Upload app.py

app.py

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+from googletrans import Translator
+from diffusers import StableDiffusionPipeline, DPMSolverMultistepScheduler
+import torch
+import gradio as gr
+
+translator = Translator()
+
+model_id = "stabilityai/stable-diffusion-2-1"
+access_token="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"
+
+pipe = StableDiffusionPipeline.from_pretrained(model_id, torch_dtype=torch.float16, use_auth_token=access_token
+)
+
+pipe.scheduler = DPMSolverMultistepScheduler.from_config(pipe.scheduler.config)
+pipe = pipe.to("cuda")
+
+def generate(prompt, inference_steps, guidance_scale, neg_prompt):
+  if not neg_prompt:
+    neg_prompt = ""
+    
+    prompt_eng = translator.translate(prompt, dest='en').text
+    image = pipe(prompt_eng, guidance_scale= int(guidance_scale), num_inference_steps = int(inference_steps), negative_prompt = neg_prompt).images[0]
+    return image
+
+gr.Interface(
+    generate,
+    title = 'Image to Image using Diffusers',
+    inputs=[
+        gr.Textbox(label="Prompt"),
+        gr.Slider(50, 700, value=50, label ="Inference steps"),
+        gr.Slider(1, 10, value=5, label ="Guidance scale"),
+        gr.Textbox(label="Negative prompt (include things you DO NOT want in the image")
+    ],
+    outputs = [
+        gr.Image(elem_id="output-image"),
+
+        ], css = "#output-image, #input-image, #image-preview {border-radius: 40px !important; background-color : gray !important;} "
+).launch()