From 7ad54c5ee934c6fd6f5411df8ec3347120471cef Mon Sep 17 00:00:00 2001 From: aalbersk Date: Tue, 20 Dec 2022 15:18:33 +0000 Subject: [PATCH 01/10] add cpu support & add intel ipex optimizations --- .../ipex/v2-inference-bf16.yaml | 68 ++++++++++ .../ipex/v2-inference-fp32.yaml | 67 ++++++++++ .../ipex/v2-inference-v-bf16.yaml | 69 ++++++++++ .../ipex/v2-inference-v-fp32.yaml | 68 ++++++++++ ldm/models/diffusion/ddim.py | 7 +- ldm/models/diffusion/ddpm.py | 8 +- ldm/models/diffusion/dpm_solver/sampler.py | 7 +- ldm/models/diffusion/plms.py | 7 +- ldm/modules/diffusionmodules/openaimodel.py | 2 + scripts/txt2img.py | 121 ++++++++++++++++-- 10 files changed, 403 insertions(+), 21 deletions(-) create mode 100644 configs/stable-diffusion/ipex/v2-inference-bf16.yaml create mode 100644 configs/stable-diffusion/ipex/v2-inference-fp32.yaml create mode 100644 configs/stable-diffusion/ipex/v2-inference-v-bf16.yaml create mode 100644 configs/stable-diffusion/ipex/v2-inference-v-fp32.yaml diff --git a/configs/stable-diffusion/ipex/v2-inference-bf16.yaml b/configs/stable-diffusion/ipex/v2-inference-bf16.yaml new file mode 100644 index 0000000..e36c3b3 --- /dev/null +++ b/configs/stable-diffusion/ipex/v2-inference-bf16.yaml @@ -0,0 +1,68 @@ +model: + base_learning_rate: 1.0e-4 + target: ldm.models.diffusion.ddpm.LatentDiffusion + params: + linear_start: 0.00085 + linear_end: 0.0120 + num_timesteps_cond: 1 + log_every_t: 200 + timesteps: 1000 + first_stage_key: "jpg" + cond_stage_key: "txt" + image_size: 64 + channels: 4 + cond_stage_trainable: false + conditioning_key: crossattn + monitor: val/loss_simple_ema + scale_factor: 0.18215 + use_ema: False # we set this to false because this is an inference only config + + unet_config: + target: ldm.modules.diffusionmodules.openaimodel.UNetModel + params: + use_checkpoint: False + use_fp16: False + use_bf16: True + image_size: 32 # unused + in_channels: 4 + out_channels: 4 + model_channels: 320 + attention_resolutions: [ 4, 2, 1 ] + num_res_blocks: 2 + channel_mult: [ 1, 2, 4, 4 ] + num_head_channels: 64 # need to fix for flash-attn + use_spatial_transformer: True + use_linear_in_transformer: True + transformer_depth: 1 + context_dim: 1024 + legacy: False + + first_stage_config: + target: ldm.models.autoencoder.AutoencoderKL + params: + embed_dim: 4 + monitor: val/rec_loss + ddconfig: + #attn_type: "vanilla-xformers" + double_z: true + z_channels: 4 + resolution: 256 + in_channels: 3 + out_ch: 3 + ch: 128 + ch_mult: + - 1 + - 2 + - 4 + - 4 + num_res_blocks: 2 + attn_resolutions: [] + dropout: 0.0 + lossconfig: + target: torch.nn.Identity + + cond_stage_config: + target: ldm.modules.encoders.modules.FrozenOpenCLIPEmbedder + params: + freeze: True + layer: "penultimate" diff --git a/configs/stable-diffusion/ipex/v2-inference-fp32.yaml b/configs/stable-diffusion/ipex/v2-inference-fp32.yaml new file mode 100644 index 0000000..ef63a09 --- /dev/null +++ b/configs/stable-diffusion/ipex/v2-inference-fp32.yaml @@ -0,0 +1,67 @@ +model: + base_learning_rate: 1.0e-4 + target: ldm.models.diffusion.ddpm.LatentDiffusion + params: + linear_start: 0.00085 + linear_end: 0.0120 + num_timesteps_cond: 1 + log_every_t: 200 + timesteps: 1000 + first_stage_key: "jpg" + cond_stage_key: "txt" + image_size: 64 + channels: 4 + cond_stage_trainable: false + conditioning_key: crossattn + monitor: val/loss_simple_ema + scale_factor: 0.18215 + use_ema: False # we set this to false because this is an inference only config + + unet_config: + target: ldm.modules.diffusionmodules.openaimodel.UNetModel + params: + use_checkpoint: False + use_fp16: False + image_size: 32 # unused + in_channels: 4 + out_channels: 4 + model_channels: 320 + attention_resolutions: [ 4, 2, 1 ] + num_res_blocks: 2 + channel_mult: [ 1, 2, 4, 4 ] + num_head_channels: 64 # need to fix for flash-attn + use_spatial_transformer: True + use_linear_in_transformer: True + transformer_depth: 1 + context_dim: 1024 + legacy: False + + first_stage_config: + target: ldm.models.autoencoder.AutoencoderKL + params: + embed_dim: 4 + monitor: val/rec_loss + ddconfig: + #attn_type: "vanilla-xformers" + double_z: true + z_channels: 4 + resolution: 256 + in_channels: 3 + out_ch: 3 + ch: 128 + ch_mult: + - 1 + - 2 + - 4 + - 4 + num_res_blocks: 2 + attn_resolutions: [] + dropout: 0.0 + lossconfig: + target: torch.nn.Identity + + cond_stage_config: + target: ldm.modules.encoders.modules.FrozenOpenCLIPEmbedder + params: + freeze: True + layer: "penultimate" diff --git a/configs/stable-diffusion/ipex/v2-inference-v-bf16.yaml b/configs/stable-diffusion/ipex/v2-inference-v-bf16.yaml new file mode 100644 index 0000000..9c20593 --- /dev/null +++ b/configs/stable-diffusion/ipex/v2-inference-v-bf16.yaml @@ -0,0 +1,69 @@ +model: + base_learning_rate: 1.0e-4 + target: ldm.models.diffusion.ddpm.LatentDiffusion + params: + parameterization: "v" + linear_start: 0.00085 + linear_end: 0.0120 + num_timesteps_cond: 1 + log_every_t: 200 + timesteps: 1000 + first_stage_key: "jpg" + cond_stage_key: "txt" + image_size: 64 + channels: 4 + cond_stage_trainable: false + conditioning_key: crossattn + monitor: val/loss_simple_ema + scale_factor: 0.18215 + use_ema: False # we set this to false because this is an inference only config + + unet_config: + target: ldm.modules.diffusionmodules.openaimodel.UNetModel + params: + use_checkpoint: False + use_fp16: False + use_bf16: True + image_size: 32 # unused + in_channels: 4 + out_channels: 4 + model_channels: 320 + attention_resolutions: [ 4, 2, 1 ] + num_res_blocks: 2 + channel_mult: [ 1, 2, 4, 4 ] + num_head_channels: 64 # need to fix for flash-attn + use_spatial_transformer: True + use_linear_in_transformer: True + transformer_depth: 1 + context_dim: 1024 + legacy: False + + first_stage_config: + target: ldm.models.autoencoder.AutoencoderKL + params: + embed_dim: 4 + monitor: val/rec_loss + ddconfig: + #attn_type: "vanilla-xformers" + double_z: true + z_channels: 4 + resolution: 256 + in_channels: 3 + out_ch: 3 + ch: 128 + ch_mult: + - 1 + - 2 + - 4 + - 4 + num_res_blocks: 2 + attn_resolutions: [] + dropout: 0.0 + lossconfig: + target: torch.nn.Identity + + cond_stage_config: + target: ldm.modules.encoders.modules.FrozenOpenCLIPEmbedder + params: + freeze: True + layer: "penultimate" diff --git a/configs/stable-diffusion/ipex/v2-inference-v-fp32.yaml b/configs/stable-diffusion/ipex/v2-inference-v-fp32.yaml new file mode 100644 index 0000000..10715f5 --- /dev/null +++ b/configs/stable-diffusion/ipex/v2-inference-v-fp32.yaml @@ -0,0 +1,68 @@ +model: + base_learning_rate: 1.0e-4 + target: ldm.models.diffusion.ddpm.LatentDiffusion + params: + parameterization: "v" + linear_start: 0.00085 + linear_end: 0.0120 + num_timesteps_cond: 1 + log_every_t: 200 + timesteps: 1000 + first_stage_key: "jpg" + cond_stage_key: "txt" + image_size: 64 + channels: 4 + cond_stage_trainable: false + conditioning_key: crossattn + monitor: val/loss_simple_ema + scale_factor: 0.18215 + use_ema: False # we set this to false because this is an inference only config + + unet_config: + target: ldm.modules.diffusionmodules.openaimodel.UNetModel + params: + use_checkpoint: False + use_fp16: False + image_size: 32 # unused + in_channels: 4 + out_channels: 4 + model_channels: 320 + attention_resolutions: [ 4, 2, 1 ] + num_res_blocks: 2 + channel_mult: [ 1, 2, 4, 4 ] + num_head_channels: 64 # need to fix for flash-attn + use_spatial_transformer: True + use_linear_in_transformer: True + transformer_depth: 1 + context_dim: 1024 + legacy: False + + first_stage_config: + target: ldm.models.autoencoder.AutoencoderKL + params: + embed_dim: 4 + monitor: val/rec_loss + ddconfig: + #attn_type: "vanilla-xformers" + double_z: true + z_channels: 4 + resolution: 256 + in_channels: 3 + out_ch: 3 + ch: 128 + ch_mult: + - 1 + - 2 + - 4 + - 4 + num_res_blocks: 2 + attn_resolutions: [] + dropout: 0.0 + lossconfig: + target: torch.nn.Identity + + cond_stage_config: + target: ldm.modules.encoders.modules.FrozenOpenCLIPEmbedder + params: + freeze: True + layer: "penultimate" diff --git a/ldm/models/diffusion/ddim.py b/ldm/models/diffusion/ddim.py index 27ead0e..c6cfd57 100644 --- a/ldm/models/diffusion/ddim.py +++ b/ldm/models/diffusion/ddim.py @@ -8,16 +8,17 @@ from ldm.modules.diffusionmodules.util import make_ddim_sampling_parameters, mak class DDIMSampler(object): - def __init__(self, model, schedule="linear", **kwargs): + def __init__(self, model, schedule="linear", device=torch.device("cuda"), **kwargs): super().__init__() self.model = model self.ddpm_num_timesteps = model.num_timesteps self.schedule = schedule + self.device = device def register_buffer(self, name, attr): if type(attr) == torch.Tensor: - if attr.device != torch.device("cuda"): - attr = attr.to(torch.device("cuda")) + if attr.device != self.device: + attr = attr.to(self.device) setattr(self, name, attr) def make_schedule(self, ddim_num_steps, ddim_discretize="uniform", ddim_eta=0., verbose=True): diff --git a/ldm/models/diffusion/ddpm.py b/ldm/models/diffusion/ddpm.py index 6090212..6d2f5a7 100644 --- a/ldm/models/diffusion/ddpm.py +++ b/ldm/models/diffusion/ddpm.py @@ -1326,7 +1326,13 @@ class DiffusionWrapper(pl.LightningModule): cc = torch.cat(c_crossattn, 1) else: cc = c_crossattn - out = self.diffusion_model(x, t, context=cc) + if hasattr(self, "scripted_diffusion_model"): + # TorchScript changes names of the arguments + # with argument cc defined as context=cc scripted model will produce + # an error: RuntimeError: forward() is missing value for argument 'argument_3'. + out = self.scripted_diffusion_model(x, t, cc) + else: + out = self.diffusion_model(x, t, context=cc) elif self.conditioning_key == 'hybrid': xc = torch.cat([x] + c_concat, dim=1) cc = torch.cat(c_crossattn, 1) diff --git a/ldm/models/diffusion/dpm_solver/sampler.py b/ldm/models/diffusion/dpm_solver/sampler.py index 7d137b8..4270c61 100644 --- a/ldm/models/diffusion/dpm_solver/sampler.py +++ b/ldm/models/diffusion/dpm_solver/sampler.py @@ -11,16 +11,17 @@ MODEL_TYPES = { class DPMSolverSampler(object): - def __init__(self, model, **kwargs): + def __init__(self, model, device=torch.device("cuda"), **kwargs): super().__init__() self.model = model + self.device = device to_torch = lambda x: x.clone().detach().to(torch.float32).to(model.device) self.register_buffer('alphas_cumprod', to_torch(model.alphas_cumprod)) def register_buffer(self, name, attr): if type(attr) == torch.Tensor: - if attr.device != torch.device("cuda"): - attr = attr.to(torch.device("cuda")) + if attr.device != self.device: + attr = attr.to(self.device) setattr(self, name, attr) @torch.no_grad() diff --git a/ldm/models/diffusion/plms.py b/ldm/models/diffusion/plms.py index 7002a36..9d31b39 100644 --- a/ldm/models/diffusion/plms.py +++ b/ldm/models/diffusion/plms.py @@ -10,16 +10,17 @@ from ldm.models.diffusion.sampling_util import norm_thresholding class PLMSSampler(object): - def __init__(self, model, schedule="linear", **kwargs): + def __init__(self, model, schedule="linear", device=torch.device("cuda"), **kwargs): super().__init__() self.model = model self.ddpm_num_timesteps = model.num_timesteps self.schedule = schedule + self.device = device def register_buffer(self, name, attr): if type(attr) == torch.Tensor: - if attr.device != torch.device("cuda"): - attr = attr.to(torch.device("cuda")) + if attr.device != self.device: + attr = attr.to(self.device) setattr(self, name, attr) def make_schedule(self, ddim_num_steps, ddim_discretize="uniform", ddim_eta=0., verbose=True): diff --git a/ldm/modules/diffusionmodules/openaimodel.py b/ldm/modules/diffusionmodules/openaimodel.py index 7df6b5a..764a34b 100644 --- a/ldm/modules/diffusionmodules/openaimodel.py +++ b/ldm/modules/diffusionmodules/openaimodel.py @@ -454,6 +454,7 @@ class UNetModel(nn.Module): num_classes=None, use_checkpoint=False, use_fp16=False, + use_bf16=False, num_heads=-1, num_head_channels=-1, num_heads_upsample=-1, @@ -518,6 +519,7 @@ class UNetModel(nn.Module): self.num_classes = num_classes self.use_checkpoint = use_checkpoint self.dtype = th.float16 if use_fp16 else th.float32 + self.dtype = th.bfloat16 if use_bf16 else self.dtype self.num_heads = num_heads self.num_head_channels = num_head_channels self.num_heads_upsample = num_heads_upsample diff --git a/scripts/txt2img.py b/scripts/txt2img.py index 1ed42a3..53e8701 100644 --- a/scripts/txt2img.py +++ b/scripts/txt2img.py @@ -25,7 +25,7 @@ def chunk(it, size): return iter(lambda: tuple(islice(it, size)), ()) -def load_model_from_config(config, ckpt, verbose=False): +def load_model_from_config(config, ckpt, device=torch.device("cuda"), verbose=False): print(f"Loading model from {ckpt}") pl_sd = torch.load(ckpt, map_location="cpu") if "global_step" in pl_sd: @@ -40,7 +40,13 @@ def load_model_from_config(config, ckpt, verbose=False): print("unexpected keys:") print(u) - model.cuda() + if device == torch.device("cuda"): + model.cuda() + elif device == torch.device("cpu"): + model.cpu() + model.cond_stage_model.device = "cpu" + else: + raise ValueError(f"Incorrect device name. Received: {device}") model.eval() return model @@ -171,6 +177,28 @@ def parse_args(): default=1, help="repeat each prompt in file this often", ) + parser.add_argument( + "--device", + type=str, + help="Device on which Stable Diffusion will be run", + choices=["cpu", "cuda"], + default="cpu" + ) + parser.add_argument( + "--torchscript", + action='store_true', + help="Use TorchScript", + ) + parser.add_argument( + "--ipex", + action='store_true', + help="Use Intel® Extension for PyTorch*", + ) + parser.add_argument( + "--bf16", + action='store_true', + help="Use bfloat16", + ) opt = parser.parse_args() return opt @@ -187,17 +215,15 @@ def main(opt): seed_everything(opt.seed) config = OmegaConf.load(f"{opt.config}") - model = load_model_from_config(config, f"{opt.ckpt}") - - device = torch.device("cuda") if torch.cuda.is_available() else torch.device("cpu") - model = model.to(device) + device = torch.device("cuda") if opt.device == "cuda" else torch.device("cpu") + model = load_model_from_config(config, f"{opt.ckpt}", device) if opt.plms: - sampler = PLMSSampler(model) + sampler = PLMSSampler(model, device=device) elif opt.dpm: - sampler = DPMSolverSampler(model) + sampler = DPMSolverSampler(model, device=device) else: - sampler = DDIMSampler(model) + sampler = DDIMSampler(model, device=device) os.makedirs(opt.outdir, exist_ok=True) outpath = opt.outdir @@ -231,9 +257,82 @@ def main(opt): if opt.fixed_code: start_code = torch.randn([opt.n_samples, opt.C, opt.H // opt.f, opt.W // opt.f], device=device) - precision_scope = autocast if opt.precision == "autocast" else nullcontext + if opt.torchscript or opt.ipex: + transformer = model.cond_stage_model.model + unet = model.model.diffusion_model + decoder = model.first_stage_model.decoder + additional_context = torch.cpu.amp.autocast() if opt.bf16 else nullcontext() + shape = [opt.C, opt.H // opt.f, opt.W // opt.f] + + if opt.bf16 and not opt.torchscript and not opt.ipex: + raise ValueError('Bfloat16 is supported only for torchscript+ipex') + if opt.bf16 and unet.dtype != torch.bfloat16: + raise ValueError("Use configs/stable-diffusion/ipex/ configs with bf16 enabled if " + + "you'd like to use bfloat16 with CPU.") + if unet.dtype == torch.float16 and device == torch.device("cpu"): + raise ValueError("Use configs/stable-diffusion/ipex/ configs for your model if you'd like to run it on CPU.") + + if opt.ipex: + import intel_extension_for_pytorch as ipex + bf16_dtype = torch.bfloat16 if opt.bf16 else None + transformer = transformer.to(memory_format=torch.channels_last) + transformer = ipex.optimize(transformer, level="O1", inplace=True) + + unet = unet.to(memory_format=torch.channels_last) + unet = ipex.optimize(unet, level="O1", auto_kernel_selection=True, inplace=True, dtype=bf16_dtype) + + decoder = decoder.to(memory_format=torch.channels_last) + decoder = ipex.optimize(decoder, level="O1", auto_kernel_selection=True, inplace=True, dtype=bf16_dtype) + + if opt.torchscript: + with torch.no_grad(), additional_context: + # get UNET scripted + if unet.use_checkpoint: + raise ValueError("Gradient checkpoint won't work with tracing. " + + "Use configs/stable-diffusion/ipex/ configs for your model or disable checkpoint in your config.") + + img_in = torch.ones(2, 4, 96, 96, dtype=torch.float32) + t_in = torch.ones(2, dtype=torch.int64) + context = torch.ones(2, 77, 1024, dtype=torch.float32) + scripted_unet = torch.jit.trace(unet, (img_in, t_in, context)) + scripted_unet = torch.jit.optimize_for_inference(scripted_unet) + print(type(scripted_unet)) + model.model.scripted_diffusion_model = scripted_unet + + # get Decoder for first stage model scripted + samples_ddim = torch.ones(1, 4, 96, 96, dtype=torch.float32) + scripted_decoder = torch.jit.trace(decoder, (samples_ddim)) + scripted_decoder = torch.jit.optimize_for_inference(scripted_decoder) + print(type(scripted_decoder)) + model.first_stage_model.decoder = scripted_decoder + + prompts = data[0] + print("Running a forward pass to initialize optimizations") + uc = None + if opt.scale != 1.0: + uc = model.get_learned_conditioning(batch_size * [""]) + if isinstance(prompts, tuple): + prompts = list(prompts) + + with torch.no_grad(), additional_context: + for _ in range(3): + c = model.get_learned_conditioning(prompts) + samples_ddim, _ = sampler.sample(S=5, + conditioning=c, + batch_size=batch_size, + shape=shape, + verbose=False, + unconditional_guidance_scale=opt.scale, + unconditional_conditioning=uc, + eta=opt.ddim_eta, + x_T=start_code) + print("Running a forward pass for decoder") + for _ in range(3): + x_samples_ddim = model.decode_first_stage(samples_ddim) + + precision_scope = autocast if opt.precision=="autocast" or opt.bf16 else nullcontext with torch.no_grad(), \ - precision_scope("cuda"), \ + precision_scope(opt.device), \ model.ema_scope(): all_samples = list() for n in trange(opt.n_iter, desc="Sampling"): From 71e90423dcc5b3dbd1d75791b84e40108b4948de Mon Sep 17 00:00:00 2001 From: aalbersk Date: Tue, 20 Dec 2022 21:23:46 +0000 Subject: [PATCH 02/10] add intel info to README --- README.md | 36 ++++++++++++++++++++++++++++++++++++ 1 file changed, 36 insertions(+) diff --git a/README.md b/README.md index f413068..87d81b0 100644 --- a/README.md +++ b/README.md @@ -137,6 +137,42 @@ Note: The inference config for all model versions is designed to be used with EM For this reason `use_ema=False` is set in the configuration, otherwise the code will try to switch from non-EMA to EMA weights. +#### Enable Intel® Extension for PyTorch* optimizations in Text-to-Image script + +If you're planning on running Text-to-Image on Intel® CPU, try to sample an image with TorchScript and Intel® Extension for PyTorch* optimizations. Intel® Extension for PyTorch* extends PyTorch by enabling up-to-date features optimizations for an extra performance boost on Intel® hardware. It can optimize memory layout of the operators to Channel Last memory format, which is generally beneficial for Intel CPUs, take advantage of the most advanced instruction set available on a machine, optimize operators and many more. + +**Prerequisites** + +Before running the script, make sure you have all needed libraries installed. (the optimization was checked on `Ubuntu 20.04`). Install [jemalloc](https://github.com/jemalloc/jemalloc), [numactl](https://linux.die.net/man/8/numactl), Intel® OpenMP and