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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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This is the model card of a 🧨 diffusers model that has been pushed on the Hub. This model card has been automatically generated.
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- **Developed by:** [More Information Needed]
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- **Funded by [optional]:** [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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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 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. -->
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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 Dataset 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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#### 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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#### 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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##
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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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[More Information Needed]
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[
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#### Hardware
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[More Information Needed]
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## Model Card Authors [optional]
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## Model Card Contact
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[
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license: creativeml-openrail-m
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tags:
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- imagepipeline
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- imagepipeline.io
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- text-to-image
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- ultra-realistic
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pinned: false
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pipeline_tag: text-to-image
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---
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## Realities-Edge-XL-ANIME-SDXL
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<img src="https://image.civitai.com/xG1nkqKTMzGDvpLrqFT7WA/bfdb5e7f-8660-4824-8c5c-91dbf7a5fea5/width=450/06296--395029870.jpeg" alt="Generated on Image Pipeline" style="border-radius: 10px;">
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**This checkpoint model is uploaded on [imagepipeline.io](https://imagepipeline.io/)**
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Model details - Extremely easy prompting, just a couple of words and no negative until you see problems, this model is very versatile. Better ANIME in XL is here.⚠️ Recommendations for better anime images:Use anime, anime style, Japanese animation, flat colors, toon, anime style in the beginning/end of prompt to help the model out - it is not necessary all the time!If small faces come out bad, use Adetailer with original pos and neg prompts which will fix it right up! Anime 2D and Anime 2.5D as well as western artstyles.NSFW capabilities Works very well with Loras - both as a base to train on and for rendering, Excels at both types of CLIP prompting. Be it maximalist OpenAI style prompts or minimalist story driven LAION prompts (written in a more natural language without constant commas). Very low risk of burned generations even on higher CFG - recommend 5.5-15 Responds amazingly to hires.fix with just a scaling of 1.0-1.5 and beyond. I like doing it with no scaling at all, but just letting it run through with a sharp upscaler and less steps. If you have the VRAM for it, push the scaling higher, go nuts! Favorite resolution ranges are 768x1344 and 1024x1296. Works good for landscapes on even bigger resolutions. Euler and DPM+ 2M SDE Karras recommended but always test your favorite! All the img2img modes work really well and balancing a low CFG with a higher-than-average Denoising Strength will produce a sharp and clear upscale full of interesting details, using regular SD upscale. I wonder what you can do with Ultimate SD upscale? Likes Clip Skip 1-4. I frequently use 2.
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[![Try this model](https://img.shields.io/badge/try_this_model-image_pipeline-BD9319)](https://imagepipeline.io/models/Realities-Edge-XL-ANIME-SDXL?id=78bc87b2-f8a5-40dc-a36f-0f116401b144/)
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## How to try this model ?
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You can try using it locally or send an API call to test the output quality.
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Get your `API_KEY` from [imagepipeline.io](https://imagepipeline.io/). No payment required.
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Coding in `php` `javascript` `node` etc ? Checkout our documentation
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[![documentation](https://img.shields.io/badge/documentation-image_pipeline-blue)](https://docs.imagepipeline.io/docs/introduction)
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```python
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import requests
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import json
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url = "https://imagepipeline.io/sdxl/text2image/v1/run"
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payload = json.dumps({
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"model_id": "78bc87b2-f8a5-40dc-a36f-0f116401b144",
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"prompt": "ultra realistic close up portrait ((beautiful pale cyberpunk female with heavy black eyeliner)), blue eyes, shaved side haircut, hyper detail, cinematic lighting, magic neon, dark red city, Canon EOS R3, nikon, f/1.4, ISO 200, 1/160s, 8K, RAW, unedited, symmetrical balance, in-frame, 8K",
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"negative_prompt": "painting, extra fingers, mutated hands, poorly drawn hands, poorly drawn face, deformed, ugly, blurry, bad anatomy, bad proportions, extra limbs, cloned face, skinny, glitchy, double torso, extra arms, extra hands, mangled fingers, missing lips, ugly face, distorted face, extra legs, anime",
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"width": "512",
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"height": "512",
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"samples": "1",
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"num_inference_steps": "30",
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"safety_checker": false,
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"guidance_scale": 7.5,
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"multi_lingual": "no",
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"embeddings": "",
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"lora_models": "",
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"lora_weights": ""
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})
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headers = {
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'Content-Type': 'application/json',
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'API-Key': 'your_api_key'
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}
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response = requests.request("POST", url, headers=headers, data=payload)
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print(response.text)
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}
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```
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Get more ready to use `MODELS` like this for `SD 1.5` and `SDXL` :
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[![All models](https://img.shields.io/badge/Get%20All%20Models-image_pipeline-BD9319)](https://imagepipeline.io/models)
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### API Reference
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#### Generate Image
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```http
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https://api.imagepipeline.io/sdxl/text2image/v1
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```
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| Headers | Type | Description |
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|:----------------------| :------- |:-------------------------------------------------------------------------------------------------------------------|
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| `API-Key` | `str` | Get your `API_KEY` from [imagepipeline.io](https://imagepipeline.io/) |
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| `Content-Type` | `str` | application/json - content type of the request body |
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| Parameter | Type | Description |
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| :-------- | :------- | :------------------------- |
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| `model_id` | `str` | Your base model, find available lists in [models page](https://imagepipeline.io/models) or upload your own|
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| `prompt` | `str` | Text Prompt. Check our [Prompt Guide](https://docs.imagepipeline.io/docs/SD-1.5/docs/extras/prompt-guide) for tips |
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| `num_inference_steps` | `int [1-50]` | Noise is removed with each step, resulting in a higher-quality image over time. Ideal value 30-50 (without LCM) |
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| `guidance_scale` | `float [1-20]` | Higher guidance scale prioritizes text prompt relevance but sacrifices image quality. Ideal value 7.5-12.5 |
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| `lora_models` | `str, array` | Pass the model_id(s) of LoRA models that can be found in models page |
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| `lora_weights` | `str, array` | Strength of the LoRA effect |
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---
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license: creativeml-openrail-m
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tags:
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- imagepipeline
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- imagepipeline.io
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- text-to-image
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- ultra-realistic
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pinned: false
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pipeline_tag: text-to-image
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---
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### Feedback
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If you have any feedback, please reach out to us at hello@imagepipeline.io
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#### 🔗 Visit Website
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[![portfolio](https://img.shields.io/badge/image_pipeline-BD9319?style=for-the-badge&logo=gocd&logoColor=white)](https://imagepipeline.io/)
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If you are the original author of this model, please [click here](https://airtable.com/apprTaRnJbDJ8ufOx/shr4g7o9B6fWfOlUR) to add credits
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