Update README.md

README.md

@@ -1,27 +1,54 @@
----
-pipeline_tag: image-to-text
-inference: true
-license: apache-2.0
-language:
-- en
-base_model: black-forest-labs/FLUX.1-dev
-library_name: transformers
-tags:
-- image-to-text
-- image-captioning
-- vison
----
-
-# MaxCushion Image-to-Text Model
-
-This model generates textual descriptions (captions) for input images.
+# 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].
 
 ## Model Details
 
-- Model Name: colt12/maxcushion
-- Task: Image-to-Text
-- Type: Vision Encoder-Decoder
+- **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]
 
 ## Usage
 
-This model can be used with the Hugging Face Inference API. Here's an example of how to use it:
+This model can be used with the `diffusers` library. Here's a basic example:
+
+```python
+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 = 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].