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import gradio as gr |
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import torch |
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from torch import autocast |
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from diffusers import StableDiffusionPipeline |
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pipe = StableDiffusionPipeline.from_pretrained("hakurei/waifu-diffusion", torch_type=torch.float16, revision="fp16") |
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pipe = pipe.to("cuda") |
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num_samples = 2 |
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|
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def infer(prompt): |
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images = pipe([prompt] * num_samples, guidance_scale=7.5)["sample"] |
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return images |
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css = """ |
|
|
.gradio-container { |
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font-family: 'IBM Plex Sans', sans-serif; |
|
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} |
|
|
.gr-button { |
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color: white; |
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border-color: black; |
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background: black; |
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} |
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input[type='range'] { |
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accent-color: black; |
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} |
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.dark input[type='range'] { |
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accent-color: #dfdfdf; |
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} |
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.container { |
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max-width: 730px; |
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margin: auto; |
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padding-top: 1.5rem; |
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|
} |
|
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#gallery { |
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min-height: 22rem; |
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margin-bottom: 15px; |
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margin-left: auto; |
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margin-right: auto; |
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border-bottom-right-radius: .5rem !important; |
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border-bottom-left-radius: .5rem !important; |
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} |
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#gallery>div>.h-full { |
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min-height: 20rem; |
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|
} |
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.details:hover { |
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text-decoration: underline; |
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|
} |
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.gr-button { |
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white-space: nowrap; |
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|
} |
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.gr-button:focus { |
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|
border-color: rgb(147 197 253 / var(--tw-border-opacity)); |
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outline: none; |
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box-shadow: var(--tw-ring-offset-shadow), var(--tw-ring-shadow), var(--tw-shadow, 0 0 #0000); |
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--tw-border-opacity: 1; |
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--tw-ring-offset-shadow: var(--tw-ring-inset) 0 0 0 var(--tw-ring-offset-width) var(--tw-ring-offset-color); |
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--tw-ring-shadow: var(--tw-ring-inset) 0 0 0 calc(3px var(--tw-ring-offset-width)) var(--tw-ring-color); |
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--tw-ring-color: rgb(191 219 254 / var(--tw-ring-opacity)); |
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--tw-ring-opacity: .5; |
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|
} |
|
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#advanced-btn { |
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font-size: .7rem !important; |
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line-height: 19px; |
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margin-top: 12px; |
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margin-bottom: 12px; |
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padding: 2px 8px; |
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border-radius: 14px !important; |
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} |
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#advanced-options { |
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display: none; |
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margin-bottom: 20px; |
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} |
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.footer { |
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margin-bottom: 45px; |
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margin-top: 35px; |
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text-align: center; |
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border-bottom: 1px solid #e5e5e5; |
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} |
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.footer>p { |
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|
font-size: .8rem; |
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display: inline-block; |
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padding: 0 10px; |
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transform: translateY(10px); |
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background: white; |
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} |
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.dark .footer { |
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border-color: #303030; |
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} |
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.dark .footer>p { |
