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README.md

@@ -1,6 +1,8 @@
 ---
 title: DMGA
 app_file: app.py
+sdk: gradio
+sdk_version: 3.34.0
 ---
 # Doodle sketch board + image generation
 - [x] How to use gradio

app.py

@@ -34,8 +34,8 @@ pipe.scheduler = UniPCMultistepScheduler.from_config(pipe.scheduler.config)
 # this command loads the individual model components on GPU on-demand.
 pipe.enable_model_cpu_offload()
 
-prompt = "closeup face photo of caucasian lady in black clothes, night city street, bokeh"
-negative_prompt = "(deformed iris, deformed pupils, semi-realistic, cgi, 3d, render, sketch, cartoon, drawing, anime:1.4), text, close up, cropped, out of frame, worst quality, low quality, jpeg artifacts, ugly, duplicate, morbid, mutilated, extra fingers, mutated hands, poorly drawn hands, poorly drawn face, mutation, deformed, blurry, dehydrated, bad anatomy, bad proportions, extra limbs, cloned face, disfigured, gross proportions, malformed limbs, missing arms, missing legs, extra arms, extra legs, fused fingers, too many fingers, long neck"
+# prompt = "closeup face photo of caucasian lady in black clothes, night city street, bokeh"
+# negative_prompt = "(deformed iris, deformed pupils, semi-realistic, cgi, 3d, render, sketch, cartoon, drawing, anime:1.4), text, close up, cropped, out of frame, worst quality, low quality, jpeg artifacts, ugly, duplicate, morbid, mutilated, extra fingers, mutated hands, poorly drawn hands, poorly drawn face, mutation, deformed, blurry, dehydrated, bad anatomy, bad proportions, extra limbs, cloned face, disfigured, gross proportions, malformed limbs, missing arms, missing legs, extra arms, extra legs, fused fingers, too many fingers, long neck"
 
 n_steps = 25
 
@@ -45,7 +45,8 @@ generator = torch.manual_seed(0)
 #     prompt=prompt, num_inference_steps=20, generator=generator, image=canny_image
 # ).images[0]
 
-def predict(prompt):
+def predict(prompt,negative_prompt):
+    # prompt, negative_prompt = inputs 
     image = pipe(
         prompt=prompt,
         negative_prompt=negative_prompt,
@@ -65,7 +66,7 @@ def predict(prompt):
     return image
 
 
-demo = gr.Interface(fn=predict, inputs="text", outputs="text")
+demo = gr.Interface(fn=predict, inputs=[gr.Textbox(value="prompt"), gr.Textbox(value="negative prompt")], outputs="image")
 
 if __name__ == "__main__":
     demo.launch()