v1, sd1.5

app.py

@@ -0,0 +1,116 @@
+import gradio as gr
+
+import torch
+from PIL import Image, ImageDraw, ImageFont, ImageOps, ImageEnhance
+from quanto import qfloat8, quantize, freeze
+from diffusers import StableDiffusionControlNetPipeline, ControlNetModel
+from diffusers.utils import make_image_grid
+
+atkbold = ImageFont.truetype("Atkinson-Hyperlegible-Bold-102.otf",50)
+
+default_width = 1024
+default_height = 768
+default_timesteps = 8
+
+def mask_image_factory(mask_text="ASK FOR\nA SNACK", width=default_width, height=default_height):
+    img = Image.new("L", (width, height), (0,))
+    draw = ImageDraw.Draw(img)
+    draw.multiline_text(
+        xy=(0,0),
+        text=mask_text,
+        fill=(255,),
+        font=atkbold,
+        align="center",
+        spacing=0,
+    )
+    cropped = img.crop(img.getbbox())
+    # Calculate aspect ratios
+    image_aspect_ratio = width / height
+    cropped_aspect_ratio = cropped.size[0] / cropped.size[1]
+
+    # Determine which dimension of cropped.size is larger
+    if cropped_aspect_ratio > image_aspect_ratio:
+        # Calculate new dimensions for padding
+        new_width = int(cropped.size[1] * image_aspect_ratio)
+        new_height = cropped.size[1]
+    else:
+        new_width = cropped.size[0]
+        new_height = int(cropped.size[0] / image_aspect_ratio)
+
+    # Pad the image to the desired aspect ratio
+    padded = ImageOps.pad(cropped, (new_width, new_height))
+
+    resized = padded.resize((width, height), resample=Image.Resampling.LANCZOS)
+    return resized
+
+preferred_device = "cuda" if torch.cuda.is_available() else ("mps" if torch.backends.mps.is_available() else "cpu")
+preferred_device = "cpu"
+preferred_dtype = torch.float32
+
+controlnet = ControlNetModel.from_pretrained(
+    "monster-labs/control_v1p_sd15_qrcode_monster",
+#    "monster-labs/control_v1p_sdxl_qrcode_monster",
+    subfolder="v2",
+    torch_dtype=preferred_dtype,
+    #torch_dtype=unet_preferred_dtype
+).to(preferred_device)
+
+#quantize(controlnet, weights=qfloat8)
+#freeze(controlnet)
+
+ctlpipe = StableDiffusionControlNetPipeline.from_pretrained(
+    "SimianLuo/LCM_Dreamshaper_v7",
+    controlnet=controlnet,
+    torch_dtype=preferred_dtype,
+    safety_checker=None,
+).to(preferred_device)
+
+#quantize(ctlpipe.unet, weights=qfloat8)
+#freeze(ctlpipe.unet)
+#quantize(ctlpipe.text_encoder, weights=qfloat8)
+#freeze(ctlpipe.text_encoder)
+
+def app(prompt, negative_prompt, mask_text, num_inference_steps, controlnet_conditioning_scale, width, height, seed, count):
+    all_images = [ctlpipe(
+        prompt=prompt,
+        negative_prompt=negative_prompt,
+        image=mask_image_factory(mask_text=mask_text, width=width, height=height),
+        num_inference_steps=int(num_inference_steps),
+        guidance_scale=8.0,
+        controlnet_conditioning_scale=float(controlnet_conditioning_scale),
+        generator=torch.manual_seed(int(seed + i)),
+        height=height,
+        width=width,
+    ).images[0] for i in range(count)]
+    if count == 1:
+        cols = 1
+        rows = 1
+    elif count == 2:
+        cols = 1
+        rows = 2
+    else:
+        cols = 2 if count % 2 == 0 else 1
+        rows = count // cols
+    return make_image_grid(all_images, cols=cols, rows=rows)
+
+
+app("corgis running in the park", "ugly, wrong", "ASK FOR\nA SNACK", 1, 1.0, default_height, default_width, 42, 1)
+
+iface = gr.Interface(
+    app,
+    [
+        gr.Textbox(label="Prompt", value="corgis running in a flower-filled meadow at golden hour"),
+        gr.Textbox(label="Negative Prompt", value="ugly, wrong"),
+        gr.Textbox(label="Mask Text", value="ASK FOR\nA SNACK"),
+        gr.Number(label="Number of Inference Steps", value=default_timesteps, minimum=1, maximum=50, step=1),
+        gr.Slider(label="ControlNet Conditioning Scale", value=0.5, minimum=-1.0, maximum=2.0, step=0.01),
+        gr.Number(label="Width", value=default_width, minimum=256, maximum=2048, precision=0),
+        gr.Number(label="Height", value=default_height, minimum=256, maximum=2048, precision=0),
+        gr.Number(label="Random Number Seed", value=42, minimum=0, maximum=2**32-1, precision=0),
+        gr.Radio(label="Number of Images to Generate with Subsequent Consecutive Seeds", choices=[1, 2, 4, 6, 10], value=2),
+    ],
+    "image",
+)
+
+iface.launch()
+    

requirements.txt

@@ -0,0 +1,5 @@
+torch
+diffusers
+accelerate
+optimum
+quanto