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background: #0b0f19; |
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} |
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.acknowledgments h4{ |
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margin: 1.25em 0 .25em 0; |
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font-weight: bold; |
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font-size: 115%; |
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} |
|
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#container-advanced-btns{ |
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display: flex; |
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flex-wrap: wrap; |
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justify-content: space-between; |
|
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align-items: center; |
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|
} |
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.animate-spin { |
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animation: spin 1s linear infinite; |
|
|
} |
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@keyframes spin { |
|
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from { |
|
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transform: rotate(0deg); |
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} |
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to { |
|
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transform: rotate(360deg); |
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} |
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} |
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#share-btn-container { |
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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; |
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} |
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#share-btn { |
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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; |
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} |
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#share-btn * { |
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all: unset; |
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|
} |
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|
.gr-form{ |
|
|
flex: 1 1 50%; border-top-right-radius: 0; border-bottom-right-radius: 0; |
|
|
} |
|
|
#prompt-container{ |
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gap: 0; |
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} |
|
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#generated_id{ |
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min-height: 700px |
|
|
} |
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|
""" |
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block = gr.Blocks(css=css) |
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examples = [ |
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[ |
|
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'Goku' |
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], |
|
|
[ |
|
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'Mikasa Ackerman' |
|
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], |
|
|
[ |
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'Saber' |
|
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], |
|
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] |
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with block as demo: |
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gr.HTML( |
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""" |
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<div style="text-align: center; max-width: 650px; margin: 0 auto;"> |
|
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<div |
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style=" |
|
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display: inline-flex; |
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align-items: center; |
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gap: 0.8rem; |
|
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font-size: 1.75rem; |
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" |
|
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> |
|
|
<img src="data:image/png;base64,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" /> |
|
|
<h1 style="font-weight: 900; margin-bottom: 7px;"> |
|
|
Waifu Diffusion Demo |
|
|
</h1> |
|
|
</div> |
|
|
<p style="margin-bottom: 10px; font-size: 94%"> |
|
|
waifu-diffusion is a latent text-to-image diffusion model that has been conditioned on high-quality anime images through fine-tuning |
|
|
</p> |
|
|
</div> |
|
|
""" |
|
|
) |
|
|
with gr.Group(): |
|
|
with gr.Box(): |
|
|
with gr.Row().style(mobile_collapse=False, equal_height=True): |
|
|
|
|
|
text = gr.Textbox( |
|
|
label="Enter your prompt", show_label=False, max_lines=1 |
|
|
).style( |
|
|
border=(True, False, True, True), |
|
|
rounded=(True, False, False, True), |
|
|
container=False, |
|
|
) |
|
|
btn = gr.Button("Run").style( |
|
|
margin=False, |
|
|
rounded=(False, True, True, False), |
|
|
) |
|
|
|
|
|
gallery = gr.Gallery(label="Generated images", show_label=False, elem_id="generated_id").style( |
|
|
grid=[2], height="auto" |
|
|
) |
|
|
|
|
|
ex = gr.Examples(examples=examples, fn=infer, inputs=[text], outputs=gallery, cache_examples=True) |
|
|
ex.dataset.headers = [""] |
|
|
|
|
|
text.submit(infer, inputs=[text], outputs=gallery) |
|
|
btn.click(infer, inputs=[text], outputs=gallery) |
|
|
|
|
|
gr.HTML( |
|
|
""" |
|
|
<div class="footer"> |
|
|
<p>Stable Diffusion model fine-tuned on 56K anime image board images by <a href="https://huggingface.co/hakurei" style="text-decoration: underline;" target="_blank">hakurei</a> |
|
|
</p> |
|
|
</div> |
|
|
<div class="acknowledgments"> |
|
|
<p><h4>LICENSE</h4> |
|
|
The model is licensed with a <a href="https://huggingface.co/spaces/CompVis/stable-diffusion-license" style="text-decoration: underline;" target="_blank">CreativeML Open RAIL-M</a> 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 <a href="https://huggingface.co/spaces/CompVis/stable-diffusion-license" target="_blank" style="text-decoration: underline;" target="_blank">read the license</a></p> |
|
|
<p><h4>Biases and content acknowledgment</h4> |
|
|
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 <a href="https://laion.ai/blog/laion-5b/" style="text-decoration: underline;" target="_blank">LAION-5B dataset</a>, 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 <a href="https://huggingface.co/CompVis/stable-diffusion-v1-4" style="text-decoration: underline;" target="_blank">model card</a></p> |
