SoumilB7/FLUX.1-dev-ProductStudio

Prompt

Item name: alphonso mango milk shake tetra pack with labels

Prompt

Item Name: set of 6 different flavored lays pack

Prompt

Item Name: Bhuja Cracker Mix, 7-ounce Bags, vegan & vegetarian

Prompt

Item Name: hotwheels car ratrod packed

LoRA — Consumer Product Photography (FLUX)

Hello guys I fine-tuned FLUX.1-dev LoRA to generate high-quality consumer product photography.

Designed for:

• Product ideation
• Packaging & branding mocks
• CPG & D2C marketing visuals
• Studio-style commercial lighting
• Sharp labels, accurate materials, clean backgrounds

Optimized for bottles, cans, tetra packs, cosmetics, beverages, food products. Purpose-built for product shoots & concept ideation, not general art.

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Usage

Load & fuse LoRA into FLUX.1-dev (4-bit NF4)

from diffusers import FluxPipeline, AutoPipelineForText2Image, FluxTransformer2DModel, BitsAndBytesConfig
from transformers import T5EncoderModel
from transformers import BitsAndBytesConfig as TransformersBitsAndBytesConfig
import torch
import gc

ckpt_id = "black-forest-labs/FLUX.1-dev"
lora_path = "SoumilB7/consumer-product-flux"
fused_transformer_path = "fused_transformer"

bnb_4bit_compute_dtype = torch.float16

nf4_config = BitsAndBytesConfig(
    load_in_4bit=True,
    bnb_4bit_quant_type="nf4",
    bnb_4bit_compute_dtype=bnb_4bit_compute_dtype,
)

transformer = FluxTransformer2DModel.from_pretrained(
    ckpt_id, subfolder="transformer",
    quantization_config=nf4_config, torch_dtype=torch.float16
)

quant_config = TransformersBitsAndBytesConfig(load_in_4bit=True, bnb_4bit_quant_type="nf4", bnb_4bit_compute_dtype=torch.float16)

text_encoder = T5EncoderModel.from_pretrained(
    ckpt_id, subfolder="text_encoder_2", quantization_config=quant_config, torch_dtype=torch.float16,
)

pipeline = FluxPipeline.from_pretrained(
    ckpt_id,
    transformer=transformer,
    text_encoder_2=text_encoder,
    torch_dtype=bnb_4bit_compute_dtype,
)

pipeline.load_lora_weights(lora_path)
pipeline.fuse_lora()
pipeline.unload_lora_weights()

del text_encoder
del transformer
gc.collect()
torch.cuda.empty_cache()

pipeline.to("cuda")

Generate image

prompt = "alphonso mango milkshake tetra pack with label, studio softbox lighting, clean background"
image = pipeline(
    prompt,
    num_inference_steps=28,
    guidance_scale=3.5,
    height=768,
    width=512,
    generator=torch.manual_seed(0)
).images[0]

print(f"Pipeline memory usage: {torch.cuda.max_memory_reserved() / 1024**3:.3f} GB")
image.save("product_example.png")
image

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Notes

• Best for studio product shots, minimal environments
• Works extremely well with simple, commercial descriptors
• Ideal for brands, founders, designers, packaging artists

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Would you like:

1. a “Prompt Guide” section like Flux Realism models?
2. a Before → After grid for the dataset vs model output?
3. a Colab notebook link block?