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app.py

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+import gradio as gr
+import os
+hf_token = os.environ.get("HF_TOKEN")
+import spaces
+from diffusers import DiffusionPipeline, UNet2DConditionModel, LCMScheduler, AutoencoderKL
+import torch
+import time
+
+class Dummy():
+    pass
+
+resolutions = ["1536 1536","1728 1280","1856 1280","1920-1088", "1088 1920","1280 1856","1280 1728" ] 
+
+# Load pipeline 
+
+vae = AutoencoderKL.from_pretrained("madebyollin/sdxl-vae-fp16-fix", torch_dtype=torch.float16)
+unet = UNet2DConditionModel.from_pretrained("briaai/BRIA-2.2-HR", torch_dtype=torch.float16)
+pipe = DiffusionPipeline.from_pretrained("briaai/BRIA-2.2", torch_dtype=torch.float16, unet=unet, vae=vae)
+pipe.scheduler = LCMScheduler.from_config(pipe.scheduler.config)
+pipe.to('cuda')
+del unet
+del vae
+
+
+pipe.force_zeros_for_empty_prompt = False
+
+print("Optimizing BRIA 2.2 FAST - this could take a while")
+t=time.time()
+pipe.unet = torch.compile(
+    pipe.unet, mode="reduce-overhead", fullgraph=True # 600 secs compilation
+)
+with torch.no_grad():
+    outputs = pipe(
+        prompt="an apple",
+        num_inference_steps=8,
+    )
+
+    # This will avoid future compilations on different shapes
+    unet_compiled = torch._dynamo.run(pipe.unet)
+    unet_compiled.config=pipe.unet.config
+    unet_compiled.add_embedding = Dummy()
+    unet_compiled.add_embedding.linear_1 = Dummy()
+    unet_compiled.add_embedding.linear_1.in_features = pipe.unet.add_embedding.linear_1.in_features
+    pipe.unet = unet_compiled
+
+print(f"Optimizing finished successfully after {time.time()-t} secs")
+
+@spaces.GPU(enable_queue=True)
+def infer(prompt,seed,resolution):
+    print(f"""
+    —/n
+    {prompt}
+    """)
+    
+    # generator = torch.Generator("cuda").manual_seed(555)
+    t=time.time()
+
+    if seed=="-1":
+        generator=None
+    else:
+        try:
+            seed=int(seed)
+            generator = torch.Generator("cuda").manual_seed(seed)
+        except:
+            generator=None
+
+    w,h = resolution.split()
+    w,h = int(w),int(h)
+    image = pipe(prompt,num_inference_steps=8,generator=generator,width=w,height=h).images[0]
+    print(f'gen time is {time.time()-t} secs')
+    
+    # Future
+    # Add amound of steps
+    # if nsfw:
+    #     raise gr.Error("Generated image is NSFW")
+    
+    return image
+
+css = """
+#col-container{
+    margin: 0 auto;
+    max-width: 580px;
+}
+"""
+with gr.Blocks(css=css) as demo:
+    with gr.Column(elem_id="col-container"):
+        gr.Markdown("## BRIA 2.2 FAST")
+        gr.HTML('''
+          <p style="margin-bottom: 10px; font-size: 94%">
+            This is a demo for 
+            <a href="https://huggingface.co/briaai/BRIA-2.2-FAST" target="_blank">BRIA 2.2 FAST </a>. 
+            This is a fast version of BRIA 2.2 text-to-image model, still trained on licensed data, and so provide full legal liability coverage for copyright and privacy infringement.
+          </p>
+        ''')
+        with gr.Group():
+            with gr.Column():
+                prompt_in = gr.Textbox(label="Prompt", value="A smiling man with wavy brown hair and a trimmed beard")
+                resolution = gr.Dropdown(value=resolutions[0], show_label=True, label="Resolution", choices=resolutions)
+                seed = gr.Textbox(label="Seed", value=-1)
+                submit_btn = gr.Button("Generate")
+        result = gr.Image(label="BRIA 2.2 FAST Result")
+
+        # gr.Examples(
+        #     examples = [ 
+        #         "Dragon, digital art, by Greg Rutkowski",
+        #         "Armored knight holding sword",
+        #         "A flat roof villa near a river with black walls and huge windows",
+        #         "A calm and peaceful office",
+        #         "Pirate guinea pig"
+        #     ],
+        #     fn = infer, 
+        #     inputs = [
+        #         prompt_in
+        #     ],
+        #     outputs = [
+        #         result
+        #     ]
+        # )
+
+    submit_btn.click(
+        fn = infer,
+        inputs = [
+            prompt_in,
+            seed,
+            resolution
+        ],
+        outputs = [
+            result
+        ]
+    )
+
+demo.queue().launch(show_api=False)

requirements.txt

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+transformers
+diffusers
+torch
+torchvision
+accelerate
+spaces
+gradio