| ###TEST03 JUSTE CHARGER FLUX-SCHNELL |
| ###https://huggingface.co/spaces/black-forest-labs/FLUX.1-schnell/blob/main/app.py |
| ### |
| import os |
| import gradio as gr |
| from huggingface_hub import login |
| from diffusers import FluxPipeline |
| import torch |
| from PIL import Image |
| import fitz # PyMuPDF pour la gestion des PDF |
| import sentencepiece |
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| import numpy as np |
| import random |
| import spaces |
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| # |
| #import gradio as gr |
| #import numpy as np |
| #import random |
| #import spaces |
| #import torch |
| #from diffusers import DiffusionPipeline |
| # |
| #dtype = torch.bfloat16 |
| #device = "cuda" if torch.cuda.is_available() else "cpu" |
| # |
| #pipe = DiffusionPipeline.from_pretrained("black-forest-labs/FLUX.1-schnell", torch_dtype=dtype).to(device) |
| # |
| #MAX_SEED = np.iinfo(np.int32).max |
| #MAX_IMAGE_SIZE = 2048 |
| # |
| #@spaces.GPU() |
| #def infer(prompt, seed=42, randomize_seed=False, width=1024, height=1024, num_inference_steps=4, progress=gr.Progress(track_tqdm=True)): |
| # if randomize_seed: |
| # seed = random.randint(0, MAX_SEED) |
| # generator = torch.Generator().manual_seed(seed) |
| # image = pipe( |
| # prompt = prompt, |
| # width = width, |
| # height = height, |
| # num_inference_steps = num_inference_steps, |
| # generator = generator, |
| # guidance_scale=0.0 |
| # ).images[0] |
| # return image, seed |
| # |
| #examples = [ |
| # "a tiny astronaut hatching from an egg on the moon", |
| # "a cat holding a sign that says hello world", |
| # "an anime illustration of a wiener schnitzel", |
| #] |
| # |
| #css=""" |
| ##col-container { |
| # margin: 0 auto; |
| # max-width: 520px; |
| #} |
| #""" |
| # |
| #with gr.Blocks(css=css) as demo: |
| # |
| # with gr.Column(elem_id="col-container"): |
| # gr.Markdown(f"""# FLUX.1 [schnell] |
| #12B param rectified flow transformer distilled from [FLUX.1 [pro]](https://blackforestlabs.ai/) for 4 step generation |
| #[[blog](https://blackforestlabs.ai/announcing-black-forest-labs/)] [[model](https://huggingface.co/black-forest-labs/FLUX.1-schnell)] |
| # """) |
| # |
| # with gr.Row(): |
| # |
| # prompt = gr.Text( |
| # label="Prompt", |
| # show_label=False, |
| # max_lines=1, |
| # placeholder="Enter your prompt", |
| # container=False, |
| # ) |
| # |
| # run_button = gr.Button("Run", scale=0) |
| # |
| # result = gr.Image(label="Result", show_label=False) |
| # |
| # 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) |
| # |
| # with gr.Row(): |
| # |
| # width = gr.Slider( |
| # label="Width", |
| # minimum=256, |
| # maximum=MAX_IMAGE_SIZE, |
| # step=32, |
| # value=1024, |
| # ) |
| # |
| # height = gr.Slider( |
| # label="Height", |
| # minimum=256, |
| # maximum=MAX_IMAGE_SIZE, |
| # step=32, |
| # value=1024, |
| # ) |
| # |
| # with gr.Row(): |
| # |
| # |
| # num_inference_steps = gr.Slider( |
| # label="Number of inference steps", |
| # minimum=1, |
| # maximum=50, |
| # step=1, |
| # value=4, |
| # ) |
| # |
| # gr.Examples( |
| # examples = examples, |
| # fn = infer, |
| # inputs = [prompt], |
| # outputs = [result, seed], |
| # cache_examples="lazy" |
| # ) |
| # |
| # gr.on( |
| # triggers=[run_button.click, prompt.submit], |
| # fn = infer, |
| # inputs = [prompt, seed, randomize_seed, width, height, num_inference_steps], |
| # outputs = [result, seed] |
| # ) |
| # |
| #demo.launch() |
| # |
| # |
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| # Force l'utilisation du CPU pour tout PyTorch |
| #torch.set_default_device("cpu") |
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| #dtype = torch.bfloat16 |
| device = "cuda" if torch.cuda.is_available() else "cpu" |
| # |
| #pipe = DiffusionPipeline.from_pretrained("black-forest-labs/FLUX.1-schnell", torch_dtype=dtype).to(device) |
