101 lines
3.7 KiB
Python
101 lines
3.7 KiB
Python
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"""
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Copyright [2022] Victor C Hall
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Licensed under the GNU Affero General Public License;
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You may not use this code except in compliance with the License.
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You may obtain a copy of the License at
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https://www.gnu.org/licenses/agpl-3.0.en.html
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Unless required by applicable law or agreed to in writing, software
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distributed under the License is distributed on an "AS IS" BASIS,
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WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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See the License for the specific language governing permissions and
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limitations under the License.
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"""
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import os
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import random
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import argparse
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from torch import no_grad
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from torch.cuda.amp import autocast
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import torch
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from diffusers import StableDiffusionPipeline, AutoencoderKL, UNet2DConditionModel, DDIMScheduler, DDPMScheduler, PNDMScheduler, EulerAncestralDiscreteScheduler
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#from diffusers.models import AttentionBlock
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from diffusers.optimization import get_scheduler
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from diffusers.utils.import_utils import is_xformers_available
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from transformers import CLIPTextModel, CLIPTokenizer
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def __generate_sample(pipe: StableDiffusionPipeline, prompt : str, cfg: float, resolution: int, gen, steps: int = 30):
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"""
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generates a single sample at a given cfg scale and saves it to disk
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"""
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with autocast():
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image = pipe(prompt,
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num_inference_steps=steps,
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num_images_per_prompt=1,
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guidance_scale=cfg,
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generator=gen,
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height=resolution,
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width=resolution,
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).images[0]
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return image
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def __create_inference_pipe(unet, text_encoder, tokenizer, scheduler, vae):
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"""
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creates a pipeline for SD inference
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"""
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pipe = StableDiffusionPipeline(
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vae=vae,
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text_encoder=text_encoder,
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tokenizer=tokenizer,
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unet=unet,
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scheduler=scheduler,
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safety_checker=None, # save vram
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requires_safety_checker=None, # avoid nag
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feature_extractor=None, # must be none of no safety checker
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)
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return pipe
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def main(args):
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text_encoder = CLIPTextModel.from_pretrained(args.diffusers_path, subfolder="text_encoder")
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vae = AutoencoderKL.from_pretrained(args.diffusers_path, subfolder="vae")
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unet = UNet2DConditionModel.from_pretrained(args.diffusers_path, subfolder="unet")
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sample_scheduler = DDIMScheduler.from_pretrained(args.diffusers_path, subfolder="scheduler")
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tokenizer = CLIPTokenizer.from_pretrained(args.diffusers_path, subfolder="tokenizer", use_fast=False)
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text_encoder.eval()
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vae.eval()
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unet.eval()
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text_encoder.to("cuda")
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vae.to("cuda")
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unet.to("cuda")
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pipe = __create_inference_pipe(unet, text_encoder, tokenizer, sample_scheduler, vae)
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seed = args.seed if args.seed != -1 else random.randint(0, 2**30)
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gen = torch.Generator(device="cuda").manual_seed(seed)
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img = __generate_sample(pipe, args.prompt, args.cfg_scale, args.resolution, gen=gen, steps=args.steps)
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img.save(f"img_{args.prompt[0:100].replace(' ', '_')}_cfg_{args.cfg_scale}.png")
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if __name__ == '__main__':
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parser = argparse.ArgumentParser()
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parser.add_argument('--diffusers_path', type=str, default=None, required=True, help='path to diffusers model (from logs)')
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parser.add_argument('--prompt', type=str, required=True, help='prompt to use')
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parser.add_argument('--resolution', type=int, default=512, help='resolution (def: 512)')
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parser.add_argument('--seed', type=int, default=-1, help='seed, use -1 for random (def: -1)')
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parser.add_argument('--steps', type=int, default=50, help='inference, denoising steps (def: 50)')
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parser.add_argument('--cfg_scale', type=int, default=7.5, help='unconditional guidance scale (def: 7.5)')
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args = parser.parse_args()
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main(args)
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