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| """Gradio entry point for local use and Hugging Face ZeroGPU.""" | |
| import faulthandler | |
| faulthandler.enable() # Include Python stacks if native startup code crashes. | |
| import spaces # Must initialize ZeroGPU before importing torch/diffusers. | |
| import base64 | |
| import html | |
| import inspect | |
| from io import BytesIO | |
| import os | |
| import secrets | |
| import time | |
| import gradio as gr | |
| import numpy as np | |
| from PIL import Image | |
| from generate import CHECKPOINTS, MODEL_ID, MODEL_SIZES, ROOT, generate_images, load_model | |
| ZERO_GPU = os.environ.get("SPACES_ZERO_GPU", "").lower() in {"1", "true", "yes"} | |
| def gpu_unavailable(error): | |
| """Recognize allocation/quota/CUDA failures without masking model errors.""" | |
| # Spaces puts the stable error category in gr.Error.title, while the | |
| # message varies by authentication/subscription tier and quota type. | |
| title = str(getattr(error, "title", "")).lower() | |
| message = str(getattr(error, "message", str(error))).lower() | |
| if title in {"zerogpu quota exceeded", "zerogpu pending credits exceeded"}: | |
| return True | |
| quota_failure = ( | |
| ("quota" in message and ("exceeded" in message or "exhausted" in message)) | |
| or "space app has reached its gpu limit" in message | |
| or "exceeded your zerogpu runs limit" in message | |
| or "too many zerogpu credits allocated" in message | |
| ) | |
| return quota_failure or any(text in message for text in ( | |
| "no gpu was available", "no gpu available", "cuda out of memory", | |
| "cuda is not available", "no nvidia driver", "not compiled with cuda", | |
| "cuda driver", "cuda-capable device", "gpu task aborted", | |
| )) | |
| # ZeroGPU requires CUDA model placement before its worker is allocated. | |
| GPU_READY = False | |
| MPS_READY = False | |
| if ZERO_GPU: | |
| try: | |
| for model_id in MODEL_SIZES: | |
| load_model(device="cuda", model_id=model_id) | |
| GPU_READY = True | |
| except Exception as error: | |
| if not gpu_unavailable(error): | |
| raise | |
| print(f"GPU unavailable at startup; using CPU: {error}") | |
| else: | |
| import torch | |
| GPU_READY = torch.cuda.is_available() | |
| MPS_READY = hasattr(torch.backends, "mps") and torch.backends.mps.is_available() | |
| def parse_settings(seed, checkpoint_text): | |
| try: | |
| seed = int(str(seed).strip()) | |
| checkpoints = sorted(set(int(part.strip()) for part in checkpoint_text.split(","))) | |
| except (ValueError, TypeError): | |
| raise gr.Error("Enter an integer seed and comma-separated integer checkpoints.") | |
| if not 0 <= seed <= 2147483647: | |
| raise gr.Error("Seed must be between 0 and 2147483647.") | |
| if not 2 <= len(checkpoints) <= 12 or checkpoints[0] != 0 or checkpoints[-1] != 1000: | |
| raise gr.Error("Use 2–12 checkpoints between 0 and 1000, including both endpoints.") | |
| return seed, checkpoints | |
| BLANK_IMAGE = Image.new("RGB", (256, 256), "#e7e5dc") | |
| def checkpoint_slots(checkpoints, frames=None): | |
| """Keep every timestep in its own fixed slot, including pending frames.""" | |
| frames = frames or {} | |
| return [(frames.get(step, BLANK_IMAGE), f"t = {step}") for step in checkpoints] | |
| def gaussian_noise(seed, model_id=MODEL_ID): | |
| noise = np.random.default_rng(int(seed)).standard_normal((MODEL_SIZES[model_id], MODEL_SIZES[model_id], 3)) | |
| return Image.fromarray(np.round((noise.clip(-1, 1) + 1) * 127.5).astype(np.uint8)) | |
| def progress_text(completed=0, started=None): | |
| elapsed = time.monotonic() - started if started is not None else 0 | |
| rate = completed / elapsed if elapsed > 0 and completed else 0 | |