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| def load_pdf(pdf_path): |
| """Traite le texte d'un fichier PDF""" |
| if pdf_path is None: |
| return None |
| text = "" |
| try: |
| doc = fitz.open(pdf_path) |
| for page in doc: |
| text += page.get_text() |
| doc.close() |
| return text |
| except Exception as e: |
| print(f"Erreur lors de la lecture du PDF: {str(e)}") |
| return None |
| |
| class FluxGenerator: |
| def __init__(self): |
| self.token = os.getenv('Authentification_HF') |
| if not self.token: |
| raise ValueError("Token d'authentification HuggingFace non trouvé") |
| login(self.token) |
| self.pipeline = None |
| self.device = "cpu" # Force l'utilisation du CPU |
| self.load_model() |
| |
| def load_model(self): |
| """Charge le modèle FLUX avec des paramètres optimisés pour CPU""" |
| try: |
| print("Chargement du modèle FLUX sur CPU...") |
| # Configuration spécifique pour CPU |
| torch.set_grad_enabled(False) # Désactive le calcul des gradients |
| |
| self.pipeline = FluxPipeline.from_pretrained( |
| "black-forest-labs/FLUX.1-schnell", |
| revision="refs/pr/1", |
| torch_dtype=torch.float32 # Utilise float32 au lieu de bfloat16 pour meilleure compatibilité CPU |
| ) |
| # device_map={"cpu": self.device} # Force tous les composants sur CPU |
| # )device |
| |
| # Désactive les optimisations GPU |
| self.pipeline.to(self.device) |
| print(f"Utilisation forcée du CPU") |
| print("Modèle FLUX chargé avec succès!") |
| |
| except Exception as e: |
| print(f"Erreur lors du chargement du modèle: {str(e)}") |
| raise |
| |
| def generate_image(self, prompt, reference_image=None, pdf_file=None): |
| """Génère une image à partir d'un prompt et optionnellement une référence""" |
| try: |
| # Si un PDF est fourni, ajoute son contenu au prompt |
| if pdf_file is not None: |
| pdf_text = load_pdf(pdf_file) |
| if pdf_text: |
| prompt = f"{prompt}\nContexte du PDF:\n{pdf_text}" |
| |
| # Configuration pour génération sur CPU |
| with torch.no_grad(): # Désactive le calcul des gradients pendant la génération |
| image = self.pipeline( |
| prompt=prompt, |
| num_inference_steps=20, # Réduit le nombre d'étapes pour accélérer sur CPU |
| guidance_scale=0.0, |
| max_sequence_length=256, |
| generator=torch.Generator(device=self.device).manual_seed(0) |
| ).images[0] |
| |
| return image |
| |
| except Exception as e: |
| print(f"Erreur lors de la génération de l'image: {str(e)}") |
| return None |
| |
| # Instance globale du générateur |
| generator = FluxGenerator() |
| |
| def generate(prompt, reference_file): |
| """Fonction de génération pour l'interface Gradio""" |
| try: |
| # Gestion du fichier de référence |
| if reference_file is not None: |
| if isinstance(reference_file, dict): # Si le fichier est fourni par Gradio |
| file_path = reference_file.name |
| else: # Si c'est un chemin direct |
| file_path = reference_file |
| |
| file_type = file_path.split('.')[-1].lower() |
| if file_type in ['pdf']: |
| return generator.generate_image(prompt, pdf_file=file_path) |
| elif file_type in ['png', 'jpg', 'jpeg']: |
| return generator.generate_image(prompt, reference_image=file_path) |
| |
| # Génération sans référence |
| return generator.generate_image(prompt) |
| |
| except Exception as e: |
| print(f"Erreur détaillée: {str(e)}") |
| return None |
| |
| # Interface Gradio simple |
| demo = gr.Interface( |
| fn=generate, |
| inputs=[ |
| gr.Textbox(label="Prompt", placeholder="Décrivez l'image que vous souhaitez générer..."), |
| gr.File(label="Image ou PDF de référence (optionnel)", type="file") |
| ], |
| outputs=gr.Image(label="Image générée"), |
| title="Test du modèle FLUX (CPU)", |
| description="Interface simple pour tester la génération d'images avec FLUX (optimisé pour CPU)" |
| ) |
| |
| if __name__ == "__main__": |
| demo.launch() |
