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@@ -21,10 +21,52 @@ base_model: black-forest-labs/FLUX.1-dev
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  instance_prompt: Smiley C4C
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  license: creativeml-openrail-m
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  ---
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- # Flux-C4C-Design-LoRA
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  <Gallery />
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  ## Trigger words
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  instance_prompt: Smiley C4C
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  license: creativeml-openrail-m
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  ---
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+ ![strangerzonehf/Flux-C4C-Design-LoRA](images/cx.png)
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  <Gallery />
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+ ## Model description for C4C Design LoRA
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+
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+ Image Processing Parameters
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+
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+ | Parameter | Value | Parameter | Value |
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+ |---------------------------|--------|---------------------------|--------|
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+ | LR Scheduler | constant | Noise Offset | 0.03 |
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+ | Optimizer | AdamW | Multires Noise Discount | 0.1 |
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+ | Network Dim | 64 | Multires Noise Iterations | 10 |
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+ | Network Alpha | 32 | Repeat & Steps | 25 & 3100 |
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+ | Epoch | 15 | Save Every N Epochs | 1 |
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+
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+ Labeling: florence2-en(natural language & English)
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+
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+ Total Images Used for Training : 14
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+
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+ ## Best Dimensions & Inference
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+
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+ | **Dimensions** | **Aspect Ratio** | **Recommendation** |
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+ |-----------------|------------------|---------------------------|
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+ | 1280 x 832 | 3:2 | Best |
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+ | 1024 x 1024 | 1:1 | Default |
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+
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+ ### Inference Range
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+
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+ - **Recommended Inference Steps:** 30–35
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+
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+ ## Setting Up
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+ ```python
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+ import torch
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+ from pipelines import DiffusionPipeline
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+
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+ base_model = "black-forest-labs/FLUX.1-dev"
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+ pipe = DiffusionPipeline.from_pretrained(base_model, torch_dtype=torch.bfloat16)
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+
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+ lora_repo = "strangerzonehf/Flux-C4C-Design-LoRA"
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+ trigger_word = "Smiley C4C"
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+ pipe.load_lora_weights(lora_repo)
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+
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+ device = torch.device("cuda")
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+ pipe.to(device)
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+ ```
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  ## Trigger words
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