| def duration(seconds): | |
| if seconds is None: | |
| return "--:--" | |
| seconds = int(seconds) | |
| hours, seconds = divmod(seconds, 3600) | |
| minutes, seconds = divmod(seconds, 60) | |
| return (f"{hours:02d}:" if hours else "") + f"{minutes:02d}:{seconds:02d}" | |
| remaining = 0 if completed == 1000 else (1000 - completed) / rate if rate else None | |
| return (f"{completed}/1000 [{duration(elapsed)}<{duration(remaining)}, " | |
| f"{f'{rate:.2f}' if rate else '?'}it/s]") | |
| def diffuse(image, seed, checkpoint_text, model_id=MODEL_ID): | |
| seed, checkpoints = parse_settings(seed, checkpoint_text) | |
| if image is None: | |
| raise gr.Error("Upload an image first.") | |
| pixels = np.asarray(image.convert("RGB").resize((MODEL_SIZES[model_id], MODEL_SIZES[model_id])), dtype=np.float32) / 127.5 - 1 | |
| noise = np.random.default_rng(seed).standard_normal(pixels.shape) | |
| alpha_bars = np.cumprod(1 - np.linspace(0.0001, 0.02, 1000)) | |
| started = time.monotonic() | |
| frames = {0: image.convert("RGB").resize((MODEL_SIZES[model_id], MODEL_SIZES[model_id]))} | |
| final_noise = None | |
| yield checkpoint_slots(checkpoints, frames), None, progress_html(0, started) | |
| for step in checkpoints: | |
| alpha = 1 if step == 0 else alpha_bars[step - 1] | |
| sample = np.sqrt(alpha) * pixels + np.sqrt(1 - alpha) * noise | |
| frame = Image.fromarray(np.round((sample.clip(-1, 1) + 1) * 127.5).astype(np.uint8)) | |
| frames[step] = frame | |
| final_noise = frame | |
| yield checkpoint_slots(checkpoints, frames), final_noise if step == 1000 else None, progress_html(step, started) | |
| def stream_denoising(seed, checkpoints, initial_image, device, model_id=MODEL_ID): | |
| started = time.monotonic() | |
| frames = {1000: initial_image if initial_image is not None else gaussian_noise(seed, model_id)} | |
| yield checkpoint_slots(checkpoints, frames), progress_html(0, started) | |
| print('using', device) | |
| for step, frame in generate_images(seed, checkpoints, initial_image, device=device, model_id=model_id): | |
| if frame is not None: | |
| frames[step] = frame | |
| yield (checkpoint_slots(checkpoints, frames) if frame is not None else gr.skip(), | |
| progress_html(1000 - step, started)) | |
| def denoise_gpu(seed, checkpoints, initial_image, model_id=MODEL_ID): | |
| yield from stream_denoising(seed, checkpoints, initial_image, "cuda", model_id) | |
| def denoise(seed, checkpoint_text, input_mode, forward_noise, model_id=MODEL_ID): | |
| seed, checkpoints = parse_settings(seed, checkpoint_text) | |
| initial_image = forward_noise if input_mode == "Forward noise" else None | |
| if input_mode == "Forward noise" and initial_image is None: | |
| raise gr.Error("Run Diffuse first to create forward noise.") | |
| if GPU_READY: | |
| try: | |
| yield from denoise_gpu(seed, checkpoints, initial_image, model_id) | |
| return | |
| except Exception as error: | |
| if not gpu_unavailable(error): | |
| raise | |
| fallback = "MPS" if MPS_READY else "CPU" | |
| print(f"CUDA unavailable; restarting on {fallback}: {error}") | |
| gr.Info(f"CUDA unavailable or quota exhausted. Running on {fallback}.") | |
| if MPS_READY: | |
| try: | |
| yield from stream_denoising(seed, checkpoints, initial_image, "mps", model_id) | |
| return | |
| except (RuntimeError, NotImplementedError) as error: | |
| message = str(error).lower() | |
| if "mps" not in message and "metal" not in message: | |
| raise | |
| print(f"MPS unavailable; restarting on CPU: {error}") | |
| gr.Info("MPS unavailable. Running on CPU.") | |
| yield from stream_denoising(seed, checkpoints, initial_image, "cpu", model_id) | |
| # Reuse the original page's palette, typography, header, and panel styling. | |
