Create app.py

app.py

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+import gradio as gr
+import torch
+from diffusers import LCMScheduler, AutoPipelineForText2Image
+
+# model_id = "Lykon/dreamshaper-7"
+# prompt = "Self-portrait oil painting, a beautiful cyborg with golden hair, 8k"
+
+def run(checkpoint, prompt):
+    model_id = checkpoint
+    adapter_id = "latent-consistency/lcm-lora-sdv1-5"
+
+    pipe = AutoPipelineForText2Image.from_pretrained(model_id, torch_dtype=torch.float32, safety_checker=None).to("cpu")
+    pipe.scheduler = LCMScheduler.from_config(pipe.scheduler.config)
+
+    # load and fuse lcm lora
+    pipe.load_lora_weights(adapter_id)
+    pipe.fuse_lora()
+
+    # disable guidance_scale by passing 0
+    image = pipe(prompt=prompt, num_inference_steps=4, guidance_scale=0, width=512, height=512).images[0]
+    return image
+
+with gr.Blocks() as demo:
+    input_checkpoint = gr.Text(value="Lykon/dreamshaper-7", label="Checkpoint")
+    input_prompt = gr.Text(value="Self-portrait oil painting, a beautiful cyborg with golden hair, 8k", label="Prompt")
+    out = gr.Image(type="pil")
+    btn = gr.Button("Run")
+    btn.click(fn=run, inputs=[input_checkpoint, input_prompt], outputs=out)
+
+demo.launch()