###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()