feat: let's see

app.py

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+import gradio as gr
+from torch import autocast
+from diffusers import StableDiffusionPipeline
+import torch
+
+model_id = "models/pawelklimkowski/tylko-sd-dream" #@param {type:"string"}
+pipe = StableDiffusionPipeline.from_pretrained(model_id, torch_dtype=torch.float16).to("cuda")
+
+def inference(prompt, num_samples):
+    all_images = [] 
+    images = pipe(prompt, num_images_per_prompt=num_samples, num_inference_steps=50, guidance_scale=7.5).images
+    all_images.extend(images)
+    return all_images
+
+with gr.Blocks() as demo:
+    gr.HTML("<h2 style=\"font-size: 2em; font-weight: bold\" align=\"center\">Run Concept</h2>")
+    with gr.Row():
+        with gr.Column():
+            prompt = gr.Textbox(label="prompt")
+            samples = gr.Slider(label="Samples",value=1)
+            run = gr.Button(value="Run")
+        with gr.Column():
+            gallery = gr.Gallery(show_label=False)
+
+    run.click(inference, inputs=[prompt,samples], outputs=gallery)
+    gr.Examples([["a photo of oklyt", 1,1]], [prompt,samples], gallery, inference, cache_examples=False)
+
+
+demo.launch(debug=True)

requirements.txt

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+transformers
+torch