Spaces:
Paused
Paused
| import os | |
| is_spaces = True if os.environ.get('SPACE_ID') else False | |
| if(is_spaces): | |
| import spaces | |
| os.environ["HF_HUB_ENABLE_HF_TRANSFER"] = "1" | |
| import sys | |
| from dotenv import load_dotenv | |
| load_dotenv() | |
| # Add the current working directory to the Python path | |
| sys.path.insert(0, os.getcwd()) | |
| import gradio as gr | |
| from PIL import Image | |
| import torch | |
| import uuid | |
| import os | |
| import shutil | |
| import json | |
| import yaml | |
| from slugify import slugify | |
| from transformers import AutoProcessor, AutoModelForCausalLM | |
| if(not is_spaces): | |
| from toolkit.job import get_job | |
| MAX_IMAGES = 150 | |
| def load_captioning(uploaded_images, concept_sentence): | |
| updates = [] | |
| if len(uploaded_images) <= 1: | |
| raise gr.Error( | |
| "Please upload at least 2 images to train your model (the ideal number with default settings is between 4-30)" | |
| ) | |
| elif len(uploaded_images) > MAX_IMAGES: | |
| raise gr.Error( | |
| f"For now, only {MAX_IMAGES} or less images are allowed for training" | |
| ) | |
| # Update for the captioning_area | |
| #for _ in range(3): | |
| updates.append(gr.update(visible=True)) | |
| # Update visibility and image for each captioning row and image | |
| for i in range(1, MAX_IMAGES + 1): | |
| # Determine if the current row and image should be visible | |
| visible = i <= len(uploaded_images) | |
| # Update visibility of the captioning row | |
| updates.append(gr.update(visible=visible)) | |
| # Update for image component - display image if available, otherwise hide | |
| image_value = uploaded_images[i - 1] if visible else None | |
| updates.append(gr.update(value=image_value, visible=visible)) | |
| #Update value of captioning area | |
| text_value = "[trigger]" if visible and concept_sentence else None | |
| updates.append(gr.update(value=text_value, visible=visible)) | |
| #Update for the sample caption area | |
| updates.append(gr.update(visible=True)) | |
| updates.append(gr.update(placeholder=f'A photo of {concept_sentence} holding a sign that reads "Hello friend"')) | |
| updates.append(gr.update(placeholder=f'A mountainous landscape in the style of {concept_sentence}')) | |
| updates.append(gr.update(placeholder=f'A {concept_sentence} in a mall')) | |
| return updates | |
| if(is_spaces): | |
| load_captioning = spaces.GPU()(load_captioning) | |
| def create_dataset(*inputs): | |
| print("Creating dataset") | |
| images = inputs[0] | |
| destination_folder = str(f"datasets/{uuid.uuid4()}") | |
| if not os.path.exists(destination_folder): | |
| os.makedirs(destination_folder) | |
| jsonl_file_path = os.path.join(destination_folder, 'metadata.jsonl') | |
| with open(jsonl_file_path, 'a') as jsonl_file: | |
| for index, image in enumerate(images): | |
| new_image_path = shutil.copy(image, destination_folder) | |
| original_caption = inputs[index + 1] | |
| file_name = os.path.basename(new_image_path) | |
| data = {"file_name": file_name, "prompt": original_caption} | |
| jsonl_file.write(json.dumps(data) + "\n") | |
| return destination_folder | |
| def run_captioning(images, concept_sentence, *captions): | |
| device = "cuda" if torch.cuda.is_available() else "cpu" | |
| torch_dtype = torch.float16 | |
| model = AutoModelForCausalLM.from_pretrained("microsoft/Florence-2-large", torch_dtype=torch_dtype, trust_remote_code=True).to(device) | |
| processor = AutoProcessor.from_pretrained("microsoft/Florence-2-large", trust_remote_code=True) | |
| captions = list(captions) | |
| for i, image_path in enumerate(images): | |
| print(captions[i]) | |
| if isinstance(image_path, str): # If image is a file path | |
| image = Image.open(image_path).convert('RGB') | |
| prompt = "<DETAILED_CAPTION>" | |
| inputs = processor(text=prompt, images=image, return_tensors="pt").to(device, torch_dtype) | |
| generated_ids = model.generate( | |
| input_ids=inputs["input_ids"], | |
| pixel_values=inputs["pixel_values"], | |
| max_new_tokens=1024, | |
| num_beams=3 | |
| ) | |
| generated_text = processor.batch_decode(generated_ids, skip_special_tokens=False)[0] | |
| parsed_answer = processor.post_process_generation(generated_text, task=prompt, image_size=(image.width, image.height)) | |
| caption_text = parsed_answer['<DETAILED_CAPTION>'].replace("The image shows ", "") | |