| PAGE_CSS = (ROOT / "styles.css").read_text() + """ | |
| .gradio-container { max-width:1440px !important; padding:0 6% !important; | |
| background:var(--paper) !important; color:var(--ink) !important; | |
| background-image:radial-gradient(#59644d12 .7px,transparent .7px) !important; | |
| background-size:8px 8px !important; font-family:var(--sans) !important; } | |
| /* Keep the brand close to the top and Configure close to its divider. */ | |
| .gradio-container > .main { padding-top:0 !important; } | |
| #lab-header { padding:0 !important; margin:0 !important; } | |
| #lab-header .html-container { padding:0 !important; margin:0 !important; } | |
| #lab-header .topbar { height:38px; align-items:flex-start; padding-top:2px; } | |
| #lab-header .brand { font-size:24px; line-height:30px; } | |
| #lab-header .topbar-right { padding-top:12px; } | |
| #lab-main { gap:22px; padding:0; } | |
| .gradio-container > .main { padding-bottom:0 !important; flex-grow:0 !important; } | |
| .gradio-container, .gradio-container > .wrap { min-height:0 !important; } | |
| .gradio-container > .wrap { flex-grow:0 !important; } | |
| .gradio-container footer { margin-top:0 !important; padding:4px 0 !important; min-height:0 !important; } | |
| .gradio-container .gap:has(> #lab-header) { gap:0 !important; } | |
| .lab-panel { border-radius:0 !important; gap:12px !important; } | |
| .lab-panel h1,.lab-panel h2 { color:var(--ink); } | |
| .lab-panel.control-panel { padding-top:8px !important; } | |
| .control-panel > .html-container, .control-panel > .block { margin-top:0 !important; } | |
| .control-panel h1 { margin:0 !important; } | |
| .lab-panel.process-panel { padding:12px 14px !important; gap:8px !important; } | |
| .config-row { display:flex !important; gap:0 !important; width:100%; max-width:none; flex-wrap:nowrap !important; } | |
| .config-label { flex:0 0 110px !important; min-width:110px !important; | |
| height:40px !important; min-height:40px !important; padding:0 12px !important; display:flex; align-items:center; justify-content:flex-start !important; opacity:1 !important; | |
| background:#f0f2eb !important; border:1px solid #cfd3c9 !important; | |
| border-radius:3px 0 0 3px !important; color:var(--green) !important; | |
| font:10px var(--mono) !important; } | |
| .config-field { flex:1 1 0 !important; min-width:0 !important; border:0 !important; padding:0 !important; background:transparent !important; } | |
| /* Gradio nests the textbox inside a label; undo the original page's | |
| .config-row label styles so that wrapper cannot become a second box. */ | |
| .config-field label { display:block !important; width:100% !important; min-width:0 !important; | |
| border:0 !important; padding:0 !important; margin:0 !important; background:transparent !important; } | |
| .config-field .input-container { width:100% !important; max-width:none !important; } | |
| .config-field textarea,.config-field input { width:100% !important; max-width:none !important; box-sizing:border-box !important; height:40px !important; min-height:40px !important; | |
| border:1px solid #cfd3c9 !important; border-radius:0 3px 3px 0 !important; | |
| padding:10px 12px !important; font:11px var(--mono) !important; background:white !important; white-space:pre !important; overflow-x:auto !important; resize:none !important; } | |
| /* The dropdown wrapper adds spacing that textboxes do not have. */ | |
| .config-row:has(#model-field) { column-gap:0 !important; } | |
| .config-row:has(#model-field) > .form { margin:0 !important; padding:0 !important; gap:0 !important; } | |
| #model-field { margin:0 !important; padding:0 !important; | |
| height:40px !important; min-height:40px !important; max-height:40px !important; } | |
