Update app.py

app.py

@@ -1,421 +1,349 @@
 import gradio as gr
-import torch
-from typing import Any, Callable, Dict, List, Optional, Tuple, Union
+from datasets import load_dataset
+from PIL import Image  
 
-from diffusers import StableDiffusionXLPipeline
-from diffusers.pipelines.stable_diffusion_xl import StableDiffusionXLPipelineOutput
-import torch
-from PIL import Image
+import re
+import os
+import requests
 
-import diffusers
 from share_btn import community_icon_html, loading_icon_html, share_js
 
-device = "cuda" if torch.cuda.is_available() else "cpu"
-
-pipe = StableDiffusionXLPipeline.from_pretrained(
-    "stabilityai/stable-diffusion-xl-base-1.0",
-    torch_dtype=torch.float32,
-    variants="fp32",
-    use_safetensor=True,
-)
-pipe.to(device)
-
-@torch.no_grad()
-def call(
-        pipe,
-        prompt: Union[str, List[str]] = None,
-        prompt2: Union[str, List[str]] = None,
-        height: Optional[int] = None,
-        width: Optional[int] = None,
-        num_inference_steps: int = 50,
-        denoising_end: Optional[float] = None,
-        guidance_scale: float = 5.0,
-        guidance_scale2: float = 5.0,
-        negative_prompt: Optional[Union[str, List[str]]] = None,
-        negative_prompt2: Optional[Union[str, List[str]]] = None,
-        num_images_per_prompt: Optional[int] = 1,
-        eta: float = 0.0,
-        generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None,
-        latents: Optional[torch.FloatTensor] = None,
-        prompt_embeds: Optional[torch.FloatTensor] = None,
-        negative_prompt_embeds: Optional[torch.FloatTensor] = None,
-        pooled_prompt_embeds: Optional[torch.FloatTensor] = None,
-        negative_pooled_prompt_embeds: Optional[torch.FloatTensor] = None,
-        output_type: Optional[str] = "pil",
-        return_dict: bool = True,
-        callback: Optional[Callable[[int, int, torch.FloatTensor], None]] = None,
-        callback_steps: int = 1,
-        cross_attention_kwargs: Optional[Dict[str, Any]] = None,
-        guidance_rescale: float = 0.0,
-        original_size: Optional[Tuple[int, int]] = None,
-        crops_coords_top_left: Tuple[int, int] = (0, 0),
-        target_size: Optional[Tuple[int, int]] = None,
-        negative_original_size: Optional[Tuple[int, int]] = None,
-        negative_crops_coords_top_left: Tuple[int, int] = (0, 0),
-        negative_target_size: Optional[Tuple[int, int]] = None,
-    ):
-        # 0. Default height and width to unet
-        height = height or pipe.default_sample_size * pipe.vae_scale_factor
-        width = width or pipe.default_sample_size * pipe.vae_scale_factor
-
-        original_size = original_size or (height, width)
-        target_size = target_size or (height, width)
-
-        # 1. Check inputs. Raise error if not correct
-        pipe.check_inputs(
-            prompt,
-            None,
-            height,
-            width,
-            callback_steps,
-            negative_prompt,
-            None,
-            prompt_embeds,
-            negative_prompt_embeds,
-            pooled_prompt_embeds,
-            negative_pooled_prompt_embeds,
-        )
-
-        # 2. Define call parameters
-        if prompt is not None and isinstance(prompt, str):
-            batch_size = 1
-        elif prompt is not None and isinstance(prompt, list):
-            batch_size = len(prompt)
-        else:
-            batch_size = prompt_embeds.shape[0]
-
-        device = pipe._execution_device
-
-        # here `guidance_scale` is defined analog to the guidance weight `w` of equation (2)
-        # of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1`
-        # corresponds to doing no classifier free guidance.
