###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()