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tmp/hugging-demos-build-model_sensenova_SenseNova-U1.5-8B-MoT-anc7wu33/build/app.py

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+from __future__ import annotations
+
+import spaces  # MUST be imported before torch / transformers / sensenova_u1
+
+import os
+import random
+
+import gradio as gr
+import numpy as np
+import torch
+from PIL import Image
+from transformers import AutoConfig, AutoModel, AutoTokenizer
+
+import sensenova_u1
+from sensenova_u1.models.neo_unify.utils import smart_resize
+
+MODEL_ID = "sensenova/SenseNova-U1.5-8B-MoT"
+
+NORM_MEAN = (0.5, 0.5, 0.5)
+NORM_STD = (0.5, 0.5, 0.5)
+
+# U1.5 trained T2I aspect-ratio buckets (from the upstream examples/t2i/inference.py
+# SUPPORTED_RESOLUTIONS table).
+T2I_RESOLUTIONS: dict[str, tuple[int, int]] = {
+    "1:1": (2048, 2048),
+    "16:9": (2720, 1536),
+    "9:16": (1536, 2720),
+    "3:2": (2496, 1664),
+    "2:3": (1664, 2496),
+    "4:3": (2368, 1760),
+    "3:4": (1760, 2368),
+    "1:2": (1440, 2880),
+    "2:1": (2880, 1440),
+    "1:3": (1152, 3456),
+    "3:1": (3456, 1152),
+}
+
+# Reference config for SenseNova-U1.5 (from the model card Quick Start):
+#   cfg_scale=4.0, timestep_shift=3.0, num_steps=50
+DEFAULT_CFG_SCALE = 4.0
+DEFAULT_TIMESTEP_SHIFT = 3.0
+DEFAULT_NUM_STEPS = 50
+
+# Editing output grid factor (= patch_size * merge_size = 32).
+EDIT_GRID_FACTOR = 32
+EDIT_TARGET_PIXELS = 2048 * 2048
+EDIT_INPUT_MAX_PIXELS = 2048 * 2048
+
+MAX_SEED = 2**31 - 1
+
+
+def _denorm(x: torch.Tensor) -> torch.Tensor:
+    mean = torch.tensor(NORM_MEAN, device=x.device, dtype=x.dtype).view(1, 3, 1, 1)
+    std = torch.tensor(NORM_STD, device=x.device, dtype=x.dtype).view(1, 3, 1, 1)
+    return (x * std + mean).clamp(0, 1)
+
+
+def _to_pil(batch: torch.Tensor) -> list[Image.Image]:
+    arr = _denorm(batch.float()).permute(0, 2, 3, 1).cpu().numpy()
+    arr = (arr * 255.0).round().astype(np.uint8)
+    return [Image.fromarray(a) for a in arr]
+
+
+def _coerce_pil(img) -> Image.Image:
+    if isinstance(img, Image.Image):
+        return img
+    if isinstance(img, tuple):
+        img = img[0]
+    if isinstance(img, str):
+        return Image.open(img)
+    return img
+
+
+def _prep_input_image(img: Image.Image, max_pixels: int) -> Image.Image:
+    if img.mode == "RGBA":
+        bg = Image.new("RGB", img.size, (255, 255, 255))
+        bg.paste(img, mask=img.split()[3])
+        img = bg
+    img = img.convert("RGB")
+    h, w = smart_resize(
+        height=img.height,
+        width=img.width,
+        factor=EDIT_GRID_FACTOR,
+        min_pixels=max_pixels,
+        max_pixels=max_pixels,
+    )
+    if (w, h) != img.size:
+        img = img.resize((w, h), Image.LANCZOS)
+    return img
+
+
+def _editing_output_size(input_img: Image.Image, target_pixels: int) -> tuple[int, int]:
+    h, w = smart_resize(
+        height=input_img.height,
+        width=input_img.width,
+        factor=EDIT_GRID_FACTOR,
+        min_pixels=target_pixels,
+        max_pixels=target_pixels,
+    )
+    return w, h
+
+
+print("[startup] loading SenseNova-U1.5-8B-MoT (this may take a few minutes)...")
