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app.py

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+from diffusers import StableDiffusionXLPipeline
+import torch
+from transformers.pipelines.image_to_text import Image
+import gradio as gr
+
+pipe = StableDiffusionXLPipeline.from_pretrained(
+    "segmind/SSD-1B",
+    torch_dtype = torch.float16,
+    use_safetensors=True,
+    variant = "fp16",
+)
+pipe.to("cuda")
+
+prompt = "astronaut riding a green horse"
+neg_prompt = "ugly, blurry, poor quality"
+
+def generate_image(prompt, neg_prompt):
+    image = pipe(
+        prompt=prompt,
+        negative_prompt=neg_prompt
+    ).images[0]
+    return image
+
+prompt = gr.Text(
+    label="Prompt",
+    show_label=False,
+    max_lines=1,
+    placeholder="Enter your prompt :",
+    container=False
+)
+
+neg_prompt = gr.Text(
+    label="Negative Prompt",
+    show_label=False,
+    max_lines=1,
+    placeholder="Enter your negative prompt",
+    container=False
+)
+
+iface = gr.Interface(
+     fn=generate_image,
+     inputs=[prompt, neg_prompt],
+     outputs="image",
+     title="Text to Image Generation",
+     examples=[
+         ["a painting of a cute cat sitting on a chair","ugly, blurry"],
+         ["an assortment riding a horse on mars","poorly down"]
+     ],
+     allow_flagging=False
+)
+
+iface.launch(share=True)
+#image = pipe(prompt=prompt, negative_prompt = neg_prompt).images[0]
+# print(image)
+
+
+
+
+
+
+

requirements.txt

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+git+https://github.com/huggingface/diffusers
+transformers 
+accelerate
+safetensors
+gradio
+torch
+
+