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  - en
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  library_name: diffusers
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  ---
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- # Model Card for Model ID
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  <!-- Provide a quick summary of what the model is/does. -->
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- This modelcard aims to be a base template for new models. It has been generated using [this raw template](https://github.com/huggingface/huggingface_hub/blob/main/src/huggingface_hub/templates/modelcard_template.md?plain=1).
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- ## Model Details
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- ### Model Description
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- <!-- Provide a longer summary of what this model is. -->
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- - **Developed by:** [More Information Needed]
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- - **Shared by [optional]:** [More Information Needed]
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- - **Model type:** [More Information Needed]
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- - **Language(s) (NLP):** [More Information Needed]
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- - **License:** [More Information Needed]
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- - **Finetuned from model [optional]:** [More Information Needed]
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- ### Model Sources [optional]
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- <!-- Provide the basic links for the model. -->
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- - **Repository:** [More Information Needed]
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- - **Paper [optional]:** [More Information Needed]
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- - **Demo [optional]:** [More Information Needed]
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  ## Uses
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- <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
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- ### Direct Use
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- <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
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- [More Information Needed]
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- ### Downstream Use [optional]
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- <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
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- [More Information Needed]
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- ### Out-of-Scope Use
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- <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
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- [More Information Needed]
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- ## Bias, Risks, and Limitations
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- <!-- This section is meant to convey both technical and sociotechnical limitations. -->
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- [More Information Needed]
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- ### Recommendations
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- <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
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- Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
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- ## How to Get Started with the Model
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- Use the code below to get started with the model.
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- [More Information Needed]
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- ## Training Details
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- ### Training Data
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- <!-- This should link to a Data 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. -->
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- [More Information Needed]
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- ### Training Procedure
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- <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
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- #### Preprocessing [optional]
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- [More Information Needed]
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- #### Training Hyperparameters
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- - **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
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- #### Speeds, Sizes, Times [optional]
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- <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
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- [More Information Needed]
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- ## Evaluation
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- <!-- This section describes the evaluation protocols and provides the results. -->
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- ### Testing Data, Factors & Metrics
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- #### Testing Data
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- <!-- This should link to a Data Card if possible. -->
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- [More Information Needed]
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- #### Factors
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- <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
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- [More Information Needed]
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- #### Metrics
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- <!-- These are the evaluation metrics being used, ideally with a description of why. -->
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- [More Information Needed]
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- ### Results
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- #### Summary
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- ## Model Examination [optional]
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- <!-- Relevant interpretability work for the model goes here -->
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- [More Information Needed]
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- ## Environmental Impact
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- <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
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- 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).
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- - **Hardware Type:** [More Information Needed]
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- - **Hours used:** [More Information Needed]
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- - **Cloud Provider:** [More Information Needed]
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- - **Compute Region:** [More Information Needed]
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- - **Carbon Emitted:** [More Information Needed]
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- ## Technical Specifications [optional]
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- ### Model Architecture and Objective
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- ### Compute Infrastructure
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- #### Hardware
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- #### Software
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- ## Citation [optional]
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- <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
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- **BibTeX:**
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- **APA:**
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- ## Glossary [optional]
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- <!-- 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 Needed]
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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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  library_name: diffusers
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  ---
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+ # D.R.E.A.M(Image generation model)
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  <!-- Provide a quick summary of what the model is/does. -->
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+ Title: D.R.E.A.M - Stable Diffusion Model Summary
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+ D.R.E.A.M (Diffusion-based Rendering for Efficient AI Models) is a fine-tuned diffusion model designed for versatile image generation. This model consists of three variants tailored to different image generation tasks, making it a powerful tool for a wide range of applications.
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+ 1. **Base Model**: The base model in D.R.E.A.M serves as a reliable workhorse, capable of generating high-quality images across various domains. It excels in tasks that demand general image generation and serves as an excellent starting point for various creative projects.
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+ 2. **Photorealistic Model**: For those seeking to create photorealistic images, D.R.E.A.M offers a specialized model that is finely tuned to produce stunningly realistic visual content. This variant is perfect for applications where realism and detail are paramount.
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+ 3. **Anime-Style Model**: The third variant of D.R.E.A.M is tailored specifically for generating anime-style images. While still a work in progress, this model shows promise in producing anime-themed content and is continually being improved for efficiency and quality.
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+ D.R.E.A.M is an evolving project, with regular updates aimed at enhancing its performance and effectiveness. Users can expect ongoing refinements to ensure that D.R.E.A.M remains a cutting-edge tool for image generation across diverse genres and styles. Stay tuned for updates and improvements as we continue to make D.R.E.A.M even better.
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+ ## Model Details
 
 
 
 
 
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+ ### Model Description
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+ <!-- Provide a longer summary of what this model is. -->
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+ - **Developed by:** cyanex1702
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+ - **Model type:** Diffusion model
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+ - **Finetuned from model :** Stable diffusion
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  ## Uses
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+ **Artistic Image Generation**: D.R.E.A.M can be used by artists and designers to create visually stunning and unique artworks, leveraging its base model for a wide range of creative image generation.
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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+ **Photorealistic Rendering**: The photorealistic model is ideal for rendering scenes and objects with exceptional realism. It can be applied in architectural visualization, product design, and other fields requiring lifelike images.
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+ **Anime and Manga Creation**: The anime-style variant of D.R.E.A.M is well-suited for generating characters, scenes, and artwork in the popular anime and manga style, making it a valuable tool for illustrators and fans of anime culture.
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+ **Content Generation for Media**: Content creators and media professionals can utilize D.R.E.A.M to generate high-quality visuals for websites, marketing materials, social media, and more, enhancing the visual appeal of their content.
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