+sensenova_u1.set_attn_backend("auto")
+print(f"[startup] attn backend: {sensenova_u1.effective_attn_backend()!r}")
+
+config = AutoConfig.from_pretrained(MODEL_ID)
+sensenova_u1.check_checkpoint_compatibility(config)
+tokenizer = AutoTokenizer.from_pretrained(MODEL_ID)
+model = AutoModel.from_pretrained(MODEL_ID, config=config, dtype=torch.bfloat16).to("cuda").eval()
+print("[startup] model ready.")
+
+
+def _estimate_duration(images, prompt, aspect_ratio, seed, randomize_seed, *args, **kwargs):
+    # Editing is heavier (image conditioning); give it more headroom.
+    has_input = images is not None and len(images) > 0
+    if has_input:
+        return 180
+    # T2I at 2048x2048 with 50 steps is the heaviest t2i case
+    w, h = T2I_RESOLUTIONS.get(aspect_ratio, (2048, 2048))
+    pixels = w * h
+    if pixels > 2048 * 2048:
+        return 180
+    return 120
+
+
+@spaces.GPU(duration=_estimate_duration)
+def generate(
+    images: list | None,
+    prompt: str,
+    aspect_ratio: str = "1:1",
+    seed: int = 42,
+    randomize_seed: bool = True,
+    progress=gr.Progress(track_tqdm=True),
+):
+    """Generate an image from a text prompt, or edit an uploaded image.
+
+    Args:
+        images: optional uploaded image(s) to edit; leave empty for text-to-image.
+        prompt: what to generate, or the edit instruction to apply to the input image.
+        aspect_ratio: output aspect ratio for text-to-image (ignored when editing).
+        seed: RNG seed for reproducible sampling.
+        randomize_seed: if True, pick a fresh random seed each run.
+    """
+    if not prompt or not prompt.strip():
+        raise gr.Error("Please enter a prompt.")
+    if randomize_seed:
+        seed = random.randint(0, MAX_SEED)
+
+    has_input = images is not None and len(images) > 0
+
+    with torch.inference_mode():
+        if not has_input:
+            width, height = T2I_RESOLUTIONS[aspect_ratio]
+            tensor = model.t2i_generate(
+                tokenizer,
+                prompt,
+                image_size=(width, height),
+                cfg_scale=DEFAULT_CFG_SCALE,
+                cfg_norm="none",
+                timestep_shift=DEFAULT_TIMESTEP_SHIFT,
+                cfg_interval=(0.0, 1.0),
+                num_steps=DEFAULT_NUM_STEPS,
+                batch_size=1,
+                seed=int(seed),
+                think_mode=False,
+            )
+        else:
+            pil_inputs = [_prep_input_image(_coerce_pil(item), EDIT_INPUT_MAX_PIXELS) for item in images]
+            out_w, out_h = _editing_output_size(pil_inputs[0], EDIT_TARGET_PIXELS)
+            tensor = model.it2i_generate(
+                tokenizer,
+                prompt,
+                pil_inputs,
+                image_size=(out_w, out_h),
+                cfg_scale=DEFAULT_CFG_SCALE,
+                img_cfg_scale=1.0,
+                cfg_norm="none",
+                timestep_shift=DEFAULT_TIMESTEP_SHIFT,
+                cfg_interval=(0.0, 1.0),
+                num_steps=DEFAULT_NUM_STEPS,
+                batch_size=1,
+                think_mode=False,
+                seed=int(seed),
+            )
+
+    images_out = _to_pil(tensor)
+    return images_out[0], seed
+
+
+# T2I examples: prompt + aspect ratio
+T2I_EXAMPLES = [
+    [
+        "A cinematic mountain lake at sunrise, realistic photography, golden mist over still water, snow-capped peaks reflected in the lake, ultra-detailed.",
+        "1:1",
+    ],
+    [
+        'A neon bar sign that clearly reads "OPEN LATE", dark interior, moody reflections, easy text rendering.',
+        "16:9",
+    ],
+    [
+        "Close portrait of an elderly woman by a farmhouse window, textured skin, gentle smile, warm natural light, emotional documentary look.",
+        "2:3",
+    ],
+    [