-        do_classifier_free_guidance = guidance_scale > 1.0
-
-        # 3. Encode input prompt
-        text_encoder_lora_scale = (
-            cross_attention_kwargs.get("scale", None) if cross_attention_kwargs is not None else None
-        )
-
-        (
-            prompt_embeds,
-            negative_prompt_embeds,
-            pooled_prompt_embeds,
-            negative_pooled_prompt_embeds,
-        ) = pipe.encode_prompt(
-            prompt=prompt,
-            device=device,
-            num_images_per_prompt=num_images_per_prompt,
-            do_classifier_free_guidance=do_classifier_free_guidance,
-            negative_prompt=negative_prompt,
-            prompt_embeds=None,
-            negative_prompt_embeds=None,
-            pooled_prompt_embeds=None,
-            negative_pooled_prompt_embeds=None,
-            lora_scale=text_encoder_lora_scale,
-        )
-
-        (
-            prompt2_embeds,
-            negative_prompt2_embeds,
-            pooled_prompt2_embeds,
-            negative_pooled_prompt2_embeds,
-        ) = pipe.encode_prompt(
-            prompt=prompt2,
-            device=device,
-            num_images_per_prompt=num_images_per_prompt,
-            do_classifier_free_guidance=do_classifier_free_guidance,
-            negative_prompt=negative_prompt2,
-            prompt_embeds=None,
-            negative_prompt_embeds=None,
-            pooled_prompt_embeds=None,
-            negative_pooled_prompt_embeds=None,
-            lora_scale=text_encoder_lora_scale,
-        )
-
-        # 4. Prepare timesteps
-        pipe.scheduler.set_timesteps(num_inference_steps, device=device)
-
-        timesteps = pipe.scheduler.timesteps
-
-        # 5. Prepare latent variables
-        num_channels_latents = pipe.unet.config.in_channels
-        latents = pipe.prepare_latents(
-            batch_size * num_images_per_prompt,
-            num_channels_latents,
-            height,
-            width,
-            prompt_embeds.dtype,
-            device,
-            generator,
-            latents,
-        )
-
-        # 6. Prepare extra step kwargs. TODO: Logic should ideally just be moved out of the pipeline
-        extra_step_kwargs = pipe.prepare_extra_step_kwargs(generator, eta)
-
-        # 7. Prepare added time ids & embeddings
-        add_text_embeds = pooled_prompt_embeds
-        add_text2_embeds = pooled_prompt2_embeds
-
-        add_time_ids = pipe._get_add_time_ids(
-            original_size, crops_coords_top_left, target_size, dtype=prompt_embeds.dtype
-        )
-        add_time2_ids = pipe._get_add_time_ids(
-            original_size, crops_coords_top_left, target_size, dtype=prompt2_embeds.dtype
-        )
-
-        if negative_original_size is not None and negative_target_size is not None:
-            negative_add_time_ids = pipe._get_add_time_ids(
-                negative_original_size,
-                negative_crops_coords_top_left,
-                negative_target_size,
-                dtype=prompt_embeds.dtype,
-            )
-        else:
-            negative_add_time_ids = add_time_ids
-            negative_add_time2_ids = add_time2_ids
-
-        if do_classifier_free_guidance:
-            prompt_embeds = torch.cat([negative_prompt_embeds, prompt_embeds], dim=0)
-            add_text_embeds = torch.cat([negative_pooled_prompt_embeds, add_text_embeds], dim=0)
-            add_time_ids = torch.cat([negative_add_time_ids, add_time_ids], dim=0)
-
-            prompt2_embeds = torch.cat([negative_prompt2_embeds, prompt2_embeds], dim=0)
-            add_text2_embeds = torch.cat([negative_pooled_prompt2_embeds, add_text2_embeds], dim=0)
-            add_time2_ids = torch.cat([negative_add_time2_ids, add_time2_ids], dim=0)
-
-        prompt_embeds = prompt_embeds.to(device)
-        add_text_embeds = add_text_embeds.to(device)
-        add_time_ids = add_time_ids.to(device).repeat(batch_size * num_images_per_prompt, 1)
-
-        prompt2_embeds = prompt2_embeds.to(device)
-        add_text2_embeds = add_text2_embeds.to(device)
-        add_time2_ids = add_time2_ids.to(device).repeat(batch_size * num_images_per_prompt, 1)
