---
tags:
- text-to-image
- lora
- diffusers
- template:sd-lora
- flux
- consumer-products
widget:
- text: >
    Item name: alphonso mango milk shake tetra pack with labels
  output:
    url: images/mango.png
- text: >
    Item Name: set of 6 different flavored lays pack
  output:
    url: images/lays.png
- text: >
    Item Name: Bhuja Cracker Mix, 7-ounce Bags, vegan & vegetarian
  output:
    url: images/bhuja_mix.png
- text: >
    Item Name: hotwheels car ratrod packed
  output:
    url: images/hotwheels.png
base_model: black-forest-labs/FLUX.1-dev
license: mit
datasets:
- SoumilB7/consumer-product-50
---

<Gallery />

# 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.


---

## Usage

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

```python
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

```python
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
```

---

## Notes

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

---

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?