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prithivMLmods
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Model Details
Model Provider
prithivMLmods
Model Tree
black-forest-labs/FLUX.1-Kontext-dev
Input Modalities
Output Modalities
Supported Functionality
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!pip install diffusers torch gradio transformers!pip install pillow gradio-imageslider huggingface_hub!pip install sentencepiece spaces peft torchvision acceleratepy
import osimport gradio as grimport numpy as npimport spacesimport torchimport randomfrom PIL import Imagefrom typing import Iterable from diffusers import FluxKontextPipelinefrom diffusers.utils import load_imagefrom huggingface_hub import hf_hub_downloadfrom gradio_imageslider import ImageSlider device = torch.device("cuda" if torch.cuda.is_available() else "cpu") # --- Main Model Initialization ---MAX_SEED = np.iinfo(np.int32).maxpipe = FluxKontextPipeline.from_pretrained("black-forest-labs/FLUX.1-Kontext-dev", torch_dtype=torch.bfloat16).to("cuda") # --- Load New Adapter ---pipe.load_lora_weights("prithivMLmods/Kontext-Watermark-Remover", weight_name="Kontext-Watermark-Remover.safetensors", adapter_name="watermark_remover") @spaces.GPUdef infer(input_image, prompt, seed=42, randomize_seed=False, guidance_scale=2.5, steps=28, progress=gr.Progress(track_tqdm=True)): """ Perform image editing, returning a pair for the ImageSlider. """ if not input_image: raise gr.Error("Please upload an image for editing.") pipe.set_adapters(["watermark_remover"], adapter_weights=[1.0]) if randomize_seed: seed = random.randint(0, MAX_SEED) original_image = input_image.copy().convert("RGB") image = pipe( image=original_image, prompt=prompt, guidance_scale=guidance_scale, width = original_image.size[0], height = original_image.size[1], num_inference_steps=steps, generator=torch.Generator().manual_seed(seed), ).images[0] return (original_image, image), seed, gr.Button(visible=True) css="""#col-container { margin: 0 auto; max-width: 960px;}#main-title h1 {font-size: 2.1em !important;}""" with gr.Blocks(css=css) as demo: with gr.Column(elem_id="col-container"): gr.Markdown("# **Photo-Mate-i2i: Watermark Remover**", elem_id="main-title") gr.Markdown("Image manipulation with FLUX.1 Kontext. This demo focuses on watermark removal.") with gr.Row(): with gr.Column(): input_image = gr.Image(label="Upload Image with Watermark", type="pil", height="300") with gr.Row(): prompt = gr.Text( label="Edit Prompt", show_label=False, max_lines=1, placeholder="e.g., 'Remove the watermark'", container=False, value="[photo content], remove any watermark text or logos from the image while preserving the background, texture, lighting, and overall realism. Ensure the edited areas blend seamlessly with surrounding details, leaving no visible traces of watermark removal." ) run_button = gr.Button("Run", variant="primary", scale=0) with gr.Accordion("Advanced Settings", open=False): seed = gr.Slider( label="Seed", minimum=0, maximum=MAX_SEED, step=1, value=0, ) randomize_seed = gr.Checkbox(label="Randomize seed", value=True) guidance_scale = gr.Slider( label="Guidance Scale", minimum=1, maximum=10, step=0.1, value=2.5, ) steps = gr.Slider( label="Steps", minimum=1, maximum=30, value=28, step=1 ) with gr.Column(): output_slider = ImageSlider(label="Before / After", show_label=False, interactive=False) reuse_button = gr.Button("Reuse this image", visible=False) gr.on( triggers=[run_button.click, prompt.submit], fn=infer, inputs=[input_image, prompt, seed, randomize_seed, guidance_scale, steps], outputs=[output_slider, seed, reuse_button] ) reuse_button.click( fn=lambda images: images[1] if isinstance(images, (list, tuple)) and len(images) > 1 else images, inputs=[output_slider], outputs=[input_image] ) demo.launch(mcp_server=True, ssr_mode=False, show_error=True)| Setting | Value |
|---|---|
| Module Type | Adapter |
| Base Model | FLUX.1 Kontext Dev - fp8 |
| Trigger Words | [photo content], remove any watermark text or logos from the image while preserving the background, texture, lighting, and overall realism. Ensure the edited areas blend seamlessly with surrounding details, leaving no visible traces of watermark removal. |
| Image Processing Repeats | 50 |
| Epochs | 25 |
| Save Every N Epochs | 1 |
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Labeling: florence-community/Florence-2-large-ft (natural language & English) Total Images Used for Training : 150 Image Pairs (75 Start, 75 End)| Setting | Value |
|---|---|
| Seed | - |
| Clip Skip | - |
| Text Encoder LR | 0.00001 |
| UNet LR | 0.00005 |
| LR Scheduler | constant |
| Optimizer | AdamW8bit |
| Network Dimension | 64 |
| Network Alpha | 32 |
| Gradient Accumulation Steps | - |
| Setting | Value |
|---|---|
| Shuffle Caption | - |
| Keep N Tokens | - |
| Setting | Value |
|---|---|
| Noise Offset | 0.03 |
| Multires Noise Discount | 0.1 |
| Multires Noise Iterations | 10 |
| Conv Dimension | - |
| Conv Alpha | - |
| Batch Size | - |
| Steps | 2900 & 400(warm up) |
| Sampler | euler |
You should use [photo content] to trigger the image generation.
