add app [fast_inferences] ✅

app.py

@@ -0,0 +1,266 @@
+import os
+import gc
+import gradio as gr
+import numpy as np
+import spaces
+import torch
+import random
+from PIL import Image
+from typing import Iterable
+from gradio.themes import Soft
+from gradio.themes.utils import colors, fonts, sizes
+
+colors.orange_red = colors.Color(
+    name="orange_red",
+    c50="#FFF0E5",
+    c100="#FFE0CC",
+    c200="#FFC299",
+    c300="#FFA366",
+    c400="#FF8533",
+    c500="#FF4500",
+    c600="#E63E00",
+    c700="#CC3700",
+    c800="#B33000",
+    c900="#992900",
+    c950="#802200",
+)
+
+class OrangeRedTheme(Soft):
+    def __init__(
+        self,
+        *,
+        primary_hue: colors.Color | str = colors.gray,
+        secondary_hue: colors.Color | str = colors.orange_red,
+        neutral_hue: colors.Color | str = colors.slate,
+        text_size: sizes.Size | str = sizes.text_lg,
+        font: fonts.Font | str | Iterable[fonts.Font | str] = (
+            fonts.GoogleFont("Outfit"), "Arial", "sans-serif",
+        ),
+        font_mono: fonts.Font | str | Iterable[fonts.Font | str] = (
+            fonts.GoogleFont("IBM Plex Mono"), "ui-monospace", "monospace",
+        ),
+    ):
+        super().__init__(
+            primary_hue=primary_hue,
+            secondary_hue=secondary_hue,
+            neutral_hue=neutral_hue,
+            text_size=text_size,
+            font=font,
+            font_mono=font_mono,
+        )
+        super().set(
+            background_fill_primary="*primary_50",
+            background_fill_primary_dark="*primary_900",
+            body_background_fill="linear-gradient(135deg, *primary_200, *primary_100)",
+            body_background_fill_dark="linear-gradient(135deg, *primary_900, *primary_800)",
+            button_primary_text_color="white",
+            button_primary_text_color_hover="white",
+            button_primary_background_fill="linear-gradient(90deg, *secondary_500, *secondary_600)",
+            button_primary_background_fill_hover="linear-gradient(90deg, *secondary_600, *secondary_700)",
+            button_primary_background_fill_dark="linear-gradient(90deg, *secondary_600, *secondary_700)",
+            button_primary_background_fill_hover_dark="linear-gradient(90deg, *secondary_500, *secondary_600)",
+            button_secondary_text_color="black",
+            button_secondary_text_color_hover="white",
+            button_secondary_background_fill="linear-gradient(90deg, *primary_300, *primary_300)",
+            button_secondary_background_fill_hover="linear-gradient(90deg, *primary_400, *primary_400)",
+            button_secondary_background_fill_dark="linear-gradient(90deg, *primary_500, *primary_600)",
+            button_secondary_background_fill_hover_dark="linear-gradient(90deg, *primary_500, *primary_500)",
+            slider_color="*secondary_500",
+            slider_color_dark="*secondary_600",
+            block_title_text_weight="600",
+            block_border_width="3px",
+            block_shadow="*shadow_drop_lg",
+            button_primary_shadow="*shadow_drop_lg",
+            button_large_padding="11px",
+            color_accent_soft="*primary_100",
+            block_label_background_fill="*primary_200",
+        )
+
+orange_red_theme = OrangeRedTheme()
+
+device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+
+print("CUDA_VISIBLE_DEVICES=", os.environ.get("CUDA_VISIBLE_DEVICES"))
+print("torch.__version__ =", torch.__version__)
+print("Using device:", device)
+
+from diffusers import FlowMatchEulerDiscreteScheduler
+from qwenimage.pipeline_qwenimage_edit_plus import QwenImageEditPlusPipeline
+from qwenimage.transformer_qwenimage import QwenImageTransformer2DModel
+from qwenimage.qwen_fa3_processor import QwenDoubleStreamAttnProcessorFA3
+
+dtype = torch.bfloat16
+
+pipe = QwenImageEditPlusPipeline.from_pretrained(
+    "FireRedTeam/FireRed-Image-Edit-1.0",
+    transformer=QwenImageTransformer2DModel.from_pretrained(
+        "prithivMLmods/Qwen-Image-Edit-Rapid-AIO-V19",
+        torch_dtype=dtype,
+        device_map='cuda'
+    ),
+    torch_dtype=dtype
+).to(device)
+
+try:
+    pipe.transformer.set_attn_processor(QwenDoubleStreamAttnProcessorFA3())
+    print("Flash Attention 3 Processor set successfully.")