+        "A cute fluffy corgi puppy wearing a tiny chef's hat, sitting at a wooden table with fresh-baked cookies, warm kitchen lighting, photorealistic.",
+        "1:1",
+    ],
+    [
+        "Lavender fields stretching to the horizon under a pastel sunset, a small stone farmhouse, highly detailed flowers, romantic countryside scene.",
+        "4:3",
+    ],
+]
+
+# Editing examples: gallery input (list of paths) + prompt
+EDIT_EXAMPLES = [
+    [["examples/edit_1.webp"], "Change the jacket of the person on the left to bright yellow."],
+    [["examples/edit_2.webp"], "Make the person in the image smile."],
+    [["examples/edit_3.webp"], "Add a bouquet of flowers."],
+    [["examples/edit_4.webp"], "Turn the image into an American comic style."],
+]
+
+
+CSS = """
+#col-container { max-width: 1100px; margin: 0 auto; }
+.dark .gradio-container { color: var(--body-text-color); }
+"""
+
+with gr.Blocks(title="SenseNova-U1.5-8B-MoT") as demo:
+    gr.Markdown(
+        """
+        # SenseNova-U1.5-8B-MoT
+
+        Unified text-to-image **and** image editing with
+        [**SenseNova-U1.5-8B-MoT**](https://huggingface.co/sensenova/SenseNova-U1.5-8B-MoT),
+        a natively unified multimodal model built on the
+        [NEO-unify](https://huggingface.co/blog/sensenova/neo-unify) architecture.
+        Leave the image upload empty for text-to-image, or upload an image and
+        write an edit instruction.
+        """
+    )
+
+    with gr.Row():
+        with gr.Column(scale=1):
+            image_input_gallery = gr.Gallery(
+                label="Upload image(s) to edit (leave empty for text-to-image)",
+                file_types=["image"],
+                height=200,
+                columns=4,
+            )
+            prompt_input = gr.Textbox(
+                label="Prompt",
+                placeholder="Describe the image to generate, or how to edit your input.",
+                lines=3,
+            )
+            aspect_ratio = gr.Dropdown(
+                label="Aspect ratio (text-to-image only — editing keeps input ratio)",
+                choices=list(T2I_RESOLUTIONS.keys()),
+                value="1:1",
+            )
+            with gr.Row():
+                seed = gr.Slider(label="Seed", minimum=0, maximum=MAX_SEED, step=1, value=42)
+                randomize_seed = gr.Checkbox(label="Randomize seed", value=True)
+            generate_button = gr.Button("Generate", variant="primary")
+        with gr.Column(scale=1):
+            output_image = gr.Image(label="Output", type="pil", format="png", interactive=False)
+            used_seed = gr.Number(label="Seed used", interactive=False)
+
+    with gr.Accordion("Text-to-Image Examples", open=True):
+        gr.Examples(
+            examples=T2I_EXAMPLES,
+            inputs=[prompt_input, aspect_ratio],
+            outputs=[output_image, used_seed],
+            fn=generate,
+            cache_examples=False,
+            run_on_click=True,
+        )
+
+    with gr.Accordion("Image Editing Examples", open=True):
+        gr.Examples(
+            examples=EDIT_EXAMPLES,
+            inputs=[image_input_gallery, prompt_input],
+            outputs=[output_image, used_seed],
+            fn=generate,
+            cache_examples=False,
+            run_on_click=True,
+        )
+
+    generate_button.click(
+        fn=generate,
+        inputs=[image_input_gallery, prompt_input, aspect_ratio, seed, randomize_seed],
+        outputs=[output_image, used_seed],
+        api_name="generate",
+    )
+    prompt_input.submit(
+        fn=generate,
+        inputs=[image_input_gallery, prompt_input, aspect_ratio, seed, randomize_seed],
+        outputs=[output_image, used_seed],
+        api_name="generate_submit",
+    )
+
+
+if __name__ == "__main__":
+    demo.launch(theme=gr.themes.Citrus(), css=CSS, mcp_server=True)