Create app.py

app.py

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+from diffusers import StableDiffusionControlNetPipeline, ControlNetModel
+from diffusers import UniPCMultistepScheduler
+import gradio as gr
+import torch
+from gradio.components import Markdown
+
+# Models
+controlnet_pose = ControlNetModel.from_pretrained(
+    "lllyasviel/sd-controlnet-openpose", torch_dtype=torch.float16
+)
+controlnet_canny = ControlNetModel.from_pretrained(
+    "lllyasviel/sd-controlnet-canny", torch_dtype=torch.float16
+)
+
+pipe = StableDiffusionControlNetPipeline.from_pretrained(
+    "dreamlike-art/dreamlike-anime-1.0",
+    controlnet=[controlnet_pose, controlnet_canny],
+    safety_checker=None, torch_dtype=torch.float16
+).to('cuda')
+pipe.scheduler = UniPCMultistepScheduler.from_config(pipe.scheduler.config)
+
+# This command loads the individual model components on GPU on-demand. So, we don't
+# need to explicitly call pipe.to("cuda").
+#pipe.enable_model_cpu_offload()
+
+# xformers
+pipe.enable_xformers_memory_efficient_attention()
+
+# Generator seed,
+generator = torch.manual_seed(1)
+
+negative_prompt = ("worst quality, low quality, lowres, bad anatomy, bad hands, "
+                   "missing fingers, extra digit, fewer digits")
+
+markdown = """
+## Generate controlled outputs with Mult-ControlNet and Stable Diffusion using 🤗Diffusers
+This Space uses pose lines and canny edged image as the additional conditioning. Please refer to the "Examples" for what kind of images are appropriate.
+The Followings are tools available to create such images. In this example, tool 1 is being used.
+1. [Character bones that look like Openpose for blender Ver4.7 Depth+Canny](https://toyxyz.gumroad.com/l/ciojz)
+2. [open-pose-editor](https://github.com/ZhUyU1997/open-pose-editor)
+"""
+# **This space using these models**:
+# - ControlNet Model (canny): [lllyasviel/sd-controlnet-canny](https://hf.co/lllyasviel/sd-controlnet-canny)
+# - ControlNet Model (openpose): [lllyasviel/sd-controlnet-openpose](https://hf.co/lllyasviel/sd-controlnet-openpose)
+# - SD Base Model: [dreamlike-art/dreamlike-anime-1.0](https://hf.co/dreamlike-art/dreamlike-anime-1.0)
+
+def generate_images(pose_image, canny_image, prompt):
+    output = pipe(
+        prompt=prompt,
+        negative_prompt=negative_prompt,
+        image=[pose_image, canny_image],
+        generator=generator,
+        num_images_per_prompt=2,
+        num_inference_steps=20,
+    )
+    all_outputs = []
+    all_outputs.append(pose_image)
+    all_outputs.append(canny_image)
+    for image in output.images:
+        all_outputs.append(image)
+    return all_outputs
+
+
+gr.Interface(
+    generate_images,
+    inputs=[
+        gr.Image(type="pil"),
+        gr.Image(type="pil"),
+        gr.Textbox(
+            label="Enter your prompt",
+            max_lines=1,
+            placeholder="Enter your prompt",
+        ),
+    ],
+    outputs=gr.Gallery().style(grid=[2], height="auto"),
+    description=markdown,
+    examples=[
+        ["p11_clip.png",
+         "c11_clip.png",
+         "masterpiece, a professional portrait of woman wearing white shirts, smile, heart hand sign"
+         ],
+        # ["p13_clip.png",
+        #  "c13_clip.png",
+        #  "masterpiece, a professional portrait of woman wearing white shirts, smile"
+        #  ],
+        ["p12_clip.png",
+         "c12_clip.png",
+         "masterpiece, a professional portrait of woman wearing white shirts, smile"
+         ],
+    ],
+    allow_flagging=False,
+).launch(enable_queue=True)