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Running
on
Zero
Running
on
Zero
Update app.py
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app.py
CHANGED
@@ -1,6 +1,6 @@
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import gradio as gr
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import torch
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from diffusers import
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from huggingface_hub import hf_hub_download
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import spaces
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from PIL import Image
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translator = Translator()
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# Constants
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base = "
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checkpoints = {
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"1-Step" : ["dmd2_sdxl_1step_unet_fp16.bin", 1],
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"4-Step" : ["dmd2_sdxl_4step_unet_fp16.bin", 4],
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}
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loaded = None
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CSS = """
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.gradio-container {
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}
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}"""
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# Ensure model and scheduler are initialized in GPU-enabled function
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if torch.cuda.is_available():
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# Function
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@spaces.GPU()
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def generate_image(
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prompt = str(translator.translate(prompt, 'English'))
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print(prompt)
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else:
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timesteps=[999, 749, 499, 249]
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results = pipe(prompt, num_inference_steps=num_inference_steps, guidance_scale=0, timesteps=timesteps)
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return results.images[0]
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examples = [
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# Gradio Interface
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with gr.Blocks(css=CSS, js=JS, theme="soft") as demo:
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gr.HTML("<h1><center>
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gr.HTML("<p><center><a href='https://huggingface.co/
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with gr.Group():
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with gr.Row():
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prompt = gr.Textbox(label='Enter Your Prompt', scale=
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ckpt = gr.Dropdown(label='Steps',choices=['1-Step', '4-Step'], value='4-Step', interactive=True)
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submit = gr.Button(scale=1, variant='primary')
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img = gr.Image(label='
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gr.Examples(
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examples=examples,
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inputs=prompt,
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)
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prompt.submit(fn=generate_image,
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inputs=[prompt,
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outputs=img,
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)
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submit.click(fn=generate_image,
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inputs=[prompt,
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outputs=img,
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)
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import gradio as gr
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import torch
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from diffusers import StableDiffusionXLPipeline, AutoencoderKL
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from huggingface_hub import hf_hub_download
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import spaces
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from PIL import Image
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translator = Translator()
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# Constants
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base = "Corcelio/openvision"
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vae = "madebyollin/sdxl-vae-fp16-fix"
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CSS = """
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.gradio-container {
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}
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}"""
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# Load VAE component
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vae = AutoencoderKL.from_pretrained(
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"madebyollin/sdxl-vae-fp16-fix",
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torch_dtype=torch.float16
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)
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# Ensure model and scheduler are initialized in GPU-enabled function
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if torch.cuda.is_available():
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pipe = StableDiffusionXLPipeline.from_pretrained(base, vae=vae, torch_dtype=torch.float16).to("cuda")
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# Function
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@spaces.GPU()
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def generate_image(
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prompt,
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negative,
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width=1024,
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height=1024,
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scale=1.5,
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steps=30):
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prompt = str(translator.translate(prompt, 'English'))
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print(f'prompt:{prompt}')
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image = pipe(
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prompt,
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negative_prompt=negative,
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width=width,
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height=height,
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guidance_scale=scale,
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num_inference_steps=steps,
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)
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print(image.images[0])
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return image.images[0]
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examples = [
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# Gradio Interface
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with gr.Blocks(css=CSS, js=JS, theme="soft") as demo:
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gr.HTML("<h1><center>OpenVision</center></h1>")
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gr.HTML("<p><center><a href='https://huggingface.co/Corcelio/openvision'>OpenVision</a> text-to-image generation</center><br><center>Multi-Languages. Midjourney Aesthetic for All Your Images</center></p>")
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with gr.Group():
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with gr.Row():
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prompt = gr.Textbox(label='Enter Your Prompt', scale=6)
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submit = gr.Button(scale=1, variant='primary')
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img = gr.Image(label='OpenVision Generated Image')
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with gr.Accordion("Advanced Options", open=False):
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with gr.Row():
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negative = gr.Textbox(label="Negative prompt", value="low quality")
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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=512,
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maximum=1280,
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step=8,
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value=1024,
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)
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height = gr.Slider(
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label="Height",
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minimum=512,
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maximum=1280,
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step=8,
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value=1024,
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)
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with gr.Row():
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scale = gr.Slider(
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label="Guidance Scale",
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minimum=0,
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maximum=50,
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step=0.1,
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value=1.5,
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)
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steps = gr.Slider(
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label="Steps",
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minimum=1,
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maximum=50,
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step=1,
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value=30,
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)
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gr.Examples(
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examples=examples,
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inputs=prompt,
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)
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prompt.submit(fn=generate_image,
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inputs=[prompt, negative, width, height, scale, steps],
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outputs=img,
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)
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submit.click(fn=generate_image,
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inputs=[prompt, negative, width, height, scale, steps],
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outputs=img,
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)
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