45:44 +04:00
MishaFein	00dccbb17a	Update Readme.md	2025-02-10 18:11:40 +02:00
MishaFein	1cb9ce4394	Update Readme.md	2025-02-10 18:10:57 +02:00
MishaFein	b2f8b8d0e3	Update Readme.md	2025-02-10 18:05:56 +02:00
MishaFein	129bc599d0	Update Readme.md	2025-02-10 18:04:28 +02:00
MishaFein	28c2631581	Update Readme.md	2025-02-10 17:57:11 +02:00
ori-liberman	baadbc02bc	Add content moderation option to background removal and image generation nodes	2025-02-10 12:49:02 +00:00
or	a73045f8e6	Added workflow sample to readme	2025-02-09 14:21:38 +02:00
tairBria	dd74d03fd0	Merge pull request #17 from Bria-AI/t2i-tailored-content-moderation content moderation t2i reimagime tailored	2025-02-09 13:12:30 +02:00
or	bfc83da41f	added text-to-image workflow and updated version	2025-02-04 17:40:08 +02:00
BriaOr	499ec5d104	Update Readme.md	2025-02-04 13:44:48 +02:00
BriaOr	fef2e49902	Update Readme.md with new API URL	2025-02-04 13:39:36 +02:00
BriaOr	2eae6dae46	Merge pull request #16 from Bria-AI/feature/content-moderation-expansion-removefg image expansion & remove fg- content moderation	2025-02-03 16:10:11 +02:00
israelweiss90	28c0cdf8d5	image expansion & remove fg- content moderation	2025-02-02 16:14:02 +00:00
Tair	54f380f1f0	content moderation t2i reimagime tailored	2025-02-02 14:52:18 +00:00
BriaOr	4f7691ff93	Merge pull request #14 from Bria-AI/bugfi/rmbg-temp-file temp file to buffer to avoid OS dependency	2025-02-02 14:08:39 +02:00
israelweiss90	1781beff09	temp file to buffer to avoid OS dependency	2025-02-02 10:56:04 +00:00
BriaOr	502206f518	Merge pull request #13 from Bria-AI/DvirYBria-gen-fill-seed Added seed go gen fill	2025-02-02 11:45:15 +02:00
DvirYBria	da8adda281	Added seed go gen fill	2025-02-02 11:35:57 +02:00
BriaOr	1c02dea96b	Update Readme.md	2025-01-30 17:08:42 +02:00
or	5b77fd90a8	updated genfill workflow	2025-01-30 16:52:21 +02:00
BriaOr	246e05ac3e	Update Readme.md	2025-01-30 15:29:41 +02:00
or	b6f2f8b297	Updated background workflow	2025-01-30 15:24:53 +02:00
BriaOr	5052a7bf94	Update Readme.md	2025-01-30 15:13:40 +02:00
BriaOr	afc9599c63	Update Readme.md	2025-01-30 15:03:42 +02:00
BriaOr	13a3dfb2bb	Update Readme.md	2025-01-30 15:02:07 +02:00
BriaOr	6573f2e43d	Rename Product shot generation_workflow.json to product shot generation_workflow.json	2025-01-30 14:52:06 +02:00
or	ac7e97c348	Updated workflows	2025-01-30 14:51:39 +02:00
BriaOr	c6fe701115	Merge pull request #10 from Bria-AI/feature/new-comfyui-nodes 4 new nodes- rmbg, replace bg, expand, remove fg	2025-01-30 14:21:15 +02:00
israelweiss90	ab17ee227d	pyproject version 2.0.1	2025-01-29 16:18:52 +00:00
israelweiss90	2fa966e887	4 new nodes- rmbg, replace bg, expand, remove fg	2025-01-29 16:08:05 +00:00
BriaOr	3b5cca2dc8	Update Readme.md	2025-01-29 16:07:42 +02:00
BriaOr	aaf0729f78	Update Readme.md	2025-01-28 17:57:17 +02:00
BriaOr	1a37414fe4	Update Readme.md	2025-01-28 17:51:10 +02:00
BriaOr	2fbbce0d1b	Update Readme.md	2025-01-28 17:47:33 +02:00
BriaOr	23f9e46eff	Update Readme.md	2025-01-28 17:39:26 +02:00
BriaOr	709d16cb72	Merge pull request #9 from Bria-AI/ranges return model id from TG info node	2025-01-21 10:22:52 +02:00
Tair	55133b0910	return model id from TG info node	2025-01-20 16:19:30 +00:00
tairBria	da4c773cb7	Merge pull request #7 from Bria-AI/t2i-comfy * base and hd * image prompt * reimagine	2025-01-20 14:42:27 +02:00
Tair	fc1d1aff05	כ	2025-01-20 12:41:46 +00:00
Tair	44adccf16d	fix	2025-01-20 09:42:52 +00:00
Tair	d465f55b3a	fix	2025-01-20 09:05:27 +00:00
Tair	fcce3cbfb3	clean	2025-01-20 08:57:31 +00:00
Tair	fb1eed93ae	fix	2025-01-20 08:56:07 +00:00
Tair	2990e8f024	fix none clause	2025-01-20 08:54:15 +00:00
Tair	bb9b1d5755	reimagine	2025-01-19 14:23:35 +00:00
Tair	4ac24cbd5a	Merge branch 'main' into t2i-comfy	2025-01-19 11:56:13 +00:00
