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