--- tags: - text-to-image - lora - diffusers - template:diffusion-lora widget: - text: 'frosted GC, a pristine white jar, adorned with a silver lid, is adorned with the words "Spirulina" in bold black lettering. The jar is set against a light blue backdrop, creating a stark contrast to the jars contents. A barcode is visible on the jar, adding a pop of color to the composition.' output: url: images/FG1.png - text: 'frosted GC, A medium shot of a white jar with a yellow lid. The jar has the words "Yellow" and "Fish Oil Bottle Mockup" written on it in black letters. There is a white background behind the jar.' output: url: images/FG2.png - text: 'frosted GC, a small, gray bottle with a black cap is seen against a stark white backdrop. The bottles label, "Cellular Nutrition mitopure" is prominently displayed in the center of the frame, with the words "Time-line" written in white font on the right side of the bottle. The label also includes the number "60" and "50" in the bottom left corner.' output: url: images/FG3.png base_model: black-forest-labs/FLUX.1-dev instance_prompt: frosted GC license: creativeml-openrail-m --- # Flux.1-Dev-Frosted-Container-LoRA **The model is still in the training phase. This is not the final version and may contain artifacts and perform poorly in some cases.** ## Model description **prithivMLmods/Flux.1-Dev-Frosted-Container-LoRA** Image Processing Parameters | Parameter | Value | Parameter | Value | |---------------------------|--------|---------------------------|--------| | LR Scheduler | constant | Noise Offset | 0.03 | | Optimizer | AdamW | Multires Noise Discount | 0.1 | | Network Dim | 64 | Multires Noise Iterations | 10 | | Network Alpha | 32 | Repeat & Steps | 14 & 2200 | | Epoch | 10 | Save Every N Epochs | 1 | Labeling: florence2-en(natural language & English) Total Images Used for Training : 16 ## Best Dimensions - 768 x 1024 (Best) - 1024 x 1024 (Default) ## Setting Up ```python import torch from pipelines import DiffusionPipeline base_model = "black-forest-labs/FLUX.1-dev" pipe = DiffusionPipeline.from_pretrained(base_model, torch_dtype=torch.bfloat16) lora_repo = "prithivMLmods/Flux.1-Dev-Frosted-Container-LoRA" trigger_word = "frosted GC" pipe.load_lora_weights(lora_repo) device = torch.device("cuda") pipe.to(device) ``` # Other Sample Image ![sample](images/FG4.png) ## Trigger words You should use `frosted GC` to trigger the image generation. ## Download model Weights for this model are available in Safetensors format. [Download](/prithivMLmods/Flux.1-Dev-Frosted-Container-LoRA/tree/main) them in the Files & versions tab.