Re-add files with LFS tracking

checkpoints/lora30k/pytorch_lora_weights.safetensors

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+version https://git-lfs.github.com/spec/v1
+oid sha256:ae276908f80efd67416c36860781cb8cfe84d94812ef09afa3fd4eef4e60d8da
+size 12732240

gen_w_lora.py

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+from diffusers import StableDiffusionPipeline
+import torch
+from transformers import CLIPTextModel
+
+
+pipe_id = "stabilityai/stable-diffusion-2-1"
+# checkpoint_dir = "/home/michaila/Projects/github/diffusers/examples/text_to_image/sd-2-1-train-finetune-LoRA-test5/checkpoint-2800/"
+# checkpoint_dir = "/home/michaila/Projects/github/diffusers/examples/text_to_image/sd-2-1-train-finetune-wText-LoRA-lr1e5-r8/checkpoint-15500/"
+# checkpoint_dir = '/home/michaila/Projects/github/diffusers/examples/text_to_image/sd-2-1-train-finetune-LoRA-ffhq-easyportr-2/checkpoint-100/'
+# checkpoint_dir = "/home/michaila/Projects/github/diffusers/examples/text_to_image/sd-2-1-train-finetune-wText-LoRA-EasyPortait_lr1e5-r8/checkpoint-22000/"
+# checkpoint_dir = "/home/michaila/Projects/github/diffusers/examples/text_to_image/sd-2-1-train-finetune-wText-LoRA-FFHQ-EasyPortrait_lr1e5-r8_768/checkpoint-30000/"
+checkpoint_dir = "checkpoints/lora30k"
+
+pipe = StableDiffusionPipeline.from_pretrained(pipe_id, torch_dtype=torch.float16).to("cuda")
+
+# pipe.load_lora_weights("/home/michaila/Projects/github/diffusers/examples/text_to_image/sd-2-1-train-finetune-LoRA-ffhq-easyportr-2/checkpoint-500", weight_name="pytorch_lora_weights.safetensors")  # , adapter_name="toy")
+# pipe.load_lora_weights(checkpoint_dir, weight_name="pytorch_lora_weights.safetensors")  # , adapter_name="toy")
+# pipe.text_encoder.load_lora_weights(checkpoint_dir, weight_name="pytorch_lora_weights.safetensors")  # , adapter_name="toy")
+state_dict, network_alphas = StableDiffusionPipeline.lora_state_dict(
+    # Path to my trained lora output_dir
+    checkpoint_dir,
+    weight_name="pytorch_lora_weights.safetensors"
+)
+pipe.load_lora_into_unet(state_dict, network_alphas, pipe.unet, adapter_name='test_lora')
+pipe.load_lora_into_text_encoder(state_dict, network_alphas, pipe.text_encoder, adapter_name='test_lora')
+pipe.set_adapters(["test_lora"], adapter_weights=[1.0])
+# pipe.set_adapters(["text_lora"], adapter_weights=[1.0])
+
+# def generate(prompt, name='example', seed=1):
+#     lora_scale = 1.0
+#     image = pipe(
+#         prompt, num_inference_steps=50, cross_attention_kwargs={"scale": lora_scale}, generator=torch.manual_seed(seed)
+#     ).images[0]
+#     image.save(f"{checkpoint_dir}/{name}.png")
+
+
+def generate(prompt, negprompt='', steps=50, name='example', seed=1):
+    lora_scale = 1.0
+    image = pipe(
+        prompt, negative_prompt=negprompt, num_inference_steps=steps, cross_attention_kwargs={"scale": lora_scale}, generator=torch.manual_seed(seed)
+    ).images[0]
+    image.save(f"{checkpoint_dir}/{'_'.join(prompt.replace('.', ' ').split(' '))}.png")
+
+
+# prompt = "a color photo of a 30 year old man with a sad expression, beard, very little hair, a slightly open mouth, his eyes look directly at the camera."
+# prompt = "a color photo of a 30 year old man with a sad expression, beard, very little hair, a fully open mouth, his eyes look directly at the camera."
+# prompt = "a 50 year old asian woman with a neutral expression, little hair, a slightly open mouth and visible teeth."
+# prompt = "a 50 year old asian woman smiling."
+# prompt = "an 20 year old white man with slightly open mouth, visible teeth. His tongue is out, clearly visible."
+# prompt = "A baby with fully closed mouth."
+# prompt = "A 25 year old female with long, blonde hair, green eyes and neutral expression looking at the camera."
+# prompt = "A black african female with long, straight blond hair and happy expression."
+# prompt = "A black female with blonde hair."
+# prompt = 'An attractive blond male'
+# prompt = 'A happy 55 year old black woman with a hat, sunglasses, earrings and visible teeth. High resolution, sharp image.' #at the camera.'
+prompt = 'A happy 25 year old woman with blond hair. Her head is looking significantly to the right.'
+
+negprompt = ''  #'bad teeth'
+# generate(prompt, name='example', seed=4)
+
+generate(prompt, negprompt=negprompt, steps=50, name='example', seed=200)
+
+
+
+

main.py

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+import torch
+from diffusers import StableDiffusionPipeline, DPMSolverMultistepScheduler
+
+model_id = "stabilityai/stable-diffusion-2-1"
+
+# Use the DPMSolverMultistepScheduler (DPM-Solver++) scheduler here instead
+pipe = StableDiffusionPipeline.from_pretrained(model_id, torch_dtype=torch.float16)
+pipe.scheduler = DPMSolverMultistepScheduler.from_config(pipe.scheduler.config)
+pipe = pipe.to("cuda")
+
+prompt = "a photo of an astronaut riding a horse on mars"
+image = pipe(prompt).images[0]
+
+image.save("astronaut_rides_horse.png")