Update app.py

app.py

@@ -6,20 +6,24 @@ from diffusers import FluxPipeline
 from huggingface_hub.utils import RepositoryNotFoundError
 
 pipeline = FluxPipeline.from_pretrained("black-forest-labs/FLUX.1-dev", torch_dtype=torch.float16).to("cuda")
+pipelineImg = FluxImg2ImgPipeline.from_pretrained("black-forest-labs/FLUX.1-dev", torch_dtype=torch.float16).to("cuda")
 
 @spaces.GPU(duration=70)
-def generate(prompt, negative_prompt, width, height, sample_steps, lora_id):
+def generate(image, prompt, negative_prompt, width, height, sample_steps, lora_id):
     try:
-        pipeline.load_lora_weights(lora_id)
+        # pipeline.load_lora_weights(lora_id)
+        init_image = load_image(image).resize((1024, 1024))
+        pipelineImg.load_lora_weights(lora_id)
     except RepositoryNotFoundError:
         raise ValueError(f"Recieved invalid FLUX LoRA.")
     
-    return pipeline(prompt=f"{prompt}\nDO NOT INCLUDE {negative_prompt}", width=width, height=height, num_inference_steps=sample_steps, generator=torch.Generator("cpu").manual_seed(42), guidance_scale=7).images[0]
+    return pipeline(prompt=f"{prompt}\nDO NOT INCLUDE {negative_prompt}", image=init_image, width=width, height=height, num_inference_steps=sample_steps, generator=torch.Generator("cpu").manual_seed(42), guidance_scale=7).images[0]
 
 with gr.Blocks() as interface:
         with gr.Column():
             with gr.Row():
                 with gr.Column():
+                    image = gr.Image(label="Input image", show_label=False, type="filepath")
                     prompt = gr.Textbox(label="Prompt", info="What do you want?", value="Keanu Reeves holding a neon sign reading 'Hello, world!', 32k HDR, paparazzi", lines=4, interactive=True)
                     negative_prompt = gr.Textbox(label="Negative Prompt", info="What do you want to exclude from the image?", value="ugly, low quality", lines=4, interactive=True)
                 with gr.Column():
@@ -35,7 +39,7 @@ with gr.Blocks() as interface:
                             sampling_steps = gr.Slider(label="Sampling Steps", info="The number of denoising steps.", value=20, minimum=4, maximum=50, step=1, interactive=True)
                             lora_id = gr.Textbox(label="Adapter Repository", info="ID of the FLUX LoRA", value="pepper13/fluxfw")
         
-        generate_button.click(fn=generate, inputs=[prompt, negative_prompt, width, height, sampling_steps, lora_id], outputs=[output])
+        generate_button.click(fn=generate, inputs=[image, prompt, negative_prompt, width, height, sampling_steps, lora_id], outputs=[output])
 
 if __name__ == "__main__":
     interface.launch()