Spaces:
Running
on
Zero
Running
on
Zero
Update app.py
Browse files
app.py
CHANGED
@@ -1,27 +1,17 @@
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import gradio as gr
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import numpy as np
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import random
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# import spaces #[uncomment to use ZeroGPU]
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from diffusers import DiffusionPipeline
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import torch
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device = "cuda" if torch.cuda.is_available() else "cpu"
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model_repo_id = "stabilityai/sdxl-turbo" # Replace to the model you would like to use
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if torch.cuda.is_available():
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torch_dtype = torch.float16
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else:
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torch_dtype = torch.float32
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pipe = DiffusionPipeline.from_pretrained(model_repo_id, torch_dtype=torch_dtype)
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pipe = pipe.to(device)
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MAX_SEED = np.iinfo(np.int32).max
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MAX_IMAGE_SIZE = 1024
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def infer(
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prompt,
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negative_prompt,
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@@ -33,10 +23,18 @@ def infer(
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num_inference_steps,
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progress=gr.Progress(track_tqdm=True),
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):
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if randomize_seed:
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seed = random.randint(0, MAX_SEED)
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generator = torch.Generator().manual_seed(seed)
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image = pipe(
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prompt=prompt,
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return image, seed
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examples = [
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"Astronaut in a jungle, cold color palette, muted colors, detailed, 8k",
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"An astronaut riding a green horse",
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@@ -66,7 +63,7 @@ css = """
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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(" # Text-to-Image Gradio Template")
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with gr.Row():
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prompt = gr.Text(
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@@ -105,7 +102,7 @@ with gr.Blocks(css=css) as demo:
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minimum=256,
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maximum=MAX_IMAGE_SIZE,
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step=32,
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value=1024,
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)
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height = gr.Slider(
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@@ -113,7 +110,7 @@ with gr.Blocks(css=css) as demo:
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minimum=256,
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maximum=MAX_IMAGE_SIZE,
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step=32,
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value=1024,
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)
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with gr.Row():
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@@ -122,7 +119,7 @@ with gr.Blocks(css=css) as demo:
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minimum=0.0,
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maximum=10.0,
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step=0.1,
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value=0.0,
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)
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num_inference_steps = gr.Slider(
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@@ -130,10 +127,11 @@ with gr.Blocks(css=css) as demo:
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minimum=1,
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maximum=50,
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step=1,
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value=2,
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)
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gr.Examples(examples=examples, inputs=[prompt])
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gr.on(
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triggers=[run_button.click, prompt.submit],
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fn=infer,
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import gradio as gr
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import numpy as np
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import random
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from diffusers import DiffusionPipeline
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import torch
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from spaces import GPU # IMPORTANTE: Ativando suporte ao ZeroGPU
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MAX_SEED = np.iinfo(np.int32).max
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MAX_IMAGE_SIZE = 1024
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model_repo_id = "stabilityai/sdxl-turbo" # Pode trocar por outro modelo se quiser
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pipe = None # O modelo só vai carregar DENTRO da função GPU
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@GPU # 🚨 Essa é a função que o Hugging Face vai usar para alocar GPU temporária
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def infer(
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prompt,
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negative_prompt,
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num_inference_steps,
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progress=gr.Progress(track_tqdm=True),
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):
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global pipe
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if pipe is None:
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# 🚨 Carregamento do modelo só quando o ZeroGPU te der acesso à GPU
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torch_dtype = torch.float16 if torch.cuda.is_available() else torch.float32
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pipe = DiffusionPipeline.from_pretrained(model_repo_id, torch_dtype=torch_dtype)
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pipe = pipe.to("cuda" if torch.cuda.is_available() else "cpu")
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if randomize_seed:
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seed = random.randint(0, MAX_SEED)
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generator = torch.Generator(device="cuda" if torch.cuda.is_available() else "cpu").manual_seed(seed)
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image = pipe(
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prompt=prompt,
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return image, seed
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examples = [
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"Astronaut in a jungle, cold color palette, muted colors, detailed, 8k",
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"An astronaut riding a green horse",
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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(" # Text-to-Image Gradio Template (ZeroGPU Ready ✅)")
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with gr.Row():
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prompt = gr.Text(
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minimum=256,
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maximum=MAX_IMAGE_SIZE,
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step=32,
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value=1024,
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)
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height = gr.Slider(
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minimum=256,
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maximum=MAX_IMAGE_SIZE,
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step=32,
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value=1024,
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)
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with gr.Row():
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minimum=0.0,
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maximum=10.0,
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step=0.1,
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value=0.0,
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)
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num_inference_steps = gr.Slider(
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minimum=1,
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maximum=50,
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step=1,
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value=2,
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)
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gr.Examples(examples=examples, inputs=[prompt])
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gr.on(
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triggers=[run_button.click, prompt.submit],
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fn=infer,
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