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1---
2license: apache-2.0
3model_name: Sharmin BD Girl
4tags:
5 - lora
6 - flux-dev
7 - image-generation
8 - fine-tuning
9 - safetensors
10datasets: []
11language: []
12metrics: []
13library_name: diffusers
14pipeline_tag: text-to-image
15---
16
17model_card:
18 model_id: Sharmin BD Girl
19 description: |
20 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.
21
22 model_details:
23 developed_by: Sharmin BD Girl
24 funded_by: [More Information Needed]
25 shared_by: Sharmin BD Girl
26 model_type: LoRA (Low-Rank Adaptation) for fine-tuning
27 languages: Not applicable
28 license: Apache-2.0
29 finetuned_from: Flux Dev
30 version: 1.0
31 date: 2025-06-15
32
33 model_sources:
34 repository: [More Information Needed]
35 paper: None
36 demo: [More Information Needed]
37
38 uses:
39 direct_use: |
40 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.
41 downstream_use: |
42 The model can be further fine-tuned or integrated into larger applications, such as art generation tools, design software, or creative platforms.
43 out_of_scope_use: |
44 - Generating harmful, offensive, or misleading content.
45 - Real-time applications without optimized hardware due to potential latency.
46 - Tasks outside the scope of the Flux Dev base model’s capabilities, such as text generation.
47
48 bias_risks_limitations:
49 bias: |
50 The model may inherit biases from the Flux Dev base model or the fine-tuning dataset, potentially affecting output fairness or quality.
51 risks: |
52 Improper use could lead to generating inappropriate content. Users must validate outputs for sensitive applications.
53 limitations: |
54 - Performance depends on prompt quality and relevance.
55 - High computational requirements for inference (recommended: 8GB+ VRAM).
56 - Limited testing in edge cases or specific domains.
57 recommendations: |
58 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.
59
60 how_to_get_started:
61 code: |
62 ```python
63 from diffusers import DiffusionPipeline
64 import torch
65
66 # Load base model
67 base_model = DiffusionPipeline.from_pretrained("flux-dev")
68
69 # Load LoRA weights
70 base_model.load_lora_weights("path/to/jhilik_mullick.safetensors")
71
72 # Move to GPU if available
73 device = "cuda" if torch.cuda.is_available() else "cpu"
74 base_model.to(device)
75
76 # Example inference
77 output = base_model("your prompt here").images[0]
78 output.save("output.png")