Update app.py

app.py

@@ -7,10 +7,21 @@ from diffusers import StableDiffusionPipeline
 pipeline = StableDiffusionPipeline.from_pretrained(
     "nroggendorff/zelda-diffusion"
 ).to("cuda")
+img2img = StableDiffusionImg2ImgPipeline(**text2img.components)
 
 @spaces.GPU
-def generate(prompt, negative_prompt, width, height, sample_steps):
-    return pipeline(prompt=prompt, negative_prompt=negative_prompt, width=width, height=height, num_inference_steps=sample_steps).images[0]
+def generate(prompt, negative_prompt, width, height, sample_steps, hrf):
+    image = pipeline(prompt=prompt, negative_prompt=negative_prompt, width=width, height=height, num_inference_steps=sample_steps).images[0]
+    if hrf:
+        return img2img(
+            prompt=prompt,
+            image=image,
+            strength=0.75,
+            width=width * 2,
+            height=height * 2
+        ).images[0]
+    else:
+        return image
 
 with gr.Blocks() as interface:
         with gr.Column():
@@ -27,10 +38,11 @@ with gr.Blocks() as interface:
                         with gr.Column():
                             width = gr.Slider(label="Width", info="The width in pixels of the generated image.", value=512, minimum=128, maximum=4096, step=64, interactive=True)
                             height = gr.Slider(label="Height", info="The height in pixels of the generated image.", value=512, minimum=128, maximum=4096, step=64, interactive=True)
+                            hrf = gr.Checkbox(label="High-Res Fix", info="Run through img2img.", value=False)
                         with gr.Column():
                             sampling_steps = gr.Slider(label="Sampling Steps", info="The number of denoising steps.", value=20, minimum=4, maximum=50, step=1, interactive=True)
         
-        generate_button.click(fn=generate, inputs=[prompt, negative_prompt, width, height, sampling_steps], outputs=[output])
+        generate_button.click(fn=generate, inputs=[prompt, negative_prompt, width, height, sampling_steps, hrf], outputs=[output])
 
 if __name__ == "__main__":
     interface.launch()