Support only CPU

Signed-off-by: Aisuko <[email protected]>

app.py

@@ -14,16 +14,16 @@ from diffusers import (
     EulerDiscreteScheduler,
 )
 
-controlnet = ControlNetModel.from_pretrained(
-    "DionTimmer/controlnet_qrcode-control_v1p_sd15", torch_dtype=torch.float16
-)
+# controlnet = ControlNetModel.from_pretrained(
+#     "DionTimmer/controlnet_qrcode-control_v1p_sd15", torch_dtype=torch.float16
+# )
 
-pipe= StableDiffusionControlNetImg2ImgPipeline.from_pretrained(
-    "runwayml/stable-diffusion-v1-5",
-    controlnet=controlnet,
-    use_safetensors=True,
-    torch_dtype=torch.float16,
-).to("cuda")
+# pipe= StableDiffusionControlNetImg2ImgPipeline.from_pretrained(
+#     "runwayml/stable-diffusion-v1-5",
+#     controlnet=controlnet,
+#     use_safetensors=True,
+#     torch_dtype=torch.float16,
+# ).to("cuda")
 
 
 SAMPLER_MAP={
@@ -36,60 +36,60 @@ SAMPLER_MAP={
 }
 
 
-def inference(
-    qr_code_content: str,
-    prompt: str,
-    negative_prompt: str,
-    guidance_scale: float = 10.0,
-    controlnet_conditioning_scale: float = 2.0,
-    strength: float = 0.8,
-    seed: int = -1,
-    init_image: Image.Image | None = None,
-    qrcode_image: Image.Image | None = None,
-    sampler = "DPM++ Karras SDE",
-):
-    if prompt is None or prompt == "":
-        raise gr.Error("Prompt is required")
-
-    if qrcode_image is None and qr_code_content == "":
-        raise gr.Error("QR Code Image or QR Code Content is required")
-
-    pipe.scheduler = SAMPLER_MAP[sampler](pipe.scheduler.config)
-
-    generator = torch.manual_seed(seed) if seed != -1 else torch.Generator()
-
-    if qr_code_content != "" or qrcode_image.size == (1, 1):
-        qr = qrcode.QRCode(
-            version=1,
-            error_correction=qrcode.constants.ERROR_CORRECT_H,
-            box_size=10,
-            border=4,
-        )
-        qr.add_data(qr_code_content)
-        qr.make(fit=True)
-
-        qrcode_image = qr.make_image(fill_color="black", back_color="white")
-        qrcode_image = qrcode_image.resize((768, 768))
-    else:
-        qrcode_image = qrcode_image.resize((768, 768))
-
-    # hack due to gradio examples
-    init_image = qrcode_image
-
-    out = pipe(
-        prompt=prompt,
-        negative_prompt=negative_prompt,
-        image=init_image,
-        control_image=qrcode_image,  # type: ignore
-        width=768,  # type: ignore
-        height=768,  # type: ignore
-        guidance_scale=float(guidance_scale),
-        controlnet_conditioning_scale=float(controlnet_conditioning_scale),  # type: ignore
-        generator=generator,
-        strength=float(strength),
-        num_inference_steps=40,
-    )
-    return out.images[0]  # type: ignore
+# def inference(
+#     qr_code_content: str,
+#     prompt: str,
+#     negative_prompt: str,
+#     guidance_scale: float = 10.0,
+#     controlnet_conditioning_scale: float = 2.0,
+#     strength: float = 0.8,
+#     seed: int = -1,
+#     init_image: Image.Image | None = None,
+#     qrcode_image: Image.Image | None = None,
+#     sampler = "DPM++ Karras SDE",
+# ):
+#     if prompt is None or prompt == "":
+#         raise gr.Error("Prompt is required")
+
+#     if qrcode_image is None and qr_code_content == "":
+#         raise gr.Error("QR Code Image or QR Code Content is required")
+
+#     pipe.scheduler = SAMPLER_MAP[sampler](pipe.scheduler.config)
+
+#     generator = torch.manual_seed(seed) if seed != -1 else torch.Generator()
+
+#     if qr_code_content != "" or qrcode_image.size == (1, 1):
+#         qr = qrcode.QRCode(
+#             version=1,
+#             error_correction=qrcode.constants.ERROR_CORRECT_H,
+#             box_size=10,
+#             border=4,
+#         )
+#         qr.add_data(qr_code_content)
+#         qr.make(fit=True)
+
+#         qrcode_image = qr.make_image(fill_color="black", back_color="white")
+#         qrcode_image = qrcode_image.resize((768, 768))
+#     else:
+#         qrcode_image = qrcode_image.resize((768, 768))
+
+#     # hack due to gradio examples
+#     init_image = qrcode_image
+
+#     out = pipe(
+#         prompt=prompt,
+#         negative_prompt=negative_prompt,
+#         image=init_image,
+#         control_image=qrcode_image,  # type: ignore
+#         width=768,  # type: ignore
+#         height=768,  # type: ignore
+#         guidance_scale=float(guidance_scale),
+#         controlnet_conditioning_scale=float(controlnet_conditioning_scale),  # type: ignore
+#         generator=generator,
+#         strength=float(strength),
+#         num_inference_steps=40,
+#     )
+#     return out.images[0]  # type: ignore
 
 def inference_ui_demo():
     return None