init

app.py

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+import gradio as gr
+import os
+
+import torch
+from diffusers import AutoPipelineForText2Image, LCMScheduler
+from torchvision.transforms.functional import to_pil_image, center_crop, resize, to_tensor
+
+device = 'cuda' if torch.cuda.is_available() else 'mps' if torch.backends.mps.is_available() else 'cpu'
+
+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(device)
+
+# load and fuse lcm lora
+pipe.load_lora_weights(adapter_id)
+pipe.fuse_lora()
+
+prompt = "Self-portrait oil painting, a beautiful cyborg with golden hair, 8k"
+
+
+@torch.no_grad()
+def generate(prompt, guidance_scale):
+    image = pipe(prompt=prompt, num_inference_steps=4, guidance_scale=guidance_scale).images[0]
+    return image
+
+
+def app():
+    return gr.Interface(generate,
+                        [gr.Textbox(
+                            label="Prompt",
+                            info="Enter your prompt",
+                            lines=3,
+                            value="Self-portrait oil painting, a beautiful cyborg with golden hair, 8k",
+                        ),
+                            gr.Slider(2, 20, value=7.5, label="Guidance Scale",
+                                      info="Higher scale depicts more creativity")],
+                        gr.Image(type="pil",
+                                 height=512,
+                                 width=512
+                                 )
+                        , allow_flagging='never', title='Gen Image')
+
+
+if __name__ == "__main__":
+    app().launch()

requirements.txt

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+torch
+torchvision
+transformers
+diffusers
+pillow
+accelerate