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61bc6a3
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Parent(s):
5e64d98
Update app.py
Browse files
app.py
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import gradio as gr
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from
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from diffusers import AutoencoderKL, StableDiffusionXLPipeline
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import torch
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import
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import
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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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### SDXL Turbo ####
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pipe_turbo = StableDiffusionXLPipeline.from_pretrained("stabilityai/sdxl-turbo",
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vae=vae,
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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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)
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return result_lighting
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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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def run_comparison(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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.gradio-container{max-width: 768px !important}
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'''
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with gr.Blocks(css=css) as demo:
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prompt = gr.Textbox(label="Prompt")
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run = gr.Button("Run")
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with gr.Row():
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image_turbo = gr.Image(label="SDXL Turbo")
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image_lightning = gr.Image(label="SDXL Lightning")
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image_hyper = gr.Image("Hyper SDXL")
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run.click(fn=run_comparison, inputs=prompt, outputs=[image_turbo, image_lightning, image_hyper])
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demo.launch()
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import gradio as gr
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from diffusers import StableDiffusionXLPipeline, UNet2DConditionModel, EulerDiscreteScheduler, LCMScheduler
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import torch
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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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### SDXL Turbo ####
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pipe_turbo = StableDiffusionXLPipeline.from_pretrained("stabilityai/sdxl-turbo", torch_dtype=torch.float16, variant="fp16")
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pipe_turbo.to("cuda")
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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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unet = UNet2DConditionModel.from_config(base, subfolder="unet").to("cuda", torch.float16)
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unet.load_state_dict(load_file(hf_hub_download(repo, ckpt), device="cuda"))
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pipe_lightning = StableDiffusionXLPipeline.from_pretrained(base, unet=unet, torch_dtype=torch.float16, variant="fp16").to("cuda")
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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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### Hyper SDXL ###
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repo_name = "ByteDance/Hyper-SD"
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ckpt_name = "Hyper-SDXL-1step-Unet.safetensors"
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unet = UNet2DConditionModel.from_config(base, subfolder="unet").to("cuda", torch.float16)
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unet.load_state_dict(load_file(hf_hub_download(repo_name, ckpt_name), device="cuda"))
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pipe_hyper = StableDiffusionXLPipeline.from_pretrained(base, unet=unet, torch_dtype=torch.float16, variant="fp16").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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def run_comparison(prompt):
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image_turbo=pipe_turbo(prompt=prompt, num_inference_steps=1, guidance_scale=0).images[0]
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image_lightning=pipe_lightning(prompt=prompt, num_inference_steps=1, guidance_scale=0).images[0]
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image_hyper=pipe_hyper(prompt=prompt, num_inference_steps=1, guidance_scale=0, timesteps=[800]).images[0]
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return image_turbo, image_lightning, image_hyper
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with gr.Blocks() as demo:
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prompt = gr.Textbox(label="Prompt")
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run = gr.Button("Run")
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with gr.Row():
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image_turbo = gr.Image(label="SDXL Turbo")
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image_lightning = gr.Image(label="SDXL Lightning")
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image_hyper = gr.Image(label="Hyper SDXL")
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run.click(fn=run_comparison, inputs=prompt, outputs=[image_turbo, image_lightning, image_hyper])
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demo.launch()
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