Upload generate.py

generate.py

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+import torch
+import gradio as gr
+from diffusers import StableDiffusionPipeline
+
+# Chemin vers ton modèle .safetensors
+model_path = "/Users/arthurdufour/Documents/ComfyUI/models/checkpoints/v1-5-pruned-emaonly.safetensors"
+
+# Charger le modèle directement (évite load_state_dict)
+pipeline = StableDiffusionPipeline.from_single_file(model_path, torch_dtype=torch.float32)
+
+# Vérification du backend MPS pour MacBook M3
+device = "mps" if torch.backends.mps.is_available() else "cpu"
+pipeline.to(device)
+
+def generate_image(positive_prompt, negative_prompt, steps, seed):
+    torch.mps.empty_cache()  # Nettoyage mémoire
+    generator = torch.manual_seed(int(seed))
+
+    try:
+        image = pipeline(
+            prompt=positive_prompt,
+            negative_prompt=negative_prompt if "negative_prompt" in pipeline.__call__.__code__.co_varnames else None,
+            num_inference_steps=int(steps),
+            width=512,
+            height=512,
+            generator=generator
+        ).images[0]
+    except Exception as e:
+        return f"Erreur : {str(e)}"
+
+    return image
+
+# Interface Gradio
+with gr.Blocks() as demo:
+    gr.Markdown("## Génération d'images Stable Diffusion (MPS)")
+
+    with gr.Row():
+        prompt_input = gr.Textbox(label="Prompt Positif", value="a horse")
+        negative_input = gr.Textbox(label="Prompt Négatif", value="text, watermark")
+
+    with gr.Row():
+        steps_slider = gr.Slider(1, 50, 20, step=1, label="Nombre de Steps")
+        seed_input = gr.Number(value=580029479038533, label="Seed")
+
+    output_image = gr.Image(label="Image Générée")
+
+    generate_button = gr.Button("Générer")
+    generate_button.click(generate_image, inputs=[prompt_input, negative_input, steps_slider, seed_input], outputs=output_image)
+
+# Lancer l'interface
+demo.launch()