import gradio as gr import numpy as np import random import torch import spaces from PIL import Image from diffsynth.pipelines.qwen_image import QwenImagePipeline, ModelConfig import os from huggingface_hub import hf_hub_download pipe = QwenImagePipeline.from_pretrained( torch_dtype=torch.bfloat16, device="cuda", model_configs=[ ModelConfig(model_id="Qwen/Qwen-Image-Edit-2509", download_source='huggingface', origin_file_pattern="transformer/diffusion_pytorch_model*.safetensors"), ModelConfig(model_id="Qwen/Qwen-Image-Edit-2509", download_source='huggingface',origin_file_pattern="text_encoder/model*.safetensors"), ModelConfig(model_id="Qwen/Qwen-Image-Edit-2509", download_source='huggingface',origin_file_pattern="vae/diffusion_pytorch_model.safetensors"), ], tokenizer_config=None, processor_config=ModelConfig(model_id="Qwen/Qwen-Image-Edit-2509", download_source='huggingface',origin_file_pattern="processor/"), ) speedup = hf_hub_download(repo_id="Tele-AI/TeleStyle", filename="weights/diffsynth_Qwen-Image-Edit-2509-Lightning-4steps-V1.0-bf16.safetensors") telestyle= hf_hub_download(repo_id="Tele-AI/TeleStyle", filename="weights/diffsynth_Qwen-Image-Edit-2509-telestyle.safetensors") pipe.load_lora(pipe.dit, telestyle) pipe.load_lora(pipe.dit,speedup) dtype = torch.bfloat16 device = "cuda" if torch.cuda.is_available() else "cpu" MAX_SEED = np.iinfo(np.int32).max @spaces.GPU def infer( content_ref, style_ref, prompt, seed=123, randomize_seed=False, true_guidance_scale=1.0, num_inference_steps=4, minedge=1024, progress=gr.Progress(track_tqdm=True), ): content_ref=Image.fromarray(content_ref) style_ref=Image.fromarray(style_ref) if randomize_seed: seed = random.randint(0, MAX_SEED) w,h=content_ref.size minedge=minedge-minedge%16 if w>h: r=w/h h=minedge w=int(h*r)-int(h*r)%16 else: r=h/w w=minedge h=int(w*r)-int(w*r)%16 print(f"Calling pipeline with prompt: '{prompt}'") print(f"Seed: {seed}, Steps: {num_inference_steps}, Guidance: {true_guidance_scale}, Size: {w}x{h}") images = [ content_ref.resize((w, h)), style_ref.resize((minedge, minedge)) , ] # Generate the image image = pipe(prompt, edit_image=images, seed=seed, num_inference_steps=num_inference_steps, height=h, width=w,edit_image_auto_resize=False,cfg_scale=true_guidance_scale)#ligtning return image, seed # --- Examples and UI Layout --- examples = [] _HEADER_ = '''

TeleStyle

Paper: TeleStyle: Content-Preserving Style Transfer in Images and Videos | Codes: GitHub

If you encounter an Error with this demo, the most possible reason is ZeroGPU out-of-memory and the solution is to decrease the Min Edge of the generated image from 1024 to a lower value. This is because ZeroGPU has a memory limit of 70GB, while all the examples are tested with 80GB H100 GPUs.

