Spaces:
Running
on
Zero
Running
on
Zero
Update
Browse files
app.py
CHANGED
@@ -17,6 +17,7 @@ bases = {
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}
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step_loaded = None
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base_loaded = "ToonYou"
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# Ensure model and scheduler are initialized in GPU-enabled function
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if not torch.cuda.is_available():
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@@ -29,7 +30,7 @@ pipe.scheduler = EulerDiscreteScheduler.from_config(pipe.scheduler.config, times
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# Function
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@spaces.GPU(enable_queue=True)
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def generate_image(prompt, base, step):
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global step_loaded
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global base_loaded
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print(prompt, base, step)
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@@ -44,6 +45,11 @@ def generate_image(prompt, base, step):
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pipe.unet.load_state_dict(torch.load(hf_hub_download(bases[base], "unet/diffusion_pytorch_model.bin"), map_location=device), strict=False)
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base_loaded = base
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output = pipe(prompt=prompt, guidance_scale=1.0, num_inference_steps=step)
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name = str(uuid.uuid4()).replace("-", "")
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path = f"/tmp/{name}.mp4"
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@@ -58,9 +64,9 @@ with gr.Blocks(css="style.css") as demo:
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with gr.Group():
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with gr.Row():
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prompt = gr.Textbox(
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label='Prompt (English)'
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scale=8
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)
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select_base = gr.Dropdown(
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label='Base model',
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choices=[
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@@ -70,6 +76,16 @@ with gr.Blocks(css="style.css") as demo:
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value=base_loaded,
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interactive=True
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)
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select_step = gr.Dropdown(
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label='Inference steps',
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choices=[
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@@ -94,12 +110,12 @@ with gr.Blocks(css="style.css") as demo:
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prompt.submit(
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fn=generate_image,
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inputs=[prompt, select_base, select_step],
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outputs=video,
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)
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submit.click(
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fn=generate_image,
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inputs=[prompt, select_base, select_step],
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outputs=video,
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)
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}
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step_loaded = None
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base_loaded = "ToonYou"
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motion_loaded = None
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# Ensure model and scheduler are initialized in GPU-enabled function
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if not torch.cuda.is_available():
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# Function
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@spaces.GPU(enable_queue=True)
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def generate_image(prompt, base, motion, step):
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global step_loaded
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global base_loaded
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print(prompt, base, step)
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pipe.unet.load_state_dict(torch.load(hf_hub_download(bases[base], "unet/diffusion_pytorch_model.bin"), map_location=device), strict=False)
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base_loaded = base
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if motion_loaded != motion:
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pipe.unload_lora_weights()
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pipe.load_lora_weights(hf_hub_download("guoyww/animatediff", motion))
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motion_loaded = motion
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output = pipe(prompt=prompt, guidance_scale=1.0, num_inference_steps=step)
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name = str(uuid.uuid4()).replace("-", "")
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path = f"/tmp/{name}.mp4"
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with gr.Group():
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with gr.Row():
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prompt = gr.Textbox(
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label='Prompt (English)'
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)
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with gr.Row():
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select_base = gr.Dropdown(
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label='Base model',
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choices=[
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value=base_loaded,
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interactive=True
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)
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select_motion = gr.Dropdown(
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label='Motion LoRAs',
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choices=[
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("None", None),
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("Zoom in", "v2_lora_ZoomIn.ckpt"),
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("Zoom out", "v2_lora_ZoomOut.ckpt"),
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],
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value=None,
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interactive=True
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)
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select_step = gr.Dropdown(
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label='Inference steps',
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choices=[
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prompt.submit(
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fn=generate_image,
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inputs=[prompt, select_base, select_motion, select_step],
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outputs=video,
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)
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submit.click(
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fn=generate_image,
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inputs=[prompt, select_base, select_motion, select_step],
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outputs=video,
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)
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