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Parent(s):
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Browse files- run_bug_4297.py +6 -6
- run_bug_4297_new.py +49 -0
run_bug_4297.py
CHANGED
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#!/usr/bin/env python3
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from diffusers import DiffusionPipeline
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import torch
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torch.backends.cudnn.deterministic = False
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torch.backends.cuda.matmul.allow_tf32 = False
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@@ -7,7 +7,8 @@ torch.backends.cudnn.allow_tf32 = False
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torch.backends.cudnn.benchmark = True
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torch.backends.cuda.enable_flash_sdp(False)
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-
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base_pipe.to("cuda") # OR, pipe.enable_sequential_cpu_offload() OR,
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#pipe.enable_model_cpu_offload()
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@@ -17,12 +18,11 @@ base_pipe.to("cuda") # OR, pipe.enable_sequential_cpu_offload() OR,
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# Reproducibility.
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torch_seed = 4202420420
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refiner_seed = 698008569
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refiner_strength = 0.50
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prompt = "happy child flying a kite on a sunny day"
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negative_prompt = ''
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# Batch size.
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batch_size = 2
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do_latent =
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prompt = [ prompt ] * batch_size
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negative_prompt = [ negative_prompt ] * batch_size
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# We're going to schedule 20 steps, and complete 50% of them using either model.
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# We need multiple Generators.
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generator = [ torch.Generator(device="cuda").manual_seed(torch_seed) ] * batch_size
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pipe = DiffusionPipeline.from_pretrained("stabilityai/stable-diffusion-xl-refiner-0
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# Using channels last layout.
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pipe.unet.to(memory_format=torch.channels_last)
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pipe.to("cuda") # OR, pipe.enable_sequential_cpu_offload() OR,
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# Generate the base image.
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#!/usr/bin/env python3
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from diffusers import DiffusionPipeline, AutoencoderKL
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import torch
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torch.backends.cudnn.deterministic = False
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torch.backends.cuda.matmul.allow_tf32 = False
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torch.backends.cudnn.benchmark = True
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torch.backends.cuda.enable_flash_sdp(False)
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vae = AutoencoderKL.from_pretrained("stabilityai/sdxl-vae", torch_dtype=torch.float16)
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base_pipe = DiffusionPipeline.from_pretrained("stabilityai/stable-diffusion-xl-base-1.0", vae=vae, torch_dtype=torch.float16, use_safetensors=True, variant="fp16")
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base_pipe.to("cuda") # OR, pipe.enable_sequential_cpu_offload() OR,
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#pipe.enable_model_cpu_offload()
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# Reproducibility.
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torch_seed = 4202420420
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refiner_seed = 698008569
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prompt = "happy child flying a kite on a sunny day"
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negative_prompt = ''
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# Batch size.
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batch_size = 2
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do_latent = True
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prompt = [ prompt ] * batch_size
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negative_prompt = [ negative_prompt ] * batch_size
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# We're going to schedule 20 steps, and complete 50% of them using either model.
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# We need multiple Generators.
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generator = [ torch.Generator(device="cuda").manual_seed(torch_seed) ] * batch_size
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pipe = DiffusionPipeline.from_pretrained("stabilityai/stable-diffusion-xl-refiner-1.0", vae=base_pipe.vae, text_encoder_2=base_pipe.text_encoder_2, torch_dtype=torch.float16, use_safetensors=True, variant="fp16")
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# Using channels last layout.
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# pipe.unet.to(memory_format=torch.channels_last)
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pipe.to("cuda") # OR, pipe.enable_sequential_cpu_offload() OR,
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# Generate the base image.
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run_bug_4297_new.py
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#!/usr/bin/env python3
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from diffusers import DiffusionPipeline
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import torch
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torch.backends.cudnn.deterministic = False
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torch.backends.cuda.matmul.allow_tf32 = False
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torch.backends.cudnn.allow_tf32 = False
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torch.backends.cudnn.benchmark = True
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torch.backends.cuda.enable_flash_sdp(False)
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# vae = AutoEncoderKL.from_pretrained("stabilityai/sdxl-vae", torch_dtype=torch.float16)
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# base_pipe = DiffusionPipeline.from_pretrained("stabilityai/stable-diffusion-xl-base-1.0", vae=vae, torch_dtype=torch.float16, use_safetensors=True, variant="fp16")
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base_pipe = DiffusionPipeline.from_pretrained("stabilityai/stable-diffusion-xl-base-1.0", vae, torch_dtype=torch.float16, use_safetensors=True, variant="fp16")
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base_pipe.to("cuda") # OR, pipe.enable_sequential_cpu_offload() OR,
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# Reproducibility.
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torch_seed = 4202420420
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refiner_seed = 698008569
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refiner_strength = 0.50
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prompt = "happy child flying a kite on a sunny day"
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negative_prompt = ''
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# Batch size.
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batch_size = 2
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do_latent = True
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# We're going to schedule 20 steps, and complete 50% of them using either model.
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total_num_steps = 20
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# We need multiple Generators.
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generator = torch.Generator(device="cuda").manual_seed(torch_seed)
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pipe = DiffusionPipeline.from_pretrained("stabilityai/stable-diffusion-xl-refiner-1.0", vae=base_pipe.vae, text_encoder_2=base_pipe.text_encoder_2, torch_dtype=torch.float16, use_safetensors=True, variant="fp16")
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# Using channels last layout.
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pipe.unet.to(memory_format=torch.channels_last)
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pipe.to("cuda") # OR, pipe.enable_sequential_cpu_offload() OR,
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# Generate the base image.
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pre_image = base_pipe(prompt=prompt, generator=generator,
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num_inference_steps=total_num_steps, negative_prompt=negative_prompt, num_images_per_prompt=batch_size, output_type="latent" if do_latent else "pil").images
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# Generate a range from 0.1 to 0.9, with 0.1 increments.
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test_strengths = [0.5]
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for refiner_strength in test_strengths:
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# Generate a new set of random states for each image.
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generator_two = torch.Generator(device="cuda").manual_seed(refiner_seed)
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# Put through the refiner now.
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images = pipe(prompt=prompt, image=pre_image, aesthetic_score=10, negative_aesthetic_score=2.4, generator=generator_two,
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num_inference_steps=total_num_steps, num_images_per_prompt=batch_size, strength=refiner_strength, negative_prompt=negative_prompt).images # denoising_start
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for idx in range(0, len(images)):
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print(f'Image: {idx}')
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images[idx].save(f'/home/patrick/images/refiner_bug/test-{refiner_strength}-{idx}--{batch_size}--{do_latent}.png', format='PNG')
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