import gradio as gr import numpy as np import random import spaces import torch import math # from diffusers import ZImagePipeline from diffusers import QwenImagePipeline, FlowMatchEulerDiscreteScheduler import os import requests import tempfile import shutil from urllib.parse import urlparse dtype = torch.bfloat16 device = "cuda" if torch.cuda.is_available() else "cpu" scheduler_config = { "base_image_seq_len": 256, "base_shift": math.log(3), # We use shift=3 in distillation "invert_sigmas": False, "max_image_seq_len": 8192, "max_shift": math.log(3), # We use shift=3 in distillation "num_train_timesteps": 1000, "shift": 1.0, "shift_terminal": None, # set shift_terminal to 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) # Load the model pipeline pipe = QwenImagePipeline.from_pretrained("Qwen/Qwen-Image-2512", scheduler=scheduler, torch_dtype=dtype).to(device) pipe.load_lora_weights( "lightx2v/Qwen-Image-2512-Lightning", weight_name="Qwen-Image-2512-Lightning-4steps-V1.0-bf16.safetensors" ) # pipe.load_lora_weights("Wuli-art/Qwen-Image-2512-Turbo-LoRA") pipe.fuse_lora() pipe.vae.enable_tiling() # Load the model pipeline # pipe = QwenImagePipeline.from_pretrained("Qwen/Qwen-Image-2512", torch_dtype=dtype).to(device) # pipe.vae.enable_tiling() # pipe.load_lora_weights("Wuli-art/Qwen-Image-2512-Turbo-LoRA") # pipe.fuse_lora() torch.cuda.empty_cache() MAX_SEED = np.iinfo(np.int32).max MAX_IMAGE_SIZE = 2048 # pipe.flux_pipe_call_that_returns_an_iterable_of_images = flux_pipe_call_that_returns_an_iterable_of_images.__get__(pipe) # flymy-ai/qwen-image-realism-lora def load_lora_auto(pipe, lora_input): lora_input = lora_input.strip() if not lora_input: return # If it's just an ID like "author/model" if "/" in lora_input and not lora_input.startswith("http"): pipe.load_lora_weights(lora_input) return if lora_input.startswith("http"): url = lora_input # Repo page (no blob/resolve) if "huggingface.co" in url and "/blob/" not in url and "/resolve/" not in url: repo_id = urlparse(url).path.strip("/") pipe.load_lora_weights(repo_id) return # Blob link → convert to resolve link if "/blob/" in url: url = url.replace("/blob/", "/resolve/") # Download direct file tmp_dir = tempfile.mkdtemp() local_path = os.path.join(tmp_dir, os.path.basename(urlparse(url).path)) try: print(f"Downloading LoRA from {url}...") resp = requests.get(url, stream=True) resp.raise_for_status() with open(local_path, "wb") as f: for chunk in resp.iter_content(chunk_size=8192): f.write(chunk) print(f"Saved LoRA to {local_path}") pipe.load_lora_weights(local_path) finally: shutil.rmtree(tmp_dir, ignore_errors=True) @spaces.GPU() def infer(prompt, negative_prompt, seed=42, randomize_seed=False, width=1024, height=1024, guidance_scale=4, num_inference_steps=28, lora_id=None, lora_scale=0.95, progress=gr.Progress(track_tqdm=True)): if randomize_seed: seed = random.randint(0, MAX_SEED) generator = torch.Generator().manual_seed(seed) if lora_id and lora_id.strip() != "": pipe.unload_lora_weights() load_lora_auto(pipe, lora_id) try: image = pipe( prompt=prompt, negative_prompt=negative_prompt, width=width, height=height, num_inference_steps=num_inference_steps, generator=generator, true_cfg_scale=guidance_scale, # Use a fixed default for distilled guidance guidance_scale=1.0 ).images[0] print("Image Generation Completed for: ", prompt, lora_id) return image, seed finally: # Unload