Tair	7a8276a8e4	image prompt	2025-01-19 11:54:02 +00:00
tairBria	68a83db7a5	Merge pull request #8 from movalex/fix/shot-by-image-get-api-url fix api url handling	2025-01-16 14:01:09 +02:00
Alexey Bogomolov	cdc1e52076	fix api url handling	2025-01-15 22:28:44 +03:00
Tair	449b6ebb84	base and hd	2025-01-13 14:06:25 +00:00
BriaOr	02ead854bf	Update Readme.md	2025-01-09 16:41:28 +02:00
or	eaca630863	updated tailored workflow	2025-01-09 14:48:48 +02:00
BriaOr	c72754d15b	Update Readme.md	2025-01-09 13:51:14 +02:00
or	731b03634a	Added T2I node to documentation	2025-01-09 13:50:31 +02:00
BriaOr	c5193119cf	Update Readme.md	2025-01-09 13:30:31 +02:00
BriaOr	7a97620e78	Merge pull request #6 from Bria-AI/Docs-update Docs update	2025-01-09 13:22:12 +02:00
BriaOr	8cf75db231	Update Readme.md	2025-01-09 11:30:25 +02:00
BriaOr	8c86560f23	Merge pull request #5 from Bria-AI/t2i-comfy T2i comfy	2025-01-09 11:16:15 +02:00
or	3af0cc4341	new doc version updated	2025-01-09 11:02:26 +02:00
Tair	bba67b3767	tailored workflow	2025-01-08 16:32:59 +00:00
or	1d10365fe1	V1 of the documentation	2025-01-08 18:22:02 +02:00
Tair	00c6822881	text to image base	2025-01-08 16:20:12 +00:00
tairBria	beadb83b5d	Merge pull request #4 from Bria-AI/t2i-comfy include_generation_prefix always false	2025-01-08 18:19:29 +02:00
or	f50bf3f2ed	Added more changes	2025-01-08 18:08:37 +02:00
Tair	67f237b37c	include_generation_prefix always false	2025-01-08 15:41:06 +00:00
BriaOr	93971fc014	Merge pull request #3 from Bria-AI/t2i-comfy tailored and some code cleaning	2025-01-08 16:14:11 +02:00
or	e4ea36e135	Added coming soon	2025-01-08 15:33:00 +02:00
or	f42b100e1b	Added modifications to the readme	2025-01-08 15:23:12 +02:00
Tair	9f3bfab023	tailored and some code cleaning	2025-01-08 13:14:17 +00:00
BriaOr	424678c8e8	Merge pull request #2 from Bria-AI/t2i-comfy nodes folder	2025-01-07 15:10:10 +02:00
Tair	e4cf57f129	nodes folder	2025-01-06 16:47:06 +00:00
BriaOr	c45ad0647d	Update Readme.md	2024-12-22 22:31:02 +02:00
or	158838158b	Added product shot generation collaterals	2024-12-22 22:30:22 +02:00
ori-liberman	6c1d953896	Merge branch 'main' of https://github.com/Bria-AI/ComfyUI-BRIA-API into main	2024-12-22 14:21:17 +00:00
ori-liberman	808721a995	Change optimize_description and enhance_ref_image types from BOOLEAN to INT	2024-12-22 14:21:15 +00:00
OriL	7510ccca3a	Update Readme.md	2024-12-22 15:21:03 +02:00
BriaOr	7d3edf6174	Update Readme.md	2024-12-22 11:42:51 +02:00
OriL	396d56eb2a	Add files via upload	2024-12-19 15:19:45 +02:00
OriL	e4a6238935	Update Readme.md	2024-12-19 14:40:40 +02:00
ori-liberman	796013d91a	Bump version to 1.0.2 in pyproject.toml	2024-12-19 08:07:59 +00:00
ori-liberman	0b935dcffc	Add ShotByTextNode and ShotByImageNode classes with API integration	2024-12-18 15:11:57 +00:00
BriaOr	99155fb898	Update Readme.md	2024-12-03 18:28:55 +02:00
or	c266970425	Updated readme and new workflows	2024-12-03 18:26:49 +02:00
or	83a1f760f4	updated genfill node	2024-12-03 18:17:00 +02:00
DvirYBria	7886ec31e2	Update Readme.md	2024-12-03 15:17:16 +02:00
or	cbd980ac92	Working GenFill node	2024-12-03 11:53:37 +02:00
DvirYBria	0ef5288ff6	Update pyproject.toml	2024-12-02 15:40:38 +02:00
DvirYBria	928091f7bb	Update __init__.py	2024-12-02 15:35:48 +02:00
DvirYBria	0a0e7eebaa	Update __init__.py	2024-12-02 15:33:52 +02:00
DvirYBria	6357dfccf0	Update __init__.py	2024-12-02 15:27:38 +02:00
BriaOr	9e6f75a7bf	Merge pull request #1 from Bria-AI/generative_fill_node Update and added GenFill node bria_api_node.py	2024-12-02 09:11:49 +01:00
				`@@ -0,0 +1 @@`
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				`@@ -0,0 +1 @@`
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				`@@ -0,0 +1 @@`
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