-
-        # 8. Denoising loop
-        num_warmup_steps = max(len(timesteps) - num_inference_steps * pipe.scheduler.order, 0)
-
-        # 7.1 Apply denoising_end
-        if denoising_end is not None and isinstance(denoising_end, float) and denoising_end > 0 and denoising_end < 1:
-            discrete_timestep_cutoff = int(
-                round(
-                    pipe.scheduler.config.num_train_timesteps
-                    - (denoising_end * pipe.scheduler.config.num_train_timesteps)
-                )
-            )
-            num_inference_steps = len(list(filter(lambda ts: ts >= discrete_timestep_cutoff, timesteps)))
-            timesteps = timesteps[:num_inference_steps]
-
-        with pipe.progress_bar(total=num_inference_steps) as progress_bar:
-            for i, t in enumerate(timesteps):
-                if i % 2 == 0:
-                  # expand the latents if we are doing classifier-free guidance
-                  latent_model_input = torch.cat([latents] * 2) if do_classifier_free_guidance else latents
-
-                  latent_model_input = pipe.scheduler.scale_model_input(latent_model_input, t)
-
-                  # predict the noise residual
-                  added_cond_kwargs = {"text_embeds": add_text_embeds, "time_ids": add_time_ids}
-                  noise_pred = pipe.unet(
-                      latent_model_input,
-                      t,
-                      encoder_hidden_states=prompt_embeds,
-                      cross_attention_kwargs=cross_attention_kwargs,
-                      added_cond_kwargs=added_cond_kwargs,
-                      return_dict=False,
-                  )[0]
-
-                  # perform guidance
-                  if do_classifier_free_guidance:
-                      noise_pred_uncond, noise_pred_text = noise_pred.chunk(2)
-                      noise_pred = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
-                else:
-                  # expand the latents if we are doing classifier free guidance
-                  latent_model_input2 = torch.cat([latents.flip(2)] * 2) if do_classifier_free_guidance else latents
-                  latent_model_input2 = pipe.scheduler.scale_model_input(latent_model_input2, t)
-
-                  # predict the noise residual
-                  added_cond2_kwargs = {"text_embeds": add_text2_embeds, "time_ids": add_time2_ids}
-                  noise_pred2 = pipe.unet(
-                      latent_model_input2,
-                      t,
-                      encoder_hidden_states=prompt2_embeds,
-                      cross_attention_kwargs=cross_attention_kwargs,
-                      added_cond_kwargs=added_cond2_kwargs,
-                      return_dict=False,
-                  )[0]
-
-                  # perform guidance
-                  if do_classifier_free_guidance:
-                      noise_pred2_uncond, noise_pred2_text = noise_pred2.chunk(2)
-                      noise_pred2 = noise_pred2_uncond + guidance_scale2 * (noise_pred2_text - noise_pred2_uncond)
-
-                noise_pred = noise_pred if i % 2 == 0 else noise_pred2.flip(2)
-
-                # compute the previous noisy sample x_t -> x_t-1
-                latents = pipe.scheduler.step(noise_pred, t, latents, **extra_step_kwargs, return_dict=False)[0]
-
-                # call the callback, if provided
-                if i == len(timesteps) - 1 or ((i + 1) > num_warmup_steps and (i + 1) % pipe.scheduler.order == 0):
-                    progress_bar.update()
-                    if callback is not None and i % callback_steps == 0:
-                        callback(i, t, latents)
-
-        if not output_type == "latent":
-            # make sure the VAE is in float32 mode, as it overflows in float16
-            needs_upcasting = pipe.vae.dtype == torch.float16 and pipe.vae.config.force_upcast
-
-            if needs_upcasting:
-                pipe.upcast_vae()
-                latents = latents.to(next(iter(pipe.vae.post_quant_conv.parameters())).dtype)
-