| #model-field .wrap, #model-field .input-container { margin:0 !important; } | |
| #model-field .container { padding:0 !important; margin:0 !important; } | |
| #model-field .wrap { box-sizing:border-box !important; | |
| height:40px !important; min-height:40px !important; max-height:40px !important; | |
| border:1px solid #cfd3c9 !important; | |
| border-radius:0 3px 3px 0 !important; box-shadow:none !important; } | |
| #model-field .wrap-inner { height:38px !important; min-height:0 !important; | |
| padding:0 12px !important; gap:0 !important; } | |
| /* The dropdown input sits inside its own bordered wrapper. */ | |
| #model-field input { border:0 !important; border-radius:0 !important; | |
| margin:0 !important; padding:0 !important; background:transparent !important; box-shadow:none !important; | |
| height:38px !important; min-height:38px !important; } | |
| .process-button { flex:0 0 auto !important; min-width:76px !important; | |
| height:28px !important; min-height:28px !important; padding:0 12px !important; | |
| background:var(--green) !important; color:white !important; border-radius:3px !important; | |
| font:10px var(--mono) !important; } | |
| #apply-timesteps { height:40px !important; border-radius:0 3px 3px 0 !important; } | |
| .process-heading { margin-bottom:0; } | |
| .process-toolbar { align-items:center !important; gap:12px !important; } | |
| /* Three columns keep progress centered independently of the controls. */ | |
| .process-toolbar { display:grid !important; grid-template-columns:minmax(0,1fr) max-content minmax(0,1fr); | |
| align-items:center !important; } | |
| .process-toolbar > .process-button { justify-self:start; } | |
| .process-toolbar > .process-button:last-child { justify-self:end; } | |
| .progress-counter { justify-self:center; min-width:0 !important; width:max-content !important; max-width:100%; | |
| border:0 !important; background:transparent !important; padding:0 !important; } | |
| .progress-counter .html-container { padding:0 !important; } | |
| .progress-counter pre { margin:0; padding:3px 5px; color:var(--coral); | |
| background:#f5f4ef; border:1px solid #d6dacf; border-radius:3px; | |
| font:11px var(--mono); text-align:center; white-space:pre; overflow-x:auto; } | |
| .checkpoint-track { background:transparent !important; border:0 !important; padding:0 !important; } | |
| /* Gradio's auto-height galleries still impose a 450px minimum. */ | |
| .checkpoint-track .grid-wrap { overflow-x:auto !important; overflow-y:hidden !important; | |
| height:auto !important; min-height:0 !important; max-height:none !important; | |
| padding:0 !important; } | |
| .checkpoint-track .grid-container { height:auto !important; min-height:0 !important; } | |
| .checkpoint-track .grid-container { gap:10px !important; grid-auto-rows:auto !important; } | |
| .checkpoint-track .thumbnail-item { min-width:0 !important; aspect-ratio:1 !important; height:auto !important; } | |
| .checkpoint-track .thumbnail-item { border-radius:0 !important; background:#e7e5dc !important; } | |
| .checkpoint-track .caption { font:11px var(--mono) !important; color:var(--green) !important; } | |
| .checkpoint-row { position:relative; gap:0 !important; overflow:visible !important; } | |
| .checkpoint-preview-host { position:relative; height:0 !important; min-height:0 !important; padding:0 !important; | |
| margin:0 !important; border:0 !important; overflow:visible !important; z-index:1000; } | |
| .checkpoint-preview-host .html-container { padding:0 !important; overflow:visible !important; } | |
| .checkpoint-preview { position:absolute; bottom:8px; width:max-content; max-width:none; z-index:1000; | |
