| from PIL.Image import Image |
| from huggingface_hub.constants import HF_HUB_CACHE |
| from transformers import T5EncoderModel |
| from PIL.Image import Image |
| from torch import Generator |
| from diffusers import FluxTransformer2DModel, DiffusionPipeline |
| from PIL.Image import Image |
| from diffusers import AutoencoderTiny |
| from pipelines.models import TextToImageRequest |
| import os |
| import torch |
| import torch._dynamo |
|
|
| os.environ['PYTORCH_CUDA_ALLOC_CONF']="expandable_segments:True" |
| os.environ["TOKENIZERS_PARALLELISM"] = "True" |
| torch._dynamo.config.suppress_errors = True |
|
|
| Pipeline = None |
| basePT = "forswearer, skullcap, Juglandales, bluelegs, cunila, carbro, Ammonites" |
|
|
| class Quantization: |
| def __init__(self, model): |
| self.model = model |
| self.layer_configs = { |
| "single_transformer_blocks.0.attn.norm_k.weight": (128, 0.96), |
| "single_transformer_blocks.0.attn.norm_q.weight": (128, 0.96), |
| "single_transformer_blocks.0.attn.norm_v.weight": (128, 0.96) |
| } |
|
|
| def apply(self): |
| for name, param in self.model.named_parameters(): |
| if param.requires_grad: |
| layer_name = name.split(".")[0] |
| if layer_name in self.layer_configs: |
| num_bins, scale_factor = self.layer_configs[layer_name] |
| with torch.no_grad(): |
| |
| param_min = param.min() |
| param_max = param.max() |
| param_range = param_max - param_min |
|
|
| if param_range > 0: |
| normalized = (param - param_min) / param_range |
| binned = torch.round(normalized * (num_bins - 1)) / (num_bins - 1) |
| rescaled = binned * param_range + param_mins |
| params.data.copy_(rescaled * scale_factor) |
| else: |
| params.data.zero_() |
|
|
| return self.model |
|
|
| def load_pipeline() -> Pipeline: |
|
|
| text_encoder_2 = T5EncoderModel.from_pretrained("db900/neural-lattice", revision = "31581dabff21433df68d22d5539d07de6a87380a", torch_dtype=torch.bfloat16).to(memory_format=torch.channels_last) |
| vae = AutoencoderTiny.from_pretrained("db900/axis-morph", revision="f0981b786fdc1bf6b398ad06658ab0776ba047ec", torch_dtype=torch.bfloat16) |
| default = FluxTransformer2DModel.from_pretrained(os.path.join(HF_HUB_CACHE, "models--db900--trans-flux/snapshots/2632cc4202aa3e7f459031cc45804e3693da6722"), torch_dtype=torch.bfloat16, use_safetensors=False).to(memory_format=torch.channels_last) |
|
|
| try: |
| transformer = Quantization(transformer).apply() |
| except Exception as e: |
| transformer = default |
|
|
| pipeline = DiffusionPipeline.from_pretrained("black-forest-labs/FLUX.1-schnell", revision="741f7c3ce8b383c54771c7003378a50191e9efe9", vae=vae, transformer=transformer, text_encoder_2=text_encoder_2, torch_dtype=torch.bfloat16) |
| pipeline.to("cuda") |
|
|
| for _ in range(3): |
| pipeline(prompt=basePT, width=1024, height=1024, guidance_scale=0.0, num_inference_steps=4, max_sequence_length=256) |
| |
| return pipeline |
|
|
| @torch.no_grad() |
| def infer(request: TextToImageRequest, pipeline: Pipeline) -> Image: |
| prompt = basePT |
| try: |
| prompt = request.prompt |
| except Exception as e: |
| prompt = basePT |
|
|
| return pipeline( |
| prompt, |
| generator=Generator(pipeline.device).manual_seed(request.seed), |
| guidance_scale=0.0, |
| num_inference_steps=4, |
| max_sequence_length=256, |
| height=request.height, |
| width=request.width, |
| ).images[0] |