-            image = pipe.vae.decode(latents / pipe.vae.config.scaling_factor, return_dict=False)[0]
-
-            # cast back to fp16 if needed
-            if needs_upcasting:
-                pipe.vae.to(dtype=torch.float16)
-        else:
-            image = latents
-
-        if not output_type == "latent":
-            # apply watermark if available
-            if pipe.watermark is not None:
-                image = pipe.watermark.apply_watermark(image)
-
-            image = pipe.image_processor.postprocess(image, output_type=output_type)
-
-        # Offload all models
-        pipe.maybe_free_model_hooks()
-
-        if not return_dict:
-            return (image,)
-
-        return StableDiffusionXLPipelineOutput(images=image)
-
-def read_content(file_path: str) -> str:
-    """read the content of target file
-    """
-    with open(file_path, 'r', encoding='utf-8') as f:
-        content = f.read()
-
-    return content
-
-def predict(dict, prompt="", negative_prompt="", guidance_scale=7.5, steps=20, strength=1.0, scheduler="EulerDiscreteScheduler"):
-    if negative_prompt == "":
-        negative_prompt = None
-    scheduler_class_name = scheduler.split("-")[0]
-
-    add_kwargs = {}
-    if len(scheduler.split("-")) > 1:
-        add_kwargs["use_karras"] = True
-    if len(scheduler.split("-")) > 2:
-        add_kwargs["algorithm_type"] = "sde-dpmsolver++"
-
-    scheduler = getattr(diffusers, scheduler_class_name)
-    pipe.scheduler = scheduler.from_pretrained("stabilityai/stable-diffusion-xl-base-1.0", subfolder="scheduler", **add_kwargs)
+word_list_dataset = load_dataset("stabilityai/word-list", data_files="list.txt", use_auth_token=True)
+word_list = word_list_dataset["train"]['text']
+
+is_gpu_busy = False
+def infer(prompt, negative, scale):
+    global is_gpu_busy
+    for filter in word_list:
+        if re.search(rf"\b{filter}\b", prompt):
+            raise gr.Error("Unsafe content found. Please try again with different prompts.")
+        
+    images = []
+    url = os.getenv('JAX_BACKEND_URL')
+    payload = {'prompt': prompt, 'negative_prompt': negative, 'guidance_scale': scale}
+    images_request = requests.post(url, json = payload)
+    for image in images_request.json()["images"]:
+        image_b64 = (f"data:image/jpeg;base64,{image}")
+        images.append(image_b64)
     
-    init_image = dict["image"].convert("RGB").resize((1024, 1024))
-    mask = dict["mask"].convert("RGB").resize((1024, 1024))
+    return images
     
-    output = pipe(prompt = prompt, negative_prompt=negative_prompt, image=init_image, mask_image=mask, guidance_scale=guidance_scale, num_inference_steps=int(steps), strength=strength)
     
-    return output.images[0], gr.update(visible=True)
-
-
-css = '''
-.gradio-container{max-width: 1100px !important}
-#image_upload{min-height:400px}
-#image_upload [data-testid="image"], #image_upload [data-testid="image"] > div{min-height: 400px}
-#mask_radio .gr-form{background:transparent; border: none}
-#word_mask{margin-top: .75em !important}
-#word_mask textarea:disabled{opacity: 0.3}
-.footer {margin-bottom: 45px;margin-top: 35px;text-align: center;border-bottom: 1px solid #e5e5e5}
-.footer>p {font-size: .8rem; display: inline-block; padding: 0 10px;transform: translateY(10px);background: white}
-.dark .footer {border-color: #303030}
-.dark .footer>p {background: #0b0f19}
-.acknowledgments h4{margin: 1.25em 0 .25em 0;font-weight: bold;font-size: 115%}
-#image_upload .touch-none{display: flex}
-@keyframes spin {
-    from {
-        transform: rotate(0deg);
-    }
-    to {
-        transform: rotate(360deg);
-    }
-}
-#share-btn-container {padding-left: 0.5rem !important; padding-right: 0.5rem !important; background-color: #000000; justify-content: center; align-items: center; border-radius: 9999px !important; max-width: 13rem; margin-left: auto;}
-div#share-btn-container > div {flex-direction: row;background: black;align-items: center}
-#share-btn-container:hover {background-color: #060606}
-#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: 0.5rem !important; padding-bottom: 0.5rem !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}
