import gradio as gr import numpy as np import random import torch import spaces from PIL import Image from diffusers import FlowMatchEulerDiscreteScheduler from optimization import optimize_pipeline_ from qwenimage.pipeline_qwenimage_edit_plus import QwenImageEditPlusPipeline from qwenimage.transformer_qwenimage import QwenImageTransformer2DModel from qwenimage.qwen_fa3_processor import QwenDoubleStreamAttnProcessorFA3 import math import os import tempfile from huggingface_hub import hf_hub_download # --- Model & Repo --- HF_MODEL = os.environ.get("HF_UPLOAD_REPO", "rahul7star/qwen-edit-img-repo") dtype = torch.bfloat16 device = "cuda" if torch.cuda.is_available() else "cpu" # --- Camera prompts --- BASE_PROMPTS = { "front": "Move the camera to a front-facing position showing the full character. Background is plain white.", "back": "Move the camera to a back-facing position showing the full character. Background is plain white.", "left": "Move the camera to a side (left) profile view. Background is plain white.", "right": "Move the camera to a side (right) profile view. Background is plain white.", "45_left": "Rotate camera 45° left showing the full character", "45_right": "Rotate camera 45° right showing the full character", #"90_left": "Rotate camera 90° left", #"90_right": "Rotate camera 90° right", "top_down": "Switch to top-down view showing the full character", "low_angle": "Switch to low-angle view", "close_up": "Switch to close-up lens", "medium_close_up": "Switch to medium close-up lens", "zoom_out": "Switch to zoom out lens", } # --- Resolution presets --- RESOLUTIONS = { "1:4": (512, 2048), "1:3": (576, 1728), "nealy 9:16": (768, 1344), "nealy 2:3": (832, 1216), "3:4": (896, 1152), } MAX_SEED = np.iinfo(np.int32).max # --- CPU-only upload function --- def upload_image_and_prompt_cpu(input_image, prompt_text) -> str: from datetime import datetime import uuid, shutil from huggingface_hub import HfApi api = HfApi() print(prompt_text) today_str = datetime.now().strftime("%Y-%m-%d") unique_subfolder = f"Upload-Image-{uuid.uuid4().hex[:8]}" hf_folder = f"{today_str}/{unique_subfolder}" with tempfile.NamedTemporaryFile(suffix=".png", delete=False) as tmp_img: if isinstance(input_image, str): shutil.copy(input_image, tmp_img.name) else: input_image.save(tmp_img.name, format="PNG") tmp_img_path = tmp_img.name api.upload_file( path_or_fileobj=tmp_img_path, path_in_repo=f"{hf_folder}/input_image.png", repo_id=HF_MODEL, repo_type="model", token=os.environ.get("HUGGINGFACE_HUB_TOKEN") ) summary_file = tempfile.NamedTemporaryFile(delete=False, suffix=".txt").name with open(summary_file, "w", encoding="utf-8") as f: f.write(prompt_text) api.upload_file( path_or_fileobj=summary_file, path_in_repo=f"{hf_folder}/summary.txt", repo_id=HF_MODEL, repo_type="model", token=os.environ.get("HUGGINGFACE_HUB_TOKEN") ) os.remove(tmp_img_path) os.remove(summary_file) return hf_folder # --- Scheduler & model load --- scheduler_config = { "base_image_seq_len": 256, "base_shift": math.log(3), "invert_sigmas": False, "max_image_seq_len": 8192, "max_shift": math.log(3), "num_train_timesteps": 1000, "shift": 1.0, "shift_terminal": None, "stochastic_sampling": False, "time_shift_type": "exponential", "use_beta_sigmas": False, "use_dynamic_shifting": True, "use_exponential_sigmas": False, "use_karras_sigmas": False, } scheduler = FlowMatchEulerDiscreteScheduler.from_config(scheduler_config) pipe = QwenImageEditPlusPipeline.from_pretrained( "Qwen/Qwen-Image-Edit-2509", scheduler=scheduler, torch_dtype=dtype ).to(device) # Load LoRA weights # pipe.load_lora_weights( # "rahul7star/qwen-char-lora", # weight_name="qwen_lora/Qwen-Image-Edit-2509-Lightning-4steps-V1.0-bf16_dim1.safetensors" # ) # pipe.fuse_lora(lora_scale=1.0) pipe.load_lora_weights( "rahul7star/qwen-char-lora", weight_name="qwen_lora/qwen-multiple-char.safetensors", ) pipe.fuse_lora(lora_scale=1.0) pipe.unload_lora_weights() pipe.transformer.