You should use remove any watermark text or logos from the image while preserving the background to trigger the image generation.
You should use texture to trigger the image generation.
You should use lighting to trigger the image generation.
You should use and overall realism. Ensure the edited areas blend seamlessly with surrounding details to trigger the image generation.
You should use leaving no visible traces of watermark removal. to trigger the image generation.
Download them in the Files & versions tab.
!pip install diffusers torch gradio transformers!pip install pillow gradio-imageslider huggingface_hub!pip install sentencepiece spaces peft torchvision accelerateLabeling: florence-community/Florence-2-large-ft (natural language & English)Total Images Used for Training : 150 Image Pairs (75 Start, 75 End)import osimport gradio as grimport numpy as npimport spacesimport torchimport randomfrom PIL import Imagefrom typing import Iterable from diffusers import FluxKontextPipelinefrom diffusers.utils import load_imagefrom huggingface_hub import hf_hub_downloadfrom gradio_imageslider import ImageSlider device = torch.device("cuda" if torch.cuda.is_available() else "cpu") # --- Main Model Initialization ---MAX_SEED = np.iinfo(np.int32).maxpipe = FluxKontextPipeline.from_pretrained("black-forest-labs/FLUX.1-Kontext-dev", torch_dtype=torch.bfloat16).to("cuda") # --- Load New Adapter ---pipe.load_lora_weights("prithivMLmods/Kontext-Watermark-Remover", weight_name="Kontext-Watermark-Remover.safetensors", adapter_name="watermark_remover") @spaces.GPUdef infer(input_image, prompt, seed=42, randomize_seed=False, guidance_scale=2.5, steps=28, progress=gr.Progress(track_tqdm=True)): """ Perform image editing, returning a pair for the ImageSlider. """ if not input_image: raise gr.Error("Please upload an image for editing.") pipe.set_adapters(["watermark_remover"], adapter_weights=[1.0]) if randomize_seed: seed = random.randint(0, MAX_SEED) original_image = input_image.copy().convert("RGB") image = pipe( image=original_image, prompt=prompt, guidance_scale=guidance_scale, width = original_image.size[0], height = original_image.size[1], num_inference_steps=steps, generator=torch.Generator().manual_seed(seed), ).images[0] return (original_image, image), seed, gr.Button(visible=True) css="""#col-container { margin: 0 auto; max-width: 960px;}#main-title h1 {font-size: 2.1em !important;}""" with gr.Blocks(css=css) as demo: with gr.Column(elem_id="col-container"): gr.Markdown("# **Photo-Mate-i2i: Watermark Remover**", elem_id="main-title") gr.Markdown("Image manipulation with FLUX.1 Kontext. This demo focuses on watermark removal.") with gr.Row(): with gr.Column(): input_image = gr.Image(label="Upload Image with Watermark", type="pil", height="300") with gr.Row(): prompt = gr.Text( label="Edit Prompt", show_label=False, max_lines=1, placeholder="e.g., 'Remove the watermark'", container=False, value="[photo content], remove any watermark text or logos from the image while preserving the background, texture, lighting, and overall realism. Ensure the edited areas blend seamlessly with surrounding details, leaving no visible traces of watermark removal." ) run_button = gr.Button("Run", variant="primary", scale=0) with gr.Accordion("Advanced Settings", open=False): seed = gr.Slider( label="Seed", minimum=0, maximum=MAX_SEED, step=1, value=0, ) randomize_seed = gr.Checkbox(label="Randomize seed", value=True) guidance_scale = gr.Slider( label="Guidance Scale", minimum=1, maximum=10, step=0.1, value=2.5, ) steps = gr.Slider( label="Steps", minimum=1, maximum=30, value=28, step=1 ) with gr.Column(): output_slider = ImageSlider(label="Before / After", show_label=False, interactive=False) reuse_button = gr.Button("Reuse this image", visible=False) gr.on( triggers=[run_button.click, prompt.submit], fn=infer, inputs=[input_image, prompt, seed, randomize_seed, guidance_scale, steps], outputs=[output_slider, seed, reuse_button] ) reuse_button.click( fn=lambda images: images[1] if isinstance(images, (list, tuple)) and len(images) > 1 else images, inputs=[output_slider], outputs=[input_image] ) demo.launch(mcp_server=True, ssr_mode=False, show_error=True)