-#share-btn-container.hidden {display: none!important}
-#prompt input{width: calc(100% - 160px);border-top-right-radius: 0px;border-bottom-right-radius: 0px;}
-#run_button{position:absolute;margin-top: 11px;right: 0;margin-right: 0.8em;border-bottom-left-radius: 0px;
-    border-top-left-radius: 0px;}
-#prompt-container{margin-top:-18px;}
-#prompt-container .form{border-top-left-radius: 0;border-top-right-radius: 0}
-#image_upload{border-bottom-left-radius: 0px;border-bottom-right-radius: 0px}
-'''
-
-image_blocks = gr.Blocks(css=css, elem_id="total-container")
-with image_blocks as demo:
-    gr.HTML(read_content("header.html"))
-    with gr.Row():
-                with gr.Column():
-                    image = gr.Image(source='upload', tool='sketch', elem_id="image_upload", type="pil", label="Upload",height=400)
-                    with gr.Row(elem_id="prompt-container", mobile_collapse=False, equal_height=True):
-                        with gr.Row():
-                            prompt = gr.Textbox(placeholder="Your prompt (what you want in place of what is erased)", show_label=False, elem_id="prompt")
-                            btn = gr.Button("Inpaint!", elem_id="run_button")
-                    
-                    with gr.Accordion(label="Advanced Settings", open=False):
-                        with gr.Row(mobile_collapse=False, equal_height=True):
-                            guidance_scale = gr.Number(value=7.5, minimum=1.0, maximum=20.0, step=0.1, label="guidance_scale")
-                            steps = gr.Number(value=20, minimum=10, maximum=30, step=1, label="steps")
-                            strength = gr.Number(value=0.99, minimum=0.01, maximum=0.99, step=0.01, label="strength")
-                            negative_prompt = gr.Textbox(label="negative_prompt", placeholder="Your negative prompt", info="what you don't want to see in the image")
-                        with gr.Row(mobile_collapse=False, equal_height=True):
-                            schedulers = ["DEISMultistepScheduler", "HeunDiscreteScheduler", "EulerDiscreteScheduler", "DPMSolverMultistepScheduler", "DPMSolverMultistepScheduler-Karras", "DPMSolverMultistepScheduler-Karras-SDE"]
-                            scheduler = gr.Dropdown(label="Schedulers", choices=schedulers, value="EulerDiscreteScheduler")
-                        
-                with gr.Column():
-                    image_out = gr.Image(label="Output", elem_id="output-img", height=400)
-                    with gr.Group(elem_id="share-btn-container", visible=False) as share_btn_container:
-                        community_icon = gr.HTML(community_icon_html)
-                        loading_icon = gr.HTML(loading_icon_html)
-                        share_button = gr.Button("Share to community", elem_id="share-btn",visible=True)
-            
-
-    btn.click(fn=predict, inputs=[image, prompt, negative_prompt, guidance_scale, steps, strength, scheduler], outputs=[image_out, share_btn_container], api_name='run')
-    prompt.submit(fn=predict, inputs=[image, prompt, negative_prompt, guidance_scale, steps, strength, scheduler], outputs=[image_out, share_btn_container])
-    share_button.click(None, [], [], _js=share_js)
-
-    gr.Examples(
-                examples=[
-                    ["./imgs/aaa (8).png"],
-                    ["./imgs/download (1).jpeg"],
-                    ["./imgs/0_oE0mLhfhtS_3Nfm2.png"],
-                    ["./imgs/02_HubertyBlog-1-1024x1024.jpg"],
-                    ["./imgs/jdn_jacques_de_nuce-1024x1024.jpg"],
-                    ["./imgs/c4ca473acde04280d44128ad8ee09e8a.jpg"],
-                    ["./imgs/canam-electric-motorcycles-scaled.jpg"],
-                    ["./imgs/e8717ce80b394d1b9a610d04a1decd3a.jpeg"],
-                    ["./imgs/Nature___Mountains_Big_Mountain_018453_31.jpg"],
-                    ["./imgs/Multible-sharing-room_ccexpress-2-1024x1024.jpeg"],
-                ],
-                fn=predict,
-                inputs=[image],
-                cache_examples=False,
-    )
+css = """
+        .gradio-container {
