Create README.md

README.md

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+This is a LORA for stable diffusion 1.5, that improves the generation quality at 4 steps. It uses direct backpropogation and HPSV2 reward scoring. 
+
+It can generate decent quality images at only 4 inference steps.
+
+![image/png](https://cdn-uploads.huggingface.co/production/uploads/6424d3394191f56e07324282/fh-m_nfzZzRLKmMnBqFiX.png)
+
+
+Load with LCM https://huggingface.co/latent-consistency/lcm-lora-sdv1-5
+
+Like this:
+
+```
+import torch
+from diffusers import LCMScheduler, AutoPipelineForText2Image
+
+model_id = "Lykon/dreamshaper-7"
+adapter_id = "latent-consistency/lcm-lora-sdv1-5"
+
+pipe = AutoPipelineForText2Image.from_pretrained(model_id, torch_dtype=torch.float16, variant="fp16")
+pipe.scheduler = LCMScheduler.from_config(pipe.scheduler.config)
+pipe.to("cuda")
+
+# load and fuse lcm lora
+pipe.load_lora_weights(adapter_id)
+pipe.fuse_lora()
+
+# load and fuse my lcm-lora-hpsv2 lora
+pipe.load_lora_weights("adams-story/lcm-lora-hpsv2-rl")
+pipe.fuse_lora()
+
+prompt = "Self-portrait oil painting, a beautiful cyborg with golden hair, 8k"
+
+# disable guidance_scale by passing 0
+image = pipe(prompt=prompt, num_inference_steps=4, guidance_scale=0).images[0]
+```