| border:2px solid #fbfaf6; background:#fbfaf6; box-shadow:0 8px 24px #25312852; } | |
| .checkpoint-preview summary { cursor:pointer; padding:3px 6px; font:10px var(--mono); } | |
| .checkpoint-preview img { width:auto; height:auto; max-width:none; display:block; } | |
| .checkpoint-preview:not([open]) { display:none; } | |
| #reverse-input { justify-self:end; width:max-content !important; min-width:0 !important; | |
| border:0 !important; background:transparent !important; padding:0 !important; } | |
| #reverse-input .wrap, #reverse-input .form, #reverse-input .container { | |
| width:max-content !important; min-width:0 !important; padding:0 !important; | |
| gap:6px !important; justify-content:flex-end !important; } | |
| #reverse-input label { flex:0 0 auto !important; width:max-content !important; | |
| margin:0 !important; padding:4px 6px !important; } | |
| @media(max-width:620px) { | |
| .gradio-container { padding:0 4% !important; } | |
| .config-label { flex-basis:82px !important; min-width:82px !important; } | |
| .process-heading { align-items:flex-start; flex-direction:column; } | |
| .process-toolbar { grid-template-columns:1fr !important; } | |
| .process-toolbar > :last-child { justify-self:end; } | |
| } | |
| """ | |
| PAGE_HEAD = """<link href="https://fonts.googleapis.com/css2?family=DM+Mono:wght@400;500&family=DM+Sans:wght@400;500;600;700&family=Space+Grotesk:wght@400;500;600;700&display=swap" rel="stylesheet">""" | |
| # Gradio 6 moved CSS/head from Blocks to launch; support both runtime versions. | |
| PAGE_OPTIONS = {"css": PAGE_CSS, "head": PAGE_HEAD} | |
| BLOCK_OPTIONS = PAGE_OPTIONS if "css" not in inspect.signature(gr.Blocks.launch).parameters else {} | |
| LAUNCH_OPTIONS = PAGE_OPTIONS if not BLOCK_OPTIONS else {} | |
| # Install after a native selection event so this also works when Spaces mounts | |
| # Blocks without using the local launch options. Traverse open shadow roots. | |
| INSTALL_PREVIEW_DISMISS = """() => { | |
| if (window.diffusionDismissInstalled) return; | |
| window.diffusionDismissInstalled = true; | |
| const closeOutside = (root, path) => { | |
| for (const preview of root.querySelectorAll(".checkpoint-preview[open]")) { | |
| if (!path.includes(preview)) preview.open = false; | |
| } | |
| for (const element of root.querySelectorAll("*")) { | |
| if (element.shadowRoot) closeOutside(element.shadowRoot, path); | |
| } | |
| }; | |
| document.addEventListener("pointerdown", event => { | |
| closeOutside(document, event.composedPath()); | |
| }, true); | |
| document.addEventListener("keydown", event => { | |
| if (event.key === "Escape") closeOutside(document, []); | |
| }); | |
| }""" | |
| def progress_html(completed=0, started=None): | |
| return "<pre>" + progress_text(completed, started) + "</pre>" | |
| def reset_tracks(seed, checkpoint_text, image, model_id=MODEL_ID): | |
| seed, steps = parse_settings(seed, checkpoint_text) | |
| initial = {0: image.convert("RGB").resize((MODEL_SIZES[model_id], MODEL_SIZES[model_id]))} if image is not None else {} | |
| return (", ".join(map(str, steps)), | |
| gr.Gallery(value=checkpoint_slots(steps, initial), columns=len(steps), allow_preview=False, interactive=False), | |
| gr.Gallery(value=checkpoint_slots(steps, {1000: gaussian_noise(seed, model_id)}), columns=len(steps), allow_preview=False, interactive=False), | |
| None, progress_html(), progress_html()) | |
| def change_source(filepath, seed, checkpoint_text, model_id=MODEL_ID): | |
| with Image.open(filepath) as uploaded: | |
| image = uploaded.convert("RGB") | |
| return (image, *reset_tracks(seed, checkpoint_text, image, model_id)) | |
| def change_model(seed, checkpoint_text, image, model_id): | |
| source_paths = { | |
