Create app.py

app.py

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+from hidiffusion import apply_hidiffusion, remove_hidiffusion
+from diffusers import DiffusionPipeline, DDIMScheduler, AutoencoderKL
+import torch
+import spaces
+
+vae = AutoencoderKL.from_pretrained("stabilityai/sd-vae-ft-mse")
+safety_checker=StableDiffusionSafetyChecker.from_pretrained("CompVis/stable-diffusion-safety-checker"),
+feature_extractor=CLIPFeatureExtractor.from_pretrained("openai/clip-vit-base-patch32")
+pretrain_model = "SG161222/Realistic_Vision_V5.1_noVAE"
+scheduler = DDIMScheduler.from_pretrained(pretrain_model, subfolder="scheduler")
+pipe = DiffusionPipeline.from_pretrained(pretrain_model, scheduler = scheduler, safety_checker=safety_checker, feature_extractor=feature_extractor, vae=vae, torch_dtype=torch.float16).to("cuda")
+
+# # Optional. enable_xformers_memory_efficient_attention can save memory usage and increase inference speed. enable_model_cpu_offload and enable_vae_tiling can save memory usage.
+#pipe.enable_model_cpu_offload()
+#pipe.enable_vae_tiling()
+
+# Apply hidiffusion with a single line of code.
+apply_hidiffusion(pipe)
+
+with gr.Blocks() as demo:
+    prompt = gr.Textbox()
+    negative_prompt = gr.Textbox()
+    btn = gr.Button("Run")
+    output = gr.Image()
+
+    btn.click(fn=run_hidiffusion, inputs=[prompt, negative_prompt], outputs=[output])
+    
+    @spaces.GPU
+    def run_hidiffusion(prompt, negative_prompt):
+        return pipe(prompt, guidance_scale=7.5, height=1024, width=1024, eta=1.0, negative_prompt=negative_prompt).images[0]