Update README.md

README.md

@@ -1,38 +1,39 @@
----
-license: other
-license_name: flux-1-dev-non-commercial-license
-license_link: https://huggingface.co/black-forest-labs/FLUX.1-dev/blob/main/LICENSE.md
-language:
-- en
-tags:
-- flux
-- diffusers
-- lora
-base_model: "black-forest-labs/FLUX.1-dev"
-pipeline_tag: text-to-image
-instance_prompt: DHANUSH
----
-
-#Flux
-
-Trained on Replicate using:
-
-https://replicate.com/ostris/flux-dev-lora-trainer/train
-
-
-## Trigger words
-You should use `TOK` to trigger the image generation.
-
-
-## Use it with the [🧨 diffusers library](https://github.com/huggingface/diffusers)
-
-```py
-from diffusers import AutoPipelineForText2Image
+import gradio as gr
 import torch
-
-pipeline = AutoPipelineForText2Image.from_pretrained('black-forest-labs/FLUX.1-dev', torch_dtype=torch.float16).to('cuda')
-pipeline.load_lora_weights('codermert/model_malika', weight_name='sarah-lora.safetensors')
-image = pipeline('your prompt').images[0]
-```
-
-For more details, including weighting, merging and fusing LoRAs, check the [documentation on loading LoRAs in diffusers](https://huggingface.co/docs/diffusers/main/en/using-diffusers/loading_adapters)
+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()