model card

README.md

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+---
+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
+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?
+
+