Update app.py
Browse files
app.py
CHANGED
@@ -23,29 +23,29 @@ scheduler = EulerAncestralDiscreteScheduler(
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pipe = StableDiffusionXLPipeline.from_pretrained(model_id, torch_dtype=torch.float16,scheduler=scheduler).to("cuda")
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print("Optimizing BRIA-2.2 - this could take a while")
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t=time.time()
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pipe.unet = torch.compile(
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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):
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print(f"""
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—/n
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{prompt}
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@@ -53,7 +53,16 @@ def infer(prompt):
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# generator = torch.Generator("cuda").manual_seed(555)
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t=time.time()
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print(f'gen time is {time.time()-t} secs')
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# Future
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@@ -82,6 +91,9 @@ with gr.Blocks(css=css) as demo:
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with gr.Group():
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with gr.Column():
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prompt_in = gr.Textbox(label="Prompt", value="A red colored sports car")
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submit_btn = gr.Button("Generate")
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result = gr.Image(label="BRIA-2.2 Result")
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@@ -105,7 +117,10 @@ with gr.Blocks(css=css) as demo:
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submit_btn.click(
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fn = infer,
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inputs = [
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prompt_in
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],
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outputs = [
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result
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)
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pipe = StableDiffusionXLPipeline.from_pretrained(model_id, torch_dtype=torch.float16,scheduler=scheduler).to("cuda")
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# print("Optimizing BRIA-2.2 - 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=30,
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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,negative_prompt,seed,resolution):
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print(f"""
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—/n
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{prompt}
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# generator = torch.Generator("cuda").manual_seed(555)
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t=time.time()
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if negative_prompt=="":
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negative_prompt = default_negative_prompt
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if seed==-1:
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generator=None
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else:
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generator = torch.Generator("cuda").manual_seed(seed)
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w,h = resolution
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image = pipe(prompt,num_inference_steps=30, negative_prompt=negative_prompt,generator=generator,width=w,height=h).images[0]
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print(f'gen time is {time.time()-t} secs')
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# Future
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with gr.Group():
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with gr.Column():
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prompt_in = gr.Textbox(label="Prompt", value="A red colored sports car")
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negative_prompt = gr.Textbox(label="Negative Prompt", value="")
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resolution = gr.Dropdown(value=(1024,1024), show_label=True, label="Resolution", choices=[(1024,1024),(1344, 768)])
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seed = gr.Textbox(label="Seed", value=-1)
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submit_btn = gr.Button("Generate")
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result = gr.Image(label="BRIA-2.2 Result")
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submit_btn.click(
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fn = infer,
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inputs = [
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prompt_in,
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negative_prompt,
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seed,
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resolution
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],
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outputs = [
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result
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