Intel® Extension for PyTorch*. + +```bash +apt-get install numactl libjemalloc-dev +pip install intel-openmp +pip install intel_extension_for_pytorch -f https://software.intel.com/ipex-whl-stable +``` + +To sample from the _SD2.1-v_ model with TorchScript+IPEX optimizations, run the following: + +# TODO HOW TO EXPLAIN -NINSTANCE +``` +MALLOC_CONF=oversize_threshold:1,background_thread:true,metadata_thp:auto,dirty_decay_ms:9000000000,muzzy_decay_ms:9000000000 python -m intel_extension_for_pytorch.cpu.launch --ninstance 1 --enable_jemalloc scripts/txt2img.py --prompt \"a corgi is playing guitar, oil on canvas\" --ckpt --config configs/stable-diffusion/ipex/v2-inference-v-fp32.yaml --H 768 --W 768 --precision full --device cpu --torchscript --ipex +``` + +To sample from the base model with IPEX optimizations, use + +``` +MALLOC_CONF=oversize_threshold:1,background_thread:true,metadata_thp:auto,dirty_decay_ms:9000000000,muzzy_decay_ms:9000000000 python -m intel_extension_for_pytorch.cpu.launch --ninstance 1 --enable_jemalloc scripts/txt2img.py --prompt \"a corgi is playing guitar, oil on canvas\" --ckpt --config configs/stable-diffusion/ipex/v2-inference-fp32.yaml --n_samples 1 --n_iter 4 --precision full --device cpu --torchscript --ipex +``` + +If you're using a CPU that supports `bfloat16`, consider sample from the model with bfloat16 enabled for a performance boost, like so + +```bash +# SD2.1-v +MALLOC_CONF=oversize_threshold:1,background_thread:true,metadata_thp:auto,dirty_decay_ms:9000000000,muzzy_decay_ms:9000000000 python -m intel_extension_for_pytorch.cpu.launch --ninstance 1 --enable_jemalloc scripts/txt2img.py --prompt \"a corgi is playing guitar, oil on canvas\" --ckpt --config configs/stable-diffusion/ipex/v2-inference-v-bf16.yaml --H 768 --W 768 --precision full --device cpu --torchscript --ipex --bf16 +# SD2.1-base +MALLOC_CONF=oversize_threshold:1,background_thread:true,metadata_thp:auto,dirty_decay_ms:9000000000,muzzy_decay_ms:9000000000 python -m intel_extension_for_pytorch.cpu.launch --ninstance 1 --enable_jemalloc scripts/txt2img.py --prompt \"a corgi is playing guitar, oil on canvas\" --ckpt --config configs/stable-diffusion/ipex/v2-inference-bf16.yaml --precision full --device cpu --torchscript --ipex --bf16 +``` + ### Image Modification with Stable Diffusion ![depth2img-stable2](assets/stable-samples/depth2img/merged-0000.png) From 872cc9e8739f2621f526c055ebeaa544e038248d Mon Sep 17 00:00:00 2001 From: Anna Alberska Date: Thu, 5 Jan 2023 13:16:23 +0100 Subject: [PATCH 03/10] add info about ninstance and license --- README.md | 11 +++++------ configs/stable-diffusion/ipex/v2-inference-bf16.yaml | 3 +++ configs/stable-diffusion/ipex/v2-inference-fp32.yaml | 3 +++ .../stable-diffusion/ipex/v2-inference-v-bf16.yaml | 3 +++ .../stable-diffusion/ipex/v2-inference-v-fp32.yaml | 3 +++ ldm/models/diffusion/ddim.py | 3 +++ ldm/models/diffusion/ddpm.py | 3 +++ ldm/models/diffusion/dpm_solver/sampler.py | 3 +++ ldm/models/diffusion/plms.py | 3 +++ ldm/modules/diffusionmodules/openaimodel.py | 3 +++ scripts/txt2img.py | 3 +++ 11 files changed, 35 insertions(+), 6 deletions(-) diff --git a/README.md b/README.md index 87d81b0..11ea753 100644 --- a/README.md +++ b/README.md @@ -151,26 +151,25 @@ pip install intel-openmp pip install intel_extension_for_pytorch -f https://software.intel.com/ipex-whl-stable ``` -To sample from the _SD2.1-v_ model with TorchScript+IPEX optimizations, run the following: +To sample from the _SD2.1-v_ model with TorchScript+IPEX optimizations, run the following. Remember to specify desired number of instances you want to run the program on ([more](https://github.com/intel/intel-extension-for-pytorch/blob/master/intel_extension_for_pytorch/cpu/launch.py#L48)). -# TODO HOW TO EXPLAIN -NINSTANCE ``` -MALLOC_CONF=oversize_threshold:1,background_thread:true,metadata_thp:auto,dirty_decay_ms:9000000000,muzzy_decay_ms:9000000000 python -m intel_extension_for_pytorch.cpu.launch --ninstance 1 --enable_jemalloc scripts/txt2img.py --prompt \"a corgi is playing guitar, oil on canvas\" --ckpt --config configs/stable-diffusion/ipex/v2-inference-v-fp32.yaml --H 768 --W 768 --precision full --device cpu --torchscript --ipex +MALLOC_CONF=oversize_threshold:1,background_thread:true,metadata_thp:auto,dirty_decay_ms:9000000000,muzzy_decay_ms:9000000000 python -m intel_extension_for_pytorch.cpu.launch --ninstance --enable_jemalloc scripts/txt2img.py --prompt \"a corgi is playing guitar, oil on canvas\" --ckpt --config configs/stable-diffusion/ipex/v2-inference-v-fp32.yaml --H 768 --W 768 --precision full --device cpu --torchscript --ipex ``` To sample from the base model with IPEX optimizations, use ``` -MALLOC_CONF=oversize_threshold:1,background_thread:true,metadata_thp:auto,dirty_decay_ms:9000000000,muzzy_decay_ms:9000000000 python -m intel_extension_for_pytorch.cpu.launch --ninstance 1 --enable_jemalloc scripts/txt2img.py --prompt \"a corgi is playing guitar, oil on canvas\" --ckpt --config configs/stable-diffusion/ipex/v2-inference-fp32.yaml --n_samples 1 --n_iter 4 --precision full --device cpu --torchscript --ipex +MALLOC_CONF=oversize_threshold:1,background_thread:true,metadata_thp:auto,dirty_decay_ms:9000000000,muzzy_decay_ms:9000000000 python -m intel_extension_for_pytorch.cpu.launch --ninstance --enable_jemalloc scripts/txt2img.py --prompt \"a corgi is playing guitar, oil on canvas\" --ckpt --config configs/stable-diffusion/ipex/v2-inference-fp32.yaml --n_samples 1 --n_iter 4 --precision full --device cpu --torchscript --ipex ``` If you're using a CPU that supports `bfloat16`, consider sample from the model with bfloat16 enabled for a performance boost, like so ```bash # SD2.1-v -MALLOC_CONF=oversize_threshold:1,background_thread:true,metadata_thp:auto,dirty_decay_ms:9000000000,muzzy_decay_ms:9000000000 python -m intel_extension_for_pytorch.cpu.launch --ninstance 1 --enable_jemalloc scripts/txt2img.py --prompt \"a corgi is playing guitar, oil on canvas\" --ckpt --config configs/stable-diffusion/ipex/v2-inference-v-bf16.yaml --H 768 --W 768 --precision full --device cpu --torchscript --ipex --bf16 +MALLOC_CONF=oversize_threshold:1,background_thread:true,metadata_thp:auto,dirty_decay_ms:9000000000,muzzy_decay_ms:9000000000 python -m intel_extension_for_pytorch.cpu.launch --ninstance --enable_jemalloc scripts/txt2img.py --prompt \"a corgi is playing guitar, oil on canvas\" --ckpt --config configs/stable-diffusion/ipex/v2-inference-v-bf16.yaml --H 