import logging import random import warnings import os import gradio as gr import numpy as np import spaces import torch from diffusers import FluxControlNetModel from diffusers.pipelines import FluxControlNetPipeline from gradio_imageslider import ImageSlider from PIL import Image from huggingface_hub import snapshot_download css = """ #col-container { margin: 0 auto; max-width: 512px; } """ if torch.cuda.is_available(): power_device = "GPU" device = "cuda" else: power_device = "CPU" device = "cpu" huggingface_token = os.getenv("HUGGINFACE_TOKEN") model_path = snapshot_download( repo_id="black-forest-labs/FLUX.1-dev", repo_type="model", ignore_patterns=["*.md", "*..gitattributes"], local_dir="FLUX.1-dev", token=huggingface_token, # type a new token-id. ) # Load pipeline controlnet = FluxControlNetModel.from_pretrained( "jasperai/Flux.1-dev-Controlnet-Upscaler", torch_dtype=torch.bfloat16 ).to(device) pipe = FluxControlNetPipeline.from_pretrained( model_path, controlnet=controlnet, torch_dtype=torch.bfloat16 ) pipe.to(device) MAX_SEED = 1000000 MAX_PIXEL_BUDGET = 1024 * 1024 def process_input(input_image, upscale_factor, **kwargs): w, h = input_image.size w_original, h_original = w, h aspect_ratio = w / h was_resized = False if w * h * upscale_factor**2 > MAX_PIXEL_BUDGET: warnings.warn( f"Requested output image is too large ({w * upscale_factor}x{h * upscale_factor}). Resizing to ({int(aspect_ratio * MAX_PIXEL_BUDGET ** 0.5 // upscale_factor), int(MAX_PIXEL_BUDGET ** 0.5 // aspect_ratio // upscale_factor)}) pixels." ) gr.Info( f"Requested output image is too large ({w * upscale_factor}x{h * upscale_factor}). Resizing input to ({int(aspect_ratio * MAX_PIXEL_BUDGET ** 0.5 // upscale_factor), int(MAX_PIXEL_BUDGET ** 0.5 // aspect_ratio // upscale_factor)}) pixels budget." ) input_image = input_image.resize( ( int(aspect_ratio * MAX_PIXEL_BUDGET**0.5 // upscale_factor), int(MAX_PIXEL_BUDGET**0.5 // aspect_ratio // upscale_factor), ) ) was_resized = True # resize to multiple of 8 w, h = input_image.size w = w - w % 8 h = h - h % 8 return input_image.resize((w, h)), w_original, h_original, was_resized @spaces.GPU#(duration=42) def infer( seed, randomize_seed, input_image, num_inference_steps, upscale_factor, controlnet_conditioning_scale, progress=gr.Progress(track_tqdm=True), ): if randomize_seed: seed = random.randint(0, MAX_SEED) true_input_image = input_image input_image, w_original, h_original, was_resized = process_input( input_image, upscale_factor ) # rescale with upscale factor w, h = input_image.size control_image = input_image.resize((w * upscale_factor, h * upscale_factor)) generator = torch.Generator().manual_seed(seed) gr.Info("Upscaling image...") image = pipe( prompt="", control_image=control_image, controlnet_conditioning_scale=controlnet_conditioning_scale, num_inference_steps=num_inference_steps, guidance_scale=3.5, height=control_image.size[1], width=control_image.size[0], generator=generator, ).images[0] if was_resized: gr.Info( f"Resizing output image to targeted {w_original * upscale_factor}x{h_original * upscale_factor} size." ) # resize to target desired size image = image.resize((w_original * upscale_factor, h_original * upscale_factor)) image.save("output.jpg") # convert to numpy return [true_input_image, image, seed] with gr.Blocks(theme="Yntec/HaleyCH_Theme_Orange", css=css) as demo: with gr.Row(): run_button = gr.Button(value="Run") with gr.Row(): with gr.Column(scale=4): input_im = gr.Image(label="Input Image", type="pil") with gr.Column(scale=1): num_inference_steps = gr.Slider( label="Number of Inference Steps", minimum=8, maximum=50, step=1, value=28, ) upscale_factor = gr.Slider( label="Upscale Factor", minimum=1, maximum=4, step=1, value=4, ) controlnet_conditioning_scale = gr.Slider( label="Controlnet Conditioning Scale", minimum=0.1, maximum=1.5, step=0.1, value=0.6, ) seed = gr.Slider( label="Seed", minimum=0, maximum=MAX_SEED, step=1, value=42, ) randomize_seed = gr.Checkbox(label="Randomize seed", value=True) with gr.Row(): result = ImageSlider(label="Input / Output", type="pil", interactive=True) examples = gr.Examples( examples=[ [42, False, "z1.webp", 28, 4, 0.6], [42, False, "z1.webp", 28, 4, 0.6], ], inputs=[ seed, randomize_seed, input_im, num_inference_steps, upscale_factor, controlnet_conditioning_scale, ], fn=infer, outputs=result, cache_examples="lazy", ) # examples = gr.Examples( # examples=[ # #[42, False, "examples/image_1.jpg", 28, 4, 0.6], # [42, False, "examples/image_2.jpg", 28, 4, 0.6], # #[42, False, "examples/image_3.jpg", 28, 4, 0.6], # #[42, False, "examples/image_4.jpg", 28, 4, 0.6], # [42, False, "examples/image_5.jpg", 28, 4, 0.6], # [42, False, "examples/image_6.jpg", 28, 4, 0.6], # [42, False, "examples/image_7.jpg", 28, 4, 0.6], # ], # inputs=[ # seed, # randomize_seed, # input_im, # num_inference_steps, # upscale_factor, # controlnet_conditioning_scale, # ], # ) gr.Markdown("**Disclaimer:**") gr.Markdown( "This demo is only for research purpose. Jasper cannot be held responsible for the generation of NSFW (Not Safe For Work) content through the use of this demo. Users are solely responsible for any content they create, and it is their obligation to ensure that it adheres to appropriate and ethical standards. Jasper provides the tools, but the responsibility for their use lies with the individual user." ) gr.on( [run_button.click], fn=infer, inputs=[ seed, randomize_seed, input_im, num_inference_steps, upscale_factor, controlnet_conditioning_scale, ], outputs=result, show_api=False, # show_progress="minimal", ) demo.queue().launch(share=False, show_api=False)