mirror of https://github.com/InstantID/InstantID
support diffuser==0.27.0
ResearcherXman
parent
9d2762a7e3
commit
e6b1f21b6e
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@ -0,0 +1,84 @@
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import cv2
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import torch
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import numpy as np
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from PIL import Image
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from diffusers.utils import load_image
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from diffusers.models import ControlNetModel
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from insightface.app import FaceAnalysis
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from pipeline_stable_diffusion_xl_instantid_img2img import StableDiffusionXLInstantIDImg2ImgPipeline, draw_kps
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def resize_img(input_image, max_side=1280, min_side=1024, size=None,
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pad_to_max_side=False, mode=Image.BILINEAR, base_pixel_number=64):
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w, h = input_image.size
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if size is not None:
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w_resize_new, h_resize_new = size
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else:
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ratio = min_side / min(h, w)
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w, h = round(ratio*w), round(ratio*h)
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ratio = max_side / max(h, w)
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input_image = input_image.resize([round(ratio*w), round(ratio*h)], mode)
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w_resize_new = (round(ratio * w) // base_pixel_number) * base_pixel_number
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h_resize_new = (round(ratio * h) // base_pixel_number) * base_pixel_number
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input_image = input_image.resize([w_resize_new, h_resize_new], mode)
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if pad_to_max_side:
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res = np.ones([max_side, max_side, 3], dtype=np.uint8) * 255
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offset_x = (max_side - w_resize_new) // 2
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offset_y = (max_side - h_resize_new) // 2
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res[offset_y:offset_y+h_resize_new, offset_x:offset_x+w_resize_new] = np.array(input_image)
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input_image = Image.fromarray(res)
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return input_image
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if __name__ == "__main__":
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# Load face encoder
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app = FaceAnalysis(name='antelopev2', root='./', providers=['CUDAExecutionProvider', 'CPUExecutionProvider'])
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app.prepare(ctx_id=0, det_size=(640, 640))
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# Path to InstantID models
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face_adapter = f'./checkpoints/ip-adapter.bin'
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controlnet_path = f'./checkpoints/ControlNetModel'
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# Load pipeline
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controlnet = ControlNetModel.from_pretrained(controlnet_path, torch_dtype=torch.float16)
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base_model_path = 'stabilityai/stable-diffusion-xl-base-1.0'
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pipe = StableDiffusionXLInstantIDImg2ImgPipeline.from_pretrained(
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base_model_path,
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controlnet=controlnet,
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torch_dtype=torch.float16,
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)
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pipe.cuda()
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pipe.load_ip_adapter_instantid(face_adapter)
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# Infer setting
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prompt = "analog film photo of a man. faded film, desaturated, 35mm photo, grainy, vignette, vintage, Kodachrome, Lomography, stained, highly detailed, found footage, masterpiece, best quality"
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n_prompt = "(lowres, low quality, worst quality:1.2), (text:1.2), watermark, painting, drawing, illustration, glitch, deformed, mutated, cross-eyed, ugly, disfigured (lowres, low quality, worst quality:1.2), (text:1.2), watermark, painting, drawing, illustration, glitch,deformed, mutated, cross-eyed, ugly, disfigured"
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face_image = load_image("./examples/yann-lecun_resize.jpg")
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face_image = resize_img(face_image)
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face_info = app.get(cv2.cvtColor(np.array(face_image), cv2.COLOR_RGB2BGR))
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face_info = sorted(face_info, key=lambda x:(x['bbox'][2]-x['bbox'][0])*(x['bbox'][3]-x['bbox'][1]))[-1] # only use the maximum face
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face_emb = face_info['embedding']
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face_kps = draw_kps(face_image, face_info['kps'])
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image = pipe(
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prompt=prompt,
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negative_prompt=n_prompt,
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image=face_image,
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image_embeds=face_emb,
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control_image=face_kps,
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controlnet_conditioning_scale=0.8,
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ip_adapter_scale=0.8,
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num_inference_steps=30,
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guidance_scale=5,
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strength=0.85
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).images[0]
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image.save('result.jpg')
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@ -454,20 +454,20 @@ class StableDiffusionXLInstantIDPipeline(StableDiffusionXLControlNetPipeline):
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# 1. Check inputs. Raise error if not correct
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self.check_inputs(
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prompt,
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prompt_2,
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image,
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callback_steps,
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negative_prompt,
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negative_prompt_2,
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prompt_embeds,
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negative_prompt_embeds,
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pooled_prompt_embeds,
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negative_pooled_prompt_embeds,
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controlnet_conditioning_scale,
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control_guidance_start,
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control_guidance_end,
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callback_on_step_end_tensor_inputs,
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prompt=prompt,
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prompt_2=prompt_2,
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image=image,
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callback_steps=callback_steps,
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negative_prompt=negative_prompt,
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negative_prompt_2=negative_prompt_2,
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prompt_embeds=prompt_embeds,
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negative_prompt_embeds=negative_prompt_embeds,
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pooled_prompt_embeds=pooled_prompt_embeds,
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negative_pooled_prompt_embeds=negative_pooled_prompt_embeds,
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controlnet_conditioning_scale=controlnet_conditioning_scale,
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control_guidance_start=control_guidance_start,
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control_guidance_end=control_guidance_end,
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callback_on_step_end_tensor_inputs=callback_on_step_end_tensor_inputs,
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)
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self._guidance_scale = guidance_scale
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@ -840,22 +840,22 @@ class StableDiffusionXLInstantIDPipeline(StableDiffusionXLControlNetPipeline):
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# 1. Check inputs. Raise error if not correct
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self.check_inputs(
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prompt,
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prompt_2,
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image,
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callback_steps,
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negative_prompt,
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negative_prompt_2,
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prompt_embeds,
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negative_prompt_embeds,
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pooled_prompt_embeds,
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negative_pooled_prompt_embeds,
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controlnet_conditioning_scale,
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control_guidance_start,
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control_guidance_end,
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callback_on_step_end_tensor_inputs,
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prompt=prompt,
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prompt_2=prompt_2,
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image=image,
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callback_steps=callback_steps,
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negative_prompt=negative_prompt,
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negative_prompt_2=negative_prompt_2,
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prompt_embeds=prompt_embeds,
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negative_prompt_embeds=negative_prompt_embeds,
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pooled_prompt_embeds=pooled_prompt_embeds,
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negative_pooled_prompt_embeds=negative_pooled_prompt_embeds,
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controlnet_conditioning_scale=controlnet_conditioning_scale,
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control_guidance_start=control_guidance_start,
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control_guidance_end=control_guidance_end,
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callback_on_step_end_tensor_inputs=callback_on_step_end_tensor_inputs,
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)
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self._guidance_scale = guidance_scale
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self._clip_skip = clip_skip
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self._cross_attention_kwargs = cross_attention_kwargs
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