Update README.md

README.md

@@ -1,39 +0,0 @@
-import gradio as gr
-import torch
-from diffusers import StableDiffusionPipeline, DPMSolverMultistepScheduler
-from safetensors.torch import load_file
-
-model_id = "runwayml/stable-diffusion-v1-5"
-lora_path = "https://huggingface.co/codermert/model_malika/resolve/main/sarah-lora.safetensors"
-
-pipe = StableDiffusionPipeline.from_pretrained(model_id, torch_dtype=torch.float16)
-pipe.scheduler = DPMSolverMultistepScheduler.from_config(pipe.scheduler.config)
-pipe = pipe.to("cuda")
-
-# LoRA dosyasını yükle
-state_dict = load_file(lora_path)
-pipe.unet.load_attn_procs(state_dict)
-
-def generate_image(prompt, negative_prompt, guidance_scale, num_inference_steps):
-    image = pipe(
-        prompt=prompt,
-        negative_prompt=negative_prompt,
-        guidance_scale=guidance_scale,
-        num_inference_steps=num_inference_steps
-    ).images[0]
-    return image
-
-iface = gr.Interface(
-    fn=generate_image,
-    inputs=[
-        gr.Textbox(label="Prompt"),
-        gr.Textbox(label="Negative Prompt"),
-        gr.Slider(minimum=1, maximum=20, step=0.5, label="Guidance Scale", value=7.5),
-        gr.Slider(minimum=1, maximum=100, step=1, label="Number of Inference Steps", value=50)
-    ],
-    outputs=gr.Image(label="Generated Image"),
-    title="Stable Diffusion with LoRA",
-    description="Generate images using Stable Diffusion v1.5 with a custom LoRA model."
-)
-
-iface.launch()