feat: init project

app.py

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+import gradio as gr
+import numpy as np
+import random
+from diffusers import DiffusionPipeline
+import torch
+
+device = "cuda" if torch.cuda.is_available() else "cpu"
+
+if torch.cuda.is_available():
+    torch_dtype = torch.float16
+else:
+    torch_dtype = torch.float32
+
+model_turbo_repo_id = "stabilityai/sdxl-turbo"
+model_repo_id = "stabilityai/stable-diffusion-2"
+pipe = DiffusionPipeline.from_pretrained(model_turbo_repo_id, torch_dtype=torch_dtype)
+pipe = pipe.to(device)
+
+# Recommended if your computer has < 64 GB of RAM
+pipe.enable_attention_slicing()
+
+MAX_SEED = np.iinfo(np.int32).max
+MAX_IMAGE_SIZE = 1024
+
+
+def create_generator(seed):
+    return torch.Generator().manual_seed(seed)
+
+
+def generate(prompt, negative_prompt, seed, is_random_seed, width, height, guidance_scale, inference_steps):
+    if is_random_seed:
+        seed = random.randint(0, np.iinfo(np.int32).max)
+
+    generator = create_generator(seed)
+
+    return pipe(
+        prompt=prompt,
+        negative_prompt=negative_prompt,
+        guidance_scale=guidance_scale,
+        num_inference_steps=inference_steps,
+        width=width,
+        height=height,
+        generator=generator
+    ).images[0]
+
+
+with gr.Blocks() as imageGenerator:
+    with gr.Column():
+        gr.Markdown(f"""
+        # Zatsit Image Generator
+        ## Générateur d'image basé sur des modèles de stable diffusion
+        Vous cherchez de l'inspiration pour vos prompts ? [lien](https://stablediffusion.fr/prompts)
+        """)
+
+        with gr.Row():
+            prompt = gr.Text(
+                label="Prompt",
+                show_label=False,
+                max_lines=4,
+                placeholder="Votre prompt",
+                container=False,
+            )
+
+            generate_btn = gr.Button("Générer", scale=0)
+
+        result = gr.Image(label="Image générée", show_label=False)
+
+        with gr.Accordion("Paramètres", open=True):
+            negative_prompt = gr.Text(
+                label="Negative prompt",
+                max_lines=4,
+                placeholder="Votre prompt négatif",
+            )
+
+            seed = gr.Slider(
+                label="Seed",
+                minimum=0,
+                maximum=MAX_SEED,
+                step=1,
+                value=70537634,
+            )
+
+            is_random_seed = gr.Checkbox(label="Seed en mode random", value=False)
+
+            width = gr.Slider(
+                label="Width",
+                minimum=256,
+                maximum=MAX_IMAGE_SIZE,
+                step=10,
+                value=1024,
+            )
+
+            height = gr.Slider(
+                label="Height",
+                minimum=256,
+                maximum=MAX_IMAGE_SIZE,
+                step=10,
+                value=1024,
+            )
+
+            guidance_scale = gr.Slider(
+                label="Guildance scale",
+                minimum=0,
+                maximum=10.0,
+                step=0.1,
+                value=0.0,
+            )
+
+            inference_steps = gr.Slider(
+                label="Nombre d'inférences",
+                minimum=0,
+                maximum=100.0,
+                step=1,
+                value=2,
+            )
+
+        gr.Examples(
+            examples=[
+                ["pikachu eating spagetti, Antonio J. Manzanedo", 3384976558, 7, 20],
+                ["Gandalf from Lord of the Rings, diffuse lighting, fantasy, intricate elegant highly detailed "
+                 "lifelike photorealistic digital painting, artstation", 248215544, 7, 42],
+                ["Ethereal gardens of marble built in a shining teal river in future city, gorgeous ornate "
+                 "multi-tiered fountain, futuristic, intricate elegant highly detailed lifelike photorealistic "
+                 "realistic  painting, long shot, studio lighting, octane render, by Dorian Cleavenger", 3868142022,
+                 7, 20],
+                ["Astronaut in a jungle, cold color palette, muted colors, detailed, 8k", 70537634, 1, 2]
+            ],
+            inputs=[prompt, seed, guidance_scale, inference_steps]
+        )
+
+    gr.on(
+        triggers=[generate_btn.click, prompt.submit],
+        fn=generate,
+        inputs=[prompt, negative_prompt, seed, is_random_seed, width, height, guidance_scale, inference_steps],
+        outputs=[result]
+    )
+
+imageGenerator.queue().launch()

requirements.txt

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+diffusers
+transformers
+accelerate
+scipy
+xformers
+gradio
+torch
+invisible_watermark