| if(concept_sentence): | |
| caption_text = f"{caption_text} [trigger]" | |
| captions[i] = caption_text | |
| yield captions | |
| model.to("cpu") | |
| del model | |
| del processor | |
| def start_training( | |
| lora_name, | |
| concept_sentence, | |
| steps, | |
| lr, | |
| rank, | |
| dataset_folder, | |
| sample_1, | |
| sample_2, | |
| sample_3, | |
| ): | |
| if not lora_name: | |
| raise gr.Error("You forgot to insert your LoRA name! This name has to be unique.") | |
| print("Started training") | |
| slugged_lora_name = slugify(lora_name) | |
| # Load the default config | |
| with open("train_lora_flux_24gb.yaml", "r") as f: | |
| config = yaml.safe_load(f) | |
| # Update the config with user inputs | |
| config['config']['name'] = slugged_lora_name | |
| config['config']['process'][0]['model']['low_vram'] = True | |
| config['config']['process'][0]['train']['skip_first_sample'] = True | |
| config['config']['process'][0]['train']['steps'] = int(steps) | |
| config['config']['process'][0]['train']['lr'] = float(lr) | |
| config['config']['process'][0]['network']['linear'] = int(rank) | |
| config['config']['process'][0]['network']['linear_alpha'] = int(rank) | |
| config['config']['process'][0]['datasets'][0]['folder_path'] = dataset_folder | |
| if(concept_sentence): | |
| config['config']['process'][0]['trigger_word'] = concept_sentence | |
| if(sample_1 or sample_2 or sample_2): | |
| config['config']['process'][0]['train']['disable_sampling'] = False | |
| config['config']['process'][0]['sample']["sample_every"] = steps | |
| config['config']['process'][0]['sample']['prompts'] = [] | |
| if(sample_1): | |
| config['config']['process'][0]['sample']['prompts'].append(sample_1) | |
| if(sample_2): | |
| config['config']['process'][0]['sample']['prompts'].append(sample_2) | |
| if(sample_3): | |
| config['config']['process'][0]['sample']['prompts'].append(sample_3) | |
| else: | |
| config['config']['process'][0]['train']['disable_sampling'] = True | |
| # Save the updated config | |
| config_path = f"config/{slugged_lora_name}.yaml" | |
| with open(config_path, "w") as f: | |
| yaml.dump(config, f) | |
| if(is_spaces): | |
| pass | |
| #do the spacerunner things here | |
| else: | |
| #run the job locally | |
| job = get_job(config_path) | |
| job.run() | |
| job.cleanup() | |
| return f"Training completed successfully. Model saved as {slugged_lora_name}" | |
| theme = gr.themes.Monochrome( | |
| text_size=gr.themes.Size(lg="18px", md="15px", sm="13px", xl="22px", xs="12px", xxl="24px", xxs="9px"), | |
| font=[gr.themes.GoogleFont('Source Sans Pro'), 'ui-sans-serif', 'system-ui', 'sans-serif'], | |
| ) | |
| css = ''' | |
| #component-1{text-align:center} | |
| .main_ui_logged_out{opacity: 0.3; pointer-events: none} | |
| .tabitem{border: 0px} | |
| ''' | |
| def swap_visibilty(profile: gr.OAuthProfile | None): | |
| print(profile) | |
| if(is_spaces): | |
| if profile is None: | |
| return gr.update(elem_classes=["main_ui_logged_out"]) | |
| else: | |
| print(profile.name) | |
| return gr.update(elem_classes=["main_ui_logged_in"]) | |
| else: | |
| return gr.update(elem_classes=["main_ui_logged_in"]) | |
| with gr.Blocks(theme=theme, css=css) as demo: | |
| gr.Markdown('''# LoRA Ease for FLUX 🧞♂️ | |
| ### Train a high quality FLUX LoRA in a breeze ༄ using [Ostris' AI Toolkit](https://github.com/ostris/ai-toolkit) and [AutoTrain Advanced](https://github.com/huggingface/autotrain-advanced)''') | |
| if(is_spaces): | |
| gr.LoginButton("Sign in with Hugging Face to train your LoRA on Spaces", visible=is_spaces) | |
| with gr.Tab("Train on Spaces" if is_spaces else "Train locally"): | |
| with gr.Column() as main_ui: | |
| with gr.Row(): | |
| lora_name = gr.Textbox(label="The name of your LoRA", info="This has to be a unique name", placeholder="e.g.: Persian Miniature Painting style, Cat Toy") | |
| #training_option = gr.Radio( | |
| # label="What are you training?", choices=["object", "style", "character", "face", "custom"] | |
| #) | |
| concept_sentence = gr.Textbox( | |
| label="Trigger word/sentence", | |
| info="Trigger word or sentence to be used", | |
| placeholder="uncommon word like p3rs0n or trtcrd, or sentence like 'in the style of CNSTLL'", | |
| interactive=True, | |
| ) | |
| with gr.Group(visible=True) as image_upload: | |
