Spaces:
Running
on
Zero
Running
on
Zero
Update app.py
Browse files
app.py
CHANGED
@@ -14,8 +14,8 @@ resolutions = ["1024 1024","1280 768","1344 768","768 1344","768 1280" ]
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# Load pipeline
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vae = AutoencoderKL.from_pretrained("madebyollin/sdxl-vae-fp16-fix", torch_dtype=torch.float16)
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unet = UNet2DConditionModel.from_pretrained("briaai/BRIA-2.
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pipe = DiffusionPipeline.from_pretrained("briaai/BRIA-2.
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pipe.scheduler = LCMScheduler.from_config(pipe.scheduler.config)
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pipe.to('cuda')
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del unet
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@@ -24,26 +24,26 @@ del vae
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pipe.force_zeros_for_empty_prompt = False
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print("Optimizing BRIA 2.
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t=time.time()
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pipe.unet = torch.compile(
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)
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with torch.no_grad():
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print(f"Optimizing finished successfully after {time.time()-t} secs")
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@spaces.GPU(enable_queue=True)
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def infer(prompt,seed,resolution):
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@@ -84,16 +84,16 @@ css = """
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"""
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with gr.Blocks(css=css) as demo:
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with gr.Column(elem_id="col-container"):
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gr.Markdown("## BRIA 2.
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gr.HTML('''
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<p style="margin-bottom: 10px; font-size: 94%">
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This is a demo for
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<a href="https://huggingface.co/briaai/BRIA-2.
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This is a fast version of BRIA 2.
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Are you a startup or a student? We encourage you to apply for our
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<a href="https://pages.bria.ai/the-visual-generative-ai-platform-for-builders-startups-plan?_gl=1*cqrl81*_ga*MTIxMDI2NzI5OC4xNjk5NTQ3MDAz*_ga_WRN60H46X4*MTcwOTM5OTMzNC4yNzguMC4xNzA5Mzk5MzM0LjYwLjAuMA..) </a>
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This program are designed to support emerging businesses and academic pursuits with our cutting-edge technology.
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</p>
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# Load pipeline
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vae = AutoencoderKL.from_pretrained("madebyollin/sdxl-vae-fp16-fix", torch_dtype=torch.float16)
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unet = UNet2DConditionModel.from_pretrained("briaai/BRIA-2.3-FAST", torch_dtype=torch.float16)
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pipe = DiffusionPipeline.from_pretrained("briaai/BRIA-2.3-BETA", torch_dtype=torch.float16, unet=unet, vae=vae)
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pipe.scheduler = LCMScheduler.from_config(pipe.scheduler.config)
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pipe.to('cuda')
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del unet
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pipe.force_zeros_for_empty_prompt = False
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# print("Optimizing BRIA 2.3 FAST - this could take a while")
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# t=time.time()
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# pipe.unet = torch.compile(
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# pipe.unet, mode="reduce-overhead", fullgraph=True # 600 secs compilation
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# )
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# with torch.no_grad():
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# outputs = pipe(
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# prompt="an apple",
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# num_inference_steps=8,
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# )
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# # This will avoid future compilations on different shapes
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# unet_compiled = torch._dynamo.run(pipe.unet)
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# unet_compiled.config=pipe.unet.config
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# unet_compiled.add_embedding = Dummy()
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# unet_compiled.add_embedding.linear_1 = Dummy()
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# unet_compiled.add_embedding.linear_1.in_features = pipe.unet.add_embedding.linear_1.in_features
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# pipe.unet = unet_compiled
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# print(f"Optimizing finished successfully after {time.time()-t} secs")
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@spaces.GPU(enable_queue=True)
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def infer(prompt,seed,resolution):
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"""
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with gr.Blocks(css=css) as demo:
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with gr.Column(elem_id="col-container"):
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gr.Markdown("## BRIA 2.3 FAST")
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gr.HTML('''
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<p style="margin-bottom: 10px; font-size: 94%">
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This is a demo for
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<a href="https://huggingface.co/briaai/BRIA-2.3-FAST" target="_blank">BRIA 2.3 FAST </a>.
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This is a fast version of BRIA 2.3 text-to-image model, still trained on licensed data, and so provides full legal liability coverage for copyright and privacy infringement.
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You can also try it for free in our <a href="https://labs.bria.ai/" target="_blank">webapp demo </a>.
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Are you a startup or a student? We encourage you to apply for our
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<a href="https://pages.bria.ai/the-visual-generative-ai-platform-for-builders-startups-plan?_gl=1*cqrl81*_ga*MTIxMDI2NzI5OC4xNjk5NTQ3MDAz*_ga_WRN60H46X4*MTcwOTM5OTMzNC4yNzguMC4xNzA5Mzk5MzM0LjYwLjAuMA..) target="_blank">Startup Plan </a>
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This program are designed to support emerging businesses and academic pursuits with our cutting-edge technology.
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</p>
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