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README.md CHANGED
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  ---
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- license: cc-by-4.0
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- title: D.R.E.A.M
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- colorFrom: blue
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- colorTo: red
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- sdk: gradio
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- sdk_version: 3.43.2
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- app_file: app.py
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- pinned: true
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- disable_embedding: true
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- inference: true
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  language:
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  - en
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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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-
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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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-
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- ### Model Description
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-
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- <!-- Provide a longer summary of what this model is. -->
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-
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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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-
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- ## Uses
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-
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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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  ---
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+ license: creativeml-openrail-m
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+ tags:
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+ - text-to-image
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+ - stable-diffusion
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+ - art
 
 
 
 
 
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  language:
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  - en
 
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  ---
 
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+ # D.r.e.a.m (Digital Rendering Engine for Artistic Melodies)
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+ ## Welcome to D.r.e.a.m (Digital Rendering Engine for Artistic Melodies).
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+ The model is currently in its training phase. This is not the final version and may contain artifacts, potentially performing poorly in some cases. The goal of this model is to create images similar to those produced by Midjourney. It is being trained using the Midjourney Normalized Dataset available on Kaggle.
 
 
 
 
 
 
 
 
 
 
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  ## Model Details
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+ - **Developed by:** Cyanex1702
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+ - **Model Type:** Diffusion-based text-to-image generation model
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+ - **Language(s):** English
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+ - **Dataset:** [DreamScape](http://https://www.kaggle.com/datasets/cyanex1702/midjouney-normalized-dataset "DreamScape")
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+ - **Training Status:** In Progress
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+
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+ ## Model Description
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+ D.r.e.a.m is a model designed to generate and modify images based on text prompts. The model leverages advanced diffusion techniques to create high-quality, artistic renderings from textual descriptions, aiming to emulate the style and creativity of Midjourney.
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+ ## Samples
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+ ![undefined_image](https://github.com/AUTOMATIC1111/stable-diffusion-webui/assets/117669695/11e26493-7e03-4c26-ad65-d64b8ac5bead)
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+ ![undefined_image (10)](https://github.com/AUTOMATIC1111/stable-diffusion-webui/assets/117669695/aaa54dfe-c0c7-4e85-9d2d-29a4e9d5ac09)
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+ ![undefined_image (8)](https://github.com/AUTOMATIC1111/stable-diffusion-webui/assets/117669695/f68c2348-3947-4e42-8ced-078520906c4d)
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+ ![undefined_image (7)](https://github.com/AUTOMATIC1111/stable-diffusion-webui/assets/117669695/62999dff-ee60-493a-95c9-f9660d554e98)
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+ ![undefined_image (6)](https://github.com/AUTOMATIC1111/stable-diffusion-webui/assets/117669695/a86d66a3-b803-4304-9c77-f58af4e9d607)
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+ ![undefined_image (5)](https://github.com/AUTOMATIC1111/stable-diffusion-webui/assets/117669695/21774bca-9561-4572-9a24-35b4c69cf829)
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+ ![undefined_image (2)](https://github.com/AUTOMATIC1111/stable-diffusion-webui/assets/117669695/a06f51ad-52a3-47cd-91b6-c4d109e39b88)
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+ ![undefined_image (1)](https://github.com/AUTOMATIC1111/stable-diffusion-webui/assets/117669695/b74875af-9af5-408d-b6e3-71d628b38d87)
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+ ![undefined_image (3)](https://github.com/AUTOMATIC1111/stable-diffusion-webui/assets/117669695/9a155bd2-7c5e-48a7-8c70-f63faf51661f)
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+ ![undefined_image (4)](https://github.com/AUTOMATIC1111/stable-diffusion-webui/assets/117669695/7e4a1beb-56fb-4cf4-9c90-14423ed81746)
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+
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+ ## Features
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+ - **Text-to-Image Generation:** Generate images from descriptive text prompts.
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+ - **Image Modification:** Modify existing images based on new text inputs.
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+ - **Creative Rendering:** Produce artistic and imaginative images.
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+
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+ ## Usage
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+ To use the model, you can input text prompts in English. The model will process these prompts and generate corresponding images. Note that due to the model's current training phase, the results may vary and contain imperfections.
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+
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+ ## Contributing
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+ We welcome contributions from the community! If you'd like to contribute.
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model_index (1).json ADDED
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+ {
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+ "_class_name": "StableDiffusionPipeline",
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+ "_diffusers_version": "0.6.0",
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+ "feature_extractor": [
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+ "transformers",
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+ "CLIPImageProcessor"
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+ ],
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+ "safety_checker": [
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+ "stable_diffusion",
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+ "StableDiffusionSafetyChecker"
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+ ],
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+ "scheduler": [
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+ "diffusers",
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+ "PNDMScheduler"
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+ ],
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+ "text_encoder": [
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+ "transformers",
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+ "CLIPTextModel"
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+ ],
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+ "tokenizer": [
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+ "transformers",
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+ "CLIPTokenizer"
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+ ],
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+ "unet": [
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+ "diffusers",
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+ "UNet2DConditionModel"
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+ ],
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+ "vae": [
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+ "diffusers",
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+ "AutoencoderKL"
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+ ]
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+ }