import gradio as gr import torch import numpy as np import diffusers import os import random import spaces from PIL import Image hf_token = os.environ.get("HF_TOKEN") from diffusers import AutoPipelineForText2Image from diffusers.utils import load_image device = "cuda" #if torch.cuda.is_available() else "cpu" pipe = AutoPipelineForText2Image.from_pretrained("briaai/BRIA-2.3", torch_dtype=torch.float16, force_zeros_for_empty_prompt=False).to(device) pipe.load_ip_adapter("briaai/Image-Prompt", subfolder='models', weight_name="ip_adapter_bria.bin") pipe.to(device) # default_negative_prompt= "" #"Logo,Watermark,Text,Ugly,Morbid,Extra fingers,Poorly drawn hands,Mutation,Blurry,Extra limbs,Gross proportions,Missing arms,Mutated hands,Long neck,Duplicate,Mutilated,Mutilated hands,Poorly drawn face,Deformed,Bad anatomy,Cloned face,Malformed limbs,Missing legs,Too many fingers" MAX_SEED = np.iinfo(np.int32).max @spaces.GPU(enable_queue=True) def predict(prompt, upload_images, ip_adapter_scale=0.5, negative_prompt="", seed=100, randomize_seed=False, center_crop=False, width=1024, height=1024, guidance_scale=5.0, num_inference_steps=50, mode="Basic", progress=gr.Progress(track_tqdm=True)): if randomize_seed: seed = random.randint(0, MAX_SEED) ip_adapter_images = [] for img in upload_images: ip_adapter_images.append(load_image(img)) # ip_adapter_images = [Image.open(image) for image in ip_adapter_images] # # Optionally resize images if center crop is not selected # if not center_crop: # ip_adapter_images = [image.resize((224, 224)) for image in ip_adapter_images] generator = torch.Generator(device="cuda").manual_seed(seed) if mode == "Style-Only": adapter_scale = {"down": {"block_2": [ip_adapter_scale, 0.0]}, "up": {"block_0": [0.0, ip_adapter_scale, 0.0]}, "mid": ip_adapter_scale} elif mode == "Style2": adapter_scale = {"down": {"block_2": [ip_adapter_scale, ip_adapter_scale]}, "up": {"block_0": [0.0, ip_adapter_scale, 0.0]}} elif mode == "Style3": adapter_scale = {"down": {"block_2": [ip_adapter_scale, 0.0], "block_1": [0.0, ip_adapter_scale]}, "up": {"block_0": [0.0, ip_adapter_scale, 0.0]}} else: adapter_scale = ip_adapter_scale pipe.set_ip_adapter_scale([adapter_scale]) image = pipe( prompt=prompt, ip_adapter_image=[ip_adapter_images], negative_prompt=negative_prompt, height=height, width=width, num_inference_steps=num_inference_steps, guidance_scale=guidance_scale, num_images_per_prompt=1, generator=generator, ).images[0] return image, seed def swap_to_gallery(images): return gr.update(value=images, visible=True), gr.update(visible=True), gr.update(visible=False) def remove_back_to_files(): return gr.update(visible=False), gr.update(visible=False), gr.update(visible=True) # examples = [ # ["high quality", ["example1.png"], 1.0, "", 1000, False, False, 1152, 896, 5.0, 30, "Regular"], # ["capybara", ["example2.png"], 0.7, "", 1000, False, False, 1152, 896, 5.0, 30, "Style-Only"], # ] css=""" #col-container { margin: 0 auto; max-width: 1024px; } #result img{ object-position: top; } #result .image-container{ height: 100% } """ with gr.Blocks(css=css) as demo: with gr.Column(elem_id="col-container"): gr.Markdown(f""" # Bria's Multi-Image-Prompt-Adapter """) with gr.Row(): with gr.Column(): # ip_adapter_images = gr.Gallery(label="Input Images", elem_id="image-gallery").style(grid=[2], preview=True) # ip_adapter_images = gr.Gallery(label="Input Images", elem_id="image-gallery", show_label=True)#.style(grid=[2]) # ip_adapter_images = gr.Gallery(columns=4, interactive=True, label="Input Images") files = gr.File( label="Input Image/s", file_types=["image"], file_count="multiple" ) uploaded_files = gr.Gallery(label="Your images", visible=False, columns=5, rows=1, height=200) with gr.Column(visible=False) as clear_button: remove_and_reupload = gr.ClearButton(value="Remove and upload new ones", components=files, size="sm") with gr.Row(): ip_adapter_scale = gr.Slider( label="Image Input Scale", info="Use 1 for creating image variations", minimum=0.0, maximum=1.0, step=0.05, value=1.0, ) mode = gr.Dropdown( ["Regular", "Style-Only"], label="Mode",#, info="Mode" ) with gr.Column(): result = gr.Image(label="Result", elem_id="result", format="png") prompt = gr.Text( label="Prompt", show_label=True, lines=1, placeholder="Enter your prompt", container=True, info='For image variation, leave empty or try a prompt like: "high quality".' ) with gr.Row(): width = gr.Slider( label="Width", minimum=256, maximum=2048, step=32, value=1024, ) height = gr.Slider( label="Height", minimum=256, maximum=2048, step=32, value=1024, ) run_button = gr.Button("Run", scale=0) with gr.Accordion("Advanced Settings", open=False): negative_prompt = gr.Text( label="Negative prompt", max_lines=1, placeholder="Enter a negative prompt", ) seed = gr.Slider( label="Seed", minimum=0, maximum=MAX_SEED, step=1, value=1000, ) randomize_seed = gr.Checkbox(label="Randomize seed", value=True) center_crop = gr.Checkbox(label="Center Crop image", value=False, info="If not checked, the IP-Adapter image input would be resized to a square.") # with gr.Row(): # width = gr.Slider( # label="Width", # minimum=256, # maximum=2048, # step=32, # value=1024, # ) # height = gr.Slider( # label="Height", # minimum=256, # maximum=2048, # step=32, # value=1024, # ) with gr.Row(): guidance_scale = gr.Slider( label="Guidance scale", minimum=0.0, maximum=10.0, step=0.1, value=7.0, ) num_inference_steps = gr.Slider( label="Number of inference steps", minimum=1, maximum=100, step=1, value=25, ) files.upload(fn=swap_to_gallery, inputs=files, outputs=[uploaded_files, clear_button, files]) remove_and_reupload.click(fn=remove_back_to_files, outputs=[uploaded_files, clear_button, files]) # # gr.Examples( # # examples=examples, # # fn=predict, # # inputs=[prompt, files, ip_adapter_scale, negative_prompt, seed, randomize_seed, center_crop, width, height, guidance_scale, num_inference_steps, mode], # # outputs=[result, seed], # # cache_examples="lazy" # # ) gr.on( triggers=[run_button.click, prompt.submit], fn=predict, inputs=[prompt, files, ip_adapter_scale, negative_prompt, seed, randomize_seed, center_crop, width, height, guidance_scale, num_inference_steps, mode], outputs=[result, seed] ) demo.queue(max_size=25,api_open=False).launch(show_api=False) # image_blocks.queue(max_size=25,api_open=False).launch(show_api=False)