Commit
•
80e4491
1
Parent(s):
e1232bd
Update app.py
Browse files
app.py
CHANGED
@@ -60,7 +60,7 @@ if TORCH_COMPILE:
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pipe.load_lora_weights(
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"lcm-sd/lcm-sdxl-lora",
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weight_name="lcm_sdxl_lora.safetensors",
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adapter_name="lcm",
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use_auth_token=HF_TOKEN,
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)
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@@ -123,7 +123,8 @@ css = """
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with gr.Blocks(css=css) as demo:
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with gr.Column(elem_id="container"):
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gr.Markdown(
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"""# Ultra-Fast SDXL
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""",
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elem_id="intro",
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)
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@@ -133,6 +134,8 @@ with gr.Blocks(css=css) as demo:
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placeholder="Insert your prompt here:", value="papercut style of a cute monster", scale=5, container=False
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)
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generate_bt = gr.Button("Generate", scale=1)
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with gr.Accordion("Advanced options", open=False):
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guidance = gr.Slider(
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label="Guidance", minimum=0.0, maximum=5, value=0.3, step=0.001
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@@ -141,8 +144,23 @@ with gr.Blocks(css=css) as demo:
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seed = gr.Slider(
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randomize=True, minimum=0, maximum=12013012031030, label="Seed", step=1
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)
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-
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-
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inputs = [prompt, guidance, steps, seed]
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generate_bt.click(fn=predict, inputs=inputs, outputs=image)
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pipe.load_lora_weights(
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"lcm-sd/lcm-sdxl-lora",
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weight_name="lcm_sdxl_lora.safetensors",
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#adapter_name="lcm",
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use_auth_token=HF_TOKEN,
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)
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with gr.Blocks(css=css) as demo:
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with gr.Column(elem_id="container"):
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gr.Markdown(
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"""# Ultra-Fast SDXL with Latent Consistency LoRA
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In this Space, SDXL is loaded with a latent consistency LoRA, giving it the super power of doing inference in as little as 4 steps. [Learn more on our blog](#) or [technical report](#).
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""",
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elem_id="intro",
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)
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placeholder="Insert your prompt here:", value="papercut style of a cute monster", scale=5, container=False
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)
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generate_bt = gr.Button("Generate", scale=1)
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image = gr.Image(type="filepath")
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with gr.Accordion("Advanced options", open=False):
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guidance = gr.Slider(
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label="Guidance", minimum=0.0, maximum=5, value=0.3, step=0.001
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seed = gr.Slider(
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randomize=True, minimum=0, maximum=12013012031030, label="Seed", step=1
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)
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with gr.Group():
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gr.Markdown('''## Using it with `diffusers`
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```py
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from diffusers import DiffusionPipeline, LCMScheduler
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pipe = DiffusionPipeline.from_pretrained("stabilityai/stable-diffusion-xl-base-1.0").to("cuda")
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pipe.scheduler = LCMScheduler.from_config(pipe.scheduler.config)
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pipe.load_lora_weights("lcm-sd/lcm-sdxl-lora")
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results = pipe(
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prompt="The spirit of a tamagotchi wandering in the city of Vienna",
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num_inference_steps=4,
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guidance_scale=0.5,
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)
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results.images[0]
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```
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''')
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inputs = [prompt, guidance, steps, seed]
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generate_bt.click(fn=predict, inputs=inputs, outputs=image)
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