+            font-family: 'IBM Plex Sans', sans-serif;
+        }
+        .gr-button {
+            color: white;
+            border-color: black;
+            background: black;
+        }
+        input[type='range'] {
+            accent-color: black;
+        }
+        .dark input[type='range'] {
+            accent-color: #dfdfdf;
+        }
+        .container {
+            max-width: 730px;
+            margin: auto;
+            padding-top: 1.5rem;
+        }
+        #gallery {
+            min-height: 22rem;
+            margin-bottom: 15px;
+            margin-left: auto;
+            margin-right: auto;
+            border-bottom-right-radius: .5rem !important;
+            border-bottom-left-radius: .5rem !important;
+        }
+        #gallery>div>.h-full {
+            min-height: 20rem;
+        }
+        .details:hover {
+            text-decoration: underline;
+        }
+        .gr-button {
+            white-space: nowrap;
+        }
+        .gr-button:focus {
+            border-color: rgb(147 197 253 / var(--tw-border-opacity));
+            outline: none;
+            box-shadow: var(--tw-ring-offset-shadow), var(--tw-ring-shadow), var(--tw-shadow, 0 0 #0000);
+            --tw-border-opacity: 1;
+            --tw-ring-offset-shadow: var(--tw-ring-inset) 0 0 0 var(--tw-ring-offset-width) var(--tw-ring-offset-color);
+            --tw-ring-shadow: var(--tw-ring-inset) 0 0 0 calc(3px var(--tw-ring-offset-width)) var(--tw-ring-color);
+            --tw-ring-color: rgb(191 219 254 / var(--tw-ring-opacity));
+            --tw-ring-opacity: .5;
+        }
+        #advanced-btn {
+            font-size: .7rem !important;
+            line-height: 19px;
+            margin-top: 12px;
+            margin-bottom: 12px;
+            padding: 2px 8px;
+            border-radius: 14px !important;
+        }
+        #advanced-options {
+            display: none;
+            margin-bottom: 20px;
+        }
+        .footer {
+            margin-bottom: 45px;
+            margin-top: 35px;
+            text-align: center;
+            border-bottom: 1px solid #e5e5e5;
+        }
+        .footer>p {
+            font-size: .8rem;
+            display: inline-block;
+            padding: 0 10px;
+            transform: translateY(10px);
+            background: white;
+        }
+        .dark .footer {
+            border-color: #303030;
+        }
+        .dark .footer>p {
+            background: #0b0f19;
+        }
+        .acknowledgments h4{
+            margin: 1.25em 0 .25em 0;
+            font-weight: bold;
+            font-size: 115%;
+        }
+        .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;
+            margin-top: 10px;
+            margin-left: auto;
+        }
+        #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;
+        }
+        
+        .gr-form{
+            flex: 1 1 50%; border-top-right-radius: 0; border-bottom-right-radius: 0;
+        }
+        #prompt-container{
+            gap: 0;
+        }
+        #prompt-text-input, #negative-prompt-text-input{padding: .45rem 0.625rem}
+        #component-16{border-top-width: 1px!important;margin-top: 1em}
+        .image_duplication{position: absolute; width: 100px; left: 50px}
+"""
+
+block = gr.Blocks(css=css)
+
+examples = [
+    [
+        'A high tech solarpunk utopia in the Amazon rainforest',
+        'low quality',
+        9
+    ],
+    [
+        'A pikachu fine dining with a view to the Eiffel Tower',
+        'low quality',
+        9
+    ],
+    [
+        'A mecha robot in a favela in expressionist style',
+        'low quality, 3d, photorealistic',
+        9
+    ],
+    [
+        'an insect robot preparing a delicious meal',
+        'low quality, illustration',
+        9
+    ],
+    [
+        "A small cabin on top of a snowy mountain in the style of Disney, artstation",
+        'low quality, ugly',
+        9
+    ],
+]
+
+
+with block:
     gr.HTML(
         """
-            <div class="footer">
-                <p>Model by <a href="https://huggingface.co/diffusers" style="text-decoration: underline;" target="_blank">Diffusers</a> - Gradio Demo by 🤗 Hugging Face
