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Update app.py
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app.py
CHANGED
@@ -1,17 +1,21 @@
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import gradio as gr
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import torch
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from diffusers import StableDiffusionXLPipeline, AutoencoderKL, KDPM2AncestralDiscreteScheduler
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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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import requests
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from translatepy import Translator
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translator = Translator()
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# Constants
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model = "Corcelio/mobius"
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vae_model = "madebyollin/sdxl-vae-fp16-fix"
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CSS = """
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.gradio-container {
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@@ -37,7 +41,8 @@ vae = AutoencoderKL.from_pretrained(
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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.scheduler = KDPM2AncestralDiscreteScheduler.from_config(pipe.scheduler.config)
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@@ -49,13 +54,21 @@ def generate_image(
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negative="low quality",
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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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clip=3):
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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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@@ -63,10 +76,12 @@ def generate_image(
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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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clip_skip=clip,
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)
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return image
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examples = [
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with gr.Blocks(css=CSS, js=JS, theme="soft") as demo:
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gr.HTML("<h1><center>Mobius💠</center></h1>")
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gr.HTML("<p><center><a href='https://huggingface.co/Corcelio/mobius'>mobius</a> text-to-image generation</center><br><center>
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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', value="best quality, HD, aesthetic", scale=6)
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submit = gr.Button(scale=1, variant='primary')
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img = gr.
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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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@@ -108,6 +123,23 @@ with gr.Blocks(css=CSS, js=JS, theme="soft") as demo:
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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",
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@@ -129,7 +161,7 @@ with gr.Blocks(css=CSS, js=JS, theme="soft") as demo:
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maximum=10,
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step=1,
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value=3,
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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, clip],
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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, clip],
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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, KDPM2AncestralDiscreteScheduler, UNet2DConditionModel
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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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import requests
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from translatepy import Translator
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import numpy as np
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import random
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translator = Translator()
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# Constants
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model = "Corcelio/mobius"
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vae_model = "madebyollin/sdxl-vae-fp16-fix"
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MAX_SEED = np.iinfo(np.int32).max
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CSS = """
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.gradio-container {
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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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unet = UNet2DConditionModel.from_pretrained(model, subfolder="unet").to("cuda", torch.float16)
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pipe = StableDiffusionXLPipeline.from_pretrained(model, vae=vae, unet=unet, torch_dtype=torch.float16).to("cuda")
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pipe.scheduler = KDPM2AncestralDiscreteScheduler.from_config(pipe.scheduler.config)
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negative="low quality",
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width=1024,
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height=1024,
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seed=-1,
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nums=1,
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scale=1.5,
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steps=30,
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clip=3):
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if seed == -1:
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seed = random.randint(0, MAX_SEED)
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generator = torch.Generator().manual_seed(seed)
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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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width=width,
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height=height,
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guidance_scale=scale,
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generator = generator,
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num_inference_steps=steps,
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num_images_per_prompt=nums,
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clip_skip=clip,
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).images
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return image, seed
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examples = [
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with gr.Blocks(css=CSS, js=JS, theme="soft") as demo:
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gr.HTML("<h1><center>Mobius💠</center></h1>")
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gr.HTML("<p><center><a href='https://huggingface.co/Corcelio/mobius'>mobius</a> text-to-image generation</center><br><center>Adding default prompts to enhance.</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(Multi-Languages)', value="best quality, HD, aesthetic", scale=6)
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submit = gr.Button(scale=1, variant='primary')
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img = gr.Gallery(label='Mobius Generated Image',columns = 1, preview=True)
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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, ugly, blurry, poor face, bad anatomy")
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with gr.Row():
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width = gr.Slider(
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label="Width",
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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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seed = gr.Slider(
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label="Seed (-1 Get Random)",
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minimum=-1,
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maximum=MAX_SEED,
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step=1,
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value=-1,
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scale=2,
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)
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nums = gr.Slider(
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label="Image Numbers",
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minimum=1,
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maximum=4,
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step=1,
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value=1,
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scale=1,
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)
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with gr.Row():
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scale = gr.Slider(
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label="Guidance",
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maximum=10,
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step=1,
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value=3,
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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, seed, nums, scale, steps, clip],
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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, seed, nums, scale, steps, clip],
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outputs=img,
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)
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