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Update app.py
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app.py
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import gradio as gr
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from
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import
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from huggingface_hub import hf_hub_download
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from safetensors.torch import load_file
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import gc
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import spaces
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vae = AutoencoderKL.from_pretrained("madebyollin/sdxl-vae-fp16-fix", torch_dtype=torch.float16)
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@@ -15,64 +13,57 @@ pipe_turbo = StableDiffusionXLPipeline.from_pretrained("stabilityai/sdxl-turbo",
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torch_dtype=torch.float16,
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variant="fp16"
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### SDXL Lightning ###
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base = "stabilityai/stable-diffusion-xl-base-1.0"
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repo = "ByteDance/SDXL-Lightning"
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ckpt = "sdxl_lightning_1step_unet_x0.safetensors"
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tokenizer=pipe_turbo.tokenizer,
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tokenizer_2=pipe_turbo.tokenizer_2,
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torch_dtype=torch.float16,
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variant="fp16"
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)#.to("cuda")
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del unet
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gc.collect()
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pipe_lightning.scheduler = EulerDiscreteScheduler.from_config(pipe_lightning.scheduler.config, timestep_spacing="trailing", prediction_type="sample")
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#pipe_lightning.to("cuda")
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text_encoder_2=pipe_turbo.text_encoder_2,
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tokenizer=pipe_turbo.tokenizer,
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tokenizer_2=pipe_turbo.tokenizer_2,
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torch_dtype=torch.float16,
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variant="fp16"
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)#.to("cuda")
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pipe_hyper.scheduler = LCMScheduler.from_config(pipe_hyper.scheduler.config)
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#pipe_hyper.to("cuda")
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del unet
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gc.collect()
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@spaces.GPU
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def
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image_turbo
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image_turbo
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css = '''
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.gradio-container{max-width: 768px !important}
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import gradio as gr
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from gradio_client import Client
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import concurrent.futures
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client_lightning = Client("AP123/SDXL-Lightning")
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client_hyper = Client("ByteDance/Hyper-SDXL-1Step-T2I")
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vae = AutoencoderKL.from_pretrained("madebyollin/sdxl-vae-fp16-fix", torch_dtype=torch.float16)
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torch_dtype=torch.float16,
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variant="fp16"
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)
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pipe_turbo.to("cuda")
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def get_lighting_result(prompt):
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result_lighting = client_lightning.predict(
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prompt, # Your prompt
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"1-Step", # Number of inference steps
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api_name="/generate_image"
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)
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return result_lighting
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def get_hyper_result(prompt):
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result_hyper = client_hyper.predict(
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num_images=1,
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height=1024,
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width=1024,
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prompt=prompt,
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seed=3413,
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api_name="/process_image"
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)
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return result_hyper
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@spaces.GPU
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def get_turbo_result(prompt):
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image_turbo = pipe_turbo(prompt=prompt, num_inference_steps=1, guidance_scale=0).images[0]
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return image_turbo
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def run_in_parallel(prompt):
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with concurrent.futures.ThreadPoolExecutor() as executor:
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# Submit tasks to the executor
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future_lighting = executor.submit(get_lighting_result, prompt)
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future_hyper = executor.submit(get_hyper_result, prompt)
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future_turbo = executor.submit(get_turbo_result, prompt)
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# Wait for all futures to complete
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results = concurrent.futures.wait(
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[future_lighting, future_hyper, future_turbo],
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return_when=concurrent.futures.ALL_COMPLETED
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)
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# Extract results from futures
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result_lighting = future_lighting.result()
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result_hyper = future_hyper.result()
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image_turbo = future_turbo.result()
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print(result_lighting)
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print(result_hyper)
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return image_turbo, result_lighting, result_hyper
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# Example usage
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prompt = "Enter your prompt here"
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image_turbo, result_lighting, result_hyper = run_in_parallel(prompt)
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css = '''
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.gradio-container{max-width: 768px !important}
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