''' with gr.Blocks() as demo: with gr.Column(elem_id="col-container"): gr.Markdown(_HEADER_) gr.Markdown("This is a demo of TeleStyle-Image, enabling Content-Preserving Style Transfer capability to Qwen-Image-Edit-2509.") with gr.Row(): with gr.Column(): with gr.Row(): content_ref = gr.Image(label="content ref", type="numpy", ) style_ref = gr.Image(label="style ref", type="numpy", ) #print(f"type(content_ref)={type(content_ref)}") #input_images = gr.Gallery(label="Input Images", show_label=False, type="pil", interactive=True) result = gr.Image(label="Result", show_label=True, type="pil") #result = gr.Gallery(label="Result", show_label=True, type="pil") with gr.Row(): prompt = gr.Text( label="Prompt", value='Style Transfer the style of Figure 2 to Figure 1, and keep the content and characteristics of Figure 1.', show_label=True, placeholder='Style Transfer the style of Figure 2 to Figure 1, and keep the content and characteristics of Figure 1.', container=True, ) run_button = gr.Button("Edit!", variant="primary") with gr.Accordion("Advanced Settings", open=True): # Negative prompt UI element is removed here seed = gr.Slider( label="Seed", minimum=0, maximum=MAX_SEED, step=1, value=123, ) randomize_seed = gr.Checkbox(label="Randomize seed", value=False) with gr.Row(): true_guidance_scale = gr.Slider( label="CFG should be 1.0", minimum=0, maximum=10.0, step=0.1, value=1.0 ) num_inference_steps = gr.Slider( label="Number of inference steps should be 4", minimum=1, maximum=50, step=1, value=4, ) minedge = gr.Slider( label="Min Edge of the generated image", minimum=256, maximum=2048, step=8, value=1024, ) with gr.Row(), gr.Column(): gr.Markdown("## Examples") gr.Markdown("changing the minedge could lead to different style similarity.") default_prompt='Style Transfer the style of Figure 2 to Figure 1, and keep the content and characteristics of Figure 1.' gr.Examples(examples=[ ['./qwenstyleref/pulpfiction_2.jpg','./qwenstyleref/styleref=6_style_ref.png',default_prompt,123,False,1.0,4,832], ['./qwenstyleref/styleref=0_content_ref.png','./qwenstyleref/110.png',default_prompt,123,False,1.0,4,832], ['./qwenstyleref/romanholiday_1.jpg','./qwenstyleref/s0099____1113_01_query_1_img_000146_1682705733350_08158389675901344.jpg.jpg',default_prompt,123,False,1.0,4,800], ['./qwenstyleref/styleref=0_content_ref.png','./qwenstyleref/125.png',default_prompt,123,False,1.0,4,832], ['./qwenstyleref/fallenangle.jpg','./qwenstyleref/styleref=s0038.png',default_prompt,123,False,1.0,4,832], ['./qwenstyleref/styleref=0_content_ref.png','./qwenstyleref/styleref=s0572.png',default_prompt,123,False,1.0,4,832], ['./qwenstyleref/startrooper1.jpg','./qwenstyleref/david-face-760x985.jpg','Style Transfer Figure 1 into marble material.',123,False,1.0,4,1024], ['./qwenstyleref/startrooper1.jpg','./qwenstyleref/125.png',default_prompt, 123,False,1.0,4,1024], ['./qwenstyleref/possession.png','./qwenstyleref/s0026____0907_01_query_0_img_000194_1682674358294_041656249089406583.jpeg.jpg',default_prompt,123,False,1.0,4,832], ['./qwenstyleref/styleref=0_content_ref.png','./qwenstyleref/Jotarokujo.webp',default_prompt,123,False,1.0,4,832], ['./qwenstyleref/wallstreet1.jpg','./qwenstyleref/034.png',default_prompt,123,False,1.0,4,1024], ['./qwenstyleref/bird.jpeg','./qwenstyleref/styleref=s0539.png',default_prompt,123,False,1.0,4,832], ], inputs=[content_ref, style_ref, prompt, seed, randomize_seed, true_guidance_scale, num_inference_steps, minedge,], #inputs=[content_ref,style_ref, prompt,], outputs=[result, seed], fn=infer, cache_examples=False ) # gr.Examples(examples=examples, inputs=[prompt], outputs=[result, seed], fn=infer, cache_examples=False) gr.on( triggers=[run_button.click], fn=infer, inputs=[ content_ref, style_ref, prompt, seed, randomize_seed, true_guidance_scale, num_inference_steps, minedge, ], outputs=[result, seed], ) if __name__ == "__main__": demo.launch(server_name='0.0.0.0')