Spaces:
Running
on
Zero
Running
on
Zero
File size: 4,883 Bytes
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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 = "John6666/wai-ani-nsfw-ponyxl-v11-sdxl" # 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
# Specific prefixes for the prompt and negative prompt
prompt_prefix = "score_9, score_8_up, score_7_up, source_anime"
negative_prompt_prefix = "score_6, score_5, score_4, source_cartoon, 3d, (censor), monochrome, blurry, lowres, watermark"
@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)
# Prepend the specific terms to the prompt and negative prompt
full_prompt = f"{prompt_prefix}, {prompt}"
full_negative_prompt = f"{negative_prompt_prefix}, {negative_prompt}"
image = pipe(
prompt=full_prompt,
negative_prompt=full_negative_prompt,
guidance_scale=guidance_scale,
num_inference_steps=num_inference_steps,
width=width,
height=height,
generator=generator,
).images[0]
return image, seed, full_prompt, full_negative_prompt
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(" # Rainbow Media Anime Generator")
gr.Markdown(' ### <a href="https://example.com" target="_blank" class="button-link">Try a more realistic model</a>')
result = gr.Image(label="Result", show_label=False)
prompt = gr.Text(
label="Prompt",
show_label=False,
lines=3,
placeholder="Enter your prompt",
container=False,
)
run_button = gr.Button("Run", variant="primary")
with gr.Accordion("Advanced Settings", open=False):
with gr.Row():
negative_prompt = gr.Text(
label="Negative prompt",
lines=3,
placeholder="Enter a negative prompt",
visible=True, # Show negative prompt by default
)
with gr.Row():
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=7.0, # 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=35, # Replace with defaults that work for your model
)
# Add text outputs to show full prompt and negative prompt
full_prompt_output = gr.Textbox(label="Full Prompt", interactive=False)
full_negative_prompt_output = gr.Textbox(label="Full Negative Prompt", interactive=False)
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, full_prompt_output, full_negative_prompt_output],
)
if __name__ == "__main__":
demo.launch()
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