rstudioModel/jhilik_mallick

---
license: apache-2.0
model_name: Jhilik Mullick
tags:
  - lora
  - flux-dev
  - image-generation
  - fine-tuning
  - safetensors
datasets: []
language: []
metrics: []
library_name: diffusers
pipeline_tag: text-to-image
---

model_card:
  model_id: Jhilik Mullick
  description: |
    Jhilik Mullick 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.

  model_details:
    developed_by: Jhilik Mullick
    funded_by: [More Information Needed]
    shared_by: Jhilik Mullick
    model_type: LoRA (Low-Rank Adaptation) for fine-tuning
    languages: Not applicable
    license: Apache-2.0
    finetuned_from: Flux Dev
    version: 1.0
    date: 2025-06-15

  model_sources:
    repository: [More Information Needed]
    paper: None
    demo: [More Information Needed]

  uses:
    direct_use: |
      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.
    downstream_use: |
      The model can be further fine-tuned or integrated into larger applications, such as art generation tools, design software, or creative platforms.
    out_of_scope_use: |
      - Generating harmful, offensive, or misleading content.
      - Real-time applications without optimized hardware due to potential latency.
      - Tasks outside the scope of the Flux Dev base model’s capabilities, such as text generation.

  bias_risks_limitations:
    bias: |
      The model may inherit biases from the Flux Dev base model or the fine-tuning dataset, potentially affecting output fairness or quality.
    risks: |
      Improper use could lead to generating inappropriate content. Users must validate outputs for sensitive applications.
    limitations: |
      - Performance depends on prompt quality and relevance.
      - High computational requirements for inference (recommended: 8GB+ VRAM).
      - Limited testing in edge cases or specific domains.
    recommendations: |
      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.

  how_to_get_started:
    code: |
      ```python
      from diffusers import DiffusionPipeline
      import torch

      # Load base model
      base_model = DiffusionPipeline.from_pretrained("flux-dev")

      # Load LoRA weights
      base_model.load_lora_weights("path/to/jhilik_mullick.safetensors")

      # Move to GPU if available
      device = "cuda" if torch.cuda.is_available() else "cpu"
      base_model.to(device)

      # Example inference
      output = base_model("your prompt here").images[0]
      output.save("output.png")