Put your product in a new scene

# Python 3.10+: pip install comfy-sdk==0.4.0
import os

from comfy_sdk import Comfy

api_key = os.environ.get("COMFY_API_KEY", "").strip()
if not api_key: raise RuntimeError("Set COMFY_API_KEY")
os.environ.setdefault("COMFY_BASE_URL", "https://cloud.comfy.org")
client = Comfy(api_key=api_key)
workflow = client.workflows.from_json({
    "1": {
        "class_type": "LoadImage",
        "inputs": {
            "image": "UPLOADED_image1_FILENAME"
        },
        "_meta": {
            "title": "Load Product Image"
        }
    },
    "2": {
        "class_type": "LoadImage",
        "inputs": {
            "image": "UPLOADED_image2_FILENAME"
        },
        "_meta": {
            "title": "Load Background Image"
        }
    },
    "12": {
        "class_type": "PrimitiveStringMultiline",
        "inputs": {
            "value": "Task: Realistic Product Compositing with Material Preservation Goal: Insert the product from Input 2 into Input 1, ensuring placement is logical and lighting interacts accurately with the product's specific material properties.\n\nInput Image(s):\n\nInput 1 (Scene)\nInput 2 (Product): {product_description}\n\nManual Material Breakdown (Critical): To ensure correct light interaction, treat the product as having the following physical properties:\n\nPrimary Material: [e.g., Matte Plastic / Brushed Aluminum / Clear Glass]\n\nReflectivity: [e.g., Low (dull reflections) / High (mirror-like)]\n\nTexture: [e.g., Smooth / Grainy / Porous]\n\nReaction to Light: [e.g., \"Should absorb light with soft highlights, no sharp glares\"]\n\nSecondary Material (if any): [e.g., Gold Foil Label / Glossy Cap]\n\nReaction to Light: [e.g., \"Should catch sharp, bright specular highlights from the environment\"]\n\nProcess:\nScene Analysis: Identify the logical ground plane/surface in Input 1.\n\nPlacement: Scale Input 2 uniformly (maintain aspect ratio) and place it on the surface.\n\nMaterial-Based Relighting:\n\nDo not simply overlay the scene colors onto the product.\nInstead, map the environment light from Input 1 onto the contours of Input 2 based on the material definitions above.\n\nExample: If the material is Matte, only the edge lighting and general ambient tone should change; do not add sharp reflections.\n\nExample: If the material is Glossy, reflect the scene's details on the surface.\n\nShadows: Generate contact shadows that match the hardness/softness of shadows in the rest of the scene.\n\nTechnical Requirements:\nAlbedo Preservation: The intrinsic color of the product (e.g., \"Brand Red\") must not be washed out by the scene's lighting color.\n\nTexture Fidelity: Do not smooth out surface details (grain, bumps) during the blending process.\n\nScaling: No non-uniform stretching.\n\nOutput: A composite where the lighting confirms the materiality of the product (e.g., makes the plastic look like plastic, not metal)."
        },
        "_meta": {
            "title": "Prompt Template"
        }
    },
    "13": {
        "class_type": "RegexReplace",
        "inputs": {
            "case_insensitive": True,
            "count": 0,
            "dotall": False,
            "multiline": False,
            "regex_pattern": "{product_description}",
            "replace": [
                "14",
                0
            ],
            "string": [
                "12",
                0
            ]
        },
        "_meta": {
            "title": "Replace Text (Regex)"
        }
    },
    "14": {
        "class_type": "PrimitiveStringMultiline",
        "inputs": {
            "value": "A dispenser bottle filled with viscuous clear liquid, with a metallic top, \"C\" logo and a clear glass body"
        },
        "_meta": {
            "title": "Describe the Product"
        }
    },
    "22": {
        "class_type": "ByteDanceSeedreamNode",
        "inputs": {
            "fail_on_partial": True,
            "height": 2048,
            "image": [
                "15:15",
                0
            ],
            "max_images": 1,
            "model": "seedream-4-5-251128",
            "prompt": [
                "13",
                0
            ],
            "seed": 406946789,
            "sequential_image_generation": "disabled",
            "size_preset": "2048x2048 (1:1)",
            "watermark": False,
            "width": 2048
        },
        "_meta": {
            "title": "ByteDance Seedream 4.5 & 5.0"
        }
    },
    "23": {
        "class_type": "SaveImage",
        "inputs": {
            "filename_prefix": "ComfyUI",
            "images": [
                "22",
                0
            ]
        },
        "_meta": {
            "title": "Save Image"
        }
    },
    "15:15": {
        "class_type": "BatchImagesNode",
        "inputs": {
            "images.image0": [
                "2",
                0
            ],
            "images.image1": [
                "15:6",
                0
            ]
        },
        "_meta": {
            "title": "Batch Images"
        }
    },
    "15:5": {
        "class_type": "GetImageSize",
        "inputs": {
            "image": [
                "2",
                0
            ]
        },
        "_meta": {
            "title": "Get Image Size"
        }
    },
    "15:6": {
        "class_type": "ResizeAndPadImage",
        "inputs": {
            "image": [
                "1",
                0
            ],
            "interpolation": "area",
            "padding_color": "white",
            "target_height": [
                "15:5",
                1
            ],
            "target_width": [
                "15:5",
                0
            ]
        },
        "_meta": {
            "title": "Resize And Pad Image"
        }
    }
})
workflow.set_input("1", "image", client.assets.from_url("https://cdn.jsdelivr.net/gh/Comfy-Org/workflow_templates@90c71fb78b3726392d010ff62a8e79e92d7296ad/input/comfy_shampoo.png"))
workflow.set_input("2", "image", client.assets.from_url("https://cdn.jsdelivr.net/gh/Comfy-Org/workflow_templates@90c71fb78b3726392d010ff62a8e79e92d7296ad/input/flower_sea.png"))

# run() also passes api_key to the partner nodes in this workflow.
job = client.run(workflow, api_key=api_key)
for output in job.get_outputs("23"):
    output.to_file(output.name)