Update app.py

app.py

@@ -1,5 +1,5 @@
-###TEST02 JUSTE CHARGER FLUX-SCHNELL
-###
+###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
@@ -9,9 +9,185 @@ 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()
+#
+#
+
+
+
+
+
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+
+
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+
 
 # Force l'utilisation du CPU pour tout PyTorch
-torch.set_default_device("cpu")
+#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"""
@@ -48,9 +224,9 @@ class FluxGenerator:
             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={"auto": self.device}  # Force tous les composants sur CPU
-            )
+                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)