|
|
</div> |
|
|
""" |
|
|
) |
|
|
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] |
|
|
|
|
|
|
|
|
|
|
|
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 |
|
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|
|
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css = """ |
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|
#col-container {max-width: 700px; margin-left: auto; margin-right: auto;} |
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|
a {text-decoration-line: underline; font-weight: 600;} |
|
|
.animate-spin { |
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|
animation: spin 1s linear infinite; |
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|
} |
|
|
@keyframes spin { |
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|
from { |
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|
transform: rotate(0deg); |
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|
} |
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|
to { |
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|
transform: rotate(360deg); |
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|
} |
|
|
} |
|
|
#share-btn-container { |
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|
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; |
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|
} |
|
|
""" |
|
|
|
|
|
with gr.Blocks(css=css) as demo: |
|
|
gr.HTML( |
|
|
""" |
|
|
<div style="text-align: center; max-width: 700px; margin: 0 auto;"> |
|
|
<div |
|
|
style=" |
|
|
display: inline-flex; |
|
|
align-items: center; |
|
|
gap: 0.8rem; |
|
|
font-size: 1.75rem; |
|
|
" |
|
|
> |
|
|
<h1 style="font-weight: 900; margin-bottom: 7px;"> |
|
|
DGS Diffusion Space |
|
|
</h1> |
|
|
</div> |
|
|
<p style="margin-bottom: 10px; font-size: 94%"> |
|
|
Demo for Cyberpunk Anime Diffusion. Based of the projects by anzorq and fffiloni <a href="https://twitter.com/hahahahohohe"> |
|
|
</p> |
|
|
</div> |
|
|
""" |
|
|
) |
|
|
gr.Markdown(''' |
|
|
👇 Buy me a coffee if you like ♥ this project~ 👇 Running this server costs me $100 per week, any help would be much appreciated! |
|
|
[](https://www.buymeacoffee.com/dgspitzer) |
|
|
''') |
|
|
with gr.Row(): |
|
|
|
|
|
with gr.Column(): |
|
|
model = gr.Dropdown(label="Model", choices=models, value=models[0]) |
|
|
prompt = gr.Textbox(label="Prompt", placeholder="{} is added automatically".format(prompt_prefixes[current_model]), elem_id="input-prompt") |
|
|
guidance = gr.Slider(label="Guidance scale", value=7.5, maximum=15) |
|
|
steps = gr.Slider(label="Steps", value=27, maximum=100, minimum=2) |
|
|
run = gr.Button(value="Run") |
|
|
gr.Markdown(f"Running on: {device}") |
|
|
with gr.Column(): |
|
|
image_out = gr.Image(height=512, type="filepath", elem_id="output-img") |
|
|
|
|
|
with gr.Column(elem_id="col-container"): |
|
|
with gr.Group(elem_id="share-btn-container"): |
|
|
community_icon = gr.HTML(community_icon_html, visible=False) |
|
|
loading_icon = gr.HTML(loading_icon_html, visible=False) |
|
|
share_button = gr.Button("Share to community", elem_id="share-btn", visible=False) |
|
|
|
|
|
model.change(on_model_change, inputs=model, outputs=[]) |
|
|
run.click(inference, inputs=[prompt, guidance, steps], outputs=[image_out, share_button, community_icon, loading_icon, prompt]) |
|
|
|
|
|
share_button.click(None, [], [], _js=share_js) |
|
|
|
|
|
gr.Examples([ |
|
|
["portrait of anime girl", 7.5, 27], |
|
|
["a beautiful perfect face girl, Anime fine details portrait of school girl in front of modern tokyo city landscape on the background deep bokeh, anime masterpiece by studio ghibli, 8k, sharp high quality anime, artstation", 7.5, 27], |
|
|
["cyberpunk city landscape with fancy car", 7.5, 27], |
|
|
["portrait of liu yifei girl, soldier working in a cyberpunk city, cleavage, intricate, 8k, highly detailed, digital painting, intense, sharp focus", 7.5, 27], |
|
|
["portrait of a muscular beard male in dgs illustration style, half-body, holding robot arms, strong chest", 7.5, 27], |
|
|
], [prompt, guidance, steps], image_out, inference_example, cache_examples=torch.cuda.is_available()) |
|
|
gr.Markdown(''' |
|
|
Models and Space by [@DGSpitzer](https://huggingface.co/DGSpitzer)❤️<br> |
|
|
[](https://twitter.com/DGSpitzer) |
|
|
 |
|
|
|
|
|
 |
|
|
|
|
|
''') |
|
|
|
|
|
demo.queue() |
|
|
demo.launch() |
|
|
|
|
|
from transformers import pipeline, set_seed |
|
|
import gradio as grad, random, re |
|
|
|
|
|
|
|
|
gpt2_pipe = pipeline('text-generation', model='Gustavosta/MagicPrompt-Stable-Diffusion', tokenizer='gpt2') |
|
|
with open("ideas.txt", "r") as f: |
|
|
line = f.readlines() |
|
|
|
|
|
|
|
|
def generate(starting_text): |
|
|
seed = random.randint(100, 1000000) |
|
|
set_seed(seed) |
|
|
|
|
|
if starting_text == "": |
|
|
starting_text: str = line[random.randrange(0, len(line))].replace("\n", "").lower().capitalize() |
|
|
starting_text: str = re.sub(r"[,:\-–.!;?_]", '', starting_text) |
|
|
|
|
|
response = gpt2_pipe(starting_text, max_length=(len(starting_text) + random.randint(60, 90)), num_return_sequences=4) |
|
|
response_list = [] |
|
|
for x in response: |
|
|
resp = x['generated_text'].strip() |
|
|
if resp != starting_text and len(resp) > (len(starting_text) + 4) and resp.endswith((":", "-", "—")) is False: |
|
|
response_list.append(resp+'\n') |
|
|
|
|
|
response_end = "\n".join(response_list) |
|
|
response_end = re.sub('[^ ]+\.[^ ]+','', response_end) |
|
|
response_end = response_end.replace("<", "").replace(">", "") |
|
|
|
|
|
if response_end != "": |
|
|
return response_end |
|
|
|
|
|
|
|
|
txt = grad.Textbox(lines=1, label="Initial Text", placeholder="English Text here") |
|
|
out = grad.Textbox(lines=4, label="Generated Prompts") |
|
|
|
|
|
examples = [] |
|
|
for x in range(8): |
|
|
examples.append(line[random.randrange(0, len(line))].replace("\n", "").lower().capitalize()) |
|
|
|
|
|
title = "Stable Diffusion Prompt Generator" |
|
|
description = 'This is a demo of the model series: "MagicPrompt", in this case, aimed at: "Stable Diffusion". To use it, simply submit your text or click on one of the examples. To learn more about the model, [click here](https://huggingface.co/Gustavosta/MagicPrompt-Stable-Diffusion).<br>' |
|
|
|
|
|
grad.Interface(fn=generate, |
|
|
inputs=txt, |
|
|
outputs=out, |
|
|
examples=examples, |
|
|
title=title, |
|
|
description=description, |
|
|
article='', |
|
|
allow_flagging='never', |
|
|
cache_examples=False, |
|
|
theme="default").launch(enable_queue=True, debug=True) |
|
|
|
