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Running
on
Zero
import torch | |
from diffusers import StableDiffusionXLPipeline | |
import gradio as gr | |
import random | |
import transformers | |
import numpy as np | |
from transformers import T5Tokenizer, T5ForConditionalGeneration | |
import spaces | |
if torch.cuda.is_available(): | |
device = "cuda" | |
print("Using GPU") | |
else: | |
device = "cpu" | |
print("Using CPU") | |
MAX_SEED = np.iinfo(np.int32).max | |
# Initialize the pipeline and download the sdxl flash model | |
pipe = StableDiffusionXLPipeline.from_pretrained("sd-community/sdxl-flash", torch_dtype=torch.float16) | |
pipe.to(device) | |
# Ensure sampler uses "trailing" timesteps. | |
pipe.scheduler = DPMSolverSinglestepScheduler.from_config(pipe.scheduler.config, timestep_spacing="trailing") | |
# superprompt-v1 | |
tokenizer = T5Tokenizer.from_pretrained("roborovski/superprompt-v1") | |
model = T5ForConditionalGeneration.from_pretrained("roborovski/superprompt-v1", device_map="auto", torch_dtype="auto") | |
model.to(device) | |
# toggle visibility the enhanced prompt output | |
def update_visibility(enhance_prompt): | |
return gr.update(visible=enhance_prompt) | |
# Define the image generation function | |
def generate_image(prompt, enhance_prompt, negative_prompt, num_inference_steps, height, width, guidance_scale, seed, num_images_per_prompt, progress=gr.Progress(track_tqdm=True)): | |
if seed == 0: | |
seed = random.randint(1, 2**32-1) | |
if enhance_prompt: | |
transformers.set_seed(seed) | |
input_text = f"Expand the following prompt to add more detail: {prompt}" | |
input_ids = tokenizer(input_text, return_tensors="pt").input_ids.to(device) | |
outputs = model.generate( | |
input_ids, | |
max_new_tokens=512, | |
repetition_penalty=1.2, | |
do_sample=True, | |
temperature=0.7, | |
top_p=1, | |
top_k=50 | |
) | |
prompt = tokenizer.decode(outputs[0], skip_special_tokens=True) | |
generator = torch.Generator().manual_seed(seed) | |
output = pipe( | |
prompt=prompt, | |
negative_prompt=negative_prompt, | |
num_inference_steps=num_inference_steps, | |
height=height, | |
width=width, | |
guidance_scale=guidance_scale, | |
generator=generator, | |
num_images_per_prompt=num_images_per_prompt | |
).images | |
return output, prompt | |
# Create the Gradio interface | |
examples = [ | |
["A white car racing fast to the moon.", True], | |
["A woman in a red dress singing on top of a building.", True], | |
["An astrounat on mars in a futuristic cyborg suit.", True], | |
] | |
css = ''' | |
.gradio-container{max-width: 1000px !important} | |
h1{text-align:center} | |
''' | |
with gr.Blocks(css=css) as demo: | |
with gr.Row(): | |
with gr.Column(): | |
gr.HTML( | |
""" | |
<h1 style='text-align: center'> | |
SDXL Flash | |
</h1> | |
""" | |
) | |
gr.HTML( | |
""" | |
Made by <a href='https://linktr.ee/Nick088' target='_blank'>Nick088</a> | |
<br> <a href="https://discord.gg/osai"> <img src="https://img.shields.io/discord/1198701940511617164?color=%23738ADB&label=Discord&style=for-the-badge" alt="Discord"> </a> | |
""" | |
) | |
with gr.Group(): | |
with gr.Column(): | |
prompt = gr.Textbox(label="Prompt", info="Describe the image you want", placeholder="A cat...") | |
enhance_prompt = gr.Checkbox(label="Prompt Enhancement with SuperPrompt-v1", value=True) | |
run_button = gr.Button("Run") | |
result = gr.Gallery(label="Generated AI Images", elem_id="gallery") | |
better_prompt = gr.Textbox(label="Enhanced Prompt", info="The output of your enhanced prompt used for the Image Generation", visible=True) | |
enhance_prompt.change(fn=update_visibility, inputs=enhance_prompt, outputs=better_prompt) | |
with gr.Accordion("Advanced options", open=False): | |
with gr.Row(): | |
negative_prompt = gr.Textbox(label="Negative Prompt", info="Describe what you don't want in the image", value="deformed, distorted, disfigured, poorly drawn, bad anatomy, incorrect anatomy, extra limb, missing limb, floating limbs, mutated hands and fingers, disconnected limbs, mutation, mutated, ugly, disgusting, blurry, amputation", placeholder="Ugly, bad anatomy...") | |
with gr.Row(): | |
num_inference_steps = gr.Slider(label="Number of Inference Steps", info="The number of denoising steps of the image. More denoising steps usually lead to a higher quality image at the cost of slower inference", minimum=1, maximum=50, value=25, step=1) | |
guidance_scale = gr.Slider(label="Guidance Scale", info="Controls how much the image generation process follows the text prompt. Higher values make the image stick more closely to the input text.", minimum=0.0, maximum=10.0, value=7.5, step=0.1) | |
with gr.Row(): | |
width = gr.Slider(label="Width", info="Width of the Image", minimum=256, maximum=1344, step=32, value=1024) | |
height = gr.Slider(label="Height", info="Height of the Image", minimum=256, maximum=1344, step=32, value=1024) | |
with gr.Row(): | |
seed = gr.Slider(value=42, minimum=0, maximum=MAX_SEED, step=1, label="Seed", info="A starting point to initiate the generation process, put 0 for a random one") | |
num_images_per_prompt = gr.Slider(label="Images Per Prompt", info="Number of Images to generate with the settings",minimum=1, maximum=4, step=1, value=2) | |
gr.Examples( | |
examples=examples, | |
inputs=[prompt, enhance_prompt], | |
outputs=[result, better_prompt], | |
fn=generate_image, | |
) | |
gr.on( | |
triggers=[ | |
prompt.submit, | |
run_button.click, | |
], | |
fn=generate_image, | |
inputs=[ | |
prompt, | |
enhance_prompt, | |
negative_prompt, | |
num_inference_steps, | |
width, | |
height, | |
guidance_scale, | |
seed, | |
num_images_per_prompt, | |
], | |
outputs=[result, better_prompt], | |
) | |
demo.queue().launch(share = False) |