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

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Co-authored-by: Sylvain Filoni <fffiloni@users.noreply.huggingface.co>

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  1. .gitattributes +35 -0
  2. README.md +12 -0
  3. app.py +154 -0
  4. requirements.txt +6 -0
.gitattributes ADDED
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README.md ADDED
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+ ---
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+ title: Text-to-Image Gradio Template
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+ emoji: 🖼
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+ colorFrom: purple
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+ colorTo: red
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+ sdk: gradio
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+ sdk_version: 5.0.1
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+ app_file: app.py
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+ pinned: false
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+ ---
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+
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+ Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
app.py ADDED
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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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+
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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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+
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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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+
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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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+
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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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+
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+ MAX_SEED = np.iinfo(np.int32).max
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+ MAX_IMAGE_SIZE = 1024
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+
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+
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+ # @spaces.GPU #[uncomment to use ZeroGPU]
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+ def infer(
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+ prompt,
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+ negative_prompt,
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+ seed,
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+ randomize_seed,
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+ width,
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+ height,
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+ guidance_scale,
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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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+
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+ generator = torch.Generator().manual_seed(seed)
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+
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+ image = pipe(
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+ prompt=prompt,
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+ negative_prompt=negative_prompt,
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+ guidance_scale=guidance_scale,
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+ num_inference_steps=num_inference_steps,
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+ width=width,
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+ height=height,
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+ generator=generator,
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+ ).images[0]
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+
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+ return image, seed
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+
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+
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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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+ "A delicious ceviche cheesecake slice",
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+ ]
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+
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+ css = """
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+ #col-container {
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+ margin: 0 auto;
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+ max-width: 640px;
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+ }
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+ """
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+
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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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+
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+ with gr.Row():
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+ prompt = gr.Text(
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+ label="Prompt",
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+ show_label=False,
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+ max_lines=1,
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+ placeholder="Enter your prompt",
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+ container=False,
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+ )
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+
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+ run_button = gr.Button("Run", scale=0, variant="primary")
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+
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+ result = gr.Image(label="Result", show_label=False)
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+
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+ with gr.Accordion("Advanced Settings", open=False):
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+ negative_prompt = gr.Text(
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+ label="Negative prompt",
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+ max_lines=1,
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+ placeholder="Enter a negative prompt",
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+ visible=False,
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+ )
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+
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+ seed = gr.Slider(
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+ label="Seed",
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+ minimum=0,
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+ maximum=MAX_SEED,
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+ step=1,
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+ value=0,
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+ )
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+
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+ randomize_seed = gr.Checkbox(label="Randomize seed", value=True)
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+
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+ with gr.Row():
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+ width = gr.Slider(
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+ label="Width",
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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, # Replace with defaults that work for your model
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+ )
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+
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+ height = gr.Slider(
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+ label="Height",
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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, # Replace with defaults that work for your model
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+ )
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+
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+ with gr.Row():
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+ guidance_scale = gr.Slider(
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+ label="Guidance scale",
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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, # Replace with defaults that work for your model
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+ )
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+
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+ num_inference_steps = gr.Slider(
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+ label="Number of inference steps",
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+ minimum=1,
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+ maximum=50,
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+ step=1,
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+ value=2, # Replace with defaults that work for your model
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+ )
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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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+ inputs=[
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+ prompt,
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+ negative_prompt,
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+ seed,
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+ randomize_seed,
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+ width,
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+ height,
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+ guidance_scale,
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+ num_inference_steps,
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+ ],
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+ outputs=[result, seed],
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+ )
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+
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+ if __name__ == "__main__":
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+ demo.launch()
requirements.txt ADDED
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+ accelerate
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+ diffusers
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+ invisible_watermark
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+ torch
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+ transformers
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+ xformers