| MODEL_ID: ROOT / "assets/celeba_hq_validation_91_256.png", | |
| "google/ddpm-cifar10-32": ROOT / "assets/cifar10_train_23_32.png", | |
| } | |
| return change_source(source_paths[model_id], seed, checkpoint_text, model_id) | |
| def show_checkpoint_preview(gallery, event: gr.SelectData): | |
| if not gallery: | |
| return "" | |
| index = int(event.index) | |
| image, caption = gallery[index] | |
| if isinstance(image, dict): | |
| image = image["path"] | |
| if not isinstance(image, Image.Image): | |
| with Image.open(image) as loaded: | |
| image = loaded.convert("RGB") | |
| buffer = BytesIO() | |
| image.save(buffer, format="PNG") | |
| encoded = base64.b64encode(buffer.getvalue()).decode("ascii") | |
| width, height = image.size | |
| preview_width = width + 4 | |
| center = 100 * (index + 0.5) / len(gallery) | |
| # Force fresh markup even when the same thumbnail is clicked again, so | |
| # a details element closed in the browser is recreated in its open state. | |
| preview_id = secrets.token_hex(8) | |
| return (f'<details open class="checkpoint-preview" data-preview-id="{preview_id}" ' | |
| f'style="left:clamp(0px, calc({center}% - {preview_width / 2}px), max(0px, calc(100% - {preview_width}px)))">' | |
| '<summary>Close preview</summary>' | |
| f'<img width="{width}" height="{height}" src="data:image/png;base64,{encoded}" alt="{html.escape(caption or "Checkpoint", quote=True)}">' | |
| '</details>') | |
| with gr.Blocks(title="Diffusion — A DDPM in motion", **BLOCK_OPTIONS) as demo: | |
| gr.HTML('<header class="topbar"><a class="brand" href="#lab-main">' | |
| '<span class="brand-mark" aria-hidden="true"><i></i><i></i><i></i><i></i></span>' | |
| '<span>diffusion model</span></a><div class="topbar-right">' | |
| '<span class="live-dot"></span></div></header>', | |
| elem_id="lab-header", container=False) | |
| with gr.Column(elem_id="lab-main"): | |
| with gr.Column(elem_classes=["lab-panel", "control-panel"]): | |
| gr.HTML("<h1>Configure</h1>") | |
| with gr.Row(elem_classes="config-row"): | |
| gr.Button("Model", interactive=False, scale=0, min_width=110, elem_classes="config-label") | |
| model = gr.Dropdown(choices=list(MODEL_SIZES), value=MODEL_ID, | |
| scale=10, min_width=0, show_label=False, container=False, elem_id="model-field", elem_classes="config-field") | |
| with gr.Row(elem_classes="config-row"): | |
| gr.Button("Timesteps", interactive=False, scale=0, min_width=110, elem_classes="config-label") | |
| checkpoints = gr.Textbox(value=", ".join(map(str, CHECKPOINTS)), show_label=False, | |
| scale=10, min_width=0, lines=1, max_lines=1, container=False, elem_classes="config-field") | |
| apply_button = gr.Button("Apply", scale=0, min_width=76, elem_id="apply-timesteps", elem_classes="process-button") | |
| with gr.Row(elem_classes="config-row"): | |
| randomize = gr.Button("Seed", scale=0, min_width=110, elem_classes="config-label") | |
| seed = gr.Textbox(value="42", show_label=False, scale=10, min_width=0, lines=1, max_lines=1, container=False, elem_classes="config-field") | |
| randomize.click(lambda: str(secrets.randbelow(2**31)), outputs=seed, queue=False) | |
| default_image = ROOT / "assets/celeba_hq_validation_91_256.png" | |
| initial_image = Image.open(default_image).convert("RGB") if default_image.exists() else None | |
| initial_frames = {0: initial_image.resize((256, 256))} if initial_image is not None else {} | |
| source = gr.State(initial_image) | |
| forward_noise = gr.State(None) | |
| with gr.Column(elem_classes=["lab-panel", "process-panel"]): | |
| gr.HTML('<div class="process-heading"><h2>Forward process: image to noise</h2>' | |
| '<span class="equation">x<sub>t</sub> = √α̅<sub>t</sub> x<sub>t−1</sub>' | |
| ' + √(1 − α̅<sub>t</sub>) ε<sub>t</sub></span></div>') | |
