Update README.md

README.md

@@ -1,3 +1,90 @@
----
-license: apache-2.0
----
+---
+license: apache-2.0
+language:
+- en
+base_model:
+- black-forest-labs/FLUX.1-dev
+- black-forest-labs/FLUX.1-schnell
+tags:
+- sexy
+- curvy
+---
+```yaml
+---
+license: apache-2.0
+model_name: Sharmin BD Gir
+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")