inference logic

.gitignore

@@ -1,2 +1,5 @@
 
-.idea
+.idea
+
+venv
+.venv

README.md

@@ -7,6 +7,8 @@ sdk: gradio
 sdk_version: 3.27.0
 app_file: app.py
 pinned: false
+tags:
+- jax-diffusers-event
 ---
 
 Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

app.py

@@ -0,0 +1,87 @@
+from PIL import Image
+import gradio as gr
+from diffusers import StableDiffusionControlNetPipeline, ControlNetModel, UniPCMultistepScheduler
+import torch
+
+controlnet = ControlNetModel.from_pretrained("ioclab/control_v1p_sd15_brightness", torch_dtype=torch.float32, use_safetensors=True)
+
+
+pipe = StableDiffusionControlNetPipeline.from_pretrained(
+    "runwayml/stable-diffusion-v1-5", controlnet=controlnet, torch_dtype=torch.float32,
+)
+
+pipe.scheduler = UniPCMultistepScheduler.from_config(pipe.scheduler.config)
+
+# pipe.enable_xformers_memory_efficient_attention()
+pipe.enable_model_cpu_offload()
+
+
+def infer(prompt, negative_prompt, num_inference_steps, conditioning_image):
+    # conditioning_image = Image.open(conditioning_image)
+    conditioning_image = Image.fromarray(conditioning_image)
+    generator = torch.Generator(device="cpu").manual_seed(1500)
+
+    output_image = pipe(
+        prompt,
+        conditioning_image,
+        height=512,
+        width=512,
+        num_inference_steps=num_inference_steps,
+        generator=generator,
+        negative_prompt=negative_prompt,
+        controlnet_conditioning_scale=1.0,
+    ).images[0]
+
+    return output_image
+
+with gr.Blocks() as demo:
+    gr.Markdown(
+        """
+    # ControlNet on Brightness
+
+    This is a demo on ControlNet based on brightness.
+    """)
+
+    with gr.Row():
+        with gr.Column():
+            prompt = gr.Textbox(
+                label="Prompt",
+            )
+            negative_prompt = gr.Textbox(
+                label="Negative Prompt",
+            )
+            num_inference_steps = gr.Slider(
+                10, 40, 20,
+                step=1,
+                label="Steps",
+            )
+            conditioning_image = gr.Image(
+                label="Conditioning Image",
+            )
+            submit_btn = gr.Button(
+                value="Submit",
+                variant="primary"
+            )
+        with gr.Column(min_width=300):
+            output = gr.Image(
+                label="Result",
+            )
+
+    submit_btn.click(
+        fn=infer,
+        inputs=[
+            prompt, negative_prompt, num_inference_steps, conditioning_image
+        ],
+        outputs=output
+    )
+    gr.Examples(
+        examples=[
+            ["a painting of a village in the mountains", "monochrome", "./conditioning_images/conditioning_image_1.jpg"],
+            ["three people walking in an alleyway with hats and pants", "monochrome", "./conditioning_images/conditioning_image_2.jpg"],
+        ],
+        inputs=[
+            prompt, negative_prompt, conditioning_image
+        ],
+    )
+
+demo.launch()

conditioning_images/conditioning_image_1_prompt.txt

@@ -0,0 +1 @@
+a painting of a village in the mountains

conditioning_images/conditioning_image_2_prompt.txt

@@ -0,0 +1 @@
+three people walking in an alleyway with hats and pants

requirements.txt

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