768 --W 768 --precision full --device cpu --torchscript --ipex --bf16 # SD2.1-base -MALLOC_CONF=oversize_threshold:1,background_thread:true,metadata_thp:auto,dirty_decay_ms:9000000000,muzzy_decay_ms:9000000000 python -m intel_extension_for_pytorch.cpu.launch --ninstance 1 --enable_jemalloc scripts/txt2img.py --prompt \"a corgi is playing guitar, oil on canvas\" --ckpt --config configs/stable-diffusion/ipex/v2-inference-bf16.yaml --precision full --device cpu --torchscript --ipex --bf16 +MALLOC_CONF=oversize_threshold:1,background_thread:true,metadata_thp:auto,dirty_decay_ms:9000000000,muzzy_decay_ms:9000000000 python -m intel_extension_for_pytorch.cpu.launch --ninstance --enable_jemalloc scripts/txt2img.py --prompt \"a corgi is playing guitar, oil on canvas\" --ckpt --config configs/stable-diffusion/ipex/v2-inference-bf16.yaml --precision full --device cpu --torchscript --ipex --bf16 ``` ### Image Modification with Stable Diffusion diff --git a/configs/stable-diffusion/ipex/v2-inference-bf16.yaml b/configs/stable-diffusion/ipex/v2-inference-bf16.yaml index e36c3b3..66f0dbd 100644 --- a/configs/stable-diffusion/ipex/v2-inference-bf16.yaml +++ b/configs/stable-diffusion/ipex/v2-inference-bf16.yaml @@ -1,3 +1,6 @@ +# Copyright (C) 2022 Intel Corporation +# SPDX-License-Identifier: MIT + model: base_learning_rate: 1.0e-4 target: ldm.models.diffusion.ddpm.LatentDiffusion diff --git a/configs/stable-diffusion/ipex/v2-inference-fp32.yaml b/configs/stable-diffusion/ipex/v2-inference-fp32.yaml index ef63a09..7b66ac8 100644 --- a/configs/stable-diffusion/ipex/v2-inference-fp32.yaml +++ b/configs/stable-diffusion/ipex/v2-inference-fp32.yaml @@ -1,3 +1,6 @@ +# Copyright (C) 2022 Intel Corporation +# SPDX-License-Identifier: MIT + model: base_learning_rate: 1.0e-4 target: ldm.models.diffusion.ddpm.LatentDiffusion diff --git a/configs/stable-diffusion/ipex/v2-inference-v-bf16.yaml b/configs/stable-diffusion/ipex/v2-inference-v-bf16.yaml index 9c20593..2b4b0e6 100644 --- a/configs/stable-diffusion/ipex/v2-inference-v-bf16.yaml +++ b/configs/stable-diffusion/ipex/v2-inference-v-bf16.yaml @@ -1,3 +1,6 @@ +# Copyright (C) 2022 Intel Corporation +# SPDX-License-Identifier: MIT + model: base_learning_rate: 1.0e-4 target: ldm.models.diffusion.ddpm.LatentDiffusion diff --git a/configs/stable-diffusion/ipex/v2-inference-v-fp32.yaml b/configs/stable-diffusion/ipex/v2-inference-v-fp32.yaml index 10715f5..8ccd92e 100644 --- a/configs/stable-diffusion/ipex/v2-inference-v-fp32.yaml +++ b/configs/stable-diffusion/ipex/v2-inference-v-fp32.yaml @@ -1,3 +1,6 @@ +# Copyright (C) 2022 Intel Corporation +# SPDX-License-Identifier: MIT + model: base_learning_rate: 1.0e-4 target: ldm.models.diffusion.ddpm.LatentDiffusion diff --git a/ldm/models/diffusion/ddim.py b/ldm/models/diffusion/ddim.py index c6cfd57..e93f7fb 100644 --- a/ldm/models/diffusion/ddim.py +++ b/ldm/models/diffusion/ddim.py @@ -1,3 +1,6 @@ +# Copyright (C) 2022 Intel Corporation +# SPDX-License-Identifier: MIT + """SAMPLING ONLY.""" import torch diff --git a/ldm/models/diffusion/ddpm.py b/ldm/models/diffusion/ddpm.py index 6d2f5a7..b6248ea 100644 --- a/ldm/models/diffusion/ddpm.py +++ b/ldm/models/diffusion/ddpm.py @@ -1,3 +1,6 @@ +# Copyright (C) 2022 Intel Corporation +# SPDX-License-Identifier: MIT + """ wild mixture of https://github.com/lucidrains/denoising-diffusion-pytorch/blob/7706bdfc6f527f58d33f84b7b522e61e6e3164b3/denoising_diffusion_pytorch/denoising_diffusion_pytorch.py diff --git a/ldm/models/diffusion/dpm_solver/sampler.py b/ldm/models/diffusion/dpm_solver/sampler.py index 4270c61..108b336 100644 --- a/ldm/models/diffusion/dpm_solver/sampler.py +++ b/ldm/models/diffusion/dpm_solver/sampler.py @@ -1,3 +1,6 @@ +# Copyright (C) 2022 Intel Corporation +# SPDX-License-Identifier: MIT + """SAMPLING ONLY.""" import torch diff --git a/ldm/models/diffusion/plms.py b/ldm/models/diffusion/plms.py index 9d31b39..bef62b3 100644 --- a/ldm/models/diffusion/plms.py +++ b/ldm/models/diffusion/plms.py @@ -1,3 +1,6 @@ +# Copyright (C) 2022 Intel Corporation +# SPDX-License-Identifier: MIT + """SAMPLING ONLY.""" import torch diff --git a/ldm/modules/diffusionmodules/openaimodel.py b/ldm/modules/diffusionmodules/openaimodel.py index 764a34b..634e3f4 100644 --- a/ldm/modules/diffusionmodules/openaimodel.py +++ b/ldm/modules/diffusionmodules/openaimodel.py @@ -1,3 +1,6 @@ +# Copyright (C) 2022 Intel Corporation +# SPDX-License-Identifier: MIT + from abc import abstractmethod import math diff --git a/scripts/txt2img.py b/scripts/txt2img.py index 53e8701..1cae967 100644 --- a/scripts/txt2img.py +++ b/scripts/txt2img.py @@ -1,3 +1,6 @@ +# Copyright (C) 2022 Intel Corporation +# SPDX-License-Identifier: MIT + import argparse, os import cv2 import torch From 334969331438036c1b4fb529c262f78499870d86 Mon Sep 17 00:00:00 2001 From: aalbersk Date: Fri, 3 Feb 2023 09:27:06 +0000 Subject: [PATCH 04/10] adjust licenses and naming --- README.md | 8 ++++---- .../{ipex => intel}/v2-inference-bf16.yaml | 0 .../{ipex => intel}/v2-inference-fp32.yaml | 0 .../{ipex => intel}/v2-inference-v-bf16.yaml | 0 .../{ipex => intel}/v2-inference-v-fp32.yaml | 0 ldm/models/diffusion/ddim.py | 3 --- ldm/models/diffusion/ddpm.py | 3 --- ldm/models/diffusion/dpm_solver/sampler.py | 3 --- ldm/models/diffusion/plms.py | 3 --- ldm/modules/diffusionmodules/openaimodel.py | 3 --- scripts/txt2img.py | 9 +++------ 11 files changed, 7 insertions(+), 25 deletions(-) rename configs/stable-diffusion/{ipex => intel}/v2-inference-bf16.yaml (100%) rename configs/stable-diffusion/{ipex => intel}/v2-inference-fp32.yaml (100%) rename configs/stable-diffusion/{ipex => intel}/v2-inference-v-bf16.yaml (100%) rename configs/stable-diffusion/{ipex => intel}/v2-inference-v-fp32.yaml (100%) diff --git a/README.md b/README.md index 11ea753..0cc7471 100644 --- a/README.md +++ b/README.md @@ -154,22 +154,22 @@ pip install intel_extension_for_pytorch -f https://software.intel.com/ipex-whl-s To sample from the _SD2.1-v_ model with TorchScript+IPEX optimizations, run the following. Remember to specify desired number of instances you want to run the program on ([more](https://github.com/intel/intel-extension-for-pytorch/blob/master/intel_extension_for_pytorch/cpu/launch.py#L48)). ``` -MALLOC_CONF=oversize_threshold:1,background_thread:true,metadata_thp:auto,dirty_decay_ms:9000000000,muzzy_decay_ms:9000000000 python -m intel_extension_for_pytorch.cpu.launch --ninstance --enable_jemalloc scripts/txt2img.py --prompt \"a corgi is playing guitar, oil on canvas\" --ckpt --config configs/stable-diffusion/ipex/v2-inference-v-fp32.yaml --H 768 --W 768 --precision full --device cpu --torchscript --ipex +MALLOC_CONF=oversize_threshold:1,background_thread:true,metadata_thp:auto,dirty_decay_ms:9000000000,muzzy_decay_ms:9000000000 python -m intel_extension_for_pytorch.cpu.launch --ninstance --enable_jemalloc scripts/txt2img.py --prompt \"a corgi is playing guitar, oil on canvas\" --ckpt --config configs/stable-diffusion/intel/v2-inference-v-fp32.yaml --H 768 --W 768 --precision full --device cpu --torchscript --ipex ``` To sample from the base model with IPEX optimizations, use ``` -MALLOC_CONF=oversize_threshold:1,background_thread:true,metadata_thp:auto,dirty_decay_ms:9000000000,muzzy_decay_ms:9000000000 python -m intel_extension_for_pytorch.cpu.launch --ninstance --enable_jemalloc scripts/txt2img.py --prompt \"a corgi is playing guitar, oil on canvas\" --ckpt --config configs/stable-diffusion/ipex/v2-inference-fp32.yaml --n_samples 1 --n_iter 4 --precision full --device cpu --torchscript --ipex +MALLOC_CONF=oversize_threshold:1,background_thread:true,metadata_thp:auto,dirty_decay_ms:9000000000,muzzy_decay_ms:9000000000 python -m intel_extension_for_pytorch.cpu.launch --ninstance --enable_jemalloc scripts/txt2img.py --prompt \"a corgi is playing guitar, oil on canvas\" --ckpt --config configs/stable-diffusion/intel/v2-inference-fp32.yaml --n_samples 1 --n_iter 4 --precision full --device cpu --torchscript --ipex ``` If you're using a CPU that supports `bfloat16`, consider sample from the model with bfloat16 enabled for a performance boost, like so ```bash # SD2.1-v -MALLOC_CONF=oversize_threshold:1,background_thread:true,metadata_thp:auto,dirty_decay_ms:9000000000,muzzy_decay_ms:9000000000 python -m intel_extension_for_pytorch.cpu.launch --ninstance --enable_jemalloc scripts/txt2img.py --prompt \"a corgi is playing guitar, oil on canvas\" --ckpt --config configs/stable-diffusion/ipex/v2-inference-v-bf16.yaml --H 768 --W 768 --precision full --device cpu --torchscript --ipex --bf16 +MALLOC_CONF=oversize_threshold:1,background_thread:true,metadata_thp:auto,dirty_decay_ms:9000000000,muzzy_decay_ms:9000000000 python -m intel_extension_for_pytorch.cpu.launch --ninstance --enable_jemalloc scripts/txt2img.py --prompt \"a corgi is playing guitar, oil on canvas\" --ckpt --config configs/stable-diffusion/intel/v2-inference-v-bf16.yaml --H 768 --W 768 --precision full --device cpu --torchscript --ipex --bf16 # SD2.1-base -MALLOC_CONF=oversize_threshold:1,background_thread:true,metadata_thp:auto,dirty_decay_ms:9000000000,muzzy_decay_ms:9000000000 python -m intel_extension_for_pytorch.cpu.launch --ninstance --enable_jemalloc scripts/txt2img.py --prompt \"a corgi is playing guitar, oil on canvas\" --ckpt --config configs/stable-diffusion/ipex/v2-inference-bf16.yaml --precision full --device cpu --torchscript --ipex --bf16 +MALLOC_CONF=oversize_threshold:1,background_thread:true,metadata_thp:auto,dirty_decay_ms:9000000000,muzzy_decay_ms:9000000000 python -m intel_extension_for_pytorch.cpu.launch --ninstance --enable_jemalloc scripts/txt2img.py --prompt \"a corgi is playing guitar, oil on canvas\" --ckpt --config configs/stable-diffusion/intel/v2-inference-bf16.yaml --precision full --device cpu --torchscript --ipex --bf16 ``` ### Image Modification with Stable Diffusion diff --git a/configs/stable-diffusion/ipex/v2-inference-bf16.yaml b/configs/stable-diffusion/intel/v2-inference-bf16.yaml similarity index 100% rename from configs/stable-diffusion/ipex/v2-inference-bf16.yaml rename to configs/stable-diffusion/intel/v2-inference-bf16.yaml diff --git a/configs/stable-diffusion/ipex/v2-inference-fp32.yaml b/configs/stable-diffusion/intel/v2-inference-fp32.yaml similarity index 100% rename from configs/stable-diffusion/ipex/v2-inference-fp32.yaml rename to configs/stable-diffusion/intel/v2-inference-fp32.yaml diff --git a/configs/stable-diffusion/ipex/v2-inference-v-bf16.yaml b/configs/stable-diffusion/intel/v2-inference-v-bf16.yaml similarity index 100% rename from configs/stable-diffusion/ipex/v2-inference-v-bf16.yaml rename to configs/stable-diffusion/intel/v2-inference-v-bf16.yaml diff --git a/configs/stable-diffusion/ipex/v2-inference-v-fp32.yaml b/configs/stable-diffusion/intel/v2-inference-v-fp32.yaml similarity index 100% rename from configs/stable-diffusion/ipex/v2-inference-v-fp32.yaml rename to configs/stable-diffusion/intel/v2-inference-v-fp32.yaml diff --git a/ldm/models/diffusion/ddim.py b/ldm/models/diffusion/ddim.py index e93f7fb..c6cfd57 100644 --- a/ldm/models/diffusion/ddim.py +++ b/ldm/models/diffusion/ddim.py @@ -1,6 +1,3 @@ -# Copyright (C) 2022 Intel Corporation -# SPDX-License-Identifier: MIT - """SAMPLING ONLY.""" import torch diff --git a/ldm/models/diffusion/ddpm.py b/ldm/models/diffusion/ddpm.py index b6248ea..6d2f5a7 100644 --- a/ldm/models/diffusion/ddpm.py +++ b/ldm/models/diffusion/ddpm.py @@ -1,6 +1,3 @@ -# Copyright (C) 2022 Intel Corporation -# SPDX-License-Identifier: MIT - """ wild mixture of https://github.com/lucidrains/denoising-diffusion-pytorch/blob/7706bdfc6f527f58d33f84b7b522e61e6e3164b3/denoising_diffusion_pytorch/denoising_diffusion_pytorch.py diff --git a/ldm/models/diffusion/dpm_solver/sampler.py b/ldm/models/diffusion/dpm_solver/sampler.py index 108b336..4270c61 100644 --- a/ldm/models/diffusion/dpm_solver/sampler.py +++ b/ldm/models/diffusion/dpm_solver/sampler.py @@ -1,6 +1,3 @@ -# Copyright (C) 2022 Intel Corporation -# SPDX-License-Identifier: MIT - """SAMPLING ONLY.""" import torch diff --git a/ldm/models/diffusion/plms.py b/ldm/models/diffusion/plms.py index bef62b3..9d31b39 100644 --- a/ldm/models/diffusion/plms.py +++ b/ldm/models/diffusion/plms.py @@ -1,6 +1,3 @@ -# Copyright (C) 2022 Intel Corporation -# SPDX-License-Identifier: MIT - """SAMPLING ONLY.""" import torch diff --git a/ldm/modules/diffusionmodules/openaimodel.py b/ldm/modules/diffusionmodules/openaimodel.py index 634e3f4..764a34b 100644 --- a/ldm/modules/diffusionmodules/openaimodel.py +++ b/ldm/modules/diffusionmodules/openaimodel.py @@ -1,6 +1,3 @@ -# Copyright (C) 2022 Intel Corporation -# SPDX-License-Identifier: MIT - from abc import abstractmethod import math diff --git a/scripts/txt2img.py b/scripts/txt2img.py index 1cae967..9d955e3 100644 --- a/scripts/txt2img.py +++ b/scripts/txt2img.py @@ -1,6 +1,3 @@ -# Copyright (C) 2022 Intel Corporation -# SPDX-License-Identifier: MIT - import argparse, os import cv2 import torch @@ -270,10 +267,10 @@ def main(opt): if opt.bf16 and not opt.torchscript and not opt.ipex: raise ValueError('Bfloat16 is supported only for torchscript+ipex') if opt.bf16 and unet.dtype != torch.bfloat16: - raise ValueError("Use configs/stable-diffusion/ipex/ configs with bf16 enabled if " + + raise ValueError("Use configs/stable-diffusion/intel/ configs with bf16 enabled if " + "you'd like to use bfloat16 with CPU.") if unet.dtype == torch.float16 and device == torch.device("cpu"): - raise ValueError("Use configs/stable-diffusion/ipex/ configs for your model if you'd like to run it on CPU.") + raise ValueError("Use configs/stable-diffusion/intel/ configs for your model if you'd like to run it on CPU.") if opt.ipex: import intel_extension_for_pytorch as ipex @@ -292,7 +289,7 @@ def main(opt): # get UNET scripted if unet.use_checkpoint: raise ValueError("Gradient checkpoint won't work with tracing. " + - "Use configs/stable-diffusion/ipex/ configs for your model or disable checkpoint in your config.") + "Use configs/stable-diffusion/intel/ configs for your model or disable checkpoint in your config.") img_in = torch.ones(2, 4, 96, 96, dtype=torch.float32) t_in = torch.ones(2, dtype=torch.int64) From c25a9a862d6ce38d8099f3750d7e9a2c739e025b Mon Sep 17 00:00:00 2001 From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com> Date: Mon, 20 Mar 2023 18:39:32 +0000 Subject: [PATCH 05/10] Bump gradio from 3.11 to 3.13.2 Bumps [gradio](https://github.com/gradio-app/gradio) from 3.11 to 3.13.2. - [Release notes](https://github.com/gradio-app/gradio/releases) - [Changelog](https://github.com/gradio-app/gradio/blob/main/CHANGELOG.md) - [Commits](https://github.com/gradio-app/gradio/compare/v3.11.0...v3.13.2) --- updated-dependencies: - dependency-name: gradio dependency-type: direct:production ... Signed-off-by: dependabot[bot] --- requirements.txt | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/requirements.txt b/requirements.txt index 0cd8b07..23b4acb 100644 --- a/requirements.txt +++ b/requirements.txt @@ -12,7 +12,7 @@ einops==0.3.0 transformers==4.19.2 webdataset==0.2.5 open-clip-torch==2.7.0 -gradio==3.11 +gradio==3.13.2 kornia==0.6 invisible-watermark>=0.1.5 streamlit-drawable-canvas==0.8.0 From 0bbbcbb45d5d635942b7d6c92c0b4d307dfba7bf Mon Sep 17 00:00:00 2001 From: hardmaru Date: Fri, 24 Mar 2023 14:59:54 +0900 Subject: [PATCH 06/10] Add files via upload Stable Diffusion 2.1 unCLIP Release --- .../stable-unclip/houses_out.jpeg | Bin 0 -> 179603 bytes .../stable-unclip/oldcar000.jpeg | Bin 0 -> 289380 bytes .../stable-unclip/oldcar500.jpeg | Bin 0 -> 272976 bytes .../stable-unclip/oldcar800.jpeg | Bin 0 -> 297144 bytes assets/stable-samples/stable-unclip/panda.jpg | Bin 0 -> 175412 bytes .../stable-unclip/plates_out.jpeg | Bin 0 -> 118215 bytes .../stable-unclip/unclip-variations.png | Bin 0 -> 1774803 bytes .../stable-unclip/unclip-variations_noise.png | Bin 0 -> 1541201 bytes doc/UNCLIP.MD | 56 ++++++++++++++++++ 9 files changed, 56 insertions(+) create mode 100644 assets/stable-samples/stable-unclip/houses_out.jpeg create mode 100644 assets/stable-samples/stable-unclip/oldcar000.jpeg create mode 100644 assets/stable-samples/stable-unclip/oldcar500.jpeg create mode 100644 assets/stable-samples/stable-unclip/oldcar800.jpeg create mode 100644 assets/stable-samples/stable-unclip/panda.jpg create mode 100644 assets/stable-samples/stable-unclip/plates_out.jpeg create mode 100644 assets/stable-samples/stable-unclip/unclip-variations.png create mode 100644 assets/stable-samples/stable-unclip/unclip-variations_noise.png create mode 100644 doc/UNCLIP.MD diff --git a/assets/stable-samples/stable-unclip/houses_out.jpeg b/assets/stable-samples/stable-unclip/houses_out.jpeg new file mode 100644 index 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z;!s4R7C41SkEu-*aWGw~uA|6a)vi#YR3bG(*&KW(3us-Z)shSV5&wVa^hwWGsUJ-M z001R)MObuXVRU6WV{&C-bY%cCFfubOFfuJNGgL7#Ix{djFf%JKF*-0X?2F2L0000b zbVXQnWMOn=I&E)cX=Zr$F*-6iIyE#aFflqXFf9tUAOHXW07*qo IM6N<$f;M&EWB>pF literal 0 HcmV?d00001 diff --git a/doc/UNCLIP.MD b/doc/UNCLIP.MD new file mode 100644 index 0000000..9a12c0c --- /dev/null +++ b/doc/UNCLIP.MD @@ -0,0 +1,56 @@ +### Stable unCLIP + +[unCLIP](https://openai.com/dall-e-2/) is the approach behind OpenAI's [DALL·E 2](https://openai.com/dall-e-2/), +trained to invert CLIP image embeddings. +We finetuned SD 2.1 to accept a CLIP ViT-L/14 image embedding in addition to the text encodings. +This means that the model can be used to produce image variations, but can also be combined with a text-to-image +embedding prior to yield a full text-to-image model at 768x768 resolution. +We provide two models, trained on OpenAI CLIP-L and OpenCLIP-H image embeddings, respectively, +available from [https://huggingface.co/stabilityai/stable-diffusion-2-1-unclip](https://huggingface.co/stabilityai/stable-diffusion-2-1-unclip/tree/main). +To use them, download from Hugging Face, and put and the weights into the `checkpoints` folder. +#### Image Variations +![image-variations-l-1](../assets/stable-samples/stable-unclip/unclip-variations.png) + +Run + +``` +streamlit run scripts/streamlit/stableunclip.py +``` +to launch a streamlit script than can be used to make image variations with both models (CLIP-L and OpenCLIP-H). +These models can process a `noise_level`, which specifies an amount of Gaussian noise added to the CLIP embeddings. +This can be used to increase output variance as in the following examples. + +![image-variations-noise](../assets/stable-samples/stable-unclip/unclip-variations_noise.png) + + +### Stable Diffusion Meets Karlo +![panda](../assets/stable-samples/stable-unclip/panda.jpg) + +Recently, [KakaoBrain](https://kakaobrain.com/) openly released [Karlo](https://github.com/kakaobrain/karlo), a pretrained, large-scale replication of [unCLIP](https://arxiv.org/abs/2204.06125). +We introduce _Stable Karlo_, a combination of the Karlo CLIP image embedding prior, and Stable Diffusion v2.1-768. + +To run the model, first download the KARLO checkpoints +```shell +mkdir -p checkpoints/karlo_models +cd checkpoints/karlo_models +wget https://arena.kakaocdn.net/brainrepo/models/karlo-public/v1.0.0.alpha/096db1af569b284eb76b3881534822d9/ViT-L-14.pt +wget https://arena.kakaocdn.net/brainrepo/models/karlo-public/v1.0.0.alpha/0b62380a75e56f073e2844ab5199153d/ViT-L-14_stats.th +wget