| ###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 | |
| import numpy as np | |
| import random | |
| import spaces | |
| # | |
| #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() | |
| # | |
| # | |
| # Force l'utilisation du CPU pour tout PyTorch | |
| #torch.set_default_device("cpu") | |
| #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) | |
| 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() |