Duplicate from gradio-templates/text-to-image-gradio-template

Co-authored-by: Sylvain Filoni <[email protected]>

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README.md

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+---
+title: Text-to-Image Gradio Template
+emoji: 🖼
+colorFrom: purple
+colorTo: red
+sdk: gradio
+sdk_version: 5.0.1
+app_file: app.py
+pinned: false
+---
+
+Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

app.py

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+import gradio as gr
+import numpy as np
+import random
+
+# import spaces #[uncomment to use ZeroGPU]
+from diffusers import DiffusionPipeline
+import torch
+
+device = "cuda" if torch.cuda.is_available() else "cpu"
+model_repo_id = "stabilityai/sdxl-turbo" # Replace to the model you would like to use
+
+if torch.cuda.is_available():
+ torch_dtype = torch.float16
+else:
+ torch_dtype = torch.float32
+
+pipe = DiffusionPipeline.from_pretrained(model_repo_id, torch_dtype=torch_dtype)
+pipe = pipe.to(device)
+
+MAX_SEED = np.iinfo(np.int32).max
+MAX_IMAGE_SIZE = 1024
+
+
+# @spaces.GPU #[uncomment to use ZeroGPU]
+def infer(
+ prompt,
+ negative_prompt,
+ seed,
+ randomize_seed,
+ width,
+ height,
+ guidance_scale,
+ num_inference_steps,
+ progress=gr.Progress(track_tqdm=True),
+):
+ if randomize_seed:
+ seed = random.randint(0, MAX_SEED)
+
+ generator = torch.Generator().manual_seed(seed)
+
+ image = pipe(
+ prompt=prompt,
+ negative_prompt=negative_prompt,
+ guidance_scale=guidance_scale,
+ num_inference_steps=num_inference_steps,
+ width=width,
+ height=height,
+ generator=generator,
+ ).images[0]
+
+ return image, seed
+
+
+examples = [
+ "Astronaut in a jungle, cold color palette, muted colors, detailed, 8k",
+ "An astronaut riding a green horse",
+ "A delicious ceviche cheesecake slice",
+]
+
+css = """
+#col-container {
+ margin: 0 auto;
+ max-width: 640px;
+}
+"""
+
+with gr.Blocks(css=css) as demo:
+ with gr.Column(elem_id="col-container"):
+ gr.Markdown(" # Text-to-Image Gradio Template")
+
+ with gr.Row():
+ prompt = gr.Text(
+ label="Prompt",
+ show_label=False,
+ max_lines=1,
+ placeholder="Enter your prompt",
+ container=False,
+ )
+
+ run_button = gr.Button("Run", scale=0, variant="primary")
+
+ result = gr.Image(label="Result", show_label=False)
+
+ with gr.Accordion("Advanced Settings", open=False):
+ negative_prompt = gr.Text(
+ label="Negative prompt",
+ max_lines=1,
+ placeholder="Enter a negative prompt",
+ visible=False,
+ )
+
+ seed = gr.Slider(
+ label="Seed",
+ minimum=0,
+ maximum=MAX_SEED,
+ step=1,
+ value=0,
+ )
+
+ randomize_seed = gr.Checkbox(label="Randomize seed", value=True)
+
+ with gr.Row():
+ width = gr.Slider(
+ label="Width",
+ minimum=256,
+ maximum=MAX_IMAGE_SIZE,
+ step=32,
+ value=1024, # Replace with defaults that work for your model
+ )
+
+ height = gr.Slider(
+ label="Height",
+ minimum=256,
+ maximum=MAX_IMAGE_SIZE,
+ step=32,
+ value=1024, # Replace with defaults that work for your model
+ )
+
+ with gr.Row():
+ guidance_scale = gr.Slider(
+ label="Guidance scale",
+ minimum=0.0,
+ maximum=10.0,
+ step=0.1,
+ value=0.0, # Replace with defaults that work for your model
+ )
+
+ num_inference_steps = gr.Slider(
+ label="Number of inference steps",
+ minimum=1,
+ maximum=50,
+ step=1,
+ value=2, # Replace with defaults that work for your model
+ )
+
+ gr.Examples(examples=examples, inputs=[prompt])
+ gr.on(
+ triggers=[run_button.click, prompt.submit],
+ fn=infer,
+ inputs=[
+ prompt,
+ negative_prompt,
+ seed,
+ randomize_seed,
+ width,
+ height,
+ guidance_scale,
+ num_inference_steps,
+ ],
+ outputs=[result, seed],
+ )
+
+if __name__ == "__main__":
+ demo.launch()

requirements.txt

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+accelerate
+diffusers
+invisible_watermark
+torch
+transformers
+xformers