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import gradio as gr |
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import jax |
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import jax.numpy as jnp |
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from diffusers import FlaxPNDMScheduler, FlaxStableDiffusionPipeline |
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from flax.jax_utils import replicate |
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from flax.training.common_utils import shard |
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from share_btn import community_icon_html, loading_icon_html, share_js |
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DTYPE = jnp.float16 |
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pipeline, pipeline_params = FlaxStableDiffusionPipeline.from_pretrained( |
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"bguisard/stable-diffusion-nano-2-1", |
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dtype=DTYPE, |
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) |
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if DTYPE != jnp.float32: |
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scheduler, scheduler_params = FlaxPNDMScheduler.from_pretrained( |
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pretrained_model_name_or_path="bguisard/stable-diffusion-nano-2-1", |
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subfolder="scheduler", |
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dtype=jnp.float32, |
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) |
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pipeline_params["scheduler"] = scheduler_params |
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pipeline.scheduler = scheduler |
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def generate_image(prompt: str, negative_prompt: str = "", inference_steps: int = 25, prng_seed: int = 0, guidance_scale: float = 9): |
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rng = jax.random.PRNGKey(int(prng_seed)) |
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rng = jax.random.split(rng, jax.device_count()) |
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p_params = replicate(pipeline_params) |
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num_samples = 1 |
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prompt_ids = pipeline.prepare_inputs([prompt] * num_samples) |
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prompt_ids = shard(prompt_ids) |
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if negative_prompt == "": |
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images = pipeline( |
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prompt_ids=prompt_ids, |
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params=p_params, |
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prng_seed=rng, |
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height=128, |
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width=128, |
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num_inference_steps=int(inference_steps), |
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guidance_scale=float(guidance_scale), |
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jit=True, |
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).images |
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else: |
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neg_prompt_ids = pipeline.prepare_inputs( |
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[negative_prompt] * num_samples) |
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neg_prompt_ids = shard(neg_prompt_ids) |
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images = pipeline( |
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prompt_ids=prompt_ids, |
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params=p_params, |
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prng_seed=rng, |
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height=128, |
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width=128, |
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num_inference_steps=int(inference_steps), |
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neg_prompt_ids=neg_prompt_ids, |
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guidance_scale=float(guidance_scale), |
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jit=True, |
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).images |
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images = images.reshape((num_samples,) + images.shape[-3:]) |
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images = pipeline.numpy_to_pil(images) |
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return images[0] |
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examples = [ |
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["A watercolor painting of a bird"], |
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["A watercolor painting of an otter"] |
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] |
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css = """ |
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.gradio-container { |
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font-family: 'IBM Plex Sans', sans-serif; |
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max-width: 730px!important; |
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margin: auto; |
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padding-top: 1.5rem; |
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} |
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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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cache_examples=True, |
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postprocess=False) |
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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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.animate-spin { |
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animation: spin 1s linear infinite; |
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} |
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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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margin-top: 10px; |
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margin-left: auto; |
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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;right:0; |
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} |
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#share-btn * { |
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all: unset; |
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} |
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#share-btn-container div:nth-child(-n+2){ |
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width: auto !important; |
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min-height: 0px !important; |
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} |
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#share-btn-container .wrap { |
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display: none !important; |
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} |
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.share_button { |
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color:#6366f1!important; |
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} |
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.gr-form{ |
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flex: 1 1 50%; border-top-right-radius: 0; border-bottom-right-radius: 0; |
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} |
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#prompt-text-input, #negative-prompt-text-input{padding: .45rem 0.625rem} |
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.image_duplication{position: absolute; width: 100px; left: 50px} |
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""" |
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block = gr.Blocks(theme="gradio/soft",css=css) |
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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; 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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> |
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<svg |
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width="0.65em" |
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height="0.65em" |
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viewBox="0 0 115 115" |
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fill="none" |
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xmlns="http://www.w3.org/2000/svg" |
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> |
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<rect width="23" height="23" fill="white"></rect> |
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<rect y="69" width="23" height="23" fill="white"></rect> |
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<rect x="23" width="23" height="23" fill="#AEAEAE"></rect> |
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<rect x="23" y="69" width="23" height="23" fill="#AEAEAE"></rect> |
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<rect x="46" width="23" height="23" fill="white"></rect> |
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<rect x="46" y="69" width="23" height="23" fill="white"></rect> |