__class__ = QwenImageTransformer2DModel pipe.transformer.set_attn_processor(QwenDoubleStreamAttnProcessorFA3()) optimize_pipeline_(pipe, image=[Image.new("RGB", (1024, 1024)), Image.new("RGB", (1024, 1024))], prompt="prompt") # --- Helpers --- def _append_prompt(base: str, extra: str) -> str: extra = (extra or "").strip() return (base if not extra else f"{base} {extra}").strip() def generate_single_view(input_images, prompt, seed, num_inference_steps, true_guidance_scale): generator = torch.Generator(device=device).manual_seed(seed) result = pipe( image=input_images if input_images else None, prompt=prompt, negative_prompt=" ", num_inference_steps=num_inference_steps, generator=generator, true_cfg_scale=true_guidance_scale, num_images_per_prompt=1, ).images return result[0] def resize_to_preset(img: Image.Image, preset_key: str) -> Image.Image: w, h = RESOLUTIONS[preset_key] return img.resize((w, h), Image.LANCZOS) def concat_images_horizontally(images, bg_color=(255, 255, 255)): images = [img.convert("RGB") for img in images if img is not None] if not images: return None h = max(img.height for img in images) resized = [] for img in images: if img.height != h: w = int(img.width * (h / img.height)) img = img.resize((w, h), Image.LANCZOS) resized.append(img) w_total = sum(img.width for img in resized) canvas = Image.new("RGB", (w_total, h), bg_color) x = 0 for img in resized: canvas.paste(img, (x, 0)) x += img.width return canvas # --- Generate all camera angles dynamically --- @spaces.GPU() def generate_turnaround( image, extra_prompt="", preset_key="nealy 9:16", seed=42, randomize_seed=False, true_guidance_scale=1.0, num_inference_steps=4, progress=gr.Progress(track_tqdm=True), ): if randomize_seed: seed = random.randint(0, MAX_SEED) if image is None: return [None]*(len(BASE_PROMPTS)+1), seed, "❌ 入力画像をアップロードしてください" input_image = image.convert("RGB") if isinstance(image, Image.Image) else Image.open(image).convert("RGB") pil_images = [input_image] results = {} total = len(BASE_PROMPTS) for i, (key, base_prompt) in enumerate(BASE_PROMPTS.items(), start=1): progress(i/total, desc=f"{key} 生成中...") prompt_full = _append_prompt(base_prompt, extra_prompt) img = generate_single_view(pil_images, prompt_full, seed+i, num_inference_steps, true_guidance_scale) results[key] = resize_to_preset(img, preset_key) concat = concat_images_horizontally(list(results.values())) return [*results.values(), concat, seed, f"✅ {len(results)}視点の画像＋連結画像を生成しました"] # --- UI --- css = """ #col-container {margin: 0 auto; max-width: 1400px;} .image-container img {object-fit: contain !important; max-width: 100%; max-height: 100%;} .notice {background: #fff5f5; border: 1px solid #fca5a5; color: #7f1d1d; padding: 12px 14px; border-radius: 10px; font-weight: 600; line-height: 1.5; margin-bottom: 10px;} """ with gr.Blocks(css=css) as demo: with gr.Column(elem_id="col-container"): input_image = gr.Image(label="入力画像", type="pil", height=500) extra_prompt = gr.Textbox( label="追加プロンプト（各視点プロンプト末尾に追加）", placeholder="high detail, anime style, soft lighting, 4k", lines=2 ) preset_dropdown = gr.Dropdown( label="出力解像度プリセット", choices=list(RESOLUTIONS.keys()), value="nealy 9:16" ) run_button = gr.Button("🎨 生成開始", variant="primary") status_text = gr.Textbox(label="ステータス", interactive=False) # Dynamic outputs for all angles result_images = [] for key in BASE_PROMPTS.keys(): result_images.append(gr.Image(label=key.capitalize(), type="pil", format="png", height=400, show_download_button=True)) result_concat = gr.Image(label="連結画像（全視点）", type="pil", format="png", height=400, show_download_button=True) with gr.Accordion("⚙️ 詳細設定", open=False): seed_slider = gr.Slider(label="Seed", minimum=0, maximum=MAX_SEED, step=1, value=0) randomize_seed_checkbox = gr.Checkbox(label="ランダムシード", value=True) guidance_scale_slider = gr.Slider(label="True guidance scale", minimum=1.0, maximum=10.0, step=0.1, value=1.0) num_steps_slider = gr.Slider(label="生成ステップ数", minimum=1, maximum=40, step=1, value=4) run_button.click( fn=generate_turnaround, inputs=[input_image, extra_prompt, preset_dropdown, seed_slider, randomize_seed_checkbox, guidance_scale_slider, num_steps_slider], outputs=[*result_images, result_concat, seed_slider, status_text] ) if __name__ == "__main__": demo.launch()