-                </p>
+            <div style="text-align: center; margin: 0 auto;">
+              <div
+                style="
+                  display: inline-flex;
+                  align-items: center;
+                  gap: 0.8rem;
+                  font-size: 1.75rem;
+                "
+              >
+                <svg
+                  width="0.65em"
+                  height="0.65em"
+                  viewBox="0 0 115 115"
+                  fill="none"
+                  xmlns="http://www.w3.org/2000/svg"
+                >
+                  <rect width="23" height="23" fill="white"></rect>
+                  <rect y="69" width="23" height="23" fill="white"></rect>
+                  <rect x="23" width="23" height="23" fill="#AEAEAE"></rect>
+                  <rect x="23" y="69" width="23" height="23" fill="#AEAEAE"></rect>
+                  <rect x="46" width="23" height="23" fill="white"></rect>
+                  <rect x="46" y="69" width="23" height="23" fill="white"></rect>
+                  <rect x="69" width="23" height="23" fill="black"></rect>
+                  <rect x="69" y="69" width="23" height="23" fill="black"></rect>
+                  <rect x="92" width="23" height="23" fill="#D9D9D9"></rect>
+                  <rect x="92" y="69" width="23" height="23" fill="#AEAEAE"></rect>
+                  <rect x="115" y="46" width="23" height="23" fill="white"></rect>
+                  <rect x="115" y="115" width="23" height="23" fill="white"></rect>
+                  <rect x="115" y="69" width="23" height="23" fill="#D9D9D9"></rect>
+                  <rect x="92" y="46" width="23" height="23" fill="#AEAEAE"></rect>
+                  <rect x="92" y="115" width="23" height="23" fill="#AEAEAE"></rect>
+                  <rect x="92" y="69" width="23" height="23" fill="white"></rect>
+                  <rect x="69" y="46" width="23" height="23" fill="white"></rect>
+                  <rect x="69" y="115" width="23" height="23" fill="white"></rect>
+                  <rect x="69" y="69" width="23" height="23" fill="#D9D9D9"></rect>
+                  <rect x="46" y="46" width="23" height="23" fill="black"></rect>
+                  <rect x="46" y="115" width="23" height="23" fill="black"></rect>
+                  <rect x="46" y="69" width="23" height="23" fill="black"></rect>
+                  <rect x="23" y="46" width="23" height="23" fill="#D9D9D9"></rect>
+                  <rect x="23" y="115" width="23" height="23" fill="#AEAEAE"></rect>
+                  <rect x="23" y="69" width="23" height="23" fill="black"></rect>
+                </svg>
+                <h1 style="font-weight: 900; margin-bottom: 7px;margin-top:5px">
+                  Stable Diffusion 2.1 Demo
+                </h1>
+              </div>
+              <p style="margin-bottom: 10px; font-size: 94%; line-height: 23px;">
+                Stable Diffusion 2.1 is the latest text-to-image model from StabilityAI. <a style="text-decoration: underline;" href="https://huggingface.co/spaces/stabilityai/stable-diffusion-1">Access Stable Diffusion 1 Space here</a><br>For faster generation and API
+                access you can try
+                <a
+                  href="http://beta.dreamstudio.ai/"
+                  style="text-decoration: underline;"
+                  target="_blank"
+                  >DreamStudio Beta</a
+                >.</a>
+              </p>
             </div>
         """
     )
+    with gr.Group():
+        with gr.Box():
+            with gr.Row(elem_id="prompt-container").style(mobile_collapse=False, equal_height=True):
+                with gr.Column():
+                    text = gr.Textbox(
+                        label="Enter your prompt",
+                        show_label=False,
+                        max_lines=1,
+                        placeholder="Enter your prompt",
+                        elem_id="prompt-text-input",
+                    ).style(
+                        border=(True, False, True, True),