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<rect x="69" width="23" height="23" fill="black"></rect> |
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<rect x="69" y="69" width="23" height="23" fill="black"></rect> |
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<rect x="92" width="23" height="23" fill="#D9D9D9"></rect> |
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<rect x="92" y="69" width="23" height="23" fill="#AEAEAE"></rect> |
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<rect x="115" y="46" width="23" height="23" fill="white"></rect> |
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<rect x="115" y="115" width="23" height="23" fill="white"></rect> |
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<rect x="115" y="69" width="23" height="23" fill="#D9D9D9"></rect> |
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<rect x="92" y="46" width="23" height="23" fill="#AEAEAE"></rect> |
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<rect x="92" y="115" width="23" height="23" fill="#AEAEAE"></rect> |
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<rect x="92" y="69" width="23" height="23" fill="white"></rect> |
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<rect x="69" y="46" width="23" height="23" fill="white"></rect> |
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<rect x="69" y="115" width="23" height="23" fill="white"></rect> |
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<rect x="69" y="69" width="23" height="23" fill="#D9D9D9"></rect> |
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<rect x="46" y="46" width="23" height="23" fill="black"></rect> |
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<rect x="46" y="115" width="23" height="23" fill="black"></rect> |
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<rect x="46" y="69" width="23" height="23" fill="black"></rect> |
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<rect x="23" y="46" width="23" height="23" fill="#D9D9D9"></rect> |
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<rect x="23" y="115" width="23" height="23" fill="#AEAEAE"></rect> |
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<rect x="23" y="69" width="23" height="23" fill="black"></rect> |
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</svg> |
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<h1 style="font-weight: 900; margin-bottom: 7px;margin-top:5px"> |
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Stable Diffusion Nano Demo |
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</h1> |
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</div> |
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<p style="margin-bottom: 10px; font-size: 94%; line-height: 23px;"> |
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Stable Diffusion Nano was built during the <a style="text-decoration: underline;" href="https://github.com/huggingface/community-events/tree/main/jax-controlnet-sprint">JAX/Diffusers community sprint 🧨</a> based on Stable Diffusion 2.1 and finetuned on 128x128 images for fast prototyping. <br> |
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</p> |
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</div> |
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""" |
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) |
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with gr.Group(): |
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with gr.Box(): |
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with gr.Row(elem_id="prompt-container").style(equal_height=True): |
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with gr.Column(scale=2): |
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prompt_input = gr.Textbox( |
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label="Enter your prompt", |
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max_lines=1, |
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placeholder="Enter your prompt", |
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elem_id="prompt-text-input", |
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show_label=False, |
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) |
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negative = gr.Textbox( |
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label="Enter your negative prompt", |
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max_lines=1, |
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placeholder="Enter a negative prompt", |
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elem_id="negative-prompt-text-input", |
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show_label=False, |
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) |
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btn = gr.Button("Generate image", label="Primary Button", variant="primary") |
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gallery = gr.Image( |
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label="Generated images", show_label=False, elem_id="gallery" |
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) |
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with gr.Row(): |
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with gr.Column(scale=2): |
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with gr.Accordion("Advanced settings"): |
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seed_input = gr.inputs.Number(default=0, label="Seed") |
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inf_steps_input = gr.inputs.Slider( |
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minimum=1, maximum=100, default=25, step=1, label="Inference Steps" |
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) |
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guidance_scale = gr.inputs.Slider( |
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label="Guidance Scale", minimum=0, maximum=50, default=9, step=0.1 |
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) |
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with gr.Column(scale=1): |
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ex = gr.Examples(examples=examples, |
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fn=generate_image, |
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inputs=[prompt_input, negative,inf_steps_input, seed_input, guidance_scale], |
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outputs=[gallery], |
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cache_examples=False) |
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ex.dataset.headers = [""] |
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share_button = gr.Button("Share to community",elem_classes="share_button") |
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negative.submit(generate_image, inputs=[ |
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prompt_input, negative, inf_steps_input, seed_input, guidance_scale], outputs=[gallery], postprocess=False) |
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prompt_input.submit(generate_image, inputs=[ |
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prompt_input, negative, inf_steps_input, seed_input, guidance_scale], outputs=[gallery], postprocess=False) |
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btn.click(generate_image, inputs=[prompt_input, negative, inf_steps_input, |
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seed_input, guidance_scale], outputs=[gallery], postprocess=False) |
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share_button.click( |
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None, |
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[], |
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[], |
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_js=share_js, |
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) |
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gr.Markdown("Model by Stable Diffusion Nano Team",elem_classes="footer") |
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with gr.Accordion(label="License", open=False): |
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gr.HTML( |
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""" |
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<div class="acknowledgments"> |
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<p><h4>LICENSE</h4> |
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The model is licensed with a <a href="https://huggingface.co/stabilityai/stable-diffusion-2/blob/main/LICENSE-MODEL" style="text-decoration: underline;" target="_blank">CreativeML OpenRAIL++</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> |
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<p><h4>Biases and content acknowledgment</h4> |
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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://huggingface.co/datasets/laion/laion2B-en-aesthetic" style="text-decoration: underline;" target="_blank">LAION-2B Aesthetic 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/bguisard/stable-diffusion-nano-2-1" style="text-decoration: underline;" target="_blank">model card</a></p> |
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</div> |
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""" |
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) |
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demo.queue(concurrency_count=10) |
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demo.launch() |
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