Update README.md

README.md

@@ -9,13 +9,11 @@ tags:
 - lora
 inference: true
 base_model: stabilityai/stable-diffusion-xl-base-1.0
+datasets:
+- 0x7o/RussianVibe-data
 ---
 
-<!-- This model card has been generated automatically according to the information the training script had access to. You
-should probably proofread and complete it, then remove this comment. -->
-
-
-# LoRA text2image fine-tuning - 0x7o/RussianVibe-XL-v2.0
+# RussianVibe XL v2.0
 
 These are LoRA adaption weights for stabilityai/stable-diffusion-xl-base-1.0. The weights were fine-tuned on the 0x7o/RussianVibe-data dataset. You can find some example images in the following. 
 
@@ -35,13 +33,13 @@ Special VAE used for training: madebyollin/sdxl-vae-fp16-fix.
 #### How to use
 
 ```python
-# TODO: add an example code snippet for running this diffusion pipeline
-```
-
-#### Limitations and bias
-
-[TODO: provide examples of latent issues and potential remediations]
-
-## Training details
-
-[TODO: describe the data used to train the model]
+from diffusers import DiffusionPipeline, DPMSolverMultistepScheduler
+import torch
+pipe = DiffusionPipeline.from_pretrained("stabilityai/stable-diffusion-xl-base-1.0", torch_dtype=torch.float16)
+pipe.load_lora_weights("0x7o/RussianVibe-XL-v2.0")
+pipe.to("cuda")
+pipe.scheduler = DPMSolverMultistepScheduler.from_config(pipe.scheduler.config)
+prompt = "The sun is setting through a window, casting a warm glow on the cityscape beyond. The sun casts a warm orange glow on the buildings in the distance, creating a beautiful and serene atmosphere."
+image = pipe(prompt, num_inference_steps=30, guidance_scale=5.0, negative_prompt="bad quality, painting, art").images[0]
+image.save("output.png")
+```