---
datasets:
- raresense/textile_print_to_Img
base_model:
- black-forest-labs/FLUX.1-Fill-dev
library_name: diffusers
---

<!-- Provide a quick summary of what the model is/does. -->

This model aims to help fashion designers showcase their textile prints on diffrent products like bag, cloth, pillow etc. 

### Model Description

<!-- Provide a longer summary of what this model is. -->
- **Developed by:** AI Team at Rare Sense Inc
- **Model type:** Inpainting
- **Finetuned from model :** Flux Fill Dev

### Model Sources

<!-- Provide the basic links for the model. -->

- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]

## Uses

- Turns fashion sketches into realistic images

## Bias, Risks, and Limitations

- Sometimes might produce inaccurate results

[More Information Needed]

## How to Get Started with the Model

Use the code below to get started with the model.

[More Information Needed]

## Training Details

### Training Data

<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->

[More Information Needed]

### Training Procedure

<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->

#### Preprocessing [optional]

[More Information Needed]


#### Training Hyperparameters

- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->

#### Speeds, Sizes, Times [optional]

<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->

[More Information Needed]

## Evaluation

#### Testing Data

<!-- This should link to a Dataset Card if possible. -->

[More Information Needed]

#### Factors

<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->

[More Information Needed]

#### Metrics

<!-- These are the evaluation metrics being used, ideally with a description of why. -->

[More Information Needed]

### Results

![Results](https://media.discordapp.net/attachments/1263412664705613824/1342439669496938598/5cc1098ddacd58093e534da862009495.jpg?ex=67d0b61e&is=67cf649e&hm=6f015410020acea019cc7f71f60cfcdd787c32056e29bbd1baa4cba9aff5d83a&=&format=webp&width=698&height=930)
![Results](https://media.discordapp.net/attachments/1263412664705613824/1342454439499530280/1.jpg?ex=67d0c3df&is=67cf725f&hm=098a016ca2dc6844bc69a1f7d9bea7cb58e2471e34db4c4610ea6341df3eaedb&=&format=webp&width=555&height=740)


![Results](https://media.discordapp.net/attachments/1263412664705613824/1342439740334673963/5ba14c285666b71502d731143101174b.jpg?ex=67d0b62f&is=67cf64af&hm=a8fe541d77a6adcfd29726ee6a065ea99a7ddc9aab785b1ce03885a2f28385db&=&format=webp&width=698&height=930)
![Results](https://media.discordapp.net/attachments/1263412664705613824/1342454440031944794/2.jpg?ex=67d0c3e0&is=67cf7260&hm=bb548911b7bc56a02a92bfe5c92c0c83cc7445c66b34d1a16c0c797258266dca&=&format=webp&width=555&height=740)


![Results](https://media.discordapp.net/attachments/1263412664705613824/1342439825311006760/d92adf65d0e7cd498f17b77f459786e7.jpg?ex=67d0b643&is=67cf64c3&hm=4b5b85d2f0760b4df1e840986e32d324649f0ad87e0094dccc420cd1d07d6644&=&format=webp&width=698&height=930)
![Results](https://media.discordapp.net/attachments/1263412664705613824/1342454438941687818/3.jpg?ex=67d0c3df&is=67cf725f&hm=140a676c3f8777b52f10aa35225bad02547c29eb840c97694d8d5ba10c2ea88d&=&format=webp&width=555&height=740)


![Results](https://media.discordapp.net/attachments/1263412664705613824/1342439916507889755/05eaf94ba74419c349b6d6948352abf6.jpg?ex=67d0b659&is=67cf64d9&hm=c0dc0d0c134b743d539355d628f281b403ccdda03eada5f42203cdaaa21e741f&=&format=webp&width=698&height=930)
![Results](https://media.discordapp.net/attachments/1263412664705613824/1342454439193083988/4.jpg?ex=67d0c3df&is=67cf725f&hm=b06046b27f004b66678f8734417dff03d6f82bccb373b486ddae43fc74183937&=&format=webp&width=555&height=740)
#### Summary



## Model Examination [optional]

<!-- Relevant interpretability work for the model goes here -->

[More Information Needed]

## Environmental Impact

<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->

Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).

- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]

## Technical Specifications [optional]

### Model Architecture and Objective

[More Information Needed]

### Compute Infrastructure

[More Information Needed]

#### Hardware

[More Information Needed]

#### Software

[More Information Needed]

## Citation [optional]

<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->

**BibTeX:**

[More Information Needed]

**APA:**

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## Glossary [optional]

<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->

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## More Information [optional]

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## Model Card Authors [optional]

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## Model Card Contact

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