Update README.md

README.md

@@ -1,25 +1,26 @@
 ---
 language: en
 tags:
-  - text-generation
-  - transformers
-license: apache-2.0
+  - stable-diffusion-xl
+  - text-to-image
+  - diffusers
+license: creativeml-openrail-m
 datasets:
-  - wikitext
+  - your-dataset-name
 metrics:
-  - perplexity
----  
+  - your-metric
+---
 
 # MaxCushion - SDXL Fine-tuned Model
 
-This is a fine-tuned Stable Diffusion XL (SDXL) model based on [FLUX.1-dev](https://huggingface.co/black-forest-labs/FLUX.1-dev). It's designed to generate high-quality images with a focus on [specific theme or style your model specializes in].
+This is a fine-tuned Stable Diffusion XL (SDXL) model based on [FLUX.1-dev](https://huggingface.co/black-forest-labs/FLUX.1-dev). It's designed to generate high-quality images with a focus on **[specific theme or style your model specializes in]**.
 
 ## Model Details
 
 - **Base Model:** [FLUX.1-dev](https://huggingface.co/black-forest-labs/FLUX.1-dev)
 - **Type:** Stable Diffusion XL (SDXL)
 - **Language(s):** English
-- **License:** [Your chosen license, e.g., CreativeML Open RAIL-M]
+- **License:** [CreativeML Open RAIL-M](https://huggingface.co/spaces/CompVis/stable-diffusion-license)
 
 ## Usage
 
@@ -30,37 +31,11 @@ from diffusers import StableDiffusionXLPipeline
 import torch
 
 model_id = "colt12/maxcushion"
-pipe = StableDiffusionXLPipeline.from_pretrained(model_id, torch_dtype=torch.float16, use_safetensors=True, variant="fp16")
+pipe = StableDiffusionXLPipeline.from_pretrained(
+    model_id, torch_dtype=torch.float16, use_safetensors=True, variant="fp16"
+)
 pipe = pipe.to("cuda")
 
 prompt = "Your prompt here"
 image = pipe(prompt).images[0]
-image.save("generated_image.png")
-```
-
-## Parameters
-
-- `prompt`: The text prompt to generate an image from.
-- `negative_prompt` (optional): Text prompt that the model should not use for image generation.
-- `num_inference_steps` (default: 30): Number of denoising steps.
-- `guidance_scale` (default: 7.5): How closely the model should follow the prompt.
-
-## Examples
-
-[Include a few example prompts and their resulting images here]
-
-## Fine-tuning Details
-
-This model was fine-tuned on [describe your dataset] using [describe your training process, e.g., tools, number of steps, learning rate, etc.].
-
-## Limitations
-
-[Describe any known limitations or biases of your model]
-
-## Ethical Considerations
-
-[Include any ethical considerations or guidelines for using your model]
-
-## Contact
-
-For questions or feedback, please [provide contact information or link to issues page].
+image.save("generated_image.png")