+except Exception as e:
+    print(f"Warning: Could not set FA3 processor: {e}")
+
+MAX_SEED = np.iinfo(np.int32).max
+
+def update_dimensions_on_upload(image):
+    if image is None:
+        return 1024, 1024
+
+    original_width, original_height = image.size
+
+    if original_width > original_height:
+        new_width = 1024
+        aspect_ratio = original_height / original_width
+        new_height = int(new_width * aspect_ratio)
+    else:
+        new_height = 1024
+        aspect_ratio = original_width / original_height
+        new_width = int(new_height * aspect_ratio)
+
+    new_width = (new_width // 8) * 8
+    new_height = (new_height // 8) * 8
+
+    return new_width, new_height
+
+@spaces.GPU
+def infer(
+    images,
+    prompt,
+    seed,
+    randomize_seed,
+    guidance_scale,
+    steps,
+    progress=gr.Progress(track_tqdm=True)
+):
+    gc.collect()
+    torch.cuda.empty_cache()
+
+    if not images:
+        raise gr.Error("Please upload at least one image to edit.")
+
+    pil_images = []
+    if images is not None:
+        for item in images:
+            try:
+                if isinstance(item, tuple) or isinstance(item, list):
+                    path_or_img = item[0]
+                else:
+                    path_or_img = item
+
+                if isinstance(path_or_img, str):
+                    pil_images.append(Image.open(path_or_img).convert("RGB"))
+                elif isinstance(path_or_img, Image.Image):
+                    pil_images.append(path_or_img.convert("RGB"))
+                else:
+                    pil_images.append(Image.open(path_or_img.name).convert("RGB"))
+            except Exception as e:
+                print(f"Skipping invalid image item: {e}")
+                continue
+
+    if not pil_images:
+        raise gr.Error("Could not process uploaded images.")
+
+    if randomize_seed:
+        seed = random.randint(0, MAX_SEED)
+
+    generator = torch.Generator(device=device).manual_seed(seed)
+    negative_prompt = "worst quality, low quality, bad anatomy, bad hands, text, error, missing fingers, extra digit, fewer digits, cropped, jpeg artifacts, signature, watermark, username, blurry"
+
+    width, height = update_dimensions_on_upload(pil_images[0])
+
+    try:
+        result_image = pipe(
+            image=pil_images,
+            prompt=prompt,
+            negative_prompt=negative_prompt,
+            height=height,
+            width=width,
+            num_inference_steps=steps,
+            generator=generator,
+            true_cfg_scale=guidance_scale,
+        ).images[0]
+
+        return result_image, seed
+
+    except Exception as e:
+        raise e
+    finally:
+        gc.collect()
+        torch.cuda.empty_cache()
+
+@spaces.GPU
+def infer_example(images, prompt):
+    if not images:
+        return None, 0
+
+    if isinstance(images, str):
+        images_list = [images]
+    else:
+        images_list = images
+
+    result, seed = infer(
+        images=images_list,
+        prompt=prompt,
+        seed=0,
+        randomize_seed=True,
+        guidance_scale=1.0,
+        steps=4
+    )
+    return result, seed
+
+css = """
+#col-container {
+    margin: 0 auto;
+    max-width: 1000px;
+}
+#main-title h1 {font-size: 2.4em !important;}
+"""
+
+with gr.Blocks() as demo:
+    with gr.Column(elem_id="col-container"):
+        gr.Markdown("# **Qwen-Image-Edit-2511 Fast**", elem_id="main-title")
+        gr.Markdown("Perform image edits using [Qwen-Image-Edit-2511](https://huggingface.co/Qwen/Qwen-Image-Edit-2511) with 4-step fast inference.")
+
+        with gr.Row(equal_height=True):
+            with gr.Column():
+                images = gr.Gallery(
+                    label="Upload Images",
+                    type="filepath",
+                    columns=2,
+                    rows=1,
+                    height=300,
+                    allow_preview=True
+                )
+
+                prompt = gr.Text(
+                    label="Edit Prompt",
+                    show_label=True,
+                    placeholder="e.g., transform into anime, upscale, change lighting...",
+                )
+
+                run_button = gr.Button("Edit Image", variant="primary")
+
+            with gr.Column():
+                output_image = gr.Image(label="Output Image", interactive=False, format="png", height=365)
+
+                with gr.Accordion("Advanced Settings", open=False):
+                    seed = gr.Slider(label="Seed", minimum=0, maximum=MAX_SEED, step=1, value=0)
+                    randomize_seed = gr.Checkbox(label="Randomize Seed", value=True)
+                    guidance_scale = gr.Slider(label="Guidance Scale", minimum=1.0, maximum=10.0, step=0.1, value=1.0)
+                    steps = gr.Slider(label="Inference Steps", minimum=1, maximum=50, step=1, value=4)
+
+    run_button.click(
+        fn=infer,
+        inputs=[images, prompt, seed, randomize_seed, guidance_scale, steps],
+        outputs=[output_image, seed]
+    )
+
+if __name__ == "__main__":
+    demo.queue(max_size=30).launch(css=css, theme=orange_red_theme, mcp_server=True, ssr_mode=False, show_error=True)