LICENSE
The model is licensed with a
CreativeML Open RAIL-M license. The authors claim no rights on the outputs you generate, you are free to use them and are accountable for their use which must not go against the provisions set in this license. The license forbids you from sharing any content that violates any laws, produce any harm to a person, disseminate any personal information that would be meant for harm, spread misinformation and target vulnerable groups. For the full list of restrictions please
read the license
Biases and content acknowledgment
Despite how impressive being able to turn text into image is, beware to the fact that this model may output content that reinforces or exacerbates societal biases, as well as realistic faces, pornography and violence. The model was trained on the
LAION-5B dataset, which scraped non-curated image-text-pairs from the internet (the exception being the removal of illegal content) and is meant for research purposes. You can read more in the
model card
"""
)
demo.queue(max_size=25).launch()
from diffusers import StableDiffusionPipeline
import gradio as gr
import torch
import os
from share_btn import community_icon_html, loading_icon_html, share_js
if torch.cuda.is_available():
torchfloat = torch.float16
else:
torchfloat = torch.float32
models = [
"DGSpitzer/Cyberpunk-Anime-Diffusion"
]
prompt_prefixes = {
models[0]: "dgs illustration style "
}
current_model = models[0]
#auth_token = os.environ.get("test") or True
#pipe = StableDiffusionPipeline.from_pretrained(current_model, use_auth_token=auth_token, torch_dtype=torchfloat, revision="fp16")
pipe = StableDiffusionPipeline.from_pretrained(current_model, torch_dtype=torchfloat, revision="fp16")
if torch.cuda.is_available():
pipe = pipe.to("cuda")
else:
pipe = pipe.to("cpu")
device = "GPU 🔥" if torch.cuda.is_available() else "CPU 🥶"
def on_model_change(model):
global current_model
global pipe
if model != current_model:
current_model = model
pipe = StableDiffusionPipeline.from_pretrained(current_model, torch_dtype=torchfloat)
if torch.cuda.is_available():
pipe = pipe.to("cuda")
def inference(prompt, guidance, steps):
promptPrev = prompt
prompt = prompt_prefixes[current_model] + prompt
image = pipe(prompt, num_inference_steps=int(steps), guidance_scale=guidance, width=512, height=512).images[0]
return image, gr.update(visible=True), gr.update(visible=True), gr.update(visible=True), gr.update(placeholder=promptPrev)
def inference_example(prompt, guidance, steps):
prompt = prompt_prefixes[current_model] + prompt
image = pipe(prompt, num_inference_steps=int(steps), guidance_scale=guidance, width=512, height=512).images[0]
return image
css = """
#col-container {max-width: 700px; margin-left: auto; margin-right: auto;}
a {text-decoration-line: underline; font-weight: 600;}
.animate-spin {
animation: spin 1s linear infinite;
}
@keyframes spin {
from {
transform: rotate(0deg);
}
to {
transform: rotate(360deg);
}
}
#share-btn-container {
display: flex; padding-left: 0.5rem !important; padding-right: 0.5rem !important; background-color: #000000; justify-content: center; align-items: center; border-radius: 9999px !important; width: 13rem;
}
#share-btn {
all: initial; color: #ffffff;font-weight: 600; cursor:pointer; font-family: 'IBM Plex Sans', sans-serif; margin-left: 0.5rem !important; padding-top: 0.25rem !important; padding-bottom: 0.25rem !important;right:0;
}
#share-btn * {
all: unset;
}
#share-btn-container div:nth-child(-n+2){
width: auto !important;
min-height: 0px !important;
}
#share-btn-container .wrap {
display: none !important;
}
"""
with gr.Blocks(css=css) as demo:
gr.HTML(
"""
