initial commit

.gitattributes

@@ -0,0 +1,35 @@
+*.7z filter=lfs diff=lfs merge=lfs -text
+*.arrow filter=lfs diff=lfs merge=lfs -text
+*.bin filter=lfs diff=lfs merge=lfs -text
+*.bz2 filter=lfs diff=lfs merge=lfs -text
+*.ckpt filter=lfs diff=lfs merge=lfs -text
+*.ftz filter=lfs diff=lfs merge=lfs -text
+*.gz filter=lfs diff=lfs merge=lfs -text
+*.h5 filter=lfs diff=lfs merge=lfs -text
+*.joblib filter=lfs diff=lfs merge=lfs -text
+*.lfs.* filter=lfs diff=lfs merge=lfs -text
+*.mlmodel filter=lfs diff=lfs merge=lfs -text
+*.model filter=lfs diff=lfs merge=lfs -text
+*.msgpack filter=lfs diff=lfs merge=lfs -text
+*.npy filter=lfs diff=lfs merge=lfs -text
+*.npz filter=lfs diff=lfs merge=lfs -text
+*.onnx filter=lfs diff=lfs merge=lfs -text
+*.ot filter=lfs diff=lfs merge=lfs -text
+*.parquet filter=lfs diff=lfs merge=lfs -text
+*.pb filter=lfs diff=lfs merge=lfs -text
+*.pickle filter=lfs diff=lfs merge=lfs -text
+*.pkl filter=lfs diff=lfs merge=lfs -text
+*.pt filter=lfs diff=lfs merge=lfs -text
+*.pth filter=lfs diff=lfs merge=lfs -text
+*.rar filter=lfs diff=lfs merge=lfs -text
+*.safetensors filter=lfs diff=lfs merge=lfs -text
+saved_model/**/* filter=lfs diff=lfs merge=lfs -text
+*.tar.* filter=lfs diff=lfs merge=lfs -text
+*.tar filter=lfs diff=lfs merge=lfs -text
+*.tflite filter=lfs diff=lfs merge=lfs -text
+*.tgz filter=lfs diff=lfs merge=lfs -text
+*.wasm filter=lfs diff=lfs merge=lfs -text
+*.xz filter=lfs diff=lfs merge=lfs -text
+*.zip filter=lfs diff=lfs merge=lfs -text
+*.zst filter=lfs diff=lfs merge=lfs -text
+*tfevents* filter=lfs diff=lfs merge=lfs -text

README.md

@@ -0,0 +1,13 @@
+---
+title: First Demo
+emoji: 🌖
+colorFrom: green
+colorTo: yellow
+sdk: gradio
+sdk_version: 4.7.1
+app_file: app.py
+pinned: false
+license: mit
+---
+
+Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

app.py

@@ -0,0 +1,127 @@
+from diffusers import (
+  ControlNetModel,
+  StableDiffusionImg2ImgPipeline,
+  StableDiffusionControlNetImg2ImgPipeline,
+)
+from compel import Compel
+from PIL import Image
+import cv2
+import gc
+import gradio
+import numpy
+import torch
+
+base_model = "SimianLuo/LCM_Dreamshaper_v7"
+controlnet_model = "lllyasviel/control_v11p_sd15_canny"
+device = "cuda"
+dtype = torch.float16
+width = 512
+height = 512
+
+controlnet = ControlNetModel.from_pretrained(
+  controlnet_model, tourch_dtype=dtype
+)
+
+pipe = StableDiffusionControlNetImg2ImgPipeline.from_pretrained(
+  base_model, controlnet=controlnet, safety_checker=None
+).to(dtype=dtype)
+pipe.enable_model_cpu_offload(device=device)
+pipe.unet.to(memory_format=torch.channels_last)
+
+compel_proc = Compel(
+  tokenizer=pipe.tokenizer,
+  text_encoder=pipe.text_encoder,
+  truncate_long_prompts=False,
+)
+
+pipe_no_controlnet = StableDiffusionImg2ImgPipeline.from_pretrained(
+  base_model, safety_checker=None
+).to(dtype=dtype)
+pipe.enable_model_cpu_offload(device=device)
+pipe_no_controlnet.enable_model_cpu_offload()
+
+compel_proc_no_controlnet = Compel(
+  tokenizer=pipe_no_controlnet.tokenizer,
+  text_encoder=pipe_no_controlnet.text_encoder,
+  truncate_long_prompts=False,
+)
+
+def predict(
+  prompt: str,
+  image: Image,
+  use_controlnet: bool,
+  generator: int,
+  num_inference_steps: int,
+  strength: float,
+  guidance_scale: float,
+  controlnet_conditioning_scale: float,
+  canny_lower_threshold: int,
+  canny_higher_threshold: int,
+):
+  if image is None:
+    return None
+
+  generator = torch.manual_seed(generator)
+  # TODO: Keep the original ratio?
+  image = image.resize((width, height))
+
+  if use_controlnet:
+    prompt_embeds = compel_proc(prompt)
+    image_array = numpy.array(image)
+    image_array = cv2.Canny(
+      image_array,
+      canny_lower_threshold,
+      canny_higher_threshold
+    )
+    image_array = image_array[:, :, None]
+    image_array = numpy.concatenate([image_array, image_array, image_array], axis=2)
+    control_image = Image.fromarray(image_array)
+    results = pipe(
+      control_image=control_image,
+      control_guidance_end=1.0,
+      control_guidance_start=0.0,
+      controlnet_conditioning_scale=controlnet_conditioning_scale,
+      generator=generator,
+      guidance_scale=guidance_scale,
+      image=image,
+      num_inference_steps=num_inference_steps,
+      output_type="pil",
+      prompt_embeds=prompt_embeds,
+      strength=strength,
+    )
+    control_image.close()
+  else:
+    prompt_embeds = compel_proc_no_controlnet(prompt)
+    results = pipe_no_controlnet(
+      generator=generator,
+      guidance_scale=guidance_scale,
+      image=image,
+      num_inference_steps=num_inference_steps,
+      output_type="pil",
+      prompt_embeds=prompt_embeds,
+      strength=strength,
+    )
+
+  gc.collect()
+
+  if len(results.images) > 0:
+    return results.images[0]
+  return None
+
+app = gradio.Interface(
+  fn=predict,
+  inputs=[
+    gradio.Textbox("Kirisame Marisa, Cute, Smiling, High quality, Realistic"), # prompt
+    gradio.Image(type="pil"), # image
+    gradio.Checkbox(True), # use_controlnet
+    gradio.Slider(0, 2147483647, 2159232, step=1), # generator
+    gradio.Slider(2, 15, 4, step=1), # num_inference_steps
+    gradio.Slider(0.0, 1.0, 0.5, step=0.01), # strength
+    gradio.Slider(0.0, 5.0, 0.2, step=0.01), # guidance_scale
+    gradio.Slider(0.0, 1.0, 0.8, step=0.01), # controlnet_conditioning_scale
+    gradio.Slider(0, 255, 100, step=1), # canny_lower_threshold
+    gradio.Slider(0, 255, 200, step=1), # canny_higher_threshold
+  ],
+  outputs=gradio.Image(type="pil")
+)
+app.launch()

requirements.txt

@@ -0,0 +1,7 @@
+diffusers
+accelerate
+compel
+gradio
+numpy
+opencv-python
+transformers