| with gr.Row(elem_classes="process-toolbar"): | |
| diffuse_button = gr.Button("Diffuse", elem_classes="process-button") | |
| forward_status = gr.HTML(value=progress_html(), container=False, | |
| elem_classes="progress-counter") | |
| upload_source = gr.UploadButton("Change image", file_types=["image"], | |
| type="filepath", elem_classes="process-button") | |
| with gr.Column(elem_classes="checkpoint-row"): | |
| forward_preview = gr.HTML("", container=False, elem_classes="checkpoint-preview-host") | |
| forward_gallery = gr.Gallery(value=checkpoint_slots(CHECKPOINTS, initial_frames), | |
| show_label=False, columns=len(CHECKPOINTS), rows=1, | |
| height="auto", object_fit="cover", allow_preview=False, interactive=False, elem_id="forward-images", | |
| elem_classes="checkpoint-track") | |
| forward_gallery.select(show_checkpoint_preview, [forward_gallery], [forward_preview], | |
| queue=False, show_progress="hidden").then( | |
| fn=None, js=INSTALL_PREVIEW_DISMISS, queue=False) | |
| diffuse_button.click(diffuse, [source, seed, checkpoints, model], | |
| [forward_gallery, forward_noise, forward_status]) | |
| with gr.Column(elem_classes=["lab-panel", "process-panel"]): | |
| gr.HTML('<div class="process-heading"><h2>Reverse process: noise to image</h2>' | |
| '<p class="reverse-equation">x<sub>t−1</sub> = 1/√α<sub>t</sub>' | |
| ' (x<sub>t</sub> − β<sub>t</sub>/√(1−α̅<sub>t</sub>)' | |
| ' ε<sub>θ</sub>(x<sub>t</sub>,t)) + σ<sub>t</sub>z</p></div>') | |
| with gr.Row(elem_classes="process-toolbar"): | |
| denoise_button = gr.Button("Denoise", elem_classes="process-button") | |
| status = gr.HTML(value=progress_html(), container=False, | |
| elem_classes="progress-counter") | |
| input_mode = gr.Radio(["Gaussian noise", "Forward noise"], value="Gaussian noise", | |
| show_label=False, container=False, scale=0, min_width=0, elem_id="reverse-input") | |
| with gr.Column(elem_classes="checkpoint-row"): | |
| reverse_preview = gr.HTML("", container=False, elem_classes="checkpoint-preview-host") | |
| reverse_gallery = gr.Gallery(value=checkpoint_slots(CHECKPOINTS, {1000: gaussian_noise(42)}), | |
| show_label=False, columns=len(CHECKPOINTS), rows=1, | |
| height="auto", object_fit="cover", allow_preview=False, interactive=False, elem_classes="checkpoint-track") | |
| reverse_gallery.select(show_checkpoint_preview, [reverse_gallery], [reverse_preview], | |
| queue=False, show_progress="hidden").then( | |
| fn=None, js=INSTALL_PREVIEW_DISMISS, queue=False) | |
| denoise_button.click(denoise, [seed, checkpoints, input_mode, forward_noise, model], | |
| [reverse_gallery, status], concurrency_limit=1, | |
| **({"stream_every": 0} if "stream_every" in | |
| inspect.signature(denoise_button.click).parameters else {})) | |
| def clear_previews(): | |
| return "", "" | |
| for control, event_name in [(apply_button, "click"), (upload_source, "upload"), | |
| (diffuse_button, "click"), (denoise_button, "click")]: | |
| getattr(control, event_name)(clear_previews, outputs=[forward_preview, reverse_preview], | |
| queue=False, show_progress="hidden") | |
| reset_outputs = [checkpoints, forward_gallery, reverse_gallery, | |
| forward_noise, forward_status, status] | |
| apply_button.click(reset_tracks, [seed, checkpoints, source, model], reset_outputs, queue=False) | |
| model.change(change_model, [seed, checkpoints, source, model], [source, *reset_outputs]) | |
| model.change(clear_previews, outputs=[forward_preview, reverse_preview], queue=False) | |
| upload_source.upload(change_source, [upload_source, seed, checkpoints, model], | |
| [source, *reset_outputs], queue=False) | |
| if __name__ == "__main__": | |
| demo.queue().launch(**LAUNCH_OPTIONS) | |