LoRA weights if they were loaded if lora_id: pipe.unload_lora_weights() lora_examples = [ ["prithivMLmods/Qwen-Image-2512-Pixel-Art-LoRA"], ["RayyanAhmed9477/qwen-image-2512-lora-advertisement"], ["lichorosario/t21person-qwen-image-2512"], ["https://huggingface.co/lichorosario/flat-cartoon-qwen-image-2512/resolve/main/flat-cartoon-qwen-image-2512_20.safetensors"] ] examples = [ "a tiny astronaut hatching from an egg on the moon", "a cat holding a sign that says hello world", "an anime illustration of a wiener schnitzel", ] css = """ #col-container { margin: 0 auto; max-width: 960px; } .generate-btn { background: linear-gradient(90deg, #4B79A1 0%, #283E51 100%) !important; border: none !important; color: white !important; } .generate-btn:hover { transform: translateY(-2px); box-shadow: 0 5px 15px rgba(0,0,0,0.2); } """ with gr.Blocks(css=css, delete_cache=(3600, 7200)) as app: # cleanup: check every 1h, delete files >2h old gr.HTML("

Qwen-Image 2512 with LoRA support

") with gr.Column(elem_id="col-container"): with gr.Row(): with gr.Column(): with gr.Row(): text_prompt = gr.Textbox(label="Prompt", placeholder="Enter a prompt here", lines=3, elem_id="prompt-text-input") with gr.Row(): custom_lora = gr.Textbox(label="Custom LoRA (optional)", info="URL or the path to the LoRA weights", placeholder="ostris/z_image_turbo_childrens_drawings") with gr.Row(): with gr.Accordion("Advanced Settings", open=False): lora_scale = gr.Slider( label="LoRA Scale", minimum=0, maximum=2, step=0.01, value=1, ) negative_prompt = gr.Textbox(label="Negative prompt", value = "低分辨率，低画质，肢体畸形，手指畸形，画面过饱和，蜡像感，人脸无细节，过度光滑，画面具有AI感。构图混乱。文字模糊，扭曲", lines=3, elem_id="prompt-text-input") with gr.Row(): width = gr.Slider(label="Width", value=1328, minimum=64, maximum=2048, step=16) height = gr.Slider(label="Height", value=1328, minimum=64, maximum=2048, step=16) seed = gr.Slider(label="Seed", value=-1, minimum=-1, maximum=4294967296, step=1) randomize_seed = gr.Checkbox(label="Randomize seed", value=True) with gr.Row(): steps = gr.Slider(label="Inference steps steps", value=4, minimum=1, maximum=20, step=1) cfg = gr.Slider(label="Guidance Scale", value=1, minimum=0, maximum=20, step=0.5) # method = gr.Radio(label="Sampling method", value="DPM++ 2M Karras", choices=["DPM++ 2M Karras", "DPM++ SDE Karras", "Euler", "Euler a", "Heun", "DDIM"]) with gr.Row(): # text_button = gr.Button("Run", variant='primary', elem_id="gen-button") text_button = gr.Button("✨ Generate Image", variant='primary', elem_classes=["generate-btn"]) with gr.Column(): with gr.Row(): image_output = gr.Image(type="pil", label="Image Output", elem_id="gallery") gr.Examples( examples=lora_examples, inputs=[custom_lora], label="Select a LoRA Model", ) # gr.Markdown(article_text) with gr.Column(): gr.Examples( examples = examples, inputs = [text_prompt], ) gr.on( triggers=[text_button.click, text_prompt.submit], fn = infer, inputs=[text_prompt, negative_prompt, seed, randomize_seed, width, height, cfg, steps, custom_lora, lora_scale], outputs=[image_output, seed] ) # text_button.click(query, inputs=[custom_lora, text_prompt, steps, cfg, randomize_seed, seed, width, height], outputs=[image_output,seed_output, seed]) # text_button.click(infer, inputs=[text_prompt, seed, randomize_seed, width, height, cfg, steps, custom_lora, lora_scale], outputs=[image_output,seed_output, seed]) app.launch(mcp_server=True, share=True)