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--- |
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license: apache-2.0 |
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language: |
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- en |
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base_model: |
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- black-forest-labs/FLUX.1-dev |
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- black-forest-labs/FLUX.1-schnell |
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tags: |
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- sexy |
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- curvy |
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--- |
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```yaml |
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--- |
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license: apache-2.0 |
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model_name: Sharmin BD Girl |
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tags: |
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- lora |
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- flux-dev |
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- image-generation |
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- fine-tuning |
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- safetensors |
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datasets: [] |
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language: [] |
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metrics: [] |
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library_name: diffusers |
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pipeline_tag: text-to-image |
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--- |
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model_card: |
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model_id: Sharmin BD Girl |
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description: | |
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Sharmin BD Girl is a LoRA (Low-Rank Adaptation) model fine-tuned on the Flux Dev base model, designed for text-to-image generation. It is stored in the `.safetensors` format for efficient and secure weight storage. |
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model_details: |
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developed_by: Sharmin BD Girl |
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funded_by: [More Information Needed] |
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shared_by: Sharmin BD Girl |
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model_type: LoRA (Low-Rank Adaptation) for fine-tuning |
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languages: Not applicable |
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license: Apache-2.0 |
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finetuned_from: Flux Dev |
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version: 1.0 |
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date: 2025-06-15 |
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model_sources: |
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repository: [More Information Needed] |
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paper: None |
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demo: [More Information Needed] |
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uses: |
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direct_use: | |
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The model can be used directly for generating images from text prompts using the Flux Dev pipeline with the LoRA weights applied. Suitable for creative applications, research, or prototyping. |
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downstream_use: | |
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The model can be further fine-tuned or integrated into larger applications, such as art generation tools, design software, or creative platforms. |
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out_of_scope_use: | |
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- Generating harmful, offensive, or misleading content. |
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- Real-time applications without optimized hardware due to potential latency. |
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- Tasks outside the scope of the Flux Dev base model’s capabilities, such as text generation. |
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bias_risks_limitations: |
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bias: | |
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The model may inherit biases from the Flux Dev base model or the fine-tuning dataset, potentially affecting output fairness or quality. |
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risks: | |
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Improper use could lead to generating inappropriate content. Users must validate outputs for sensitive applications. |
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limitations: | |
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- Performance depends on prompt quality and relevance. |
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- High computational requirements for inference (recommended: 8GB+ VRAM). |
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- Limited testing in edge cases or specific domains. |
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recommendations: | |
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Users should evaluate outputs for biases and appropriateness. For sensitive applications, implement additional filtering or validation. More information is needed to provide specific mitigation strategies. |
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how_to_get_started: |
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code: | |
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```python |
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from diffusers import DiffusionPipeline |
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import torch |
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# Load base model |
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base_model = DiffusionPipeline.from_pretrained("flux-dev") |
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# Load LoRA weights |
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base_model.load_lora_weights("path/to/jhilik_mullick.safetensors") |
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# Move to GPU if available |
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device = "cuda" if torch.cuda.is_available() else "cpu" |
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base_model.to(device) |
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# Example inference |
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output = base_model("your prompt here").images[0] |
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output.save("output.png") |