| with gr.Row(): | |
| images = gr.File( | |
| file_types=["image"], | |
| label="Upload your images", | |
| file_count="multiple", | |
| interactive=True, | |
| visible=True, | |
| scale=1, | |
| ) | |
| with gr.Column(scale=3, visible=False) as captioning_area: | |
| with gr.Column(): | |
| gr.Markdown("""# Custom captioning | |
| You can optionally add a custom caption for each image (or use an AI model for this). [trigger] will represent your concept sentence/trigger word. | |
| """) | |
| do_captioning = gr.Button("Add AI captions with Florence-2") | |
| output_components = [captioning_area] | |
| caption_list = [] | |
| for i in range(1, MAX_IMAGES + 1): | |
| locals()[f"captioning_row_{i}"] = gr.Row(visible=False) | |
| with locals()[f"captioning_row_{i}"]: | |
| locals()[f"image_{i}"] = gr.Image( | |
| type="filepath", | |
| width=111, | |
| height=111, | |
| min_width=111, | |
| interactive=False, | |
| scale=2, | |
| show_label=False, | |
| show_share_button=False, | |
| show_download_button=False | |
| ) | |
| locals()[f"caption_{i}"] = gr.Textbox( | |
| label=f"Caption {i}", scale=15, interactive=True | |
| ) | |
| output_components.append(locals()[f"captioning_row_{i}"]) | |
| output_components.append(locals()[f"image_{i}"]) | |
| output_components.append(locals()[f"caption_{i}"]) | |
| caption_list.append(locals()[f"caption_{i}"]) | |
| with gr.Accordion("Advanced options", open=False): | |
| steps = gr.Number(label="Steps", value=1000, minimum=1, maximum=10000, step=1) | |
| lr = gr.Number(label="Learning Rate", value=4e-4, minimum=1e-6, maximum=1e-3, step=1e-6) | |
| rank = gr.Number(label="LoRA Rank", value=16, minimum=4, maximum=128, step=4) | |
| with gr.Accordion("Sample prompts", visible=False) as sample: | |
| gr.Markdown("Include sample prompts to test out your trained model. Don't forget to include your trigger word/sentence (optional)") | |
| sample_1 = gr.Textbox(label="Test prompt 1") | |
| sample_2 = gr.Textbox(label="Test prompt 2") | |
| sample_3 = gr.Textbox(label="Test prompt 3") | |
| output_components.append(sample) | |
| output_components.append(sample_1) | |
| output_components.append(sample_2) | |
| output_components.append(sample_3) | |
| start = gr.Button("Start training") | |
| progress_area = gr.Markdown("") | |
| with gr.Tab("Train locally" if is_spaces else "Instructions"): | |
| gr.Markdown(f'''To use FLUX LoRA Ease locally with this UI, you can clone this repository (yes, HF Spaces are git repos!) | |
| ```bash | |
| git clone https://huggingface.co/spaces/flux-train/flux-lora-trainer | |
| cd flux-lora-trainer | |
| pip install requirements_local.txt | |
| ``` | |
| Then you can install ai-toolkit | |
| ```bash | |
| git clone https://github.com/ostris/ai-toolkit.git | |
| cd ai-toolkit | |
| git submodule update --init --recursive | |
| python3 -m venv venv | |
| source venv/bin/activate | |
| # .\venv\Scripts\activate on windows | |
| # install torch first | |
| pip3 install torch | |
| pip3 install -r requirements.txt | |
| cd .. | |
| ``` | |
| Login with Hugging Face to access FLUX.1 [dev], choose a token with `write` permissions to push your LoRAs to the HF Hub | |
| ```bash | |
| huggingface-cli login | |
| ``` | |
| Now you can run FLUX LoRA Ease locally by doing a simple | |
| ```py | |
| python app.py | |
| ``` | |
| If you prefer command line, you can run Ostris' [AI Toolkit](https://github.com/ostris/ai-toolkit) yourself directly. | |
| ''') | |
| dataset_folder = gr.State() | |
| images.upload( | |
| load_captioning, | |
| inputs=[images, concept_sentence], | |
| outputs=output_components, | |
| queue=False | |
| ) | |
| start.click( | |
| fn=create_dataset, | |
| inputs=[images] + caption_list, | |
| outputs=dataset_folder, | |
| queue=False | |
| ).then( | |
| fn=start_training_spaces if is_spaces else start_training, | |
| inputs=[ | |
| lora_name, | |
| concept_sentence, | |
| steps, | |
| lr, | |
| rank, | |
| dataset_folder, | |
| sample_1, | |
| sample_2, | |
| sample_3, | |
| ], | |
| outputs=progress_area, | |
| queue=False | |
| ) | |
| do_captioning.click( | |
| fn=run_captioning, inputs=[images, concept_sentence] + caption_list, outputs=caption_list | |
| ) | |
| demo.load(fn=swap_visibilty, outputs=main_ui, queue=False) | |
| if __name__ == "__main__": | |
| demo.queue() | |
| demo.launch(share=True) |