+                        rounded=(True, False, False, True),
+                        container=False,
+                    )
+                    negative = gr.Textbox(
+                        label="Enter your negative prompt",
+                        show_label=False,
+                        max_lines=1,
+                        placeholder="Enter a negative prompt",
+                        elem_id="negative-prompt-text-input",
+                    ).style(
+                        border=(True, False, True, True),
+                        rounded=(True, False, False, True),
+                        container=False,
+                    )
+                btn = gr.Button("Generate image").style(
+                    margin=False,
+                    rounded=(False, True, True, False),
+                    full_width=False,
+                )
+
+        gallery = gr.Gallery(
+            label="Generated images", show_label=False, elem_id="gallery"
+        ).style(grid=[2], height="auto")
+
+        with gr.Group(elem_id="container-advanced-btns"):
+            #advanced_button = gr.Button("Advanced options", elem_id="advanced-btn")
+            with gr.Group(elem_id="share-btn-container"):
+                community_icon = gr.HTML(community_icon_html)
+                loading_icon = gr.HTML(loading_icon_html)
+                share_button = gr.Button("Share to community", elem_id="share-btn")
+
+        with gr.Accordion("Advanced settings", open=False):
+        #    gr.Markdown("Advanced settings are temporarily unavailable")
+        #    samples = gr.Slider(label="Images", minimum=1, maximum=4, value=4, step=1)
+        #    steps = gr.Slider(label="Steps", minimum=1, maximum=50, value=45, step=1)
+             guidance_scale = gr.Slider(
+                label="Guidance Scale", minimum=0, maximum=50, value=9, step=0.1
+             )
+        #    seed = gr.Slider(
+        #        label="Seed",
+        #        minimum=0,
+        #        maximum=2147483647,
+        #        step=1,
+        #        randomize=True,
+        #    )
+
+        ex = gr.Examples(examples=examples, fn=infer, inputs=[text, negative, guidance_scale], outputs=[gallery, community_icon, loading_icon, share_button], cache_examples=False)
+        ex.dataset.headers = [""]
+        negative.submit(infer, inputs=[text, negative, guidance_scale], outputs=[gallery], postprocess=False)
+        text.submit(infer, inputs=[text, negative, guidance_scale], outputs=[gallery], postprocess=False)
+        btn.click(infer, inputs=[text, negative, guidance_scale], outputs=[gallery], postprocess=False)
+        
+        #advanced_button.click(
+        #    None,
+        #    [],
+        #    text,
+        #    _js="""
+        #    () => {
+        #        const options = document.querySelector("body > gradio-app").querySelector("#advanced-options");
+        #        options.style.display = ["none", ""].includes(options.style.display) ? "flex" : "none";
+        #    }""",
+        #)
+        share_button.click(
+            None,
+            [],
+            [],
+            _js=share_js,
+        )
+        gr.HTML(
+            """
+                <div class="footer">
+                    <p>Model by <a href="https://huggingface.co/stabilityai" style="text-decoration: underline;" target="_blank">StabilityAI</a> - backend running JAX on TPUs due to generous support of <a href="https://sites.research.google/trc/about/" style="text-decoration: underline;" target="_blank">Google TRC program</a> - Gradio Demo by 🤗 Hugging Face
+                    </p>
+                </div>
+           """
+        )
+        with gr.Accordion(label="License", open=False):
+            gr.HTML(
+                """<div class="acknowledgments">
+                    <p><h4>LICENSE</h4>
+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>
+                    <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>
+                """
+            )
+        
 
-image_blocks.queue(max_size=25).launch()
+block.queue(concurrency_count=80, max_size=100).launch(max_threads=150)