https://arena.kakaocdn.net/brainrepo/models/karlo-public/v1.0.0.alpha/85626483eaca9f581e2a78d31ff905ca/prior-ckpt-step%3D01000000-of-01000000.ckpt +cd ../../ +``` +and the finetuned SD2.1 unCLIP-L checkpoint from [here](https://huggingface.co/stabilityai/stable-diffusion-2-1-unclip/blob/main/sd21-unclip-l.ckpt), and put the ckpt into the `checkpoints folder` + +Then, run + +``` +streamlit run scripts/streamlit/stableunclip.py +``` +and pick the `use_karlo` option in the GUI. +The script optionally supports sampling from the full Karlo model. To use it, download the 64x64 decoder and 64->256 upscaler +via +```shell +cd checkpoints/karlo_models +wget https://arena.kakaocdn.net/brainrepo/models/karlo-public/v1.0.0.alpha/efdf6206d8ed593961593dc029a8affa/decoder-ckpt-step%3D01000000-of-01000000.ckpt +wget https://arena.kakaocdn.net/brainrepo/models/karlo-public/v1.0.0.alpha/4226b831ae0279020d134281f3c31590/improved-sr-ckpt-step%3D1.2M.ckpt +cd ../../ +``` \ No newline at end of file From ae978e621da0e62c29b29958fc4b02afd3521c30 Mon Sep 17 00:00:00 2001 From: hardmaru Date: Fri, 24 Mar 2023 15:04:33 +0900 Subject: [PATCH 07/10] Update modelcard.md --- modelcard.md | 3 +++ 1 file changed, 3 insertions(+) diff --git a/modelcard.md b/modelcard.md index 787f15c..65d65b3 100644 --- a/modelcard.md +++ b/modelcard.md @@ -96,6 +96,9 @@ We currently provide the following checkpoints, for various versions: - `512-base-ema.ckpt`: Fine-tuned on `512-base-ema.ckpt` 2.0 with 220k extra steps taken, with `punsafe=0.98` on the same dataset. - `768-v-ema.ckpt`: Resumed from `768-v-ema.ckpt` 2.0 with an additional 55k steps on the same dataset (`punsafe=0.1`), and then fine-tuned for another 155k extra steps with `punsafe=0.98`. + +SD-unCLIP 2.1 is a finetuned version of Stable Diffusion 2.1, modified to accept (noisy) CLIP image embedding in addition to the text prompt, and can be used to create image variations ([Examples](../assets/stable-samples/stable-unclip/unclip-variations_noise.png)) or can be chained with text-to-image CLIP priors. The amount of noise added to the image embedding can be specified via the `noise_level` (0 means no noise, 1000 full noise). + ### Version 2.0 - `512-base-ema.ckpt`: 550k steps at resolution `256x256` on a subset of [LAION-5B](https://laion.ai/blog/laion-5b/) filtered for explicit pornographic material, using the [LAION-NSFW classifier](https://github.com/LAION-AI/CLIP-based-NSFW-Detector) with `punsafe=0.1` and an [aesthetic score](https://github.com/christophschuhmann/improved-aesthetic-predictor) >= `4.5`. From e272fe649a08b3de3857013007b264b349fe297e Mon Sep 17 00:00:00 2001 From: hardmaru Date: Fri, 24 Mar 2023 15:05:01 +0900 Subject: [PATCH 08/10] Update README.md --- README.md | 6 ++++++ 1 file changed, 6 insertions(+) diff --git a/README.md b/README.md index 0cc7471..e2b5d28 100644 --- a/README.md +++ b/README.md @@ -8,6 +8,12 @@ new checkpoints. The following list provides an overview of all currently availa ## News +**March 24, 2023** + +*Stable UnCLIP 2.1* + +- New stable diffusion finetune (_Stable unCLIP 2.1_, [HuggingFace](https://huggingface.co/stabilityai/)) at 768x768 resolution, based on SD2.1-768. This model allows for image variations and mixing operations as described in [*Hierarchical Text-Conditional Image Generation with CLIP Latents*](https://arxiv.org/abs/2204.06125), and, thanks to its modularity, can be combined with other models such as [KARLO](https://github.com/kakaobrain/karlo). Comes in two variants: [*Stable unCLIP-L*](https://huggingface.co/stabilityai/stable-diffusion-2-1-unclip/blob/main/sd21-unclip-l.ckpt) and [*Stable unCLIP-H*](https://huggingface.co/stabilityai/stable-diffusion-2-1-unclip/blob/main/sd21-unclip-h.ckpt), which are conditioned on CLIP ViT-L and ViT-H image embeddings, respectively. Instructions are available [here](doc/UNCLIP.MD). + **December 7, 2022** *Version 2.1* From f2aa661ea3a6050898832c387dfe9f484cd03844 Mon Sep 17 00:00:00 2001 From: hardmaru Date: Fri, 24 Mar 2023 15:06:15 +0900 Subject: [PATCH 09/10] Update UNCLIP.MD --- doc/UNCLIP.MD | 4 +++- 1 file changed, 3 insertions(+), 1 deletion(-) diff --git a/doc/UNCLIP.MD b/doc/UNCLIP.MD index 9a12c0c..bc4c3b2 100644 --- a/doc/UNCLIP.MD +++ b/doc/UNCLIP.MD @@ -11,6 +11,8 @@ To use them, download from Hugging Face, and put and the weights into the `check #### Image Variations ![image-variations-l-1](../assets/stable-samples/stable-unclip/unclip-variations.png) +If you would like to try a demo of this model, please visit https://clipdrop.co/stable-diffusion-reimagine + Run ``` @@ -53,4 +55,4 @@ cd checkpoints/karlo_models wget https://arena.kakaocdn.net/brainrepo/models/karlo-public/v1.0.0.alpha/efdf6206d8ed593961593dc029a8affa/decoder-ckpt-step%3D01000000-of-01000000.ckpt wget https://arena.kakaocdn.net/brainrepo/models/karlo-public/v1.0.0.alpha/4226b831ae0279020d134281f3c31590/improved-sr-ckpt-step%3D1.2M.ckpt cd ../../ -``` \ No newline at end of file +``` From 4e409af95c66f4ec27315360a7b1911a6641912d Mon Sep 17 00:00:00 2001 From: hardmaru Date: Fri, 24 Mar 2023 15:07:06 +0900 Subject: [PATCH 10/10] Update UNCLIP.MD --- doc/UNCLIP.MD | 8 +++++--- 1 file changed, 5 insertions(+), 3 deletions(-) diff --git a/doc/UNCLIP.MD b/doc/UNCLIP.MD index bc4c3b2..fc2fc2a 100644 --- a/doc/UNCLIP.MD +++ b/doc/UNCLIP.MD @@ -5,14 +5,16 @@ trained to invert CLIP image embeddings. We finetuned SD 2.1 to accept a CLIP ViT-L/14 image embedding in addition to the text encodings. This means that the model can be used to produce image variations, but can also be combined with a text-to-image embedding prior to yield a full text-to-image model at 768x768 resolution. + +If you would like to try a demo of this model on the web, please visit https://clipdrop.co/stable-diffusion-reimagine + We provide two models, trained on OpenAI CLIP-L and OpenCLIP-H image embeddings, respectively, available from [https://huggingface.co/stabilityai/stable-diffusion-2-1-unclip](https://huggingface.co/stabilityai/stable-diffusion-2-1-unclip/tree/main). -To use them, download from Hugging Face, and put and the weights into the `checkpoints` folder. +To use them, download from Hugging Face, and put and the weights into the `checkpoints` folder. + #### Image Variations ![image-variations-l-1](../assets/stable-samples/stable-unclip/unclip-variations.png) -If you would like to try a demo of this model, please visit https://clipdrop.co/stable-diffusion-reimagine - Run ```

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